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| author | SoloDShelby | 2024-07-19 14:41:40 +0300 |
|---|---|---|
| committer | SoloDShelby | 2024-07-19 14:41:40 +0300 |
| commit | 3fa31b50af2861382fbe2c76406f5a04c3fefc93 (patch) | |
| tree | 34d581648b0e0d3fc8dbe6577752a4fd433a3258 /gnqa/paper1_eval/src | |
| parent | 74616897e30c7daafe5e74d34073466464921316 (diff) | |
| download | gn-ai-3fa31b50af2861382fbe2c76406f5a04c3fefc93.tar.gz | |
Evaluation code for paper 1
Diffstat (limited to 'gnqa/paper1_eval/src')
290 files changed, 61293 insertions, 0 deletions
diff --git a/gnqa/paper1_eval/src/_config.cfg b/gnqa/paper1_eval/src/_config.cfg new file mode 100644 index 0000000..daf260a --- /dev/null +++ b/gnqa/paper1_eval/src/_config.cfg @@ -0,0 +1,12 @@ +[DEFAULT] +DATA_DIR = './data/' + +[out.response.dataset] +human_dir = /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/ +gpt4o_dir = /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/gpt4o/ + +[key.api] +fahamuai = SFMyNTY.g2gDYQJuBgD5foxyjwFiAAFRgA.oeVeuUe6CVM5kPW9hp3iBLYxCXDcqTLxjsyazVYrGBw +togetherai = c2257a61d2f4fa978e8605af87190667644072c1a1613484697f7652051c2160 +openai = 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 +openai2 = sk-proj-AxYJ7OutycVxqWRz2pgJT3BlbkFJKu9bVqXTA6tpiLafOiGN diff --git a/gnqa/paper1_eval/src/_config.example.cfg b/gnqa/paper1_eval/src/_config.example.cfg new file mode 100644 index 0000000..f82fc42 --- /dev/null +++ b/gnqa/paper1_eval/src/_config.example.cfg @@ -0,0 +1,11 @@ +[API] +rag_key = asdfadsfjlak;dsjflkajdsf;kl +DATA_DIR = './data/' + + +[out.response.human] +dataset_dir = ${PD}/data/datasets/human/ +[out.response.gpt4o] +dataset_dir = ${PD}/data/datasets/gpt4o/ + + diff --git a/gnqa/paper1_eval/src/apis/__pycache__/gnqaclient.cpython-310.pyc b/gnqa/paper1_eval/src/apis/__pycache__/gnqaclient.cpython-310.pyc Binary files differnew file mode 100644 index 0000000..2157a76 --- /dev/null +++ b/gnqa/paper1_eval/src/apis/__pycache__/gnqaclient.cpython-310.pyc diff --git a/gnqa/paper1_eval/src/apis/__pycache__/process.cpython-310.pyc b/gnqa/paper1_eval/src/apis/__pycache__/process.cpython-310.pyc Binary files differnew file mode 100644 index 0000000..65779da --- /dev/null +++ b/gnqa/paper1_eval/src/apis/__pycache__/process.cpython-310.pyc diff --git a/gnqa/paper1_eval/src/apis/__pycache__/resp.cpython-310.pyc b/gnqa/paper1_eval/src/apis/__pycache__/resp.cpython-310.pyc Binary files differnew file mode 100644 index 0000000..256f1a7 --- /dev/null +++ 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Identification of a QTL in Mus musculus for Alcohol Preference, Withdrawal, and Ap3m2 Expression Using Integrative Functional Genomics and Precision Genetics.txt", + "80a77e9d-0d80-4548-9253-c14b8bb5cff7": "2014 - Pharmacological Inhibition of Poly(ADP-Ribose) Polymerases Improves Fitness and Mitochondrial Function in Skeletal Muscle.txt", + "d3dae048-6a34-4ab5-9c47-4f11da232b97": "2008 - Studies on Syntaxin 12 and Alcohol Preference Involving C57BL6J and DBA2J Strains of Mice.txt", + "0140d814-c394-4293-89bf-81f94fd22012": "2006 - Characterization of genetic differences within the centrally projecting Edinger–Westphal nucleus of C57BL6J and DBA2J mice by expression profiling_William J Giardino, Dawn M Cote, Ju Li, Andrey.txt", + "32338b01-15af-4ec9-9bc4-e9c58b53068e": "2015 - Genetic properties of the MAGIC maize population a new platform for high definition QTL mapping in Zea mays.txt", + "a68c1746-5f14-4226-af75-10c0988b2d38": "2005 - Quantitative Trait Locus (QTL) mapping of mechanical sensitivity and peripheral nerve injury-induced neuropathic mechanical allodynia in Recombinant Inbred (RI) mice using www.webqtl.org.txt", + "5964906e-b016-4a60-b043-db826b438048": "2008 - Variation in mouse basolateral amygdala volume is associated with differences in stress reactivity and fear learning.txt", + "12780adb-bba7-43fe-9e36-2dc6b13afa93": "2016 - Influenza H3N2 infection of the collaborative cross founder strains reveals highly divergent host responses and identifies a unique phenotype in CASTEiJ mice.txt", + "3517925b-cdcd-477c-9632-09dadec2dffd": "2016 - Decreased expression levels of Ifi genes is associated to the increased resistance to spontaneous arthritis disease in mice deficiency of IL-1RA.txt", + "e70edf15-a163-4db3-9346-6d3b69cdc6d3": "2012 - Hepatocellular carcinoma as extracolonic manifestation of Lynch syndrome indicates SEC63 as potential target gene in hepatocarcinogenesis.txt", + "a6c28701-9fcd-4e0a-9d1d-26c63b28a6e7": "2012 - Genetic Control of a Central Pattern Generator Rhythmic Oromotor Movement in Mice Is Controlled by a Major Locus near Atp1a2.txt", + "7a81de58-67b5-4185-bc89-bcf50a7032d4": "2017 - Sex difference in the expression and gene network of epidermal growth factor receptor in pituitary gland in mice.txt", + "7ca1b7d0-cbae-4c68-b8e8-610435d84f9f": "2013 - Congenic Mice Provide Evidence for a Genetic Locus That Modulates Spontaneous Arthritis Caused by Deficiency of IL-1RA.txt", + "d2749f7d-2943-4c3e-ae5a-ac3999aad956": "2005 - Regression-based quantitative trait loci mapping robust, efficient and effective.txt", + "e2799196-122f-4b4f-a2a6-68adf24a236f": "2008 - Using hippocampal microRNA expression differences between mouse inbred strains to characterise miRNA function.txt", + "adf2ef89-dd1c-4b90-8eea-9d14a3952f6a": "2013 - Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli_Irit Gat-Viks, Nicolas Chevrier, [...], and Aviv.txt", + "448232de-41b9-4c26-82fc-d3e2126ad1dd": "2011 - THREE APPROACHES TO INVESTIGATING AN EPIGENETIC BASIS TO NICOTINE CONSUMPTION IN ADOLESCENT MICE AGOUTI VIABLE YELLOW PROGRAMMING, METHYL DONOR SUPPLEMENTATION, AND MATERNAL CARE.txt", + "a67372ac-02b7-41c4-bb55-5152444c5479": "2006 - Gene expression profiling in the striatum of inbred mouse strains with distinct opioid-related phenotypes.txt", + "91f1c2e6-da3e-4709-ab7f-117297f1aea8": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.txt", + "dee4f645-0cce-42f8-8cb5-66efb1378d82": "2004 - P2P-R expression is genetically coregulated with components of the translation machinery and with PUM2, a translational repressor that associates with the P2P-R mRNA.txt", + "6d78d290-1edb-40aa-9bf9-a77998e47671": "2012 - Methods for scoring the collective effect of SNPs Minor alleles of common SNPs quantitatively affect traits diseases and are under both positive and negative selection.txt", + "e34e7f49-66e0-4c99-8f7d-efe109589bf3": "53b7b8006154d7cb2f4be3d20696a79d.pdf", + "d74b9d37-258f-440c-afdd-cb3ed7c14f34": "glm.pdf" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/apis/gnqaclient.py b/gnqa/paper1_eval/src/apis/gnqaclient.py new file mode 100644 index 0000000..0024c31 --- /dev/null +++ b/gnqa/paper1_eval/src/apis/gnqaclient.py @@ -0,0 +1,226 @@ +# pylint: skip-file +import json +import string +import os +import datetime +import time +import requests + +from requests.adapters import HTTPAdapter +from requests.packages.urllib3.util.retry import Retry +from requests import HTTPError,Session +from urllib.parse import urljoin,quote +from urllib.request import urlretrieve +from errors.rag_err import UnprocessableEntity, LLMError + +basedir = os.path.join(os.path.dirname(__file__)) + + +class TimeoutHTTPAdapter(HTTPAdapter): + def __init__(self, timeout, *args, **kwargs): + """TimeoutHTTPAdapter constructor. + Args: + timeout (int): How many seconds to wait for the server to send data before + giving up. + """ + self.timeout = timeout + super().__init__(*args, **kwargs) + + def send(self, request, **kwargs): + """Override :obj:`HTTPAdapter` send method to add a default timeout.""" + timeout = kwargs.get("timeout") + if timeout is None: + kwargs["timeout"] = self.timeout + + return super().send(request, **kwargs) + + +class GeneNetworkQAClient(Session): + """GeneNetworkQA Client + + This class provides a client object interface to the GeneNetworkQA API. + It extends the `requests.Session` class and includes authorization, base URL, + request timeouts, and request retries. + + Args: + account (str): Base address subdomain. + api_key (str): API key. + version (str, optional): API version, defaults to "v3". + timeout (int, optional): Timeout value, defaults to 5. + total_retries (int, optional): Total retries value, defaults to 5. + backoff_factor (int, optional): Retry backoff factor value, defaults to 30. + + Usage: + from genenetworkqa import GeneNetworkQAClient + gnqa = GeneNetworkQAClient(account="account-name", api_key="XXXXXXXXXXXXXXXXXXX...") + """ + + BASE_URL = 'https://genenetwork.fahamuai.com/api/tasks' + + def __init__(self, account, api_key, version="v3", timeout=30, total_retries=5, backoff_factor=30): + super().__init__() + self.headers.update( + {"Authorization": "Bearer " + api_key}) + self.answer_url = f"{self.BASE_URL}/answers" + self.feedback_url = f"{self.BASE_URL}/feedback" + + adapter = TimeoutHTTPAdapter( + timeout=timeout, + max_retries=Retry( + total=total_retries, + status_forcelist=[429, 500, 502, 503, 504], + backoff_factor=backoff_factor, + ), + ) + + self.mount("https://", adapter) + self.mount("http://", adapter) + + @staticmethod + def format_bibliography_info(bib_info): + + if isinstance(bib_info, str): + # Remove '.txt' + bib_info = bib_info.removesuffix('.txt') + elif isinstance(bib_info, dict): + # Format string bibliography information + bib_info = "{0}.{1}.{2}.{3} ".format(bib_info.get('author', ''), + bib_info.get('title', ''), + bib_info.get('year', ''), + bib_info.get('doi', '')) + return bib_info + + @staticmethod + def ask_the_documents(extend_url, my_auth): + try: + response = requests.post( + base_url + extend_url, data={}, headers=my_auth) + response.raise_for_status() + except requests.exceptions.RequestException as e: + # Handle the exception appropriately, e.g., log the error + raise RuntimeError(f"Error making the request: {e}") + + if response.status_code != 200: + return negative_status_msg(response), 0 + + task_id = get_task_id_from_result(response) + response = get_answer_using_task_id(task_id, my_auth) + + if response.status_code != 200: + + return negative_status_msg(response), 0 + + return response, 1 + + @staticmethod + def negative_status_msg(response): + return f"Error: Status code -{response.status_code}- Reason::{response.reason}" + # return f"Problems\n\tStatus code => {response.status_code}\n\tReason => {response.reason}" + + def ask(self, exUrl, *args, **kwargs): + askUrl = self.BASE_URL + exUrl + res = self.custom_request('POST', askUrl, *args, **kwargs) + if (res.status_code != 200): + return self.negative_status_msg(res), 0 + task_id = self.getTaskIDFromResult(res) + return res, task_id + + def answer(self, taskid, *args, **kwargs): + query = self.answer_url + self.extendForTaskID(taskid) + res = self.custom_request('GET', query, *args, **kwargs) + if (res.status_code != 200): + print('The result is {0}',format(res)) + return self.negative_status_msg(res), 0 + return res, 1 + + def get_answer(self, taskid, *args, **kwargs): + query = self.answer_url + self.extendTaskID(taskid) + res = self.custom_request('GET', query, *args, **kwargs) + if (res.status_code != 200): + print('The result is {0}',format(res)) + return self.negative_status_msg(res), 0 + return res, 1 + + def custom_request(self, method, url, *args, **kwargs): + + max_retries = 50 + retry_delay = 3 + + for i in range(max_retries): + try: + response = super().request(method, url, *args, **kwargs) + response.raise_for_status() + + except requests.exceptions.HTTPError as error: + if error.response.status_code ==500: + raise LLMError(error.request, error.response, f"Response Error,status_code:{error.response.status_code},Reason: Use of Invalid Token") + elif error.response.status_code ==404: + raise LLMError(error.request,error.response,f"404 Client Error: Not Found for url: {self.BASE_URL}") + raise error + + except requests.exceptions.RequestException as error: + raise error + + + if response.ok: + if method.lower() == "get" and response.json().get("data") is None: + time.sleep(retry_delay) + continue + else: + return response + else: + time.sleep(retry_delay) + return response + + @staticmethod + def get_task_id_from_result(response): + task_id = json.loads(response.text) + result = f"?task_id={task_id.get('task_id', '')}" + return result + + @staticmethod + def get_answer_using_task_id(extend_url, my_auth): + try: + response = requests.get( + answer_url + extend_url, data={}, headers=my_auth) + response.raise_for_status() + return response + except requests.exceptions.RequestException as error: + # Handle the exception appropriately, e.g., log the error + raise error + + @staticmethod + def filter_response_text(val): + """ + Filters out non-printable characters from the input string and parses it as JSON. + + Args: + val (str): Input string to be filtered and parsed. + + Returns: + dict: Parsed JSON object. + # remove this + """ + return json.loads(''.join([str(char) for char in val if char in string.printable])) + + def getTaskIDFromResult(self, res): + return json.loads(res.text) + + def extendTaskID(self, task_id): + return '?task_id=' + str(task_id['task_id']) + + def extendForTaskID(self, task_id): + return '?task_id=' + str(task_id) + + def get_gnqa(self, query): + qstr = quote(query) + res, task_id = api_client.ask('?ask=' + qstr) + res, success = api_client.get_answer(task_id) + + if success == 1: + resp_text = filter_response_text(res.text) + answer = resp_text.get('data', {}).get('answer', '') + context = resp_text.get('data', {}).get('context', '') + return answer, context + else: + return res, "Unfortunately, I have nothing." diff --git a/gnqa/paper1_eval/src/apis/process.py b/gnqa/paper1_eval/src/apis/process.py new file mode 100644 index 0000000..37f2d73 --- /dev/null +++ b/gnqa/paper1_eval/src/apis/process.py @@ -0,0 +1,152 @@ +"""this module contains code for processing response from fahamu client.py""" +import os +import string +import json + +from urllib.parse import urljoin +from urllib.parse import quote +import logging +import requests + +from apis.gnqaclient import GeneNetworkQAClient +from apis.resp import DocIDs + + +BASE_URL = 'https://genenetwork.fahamuai.com/api/tasks' + + +# pylint: disable=C0301 + + +def format_bibliography_info(bib_info): + """Function for formatting bibliography info""" + if isinstance(bib_info, str): + return bib_info.removesuffix('.txt') + elif isinstance(bib_info, dict): + return f"{bib_info['author']}.{bib_info['title']}.{bib_info['year']}.{bib_info['doi']} " + return bib_info + + +def filter_response_text(val): + """helper function for filtering non-printable chars""" + return json.loads(''.join([str(char) + for char in val if char in string.printable])) + + +def parse_context(context, get_info_func, format_bib_func): + """function to parse doc_ids content""" + results = [] + for doc_ids, summary in context.items(): + combo_txt = "" + for entry in summary: + combo_txt += "\t" + entry["text"] + doc_info = get_info_func(doc_ids) + bib_info = doc_ids if doc_ids == doc_info else format_bib_func( + doc_info) + results.append( + {"doc_id": doc_ids, "bibInfo": bib_info, "comboTxt": combo_txt}) + return results + + +def rate_document(task_id, doc_id, rating, auth_token): + """This method is used to provide feedback for a document by making a rating.""" + # todo move this to clients + try: + url = urljoin(BASE_URL, + f"""/feedback?task_id={task_id}&document_id={doc_id}&feedback={rating}""") + headers = {"Authorization": f"Bearer {auth_token}"} + + resp = requests.post(url, headers=headers) + resp.raise_for_status() + + return {"status": "success", **resp.json()} + except requests.exceptions.HTTPError as http_error: + raise RuntimeError(f"HTTP Error Occurred:\ + {http_error.response.text} -with status code- {http_error.response.status_code}") from http_error + except Exception as error: + raise RuntimeError(f"An error occurred: {str(error)}") from error + + +def load_file(filename, dir_path): + """function to open and load json file""" + file_path = os.path.join(dir_path, f"{filename}") + if not os.path.isfile(file_path): + raise FileNotFoundError(f"{filename} was not found or is a directory") + with open(file_path, "rb") as file_handler: + return json.load(file_handler) + + +def fetch_pubmed(references, file_name, data_dir=""): + """method to fetch and populate references with pubmed""" + + try: + pubmed = load_file(file_name, os.path.join(data_dir, "gn-meta/lit")) + for reference in references: + if pubmed.get(reference["doc_id"]): + reference["pubmed"] = pubmed.get(reference["doc_id"]) + return references + + except FileNotFoundError: + logging.error("failed to find pubmed_path for %s/%s", + data_dir, file_name) + return references + + +def get_gnqa(query, auth_token, tmp_dir=""): + """entry function for the gn3 api endpoint()""" + + api_client = GeneNetworkQAClient(requests.Session(), api_key=auth_token) + res, task_id = api_client.ask('?ask=' + quote(query), auth_token) + if task_id == 0: + raise RuntimeError(f"Error connecting to Fahamu Api: {str(res)}") + res, success = api_client.get_answer(task_id) + if success == 1: + resp_text = filter_response_text(res.text) + if resp_text.get("data") is None: + return task_id, "Please try to rephrase your question to receive feedback", [] + answer = resp_text['data']['answer'] + context = resp_text['data']['context'] + references = parse_context( + context, DocIDs().getInfo, format_bibliography_info) + #references = fetch_pubmed(references, "pubmed.json", tmp_dir) + + return task_id, answer, references + else: + return task_id, "Please try to rephrase your question to receive feedback", [] + +def get_response_from_taskid(auth_token, task_id): + api_client = GeneNetworkQAClient(requests.Session(), api_key=auth_token) + res, success = api_client.answer(task_id) + if success == 1: + resp_text = filter_response_text(res.text) + if resp_text.get("data") is None: + return task_id, "Please try to rephrase your question to receive feedback", [] + answer = resp_text['data']['answer'] + context = resp_text['data']['context'] + references = parse_context( + context, DocIDs().getInfo, format_bibliography_info) + #references = fetch_pubmed(references, "pubmed.json", tmp_dir) + + return task_id, answer, references + else: + return task_id, "Please try to rephrase your question to receive feedback", [] + + +def fetch_query_results(query, user_id, redis_conn): + """this method fetches prev user query searches""" + result = redis_conn.get(f"LLM:{user_id}-{query}") + if result: + return json.loads(result) + return { + "query": query, + "answer": "Sorry No answer for you", + "references": [], + "task_id": None + } + + +def get_user_queries(user_id, redis_conn): + """methods to fetch all queries for a specific user""" + + results = redis_conn.keys(f"LLM:{user_id}*") + return [query for query in [result.partition("-")[2] for result in results] if query != ""] diff --git a/gnqa/paper1_eval/src/apis/resp.py b/gnqa/paper1_eval/src/apis/resp.py new file mode 100644 index 0000000..5d6ee24 --- /dev/null +++ b/gnqa/paper1_eval/src/apis/resp.py @@ -0,0 +1,75 @@ + +# pylint: skip-file +import string +import json +import os + + +basedir = os.path.abspath(os.path.dirname(__file__)) + + +class DocIDs(): + def __init__(self): + # open doc ids for GN refs + self.doc_ids = self.loadFile("doc_ids.json") + # open doc ids for Diabetes references + self.sugar_doc_ids = self.loadFile("all_files.json") + # format is not what I prefer, it needs to be rebuilt + self.formatDocIDs(self.sugar_doc_ids) + + def loadFile(self, file_name): + file_path = os.path.join(basedir, file_name) + if os.path.isfile(file_path): + f = open(file_path, "rb") + result = json.load(f) + f.close() + return result + else: + raise Exception("\n{0} -- File does not exist\n".format(file_path)) + + def formatDocIDs(self, values): + for _key, _val in values.items(): + if isinstance(_val, list): + for theObject in _val: + docName = self.formatDocumentName(theObject['filename']) + docID = theObject['id'] + self.doc_ids.update({docID: docName}) + + def formatDocumentName(self, val): + result = val.removesuffix('.pdf') + result = result.removesuffix('.txt') + result = result.replace('_', ' ') + return result + + + def getInfo(self, doc_id): + if doc_id in self.doc_ids.keys(): + return self.doc_ids[doc_id] + else: + return doc_id + +class RespContext(): + def __init__(self, context): + self.cntxt = context + self.theObj = {} + + def parseIntoObject(self, info): + # check for obj, arr, or val + for key, val in info.items(): + if isinstance(val, list): + self.parseIntoObject(val) + elif isinstance(val, str) or isinstance(val, int): + self.theObj[key] = val + self.theObj[key] = self.val + + +def createAccordionFromJson(theContext): + result = '' + # loop thru json array + ndx = 0 + for docID, summaryLst in theContext.items(): + # item is a key with a list + comboTxt = '' + for entry in summaryLst: + comboTxt += '\t' + entry['text'] + return result diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_aging_1.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_aging_1.json new file mode 100644 index 0000000..1e25ced --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_aging_1.json @@ -0,0 +1,120 @@ +{ + "question": [ + "What are the main genetic factors that influence aging?", + "How do genes affect the aging process in humans?", + "What lifestyle choices can help slow down genetic aging?", + "How do scientists study the genetics of aging in animals?", + "Are there specific genes that have been linked to longer lifespans?" + ], + "answer": [ + "The main genetic factors that influence aging include genes such as APOE, FOXO3A, HLA-DQA1/DRB1, LPA, CHRNA3/5, CDKN2A/B, SH2B3, and MTP. These genes are associated with various biological processes such as metabolism, insulin signaling, and DNA damage and repair. Polymorphisms in these genes have been associated with longevity and healthy aging.", + "Genes influence the aging process in humans by governing levels of physiological capacity, repair, and turnover. They indirectly determine potential longevity. Genetic factors can explain about 20-25% of the variability of human survival to the mid-eighties. Certain genes, such as APOE and FOXOA3, have been associated with long life. However, aging is a complex process that involves the interactions of multiple genes with each other and the environment. It's also important to note that aging is likely a highly polygenic trait, meaning it's probably influenced by many genes.", + "Lifestyle choices that can help slow down genetic aging include mindful changes in behavior, reducing environmental exposure, healthy food and supplement use, weight loss, and regular exercise. A Mediterranean diet containing fruits, whole grains, fiber, antioxidants, soy protein, and healthy fats is also suggested. Additionally, giving up smoking and increasing educational attainment can positively correlate with lifespan.", + "Scientists study the genetics of aging in animals by using various animal models, including yeast, worms, flies, and mice. These models are genetically manipulated to investigate lifespan and age-related diseases. Techniques such as genotyping, genetic analysis, and forward genetic approaches are used to identify specific genes and molecular pathways related to aging. In some cases, cross-breeding of different strains is performed to identify aging-relevant genetic determinants. Additionally, in vivo studies are conducted to test the effect of certain manipulations or treatments on the whole organism.", + "Yes, specific genes have been linked to longer lifespans. These include the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the AKT1 locus. Other genes associated with longevity include HLA-DQA1/DRB1 and LPA. However, these genes account for only a small portion of the genetic contribution to longevity." + ], + "contexts": [ + [ + "\t\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "\t\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals.", + "\tIntroduction\n\nWith the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging.", + "\tINTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining 20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging.", + "\t\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26].\tIndividual Genotype\n\nIndividual differences in biological ageing may be due in part to the specific variations of the genotype but also genome-environment interactions [21,37].The maintenance of genomic stability and integrity is considered an essential factor required for cell viability and the overall longevity of an organism.The accumulation of physical damage is one of the leading causes of the ageing process.When considering oxidative damage as one of the causes of the damage of genetic material, these changes alter vital processes, such as replication, transcription, and translation, leading to genomic instability and personalized processes of ageing [38,39].\tInfluence of Genetic Factors in Ageing and Lifespan\n\nAgeing is defined as the decline of physiological functions in several tissues and organs inducing an increasing probability of death [17].The understanding of genetic factors involved in ageing has been limited due to the complexity of this process and the heterogeneity among individuals and even among tissues [18][19][20].Tissue cells adopt a senescent phenotype as a consequence of multiple intrinsic, extrinsic, and stochastic factors [21].The combination of these genetic factors is related to longevity and healthy ageing [22].Although this decline is somewhat predictable, some individuals show a much slower decline and get to live past the age of 100.Studies in these individuals showed polymorphisms in some genes which are associated with long life, such as APOE and FOXO3.However, these associations have not been consistent across different populations, suggesting that ageing is rather polygenic [23].", + "\t\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes.", + "\t\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10).", + "\t\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13].", + "\tTranslational\n\nA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors.", + "\t\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics.", + "\t\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity.", + "\t\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways.", + "\tIntroduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches.", + "\tThe mechanisms that underlie healthy agingparticularly, the cognitive as-\n\npectsremain poorly understood. Research suggests that genetics play a significant role in determining an individuals\nsusceptibility or resilience to cognitive decline and dementia\n(Harris and Deary 2011; Ridge et al. , 2013). Identification of precise genetic factors involved would provide insight into\n\nCell Reports 32, 108091, September 1, 2020 2020 The Author(s). 1\nThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). ll\nOPEN ACCESS\n\nReport\n\nFigure 1.", + "\tGenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhes, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging.", + "\t\n\nI NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity.", + "\t\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation.\t\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + ], + [ + "\t\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals.", + "\t\nAging is a complex process affecting different species and individuals in different ways.Comparing genetic variation across species with their aging phenotypes will help understanding the molecular basis of aging and longevity.Although most studies on aging have so far focused on short-lived model organisms, recent comparisons of genomic, transcriptomic, and metabolomic data across lineages with different lifespans are unveiling molecular signatures associated with longevity.Here, we examine the relationship between genomic variation and maximum lifespan across primate species.We used two different approaches.First, we searched for parallel amino-acid mutations that co-occur with increases in longevity across the primate linage.Twenty-five such amino-acid variants were identified, several of which have been previously reported by studies with different experimental setups and in different model organisms.The genes harboring these mutations are mainly enriched in functional categories such as wound healing, blood coagulation, and cardiovascular disorders.We demonstrate that these pathways are highly enriched for pleiotropic effects, as predicted by the antagonistic pleiotropy theory of aging.A second approach was focused on changes in rates of protein evolution across the primate phylogeny.Using the phylogenetic generalized least squares, we show that some genes exhibit strong correlations between their evolutionary rates and longevity-associated traits.These include genes in the Sphingosine 1-phosphate pathway, PI3K signaling, and the Thrombin/protease-activated receptor pathway, among other cardiovascular processes.Together, these results shed light into human senescence patterns and underscore the power of comparative genomics to identify pathways related to aging and longevity.\t\n\nAging is a complex process affecting different species and individuals in different ways.Comparing genetic variation across species with their aging phenotypes will help understanding the molecular basis of aging and longevity.Although most studies on aging have so far focused on short-lived model organisms, recent comparisons of genomic, transcriptomic, and metabolomic data across lineages with different lifespans are unveiling molecular signatures associated with longevity.Here, we examine the relationship between genomic variation and maximum lifespan across primate species.We used two different approaches.First, we searched for parallel amino-acid mutations that co-occur with increases in longevity across the primate linage.Twenty-five such amino-acid variants were identified, several of which have been previously reported by studies with different experimental setups and in different model organisms.The genes harboring these mutations are mainly enriched in functional categories such as wound healing, blood coagulation, and cardiovascular disorders.We demonstrate that these pathways are highly enriched for pleiotropic effects, as predicted by the antagonistic pleiotropy theory of aging.A second approach was focused on changes in rates of protein evolution across the primate phylogeny.Using the phylogenetic generalized least squares, we show that some genes exhibit strong correlations between their evolutionary rates and longevity-associated traits.These include genes in the Sphingosine 1-phosphate pathway, PI3K signaling, and the Thrombin/protease-activated receptor pathway, among other cardiovascular processes.Together, these results shed light into human senescence patterns and underscore the power of comparative genomics to identify pathways related to aging and longevity.", + "\tINTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining 20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging.", + "\t\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process.", + "\t\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes.\t\n\nAging is an extremely complex process associated with interplay of genetic, biochemical, and metabolic factors in an organism in a given environment.Although genetic studies of various animal models suggest that even a single-gene mutation can remarkably extend lifespan (Kenyon 2005;Johnson 2006) and, thus, modulate aging, no such genes are revealed in humans so far.Given that a human organism is a much more complex system than a model organism (Christensen et al. 2006), it is evident that genetic effects on the aging process should be mediated via coordinate action of a large number of inter-related processes (Kirkwood 2011).Coordinated function is rather relevant to complex biological (Soltow et al. 2010;Slagboom et al. 2011) and genetic (Bloss et al. 2011) networks than to individual genes.\t\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010).", + "\t\n\nGenes do not drive the aging process but by governing the levels of excess physiological capacity, repair, and turnover they indirectly determine potential longevity.There are no genes that specifically drive longevity but there are genes that govern biological processes that increase the likelihood of survival to reproductive maturity.The variations in excess physiological capacity, repair, and turnover accounts for the variations found in longevity both within and between species.\t\n\nThe studies in lower animals made in recent years that have led to the view that genes are involved in aging have not revealed a reversal or arrest of the inexorable expression of molecular disorder that is the hallmark of aging.These studies are more accurately interpreted to have impact on our understanding of longevity determination because all of the experimental results have altered biological variables before the aging process begins.None of these studies in invertebrates has demonstrated that the manipulation of genes has slowed, stopped, or reversed recognized biomarkers of the aging process.", + "\t\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.\t\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.", + "\tIV. Genome-Environment Interactions as Targets for Dietary Interventions and Drug Discovery\n\n\"[It's] possible that we could change a human gene and double our life span. \"-CynthiaKenyon (Duncan, 2004) According to the GenAge database of aging-related genes (http://genomics.senescence.info/genes/),more than 700 genes have been identified that regulate lifespan in model organisms (de Magalha es et al., 2009a).Many of these genes and their associated pathways-such as the insulin/IGF1/GH pathway-have been shown to affect longevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolutionarily conserved and may have potential therapeutic applications (Baur et al., 2006).For example, evidence suggests the use of lowered IGF signaling (e.g., by targeting IGF receptors) to treat certain age-related diseases such as cancer (Pollak et al., 2004), Alzheimer's disease (Cohen et al., 2009), and autoimmune diseases (Smith, 2010).Moreover, a number of genes and pathways associated with longevity and CR are part of nutrient-sensing pathways that also regulate growth and development, including the insulin/IGF1/GH pathway (Narasimhan et al., 2009;Stanfel et al., 2009).Many of these genes modulate the response to environmental signals, such as food availability, and act in signaling pathways that if understood can be targeted (Fig. 1).The genetic regulation of aging is therefore an emerging field with multiple applications in the human nutrition, cosmetic, and pharmaceutical industries.\t\n\nThe remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design.", + "\tTranslational\n\nA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors.", + "\t\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics.", + "\tGenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhes, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging.\t\n\nAlthough the models data set comprises all genes (to our knowledge) shown by the time of the latest update to statistically increase longevity or alter the aging process in a noticeable way, in the human data set we try to evaluate whether a given intervention is affecting the aging process itself or not.For example, many mutations may increase longevity by decreasing the incidence of specific diseases, rather than by altering the basic process of aging (de Magalhes et al ., 2005a(de Magalhes et al ., , 2005b)).Therefore, the human data set is not merely an extension of the work conducted in model organisms and of its bibliography, but a manually selected list of the most pertinent human aging candidate genes, each presented with a higher annotation level.We cite studies on whether the functions of aging-associated genes in model organisms are conserved in their human orthologues.Likewise, we cite flaws in previous studies based on new published observations, although we have a neutral stance on conflicting findings from different research groups.Our policy is to cite all conflicting reports and let visitors make their own decisions on how to interpret them.By contrast, each entry in GenAge model organisms has only one reference: the first publication reporting an association of the gene with longevity or aging.Moreover, one of the latest enhancements in the human data set was the inclusion of Gene Ontology annotation.Gene Ontology terms and annotation files were obtained from the Gene Ontology Consortium website (http://www.geneontology.org/ ) and provide an additional layer of description for the gene products in a cellular context (Ashburner et al ., 2000).", + "\t\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.\t\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + ], + [ + "\t\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.\t\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "\t\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals.", + "\tIntroduction\n\nWith the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging.", + "\t\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18].\tConclusions and Perspectives\n\nThe advent of new technologies has allowed the identification of conserved pathways involved in the aging process, as well as the association of genomic variants with human longevity.Nevertheless, heritability of human longevity has been estimated from 20% to 30%, reinforcing the fact that external factors such as diet, environment, and physical activity play a critical role in the human life span.", + "\t\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10).\t\nLiving to a late age without suffering any major health problems is a genetically influenced trait.To identify the genes contributing to this important phenotype, a 10 cM genome screen was performed in 95 pairs of male fraternal twins concordant for healthy aging.Individuals meeting these criteria were defined as those attaining the age of 70 free of cardiovascular disease (coronary surgery, diabetes, heart attack, and stroke) and prostate cancer.Six chromosomal regions were identified with logarithm of odds (LOD) scores greater than 1.2 ( p , .01).A region on chromosome 4 at marker D4S1564 produced a LOD score of 1.67; this was the same marker previously linked to extreme longevity segregating as an autosomal dominant trait in centenarian families.Our results provide independent evidence that a locus on the long arm of chromosome 4 is associated with better physical aging and/or longevity.", + "\t\n\nMany factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone.Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases.If such genes exist, their effects were too small to be detected in this study.The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease.", + "\tIntroduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005).", + "\t\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13].", + "\tIV. Genome-Environment Interactions as Targets for Dietary Interventions and Drug Discovery\n\n\"[It's] possible that we could change a human gene and double our life span. \"-CynthiaKenyon (Duncan, 2004) According to the GenAge database of aging-related genes (http://genomics.senescence.info/genes/),more than 700 genes have been identified that regulate lifespan in model organisms (de Magalha es et al., 2009a).Many of these genes and their associated pathways-such as the insulin/IGF1/GH pathway-have been shown to affect longevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolutionarily conserved and may have potential therapeutic applications (Baur et al., 2006).For example, evidence suggests the use of lowered IGF signaling (e.g., by targeting IGF receptors) to treat certain age-related diseases such as cancer (Pollak et al., 2004), Alzheimer's disease (Cohen et al., 2009), and autoimmune diseases (Smith, 2010).Moreover, a number of genes and pathways associated with longevity and CR are part of nutrient-sensing pathways that also regulate growth and development, including the insulin/IGF1/GH pathway (Narasimhan et al., 2009;Stanfel et al., 2009).Many of these genes modulate the response to environmental signals, such as food availability, and act in signaling pathways that if understood can be targeted (Fig. 1).The genetic regulation of aging is therefore an emerging field with multiple applications in the human nutrition, cosmetic, and pharmaceutical industries.\t\n\nWith an aging population, there is a great and urgent need to develop approaches and therapies targeting the aging process and age-related diseases (Butler et al., 2008).Delaying the process of aging, even slightly, would have profound social, medical and economic benefits (Olshansky et al., 2006;Butler et al., 2008).For example, slowing aging by a mere 7 years would cut mortality of age-related diseases by half at every age.Therefore, the potential benefits from research on the basic biology and genetics of aging are unparalleled in terms of improving quality of life and health.Although much debate remains regarding the molecular causes of aging, findings from model organisms show that aging is surprisingly plastic and can be manipulated by both genetic and environmental factors (Finch and Ruvkun, 2001;Kenyon, 2010).In principle, therefore, it is possible to manipulate human aging.Unlocking this capacity to manipulate aging in people would result in unprecedented human health benefits, and it opens new opportunities for industry.", + "\t\n\nA better understanding of pathways that contribute at middle age to the divergence of healthy and unhealthy ageing humans may be substantiated by in depth studies of the cells and tissues of longevity family members in the context of their genomic background.", + "\tRelevance to nurse practitioner practice\n\nCurrently, there is no cure for genetic variants associated with rapid aging, but novel agents that may slow down the aging process are being tested.The authors of this article advocate individual participation in association studies of aging and pharmacologic risk mitigation or reversal of symptoms for those with known genetic disease risk.Direct to consumer epigenetic biological aging tests and telomere length tests are available; but they are not approved by the Food and Drug Administration.Health care providers may want to consider the simple but key clinical and personal changes, suggested above, to enhance DNA health, wellness, and longevity.Simple mindful changes in behavior, environmental exposure, food/supplement use, weight loss, and regular exercise can reduce adduct exposure damage and impact telomere length, potentially increasing longevity.A Mediterranean diet containing fruits and whole grains along with fiber, antioxidants, soy protein, and healthy fats (from avocados, fish, flax, and walnuts) is suggested to reduce DNA adducts and protect telomeres.In light of our current pandemic, focus on population health, and restrictions to health care access, especially in rural communities, health care providers could incorporate these lifestyle and dietary principles in telehealth visits with patients to reduce disease risk and optimize healthy aging.\t\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics.", + "\t[PubMed: 18208581]\n3. de Magalhes JP, Wuttke D, Wood SH, Plank M & Vora C Genome-environment interactions that\nmodulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88101 (2012). [PubMed:\n22090473]\n4. McDaid AFet al.Bayesian association scan reveals loci associated with human lifespan and linked\nbiomarkers. Nat. Commun. 8, 15842 (2017). [PubMed: 28748955]\n5. Fontana L & Partridge L Promoting health and longevity through diet: From model organisms to\nhumans. Cell 161, 106118 (2015). [PubMed: 25815989]\n6.", + "\t\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways.", + "\tThe mechanisms that underlie healthy agingparticularly, the cognitive as-\n\npectsremain poorly understood. Research suggests that genetics play a significant role in determining an individuals\nsusceptibility or resilience to cognitive decline and dementia\n(Harris and Deary 2011; Ridge et al. , 2013). Identification of precise genetic factors involved would provide insight into\n\nCell Reports 32, 108091, September 1, 2020 2020 The Author(s). 1\nThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). ll\nOPEN ACCESS\n\nReport\n\nFigure 1.", + "\t\n\nWith modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms." + ], + [ + "\t\n\nWhen considering the advantages and disadvantages of dogs as a model for geroscience research, it is useful to note that the vast majority of mammalian studies on the basic biology of aging are performed in a relatively small number of inbred mouse strains.Typical average lifespan for most of these mouse strains is approximately 2-3 years, and animals are generally kept in highly controlled, pathogen-free facilities and fed one of a small number of standard, refined mouse chow diets.A majority of laboratory-aged mice die from cancers, many of which are not commonly occurring in human populations.Although there have been recent efforts to define comprehensive measures of frailty and healthspan in aging mice (Parks et al. 2012;Richardson et al. 2016), consensus is still lacking, and functional measures of key organ systems are rarely utilized by the broader field.For example, changes in cardiac and cognitive/behavioral function are rarely assessed in studies of aging mice, but are obviously important components of human aging.As we discuss below, the challenges of the mouse as an aging model underscore the value of studying aging in companion dogs.In the following paragraphs, we expand on some of the additional features of companion dogs that make them uniquely well suited for geroscience research.", + "\t\n\nStudies on the aging of mammals are rather limited by the long life span of the commonly used model organisms.Thus, both nonvertebrate and invertebrate organisms, with their shorter life span and ease of genetic and environmental manipulations, gained popularity among researchers in the aging field as experimental models for aging studies.Among them, budding yeast or Saccharomyces cerevisiae is a highly informative organismal model for aging studies with its genetic tools, short life span, and fully sequenced genome (20,21).Despite being unicellular, yeast has been an excellent model to identify and characterize conserved basic biological processes, including aging.Yeast has been extensively used to identify genes and interventions responsible for life span extension and to gain insights into the aging processes of all eukaryotic organisms.In parallel, over the years, studies on invertebrate organisms, such as Drosophila melanogaster (flies) and Caenorhabditis elegans (worms), and certain vertebrate models, such as mice, zebrafish, naked mole rats, and, most recently, African turquoise killifish, have also provided invaluable information to help us understand the complexity of the process of aging and the influence of overlapping pathways on the outcome (22,23).", + "\tAfter specific tissues (brain regions and hind\nlimbs) were dissected immediately upon sacrifice,\nAging Clin Exp Res\n\ncarcasses were preserved in buffered formalin until comprehensive pathological examinations were completed. Genotyping and genetic analyses\nMice that began the phenotyping procedure in each of the 3\nage groups were genotyped at 96 microsatellite markers\n[1317]. Distortions in the frequency of alleles with aging\nwere detected by Chi-squared tests conducted for each\nlocus.", + "\tExperimental Goals and Significance\nThe experimental goal of the work performed in this dissertation was to identify\nspecific gene(s) and molecular pathways underlying HSC aging in two commonly used\nstrains of inbred mice using a forward genetic approach.", + "\t\n\nCross-breeding of N. furzeri strains with different life spans is currently being performed in our laboratory and should enable the identification of quantitative trait loci and facilitate cloning of aging-relevant genetic determinants.The present study illustrates the challenges that will have to be addressed in an N. furzeri genome project that we would like to establish in order to make maximal use of this fish species as a vertebrate model for aging research.", + "\t\n\nMost studies in this area have been performed in the classical animal models of aging, such as C. elegans, yeast, and rodents (mice and rats); however, in this chapter we have attempted to include only representative studies in humans, which were performed in samples of skeletal muscle, heart, and brain obtained from elderly individuals.\tAnimal Models for HGPS\n\nIn recent years, animal models have been at the forefront of aging research, making important contributions to a better understanding of this process at the organismal level.Some animals have been preferred in aging research, ranging from invertebrate (Caenorhabditis elegans and Drosophila melanogaster) to mammal species (murine and primate species).Nonetheless, scientists preferably chose mouse models for the study of age-related diseases for various reasons: (a) mice are closely related to humans, with nearly 99% of human orthologous in mice; (b) their relatively short lifespan and small size allow surveillance of the aging process within a pertinent time frame and make their housing less expensive; (c) the feasibility of performing genetic manipulations facilitates the engineering of transgenic strains (gain-and loss-of function mice) that model premature aging disorders.In this section, we describe the major HGPS mouse models previously developed (see Table 10.1 for details).", + "\tJournal of Theoretical Biology 12:1245. Hughes, K. A., and B. Charlesworth. 1994. A genetic analysis of senescence in Drosophila. Nature 367:6466. Hutchinson, E. W., and M. R. Rose. 1990. Quantitative genetic analysis of Drosophila stocks\nwith postponed aging. Pages 6687 in D. E. Harrison, ed. Genetic Effects on Aging II. Caldwell, NJ: Telford. Kannisto, V., J. Lauristen, and J. W. Vaupel. 1994. Reduction in mortality at advanced ages: Several decades of evidence from 27 countries. Population Development Review 20:793810. 580\n\n\n\nA P P L I C AT I O N S\nKennedy, B. K., and L. Guarente. 1996.\tGenetic analysis of aging in Saccharomyces cerevisiae. Trends in Genetics 12:355359. Khazaeli, A. A., S. D. Pletcher, and J. W. Curtsinger. 1998. The fractionation experiment:\nReducing heterogeneity to investigate age-specific mortality in Drosophila. Mechanics of\nAgeing and Development 16:301317. Khazaeli, A. A., W. Van Voorhies, and J. W. Curtsinger. 2005. The relationship between life\nspan and adult body size is highly strain-specific in Drosophila melanogaster. Experimental\nGerontology 40:37785. Kim, S. K. 2007. Common aging pathways in worms, flies, mice and humans. Journal of\nExperimental Biology 210:16071612. Kirkwood, T. B. L. 1977. Evolution of aging. Nature 270:301304.", + "\tIn Vivo\n\nIn vivo studies can further test the effect of a manipulation or treatment, either targeted or scattered, on the whole organism.Most of these biological models offer many advantages over humans, for instance, their basic biology and genomes are well documented and are easier to manipulate genetically.Furthermore, they have much shorter life spans than humans, enabling longitudinal studies, while ethical issues, long natural life span, environmental influences, genetic heterogeneity, and various other limiting factors complicate the use of human subjects in aging research.Regardless of the advantages listed earlier and the eminent contribution to our understanding of the aging process, the use of animal models in aging studies has its own limitations.Aging is not a simple process, and there is no genuine agreement about what it is and how to define it (14,15), despite the agreement on being a multifactorial and complex phenomenon.Additionally, there is conflicting evidence about aging as a process that is similar across all organisms or particular to each species (15,16).Therefore, it is important to draw attention to the fact that animal models are usually chosen for convenience rather than for specific features applicable to human aging.Hence, choosing the suitable animal model to answer the specific question we aim to understand is of high importance in these types of studies.Among the most prevalent aging model organisms are Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Mus musculus.As a single-celled organism, S. cerevisiae is easily grown, manipulated, and observed; together with a well-characterized genome that bares much resemblance to bigger and more complex organisms, this model organism among others is a convenient platform for the study of the aging phenotype.Another important model system for studying a range of biological processes, including aging, is the nematode C. elegans.C. elegans has a short adult life span of ~2 weeks and a well-documented anatomy which is visible using a microscope.This enables easy observations of aging-related changes in the whole organism, in specific tissues and organs, and even on molecular and cellular levels (17)(18)(19)(20)(21).The classic genetic model organism, D. melanogaster, is also used FiGURe 1 | Key elements in the DNA damage response (DDR) pathway.In case of double-strand breaks (DSB), the DNA damage sensor MRN complex recruits the protein kinase ATM which activates H2AX at the damaged site.H2AX connects to MDC1, and this complex amplifies the activity of the MRN complex which, in a positive feedback, amplifies the ATM activity and the dispersal of H2AX along the chromosome.MDC1 and 53BP1 further mediates the activation of CHK2 which carries the signal to distant locations on the genome.For single-strand breaks (SSB), the protein kinase ATR is activated and amplified by the 9-1-1 complex and TOPBP1, which also mediates the activation of CHK1.The signaling pathway cascades toward the key factors p53 and CDC25.When the lesion is repaired, the DDR complexes are dismantled (2,4,9). in the study of aging.Studies conducted in these flies have identified single gene mutations that influence their life span.One of the strengths of Drosophila as a model organism is the capability to illustrate how genes that have an established role in regulating organismal life span particularly influence cellular and tissue function, how they work together, and how their tissue-specific functions might be linked (22)(23)(24)(25).That said, Drosophila is far from being a good model for human aging as they share only 60% of the human genome.A better similarity is achieved with M. musculus, the mouse.It is the most commonly used model in biological research for various reasons.Mice are small, have a short generation time, and an accelerated life span which means they are not expensive and require only little space and time, compared to larger animal models.Another important reason is the fact that the mouse genome is well documented and can be easily manipulated.In addition, they are biologically similar to humans, exhibiting many of the same diseases and conditions.Nevertheless, mice do not develop several important age-related diseases naturally (e.g., atherosclerosis and diabetes), a fact that limits their potential as an aging model.All the organisms described earlier are short-lived, which is one of their desired traits as model organisms.However, that may not be appropriate for the study of human aging.Thus, in recent years there have been more studies conducted on non-model long-living organisms such as the naked mole rats and bats, which may be more appropriate models in understanding healthy human aging.The naked mole rat (Heterocephalus glaber) is a very important non-model organism in cancer and aging studies.This subterranean, mouse-sized, eusocial rodent is known as the longest-living rodent, living 4-17 years in the wild and with captive individuals demonstrating exceptional longevity that exceeds 30 years (26)-almost an order of magnitude longer than mice.Moreover, until a few years ago no cancer cases were reported in NMRs, and researchers failed to induce tumorigenesis, placing this rodent as a novel model for cancer studies.Bats are the second most speciose mammalian order after rodents.Little brown bats (Myotis) are the smallest bats (3-30 g) with the highest longevity records (Myotis myotis live for 37.1 years and M. brandti live for 41 years).Nevertheless, longevity is generally high in all bat lineages, which makes them an interesting model in biogerontology.One of the most interesting non-model organisms adopted for aging research is the Bowhead whale (Balaena mysticetus), which is estimated to be the longest-living mammal, reaching the age of ~200 years and also one of the biggest species, with length and weight of 20 m and 100 tons (6,27).Bowhead whales live in arctic environment and are well adapted to these harsh surroundings.They are considered to be resistance to cancer and age-related diseases, and thus, though research is very technically complicated, the study of Bowhead whale in the context of longevity could improve our understanding of molecular mechanisms of healthy aging (27).", + "\t\n\nOur own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice.", + "\t\n\nThe present study offers certain alternatives relative to studies using clinical samples by employing inbred mouse strains.The use of inbred mice achieves several advantages such as isogenicity and genomewide homozygosity among individuals within a strain, which significantly reduces gene expression variability between individuals.This variability was confounding in the aging human study (18).Aging mouse models have been used, for example, to evaluate global gene expression changes in skeletal muscle (35).One of the primary findings with aged skeletal muscle suggested that stress-response genes, including heat shock-response and oxidative stress-inducible genes, were upregulated.A similar study focusing on retinal tissue also found an upregulation in stressresponse genes with age (25).While both of these previous studies used C57BL/6J (B6) mice, there was a common agedependent upregulation of stress-response genes across different tissues.", + "\tJournal of Theoretical Biology 12:1245. Hughes, K. A., and B. Charlesworth. 1994. A genetic analysis of senescence in Drosophila. Nature 367:6466. Hutchinson, E. W., and M. R. Rose. 1990. Quantitative genetic analysis of Drosophila stocks\nwith postponed aging. Pages 6687 in D. E. Harrison, ed. Genetic Effects on Aging II. Caldwell, NJ: Telford. Kannisto, V., J. Lauristen, and J. W. Vaupel. 1994. Reduction in mortality at advanced ages: Several decades of evidence from 27 countries. Population Development Review 20:793810. 580\n\n\n\nA P P L I C AT I O N S\nKennedy, B. K., and L. Guarente. 1996.\tGenetic analysis of aging in Saccharomyces cerevisiae. Trends in Genetics 12:355359. Khazaeli, A. A., S. D. Pletcher, and J. W. Curtsinger. 1998. The fractionation experiment:\nReducing heterogeneity to investigate age-specific mortality in Drosophila. Mechanics of\nAgeing and Development 16:301317. Khazaeli, A. A., W. Van Voorhies, and J. W. Curtsinger. 2005. The relationship between life\nspan and adult body size is highly strain-specific in Drosophila melanogaster. Experimental\nGerontology 40:37785. Kim, S. K. 2007. Common aging pathways in worms, flies, mice and humans. Journal of\nExperimental Biology 210:16071612. Kirkwood, T. B. L. 1977. Evolution of aging. Nature 270:301304.", + "\t\n\n(ii) Uncertainties exist as to the most suitable model systems for molecular biological studies on aging.Although material from humans should be employed where possible, for practical reasons animal model systems like rats and mice are indispensible.There is evidence that, provided their health status and husbandry is optimal, rodents age much in the same way as humans do (Burek 1978).For studying certain fundamental processes, such as the occurrence of various types of DNA rearrangement, lower organisms and cell lines can also be employed.Various aspects of mammalian development and differentiation have been revealed in such model systems, which could facilitate the interpretation of observed phenomena and their relevance to the aging process.However, in these cases results obtained cannot be extrapolated directly to the human situation with respect to physiological consequences.", + "\tTo identify genes and molecular\npathways regulating memory capabilities during aging, here we\nperform a forward systems genetic analysis on an aged cohort of\nstrains from the BXD GRP. 2. Methods\n2.1. Animals\nMale and female mice were group housed (2e5 per cage) and\nmaintained in colony housing (12-hour light/dark cycle) with ad\nlibitum access to food and water.", + "\t\n\nTaking advantage of the long-range contiguity of the N. furzeri reference sequence, we set out to study whether aging-related genes show positional gene enrichment (PGE) in sgrs.To this end, we identified aging-related DEGs in three tissues (brain, liver, and skin) by applying two different approaches: (1) we compared young versus old MZM-0410 (5 weeks versus 39 weeks, corresponding to 10% versus 75% of maximum lifespan), and (2) we compared GRZ versus MZM-0410 at 12 weeks.As aging rates differ between these strains (Terzibasi et al., 2008), the same chronological age in the second approach corresponds to 50% of the maximum lifespan in GRZ and 24% in MZM-0410 (Data S4A-S4G).", + "\tFor example, investigators funded by\nNIH National Institute on Aging have been working assiduously for years using a complex 4-way\nmouse F2 intercross to understand how a multitude of interventions affect longevity\n\nAccepted Article\n\n(www.nia.nih.gov/research/dab/interventions-testing-program-itp). Scientists at three sites have\nsystematically phenotyped ~15,000 animals using a range of diets, pharmaceuticals, and\nsupplements. Fortunately, they had the foresight to save tails, and the genetic component is now\nbeing bolted on at a cost of about $20/animala bargain given the cost of obtaining longevity data.", + "\t\n\nVarious animal models have been critical for uncovering key pathways related to aging.Genetically tractable models such as yeast have been used to investigate both replicative lifespan, measured by the maximum number of mitotic divisions a cell can undergo, and chronological lifespan, measured by the length of time a cell can survive in a post-mitotic state (Kaeberlein et al., 2007).Other studies have taken advantage of the short lifespans of worms and flies (Brandt and Vilcinskas, 2013;Tissenbaum, 2012).Studies in these models have contributed greatly to the field but cannot fully recapitulate the complex nature of human aging, particularly with respect to age-related diseases and the decline of healthspan.Therefore, vertebrate models such as mice have been utilized, taking advantage of genetic proximity to humans and the availability of gene knockout and premature aging models (Quarrie and Riabowol, 2004).Unfortunately, mouse lifespan is too long for efficient laboratory studies of normal aging, creating the need for alternative short-lived vertebrate models such as the African turquoise killifish (Nothobranchius furzeri) (Harel et al., 2015) which lives for 4-6 months and recapitulates many of the age-related pathological changes found in humans.", + "\t\n\nInstead, we recommend the use of organisms that have had their aging slowed or postponed, but that do not merely have life \"stretching\", unlike cooled poikilotherms.That is, we propose that aging studies normally be based on the comparison of normal healthy animals with an experimental group that lives even longer, with increased total biological activity, from reproduction to locomotion to metabolic work.Such organisms exist among Drosophila stocks, including some of the mutants with increased lifespan and the selectively bred populations." + ], + [ + "\t\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.\t\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "\t\n\nStudies revealed from 300 to 750 genes related to longevity that are critically involved in a variety of life activities, such as growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [5,6].Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability, and the endocrine-related pathway [7][8][9].In addition, the candidates for longevity encompass genes related to drug metabolism, the ones involved in protein folding, stabilization, and degradation, as well those related to coagulation and regulation of circulation [10], etc.In most cases, these genes or their polymorphic sites were examined in multiple population replication studies, which discovered certain longevity-associated genes or pathways [4][5][6][7][8][9][10].", + "\t\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18].", + "\t\n\nsmall number of genes or interventions are known to increase life span in different model organisms.A selection of these are shown here.\t\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases.\t\n\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases.", + "\t\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained.", + "\t\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes.", + "\t\nClear evidence exists for heritability of human longevity, and much interest is focused on identifying genes associated with longer lives.To identify such longevity alleles, we performed the largest genomewide linkage scan thus far reported.Linkage analyses included 2118 nonagenarian Caucasian sibling pairs that have been enrolled in fifteen study centers of eleven European countries as part of the Genetics of Healthy Ageing (GEHA) project.In the joint linkage analyses we observed four regions that\t\n\nClear evidence exists for heritability of human longevity, and much interest is focused on identifying genes associated with longer lives.To identify such longevity alleles, we performed the largest genomewide linkage scan thus far reported.Linkage analyses included 2118 nonagenarian Caucasian sibling pairs that have been enrolled in fifteen study centers of eleven European countries as part of the Genetics of Healthy Ageing (GEHA) project.In the joint linkage analyses we observed four regions that", + "\t\nLiving to a late age without suffering any major health problems is a genetically influenced trait.To identify the genes contributing to this important phenotype, a 10 cM genome screen was performed in 95 pairs of male fraternal twins concordant for healthy aging.Individuals meeting these criteria were defined as those attaining the age of 70 free of cardiovascular disease (coronary surgery, diabetes, heart attack, and stroke) and prostate cancer.Six chromosomal regions were identified with logarithm of odds (LOD) scores greater than 1.2 ( p , .01).A region on chromosome 4 at marker D4S1564 produced a LOD score of 1.67; this was the same marker previously linked to extreme longevity segregating as an autosomal dominant trait in centenarian families.Our results provide independent evidence that a locus on the long arm of chromosome 4 is associated with better physical aging and/or longevity.", + "\tIntroduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005).", + "\t\nIn animal models, single-gene mutations in genes involved in insulin/IGF and target of rapamycin signalling pathways extend lifespan to a considerable extent.The genetic, genomic and epigenetic influences on human longevity are expected to be much more complex.Strikingly however, beneficial metabolic and cellular features of long-lived families resemble those in animals for whom the lifespan is extended by applying genetic manipulation and, especially, dietary restriction.Candidate gene studies in humans support the notion that human orthologues from longevity genes identified in lower species do contribute to longevity but that the influence of the genetic variants involved is small.Here we discuss how an integration of novel study designs, labour-intensive biobanking, deep phenotyping and genomic research may provide insights into the mechanisms that drive human longevity and healthy ageing, beyond the associations usually provided by molecular and genetic epidemiology.Although prospective studies of humans from the cradle to the grave have never been performed, it is feasible to extract life histories from different cohorts jointly covering the molecular changes that occur with age from early development all the way up to the age at death.By the integration of research in different study cohorts, and with research in animal models, biological research into human longevity is thus making considerable progress.\t\n\nIn animal models, single-gene mutations in genes involved in insulin/IGF and target of rapamycin signalling pathways extend lifespan to a considerable extent.The genetic, genomic and epigenetic influences on human longevity are expected to be much more complex.Strikingly however, beneficial metabolic and cellular features of long-lived families resemble those in animals for whom the lifespan is extended by applying genetic manipulation and, especially, dietary restriction.Candidate gene studies in humans support the notion that human orthologues from longevity genes identified in lower species do contribute to longevity but that the influence of the genetic variants involved is small.Here we discuss how an integration of novel study designs, labour-intensive biobanking, deep phenotyping and genomic research may provide insights into the mechanisms that drive human longevity and healthy ageing, beyond the associations usually provided by molecular and genetic epidemiology.Although prospective studies of humans from the cradle to the grave have never been performed, it is feasible to extract life histories from different cohorts jointly covering the molecular changes that occur with age from early development all the way up to the age at death.By the integration of research in different study cohorts, and with research in animal models, biological research into human longevity is thus making considerable progress.\tGENETIC STUDIES OF HUMAN LONGEVITY\n\nGenetic and genomic studies into longevity have been performed based on a hypothesis, referred to as a candidate gene approach.Alternatively, explorative genome-wide analyses have been applied in which genetic variation and gene transcription across the complete genome are being studied for associations with longevity and related traits.Genetic studies into human disease and longevity include candidate gene approaches, genome-wide association studies (GWASs) and genome-wide linkage studies.", + "\t\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity.", + "\tIntroduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches.", + "\t\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained.", + "\t\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + ] + ], + "task_id": [ + { + "task_id": "45610E53D08C987ADFE70F7C8144A035" + }, + { + "task_id": "0104C29CA7CB011AB547E22520E72DDF" + }, + { + "task_id": "8D7229969C792686F7C492F793386B06" + }, + { + "task_id": "00E6DDA8461F4DDAE6AC1EBF19B51359" + }, + { + "task_id": "2AF034316EF15DE09CCA38F533D32DB7" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_aging_2.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_aging_2.json new file mode 100644 index 0000000..7eb37df --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_aging_2.json @@ -0,0 +1,91 @@ +{ + "question": [ + "How do telomeres affect the aging process?", + "What role does DNA repair play in aging?", + "Can genetic research lead to treatments that slow down aging?", + "How does mitochondrial DNA influence aging?", + "Are there any known genetic mutations that cause premature aging?" + ], + "answer": [ + "Telomeres play a central role in aging by adjusting the cellular response to stress and DNA damage. As cells divide, telomeres shorten, and when they become critically short, they can trigger cell cycle arrest, genomic instability, and cell death, a process known as senescence. This telomere shortening is considered a major cause of replicative senescence, which is linked to aging. Additionally, telomere dysfunction can lead to mitochondrial dysfunction, contributing to aging. However, the relationship between telomere length and aging is complex and not straightforward.", + "DNA repair plays a significant role in aging. Evidence suggests that the rate of DNA repair declines with age, leading to an accumulation of DNA damage. This damage can cause changes in gene expression, cellular functions, and can lead to age-related diseases. Some studies also suggest that defects in DNA repair pathways can lead to premature aging. Furthermore, the balance between DNA damage and its repair is considered a major determinant of longevity and cell viability. Therefore, DNA repair mechanisms are crucial in maintaining genomic stability and cellular health, influencing the aging process.", + "Yes, genetic research can potentially lead to treatments that slow down aging. Discoveries about the biological determinants of aging, such as gerontogenes, could lead to therapies that slow the aging process. Additionally, understanding how the environment modulates aging-related genes could lead to the creation of anti-aging therapies applicable to humans, potentially through diet, lifestyle, and pharmacological interventions.", + "Mitochondrial DNA (mtDNA) influences aging through the accumulation of mutations and the reduction of mtDNA copy number over time. These changes disrupt mitochondrial energetics, which may contribute to aging and age-associated phenotypes. The accumulation of mtDNA mutations can compromise cellular energy metabolism and increase intracellular oxidative stress, leading to degenerative changes in tissues. Additionally, certain mtDNA polymorphisms have been associated with longevity and age-related diseases.", + "Yes, there are several known genetic mutations that cause premature aging. These include mutations in genes involved in DNA metabolism or regulation, such as those seen in Werner syndrome (WS), Bloom syndrome (BLM), Cockayne syndrome (CS), ataxia-telangiectasia (AT), Hutchinson-Gilford progeria syndrome (HGPS), and restrictive dermopathy (RD). Other examples include mutations in the LMNA gene causing Hutchinson-Gilford progeria syndrome, and mutations in RecQ genes causing Werner syndrome, Bloom syndrome, and Rothmund-Thomson syndrome." + ], + "contexts": [ + [ + "\t\n\nIntegration of oxidative stress and cell senescence (and, by extension, telomere shortening and in vitro senescence in general) is thus very well established in vitro.The only question remaining is whether this relationship (and the underling phenomenon) is also true, and biologically significant, in vivo.Certain correlative data would point in that direction.Short telomeres have been identified as markers in human disease in which oxidative stress is also thought to be involved (for example, [311], reviewed in Ref. [312]).Even more interesting is the finding that telomere length is a statistical predictor of survival in humans over 60 [313] and mortality and morbidity in several age-related diseases [314].It is tempting to speculate that this may be the result of an underlying oxidative stress, though further work will be needed to prove this point.", + "\t\n\nTelomeres play a central role in cell fate and aging by adjusting the cellular response to stress and growth stimulation on the basis of previous cell divisions and DNA damage.At least a few hundred nucleotides of telomere repeats must \"cap\" each chromosome end to avoid activation of DNA repair pathways.Repair of critically short or \"uncapped\" telomeres by telomerase or recombination is limited in most somatic cells and apoptosis or cellular senescence is triggered when too many \"uncapped\" telomeres accumulate.The chance of the latter increases as the average telomere length decreases.The average telomere length is set and maintained in cells of the germline which typically express high levels of telomerase.In somatic cells, telomere length is very heterogeneous but typically declines with age, posing a barrier to tumor growth but also contributing to loss of cells with age.Loss of (stem) cells via telomere attrition provides strong selection for abnormal and malignant cells, a process facilitated by the genome instability and aneuploidy triggered by dysfunctional telomeres.The crucial role of telomeres in cell turnover and aging is highlighted by patients with 50% of normal telomerase levels resulting from a mutation in one of the telomerase genes.Short telomeres in such patients are implicated in a variety of disorders including dyskeratosis congenita, aplastic anemia, pulmonary fibrosis, and cancer.Here the role of telomeres and telomerase in human aging and agingassociated diseases is reviewed.\t\nTelomeres play a central role in cell fate and aging by adjusting the cellular response to stress and growth stimulation on the basis of previous cell divisions and DNA damage.At least a few hundred nucleotides of telomere repeats must \"cap\" each chromosome end to avoid activation of DNA repair pathways.Repair of critically short or \"uncapped\" telomeres by telomerase or recombination is limited in most somatic cells and apoptosis or cellular senescence is triggered when too many \"uncapped\" telomeres accumulate.The chance of the latter increases as the average telomere length decreases.The average telomere length is set and maintained in cells of the germline which typically express high levels of telomerase.In somatic cells, telomere length is very heterogeneous but typically declines with age, posing a barrier to tumor growth but also contributing to loss of cells with age.Loss of (stem) cells via telomere attrition provides strong selection for abnormal and malignant cells, a process facilitated by the genome instability and aneuploidy triggered by dysfunctional telomeres.The crucial role of telomeres in cell turnover and aging is highlighted by patients with 50% of normal telomerase levels resulting from a mutation in one of the telomerase genes.Short telomeres in such patients are implicated in a variety of disorders including dyskeratosis congenita, aplastic anemia, pulmonary fibrosis, and cancer.Here the role of telomeres and telomerase in human aging and agingassociated diseases is reviewed.In the future attention undoubtedly will be centered on the genome, and with greater appreciation of its significance as a highly sensitive organ of the cell, monitoring genomic activities and correcting common errors, sensing the unusual and unexpected events, and responding to them, often by restructuring the genome.\t\n\nHigher \"background\" levels of activated p53 could decrease the threshold for activation of senescence or apoptosis in \"old\" cells, in line with the increased sensitivity to stress and more fragile nature of cells and tissues from the elderly.The role of telomeres in cellular aging relative to other proposed molecular mechanisms of aging including oxidative stress resulting from mitochondrial dysfunction or loss of ribosomal function remains to be precisely FIG. 4. Diagram of factors affecting the telomere length in primary somatic cells from human tissues.According to the model shown, telomeres in \"young\" somatic cells have long tracts of telomere repeats that favor folding into a \"closed\" structure that is invisible to the DNA damage response pathways and telomerase.As the telomere length at individual chromosome ends decreases, the likelihood that telomeres remain \"closed\" also decreases (see also Fig. 3).At one point telomeres become too short and indistinguishable from broken ends.Such ends will be processed by enzymes in the DNA repair compartment (proposed to occupy a different nuclear domain than long telomeres).Depending on the cell type and the genes that are expressed in the cell, a limited number of short ends can be elongated by limiting levels of telomerase or recombination.However, with continued cell division and telomere loss, eventually too many short ends accumulate for the limited capacity of these \"telomere salvage pathways. \"At this point, defective telomeres will trigger levels of DNA damage signals such as p53 to which cells respond by either apoptosis or senescence.Rare (mutant) cells that do not upregulate functional DNA damage responses (e.g., by loss of functional p53) continue cell divisions in the presence of dysfunctional telomeres causing genome instability via chromosome fusions, chromosome breaks, and repetitive break-fusion bridge cycles.delineated.The development of an integrated view of the various molecular mechanisms of aging that have been proposed remains as formidable a challenge.However, it has become clear that telomeres are directly responsible for sustained DNA damage signals in senescent cells (54,203), and DNA damage foci originating from telomeres in senescent cells can readily be detected in vivo (104).\tIII. LOSS OF TELOMERIC DNA WITH AGE: OVERVIEW\n\nLoss of telomeric DNA at the cellular level is well established and was shown to be related to replicative history and life span in somatic cells (see sect.II and Figs. 2 and 4).However, at the level of tissues or of the entire organism, what is the impact of telomere shortening?Does aging cause telomere shortening, or does telomere shortening cause aging (98)?The issue of organismal aging as a consequence of short telomeres was raised as a concern when Dolly, \"cloned\" by transfer of an adult mammary gland nucleus into an enucleated egg, was shown to have short telomeres (189).In contrast, nuclear transfer experiments using nuclei from senescent bovine fibroblasts yielded offspring with longer than expected telomeres and a \"youthful\" phenotype (117).Differences in donor nucleus cell type, nuclear transfer methodology, or species could explain these discrepant results (1,103,112).However, the \"immortal\" growth properties of embryonic stem cell lines derived from preimplantation embryos of many species suggest that telomere length can be maintained or telomere loss attenuated in early development.The loss of telomere repeats in human cells with age varies greatly between cells and tissues, and the amount of information for different tissues is often very limited.It has been proposed that the number of cell divisions in stem cells is 100 divisions over a human lifetime and that this efficiency is achieved by a strict hierarchy at the level of stem cells with the most primitive cells dividing the least and having the longest telomeres (115).A diagram representation of this model is shown in Figure 7.\t\n\nThe correlation between telomere length and replicative potential became a mechanistic link when it was demonstrated that the replicative potential of primary human fibroblasts can be extended indefinitely by artificially elongating telomeres.The latter was achieved in primary human fibroblasts by overexpression of the telomerase reverse transcriptase (hTERT) gene (25,211).These experiments established that progressive telomere loss is indeed the major cause of replicative senescence as had been proposed earlier (3,84).\tA. Telomeres From Cytogenetics to Replicative\n\nSenescence: Historic Background That chromosome ends play an important role in ensuring chromosome stability was first proposed in the 1930s by Barbara McClintock working with maize (142) and Hermann Muller working with fruitflies (155).Both investigators proposed that chromosome ends have special structures required for chromosome stability.Muller coined the term telomere, from the Greek for \"end\" (telos) and \"part\" (meros).McClintock noted that without these special end structures, chromosomes would fuse and often break upon mitosis, and she observed that the resulting chromosome instability was detrimental to cells.These pioneering studies established that functional \"telomeres\" are required to protect chromosome ends, to provide chromosome stability, and to ensure faithful segregation of genetic material into daughter cells upon cell division.These conclusions have stood the test of time, and since this work was published, an enormous amount of data on telomeres and their function have been produced.Some of the most striking contributions are reviewed here.However, despite this progress, it is also clear that many mysteries around telomeres and their function remain.The increasing amount of detail about individual molecules and pathways involved in telomere biology and DNA damage responses has not at all diminished the challenge of understanding how telomeres are integrated and involved in DNA damage responses, cellular fitness, and human aging.While it has become clear that telomeres play a central role in the cellular response to stress and DNA damage, neither the relative importance to other factors nor all the connections between proteins and signaling pathways that directly or indirectly involve telomeres are fully understood.The future of telomere research is bright!In the early 1960s, Leonard Hayflick observed that human cells placed in tissue culture stop dividing after a limited number of cell divisions by a process now known as replicative senescence (90,92;reviewed in Ref. 89).He proposed that the cell culture phenomenon could be used as a model to study human aging at a molecular and cellular level.However, the role of replicative senescence in human aging and the relevance of the in vitro studies remained subject to much debate.Cells presumably divide either to balance normal cell loss or in response to injury.Many cells in the human body can divide many more times than needed during a normal lifetime.A mitotic \"reserve capacity\" was used as an argument against the idea that replicative senescence has any relevance to human aging.However, one would not expect all (stem) cells in the body to have a similar replicative history (or potential), and cells that no longer exist (or can no longer divide) are easily overlooked.It has furthermore been difficult to estimate the actual turnover of the stem cells in tissues such as the intestine and hematopoietic stem cells over a normal lifetime with any degree of accuracy.Estimates range from more than 1,000 times for intestinal epithelial cells in rodents (170) to less than 100 times for hematopoietic stem cells in humans (115).Recent studies of the levels of 14 C remaining in tissues from nuclear weapons test during the Cold War have shown that the turnover of blood cells far exceeds that of the cells in the gut (197), and these data seem incompatible with thousands of cell divisions.Uncertainties about actual turnover and the fact that model organisms such as worms and flies clearly \"age\" without cell renewal being a major factor have been used to question the role of cell turnover and replicative senescence in human aging.However, as will be discussed, the tight association of telomeres to overall cellular fitness does not exclude a role for telomeres even in the aging of tissues that contain mostly long-lived postmitotic cells such as the brain, heart, or kidney.For example, it is possible that damage to telomeric DNA by reactive oxygen species (ROS) produced by either dysfunctional mitochondria (85,220) or by signaling pathways (e.g., overexpression of oncogenes such as Ras, Refs.152,239) contributes or predisposes cells to apoptosis and senescence.Thus DNA damage signals originating from telomeres could be replication independent, and the sensitivity of cells to DNA damage could increase as the overall telomere length declines.More information is needed on the role of telomeres in the cellular response to various types of insults (177).", + "\tImpact on aging\n\nThere is no straightforward relationship between telomere length or stringency of control of telomerase expression and organismal life span (Campisi, 2001).On the other hand, two human syndromes with features of premature aging -Werner syndrome (WS) and dyskeratosis congenita (DKC) -have been linked directly (DKC) or indirectly (WRN) to telomere length and presumably telomere structure (Chang et al., 2004;Mitchell, Wood, & Collins, 1999).Thus, functional telomeres may directly increase longevity by maintaining genomic stability and suppressing cancer while also indirectly postponing aging phenotypes by preventing apoptosis and/or senescence (Blasco, 2003;Campisi, 2003aCampisi, , 2003b)).Whatever the case, the cellular responses to telomere dysfunction -apoptosis and senescence -have been proposed to contribute to aging phenotypes (Campisi, 2003a).", + "\t\n\nRegarding cancer and aging, Serrano and Blasco (2007) suggested that an equilibrium between mechanisms diminishing cellular damage and mechanisms preventing excessive cellular proliferation is required between both processes [43].The authors argue that the p53 pathway may be seen as an anti-aging mechanism as it is a key defense mechanism against cellular damage protecting from both aging and cancer.One effect of aging at the cellular level is reduced telomerase activity and progressive shorter telomeres in somatic cells [45].Shortened telomeres are highly recombinogenic, leading to a genome-susceptible cancer development [46,47].Genomic instability driven by dysfunctional telomeres is also associated with the transition from benign to malignant tumors [48].Conversely, telomere dysfunction also acts to induce the p53 gene to suppress tumor development by initiating cell-cycle arrest, cellular senescence or, apoptosis.Our analysis has identified several genes involved in the regulation and activity of the p53 pathway as being affected by age.In skin, the telomerase reverse transcriptase (TERT) showed an age-related expression in association with a genetic variant (rs10866530).In addition p21, a gene directly regulated by p53 and also involved in telomere-driven aging, was shown to be differentially expressed with age [49].In brain, theZBTB16, CA9,and HEY2, genes associated to the p53 pathway directly or via SIRT1, all showed age-related expression.The activity of p53 has been shown to enhance the transcription of inhibitors of the insulin receptor pathway, preventing cell growth and division after stress signaling [50,51] and many genes from the insulin signaling pathway have been extensively associated with longevity in multiple studies and organisms.Our results suggest that the link between aging and cancer is evident in multiple tissues through differential expression of genes with age.", + "\tevidence From In Vitro Studies\n\nIn most organisms, telomere elongation is controlled by the enzyme telomerase under tight regulation to ensure sufficient number of replications, yet when this number is reached, telomere elongation is seized (2,83).Once telomeres reach the critical length, the cells undergo senescence and stop proliferating (84).This process is believed to be the trigger for the aging process, according to the telomere theory (11,85,86).It is further supported by Bodnar et al. who proved that telomere elongation caused by ectopic expression of telomerase avoids the senescence phenotype (87).His work relied on one of the earliest studies linking telomere shortening to aging which was performed by Harley et al. on human fibroblast cells (88).In their paper, they describe the shortening of telomeres in aging fibroblasts alongside chromosomal abnormalities, specifically the fusion of two chromosomes at the telomeric region and chromosomal rearrangement, while hinting at a biological significance to the shortening process.Since this early study, numerous studies have emerged strengthening this association and aiming to elucidate the exact underlying mechanism of telomere shortening.Murillo-Ortiz et al. ( 89) studied telomere alterations using T, B, and NK cells from 20 to 25-year-old and 60 to 65-year-old donors.Treatment with concanavalin A (a mitogen of T cells) caused increase in telomere length and number of replications in the samples from the young donors, but did not improve the samples from the older donors, which exhibited loss of telomere parts, decrease in telomere length, and decreased proliferation potential (89).Age-related changes in telomere length were also established in bone marrow hMSC in a long-term in vitro study (90).COMET assay revealed higher levels of damage in cells from older donors (91).Similar results were obtained in the study of CD34 and CD34 + cells isolated from healthy donors of different ages.However, some of the cells exhibited telomere shortening that was not correlated with age.It seems that CD34 + cells from older donor suffer from increased non-telomeric DNA damage, but the variation among the cultures hints for multiple factors contributing to DNA damage (92).\t\n\nThe Question of Telomere-Related Senescence in S. cerevisiae For S. cerevisiae, various studies were performed on the effect of missing/broken telomere and mutated telomerase on the physiology of the organism.Genetic manipulations of S. cerevisiae cells caused decreased growth, irregular shape, and eventually, cellular senescence (69).Several genes, such as EST1 (telomere elongation protein), EST2 (telomere reverse transcriptase), EST3 (telomere replication protein), TLC1 (template RNA component), RAD9, RAP1 (DNA binding protein), CDC13 (cell division control protein 13), TEL1 (serine/threonine protein kinase), MEC1 (serine/ threonine protein kinase), and MRC1 (macrophage mannose receptor 1 precursor) were studied in connection to telomererelated senescence; however, despite the extensive experimental work put into using mutated cells, the role of eroded telomeres in \"natural\" cellular senescence in yeast remained questionable (93).For example, EST1-4 (ever short telomere) mutants began to lose viability after 60 doublings, but late knockout cultures continued to maintain proliferation potential (94).Cells with mutated telomerase exhibited irregular morphology and short telomeres, but these changes did not cause deadly damage and determinate senescence (95).One hypothesis connects aging to telomere erosion through the transcription of subtelomeric genes.Genes located in subtelomeric regions are affected by transcriptional silencing which was found to change in an age-related manner.Kim et al. (96) found that silencing of genes in subtelomeric regions declined during the cell's senescence, hinting at a connection between the transcription of subtelomeric regions and cellular senescence in yeast (96).The work of Austriaco and Guarente (97) reinforced this model, as they found that mutated telomerase extended life span (relatively to the wild type), probably by hanging the silencing procedure in the subtelomeric locations (97).\tCONCLUSiON\n\nHealthy aging and cellular senescence are complex processes of great interest to researchers.The multigenic nature of both of them complicates studies and necessitates creative and novel approaches in the path for understanding those phenomena.The three spear-headed strategies implemented for this purpose have brought forth much information and knowledge, yet there is still much to learn in these fields.The doubting and contradicting results in in vivo studies are influenced both by physiological and genetic differences between the model organisms and humans and the differences in the possible research methodologies between in vitro and in vivo studies.In many cases, the age-related phenotypes searched for and studied in vitro are not visible in vivo or not relevant for the model organism (Table 1. ).Molecular processes such as DNA damage repair, telomere shortening, and epigenetic alterations discussed earlier are the driving forces of the aging process in human, but their significance is varied in other organisms.Many evidence for age-related accumulation of DNA damage were found in in vitro studies, both in human and mice cell cultures.The connection between DNA damage and aging is emphasized by the secretion of senescenceassociated proteins during cellular senescence, a phenotype which is activated by DNA damage and is common for both human and mice.Human progeroid diseases also show the connection between early aging and faulty DNA repair.In yeast, flies and mice, however, although some evidence for age-related damage and faulty DNA repair mechanisms were found, contradicting and debating results highlight the complexity of the use of these model organisms in this aging research.The study of telomeres in relation to aging demonstrates the questions derived from both physiological differences between organisms and differences in research approaches.The connection between telomere attrition and aging is very present in human aging (both in in vitro studies and as telomeropathies such as DKC, Werner syndrome, and Hutchinson-Gilford progeria) but not relevant in model organisms.In C. elegans, the evidence are contradicting.In drosophila, maybe because of the unique telomere structure, there are no evidence connecting telomere attrition to aging.In yeast and mice, genetic manipulations enabled the study of telomere-aging relations, but such relations were not seen in wild-type subjects.The study of telomere-related aging in mice especially feature the difficulties of comparing human and model organisms, since the telomeres of most laboratory mice are 5-10 times longer than in humans, but their life span is much shorter.", + "\t\n\nAnother attractive model of ageing is formulated by the ''telomere shortening theory'' [11].The activity of the telomerase enzyme complex responsible for maintaining the structure of the chromosome ends (telomeres) at each round of cell division likewise affects lifespan in a number of model organisms [11,12].Still, the ageing process of postmitotic cells (like neurons) contradicts the theory.Furthermore, the somatic cells of adult C. elegans do not divide, meaning that the shortening of telomeric regions is not an issue even in the case of a complete absence of telomerase activity [13].Regardless, the adult nematode ages and dies in about 2 weeks.Thus, the effect of telomere length on ageing appears to be rather complex.", + "\t\n\nIn aging research there has been a great deal of interest in the idea that telomere shortening is a critical feature that leads to senescence.By contrast, the mitochondrial theory of aging posits that mitochondrial dysfunction is the cause of aging [56].Telomere processing and mitochondrial bioenergetics have so far been separate fields, with very limited interaction.The emerging evidence for some crosstalk between these fields of study is very exciting.Recently it has been shown that telomere dysfunction can lead to mitochondrial dysfunction [46] and vice versa [57].It is therefore of great interest that specific proteins, such as RECQL4, have now been identified that operate in both compartments.", + "\t\n\nTelomere shortening is considered as the major cause of replicative senescence [82,83].It has been reported that the rate of telomere shortening is directly related to the cellular level of oxidative stress [84].Telomere shortening is significantly increased under mild oxidative stress as compared to that observed under normal conditions, whereas overexpression of the extracellular SOD in human fibroblasts decreases the peroxide content and the rate of telomere shortening [79].ROS can affect telomere maintenance at multiple levels.The presence of 8-oxoguanine (8-oxoG), an oxidative derivative of guanine, in telomeric repeat-containing DNA oligonucleotides has been shown to impair the formation of intramolecular G quadruplexes and reduces the affinity of telomeric DNA for telomerase, thereby interfering with telomerase-mediated extension of single-stranded telomeric DNA [85].ROS also affect telomeres indirectly through their interaction with the catalytic subunit of telomerase, telomerase reverse transcriptase (TERT).Increased intracellular ROS lead to loss of TERT activity, whereas ROS scavengers such as N-acetylcysteine (NAC) block ROSmediated reduction of TERT activity and delay the onset of cellular senescence [86].Furthermore, the presence of 8-oxoG in the telomeric sequence reduces the binding affinity of TRF1 and TRF2 to telomeres [87].TRF1 and TRF2 are components of the telomere-capping shelterin complex that protects the integrity of telomeres [88].In addition, ROS-induced DNA damage elicits a DNA damage response, leading to the activation of p53 [89], a critical regulator of senescence.It has been shown that p53 transactivates E3 ubiquitin ligase Siah1, which in turn mediates ubiquitination and degradation of TRF2.Consequently, knockdown of Siah1 expression stabilizes TRF2 and delays the onset of replicative senescence [90].The p53-Siah1-TRF2 regulatory axis places p53 both downstream and upstream of DNA damage signaling initiated by telomere dysfunction.By regulating telomere maintenance or integrity directly or indirectly, ROS plays a critical role in senescence.", + "\tThe cell-autonomous theory on the\nother hand posits that individual cells are the targets of the aging process, via a timedependent increase in homeostatic dysfunction. The potential mechanisms include\nincreases in the production of reactive oxygen species, telomere shortening and, not\nsurprisingly, genomic instability. An implication of this theory is that long-lived cells in\nthe organism, such as neurons, muscle, and importantly stem cells, would be the\npredominant substrates of aging, while those cells that undergo rapid and continuous\nturnover would be removed before they could exert an effect on tissue function.", + "\tTelomere Theory of Aging: Mitotic Clocks and Cancer\n\nTelomere stability has been implicated in the control of replicative senescence in human cells (Harley, 1995).The average telomere length of human germ cells is longer than that of differentiated somatic cells.As somatic cells age in vivo or in vitro, telomere arrays shorten in a progressive manner (Harley et al., 1990); telomere shortening in humans correlates with the developmental regulation of telomerase activity.Somatic cells have low or undetectable telomerase activity (Counter et al., 1992), and thus upon successive replication cycles, telomere sequences shorten as a result of incomplete replication of the 5 end of the daughter strand (Harley, 1995;Forsyth et al., 2002).Telomere shortening is proposed as the predominant \"mitotic clock\" that measures and controls the replicative life span of somatic cells.The telomere clock theory of aging states that erosion of the chromosome end triggers significant genome instability inducing cell senescence (Olovnikov, 1973;Hayflick, 1997).Numerous studies provide support for the telomere clock theory of cell aging (Harley et al., 1990;Harley, 1991;Harley, 1995;Forsyth et al., 2002).Telomere shortening is correlated with increased frequency of chromosome rearrangements (Counter et al., 1992) and p53-induced apoptosis (Karlseder et al., 1999).Of significant interest was the finding that telomerase activity resumes in the majority of immortalized cell lines and human tumors (Shay and Bacchetti, 1997) and that telomere array length stabilizes, and in some cases lengthens, in cancerous cells (Counter et al., 1992;Kim et al., 1994).Thus, telomere stabilization and abrogation of the normal telomere clock via abnormal telomerase activity (or an alternate pathway, see below) in cancerous cells may contribute to the immortalization capacity of metastatic cells (Harley et al., 1994; for a recent review, see Shay et al., 2001).Interestingly, transfection of TERT into human epithelial or fibroblast cells (Bodnar et al., 1998) has produced cell lines that are immortalized without being transformed.", + "\tTelomeres and Reproductive Aging\n\n7][8] Telomeres are repetitive sequences and associated proteins, which cap and protect chromosome ends. 94][15] When telomeres become critically short, the uncapped, blunt chromosome end triggers cell cycle arrest, genomic instability, and cell death, a cellular process called senescence. 8,16elomere attrition plays a central role in oocyte aging. 5,17,18elomere length in most mouse strains is 5 to 10 times longer than that of humans, and intriguingly, most mouse strains do not exhibit appreciable oocyte aging.Rather, age-related changes in the uterus and/or hypothalamus precede oocyte aging. 19,20However, pharmacologic or genetic shortening of telomeres phenocopies the reproductive aging observed in women.As telomeres shorten in telomerase-null mice, their oocytes develop abnormal meiotic spindles, 21 arrested and fragmented embryos, 22 decreased chiasmata and synapsis, 23 and infertility. 24Observational studies in women have associated leukocyte telomere DNA attrition with earlier menopause, 25 recurrent miscarriage, 26 and Down syndrome. 27,28ocyte telomere length has been associated with failed in vitro fertilization (IVF) cycles, 29 embryo fragmentation, 22 and aneuploidy 30 in fertility treatment cycles.\t\n\nImplantation rate decreases and miscarriage rate increases with advancing maternal age.The oocyte must be the locus of reproductive aging because donation of oocytes from younger to older women abrogates the effects of aging on fecundity.Nuclear transfer experiments in a mouse model of reproductive aging show that the reproductive aging phenotype segregates with the nucleus rather than the cytoplasm.A number of factors within the nucleus have been hypothesized to mediate reproductive aging, including disruption of cohesions, reduced chiasma, aneuploidy, disrupted meiotic spindles, and DNA damage caused by chronic exposure to reactive oxygen species.We have proposed telomere attrition as a parsimonious way to explain these diverse effects of aging on oocyte function.Telomeres are repetitive sequences of DNA and associated proteins, which form a loop (t loop) at chromosome ends.Telomeres prevent the blunt end of DNA from triggering a DNA damage response.Previously, we showed that experimental telomere shortening phenocopies reproductive aging in mice.Telomere shortening causes reduced synapsis and chiasma, chromosome fusions, embryo arrest and fragmentation, and abnormal meiotic spindles.Telomere length of polar bodies predicts the fragmentation of human embryos.Telomerase, the reverse transcriptase capable of reconstituting shortened telomeres, is only minimally active in oocytes and preimplantation embryos.Intriguingly, during the first cell cycles following activation, telomeres robustly elongate via a DNA double-strand break mechanism called alternative lengthening of telomeres (ALTs).Alternative lengthening of telomere takes place even in telomerase-null mice.This mechanism of telomere elongation previously had been found only in cancer cells lacking telomerase activity.We propose that ALT elongates telomeres across generations but does so at the cost of extensive genomic instability in preimplantation embryos.", + "\t\n\nWe examined the ant genomes and transcriptomes for signatures related to aging.Telomere shortening is a hallmark of cellular senescence in multicellular eukaryotes, and the enzyme telomerase (TERT), which counteracts telomere shortening, prolongs life span upon overexpression (8).TERT RNA levels were highest in eggs and lower in adults in both C. floridanus and H. saltator, but they were up-regulated in H. saltator gamergates (Fig. 3A).This may be explained by the gamergates acquiring many physiological characteristics of queens, including longer life span (9).Aging has also been linked to the sirtuin lysine deacetylases enzymes SIRT1 and SIRT6, homologous to the Saccharomyces cerevisiae Sir2p implicated in replicative senescence (10).In H. saltator gamergates, both of these genes are expressed at higher levels compared to workers (Fig. 3B).These results suggest that the regulation of life span in gamergates may share common mechanisms with other organisms." + ], + [ + "\t\nThe biology of aging is an area of intense research, and many questions remain about how and why cell and organismal functions decline over time.In mammalian cells, genomic instability and mitochondrial dysfunction are thought to be among the primary drivers of cellular aging.This review focuses on the interrelationship between genomic instability and mitochondrial dysfunction in mammalian cells and its relevance to age-related functional decline at the molecular and cellular level.The importance of oxidative stress and key DNA damage response (DDR) pathways in cellular aging is discussed, with a special focus on poly (ADP-ribose) polymerase 1, whose persistent activation depletes cellular energy reserves, leading to mitochondrial dysfunction, loss of energy homeostasis, and altered cellular metabolism.Elucidation of the relationship between genomic instability, mitochondrial dysfunction, and the signaling pathways that connect these pathways/processes are key to the future of research on human aging.An important component of mitochondrial health preservation is mitophagy, and this and other areas that are particularly ripe for future investigation will be discussed\nAccepted ArticleThis article is protected by copyright.All rights reserved.defects in DNA repair, and improved understanding of the signaling pathways that connect these processes are important for future research on human aging. DNA damage response pathwaysAll cells are continuously exposed to endogenous agents that cause DNA damage, including reactive oxygen species (ROS), reactive nitrogen species (RNS) and environmental sources of DNA damaging agents, such as radiation, chemical mutagens and carcinogens.It is estimated that approximately 10 5 DNA lesions accumulate in the human genome per cell per day [4].Figure 1 summarizes the classes of DNA damage and the primary cellular mechanism responsible for repairing each class of DNA damage [5].In mammalian cells, nucleotide excision repair (NER) is the primary pathway for repair of bulky DNA lesions, including those generated by ultraviolet light, environmental and chemical mutagens [6].Base excision repair (BER) removes damaged bases caused by oxidation, alkylation, deamination, and spontaneous hydrolysis of the glycosidic bond [7].Single-strand DNA breaks (SSBs) and double-strand DNA breaks (DSBs) are among the most genotoxic DNA lesions.DSBs can lead to chromosomal rearrangements and genomic instability that can trigger cell death and/or senescence [8].Mammalian cells express four distinct DSB repair (DSBR) pathways: homologous recombination (HR), non-homologous end joining (NHEJ), alternative end joining (Alt-EJ) and single strand annealing (SSA).Since NHEJ ligates free ends it is a mutagenic process whereas HR is thought to be error free.Notably, NHEJ is less mutagenic than Alt-EJ, and SSA pathways, which are highly error-prone and promote chromosomal rearrangements and genomic instability [9,10].The mechanisms and factors that determine which pathway repairs a specific DSB in a specific cell include cell cycle phase, efficiency of DNA end-resection, and status of RecQ helicase expression, and post-translational modification [9][10][11].When a cell's capacity to repair DNA lesions is compromised or exceeded, persistent DNA lesions can accumulate and block DNA replication forks and inhibit cell cycle progression in proliferating cells.Replication fork blockage can, in some cases, be overcome by activating secondary origins of replication or by enabling lesion bypass by an error-prone translesion DNA polymerases [12].Cells that harbor a defect in one or more DNA repair pathways, accumulate persistent DNA damage and typically exhibit an elevated mutation rate [2,13].Many theories have been advanced to explain why and how organisms age, and one of the prevalent ones proposes that time-dependent accumulation of DNA damage and genetic mutations plays a major causal role in aging.Consistent with this hypothesis, several heritable human disorders characterized by accelerated aging are caused by mutant alleles in DNA repair genes which impairs DNA repair capacity [14].Thus, human premature aging disorders are strongly associated with defects in DSBR,", + "\t\n\nThe lacI/lacZ reporter gene mouse models have taught us that different tissues exhibit different mutation rates with age.Specific DNA repair pathways have been shown to decline with age, depending on the tissues.Except for the BER pathway, few studies have shown decline of other DNA repair pathways or repair enzymes in the mouse aging liver.As several DNA repair enzymes are posttranslationally modified upon DNA damage (thus altering their activities), appropriate experiments are warranted to follow such posttranslational changes at the protein levels in the liver of aging mice.Noteworthy, the genetic background of the mice under study and the husbandry conditions (including diet) will also impact on the phenotypes.Thus, depending on the stress imposed on mice, the severity of the phenotype will vary.Nevertheless, the control of ROS levels, structural changes at the telomere, DNA damage and mutation rate, mitochondrial dysfunction will ultimately impact on health, and such processes underline the complexity of aging.\t\n\nIt remains unclear why only certain DNA repair mutants show phenotypes related to premature aging.It is interesting to note that the DNA repair-deficient mouse models that exhibit reduced health and/or life span in addition to early appearance of age-related phenotypes also display major changes in the expression of liver genes involved in stress response, cell proliferation and apoptosis, glucose and/or lipid metabolism, and inflammatory response.This suggests that NEIL1 (associated with BER), CSB, ERCC1, XPA, XPD (associated with NER), DNA-PKcs/Ku complex (associated with NHEJ), and WRN (associated with NHEJ, HR, or BER) are also implicated (directly or indirectly) with the transcription of a subset of genes (or pathways) important for the aging phenotypes at least in the liver.Such data imply the possibility of targeting specific biochemical pathways (in addition to ROS levels, telomere structural changes, mitochondrial dysfunction) to control or slow down the progression of age-related diseases.The impact of calorie restriction, dietary restriction mimetics, or antioxidants is already under scrutiny in different mouse models of aging [129,130,137,138].", + "\tDiscussion\n\nAlthough great attention has been paid to the potential relationship between aging and DNA DSB repair, the major descriptive and mechanistic studies were performed in rodent models. 3,4,6,11,17,23Relevant research in humans was mainly focused on age-related change in the recruitment kinetics of essential DNA damage response factors, assayed by immune-staining; 26 age-related change of genomic instability, measured by comet assay; 7 age-related change of expression profile of important DNA repair factors, analyzed by RNA array and proteomic tools. 27,28Although the previous work greatly advanced our understanding of age-associated changes of DNA DSB repair, due to a lack of proper tools for the analysis of NHEJ and HR efficiency and fidelity separately, and the hardship of acquiring a sufficient number of human samples, whether NHEJ efficiency and fidelity, and HR efficiency change with age in humans and the consequences of any such change, and its underlying molecular mechanism are not well understood.Here, we established 50 eyelids fibroblast cell lines derived from donors who are evenly distributed by age.With these cell lines, using our well-characterized reporters for the analysis of NHEJ and HR capacities, for the first time, we conclusively demonstrate that both DNA repair pathways decline with age.The impaired recruitment of Rad51 to DNA damage sites during aging hampers the ability of aged cells to choose the precise HR pathway, forcing cells to utilize the error-prone NHEJ pathway.Simultaneously, because of decreased expression of XRCC4, DNA Lig4 and DNA Lig3 during aging, NHEJ becomes more inefficient and inaccurate with age, leaving more damage sites repaired with a loss of more genetic information.The declined DNA DSB repair by both pathways then leads to accumulation of DNA mutations, posing more damages to both NHEJ and HR repair machineries, eventually exacerbating the age-related rise of genomic instability (Figure 8).Our previous reports indicate that the efficiency of DNA DSB repair by NHEJ and HR declines, and NHEJ becomes more error-prone with replicative cellular senescence. 21,29In presenescent cells, HR efficiency declines by 38-fold, whereas NHEJ changes by only ~two to threefold.Consistent with the above results, our current aging study also shows a sharp decline of HR efficiency during aging, with the biggest difference of an ~30-fold change, whereas the change of NHEJ with age is relatively mild, albeit statistically significant.However, contradictorily, knocking out major NHEJ factors, such as DNA-PKcs, Ku70, Ku80 or Artemis in mice leads to a phenotype of progeria, 4 whereas knocking out HR factors usually leads to a phenotype of embryonic lethality, 4,30,31 suggesting that NHEJ is more likely to be involved in aging.Considering an organism's life history is likely critical for reconciling these observations.During embryogenesis cells are rapidly dividing and therefore undergoing replication stress; complete loss of HR, which is a dominant pathway for relieving replication stress, 32 may cause cells to enter apoptosis by activating P53, leading to embryonic lethality.However, the embryonic lethality could mask the roles of HR in aging.Indeed, partial loss of HR might also lead to agingassociated phenotypes.For instance, BRCA1 heterozygous mice are short lived and have a premature aging phenotype in the ovaries. 33,34Intriguingly, once an organism has developed into adulthood, a gradual suppression of the HR pathway with age is needed to counteract the potential tumorigenesis as uncontrolled or overactive single-strand annealing (SSA), which shares almost identical repair machinery with the HR pathway, 35 may cause loss of large genomic fragments due to the prevalence of repetitive sequences in human genomes.", + "\tPARP1 in DNA Repair. As discussed above, a substantial body of evidence demonstrates a causative role of DNA repair and genome maintenance mechanisms in mammalian longevity.", + "\t\n\nA similar duality is emerging in mammals, where defective DNA repair is often associated with premature aging (Lombard et al., 2005), yet the lack of a DNA damage response can be beneficial in situations of chronic DNA damage due to telomere dysfunction (Choudhury et al., 2007;Schaetzlein et al., 2007).Furthermore, exposure to genotoxic stress early in life seems to accelerate changes in gene expression that have been associated with age-related diseases such as amyloidogenesis (Wu et al., 2008).Interestingly, we found that constitutive overexpression of a set of age-deregulated SIRT1 target genes promotes apoptosis in primary neurons (Figure S11); however more work is needed to determine the physiological relevance of this observation.\t\n\nThere is some evidence that related processes occur in mammals.First, cells damaged by oxidative stress in vitro undergo stochastic transcriptional changes that parallel those in aged heart tissue (Bahar et al., 2006).Second, a deficiency in the DNA repair factor ERCC1 accelerates aging phenotypes and generates gene expression profiles reminiscent of aged animals (Niedernhofer et al., 2006).Third, cells that senesce because of replicative aging in vitro or in aged tissues in vivo exhibit alterations in heterochromatin (Herbig et al., 2006;Narita et al., 2006) and secrete growth factors that can drive tumorigenesis (Campisi, 2005).Finally, oxidative DNA damage at promoters correlates with gene repression in the aging human brain (Lu et al., 2004) and has been linked to both transcriptional and epigenetic changes that may contribute to Alzheimer's disease (Wu et al., 2008).", + "\t\n\nThe paradigm of the DNA damage theory of stem cell aging states that aging-associated changes in the DNA repair system in HSCs, together with changes in cell-cycle regulation due to increased DNA damage with age (Pietras et al., 2011;Rossi et al., 2007a), are thought to result in elevated DNA mutations, which then causally contribute to the decrease in HSC function with age.The paradigm is in part based on the finding that mice lacking a distinct set of DNA damage repair proteins display reduced function of HSCs, including an impaired repopulating potential and an overall depletion of the HSC pool (Ito et al., 2004;Navarro et al., 2006;Nijnik et al., 2007;Parmar et al., 2010;Prasher et al., 2005;Reese et al., 2003;Rossi et al., 2007a;Ruzankina et al., 2007;Zhang et al., 2010;Geiger et al., 2013), although in naturally aged mice, there is actually an expansion of the number of phenotypic stem cells instead of a depletion of the HSC pool.HSC aging also correlates with an increase in DNA double-strand breaks (DSBs).Both human and mouse HSCs present upon aging with a 2-to 3-fold elevated number of gH2AX foci, a bona fide surrogate marker for unresolved DSBs (Rossi et al., 2007a;R ube et al., 2011).Unresolved DSBs accumulated in quiescent, but not cycling, HSCs upon aging (Beerman et al., 2014).gH2AX foci though were very recently shown to co-localize in HSCs with proteins associated with replication and ribosomal biogenesis stress (Flach et al., 2014), rendering gH2AX foci as a general marker for persistent DNA DSBs in HSCs questionable.", + "\tAging\n\nThe oxidative stress theory of aging proposes that accumulation of oxidative DNA damage over the life span of an organism leads to gradual decline of cellular functions and eventual death (Bohr, 2002).This model is supported by several circumstantial evidences including the observation that lower free radical production and/or antioxidant treatment protects against agerelated deterioration, and cognitive decline (Lemon et al., 2003).Further, deficit or decrease in the repair of oxidative DNA damage appears to correlate with premature aging and age-related diseases (Bohr et al., 2007).It appears likely that overall genome repair, specifically the balance between DNA damage and its repair is a major determinant of the longevity and cell viability.A specific defect in processing 5 0 dRP residue at the strand break in Sir2 (SIRT6 homolog)-deficient mice displayed age-related degenerative phenotype (Mostoslavsky et al., 2006).The activities of DGs OGG1, NTH1 and uracil DNA glycosylase (UNG) in brain mitochondria decrease significantly with age (Gredilla et al., 2010).", + "\t\n\nPrevious evidence for an age-related decline in DNA repair was obtained largely from cell culture systems.For example, decreased repair has been observed in some but not all cases in mammalian cells undergoing senescence in culture [58,59], as well as cultures of primary cells taken from older versus younger individuals [26,[60][61][62][63][64].Additionally, there is a general correlation between mammalian lifespan and DNA repair (for review [65]).Further support for a relationship between DNA repair and aging comes from the existence of several human diseases caused by DNA repair defects that result in shortened lifespan in affected humans as well as rodent models, despite the much shorter normal rodent lifespan [24,25,66,67].Finally, a recent study reported that the in vivo repair of CPDs is decreased in the skin of old compared with that of young men, suggesting that the previous cell culture results are reflective of in vivo biology [27].\t\n\nWe also asked whether repair of UVC damage is less efficient in the nuclei of aging than in those of young adult C. elegans.There is evidence that nuclear genome integrity may be related to the aging process in mammals [24,25] and that repair rates decline in mammalian cells in culture [25,26].However, very few in vivo, whole organism data have been reported that address this hypothesis [27].Furthermore, there is little evidence to support the hypothesis that DNA repair capacity is related to age in C. elegans, despite the extensive use of this organism as a model for aging [5,6].In this study, we observed a 30% to 50% decrease in DNA repair in aging C. elegans (assayed at 6 days after L4 molt, corresponding to 60% of the population's mean adult lifespan), and then performed gene expression profiling in young and aging adults to generate hypotheses to explain the mechanism of that decline.\tRepair in nuclear genes is decreased in aging nematodes\n\nPrevious studies conducted in cells in culture have suggested that DNA repair declines with age in mammals [24,25].We found that repair in all ten nuclear targets was lower in aging (6 days after L4) adults than repair of those same targets in young (1 day after L4) glp-1 adults (P < 0.0001; Table 1).This difference was greatest in low and medium expression genes (about 50% decrease) but was also robust in high expression genes (about 33% decrease).We chose day 6 to represent the aging adult population because at this age more than 98% of the population is still alive, but the population as a whole has reached 60% of its mean adult lifespan (10 days; Figure 6) and 43% of its maximum adult lifespan (14 days; Figure 6).One-day-old adults have reached 10% of the mean adult lifespan, and 7% of the maximum adult lifespan.glp-1 adults raised at 25C exhibit signs of old age at 6 days, including constipation, cuticular blisters, and reduced mobility and feeding, but they have not yet begun to die in significant numbers (Figure 6 and Additional data file 2).It is therefore unlikely that repair rates are significantly confounded by DNA degradation occurring in dead animals.Initial lesion frequencies were not significantly different between young and aging adults (Table 1).", + "\t\n\nAlthough these age-related diseases are strongly influenced by DNA damage, there is still much debate about the extent to which DNA damage contributes to ageing.On the one hand, there is a clear link between oxidative stress and lifespan in invertebrates.In mammals, calorie restriction -a dietary intervention known to extend lifespanreduces ROS production and increases the expression of enzymes that metabolize ROS, such as superoxide dismutases (SODs) and catalase (reviewed in Ref. 80) (see figure).Decreased DNA damage and increased lifespan have also been observed in mice that overexpress catalase in mitochondria 81 .Similarly, mice with mutations in DNA-repair enzymes that are involved in transcription-coupled repair or base-excision repair show signs of premature ageing 60,82 .In humans, several defective DNA-repair pathways can cause accelerated ageing (progeroid) syndromes.On the other hand, certain mouse strains with defective DNA-repair systems accumulate high levels of DNA damage and yet have a normal lifespan (reviewed in Ref. 83).Similarly, a reduction in SOD levels in mice leads to increased oxidative DNA damage but does not affect the ageing process 84 .", + "\t\n\nThe role of faulty DNA repair machinery in age-related genomic instability was also found in S. cerevisiae and Drosophila.Mutations in the sgs1 and srs2 genes [encoding for RecQ helicase, homologous to the human WRN (43)] shortened S. cerevisiae life span through two distinct pathways: sgs1-and srs2-mutated cells stopped dividing randomly in an age-independent manner that required the RAD9 (cell cycle checkpoint control protein) DNA damage checkpoint, but late-generation sgs1-and srs2-mutated cells exhibited premature aging.The double sgs1/srs2-mutated yeast cells showed a high rate of terminal G2/M arrest.This arrest was suppressed by knockouts of RAD51 (DNA repair protein RAD51 homolog 1), RAD52 (DNA repair protein), and RAD57 (DNA repair protein), hinting for malfunctioning HR.In a similar study, knockout of DNA2, encoding RecQ helicase-like protein, caused premature aging phenotypes including longer cell cycle time, transcriptional silencing, genomic alterations, and eventually shorter life span (44).Shaposhnikov et al. (45) used D. melanogaster to evaluate the effect of overexpression of DNA repair genes in several locations in the body and several time points during the life period on the Drosophila life span.Beneficial effects on life span were observed with overexpression of Hus1 (checkpoint clamp component), mnk (MAPK interacting protein kinases), mei-9 (meiotic 9, D. melanogaster), mus210 (Xeroderma pigmentosum, complementation group C, D. melanogaster), spn-B (spindle B, D. melanogaster), and WRNexo (WRN exonuclease, D. melanogaster), which control the processes of DNA damage recognition and repair (45).Myc, a key regulator protein of cell growth and proliferation, was shown to act as a pro-aging factor, probably by its ability to increase genomic instability.Overexpression of Myc in Drosophila increased the frequency of large genome rearrangements associated with faulty repair of DNA DSBs and decreased adult life span.Myc knockdowns demonstrated reduced mutation rate and extended life span (46).In aged mice, increased levels of DNA breaks or unrepaired DNA damage as illustrated by the formation of H2AX (phosphorylated variant histone H2A) foci were observed (47)(48)(49).A positive effect on longevity was observed with overexpression of the human enzyme hMTH1 (MutT Human Homolog 1), which eliminates oxidized purine18 and deacetylase Sirt6 (50).Overexpression of SIRT6 promotes DSB repair by the activation of PARP1 [Poly (ADP-ribose) polymerase 1] and facilitating the recruitment of Rad51 (51) and NBS1 (Nijmegen Breakage Syndrome 1) (52) to DNA lesions.", + "\t\n\n40.Goukassian D, Gad F, Yaar M, Eller MS, Nehal US, Gilchrest BA. 2000.Mechanisms and implications of the age-associated decrease in DNA repair capacity.FASEB J. 14:1325-34", + "\tHow does the rate of DNA damage accumulation influence ovarian ageing? Detailed analysis of full genome expression profiles of multiple organs in a variety of DNA repair-deficient, progeroid mouse models has disclosed that these mutants strongly resemble genome-wide expression profiles of normal ageing, capturing a tremendous amount of underlying biological processes, which are shared between accelerated and natural ageing [31,39,40].This is consistent with the numerous parallels at the pathological, histological, physiological and functional levels, supporting the notion that the accelerated ageing to a large extent resembles the normal ageing process.The expression profile analysis also revealed that repair-deficient, premature ageing mouse mutants systemically suppress key somato-, lacto-and thyrotrophic hormonal axes, including the GH/IGF1 pathway, explaining why all progeroid repair mice -and the corresponding human patients-show dramatic early cessation of growth.Attenuation of the GH/IGF1 axis is also found with normal ageing [41].Energy appears to be redirected from growth to maintenance and defence mechanisms, such as the NRF2-controlled anti-oxidant system and stress resistance.This so-called 'survival' response resembles the response triggered by dietary restriction, which is for long known to retard the process of ageing and promote longevity in a very wide variety of organisms, ranging from yeast to mammals, including in one study non-human primates [42].Persistent DNA damage even triggers this response at the level of individual cells in culture, indicating its universal, highly conserved nature [43].The most plausible interpretation of this response is that organisms facing accelerated ageing due to rapid accumulation of DNA damage, caused by an inborn DNA repair deficiency, attempt in this way to delay ageing in order to extend their short lifespan and live as long as possible.This finding provided a link between high DNA damage loads and the insulin/IGF1 signal transduction pathway, which controls, metabolism, growth and lifespan and influences the ageing process.", + "\t\n\nIt is well known that a link between DNA damage and mammalian ageing exists (Sedelnikova et al., 2004;Karanjawala and Lieber, 2004;Lans and Hoeijmakers, 2006).Recent studies have shown that double-strand breaks (DSBs) typically accumulate in HGPS and RD cells and that the resultant genome instability might contribute to premature aging (Liu et al., 2005;Manju et al., 2006).DNA repair pathway defects were observed in HGPS and in a RD mouse model (Zmpste24/).Prelamin A accumulation was also associated with impairing of DNA repair factors recruitment at damage sites (Liu et al., 2005).A second study identified the overexpression of many essential p53 targets in the Zmpste24/ mouse model, which caused at least part of their Progeria-like phenotype (Bergo et al., 2002;Penda s et al., 2002;Varela et al., 2005).Indeed, double knock-out Zmpste24/, p53/ mice showed a partially rescued phenotype (Varela et al., 2005).It is known indeed that p53 activation is triggered by DNA damage (Burma et al., 1999;d'Adda di Fagagna et al., 2003), and that, to some extent, p53 activation can have deleterious effects on bone development, as observed in Progeria (Zambetti et al., 2006).Further proofs of the links existing between altered bone development, DNA repair, accelerated aging, and reduced cancer are the phenotypes of several DNA repair mouse models, as XPD mutant mice (de Boer et al., 2002), Ku80 defective mice (Difilippantonio et al., 2000) and p53 truncation mutants (Tyner et al., 2002).Furthermore, Manju et al. demonstrated that several Lamin mutants causing Progeria and muscle-specific disorders induce defects in ATR signaling pathways such as reduced phosphorylation of g-H2AX and inadequate recruitment of 53BP1 to repair sites in response to DNA damage in cultured cells (Manju et al., 2006).More recently, it has been shown that whereas DSBs repair proteins Rad51 and Rad50 were absent at Laminopathy-related DNA damage sites in patients' cells, xeroderma pigmentosum group A (XPA) protein, a unique nucleotide excisionrepair protein, colocalizes with DSB sites (Liu et al., 2007), maybe pointing to ''unifying'' pathophysiologic clues between different disorders characterized by features of premature ageing.", + "\t\n\nOther modulators of the DNA damage response appear to impact aging.For example, inhibition of PARP1 leads to lifespan extension in certain model organisms [21].Concomitant with the age-associated activation of PARP1 is the observation that persistent DNA damage foci containing the proteins 53BP1, gH2AX, and FOXO4 accumulate in aging cells [4,60].Notably, signaling from these foci may contribute to the senescence-associated secretory phenotype [47].Another approach to tackle this signaling cascade is therefore to break up these foci.Treatment with a FOXO4mimicking peptide leads to the removal of p53-and FOXO4-containing foci, thus facilitating apoptosis of senescent cells, regrowth of lost hair, and lifespan extension in models of severe premature aging [60].", + "\tCONCLUSION\n\nAccumulation of DNA lesions during aging is likely a major driver of aging and age-related diseases.Known prolongevity interventions and pathways could reduce DNA damage load.Dissecting these mechanisms might facilitate the development of novel age-related intervention strategies.Conversely, elucidating the downstream molecular and cellular mechanisms by which DNA damage drives aging and age-related diseases might also lead to novel antiaging therapies.The use of mouse models that mimic progeroid syndromes can dramatically accelerate aging research, not only by shedding light on the molecular mechanisms underlying the aging process, but also by screening for novel interventions.For instance, premature aging Ercc1 / mice with a life span of 0.5 year have the broadest spectrum of age-related pathologies recorded, which also includes the progressive frailty that is frequently observed in natural human aging.Ercc1 / mice could be used to systematically screen interventions for their ability to reduce age-related pathology much faster than in wild-type mice.\t\n\nrepair capacity and thereby reduce DNA damage load and its consequences could be promising.DNA repair, however, is comprised of multiple, complex pathways for which capacity-limiting proteins have not been identified; this hampers the development of interventions that enhance repair.If DNA damage is a main driver of aging, then known life span-extending pathways and interventions might promote longevity by reducing DNA damage load.Several lines of evidence support this hypothesis.Dietary restriction (DR), reduced calorie intake without malnutrition, is the only robust universal intervention with widespread documented longevity-and health-promoting effects in numerous species (117).DR reduces mutation accumulation (118), which suggests improved DNA repair or reduced generation of endogenous genotoxic metabolic (by-)products by direct DR-mediated alterations in metabolism.Suppression of insulin and IGF1 signaling are among the best-documented prolongevity pathways in model organisms ranging from worms and flies to mammals (119).These pathways also directly impinge on energy metabolism; hence, generation of genotoxic metabolic (by-)products could be reduced.Additionally, insulin/IGF1 longevity pathways can also impinge on DNA repair to provide a complementary protective mechanism against aging.Insulin/IGF1 signaling is reduced by DR in long-lived mouse mutants with defects in these signaling pathways (120), which leads to reduced AKT activity.AKT activity needs both T308 and S473 phosphorylation (121); insulin/IGF1 signaling induces T308 phosphorylation (121).The proteins responsible for S473 phosphorylation are less clear, but DSB-induced checkpoint kinases DNA-PK and ATM can phosphorylate AKT at S473 (122)(123)(124)(125)(126). Thus, DNA damage repair and signaling might be integrated with nutrient status.Indeed, active AKT negatively modulates DNA repair (127) by inhibiting p53 activity (128).Also, the FoxO transcription factors, repressed by AKT (129), have also been implicated in promoting DNA repair (130,131).This provides yet another mechanism by which repair might be affected by DR.Furthermore, AKT has been shown to phosphorylate and inhibit several key DDR factors including Chk1 and TopBP1 (127).Thus, DR could improve DNA repair or signaling via altered insulin/IGF1 signal transduction pathways.This could provide opportunities to improve DNA repair via existing prolongevity mechanisms." + ], + [ + "\t\n\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.\t\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study.\tIntroduction\n\nWith the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging.", + "\tConclusions\n\nIn the absence of a consensus phenotype for aging, genetic research is impeded (Melzer et al. 2007).At present, it is difficult to determine whether preventative and therapeutic strategies (such as calorie restriction) have beneficial effects in humans because there are no validated biomarkers that can serve as surrogate markers of aging (Matkovic et al. 1990).To have the \"phenome of aging\" (Xue et al. 2007) much better defined, we propose using the musculoskeletal aging phenotypes as an example and starting point.", + "\t\nStudies of the basic biology of aging have identified several genetic and pharmacological interventions that appear to modulate the rate of aging in laboratory model organisms, but a barrier to further progress has been the challenge of moving beyond these laboratory discoveries to impact health and quality of life for people.The domestic dog, Canis familiaris, offers a unique opportunity for surmounting this barrier in the near future.In particular, companion dogs share our environment and play an important role in improving the quality of life for millions of people.Here, we present a rationale for increasing the role of companion dogs as an animal model for both basic and clinical geroscience and describe complementary approaches and ongoing projects aimed at achieving this goal.", + "\t\n\nOn the other hand, the same evolutionary-motivated strategy suggesting to focus on more heterogeneous phenotypes (as opposite to more homogenous) can be highly beneficial for unraveling genetic predisposition to fundamental mechanisms of intrinsic biological aging and, consequently, to geriatric diseases.Indeed, aging is associated with systemic remodeling of an organism's functioning which increases chances of virtually all geriatric disorders (Franco et al. 2009;Franceschi et al. 2000;Martin et al. 2007;Cutler and Mattson 2006).Experiments with laboratory animals (Johnson 2006) and heritability estimates in humans (Christensen et al. 2006;Iachine et al. 1998) show that aging can be genetically regulated (Finch and Tanzi 1997;Martin et al. 2007;Vaupel 2010).Accordingly, yielding insights in genetic predisposition to aging-related processes in an organism could be a major breakthrough in preventing and/or ameliorating not one geriatric trait, but perhaps a major subset of such traits (Martin et al. 2007) that can greatly advance progress in solving the problem of extending healthy lifespan in humans.", + "\t\n\nThe studies in lower animals made in recent years that have led to the view that genes are involved in aging have not revealed a reversal or arrest of the inexorable expression of molecular disorder that is the hallmark of aging.These studies are more accurately interpreted to have impact on our understanding of longevity determination because all of the experimental results have altered biological variables before the aging process begins.None of these studies in invertebrates has demonstrated that the manipulation of genes has slowed, stopped, or reversed recognized biomarkers of the aging process.", + "\t\n\nAny discovery about the biological determinants of the rate of aging raises the possibility of therapies to slow aging.Therefore the discovery of a gerontogene with even very rare mutations that increased longevity would cause speculation about future trends in mortality.However, the discovery of such a gene would be relevant only to long-term (and, therefore, very speculative) projections.\tGENETIC ANALYSIS OF LONGEVITY, OF AGING, AND OF AGE-SENSITIVE TRAITS IN MICE\n\nBiogerontology has just begun to benefit from the attention and skills of professional geneticists.Geneticists can attack problems of aging from several related but fundamentally distinct directions.Studies of rare mutations at individual loci, such as the Werner's syndrome locus WRN, whose mutant form produces, in middle-aged people, several of the diseases typically not seen until old age, can give attractive points of entry into the pathophysiology of age-related diseases.In mice there are now four reports of mutations-two naturally occurring and two artificially produced-that lead to impressive increases in mean and maximal longevity (Miskin and Masos, 1997;Brown-Borg et al., 1996;Miller, 1999;Migliaccio et al., 1999), and thus provide extremely valuable models for testing mechanistic ideas and the control of aging.Some of these, such as the dw/dw and df/df dwarfing mutations that affect levels of growth hormone and thyroid hormone, provide clues to endocrine-dependent pathways that could regulate age effects in multiple cells and tissues.The recent report (Migliaccio et al., 1999) that mouse life span can be extended by an induced mutation that diminishes cell susceptibility to apoptotic death after injury should stimulate new inquiries into the effects of altered cell turnover on age-dependent changes.Each of these mutations, however, is exceptionally rare in natural populations; despite their effect on longevity, perhaps mediated by a direct effect on aging, each of the mutations is likely to have, overall, a negative effect on reproductive success and thus fail to become fixed in natural mouse populations.", + "\t\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13].", + "\t\n\nWith an aging population, there is a great and urgent need to develop approaches and therapies targeting the aging process and age-related diseases (Butler et al., 2008).Delaying the process of aging, even slightly, would have profound social, medical and economic benefits (Olshansky et al., 2006;Butler et al., 2008).For example, slowing aging by a mere 7 years would cut mortality of age-related diseases by half at every age.Therefore, the potential benefits from research on the basic biology and genetics of aging are unparalleled in terms of improving quality of life and health.Although much debate remains regarding the molecular causes of aging, findings from model organisms show that aging is surprisingly plastic and can be manipulated by both genetic and environmental factors (Finch and Ruvkun, 2001;Kenyon, 2010).In principle, therefore, it is possible to manipulate human aging.Unlocking this capacity to manipulate aging in people would result in unprecedented human health benefits, and it opens new opportunities for industry.\tIV. Genome-Environment Interactions as Targets for Dietary Interventions and Drug Discovery\n\n\"[It's] possible that we could change a human gene and double our life span. \"-CynthiaKenyon (Duncan, 2004) According to the GenAge database of aging-related genes (http://genomics.senescence.info/genes/),more than 700 genes have been identified that regulate lifespan in model organisms (de Magalha es et al., 2009a).Many of these genes and their associated pathways-such as the insulin/IGF1/GH pathway-have been shown to affect longevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolutionarily conserved and may have potential therapeutic applications (Baur et al., 2006).For example, evidence suggests the use of lowered IGF signaling (e.g., by targeting IGF receptors) to treat certain age-related diseases such as cancer (Pollak et al., 2004), Alzheimer's disease (Cohen et al., 2009), and autoimmune diseases (Smith, 2010).Moreover, a number of genes and pathways associated with longevity and CR are part of nutrient-sensing pathways that also regulate growth and development, including the insulin/IGF1/GH pathway (Narasimhan et al., 2009;Stanfel et al., 2009).Many of these genes modulate the response to environmental signals, such as food availability, and act in signaling pathways that if understood can be targeted (Fig. 1).The genetic regulation of aging is therefore an emerging field with multiple applications in the human nutrition, cosmetic, and pharmaceutical industries.\t\n\nThe remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design.\t\n\nEven if sirtuins and resveratrol do not live up to their expectations, this research is pioneering in terms of genome-environment interactions and nutritional manipulations of aging.These studies also show the path from basic discovery on the biology of aging to potential antiaging and pharmacological interventions and can therefore be applied to other genes and pathways.The lessons learned from the pitfalls of SIRT1 and resveratrol research can also help others to translate basic research on the biology of aging to the clinic, such as avoiding the use of short-lived rodent strains (e.g., by using unhealthy diets), which may lead to findings that only apply to a subset of individuals.\t\n\nIt seems that organisms from yeast to mammals have evolved genetic programs to cope with periods of starvation that can also postpone aging and age-related diseases, but how can we take advantage of those mechanisms to improve human health?Because assaying the longevity effects of CR in humans is practically impossible, studying its molecular mechanisms in lower life forms could be beneficial to humans through the identification of candidate genes, pathways and molecular mechanisms.Although CR will not be suitable for everyone, targeting its mechanisms and developing CR mimetics may lead to drug development for a number of age-related and metabolic diseases.", + "\t\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics.\tRelevance to nurse practitioner practice\n\nCurrently, there is no cure for genetic variants associated with rapid aging, but novel agents that may slow down the aging process are being tested.The authors of this article advocate individual participation in association studies of aging and pharmacologic risk mitigation or reversal of symptoms for those with known genetic disease risk.Direct to consumer epigenetic biological aging tests and telomere length tests are available; but they are not approved by the Food and Drug Administration.Health care providers may want to consider the simple but key clinical and personal changes, suggested above, to enhance DNA health, wellness, and longevity.Simple mindful changes in behavior, environmental exposure, food/supplement use, weight loss, and regular exercise can reduce adduct exposure damage and impact telomere length, potentially increasing longevity.A Mediterranean diet containing fruits and whole grains along with fiber, antioxidants, soy protein, and healthy fats (from avocados, fish, flax, and walnuts) is suggested to reduce DNA adducts and protect telomeres.In light of our current pandemic, focus on population health, and restrictions to health care access, especially in rural communities, health care providers could incorporate these lifestyle and dietary principles in telehealth visits with patients to reduce disease risk and optimize healthy aging.", + "\t\n\nTaking advantage of advances in genomics and bioinformatics, we have used the evidence available to argue for a new theory of aging.To test that theory, still more sophisticated experiments and analyses will be necessary, but we are sure that the talented and dedicated scientists of the future will rise to the challenge.Regardless of what they find, we are now seeing the dawn of a new age in aging research.Borrowing elements from both Szilard's and Orgel's models, somatic mutations increase at an accelerating rate with age, a feedback loop mediated partially by altered protein sequences but primarily by a dysregulation of gene expression.The redundancy of the organism, both cellular and genetic, may inhibit these consequences of somatic mutations from directly contributing to aging, but is itself subject to degradation by somatic mutations.This model may most accurately reflect human aging, predicting both a period of latency (reflecting the lack of an aging phenotype during development and early adulthood) and an accelerating decline afterwards (reflecting the slow-thenrapid deterioration that begins in middle age).", + "\t\n\nWith modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms.\tCONCLUSION: AGING DOES NOT HAVE TO BE UNSTOPPABLE\n\nThirty years ago, the genetic or biochemical postponement of aging was regarded as impossible in any organism.But the last few decades have seen aging become an easily ameliorated condition in model organisms, especially Drosophila.The toy electrical machines of Michael Faraday pointed to the future electrification of industry.The rockets of Robert Godard pointed toward space travel.Likewise, tiny Methuselahs show that aging can be substantially postponed.There is no biological necessity to any particular rate of aging, only the practical difficulty of changing that rate." + ], + [ + "\tOxidative stress and mitochondrial DNA\n\nNot long after it was discovered that mitochondria have their own genetic apparatus, Harman proposed that mitochondria play a central role in the free radical theory of aging [16].This idea was developed further by Miquel et al. [330], and the notion that mtDNA mutagenesis played a role in aging took hold.The phenotypical importance of mutations in mtDNA was demonstrated by Wallace et al. [331] and Holt et al. [332], who first showed that Leber's hereditary optic neuropathy and mitochondrial myopathies were caused by mtDNA mutations (reviewed in [333]).Because mtDNA is so close to the site of mitochondrial ROS production, it is exposed to considerably higher oxidative stress, resulting in 3-fold higher levels of DNA oxidative damage (the previously quoted 20-fold figure is apparently due to an isolation artifact [334,335]).In the 1990s a series of papers reported that the frequency of mitochondrial DNA deletions increases dramatically with age, being essentially undetectable in young individuals and reaching levels as high as 2% of mtDNA in old individuals.This age-related increase in mtDNA deletions was found in organisms as diverse as worms, mice, and humans (reviewed in [24,336]).The same is also true with mtDNA point mutations [337,338].Certain mtDNA polymorphisms have been found in increased frequency in centenarians, implying a protective effect during aging [339][340][341].Similar protective effects of mtDNA polymorphisms have been reported for the age-related neurodegenerative condition, Parkinson's disease [342].", + "\t\n\ndoi: 10.1196/annals.1293.002cells and individuals.We previously identified a mitochondrial genotype, 5178C~A (ND2, Leu237Met), representing haplogroup D, to be associated with longevity in Japanese centenarians.Our proposal that certain mitochondrial polymorphisms are associated with longevity is further supported by observations that haplogroups J and U are overrepresented in European centenarians. 2Based on these findings, we have hypothesized that other haplogroups are associated with age-related neurodegeneration in Parkinson's disease or Alzheimer's disease.We also postulated that common metabolic disorders, such as obesity and type-2 diabetes mellitus, are attributable at least in part to mitochondrial polymorphisms.To examine these hypotheses, we have started comprehensive sequence analysis of the entire mitochondrial genome of centenarians, young obese or non-obese adults, patients with Parkinson's disease or Alzheimer's disease, and diabetic patients with or without angiopathy, using 96 individuals for each of these groups", + "\t\n\nBuilding on previous work in this system, the current study tests three primary hypotheses about how variation in mtDNA and mitochondrial function relate to variation in life-history traits and aging within this system (Fig. 1): (1) First, we test whether rates of cellular oxygen consumption in isolated immune cells exhibit patterns that are consistent with the hypothesis that cellular processes drive whole-organism senescence and aging, and if these patterns differ between the SA and FA ecotypes and between sexes.By measuring basal, ATP-production associated, and maximal rates of cellular oxygen consumption, we further test for evidence that phenotypic divergence is dependent on a specific aspect of oxidative phosphorylation within immune cells.The energetics of these cells are particularly important given their essential role in modulating disease and infection, important factors contributing to senescence (Metcalf et al., 2019).We predict that SA snakes will maintain levels of cellular oxygen consumption across age, whereas the FA snakes will show a decline with age, especially in ATP-associated rates, possibly due to continual degradation of electron transport chain functionality from accumulating oxidative damage and reduced DNA repair mechanisms (Robert and Bronikowski, 2010;Schwartz and Bronikowski, 2013). ( 2) Second, we expand our mitochondrial genomics dataset to quantify mtDNA genetic structure across the landscape and test whether mtDNA haplotypes, and alleles at a nonsynonymous SNP in the Cytochrome B (CytB) gene correlate with aging ecotypes. (3) Third, we test the hypothesis that variation in mtDNA correlates with whole-organism variation in metabolic rates, suggesting a pathway linking mitochondrial genetic variation in mtDNA to whole-organism energetics.We first test whether different haplotypes differ in resting metabolic rate.Then, we test the effects of the nonsynonymous SNP in CytB on resting metabolic rate.The CytB gene encodes a component of complex III of the ETC, and was previously found to segregate between these life-history ecotypes (Schwartz et al., 2015).This SNP results in an amino acid substitution from isoleucine (aliphatic, hydrophobic) to threonine (hydrophilic) on a region that comes into close contact with a nuclear-encoded subunit (Schwartz et al., 2015).We combine previously published and new data on whole-organism resting metabolic rates (oxygen consumption) to test for the effects of this nonsynonymous mutation in three populations where we find heterogeneity at this nucleotide, thus allowing us to disentangle the effects of shared environment (population) from sequence variation (SNP).We predict that this SNP will correlate with variation in whole-organism metabolic rate, demonstrating a putatively adaptive difference between the derived and ancestral sequence.By utilizing this integrative data setfrom genes to organelles to whole organisms to populationsin a known life-history context, we are able to test hypotheses across levels of organization to provide a more complete picture of the complicated story of mitochondria and life history (Havird et al., 2019).", + "\t\n\nEven with these levels of mtDNA protection, mtDNA mutation frequency increases with age in animal models and humans alike (Cortopassi and Arnheim 1990;Larsson 2010), although the role of mtDNA mutations remains unclear (Khrapko and Vijg 2009;Pohjoismaki et al. 2018;Theurey and Pizzo 2018).However, recent reports have shown that mtDNA point mutations in aged tissues largely arise from replication infidelity (i.e., DNA polymerase errors), rather than ROS-induced damage (Ameur et al. 2011;Kennedy et al. 2013;Vermulst et al. 2007).To test if replicative infidelity causes aging, mice with mutant mitochondrial DNA polymerase that are deficient in proofreading during DNA replication, causing supraphysiological mutation loads (roughly 2500-fold in the homozygous polg mut/mut compared to 500-fold higher in the polg +/mut ), were examined (Vermulst et al. 2007).While the homozygous mice (polg mut/mut ) showed signs of accelerated aging phenotypes and significantly reduced lifespan, the heterozygous mice (polg +/mut ) had a normal lifespan albeit exhibiting premature aging phenotypes (Trifunovic et al. 2004).One plausible explanation for this discrepancy lies with increased mtDNA deletions in the homozygous mice (polg mut/mut ) (Vermulst et al. 2007(Vermulst et al. , 2008)).These cumulative results suggest that the connections between oxidative stress, mtDNA mutations, and aging are more complicated than originally appreciated and require further investigation to fully understand their relation (Pomatto and Davies 2018).It is evident, however, that the mtDNA mutations are linked to more than 300 diseases connected to aging, including Alzheimer's Disease, and that proper communication between the mitochondria and the nucleus plays a key role (DeBalsi et al. 2017;Grazina et al. 2006;Lane 2011;Onyango et al. 2006;Quirs et al. 2016;Swerdlow et al. 2017).", + "\t\n\nConclusions: Our population-based study indicates that both mtDNA quality and quantity are influenced by age.An open question for the future is whether interventions that would contribute to maintain optimal mtDNA copy number and prevent the expansion of heteroplasmy could promote healthy aging.\t\nBackground: The accumulation of mitochondrial DNA (mtDNA) mutations, and the reduction of mtDNA copy number, both disrupt mitochondrial energetics, and may contribute to aging and age-associated phenotypes.However, there are few genetic and epidemiological studies on the spectra of blood mtDNA heteroplasmies, and the distribution of mtDNA copy numbers in different age groups and their impact on age-related phenotypes.In this work, we used whole-genome sequencing data of isolated peripheral blood mononuclear cells (PBMCs) from the UK10K project to investigate in parallel mtDNA heteroplasmy and copy number in 1511 women, between 17 and 85 years old, recruited in the TwinsUK cohorts.Results: We report a high prevalence of pathogenic mtDNA heteroplasmies in this population.We also find an increase in mtDNA heteroplasmies with age ( = 0.011, P = 5.77e-6), and showed that, on average, individuals aged 70-years or older had 58.5% more mtDNA heteroplasmies than those under 40-years old.Conversely, mtDNA copy number decreased by an average of 0.4 copies per year ( = 0.395,P = 0.0097).Multiple regression analyses also showed that age had independent effects on mtDNA copy number decrease and heteroplasmy accumulation.Finally, mtDNA copy number was positively associated with serum bicarbonate level (P = 4.46e-5), and inversely correlated with white blood cell count (P = 0.0006).Moreover, the aggregated heteroplasmy load was associated with blood apolipoprotein B level (P = 1.33e-5), linking the accumulation of mtDNA mutations to age-related physiological markers.Conclusions: Our population-based study indicates that both mtDNA quality and quantity are influenced by age.An open question for the future is whether interventions that would contribute to maintain optimal mtDNA copy number and prevent the expansion of heteroplasmy could promote healthy aging.\t\n\nAging is commonly characterized as a time-dependent progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death [14].One important factor in aging is the accumulation of DNA damage over time [15].mtDNA has been considered a major target of aging-associated mutation accumulation, possibly because it experiences higher oxidative damages, more turnover, and has lower replication fidelity compared to nuclear DNA (nDNA) [16][17][18].Mice carrying elevated mtDNA mutation burden present premature signs of aging including hair loss, kyphosis, and premature death (lifespan shortened by up to 50%) [19,20].In human studies, mtDNA heteroplasmy incidence increases with age [21][22][23], while lower mtDNA copy number has been reported in aged populations [12,24].Ding et al. reported an trend of increased heteroplasmies and decreased mtDNA copy number with age in their study population [25].However, previous studies were limited in one or more ways: i) limited power in detecting low-to-medium frequency heteroplasmies in blood due to low sequencing depth; ii) relatively small sample sizes, limiting statistical power; iii) small age range; iv) whole blood as the source of DNA, which contains several sources of contaminants for mtDNA analysis; and/or v) assessing either mtDNA mutation or copy number, but not both in the same biological samples.Thus, it is largely unknown whether the impacts of age on mtDNA mutation burden and on copy number are independent from each other.\t\n\nBackground: The accumulation of mitochondrial DNA (mtDNA) mutations, and the reduction of mtDNA copy number, both disrupt mitochondrial energetics, and may contribute to aging and age-associated phenotypes.However, there are few genetic and epidemiological studies on the spectra of blood mtDNA heteroplasmies, and the distribution of mtDNA copy numbers in different age groups and their impact on age-related phenotypes.In this work, we used whole-genome sequencing data of isolated peripheral blood mononuclear cells (PBMCs) from the UK10K project to investigate in parallel mtDNA heteroplasmy and copy number in 1511 women, between 17 and 85 years old, recruited in the TwinsUK cohorts.", + "\t\n\nHence, progressive age-dependent damage in mitochondrial genomes and functions is an important contributor to human aging.\t\n\nIn 1989, based on expanding molecular biology studies of diseases caused by mtDNA mutations, my colleagues and I (216) proposed the \"mitochondrial theory of aging\" that the somatic accumulation of mitochondrial mutations and the subsequent cytoplasmic segregation of these mutations during life is a major contributor to the gradual loss of cellular bioenergetic capacity within tissues and organs associated with general senescence and diseases of aging.The hypothesis encompasses the concept that a decline in bioenergetic capacity in tissues will contribute to age-associated diseases, such as those that affect the cardiac, vascular, and neuromuscular systems.\t\n\nAccumulated evidence to date exhorts to unify both ideas of the free radical theory of aging and mitochondrial theory of aging to be \"the redox mechanism of mitochondrial aging\" (281), that the mtDNA's oxidative damage results in cumulative increase in somatic mutations in mtDNA leading to bioenergetic deficit, cell death, and aging.The germline mutations in mtDNA as well as nDNA specific for the patients with mitochondrial diseases accelerate the oxidative damage and somatic mutations synergistically leading to their phenotypic expression as premature aging or death.", + "\t\n\nAging is a complex process as a time-dependent progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death [74], and as we described above, aging is highly associated with mtDNA mutations; in fact heteroplasmy incidence increases with age, while lower mtDNA copy number has been reported in aged populations as well as mitochondria morphology, abundance, and oxidative phosphorylation activity [75,76].Interestingly, in aging the significant amount of these mutations converges in sites that encode structural subunits of the ETC such as complexes I and III [77], leading to OxPhos uncoupling and mitochondrial dysfunction in aged population.Since there are several limitations to study mitochondrial metabolism in human samples, in this section we briefly described the implications of mitochondrial metabolism for aging in the most studied and high energy demand human tissues, such as skeletal muscle, heart, and brain.", + "\tINTRODUCTION\n\nAbout 10 years ago it was proposed that aging is caused by life-long accumulation of somatic mitochondrial DNA (mtDNA) mutations (1), which compromises cellular energy metabolism and/or increases intracellular oxidative stress (2).Ultimately, this could result in the development of the multiple degenerative changes in tissues that become manifest in old age.It has been shown that mtDNA deletions and, with less certainty, mtDNA point mutations, increase with advancing age (recently reviewed in 3,4).These data are consistent with the mitochondrial theory of aging but do not exclude the possibility that accumulation of mtDNA mutations accompanies, but does not cause aging.", + "\t\nAging is an intricate phenomenon characterized by progressive decline in physiological functions and increase in mortality that is often accompanied by many pathological diseases.Although aging is almost universally conserved among all organisms, the underlying molecular mechanisms of aging remain largely elusive.Many theories of aging have been proposed, including the freeradical and mitochondrial theories of aging.Both theories speculate that cumulative damage to mitochondria and mitochondrial DNA (mtDNA) caused by reactive oxygen species (ROS) is one of the causes of aging.Oxidative damage affects replication and transcription of mtDNA and results in a decline in mitochondrial function which in turn leads to enhanced ROS production and further damage to mtDNA.In this paper, we will present the current understanding of the interplay between ROS and mitochondria and will discuss their potential impact on aging and age-related diseases.\t\n\nAging is an intricate phenomenon characterized by progressive decline in physiological functions and increase in mortality that is often accompanied by many pathological diseases.Although aging is almost universally conserved among all organisms, the underlying molecular mechanisms of aging remain largely elusive.Many theories of aging have been proposed, including the freeradical and mitochondrial theories of aging.Both theories speculate that cumulative damage to mitochondria and mitochondrial DNA (mtDNA) caused by reactive oxygen species (ROS) is one of the causes of aging.Oxidative damage affects replication and transcription of mtDNA and results in a decline in mitochondrial function which in turn leads to enhanced ROS production and further damage to mtDNA.In this paper, we will present the current understanding of the interplay between ROS and mitochondria and will discuss their potential impact on aging and age-related diseases.", + "\t\n\nMitochondrial genomes harboring large deletions are known to accumulate both in patients with heteroplasmic mtDNA mutations and in normal individuals during aging, particularly in postmitotic tissues such as muscle and brain (3).These observations support the mitochondrial theory of aging, which states that the slow accumulation of impaired mitochondria is the driving force of the aging process.This idea is attractive because it can be reconciled with the free radical theory of aging, which argues that oxidative damage plays a key role in senescence.Among the numerous mechanisms known to generate oxidants, leakage of superoxide anion and hydrogen peroxide from the mitochondrial electron transport chain are the chief candidates.Increased damage to mtDNA could exacerbate this leakage of reactive oxygen species (ROS) (4).", + "\t\n\nMitochondrial DNA (mtDNA) rearrangements have been shown to accumulate with age in the post-mitotic tissues of a variety of animals and have been hypothesized to result in the age-related decline of mitochondrial bioenergetics leading to tissue and organ failure.Caloric restriction in rodents has been shown to extend life span supporting an association between bioenergetics and senescence.In the present study, we use full length mtDNA amplification by long-extension polymerase chain reaction (LX-PCR) to demonstrate that mice accumulate a wide variety of mtDNA rearrangements with age in post mitotic tissues.Similarly, using an alternative PCR strategy, we have found that 2-4 kb minicircles containing the origin of heavy-strand replication accumulate with age in heart but not brain.Analysis of mtDNA structure and conformation by Southern blots of unrestricted DNA resolved by field inversion gel electrophoresis have revealed that the brain mtDNAs of young animals contain the traditional linear, nicked, and supercoiled mtDNAs while old animals accumulate substantial levels of a slower migrating species we designate age-specific mtDNAs.In old caloric restricted animals, a wide variety of rearranged mtDNAs can be detected by LX-PCR in post mitotic tissues, but Southern blots of unrestricted DNA reveals a marked reduction in the levels of the agespecific mtDNA species.These observations confirm that mtDNA mutations accumulate with age in mice and suggest that caloric restriction impedes this progress.\t\n\nIt has often been hypothesized that quantitation of a single mtDNA deletion from old tissue represents 'the tip of the iceberg', and that the cumulative mitochondrial somatic mutational load is large in senescent organisms (1).By observing an array of mitochondrial sequence rearrangements with age, our data lend strong experimental support to this hypothesis.Further, the observation that there are substantial mtDNA conformational variants with age, and that the regimen of CR can modulate the level of the conformational variant in the brain, may indicate that mtDNA from the brain is more sensitive to oxidative damage as a result of ROS production.The current results in mouse are consistent with our previous studies in aging humans, in skeletal muscle (10), heart (15), and brain (5).The association of somatic mtDNA changes with age regardless of organismal maximum or mean lifespan, and modulation of some of these changes via CR, are consistent with the hypothesis that mtDNA changes with age may play a role in the senescence of multicellular organisms.\t\n\nAs a further step toward determining if mtDNA rearrangements play a significant role in senescence, it would be important to demonstrate that the accumulation of mtDNA rearrangements is retarded when mortality rate is reduced through genetic, or environmental modifications which extend lifespan.One of the few experimental aging models in which lifespan can be genetically extended is the age-1 mutant of Caenorhabditis elegans.In this mutant, mtDNA rearrangements have been observed to accumulate at a slower rate than in wild-type animals (9).In mammals, the only reproducible treatment to date which extends lifespan is that of CR (32).When the total number of calories consumed by the animal is reduced over the lifespan relative to AL fed animals, the mean and maximum lifespan can be extended by up to 50% (33).The mechanism by which CR extends lifespan is unknown, but CR is associated with a decrease in total body fat, increased fitness, and decreased pathology.\t\nMitochondrial DNA (mtDNA) rearrangements have been shown to accumulate with age in the post-mitotic tissues of a variety of animals and have been hypothesized to result in the age-related decline of mitochondrial bioenergetics leading to tissue and organ failure.Caloric restriction in rodents has been shown to extend life span supporting an association between bioenergetics and senescence.In the present study, we use full length mtDNA amplification by long-extension polymerase chain reaction (LX-PCR) to demonstrate that mice accumulate a wide variety of mtDNA rearrangements with age in post mitotic tissues.Similarly, using an alternative PCR strategy, we have found that 2-4 kb minicircles containing the origin of heavy-strand replication accumulate with age in heart but not brain.Analysis of mtDNA structure and conformation by Southern blots of unrestricted DNA resolved by field inversion gel electrophoresis have revealed that the brain mtDNAs of young animals contain the traditional linear, nicked, and supercoiled mtDNAs while old animals accumulate substantial levels of a slower migrating species we designate age-specific mtDNAs.In old caloric restricted animals, a wide variety of rearranged mtDNAs can be detected by LX-PCR in post mitotic tissues, but Southern blots of unrestricted DNA reveals a marked reduction in the levels of the agespecific mtDNA species.These observations confirm that mtDNA mutations accumulate with age in mice and suggest that caloric restriction impedes this progress." + ], + [ + "\t\n\nStudies of genes and molecular processes that are associated with segmental progeroid disorders, such as Hutchinson-Gilford progeria syndrome (HGPS, progeria, OMIM#176670), could be of importance when studying the genetic mechanisms of aging (Martin, 2005;Baker et al., 1981).For example, most cases of HGPS are caused by a de novo point mutation in the LMNA gene (LMNA c.1824C>T; p.G608G).This mutation activates a cryptic splice site that results in aberrant splicing of the lamin A transcript (Eriksson et al., 2003).Interestingly, it has been shown that the products of this aberrant splicing, the truncated transcript and resultant protein (named progerin), increase in number with aging in HGPS (Goldman et al., 2004;Cao et al., 2007;Rodriguez et al., 2009).In addition, several reports have found progerin, and increasing levels of progerin, in normal cells over the course of normal aging (Scaffidi & Misteli, 2006;McClintock et al., 2007;Cao et al., 2007;Rodriguez et al., 2009), which suggests a similar genetic mechanism in HGPS and normal aging.Moreover, genome-scale expression profiling in cells from HGPS patients, as well as in physiological aging, has revealed widespread transcriptional misregulation in multiple mammalian tissues (Ly et al., 2000;Csoka et al., 2004;Zahn et al., 2007;Scaffidi & Misteli, 2008;Cao et al., 2011;McCord et al., 2013).", + "\tDNA Repair and Accelerated Aging Syndromes\n\nThe association of human syndromes of accelerated aging with inherited mutations in DNA repair genes strongly implicates DNA damage in the human aging process.These disorders, known as segmental progeroid syndromes, are characterized by accelerated onset of a subset of human aging phenotypes that frequently include neurodegeneration (50).Mutations in genes involved in singleor double-strand DNA break repair result in cerebellar degenerative syndromes known as ataxias, which are manifested by movement disorders.The continued proliferation of cerebellar granule cells during postnatal development may underlie the vulnerability of the cerebellum to inherited deficits in genome stability.In contrast, inherited mutations in DNA helicases, such as Werner and Rothmund-Thomson syndromes, give rise to features of accelerated aging that often do not include nervous system dysfunction.This may reflect the role of RecQ-like helicases in recombinant events in replicating cells.Inherited mutations in enzymes involved in nucleotide and base excision repair, including xeroderma pigmentosum and Cockayne syndrome, are characterized by accelerated aging phenotypes that include neurodegeneration, mental retardation, and delayed psychomotor development (50).A new human progeroid syndrome that is caused by a loss of function mutation in the XPF-ERCC1 endonuclease that repairs helix-distorting DNA lesions was recently described.Mice deficient in ERCC1 recapitulate the progeroid features and exhibit a gene expression profile in the liver that overlaps with that of normal aging mice (correlation coefficient 0.32), suggesting that this type of DNA damage may contribute to the aging process (51).Segmental progerias typically have a short life span of less than 20 years, which may account for the absence of Alzheimer-type neuropathological Double-strand break (DSB): a severe form of DNA damage involving scission of both DNA strands, usually induced by ionizing radiation or ROS NHEJ: nonhomologous end joining changes.However, individuals with Werner syndrome, a longer-lived progeroid syndrome, can have variable neuropathology, with one 57-year-old case reportedly showing unusually high levels of amyloid -protein deposition in the brain (52).", + "\t\n\nHutchinson-Gilford progeria syndrome (HGPS) and Werner syndrome are rare human genetic disorders characterized by premature aging phenotypes with a shortened life span.This group of diseases resembles physiological aging to a certain extent, serving as excellent models to gain insight into the biology of aging in humans (24,25).These diseases are due to either a mutation in genes encoding the DNA repair machinery or the A-type lamin, leading to disorganized chromatin structures.The causative mutations behind these progeria syndromes indicate that genomic instability and chromatin deterioration are causes of human aging.Furthermore, the knowledge we gain from understanding the molecular pathology of these human premature aging diseases provides us with useful information to understand the complex aging process.Individuals with HGPS do not recapitulate all aging phenotypes because they usually show segmental progeria affecting multiple tissues.By recapitulating some molecular and cellular changes that are characteristics of the natural aging process, these models provide us with a unique opportunity to understand the aging process in a human model (24,25).", + "\t\n\nResearchers in recent studies have focused on gene mutations accompanying known progeroid syndromes, such as Hutchinson-Gilford progeria, Werner syndrome, Rothmund-Thomson syndrome, Cockayne syndrome, ataxia telangiectasia, and Down syndrome. 143The most common skin disorders of these syndromes, which are characterized by an acceleration of the aging phenotype, are alopecia, skin atrophy and sclerosis, telangiectasia, poikiloderma, thinning and graying of hair, and several malignancies.Most of these syndromes are inherited in an autosomal recessive way and mostly display defects in DNA replication, recombination, repair, and transcription.Expression gene patterns of skin cells derived from old and young donors with Werner syndrome, 144 show that 91% of the analyzed genes have similar expression changes in Werner syndrome and in normal aging, implying transcription alterations common to Werner syndrome and normal aging represent general events in the aging process.", + "\tDNA Repair-Related Progeroid Syndromes\n\nAs mentioned previously, premature aging syndromes are often caused by mutations in genes whose function is to preserve genomic integrity.In this respect, the RecQ family of DNA helicases has been found to function in DNA damage repair, including base excision repair and in DNA double-strand break (DBS) repair, as well as in DNA replication subjected to a normal or stressed state [36].Mutations in three RecQ genes (WRN, BLM, and RECQL4) give rise to the Werner syndrome (WS), Bloom syndrome (BS), and Rothmund-Thomson syndrome (RTS), respectively [37].Additional genetic defects in the DNA damage repair system also cause the following disorders: Cockayne syndrome (CS), xeroderma pigmentosum (XP), and trichothiodystrophy (TTD).\t\n\nAn alternative strategy to the investigation of aging using the humans themselves is the study of progeroid syndromes, a group of very rare genetic disorders characterized by accelerated aging and the presence of clinical features that resemble physiological aging, including osteoarthritis and osteoporosis, loss of muscle mass, hair loss, short stature, skin tightness, and cardiovascular diseases [4].In addition to the genuine medical interest in improving the quality of life of these patients, the study of progeroid syndromes has attracted great interest in the past 10 years, in that they constitute an invaluable source of information for understanding the molecular basis of human aging.\tConclusions\n\nRecent advances in the study of progeroid syndromes, especially HGPS, have provided novel insights into our understanding of the aging process in humans.The main progeroid syndromes revised in this chapter are caused by mutations in genes encoding for DNA repair enzymes or the nuclear lamina protein lamin A, which reinforces the notion that genome instability is a critical determinant of aging.The study models that recapitulate progeroid syndromes have dramatically stimulated aging research; while cellular models have allowed the dissection of basic cellular and molecular processes linked to aging, mice models have facilitated screening of therapeutic drugs.It is expected that upcoming technologies and the design of novel optimized animal models will help to accomplish a translational medicine approach in aging research, with HGPS being the ideal model for such a goal.", + "\tProgeroid syndromes\n\nPatients suffering from progeroid syndromes, or accelerated aging phenotypes, display an array of physical and biological features that vary widely between tissues and diseases and among individuals.Some of the main characteristics for the specific disorders of interest to this review are cited below (for further review of molecules involved and clinical presentation, see Ref. 96).A general dilemma in studies on the role of telomeres in progeroid syndromes (and aging) is that telomere involvement could be direct as well as indirect.For example, the increased cell death resulting from defective DNA repair could result in telomere shortening via increased compensatory (stem) cell turnover or via direct effects on (repair of) telomeric DNA.For many segmental aging disorders, it has proven to be very difficult to distinguish between direct and indirect effects on telomere length.Perhaps phenotypically the most striking segmental aging genetic disorder in humans, Hutchinson-Gilford Progeria syndrome (HGPS), is caused by point mutations in lamin A, a key component of nuclear scaffolding (34,72).Lamin A deficiency results in absence of hair, craniofacial deformities (\"pinched\" facial features), emaciated and wrinkled appearance, as well as cardiovascular defects that eventually lead to stroke or heart attack at a very young age.The disease is characterized by specific defects in FIG. 8. Defects in human telomerase.The human telomerase complex is minimally composed of two proteins, telomerase reverse transcriptase (hTERT, green) and dyskerin (or DKC1, blue), that both bind specifically to a folded RNA molecule (or hTERC, black) containing a telomere repeat anchoring sequence and a template (red box).Known mutations in each component have now been linked to autosomal dominant dyskeratosis congenita (AD DC), bone marrow failure (BMF), and idiopathic pulmonary fibrosis (IPF) (6,63,127,134,151,217,231,234).The telomerase complex is thought to dimerize, bind to the single-strand G-rich telomere end, and catalyze the addition of new repeats (see also Figs. 3 and 4).The complex translocates along (newly added) telomere tracts for further elongation.Mutations affecting telomerase function lead to failure to assemble a functional complex.In the majority of cases, the level of telomerase activity is reduced by 50%.Such a reduction in telomerase activity compromises telomere length maintenance and increases apoptosis and senescence in proliferating cells (see Fig. 4).nuclear shape (183).Because expression of (defective) lamin A is limited to certain cell types, some cells and tissues are more affected than others.While there is evidence that DNA damage responses in cells expressing mutant lamin A are abnormal (133), the role of telomeres in this disorders (if any) remains to be clarified.A number of other segmental aging disorders have been more directly linked to telomere (dys)function.Among these, Fanconi anemia (FA) and ataxia telangiectasia (AT) are generally autosomal recessive diseases caused by mutations in, respectively, Fanconi genes (encoding any of 12 Fanconi anemia complementation group proteins) and the ataxia telangiectasia mutated gene (encoding the ATM protein).These proteins are implicated in DNA damage and repair pathways; in addition, ATM is known to phosphorylate FANCD2 (for reviews, see Refs.64,118,190).Both diseases are associated with accelerated telomere shortening (29,121,123,146), and abnormalities in telomere replication or repair are thought to play a role in the pathogenesis, particularly in the progression of the disease to immunodeficiency and bone marrow failure, as well as in the increased predisposition to malignancy in young adults.Other syndromes related to the Fanconi DNA damage response pathway include Nijmegen breakage syndrome (NBS) and Seckel syndrome.Other \"progeroid\" genes that have been implicated in DNA replication and repair are the family of genes encoding the RecQ DNA helicases.One of the functions of these enzymes is to assist in the resolution and repair of broken or stalled replication forks.Telomeric DNA is known to readily form higher order DNA structures such as G quadruplex structures in vitro (159), and it seems plausible, based on work in C. elegans (42), that specialized helicases are required to resolve structures of G-rich DNA arising sporadically during lagging strand DNA synthesis (62).Helicases that could be involved include RecQ protein-like 2 (RecQL2), RecQL3, and RecQL4 with known mutations that give rise to Werner (WRN), Bloom (BLM), and Rothmund Thompson syndromes, respectively.Accelerated telomere shortening is observed in Werner's syndrome (51), and pathology in animal model systems is accentuated in the context of telomerase deficiency (40,156).", + "\t\n\nThe relationship between DNA damage accumulation and aging has gained maximum credibility through studies conducted on various human progeria syndromes, which are genetic disorders where patients precociously develop features resembling natural aging.Most of the reported progeria syndromes, including Werner syndrome (WS), Bloom's syndrome (BS), Rothmund-Thomson syndrome (RTS), Cockayne syndrome type A and type B (CSA and CSB), Xeroderma pigmentosum (XP), Trichothiodystrophy (TTD) and Hutchinson-Gilford progeria syndrome (HGPS) are caused by mutations of genes that are directly or indirectly involved in DNA repair.Of these, WS, BS and RTS are associated with defects in RecQ helicases, i.e.RECQL2 (WRN), RECQL3 (BLM) and RECQL4 respectively, whereas CS, XP and TTD shared similar defects in NER pathway.RecQ helicases are a group of highly conserved proteins from bacteria to humans.The roles of RecQ helicases in DNA metabolism, including DNA replication, transcription, repair and recombination, have been extensively investigated and are demonstrated to be the underlying pathological basis of WS, BS and RTS [139][140][141][142].Most recently, delayed DNA damage checkpoint response and defective DNA repair were found to contribute to the progeria phenotypes in HGPS as well [143].", + "\t\n\nThey arise from mutations in one or several genes involved in DNA metabolism or in its regulation.Accelerated aging also may result from partial genome imbalances as seen in the chromosomal disorders of Down, Klinefelter and Turner syndromes.\t\n\nThese defects result in part from accumulated damage to DNA.Such damage may result inability to maintain replicative fidelity of the genome [2][3][4].Thus, organisms with mutations to genes directly involved in basic genome structure, maintenance and replicative fidelity would understandably have an accelerated aging phenotype and/or shortened life spans.Individuals with a progeroid syndrome have a premature aging phenotype and, depending on the specific mutations involved, the effects on lifespan may range from moderate to severe.Examples include Werner syndrome (WS), Bloom syndrome (BLM), Cockayne syndrome (CS), ataxia-telangiectasia (AT), Hutchinson-Gilford progeria syndrome (HGPS), and restrictive dermopathy (RD).", + "\t\n\nThe identification of these diseases spurred the creation of numerous animal models, and the characterization of engineered laboratory mutants led to the identification of many new human diseases of systemic and segmental accelerated aging.The animal models are useful for discovering how, when, and where (in what tissues) DNA damage contributes to aging, an area in which much work is still needed.The models, because of their accelerated aging, are useful for rapid hypothesis and drug testing.The models for the large part faithfully recapitulate the human genetic diseases; however, it is notable that mice tend to display a milder phenotype than humans.This might arise from the environmental contribution to human disease, which is not well reproduced in experimental model systems.Collectively, however, these human diseases and their conservation in multiple animal model systems strongly support the role of DNA damage as a proximal contributor to aging.", + "\t\n\nThe number of identified genes associated with progeroid syndromes has increased in recent years, possibly shedding light as well on mechanisms underlying ageing in general.\t\n\nSeveral heritable premature aging syndromes have for a long time been linked to defects in genome maintenance, due to altered DNA repair mechanisms.These mainly include the following autosomal recessive syndromes: (i) Werner syndrome, due to mutations in RecQL2 DNA helicase; (ii) Cockayne syndrome (CS) type A and B, due to mutations in the genes encoding the group 8 or 6 excision-repair cross-complementing proteins (ERCC8 and ERCC6), respectively; (iii) Rothmund-Thomson syndrome (RTS), due to RecQL4 mutations; (iv) trichothiodystrophy (TTD), due to mutations in the genes ERCC2/XPD and ERCC3/XPB, encoding the two helicase subunits of the transcription/repair factor TFIIH, as well as in TFB5, encoding the tenth subunit of TFIIH (Giglia-Mari et al., 2004); (v) ataxia-telangiectasia, due to mutations in the ataxia-telangiectasia mutated gene (ATM); (vi) xeroderma pigmentosum (XP), a genetically heterogeneous autosomal recessive disorder in which can be distinguished at least seven complementation groups, due to mutations of different DNA excisionrepair proteins (Hasty et al., 2003;Kipling et al., 2004).All these progeroid diseases, involving heritable defects in DNA repair, suggest a central role of genome integrity maintenance in the aging process.\tConclusion\n\nFrom a pathophysiological point of view, the known Progeroid syndromes are caused either by mutations in genes encoding DNA repair proteins, such as in WS, Bloom syndrome (BS), Rothmund-Thomson syndrome, Cockayne syndrome, xeroderma pigmentosum or trichothiodystrophy (Hasty et al., 2003;Wood et al., 2005), or by mutations in genes encoding Lamins A/C or partners involved in their biological pathway, such as HGPS or RD (De Sandre-Giovannoli et al., 2003;Eriksson et al., 2003;Navarro et al., 2004Navarro et al., , 2005)).\t\nProgeroid syndromes are heritable human disorders displaying features that recall premature ageing.In these syndromes, premature aging is defined as ''segmental'' since only some of its features are accelerated.A number of cellular biological pathways have been linked to aging, including regulation of the insulin/growth hormone axis, pathways involving ROS metabolism, caloric restriction, and DNA repair.Different animal models, ranging from yeast, to nematodes, to mice, have been instrumental in obtaining evidence for these connections (Hasty et al., 2003).Several heritable premature aging syndromes have for a long time been linked to defects in genome maintenance, due to altered DNA repair mechanisms.These mainly include the following autosomal recessive syndromes: (i) Werner syndrome, due to mutations in RecQL2 DNA helicase; (ii) Cockayne syndrome (CS) type A and B, due to mutations in the genes encoding the group 8 or 6 excision-repair cross-complementing proteins (ERCC8 and ERCC6), respectively; (iii) Rothmund-Thomson syndrome (RTS), due to RecQL4 mutations; (iv) trichothiodystrophy (TTD), due to mutations in the genes ERCC2/XPD and ERCC3/XPB, encoding the two helicase subunits of the transcription/repair factor TFIIH, as well as in TFB5, encoding the tenth subunit of TFIIH (Giglia-Mari et al., 2004); (v) ataxia-telangiectasia, due to mutations in the ataxia-telangiectasia mutated gene (ATM); (vi) xeroderma pigmentosum (XP), a genetically heterogeneous autosomal recessive disorder in which can be distinguished at least seven complementation groups, due to mutations of different DNA excisionrepair proteins (Hasty et al., 2003;Kipling et al., 2004).All these progeroid diseases, involving heritable defects in DNA repair, suggest a central role of genome integrity maintenance in the aging process.The number of identified genes associated with progeroid syndromes has increased in recent years, possibly shedding light as well on mechanisms underlying ageing in general.Among these, premature aging syndromes related to alterations of the LMNA gene have recently been identified.LMNA encodes Lamins A/C, ubiquitous nuclear proteins belonging to the intermediate filament superfamily.These premature aging disorders have thus been classified as ''Laminopathies'', the large group of diseases associated to Lamin A/C defects.This group of heterogeneous disorders includes three main subgroups: (1) neuromuscular disorders (Emery-Dreifuss muscular dystrophy, limb-girdle", + "\t\n\nHowever, only those genetic disorders that exhibit premature aging, neurodegeneration (mental defects), and some form of chromosomal/DNA damage all together will be empha-sized here.Perhaps the most appropriate disorder under this category is Down's syndrome.It has several features of premature aging and the genetic defect is trisomy of the distal part of the long arm of chromosome 21.The critical segment of chromosome 21 is shown to have three genes coding for copper-and zinc-dependent superoxide dismutase, oncogene ets-2, and cystathione ~-synthase (Delabar et al., 1987).Since elevated levels of superoxide dismutase are found in various tissues of these individuals, it is postulated that the accelerated aging of these patients may be caused by overproduction of superoxide dismutase, which is responsible for the production of H20 2 while scavenging the oxygen-free radicals.The brains of Down's syndrome individuals are particularly vulnerable to oxidative DNA damage because the high levels of superoxide dismutase found in this tissue are not accompanied by an elevation in the glutathione peroxidase and catalase (Balazs and Brookshank, 1985) that would have normally helped in removing the overproduced H202.Other genetic syndromes characterized by signs of nervous debility, premature aging, and DNA damage/ decreased DNA-repair capacity, are Ataxia Telangiectasia (AT) and Cockayne syndrome (CS).", + "\tRare genetic disorders of aging\n\nProgeria, also known as Hutchinson-Gilford progeria syndrome, affects one in four million births worldwide with equal distribution between sex and race, causing a child's body to age more rapidly (Genetics Home Reference, 2019a).Symptoms typically occur within the first year of life, and most children do not live past 13 years.Mutation in the LMNA gene (not an adduct or telomere factor) contributes to abnormal lamin A protein, called progerin, causing cell instability and cells to easily breakdown (Genetics Home Reference, 2019a).There is no current cure for progeria but farnesyltransferase inhibitors, a cancer drug, has shown promise in reversing cell damage (Genetics Home Reference, 2019a).Other supportive treatments include cardiovascular diseaserelated issues, growth hormones, and bone/joint health.Adalia Rose has taken to social media, with multiple YouTube and Facebook postings, to help others understand her case of progeria.", + "\t\n\nMitochondrial DNA (mtDNA) mutations are thought to have a causal role in many age-related pathologies.Here we identify mtDNA deletions as a driving force behind the premature aging phenotype of mitochondrial mutator mice, and provide evidence for a homology-directed DNA repair mechanism in mitochondria that is directly linked to the formation of mtDNA deletions.In addition, our results demonstrate that the rate at which mtDNA mutations reach phenotypic expression differs markedly among tissues, which may be an important factor in determining the tolerance of a tissue to random mitochondrial mutagenesis.", + "\tINTRODUCTION\n\nIn genetics, identification of genotype-phenotype relationships relies on generated or selected mutants, which highlight underlying mechanisms.For the biology of aging, mutants that display delayed or accelerated aging have been invaluable.Rare heritable syndromes have been identified in the human population that exhibit multiple features of premature aging.A search in the Online Mendelian Inheritance in Man database (OMIM version February 25, 2015) using the keywords \"premature aging,\" \"progeria,\" or \"progeroid\" yielded 20 syndromes with at least one known mutated gene.Certainly this list is far from complete; for example, ataxia telangiectasia, fanconi anemia, and maternally transmitted mitochondrial syndromes such as maternally inherited diabetes and deafness and mitochondrial encephalomyopathy (MIDD/MELAS) are missing.Additionally, many more conditions await identification as unrecognized progeroid syndrome.The application of powerful exome and whole genome sequencing technologies will dramatically accelerate molecular resolution of genetic defects in rare patients with features of accelerated aging, and through this process, many new genes underlying these conditions will be identified.However, when we assign a primary function to each of the causally mutated genes in the known syndromes, it appears that the majority is linked to perturbed genome integrity, a second class represents metabolism, and one syndrome appears connected with cell adhesion (Figure 1).Recently, evidence has emerged for bidirectional interactions between the main aging-related processes: For instance, most DNA damage is derived from endogenous metabolic sources, and compromised genome function indirectly affects many cellular processes including metabolism (1, 2).This suggests the existence of a tightly interwoven network that underlies aging, which is the focus of this review.Progeria-associated syndromes classified by primary function of the causal genetic defect.These 20 human syndromes, listed outside of the circle, were selected from the OMIM database using the keywords \"premature aging,\" \"progeria,\" and \"progeroid. \"Related primary functions were combined in the categories genome integrity, metabolism, and adhesion (inner circle).Abbreviations: DSB, DNA double-strand break; MDPL, mandibular hypoplasia, deafness, progeroid features, and lipodystrophy; PI3K, phosphoinositide-3-kinase; PS, phosphatidylserine; XFE, XpF-Ercc1.GAPO indicates growth retardation, alopecia, pseudoanodontia, and optic atrophy.SHORT indicates short stature, hyperextensibility, hernia, ocular depression, Rieger anomaly, and teething delay." + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_aging_3.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_aging_3.json new file mode 100644 index 0000000..7c66ed3 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_aging_3.json @@ -0,0 +1,122 @@ +{ + "question": [ + "What recent discoveries have been made about the genetics of aging?", + "How do epigenetic changes affect aging?", + "What is the role of the gene FOXO3 in longevity?", + "How does the environment interact with genes to influence aging?", + "What are senescent cells and how do they contribute to aging?" + ], + "answer": [ + "Recent discoveries in the genetics of aging include the identification of numerous genes that can alter the aging process in animal models and humans. Genetic research has also found that immune-related pathways might be critically involved in aging. Specific genes, such as the apoE polymorphisms, have been found to influence both cardiovascular disease and Alzheimer's disease. Additionally, whole-genome sequencing in centenarians has revealed genetic variants associated with longevity. Studies have also shown that aging can be influenced by the interactions of multiple genes with each other and the environment.", + "Epigenetic changes significantly influence the aging process. These changes occur at various levels, including alterations in DNA methylation, histone modifications, and noncoding RNA expression. They can lead to altered gene expression, reactivation of transposable elements, and genomic instability. These changes can also influence lifespan, with diet and environmental factors potentially altering epigenetic information. Importantly, these changes can be partially or fully reversible, suggesting potential avenues for therapeutic intervention in aging and age-associated diseases.", + "FOXO3, a transcription factor, is associated with lifespan extension in various species, including humans. It is regulated by the insulin/IGF signaling pathway, the nutrient sensor AMPK, and stress. This gene controls the expression of genes involved in stress response, metabolism, immunity, and neuronal function. The FOXO3 variant has been associated with extreme longevity in humans, particularly in centenarians. It's also involved in the response to DNA damage and apoptosis. However, the exact molecular mechanisms affecting longevity require further study.", + "The environment interacts with genes to influence aging through various factors such as diet, lifestyle, and exposure to certain elements. Environmental factors can trigger signaling pathways and modulate gene expression, which in turn can affect the aging process. For instance, diet can influence aging trajectories in humans, with early nutrition affecting late-life diseases. Environmental factors like caloric restriction and smoking can also impact the rate of aging. Furthermore, environmental exposure to elements like airborne particulates, smoking, and alcohol consumption can contribute to age-related diseases. Understanding these genome-environment interactions can help in the development of anti-aging therapies and interventions.", + "Senescent cells are cells that have permanently stopped dividing but remain metabolically active. They are a hallmark of aging and accumulate in tissues over time. Senescent cells contribute to aging and age-related diseases through several mechanisms. They secrete pro-inflammatory factors and proteases that can alter the local tissue environment, promoting chronic inflammation and inducing senescence in healthy cells. This can lead to tissue dysfunction, chronic disease, and a decrease in the regenerative potential of stem cells. Additionally, senescent cells are resistant to apoptosis, leading to their accumulation and disruption of the tissue microenvironment." + ], + "contexts": [ + [ + "\t\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals.", + "\t\n\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.\t\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study.\tIntroduction\n\nWith the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging.", + "\t\nAging is a complex process affecting different species and individuals in different ways.Comparing genetic variation across species with their aging phenotypes will help understanding the molecular basis of aging and longevity.Although most studies on aging have so far focused on short-lived model organisms, recent comparisons of genomic, transcriptomic, and metabolomic data across lineages with different lifespans are unveiling molecular signatures associated with longevity.Here, we examine the relationship between genomic variation and maximum lifespan across primate species.We used two different approaches.First, we searched for parallel amino-acid mutations that co-occur with increases in longevity across the primate linage.Twenty-five such amino-acid variants were identified, several of which have been previously reported by studies with different experimental setups and in different model organisms.The genes harboring these mutations are mainly enriched in functional categories such as wound healing, blood coagulation, and cardiovascular disorders.We demonstrate that these pathways are highly enriched for pleiotropic effects, as predicted by the antagonistic pleiotropy theory of aging.A second approach was focused on changes in rates of protein evolution across the primate phylogeny.Using the phylogenetic generalized least squares, we show that some genes exhibit strong correlations between their evolutionary rates and longevity-associated traits.These include genes in the Sphingosine 1-phosphate pathway, PI3K signaling, and the Thrombin/protease-activated receptor pathway, among other cardiovascular processes.Together, these results shed light into human senescence patterns and underscore the power of comparative genomics to identify pathways related to aging and longevity.", + "\t\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26].", + "\t\n\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases.\t\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases.", + "\t\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010).", + "\t\n\nIn this light, we pursued a genomic study of an alternate but related aging phenotype-healthy aging-in order to expose its potential to uncover genetic factors for protection against age-associated disease.It is important to differentiate longevity from our healthy aging phenotype, which, as we have defined it for our healthy aging cohort (Wellderly), attempts to understand the genetics of disease-free aging in humans without medical interventions.Toward this end, we performed whole-genome sequencing (WGS) of the Wellderly and compared their genetic characteristics to an ethnicity-matched population control.Our findings suggest that healthy aging is associated with a diseaseprotective genetic profile that overlaps with but differs from that observed in exceptional longevity cohorts.These findings include no enrichment of true longevity variants, a lower genetic risk from common susceptibility alleles for Alzheimer and coronary artery disease, and no decrease in the rate of rare pathogenic variants.We identify suggestive common and rare variant genetic associations that implicate genetic protection against cognitive decline in healthy aging.Our data are made available for the discovery of additional disease protective genetic factors by the research community.", + "\t\n\nThe studies in lower animals made in recent years that have led to the view that genes are involved in aging have not revealed a reversal or arrest of the inexorable expression of molecular disorder that is the hallmark of aging.These studies are more accurately interpreted to have impact on our understanding of longevity determination because all of the experimental results have altered biological variables before the aging process begins.None of these studies in invertebrates has demonstrated that the manipulation of genes has slowed, stopped, or reversed recognized biomarkers of the aging process.", + "\tGENETIC ANALYSIS OF LONGEVITY, OF AGING, AND OF AGE-SENSITIVE TRAITS IN MICE\n\nBiogerontology has just begun to benefit from the attention and skills of professional geneticists.Geneticists can attack problems of aging from several related but fundamentally distinct directions.Studies of rare mutations at individual loci, such as the Werner's syndrome locus WRN, whose mutant form produces, in middle-aged people, several of the diseases typically not seen until old age, can give attractive points of entry into the pathophysiology of age-related diseases.In mice there are now four reports of mutations-two naturally occurring and two artificially produced-that lead to impressive increases in mean and maximal longevity (Miskin and Masos, 1997;Brown-Borg et al., 1996;Miller, 1999;Migliaccio et al., 1999), and thus provide extremely valuable models for testing mechanistic ideas and the control of aging.Some of these, such as the dw/dw and df/df dwarfing mutations that affect levels of growth hormone and thyroid hormone, provide clues to endocrine-dependent pathways that could regulate age effects in multiple cells and tissues.The recent report (Migliaccio et al., 1999) that mouse life span can be extended by an induced mutation that diminishes cell susceptibility to apoptotic death after injury should stimulate new inquiries into the effects of altered cell turnover on age-dependent changes.Each of these mutations, however, is exceptionally rare in natural populations; despite their effect on longevity, perhaps mediated by a direct effect on aging, each of the mutations is likely to have, overall, a negative effect on reproductive success and thus fail to become fixed in natural mouse populations.\t\n\nAny discovery about the biological determinants of the rate of aging raises the possibility of therapies to slow aging.Therefore the discovery of a gerontogene with even very rare mutations that increased longevity would cause speculation about future trends in mortality.However, the discovery of such a gene would be relevant only to long-term (and, therefore, very speculative) projections.", + "\t\n\nThe remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design.", + "\t\n\nHere, we review advances in genomic analysis within and across species to help refine the genetic foundations of age-associated diseases and longevity.As such, independent evolutionary occurrences of this species-specific lifespan change can empower comparative approaches to refine the shared mechanisms associating with longevity phenotypes.These evolutionary-refined gene sets can then be leveraged to focus statistical analysis within human cases of extreme longevity to discover core mechanisms of regulation.", + "\t\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways.", + "\t\n\nWith modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms.", + "\tGenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhes, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging.", + "\tConclusions and prospects\n\nOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges.", + "\t\n\nAlthough many theories have tried to explain aging, only few experimental advances were made prior to the last two decades.Since then rapid progress in the genetics of aging has been made in invertebrate models such as C. elegans and D. melanogaster, demonstrating the existence of regulatory pathways that control the rate of aging in these organisms [1][2][3][4][5][6][7][8][9][10][11][12][13][14].They include the insulin-like pathway, the Jun kinase pathway and the Sir2 deacetylase pathway.Moreover, it was rapidly shown that some of these pathways are conserved from yeast to humans." + ], + [ + "\t\n\nIn summary, our data suggest that epigenetic mechanisms can be crucial for normal aging and be important players responsible for neuron-specifi c changes accumulated during this process.", + "\t\n\nTogether, the examples above provide strong evidence that epigenetics-both DNA methylation and histone modifications-influence aging and that these impacts can differ between the sexes.The data from human DNA methylation studies suggest that alterations to the epigenome occur at a slower pace in females than in males.The data from model organisms are limited; additional studies will be needed to get a clear picture of how age-associated epigenetic changes might contribute to the sex-differences in aging observed.\tEpigenetics\n\nIn addition to increased DNA damage, mutations, and telomere attrition, large-scale epigenetic changes have been associated with increased age in a number of species.The epigenetic changes seen in old compared to young animals are quite diverse and include changes in histone modifications, DNA methylation, and levels of chromatin remodeling and modifying enzymes [for recent reviews see (63) or (64)].Heterochromatin, the silent form of chromatin required for proper centromere and telomere function and repression of transposable elements, is often lost during aging.Increased transcriptional noise associated with epigenetic changes during aging has been proposed to cause at least some of the degenerative phenotypes observed with increased age.While a variety of epigenetic changes occur with age, the relative importance of each of these changes and the impact of sex and genetic background on these changes is poorly understood.", + "\t\n\nFigure1.Epigenetics of aging and aging-related diseases.During aging, various epigenetic alterations occur including accumulation of histone variants, changes in chromatin accessibility mediated by chromatin remodeling complexes, loss of histones and heterochromatin, imbalance of activating/repressing histone modifications and aberrant expression/activity of miRNAs.These deregulations can affect transcription and, subsequently, translation, as well as the stabilization or degradation of molecular components.Consequently, these aberrant epigenetic processes can promote morbidities, which are frequently observed in the elderly populations, including inflammation, cancer, osteoporosis, neurodegenerative diseases, and diabetes.\t\n\nFigure1.Epigenetics of aging and aging-related diseases.During aging, various epigenetic alterations occur including accumulation of histone variants, changes in chromatin accessibility mediated by chromatin remodeling complexes, loss of histones and heterochromatin, imbalance of activating/repressing histone modifications and aberrant expression/activity of miRNAs.These deregulations can affect transcription and, subsequently, translation, as well as the stabilization or degradation of molecular components.Consequently, these aberrant epigenetic processes can promote morbidities, which are frequently observed in the elderly populations, including inflammation, cancer, osteoporosis, neurodegenerative diseases, and diabetes.", + "\t\nOver the past decade, a growing number of studies have revealed that progressive changes to epigenetic information accompany aging in both dividing and nondividing cells.Functional studies in model organisms and humans indicate that epigenetic changes have a huge influence on the aging process.These epigenetic changes occur at various levels, including reduced bulk levels of the core histones, altered patterns of histone posttranslational modifications and DNA methylation, replacement of canonical histones with histone variants, and altered noncoding RNA expression, during both organismal aging and replicative senescence.The end result of epigenetic changes during aging is altered local accessibility to the genetic material, leading to aberrant gene expression, reactivation of transposable elements, and genomic instability.Strikingly, certain types of epigenetic information can function in a transgenerational manner to influence the life span of the offspring.Several important conclusions emerge from these studies: rather than being genetically predetermined, our life span is largely epigenetically determined; diet and other environmental influences can influence our life span by changing the epigenetic information; and inhibitors of epigenetic enzymes can influence life span of model organisms.These new findings provide better understanding of the mechanisms involved in aging.Given the reversible nature of epigenetic information, these studies highlight exciting avenues for therapeutic intervention in aging and age-associated diseases, including cancer.\t\n\nOver the past decade, a growing number of studies have revealed that progressive changes to epigenetic information accompany aging in both dividing and nondividing cells.Functional studies in model organisms and humans indicate that epigenetic changes have a huge influence on the aging process.These epigenetic changes occur at various levels, including reduced bulk levels of the core histones, altered patterns of histone posttranslational modifications and DNA methylation, replacement of canonical histones with histone variants, and altered noncoding RNA expression, during both organismal aging and replicative senescence.The end result of epigenetic changes during aging is altered local accessibility to the genetic material, leading to aberrant gene expression, reactivation of transposable elements, and genomic instability.Strikingly, certain types of epigenetic information can function in a transgenerational manner to influence the life span of the offspring.Several important conclusions emerge from these studies: rather than being genetically predetermined, our life span is largely epigenetically determined; diet and other environmental influences can influence our life span by changing the epigenetic information; and inhibitors of epigenetic enzymes can influence life span of model organisms.These new findings provide better understanding of the mechanisms involved in aging.Given the reversible nature of epigenetic information, these studies highlight exciting avenues for therapeutic intervention in aging and age-associated diseases, including cancer.\t\n\nFig. 1.Overview of epigenetic changes during aging.In young individuals, the cells within each cell type have a similar pattern of gene expression, determined in large part by each cell having similar epigenetic information.During aging, the epigenetic information changes sporadically in response to exogenous and endogenous factors.The resulting abnormal chromatin state is characterized by different histone variants being incorporated, altered DNA methylation patterns, and altered histone modification patterns, resulting in the recruitment of different chromatin modifiers.The abnormal chromatin state in old cells includes altered transcription patterns and transcriptional drift within the population.The abnormal chromatin state in old cells also leads to new transposable elements being inserted into the genome and genomic instability, including DNA mutations.\tTRANSGENERATIONAL EPIGENETIC CHANGES THAT AFFECT AGING\n\nAccording to biological dogma, genetics governs all the inherited traits across generations, and epigenetic modifications are reset upon passage through the germ line.However, over the years, this notion was challenged when evidence of epigenetic inheritance through meiosis became acknowledged in certain processes, such as flower symmetry and color in plants, or coat color and size in mice (198,199).Recently, longevity mediated by histone methylation was shown to be epigenetically inherited for several generations (198), implicating transgenerational epigenetic inheritance for the first time in the regulation of life span.Deficiencies in either of the three components of H3 K4me3 methylase complex (ASH-2, WDR-5, or SET-2), in only the parental generation, resulted in life span extension in C. elegans in the three subsequent generations, in the absence of methylase deficiency in these offsprings.However, only the parents with the deficiencies in the H3 K4me3 regulatory complex, and not their wild-type long-lived offspring, had reduced global H3 K4me3 levels.Hence, altered histone methylation per se was not transgenerationally inherited.Instead, microarray analysis revealed that there were persistent changes in gene expression throughout the generations upon manipulation of the H3 K4me3 regulatory complex in the parents (198), which could potentially be responsible for the transgenerational inheritance of long life span.Further experimentation is needed to identify the pathways responsible for the transgenerational inheritance of longevity and to explore whether this epigenetic memory is generalizable to other species.A useful approach to study the inheritance of aging phenotypes would be to follow the lead of a recent study examining epigenetic germ line inheritance of dietinduced obesity and insulin resistance in mice (200).This study used in vitro fertilization to ensure exclusive inheritance through the gametes and showed that the parental high-fat diet renders the offspring more susceptible to developing obesity and diabetes.It is tempting to speculate that this novel mode of inheritance may illustrate how epigenetics could have contributed to evolution, whereby the ancestors' environmental exposure determined the fate of the descendants.Given the intriguing nature of the subject, more studies will undoubtedly further explore this exciting direction in the near future.", + "\tEpigenetic modifications, most commonly in the form of changes in the methylation\nstatus of DNA and biochemical modifications of core histones, have been linked to the\naging process and are increasingly recognized as part of normal and pathologic aging\nphysiology (Issa, 2003). Manel Estellers group studied the epigenetic profiles of 80\npairs of monozygotic twins ranging in age from 3-74 years old and found that older twins\nexhibited large differences in their overall content and distribution of 5-methylcytosine\nDNA and histone acetylation compared to young twins which were largely\nindistinguishable epigenetically (Fraga et al. , 2005).", + "\t\n\nClearly, epigenetic changes are both responsive to and effectors of the aging process.With DNA damage and environmental stresses like inflammation leading to changes in chromatin, the epigenome clearly adapts to age-related changes in the genome and the local milieu.Perhaps the epigenome is a general sensor of cellular dysfunction, sensing metabolic and proteomic changes that accompany aging as well.However, the epigenome is also an effector of the aging process, enforcing different patterns of gene expression in old cells and young cells and, in many cases, resulting in cellular phenotypes associated with aging such as senescence and metaplasia (Martin, 2009).In that sense, the epigenome is rather like a lens through which genomic information is filtered (Figure 3), a lens that deteriorates with age because of both loss of integrity of genomic information and direct environmental stresses within and outside of the cell.Within the ''epigenome as lens'' metaphor, the process of rejuvenation is the restoration of a youthful state by actions on the epigenomic lens (Figure 3).The loss of integrity of the genomic information remains, but the rejuvenating interventions are sufficient to overcome and possibly reverse at least some of the agerelated epigenetic changes.Similarly, an altered epigenome and gene expression programs may also be able to reverse or compensate for some age-dependent biochemical changes, such as protein aggregation, macromolecular oxidation, and glycation, to maintain cellular functions (Douglas and Dillin, 2010).", + "\tRole of Epigenetic Alterations\n\nA wide range of epigenetic alterations affects the cells during the life span, which may modulate vascular aging phenotypes. 138Epigenetic changes that may contribute to vascular aging processes involve alterations in DNA methylation patterns, posttranslational modification of histones, microRNAs (miRNAs), long noncoding RNAs, and chromatin remodeling.", + "\tEpigenetics of aging\n\nIncreasing evidence supports a role for epigenetics in the biology of aging.X-inactivated genes in the mouse show an increased frequency of reactivation with aging, consistent with age-related epigenetic change [39,40].The frequency of epigenetic changes in mice may be one to two orders of magnitude greater than the rate of somatic DNA mutation [41].This fits with a role of epigenetics in late-onset disorders such as frailty, a syndrome of decreased resiliency and reserves, in which a mutually exacerbating cycle of declines across multiple systems results in negative energy balance, sarcopenia, and diminished strength and tolerance for exertion [42].Accumulation of DNA sequence changes might not occur at enough high rate during the lifespan to induce common disease, but epigenetic changes may occur at a frequency that could contribute to this effect.Very few studies have demonstrated epigenetic changes in humans with age due to technical and biosample limitations.A recent study has shown differences in local and global methylation by age by examining the similarity in methylation patterns between MZ twins aged 3 years old and MZ twins aged 50.Although these analyses were not in the same individuals (the same twins were not followed longitudinally), the similarity in methylation patterns between young twins compared to the dissimilar patterns among older twins argues strongly for age-related changes in the epigenome [43].Direct evidence comes from a recent study showing changes in DNA methylation in the same individual over time, described in more detail below.", + "\tIntroduction\n\nEpigenetics is destined to change across the lifespan.Loss of global DNA methylation and promoter hypermethylation of several specific genes occur during aging.Epigenetics plays an important role in cellular senescence, human tumorigenesis, and several agerelated diseases (Fraga et al. 2007;Bollati et al. 2009;Kim et al. 2010;Choi et al. 2009;Moore et al. 2008;Rakyan et al. 2010;Chambers et al. 2007).Indeed, epigenomic alterations are now increasingly recognized as part of aging and its associated pathologic phenotype (Petronis 2010;Bellizzi et al. 2011).However, the role of epigenetics in the modulation of healthy aging and longevity has not been clearly studied in humans.", + "\t\n\nEpigenetic changes linked to aging also impact specific diseases of aging, including cancer.While some age-associated epigenetic changes, such as increased abundance of histone modification H4K20me3 [10] and decreased H3K27me3 [38,39], may activate tumour suppressor mechanisms and prevent cancer, others may be tumour promoting.Like cancer, aged tissue has been reported to exhibit global DNA hypomethylation and more focal hypermethylation at CpG islands [10].Most notably, so-called bivalent gene promoters, marked with both activating H3K4me3 and repressing H3K27me3 (hence \"bivalent\") in embryonal stem (ES) cells, acquire DNA methylation in aged tissues and are also methylated and stably silenced in cancer [15][16][17][18][19].In ES cells, these bivalent-marked genes are thought to be poised for activation due to loss of the repressive H3K27me3 mark during stem and progenitor cell differentiation and development.By virtue of their pro-differentiation functions these genes tend to have tumour suppressor-like properties, meaning that their methylation and stable silencing may promote proliferation, self-renewal and malignancy.In the haematopoietic system, some CpG islands progressively increase methylation from young to old to neoplasia, namely myelodysplastic syndrome (MDS) and ultimately acute myeloid leukemia [40].Sf3b1, the mouse ortholog of a gene frequently mutated in human MDS, is methylated and underexpressed in aged mouse HSCs [36].Hence, age-associated methylation changes might predispose to transformation of aged cells by promoting silencing of tumour suppressor genes.\t\n\nAging is associated with changes to the epigenome [10,11].These changes include age-associated accumulation of histone variants, for example histone H3.3 in neurons and macroH2A in lung, liver and muscle, as well as other chromatin-associated proteins and changes to histone and DNA modifications [12][13][14].Aging also affects specific gene regulatory elements, such as enhancers, promoters and CpG islands [15][16][17][18][19][20][21][22][23].Underscoring the importance of such age-associated epigenetic changes, recent human studies have identified collections of specific CpGs whose age-associated change in methylation status in multiple tissues correlates strongly with chronological age.An advanced methylation age compared to actual chronological age is thought to reflect accelerated biological age and is linked to increased mortality [24][25][26][27][28].", + "\t\n\nVasily V. Ashapkin and coworkers studied a direct relationship on how aging affects the epigenetic phenomenon.It has been established that hypermethylation of genes associated with promoter CpG islands, and hypomethylation of CpG poor genes, repeat sequences, transposable elements and intergenic genome sections occur during aging in mammals.Moreover, the methylation levels of certain CpG sites display strict correlation with age and can be used as \"epigenetic clock\" to predict biological age.Multi-substrate deacetylases SIRT1 and SIRT6 affect aging via locus-specific modulations of chromatin structure and activity of multiple regulatory proteins involved in aging.In addition, the random changes in DNA methylation or chromatin remodeling on aging lead to gradual increase in transcriptional noise introducing phenotypic variation among cells.Therefore, most likely based on the author's interpretation, such variation could become detrimental to tissue functioning, leading to highly variable progressive decline in organ functions during aging.Multiple data of age-dependent induction of NF-B regulated gene sets in various tissues suggest NF-B to be a master regulator of gene expression programs in mammalian aging.Vasily V. Ashapkin and coworkers summarized how the upregulation of multiple miRNAs occurs at mid age leading to downregulation of genes functionally involved in the control of intermediate metabolism, apoptosis, DNA repair, oxidative defense, and mitochondrial oxidative phosphorylation.Strong evidence shows that all epigenetic systems contribute to the life span control in various organisms.Similar to other cell systems, epigenome is prone to gradual degradation due to the genome damage, stressful agents and other aging factors.Critical analysis by Vasily V. Ashapkin et al., demonstrated that unlike mutations and other kinds of the genome damage, age-related epigenetic changes could be fully or partially reversed to a \"young\" aged state and requires more detailed analysis in the context of the aged associated genetic modification especially during the courses of the development and maturation of human diseases.", + "\tEPIGENETIC REGULATION OF AGING\n\nIn addition to gene expression changes, the states of epigenetic modifications have emerged to be significantly important in modulating lifespan (see the accompanying review by Liu and Zhou in this issue [45]).Epigenetic modifications include DNA and histone modifications that are potentially heritable and reversible without changing the genetic code [46].With the application of recent high-throughput approaches, such as bisulfite sequencing, ChIP-seq or ChIPchip, etc. (Section 1), epigenetic controls have become wellrecognized as important regulatory mechanisms during the lifetime of an organism [46,47].For example, using the anti-O-GlcNAc ChIP-on-chip whole-genome tiling arrays on C.elegans, Love et al. [48] found 800 genes displaying differential cycling of O-GlcNAc which have functions closely related to aging.By examining DNA methylation at CpG sites throughout the human genome, Hernandez et al. [49] identified hundreds of CpG sites with levels of DNA methylation in the human brain highly correlated with chronological age.", + "\tThe impact of epigenetic changes accumulated during aging on the aging phenotype\n\nA key question about the role of epigenetics in aging is whether epigenetic changes accumulated during aging have a causal role in establishing the aging phenotype or if the two phenomena are unrelated.To settle this matter, it is important to consider the region in the genome/chromatin where these changes occur.Changes occurring in non-coding sequences will potentially have a smaller biological impact than those occurring in coding sequences as modifications of the latter type generally involve changes in gene expression.It is also important to consider the cells and tissues in which these occur because epigenetic patterns are celland tissue-specific so that changes occurring in a specific cell or tissue would not necessarily imply the same functional consequences in different cells or tissues.\tEpigenetic changes during ontogenic development and aging\n\nThe relationship between epigenetics and aging was proposed many years ago (Table 1).A pioneering study by Berdyshev et al. (1967) showed that genomic global DNA methylation decreases with age in spawning humpbacked salmon.Subsequently, Vanyushin et al. (1973) also detected a global loss of cytosine methylation during aging in rat brain and heart.More recently, Wilson et al. (1987) confirmed the gradual loss of DNA methylation with age in various mouse tissues and in human bronchial epithelial cells.Similarly, Fuke et al. (2004) recently found an agedependent decrease in global methylation levels in human leukocytes.The definitive corroboration on intra-individual epigenetic variation over time in humans, was recently provided in a longitudinal study of DNA methylation patterns in which successive DNA samples were collected more than 10 years apart in more than 100 individuals (Bjornsson et al., 2008)." + ], + [ + "\t\n\nForkhead box O3a (mFoxo3a) is a transcription factor that is characterized by a fork head DNA-binding domain and has been associated with longevity in humans as well as with several cancers.Similar to the situation with mSirt1, no daily rhythm in expression was detected, and no differences among the ages of mice was determined (Figure 4B).", + "\tWillcox BJ, Donlon TA, He Q et al (2008) FOXO3A genotype is\nstrongly associated with human longevity. Proc Natl Acad Sci\nUSA 105(37):1398713992. doi:10.1073/pnas.0801030105\n4. Anselmi CV, Malovini A, Roncarati R et al (2009) Association of\nthe FOXO3A locus with extreme longevity in a southern Italian\ncentenarian study. Rejuvenation Res 12(2):95104. doi:10.1089/\nrej.2008.0827\n5. Flachsbart F, Caliebe A, Kleindorp R et al (2009) Association of\nFOXO3A variation with human longevity confirmed in German\ncentenarians. Proc Natl Acad Sci USA 106(8):27002705. doi:10. 1073/pnas.0809594106\n6.", + "\tCross-species, cross-condition comparisons reveal shared longevity gene-expression signatures\n\nBased upon the hypothesis that longevity may be mediated by common sets of target genes that are effectors of upstream signaling pathways, and that the transcriptional targets of FOXO are likely to include direct mediators of increased longevity, the gene expression profiles resulting from MnSOD over-expression in Drosophila were compared to those of genes regulated by daf-2 in a daf-16 dependent manner in C. elegans [74,75].Remarkably, comparison of MnSOD target genes (genes whose expression was altered at both time points) to those genes regulated by daf-2 in a daf-16 dependent manner [74] revealed 25 genes (Figure 7) out of 3,542 unique fly genes with a stringent worm ortholog that were upregulated in both conditions, and this overlap is non-random (p << 0.001; Additional data file 5).When the list of MnSODregulated genes was expanded to include those genes altered at the same chronological age, but not the same 'physiological age', five additional conserved genes (CG15099, Jra, PHGPx, n-syb, Hrb98DE) were identified (Additional data file 7).\tMnSOD-regulated targets downstream of dFOXO\n\nThe cross-species, cross-condition comparison described above was aimed at identifying genes and processes that broadly mediate lifespan and, hence, are robust signatures of longevity mechanisms.However, certain downstream targets of dFOXO may have been missed by a comparison of stringent orthologs.In order to identify species specific MnSODregulated targets that act downstream of dFOXO as well as potential lifespan promoting mechanisms that might be unique to Drosophila, the transcriptional profile of MnSOD over-expression was compared to those resulting from altered insulin signaling in Drosophila.These comparisons are described in Additional data file 10.", + "\t\n\nAge-associated changes in transcriptional factors represent a critical aspect of aging [2].Some conserved pro-longevity factors are FOXO/DAF-16, NRF/SKN-1, HSF-1, XBP-1, REST/SPR-4, and p53/CEP-1.FOXO/DAF-16 promotes longevity in a variety of species from worms to humans, and it is regulated by the insulin/IGF signaling pathway, the nutrient sensor AMPK, and stress [56,57].This transcription factor controls the expression of genes involved in stress response, metabolism, immunity, and neuronal function in a variety of organisms, and interestingly, the FOXO3 locus is associated with extreme longevity in humans (centenarians) [2,58,59].", + "\t\n\nIncreasing S-adenosylmethionine (SAM) synthesis by FOXO-dependent glycine N methyltransferase (Gnmt) extends the lifespan in Drosophila and thus overexpression of Gnmt increases longevity, cooperatively with Notes: These transcripts are significantly affected more than two-fold (>LogFC 1) dietary restriction and lowered IIS [137].We see a 6.3 LogFC (increase) in Gnmt in three week diapausing flies (Additional file 3: Dataset S1, Additional file 9: Table S4).Another gene implicated in Drosophila lifespan extension is Tequila a multiple-domain serine protease known to be upregulated during infection [138].These authors showed that knockdown of Tequila in insulin producing cells increases longevity, probably due to decreased systemic IIS.", + "\t\n\nIn addition to testing genes known to be associated with age-related diseases and phenotypes for association with longevity, genes known to promote longevity in model organisms have been examined in human populations.Mutations in insulin or insulinlike signalling pathway genes have been shown to extend lifespan in Caenorhabditis elegans [20], Drosophila melanogaster [21,22] and mice [23,24].The insulin-signalling pathway negatively regulates the forkhead (FOXO) transcription factor [25].When insulin or insulin-like growth factor signalling is low, FOXO is activated and lifespan extension occurs [26].An overrepresentation of rare insulin-like growth factor I receptor (IGFIR) mutations has been observed in centenarians [27].These mutations are associated with reduced activity of IGFIR as measured in transformed lymphocytes [27].", + "\tGiannakou, M., M. Goss, and L. Partridge. 2008. Role of dFOXO in lifespan extension by\ndietary restriction in Drosophila melanogaster: Not required, but its activity modulates the\nresponse. Aging Cell 7:187198. Gillespie, J. H. 1973. Natural selection with varying selection coefficients: A haploid model. Genetical Research 21:115120. Greenwood, M., and J. O. Irwin. 1939. Biostatistics of senility. Human Biology 11:123. Guarente, L., and C. Kenyon. 2000. Genetic pathways that regulate aging in model organisms. Nature 408:255262. Haldane, J. B. S. 1941. New Paths in Genetics. London: Allen and Unwin. Hamilton, W. D. 1966. The moulding of senescence by natural selection.", + "\t\n\nMuch work has been done implicating FOXO3 as an ageing gene in model organisms (Kenyon et al., 1993;Hwangbo et al., 2004), however we found the association in humans at that locus may be driven by expression of SESN1 (admittedly a finding restricted to peripheral blood tissue).SESN1 is a gene connected to the FOXO3 promoter via chromatin interactions and is involved in the response to reactive oxygen species and mTORC1 inhibition (Donlon et al., 2017).While finemapping studies have specifically found genetic variation within the locus causes differential expression of FOXO3 itself (Flachsbart et al., 2017;Grossi et al., 2018), this does not rule out the effect of co-expression of SESN1.More powered tissue-specific expression data and experimental work on SESN1 vs. FOXO3 could elucidate the causal mechanism.For now, results from model organisms seem to leave the preponderance of evidence for FOXO3.", + "\tHe, R. Chen, J. S. Grove,\nK. Yano, K. H. Masaki, D. C. Willcox, B. Rodriguez, and\n291\nBIBLIOGRAPHY\nJ. D. Curb. Foxo3a genotype is strongly associated with human longevity. Proceedings of the National Academy of Sciences,\n105(37):1398713992, Sep 2008. [370] David Withers, Edward Kawas, Luke McCarthy, Benjamin Vandervalk, and Mark Wilkinson. Semantically-guided workow construction in taverna: The sadi and biomoby plug-ins. Leveraging Applications of Formal Methods, Verification, and Validation,\npage 301312, 2010.", + "\t\n\nSeveral of the genes we identify have previously been shown to influence lifespan in experiments on model organisms.For example, knockouts of the orthologs of APOE, LDLR, CDKN2B, and RBM38 in mice shortens their lifespan [24][25][26][27] , while knockout of IGF1R has the opposite effect 28 .Similarly, overexpression of the FOXO3 orthologue in Drosophila melanogaster 29 and the SNCA orthologue in Caenorhabditis elegans 30 have shown to extend their respective lifespans.Many of our genes are also enriched for pathways previously related to ageing in eukaryotic model organisms, including genomic stability, cellular senescence, and nutrient sensing 31 .For example, FOXO3 and IGF1R are well-known players modulating survival in response to dietary restriction 32 , but we also highlight genes involved in the response to DNA damage and apoptosis, such as CDKN2B, USP28, E2F2, and BCL3.In addition to hallmarks discovered in model organisms, our results suggest that haem metabolism may play a role in human ageing.This pathway includes genes involved in processing haem and differentiation of erythroblasts 33 .Although the enrichment is largely driven by genes linked to the LDLR locus, genes linked to other loci of interest (such as FOXO3, CDKN2B, LINC02513) are involved in similar biological pathways: myeloid differentiation, erythrocyte homeostasis, and chemical homeostasis.\t\n\nImportantly, the genes we have highlighted show natural variation in the human population and some of them show altered levels of expression with increasing age, which makes them good candidates for therapeutic intervention.However, colocalisation of gene expression could be due to pleiotropy rather than causality, and there is a need to validate the effects of genetic variants in experimental models to confirm their role in disease aetiology.For example, we have found life-extending variants colocalise with decreased expression of FOXO3 in blood, which itself becomes increasingly expressed with increasing age, but experiments suggest the gene has many protective functions including detoxification of reactive oxygen species and DNA damage repair 15 .The observed inverse relationship between healthy life and FOXO3 expression may reflect healthy individuals have less oxidative damage and require less FOXO3 to mitigate this damage.\t\n\nTo determine the age-related expression of the identified cisand trans-acting genes, we performed a look-up in the dataset of Peters et al. 14 .This large dataset contains the associations of genes with age in whole blood, so we limited ourselves to the cis-and trans-acting genes identified in the whole-blood datasets.We found that FOXO3 expression is increased with age in this dataset, which is in line with the life-extending variant decreasing expression (Supplementary Data 6).Moreover, one cis-(ILF3) and two trans-acting genes (E2F2 and PDZK1IP1) in the LDLR locus show a similar effect (i.e.increased or decreased expression with age combined with the life-extending variant decreasing or increasing expression, respectively).The most interesting, however, seems to be the LINC02513 locus, which showed multiple trans-acting genes to be strongly downregulated with age, while the lead life-extending variant increases expression.LEF1, CCR7, and ABLIM1 even belong to the most significantly affected genes in the whole transcriptomic dataset.This indicates that this long intergenic non-protein coding RNA may serve as a master regulator of age-related transcription in whole blood.", + "\t\n\nIt is thought that inflammatory triggers during aging may induce the loss of muscle cells and myonuclei during the process of human aging through an apoptotic mechanism (9,30).Indeed, several genes known to play a role in the regulation of apoptosis are components of the upregulated genes in this signature.The forkhead box O3A (FOXO3A) is one such gene upregulated in the aged signature.FOXO3A activation has been shown to induce apoptosis by activating the expression of genes necessary for cell death (14,48).Recent studies have shown the influence of FOXO transcription factors in the transcriptional activation of the ubiquitin protein ligase atrogin-1 during fasting-and glucocorticoid-induced atrophy (45).Welle et al. ( 59) also found increased FOXO1 mRNA in aged muscle using standard microarray analysis.Another recent study has shown that nuclei of aged muscle contain more FOXO1 than those of young muscle (35), and another shows increased atrogin mRNA in aged rats (39).Thus the FOXO proteins may very well play a role in the loss of muscle mass or muscle nuclei with aging.", + "\tGiannakou, M., M. Goss, and L. Partridge. 2008. Role of dFOXO in lifespan extension by\ndietary restriction in Drosophila melanogaster: Not required, but its activity modulates the\nresponse. Aging Cell 7:187198. Gillespie, J. H. 1973. Natural selection with varying selection coefficients: A haploid model. Genetical Research 21:115120. Greenwood, M., and J. O. Irwin. 1939. Biostatistics of senility. Human Biology 11:123. Guarente, L., and C. Kenyon. 2000. Genetic pathways that regulate aging in model organisms. Nature 408:255262. Haldane, J. B. S. 1941. New Paths in Genetics. London: Allen and Unwin. Hamilton, W. D. 1966. The moulding of senescence by natural selection.", + "\tB. Prioritizing Targets for Drug Discovery and Network Approaches\n\nGenome analyses from CR, aging, and human longevity genes provide biological targets for drug discovery.Screening natural products, existing drugs, and chemical libraries for molecules that affect \"druggable\" targets associated with aging may lead to compounds of therapeutic value.Given the hundreds of genes associated with aging and CR, however, it is important to identify the most promising targets.Integrating information from different datasets can help prioritize candidates (Fig. 2).It is interesting to note the two genes shown in model organisms to be related with aging, associated with human longevity, and essential to CR effects: IGF1R and FOXO3 (Fig. 2).IGFR1 is part of the insulin/ IGF1/GH pathway, the down-regulation of which has been associated with life-extension in several model systems and, as mentioned above, is already a target of pharmacological interventions.The FOXO transcription factor FOXO3 is a homolog of dFOXO and of daf-16, in which mutations suppress the life-extending effects of daf-2 (Kenyon et al., 1993).FOXO transcription factors are, in fact, part of the same insulin/IGF1/GH pathway (Fig. 1) that modulates lifespan across organisms (Kenyon, 2010).A strong association between FOXO3 and human longevity has been reported (Willcox et al., 2008) and subsequently validated in other populations (for review, see Kenyon, 2010).FOXO3 was also associated AGING GENES AS TARGETS FOR DRUG DISCOVERY with insulin levels and prevalence of cancer, heart disease, and type 2 diabetes (Willcox et al., 2008).Further work is necessary to understand the modulation of FOXO3 and its molecular mechanisms affecting longevity, but it is a promising target for drug development.", + "\t\n\nThe effect of reduced IIS signalling on lifespan extension in model systems is through changes in gene expression and especially genes orthologous to human FOXO transcription factor, HSF-1, a heat shock transcription factor, and NFE2L2 [25], a xenobiotic response factor.The initial human candidate longevity gene studies were dominated by contradictory results [26].The more consistent evidence obtained by repeated observation in independent cohort studies for association to longevity was found for the APOE locus and, more recently, the FOXO1 and 3 [27 -29] and AKT1 loci [30].The effect size of the association of the FOXO3 variant appears to vary with the age of the cases, being most prominent in centenarians.Other intriguing observations that need to be replicated but fit observations in humans at the phenotype level discussed above were made in the Ashkenazi Jewish Centenarian Study in which a higher serum thyroid-stimulating hormone level and TSHR genetic variation marked the centenarian population [31].Recently, an association with longevity was found for genetic variation in RNA-editing genes [32].", + "\t\n\nStudies have shown that ageing is accompanied by increased insulin/IGF signalling (IIS).FOXO (forkheadrelated transcription factor) is a transcription factor downstream of IIS that transcriptionally regulates longevityrelated genes such as hsp (heat-shock factor), inhibits ageing-related genes, and participates in feedback control of IIS (Hwangbo et al. 2004).However, the transcriptional activity of FOXO can be inhibited by increased IIS in ageing Drosophila.Several classic landmark studies have revealed that reduced signalling by insulin-like peptides through loss of CHICO (a Drosophila insulin receptor substrate protein) (Clancy et al. 2001) or mutation of InR (a Drosophila gene insulin-like receptor) (Tatar et al. 2001) can increase the lifespan of D. melanogaster (Tatar et al. 2003).Therefore, FOXO is considered an important contributor to extreme The data are presented as the mean SEM. ***P < 0.001 versus 3-day-old Drosophila.n = 100 per group Fig. 5 Relative mRNA expression of genes in the longevity-regulating pathway, the peroxisome pathway, and the mTOR-signalling pathway in 3-day-old/30-day-old Drosophila.The relative mRNA levels of the genes were normalized to the levels of tubulin and are expressed as the fold changes relative to the levels in the 3-day group.n = 6 per group.The data are presented as the mean SEM. *P < 0.05, **P < 0.01 versus 3-day-old Drosophila Fig. 6 Relative mRNA expression of predicted genes in sub-network 1 of Fig. 3 in 3-day-old/30-day-old Drosophila.The relative mRNA levels of key genes were normalized to the levels of tubulin and are expressed as the fold changes relative to the levels in the 3-day group.n = 6 per group.The data are presented as the mean SEM. *P < 0.05, **P < 0.01 versus 3-day-old Drosophila longevity and health.Akt1, Bsk, Cat and P38b are functionally crucial in the FOXO-signalling pathway.Moreover, food-finding latency is shortened in old D. melanogaster with increased IIS, leading to lower fat reserves and lower starvation resistance (Egenriether et al. 2015).It was confirmed that starvation resistance was significantly reduced in 30-day-old D. melanogaster strain w 1118 , indicating that the 30-day-old D. melanogaster strain w 1118 showed a tendency toward senescence.", + "\tFOXO3A and EXO1\n\nThe recently confirmed longevity gene FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008) and the longevity candidate EXO1 (Nebel et al., 2009) yielded comparatively high P CCA values of 0.007 and 0.035, respectively, and were therefore far too large to qualify for follow-up in stage 2.", + "\t\n\nIn addition to testing genes known to be associated with age-related diseases and phenotypes for association with longevity, genes known to promote longevity in model organisms have been examined in human populations.Mutations in insulin or insulinlike signalling pathway genes have been shown to extend lifespan in Caenorhabditis elegans [20], Drosophila melanogaster [21,22] and mice [23,24].The insulin-signalling pathway negatively regulates the forkhead (FOXO) transcription factor [25].When insulin or insulin-like growth factor signalling is low, FOXO is activated and lifespan extension occurs [26].An overrepresentation of rare insulin-like growth factor I receptor (IGFIR) mutations has been observed in centenarians [27].These mutations are associated with reduced activity of IGFIR as measured in transformed lymphocytes [27]." + ], + [ + "\tINTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining 20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging.", + "\t\n\nIn addition, environmental factors influence the organism's ability to withstand the increase in entropy with aging: for example, caloric restriction and smoking can exert opposite effects on the rate of aging (Colman et al. 2009;Fraser and Shavlik 2001).Both protective alleles and a benevolent environment contribute to excess physiological capacity, which in turn indirectly determines an individual's healthy life span and longevity (Martin et al. 2007).The wellrecognized increase in variability with aging reflects the precarious balance between the stochastic destruction, environmental influences, and correcting effect of genes responsible for repair.", + "\tStochasticity in Aging\n\nAging has a strong nongenetic and apparently nonenvironmental component.The nongenetic, nonenvironmental component of life span is evident from studies of isogenic organisms aged in the same environment, because the animals have different life spans.For example, individual isogenic C. elegans aged on the same Petri dish can have an order of magnitude difference in life span (36).This nongenetic, nonenvironmental component is comprised of experimentally difficult-to-track variables including chance events centered around the partitioning of resources and epigenetic information between cells, accumulated molecular damage, and differences in the perception of environmental or biological signals (37).These differences can begin as early as gametogenesis (38,39).Importantly, these differences affect the biological program of gene expression.", + "\tIndividual Genotype\n\nIndividual differences in biological ageing may be due in part to the specific variations of the genotype but also genome-environment interactions [21,37].The maintenance of genomic stability and integrity is considered an essential factor required for cell viability and the overall longevity of an organism.The accumulation of physical damage is one of the leading causes of the ageing process.When considering oxidative damage as one of the causes of the damage of genetic material, these changes alter vital processes, such as replication, transcription, and translation, leading to genomic instability and personalized processes of ageing [38,39].", + "\t\nThe underlying cause of aging remains one of the central mysteries of biology.Recent studies in several different systems suggest that not only may the rate of aging be modified by environmental and genetic factors, but also that the aging clock can be reversed, restoring characteristics of youthfulness to aged cells and tissues.This Review focuses on the emerging biology of rejuvenation through the lens of epigenetic reprogramming.By defining youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges.", + "\t\n\nAging is an extremely complex process associated with interplay of genetic, biochemical, and metabolic factors in an organism in a given environment.Although genetic studies of various animal models suggest that even a single-gene mutation can remarkably extend lifespan (Kenyon 2005;Johnson 2006) and, thus, modulate aging, no such genes are revealed in humans so far.Given that a human organism is a much more complex system than a model organism (Christensen et al. 2006), it is evident that genetic effects on the aging process should be mediated via coordinate action of a large number of inter-related processes (Kirkwood 2011).Coordinated function is rather relevant to complex biological (Soltow et al. 2010;Slagboom et al. 2011) and genetic (Bloss et al. 2011) networks than to individual genes.\t\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes.", + "\t\n\nGenes significantly affected by age (P 0.05) in both the active and sedentary environment", + "\t\n\nGenes do not drive the aging process but by governing the levels of excess physiological capacity, repair, and turnover they indirectly determine potential longevity.There are no genes that specifically drive longevity but there are genes that govern biological processes that increase the likelihood of survival to reproductive maturity.The variations in excess physiological capacity, repair, and turnover accounts for the variations found in longevity both within and between species.", + "\t\n\nIn the most general terms, three types of environmental factors can influence human health during aging: physical, chemical, and biological.Physical factors include temperature and solar radiation.Chemical factors from natural and biological sources include trace toxins (asbestos, lead, tobacco smoke), but also trace morphogens that can cause subtle abnormalities in development.Biological factors include diet and infectious organisms, but also stress from social interactions.We know little about the concentrations of a vast number of bioactive substances that may be present sporadically in the environment.It seems fair to say that our concept of the environment will evolve rapidly with new technical developments and may come to include multigenerational effects.For example, in the case of diabetes, the maternal physiological state existing before pregnancy can influence fetal growth.Moreover, the ovary acquires its full stock of eggs in the fetus: thus, the egg cell from which all of our cells stem was exposed to the environment of our maternal grandmother (Finch and Loehlin, 1998).The depth of the transgenerational environment is a completely obscure aspect of human experience.", + "\t\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13].", + "\t\n\nIn this review, we give an overview of the major environmental factors that modulate aging in animals, in particular those with underlying gene-environment interactions with potential for improving human health and drug discovery.Moreover, we provide a snapshot of the relevance of these to human biology and to antiaging applications in diet, industry, pharmacy, and healthcare.\t\n\nThe remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design.\tIV. Genome-Environment Interactions as Targets for Dietary Interventions and Drug Discovery\n\n\"[It's] possible that we could change a human gene and double our life span. \"-CynthiaKenyon (Duncan, 2004) According to the GenAge database of aging-related genes (http://genomics.senescence.info/genes/),more than 700 genes have been identified that regulate lifespan in model organisms (de Magalha es et al., 2009a).Many of these genes and their associated pathways-such as the insulin/IGF1/GH pathway-have been shown to affect longevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolutionarily conserved and may have potential therapeutic applications (Baur et al., 2006).For example, evidence suggests the use of lowered IGF signaling (e.g., by targeting IGF receptors) to treat certain age-related diseases such as cancer (Pollak et al., 2004), Alzheimer's disease (Cohen et al., 2009), and autoimmune diseases (Smith, 2010).Moreover, a number of genes and pathways associated with longevity and CR are part of nutrient-sensing pathways that also regulate growth and development, including the insulin/IGF1/GH pathway (Narasimhan et al., 2009;Stanfel et al., 2009).Many of these genes modulate the response to environmental signals, such as food availability, and act in signaling pathways that if understood can be targeted (Fig. 1).The genetic regulation of aging is therefore an emerging field with multiple applications in the human nutrition, cosmetic, and pharmaceutical industries.\tIII. Diet, Health, and Aging\n\nThe previous examples of how diet can modulate aging (e.g., social insects and the dauer pathway) are extreme cases not observed in humans.There is evidence, however, that the environment, and diet in particular, can influence aging trajectories in humans.Such environmental influences can be observed from an early age with long-lasting effects.Early nutrition can affect latelife diseases, such as cardiovascular disease (Barker and Osmond, 1986) and mortality (Gluckman et al., 2008;Hanson and Gluckman, 2008).Likewise, infections in early life can increase inflammatory levels and, together with diet, contribute to late-life diseases (Finch, 2010).The specific genes and mechanisms involved are largely unknown, but these epidemiological studies clearly demonstrate that early life environment can affect aging, and these effects are most likely mediated by geneenvironment interactions.", + "\t\nAs our society is growing older, the consequences of aging have begun to gain particular attention.Improvement of quality of life at old age and prevention of age-associated diseases have become the main focus of the aging research.The process of aging in humans is complex and underlies multiple influences, with the probable involvement of heritable and various environmental factors.In particular, hormones are decisively involved in the generation of aging.Over time, important circulating hormones decline due to a reduced secretion of the pituitary, the adrenal glands and the gonads or due to an intercurrent disease.Among them, serum levels of growth factors and sexual steroids show significant aging-associated changes.Within the scope of the Explorative Project 'Genetic aetiology of human longevity' supported by the German National Genome Research Network 2 (NGFN-2) an in vitro model of human hormonal aging has been developed.Human SZ95 sebocytes were maintained under a hormone-substituted environment consisting of growth factors and sexual steroids in concentrations corresponding to those circulating in 20-and in 60-year-old women.Eight hundred and ninety-nine genes showed a differential expression in SZ95 sebocytes maintained under the 20-and 60-year-old hormone mixture, respectively.Among them genes were regulated which are involved in biological processes which are all hallmarks of aging.The most significantly altered signaling pathway identified was that of the transforming growth factor-b (TGF-b).A disturbed function of this cascade has been associated with tumorigenesis, i.e. in pancreatic, prostate, intestine, breast, and uterine cancer.Interestingly, genes expressed in signaling pathways operative in age-associated diseases such as Huntington's disease (HD), dentatorubral-pallidoluysian atrophy (DRPLA), and amyotrophic lateral sclerosis (ALS) were also identified.These data demonstrate that skin and its appendages may represent an adequate model for aging research.Hormones interact in a complex fashion, and aging may be partly attributed to the changes in their circulating blood levels.Furthermore, a disturbed hormone status may partially act towards the manifestation of neurodegenerative diseases.Thus, these results could be a basis for an integrated and interdisciplinary approach to the analysis of the aging process.", + "\tTranslational\n\nA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors.", + "\t\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics.", + "\t[PubMed: 18208581]\n3. de Magalhes JP, Wuttke D, Wood SH, Plank M & Vora C Genome-environment interactions that\nmodulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88101 (2012). [PubMed:\n22090473]\n4. McDaid AFet al.Bayesian association scan reveals loci associated with human lifespan and linked\nbiomarkers. Nat. Commun. 8, 15842 (2017). [PubMed: 28748955]\n5. Fontana L & Partridge L Promoting health and longevity through diet: From model organisms to\nhumans. Cell 161, 106118 (2015). [PubMed: 25815989]\n6.", + "\tGenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhes, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + ], + [ + "\tSenescence and apoptosis are thought to contribute\nto aging and age-related disorders by decreasing the proliferative potential of progenitor\nstem cells, altering tissue regenerative capacity, decreasing tissue function and by altered\ntissue architecture and microenvironment caused by altered gene expression and secretion of\ninflammatory cytokines, growth factors, and proteases (Campisi 2003; Coppe et al. 2008;\nGarfinkel et al. 1994; Krtolica and Campisi 2002; Kuilman et al. 2008; Novakova et al. 2010; Ohtani and Hara 2013).", + "\tIntroduction\n\nReplicative cellular senescence was first described as an irreversible growth arrest triggered by the accumulation of cell divisions (Hayflick & Moorhead, 1961).Subsequently it has emerged as a potent tumor suppression mechanism, and recent evidence points to important connections with aging (Collado et al., 2007;Baker et al., 2011).Progression of both cancer and aging includes a significant epigenetic component, such as changes in DNA methylation and chromatin remodeling (Decottignies & d'Adda di Fagagna, 2011).", + "\t\nAccumulation of senescent cells over time contributes to aging and age-related diseases.However, what drives senescence in vivo is not clear.Here we used a genetic approach to determine if spontaneous nuclear DNA damage is sufficient to initiate senescence in mammals.Ercc1 -/ mice with reduced expression of ERCC1-XPF endonuclease have impaired capacity to repair the nuclear genome.Ercc1 -/ mice accumulated spontaneous, oxidative DNA damage more rapidly than wild-type (WT) mice.As a consequence, senescent cells accumulated more rapidly in Ercc1 -/ mice compared to repair-competent animals.However, the levels of DNA damage and\t\n\nAccumulation of senescent cells over time contributes to aging and age-related diseases.However, what drives senescence in vivo is not clear.Here we used a genetic approach to determine if spontaneous nuclear DNA damage is sufficient to initiate senescence in mammals.Ercc1 -/ mice with reduced expression of ERCC1-XPF endonuclease have impaired capacity to repair the nuclear genome.Ercc1 -/ mice accumulated spontaneous, oxidative DNA damage more rapidly than wild-type (WT) mice.As a consequence, senescent cells accumulated more rapidly in Ercc1 -/ mice compared to repair-competent animals.However, the levels of DNA damage and", + "\t\n\nCellular senescence is one of the hallmarks of aging [87] and the accumulation of senescent cells in human tissues with age has been implicated as a driver of agingrelated diseases.Indeed, pharmacological approaches targeting senescent cells, like senolytics, are a major and timely area of research that could result in human clinical applications [5,88].It is imperative that we fully understand and deconstruct cellular senescence in order to target aging-related diseases.We hope that CellAge will help researchers understand the role that CS plays in aging and aging-related diseases and contributes to the development of drugs and strategies to ameliorate the detrimental effects of senescent cells.\tBackground\n\nIn the 1960s, Leonard Hayflick and Paul Moorhead demonstrated that human fibroblasts reached a stable proliferative growth arrest between their fortieth and sixtieth divisions [1].Such cells would enter an altered state of \"replicative senescence,\" subsisting in a nonproliferating, metabolically active phase with a distinct vacuolated morphology [2].This intrinsic form of senescence is driven by gradual replicative telomere erosion, eventually exposing an uncapped free double-stranded chromosome end and triggering a permanent DNA damage response [3,4].Additionally, acute premature senescence can occur as an antagonistic consequence of genomic, epigenomic, or proteomic damage, driven by oncogenic factors, oxidative stress, or radiation [5].Initially considered an evolutionary response to reduce mutation accrual and subsequent tumorigenesis, the pleiotropic nature of senescence has also been positively implicated in processes including embryogenesis [6,7], wound healing [8], and immune clearance [9,10].By contrast, the gradual accumulation and chronic persistence of senescent cells with time promotes deleterious effects that are considered to accelerate deterioration and hyperplasia in aging [11].Senescent cells secrete a cocktail of inflammatory and stromal regulators-denoted as the senescence-associated secretory phenotype, or SASP-which adversely impact neighboring cells, the surrounding extracellular matrix, and other structural components, resulting in chronic inflammation, the induction of senescence in healthy cells, and vulnerable tissue [12,13].Mice expressing transgenic INK-ATTAC, which induces apoptosis of p16-positive senescent cells, also have increased lifespan and improved healthspan [14].It is, therefore, no surprise that in recent years gerontology has heavily focused on the prevention or removal of senescent cells as a means to slow or stop aging and related pathologies [15][16][17].\t\n\nBackground: Cellular senescence, a permanent state of replicative arrest in otherwise proliferating cells, is a hallmark of aging and has been linked to aging-related diseases.Many genes play a role in cellular senescence, yet a comprehensive understanding of its pathways is still lacking.", + "\tJ\nAm Geriatr Soc 45: 482-8. Campisi J (2005). Senescent cells, tumor suppression, and organismal aging: good\ncitizens, bad neighbors. Cell 120: 513-22. Chambers SM, Boles NC, Lin KY, Tierney MP, Bowman TV, Bradfute SB et al (2007a). Hematopoietic Fingerprints: An Expression Database of Stem Cells and Their Progeny. Cell Stem Cell 1: 578-591. 128\nChambers SM, Shaw CA, Gatza C, Fisk CJ, Donehower LA, Goodell MA (2007b). Aging hematopoietic stem cells decline in function and exhibit epigenetic dysregulation. PLoS Biol 5: e201. Chen DJ, Nirodi CS (2007).\tMany stimuli\nhave been shown to induce the senescence response including, but not limited to,\ntelomere erosion, certain types of DNA damage, such as DNA breaks and oxidative\nlesions, epigenetic changes to chromatin organization, as well as exposure to ionizing\nirradiation (Campisi, 2005; Wang et al. , 2006). There is increasing evidence that\nsenescent cells accumulate with age. Senescence-associated -galactosidase, an enzyme\ncommonly used as a marker to detect the senescent phenotype, was shown to increase\nwith age in various mammalian tissues (Krtolica and Campisi, 2002).", + "\tDissecting the Role of Cellular Senescence\n\nAnother hallmark of the ageing process is the induction and accumulation of cells in a senescent state [2].Cellular senescence is characterised by a stable arrest of the cell cycle while maintaining viability and metabolic activity.Senescent cells are also known to activate what is known as the senescence-associated secretory phenotype (SASP), which is a plethora of secreted factors comprising pro-inflammatory cytokines, chemokines, growth factors and matrix remodelling enzymes [88,89].Beyond telomere attrition in the case of replicative senescence, cellular senescence can be induced by many other cellular stresses like oncogene activation, loss of tumour suppressors, oxidative stress, persistent DNA damage response, ionising radiation and cytotoxic chemicals [88,89].Cellular senescence is thought to primarily act as a potent cell-autonomous tumour-suppressive mechanism by preventing the expansion of pre-malignant cells.However, research over the past decade has revealed that cellular senescence is a pleiotropic phenotype that has many context-dependent paracrine effects mediated by the SASP, such as aiding in tissue regeneration or, paradoxically, promoting tumorigenesis and the acquisition of malignancy [88][89][90].", + "\t\n\nHow might apoptosis and senescence be antagonistically pleiotropic and contribute to aging?In the case of apoptosis, this process clearly is beneficial because it culls damaged or defective cells from tissues.However, it also eventually depletes tissues of cells and/or depletes stem cell reserves.In the case of senescence, this process is beneficial because it prevents the proliferation of preneoplastic, damaged or defective cells.However, senescent cells persist and adopt an altered phenotype in conjunction with the senescence growth arrest (Krtolica & Campisi, 2002;Rinehart & Torti, 1997).This phenotype includes the secretion of degradative enzymes, cytokines and growth factors that can perturb the surrounding tissue, leading to a loss of tissue homeostasis and development of age related pathologies.", + "\t\n\nSeveral representative applications merit an integrative genomics approach to aging.One application is to determine which molecular and cellular factors responsible for the process of cellular senescence also underlie functional cognitive decline.Cellular senescence is an anticancer and wound healing mechanism characterized by arrested cellular proliferation and secretion of pro-inflammatory cytokines, chemokines, growth factors, and proteases (the senescence associated secretory phenotype, or SASP).Senescent cells accumulate with age in many tissues, where the SASP promotes chronic inflammation and exacerbates age-associated degeneration and hyperplasia.Recent evidence suggests that neurological aging and neurodegeneration are accompanied by an accumulation of secretory cells in brain, suggesting that cellular senescence may contribute to brain aging [2] through a shared mechanism.Overlapping mechanisms can be detected using functional genomics studies of both the biology of cellular senescence and cognitive aging.", + "\t\n\nMarkers of senescence are detected at higher levels in tissues of older mice, humans, and other primates, including skin, liver, pancreatic islets, bone marrow, intestine, kidney, ovary, heart, and retina tissues.Senescent cells have altered metabolism (83).They also secrete proinflammatory factors and proteases able to alter the local tissue environment (84), providing plausible mechanisms by which senescent cells could promote aging and age-related degenerative diseases.Indeed, senescent cells are found at sites of numerous tissue-specific, age-related diseases, including atherosclerosis, osteoarthritis, sarcopenia, ulcer formation, cancer, and Alzheimer disease, which is suggestive of a causative role.However, the most convincing evidence that senescent cells cause aging comes from recent genetic (85) and pharmacologic studies (86) revealing that clearance of senescent cells can prevent or delay tissue dysfunction and extend health span.\t\n\nOf note, senescent cells accumulate with age in mammals (51).Compelling evidence shows that BER (47), NER (52), and NHEJ (53) are reduced in senescent cells relative to earlier passage nonsenescent cells.Thus, DNA repair may be reduced in a subset of cells that increase in number as an organism ages.Furthermore, genotoxic stress and ex vivo culture conditions induce senescence of cells, which impacts measurement of DNA repair.New tools to measure DNA repair in vivo are needed to determine if diminution of repair occurs in all cells and cell types as an organism ages.", + "\tCellAge--a database of cell senescence genes\n\nCell senescence, also known as cellular senescence (CS), is the irreversible cessation of cell division of normally prolif-erating cells.Senescent cells accumulate as an organism ages and may be an important contributor to ageing and agerelated disease (34).However, the connection between organismal ageing and CS remains controversial (35).CellAge (http://genomics.senescence.info/cells/) is a new database of CS-associated genes, built to elucidate mechanisms of CS and its role in ageing.It is described here for the first time.", + "\t\n\nInterestingly, when senescent cells are abolished either through genetic manipulation or via senolytic drugs, biological aging is significantly halted in mice [53,54].Therefore, trials are now under way to test the ability of senolytics to postpone age-associated pathologies in humans [55].Notably, multiple drugs are being pursued that either directly or indirectly impact DNA repair or the consequence of DNA damage.", + "\t\n\nIrreparably damaged cells may also enter senescence.Senescence occurs in response to various insults, including genotoxic (e.g., oxidative) stress, telomere erosion, and oncogenic and replicative stress, which often occur as a result of persistent DNA lesions (111).Cellular senescence is elevated in many accelerated-aging mouse models and in a plethora of human age-associated pathologies, including osteoporosis, atherosclerosis, glomerular disease, diabetic venous ulcers, chronic obstructive pulmonary disease and emphysema, osteoarthritis, herniated intervertebral discs, and vascular calcification (112).Senescent cells are resistant to apoptosis and accumulate exponentially with age as a consequence of inefficient clearance.Unlike apoptotic tissues, senescent tissues largely retain their function.Therefore, senescence is thought to be antagonistically pleiotropic: It is beneficial early in life during development and later in life during wound healing after injury, but it becomes deleterious late in life, as the tissue increasingly accumulates nondividing senescent cells, which disturb the tissue microenvironment (113).This disruption is primarily caused by the secretion of a range of proinflammatory cyto-and chemokines, a state that has been defined as the senescence-associated secretory phenotype (SASP) (103).Major SASP factors include IL1, IL6, IL8, and various matrix metalloproteases (MMPs), all of which individually are thought to drive aging and age-related diseases.Thus, DNA damage is a major determinant in controlling cell death, stem cell exhaustion, and cellular senescence, which are considered important events in the development of age-related pathology and aging.", + "\t\n\nAnother group of studies concentrated on a classic in vitro model for aging: the replicative senescence of primary cultured cells.The process of cellular senescence was first described in a seminal study by Hayflick and Moorhead (1961), who observed that normal human fibroblasts were able to enter a state of irreversible growth arrest after serial cultivation in vitro, while cancer cells were able to proliferate indefinitely.They proposed that there were some factors whose gradual loss through cell proliferation limited the number of cell divisions and that this process could contribute to organismal aging.It is still not completely clear how the latter might occur, but two main processes have been suggested: the accumulation of senescent cells in tissues and the limitation of regenerative potential of adult stem cell pools (Fraga et al., 2007).Wilson and Jones (1983) first showed how global DNA methylation also decreased with the number of cell passages in cultures of diploid fibroblasts of mice, hamsters and humans, while immortal cell lines had stable levels of methylation.The greatest loss of methylation was observed in mouse cells, which survived the fewest divisions, implying that the rate of methylation loss may be correlated with functional senescence.", + "\t\n\nSenescence primarily occurs in the G0/G1 phase of the cell cycle and is a vital tumor suppressive mechanism that prevents passing damaged DNA to daughter cells or potential neoplastic transformation of damaged cells [144,145].Since being first described by Leonard Hayflick as an in vitro phenomenon in human fibroblasts, the potential role of senescence in in vivo aging and disease has been difficult to assess and somewhat controversial [146].However, recent studies have shown that senescent cells accumulate in normal arterial tissue over the lifespan of humans [147,148].Likewise, the accumulation of senescent cells has been reported in diseased tissues, such as atherosclerotic plaques [149] and abdominal aortic aneurysms [150].Baker et al. showed that clearance of senescent cells reversed aged and diseased phenotypes in a mouse model of accelerated aging [151].This important study strongly suggested that there were phenotypic properties of senescent cells that were problematic to tissues, and potentially contribute to aging and chronic disease.", + "\tConcluding remarks and future perspectives\n\nAging research has rapidly expanded over the past two decades, with studies ranging from lifespan-extending [68,69,71].However, when their effect on cell death and senescence leads to stem cell loss and tissue degeneration, they might contribute to aging [66,67]." + ] + ], + "task_id": [ + { + "task_id": "C5BB60D5C5515AB310E870B4FC5C4CF9" + }, + { + "task_id": "6B836AD417A7B5B70185949BC5CB9E7B" + }, + { + "task_id": "70EBE57343443E5779A736414CEA2EF4" + }, + { + "task_id": "3DD38AA2307344E3FD450E5C57A7C7BF" + }, + { + "task_id": "5321131FE834527EC39E714C50359EF4" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_aging_4.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_aging_4.json new file mode 100644 index 0000000..9bb53f4 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_aging_4.json @@ -0,0 +1,105 @@ +{ + "question": [ + "Are there any known lifestyle interventions that can positively impact genes related to aging?", + "What is the 'epigenetic clock,' and how is it used in aging research?", + "How do researchers use model organisms like yeast or worms to study human aging?", + "Are there any promising anti-aging therapies being developed based on genetic research?", + "How do caloric restriction and diet impact the genetics of aging?" + ], + "answer": [ + "Yes, there are several lifestyle interventions that can positively impact genes related to aging. These include mindful changes in behavior, environmental exposure, food/supplement use, weight loss, and regular exercise. A Mediterranean diet containing fruits, whole grains, fiber, antioxidants, soy protein, and healthy fats is also suggested to reduce DNA damage and protect telomeres, potentially increasing longevity.", + "The 'epigenetic clock' is a predictive tool that uses DNA methylation patterns at specific sites, known as CpG sites, to estimate biological age. It is used in aging research to predict the age of unknown samples, calculate", + "Researchers use model organisms like yeast or worms to study human aging due to their short lifespan and well-characterized genetic structures. These organisms allow for genome-wide studies and genetic manipulation to identify genes and pathways that influence lifespan. The findings can then be translated into mammalian models. The assumption is that if a gene or pathway modulates longevity in these simple organisms, it might have a similar function in more complex organisms like humans. This approach aids in the identification of potential therapeutic targets for age-associated diseases in humans.", + "Yes, there are promising anti-aging therapies being developed based on genetic research. These include nutritional supplements that target genes/pathways involved in aging, drugs that mimic the life-extension effects of caloric restriction, and multitarget drugs and combinatorial therapies developed through network approaches. Additionally, genomic methods are being used to identify biochemical pathways for increasing lifespan, which can then be targeted for pharmaceutical development.", + "Caloric restriction (CR) has been shown to extend lifespan and reduce the incidence of age-related diseases and tumors in mammals. It also delays the onset of many features of aging, including age-related diseases. CR increases the response to oxidative stress and reduces the shortening of telomeres in chromosomes, which directly intervenes in the repair of DNA damage. It also impacts metabolism, particularly the insulin/insulin-like growth factor 1 (IGF-1) pathways. Many genes and pathways associated with longevity and CR are part of nutrient-sensing pathways that also regulate growth and development. Therefore, understanding these pathways could lead to potential therapeutic applications for age-related diseases." + ], + "contexts": [ + [ + "\t\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.\t\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "\t\n\nStudies revealed from 300 to 750 genes related to longevity that are critically involved in a variety of life activities, such as growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [5,6].Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability, and the endocrine-related pathway [7][8][9].In addition, the candidates for longevity encompass genes related to drug metabolism, the ones involved in protein folding, stabilization, and degradation, as well those related to coagulation and regulation of circulation [10], etc.In most cases, these genes or their polymorphic sites were examined in multiple population replication studies, which discovered certain longevity-associated genes or pathways [4][5][6][7][8][9][10].", + "\t\nStudies of the basic biology of aging have identified several genetic and pharmacological interventions that appear to modulate the rate of aging in laboratory model organisms, but a barrier to further progress has been the challenge of moving beyond these laboratory discoveries to impact health and quality of life for people.The domestic dog, Canis familiaris, offers a unique opportunity for surmounting this barrier in the near future.In particular, companion dogs share our environment and play an important role in improving the quality of life for millions of people.Here, we present a rationale for increasing the role of companion dogs as an animal model for both basic and clinical geroscience and describe complementary approaches and ongoing projects aimed at achieving this goal.", + "\t\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process.", + "\t\n\nsmall number of genes or interventions are known to increase life span in different model organisms.A selection of these are shown here.\t\n\nThe most direct method to address how well the features that determine longevity have been conserved is to identify genes or interventions that function similarly to modulate life span in different organisms.Components of insulin/IGF-1like signaling pathway, the sirtuin family of protein deacetylases, and the nutrient-responsive TOR kinase, among others, have been found to have this property (Table 1).Until recently, however, the genetic analysis of longevity was largely limited to mutagenesis screens for secondary phenotypes (such as stress resistance) or targeted studies of specific *Address correspondence to this author at the Department of Pathology, University of Washington, Seattle, WA 98195, USA; Tel: 206-543-4849; Fax: 206-543-3644; E-mail: kaeber@u.washington.edugenes, based on prior knowledge.While many important insights were gained from such studies, they, by necessity, self-selected for mutants with specific properties that are (at best) secondarily related to longevity.Thus, it remains unclear to what degree the pathways regulating longevity are evolutionarily conserved and whether the known longevity genes represent most of the important players or only a small fraction.\t\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases.\t\n\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases.", + "\tIntroduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005).", + "\tIV. Genome-Environment Interactions as Targets for Dietary Interventions and Drug Discovery\n\n\"[It's] possible that we could change a human gene and double our life span. \"-CynthiaKenyon (Duncan, 2004) According to the GenAge database of aging-related genes (http://genomics.senescence.info/genes/),more than 700 genes have been identified that regulate lifespan in model organisms (de Magalha es et al., 2009a).Many of these genes and their associated pathways-such as the insulin/IGF1/GH pathway-have been shown to affect longevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolutionarily conserved and may have potential therapeutic applications (Baur et al., 2006).For example, evidence suggests the use of lowered IGF signaling (e.g., by targeting IGF receptors) to treat certain age-related diseases such as cancer (Pollak et al., 2004), Alzheimer's disease (Cohen et al., 2009), and autoimmune diseases (Smith, 2010).Moreover, a number of genes and pathways associated with longevity and CR are part of nutrient-sensing pathways that also regulate growth and development, including the insulin/IGF1/GH pathway (Narasimhan et al., 2009;Stanfel et al., 2009).Many of these genes modulate the response to environmental signals, such as food availability, and act in signaling pathways that if understood can be targeted (Fig. 1).The genetic regulation of aging is therefore an emerging field with multiple applications in the human nutrition, cosmetic, and pharmaceutical industries.\t\n\nEven if sirtuins and resveratrol do not live up to their expectations, this research is pioneering in terms of genome-environment interactions and nutritional manipulations of aging.These studies also show the path from basic discovery on the biology of aging to potential antiaging and pharmacological interventions and can therefore be applied to other genes and pathways.The lessons learned from the pitfalls of SIRT1 and resveratrol research can also help others to translate basic research on the biology of aging to the clinic, such as avoiding the use of short-lived rodent strains (e.g., by using unhealthy diets), which may lead to findings that only apply to a subset of individuals.\t\n\nThe remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design.", + "\tINTRODUCTION\n\nGenomic studies into human longevity are inspired by the fact that, in animal models, healthy lifespan has proved to be remarkably plastic, and major pathways of lifespan regulation have been identified.Considerable lifespan extension has been induced in models as diverse as yeast, worms, fish, flies and rodents by applying genetic manipulation and dietary restriction (DR) (see [1] for review).Reduced activity of nutrient-sensing pathways such as insulin/insulin-like growth factor (IGF-1) signalling (IIS) and target of rapamycin (TOR) signalling mediated lifespan extension, and also the extension of lifespan by DR [2].An interesting observation from the perspective of human ageing is that, in rodents and monkeys, diets restricted in glucose, fat or protein uptake reduced or delayed the risk of cancer and metabolic disease, thus extending the healthspan of the animals [2].Following the discovery of genes and pathways involved in animal lifespan extension, human research has focused on the corresponding candidate human genes with genetic, genomic and epigenetic studies into ageing and longevity.The designs of these studies differ with respect to the selection of naturally occurring phenotypes and the study populations, which include population-based, patient-based, family-based and exposure-based cohorts.\t\nIn animal models, single-gene mutations in genes involved in insulin/IGF and target of rapamycin signalling pathways extend lifespan to a considerable extent.The genetic, genomic and epigenetic influences on human longevity are expected to be much more complex.Strikingly however, beneficial metabolic and cellular features of long-lived families resemble those in animals for whom the lifespan is extended by applying genetic manipulation and, especially, dietary restriction.Candidate gene studies in humans support the notion that human orthologues from longevity genes identified in lower species do contribute to longevity but that the influence of the genetic variants involved is small.Here we discuss how an integration of novel study designs, labour-intensive biobanking, deep phenotyping and genomic research may provide insights into the mechanisms that drive human longevity and healthy ageing, beyond the associations usually provided by molecular and genetic epidemiology.Although prospective studies of humans from the cradle to the grave have never been performed, it is feasible to extract life histories from different cohorts jointly covering the molecular changes that occur with age from early development all the way up to the age at death.By the integration of research in different study cohorts, and with research in animal models, biological research into human longevity is thus making considerable progress.\t\n\nIn animal models, single-gene mutations in genes involved in insulin/IGF and target of rapamycin signalling pathways extend lifespan to a considerable extent.The genetic, genomic and epigenetic influences on human longevity are expected to be much more complex.Strikingly however, beneficial metabolic and cellular features of long-lived families resemble those in animals for whom the lifespan is extended by applying genetic manipulation and, especially, dietary restriction.Candidate gene studies in humans support the notion that human orthologues from longevity genes identified in lower species do contribute to longevity but that the influence of the genetic variants involved is small.Here we discuss how an integration of novel study designs, labour-intensive biobanking, deep phenotyping and genomic research may provide insights into the mechanisms that drive human longevity and healthy ageing, beyond the associations usually provided by molecular and genetic epidemiology.Although prospective studies of humans from the cradle to the grave have never been performed, it is feasible to extract life histories from different cohorts jointly covering the molecular changes that occur with age from early development all the way up to the age at death.By the integration of research in different study cohorts, and with research in animal models, biological research into human longevity is thus making considerable progress.", + "\tRelevance to nurse practitioner practice\n\nCurrently, there is no cure for genetic variants associated with rapid aging, but novel agents that may slow down the aging process are being tested.The authors of this article advocate individual participation in association studies of aging and pharmacologic risk mitigation or reversal of symptoms for those with known genetic disease risk.Direct to consumer epigenetic biological aging tests and telomere length tests are available; but they are not approved by the Food and Drug Administration.Health care providers may want to consider the simple but key clinical and personal changes, suggested above, to enhance DNA health, wellness, and longevity.Simple mindful changes in behavior, environmental exposure, food/supplement use, weight loss, and regular exercise can reduce adduct exposure damage and impact telomere length, potentially increasing longevity.A Mediterranean diet containing fruits and whole grains along with fiber, antioxidants, soy protein, and healthy fats (from avocados, fish, flax, and walnuts) is suggested to reduce DNA adducts and protect telomeres.In light of our current pandemic, focus on population health, and restrictions to health care access, especially in rural communities, health care providers could incorporate these lifestyle and dietary principles in telehealth visits with patients to reduce disease risk and optimize healthy aging.", + "\t[PubMed: 18208581]\n3. de Magalhes JP, Wuttke D, Wood SH, Plank M & Vora C Genome-environment interactions that\nmodulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88101 (2012). [PubMed:\n22090473]\n4. McDaid AFet al.Bayesian association scan reveals loci associated with human lifespan and linked\nbiomarkers. Nat. Commun. 8, 15842 (2017). [PubMed: 28748955]\n5. Fontana L & Partridge L Promoting health and longevity through diet: From model organisms to\nhumans. Cell 161, 106118 (2015). [PubMed: 25815989]\n6.", + "\t\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways.", + "\t\n\nWith modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms." + ], + [ + "\t\n\nThe first generation of epigenetic aging clocks used penalized regression models to predict chronological age on the basis of DNA methylation data, e.g., the widely used clocks from Hannum (2013) and Horvath (2013) apply to blood and 51 human tissues/ cell types, respectively [12][13][14].A derivative of the Horvath clock, intrinsic epigenetic age acceleration (IEAA) has since been developed, conditioning out (i.e., removing) estimates of blood cell composition.An increasing literature supports the view that IEAA relates to properties of hematopoietic stem cells [2,8,15].The second generation of epigenetic clocks move beyond estimating chronological age by incorporating information on morbidity and mortality risk (e.g., smoking, plasma protein levels, white blood cell counts), and chronological age.Two such predictors, termed PhenoAge (a DNAm predictor trained on a measure that itself was trained on mortality, using 42 clinical measures and age as input features) and GrimAge (trained on mortality, including a DNAm measure of smoking as a constituent part), outperform both Hannum and Horvath clocks in predicting mortality and are associated with various measures of morbidity and lifestyle factors [16,17].DNAm GrimAge outperforms PhenoAge and the first generation of epigenetic clocks when it comes to predicting time to death [8,18,19].\t\nBackground: Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality.Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.Results: Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively.We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function.Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs.A polygenic score for GrimAge acceleration showed strong associations with adiposityrelated traits, educational attainment, parental longevity, and C-reactive protein levels.Conclusion: This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.", + "\tDiscussion\n\nWe developed precise epigenetic clocks (ABEC and eABEC) using blood-based DNAm data from EPIC.Our epigenetic clocks showed a more precise chronological age prediction than existing blood-based epigenetic clocks (e.g., the Hannum Blood-based clock and Horvath Skin & Blood clock; Fig. 5).The reason for the higher precision is more likely due to the large training set (n = 2227, Table 1) and the wide age-span of the samples (19 to 88 years for the training set of eABEC, Table 1), which is consistent with the findings by Zhang and colleagues [34].Compared to eABEC, both Hannum Blood- [3,19].Other clocks (the Horvath Pan-tissue clock and Levine PhenoAge clock) may not be directly comparable to eABEC for chronological age prediction.For instance, the Horvath Pan-tissue clock was designed to measure epigenetic aging not only in blood but in multiple tissues [20], and the Levine Pheno-Age was designed to predict phenotypic age (estimated using 10 clinical biomarkers, e.g., albumin, creatinine, serum glucose, and seven others) based on DNAm [16].", + "\tAn Epigenetic Clock\n\nThe aging transcriptome could be used to gauge the physiological age of worms, and in that way serve as an epigenetic clock revealing how much of life span has been spent and how much remains (23).Middle-aged worms show an aging transcriptome half-way between the aging expression profiles of young and old worms.This provides an independent way to assess the age of an animal independent of its life span.This is important as there are at least 2 explanations to account for increased life span due to a longevity mutation.One is that the mutation slows down the process of aging so that worms die at the same physiological age, but that it takes worms longer to reach old age.According to this possibility, the aging transcriptome of a longevity mutant at 2 weeks might resemble the aging transcriptome of wild-type worms at 1 week of age.Another is that the longevity mutant allows the worm to survive damage accumulation in old age, so that the worms age at a normal rate but they avoid death until succumbing at a later time.For instance, improved health care increases life span by enabling people to avoid disease and live longer, not by aging slower.In this scenario, the rate of aging in the longevity mutant and wild-type worms at 2 weeks could be similar, but with higher survivability in the longevity mutant due to an ability to better withstand damage accumulation.", + "\tEpigenetic Clock\n\nChronological age is the number of years a person has lived, and biological or physiological age refers to a measure of how well your body functions compared to your chronological age.Biological age is influenced by multiple factors (genes, lifestyle, behavior, environment, among others) and correlates with mortality and health status.The epigenetic clock is one potentially reliable predictor of biological age.\t\n\nA recent study conducted in the Dunedin cohort [73] combined measurements of telomere lengths, epigenetic clocks and composite biomarkers and compared them to clinically relevant outcomes, such as health status, physical function, cognitive decline, and personal signs of ageing.The 71-cytosine-phosphate-guanine epigenetic clock and biomarker composites were consistently related to these outcomes.In another study, neural networks were applied to predict an age by using measurements from necessary blood tests, such as albumin, glucose, alkaline phosphatase, urea, and erythrocytes [74].", + "\tThe changing ticking rate of the epigenetic clock\n\nThe linear combination of the 353 clock CpGs (resulting from the regression coefficients) varies greatly across ages as can be seen from Figure 6B,C.The red calibration curve (formula in Additional file 2) reveals a logarithmic dependence until adulthood that slows to a linear dependence later in life (Figure 6B).I interpret the rate of change (of this red curve) as the ticking rate of the epigenetic clock.Using this terminology, I find that organismal growth (and concomitant cell division) leads to a high ticking rate that slows down to a constant ticking rate (linear dependence) after adulthood.", + "\tBackground\n\nRecently, a great deal of work has been performed in an effort to understand the nature of aging, the mechanisms that drive the process, and the biomarkers that may be predictive of, or affected by, age.In this effort, a seminal manuscript was published in 2013 which described the ability to use DNA methylation signatures in somatic tissues to predict an individual's chronological age [1].In this work, Dr. Horvath demonstrated that the epigenetic mechanisms that reflect the aging process are tightly conserved between individual tissues and across multiple species.Remarkably, these patterns are sufficiently consistent to enable accurate age prediction with Horvath's age calculator despite the significant contrast in epigenetic profiles between various somatic tissues.", + "\tRelationship to mortality prediction\n\nAlthough the epigenetic clock method was only published in 2013, there is already a rich body of literature that shows that it relates to biological age.Using four human cohort studies, we previously demonstrated that both the Horvath and Hannum epigenetic clocks are predictive of all-cause mortality [23].Published results in Marioni et al. [23] show that DNAm age adjusted for blood cell counts (i.e.IEAA) is prognostic of mortality in four cohort studies.We recently expanded our original analysis by analyzing 13 different cohorts (including three racial/ethnic groups) and by evaluating the prognostic utility of both IEAA and EEAA.All considered measures of epigenetic age acceleration were predictive of age at death in univariate Cox models (p AgeAccel = 1.9 10 -11 , p IEAA = 8.2 10 -9 , p EEAA = 7.5 10 -43 ) and multivariate Cox models adjusting for risk factors and pre-existing disease status (p AgeAccel = 5.4 10 -5 , p IEAA = 5.0 10 -4 , p EEAA = 3.4 10 -19 ) where the latter adjusted for chronological age, body mass index, education, alcohol, smoking pack years, recreational physical activity, and prior history of disease (diabetes, cancer, hypertension).These results will be published elsewhere.Further, the offspring of centenarians age more slowly than age matched controls according to Age Accel and IEAA [26] which strongly suggests that these measures relate to heritable components of biological age.Two independent research groups have shown that epigenetic age acceleration predicts mortality [24,25].\t\n\nWe addressed this concern in multiple ways.First, we re-analyzed the WHI data by removing the 47 CpGs (out of 353 epigenetic clock CpGs) from the analysis.The epigenetic clock software imputes the 47 missing CpGs using a constant value (the mean value observed in the original training set).Using the resulting modified epigenetic clock, we validate our findings of racial/ethnic differences in terms of IEAA and EEAA (Additional file 8A-C).However, this type of robustness analysis is limited because the removal of a subset of DNA methylation probes, potentially influenced by proximal genetic variation, is not as good a control as directly having matched genetic data.Second, we used a completely independent epigenetic biomarker based on a published signature of age-related CpGs from Teschendorff et al. [13].Again, these results corroborate our findings (Additional file 8D, E).Third, we validated our findings using the original blood-based aging measure by Hannum [19] (Additional file 8F, G).Fourth, we highlight that both the Horvath and Hannum age estimators were developed based on training data from mixed populations.The training data underlying the Horvath clock involved four racial/ethnic groups (mainly Caucasians, Hispanics, African Americans, and to a lesser extent East Asians).The Hannum clock was trained on Caucasians and Hispanics.While race/ethnicity can lead to a significant offset between DNAm age and chronological age (which is interpreted as age acceleration), these two variables are highly correlated in all racial/ethnic groups.\t\n\nThe following evidence shows that the epigenetic clock captures aspects of biological age.First, the epigenetic age of blood has been found to be predictive of all-cause mortality even after adjusting for chronological age and a variety of known risk factors [23][24][25].Second, the blood of the offspring of Italian semi-supercentenarians (i.e.participants who reached an age of at least 105 years) has a lower epigenetic age than that of age-matched controls [26].Third, the epigenetic age of blood relates to frailty [27] and cognitive/physical fitness in the elderly [28].The utility of the epigenetic clock method has been demonstrated in applications surrounding obesity [29], Down's syndrome [30], HIV infection [31], Parkinson's disease [32], Alzheimer's disease-related neuropathologies [33], lung cancer [34], and lifetime stress [35].Here, we apply the epigenetic clock to explore relationships between epigenetic age and race/ethnicity, sex, risk factors of coronary heart disease (CHD), and the CHD outcome itself.", + "\t\n\nConclusions: This study indicates that the epigenetic clock can be improved by increasing the training sample size and that its association with mortality attenuates with increased prediction of chronological age.", + "\tBackground:\n\nThe Horvath epigenetic clock is widely used.It predicts age quite well from 353 CpG sites in the DNA methylation profile in unknown samples and has been used to calculate \"age acceleration\" in various tissues and environments.\t\nBackground:The Horvath epigenetic clock is widely used.It predicts age quite well from 353 CpG sites in the DNA methylation profile in unknown samples and has been used to calculate \"age acceleration\" in various tissues and environments.Results: The model systematically underestimates age in tissues from older people.This is seen in all examined tissues but most strongly in the cerebellum and is consistently observed in multiple datasets.Age acceleration is thus agedependent, and this can lead to spurious associations.The current literature includes examples of association tests with age acceleration calculated in a wide variety of ways. Conclusions:The concept of an epigenetic clock is compelling, but caution should be taken in interpreting associations with age acceleration.Association tests of age acceleration should include age as a covariate.\tDiscussion\n\nThe Horvath epigenetic clock [8] has been of practical use in predicting the age of unknown samples and as a quality check in epigenetic research.Additional widely used age predictors specific for blood were published by Hannum [6] and Levine [42] (phenotype-based).Here we analyze the Horvath model, but the methods and many of the conclusions may be more widely applicable, in particular the Hannum clock model shows a similar underestimation of ages in elderly subjects.\t\n\nIn addition to age prediction, the Horvath [8] paper also featured the idea of \"age acceleration\" in which discrepancies between DNA methylation (DNAm) age and chronological age might tell us something about the biological aging status of the organism.A number of positive association findings with age association, particularly mortality [43], make it compelling to think of the epigenetic clock as an index of an underlying aging program that adapts to health and environment.In light of the methodological variety though, we are concerned that the different epigenetic clocks, and the variety of age acceleration methods to choose from, lay a trap of potentially hidden multiple testing, as the temptation will be to survey the available methods for interesting results.\tConclusions:\n\nThe concept of an epigenetic clock is compelling, but caution should be taken in interpreting associations with age acceleration.Association tests of age acceleration should include age as a covariate.", + "\tEpigenetic clocks\n\nFour epigenetic clocks were studied: the blood clock developed by Hannum et al. (Hannum Bld) [2], the multi-tissue clock developed by Horvath (Horvath MT) [3], the skin/ blood clock developed by Horvath et al. (Horvath Skn/Bld) [4], and the blood/saliva clock developed by Zhang et al. (Zhang Bld/Slv) [5].These clocks are described in Table 1.Together, the four epigenetic clocks comprised 1147 unique CpGs.One CpG from Horvath Skn/Bld (cg14614643) did not pass QC in our DNAm data and was therefore excluded from our analyses (i.e., 1146 CpGs were included).The four epigenetic clocks were used to predict chronological age in all 3132 samples for which methylome data were available.To this end, the coefficients of all clock CpGs were downloaded (available in their respective publications [2][3][4][5]).Beta-values of the clock CpGs were used as input for all clocks.For Horvath MT and Horvath Skn/Bld, predicted ages were transformed according to the authors' instructions [3,4].For Zhang Bld/Slv, DNAm values were normalized according to the authors' instructions, so that all samples had a mean of 0 and a standard deviation of 1 across all 450K CpGs [5].\tEpigenetic clocks accurately predict chronological age and show high similarity\n\nOur analyses were performed on whole blood samples from 3132 unrelated individuals, aged 18 to 87, originating from 6 Dutch cohorts (Table 2), for which both DNAm data and gene expression data were obtained, measured by Illumina 450K arrays and RNAseq, respectively.Only samples for which both DNAm and gene expression data passed QC were analyzed.First, we applied 4 epigenetic clocks (Table 1) to the DNAm data to predict age.All clocks accurately predicted age in our data.The Pearson correlation (r) between chronological age and predicted age was greater than 0.90 for all clocks, but there were differences in the prediction errors (Fig. 1A).Hannum Bld and Horvath MT showed the highest age prediction error (mean absolute error (MAE) = 4.5 years), followed by Horvath Skn/Bld (MAE = 3.1 years), and the prediction error was lowest for Zhang Bld/Slv (MAE = 2.7 years).We found that the errors in age prediction of the epigenetic clocks were highly correlated between clocks, with the pairwise correlation coefficients ranging from 0.57 to 0.79 (Fig. 1B).Thus, a person whose predicted age exceeds their chronological age according to one clock was likely to have a similar deviation according to another clock.However, this was not the case for extreme differences between predicted and chronological age, which were generally not reproduced between clocks (Additional file 1: Fig. S1A-B).For example, of the individuals for whom the prediction error of Hannum Bld was 10 years or higher, 32% had a prediction error above 10 years according to Horvath MT, and only 4% according to Zhang Bld/Slv (Additional file 1: Fig. S1A-B, top row).However, the individuals marked as extreme by Zhang Bld/Slv were more consistent with the other clocks, with up to 91% overlap (Additional file 1: Fig. S1A-B, bottom row).These findings indicate that extreme deviations between chronological and predicted age should be interpreted with caution.\tConclusions\n\nThe ability of epigenetic clocks to predict chronological age involves their ability to detect changes in proportions of naive and activated immune blood cells.This finding may contribute to the interpretation of associations between clock-derived measures and age-related health outcomes." + ], + [ + "\t\nYeast is a useful model organism to study the genetic and biochemical mechanisms of aging.Genomic studies of aging in yeast have been limited, however, by traditional methodologies that require a large investment of labor and resources.In this chapter, we describe a newly-developed method for quantitatively measuring the chronological life span of each strain contained in the yeast ORF deletion collection.Our approach involves determining population survival by monitoring outgrowth kinetics using a Bioscreen C MBR shaker/incubator/plate reader.This method has accuracy comparable to traditional assays, while allowing for higher throughput and decreased variability in measurement.\t\n\nYeast is a useful model organism to study the genetic and biochemical mechanisms of aging.Genomic studies of aging in yeast have been limited, however, by traditional methodologies that require a large investment of labor and resources.In this chapter, we describe a newly-developed method for quantitatively measuring the chronological life span of each strain contained in the yeast ORF deletion collection.Our approach involves determining population survival by monitoring outgrowth kinetics using a Bioscreen C MBR shaker/incubator/plate reader.This method has accuracy comparable to traditional assays, while allowing for higher throughput and decreased variability in measurement.", + "\t\nThe genetic analysis of life span has only begun in mammals, invertebrates, such as Caenorhabditis elegans and Drosophila, and yeast.Even at this primitive stage of the genetic analysis of aging, the physiological observations that rate of metabolism is intimately tied to life span is supported.In many examples from mice to worms to flies to yeast, genetic variants that affect life span also modify metabolism.Insulin signaling regulates life span coordinately with reproduction, metabolism, and free radical protective gene regulation in C. elegans.This may be related to the findings that caloric restriction also regulates mammalian aging, perhaps via the modulation of insulin-like signaling pathways.The nervous system has been implicated as a key tissue where insulin-like signaling and free radical protective pathways regulate life span in C. elegans and Drosophila.Genes that determine the life span could act in neuroendocrine cells in diverse animals.The involvement of insulin-like hormones suggests that the plasticity in life spans evident in animal phylogeny may be due to variation in the timing of release of hormones that control vitality and mortality as well as variation in the response to those hormones.Pedigree analysis of human aging may reveal variations in the orthologs of the insulin pathway genes and coupled pathways that regulate invertebrate aging.Thus, genetic approaches may identify a set of circuits that was established in ancestral metazoans to regulate their longevity.", + "\tIntroduction\n\nThe budding yeast Saccharomyces cerevisiae has been used as a model of cellular aging for more than 6 decades (Fabrizio and Longo 2007;Jazwinski 2005;Kaeberlein et al. 2007;Steinkraus et al. 2008).S. cerevisiae has several features that make it useful as a model organism for aging research, including short life span, well-characterized genetic and molecular methods, low relative cost, cell type homogeneity, and a vast organismal information base.These advantages have facilitated unbiased screens for genes that influence life span in yeast, as well as candidate gene approaches.Several dozen genetic determinants of yeast longevity have been identified from these studies, at least some of which appear to play a conserved role in the aging of multicellular eukaryotes.\t\n\nSince these early morphology-based studies, yeast replicative aging has become a prominent model for aging genetics and has been instrumental in the discovery and characterization of several of the best studied genetic pathways involved in life span determination.These pathways include dietary restriction (DR), sirtuins, TOR signaling, and mitochondrial metabolism (Table 12.1).\t\nIn the past several decades the budding yeast Saccharomyces cerevisiae has emerged as a prominent model for aging research.The creation of a single-gene deletion collection covering the majority of open reading frames in the yeast genome and advances in genomic technologies have opened yeast research to genome-scale screens for a variety of phenotypes.A number of screens have been performed looking for genes that modify secondary age-associated phenotypes such as stress resistance or growth rate.More recently, moderate-throughput methods for measuring replicative life span and high-throughput methods for measuring chronological life span have allowed for the first unbiased screens aimed at directly identifying genes involved in determining yeast longevity.In this chapter we discuss large-scale life span studies performed in yeast and their implications for research related to the basic biology of aging.", + "\t\n\nThe use of humans in aging studies is complicated due to several factors, including ethical, environmental, and social issues, and even economic reasons, and more importantly, due to the human long natural life span.The human aging process takes decades to develop, making it virtually impossible to perform longitudinal studies by following subjects throughout their lives.Thus, the most widely employed models of aging are short-lived organisms, including yeast, roundworm, fruit fly, and mice.Indeed, large-scale genetic screenings have identified numerous genes and drugs that significantly lengthen life span in these organisms; however, the biological relevance of such longevity genes to human aging remains not fully established [3].\tIntroduction\n\nResearch into the underlying mechanisms of organismal ageing has advanced at a tremendous rate over the past decade.Studying the ageing process presents a significant challenge as it is a systemic phenomenon that affects numerous organs and tissue systems in humans.Due to the complex nature of the ageing process, it has been most extensively modelled using short-lived non-vertebrate systems such as nematode worms (C.elegans), yeast (C.cerevisiae) and flies (D. melanogaster), as well as longer-lived vertebrate models, such as the mouse (M.musculus) and zebrafish (D. rerio) [1].Importantly, research using these model organisms alongside both traditional and novel genetic manipulation techniques has delineated nine hallmarks of ageing that are common across various species, including humans [2].Tremendous effort is now being expended into understanding the relationship between these different hallmarks and how their interactions impact on the ageing process.This has created a constant necessity for studying multiple interactions between complex genetic pathways, sometimes under the influence of fluctuating factors, such as epigenetic mechanisms, and especially in vertebrate models where traditional genetic engineering techniques are less efficient or involve higher costs due to longer lifespans (the maximal lifespan of mice is around 3-4 years and 5 years for zebrafish).It has therefore become of great interest for the ageing research community to develop new in vivo and in vitro genetically engineered models capable of addressing complex research questions in a time-cost efficient manner.", + "\tCONCLUSION\n\nOur understanding of the basic mechanisms of aging have benefited greatly from the use of simple model systems such as yeast and worms.The development of technologies that allow direct analysis of longevity on a genome-wide scale in these organisms has provided a wealth of new data regarding the genes and pathways that modulate longevity.Some of these genes and pathways are specific to each organism; however, others appear to be evolutionarily conserved.Future efforts will move toward translating the data from genomic longevity studies in yeast and worms into mammalian models.Any gene that functions similarly to modulate longevity and disease in yeast, worms, and mice will be an outstanding candidate for therapeutic intervention targeting age-associated diseases in people.\t\n\nGenomic comparisons of longevity across species also provide an opportunity to identify novel factors that modulate aging and age-associated disease in humans.The evolutionary distance between yeast and worms is approximately equivalent to the evolutionary distance between worm and humans.Therefore, if an ortholog pair has maintained a conserved longevity determining function between yeast and worms, it is reasonable to speculate that the function will also be retained in mammals.At lease one effort is underway to directly test this assumption (http://www.pathology.washington.edu/research/bioage/ellison/).A consortium of laboratories at the University of Washington is utilizing the data from the genome-wide yeast and worm longevity screens described above to identify candidate genes for longevity studies as gene knock-outs in mice [1].A CRE-based conditional knock-out system is being employed for these studies, to allow either complete knock-out of a particular gene or tissue specific (or post-development) gene deletion.Along with longevity, a select group of potential agingrelated biomarkers will be assayed for each of these mouse models.In addition, it should be possible to assay several of these mouse lines for resistance to specific age-associated diseases, such as diabetes and neurological disorders, by crossing them into the appropriate transgenic disease background.\t\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases.\t\n\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases.\t\n\nWhen considering the use of simple eukaryotes to study aging and age-related disease, it is pertinent to ask whether, and to what degree, the aging process is evolutionarily conserved.Does a yeast cell age by the same mechanism(s) as a mouse?Is the longevity of a nematode determined in the same way as that of a person?The complete answers to these questions remain largely unknown; however, discoveries made over the last several years have unequivocally demonstrated that at least some of the factors regulating longevity are shared between yeast, worms, flies, and mice.The degree to which these pathways will be relevant to human longevity and age-associated disease is an important unanswered question.", + "\t\n\nMany of the genes and gene networks that modulate aging are conserved across animal phyla.For this reason, the highly tractable model systems Drosophila and Caenorhabditis have provided fundamental advances in our understanding of the genetic control of cellular processes that affect aging.There is a growing realization that increasing the evolutionary breadth in animal systems used in aging studies will lead to discovery of effects and mechanisms that are more likely to be robust and reveal fundamental principles of aging.The use of diverse models may also reveal previously unknown genetic factors involved in healthy aging in humans.The lineages leading to Drosophila melanogaster and Caenorhabditis elegans have each undergone significant genome reduction, and these standard model systems lack many vertebrate gene homologs that are present in other invertebrates [2][3][4][5][6][7][8][9].In addition, arthropods and nematodes are more closely related to each other than originally thought [10,11], limiting the evolutionary range in comparative studies of aging [12] and thus the degree to which conclusions can be reliably generalized from these models to humans.", + "\t\n\nIt seems that organisms from yeast to mammals have evolved genetic programs to cope with periods of starvation that can also postpone aging and age-related diseases, but how can we take advantage of those mechanisms to improve human health?Because assaying the longevity effects of CR in humans is practically impossible, studying its molecular mechanisms in lower life forms could be beneficial to humans through the identification of candidate genes, pathways and molecular mechanisms.Although CR will not be suitable for everyone, targeting its mechanisms and developing CR mimetics may lead to drug development for a number of age-related and metabolic diseases.", + "\tINTRODUCTION\n\nGenomic studies into human longevity are inspired by the fact that, in animal models, healthy lifespan has proved to be remarkably plastic, and major pathways of lifespan regulation have been identified.Considerable lifespan extension has been induced in models as diverse as yeast, worms, fish, flies and rodents by applying genetic manipulation and dietary restriction (DR) (see [1] for review).Reduced activity of nutrient-sensing pathways such as insulin/insulin-like growth factor (IGF-1) signalling (IIS) and target of rapamycin (TOR) signalling mediated lifespan extension, and also the extension of lifespan by DR [2].An interesting observation from the perspective of human ageing is that, in rodents and monkeys, diets restricted in glucose, fat or protein uptake reduced or delayed the risk of cancer and metabolic disease, thus extending the healthspan of the animals [2].Following the discovery of genes and pathways involved in animal lifespan extension, human research has focused on the corresponding candidate human genes with genetic, genomic and epigenetic studies into ageing and longevity.The designs of these studies differ with respect to the selection of naturally occurring phenotypes and the study populations, which include population-based, patient-based, family-based and exposure-based cohorts.", + "\tINTRODUCTION A Brief History of Longevity Genetics Research in C. elegans\n\nProgress in aging research has identified genetic and environmental factors that regulate longevity across species [1][2][3].The nematode worm Caenorhabditis elegans has become an invaluable model system for investigating the molecular mechanisms of aging and longevity, offering the advantages of its relatively low cost, short lifespan, and conservation of key nutrient and stress-responsive signaling pathways in mammals.", + "\t\n\nIn addition to these advanced tools, new studies in emerging aging models, such as eusocial insects, and in yet-uncharacterized models will provide additional opportunities for insight into key epigenetic mechanisms in aging.In the case of the Indian jumping ant Harpegnathos saltator, a worker can replace a queen in the colony, resulting in a change in longevity, acquisition of reproductive function, and loss of worker behavior, all of which can ultimately be reversed.The epigenetic mechanisms that underlie this transition are of great interest, including characterization and manipulation of epigenetic patterning during development, which lead to key behavioral differences in these organisms (Simola et al., 2016).Particularly long lifespans have been observed in several types of deepwater fishes, various crustaceans, bow head whales, several turtles, and naked mole rats (relative to other rodents) among others.While some may be unfeasible for creation of laboratory models, tissue and cellular studies of these or similar organisms may prove to be insightful.In addition, short-lived model organisms such as yeast, worms, and killifish are useful for quick lifespan estimations (Table 1).Together with the technological advances highlighted above, new experimental avenues and models in aging research will provide key insight into the epigenetic pathways that underlie longevity and aging and will likely identify factors and pathways that can be targeted to improve health and lifespan in humans.", + "\t\n\nSaccharomyces cerevisiae has directly or indirectly contributed to the identification of arguably more mammalian genes that affect aging than any other model organism.Aging in yeast is assayed primarily by measurement of replicative or chronological life span.Here, we review the genes and mechanisms implicated in these two aging model systems and key remaining issues that need to be addressed for their optimization.Because of its well-characterized genome that is remarkably amenable to genetic manipulation and highthroughput screening procedures, S. cerevisiae will continue to serve as a leading model organism for studying pathways relevant to human aging and disease.", + "\t\n\nAlthough many theories have tried to explain aging, only few experimental advances were made prior to the last two decades.Since then rapid progress in the genetics of aging has been made in invertebrate models such as C. elegans and D. melanogaster, demonstrating the existence of regulatory pathways that control the rate of aging in these organisms [1][2][3][4][5][6][7][8][9][10][11][12][13][14].They include the insulin-like pathway, the Jun kinase pathway and the Sir2 deacetylase pathway.Moreover, it was rapidly shown that some of these pathways are conserved from yeast to humans." + ], + [ + "\t\n\nKnowledge of genetic interrelationship between the biomarkers of aging may lead to the discovery of a downstream common pathway that summarizes aging processes; the list of biomarkers should be as comprehensive as possible via incorporating other well-known systems involved in aging in addition to the musculoskeletal system.Further development of the pleiotropy-based approaches will be useful for other studies of multiple related phenotypes which employ genome-wide associations to decipher genetics in the absence of disease endophenotypes, which is the case of human aging.With the advent of these approaches, new candidate genes may emerge for further pursuit.In its turn, discovery of the \"phenome of aging\" may translate into innovative diagnostic and therapeutic interventions to improve the overall health of older men and women.", + "\t\n\nFig. 4. Functional genomics technologies promise to go deeply into the understanding and the development of therapeutic strategies for sarcopenia.", + "\tRejuvenation without Dedifferentiation\n\nRecent studies have begun to test the potential of different interventions to restore youthfulness to aged cells or tissues.", + "\tWhat does this study add? Combining genomics with in vitro human skin cell cultures is a promising approach for the identification of new antiageing and antidiscoloration compounds.\tWhat's already known about this subject? Genomics data from the study of skin biopsies has identified new biomarkers for targeting skin ageing and discoloration for therapeutic intervention. In vitro human skin cell cultures are routinely used for the rapid evaluation of cosmetic compounds.", + "\tImplications and Interventions for Antiaging Medicine\n\nOne of the aims of this work is to make others aware that age-related changes and pathologies can derive from early-onset developmental mechanisms, as supported by recent results (1, 2).Hopefully, researchers and clinicians will try to understand age-related pathologies by looking at the physiology and genetics of normal developmental processes.Assuming a link between development and aging also has major implications for how experiments are designed and interpreted in gerontology.If we see aging as triggered by development, rather than a mere accumulation of damage, then to study aging it is necessary to understand the life span as a whole and not merely its last segment.Herein, we offer a few ideas about how this can be achieved, including suggestions for experiments.", + "\t\n\nKnowledge of genetic and molecular pathways related to aging and its modulation can also be translated into predictions on health effects of dietary components (Mu ller and Kersten, 2003).Therefore, in addition to pharmaceuticals, another marketplace for basic aging research involves supplements, which avoids the need for clinical trials.Indeed, companies are now focusing on nutritional supplements that target genes/pathways involved in aging.One example is Genescient (http://www.genescient.com/), a biotechnology company; its strategy involves choosing supplements that affect pathways that may be important in long-lived flies as assayed from gene expression analyses (Rose et al., 2010).\t\n\nWe now know of hundreds of genes that regulate aging in model organisms, dozens associated with longevity in humans, and hundreds differentially expressed with age.This vast amount of information yields increased power for personalized and stratified medicine, for identifying biomarkers of aging, and for drug development to extend lifespan and ameliorate age-related diseases.Overall, it gives us a blueprint (albeit still imperfect) of how aging is controlled that we can use to potentially manipulate the basic aging process, whatever its underlying molecular mechanisms may be.Moreover, our knowledge of nutrient-sensing pathways that mediate the effects of CR has greatly increased in recent years, opening new opportunities for drug discovery and ultimately for perhaps developing an antiaging pill that retards aging with minimal side effects.\t\nAging is the major biomedical challenge of this century.The percentage of elderly people, and consequently the incidence of age-related diseases such as heart disease, cancer, and neurodegenerative diseases, is projected to increase considerably in the coming decades.Findings from model organisms have revealed that aging is a surprisingly plastic process that can be manipulated by both genetic and environmental factors.Here we review a broad range of findings in model organisms, from environmental to genetic manipulations of aging, with a focus on those with underlying gene-environment interactions with potential for drug discovery and development.One well-studied dietary manipulation of aging is caloric restriction, which consists of restricting the food intake of organisms without triggering malnutrition and has been shown to retard aging in model organ-isms.Caloric restriction is already being used as a paradigm for developing compounds that mimic its life-extension effects and might therefore have therapeutic value.The potential for further advances in this field is immense; hundreds of genes in several pathways have recently emerged as regulators of aging and caloric restriction in model organisms.Some of these genes, such as IGF1R and FOXO3, have also been associated with human longevity in genetic association studies.The parallel emergence of network approaches offers prospects to develop multitarget drugs and combinatorial therapies.Understanding how the environment modulates aging-related genes may lead to human applications and disease therapies through diet, lifestyle, or pharmacological interventions.Unlocking the capacity to manipulate human aging would result in unprecedented health benefits.\t\n\nCurrent progress in genomics, high-throughput methods, informatics, and systems biology should help to develop network approaches that test target combinations resulting in the emerging paradigm of network pharmacology (Keith et al., 2005;Hopkins, 2008).Systematic drug-design strategies directed against multiple targets hold much promise in the field of aging (Csermely et al., 2005), although challenges remain in developing accurate computer models of relevant pathways and suitable in vitro and in vivo models for testing.In the same vein, progress in personalized medicine and in predicting individual responses (e.g., using SNPs) to the environment (including diet, lifestyle, and drugs), will be key to maximizing environmental interventions that improve health and counteract aging.Therefore, network approaches to both aging and pharmacology are promising future avenues (Simko et al., 2009).\t\n\nAging is the major biomedical challenge of this century.The percentage of elderly people, and consequently the incidence of age-related diseases such as heart disease, cancer, and neurodegenerative diseases, is projected to increase considerably in the coming decades.Findings from model organisms have revealed that aging is a surprisingly plastic process that can be manipulated by both genetic and environmental factors.Here we review a broad range of findings in model organisms, from environmental to genetic manipulations of aging, with a focus on those with underlying gene-environment interactions with potential for drug discovery and development.One well-studied dietary manipulation of aging is caloric restriction, which consists of restricting the food intake of organisms without triggering malnutrition and has been shown to retard aging in model organ-isms.Caloric restriction is already being used as a paradigm for developing compounds that mimic its life-extension effects and might therefore have therapeutic value.The potential for further advances in this field is immense; hundreds of genes in several pathways have recently emerged as regulators of aging and caloric restriction in model organisms.Some of these genes, such as IGF1R and FOXO3, have also been associated with human longevity in genetic association studies.The parallel emergence of network approaches offers prospects to develop multitarget drugs and combinatorial therapies.Understanding how the environment modulates aging-related genes may lead to human applications and disease therapies through diet, lifestyle, or pharmacological interventions.Unlocking the capacity to manipulate human aging would result in unprecedented health benefits.\t\n\nIn conclusion, we now know of many target genes that either individually or collectively could be used for screening molecules (nutritional compounds and drugs) that may modulate aging.Even if proving that a particular diet or drug can delay aging is not feasible from a scientific and regulatory perspective, there is a huge potential to identify molecules that ameliorate age-related diseases and/or dysfunction.This represents a tremendous opportunity for companies working in nutrition and pharmacology in a field on an upward trajectory.\t\n\nMarred by decades of \"quackery\" (including grafting testicles from young animals into men), the science of aging has come a long way in gaining respectability (Stipp, 2010).Already more than 20 companies worldwide are focusing specifically on the aging process (http://whoswho.senescence.info/corp.php), in addition to \"big pharma,\" with agingoriented research and development projects.Although this number is modest, it shows the growing potential of a field that is bound to increase.In 2008, GlaxoSmithKline purchased Sirtris for $720 million (Sipp, 2008), a huge amount for a company with no clinical data; presumably the purchase was based on the extraordinary potential suggested by a compound capable of delaying aging.Even though questions have been raised about their efficiency, resveratrol and other drugs targeting SIRT1 showcase how a gene initially identified as a regulator of aging in yeast can be used as a pharmaceutical target for multiple human diseases.It demonstrates confidence in the field and in the idea that aging is not immutable.The recent problems raised concerning SIRT1 and resveratrol research also serve as a cautionary tale of the hurdles in translation of laboratory discoveries to the clinic.\tVI. Concluding remarks\n\nAging is the major driving factor of disease in the 21st century.Manipulation of aging-related genes by diet, lifestyle, and pharmaceuticals could dramatically improve human health and could be used to develop drugs against age-related diseases such as cancer, heart disease, type 2 diabetes, obesity, and neurodegenerative diseases.The hundreds of aging-related genes and genes related to CR already identified offer enormous opportunities for target discovery (Fig. 2).Although agingrelated genes cannot be modified in humans, understanding how these can be manipulated by diet or pharmaceuticals can have a profound impact on health.In other words, work on the genetics of aging allows the identification of novel genomic targets for drug development, opening the door for aging pharmacogenomics.\tC. Translation to Extend Human Healthspan\n\nAlthough a number of genes and even a few drugs have emerged as candidates for targeting the aging process pharmacologically, several problems are associated with translation to human aging.In principle, human clinical trials on aging cannot be performed.One major problem is that aging cannot be quantified, and even a trial running for several years would struggle to identify endpoints.Lifespan or survival could be quantified, as well as health biomarkers such as low blood pressure, insulin sensitivity, inflammatory markers, glucose metabolism, etc., but these may or may not reflect alterations in the aging process.\t\n\nOverall, demonstrating that a particular intervention is affecting human aging, as done in model organisms, is virtually impossible.Interventions, including drugs, emerging from basic research on aging will probably target specific age-related pathological conditions and/or dysfunction.Subsequent studies of health biomarkers and multiple age-related diseases may reveal broader effects.Success in animal models or short-term human studies may be sufficient to convince potential patients of the usefulness of particular dietary supplements or approaches, as exemplified by those voluntarily undergoing CR (http://www.crsociety.org/),which can serve as basis for further studies (Soare et al., 2011).", + "\tConcluding Remarks\n\nGenome instability plays a significant role in the progression of aging and protecting our aging genomes is therefore of fundamental importance for healthy aging.A major issue for the development of interventions targeting aging is the long trial time and difficulty in determining positive outcomes (see Outstanding Questions).Premature-aging diseases could represent an interesting group of disorders where aging interventions could be tested and outcomes could be determined at a much lower cost and potentially in less time.Here, treatments such as rapamycin, dietary interventions, sirtuin-activating compounds, metformin, NAD precursors, and senolytics could be more diligently tested in DNA repair disorders.A large number of therapies are emerging that may directly or indirectly lead to less DNA damage and the vast ongoing research across the globe will undoubtedly eventually be able to target this for the benefit of humankind.In sum, the future is bright.", + "\t\n\nAging is a multifold process affected by many genes and thus many biochemical pathways.This conclusion is underscored by the failure to find simple central controls for the aging process during the 20th Century.This situation poses a fundamental challenge to anti-aging medicine: how to develop effective therapies for a genomically complex pathology.We propose such a strategy.As a first step, we recommend the use of model systems in which significant genetic intervention is not proscribed or impractical.Second, we propose that work with such model systems begin with selected lines that have genetic enhancements that allow increased lifespan.Third, genomic methods should be used to identify a number of biochemical pathways for increasing lifespan.Fourth, biochemical pathways that have been identified in model systems would then be available for pharmaceutical development, first in rodents, eventually in a clinical human population.This may seem to be a cumbersome R&D strategy, but starting with human populations or inadequately pre-screened compounds would be unlikely to succeed because of the complexity of the aging problem.\t\nAging is a multifold process affected by many genes and thus many biochemical pathways.This conclusion is underscored by the failure to find simple central controls for the aging process during the 20th Century.This situation poses a fundamental challenge to anti-aging medicine: how to develop effective therapies for a genomically complex pathology.We propose such a strategy.As a first step, we recommend the use of model systems in which significant genetic intervention is not proscribed or impractical.Second, we propose that work with such model systems begin with selected lines that have genetic enhancements that allow increased lifespan.Third, genomic methods should be used to identify a number of biochemical pathways for increasing lifespan.Fourth, biochemical pathways that have been identified in model systems would then be available for pharmaceutical development, first in rodents, eventually in a clinical human population.This may seem to be a cumbersome R&D strategy, but starting with human populations or inadequately pre-screened compounds would be unlikely to succeed because of the complexity of the aging problem.", + "\tIntegrating genomics and biomarker research\n\nOnce the use of established biomarkers of biological age is standardized, the biomarker information can be integrated into studies aimed at finding causal determinants of aging and longevity.An example of an integrated approach to identify lifespan regulating loci is represented by testing whether genetic variants associated with potential biomarkers also associate with longevity.To date, GWAS have identified many genetic variants that associate with age-associated traits, such as leukocyte telomere length and features from glycome and metabolome profiles [84][85][86].The joint effect of the majority of these variants on aging and longevity still needs to be determined.One study identified a haplotype in the TERT gene that was associated with increased telomere length and longevity, which indicates that genetic variants associated with telomere length regulation might also play a role in longevity [87]." + ], + [ + "\t\nThe genetic analysis of life span has only begun in mammals, invertebrates, such as Caenorhabditis elegans and Drosophila, and yeast.Even at this primitive stage of the genetic analysis of aging, the physiological observations that rate of metabolism is intimately tied to life span is supported.In many examples from mice to worms to flies to yeast, genetic variants that affect life span also modify metabolism.Insulin signaling regulates life span coordinately with reproduction, metabolism, and free radical protective gene regulation in C. elegans.This may be related to the findings that caloric restriction also regulates mammalian aging, perhaps via the modulation of insulin-like signaling pathways.The nervous system has been implicated as a key tissue where insulin-like signaling and free radical protective pathways regulate life span in C. elegans and Drosophila.Genes that determine the life span could act in neuroendocrine cells in diverse animals.The involvement of insulin-like hormones suggests that the plasticity in life spans evident in animal phylogeny may be due to variation in the timing of release of hormones that control vitality and mortality as well as variation in the response to those hormones.Pedigree analysis of human aging may reveal variations in the orthologs of the insulin pathway genes and coupled pathways that regulate invertebrate aging.Thus, genetic approaches may identify a set of circuits that was established in ancestral metazoans to regulate their longevity.", + "\tConclusions\n\nIn the absence of a consensus phenotype for aging, genetic research is impeded (Melzer et al. 2007).At present, it is difficult to determine whether preventative and therapeutic strategies (such as calorie restriction) have beneficial effects in humans because there are no validated biomarkers that can serve as surrogate markers of aging (Matkovic et al. 1990).To have the \"phenome of aging\" (Xue et al. 2007) much better defined, we propose using the musculoskeletal aging phenotypes as an example and starting point.", + "\t\n\nHistorically, the effects of CR have been viewed as being associated with the aging process [1][2][3].This standpoint argues that effects of CR extend beyond any one disease process (e.g., tumorigenesis), but that CR has multiplex effects on a range of physiological systems, ultimately amounting to an inhibitory effect on the progression of aging.The association between CR and aging, however, remains poorly understood, largely because the aging process itself remains poorly defined [17].While an uncontroversial definition of aging may not be developed anytime soon, it should be possible to add rigor to the concept by generating quantitative models of aging that are operationally useful.In this regard, whole-genome microarray datasets would seem especially valuable [18], and can be used to generate models that test, quantitatively, the assertion that CR acts to oppose the progression of aging [11].Conclusions generated from previous investigations conflict regarding the association between the effects of CR and aging.On the one hand, an early investigation revealed that age-associated expression patterns in muscle were \"either completely or partially prevented by caloric restriction\" [19], and this conclusion was supported in subsequent studies [14,20,21].Other investigations, however, have yielded different conclusions.For instance, effects of CR were entirely unrelated to those of aging in muscle tissue from Rhesus monkeys [22], and in one aptly designed experiment examining mouse cardiac tissue, only 79 of 1075 age-responsive genes (7.3%) were significantly altered by CR [23].Clearly, experimental design and statistical methodology are two important considerations for evaluating this diverse set of results.Many studies, for instance, have not evaluated whether the observed overlap between CR and aging effects is larger than expected by chance alone.This statistical evaluation would not be straight-forward in many cases, since experiments involved a shared control treatment that was used to evaluate the effects of both aging and CR (e.g., a young control treatment, an old control treatment, and an old CR treatment).Given this design, the effect of CR is not estimated independently of the effect of aging, and some correspondence between CR and aging effects would be expected by chance [12].\t\n\nThe association between CR and aging was next examined at the global scale, among all genes, and also with respect to each of the four most well-studied tissue types (liver, heart, muscle and central nervous system) (Figure 8).In liver, there was a slight, positive association between the effects of CR and aging (r = 0.04) (Figure 8A).This association was significant (P < 6.72 10 -12 ), although given the large number of genes involved in the comparison, this Relationship between caloric restriction and aging in liver, heart, muscle and the central nervous system Figure 8 Relationship between caloric restriction and aging in liver, heart, muscle and the central nervous system.The association between CR and aging was evaluated for the (A) liver, (B) heart, (C) muscle and (D) central nervous system (hippocampus + cortex).The CR effect is positive for genes up regulated by CR and negative for genes down regulated by CR (see Figure 7 legend).Likewise, the age effect is positive for genes up regulated with age and negative for genes down regulated with age (see Figure 7 legend).The abundance of genes in relation to the CR and age effect is reflected by the color intensity, with deep blue colors corresponding to regions with the largest number of genes.The dashed red line is based upon a least-squares regression fit that quantifies the overall relationship between the CR and aging effects.In each panel, the estimated Pearson correlation is shown in the upper-right, and the percentage values (green font) indicate the fraction of genes that belong to each quadrant.The effects of CR and aging were computed in each organ system based upon p-values generated by combining results from at least 3 independent experiments.In liver, CR and aging effects are based upon 9 and 7 experiments, respectively.In heart, CR and aging effects are based upon 5 and 10 experiments, respectively.For muscle and central nervous system, CR and aging effects are each based upon 3 -6 experiments.For each organ, distinct sets of data were used to estimate the CR and aging effects, such that CR and aging effects are a priori independent.significance test was not too informative.In the heart, muscle and central nervous system, the expected negative association between CR and aging did emerge, albeit weakly, with the estimated correlation coefficient less than or equal to -0.10 in each case.The strongest association was found in heart (Figure 8B), in which age-related expression patterns were weakly opposed by CR (r = -0.096;P = 2.20 10 -16 ).In muscle and central nervous system (Figures 8C and 8D), the association between CR and aging was again weak (r < -0.048), and non-significant in the case of muscle (P = 0.054), despite the large number of genes upon which the association was based.With respect to central nervous system, a large fraction of genes (56.6%) were both increased by CR and decreased with age (i.e., within the lower-right quadrant of Figure 8D), although very few genes (8.9%) were decreased by CR and increased with age (i.e., within the upper-left quadrant of Figure 8D).", + "\t\n\nThen we have those pharmaceutical strategies that are www.impactaging.combased on emulating the pathways implicated in the response of lifespan to dietary restriction, particularly sirtuin-targeting agents like resveratrol [e.g.25].Again, like hormone manipulation, these pathways are heavily bound up with the regulation of reproduction, making the curtailment of the cost of reproduction the most likely mechanism by which the beneficial effects of emulating dietary restriction are achieved [cf. 26].This is a strategy in which longevity is increased by metabolic refrigeration, pseudo-hibernation, or curtailing functions [11].From the standpoint of evolutionary biology, this is, again, not an extension of the period of adaptation.It is instead trading one set of adaptations off against another.Most people do not regard curtailing their metabolism, cognition, affective stability or reproductive functions as a useful approach to the problem of aging.Nonetheless, some are willing to trade-off some of their adaptive functions for an increased lifespan, and for them this \"anti-aging\" strategy will have its attractions.", + "\tMetabolism\n\nStudies show that calorie restriction is the most consistent means to prolong life expectancy and health across several experimental models [55], ranging from yeasts to primates.It not only increases life expectancy, but it also delays the onset of many features and hallmarks of ageing, including age-related diseases.Transcriptional profiles are currently being applied and investigated.One of them is a caloric restriction (CR), which increases the response to oxidative stress and reduces the shortening of telomeres in chromosomes; this has a direct intervention in the repair of DNA damage.Data from human trials (such as CALERIE, Biosphere-2 and CRON) indicate that moderate CR accompanied by adequate nutrition has positive effects on health and dramatically reduces the multiple metabolic factors involved in the pathogenesis of disease chronicles, including type 2 diabetes, heart and cerebrovascular diseases, and cancer [56].", + "\t\n\nOn the other hand, the beneficial effects of caloric restriction are associated with alterations in metabolism, particularly the insulin/insulin-like growth factor 1 (IGF-1) pathways, which could reflect an evolution mechanism to ensure survival of a species during period of food shortage [3].Many genetic manipulations affecting nutrient-sensing pathways including the insulin and mTOR (mammalian target of rapamycin) pathways mimic the effect of caloric restriction on lifespan in yeast, worm, flies and mice and support this hypothesis [3].This review will firstly discuss in general terms how trace elements affect ageing and then use Selenium (Se) as an example to illustrate how trace elements influence the ageing process.Furthermore, the review will also illustrate how the so-called \"Omics technologies\" can be used to unravel the modes of action of trace elements and to identify biomarkers to define the optimal intake for health at the molecular level.\t\n\nEvidence is building up showing that caloric restriction, without malnutrition, extends lifespan in species ranging from yeast to non-human primates [3], but it appears, on the contrary, that inadequate/sub-optimal intake of micronutrients contribute to the development of chronic diseases.In his \"Triage theory\", B. Ames suggested that this could reflect the need for an organism to re-allocate micronutrients according to triage priorities to favour short-term survival over long-term wellbeing [4,5].The consequences of this re-allocation may remain unnoticed in the day-to-day experience but are likely to show up late in life as cancers, Alzheimer's disease, Parkinson's disease, diabetes and cardiovascular diseases.", + "\t\n\nCaloric restriction (CR) is the only intervention shown to extend lifespan in mammals (5).It is also the most effective means known of reducing cancer incidence and increasing the mean age of onset of age-related diseases and tumors (6).Our studies made use of an experimental design that allowed us to clearly distinguish the effects of diet from those of age on genome-wide expression patterns.Another distinctive aspect of the study allowed us to resolve changes in gene expression induced directly by CR from those that arise over time as a consequence of the interaction between CR and aging.", + "\tGenDR-genomics of DR\n\nDR, of which caloric restriction is the most widely studied regimen, is the most robust non-genetic intervention shown to extend lifespan in a multitude of species, from yeast to mammals (12,14).However, the exact mechanisms of how DR extends lifespan remain unknown.To decipher the mechanisms of DR in a systematic fashion, we established GenDR (http://genomics.senescence.info/diet/), the first database of DR-associated genes.Because GenDR and related analysis of DR networks have been recently described elsewhere (15), they will only be briefly described herein.To create GenDR, we compiled from the literature a list of DR-essential genes from model organisms.DR-essential genes were defined as those which, if genetically modified, interfere with DR-mediated lifespan extension and, ideally, do not affect the lifespan of animals on an ad libitum diet (or at least do not appear to be merely causing disease).A subset of these genes act as genetic DR mimetics, as their manipulation leads to an increased lifespan for ad libitum fed animals, which is not further extended by DR.One such example is the growth hormone receptor gene in mice (16), in fact the only mouse gene currently in GenDR.In GenDR, the respective homologues of DR-essential genes are included for all the common model organisms, as well as for humans (15).A complementary data set in GenDR is a list of genes consistently differentially expressed in mammals under DR.In a recent meta-analysis, a common signature of genes differentially expressed in DR across different mammalian species, strains, tissues and experiments was derived.This signature provides a set of genes that are most robustly responding to DR (17).", + "\t\n\nBackground: Dietary restriction (DR), a reduction in food intake without malnutrition, increases most aspects of health during aging and extends lifespan in diverse species, including rodents.However, the mechanisms by which DR interacts with the aging process to improve health in old age are poorly understood.DNA methylation could play an important role in mediating the effects of DR because it is sensitive to the effects of nutrition and can affect gene expression memory over time.", + "\tIV. Genome-Environment Interactions as Targets for Dietary Interventions and Drug Discovery\n\n\"[It's] possible that we could change a human gene and double our life span. \"-CynthiaKenyon (Duncan, 2004) According to the GenAge database of aging-related genes (http://genomics.senescence.info/genes/),more than 700 genes have been identified that regulate lifespan in model organisms (de Magalha es et al., 2009a).Many of these genes and their associated pathways-such as the insulin/IGF1/GH pathway-have been shown to affect longevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolutionarily conserved and may have potential therapeutic applications (Baur et al., 2006).For example, evidence suggests the use of lowered IGF signaling (e.g., by targeting IGF receptors) to treat certain age-related diseases such as cancer (Pollak et al., 2004), Alzheimer's disease (Cohen et al., 2009), and autoimmune diseases (Smith, 2010).Moreover, a number of genes and pathways associated with longevity and CR are part of nutrient-sensing pathways that also regulate growth and development, including the insulin/IGF1/GH pathway (Narasimhan et al., 2009;Stanfel et al., 2009).Many of these genes modulate the response to environmental signals, such as food availability, and act in signaling pathways that if understood can be targeted (Fig. 1).The genetic regulation of aging is therefore an emerging field with multiple applications in the human nutrition, cosmetic, and pharmaceutical industries.\t\n\nBy far the most widely studied dietary manipulation of aging is caloric restriction (CR), also called dietary restriction.CR consists of restricting the food intake of organisms normally fed ad libitum without triggering malnutrition and is the only dietary intervention shown, to date, to increase longevity and modulate the process of aging in several model organisms (Bishop and Guarente, 2007;Fontana et al., 2010;Spindler, 2010).Even in mammals, such as mice and rats, CR can extend longevity by up to 50%, delay physiological aging, and postpone or diminish the morbidity of most age-related diseases (Masoro, 2005).Ongoing studies in rhesus monkeys suggest that CR can lower the incidence of aging-related deaths in primates (Colman et al., 2009).", + "\tGenDR--a database of dietary restriction-related genes\n\nDietary restriction (DR) delays the ageing process and extends lifespan in a multitude of species from yeast to mammals (22).However, the exact mechanisms of how DR extends lifespan are still unknown.As previously described (23), GenDR (http://genomics.senescence.info/diet/) is a database of DR-related genes.Herein, the use and function of GenDR will be briefly outlined along with updates since the 2013 HAGR paper (3).", + "\tINTRODUCTION\n\nGenomic studies into human longevity are inspired by the fact that, in animal models, healthy lifespan has proved to be remarkably plastic, and major pathways of lifespan regulation have been identified.Considerable lifespan extension has been induced in models as diverse as yeast, worms, fish, flies and rodents by applying genetic manipulation and dietary restriction (DR) (see [1] for review).Reduced activity of nutrient-sensing pathways such as insulin/insulin-like growth factor (IGF-1) signalling (IIS) and target of rapamycin (TOR) signalling mediated lifespan extension, and also the extension of lifespan by DR [2].An interesting observation from the perspective of human ageing is that, in rodents and monkeys, diets restricted in glucose, fat or protein uptake reduced or delayed the risk of cancer and metabolic disease, thus extending the healthspan of the animals [2].Following the discovery of genes and pathways involved in animal lifespan extension, human research has focused on the corresponding candidate human genes with genetic, genomic and epigenetic studies into ageing and longevity.The designs of these studies differ with respect to the selection of naturally occurring phenotypes and the study populations, which include population-based, patient-based, family-based and exposure-based cohorts.", + "\tIn comparison, caloric\nrestriction, intermittent fasting, or a ketogenic diet generally improve lifespan and health\n811 These dietary effects are not solely dependent on patterns of caloric intake, but are\nmodulated by dietary macro- and micronutrient composition, the amount of time spent in\ndifferent metabolic states, age of onset, periodicity of access to food, sex, and of greatest\nimportance to us in this studydifferences in genometype (strain) and gene-by-dietary\ninteractions 12,13. While the effects of differences in dietary composition and caloric restriction on lifespan\nhave been studied extensively, key results remain controversial 1416.", + "\tNutrition, phenotype and longevity\n\nNo issue so 'vividly' illustrates the power of diet to alter health as the consistent observation of the effect of caloric restriction (CR) on longevity.To date, neither drug, gene nor environmental intervention have been successfully demonstrated to prolong longevity in animals; however, the simple reduction of food calories can increase life span by 30-40% across a number of model organisms, including yeast, Drosophilia, Caenorhabditis elegans, rodents and monkeys [5][6][7].This effect of CR raises one of the most intriguing questions facing life scientists today.Despite the demonstrated positive age-related benefits of a reduction in energy intake -including decreased insulin resistance [8], increased production of glucocorticoids [9] and increased production of heat-shock proteins [10] -the mechanisms by which CR contributes to increased longevity remain unknown.How CR leads to longer life span cannot be attributed to any single factor without considering the simultaneous effects of the others.CR could alter multiple age-related processes, from energy metabolism to oxidative stress and DNA repair.Unravelling the multiparametric links of CR and aging led to the seminal genomic experiment for nutrition: the gene expression analysis of young and old tissues in normal and CR animals [11 ] is a pioneering example of the use of DNA arrays to explore the effects of CR and aging on gene expression in mouse skeletal muscle.The experiment is compelling for its simplicity and its implications, that is, the gene expression profiles for a clear phenotypic difference were compared (young versus old versus CR old mice).The power of the technique was evident by the discovery of a wide range of affected genes, including those involved in protein and energy metabolism, biosynthesis (e.g. of fatty acids), and macromolecular damage, implying immediately that the effects of aging and CR are broad, yet interrelated.More detailed experiments are now being pursued around the world following the identification of the genes that are altered during aging and protected by CR.The publication of this experiment also followed the now routine approach of supplying the raw database through an accessible internet site.", + "\t\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways.", + "\tWe present a metabolic model in which the anti-aging effects of DR\nare consistent with the ability to efficiently utilize dietary resources. NIH-PA Author Manuscript\n\nKeywords\naging; food restriction; lifespan; fertility; metabolic efficiency; quantitative trait loci; genetic\nmapping; physiology\n\nIntroduction\nDietary restriction (DR) extends lifespan and slows aging across a variety of taxa and also\ncauses myriad other physiological changes (Weindruch and Walford, 1998; Mobbs et al. ,\n2007).", + "\t\n\nIn summary, we postulate that due to metabolic changes and the subsequent increase in stress response, physiological processes evoked by starvation show strong correlation with anti-aging processes (Table 2).These observations suggest that starvation may evoke the same stress response reaction as caloric restriction, which is the only treatment known to prolong lifespan in all organisms tested to date.The difference is that starvation results in a much stronger or prolonged induction.This accentuated response during starvation may facilitate the experimental identification of basic molecular mechanisms linking nutrition and health using a nutrigenomic approach." + ] + ], + "task_id": [ + { + "task_id": "4BA9088EE51B4FEB12D723A3AA9E1585" + }, + { + "task_id": "1883A6F02376841E63AE392D386836D7" + }, + { + "task_id": "CC48E25A01F0D10FF57B5E5B9EC8F179" + }, + { + "task_id": "5E49F30B15AA7350275E583CF98E70BF" + }, + { + "task_id": "94989E7D750B3FB87B9E5DA1F47DFD1F" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_1.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_1.json new file mode 100644 index 0000000..53e8b1e --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_1.json @@ -0,0 +1,127 @@ +{ + "question": [ + "How do genetic mutations in the insulin gene affect glucose metabolism?", + "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?", + "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?", + "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?", + "How do genetic differences contribute to variations in diabetes prevalence among different populations?" + ], + "answer": [ + "Genetic mutations in the insulin gene can affect glucose metabolism by disrupting insulin secretion, insulin action, and insulin processing. For instance, mutations in genes like IGF2BP2, SLC30A8, and CDKN2A/CDKN2B can lead to a lower disposition index, affecting insulin secretion. Variants in genes like GCKR can influence fasting glucose levels, insulin levels, and triglyceride levels. Additionally, mutations in genes like TCF7L2, KCNJ11, and HHEX, which are involved in -cell metabolism, can affect glucose-sensing and insulin secretion. Furthermore, a variant of the MTNR1B gene can lead to a reduction of the early insulin response to glucose, affecting insulin secretion over time.", + "The most common genetic loci associated with an increased risk of Type 2 diabetes include TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX.", + "Genome-wide association studies (GWAS) help in identifying diabetes-related genes by scanning the genomes of many people to find genetic variations associated with the disease. These studies have identified numerous risk loci, or regions of the genome, associated with type 2 diabetes. The identified loci contain genes that may influence the disease's pathophysiology. GWAS also help in understanding the genetic basis of inter-individual variation in glycemic traits, such as levels of glucose, insulin, and hemoglobin A1c. The insights gained from these studies can contribute to the development of novel strategies for patient care.", + "The HLA region on chromosome 6p21.3 plays a significant role in the genetic predisposition to Type 1 diabetes. This region contains the HLA class II genes (HLA-DRB1, DQB1, and DQA1), which are the most potent diabetes-predisposing genes in the entire genome. These genes encode the highly polymorphic antigen-presenting proteins that are central to susceptibility to Type 1 diabetes. Certain alleles of these genes, particularly HLA-DR3, DR4, and DQB1*0302, are associated with a higher risk of developing the disease. However, the exact mechanism by which these genes confer susceptibility to diabetes is not yet fully understood.", + "Genetic differences contribute to variations in diabetes prevalence among different populations through the presence of different risk alleles and allele frequencies. Certain genetic loci associated with type-2 diabetes (T2D) and obesity have been subject to recent selection pressures, leading to population-specific genetic risk factors. For instance, East Asians and sub-Saharan Africans show pronounced differentiation at T2D loci, suggesting natural selection at these loci. Similarly, South Asians and Europeans show an excess of obesity loci with evidence of recent positive selection. These genetic variations, combined with environmental and lifestyle factors, contribute to the different rates of diabetes prevalence among various populations." + ], + "contexts": [ + [ + "\tNature 503, 290294 (2013). 33. Dimas, A. S. et al. Impact of type 2 diabetes susceptibility variants on\nquantitative glycemic traits reveals mechanistic heterogeneity. Diabetes 63,\n21582171 (2014). 34. Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and\ntheir impact on type 2 diabetes risk. Nat. Genet. 42, 105116 (2010). 35. Lotta, L. A. et al. Integrative genomic analysis implicates limited peripheral\nadipose storage capacity in the pathogenesis of human insulin resistance. Nat. Genet. 49, 1726 (2017). 36. Manning, A. K. et al.", + "\t\n\nGenes reviewed were categorized into three groups: genes affecting insulin secretion, genes affecting insulin resistance, and genes affecting mitochondria function.Findings from these studies are summarized in Tables 12.2-12.4.Polymorphisms of genes, such as plasminogen activator inhibitor type 1 (PAI-1) gene and forkhead box C2 (FOXC2) gene, studied in women with GDM (Leipold, Knoefl er, Gruber, Klein, et al., 2006;Pappa et al., 2011;Shaat et al., 2007) but not associated with T2DM (Carlsson, Groop, & Ridderstrle, 2005;Osawa et al., 2003) were not included in this review.", + "\t\n\nMost of the diabetes-associated SNPs were found in non-coding regions of the genome and are thus likely to affect gene regulation.In order to understand how these genes affect type 2 diabetes and how the SNPs associated with diabetes affect gene expression, we need to first understand the physiological processes that regulate the expression of these genes.We examined the expression patterns of these potential new diabetes-susceptibility genes to determine which are expressed in tissues important for the development of type 2 diabetes.This may also suggest the potential mechanism(s) by which alterations in these genes affect diabetes risk (e.g.insulin secretion versus insulin sensitivity).We also sought to determine whether any of these genes are regulated by conditions known to alter the expression of metabolically relevant genes.We examined the expression of these genes under fasting and non-fasting conditions (e.g. in response to insulin), which might be altered if they affect peripheral insulin sensitivity.Consumption of diets high in fats and sugars is associated with risk of developing type 2 diabetes [34] and many genes that are critical for -cell function are regulated by glucose [35].Thus, we also compared their expression in fasted mice consuming a normal chow diet or a diet high in fat and sugar, and examined the expression of these genes in mouse pancreatic islets cultured under low and high glucose concentrations.Here we show that most of the diabetesassociated genes are expressed in many metabolically relevant tissues and the expression levels of several of these genes were decreased by high fat feeding or were increased in the fed state in the brain.In addition, we found most of these genes are down-regulated by increased glucose concentrations in mouse islets.", + "\t\n\nThese studies provide valuable insights into the molecular circuitry of the beta cell and pinpoint pathways crucial for the maintenance of normal glucose homeostasis.Could (a more subtle) variation in the same genes inuence susceptibility to multifactorial T2D?In the case of glucokinase and the hepatocyte nuclear factor (HNF) genes (see Chapter 4), this does not seem to be so, although regions of linkage to T2D overlapping the HNF-1a and HNF-4a loci 35,36 hint at the possibility of variants in regulatory regions not yet scanned.", + "\t\n\nMutations in transcription factors have also been reported to contribute to the genetic risk for T2DM through various mechanisms: dysregulation of target genes involved in glucose or lipid metabolism (HNFs, PPARG , IPF -1 , IB1, TIEG2/KLF11 ), impaired -cell development and differentiation ( IPF -1 , NEUROD1 / 2, TIEG2/KLF11 ), and increased -cell apoptosis ( IB1 / MAPK8IP1 ).Deleterious mutations that signifi cantly impair the transactivation activity of these transcription factors can be responsible in some families for monogenic -like forms of diabetes with late age of onset, which may represent an intermediary phenotype between MODY and the most common forms of T2DM.This is the case for the TIEG2/KLF11 gene encoding the Kr ppel -like factor 11 (KLF11), an SP1 -like pancreas expressed transcription factor that is induced by the transforming growth factor (TGF ) and regulates cell growth in the exocrine pancreas.A common polymorphism (Q62R) in KLF11 was reported to be associated with polygenic T2DM developing in adulthood and to affect the function of KLF11 in vitro [99] .Insulin levels were found to be lower in carriers of the minor allele at Q62R [99] but attempts of replication in other populations only found a minor, or no detectable effect of the Q62R common variant on diabetes risk [100] .Sequencing of KLF11 gene in families enriched for earlyonset T2DM uncovered two missense mutations which segregated with diabetes in three pedigrees [99] , but proof of their causality was only based on in vitro experiments.These fi ndings suggest a role for the TGF - signaling pathway in pancreatic diseases affecting endocrine islets (diabetes) or exocrine cells (cancer) [101] .", + "\t\n\nIn studies where overt T2D has been the phenotype the majority of associated polymorphisms have encoded proteins known to be involved in -cell metabolism; for example TCF7L2, KCNJ11 and HHEX have shown robust association [170,171].This suggests that these genes could prove useful in predicting -cell preservation during the course of T2D.The glucokinase gene (GCK) coding for the initial glucose-sensing step in the -cell can have activating mutations causing hypoglycemia that might provide structural and functional models leading to drug targets for treating T2D [172].In the GoDARTs study, investigators examined the medication response of metformin and sulphonylurea based on the TCF7L2 variants mainly affecting the -cell.The carriers of the at risk 'T' allele responded less well to sulphonylurea therapy than metformin [173].Also it is of significant public health interest that in the Diabetes Prevention Program, lifestyle modifications were shown to reduce the risk of diabetes conferred by risk variants of TCF7L2 at rs7093146, and in placebo participants who carried the homozygous risk genotype (TT), there was 80% higher risk for developing diabetes compared to the lifestyle intervention group carrying the same risk genotypes [35].These findings could herald significant future progress in the field of T2D pharmacogenomics, possibly leading to the development and use of agents tailored on the basis of genotype.", + "\t\n\nImportantly, our findings demonstrate that more than 50% of the genes in which genetic variants have been known to increase risk of T2DM showed altered expression in different tissues.The perturbation was highest, as expected, in pancreatic islets, where eight genes i.e.HHEX, HNF1B, KCNQ1, NOTCH2, TCF7L2, THADA, TSPAN8 and WFS1, showed aberrant expression.All of these genetic loci, apart from the less studied TSPAN8, have been implicated in pathways primarily involved in insulin secretion, cell proliferation and regeneration [30].Of note, genetic variants in the THADA and WFS1 have recently been shown to impair glucagon-like peptide-1stimulated insulin secretion [31,32].Furthermore, many of these loci have also shown effects on insulin sensitivity [33].In line with this, five genes, i.e.HNF1B, IRS1, KCNJ11, NOTCH2 and WFS1, were also differentially expressed in skeletal muscle.Of all T2DM genes, IRS1 seems to have a clear effect on insulin sensitivity; the T2DM-associated allele was associated with decreased IRS1 protein expression as well as reduced phosphatidylinositol-3-kinase-activity and insulin-stimulated glucose uptake in humans [12].", + "\t\nThe intersection of genome-wide association analyses with physiological and functional data indicates that variants regulating islet gene transcription influence type 2 diabetes (T2D) predisposition and glucose homeostasis.However, the specific genes through which these regulatory variants act remain poorly characterized.We generated expression quantitative trait locus (eQTL) data in 118 human islet samples using RNA-sequencing and highdensity genotyping.We identified fourteen loci at which cis-exon-eQTL signals overlapped active islet chromatin signatures and were coincident with established T2D and/or glycemic trait associations.At some, these data provide an experimental link between GWAS signals and biological candidates, such as DGKB and ADCY5.At others, the cis-signals implicate genes with no prior connection to islet biology, including WARS and ZMIZ1.At the ZMIZ1 locus, we show that perturbation of ZMIZ1 expression in human islets and beta-cells influences exocytosis and insulin secretion, highlighting a novel role for ZMIZ1 in the maintenance of glucose homeostasis.Together, these findings provide a significant advance in the mechanistic insights of T2D and glycemic trait association loci.\t\n\nThe intersection of genome-wide association analyses with physiological and functional data indicates that variants regulating islet gene transcription influence type 2 diabetes (T2D) predisposition and glucose homeostasis.However, the specific genes through which these regulatory variants act remain poorly characterized.We generated expression quantitative trait locus (eQTL) data in 118 human islet samples using RNA-sequencing and highdensity genotyping.We identified fourteen loci at which cis-exon-eQTL signals overlapped active islet chromatin signatures and were coincident with established T2D and/or glycemic trait associations.At some, these data provide an experimental link between GWAS signals and biological candidates, such as DGKB and ADCY5.At others, the cis-signals implicate genes with no prior connection to islet biology, including WARS and ZMIZ1.At the ZMIZ1 locus, we show that perturbation of ZMIZ1 expression in human islets and beta-cells influences exocytosis and insulin secretion, highlighting a novel role for ZMIZ1 in the maintenance of glucose homeostasis.Together, these findings provide a significant advance in the mechanistic insights of T2D and glycemic trait association loci.", + "\t\n\nIn conclusion, our study in the DESIR prospective cohort shows that carriers of the GCKR-L446 variant have lower fasting glycemia and insulin resistance and are protected against the development of diabetes despite higher TG levels and a risk of dyslipidemia.This suggests, for the first time, a molecular mechanism by which these two components of the so-called metabolic syndrome can be dissociated.Based on rodent models, such as the adenoviral-mediated hepatic overexpression of GCK or GCKR in mice with diet-induced diabetes (5,19), more active GCKR may result in improved interaction with GCK, leading to more efficiently releasable pools of GCK enzyme, with subsequent beneficial effects on glucose metabolism but otherwise with a concomitant alteration of lipid profile.", + "\t\n\nAgainst this background, it is intriguing that we and others have found that a variant of the MTNR1B gene is associated with elevated plasma glucose levels, a reduction of the early insulin response to both oral and intravenous glucose, a faster deterioration of insulin secretion over time, and increased future risk of T2D (Bouatia-Naji et al., 2009;Lyssenko et al., 2009;Prokopenko et al., 2009).This association has subsequently been confirmed in other populations (Jonsson et al., 2013;Renstro m et al., 2015;Ro nn et al., 2009).Despite the very robust genetic association, a molecular understanding of why melatonin signaling is involved in the pathogenesis of T2D has still not been reached.To resolve this issue, we performed experimental studies in human islets, INS-1 832/13 b cells, and mice, as well as clinical studies in humans.We show that the rs10830963 risk variant of MTNR1B is an expression quantitative trait locus (eQTL) conferring increased expression of MTNR1B mRNA in human islets.Experiments in INS-1 832/13 b cells and Mt2 knockout mice (Mt2 / ) establish that melatonin signaling results in inhibition of insulin release.Translation to humans in a recallby-genotype study demonstrates that melatonin treatment inhibits insulin secretion in all subjects, but carriers of the risk variant are more sensitive to this inhibitory effect of melatonin.Together, these observations support a model in which a genetically determined increase in melatonin signaling underlies impaired insulin secretion, a pathogenetic hallmark of T2D.", + "\tChange in Body-Mass Index and Insulin Secretion and Action\n\nWe examined the effect of the genotyped DNA variants on changes in the BMI and insulin secretion (disposition index) and action over time in 2444 subjects from the Botnia study who did not have diabetes.At baseline, carriers of risk genotypes in the IGF2BP2 and SLC30A8 genes and at the CDKN2A/CDKN2B locus had a lower disposition index, which was maintained unchanged throughout the 8-year observation period (P<0.05) (Fig. 3H, 3I, and 3M in the Supplementary Appendix).", + "\t\n\nWhile the above findings show no evidence of association between relevant mitochondrial gene sets and T2D, these genes could still display causal associations with specific intermediate phenotypes linked to the disease.Support for this comes from reported mitochondrial dysfunction in insulin-resistant individuals [8].Therefore, we tested the same three gene sets described above for enrichment of associations with seven different glucose and insulin-related traits characteristic of T2D, using GWA metaanalyses of up to 46,186 non-diabetic individuals [37,38] (Soranzo N. et al., unpublished data).The quantitative traits analyzed include fasting levels of glucose and insulin, glucose and insulin levels 2 hours following a 75-gram oral glucose tolerance test, indices of b-cell function (HOMA-B) and insulin resistance (HOMA-IR) [49], and glycated hemoglobin levels (HbA 1C ), which reflect long-term plasma glucose concentrations (see Materials and Methods).", + "\t\n\nUsing the same data, the DIAGRAM investigators were also able to extend previous analyses which derive biological insights from the association effects of T2D-risk variants on related traits, such as body mass index, fasting glucose (in non-diabetic individuals), and indices of betacell function and insulin action [6, 27, 28].They were able to confirm: (1) partial, but not complete, overlap between variants that influence individual risk of T2D, and those that modulate physiological variation in fasting glucose amongst healthy individuals; (2) that the only signals which are driven by a primary effect on obesity are those at FTO and MC4R; and (3) that, whilst most risk loci operate via beta-cell dysfunction, a growing number (see Table 1) exert their T2D-risk effects through an obesity-independent deterioration in insulin sensitivity.This list of \"insulin resistance\" loci offers interesting insights into key players mediating the actions of insulin in peripheral tissues.In the case of the GRB14 locus for example, which emerged from GWAS in South Asians as well as Europeans [8], RNA expression data from fat confirms GRB14 as the strongest candidate transcript at the locus: its product is an adaptor protein that binds to the insulin receptor to inhibit tyrosine kinase signaling [29].", + "\t\n\naffected by genetic factors (5) with an estimated heritability of 0.53 (0.33-0.70) (6).These findings indicate that genetic factors exert substantial effects on GLP-1-induced insulin response and, as a consequence, may affect an individual's response to the GLP-1-based therapies.", + "\t\nAims/hypothesis: Impaired insulin secretion, insulin action, insulin-independent glucose effectiveness, glu-cose tolerance and the associated abnormalities in insulin and glucose metabolism phenotypes are precursors of type 2 diabetes.Genome-wide multipoint variance component linkage scans were carried out using 654 markers to identify quantitative trait loci for insulin sensitivity, acute insulin response to glucose, disposition index and glucose effectiveness training responses in whites and blacks in the HERITAGE Family Study.Methods: These phenotypes were obtained from an IVGTT with the minimal model.The distributions of insulin sensitivity, acute insulin response to glucose and disposition index training responses (posttraining minus baseline) were approximately normalised using a square-root transformation.All phenotypes were adjusted for the effects of age, BMI and their respective baseline values within sex and generation by race prior to linkage scans.Results: In blacks, a promising linkage with a maximum lod score of 3.1 on 19q (54-62 Mb) for glucose effectiveness training response was found.Six interesting linkages with lod scores of at least 1.0 were found for disposition index training response in whites.They included 1p (30 Mb), 3q (152 Mb),.Conclusions/ interpretation: Quantitative trait loci for 20 weeks of endurance exercise training responses in insulin action and glucose metabolism phenotypes were found on chromosome 19q as well as 6p and 7q, with nominal (6p, 7q) but consistent (6p) linkages across the races.Keywords Acute insulin response to glucose .Disposition index .Exercise training response .Glucose effectiveness .Insulin sensitivity .IVGTT .Minimal model .Quantitative trait loci Abbreviations AIR g : acute insulin response to glucose .DI: disposition index .GYS1: glycogen synthase 1 gene .LDB: location database .PPAR: peroxisome proliferatoractivated receptor .S I : insulin sensitivity .S G : glucose effectiveness P.An (*) .T. Rice .\t\n\nAims/hypothesis: Impaired insulin secretion, insulin action, insulin-independent glucose effectiveness, glu-cose tolerance and the associated abnormalities in insulin and glucose metabolism phenotypes are precursors of type 2 diabetes.Genome-wide multipoint variance component linkage scans were carried out using 654 markers to identify quantitative trait loci for insulin sensitivity, acute insulin response to glucose, disposition index and glucose effectiveness training responses in whites and blacks in the HERITAGE Family Study.Methods: These phenotypes were obtained from an IVGTT with the minimal model.The distributions of insulin sensitivity, acute insulin response to glucose and disposition index training responses (posttraining minus baseline) were approximately normalised using a square-root transformation.All phenotypes were adjusted for the effects of age, BMI and their respective baseline values within sex and generation by race prior to linkage scans.Results: In blacks, a promising linkage with a maximum lod score of 3.1 on 19q (54-62 Mb) for glucose effectiveness training response was found.Six interesting linkages with lod scores of at least 1.0 were found for disposition index training response in whites.They included 1p (30 Mb), 3q (152 Mb),.Conclusions/ interpretation: Quantitative trait loci for 20 weeks of endurance exercise training responses in insulin action and glucose metabolism phenotypes were found on chromosome 19q as well as 6p and 7q, with nominal (6p, 7q) but consistent (6p) linkages across the races.Keywords Acute insulin response to glucose .Disposition index .Exercise training response .Glucose effectiveness .Insulin sensitivity .IVGTT .Minimal model .Quantitative trait loci Abbreviations AIR g : acute insulin response to glucose .DI: disposition index .GYS1: glycogen synthase 1 gene .LDB: location database .PPAR: peroxisome proliferatoractivated receptor .S I : insulin sensitivity .S G : glucose effectiveness P.An (*) .T. Rice .", + "\t\n\nCell Metabolism 21, March 3, 2015 2015 Elsevier Inc. 359 Cell Metabolism Perspective ADCY5, which were primarily found to be associated with the variation of fasting glucose levels (Bouatia-Naji et al., 2009;Dupuis et al., 2010;Prokopenko et al., 2009), and GCKR, which was primarily found to be associated with the variation of fasting glucose levels, fasting insulin levels, and triglyceride levels (Saxena et al., 2007;Dupuis et al., 2010) (Figure 2).Interestingly, the overlap between loci influencing glucose-or insulin-related traits and T2D-susceptibility loci was unexpectedly limited (Dupuis et al., 2010).This result suggests that genes and related pathways that influence normal physiological levels of metabolic traits can be different from those leading to pathophysiological levels of metabolic traits that define T2D.A recent study strengthened this conclusion showing that the combination of established SNPs raising fasting glucose levels was significantly associated with the incidence of impaired fasting glucose levels over the 9-year follow-up of the study, but not with the risk of developing overt T2D (Vaxillaire et al., 2014).", + "\t\n\nPatients with established type 2 diabetes display both b-cell dysfunction and insulin resistance.To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity.We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures.We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses.Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes.The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity.The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia.ARAP1 constituted a third cluster characterized by defects in insulin processing.A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels.The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits.By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.", + "\t\n\nIn conclusion, having only considered subjects with a BMI less than 25 kg/m 2 provides strong evidence of the importance of the genetic effect of Gly972Arg on diabetes risk.Although its contribution to the overall risk in the general population could be minimal, this evidence supports the line of research seeking to clarify the role of IRS1 in lean patients with diabetes.Further studies of this genetic effect are needed to evaluate its potential interaction with other factors-especially with genetic variation, risk factor as obesity-that participate in the same metabolic pathway." + ], + [ + "\t\n\nFigure2| effect sizes of the 11 common variants confirmed to be involved in type 2 diabetes risk.The x axis gives the year that published evidence reached the levels of statistical confidence that are now accepted as necessary for genetic association studies.CDKAL1, CDK5 regulatory subunitassociated protein 1-like 1; CDKN2, cyclin-dependent kinase inhibitor 2A; FTO, fat mass and obesity-associated; HHEX, haematopoietically expressed homeobox; IDE, insulin-degrading enzyme; IGF2BP2, insulin-like growth factor 2 mRNA-binding protein 2; KCNJ11, potassium inwardly-rectifying channel, subfamily J, member 11; PPARG, peroxisome proliferator-activated receptor- gene; SLC30A8, solute carrier family 30 (zinc transporter), member 8; TCF2, transcription factor 2, hepatic; TCF7L2, transcription factor 7-like 2 (T-cell specific, HMg-box); WFS1, Wolfram syndrome 1.", + "\tCorrelation of the Susceptibility Loci with the Pathogenesis of T2D\n\nWith the large number of aforementioned genetic loci susceptible to T2D, the question pertains to how they participate in the pathogenesis of T2D.A great number of studies have suggested that genetic variants in or near KCNJ11, TCF7L2, WFS1, HNF1B, IGF2BP2, CDKN2A-CDKN2B, CDKAL1, SLC30A8, HHEX/IDE, KCNQ1, THADA, TSPAN8/LGR5, CDC123/CAMK1D, JAZF1, MTNR1B, DGKB/TMEM195, GCK, PROX1, ADCY5, SRR, CENTD2, ST6GAL1, HNF4A, KCNK16, FITM2-R3HDML-HNF4A, GLIS3, GRB14, ANK1, BCAR1, RASGRP1, and TMEM163 may confer T2D risk through impaired -cell function [16,24,44,68,[111][112][113][114], whereas PPAR, ADAMTS9, IRS1, GCKR, RBMS1/ITGB6, PTPRD, DUSP9, HMGA2, KLF14, GRB14, ANKRD55, and GRK5 have an impact on insulin action [21,24,115,116] (Tables 1, 2, and 3).FTO and MC4R, previously identified genes associated with obesity, appear to confer T2D risk through their primary effects on BMI, but recent GWAS have shown that their effects on T2D were independent of BMI, though FTO may have a small but detectable influence on T2D risk through insulin action [117,118].\t\n\nIn 2010, a meta-analysis of 21 genome-wide association studies performed by Dupuis and colleagues identified ADCY5, PROX1, GCK, GCKR, and DGKB/TMEM195 as new genetic loci for T2D susceptibility [22].Among these loci, DGKB/TMEM195, GCK, PROX1, and ADCY5 mainly affect -cell functions, whereas the locus mapped in GCKR shows a primary effect on insulin action [22].In the same year, another genome-wide association study by Qi and colleagues discovered new variants near RBMS1 and ITGB6 genes at 2q24, and these variants were found to affect glucose metabolism and insulin resistance [23].In addition, an expanded meta-analysis of existing GWAS by Voight and colleagues identified 12 new signals with a combined < 5 10 8 , including BCL11A, ZBED3, KLF14, TP53INP1, TLE4, CENTD2, HMGA2, HNF1A, PRC1, ZFAND6, DUSP9, and KCNQ1 [24].HNF1A was previously recognized as the causal gene of MODY3 [62] and also harbored the common variant (G319S) that contributes to early-onset T2D [63,64].DUSP9, mapped on chromosome X, encodes a member of the family of mitogen-activated protein kinase phosphatase 4, MKP4, which is important in cell cycle regulation and plays pivotal roles in regulating insulin action [65][66][67].", + "\t\n\nOne obvious locus to consider is TCF7L2 in the context of type 2 diabetes.Common genetic variation located within the gene encoding transcription factor 7 like 2 (TCF7L2) has been consistently reported to be strongly associated with the disease.Such reports range from 2006, when we first published the association [3], to the recent transethnic meta-analysis GWAS of type 2 diabetes [4].", + "\t\n\nTesting of these loci for association with T2D as a dichotomous trait in up to 40,655 cases and 87,022 nondiabetic controls demonstrated that the fasting glucose-raising alleles at seven loci (in or near ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 and the known T2D genes TCF7L2 and SLC30A8) are robustly associated (P < 5 10 8 ) with increased risk of T2D (Table 2).The association of a highly correlated SNP in ADCY5 with T2D in partially overlapping samples is reported by our companion manuscript 29 .We found less significant T2D associations (P < 5 10 3 ) for variants in or near CRY2, FADS1, GLIS3 and C2CD4B (Table 2).These data clearly show that loci with similar fasting glucose effect sizes may have very different T2D risk effects (see, for example, ADCY5 and MADD in Table 2).", + "\t\n\nDespite identification of many putative causative genetic variants, few have generated credible susceptibility variants for type 2 diabetes.Indeed, the most important finding using linkage studies is the discovery that the alteration of TCF7L2 (TCF-4) gene expression or function (33) disrupts pancreatic islet function and results in enhanced risk of type 2 diabetes.Candidate gene studies have also reported many type 2 diabetes-associated loci and the coding variants in the nuclear receptor peroxisome proliferator-activated receptor-g (34), the potassium channel KCNJ11 (34), WFS1 (35), and HNF1B (TCF2) (36) are among the few that have been replicated (Table 2).Recently, there have been great advances in the analysis of associated variants in GWA and replication studies due to highthroughput genotyping technologies, the International HapMap Project, and the Human Genome Project.Type 2 susceptibility loci such as JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2, and ADCY5 (37,38) are among some of the established loci (Table 2).CDKN2A/B, CDKAL1, SLC30A8, IGF2BP2, HHEX/IDE, and FTO are other established susceptibility loci for diabetes (Table 2) (34,39,40).GWA studies have also identified the potassium voltage-gated channel KCNQ1 (32) as an associated gene variant for diabetes.A recent GWA study reporting a genetic variant with a strong association with insulin resistance, hyperinsulinemia, and type 2 diabetes, located adjacent to the insulin receptor substrate 1 (IRS1) gene, is the C allele of rs2943641 (41).Interestingly, the parental origin of the single nucleotide polymorphism is of importance because the allele that confers risk when paternally inherited is protected when maternally transmitted.GWA studies for glycemic traits have identified loci such as MTNR1B (42), GCK (glucokinase) (42), and GCKR (glucokinase receptor) (42); however, further investigation of genetic loci on glucose homeostasis and their impact on type 2 diabetes is needed.Indeed, a recent study by Soranzo et al. (42) using GWA studies identified ten genetic loci associated with HbA 1c .Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin may be associated with changes in levels of HbA 1c .", + "\t\n\nG enome-wide association studies (GWAS) have iden- tified several type 2 diabetes mellitus (T2DM) susceptibility loci including CDKAL1, CDKN2B, IGF2BP2, HHEX, SLC30A8, PKN2, LOC387761 (1)(2)(3)(4)(5), and KCNQ1, which was recently identified by similar GWAS approach in two independent Japanese samples (6,7).Although these associations have been well replicated in Japanese populations (8), the role of these loci in other East Asian populations remains less clear.For example, a study in China by Wu et al. (9) did not find significant associations between single-nucleotide polymorphisms (SNPs) in IGF2BP2 and SLC30A8 with T2DM, whereas an association between SNPs at the HHEX locus and T2DM was reported among Chinese living in Shanghai, but not among Chinese in Beijing.Another study in Hong Kong Chinese (10) also did not find an association with SNPs at the IGF2BP2 locus; however, they reported an association between T2DM with SNPs at the HHEX and SLC30A8 loci.", + "\t\n\nMinor susceptibility might operate in some populations from other genes, including insulin receptor substrate 1 ( IRS -1 ), adiponectin ( ACDC ) or ectonucleotide pyrophosphatase/phosphodiesterase 1 enzyme ( ENPP1 ) in a context of obesity or diabesity. In genome scans of diabetic families, loci for T2DM have been found at several sites, including chromosomes 1q, 2q ( NIDDM1 ), 2p, 3q, 12q, 11q, 10q and 20.NIDDM1 has been identifi ed as coding for calpain 10, a non -lysosomal cysteine protease with actions at the mitochondria and plasma membrane, and also in pancreatic -cell apoptosis. In 2007, fi ve large genome -wide association studies in European descent populations have identifi ed new potential T2DM genes, including the Wnt signaling related transcription factors TCF7L2 and HHEX , the zinc transporter ZnT8 ( SLC30A8 ), the CDK5 regulatory subunit -associated protein 1 -like 1 ( CDKAL1 ) and a regulatory protein for IGF2 ( IGF2BP2 ).A consensus of close to 20 confi rmed T2DMsusceptibility loci to date provided novel insights into the biology of T2DM and glucose homeostasis, but individually with a relatively small genetic effect.Importantly, these genes implicate several pathways involved in -cell development and function. Compared with clinical risk factors alone, the inclusion of common genetic variants (at least those identifi ed to date) associated with the risk of T2DM has a small effect on the ability to predict future development of T2DM.At the individual level, however, a combined genotype score based on 15 risk alleles confers a 5 -8 fold increased risk of developing T2DM.Identifying the subgroups of individuals at higher risk is important to target these subjects with more effective preventative measures.", + "\t\n\nTesting of these loci for association with T2D as a dichotomous trait in up to 40,655 cases and 87,022 nondiabetic controls demonstrated that the fasting glucose-raising alleles at seven loci (in or near ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 and the known T2D genes TCF7L2 and SLC30A8) are robustly associated (P < 5 10 8 ) with increased risk of T2D (Table 2).The association of a highly correlated SNP in ADCY5 with T2D in partially overlapping samples is reported by our companion manuscript 29 .We found less significant T2D associations (P < 5 10 3 ) for variants in or near CRY2, FADS1, GLIS3 and C2CD4B (Table 2).These data clearly show that loci with similar fasting glucose effect sizes may have very different T2D risk effects (see, for example, ADCY5 and MADD in Table 2).", + "\tType 2 Diabetes\n\nCommon variants in 11 genes were significantly associated with the risk of future type 2 diabetes in the MPP cohort, including TCF7L2 (odds ratio, 1.30; P = 9.510 13 ), PPARG (odds ratio, 1.20; P = 4.010 4 ), FTO (odds ratio, 1.14; P = 9.210 5 ), KCNJ11 (odds ratio, 1.13; P = 3.610 4 ), NOTCH2 (odds ratio, 1.13; P = 0.02), WFS1 (odds ratio, 1.12; P = 0.001), CDKAL1 (odds ratio, 1.11; P = 0.004), IGF2BP2 (odds ratio, 1.10; P = 0.008), SLC30A8 (odds ratio, 1.10; P = 0.008), JAZF1 (odds ratio, 1.08; P = 0.03), and HHEX (odds ratio, 1.07; P = 0.03) (Table 2).Although these findings could not be fully replicated in the smaller Botnia study, there was little heterogeneity between the studies with respect to the risk conferred by different genotypes.\t\n\nOf the 16 loci that have been associated with type 2 diabetes previously, [8][9][10][11][12][13][14][15] we showed that 11 -TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEXwere associated with an enhanced risk of future diabetes.Many of the variants that we genotyped appear to influence beta-cell function, possibly through effects on proliferation, regeneration, and apoptosis.There was a time-dependent increase in the BMI and a decrease in insulin sensitivity in the subjects from the Botnia study, an increase in insulin resistance that was reflected by an increase in insulin secretion.However, this increase was inadequate to compensate for the increase in insulin resistance in carriers with a high genetic risk, which resulted in a markedly impaired disposition index.Only variants in FTO were associated with an increased BMI.Both FTO and PPARG together with TCF7L2 and KCNJ11 predicted transition from impaired fasting glucose levels or impaired glucose tolerance to manifest diabetes, which suggests that a combination of increased obesity and insulin resistance with a deterioration in beta-cell function contribute to the manifestation of diabetes in these subjects.Collectively, our findings emphasize the critical role of inherited defects in beta-cell function for the development of type 2 diabetes.", + "\t\n\nTo date, more than 70 genes have been identified as involved in T2DM, primarily by association analysis [34].In addition, via GWAS arrays, more than 100 SNPs have been identified for T2DM [35].From the 50 novel loci associated with T2DM previously identified, more than 40 loci have been associated with T2DM-related traits, including fasting proinsulin, insulin and glucose (Table 1) [36][37][38][39].However, for T2DM-related traits, such as the HOMA index or pancreatic cell function, there are virtually no published data examining the relationship between these traits or the genotype and environment interactions.Clinical investigations of some loci have suggested that the genetic components of T2DM risk act preferentially through cell function [40].Among all 40 loci associated with T2DM-related traits, only transcription factor-7-like 2 (TCF7L2) was shown to clearly contribute to T2DM risk [41].Several studies in white European [42], Indian [43], Japanese [44], Mexican American [45] and West African [46] individuals have shown a strong association between TCF7L2 and T2DM.It is also noteworthy that these populations represent the major racial groups with a high prevalence of T2DM.In all populations, TCF7L2 showed a strong association, with the odds of developing T2DM increased by 30%-50% for each allele inherited.This finding indicates an approximately double odds ratio compared to most other diabetes susceptibility polymorphisms.TCF7L2 is a transcription factor involved in the Wnt signaling pathway that is ubiquitously expressed, and it has been observed that TCF7L2 risk alleles result in the overexpression of TCF7L2 in pancreatic cells.This overexpression causes reduced nutrient-induced insulin secretion, which results in a direct predisposition to T2DM as well as an indirect predisposition via an increase in hepatic glucose production [47].", + "\tCommon Variants\n\nThe development of GWAS spurred considerable progress identifying common variants [minor allele frequency (MAF)>0.05]associated with T2D (Table 1) and glycemic traits (Table 2).After early candidate gene and linkage studies identified common variants associated with T2D in PPARG, KCNJ11-ABCC8 and TCF7L2, the first five GWAS for T2D detected six additional loci, and by early 2008, GWAS and meta-analyses had identified 15 loci for T2D and G6PC2 as a locus for fasting glucose (10).Also in 2008, reports of the first non-European-based GWAS for T2D established KCNQ1 as a T2D locus with variants common in East Asians (MAF = 0.33) but low frequency in Europeans (MAF 0.01) (11,12).KCNQ1 risk variants showed similar effect sizes in both populations, demonstrating the role of allele frequency in power to detect loci (13).In 2010, a meta-analysis of European-ancestry individuals identified a second signal of T2D-associated variants near KCNQ1 that are not in marked linkage disequilibrium (LD) with the initial variants (r 2 < 0.05) and independent from them based on conditional analyses (14).By the end of 2011, further GWAS and meta-analyses in several populations had identified 55 loci for T2D (15,16).Also by 2011, GWAS had identified 32 total loci for one or more glycemic traits, including 17 for fasting glucose (15,17), 2 for fasting insulin (18), 5 for 2hGlu (19), 11 for HbA1c (20)(21)(22) and 9 for proinsulin, including 1 identified only in women (23).Incomplete overlap of loci between T2D and glycemic traits showed that not all effects on glucose levels in healthy individuals translate to the risk of T2D and vice versa.Based on the overlap between traits and the biological function of nearby genes, most identified T2D loci appeared to have a primary role in pancreatic islet -cell function, with far fewer impacting insulin resistance.", + "\t\n\nThe most replicated locus for susceptibility to T2D is TCF7L2, in which two intronic markers, rs12255372 and rs7903146, are associated with the disease across multiple, ethnically diverse populations [87][88][89][90][91][92][93][94][95][96][97][98][99][100].Because TCF7L2 is expressed in pancreatic -cells, and insulin secretion is reduced in individuals with the risk alleles at rs12255372 and rs7903146, carriers of these alleles may respond sub-optimally to sulfonylurea therapy due to decreased -cell function [101].A study involving 4469 participants from the Genetics of Diabetes Audit and Research Tayside (GoDARTs) provided evidence in support of this hypothesis by finding that individuals with the variant TT genotype at rs12255372 were less likely to respond to sulfonylurea treatment with a target HbA1c < 7% compared to carriers of the GG genotype (57% vs. 40%) [101].Further, individuals with the TT genotype were much less likely to achieve a target HbA1c of 7% within one year of initiating sulfonylurea treatment compared with carriers of the GG genotype [101].Similar results were observed with marker rs7903146.These results suggest that the TCF7L2 locus may not only affect susceptibility to T2D, but may also modulate response to sulfonylurea therapy; in both cases, the pathophysiology likely stems from impaired insulin secretion due to deteriorating -cell function.", + "\t\n\nThrough genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05).Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance.Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations.This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies.Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.", + "\tRESULTS-\n\nWe confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 10 12 P unadjusted 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations.", + "\t\n\nRESULTS-We confirmed the association of all eight loci with type 2 diabetes with odds ratio (OR) ranging from 1.18 to 1.89 (P 1.6 10 3 to 4.6 10 34 ).The strongest association with the highest effect size was observed for TCF7L2 (OR 1.89 [95% CI 1.71-2.09],P 4.6 10 34 ).We also found significant association of PPARG and TCF7L2 with homeostasis model assessment of -cell function (P 6.9 10 8 and 3 10 4 , respectively), which looked consistent with recessive and under-dominant models, respectively.CONCLUSIONS-Our study replicates the association of wellestablished common variants with type 2 diabetes in Indians and shows larger effect size for most of them than those reported in Europeans.Diabetes 59:2068-2074, 2010 T ype 2 diabetes is a complex metabolic disorder with both genetic and environmental factors such as food habits and lifestyle contributing to its pathogenesis (1).Due to its complex etiology, the progress of discovery of genetic components for type 2 diabetes had been very slow until the advent of high throughput genome-wide association (GWA) studies (2).Until recently, only a few common variants in PPARG (3), KCNJ11 (4), and TCF7L2 (5) were shown to be associated with type 2 diabetes.With the advent of GWA studies, there are at least 20 loci identified today that are associated with the risk of type 2 diabetes (6).The first GWA study in the French population revealed SLC30A8 and HHEX as new loci for type 2 diabetes in addition to replicating the strong association with TCF7L2 (7).Further, GWA studies added several new genes including CDKAL1, CDKN2A, IGF2BP2, and FTO to the list of type 2 diabetes-associated loci and confirmed the associations for PPARG, KCNJ11,.\t\n\nOBJECTIVE-Common variants in PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 genes have been shown to be associated with type 2 diabetes in European populations by genome-wide association studies.We have studied the association of common variants in these eight genes with type 2 diabetes and related traits in Indians by combining the data from two independent case-control studies.", + "\t\n\nFG-associated loci from GWAS studies have also helped define the relationship between T2D and abnormal insulin processing and secretion in -cells.Among other glycemic trait analyses by the MAGIC, nine genome-wide significant loci were described for corrected insulin response (CIR), seven of which were previously associated with both T2D and other glycemic traits (MTNR1B, GCK, HHEX/ IDE, CDKAL1, CDKN2A/2B, ANK1, C2CD4A/B) (Prokopenko et al. 2014).Two other loci included G6PC2 associated with glycemic trait variability in nondiabetic individuals and the novel GRB10 association, which showed potential tissuespecific methylation and parental imprinting that might mask its association with T2D).Meta-analysis of GWA studies by MAGIC for fasting proinsulin levels adjusted for FI identified eight loci, of which four demonstrated that both proinsulin-raising (for TCF7L2, SLC30A8, and VPS13C/C2CD4A/B) and proinsulin-lowering alleles (for ARAP1) influenced T2D risk through a decrease in insulin secretion caused by distal or proximal impairment of proinsulin conversion, respectively (Strawbridge et al. 2011).Similarly, Dimas and colleagues described associations at the HHEX/IDE and MTNR1B loci with defects in early insulin secretion through reduced insulinogenic index for the T2D risk allele and showed that the T2D risk allele at ARAP1 was related to defects in the first steps of insulin production, through association with 32,33 split proinsulin (Dimas et al. 2014).", + "\tUnderstanding the biology of T2D-susceptibility loci\n\nThis analysis takes the number of independent loci showing genomewide significant associations with T2D beyond 35.For some, such as those at KCNJ11 and SLC30A8, the molecular mechanisms responsible for the susceptibility effect can be assigned with some confidence 42 .At others, the identities of the causal variants, the genes through which they act and the pathophysiological processes which they influence remain obscure.We used several approaches designed to link DIAGRAM+ and previously reported T2D association signals to biological insights relevant to T2D pathogenesis." + ], + [ + "\tGenome-Wide Association Study (GWAS). With the advent of GWAS, exploration of the genetic basis for T2D susceptibility has made significant breakthroughs.In 2007, the results of five genome-wide association studies were published.These studies increased the number of confirmed T2D susceptibility loci to nine (PPAR, KCNJ11, TCF7L2, CDKAL1, CDKN2A/B, IGF2BP2, HHEX/IDE, FTO, and SLC30A8) [13][14][15][16][17][18].Except for PPAR and FTO, which mainly affect insulin sensitivity, all the other genes may affect cell function, although the exact mechanisms remain largely unknown [16].HHEX, which is located on chromosome 10q, is a member of the homeobox family and encodes a transcription factor that maybe involved in Wnt signaling [58].Nevertheless, these studies established the utility of GWAS approach in elucidating complex genetic traits.", + "\t\n\nThe application of genome wide association studies (GWAS) has robustly revealed dozens of genetic contributors to T1D [23][24][25][26][27][28][29], the results of which have largely been independently replicated [30][31][32][33][34][35][36].The most recently reported meta-analysis of this trait identified in excess of forty loci [29], including 18 novel regions plus confirmation of a number of loci uncovered through crossdisease comparisons [34][35][36].As such, the risks conferred by these additional loci are relatively modest compared to the 'low-hanging fruit' described in the first studies and could only be ultimately uncovered when larger sample sizes were utilized.", + "\t\n\nBy contrast, knowledge of the genetic basis of diabetes is incomplete, despite Herculean efforts (8)(9)(10)(11)(12).Genome-wide association studies have accelerated the discovery of single-nucleotide polymorphisms (SNPs) at numerous loci.Comparison of the frequencies of these SNPs in case-control studies has enabled the calculation of the odds of their association with specific disease phenotypes.To date, genome-wide studies have added more than 4,000 SNPs involving some 200 diseases, including .30diabetes-related SNPs (diabetoSNPs).The analysis of dia-betoSNPs has intrinsic appeal as a tool for diabetes prediction, and could also yield potential clues to ethnic disparities in the susceptibility to type 2 diabetes.Because the diabetoSNPs individually confer modest effects, investigators have adopted an approach based on cumulative genetic risk score (GRS) at several loci to improve sensitivity (13)(14)(15)(16).Using available information on the relative odds of diabetes per risk allele (11,12), investigators can further calculate a weighted GRS.", + "\t\n\nGenomic variations and DNA profiling of those at risk for type 2 diabetes Despite many candidate gene studies and genome-wide linkage studies, very few susceptibility loci for type 2 diabetes have been identified until the recent emergence of genomic-wide association (GWA) data and large-scale replication studies (Table 2).Meta-analysis of GWA studies provides the unique opportunity to investigate the heterogeneity or consistency of genomic associations across diverse datasets and study populations.Recently, Voight et al. (32), using large-scale association analyses combining the data from eight GWA studies, identified 12 new susceptibility loci for type 2 diabetes.", + "\t\n\nBackground: Genome-wide association studies (GWAS) identify regions of the genome that are associated with particular traits, but do not typically identify specific causative genetic elements.For example, while a large number of single nucleotide polymorphisms associated with type 2 diabetes (T2D) and related traits have been identified by human GWAS, only a few genes have functional evidence to support or to rule out a role in cellular metabolism or dietary interactions.Here, we use a recently developed Drosophila model in which high-sucrose feeding induces phenotypes similar to T2D to assess orthologs of human GWAS-identified candidate genes for risk of T2D and related traits.Results: Disrupting orthologs of certain T2D candidate genes (HHEX, THADA, PPARG, KCNJ11) led to sucrose-dependent toxicity.Tissue-specific knockdown of the HHEX ortholog dHHEX (CG7056) directed metabolic defects and enhanced lethality; for example, fat-body-specific loss of dHHEX led to increased hemolymph glucose and reduced insulin sensitivity.", + "\tGenome-Wide Association Studies (GWAS)\n\nCompletion of the Human Genome Project in 2003 [44] led to subsequent advances in biomedical research.Since 2007, a new technology in the form of 'genome-wide chips' has facilitated remarkable progress in T2D genetic research with the first publication of five large GWA scans within the span of four months, showing that more than 500,000 SNP markers distributed across the genome [45][46][47][48][49].This approach has been successful in locating genes for other diseases besides T2D and obesity [40] namely, type 1 diabetes [50], prostate cancer [51], rheumatoid arthritis [52], Crohns disease [53,54], and cardiovascular disease [55] and is being applied to other complex disorders.Use of this 'hypothesis-free' approach involved in GWAS has opened new areas of biology to explore as discoveries of more than seventy entirely new T2D loci clearly suggest that associations are not limited to candidate genes and by applying GWAS and re-sequencing approaches, new genes involved in disease pathogenesis can be identified [56] (Table 1).", + "\t\n\nGenome-wide association studies (GWAS) have made a significant contribution to our current knowledge of the role(s) of genetic variation in population-level susceptibility to T1D (Mychaleckyj et al., 2010).", + "\t\n\nOver the past few years, genome-wide association studies (GWAS) have been extremely successful in detecting loci associated with complex disease traits such as obesity and T2D.GWAS is a hypothesis-free method where many genetic markers (usually more than one million single nucleotide polymorphisms [SNPs]) spread over the entire genome are tested for association with disease traits.This method differs from the traditional biologic candidate gene approach in that it is agnostic to prior biological knowledge about a specific gene's role in disease and is hence unbiased in this respect.This approach instead relies heavily on replication of association signals across multiple populations and generally requires very large sample sizes to overcome the power constraints inherent in conducting so many association tests [72].GWAS have confirmed the three previously identified signals for T2D which localize to transcription factor 7-like 2 (TCF7L2), peroxisome proliferative activated receptor, gamma (PPARG), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11), and identified many new susceptibility loci [73][74][75][76][77][78].More than 40 T2D loci have been discovered and replicated to date, most of which localize to genes that appear to influence beta-cell function [79].These findings highlight the role of inherited defects in beta-cell function rather than defects in genes causing insulin resistance in the etiology of T2D [80,81].", + "\tIntroduction\n\nGenome-wide association studies (GWAS) have identified approximately 80 loci robustly associated with predisposition to type 2 diabetes (T2D) [1][2][3] and a further 70 influencing a range of continuous glycemic traits [4][5][6][7][8][9][10] in non-diabetic subjects.There is substantial, though far from complete, overlap between these two sets of loci.Physiological studies in non-diabetic individuals indicate that most of these loci primarily influence insulin secretion rather than insulin sensitivity, highlighting a key role for the pancreatic islets of Langerhans in the mechanistic underpinnings of these association signals [11,12].These findings have motivated efforts to catalogue the epigenomic and transcriptional landscape of human islets and to apply these findings to deliver biological insights into disease pathogenesis.Recently, it has been shown, for example, that GWAS signals for T2D and fasting glucose show significant co-localization with islet enhancers [13,14].", + "\t\nIt has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes.Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups.This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders.", + "\t\n\nGenome-wide association studies (GWAS) have discovered germline genetic variation associated with type 2 diabetes risk (1)(2)(3)(4).One of the largest GWAS, involving DNA taken from individuals of European descent and conducted by the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium, identified 65 loci associated with type 2 diabetes risk (1).However, for most of these loci, the precise identity of the affected gene and the molecular mechanisms underpinning the altered risk are not known.", + "\t\nGenome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.\t\n\nGenome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.", + "\t\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "\t\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 10 8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total 88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes.\t\n\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 10 8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total 88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes.", + "\t\n\nGenome-wide association (GWA) studies represent the single most effective technique for identifying genetic risk loci causing complex diseases.Since the publication of the first GWA studies for type 2 diabetes (T2D) in 2007, nearly 90 statistically robust risk loci have been identified.The T2D risk loci identified by GWA studies contained several genes that are targets of current diabetic therapies; however, the majority of genes in these loci had not previously been implicated in the pathophysiology of T2D.Mechanistic insights about the physiological role of T2D loci in the disease predisposition have been gained from investigation of their contribution into glycemic trait variability in nondiabetic individuals.Current efforts to identify the causative genetic mutations in these loci and the molecular mechanisms through which they exert their effects have the potential to make far-reaching contributions to our understanding of molecular basis of T2D and the development of novel strategies for patient care.\t\nGenome-wide association (GWA) studies represent the single most effective technique for identifying genetic risk loci causing complex diseases.Since the publication of the first GWA studies for type 2 diabetes (T2D) in 2007, nearly 90 statistically robust risk loci have been identified.The T2D risk loci identified by GWA studies contained several genes that are targets of current diabetic therapies; however, the majority of genes in these loci had not previously been implicated in the pathophysiology of T2D.Mechanistic insights about the physiological role of T2D loci in the disease predisposition have been gained from investigation of their contribution into glycemic trait variability in nondiabetic individuals.Current efforts to identify the causative genetic mutations in these loci and the molecular mechanisms through which they exert their effects have the potential to make far-reaching contributions to our understanding of molecular basis of T2D and the development of novel strategies for patient care. IntroductionType 2 diabetes (T2D) is a common, chronic disorder whose prevalence is increasing rapidly across the globe.Like other complex diseases, T2D represents a challenge for genetic studies aiming to uncover the underlying pathophysiological mechanisms.It is predicted that T2D will affect 592 million individuals by 2035 (Federation 2013) in developed and low-and middle-income countries.While the recent increase in T2D prevalence has been attributed to a sedentary \"westernized\"", + "\t\n\nFamily-based studies of the genetic determinants of type 2 diabetes and related precursor quantitative traits (QTs, e.g.plasma insulin and glucose levels) and GWA studies have now provided an abundance of evidence for potentially causative genes.These results have been drawn together onto a single map of the human genome sequence [86].The goal is to look for genomic locations where the presence of a potential underlying type 2 diabetes gene has been attested to repeatedly-diabetes genetic 'hot spots'.Such replication increases our confidence of the presence of an underlying gene.While GWA studies look for diabetes genes using a different approach to linkage analysis, the ultimate goal is the same-to find the genetic determinants of the disease.Therefore, the results of linkage and association must eventually match each other.The current analysis identifies multiple linkage locations that differ from those found in the recent GWA studies [87-89], and suggests the location of additional major type 2 diabetes susceptibility genes.", + "\tINTRODUCTION\n\nMultiple genome-wide association studies (GWASs) have correlated type 2 diabetes mellitus (T2DM) with genetic variants, yielding a large number of loci and associated gene products that are linked to the disease phenotype-often with little or no insight into the mechanism underlying that link (Hivert et al., 2014).The current challenge is to establish robust systems to systematically evaluate the role of these loci using disease-relevant cells.Previous studies have used patient samples, cell lines, or animal models to seek mechanistic insight but with significant limitations.Large variation is observed in primary patient samples, perhaps due to genetic heterogeneity, whereas animal models present major physiological and metabolic differences that hamper understanding of the precise function of human genes in T2DM.Therefore, a robust system to systematically evaluate the role of T2DM-associated genes using disease-relevant human cells will provide an important tool for diabetes research and spur the development of precision (allele-specific) therapies, exemplified by the use of sulfonylurea drugs to treat patients carrying certain KCNJ11 mutations (Gloyn et al., 2004)." + ], + [ + "\tB. HLA Genes\n\nEarly studies indicated that the HLA region on chromosome 6p21 (commonly termed IDDM1, for insulin-dependent diabetes mellitus locus) is a critical susceptibility locus for many human autoimmune diseases, including T1D (305,399).These initial findings revolutionized our understanding of T1D etiology in two ways, as stated by Nerup et al. (305) in conclusion of their 1974 report: 1) T1D is a distinct disease entity, corroborating histopathological evidence; and 2) an aberrant cellular immune response, potentially triggered by viral infection, instigates onset.Numerous new susceptibility loci have emerged since, but none of them matches the strong association found with the HLA region.It is unlikely that new loci will ever be discovered that confer such a dramatic risk to T1D development (96).In genetic studies, the odds ratio is the statistic used to calculate whether a single nucleotide polymorphism (SNP) given is associated with the disease.An odds ratio of one implies that the event is equally likely in both patient and control groups.Odds ratios of alleles predisposing to complex disorders are typically modest, often in the range of 1.2-1.3,and even the HLA region has a predicted value of only 6.8.This suggests that if genetic predisposition is indeed a dominant factor in T1D development, a vast amount of common SNPs are still waiting to be discovered (96,159).After several decades of continuous progress since the discovery of HLA association (for historical perspective, see Ref. 285), the class II genes remain the strongest genetic contributor (138,323,429,433,439).Several HLA class II genes are pivotal as their alleles were found to determine a susceptibility hierarchy ranging from protection to strongly at-risk (15,73,105,134,135,237,309,393).The DRB1*1501-DQA1*0102-DQB1*0602 haplotype, found in 20% of the population but only 1% of patients, confers dominant protection against T1D (134).At the susceptible end of this spectrum are individuals with the DR3/4-DQ8 heterozygous haplotype (DR3 is DRB1*03-DQB1*0201, DR4 is DRB1*04-DQB1*0302, DQ8 is DQA1*0301, DQB1*0302).It is important to note that only 30 -50% of patients with T1D have the DR3/4-DQ2/8 genotype.A study in the Denver, Colorado area (15) identified this high-risk haplotype in 2.4% of newborns and more than 20% of the children affected by T1D, and its presence marks a 55% risk of developing overt diabetes by age 12. DR3/4-DQ2/8 siblings who are HLA identical to a diabetic proband have a risk as high as 80% for persistent anti-islet autoantibodies and 60% for progression to diabetes by age 15 (15).", + "\t\n\nIt has been long established that approximately half of the genetic risk for T1D is conferred by the genomic region harboring the HLA class II genes (primarily HLA-DRB1, -DQA1 and -DQB1 genes), which encode the highly polymorphic antigen-presenting proteins.Other established loci prior to the application of GWAS are the genes encoding insulin (INS) [9][10][11][12], cytotoxic Tlymphocyte-associated protein 4 (CTLA4) [13][14][15][16], protein tyrosine phosphatase, non-receptor type 22 (PTPN22) gene [17,18], interleukin 2 receptor alpha (IL2RA) [19][20][21] and ubiquitinassociated and SH3 domain-containing protein A (UBASH3A) [22].", + "\tDiscussion\n\nThe study of the HLA region in type 1 diabetes is a model for the identification of the actual diseasepredisposing variants in complex diseases, as well as for determining when all the genetic factors in a region have been identified (17) .\tIntroduction\n\nIt long has been established that approximately half of the genetic risk for T1D is conferred by the genomic region harboring the human leukocyte antigen HLA class II genes (primarily HLA-DRB1, -DQA1 and -DQB1 genes), which encode the highly polymorphic antigen-presenting proteins.The greatest risk arises when both haplotypes are present in the same individual (1,2,3,4) .", + "\t\n\nStudies by Valdes et al. have reported that HLA class I alleles associate with age-of-onset of T1D (Valdes et al., 2012(Valdes et al., , 1999)).Several alleles in the HLA class I region (Table 2) appear to confer high risk, but this effect is modified when accounting for LD with class II haplotypes (Noble et al., 2002).The HLA-B*39:06 allele, for instance, has the strongest risk of T1D susceptibility with an odds ratio of 10.31, while HLA-B*57:01 appears to be highly protective with an OR of 0.19 even after considering the LD with DQ and DR (Noble et al., 2010).Notably, Mikk et al. suggested that B*39:06 can significantly improve the prognosis of T1D disease, especially in patients with the DRB1*04:04-DQA1*03:01-DQB1*03:02 class II haplotypes (Mikk et al., 2014).Therefore, it is important to account for LD when elucidating for genetic risk within the class I locus.\t\n\nAs such, the HLA-encoding region is the most strongly associated T1D locus (Mychaleckyj et al., 2010).However, the molecular understanding of how HLA contributes to T1D remains unclear due the large number of distinctive HLA alleles and unusual frequencies that make the overall mechanism difficult to interpret (Sanchez-Mazas and Meyer, 2014).This has raised new questions, particularly with respect to the approximation of genetic distances, and other significant statistics in population genetics studies (Buhler and Sanchez-Mazas, 2011;Sanchez-Mazas and Meyer, 2014).As such, improving our understanding of the basic biology of the HLA locus is an essential facet of research into the mechanisms and causes of T1D.", + "\t\n\nAssociation to T1D at the HLA Prior to the advent of genome-wide linkage scans, the role of the Human Leukocyte Antigen (HLA) gene region in immune regulation, and ready availability of serologic markers, led investigators to discover the association between certain HLA alleles and T1D in the early 1970s (33,130,158).The global importance of the HLA on T1D has since been confirmed in genome-wide scans for linkage: All such scans performed to date show a major locus at the HLA (28,32,36,78,119).The fraction of all genetic risk, which can be attributed to the contribution of HLA genes to T1D susceptibility, is about 44%, with a S of 3.4 (160).", + "\tGenetic association studies in type 1 diabetes\n\nThe first locus to be successfully associated with type 1 diabetes susceptibility was the HLA locus on chromosome 6p (94)(95)(96).HLA genes fall into two major classes, class I and class II [see Redondo et al. (20) for review of nomenclature].Other genes, many related to the immune system, are also located in the HLA region.Early studies indicated that the strongest associations were with class II genes and, in particular, the HLA-D genes (97) encoding DRb (HLA-DRB), DQa (HLA-DRA) and DQb (HLA-DQB).The focus was initially narrowed to the DR3 and DR4-containing chromosomes, which confer strong risk (see, e.g., Platz et al. (98) and Schober et al. (99); DR2 was found to be protective (98).These findings have been consistently reproducible, with very strong associated risks: 90% of patients carry a DR3-or DR4-containing haplotype compared with 20% of the general population (20), for an odds ratio of approximately (0.9 0.8)/ (0.1 0.2) 36.The odds ratio for compound heterozygotes carrying both DR3 and DR4 is even higher, estimated at approximately 75 (35% of patients vs. 2.4% of controls).\t\n\nSubsequent studies attempting to further localize the risk alleles have been complicated by long-range linkage disequilibrium, which can extend for 500 kb to over 1 Mb in the case of DR3 (104).Thus, an allele at one location in the HLA may show association with diabetes because of correlation with a causal allele elsewhere.For example, it seems likely that the HLA region contains additional alleles, outside the class II genes, that affect diabetes risk (105).However, linkage disequilibrium makes it difficult to localize these genes precisely (106).One approach is to compare individuals who are identical for the major associated haplotypes but differ at other regions in the HLA (107,108).By examining the HLA regions of such individuals in detail, it may be possible to eventually sort out the intricacies of the relationship between alleles in HLA and type 1 diabetes susceptibility.However, large numbers of patients will be needed to identify the few people in whom linkage disequilibrium has broken down, and these individuals will need to be extensively characterized, perhaps by complete resequencing, before definitive conclusions can be drawn.Once the relevant alleles are definitively identified, the next challenge will be to elucidate the mechanisms by which these alleles mod-ulate autoimmunity and lead to diabetes.Given the known function of class II genes in antigen presentation, a probable explanation is differing efficiency in presentation of either islet cell antigens or foreign peptides that mimic islet cell antigens.", + "\t\n\nGenetic, functional, structural, and animal model studies all indicate that the highly polymorphic HLA class II molecules, namely the DR and DQ - heterodimers, are central to susceptibility to type 1 diabetes (4,5).The genes encoding these proteins are located in the HLA region, which spans 4,000 kb of DNA on human chromosome 6p21.3.The HLA region comprises 200 genes, and 40% of the expressed genes are predicted to have immune re-sponse functions (6,7).In addition to the class II genes HLA-DRB1 and HLA-DQB1, any one (or more) of these MHC genes, including the other HLA genes, could contribute to the overall risk for type 1 diabetes.The exact mechanism(s) by which the HLA class II molecules confer susceptibility to immune-mediated destruction of the pancreatic islets is still not known in its entirety, but the binding of key peptides from autoantigens (preproinsulin, GAD, insulinoma-associated 2 antigen, and zinc transporter, ZnT8, so far identified) to HLA class II molecules in the thymus and in the periphery are likely to play an important role.Theoretically, targeting this process of antigen presentation and T-cell activation may be an effective therapeutic approach to preventing type 1 diabetes.In practice, HLA screening is used to identify people at risk for developing type 1 diabetes, for inclusion in, and exclusion from, clinical studies (8) and clinical trials (9).\t\n\nOther features of the HLA-type 1 diabetes association were also examined; however, only support for an HLA effect by age at diagnosis was found (15)(16)(17)(18).Presumably, the risk conferred by specific HLA class I and class II alleles and haplotypes reflects the specificity of peptide binding and presentation (19,20).New genomic knowledge will better define the naturally processed peptides from autoantigens in type 1 diabetes.Intriguingly, a decrease in high-risk HLA genetic contribution in new-onset cases over the last decades has been observed in several studies, suggesting a change in environmental impact on penetrance as the incidence of type 1 diabetes increases (21)(22)(23).", + "\t\n\nLinkage studies have demonstrated that the HLA re- gion, termed IDDM1, is the major genetic determinant of IDDM susceptibility (see, e.g., Davies et al. 1994).From affected-sib-pair HLA haplotype sharing data, Risch (1987) estimated that the HLA component of IDDM susceptibility (Xs for HLA) accounts for a 3.42- fold increased risk in siblings over the population prevalence, compared to an observed 15-fold increased risk in siblings due to all genetic factors (Xs).Under a multi- plicative model, Risch calculated that HLA contributes -44% to the genetic risk for IDDM.", + "\t\n\n1. Finding the region does not readily give you the gene or mechanism.More than 25 years ago, it was discovered that alleles at the human leukocyte antigen (HLA) class I HLA-B locus were associated with Type I diabetes, using case-control association studies [4850].HLA loci were candidates for predisposition to autoimmunity because HLA molecules have a critical role in the regulation of the immune response by binding and presenting foreign or selfantigens to T lymphocytes.Later studies showed that HLA class II loci, including HLA-DRB1, DQB1 and DQA1, were even more strongly associated with diabetes.As a result of several genome-wide linkage screens [61,62,73,83], it is now clear that the most potent diabetes-predisposing genes in the entire genome are located in the HLA region on chromosome 6p21.3(these HLA region susceptibility genes are now collectively referred to as IDDM1).However, because of the extensive degree of linkage disequilibrium among the various HLA loci, it has been difficult to determine which precise locus produces diabetes susceptibility (for review, see [92]).Many studies have shown that diabetics of European ancestry have higher frequencies of HLA-DR3 and DR4 (variants at DRB1).For example, 96 % of Cana-dian Type I diabetic children had at least one of these alleles, compared with 46 % of the general population [93].However, DR4 haplotypes in diabetics were found to have a higher frequency of DQB1*0302 at the nearby HLA-DQB1 locus than DR4 haplotypes in control subjects [51], which suggested that DQB1 rather than DRB1 might be the primary diabetes susceptibility locus.Similarly, several HLA haplotypes positively associated with Type I diabetes (including DR4-DQB1*0302) were found to encode an amino acid other than aspartate at position 57 of the DQB1 chain, again implying that DQB1 was the primary susceptibility locus [52].However, an elegant study showed that DR4 haplotypes encoding both DRB*0401 (a subtype of DR4) and DQB*0302 were more diabetogenic than DR4 haplotypes encoding only one of these [53] thus, DRB1 and DQB1 together could confer susceptibility.The HLA-DQA1 locus also appears to be involved in susceptibility [54,55].In addition to susceptibility alleles, there are also protective alleles.For example, DR2 haplotypes carrying DRB1*1501 and DQB1*0602 confer strong (apparently dominant) protection against diabetes.Because it is not yet known which antigens (presented with HLA to the immune system) are critical to initiating autoimmune diabetes, the mechanism by which HLA genes produce susceptibility to (or protection from) diabetes has not yet been established.One recent model is that susceptible HLA-DR and DQ molecules bind diabetogenic antigens with low affinity and allow escape from the thymus into the periphery of self-reactive T cells, while protective HLA molecules bind with high affinity, resulting in thymic negative selection of autoreactive T cells [94].This model could explain the dominant effect of protective alleles.It has also been suggested that, in addition to HLA, other genes within the HLA region are associated with Type I diabetes [9597], but these associations could be secondary to linkage disequilibrium with HLA [98101].Numerous linkage studies have also shown the existence of susceptibility genes in the HLA region.In 538 diabetic sibpairs, 54 % shared two HLA haplotypes and only 7.3 % shared zero haplotypes, both frequencies significantly different from the 25 % expected [102].From these data, one can estimate the increased risk to siblings of diabetics attributable to HLA region genes to be about 3.4 (HLA l sib = ratio of expected to observed sharing of zero haplotypes in siblings = 0.25/0.073= 3.4) [3].Because the total increase in risk to siblings is about 15 (see above), the HLA contribution to total familial clustering of diabetes is about 44 % (assuming that l sib values are multiplicative, 15/3.4 = 4.4, and 3.4/[3.4+ 4.4] = 44 %).In summary, it appears that the largest genetic contribution to Type I diabetes is through HLA-DRB, DQB and DQA alleles, which confer varying degrees of susceptibility or resistance.However, after more than 25 years of study, it is still not clear how and in which combinations the HLA genes produce their predisposing or protective effects.", + "\t\n\nIn humans, certain alleles of DR and DQ loci of the HLA region (human MHC) have been shown to be associated with, and linked to, IDDM (4).Recent studies indicated that up to 50% of IDDM susceptibility is determined by genes in the HLA region (5,6) and that genetic markers located as far as 20 centiMorgan (cM) away from the class II HLA region still show linkage with putative susceptibility genes (5).These data indicate the importance of MHC-linked genes-in the predisposi- tion to the disease.", + "\t\n\nFollowing decades of effort to unravel the \"enigma\" of T1D genetics, nearly 50 loci have (thus far) been associated with susceptibility to the disease (Fig. 3) (Cooper et al. 2008;Concannon et al. 2009;Pociot et al. 2010).Nevertheless, no single gene is in-and-of-itself either necessary or sufficient to predict the development of T1D.The first T1D susceptibility locus identified, the Human Leukocyte Antigen (HLA) complex, provides the greatest contribution (i.e., 60%) to the overall genetic susceptibility.There are three classes of HLA genes, with class II genes having the strongest association with T1D (Redondo et al. 2001).Because class II HLA genes encode for molecules that participate in antigen presentation, the effect of MHC allelic variability on T1D risk may, for example, be explained by differences in the presentation of b-cell antigens, either by promoting anti-self-reactivity or by the failure to impart regulated immune responses (Mallone et al. 2005).The great majority of T1D patients carry the HLA-DR3 or -DR4 class II antigens, with 30% being DR3/DR4 heterozygous.In Caucasians, the DR3/DR4 genotype confers the highest T1D risk, followed by DR4 and DR3 homozygosity, respectively.Conversely, the class II allele, DQB1 0602, in linkage disequilibrium with DR2, is associated with protection from the development of T1D and is found in ,1% of patients with T1D (Redondo et al. 2001).", + "\t\n\nThe major genetic risk factors are the HLA class II haplotypes HLA-DR3-DQ2 and HLA-DR4-DQ8 on chromosome 6 (REFS 49-51).The risk of develop ing celltargeted autoimmunity on the extended HLADRDQ haplotype is complicated by a large number of HLA-DRB1 alleles in humans.Specifically, on the HLA-DQ8 haplotype, HLA-DRB1*04:01 and HLA-DRB1*04:05 are associated with greater suscep tibility to T1DM than is HLA-DRB1*04:04, whereas HLA-DRB1*04:03 is protective [52][53][54] .These haplotypes are often associated with insulin autoantibodies 55 , but the extended haplotype HLA-DRB1*03:01-DQ2 (HLA-DQA1*05:01-DQB1*02:01) was associated with GAD65 autoantibody 55,56 .These genetic risk factors are common in western populations and have a low pene trance 57,58 , which might explain why many people do not develop islettargeted autoimmunity or T1DM despite having these T1DM risk factors.", + "\t\n\nOf the 49 T1D susceptibility region, the HLA association is the strongest with Odd Ratios (ORs) ranging from 0.02 to >11 for specific haplotypes (Noble and Erlich, 2012;Todd et al., 2010).This region contributes to about 50% of genetic susceptibility to T1D, specifically the HLA class II DR-DQ haplotypes (Erlich et al., 2008).Particularly, the DR4-DQ8 and DR3-DQ2 haplotype combinations are present in about 90% of children with T1D (Held et al., 1999;Tait and Boyle, 1986;Deschamps et al., 1980).A genotype containing both haplotypes (DR4-DQ8/DR3-DQ2) carries the highest risk of diabetes, and is commonly seen in 5% of early-onset disease (Gale and Gillespie, 2014).Other strong associations to T1D susceptibility come from polymorphisms in the insulin INS gene (OR = 3.5), the PTPN22 gene (OR = 3.8), the IL2RA and COBL genes (OR = 2.5; 2.4, respectively) (Gillespie, 2014;Pociot et al., 2010;Todd et al., 2010).The rest of the genomic regions that confer susceptibility to T1D have smaller effects with ORs between 8 Put together, the haplotype is the group of genes that a progeny inherits from one parent 1.1 and 1.9 (Gillespie, 2014;Todd et al., 2010).The names of the T1D susceptibility regions are listed in Table 1 along with the names of the disease associated SNPs and genes.T1D has also been shown to be associated with some other autoimmune conditions like Rheumatoid arthritis, Graves' disease and Malignant anaemia (Heras et al., 2010;Knip and Siljandera, 2008).Markers for these other diseases can be found within the susceptibility regions forT1D.The names of diseases that share T1D susceptibility regions are shown in Table 2.", + "\t\n\nIn the first case-control set, having conditioned on HLA-DQB1, HLA-DRB1 and HLA-B using allele HLA-A*02 as a reference, HLA-A*01, HLA-A*11 and HLA-A*31 were protective and HLA-A*24 was predisposing for type 1 diabetes; HLA-A*03 was more predisposing than HLA-A*11 and HLA-A*31 (Supplementary Table 4).Once these alleles were accounted for, there was no further detectable HLA-A effect in the case-control set (P 5 0.15).In the second case-control set, having conditioned on HLA-DRB1 and HLA-DQB1, both HLA-A*01 and HLA-A*11 were again more protective than HLA-A*02.HLA-A*24 was still the most predisposing for type 1 diabetes and may also be associated with an earlier age-at-diagnosis (P 5 0.01; Supplementary Tables 4 and 5).\t\n\nThe major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune.In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region [4][5][6][7][8][9][10][11] .Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear.Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods-recursive partitioning and regression-to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios .1.5;P combined 5 2.01 3 10 219 and 2.35 3 10 213 , respectively) in addition to the established associations of the MHC class II genes.Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples.Taken together with previous studies [4][5][6][7][8][10][11][12][13][14][15][16] , we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes.\t\nThe major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune.In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region [4][5][6][7][8][9][10][11] .Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear.Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods-recursive partitioning and regression-to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios .1.5;P combined 5 2.01 3 10 219 and 2.35 3 10 213 , respectively) in addition to the established associations of the MHC class II genes.Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples.Taken together with previous studies [4][5][6][7][8][10][11][12][13][14][15][16] , we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes.The MHC spans 4 megabases (Mb) and contains 149 genes, of which eight (the class II loci HLA-DRB1, HLA-DQB1, HLA-DQA1, HLA-DPB1, HLA-DPA1; the class I loci HLA-A, HLA-B and HLA-C) are the highly polymorphic immune response genes.There are many other candidate genes with common variants-any one of which or a combination thereof-that might also be involved in disease susceptibility.We studied 850 type-1-diabetes-affected sibling-pair (ASP) families from the United Kingdom and the United States and a first set of 2,049 type 1 diabetes patients and 1,912 controls from across Great Britain, in which we genotyped a combined total of 254 polymorphic MHC loci, including HLA-DQB1, HLA-DRB1, HLA-A and HLA-B (Table 1 and Supplementary Table 1).A second independent set of 1,050 type 1 diabetes cases and 1,125 controls was used for validation.After these analyses were completed, 1,475 additional single nucleotide polymorphisms (SNPs) in 1,964 of our type 1 diabetes cases and 2,923 controls became available as part of our collaboration with the Wellcome Trust Case Control Consortium (WTCCC) 17 (Table 1)." + ], + [ + "\t\n\nThe genetic influences on the prevalence of type 2 diabetes in the Asian population are mainly related to insulin secretion capacity [4] ; other genes involved in the risk of type 2 diabetes are not substantially different in other ethnic groups [5] .The most relevant genes contributing to ethnic differences are associated with insulin secretion capacity, and they are GLIS3 , PEPD , KCNK16 , HNF4A and KCNQ1 according to meta-analyses of genome-wide association studies [4,6] .The risk allele of the KCNQ1 polymorphism is associated with impairment of insulin secretion.KCNK16 and GLIS3 have been reported to be associated with decreased -cell function and -cell mass, leading to the prevalence of type 2 diabetes [4] .These genetic differences are related to the much lower insulin secretory capacity in Asians than Caucasians.The ancestral Asian diets consisted largely of complex carbohydrates with high fiber and very low fat content, and these people had very efficient insulin utilization.In Asians, the insulin secretion capacity has been consistently very low in early ages.However, eating patterns and lifestyles have changed rapidly over the last 20 years and insulin resistance has markedly increased.Therefore, the ethnic differences may be related not only to environmental factors such as eating patterns, physical activity, and stress, but also to genetic factors.Moreover, the interaction between genetic and environmental factors plays an important role in the prevalence of type 2 diabetes [7] .", + "\t\n\nAs described above, genetic studies of T2D in European populations have made significant progress in our understanding of T2D susceptibility.However, existing data can only provide partial explanation for the heritability of T2D.It is well known that discrepancies exist in allelic frequencies and effect sizes in different ethnic groups.It is, therefore, important to understand whether these variants are also applicable to other ethnic populations.Asians.Epidemiological studies have documented consistent increases in the prevalence of diabetes in Asia, especially in China, with diabetes prevalence having increased from 2.6% in 2000 to 9.7% in 2010 [69].However, our understanding of the genetic basis of T2D in East Asia remains limited.It is therefore imperative to identify specific genes associated with this disease in East Asians.", + "\t\n\nGenetic explorations in traditionally understudied populations have succeeded in identifying novel T2D variants in Mexican populations (6,14), as well as in East Asians (15), Greenlanders (16), and African Americans (8).In Mexico, T2D is one of the leading causes of death and has a prevalence twice that of non-Hispanic whites in the U.S. and is among the highest worldwide (17,18).Although different environmental and lifestyle risk factors in Mexico partially explain the increased prevalence of T2D, unique genetic influences also contribute (6,14).Here, we explored protein-coding variants present at higher frequency in people of Latino descent to shed further light on genetic risk factors for T2D in Mexico.We identified a novel T2D association with a protective, splice-acceptor variant that disrupts expression of IGF2 isoform 2, providing a clear hypothesis for future mechanism of action and therapeutic inquiries.", + "\t\n\nDespite heterogeneity across populations in risk allele frequency or effect size in type 2 diabetes genes, the combined effects of multiple genetic variants using genetic scores based on the number of risk alleles appear to be similar across different ethnic groups.Typically, each risk allele increment is associated with a 10-20% increased risk of type 2 diabetes (41,42).These data suggest that the overall contribution of the identified genetic loci to type 2 diabetes is similar between Caucasians and other ethnic groups, and that these loci do not appear to explain ethnic differences in diabetes risk.In predicting future risk of diabetes, the clinical utility of these cumulative genetic risk scores appears to be limited in either high-or low-risk populations.\tGENETIC SUSCEPTIBILITY AND GENE-ENVIRONMENT INTERACTIONS-\n\nThe recent advent of genome-wide association studies (GWAS) has led to major advances in the identification of common genetic variants contributing to diabetes susceptibility (40).To date, at least 40 genetic loci have been convincingly associated with type 2 diabetes, but these loci confer only a modest effect size and do not add to the clinical prediction of diabetes beyond traditional risk factors, such as obesity, physical inactivity, unhealthy diet, and family history of diabetes.Many diabetes genes recently discovered through GWAS in Caucasian populations have been replicated in Asians; however, there were significant interethnic differences in the location and frequency of these risk alleles.For example, common variants of the TCF7L2 gene that are significantly associated with diabetes risk are present in 20-30% of Caucasian populations but only 3-5% of Asians (41,42).Conversely, a variant in the KCNQ1 gene associated with a 20-30% increased risk of diabetes in several Asian populations (43,44) is common in East Asians, but rare in Caucasians.It is intriguing that most diabetes susceptibility loci that have been identified are related to impaired b-cell function, whereas only a few (e.g., peroxisome proliferator-activated receptor-g, insulin receptor substrate 1, IGF-1, and GCKR) are associated with insulin resistance or fasting insulin, which points toward b-cell dysfunction as a primary defect for diabetes pathogenesis.It should be noted that most of the single nucleotide polymorphisms uncovered may not be the actual causal variants, which need to be pinpointed through fine-mapping, sequencing, and functional studies.", + "\t\n\nIn addition to these environmental and lifestyle risk factors, genetic predisposition towards T2D may provide additional insights into the differences in T2D prevalence observed between populations in SSA.At present, there are around 100 loci for which there is robust (genome-wide significant) evidence of association with traits related to T2D, including obesity and fasting hyperglycaemia, identified in predominantly European and Asian populations.However, the relevance of many recent genomic findings to populations in SSA has not been systematically studied.Given the marked genomic diversity among populations in SSA, understanding the genomic basis of T2D, its complications, and its risk factors in populations of African descent is likely to provide additional insights into disease aetiology and potential therapeutic strategies [8,9].These observations highlight the need for epidemiological studies with the statistical resolution to reliably assess the burden and epidemiology of T2D and inform potential preventative and therapeutic strategies relevant to SSA.", + "\tII. Genetics of Type 2 Diabetes\n\nType 2 diabetes clearly represents a multifactorial disease, and several findings indicate that genetics is an important contributing factor.First, certain ethnic minorities and indigenous groups with low population admixture (e.g., Pima Indians, Micronesians and other Pacific Islanders, Australian Aborigines, and Mexican-Americans) show exceptionally high type 2 diabetes prevalence (up to 21% in Pima Indians) (10 -12).Second, type 2 diabetes clusters within families and first-degree relatives have, compared with the general population, an up to 3.5-fold higher risk to develop the disease (13,14).Finally, twin studies demonstrated a markedly higher concordance for type 2 diabetes in monozygotic compared with dizygotic twins (70 vs. 10%) (15).Type 2 diabetes does not follow simple Mendelian inheritance and, therefore, is considered a polygenic disease.According to the generally accepted common variant-common disease hypothesis (16), complex diseases, such as type 2 diabetes, are caused by the simultaneous occurrence of common DNA sequence variations (minor allele frequencies 5%) in many genes.Each of these DNA alterations is supposed to exert only moderate effects on the affected genes' function and/or expression, but in their sum, these variations confer an increased susceptibility toward the adverse environmental factors mentioned above.Single nucleotide polymorphisms (SNPs), exchanges of single base pairs, cover approximately 90% of the sequence variation within the human genome (SNP Fact Sheet of the Human Genome Project; available at http://www.ornl.gov/sci/techresources/Human_Genome/faq/snps.shtml) and are therefore regarded as the major determinants of the individual predisposition to complex diseases.Thus, strong efforts are currently ongoing to map and catalog these sequence variations (The International HapMap Project at http://www.hapmap.org/index.html.en).However, the less frequent copy number variations (due to deletion and/or duplication of DNA segments one kilobase to several megabases in size) and smaller DNA insertions, deletions, duplications, and inversions may also play a role.All of these findings initiated an intensive search for the genes, or better gene variants, responsible for the genetic predisposition to type 2 diabetes.", + "\t\n\nDespite comparatively limited cohort sizes, analyses of type 2 diabetes risk in Hispanic populations have driven diabetes gene discovery by leveraging high disease prevalence, population-specific haplotypic variation, and a private mutation spectrum.There is evidence that these findings are relevant across ancestry: effects of variation in Hispanic populations are significantly directionally consistent with analyses in European ancestry, even at fairly modest levels of significance (p < 0.01) [12, 42, 43].Furthermore, due to differential LD structure, inclusion of Hispanic populations in trans-ethnic fine mapping and meta-analyses provides an opportunity to narrow windows of association and localize causal alleles [12].", + "\t\n\nThe genetic structure of the Arab population and prevalence of consanguineous marriages predispose them to T2D risk.There is a requirement for carrying out genome analysis and association studies for identification of T2D risk genes so that at-risk individuals can be identified early and appropriate measures can be taken to prevent disease progression.Environmental factors also play a significant role in T2D development.Gene variants that are too rare to be picked up by GWAS may have relatively large effects on the risk of developing T2D.Moreover, variants which are considered to be rare across populations may be more common in a particular subpopulation [106].Recent advances in next-generation sequencing technologies allow vast amounts of genetic data to be analyzed and processed rapidly, thus substantially saving time and facilitating progress in genetic studies.\t\n\nAlthough initial GWAS were mainly carried out in European populations [30][31][32], more studies focused on other ethnic groups such as Hispanics [33], Asians [34,35], African-Americans [36], Asian-Americans [37] and Arabs [38], among others, are also coming up.A catalogue of all major GWAS is maintained by The National Human Genome Research Institute and can be accessed through their website [39].Meta-analysis and comparison of results of GWAS across populations can also help identify additional statistically significant genetic associations of relevance to T2D [40,41].Since obesity predisposes to T2D, the FTO gene vari-ant which affects BMI is also considered as a risk factor for T2D [42].Variants in other genes which influence glucose and insulin levels have also been investigated for their role in conferring susceptibility to diabetes, for example, glucose-raising genes such as MTNR1B, GCK, MADD and insulin-related genes such as GCKR, IGF1, IRS1 [27,43,44].Other than GWAS, case-control association studies in different ethnic groups have also helped identify haplotypes which may predispose to diabetes in the affected individuals [45,46].", + "\tGenetic Predisposition\n\nThe fact that type 2 diabetes is a genetic disease is well known to clinicians by how it occurs in families, and by there being ethnic populations who are particularly high risk.The genetic link was clearly shown more than two decades ago by a famous study of identical twins in the U.K. that found essentially a 100% concordance rate for this diseaseif one twin developed type 2 diabetes, then the other one invariably developed it (9).However, this kind of study provides no insight into how genetics act in the disease.Is there a defective gene that directly impairs the glucose homeostasis system?Alternatively, does it cause insulin resistance or some other defect that acts indirectly by exceeding the capacity of an otherwise normal glucose homeostasis system to compensate?Also, are there one or many genetic defects that predispose to this disease?", + "\tEvidence from population studies\n\nThe high prevalence of T2D in some populations, such as Nauruan Islanders and Pima Indians, is also consistent with a genetic aetiology.1011 Neel proposed the `thrifty genotype' hypothesis to explain the persistence at a high frequency of genotypes associated with adverse phenotypes in modern societies, on the basis that those same genes, by promoting ecient energy storage, had proved benecial in times of intermittent food supply. 12igration studies provide additional ammunition for the nature nurture debate.Individuals from the Indian subcontinent, for example, have high prevalence rates of T2D whether in urban India 13 or as migrants. 14Migrant populations do not, however, immediately acquire all of the environmental attributes of their new homes, so these eects may reect dietary and cultural as well as genetic factors.", + "\t\n\nGenome-wide association studies (GWAS) have made a significant contribution to our current knowledge of the role(s) of genetic variation in population-level susceptibility to T1D (Mychaleckyj et al., 2010).", + "\t\n\nAnother caveat is that most genome-wide association and prediction studies have been conducted in populations of European descent [44, 51, 52], and case-control and prospective genetic studies in African-American [57,58] or Asian [59-61] populations are still rare.It has been hypothesised that different risk alleles and allele frequencies in various ethnic groups could contribute to global differences in incidence rates of type 2 diabetes [62], but this needs to be corroborated in further studies.", + "\t\n\nWhilst the activities of the DIAGRAM consortium have focused on samples of European descent, the past year has seen considerable expansion of efforts to identify common variants influencing T2D-risk in other populations, including those of South and East Asian origin, and African-Americans.For example, Kooner and colleagues [8] completed a GWA metaanalysis in over 5500 T2D cases and 14,400 controls from the UK, Singapore, and Pakistan, all with origins in the Indian subcontinent.This analysis identified 6 novel association signals, including variants near the genes encoding the GRB14 adaptor protein and hepatocyte nuclear factor-4A, the latter already implicated in monogenic forms of diabetes [8].Equivalent efforts in East Asian subjects have been similarly productive, adding a further 8 loci to the global tally [9,10].Studies in individuals of African descent are of particular interest given their extensive genetic diversity, and during the past year the first large-scale association studies from African-American subjects have emerged [11,12].These studies have highlighted some of the particular challenges associated with genetic studies in African-descent populations (such as limited linkage disequilibrium, and genetic admixture) but did reveal a number of novel genome-wide significant signals, including those mapping near RND3 and BCL2.", + "\tDISCUSSION\n\nA number of genetic variants have recently been identified as associated with T2DM (1-6).Most of these variants were identified in GWASs in Europeans, but associations for many are consistent in other ethnic groups, including American Indians (18,19).However, some associations are heterogeneous across ethnic groups (5,6,20).In Pima Indians, for example, TCF7L2 variants, which are strongly associated in most ethnic groups, show little association with diabetes (20).In addition because of ethnic differences in allele frequencies, relative importance of different diabetessusceptibility alleles varies.For these reasons, GWASs in non-European populations might yield additional T2DM susceptibility variants.Indeed, studies in East Asians and South Asians have identified additional diabetes associations (4-6).", + "\t\n\nIf only a subset of type 2 diabetes susceptibility genes was required for the disease in any individual and the frequencies of these susceptibility genes were different in each population, linkage results would be variable.This might easily arise if hyperglycaemia was a collection of subtly different phenotypes, each resulting from different subsets of underlying genes.Heterogeneity for diabetes as a broad phenotype is already apparent in the distinct features of type 1 diabetes, type 2 diabetes and MODY/monogenic diabetes [114].The non-monogenic form of type 2 diabetes is likely to feature further levels of heterogeneity.Phenotypic heterogeneity may be largely independent of the ethnic background however, since there was a mixture of racial groups in all replication clusters (Tables 1 and 2).Even though association studies [88,115] suggest that there will be some differences in the frequency of individual type 2 diabetes genes between ethnic backgrounds, many type 2 diabetes genes may be shared between individuals of different continents of origin.", + "\t\nDifferent populations suffer from different rates of obesity and type-2 diabetes (T2D).Little is known about the genetic or adaptive component, if any, that underlies these differences.Given the cultural, geographic, and dietary variation that accumulated among humans over the last 60,000 years, we examined whether loci identified by genome-wide association studies for these traits have been subject to recent selection pressures.Using genomewide SNP data on 938 individuals in 53 populations from the Human Genome Diversity Panel, we compare population differentiation and haplotype patterns at these loci to the rest of the genome.Using an ''expanding window'' approach (100-1,600 kb) for the individual loci as well as the loci as ensembles, we find a high degree of differentiation for the ensemble of T2D loci.This differentiation is most pronounced for East Asians and sub-Saharan Africans, suggesting that these groups experienced natural selection at loci associated with T2D.Haplotype analysis suggests an excess of obesity loci with evidence of recent positive selection among South Asians and Europeans, compared to sub-Saharan Africans and Native Americans.We also identify individual loci that may have been subjected to natural selection, such as the T2D locus, HHEX, which displays both elevated differentiation and extended haplotype homozygosity in comparisons of East Asians with other groups.Our findings suggest that there is an evolutionary genetic basis for population differences in these traits, and we have identified potential group-specific genetic risk factors.\t\n\nDifferent populations suffer from different rates of obesity and type-2 diabetes (T2D).Little is known about the genetic or adaptive component, if any, that underlies these differences.Given the cultural, geographic, and dietary variation that accumulated among humans over the last 60,000 years, we examined whether loci identified by genome-wide association studies for these traits have been subject to recent selection pressures.Using genomewide SNP data on 938 individuals in 53 populations from the Human Genome Diversity Panel, we compare population differentiation and haplotype patterns at these loci to the rest of the genome.Using an ''expanding window'' approach (100-1,600 kb) for the individual loci as well as the loci as ensembles, we find a high degree of differentiation for the ensemble of T2D loci.This differentiation is most pronounced for East Asians and sub-Saharan Africans, suggesting that these groups experienced natural selection at loci associated with T2D.Haplotype analysis suggests an excess of obesity loci with evidence of recent positive selection among South Asians and Europeans, compared to sub-Saharan Africans and Native Americans.We also identify individual loci that may have been subjected to natural selection, such as the T2D locus, HHEX, which displays both elevated differentiation and extended haplotype homozygosity in comparisons of East Asians with other groups.Our findings suggest that there is an evolutionary genetic basis for population differences in these traits, and we have identified potential group-specific genetic risk factors.\t\n\nIn conclusion, our results have shown that genetic regions surrounding loci associated with T2D, and to a lesser extent, obesity, have been subject to unusually high levels of change in the last 50,000-100,000 years.Most notably, sub-Saharan Africans and East Asians appear to have undergone selection at T2D loci.Identifying specific targets of recent selection in the human genome can aid in determining population-specific risk variants, especially insofar as disease prevalence differs between populations (Ayodo et al. 2007).We anticipate that future studies will be at a finer scale at both the population, genetic, and phenotypic level, potentially further elucidating the genetic basis of obesity and T2D, and the population-specific genetic or non-genetic mechanisms that lead to different rates, types, and consequences of obesity and T2D." + ] + ], + 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\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_2.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_2.json new file mode 100644 index 0000000..28b935c --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_2.json @@ -0,0 +1,103 @@ +{ + "question": [ + "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?", + "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?", + "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?", + "How do microRNAs regulate gene expression related to diabetes?", + "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?" + ], + "answer": [ + "The PPAR-gamma gene (PPARG) is a key regulator of adipocyte differentiation and insulin sensitivity, playing a significant role in glucose and lipid metabolism. Variants of this gene can influence the risk of type 2 diabetes (T2D). For instance, loss-of-function (LOF) mutations in PPARG are associated with lipodystrophy and insulin resistance. The common P12A variant is associated with a decreased risk of T2D. However, some rare variants can increase the risk of T2D. Additionally, these genetic variants can influence the effectiveness of antidiabetic medications targeting PPARG, such as thiazolidinediones.", + "CRISPR/Cas9 technology can be used to study genetic forms of diabetes by editing genes that are associated with the disease. This allows researchers to understand the impact of these genes on cell function, growth, and survival. In terms of treatment, CRISPR/Cas9 might be used to genetically", + "The text does not provide specific information on the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes.", + "MicroRNAs (miRNAs) regulate gene expression related to diabetes by binding to the mRNA of target genes, which can inhibit their translation and lead to a decrease in the production of the corresponding proteins. These miRNAs can influence various biological processes such as insulin production and secretion, glucose homeostasis, cell growth and death, and immune response. For example, miR-29 has been shown to inhibit insulin-stimulated glucose uptake and downregulate gluconeogenesis. Other miRNAs like miR-18a and miR-206 can regulate genes like IGF-1, which is involved in glucose metabolism and insulin signaling. Dysregulation of these miRNAs can lead to the development of type 2 diabetes.", + "Studying the genetic basis of MODY has led to the identification of several genes associated with the disease, including the most common ones: glucokinase, HNF1A, and HNF4A. This has helped in understanding the disease's autosomal dominant inheritance pattern and its onset at a young age due to -cell dysfunction. The genetic subtypes have also aided in identifying patients who will respond to specific therapies, opening the possibility of tailored drug therapy. Furthermore, the genetic research has moved physicians and patients towards precision genomic medicine, taking into account individual genetic data for diagnosis and treatment." + ], + "contexts": [ + [ + "\tAt the skeletal muscle level in particular, the total mass\nof muscle and its function as the site of 70% of insulin-mediated glucose disposal\nsuggest physiologically important effects of PPAR (Semple et al 2006). Furthermore, synthetic PPAR agonists, the insulin-sensitizing thiazolidinediones (TZDs),\nare therapeutic agents used in the treatment of type 2 diabetes. However, clinical\nuse of TZDs is limited by the occurrence of fluid retention, haemodilution, and\nheart failure in up to 15% of the patients (Mudaliar et al 2003). By far the most studied PPAR polymorphism is the Pro12Ala in the unique\nPPAR 2 N-terminal domain.\tEndocr Pract\n9:406416\nMuller Y, Bogardus C, Beamer B, Shuldiner A, Baier L 2003 A functional variant in the peroxisome proliferator-activated receptor 2 promoter is associated with predictors of obesity and\ntype 2 diabetes in Pima Indians. Diabetes 52:18641871\nNelson T, Fingerlin T, Moss L, Barmada M, Ferrell R, Norris J 2007 Association of the peroxisome proliferatoractivated receptor gene with type 2 diabetes mellitus varies by physical\nactivity among non-Hispanic whites from Colorado. Metabolism 56:388393\nNewton-Cheh C, Hirschhorn JN 2005 Genetic association studies of complex traits: design\nand analysis issues.\tPPAR is a fatty acid- and eicosanoiddependent nuclear receptor that binds to specific DNA response elements (PPREs)\nas heterodimer with the retinoid X receptor and, in the presence of ligands, regulates the expression of the target gene. Although the role of PPAR in adipose\ntissue development and function is established, its low levels in tissues important\nto glucose homeostasis, including skeletal muscle, liver, and pancreatic cells, raise\nthe question of its possible physiological and pharmacological importance at those\nGENEENVIRONMENT INTERACTION AND THE METABOLIC SYNDROME\n\n105\n\nsites (Semple et al 2006).\tPPAR is considered as a strong, if not the strongest, candidate gene for\nthe metabolic syndrome. The PPAR gene is located at 3p25, a region showing\nevidence for linkage with diabetes and obesity susceptibility. Frameshift and missense heterozygous mutations have been liked to insulin resistance and type 2\ndiabetes, obesity, lipodystrophy and hypertension (Ristow et al 1998, Barroso et al\n1999, Hegele et al 2002, Savage et al 2002).", + "\tInteractions with the peroxisomeproliferator-activated receptors\n\nThe transcription factor peroxisome-proliferatoractivated receptor gamma (PPARg) is known to influence insulin sensitivity, and acts partly via a modulation of the circulating adiponectin level (PPARg agonists increase the adiponectin level) (Ref.38).The PPARgP12A SNP is a wellestablished genetic variant that modulates insulin sensitivity and the risk of type 2 diabetes (Ref.39).In a Chinese family study, Yang et al. demonstrated a genetic interaction between the ADIPOQ exon 2 45TG SNP and the P12A SNP of the PPARg gene with respect to insulin sensitivity (Ref.40).Likewise Tanko et al. reported a similar interaction between the PPARg P12A SNP and the 211377CG SNP of the ADIPOQ gene promoter (Ref.41).Indeed, a significant interaction was detected between the PPARg Ala12 and the ADIPOQ 211377C alleles and higher BMI, and the two alleles were associated with higher insulin sensitivity and displayed interaction with respect to insulin sensitivity.Such associations of 'insulinsensitising' alleles with increased BMI is not uncommon: it was previously reported for the UCP3 and the Isl1 genes in obese subjects ( Refs 42,43) and interpreted as a protective effect that delays the occurrence of type 2 diabetes and thus contributes to the reaching of a higher degree of obesity.", + "\t\n\nPeroxisome proliferator-activated receptor gamma (PPARG) is a master transcriptional regulator of adipocyte differentiation and a canonical target of antidiabetic thiazolidinedione medications.In rare families, loss-of-function (LOF) mutations in PPARG are known to cosegregate with lipodystrophy and insulin resistance; in the general population, the common P12A variant is associated with a decreased risk of type 2 diabetes (T2D).Whether and how rare variants in PPARG and defects in adipocyte differentiation influence risk of T2D in the general population remains undetermined.By sequencing PPARG in 19,752 T2D cases and controls drawn from multiple studies and ethnic groups, we identified 49 previously unidentified, nonsynonymous PPARG variants (MAF < 0.5%).Considered in aggregate (with or without computational prediction of functional consequence), these rare variants showed no association with T2D (OR = 1.35;P = 0.17).The function of the 49 variants was experimentally tested in a novel high-throughput human adipocyte differentiation assay, and nine were found to have reduced activity in the assay.Carrying any of these nine LOF variants was associated with a substantial increase in risk of T2D (OR = 7.22; P = 0.005).The combination of large-scale DNA sequencing and functional testing in the laboratory reveals that approximately 1 in 1,000 individuals carries a variant in PPARG that reduces function in a human adipocyte differentiation assay and is associated with a substantial risk of T2D.", + "\tPPARG\n\nPeroxisome proliferator-activated receptor gamma (PPARG) gene, located at 3p25, encodes peroxisome proliferator-activated receptor gamma protein, which is important in the control of insulin sensitivity, glucose homeostasis, and blood pressure (Barroso et al., 1999).Similar to previous meta-analyses, data from a recent meta-analysis involving 32,849 cases and 47,456 controls in 60 studies showed that PPARG polymorphism rs1801282 (Pro12Ala) was associated with a reduction in T2DM risk ( OR 0.86, 95%; CI 0.81 0.90; Gouda et al., 2010).Most recently, other variant genotypes, including rs3856806 (1431C T), have been associated with T2DM in a Chinese Han population (Lu et al., 2011).\tPPARGC1A\n\nPeroxisome proliferator activated receptor gamma coactivator 1-alpha (PPARGC1A) gene, located at 4p15.1, codes peroxisome proliferator activated receptor gamma coactivator 1 protein.Its expression might infl uence insulin sensitivity as well as energy expenditure, thereby contributing to the development of obesity, a risk factor for diabetes mellitus (Esterbauer, Oberkofl er, Krempler, & Patsch, 1999).The most recent meta-analysis showed that rs8192678 (Gly482Ser) and rs2970847 (Thr394Thr) polymorphisms of PPARGC1A were signifi cantly associated with the risk of T2DM, especially in the Asian Indian population (Yang, Mo, Chen, Lu, & Gu, 2011).Studies on PPARGC1A genetic polymorphisms and GDM are limited.Polymorphisms, rs8192678 and rs2970847, have not been associated with GDM in European Whites living in Vienna (Leipold, Knoefl er, Gruber, Huber, et al., 2006) or Scandinavian women (Shaat et al., 2007).", + "\tSUMMARY\n\nIn just over a decade, PPARg has evolved from somewhat humble beginnings as a simple regulator of adipogenesis to become a key therapeutic target in the fight against the 21st Century epidemics of obesity, insulin resistance and the metabolic syndrome.Whilst pharmacological and animal studies have yielded a wealth of information regarding the role of this receptor in the regulation of energy, glucose and lipid homeostasis, there can be no doubt that defining the metabolic consequences induced by polymorphisms and mutations in the human PPARg gene has contributed significantly to our understanding of the biology of this receptor.To this end, PPARg has proved to be a fruitful 'hunting ground' with many different genetic variants already identified, each providing novel insights into the role of PPARg in normal physiology and disease.Given the significant species-specific differences that exist in metabolism, particularly in relation to lipid homeostasis, it is critical that we continue to identify and study these human 'experiments of nature' in order to complement the impressive pharmacological and functional genomic approaches that are currently being employed to permit the development of more superior ligands with enhanced therapeutic impact.Given the apparent inexorable rise in the prevalence of obesity, insulin resistance and T2DM, the need for such novel therapies could not be more urgent.", + "\t\n\nAnother biologic candidate gene that was extensively studied is the peroxisome proliferator -activated receptor gene ( PPARG ), where mutations that severely decrease the transactivation potential were found to cosegregate with extreme insulin resistance, diabetes and hypertension in two families, with autosomaldominant inheritance [89] .A common amino -acid polymorphism (Pro12Ala) in PPARG has been associated with T2DM; homozygous carriers of the Pro12 allele are more insulin resistant than those having one Ala12 allele and have a 1.25 -fold increased risk of developing diabetes [90] .This common polymorphism has a modest, yet extensively replicated effect on the risk of T2DM.There is also evidence for interaction between this polymorphism and the insulin secretion in response to fatty acids [91] , and BMI [92] ; the protective effect of the alanine allele was lost in subjects with a BMI greater than 35 kg/m 2 .A widespread Gly482Ser polymorphism of PGC1 - (known as PPARGC1 ), a transcriptional coactivator of a series of nuclear receptors including PPARG , has been associated with a 1.34 genotype relative risk of T2DM [93] .In this study, a test for interaction with the Pro12Ala variant in PPARG gave no indication for additive effects on diabetes status.", + "\t\n\nHere, we report the most relevant PPAR SNPs, mutations, and rare variants, discussing their role on PPAR activity in adipose tissue and their association with T2D and obesity.In addition, we describe the role of alternative splicing and post-translational modifications on PPAR activity, with a specific focus on their impact on human metabolic disorders.Finally, considering PPAR as a pharmacological target, we discuss how nucleotide changes, PTMs, and alternative splicing can affect drug responsiveness in patients.\t\n\nPPAR is the most studied member of the PPAR family due to its primary role in dictating the expression of a network of genes responsible for lipid and glucose homeostasis as well as inflammation [17][18][19][20][21][22].Most importantly, PPAR is sufficient and essential for adipocyte formation and, as such, it is widely considered the master regulator of adipogenesis [9,[23][24][25].Because of its central role in many biological processes, different single-nucleotide polymorphisms (SNPs) and/or rare variants and mutations in the PPARG gene, or posttranslational modifications (PTMs; e.g., phosphorylation and SUMOylation), have been associated with alterations of the metabolic status including insulin resistance, obesity, and type 2 diabetes (T2D) [26][27][28][29][30].In this regard, PPAR has been the focus of intense research as a therapeutic target, and diverse synthetic partial or full agonists targeting this receptor have so far been developed [17,[31][32][33][34][35][36].Thiazolidinediones (TZDs), included in FDA-approved insulin-sensitizing drugs, are used in type 2 diabetes (T2D) treatment due to their positive action on glycemic control, on adipocyte differentiation, and on blood-circulating fatty acids levels [37][38][39][40].Nevertheless, adverse or side effects such as weight gain, edema, and fluid retention [41,42] have been reported, indicating that further knowledge on PPAR activity is needed and that new drugs targeting PPAR should be designed and tested.\tPPARG Genetic Variants and Their Effects on Drug Effectiveness in Metabolic Dysfunctions\n\nThe role of PPARG in the regulation of lipid and glucose homeostasis [20,49,50], inflammatory pathways [21], and its master role in governing the genesis of adipose tissue [19,25] indicate why it is so relevant in the etiology of metabolic diseases.Nucleotide variants in PPARG, alone or in combination (i.e., haplotype), can strongly affect PPAR activity in light of its functioning as a transcription factor.Therefore, its ability to orchestrate gene expression in different tissues can be compromised by nucleotide variations [51].In this regard, only a few studies have addressed the impact of nucleotide variants on the expression of PPAR itself or of its target genes.Instead, most of the effort has focused on identifying single-nucleotide polymorphisms (SNPs) or mutations in the PPARG gene with significant associations with complex traits and/or metabolic disorders [52][53][54][55][56][57][58][59][60][61][62].Moreover, as PPAR is targeted by synthetic agonists, (TZDs or glitazones) used in T2D treatment, different studies in the era of personalized medicine have attempted to demonstrate whether and how pharmacological efficacy can be affected by the presence of such variations in patients with diabetes [63].\tGain-and Loss-of-Function Mutations Affecting Metabolic Phenotype\n\nPPARG low frequency (~1:500) variants and rare point mutations, mostly associated with metabolic dysfunctions by independent studies, have also been extensively tested for their ability to affect PPAR activity, adipocyte differentiation, and TZD efficacy [54,58,61,[107][108][109][110][111][112][113].Among them, a very rare heterozygous PPAR mutation, P113Q (in PPAR2), has been identified in a German cohort [110,113,114] but not detected in French and American individuals or in Icelandic children [115][116][117].Ristow and colleagues [113] reported marked obesity (BMI 37.9-47.3)and T2D in patients carrying this mutation, also showing that it reduces PPAR phosphorylation in Ser112, in turn inducing PPAR transactivation capacity, adipocyte differentiation, and triglycerides accumulation [113].Subsequently, this gain-offunction mutation has been related to a marked reduction of body glucose uptake, suggesting it to be a rare cause of severe insulin resistance [114].However, insulin resistance and T2D have been mostly associated with loss-of-function (LOF) PPAR mutations [54,61,111], frequently identified as causing familial partial lipodystrophy type 3 (FPLD3).This autosomal dominant-inherited disorder is characterized by gradual loss of subcutaneous adipose tissue from limbs and the gluteal region, and is accompanied by dramatic metabolic complications, such as severe insulin resistance and early onset of T2D [108,112].A group of FPLD3-associated mutations resides within PPAR LBD and affects the protein structure to variable degrees.In particular, the heterozygous mutation in PPARG exon 6 R425C (in PPAR2) was identified in a patient who developed diabetes mellitus and hypertriglyceridemia at a young age and lipodystrophy of the extremities and face in adulthood [112].Interestingly, R425 is a wellconserved amino acid residue involved in the formation and stabilization of the tertiary structure, falling in a domain that is crucial for the interaction with RXR [112].Therefore, the R425C substitution strongly modifies receptor activity by altering the ability of the protein to form a functional heterodimer [112].Consequently, the mutated receptor strongly reduces the capacity of precursor cells to differentiate in mature adipocytes, also reducing rosiglitazone responsiveness, even at high doses [110].\t\nPurpose of Review Full and partial synthetic agonists targeting the transcription factor PPAR are contained in FDA-approved insulin-sensitizing drugs and used for the treatment of metabolic syndrome-related dysfunctions.Here, we discuss the association between PPARG genetic variants and drug efficacy, as well as the role of alternative splicing and post-translational modifications as contributors to the complexity of PPAR signaling and to the effects of synthetic PPAR ligands.Recent Findings PPAR regulates the transcription of several target genes governing adipocyte differentiation and glucose and lipid metabolism, as well as insulin sensitivity and inflammatory pathways.These pleiotropic functions confer great relevance to PPAR in physiological regulation of whole-body metabolism, as well as in the etiology of metabolic disorders.Accordingly, PPARG gene mutations, nucleotide variations, and post-translational modifications have been associated with adipose tissue disorders and the related risk of insulin resistance and type 2 diabetes (T2D).Moreover, PPAR alternative splicing isoformsgenerating dominant-negative isoforms mainly expressed in human adipose tissue-have been related to impaired PPAR activity and adipose tissue dysfunctions.Thus, multiple regulatory levels that contribute to PPAR signaling complexity may account for the beneficial as well as adverse effects of PPAR agonists.Further targeted analyses, taking into account all these aspects, are needed for better deciphering the role of PPAR in human pathophysiology, especially in insulin resistance and T2D.Summary The therapeutic potential of full and partial PPAR synthetic agonists underlines the clinical significance of this nuclear receptor.PPARG mutations, polymorphisms, alternative splicing isoforms, and post-translational modifications may contribute to the pathogenesis of metabolic disorders, also influencing the responsiveness of pharmacological therapy.Therefore, in the context of the current evidence-based trend to personalized diabetes management, we highlight the need to decipher the intricate regulation of PPAR signaling to pave the way to tailored therapies in patients with insulin resistance and T2D.Keywords PPARG genetic variants .Dominant-negative isoforms .Post-tranlational modifications .Adipose tissue dysfunctions .Drug responsiveness .Type 2 diabetes This article is part of the Topical Collection on Genetics * Alfredo Ciccodicola\t\n\nPurpose of Review Full and partial synthetic agonists targeting the transcription factor PPAR are contained in FDA-approved insulin-sensitizing drugs and used for the treatment of metabolic syndrome-related dysfunctions.Here, we discuss the association between PPARG genetic variants and drug efficacy, as well as the role of alternative splicing and post-translational modifications as contributors to the complexity of PPAR signaling and to the effects of synthetic PPAR ligands.Recent Findings PPAR regulates the transcription of several target genes governing adipocyte differentiation and glucose and lipid metabolism, as well as insulin sensitivity and inflammatory pathways.These pleiotropic functions confer great relevance to PPAR in physiological regulation of whole-body metabolism, as well as in the etiology of metabolic disorders.Accordingly, PPARG gene mutations, nucleotide variations, and post-translational modifications have been associated with adipose tissue disorders and the related risk of insulin resistance and type 2 diabetes (T2D).Moreover, PPAR alternative splicing isoformsgenerating dominant-negative isoforms mainly expressed in human adipose tissue-have been related to impaired PPAR activity and adipose tissue dysfunctions.Thus, multiple regulatory levels that contribute to PPAR signaling complexity may account for the beneficial as well as adverse effects of PPAR agonists.Further targeted analyses, taking into account all these aspects, are needed for better deciphering the role of PPAR in human pathophysiology, especially in insulin resistance and T2D.Summary The therapeutic potential of full and partial PPAR synthetic agonists underlines the clinical significance of this nuclear receptor.PPARG mutations, polymorphisms, alternative splicing isoforms, and post-translational modifications may contribute to the pathogenesis of metabolic disorders, also influencing the responsiveness of pharmacological therapy.Therefore, in the context of the current evidence-based trend to personalized diabetes management, we highlight the need to decipher the intricate regulation of PPAR signaling to pave the way to tailored therapies in patients with insulin resistance and T2D.Keywords PPARG genetic variants .Dominant-negative isoforms .Post-tranlational modifications .Adipose tissue dysfunctions .Drug responsiveness .Type 2 diabetes This article is part of the Topical Collection on Genetics * Alfredo Ciccodicola", + "\t\n\nThere is substantial evidence that PPAR- contributes to the risk for type 2 diabetes by influencing insulin sensitivity, insulin secretion and susceptibility to obesity [6].The 12Ala allele of the PPAR-2 gene, that has been shown to have a decreased transcriptional activity, is also associated with improved insulin sensitivity and lower risk for diabetes [17].This finding is in agreement with results from heterozygous PPAR- null mice exhibiting increased insulin sensitivity compared with wild-type mice [46].The 12Ala allele was associated with a higher reduction in the 2-h serum insulin level, probably indicating an increase in insulin sensitivity.However, it cannot be excluded that a decrease in insulin levels could also be due to impaired insulin secretion, because the 12Ala allele has been associated with reduced insulin secretion capacity in previous studies [20,47].\t\n\nPPAR is a ligand-activated transcription factor, a member of the nuclear hormone receptor superfamily, that functions as a heterodimer with a retinoid X receptor (RXR) to promote transcription of numerous target genes [5,6].PPAR-2, an isoform of PPAR- with 28 additional amino acids at its N-terminus, is expressed almost exclusively in adipose tissue [7].It plays a key role in adipogenesis [8,9,10,11], is involved in the regulation of insulin sensitivity [12,13], and is the major functional receptor for the thiazolidinedione class of insulin-sensitising drugs [11,14].Therefore, the PPAR- gene has been viewed as a \"thrifty gene\", with an important role in the development of type 2 diabetes and diabetes-related traits [7,15].Additionally, the Pro12Ala substitution in exon B of the PPAR-2 gene, first reported in Caucasians [16], has been associated with diabetes mellitus [17,18,19,20,21,22,23,24,25,26,27,28].Although not all associations have been consistent, a meta-analysis of published studies has confirmed a modest (1.25-fold), but statistically significant, increase in diabetes risk for the Pro12Pro genotype [4,19].\t\n\nIn summary, we have demonstrated that the Pro12Pro genotype of the PPAR-2 gene and the 482Ser allele of the PGC-1 gene predict the conversion from IGT to type 2 diabetes.Our study also shows that the interaction between drug treatment (acarbose) and genetic variation may be significant with respect to the conversion from impaired glucose tolerance to type 2 diabetes.\t\n\nPPAR- plays a key role in adipocyte differentiation [10,11], and can therefore influence body fat mass and obesity.In our study subjects, those with the 12Ala allele had a somewhat higher BMI at baseline, and tended to lose more weight.This finding is in accordance with our results from the Finnish Diabetes Prevention Study [48].In that study, subjects belonging to the intervention group (lifestyle changes) and who had the Ala12 allele lost significantly more weight (and were protected from type 2 diabetes) than subjects with the Pro12Pro genotype, although in the control group the 12Ala allele did not confer protection against diabetes.In the present study, the effect of the Pro12Pro genotype in increasing the risk for diabetes was independent of baseline weight change and other OR=odds ratio.Smoking was coded as 0 = never smokers and ex-smokers and 1 = current smokers at baseline.PPAR-2 genotypes were coded as 0 = the 12Ala allele and 1 = the Pro12Pro genotype confounding factors in women in the acarbose group, indicating that women possessing the Pro12Pro genotype were less responsive to acarbose treatment.This implies that the effect of acarbose treatment was modified by the Pro12Ala polymorphism.Several mechanisms could explain why the Gly482Ser polymorphism of the PGC-1 gene regulates the conversion from IGT to diabetes.PGC-1 increases and coordinates the expression of different genes that stimulate mitochondrial biogenesis, adaptive thermogenesis, fibre-type switching [32], expression of GLUT-4 in skeletal muscle [33], and regulation of gluconeogenesis in the liver [34].Thus, a reduction in the activity of PGC-1, possibly also as a result of the Gly482Ser mutation, might lead to insulin resistance in skeletal muscle.Additionally, a quantitative trait linkage analysis in Pima Indians suggested a link between basal insulin concentrations and chromosome 4p15. 1 [49] in cases where the PGC-1 gene has been mapped [50].In the present study the Gly482Ser variant was not related to fasting and 2-h plasma glucose, serum insulin, or their changes, or to BMI and weight change.However, compared to the Gly482Gly genotype, the 482Ser allele was associated with a 1.6-fold higher risk for diabetes in the placebo group but not in the acarbose group.The 482Ser allele had a significant interaction with treatment and acarbose treatment was able to reduce the risk of diabetes particularly among carriers of the 482Ser allele." + ], + [ + "\t\n\nThe advancements in both differentiation protocols and genome-editing technologies make it now possible to study the effect of genetic perturbations on human -cell development.\tA measure of -cell exocytosis based on electrical current. the scalability of such studies.Moreover, a genome-wide CRISPR loss-of-function screen performed in 2019 identified 373 potential regulators of insulin production in the mouse insulinoma-derived Min6 -cell line 178 .Extending genome-wide screens to human -cell models and increasing the diversity of cellular read-outs will provide orthogonal data sets for integration with existing genetic and genomic resources, in order to elucidate downstream biology.As the current protocols for hiPSC differentiation are expensive, are time-consuming and have variability in differentiation efficiency, continued advancements in differentiation protocols will enable similar approaches in these cell models.", + "\tRegulation of GWAS diabetes genes by glucose in pancreatic islets\n\nMany of the recently discovered type 2 diabetes genes have been suggested to affect the development and/or function of pancreatic islets [6].The function, growth and survival of -cells can be regulated acutely and chronically by glucose [34].Thus, we examined whether the new type 2 diabetes susceptibility genes are regulated by overnight incubation in low (5 mM) or high (25 mM) glucose (Figure 5).Most genes were significantly or tended to be downregulated under conditions of high glucose.Cdkal1, Cdkn2a (Arf, P = 0.07), Ide, Jazf1, Camk1d, and Tspan8 (P = 0.06) expression levels were decreased ~50-60%.Meanwhile, the expression of Cdkn2b, Hhex (P = 0.10), Cdc123, Adamts9 (P = 0.09), and Thada were reduced 30-40%.To ensure the islets incubated in high glucose did not have globally decreased expression, we examined the expression of Txnip, which has been shown to be highly upregulated by glucose [35] and found that its expression was still significantly elevated in the islets cultured in high glucose (Figure 5).Mouse islets consist of -cells and other cell types.Thus, the MIN6 -cell line was also examined.We found that all the genes were expressed in this cell line (not shown), although this does not preclude that they also are expressed in other cell types within the islet.", + "\tEmploying hPSCs and genome editing tools to study diabetes and metabolic syndromes\n\nIn general, the strategy to carry out in vitro disease modeling of diabetes and related metabolic syndromes with hPSCs and genome editing tools would be to 1) derive hiPSCs from patients with these conditions, 2) generate \"repaired/corrected\" isogenic controls [53] and then 3) differentiate them into pancreatic cells or target cells of relevance, such as immune cells in the case of T1D or myocytes, adipocytes and hepatocytes in the case of T2D (Figure 2).If patient material is inaccessible, one could introduce (naturally occurring) mutations or gene variants into hESCs and differentiate them accordingly to study disease mechanisms.Since excellent reviews have been published recently, we will provide a brief overview to familiarize the reader with the classification of diabetes and metabolic disorders.\tCONCLUSIONS\n\nhPSCs and the advancing genome editing tools appear to be a timely and potent combination for probing molecular mechanism(s) underlying diseases such as diabetes and metabolic syndromes.Studying monogenic forms of diabetes and syndromes of insulin resistance using these tools would be extremely useful given the lack of an autoimmune attack and confounding effects of insulin resistance and obesity.One caveat of this methodology at the moment is the \"low\" efficiency of deriving human beta cells in vitro [75,76], possibly due to our incomplete knowledge on human pancreatic development.Another explanation would be the lack of in vivo environmental cues emanating from proximal tissues such as the vasculature.Nonetheless, successful disease modeling of MODY2 [7] and Wolfram Syndrome [8] already suggests a high possibility of success.These technologies have the potential to elucidate the underlying pathophysiology that stem from defects in 1) beta cell development, metabolism or survival or 2) development of adipocyte.For instance in the case of MODY2, it is now clear that GCK mutation affects glucose-stimulated insulin secretion but not insulin synthesis or beta cell proliferation [7].With the latest advances in the derivation of mature and functional human pancreatic beta-like cells from hPSCs in vitro [75e77], eventually circumventing the requirement for in vivo maturation, disease modeling of diabetes is expected to progress exponentially.The knowledge gained from these hiPSC-based disease modeling studies can potentially be translated into the clinics by guiding clinicians on the appropriate type of medication to use for each condition based on the mechanism of action of the disease.Findings from these proposed studies could also offer clues to the pathophysiology of the \"garden variety\" of type 2 diabetes which is known to manifest defects in each of these tissues.hPSCs and genome editing tools may also provide an opportunity to better understand the relevance of gene variants identified from GWAS studies, in causing T1D, T2D, obesity and metabolic syndromes, given that they exhibit only modest effects and w85% of the variants map onto noncoding regions such as enhancers or regulatory elements [104].Investment into hPSCs and genome editing would allow a better mechanistic understanding of the pathophysiology of monogenic and complex diseases relevant for organismal homeostasis and therefore an improved approach to stratified personalized medicine.By identifying the impact of gene variants on disease predisposition, prophylactic measures in the form of lifestyle alterations or medication could be adopted early on in life to delay or even prevent the onset of diabetes and/or metabolic diseases.It is also likely that these hiPSCbased disease modeling studies would provide insights into approaches to predict the susceptibility of disease.Henceforth, the translational potential of studying human diabetes and metabolic syndrome disease mechanisms is huge, with opportunities for early prophylactic intervention that could have long-term implications for global health care and reduction of economic burden.While the derivation of hiPSCs from human tissues is relatively easier and gaining popularity compared to just a few years ago [2], it is likely that the modern technology of generating site-specific nucleases will also rapidly mature to make in vitro disease modeling a routine approach.\tEmploying hPSCs and genome editing tools to study type 1 diabetes (T1D)\n\nPatients with T1D are unable to secrete insulin due to near complete destruction of their pancreatic beta cells.More than 50 risk variants/ susceptibility alleles have been found to be associated with susceptibility to this disease [71] (https://www.niddkrepository.org/studies/t1dgc/) (Table 1).The strongest association is with the human leukocyte antigens (HLAs), which accounts for a large proportion of the genetic risk for T1D [71].Most of the T1D genes affect adaptive and innate autoimmunity leading to incomplete self-tolerance to beta cell antigens and immune-mediated destruction of beta cells [71].T1D-hiPSCs can be differentiated into T lymphocytes [72e74] and pancreatic beta cells [75e77] to allow co-culture experiments aimed at progressively evaluating their interactions in vitro (Figure 2) [78].A similar strategy can be applied to hiPSCs derived from T1D-susceptible patients to examine the impact of susceptible gene variants (Table 1) on the vulnerability of pancreatic beta cells to immune attack.For instance, hiPSCs derived from patients with a gene variant in PTPN22 can be differentiated into lymphocytes to study lymphocyte function [79e81].hiPSCs from subjects with gene variants in ERBB3, which is expressed in monocytes and dendritic cells, and may affect antigen presenting cell (APC) function [82], can be differentiated into selective immune cells to study how they affect APC function.hiPSCs from patients with gene variants in UBASH3A (also known as STS2), which is specifically expressed in lymphocytes [83], are well suited for differentiation into lymphocytes to study the function of this gene.\t\n\nBackground: Diabetes and metabolic syndromes are chronic, devastating diseases with increasing prevalence.Human pluripotent stem cells are gaining popularity in their usage for human in vitro disease modeling.With recent rapid advances in genome editing tools, these cells can now be genetically manipulated with relative ease to study how genes and gene variants contribute to diabetes and metabolic syndromes.Scope of review: We highlight the diabetes and metabolic genes and gene variants, which could potentially be studied, using two powerful technologies e human pluripotent stem cells (hPSCs) and genome editing tools e to aid the elucidation of yet elusive mechanisms underlying these complex diseases.Major conclusions: hPSCs and the advancing genome editing tools appear to be a timely and potent combination for probing molecular mechanism(s) underlying diseases such as diabetes and metabolic syndromes.The knowledge gained from these hiPSC-based disease modeling studies can potentially be translated into the clinics by guiding clinicians on the appropriate type of medication to use for each condition based on the mechanism of action of the disease.\t\n\nOne strategy to study these monogenic syndromes would be to derive hiPSCs from these patients, differentiate them into pancreatic progenitors and then transplant these progenitors into immunocompromised (SCID-Beige or NSG) mice for in vivo maturation (Figure 2).This methodology has been recently used to successfully model MODY2, demonstrating that beta cells derived from hiPSCs with GCK mutation are indeed less sensitive to glucose levels [7].Endoplasmic reticulum (ER) stress-related diabetes in patients with Wolfram syndrome has also been modeled using hiPSC-derived beta cells, demonstrating that WFS1 protein maintains ER function in beta cells by acting upstream of the unfolded protein response (UPR) pathways [8].phenotypes occurring in humans.Likewise, the stepwise analysis of human pancreatic development with this strategy would likely provide mechanistic insights into the ability of a single gene mutation (PDX1, PTF1A, HNF1B, GATA6 and GATA4) to promote pancreatic agenesis/ atrophy.Further, studying mutations in KCNJ11 and ABCC8 using hiPSC-derived beta cells may elucidate the mechanistic differences between permanent and transient neonatal diabetes [64].Overall, insulin production and secretion could be compared between diseased and gene-corrected pancreatic cells to understand the underlying cause of each type of monogenic diabetes (Figure 2).", + "\t\n\nMoving beyond cancer phenotypes, indirect in vivo screens are beginning to be used in other disease models.A genome-scale knockout screen in pancreatic beta-cells transplanted into a mouse model for Type 1 Diabetes identified genetic factors preventing autoimmune clearance of transplants.Inhibition of an identified gene hit, Rnls , with pargyline [101] prevented an autoimmune reaction and confirmed that the screen was able to identify candidates of therapeutic relevance [11] .", + "\t\n\nunderstand each cell type's genomic architecture and better characterize their roles in islet resilience and failure.Experimental manipulation of the regulatory elements and/or the target genes identified by (epi)genomic approaches described above and modeling the putative pathways and processes they implicate in human islet cell lines (e.g., EndoC-bH1-H3) is essential to progress from correlation to causation.Similarly, transitioning from \"the\" mouse (C57BL/6) to multiple mouse models for insights into the effects of naturally occurring genetic variation on islet function and physiology [61] and for manipulation of key genomic elements should also help characterize the dynamic range of islet behavior and response.T2D is a heterogeneous, complex, and progressive disorder, as multiple subtypes have been identified and associated with different genetic risk and clinical outcome profiles.Future islet genomics studies that focus on identifying the distinct subgroups of individuals with distinct genes/pathways that are disrupted and/or contributing to islet (dys)function at basal and/or responsive states are needed.Furthermore, priority should be given to profiling more islets from pre-diabetic and T2D individuals to characterize the transition between basal to stressed to T2D state and determine if there are intermediate signatures for islet failure and T2D onset.Together, this multi-pronged approach toward studying T2D genetics and islet pathophysiology will help identify additional targets and opportunities for intervention that can be exploited for more precise and effective preventative, treatment, and management options for T2D.", + "\t\n\nIn addition, knock-out and transgenic mice have become powerful tools in elucidating the influence of specific genes in glucose metabolism and the pathogenesis of diabetes.This includes understanding which transcription factors are involved in pancreas development (Habener et al., 2005) and elucidation of insulin signalling pathways (Kahn, 2003;Wang and Jin, 2009).Tissue-specific knockouts have proven to be particularly useful in studying insulin signalling (Neubauer and Kulkarni, 2006) as the global insulin receptor knock-out is non-viable (Accili et al., 1996).", + "\t\n\nA recent sequencing study provides an example of detection of rare variants in type 1 diabetes.Targeted sequencing in a series of candidate coding regions resulted in IFIH1 being identified as the causal gene in a region associated with type 1 diabetes by GWA studies (58).IFIH1 encodes a cytoplasmic helicase that mediates induction of the interferon response to viral RNA.The discovery of IFIH1 as a contributor to susceptibility to type 1 diabetes has strengthened the hypothesis (70) about a mechanism of disease pathogenesis involving virusgenetic interplay and raised type 1 interferon levels as a cofactor in -cell destruction.Nonetheless, it should be recognized that a component of the missing heritability (familial aggregation) in type 1 diabetes could well be due to unrecognized intra-familial environmental factors.Disease pathogenesis.Contemporary models of pathogenesis of type 1 diabetes support the involvement of two primary dramatis personae: the immune system and the -cell.The known and newly identified genetic risk factors for type 1 diabetes present exciting opportunities to build on to the current cast of disease mechanisms and networks.Most of the listed genes of interest (Table 2) and those in extended regions are assumed to regulate immune function.Some of these genes, however, may also have roles in the -cell (insulin being the most obvious example).Another gene, PTPN2, encoding a protein tyrosine phosphatase, was identified as affecting the risk for type 1 diabetes as well as for Crohn disease (47,71).PTPN2 is expressed in immune cells, and its expression is highly regulated by cytokines.However, PTPN2 is expressed also in -cells, where it modulates interferon (IFN)- signal transduction and has been shown to regulate cytokineinduced apoptosis (72).Other candidate genes, such as NOS2A, IL1B, reactive oxygen species scavengers, and candidate genes, identified in large GWA studies of type 2 diabetes, have not been found to be significant contributors to the susceptibility of type 1 diabetes (73).", + "\t\n\nHuman genetic studies have emphasised the dominant contribution of pancreatic islet dysfunction to development of Type 2 Diabetes (T2D).However, limited annotation of the islet epigenome has constrained efforts to define the molecular mechanisms mediating the, largely regulatory, signals revealed by Genome-Wide Association Studies (GWAS).We characterised patterns of chromatin accessibility (ATAC-seq, n = 17) and DNA methylation (whole-genome bisulphite sequencing, n = 10) in human islets, generating high-resolution chromatin state maps through integration with established ChIP-seq marks.We found enrichment of GWAS signals for T2D and fasting glucose was concentrated in subsets of islet enhancers characterised by open chromatin and hypomethylation, with the former annotation predominant.At several loci (including CDC123, ADCY5, KLHDC5) the combination of fine-mapping genetic data and chromatin state enrichment maps, supplemented by allelic imbalance in chromatin accessibility pinpointed likely causal variants.The combination of increasingly-precise genetic and islet epigenomic information accelerates definition of causal mechanisms implicated in T2D pathogenesis.", + "\tGene Therapy in Diabetes\n\nThe series of experiments leading to cloning and expression of insulin in the cultures cells in the 1970s was a tremendous revolution in the field of medicine and application of gene therapy in the treatment of diabetes was suggested as a possible cure.Regulating the sugar levels is the most important aspect in the treatment which also reduces the complications associated with the disease.Somatic gene therapy involving the somatic cells of the body includes two methods of gene delivery.The first one known as ex vivo gene therapy is described as the one in which the tissues are removed from the body; the therapeutic gene is inserted in vitro and then reimplanted back in the body while the in vivo therapy involves the insertion of gene therapy vectors directly to the patients by subcutaneous, intravenous, or intrabronchial routes, or by local injection [57].The application of ex vivo therapy aims at the generation of cells which possess the properties of cells, for example, insulin producing cells [58].This therapy has also been used to generate cells for transplantation.However, the concern lies in the aspect of surgically removing the tissue from the patient and reimplantation of the genetically modified tissues back into the body of the patients [57].Furthermore, type 1 diabetes results from autoimmune destruction of insulin synthesizing pancreatic cells and islet transplantation has been explored as a possible solution for the treatment.The invention of insulin gene therapy substitutes cell function by generating insulin secretory non- cells, not vulnerable to autoimmune reactions, offering a prospective therapeutic approach for type 1 diabetes [59].", + "\t\nThe inheritance of variants that lead to coding changes in, or the mis-expression of, genes critical to pancreatic beta cell function can lead to alterations in insulin secretion and increase the risk of both type 1 and type 2 diabetes.Recently developed clustered regularly interspaced short palindromic repeats (CRISPR/Cas9) gene editing tools provide a powerful means of understanding the impact of identified variants on cell function, growth, and survival and might ultimately provide a means, most likely after the transplantation of genetically \"corrected\" cells, of treating the disease.Here, we review some of the disease-associated genes and variants whose roles have been probed up to now.Next, we survey recent exciting developments in CRISPR/Cas9 technology and their possible exploitation for b cell functional genomics.Finally, we will provide a perspective as to how CRISPR/Cas9 technology may find clinical application in patients with diabetes.\t\n\nThe inheritance of variants that lead to coding changes in, or the mis-expression of, genes critical to pancreatic beta cell function can lead to alterations in insulin secretion and increase the risk of both type 1 and type 2 diabetes.Recently developed clustered regularly interspaced short palindromic repeats (CRISPR/Cas9) gene editing tools provide a powerful means of understanding the impact of identified variants on cell function, growth, and survival and might ultimately provide a means, most likely after the transplantation of genetically \"corrected\" cells, of treating the disease.Here, we review some of the disease-associated genes and variants whose roles have been probed up to now.Next, we survey recent exciting developments in CRISPR/Cas9 technology and their possible exploitation for b cell functional genomics.Finally, we will provide a perspective as to how CRISPR/Cas9 technology may find clinical application in patients with diabetes.\tGWAS-Identified Genes\n\nFollowing the successful identification of genetic loci by GWAS, several candidate genes within or surrounding genetic loci which are thought to play roles in b cell function, in particular, in proinsulin processing and secretion, have been examined in mechanistic studies.Gene editing tools have quickly replaced techniques such as shRNA-based silencing and HDR-mediated deletion to become a mainstream technique in studies of gene function.For example, the critical b cell-enriched NEUROD1 and SLC30A8 genes were deleted in EndoC-bH1 cells using these approaches in recent studies (243).Similarly, pancreatic duodenum homeobox-1 (PDX1), an important regulator of the INS gene, was also mutated by CRISPR-Cas9 resulting in a line with defective glucose-induced Ca 2+ influx and insulin secretion (244).Our laboratory has inactivated the type 2 diabetes-related STARD10 and FCHSD2 genes in EndoC-bH1 cells using a lentiviral approach and demonstrated effects on insulin secretion (and see above) (117).Furthermore, Fang et al. used CRISPR screening technology and identified several genes involved in insulin regulation in mouse MIN6 cells (172).\t\n\nIn vivo delivery of CRISPR editing tools into pancreatic b cells in people with diabetes is likely to face enormous challenges for two main reasons: 1. b cells are postmitotic, thus disfavouring HDR-mediated CRISPR editing.2. Selective targeting to these cells will be required, likely involving cell type-tropic viruses (272), raising evident concerns over off-target effects and toxicity.Hence, the most likely and feasible way of CRISPR editing has to be an ex vivo system where b cells can first be engineered by CRISPR editing and then transplanted into patients (Figure 2).\t\n\ninsulin secretion.We begin by providing examples of genes and loci associated with altered T2D risk.Finally, we review the CRISPR tools that may offer the potential to correct these variants in the human b cell.\tIn Vitro and In Vivo b Cell Models for Studying Genetic Variants\n\nIn order to understand the pathogenic role of diabetes-associated genetic variants, tractable b cell models are essential.Mouse models, either transgenic or knock-out, are valuable for examining the roles of single genes, but their use is more limited in studies of intergenic regions given more substantial inter-species (mouse versus human) differences in these regions.As sources of human b cells, there are currently three possibilities.Firstly, primary islets isolated from organ donors: This source is, however, limited in terms of the availability and quality of islets (226).Secondly, clonal human b cells.Immortalized human EndoC-bH1 cells were developed in recent years after infection of foetal islets with large T antigen and further inoculation of islets in immunocompromised mice (227).Later generation EndoC-bH2 (228) and EndoC-bH3 (229) cell lines were subsequently established with more advanced features including regulated deletion of the immortalizing gene.The limitation of these cell lines, however, is their extremely slow growth rate which hampers their use.Given this slow growth rate -and the fact that these lines poorly tolerate expansion from a single cell-it is virtually impossible to modify them by HDR via CRISPR editing.A third possibility are therefore islet-like cells differentiated from human embryonic stem cells (hESC) or patient-derived induced pluripotent stem cells (iPSC).In light of the limitations of the above cellular models, laboratories are now focusing on hESC or iPSC in studies of gene function throughout b cell development by differentiating hESC/iPSC cells into mature b cells (230,231).Such directed differentiation protocols have recently been improved (21,159)." + ], + [ + "\t\n\nThe insulin receptor substrate 1 (IRS-1) expressed in tissues sensitive to insulin is crucial to glucose transporter 4 translocation (GLUT-4).IRS-1 polymorphism has been found related to insulin resistance, obesity and type 2 diabetes mellitus.In a study on GDM, the frequency of IRS-1 gene polymorphism was significantly higher in women with GDM than in pregnant women with a normal glucose tolerance, suggesting a role for this polymorphism in the onset of GDM as well as type 2 diabetes mellitus (17).The switch on IRS-1 of the amino acid GLY972 Arg (rs1801278) impairs insulin secretion, and a study on 1306 GDM patients and 1973 pregnant women without GDM found a significant association between the presence of this polymorphism and the risk of GDM (18).", + "\t\n\nAssociation of ADIPOQ gene polymorphisms with Type 2 diabetes.", + "\t\n\nThese six variants of PGC-1 gene were first studied to be associated with changes in insulin/glucose levels among Danish Caucasians (Ek et al. 2001).In the present study, genetic association analysis revealed increased risk of the A-allele (2.7-fold risk) and AA genotype (3.78-fold risk) of rs3736265 polymorphism towards T2D susceptibility in Jat Sikhs only, which can be attributed to ethnic heterogeneity.In contrast, a study on Danish Caucasians (Ek et al. 2001) revealed protective role of this allele while Han Chinese population (Zhu et al. 2009) showed no association.The AA genotype of rs3755863 polymorphism tends to pose 2.7-fold T2D risk in Jat Sikh group.On the other hand, studies on Caucasians showed protective effect whereas Chinese population failed to report any association with T2D (Barroso et al. 2003;Zhang et al. 2007).", + "\t\n\nType 2 diabetes (T2DM) is a complex disease resulting from the contribution of both environmental and genetic factors.Recently, the list of genes implicated in the susceptibility to T2DM has substantially grown, also as a consequence of the great development of the genome-wide association studies in the last decade.Common polymorphisms in TCF7L2 gene have shown to have a strong effect with respect to many other involved genes.The aims of our study were to confirm the role of TCF7L2 in the susceptibility to T2DM in the Italian population and to investigate whether TCF7L2 genotypes also contribute to the clinical phenotypes variability and to diabetic complications development.Three TCF7L2 polymorphisms (rs7903146, rs7901695 and rs12255372) have been analyzed by allelic discrimination assays in a cohort of 154 Italian patients with T2DM and 171 healthy controls.A case-control association study and a genotype-phenotype correlation study have been carried out.Consistent with previous studies, all three SNPs showed a strong association with susceptibility to T2DM, both at genotypic (P = 0.003, P = 0.004 and P = 0.012) and at allelic level (P = 0.0004, P = 0.0004 and P = 0.003).Moreover, we observed associations between TCF7L2 variants and the following diabetic complications: diabetic retinopathy, cardiovascular disease and coronary artery disease.We also found a strong correlation between the rs7903146 and the presence of cardiovascular autonomic neuropathy (P = 0.02 with a high OR = 8.28).", + "\t\n\nIn a GWAS of the French population, polymorphism rs13266634 of SLC30A8 gene has been associated with T2DM (Sladek et al., 2007).In a large meta-analysis including 42,609 cases and 69,564 controls from various ethnic groups from Europe, Asia, and Africa, polymorphism rs13266634 was also associated with T2DM in both Europeans and Asians (Jing, Sun, Bi, Shen, & Zhu, 2011).", + "\t\n\nGene polymorphisms affecting drug response for some commonly used antidiabetic agents.", + "\t\n\nIn fact, only two of the many candidate-gene associations claimed for T2D have stood the test of time.The Pro12Ala variant in the peroxisome proliferator-activated receptor gamma (PPARG) gene (encoding the target for the thiazolidinedione class of drugs used to treat T2D) [11] and the Glu23Lys variant in KCNJ11 (the potassium inwardly rectifying channel, subfamily J, member 11, which encodes part of the target for another class of diabetes drug, the sulphonylureas) [12] are both common polymorphisms shown in multiple studies to influence risk of T2D.Their effect sizes are only modest, each copy of the susceptibility allele increasing risk of disease by 15-20%.Interestingly, rare mutations in both KCNJ11 and PPARG are also known to be causal for certain rare monogenic syndromes (neonatal diabetes and lipodystrophies) characterized by severe metabolic disturbance of b-cell function and insulin resistance, respectively [13,14].", + "\t\n\nNo other recent associations of polymorphisms with T2D have been replicated to date (Table 5).However, a recent meta-analysis (106) identified some early reproducibility of an association between variation in GLUT1 and T2D, originally reported in 1988 (104).It is likely that this association has not been pursued further for several reasons, but one possibility is a study that reported the rejection of linkage to GLUT1 at high levels of significance (46).However, linkage has limited power to assess associations with common variants and modest effect (and hence low S ); complete evaluation of this association would require comprehensive testing of variation in this gene in large samples.", + "\t\n\nbutions of these four common polymorphisms in type 2 diabetes patients were similar to those of normal nondiabetic controls.However, these four common polymorphisms were variably associated with several diabetes-related phenotypes, such as high-density lipoprotein (HDL) cholesterol, fasting plasma glucose, and homeostasis model assessment of insulin resistance.In particular, subjects harboring g.1062C were associated with a lower serum HDL cholesterol level after adjusting for other variables (P 0.0004 or 0.01 after Bonferroni correction for 24 tests).", + "\t\n\nHowever, there have been some successes.In T2D, the presence of common polymorphisms in known diabetes drug targets has presented obvious candidates for pharmacogenetic analysis.Evidence of a relationship between ABCC8/KCNJ11 genotype and sulfonylurea response is encouraging.Recent analyses in large cohorts have reported, for example, a 45% increased risk of glibenclamide treatment failure amongst risk compared to non-risk allele homozygotes (Sesti et al. 2006) and a greater decrease in fasting plasma glucose following gliclazide treatment amongst risk allele carriers (Feng et al. 2008).An effect upon gliclazide response is consistent with functional data which demonstrates that the risk variant K ATP channel has 3.5 times increased sensitivity to gliclazide inhibition (Hamming et al. 2009).", + "\tDNA polymorphisms associated with type 2 diabetes\n\nWe found 7 known genes (GPC1, ATSV, AGXT, HDLBP, NEDD5, PPP1R7 and serine/threonine (S/T) kinase-like), none of which were obvious candidates, and 15 ESTs in the NIDDM1 interval (Fig. 1).We identified single-nucleotide polymorphisms (SNPs) and other types of DNA polymorphism in the 7 known genes and in 4 of the 15 ESTs (Fig. 1).We carried out the initial analyses, examining association of alleles and haplotypes comprised of alleles at adjacent polymorphisms with type 2 diabetes, using just the random sample and the two groups of patients described above.There was a nominally significant difference (P=0.003,uncorrected for the 44-haplotype/group comparisons) in the haplotype frequency distribution of markers UCSNP-1, -2 and -19 between the group of patients with evidence for linkage at NIDDM1 and the random sample (Table A, see http://genetics.nature.com/supplementary_info/). The characterization of additional SNPs in the interval between UCSNP-19 and UCSNP-1 and -2 (Figs 1 and 2) revealed a cluster of four SNPs having significant differences in allele frequencies between the random sample and patients: UCSNP-26, P=0.02; UCSNP-25, P=0.03; UCSNP-23, P=0.02; and UCSNP-22, P=0.01 (Table 1).These results, however, cannot be considered independent observations of association due to linkage disequilibrium among the four SNPs.We also observed significant differences in allele frequencies at UCSNP-29, -35, -37, -38 and -40 between patient and random samples.These results suggested there might be a diabetes-susceptibility gene in the vicinity of these SNPs, thus prompting us to examine this region in more detail.We therefore resequenced this region in ten diabetic Mexican American subjects to gain a better understanding of all of the genetic variation that was present and the relationship between each polymorphism and type 2 diabetes (Fig. 2; and Table B, see http://genetics.nature.com/supplementary_info/).", + "\t\n\nwww.nature.com/clinicalpractice/endmetPPARG (peroxisome proliferator-activated receptor gene; this encodes the target for thiazolidinediones) 11 and the Glu32Lys variant in KCNJ11 (which encodes part of another diabetes therapeutic target, this time for sulfonyl ureas) 4 are both common single-nucleotide polymorphisms (SNPs) that have been shown to influence risk of diabetes in multiple studies.Their effect sizes are modest (each extra copy of a susceptibility allele increases the risk of disease by about 15-20%), however, and their contribution to the observed familial aggregation of diabetes limited. ][14] The harvest of equivalent efforts in obesity has been even more limited.The only locus contributing to a respectable proportion of cases of severe adult obesity is the one that includes MC4R (melanocortin 4 receptor gene). 6The variants responsible are themselves rare, however, and have limited impact on variation in weight within the wider population. 5,6", + "\tConclusions\n\nIn this Review, we have summarized the available evidence on the role of polymorphisms in the genes encoding for insulin-signaling inhibitors molecules in determining genetic predisposition to T2D and related diseases.Overall, solid evidence seems to exist only for rs1044498 of the ENPP1 gene and for rs2295490 of the TRIB3 gene, whose association with T2D risk and insulin resistance, even if not confirmed (for ENPP1) [33] or not yet investigated (for TRIB3) [33] by GWAS studies, has been consistently reported by several original studies [16-20, 22-33, 38-43, 100, 101, 103, 104] and large meta-analyses [32,104].It is worth underlining that both rs1044498 and rs2295490 have been reported to be associated not only with defective insulin action in peripheral target tissues but also with impaired insulin secretion and decreased beta-cell homeostasis [14,15,101,103,104].These observations suggest that the two major pathogenic defects of T2D share common genetic causes and support the hypothesis that they should be seen as different aspects of the same process rather than as separate events [105].In addition, several studies have shown that the effect of rs1044498 and rs2295490 is more evident on early-onset T2D [26,28,104]; notably similar data have been obtained for rs1801278 of IRS1 gene [106]; these data hint to the possibility that focusing on early-onset cases may represent a successful strategy to study the contribution of insulin-signaling gene variants to T2D pathogenesis.Interestingly, a very recent study [107] has investigated the combined role of rs1044498 of the ENPP1 gene and for rs2295490 of the TRIB3 gene together with rs1801278 of IRS1 gene, on CVD, age at MI, and in vivo insulin sensitivity reporting a significant additive effect among the risk variants; notably the joint predictive power of ENPP1 rs1044498, IRS1 rs1801278, and TRIB3 rs 2295490 SNPs was even more evident among obese individuals [107].These results not only further reinforce the importance of rs1044498 and rs2295490 in determining the risk of insulin resistance and related diseases but further underlie that in any single individual the effect of each specific variant is also significantly influenced by the interaction with other variants as well as by environmental factors [108,109].Indeed T2D, CVD, IR, obesity, and related metabolic disorders are characterized by extremely heterogeneous phenotypes; thus some of the earlier positive findings reported in this Review that were not confirmed in subsequent, larger studies may have been \"real\" associations, even if limited to a specific subset of subjects in a definite environmental and genetic setting.In fact the extreme hetereogeneity of T2D and related diseases may represent one of the main reasons for the apparent discrepancy between the results of GWAS and those of classical \"candidate-gene\" studies, as the design of GWAS does not take into account several factors, including sexual dimorphism, age at disease onset, and obesity status, that have been shown to have an important role in the pathogenesis of metabolic diseases.In recent years, several methods for screening gene-environment interaction have been proposed [110] and their wider implementation is likely to shed further light on the genetics of metabolic diseases.Furthermore, novel technologies, such as next generation sequencing, that allow to address the role of relatively rare variants, will significantly contribute to obtain a clearer picture of the genetics basis of T2D and related diseases [111].Finally, the data on the genetics of insulin-signaling inhibitors molecules, recapitulated in this Review article, may supply useful elements to interpret the results of novel, more technically advanced, genetic studies; indeed it is becoming increasingly evident that genetic information on complex metabolic diseases should be interpreted taking into account the composite biological pathways underlying their pathogenesis [112].In addition, as suggested by recent studies on ENPP1 rs1044498 [35][36][37], a deeper knowledge of the genetic variants affecting the pathogenesis of T2D and related metabolic diseases may have important implications also for the implementation of tailored therapeutical approaches.\t\n\nA small Iranian study evaluated the specific contribution of seven polymorphisms found in the 2 Kb at the 3 extension of PTPN1 (plausibly, the promoter region) to the development of T2D [84].Only rs6126029A/C (g.-1023) showed nominal association with T2D, but this association was not confirmed after correction for established T2D risk factors [84].Functional analyses in HepG2 cell lines also showed that rs6126029A/C did not influence PTPN1 expression [84].The IVS5+3666del-/T SNP was only found in one study, and it was associated with morbid obesity in a French cohort, with no effects on T2D development or on glucose/insulin parameters [80].", + "\t\n\nTaken together, it seems therefore reasonable to believe that minor changes in a single enzyme or protein function due to a single nucleotide polymorphism are unlikely to generate defects in blood glucose and insulin concentrations across a population as a major clinical outcome.This is in contrast to insulin secretion where relatively minor effects due to gene polymorphism on b-cell viability, survival or function would, over time, have a measurable effect on the rate of insulin secretion from pancreatic islets, and present clinically as hyperglycemia.", + "\t\n\nWe recognize that our study has limitations as the limited size of the sample in the groups of study.The functional effect of the polymorphisms only was determined by informatics tools, so experimental designs are needed in order to corroborate this functional effect.In spite of these limitations, our study contributes to a new argument in which the 5UTR 44 C/G polymorphism may have a role as a risk factor for T2DM.", + "\tDiscussion\n\nThe main result of our study shows that, among lean individuals, carriers of polymorphism Gly972Arg of the IRS1 gene are at 3 times greater odds of having T2D, as compared with noncarriers.This association with T2D exists independently of potentially associated environmental factors like BMI, family history of diabetes, and sex.This observation suggests a possible relationship of polymorphism Gly972Arg in the pathogenesis of T2D.The other 3 tested SNPs on this gene were not associated with the presence of T2D.The SNP-SNP and SNP-environment interactions were not significant.\t\n\nBased on our previous observation suggesting a greater genetic predisposition among lean diabetics [20], in the present analysis, we aimed to evaluate the association of the Gly972Arg polymorphism and other polymorphic variants on the IRS1 gene with T2D in a representative sample of the Mexican population with body mass index (BMI) less than 25 kg/m 2 .", + "\t\n\nPrevious attempts to relate the Gly482Ser polymorphism to type 2 diabetes have shown an 1.34-fold increase in risk among Danish Caucasians [38] and a significant association among Japanese subjects [39].In contrast, the 482Ser allele did not predict diabetes in French Caucasians or Pima Indians [40,41].These studies were carried out in single populations.Because several different populations were used in the STOP-NIDDM trial, our data provides strong evidence that the Gly482Ser polymorphism of the PGC-1 gene contributes to the risk of type 2 diabetes.In agreement with this, the reduced expression of PGC-1 in adipose tissue has been associated with insulin resistance [51].Moreover, recent studies have reported that down-regulation of the PGC-1 gene and coordinated changes in other genes involved in oxidative phosphorylation in man are associated with IGT, diabetes mellitus [35] and insulin resistance [37].", + "\tCONCLUsION\n\nTo conclude, rs7903146 and rs680 polymorphisms were found independently to be significantly associated with T2DM risk in Indian adults.MDR identified the gene-gene interaction between TCF7L2 and SLC30A8 polymorphisms in confirming T2DM risk.Further studies should address the biological mechanisms affecting glucose homeostasis." + ], + [ + "\tDISCUSSION\n\nIn this study, we employed high throughput sequencing to identify differentially expressed miRNAs associated with IGT and untreated diabetes in whole blood of South African mixed ancestry women, which in an earlier study we had established a high prevalence of undiagnosed IGT and DM [18).We observed evidence for differential expression of 61 in IGT, 109 in screendetected diabetes both when compared to individuals with normal glucose tolerance, of which 25 were common in both conditions.Although several of these dysregulated miRNAs have been linked to diabetic and non-diabetic hyperglycaemia, we also uncovered 57 novel miRNAs.Of note is hsa-miR-novel-chr2_50989 which had the highest fold change in screen-detected DM and remained in the top ten differentially expressed miRNAs in IGT.Functional annotation of genes that are potentially regulated by the miRNAs implicated showed that signal transduction pathways (PI3K-Akt, MAPK, HIF-1, cAMP, FoxO, ErbB, Ras, Rap1 and insulin resistance); carbohydrate metabolism; glycan biosynthesis and metabolism, cell communication, cell growth and death; immune system; endocrine system and metabolic diseases are likely involved in the development of hyperglycaemia in this population.\t\n\nA number of miRNAs such as the let-7 family, 30ep-5p [26,31,32] found in this study and others have been shown to be involved in these pathways.These miRNAs have be reported to exert their function by suppressing the expression of insulin receptor genes [17,32].Although many similarities were found between this study and others, our study is unique for uncovering that some of these miRNAs were differentially expressed between diabetic and non-diabetic dysglycaemia.Indeed, using OGTT to characterise asymptomatic participants, we identified three miRNAs that potentially distinguish between diabetic and non-diabetic hyperglycaemia.For example, miR-126-3p, and miR-28-3p were upregulated in IGT when compared to screen-detected DM, whilst miR-486-5p was down-regulated in screen-detected DM in comparison to either IGT or NGT.miR-126 is expressed by cells that modulate inflammatory response and vascular homeostasis through enhanced production of anti-inflammatory chemokines, and has been shown to be reduced in T2DM [33][34][35][36][37].The downgrelation of miR-126 has been shown to be mostly pronounced in poorly controlled T2DM and in T2DM with complications when compared to sujects with T2DM without complication [38].Similarly, in a study that investigated miR-126 in serum of DM patients with varying degrees of retinopathy, miR-126 was reduced in patients versus the controls, but lowest in patients with proliferative diabetic retinopathy [39].Taken together, our findings of upregulated miR-126 and others in IGT versus screen-detected DM most probably point towards a cascading reduction with respect to diabetes related complications suggesting a potential role for miR-126 in distinguishing prediabetes from diabetes.Indeed, Liu et al [40]), examined the usefulness of miR-126 in predicting prediabetes and T2DM and reported lower levels in T2DM compared to prediabetes, even though both were significanlty lower than in healthy controls.It is important to note that a number of miRNAs including novel ones with potential to distinguish between hyperglycaemia and normal glucose tolerance were uncovered in the current study.For example, miR-hsa-miR-1299 had the highest fold change in IGT versus controls and was not detected in individuals with DM, whilst mir-novel-chr2_55842 was amongst the 10 th most differentially expressed in IGT only.In hepato-hepatocellular carcinoma, miR-1299 inhibits cell proliferation by targeting cyclin-dependent kinase 6, [41] however there is limited information about miR-1299 in diabetes.Therefore, further studies are needed to elucidate the molecular mechanisms of miR-1299 and other novel miRNAs identified in this study.\t\n\nSome of the dysregulated miRNAs found in our study corroborate findings of many other studies that have aimed to characterize miRNAs in different tissue types of individuals with DM and/or prediabetes.A recent systematic study of dysregulated miRNAs in T2DM identified a total of 158 dysregulated miRNAs in adipose, islet, skeletal muscle, whole blood, PBMC, plasma and serum [26].Similarly we found 36 (23%) of these miRNAs dysregulated in T2DM and IGT (Supplementary Table 4).Furthermore, three additional miRNAs (miR-27b, miR-98, and miR-21) previously reported to be dysregulated in mixed ethnic ancestry women with IGT or T2DM [27] were also differentially expressed in screen-detected DM in our sample.The miRNAs found in the current study and others have been shown to play a direct role in insulin production and secretion [21][22][23][24][25]28].This was confirmed by bioinformatics techniques we applied to identify the potential biological functions affected by the miRNA signatures.p53 signaling, PI3K/ Akt, p53 signaling and MAPK were respectively the 2 nd , 3 rd and 6 th targeted significant pathways in enrichment analysis by KEGG.The PI3K/Akt/ and MAPK pathways plays a major signaling role in the cellular response to extracellular stimuli, including glucose homeostasis, cell proliferation and survival [29].In glucose homeostasis, the activation of these pathways is directly under the control of insulin receptors upon insulin stimulation [30].\t\nEarly identification of individuals with elevated risk of developing diabetes mellitus, followed by the implementation of effective prevention interventions can delay the onset of the disease and related complications.In this regard, recent studies have shown that miRNAs are useful as early markers of certain disease types, including diabetes.We used high throughput sequencing to assess miRNA expression profiles from whole blood of 12 individuals with screen-detected diabetes, 12 with prediabetes and 12 with normal glucose tolerance, matched for age, blood pressure, smoking and body mass index.We identified a total of 261 (57 novel) differentially expressed miRNA profiles between the study groups.Comparison of the miRNA expression profiles between prediabetess and diabetes revealed 25 common miRNA, but highlighted some interesting differences.For instance, three miRNAs (miR-126-3p, miR-28-3p miR-486-5p) were dysregulated in prediabetes compared to screen-detected diabetes.Target gene analysis showed thousands of potential genes and KEGG pathway analysis revealed 107 significant pathways of which some are involved signal transduction, cell-cell communications, cell growth and death, immune response, endocrine system and metabolic diseases.This first detailed African study has shown both known and novel differentially expressed miRNAs in relation to glucose tolerance.\t\n\nEarly identification of individuals with elevated risk of developing diabetes mellitus, followed by the implementation of effective prevention interventions can delay the onset of the disease and related complications.In this regard, recent studies have shown that miRNAs are useful as early markers of certain disease types, including diabetes.We used high throughput sequencing to assess miRNA expression profiles from whole blood of 12 individuals with screen-detected diabetes, 12 with prediabetes and 12 with normal glucose tolerance, matched for age, blood pressure, smoking and body mass index.We identified a total of 261 (57 novel) differentially expressed miRNA profiles between the study groups.Comparison of the miRNA expression profiles between prediabetess and diabetes revealed 25 common miRNA, but highlighted some interesting differences.For instance, three miRNAs (miR-126-3p, miR-28-3p miR-486-5p) were dysregulated in prediabetes compared to screen-detected diabetes.Target gene analysis showed thousands of potential genes and KEGG pathway analysis revealed 107 significant pathways of which some are involved signal transduction, cell-cell communications, cell growth and death, immune response, endocrine system and metabolic diseases.This first detailed African study has shown both known and novel differentially expressed miRNAs in relation to glucose tolerance.\t\n\nOverall, in addition to complementing earlier studies on miRNAs in prediabetes and diabetes, our findings provide evidence of known and novel differentially expressed miRNAs in African mixed ancestry individuals with IGT and screen-detected DM.We further observed that the aberrant expression profiles of miRNAs were linked to several biological processes, such as signal transduction, cell-cell communications, cell growth and death, immune response, endocrine system and metabolic diseases.Larger prospective studies in this and other racial populations from Africa are needed to characterize the molecular mechanisms of African-specific differentially expressed miRNAs, as well as assess their potential to predict worsening of glucose tolerance status.\t\n\nDespite the growing evidence of the important role and potential diagnostic value of miRNAs in dysglycaemia, such properties are yet to be demonstrated in the African setting.Therefore, in the present study we aimed to identify dysregulated miRNA in a South African mixed ancestry population previously reported to be at high risk of diabetes [18].To avoid potential bias from treatment induced alterations in miRNA expression, we focused on individuals with normal glucose tolerance (NGT), prediabetes individuals with IGT only and those with screen-detected diabetes who had not initiated glucose lowering drug treatment.", + "\t\n\nSome recently-identified miRNAs have been associated with insulin secretion, insulin resistance, and inflammation, and differences have emerged in some circulating miRNA levels between individuals with and without type 2 diabetes (40).Zhao and others (41) examined some miRNAs in pregnant women at 16-19 weeks of gestation (WG), finding a significantly lower expression of 3 miRNAs (miR-29a, miR-132 and miR222) in women who went on to develop GDM at 24-28 WG than in those who did not develop GDM.MiR-29 plays a part in glucose homeostasis: its overexpression inhibits insulinstimulated glucose uptake and downregulates gluconeogenesis (42).MiR-132 targets the insulin-mediated regulation of cytochrome P450 (which is involved in hepatic metabolism), and it has a role in trophoblast expansion (its reduced expression impairs normal trophoblast development) (42,43).MiR-222 is involved in regulating the cell cycle (controlling the cyclindependent kinase inhibitor).", + "\t\n\nUpon further epigenetic regulatory elements in diabetes, micro-RNAs, such as miR-15a and miR-29b, were found to be downregulated in type 2 diabetes, whereas miR-27a and miR-320a were upregulated and might open the possibility for new diagnostic markers [187, [231][232][233].", + "\t\n\nIn addition to predicting targets of the differentially expressed miRNAs in T2DMED based on a literature review, IGF-1, as one of the target genes of miR-18a or miR-206, was confirmed via luciferase assay.T2DMED rats with downregulation of IGF-1 in their CCs have been reported (El-Sakka et al. 1999).In experiments with human diabetic erectile tissue, researchers also found a decreased expression of IGF-1, which was mainly located in the layers of smooth muscle cells (Castela et al. 2012).In this study, we also verified this reduction via ELISA.IGF-1 is essential to the regeneration of NOS-containing nerve fibres in the dorsal and intracavernosal nerves (Jung et al. 1999).Intervention of IGF-1 expression in the penis could ameliorate ED in T2DMED rats (Pu et al. 2007).Thus, miR-18a and/or miR-206 suppression of IGF-1 may be an interesting research direction for T2DMED.\t\n\nThe genes regulated by the four miRNAs relate to several KEGG pathways which might be involved in the mechanisms of T2DMED\t\n\nexpression of miR-18a, miR-206, miR-122, and miR-133 were confirmed by qRT-PCR (p < 0.05 and FDR <5 %).According to bioinformatic analysis, the four miRNAs were speculated to play potential roles in the mechanisms of T2DMED via regulating 28 different genes and several pathways, including apoptosis, fibrosis, eNOS/cGMP/ PKG, and vascular smooth muscle contraction processes, which mainly focused on influencing the functions of the endothelium and smooth muscle in the CC.IGF-1, as one of the target genes, was verified to decrease in the CCs of T2DMED animals via ELISA and was confirmed as the target of miR-18a or miR-206 via luciferase assay.Finally, these four miRNAs deserve further confirmation as biomarkers of T2DMED in larger studies.Additionally, miR-18a and/or miR-206 may provide new preventive/therapeutic targets for ED management by targeting IGF-1.\t\nexpression of miR-18a, miR-206, miR-122, and miR-133 were confirmed by qRT-PCR (p < 0.05 and FDR <5 %).According to bioinformatic analysis, the four miRNAs were speculated to play potential roles in the mechanisms of T2DMED via regulating 28 different genes and several pathways, including apoptosis, fibrosis, eNOS/cGMP/ PKG, and vascular smooth muscle contraction processes, which mainly focused on influencing the functions of the endothelium and smooth muscle in the CC.IGF-1, as one of the target genes, was verified to decrease in the CCs of T2DMED animals via ELISA and was confirmed as the target of miR-18a or miR-206 via luciferase assay.Finally, these four miRNAs deserve further confirmation as biomarkers of T2DMED in larger studies.Additionally, miR-18a and/or miR-206 may provide new preventive/therapeutic targets for ED management by targeting IGF-1.\t\n\nIn conclusion, for the first time, we reported the differentially expressed miRNAs in a classical murine model of T2DMED.Four differentially expressed miRNAs (miR-18a, miR-206, miR-122 and miR-133) were confirmed by qRT-PCR and are speculated to play crucial roles in influencing the functions of the endothelium and smooth muscle via regulating 28 different genes and several pathways, including apoptosis, fibrosis, eNOS/cGMP/PKG, and vascular smooth muscle contraction processes.IGF-1, as one of the target genes, was verified to decrease in the CCs of T2DMED animals and was confirmed as the target of miR-18a or miR-206 via luciferase assay.These four miRNAs deserve further confirmation as biomarkers of T2DMED in larger studies and may provide new perspectives for understanding the molecular aetiology of T2DMED in the future.Particularly, miR-18a and/or miR-206 may provide new preventive/therapeutic targets for ED management by targeting IGF-1.", + "\t\n\nRecent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell.MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis.We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility.We used Illumina small RNA sequencing to profile the miRNA fraction in three preparations each of primary human islets and of enriched beta-cells generated by fluorescenceactivated cell sorting.In total, 366 miRNAs were found to be expressed (i.e..100cumulative reads) in islets and 346 in betacells; of the total of 384 unique miRNAs, 328 were shared.A comparison of the islet-cell miRNA profile with those of 15 other human tissues identified 40 miRNAs predominantly expressed (i.e..50% of all reads seen across the tissues) in islets.Several highly-expressed islet miRNAs, such as miR-375, have established roles in the regulation of islet function, but others (e.g.miR-27b-3p, miR-192-5p) have not previously been described in the context of islet biology.As a first step towards exploring the role of islet-expressed miRNAs and their predicted mRNA targets in T2D pathogenesis, we looked at published T2D association signals across these sites.We found evidence that predicted mRNA targets of islet-expressed miRNAs were globally enriched for signals of T2D association (p-values ,0.01, q-values ,0.1).At six loci with genome-wide evidence for T2D association (AP3S2, KCNK16, NOTCH2, SCL30A8, VPS26A, and WFS1) predicted mRNA target sites for islet-expressed miRNAs overlapped potentially causal variants.In conclusion, we have described the miRNA profile of human islets and beta-cells and provide evidence linking islet miRNAs to T2D pathogenesis.\t\nRecent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell.MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis.We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility.We used Illumina small RNA sequencing to profile the miRNA fraction in three preparations each of primary human islets and of enriched beta-cells generated by fluorescenceactivated cell sorting.In total, 366 miRNAs were found to be expressed (i.e..100cumulative reads) in islets and 346 in betacells; of the total of 384 unique miRNAs, 328 were shared.A comparison of the islet-cell miRNA profile with those of 15 other human tissues identified 40 miRNAs predominantly expressed (i.e..50% of all reads seen across the tissues) in islets.Several highly-expressed islet miRNAs, such as miR-375, have established roles in the regulation of islet function, but others (e.g.miR-27b-3p, miR-192-5p) have not previously been described in the context of islet biology.As a first step towards exploring the role of islet-expressed miRNAs and their predicted mRNA targets in T2D pathogenesis, we looked at published T2D association signals across these sites.We found evidence that predicted mRNA targets of islet-expressed miRNAs were globally enriched for signals of T2D association (p-values ,0.01, q-values ,0.1).At six loci with genome-wide evidence for T2D association (AP3S2, KCNK16, NOTCH2, SCL30A8, VPS26A, and WFS1) predicted mRNA target sites for islet-expressed miRNAs overlapped potentially causal variants.In conclusion, we have described the miRNA profile of human islets and beta-cells and provide evidence linking islet miRNAs to T2D pathogenesis.", + "\t\n\nFigure 4. Candidate miRNA regulatory hubs in a type 2 diabetes gene network. (A) Each data point represents a 59-reference miRNA or a 59-shifted isomiR from primary human beta cells, and the y-axis shows the negative Log2 of the p-value of the predicted miRNA targeting score among genes in a type 2 diabetes (T2D) network.The dashed red line denotes the significance threshold (empirical P = 0.05). (B) Effects of miR-29 mimic and inhibitor in MIN6 cells on the mRNA levels of four T2D genes are shown.The x-axis lists the gene symbols for each of four predicted miR-29 target genes and the y-axis depicts the relative quantitative value (RQV; expression determined by RT-qPCR and normalized to Rps9) in response to the miR-29 mimic (blue) or the miR-29 inhibitor (red) relative to mock transfection.The data shown represent at least two independent experiments, each conducted in triplicate.P-values were calculated based on Student's t-tests. *, P,0.05; **, P,0.01.doi:10.1371/journal.pone.0073240.g004\tCandidate 59-shifted isomiR Regulatory Hubs in Type 2 Diabetes\n\nGenome-wide association studies for type 2 diabetes (T2D) have primarily (though not exclusively) implicated genes with critical function in the pancreatic beta cell [45,46].Therefore, we sought to determine if any of the highly expressed human beta cell miRNAs, including 59-shifted isomiRs, serve as regulatory hubs in T2D.We first assembled a list of genes (n = 92) implicated in T2D and related conditions including maturing onset diabetes of the young (MODY) (Methods).We then implemented a Monte Carlo simulation strategy (Methods) to determine for each miRNA whether the predicted regulatory impact on T2D genes is significantly (uncorrected P,0.05) greater than expected by chance (such miRNAs are termed ''candidate regulatory hubs'').We identified 10 candidate miRNA regulatory hubs (Fig. 4A; Table S3 in File S2).The top two were the 59-reference miRNAs miR-29 and let-7, both of which have been implicated in beta cell function and glucose homeostasis [47][48][49].Though miR-29 has been shown to regulate glucose-stimulated insulin secretion, its target genes in the beta cell are largely unknown.To validate the in silico approach, we selected several predicted targets (Camk1d, Glis3, and Jazf1), and one previously validated target (Slc16a1 [48]), of miR-29 from among the T2D gene list for evaluation in MIN6 cells.Specifically, we transiently transfected MIN6 cells with a miR-29 mimic or inhibitor (antagomiR) and measured the mRNA levels of each of the four genes by real-time quantitative PCR (RT-qPCR).Three of the four genes were significantly (p,0.05) down regulated by the over-expression of miR-29 and three genes were significantly (p,0.05) up regulated by the antagomiR-mediated inhibition of miR-29 (Fig. 4B).These findings are consistent with previous reports that miR-29 is involved in the regulation of beta cell function [48,50], and they serve as a validation of the in silico regulatory hub analysis.", + "\t\n\nFigure 2. miRNA expression profile changes in T2D compared with control subjects using the Exiqon chip platform and TaqMan confirmation (FDR <10%). (a) Data are plotted to show the pattern of change of these significantly up-/down-regulated miRNA.Black lines represent those miRNA that increase/decrease progressively with IGT and T2D (DM), green lines represent miRNAs that are increased/decreased with IGT and then revert with T2D, while orange lines show miRNAs increased/decreased only in the T2D state. (b) miRNAs that show the expression profile during myocyte differentiation (cell data derived from Chen et al. [55]) is the opposite pattern to that observed in the muscle of patients with T2D (green = down-regulated probe sets, red = up-regulated probe sets; the color range is from -3-fold to +3-fold change).MG refers to the data produced by Chen et al. during myogenesis. (c) Expression level of miR-1, miR-133a, miR-133b and miR-206 in muscle biopsies from healthy individuals (NGT, n = 10, white bars), individuals with impaired glucose tolerance (IGT, n = 10, grey bars) and individuals with type 2 diabetes (T2D, n = 10, black bars).miR-133a (P < 0.001) and miR-206 (P = 0.04) were significantly reduced in T2D patients when compared with expression levels in healthy controls.Data are expressed as fold change from NGT and shown as mean standard error. **P < 0.001, *P < 0.05. (d) Expression level of miR-133a in muscle versus indices of glucose homeostasis in subjects with and without T2D.Expression of miR-133a is positively correlated with fasting glucose, R 2 = 0.41 (P < 0.001, n = 30).Data are shown as Ct levels normalized to RNU48 and plotted versus fasting glucose levels (mmol/L).", + "\t\n\nT2D loci were also identified at clusters of noncoding RNAs with roles in islet cell function.One locus includes a set of microRNAs specifically expressed in islet cells, the maternally expressed noncoding RNA MEG3, and the paternally expressed gene DLK1.Targets of these microRNAs increase cell apoptosis 40 , and reduced Meg3 expression impairs insulin secretion 41 .DLK1 inhibits adipocyte differentiation, thereby protecting against obesity 3 , and promotes pancreatic ductal cell differentiation into cells, increasing insulin secretion 42,43 .Other variants near MEG3 have been associated with type 1 diabetes 44 (EAS and EUR LD r 2 = 0 with EAS lead variant).The other noncoding RNA locus is the MIR17HG cluster of miRNAs, which regulate glucose-stimulated insulin secretion and pancreatic cell proliferation stress 45 ; one of these microRNAs, miR-19a, affects hepatic gluconeogenesis 46 .Yet another T2D locus is located near TRAF3, which is a direct target of the MIR17HG microRNA cluster and promotes hyperglycaemia by increasing hepatic glucose production 47,48 .The T2D association results suggest that these noncoding RNAs influence disease susceptibility." + ], + [ + "\tConclusion\n\nIn our sequencing study involving 6888 individuals, 2.2% of individuals with early onset diabetes and 0.7% of individuals with late onset diabetes harbored a likely pathogenic mutation in monogenic diabetes genes.Our results confirm previous reports that MODY is under-diagnosed [19,75], particularly in individuals presenting with early onset diabetes and clinically labeled as T2D and, in such cases, genetic testing can provide an etiological diagnosis.With the continuing reduction in costs of DNA sequencing, genetic screening of all known monogenic diabetes genes in individuals with early onset diabetes should be routinely considered since it can identify individuals with undiagnosed MODY as well as atypical forms of monogenic diabetes.Knowledge of mutations in monogenic diabetes genes has the potential to influence diagnosis and therapy for individuals with diabetes as well as to enable the genetic testing of relatives.", + "\tConclusions\n\nGenomics research in monogenic diabetes and the implementation of NGS-based approaches for precision diagnosis of MODY subtypes undoubtedly move the physicians and patients towards the era of precision genomic medicine that takes into account the individual genetic data.Specific issues are emerging such as the right estimate of variant pathogenicity and age-dependent penetrance, the multi-genic causality, and the composite phenotypes.Lessons learned from MD with recent findings in common T2D genetic architecture support a continuum of diabetes phenotypes from rare monogenic to common adult-onset diabetes which impacts the strategies for both diagnosis and longitudinal investigation of diverse clinical subtypes along the life course.Beyond facing youngonset diabetes, practitioners should systematically promote a comprehensive genetic testing of MD-MODY subtypes, with benefits of optimal patient care and of strong reduction of global medical costs.\t\nPurpose of Review Non-autoimmune monogenic diabetes (MD) in young people shows a broad spectrum of clinical presentations, which is largely explained by multiple genetic etiologies.This review discusses how the application of state-of-the-art genomics research to precision diagnosis of MD, particularly the various subtypes of maturity-onset diabetes of the young (MODY), has increasingly informed diabetes precision medicine and patient care throughout life.Recent Findings Due to extended genetic and clinical heterogeneity of MODY, diagnosis approaches based on next-generation sequencing have been worthwhile to better ascribe a specific subtype to each patient with young-onset diabetes.This guides the best appropriate treatment and clinical follow-up.Summary Early etiological diagnosis of MD and individualized treatment are essential for achieving metabolic targets and avoiding long-term diabetes complications, as well as for drastically decreasing the financial and societal burden of diabetesrelated healthcare.Genomic medicine-based practices help to optimize long-term clinical follow-up and patient care management.\t\n\nPurpose of Review Non-autoimmune monogenic diabetes (MD) in young people shows a broad spectrum of clinical presentations, which is largely explained by multiple genetic etiologies.This review discusses how the application of state-of-the-art genomics research to precision diagnosis of MD, particularly the various subtypes of maturity-onset diabetes of the young (MODY), has increasingly informed diabetes precision medicine and patient care throughout life.Recent Findings Due to extended genetic and clinical heterogeneity of MODY, diagnosis approaches based on next-generation sequencing have been worthwhile to better ascribe a specific subtype to each patient with young-onset diabetes.This guides the best appropriate treatment and clinical follow-up.Summary Early etiological diagnosis of MD and individualized treatment are essential for achieving metabolic targets and avoiding long-term diabetes complications, as well as for drastically decreasing the financial and societal burden of diabetesrelated healthcare.Genomic medicine-based practices help to optimize long-term clinical follow-up and patient care management.\tIntroduction\n\nMaturity-onset diabetes of the young (MODY), a dominantly inherited familial form of diabetes typically diagnosed before 25 years of age in non-obese subjects, represents the most frequent subgroup of early-onset non-autoimmune diabetes [1,2].MODY is a monogenic disease but with a high clinical and genetic heterogeneity, although always caused by a primary inherited or de novo genetically induced defect in insulin secretion responsible for chronic hyperglycemia.This pathophysiological feature common to all MODY cases arises from a functional impairment of one of the diverse pancreatic -cell expressed key regulators of insulin biosynthesis and secretion [2,3].More than fifteen MODY genetic subtypes have been characterized raising the issue of an accurate etiological genetic diagnosis at an early age enabling a genuine personalized medicine of diabetes.MODY patients are usually diagnosed under the age of 25-30 years, but overt diabetes or moderate chronic hyperglycemia can happen at any age from childhood to young adulthood or at later age.The broad range of phenotypic features and variability in the clinical presentations are largely dependent on the underlying genetic defect that actually determines both pathophysiology and long-term progression of diabetes.\t\n\nIn this review, we highlight the recent advances in the field of genomics of monogenic diabetes (MD) with the current challenges of accurately defining and recognizing the various MODY subtypes and of translating molecular diagnosis into personalized care over the lifetime.\t\n\nThe known genetic causes of MODY have pointed out major pancreatic -cell expressed genes regulating insulin secretion, such as alterations in GCK and a network of transcription factors important for the control of -cell function.Recent works have further provided new clues for better understanding specific functional mechanisms related to MODY genetic defects.\tA Global View on MODY Genetics\n\nClinical Heterogeneity and Genetic Subtypes of MODY More than 25 years of comprehensive investigation of MODY genetic components, through the study of patient cohorts and multiplex families, have provided great advances in the knowledge and functional characterization of major MODY genes with mostly various protein-coding changes.So far, at least 15 genes causing MODY, involving different mutation types, have been formally identified (details on these genes are given in Table 1).In these genes, a single, mostly highly penetrant, rare mutation is sufficient to cause a MODY phenotype.The major MODY genes encode pancreatic -cell expressed proteins involved in developmental processes, in the maturation and maintenance of cell function (through transcription factors regulating the transcriptional network of pancreatic -cells), in the control of -cell glucose sensing (through the glucokinase enzyme), in -cell signaling, and in insulin production and secretion [2].From our current knowledge of the underlying pathogenic mechanisms, it is well substantiated that MODY-causing mutations cluster into key genes and interconnected biological pathways that represent core regulatory networks for pancreatic -cell identity and function (as for -cell transcriptional network, or regulatory proteins of reticulum endoplasmic homeostasis) [14].Along the same line, -cell dysfunction is the main driver of MODY, together with decreased -cell mass and cellular death.", + "\tU N C O R R E C T E D A C C E P T E D A R T I C L E BACKGROUND\n\nMaturity-onset diabetes of the young (MODY) is a monogenic form of diabetes mellitus characterised by autosomal dominant inheritance, a young age of onset (often diagnosed before 25 years of age) and pancreatic -cell dysfunction (MODY; MIM# 606391) (Fajans and Bell, 2011;Hattersley, 1998;Molven and Njolstad, 2011;Tattersall, 1974).Heterozygous mutations in the genes encoding the glycolytic enzyme glucokinase (Froguel, et al., 1992;Hattersley, et al., 1992) and the transcription factors, hepatocyte nuclear factor (HNF)-1 alpha (HNF1A; MIM# 142410) (HNF1A MODY, formerly MODY3) (Yamagata, et al., 1996a), HNF-4 alpha (HNF4A; MIM# 600281) (HNF4A MODY, formerly MODY1) (Yamagata, et al., 1996b) and HNF1B (formerly MODY5) (Horikawa, et al., 1997) have been shown to cause MODY.A distinct clinical phenotype is associated with each genetic aetiology (Edghill, et al., 2006;Stride and Hattersley, 2002).Mutations in the genes pancreatic and duodenal homeobox 1 (PDX1) (Stoffers, et al., 1997), NEUROD1 (Malecki, et al., 1999), CEL (Torsvik, et al., 2010), KCNJ11 (Yorifuji, et al., 2005) INS (Edghill, et al., 2008), and ABCC8 (Bowman, et al., 2012) are rare causes of autosomal dominant diabetes.Other potential forms of MODY include mutations in the transcription factor genes KLF11 (Neve, et al., 2005), PAX4 (Plengvidhya, et al., 2007) and BLK (Borowiec, et al., 2009), but the identification of additional families showing co-segregation of mutations with diabetes is required to confirm these as \"MODY genes\".", + "\tIntroduction\n\nMaturity onset diabetes of the young (MODY) is the most common monogenic subtype of diabetes that is characterized by an early-onset of diabetes, no requirement for insulin at diagnosis, and no signs of autoimmunity or insulin resistance [1] .MODY is inherited in an autosomal dominant manner.It is a clinically heterogeneous group of disorders caused by -cell dysfunction.It is estimated that MODY accounts for up to 1.8% of patients with diabetes [2] .Mutations in 13 genes are known to cause MODY; the most prevalent are HNF1A , GCK and HNF4A [3,4] .The MODY subtypes differ in age of onset of diabetes, the pattern of hyperglycemia, response to treatment, and associated extrapancreatic manifestations [5] .As compared to type 2 diabetes, the clinical symptoms present often at a relatively young age in patients without overweight, who have a positive family history.As compared to type 1 diabetes, progression may be less severe, and the required dosage of insulin low.", + "\tCANDIDATE GENES IDENTIFIED IN HUMAN AND RODENT MODELS OF T2D\n\nMaturity onset diabetes of the young Foremost among the monogenic forms of T2D is MODY.The early age of onset and autosomal dominant pattern of inheritance have facilitated gene identication in the majority of MODY families through classical Mendelian positional cloning approaches, as described in Chapter 4.", + "\tMaturity Onset Diabetes of the Young (MODY)\n\nIn 1960, Fajans & Conn (50) first described maturity-onset diabetes of the young (MODY).MODY is characterized clinically by autosomal dominant transmission, early onset (usually before the age of 25 years), the correction of fasting hyperglycemia without insulin for at least two years following diagnosis, and nonketotic disease (49).Thus, the main distinguishing features compared to more typical cases of T2D include a strong family history (typically spanning several generations), a younger age of onset, and the absence of obesity.The familial pattern of inheritance and lack of requirement for insulin therapy to prevent ketosis distinguish MODY clinically from T1D. MODY usually presents as asymptomatic hyperglycemia in young adults and often has a mild course.Some patients, however, progress rapidly and require insulin therapy, and microvascular and renal complications can ensue.", + "\t\n\n1 Genetic causes of maturity-onset diabetes of the young A BLK, PAX4 and KLF11, although classified as MODY genes (#11, #9, and #7 respectively) in OMIM, are not listed as MODY-causing because of recently disputed or refuted gene-disease relationships (see section \"Rare types of MODY\").APPL1 was proposed as MODY14 based on two families reported in 201533, but evidence is otherwise limited.RFX6 does not have a MODY number in OMIM, but is included here as multiple loss-of-function variants were recently implicated in a phenotype very similar to that of other MODY genes but with lower penetrance 11 .OHA: Oral Hypoglycemia Agents", + "\tMaturity\n\n-onset diabetes of the young (MODY) is a heterogeneous single gene disorder characterized by non-insulin-dependent diabetes, an early onset and autosomal dominant inheritance.Mutations in six genes have been shown to cause MODY.Approximately 15-20% of families fitting MODY criteria do not have mutations in any of the known genes.These families provide a rich resource for the identification of new MODY genes.This will potentially enable further dissection of clinical heterogeneity and bring new insights into mechanisms of -cell dysfunction.To facilitate the identification of novel MODY loci, we combined the results from three genome-wide scans on a total of 23 families fitting MODY criteria.We used both a strict parametric model of inheritance with heterogeneity and a model-free analysis.We did not identify any single novel locus but provided putative evidence for linkage to chromosomes 6 (nonparametric linkage [NPL]score 2.12 at 71 cM) and 10 (NPL score 1.88 at 169 -175 cM), and to chromosomes 3 (heterogeneity LOD [HLOD] score 1.27 at 124 cM) and 5 (HLOD score 1.22 at 175 cM) in 14 more strictly defined families.Our results provide evidence for further heterogeneity in MODY.Diabetes 52:872-881, 2003 M aturity-onset diabetes of the young (MODY) is characterized by -cell dysfunction, no requirement for insulin in the first years of the disease, an autosomal dominant mode of inheritance, and an early age at onset of diabetes (25 years) ( 1).The identification of MODY genes has helped explain the phenotypic heterogeneity associated with the disorder.MODY is a genetically diverse subgroup of diabetes, and to date six distinct MODY genes have been identified: these encode the glycolytic enzyme glucokinase (GCK) (2,3), hepatocyte nuclear factor (HNF)-1 (4), HNF-1 (5), HNF-4 (6), insulin promoter factor (IPF)-1 (7), and NeuroD1/BETA2 (8).The relative distribution of MODY1-6 depends on the population investigated, although in all studies mutations in GCK and HNF1 are the two most prevalent forms (9 -11).Mutations in each gene result in distinct clinical and physiological characteristics (12).Glucokinase mutations present with stable mild fasting hyperglycemia throughout life as a result of reduced glucose sensing in the -cell (13).In contrast, mutations in the transcription factors (HNF-1, HNF-4, HNF-1, and IPF-1) cause a progressive -cell failure that may become severe (14).", + "\tTypes of monogenic diabetes\n\nMaturity-onset diabetes of the young MODY comprises most monogenic diabetes cases, with classical characteristics of young diagnosis age, family history of diabetes in an autosomal dominant pattern of transmission, and insulin independence, with some types having additional features (Table 1).While 14 genes have now been designated as MODY genes in OMIM and/or the literature, three of these (BLK, PAX4, and KLF11) have been proposed for elimination based on a recent study (10) (see Table 1 for the remaining 11 along with RFX6, recently proposed as an additional MODY gene; ref . 11).Variants in GCK, HNF1A, and HNF4A are responsible for most MODY cases, followed by HNF1B (12).Given the known genetic etiology of (42), but evidence is otherwise limited.RFX6 does not have a MODY number in OMIM, but is included here as multiple loss-of-function variants were recently implicated in a phenotype very similar to that of other MODY genes but with lower penetrance (11).OHA, oral hypoglycemia agents.", + "\t\n\nThere is now clear evidence of a strong genetic component to the disease due to prevalence differences between racial groups, a higher concordance rate among monozygotic than dizygotic twins and a sibling risk ratio of approximately 3.5 [119].Maturity-onset diabetes of the young (MODY) is the autosomal dominantly inherited form of diabetes without insulin dependency, characterized by -cell dysfunction and is diagnosed at a relatively young age (<25 years) [120,121].MODY is made up of subtypes defined on the basis of genetic etiology.These genetic subtypes have aided the identification of patients who will respond to a given therapy from those who are unlikely to respond.As such, this opens the possibility of tailored drug therapy both at the individual level for MODY and for the general treatment of T1D and T2D as a whole.Identifying further forms of this monogenic diabetes will provide crucial insights into patterns of -cell dysfunction and the associated therapeutic response.Of the seven MODY genes identified to date, the most common forms present as a consequence of mutations in the genes encoding the glycolytic enzyme, glucokinase, and the transcription factor, hepatic nuclear factor-1 (HNF1) [122,123].", + "\t\n\nMaturity-onset diabetes of the young (MODY) is a rare, autosomal dominant form of diabetes.There are six primary forms of MODY, each a consequence of mutations in six different genes [37].In addition to the autosomal dominant inheritance, MODY is characterized by onset before the age of 25 and -cell dysfunction typically in the absence of insulin resistance or obesity.MODY3 arises from mutations in the hepatocyte nuclear factor 1 homeobox A gene (HNF1A), and patients with this disease are hyper-sensitive to the hypoglycemic effects of sulfonylureas [38].In an early case study, Pearson et al. [39] identified three MODY3 patients with HNF1A mutations, in whom cessation and reintroduction of sulfonylureas caused dramatic changes in HbA1c levels, or severe hypoglycemia, in response to introduction of sulfonylureas into the treatment regimen.A subsequent study found that MODY3 patients had a 5.2-fold or 3.9-fold greater response to gliclazide compared to metformin or patients with T2D, respectively [40].These patients also had a stronger insulin secretory response to tolbutamide and were more insulin-sensitive compared to individuals with common T2D [40].", + "\t\n\nBoth genetic susceptibility and environmental drivers, notably obesity and sedentary lifestyles, determine the overall risk of T2D (4)(5)(6).Supporting a genetic component, rare monogenic forms of the disease exist with Mendelian inheritance (7,8).Thus, maturity onset of diabetes of the young (MODY) is a rare form of diabetes with mutations often residing in exons encoding the functional domains of transcription factors such as hepatocyte nuclear factor hepatocyte nuclear factor 1 homeobox A (HNF1A) (9) and HNF4A (10), or of proteins involved in b cell glucose metabolism such as glucokinase (GCK) (11) (Table 1).", + "\tIntroduction\n\nThe maturity onset diabetes of the young (MODY) is a monogenic form of diabetes characterized by an autosomal dominant inheritance; the onset usually happens before the 25 years of age and is characterized by an impaired insulin secretion with minimal or no defect of the insulin action (Fajans and Bell 2001).Some studies suggest that 1-2% of patients with type 2 diabetes (T2D) may in fact have MODY (Shields et al. 2010).Data available suggest that people carrying one mutated allele are born with completely normal physiological and biochemical functions of the pancreatic b-cells, and diabetes will occur at some stage during adolescence (Bell and Polonsky 2001;Fajans and Bell 2001).Penetrance of diabetes in patients with mutations in MODY is quite high (more than 95% by the age of 55 years) (Frayling et al. 2001;Murphy et al. 2008).Recent studies have demonstrated heterozygous mutations in genes encoding 11 forms of MODY, including the hepatocyte nuclear factor-4a encoding the gene (HNF4A)(MODY 1), the glucokinase gene or GCK (MODY 2), the hepatocyte nuclear factor-1a that encodes HNF1A (MODY 3), the pancreas/duodenum homeobox protein 1 (PDX1, also known as IPF-1) (MODY 4), the hepatocyte nuclear factor-1b encoding the gene HNF1B (MODY 5), the neurogenic differentiation 1 that encodes the gene (NEUROD1)(MODY 6), the Kruppel-like factor 11 (KLF11) (MODY 7), the carboxylester lipase encoding the gene (CEL) (MODY 8), the paired box gene 4 (PAX4) (MODY 9), insulin gene (INS) (MODY 10), the tyrosine kinase B-lymphocyte specific gene (BLK) (MODY 11), the potassium voltage-gated channel subfamily J member 11 (KCNJ11 gene) (MODY13), and the adapter protein containing PH domain, PTB domain and leucine zipper motif 1, also known as DCC-interacting protein 13-a encoded by the APPL1 gene (MODY14).Those cases of as yet unknown genetic derangement have been classified as MODYX (Online Mendelian Inheritance in Man [OMIM], MIM entry 606391).", + "\t\n\nIn contrast, maturity-onset diabetes of the young (MODY) is a rare monogenic form of type 2 diabetes that has an autosomal dominant mode of inheritance.At least five different genes, located on chromosomes 20, 7, 12, 13, and 17, independently cause MODY within single pedigrees (5)(6)(7)(8)(9).MODY genes may also play a minor role in the common form of type 2 diabetes (10)." + ] + ], + "task_id": [ + { + "task_id": "E240519BE7D595118C1489579F47EA95" + }, + { + "task_id": "F03565FC1DE138132AB1F4CB52D248F7" + }, + { + "task_id": "E36FE156B8571A149192268FE357FAF5" + }, + { + "task_id": "5329674E4693E27C6109ACCA28EBBF2F" + }, + { + "task_id": "80A67B6A36E27EC64F7C664F8E972C68" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_3.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_3.json new file mode 100644 index 0000000..a01447e --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_3.json @@ -0,0 +1,124 @@ +{ + "question": [ + "What genes are most commonly associated with an increased risk of developing diabetes?", + "How can genetic testing help predict a person's risk for diabetes?", + "What role do family genetics play in the likelihood of getting diabetes?", + "Can lifestyle changes affect genetic risk factors for diabetes?", + "What recent breakthroughs have been made in understanding the genetic causes of diabetes?" + ], + "answer": [ + "The genes most commonly associated with an increased risk of developing diabetes include TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX.", + "Genetic testing can help predict a person's risk for diabetes by identifying specific genetic variants or risk alleles associated with the disease. These genetic markers, when combined into a polygenic score, can provide information on individual patterns of disease predisposition. This can be particularly useful if measured early in life, as it can enable early interventions for preventing diabetes. However, the predictive value of these genetic factors is currently considered to be small compared to traditional risk factors like obesity and fasting glucose levels.", + "Family genetics play a significant role in the likelihood of getting diabetes. Studies have shown that siblings of individuals with diabetes have a significantly higher risk of developing the disease compared to the general population. Genetic factors account for a substantial fraction of susceptibility to type 1 diabetes. In type 2 diabetes, the risk is higher if one or both parents have the disease. However, the presence of certain genetic factors does not guarantee the development of diabetes, as environmental factors also play a significant role.", + "Yes, lifestyle changes can affect genetic risk factors for diabetes. Studies suggest that a healthy lifestyle or lifestyle modification can partially or totally control genetic predisposition to obesity and Type 2 Diabetes Mellitus (T2DM). However, the effectiveness of these changes can vary among individuals due to genetic influences.", + "Recent breakthroughs in understanding the genetic causes of diabetes include the identification of many genes that predispose to both major types of diabetes, thanks to advances in genetics. Genome-wide association studies have been particularly effective in uncovering genetic determinants of complex diseases like diabetes. More recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes. These methods have led to the identification of novel variants and loci contributing to trait variation and disease risk." + ], + "contexts": [ + [ + "\t\n\nIn the past 10 years, geneticists have devoted a large amount of effort to finding type 2 diabetes genes.These efforts have included many candidate-gene studies, extensive efforts to fine map linkage signals 3 , and an international linkage consortium that was perhaps the best example of a multi-centre collaboration in common-disease genetics.Of these efforts, only the candidate-gene studies produced unequivocal evidence for common variants involved in type 2 diabetes.These are the E23K variant in the potassium inwardly-rectifying channel, subfamily J, member 11 (KCNJ11) gene [4][5][6] , the P12A variant in the peroxisome proliferatoractivated receptor- (PPARG) gene 7 , and common variation in the transcription factor 2, hepatic (TCF2) 8,9 and the Wolfram syndrome 1 (WFS1) 10 genes.All of these genes encode proteins that have strong biological links to diabetes.Rare, severe mutations in all four cause monogenic forms of diabetes [11][12][13][14] , and two are targets of anti-diabetic therapies: KCNJ11 encodes a component of a potassium channel with a Genome-wide association studies provide new insights into type 2 diabetes aetiology Timothy M. Frayling Abstract | Human geneticists are currently in the middle of a race.Thanks to a new technology in the form of 'genome-wide chips', investigators can potentially find many novel disease genes in one large experiment.Type 2 diabetes has been hot out of the blocks with six recent publications that together provide convincing evidence for six new gene regions involved in the condition.Together with candidate approaches, these studies have identified 11 confirmed genomic regions that alter the risk of type 2 diabetes in the European population.One of these regions, the fat mass and obesity associated gene (FTO), represents by far the best example of an association between common variation and fat mass in the general population.key role in -cell physiology that is a target for the sulphonylurea class of drugs, and PPARG encodes a transcription factor involved in adipocyte differentiation that is a target for the thiazolodinedione class of drugs.\tSix new gene regions identified\n\nTogether, the six recent GWAS papers provide convincing evidence for six new gene regions involved in type 2 diabetes [16][17][18][19][20][21] ; a seventh publication describes how one of these variants alters BMI and represents by far the best example of an association between common genetic variation and obesity 22 .There are now 11 gene regions in which common variation alters type 2 diabetes risk with the levels of statistical confidence that are required by genetic association studies (FIGS 2,3).This progress is all the more remarkable in view of the weak genetic component to type 2 diabetes risk, as compared with many other common diseases that are currently being studied using GWAS.The sibling relative risk is 3-4 at the most for type 2 diabetes, in contrast with 5-10 for rheumatoid arthritis, 15 for type 1 diabetes, 7-10 for bipolar disorder, 17-35 for Crohn disease, 2-7 for early myocardial infarction and 2.5-3.5 for hypertension 21 .", + "\t\n\nGenes whose variants are commonly associated with both type 2 diabetes mellitus and cardiovascular disease.", + "\tGenomic Analyses for Diabetes Risk\n\nGenes signifying increased risk for both type 1 and type 2 diabetes have been identified.Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes.Several T1D candidate genes for increased risk of developing type 1 diabetes have been suggested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood. 12Also, several candidate genes for increased risk of developing type 2 diabetes have been identified, including peroxisome proliferatoractivated receptor gamma (PPAR2), angiotensin converting enzyme (ACE), methylene tetrahydrofolate reductase (MTHR), fatty acid binding protein-2 (FABP2), and fat mass and obesity associated gene (FTO). 13he conclusions of a \"Workshop on Metformin Pharmacogenomics,\" sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, were published in 2014. 14The meeting was intended to review metformin pharmacogenomics and identify both novel targets and more effective agents for diabetes.The idea behind the meeting was that understanding the genes and pathways that determine the response to metformin has the potential to reveal new drug targets for the treatment of diabetes.The group noted that there have been few genes associated with glycemic control by metformin, and the most reproducible associations have been in metformin transporter genes.They acknowledged that nongenetic factors also contribute to response to metformin and that broader system biology approaches will be required to model the combined effects of multiple gene variants and their interaction with nongenetic factors.They concluded that the overall challenge to the field of precision medicine as it relates to antidiabetes treatment is to identify the individualized factors that can lead to improved glycemic control.", + "\tIntroduction\n\nIt is well recognized that type II diabetes mellitus has a substantial genetic component (Barnett et al. 1981;Knowler et al. 1981;Hanson et al. 1995a).Genes that predispose to some types of diabetes have been identi-fied; these include several loci for type I diabetes (Davies et al. 1994) and for maturity-onset diabetes of the young (Froguel et al. 1992;Yamagata et al. 1996aYamagata et al. , 1996b;;Stoffers et al. 1997).However, the genes that cause the most common forms of diabetes remain unknown, and it is, therefore, likely that additional important diabetessusceptibility loci remain to be identified.Moreover, the specific risk factors through which such genes influence the development of type II diabetes are also unknown.Obesity, as quantified by body-mass index (BMI) (kg/ m 2 ), is a strong risk factor for type II diabetes (Knowler et al. 1981) and is also likely to have genetic determinants (Price et al. 1994).The present study represents a genomewide search for loci linked to diabetes and BMI in Pima Indians, a Native American population with a high prevalence of type II diabetes and obesity (Bennett et al. 1971;Knowler et al. 1978Knowler et al. , 1991)).", + "\tGENETIC SUSCEPTIBILITY AND GENE-ENVIRONMENT INTERACTIONS-\n\nThe recent advent of genome-wide association studies (GWAS) has led to major advances in the identification of common genetic variants contributing to diabetes susceptibility (40).To date, at least 40 genetic loci have been convincingly associated with type 2 diabetes, but these loci confer only a modest effect size and do not add to the clinical prediction of diabetes beyond traditional risk factors, such as obesity, physical inactivity, unhealthy diet, and family history of diabetes.Many diabetes genes recently discovered through GWAS in Caucasian populations have been replicated in Asians; however, there were significant interethnic differences in the location and frequency of these risk alleles.For example, common variants of the TCF7L2 gene that are significantly associated with diabetes risk are present in 20-30% of Caucasian populations but only 3-5% of Asians (41,42).Conversely, a variant in the KCNQ1 gene associated with a 20-30% increased risk of diabetes in several Asian populations (43,44) is common in East Asians, but rare in Caucasians.It is intriguing that most diabetes susceptibility loci that have been identified are related to impaired b-cell function, whereas only a few (e.g., peroxisome proliferator-activated receptor-g, insulin receptor substrate 1, IGF-1, and GCKR) are associated with insulin resistance or fasting insulin, which points toward b-cell dysfunction as a primary defect for diabetes pathogenesis.It should be noted that most of the single nucleotide polymorphisms uncovered may not be the actual causal variants, which need to be pinpointed through fine-mapping, sequencing, and functional studies.", + "\t\n\nInitial linkage studies in affected families indentified CAPN10 and TCF7L2 as risk-conferring genes in T2D [27].Association studies using candidate gene approach identified additional risk genes -PPARG and KCNJ11 (the targets of many current diabetes medications), IRS1, WFS1, HNF1A, HNF1B and HNF4A, among others [28].The more recent GWAS have added a plethora of genetic risk variants, but with small indifuture science group Genetics, genomics & personalized medicine in Type 2 diabetes: a perspective on the Arab region Review vidual effect size.To date, GWAS for T2D have identified over 50 genetic risk variants, but their causal relationship in the etiology of the disease remains elusive.However, it is important to note that most loci harboring disease-causing variants have been found to be associated with defective functioning of the -cells of the pancreatic islets, thus implicating this pathway as a major factor in the pathology of T2D [29].So far, the strongest association signal for T2D has been found for the TCF7L2 gene, which has been replicated across GWAS of different ethnic groups.Other important genes which have been replicated across GWAS of different populations include HHEX, SLC30A8, CDKN2A/B, IGF2BP2, HMGA2, KCNQ11 and NOTCH2-ADAM30 [28].", + "\t\n\nGenetic determinants of diabetes and metabolic syndromes.", + "\t\n\nAmong type 2 diabetes susceptibility genes few, if any, individual loci are expected to carry alleles of major effect explaining a substantial proportion of cases, although a few genes could have a substantial population effect but not give a strong genetic signal if the causal alleles were common and the increase in risk were modest [6,7].Such genes have proven hard to detect using linkage-based approaches, although recent rapid advances in genetic association methodologies have led to some successes.The P12A polymorphism in the gene encoding the peroxisome proliferator-activated receptor-g (PPARG) [7], the E23K polymorphism in the gene encoding the islet ATPdependent potassium channel Kir6.2 (ABCC8-KCNJ11) [8][9][10] and common variants in the gene encoding the transcription factor 7-like 2 gene (TCF7L2) [11,12] were all found using well-powered association mapping, and all have been reproducibly associated with diabetes in diverse samples at highly significant p-values.", + "\t\n\nIn support of our focus on developmental genes, pathway analysis of recent genome-wide association studies, which so far have yielded few T2D candidate genes, provided an integrated interpretation of the highest ranked risk genes for T2D [97].This analysis found that lipid metabolism and developmental genes were significantly over-represented in the upper ranked genes of the T2D genome-wide association studies, an observation based on thousands of samples, and one strongly consistent with the present independent analysis.Combined, we believe this presents strong evidence that developmental genes may play a role in setting or regulating the long-term responses of skeletal muscle to diabetes.", + "\tResults\n\nStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function.", + "\t\n\nRecently, spectacular advance was made in identifying susceptible genes involved in T2D through genome-wide association strategy (GWAS) [10,11].Consequently, a number of novel genetic variants (PPARG, KCNJ11, IGF2BP2, KCNQ1, TCF7L2, CDKAL1, and MTNR1B) were shown to increase the risk of T2D in reproducible studies.Therefore, several studies have examined the association of these newly identified loci using a candidate gene approach for GDM.It has been reported that the pathophysiological changes of GDM are similar to those observed in T2D, which is characterized by peripheral insulin resistance accompanied by an insulin secretory defect [12,13].Functional studies showed that these new diabetogenic genes took part in many steps of the process, for instance, impaired b-cell function (CDKAL1, IGF2BP2, KCNQ1, KCNJ11, MTNR1B), insulin resistance (PPARG, TCF7L2), and abnormal utilization of glucose (GCK) [14][15][16][17][18][19][20][21][22][23].", + "\t\n\nGenome-wide association studies (GWAS) have discovered germline genetic variation associated with type 2 diabetes risk (1)(2)(3)(4).One of the largest GWAS, involving DNA taken from individuals of European descent and conducted by the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium, identified 65 loci associated with type 2 diabetes risk (1).However, for most of these loci, the precise identity of the affected gene and the molecular mechanisms underpinning the altered risk are not known.", + "\t\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1", + "\t\n\nNearly all of the recent discoveries have used genome wide association study (GWAS) techniques to identify single nucleotide polymorphisms (SNPs) that exist at higher frequency in DNA from people with established T2DM (''cases'') than in non-diabetic individuals (''controls'').Where the physiological roles of these variants have so far been determined, the majority encode proteins linked with the b-cell.For example, of 19 validated T2DM genes, 14 have been shown to influence glucose or incretin stimulated insulin secretion (reviewed in [6]).In addition, these variants have relatively large effects on diabetes risk compared with other variants, with the seven variants with the greatest association with diabetes risk (TCF7L2, CDKAL1, HHEX, CDKNA/2B, IGF2BP2, SLC30A8, JAZF1) all affecting b-cell insulin secretion.The rapid rise in prevalence of type 2 diabetes mellitus (T2DM) has been driven by changes in environmental factors -primarily increased caloric intake and reduced energy expenditure -resulting in reduced whole body insulin sensitivity (often termed insulin resistance).Insulin resistance has been proposed to be a major driver of progression to T2DM.However, of 38 individual susceptibility loci for T2DM recently identified by genome wide association studies, by far the majority code for proteins involved in b-cell function.In this review, we discuss the possible reasons for the paucity of insulin resistance genes and ask whether the new genetic susceptibility data should focus attention on b-cell targets in the development of therapies for T2DM.", + "\t\n\nMore than 65 loci, encoding up to 500 different genes, have been implicated by genome-wide association studies (GWAS) as conferring an increased risk of developing type 2 diabetes (T2D).Whilst mouse models have in the past been central to understanding the mechanisms through which more penetrant risk genes for T2D, for example, those responsible for neonatal or maturity-onset diabetes of the young, only a few of those identified by GWAS, notably TCF7L2 and ZnT8/SLC30A8, have to date been examined in mouse models.We discuss here the animal models available for the latter genes and provide perspectives for future, higher throughput approaches towards efficiently mining the information provided by human genetics.\t\nMore than 65 loci, encoding up to 500 different genes, have been implicated by genome-wide association studies (GWAS) as conferring an increased risk of developing type 2 diabetes (T2D).Whilst mouse models have in the past been central to understanding the mechanisms through which more penetrant risk genes for T2D, for example, those responsible for neonatal or maturity-onset diabetes of the young, only a few of those identified by GWAS, notably TCF7L2 and ZnT8/SLC30A8, have to date been examined in mouse models.We discuss here the animal models available for the latter genes and provide perspectives for future, higher throughput approaches towards efficiently mining the information provided by human genetics.", + "\t\n\nGenomic information associated with Type 2 diabetes.", + "\tBackground\n\nMultiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus.We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone.", + "\t\n\nAs 80% of type 2 diabetes patients are obese, a further research focus is the identification of genes encoding 'diabesity', predisposing the carrier to both pathological conditions.Philippe Froguel (Lille, France) found in a French population with a BMI of 40, as well in Germans with early onset obesity, a linkage with markers on chromosome 2p, 8 and around D10S1781.Leptin gene polymorphisms on chromosome 7 were only associated with blood leptin levels and diet success in the extremely obese.Stephen Rich (Winston-Salem, NC) showed how quantitative trait linkage (QTL) can greatly increase the accuracy of genetic studies.Rich found a clustering for type 2 diabetes candidate genes in families with diabetic nephropathy (Caucasians s 52.7 and African Americans s 58.1) as well as a correlation with arterial-wall width.Takashi Kadowaki (Tokyo, Japan) illustrated the important role of animal models in the understanding of diabetes.Using glucokinase-, IRS2-and PPAR-knockout mice, he was able to measure the effect of different dietary fats on insulin resistance, -cell hyperplasia, overt diabetes and arterial hypertension." + ], + [ + "\t\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today.", + "\t\n\nProgress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions.\t\n\nOf course, individual small effects can amount to more when considered collectively, and it is true that genetic testing (for the 17 known genes, for example) can identify subsets of individuals who have inherited particularly high or low numbers of risk alleles and therefore have marked differences in individual risk (87).However, the numbers of individuals in these \"extreme\" high-and low-risk groups are comparatively small, and for many, their risk will already be obvious through conventional factors (family history, BMI, and previous gestational diabetes, for example).When the information from the known type 2 diabetes-susceptibility variants is examined using approaches such as receiver-operating curve analysis, which are better suited for evaluating the performance of diagnostic tests at the population level, the results look far less spectacular (72,87).", + "\tClinical Utility of Genetic Information: Prediction of Type 2 Diabetes\n\nOne of most important clinical utilities of genetic information is to predict the risk of developing T2D among nondiabetic individuals.This will facilitate the early interventional strategies to prevent or delay the onset of the disease.A vast number of recent studies have constructed genetic risk score models by summing up numerous independently inherited susceptible variants for T2D to evaluate the predictive ability from the current genetic information.For example, the area under the receiver operating characteristic (ROC) curves (AUCs) is used to assess discriminative accuracy of this approach.The AUC value can range from 0.5 to 1.0, where the AUC of 0.5 stands for the lack of discrimination and AUC of 1 stands for perfect discrimination.An AUC value of greater than 0.75 is considered to be clinically useful [140].\t\nWith rapidly increasing prevalence, diabetes has become one of the major causes of mortality worldwide.According to the latest studies, genetic information makes substantial contributions towards the prediction of diabetes risk and individualized antidiabetic treatment.To date, approximately 70 susceptibility genes have been identified as being associated with type 2 diabetes (T2D) at a genome-wide significant level ( < 510 8 ).However, all the genetic loci identified so far account for only about 10% of the overall heritability of T2D.In addition, how these novel susceptibility loci correlate with the pathophysiology of the disease remains largely unknown.This review covers the major genetic studies on the risk of T2D based on ethnicity and briefly discusses the potential mechanisms and clinical utility of the genetic information underlying T2D.", + "\tGENOMICS IN THE PREDICTION, PREVENTION, AND DIAGNOSIS OF DIABETES\n\nThe incidence and prevalence of diabetes have doubled over the past two decades (13), and there are now about 30 million adults in the U.S. living with this condition, 95% of whom have type 2 diabetes (14).Genome-wide association (GWA) studies test hundreds of thousands or even millions of common (minor allele frequency [MAF] .5%)and lowfrequency (MAF 1-5%) variants across both protein coding (exonic) and noncoding (intronic) regions of the genome.Large GWA studies have identified more than 50 genetic loci associated with various glycemic traits and at least 90 loci associated with type 2 diabetes (15)(16)(17)(18).These genetic variants, which may explain as much as 10% of the variance in disease susceptibility, have advanced our understanding of the biology of diabetes, but each genetic locus confers only a small increase in risk.For example, the common variant from these GWA studies most strongly associated with type 2 diabetes, an intronic variant in TCF7L2 (rs7903146), is associated with a 37% increased relative risk per copy of the variant allele (19).Rare variants (MAF ,1%) and variants that are common only in specific ancestral populations have been associated with a greater increase in diabetes risk, but they account for less of the overall burden of diabetes (20)(21)(22).", + "\t\n\nThe promise of genetic risk scoring for diabetes can be evaluated in the framework of three perspectives.First is the potential for robust prediction of diabetes risk.Second is the prospect of designing targeted preventive and therapeutic interventions (personalized medicine).Thirdly, increased knowledge could provide genomic clues to ethnic disparities in diabetes.Regarding robustness of prediction, results from the Framingham Offspring Study showed that clinical risk assessment (using age, sex, family history, BMI, fasting glucose level, systolic blood pressure, high-density lipoprotein cholesterol level, and triglyceride level) performed as well as cumulative genotype score at 18 loci in predicting incident type 2 diabetes during 28 years of follow-up of initially normoglycemic subjects (14).Also, cumulative genotype score at 34 loci did not add significantly to clinical risk factors in predicting progression from impaired glucose tolerance to type 2 diabetes among the multiethnic cohort enrolled in the Diabetes Prevention Program (15).One current limitation is the incomplete framework from which GRS is constructed.For example, the 17 SNPs studied in the present report (17) represent just about half of the .30diabe-toSNPs identified to date.Even the latter do not represent all possible risk loci, and important information on structural variants that might increase diabetes risk is often lacking.Thus, current experience renders the promise of robust genetic prediction and personalized diabetes intervention a distant hope.", + "\t\n\nRegardless, one expects many of the important susceptibility genes for type 2 diabetes will be uncovered in the next 10 years.Once that occurs, intense effort will be focused on developing targeted therapies.Also, medical care will shift to genetic testing of persons with type 2 diabetes, followed by giving them the most effective proven therapy for that genetic form of the disease.Also, their family members will undergo genetic testing while still normally glucose tolerant to determine if they carry a genetic predisposition.If so, specific treatment plans will be developed for prevention of the disease, again based on proven efficacy for each genetic defect.", + "\t\n\nTwo more recent population -based studies using a longitudinal design with prospectively investigated cohorts have examined the predictive value of a genotype score in addition to common risk factors for prediction of T2DM [194,195] .Meigs et al. [194] reported that a genotype score based on 18 risk alleles predicted new cases of diabetes in the community but provided only a slightly better prediction of risk than knowledge of common clinical risk factors alone [195] .A similar conclusion was drawn in the paper by Lyssenko et al. [196] , along with an improved value of genetic factors with an increasing duration of follow -up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured.They also showed that -cell function adjusted for insulin resistance (using the disposition index) was the strongest predictor of future diabetes, although subjects in the prediabetic stage presented with many features of insulin resistance.It is also noteworthy that many of the variants that were genotyped appear to infl uence -cell function.The addition of DNA data to the clinical model improved not only the discriminatory power, but also the reclassifi cation of the subjects into different risk strategies.Identifying subgroups of the population at substantially different risk of disease is important to target these subgroups of individuals with more effective preventative measures.As more genetic variants are now identifi ed, tests with better predictive performance should become available with a valuable addition to clinical practice.", + "\t\n\nPredicting T2DM in healthy individuals has been attempted using a diabetes risk score that is derived from common clinical information, such as adiposity, blood pressure, and family history of T2DM.However, using the risk score is inevitably limited in predicting T2DM because T2DM has a strong genetic basis; concordance of T2DM is about 70% for monozygotic twins, compared to about 20-30% for dizygotic twins. 2 Limitations in predicting T2DM have driven researchers to employ genetic risk assessments.Moreover, unlike clinical markers, genetic markers do not change with time, so they possess the advantage of identifying high-risk individuals long before disease onset, which could enable early interventions for preventing T2DM.Conventionally, family-based linkage studies have played an important role in identifying genes having a large effect in monogenic disorders, such as maturity-onset diabetes of the young. 3However, linkage studies have low power for polygenic diseases that are influenced by multiple genes, as is the case with the majority of those with T2DM.Therefore, using monogenic mutations would have very limited value for predicting risk of disease in the general population because of their low frequency.", + "\tDiscussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured.\t\n\nIn conclusion, the inclusion of common genetic variants that are associated with type 2 diabetes very slightly improved the prediction of future type 2 diabetes, as compared with the inclusion of clinical risk factors alone.Although this effect might be too small to allow for individual risk prediction, it could be useful in reducing the number of subjects who would need to be included in intervention studies aimed at the prevention of type 2 diabetes.Supported by grants from the Swedish Research Council (including Linn grant 31475113580), the Heart and Lung Foundation, the Swedish Diabetes Research Society, a Nordic Center of Excellence Grant in Disease Genetics, the Diabetes Program at the Lund University, the Finnish Diabetes Research Society, the Sigrid Juselius Foundation, the Phlsson Foundation, the Crafoord Foundation, the Folkhlsan Research Foundation, the Novo Nordisk Foundation, the European Network of Genomic and Genetic Epidemiology, the Wallenberg Foundation, and the European Foundation for the Study of Diabetes.\t\nA bs tr ac t\nBackgroundType 2 diabetes mellitus is thought to develop from an interaction between environmental and genetic factors.We examined whether clinical or genetic factors or both could predict progression to diabetes in two prospective cohorts. MethodsWe genotyped 16 single-nucleotide polymorphisms (SNPs) and examined clinical factors in 16,061 Swedish and 2770 Finnish subjects.Type 2 diabetes developed in 2201 (11.7%) of these subjects during a median follow-up period of 23.5 years.We also studied the effect of genetic variants on changes in insulin secretion and action over time. ResultsStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function.The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes, with a slight increase in the area under the receiveroperating-characteristic curve from 0.74 to 0.75; however, the magnitude of the increase was significant (P = 1.010 4 ).The discriminative power of genetic risk factors improved with an increasing duration of follow-up, whereas that of clinical risk factors decreased. ConclusionsAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up.", + "\t\n\nGenetic variants can also identify patients at higher risk, predict rates of C-peptide decline, and predict response to various therapies (41).With a better understanding of inheritance profiles, it may become possible to realize new targets for individualized intervention.", + "\t\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1", + "\t\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized.\t\n\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized.", + "\t\n\nTwo trials in the field of T2D have assessed weight change in response to genetic testing.In the Genetic Counseling and Lifestyle Change for Diabetes Prevention Study (107), 177 patients with metabolic syndrome were randomized to receive genetic testing for T2D susceptibility based on 36 T2D-associated SNPs plus brief genetic counseling versus no genetic testing.Diabetes risk for genotyped participants was summarized with a risk score categorizing their genetic risk as low, average, high.All patients were then enrolled in a 12-week lifestyle medication program modeled on the evidencebased DPP (108).The lifestyle intervention was effective: the group overall lost a mean of 8.5 6 10.1 pounds, with 31% losing at least 5% of their body weight.Communicating genetic risk did not change this effectiveness, however.The genotyped and control arms did not differ with respect to weight loss, attendance at the 12 DPP sessions, or motivation or confidence to make health behavior changes (107).In a second randomized trial, 601 patients with obesity or overweight received T2D risk estimates based on family history, BMI, and fasting plasma glucose, followed by either T2D genetic susceptibility results from four T2D-associated SNPs or eye disease counseling as a control (109).All participants received brief lifestyle counseling but were not otherwise enrolled in a weight loss program.Although the group receiving genetic risk information reported lower calorie and fat intake after 3 months, the two groups did not differ in these behaviors or in physical activity, weight loss, insulin resistance, or perceived risk after 6 months.", + "\t\n\nConclusions and Future Directions GWAS and GWAS meta-analyses have by far been the most efficient way to identify new T2D genes (Figure 2), but their predictive value for future occurrence of T2D has been very limited compared to classic risk factors such as obesity and fasting glucose levels (Walford et al., 2014).Although it might be good news that our genome does not fully dictate our future, the knowledge of its specificities may help us to improve our health.Early genetic studies showed that the higher risk for T2D conferred by TCF7L2 variant can be reversed by lifestyle intervention (Florez et al., 2006), opening avenues for strategies targeted on genetically selected individuals with pre-diabetes.TCF7L2 has also been shown to be associated with a lower efficiency of oral sulfonylureas in newly diagnosed T2D patients (Pearson et al., 2007), but a more recent Danish study suggested that in contrast to clinical markers, all known T2D-associated variants do not significantly affect the time to prescription of the first drug after disease onset (Hornbak et al., 2014).In other words, frequent SNPs are not helpful to predict patients' futures, though the good use of genetic data may contribute to provide better care to newly diagnosed T2D patients who are currently all treated the same (with metformin).", + "\tBackground\n\nMultiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus.We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone." + ], + [ + "\tA. Genetic Screening\n\nWe have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is 10 -15 times greater.Although 70% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3).", + "\tGenes\n\n2][43][44][45][46][47] Twin studies need to be considered carefully, however, as the intrauterine environments of dizygotic-twin (separate placentas), monozygotic-twin (60-70% share one placenta), and singleton pregnancies (one placenta without competition for maternal nutrients) will all be diff erent, and this can be a confounder in the inter pretation of eff ects. 44A large study from Sweden on familial risk of type 2 diabetes showed that the relative risks were highest in individuals with at least two aff ected siblings, irrespective of parental diabetes status. 42This fi nding suggests that a recessive pattern of inheritance from uncommon genetic defects, the sharing of similar intrauterine, postnatal, or both environments by siblings (eg, breastfeeding or bottle feeding or childhood nutrition), or a combination of these factors is important.9][50] A greater number of these loci are associated with impaired -cell function (KCNJ11, TCF7L2, WFS1, HNF1B, SLC30A8, CDKAL1, IGF2BP2, CDKN2A, CDKN2B, NOTCH2, CAMK1D, THADA, KCNQ1, MTNR1B, GCKR, GCK, PROX1, SLC2A2, G6PC2, GLIS3, ADRA2A, and GIPR) than impaired insulin sensitivity (PPARG, IRS1, IGF1, FTO, and KLF14) or obesity (FTO). 38,48,50Of these, TCF7L2 is the strongest susceptibility locus for type 2 diabetes, being associated with -cell dysfunction. 48Most patients with monogenic forms of diabetes also have gene defects that aff ect islet -cell function. 51,52Nevertheless, only around 10% of the heritability of type 2 diabetes can be explained by susceptibility loci identifi ed so far, with each locus having a low eff ect size. 36The remaining heritability might be related to a large number of less common variants (allele frequency <5%) that are diffi cult to fi nd with current approaches of genome-wide association studies, and/or epigenetic phenomena.", + "\t\n\nObserved increased risk in African Americans is likely to result from a combination of shared environmental and genetic factors.Although there are few published studies specifically investigating familial aggregation of type 2 diabetes in African-American families, Rotimi et al. (10) found that relatives of African-American probands with type 2 diabetes had a 2.95-fold (95% CI 1.55-5.62)higher prevalence of diabetes when compared with relatives of unaffected individuals.In the GENNID (Genetics of Noninsulin Dependent Diabetes Mellitus) African-American families, the majority of first-degree relatives of African-American individuals with type 2 diabetes had abnormal glucose tolerance (11), with 27% found to have undiagnosed diabetes and 31% impaired fasting glucose and/or impaired glucose tolerance.", + "\t\n\nmore frequently than by chance alone among siblings who share the phenotype of type 1 diabetes.Nuclear families, or even just the affected sibling pairs themselves, are genotyped with panels of markers spanning the genome at a modest density.Linkage between a marker and a susceptibility locus for type 1 diabetes is determined by accumulating evidence across families.Since affected sibling pairs are relatively rare in type 1 diabetes, data from linkage studies are collected from a rather unique subgroup of families with type 1 diabetes.In general, linkage studies are the method of choice when the risk factors being sought have large effect sizes but are relatively rare.As risk factors become more common and have smaller effect sizes, association methods emerge as a potentially more powerful approach (Fig. 1).Since the genetic basis of type 1 diabetes is probably a complex mixture of small, moderate, and large genetic effects, multiple strategies are needed and vary according to the population being studied and their exposure to unknown environmental factors.", + "\tEvidence from family and twin studies\n\nThe obvious familial aggregation of T2D is clearly consistent with a genetic component to disease susceptibility, although a shared environment may also contribute.The extent of familial aggregation is often summarised in terms of the sibling relative risk (l s , the ratio of disease prevalence in the siblings of aected individuals compared with that in the general population).l s for T2D in European populations is approximately 3.5 (35% versus 10%) 4 , a modest value compared with the equivalent gure of around 15 for type 1 diabetes.The patterns of segregation in families with T2D are (with rare exceptions, such as maturity onset diabetes of the young MODY see below) consistent with a complex, multifactorial inheritance. 5orts to estimate the heritability of T2D by a comparison of the concordance rates in mono-and dizygotic twins have varied greatly as a result of dierences in ascertainment scheme, diagnostic criteria and follow-up duration.69 Concordance for diabetes is generally higher in identical twins (supporting a genetic basis for disease), although the extremely high concordance rates in some early studies 6 were undoubtedly inated by ascertainment bias.", + "\tThe genetics of type 1 diabetes\n\nThere is a strong genetic risk to T1D.This is exemplified by (Redondo et al., 2001) who demonstrated a strong concordance of genetic inheritance (65%) and T1D susceptibility in monozygotic twin pairs.That is, when one sibling is afflicted, there is a high probability that the other twin will develop T1D by the age of 60 years.Additionally, autoantibody positivity and islet destruction was observed after a prospective long-term follow-up of monozygotic twins of patients with T1D, despite initial disease-discordance among the twins (Redondo et al., 2008).", + "\tHeritability\n\nFamily history is an important risk factor for the development of T1D and T2D.In rare cases, there are families in which diabetes is inherited as a monogenic disease.More generally, the sibling of a patient with T1D has a 15-fold higher risk of developing the disease (6%) than does an unrelated individual (0.4%) (53).In T2D, the absolute risk to siblings is 30%-40%, as compared to a population prevalence of 7%, providing a relative risk to siblings of four to sixfold.In T1D and T2D, rates of concordance are much higher for monozygotic twins as compared to dizygotic twins.Specifically, in T1D, the concordance rate for monozygotic twins is estimated to range from 21%-70%, higher than the 0%-13% range reported for dizygotic twins (145).For T2D, Barnett et al. (8) found that 48 of 53 identical twin pairs were concordant for T2D if followed for long enough, and Poulsen et al. (141) described a concordance rate of 43% in Danish dizygotic twins as compared to 63% in monozygotic twins.Interestingly, while the relative risk to a sibling ( S , which tracks with power in a linkage study) is higher in T1D than T2D, the absolute risk and concordance in monozygotic twins are higher in T2D than in T1D.", + "\tType 1 diabetes is a genetic disease\n\nFamily studies have indicated that genetic factors are important determinants of type 1 diabetes risk.First, the risk to a sibling of an affected individual is approximately 6%, as compared with an average risk of 0.4% (depending on the population), or a relative increased risk of 15-fold (17).The increased risk to siblings is referred to as l s (18) and is one measure of the degree of familial clustering of the disease.\t\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.\t\n\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.\tType 1 diabetes has unusual epidemiological features related to gender\n\nType 1 diabetes also displays unusual patterns of inheritance that may yield insights into etiology and provide clues to the best methods for analyzing genetic studies.The risk to the offspring is generally greater from a mother or father who was diagnosed at an early age (again suggesting that early-onset cases are more heavily genetically 'loaded').However, the risk of diabetes is approximately two to four times higher for a child whose father has type 1 diabetes than one whose mother is affected [see (52,53) and references therein].This parental difference is largely due to a low risk for offspring of mothers who were diagnosed at a later age (53).The difference could be explained by at least three different factors.First, the risk alleles could only be active when transmitted by the father (such as is seen in imprinting, where only one of the parental alleles is expressed).Alternatively, a maternal environmental factor during pregnancy could be protective.However, it is difficult to see how this protective effect would be restricted to mothers diagnosed at a later age, especially since the protective effect was unrelated to the mother's duration of diabetes or even diabetic status at delivery (53).Finally, mothers who are diagnosed at a later age could represent more 'environmental' cases of diabetes, and thus be less likely to pass on risk genes to their offspring.", + "\t\n\nCopyright 2008 Massachusetts Medical Society.All rights reserved.Panel A shows the incidence of type 2 diabetes in four quartiles (Q) of body-mass index (BMI) among Malm subjects who had a family history of diabetes and those without such a history.An increase in the quartile of the BMI gradually increased the risk of diabetes, as compared with the lowest quartile, with an odds ratio of 1.50 for the second quartile (95% confidence interval [CI], 1.26 to 1.78; P = 6.710 6 ), of 2.36 for the third quartile (95% CI, 2.00 to 2.78; P = 1.510 24 ), and of 4.96 for the fourth quartile (95% CI, 4.25 to 5.79; P = 1.110 90 ).Panel B shows the incidence of type 2 diabetes in relation to insulin secretion (disposition index) among subjects with a family history of diabetes and those without such a history.Subjects with a disposition index below the median of 23,393 (26.1% of highrisk subjects and 9.4% of low-risk subjects) had an increase in the risk of type 2 diabetes by a factor of 3.23 (95% CI, 2.41 to 4.34; P = 5.810 15 ), as compared with those above the median.A family history of diabetes significantly increased the risk of diabetes in subjects with impaired insulin secretion (35.5% vs. 9.9%), with an odds ratio of 4.86 (3.12 to 7.56, P = 2.310 12 ).Panel C shows the incidence of type 2 diabetes in carriers of an increasing number of risk alleles in 11 genes, which individually predicted future risk of type 2 diabetes, in relation to quartiles of BMI.There was a stepwise increase in diabetes risk with an increasing number of risk alleles and increasing quartiles of BMI so that participants carrying more than 12 risk alleles showed a doubling of the risk conferred by BMI alone.In the highest quartile of BMI (31.8% vs. 5.1%), this yielded an odds ratio of 8.0 (95% CI, 5.71 to 11.19; P = 9.110 34 ).Panel D shows the incidence of type 2 diabetes in carriers of an increasing number of risk alleles in the 11 genes, which individually predicted future risk of type 2 diabetes, in relation to low insulin secretion.Carriers of more than 12 risk alleles and a low disposition index (37.9%vs. 10.1%) had an odds ratio of 5.81 (95% CI, 3.18 to 10.61; P = 1.110 8 ).", + "\tEvidence for a genetic basis: family and twin studies of Type I diabetes\n\nWhat is the evidence that Type I diabetes has a genetic basis?The simplest evidence comes from the fact that the frequency of the disorder is higher in close relatives of diabetic patients than in the general population (note: the reference population in the discussion which follows are people of European ancestry, who have the highest prevalence of Type I diabetes).For example, the frequency of Type I diabetes in siblings of diabetics is about 6 % by age 30 [1], while the frequency in the general population is about 0.4 % by age 30 [2].Thus, Type I diabetes is about 6/0.4,i. e. 15 times more common in siblings of diabetic patients than in the general population.This ratio between frequency in siblings compared with the general population is referred to as l sib [3].", + "\tType 1 Diabetes\n\nThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk.\tGenetics\n\nBoth type 1 and type 2 diabetes are polygenic diseases where many common variants, largely with small effect size, contribute to overall disease risk.Disease heritability (h 2 ), defined as sibling-relative risk, is 3 for type 2 diabetes and 15 for type 1 diabetes (17).The lifetime risk of developing type 2 diabetes is ;40% if one parent has type 2 diabetes and higher if the mother has the disease (18).The risk for type 1 diabetes is ;5% if a parent has type 1 diabetes and higher if the father has the disease (19).Maturity-onset diabetes of the young (MODY) is a monogenic disease and has a high h 2 of ;50 (20).Mutations in any 1 of 13 different individual genes have been identified to cause MODY (21), and a genetic diagnosis can be critical for selecting the most appropriate therapy.For example, children with mutations in KCJN11 causing MODY should be treated with sulfonylureas rather than insulin.", + "\t\n\nGenetic factors have an important role in the development of diabetes, with some forms of the disease resulting from mutations in a single gene.Others are multifactorial in origin.The monogenic forms of diabetes account for approximately 5% of cases and are caused by mutations in genes encoding insulin 3 , the insulin receptor 4 , the glycolytic enzyme glucokinase 5 , and the transcription factors hepatocyte nuclear factor-1 (HNF-1), HNF-1, HNF-4, insulin promoter factor-1 and NeuroD1/BETA2 (refs 6-10).Mutations in maternally inherited mitochondrial genes can also cause diabetes, often in association with hearing loss 11 .", + "\t\n\nStudies [71][72][73][74] in Mexican and Asian populations have identified several mutations associated with type 2 diabetes in young people.The high prevalence of type 2 diabetes in the parents of young people diagnosed with type 2 diabetes could reflect a stronger genetic predisposition, even when monogenic diabetes is excluded.This hypothesis suggests that efforts to define genes that cause type 2 diabetes by linkage might be more powerful if focused on young adults with diabetes, raising the question of whether type 2 diabetes in older populations has a relatively smaller genetic contribution and a stronger environmental contribution. 66", + "\tFamily studies\n\nThe 29 index patients had 130 first-degree relatives (58 parents, 63 siblings, and nine children).Ten families were negative for fasting hyperglycaemia except for one sibling with juvenile-onset diabetes mellitus only.However, a family history of maturity-onset diabetes was present in seven families in members other than first-degree relatives.No relative had a history of psychiatric illness on direct questioning.There was no maternal history of diabetes or deafness.The parents of three index patients were consanguineous: one family was English, one Pakistani, and one of mixed Arabic/African descent.All the other index patients were caucasians.", + "\t\n\nWe found that the presence or absence of parental diabetes and the genotype score were independently associated with the risk of diabetes.This suggests that family history as a risk factor for diabetes conveys more than heritable genetic information; it probably includes nongenetic familial behaviors and norms.The lower relative risks for diabetes associated with observed parental diabetes as compared with those associated with self-reported family history (approximately 1.8 vs. approximately 2.2) support the contention that family history contains more risk information than is implied by inheritance of the diabetes phenotype alone.One of the limitations of our study is that the 18 SNPs we included are probably insufficient to account for the familial risk of diabetes.They account for a minority of diabetes heritability, and the SNP array platforms from which they were chosen capture only approximately 80% of common variants in Europeans.In addition, we have not considered structural variants that might confer a risk of diabetes.It is possible that the addition of rare risk alleles with large effects, or a much larger number of common risk alleles with small individual effects, could improve discrimination. 36Indeed, as many as 500 loci may underlie the genetic risk of type 2 diabetes. 16Also, we did not study interactions among genes or between genes and the environment that might alter the genetic risk in exposed persons.As more diabetes risk variants become known, their incorporation into the genotype score may explain more of the genetic risk implied by parental diabetes.", + "\t\n\nGenetics is one example of the 'other risk factors' involved in the pathogenesis of DR.Twin and epidemiological studies have strongly suggested a genetic component in the etiology of DR (6 -10), with heritability scores ranging from 27 to 52% in both type 1 and type 2 diabetes (7 -10).There is an increased risk of severe DR among family members of DR subjects (8,9) and in siblings of affected subjects (8,9).Furthermore, several studies have also shown a discrepant rate of the prevalence of DR among different racial ethnic groups in the US population, with a significantly higher prevalence observed among Hispanic, African-American and Chinese-American when compared with Caucasian populations (11).While these differences may partially be attributed to lifestyle factors, evidence from familial aggregation, ethnic differences and heritability clearly supports a genetic contribution in the etiology of DR." + ], + [ + "\t\n\nIn addition to lifestyle factors, it is known that type 2 diabetes has a strong genetic component.Recent genomewide association studies have identified >60 genetic variants that are associated with type 2 diabetes but individual effects of genetic variants are considered to be small [139,140].", + "\t\n\nAn individual's risk of developing T2D is influenced by a combination of lifestyle, environmental, and genetic factors.Uncovering the genetic contributors to diabetes holds promise for clinical impact by revealing new therapeutic targets aimed at the molecular and cellular mechanisms that lead to disease.Genome-wide association studies performed during the past decade have uncovered more than 100 regions associated with T2D (5)(6)(7)(8)(9)(10)(11)(12).Although these studies have provided a better understanding of T2D genetics, the majority of identified variants fall outside protein-coding regions, leaving the molecular mechanism by which these variants confer altered disease risk obscure.Consequently, T2D genome-wide association studies have identified few loci with clear therapeutic potential.", + "\t\n\nThe notion that lifestyle modifi cation can eliminate the increased risk for development of T2DM in subjects with genetic susceptibility is also supported by fi ndings of Barwell et al. (2008) who reported that women with family history of T2DM experience greater improvement in insulin sensitivity following an exercise intervention than women with no family history.Although lifestyle modifi cation has been found effi cient in obesity and T2DM prevention even among genetically susceptible individuals, considerable heterogeneity in intervention responses has been observed.Genetic infl uences have been suggested to contribute to this heterogeneity.Risk allele carriers in several obesity-and T2DM-associated genes, for instance, have been found to experience suppressed weight reduction and improvement in various metabolic parameters in response to exercise or combined lifestyle interventions ( Franks et preference for foods of high energy density ( Haupt et al., 2009b ;Speakman et al., 2008 ;Timpson et al., 2008 ).In summary, healthy lifestyle or lifestyle modifi cation may keep genetic predisposition to obesity and T2DM under control.Genetics has, however, been suggested to infl uence the outcome of a lifestyle intervention or even to determine individual PA level, food intake, and motivation for lifestyle change.\tLifestyle and Genetics in Obesity and Type 2 Diabetes \n\nRecent advancement in human genetics has led to the identifi cation of a relatively big number of obesity-and T2DM-associated loci.Their contribution to disease risk has, however, been shown to be small and their predictive value low, suggesting that lifestyle plays crucial role in obesity and T2DM development ( Vimaleswaran and Loos, 2010 ).Indeed, studies investigating the gene-lifestyle interactions in obesity and T2DM have suggested that the biological eff ect of genetic predisposition may be partially or totally abolished by healthy lifestyle or lifestyle modifi cation and vice versa.Epidemiological studies have reported that the negative eff ect of several obesity-and T2DM-associated genes may be attenuated in individuals with higher PA levels or healthy lifestyle, whereas low PA and western dietary pattern have been found to accentuate it. ( 1 ).\t\n\nGene-lifestyle interaction studies supporting the protective role of diet, exercise or combined lifestyle interventions in individuals genetically susceptible to obesity and type 2 diabetes.This document was downloaded for personal use only.Unauthorized distribution is strictly prohibited.\tConclusions \n\nObesity and T2DM are clearly the results of a complex interplay between inherited factors and the environment.Recent advancements made through the GWA approach have substantially contributed to our understanding of obesity and T2DM genetics, however, most of the loci identifi ed to date have modest eff ect on disease risk.Hence, lifestyle factors, namely physical inactivity and food overconsumption seem to have major importance for the development of both diseases.Healthy lifestyle and lifestyle modifi cation, on the other hand, appear to be the most effi cient tools for obesity and T2DM prevention.In addition, gene-lifestyle interaction studies suggest that lifestyle determines whether an individual is likely to develop the disease and that genetic susceptibility may be partially or totally kept under control by lifestyle modifi cation.Since genetics seems to infl uence individual response to a lifestyle intervention and even the motivation for lifestyle change, personalized interventions according to genotype may be considered in the future.By then lifestyle modifi cation targeting dietary change and increased physical activity may be recommended for successful obesity and T2DM prevention irrespectively of genetic susceptibility.\tLifestyle and Genetics in Obesity and type 2 Diabetes\n\nvaluable insights into the interactions between genetic predisposition and lifestyle factors, namely physical activity (PA) and food consumption.This current progress may have essential contribution to our understanding of the pathophysiology of both diseases, as well as, to the development and implementation of future treatment and prevention strategies.It is, therefore, the aim of the present review to summarize the available literature on the eff ect of the interactions between lifestyle and genetics on obesity and T2DM.", + "\t\n\nLifestyle behaviors and genetic loci have clear and distinguishable effects on T2D risk; however, the pattern of disease occurrence within and between populations that differ in their genetic and environmental underpinnings suggests T2D is caused in part by the interaction between adverse lifestyle behaviors and the genetic profile of an individual.For many, this seems a reasonable assumption, but there is little robust empirical evidence supporting the presence of such interactions.\t\n\nNotwithstanding the important role lifestyle factors play in the etiology of T2D, persons living similar lifestyles can vary considerably in their susceptibility to the disease, with the variance being least among biologically related individuals, suggesting a genetic basis to the disease.In the past 4 years, major advances have been made in unraveling the genetic architecture of T2D.This search has cumulated in the discovery and confirmation of more than 30 common predisposing loci [10], but the variance in disease risk explained by these variants is much lower than predicted from heritability studies [11].Thus, the genetic associations discovered to date are likely to represent no more than the tip of the iceberg with respect to the genetic landscape of T2D.\t\n\nThe availability of detailed information on gene environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them.", + "\t\n\nAt 1-week follow-up, 44% of participants indicated that the primary risk factor for them was genes/family history, followed by diet (26%) and lifestyle (19%).There was not a significant difference in the proportion of participants at increased genomic risk who indicated genes/ family history as the primary cause (p = 0.5144).In addition, no statistically significant difference in IPQ-R subscales and risk perception between those at increased and nonincreased genomic risk for T2DM or between those with and without a family history for other factors related to illness perception was observed.", + "\tDiscussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured.", + "\t\n\nAlthough the expected range of effects that are realistic for gene-lifestyle interactions in type 2 diabetes remains unclear, a doubling of the genetic risk estimate in the group exposed to adverse lifestyle factors compared with those who are unexposed (b GE = 2) is at the upper end of the interaction effect estimate ranges reported for common variants and common exposures (10).It is reasonable to conclude, therefore, that most of the interaction studies published to date report \"lucky\" true-positive results or false-positive results that may be underpinned by analytical and reporting biases.The replication of few examples of genelifestyle interactions in type 2 diabetes suggests that the literature is composed largely of the latter.Despite this, recent developments in the ways genetic association studies are performed, such as adoption of hypothesis-free approaches, the availability of comprehensive genotype arrays in large sample collections, global collaborations, and more rigorous analysis and reporting of data, have led to the emergence of many reproducible genetic association signals for type 2 diabetes and related glycemic traits, which has spurred a number of large-scale studies of gene-lifestyle interactions.", + "\t\n\nGenetic and epigenetic factors determine cell fate and function.Recent breakthroughs in genotyping technology have led to the identification of more than 20 loci associated with the risk of type 2 diabetes (Sambuy 2007;Zhao et al. 2009).However, all together these loci explain <5% of the genetic risk for diabetes.Epigenetic events have been implicated as contributing factors for metabolic diseases (Barker 1988;Kaput et al. 2007).Unhealthy diet and a sedentary lifestyle likely lead to epigenetic changes that can, in turn, contribute to the onset of diabetes (Kaput et al. 2007).At present, the underlying molecular mechanisms for disease progression remain to be elucidated.", + "\t\n\nThird, there is the issue as to whether early diagnosis can be shown to result in beneficial outcomes, for example by motivating improvements in lifestyle or treatments that reduce the risk of disease.In the case of TD, the potential for lifestyle modification and/or pharmaceutical intervention (e.g., with metformin) to reduce diabetes progression is clear (, ), and these benefits seem to accrue irrespective of genetic risk.In the Diabetes Prevention Program, for example, lifestyle intervention was effective at reducing diabetes incidence compared with placebo even among those with the highest quartile of TD rsPS ().However, there is limited evidence to date that the communication of genetic risk is sufficient to motivate most individuals to undertake the kind of long-term behavioral modification required for sustained benefit (-).There is also some (at least theoretical) risk of harm if the communication of risk information is mishandled.This could arise through failure to use ethnically appropriate scores, or to incorporate other relevant health information.For example, an overweight person with a low TD polygenic score may be at far greater risk of disease than the polygenic score alone would suggest.Some individuals may be liable to interpret high genetic risk in a deterministic and fatalistic way, failing to appreciate that remediation of risk through lifestyle modification is no less likely to be effective in their case.", + "\t\n\nTwo trials in the field of T2D have assessed weight change in response to genetic testing.In the Genetic Counseling and Lifestyle Change for Diabetes Prevention Study (107), 177 patients with metabolic syndrome were randomized to receive genetic testing for T2D susceptibility based on 36 T2D-associated SNPs plus brief genetic counseling versus no genetic testing.Diabetes risk for genotyped participants was summarized with a risk score categorizing their genetic risk as low, average, high.All patients were then enrolled in a 12-week lifestyle medication program modeled on the evidencebased DPP (108).The lifestyle intervention was effective: the group overall lost a mean of 8.5 6 10.1 pounds, with 31% losing at least 5% of their body weight.Communicating genetic risk did not change this effectiveness, however.The genotyped and control arms did not differ with respect to weight loss, attendance at the 12 DPP sessions, or motivation or confidence to make health behavior changes (107).In a second randomized trial, 601 patients with obesity or overweight received T2D risk estimates based on family history, BMI, and fasting plasma glucose, followed by either T2D genetic susceptibility results from four T2D-associated SNPs or eye disease counseling as a control (109).All participants received brief lifestyle counseling but were not otherwise enrolled in a weight loss program.Although the group receiving genetic risk information reported lower calorie and fat intake after 3 months, the two groups did not differ in these behaviors or in physical activity, weight loss, insulin resistance, or perceived risk after 6 months.", + "\t\n\nThe missing heritability of T2DM could be accounted for by the interactions between susceptibility loci and various environmental determinants, whereby the impact of a given genetic variant is modified by the environmental milieu (and vice versa).Evidence that lifestyle factors modify the genetic effects on T2DM risk has been generated from both observational studies and clinical trials 82 .However, genetic background might also affect the individual's response to lifestyle interventions 83 .In addition, replication data are sparse, and comprehensive, large-scale studies have failed to provide a compelling basis for the significant interaction effect 84,85 .This failure might have occurred because the interaction effects are of small magnitude or might be due to the limited statistical power and multiple sources of bias and confounding factors in the current research methods 86 .\tGenomics and gene-environment interactions\n\nEven though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mellitus are more susceptible to T2DM than others, which suggests that individual differences in response to lifestyle interventions exist 76 .Substantial evidence from twin and family studies has suggested a genetic basis of T2DM 77 .Over the past decade, successive waves of T2DM genome-wide association studies have identified >100 robust association signals, demonstrating the complex polygenic nature of T2DM 5 .Most of these loci affect T2DM risk through primary effects on insulin secretion, and a minority act through reducing insulin action 78 .Individually, the common variants (minor allele frequency >5%) identified in these studies have only a modest effect on T2DM risk and collectively explain only a small portion (~20%) of observed T2DM heritability 5 .It has been hypothesized that lower-frequency variants could explain much of the remaining heritability 79 .However, results of a large-scale sequencing study from the GoT2D and T2D-GENES consortia, published in 2016, do not support such a hypothesis 5 .Genetic variants might help reveal possible aetiological mechanisms underlying T2DM development; however, the variants identified thus far have not enabled clinical prediction beyond that achieved with common clinical measurements, including age, BMI, fasting levels of glucose and dyslipidaemia.A study published in 2014 linked susceptibility variants to quantitative glycaemic traits and grouped these variants on the basis of their potential intermediate mechanisms in T2DM pathophysiology: four variants fitted a clear insulin resistance pattern; two reduced insulin secretion with fasting hyperglycaemia; nine reduced insulin secretion with normal fasting glycaemia; and one altered insulin processing 80 .Considering such evidence, the genetic architecture of T2DM is highly polygenic, and thus, substantially larger association studies are needed to identify most T2DM loci, which typically have small to modest effect sizes 81 .", + "\t\n\nAlthough precision diabetes medicine is much more than genetics, the majority of relevant research has focused on evaluating the role of genetic variants in precision prevention.Large epidemiological studies (75) and intervention trials (76,77) strongly suggest that standard approaches for lifestyle modification are equally efficacious in preventing diabetes regardless of the underlying genetic risk.This contrasts with the extensive epidemiological evidence suggesting that the relationship of lifestyle with obesity is dependent on genetic risk (78-81); however, with few exceptions (e.g., [74]), analyses in large randomized controlled trials have failed to show that these same genetic variants modify weight loss in response to lifestyle intervention (82).It is also important to recognize that knowledge of increased genetic risk for diabetes may not motivate improvements in lifestyle behaviors.Indeed, knowledge of increased genetic risk for diabetes may decrease motivation to modify behavior in genetic fatalists (83).", + "\t\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + ], + [ + "\tConcluding remarks\n\nFor the past two decades, genetics has been widely advocated as a tool for unravelling the pathogenesis of common forms of diabetes, but the complexity of the problem defied easy solutions.Recent advances have made it possible to find many of the genes that predispose to both major types of diabetes.Much work is still needed to translate knowledge of these genes into benefits for patients.The greatest benefit is likely to come from new\tIntroduction\n\nWe are all witnesses to a period of astonishing progress in our understanding of the genetic basis of diabetes, and the advances of recent months are arguably the most important made since the role of the HLA region was recognised in type 1 diabetes.The number of genetic regions causally implicated is now 11 each for type 1 and type 2 diabetes [1][2][3][4][5][6][7][8][9], and is set to rise further.The bewildering pace of new discovery stands in stark contrast to the slow progress that characterised the previous two decades, with a total combined output of three confirmed genes for type 2 diabetes and six for type 1 (Fig. 1).At last, it seems, our understanding of the genetic basis of complex, multifactorial forms of diabetes is catching up with that of rarer, single-gene disorders.", + "\t\n\nThis technology recently facilitated rapid progress in type 2 diabetes genetic research.This is all the more remarkable because type 2 diabetes does not have a strong genetic component compared with some other common traits, and was previously described as 'a geneticist's nightmare' 1,2 .Nevertheless, early results have been excellent, yielding six new replicating gene regions.", + "\tFuture directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4.", + "\t\n\nRecent advances in GWAS have substantially improved our understanding of the pathophysiology of diabetes, but the currently identified genetic susceptibility loci are insufficient to explain differences in diabetes risk across different ethnic groups or the rapid rise in diabetes prevalence over the past several decades.Clinical utility of these loci in predicting future risk of diabetes is also limited.", + "\t\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner.", + "\t\n\nAll of these genetic research efforts of the last decade have led to the identification of at least 27 (confirmed and potential) type 2 diabetes susceptibility genes, and their time-course of discovery or initial publication is depicted in Fig. 1.", + "\t\n\nUnfortunately, these questions are not yet answered.The early 1990s was the beginning of the era of molecular biol- ogy, and it was generally assumed that within a few years this powerful new technology would identify the genetic defects in type 2 diabetes.Indeed, the genetic basis for many monogenic forms of diabetes has been discovered such as mitochondrial genome defects and the association with diabetes and deafness, Wolfram's syndrome, several rare syndromes of extreme insulin resistance and obesity, and many of the MODY syndromes (maturity onset diabetes of youth).Still, these account for only a small proportion of diabetes.", + "\tNew d iscoveries in the g enetic e tiology of T 2 DM\n\nImportant advances in T2DM genetics have been made with the completion of GWA studies based on HapMap -selected common SNPs.This has become reality with the outstanding breakthroughs made in the knowledge and assessment of human genome variations, their mapping and their links with the genetic background of common diseases [167] , and in the development and accessibility to very high throughput genotyping techniques based on microarray technology and to biostatistical tools for large cohort data analyses.", + "\t\n\nOver the past two years, there has been a spectacular change in the capacity to identify common genetic variants that contribute to predisposition to complex multifactorial phenotypes such as type 2 diabetes (T2D).The principal advance has been the ability to undertake surveys of genome-wide association in large study samples.Through these and related efforts, $20 common variants are now robustly implicated in T2D susceptibility.Current developments, for example in high-throughput resequencing, should help to provide a more comprehensive view of T2D susceptibility in the near future.Although additional investigation is needed to define the causal variants within these novel T2Dsusceptibility regions, to understand disease mechanisms and to effect clinical translation, these findings are already highlighting the predominant contribution of defects in pancreatic b-cell function to the development of T2D.", + "\t\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow.", + "\t\n\nIn recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management.", + "\t\nIt has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes.Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups.This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders.\t\n\nIt has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes.Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups.This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders.", + "\t\nGenome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.", + "\t\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.\t\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "\t\n\nMuch has been made over the past decade of the potential for genetics to advance our understanding of the pathogenesis of type 2 diabetes and to 'revolutionise' management of this condition [1].Others have argued that these claims are premature [2]; indeed, some have questioned the contribution of genetic predisposition to the pathogenesis of common forms of type 2 diabetes [3].", + "\t\n\n During the last decade, there have been major advances in our understanding of the genetic basis of the most common subtypes of type 1 (T1D) and type 2 diabetes (T2D), with .500robust associations identified Although individual variants typically have only a modest effect on risk, when combined into a polygenic score, they offer increasing power to capture information on individual patterns of disease predisposition with the potential to influence clinical management", + "\tTHE GENETICS OF TYPE 1 DIABETES\n\nThe study of the genome to map disease-susceptibility regions for T1D and other multifactorial diseases has been facilitated by recent advances in next generation DNA sequencing methods." + ] + ], + "task_id": [ + { + "task_id": "B36C57B013A41A55687021F0B0381728" + }, + { + "task_id": "E2DB78EE98507BDA17501CDAC1C32074" + }, + { + "task_id": "2D8A4643C25BA9AEECDF9422D43C0EDE" + }, + { + "task_id": "3F639DE5D9A1A9BE45168C06A00C571F" + }, + { + "task_id": "A34A4714BF78D63988FD46A4FA033340" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_4.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_4.json new file mode 100644 index 0000000..a579777 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_4.json @@ -0,0 +1,114 @@ +{ + "question": [ + "How do genes influence how our bodies respond to sugar and insulin?", + "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?", + "How can new gene therapies potentially cure or treat diabetes?", + "What is the difference between monogenic and polygenic diabetes?", + "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" + ], + "answer": [ + "Genes play a crucial role in how our bodies respond to sugar and insulin. Certain gene variants can impair insulin sensitivity and affect glucose, fatty acid, and energy metabolism at both cellular and whole-body levels. For instance, abnormalities in the genes encoding for proteins involved in the insulin-signaling network can cause insulin resistance and related traits. This can lead to metabolic diseases like type 2 diabetes, obesity, and cardiovascular diseases. Additionally, genetic mutations can severely alter insulin sensitivity. Some genes also play a role in insulin secretion by pancreatic -cells, which is crucial for maintaining normal glucose levels.", + "Yes, there are specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes. For Type 1 diabetes, the greatest genetic risk is conferred by specific alleles, genotypes, and haplotypes of the HLA class II (and class I) genes. There are also about 50 non-HLA region loci that affect the Type 1 diabetes risk. For Type 2 diabetes, the risk is higher if one parent has the disease, particularly if the mother has it. The disease is polygenic, with many common variants contributing to overall disease risk.", + "New gene therapies can potentially treat diabetes by targeting specific genetic variations that affect the response to certain drugs. For example, genetic variation in the organic cation transporter 1 (OCT1) has been found to affect the response to the diabetes drug metformin. Understanding these gene-drug interactions can lead to more personalized and effective treatment strategies. Additionally, therapies that slow the loss of -cell function, which is a characteristic of type 2 diabetes, could provide more durable glucose control. Incretin-based therapies, which improve -cell health, could potentially slow disease progression. Furthermore, the use of nanotechnology in gene therapies could introduce novel strategies for glucose measurement and insulin delivery.", + "Monogenic diabetes is a form of the disease that results from mutations in a single gene. It is characterized by high phenotypic penetrance, meaning the presence of the mutation almost certainly leads to the development of the disease. On the other hand, polygenic diabetes is a form of the disease that results from the combined effect of mutations in multiple genes. Each of these mutations contributes a small amount of risk, and the disease typically also requires a permissive environment to develop.", + "Studying the DNA of people with diabetes helps scientists identify key biological processes and genes involved in the disease's pathogenesis. This can lead to the discovery of novel drug targets for the disease. Additionally, understanding genetic variants can influence an individual's response to therapy, paving the way for personalized medicine. Furthermore, advancements in genomics and genetic testing can help identify individuals at risk of developing diabetes, enabling early intervention and prevention strategies." + ], + "contexts": [ + [ + "\t\n\nElucidating the potential mechanisms involved in the detrimental effect of excess body weight on insulin action is an important priority in counteracting obesityassociated diseases.The present study aimed to disentangle the epigenetic basis of insulin resistance by performing a genome-wide epigenetic analysis in visceral adipose tissue (VAT) from morbidly obese patients depending on the insulin sensitivity evaluated by the clamp technique.The global human methylome screening performed in VAT from 7 insulin-resistant (IR) and 5 insulin-sensitive (IS) morbidly obese patients (discovery cohort) analyzed using the Infinium HumanMethyla-tion450 BeadChip array identified 982 CpG sites able to perfectly separate the IR and IS samples.The identified sites represented 538 unique genes, 10% of which were diabetes-associated genes.The current work identified novel IR-related genes epigenetically regulated in VAT, such as COL9A1, COL11A2, CD44, MUC4, ADAM2, IGF2BP1, GATA4, TET1, ZNF714, ADCY9, TBX5, and HDACM.The gene with the largest methylation fold-change and mapped by 5 differentially methylated CpG sites located in island/shore and promoter region was ZNF714.This gene presented lower methylation levels in IR than in IS patients in association with increased transcription levels, as further reflected in a validation cohort (n 5 24; 11 IR and 13 IS).This study reveals, for the first time, a potential epigenetic regulation involved in the dysregulation of VAT that could predispose patients to insulin resistance and future type 2 dia-1 Both authors equally contributed to this work.\t\nElucidating the potential mechanisms involved in the detrimental effect of excess body weight on insulin action is an important priority in counteracting obesityassociated diseases.The present study aimed to disentangle the epigenetic basis of insulin resistance by performing a genome-wide epigenetic analysis in visceral adipose tissue (VAT) from morbidly obese patients depending on the insulin sensitivity evaluated by the clamp technique.The global human methylome screening performed in VAT from 7 insulin-resistant (IR) and 5 insulin-sensitive (IS) morbidly obese patients (discovery cohort) analyzed using the Infinium HumanMethyla-tion450 BeadChip array identified 982 CpG sites able to perfectly separate the IR and IS samples.The identified sites represented 538 unique genes, 10% of which were diabetes-associated genes.The current work identified novel IR-related genes epigenetically regulated in VAT, such as COL9A1, COL11A2, CD44, MUC4, ADAM2, IGF2BP1, GATA4, TET1, ZNF714, ADCY9, TBX5, and HDACM.The gene with the largest methylation fold-change and mapped by 5 differentially methylated CpG sites located in island/shore and promoter region was ZNF714.This gene presented lower methylation levels in IR than in IS patients in association with increased transcription levels, as further reflected in a validation cohort (n 5 24; 11 IR and 13 IS).This study reveals, for the first time, a potential epigenetic regulation involved in the dysregulation of VAT that could predispose patients to insulin resistance and future type 2 dia-1 Both authors equally contributed to this work.", + "\tElucidate the pathogenesis linking obesity and type 2 diabetes\n\nA better understanding of mechanisms linking obesity, insulin resistance, and type 2 diabetes may ultimately facilitate more individualized treatment.One future research priority is to clarifty how identified gene variants affect glucose, fatty acid, and energy metabolism at both cellular and whole-body levels.Rather than searching for a single factor or theory explaining the predisposition to -cell decompensation in obese individuals, a multifactorial, synergistic explanation seems more compatible with current knowledge.Multiple mechanisms may link -cell dysfunction to systemic insulin resistance, including differing cellular responses to nutrient excess and impaired brain neurocircuits governing energy homeostasis.One way to approach this complex pathophysiology is to examine glucose-tolerant obese patients and study the association with and progression to -cell decompensation.", + "\t\n\nWe began the investigation by focusing on insulin-signaling genes, a natural and well-established candidate for finding a signature set of genes associated with insulin resistance or diabetes [9].In particular, by examining microarray data, we attempted to detect a statistically significant, transcriptional alteration in a set of insulin-signaling genes in diabetic tissue compared to normal.Surprisingly, using existing analytical methods, we were unable to detect such alterations in microarray data produced in several human studies.Using sophisticated and remarkably sensitive techniques, previous studies identified the oxidative phosphorylation pathway as transcriptionally down-regulated in diabetic muscle tissue compared to normal [10,11].However, insulin-signaling gene sets were not detected to be transcriptionally altered, using state of the art analyses, more than expected by chance.\tAuthor Summary\n\nType 2 diabetes mellitus currently affects millions of people.It is clinically characterized by insulin resistance in addition to an impaired glucose response and associated with numerous complications including heart disease, stroke, neuropathy, and kidney failure, among others.Accurate identification of the underlying molecular mechanisms of the disease or its complications is an important research problem that could lead to novel diagnostics and therapy.The main challenge stems from the fact that insulin resistance is a complex disorder and affects a multitude of biological processes, metabolic networks, and signaling pathways.In this report, the authors develop a network-based methodology that appears to be more sensitive than previous approaches in detecting deregulated molecular processes in a disease state.The methodology revealed that both insulin signaling and nuclear receptor networks are consistently and differentially expressed in many models of insulin resistance.The positive results suggest such network-based diagnostic technologies hold promise as potentially useful clinical and research tools in the future.affected in the disease state. (3) Evaluate the hypothesis that genes in a given gene set are observed in a higher proportion (i.e., enriched) than expected by chance in the HSN and repeat for each gene set in the assembly.Repeat (2) and (3) for every insulin resistant or diabetic condition compared to normal in the dataset. (4) Order the gene sets of interest based on the number of different HSNs where they appear enriched. (5) For each gene set, assign a p-value to the number of conditions where it is enriched.The gene sets with a significant p-value are taken as transcriptionally affected across a broad set of diabetes-related models.Consistent with the stated goal of GNEA, gene sets enriched in a few conditions, while potentially interesting in their own right, will not generally be assigned a significant p-value (Figure 1).", + "\tIn addition, we have\ndetermined the effects of these modifications on the pattern of gene expression\nin each tissue, and how insulin signaling might interact with nuclear receptor\nsignaling in insulin resistance. Tissues of particular importance in development\nof type 2 diabetes and the metabolic syndrome include the liver, brain and fat. In liver, for example, insulin action through IRS-1 and Akt is involved in control\nof glucose production, while insulin action through IRS-2 and atypical PKCs is\nmore involved in hepatic lipogenesis.", + "\tExercise training and the Ala allele must act either independently or in synergy\nto modify glucose homeostasis through increasing glucose uptake or by decreasing\nhepatic glucose output. At the whole body level, exercise training has been shown\nto increase insulin sensitivity (Borghouts & Keizer 2000, Short et al 2003, Duncan\net al 2003) and has also been shown to decrease basal hepatic glucose production\nin patients with type 2 diabetes (Segal et al 1991).", + "\tIV. Gene Variants Affecting Insulin Sensitivity\n\nInsulin resistance provokes a critical challenge for the pancreatic -cell that has to be compensated for by increments in insulin secretion to maintain normoglycemia.Thus, genetically determined -cell defects may only become apparent in the presence of insulin resistance (9,247).Insulin resistance is therefore considered an early and crucial step in the pathogenesis of type 2 diabetes.Undoubtedly, insulin resistance is strongly associated with obesity.Although the cause-effect relationship is far from being clear, insulin resistance is often suggested to result from obesity and to be predominantly caused by environmental factors, such as high-caloric diet and/or physical inactivity (248,249).However, the genetic investigations of the last 10 yr revealed that certain gene variants impair insulin sensitivity without influencing the overall fat mass.Recent advances in the field, mainly based on candidate gene approaches, also strengthen the role of genetics in the establishment of insulin resistance.", + "\t\n\nKey components of the insulin signaling pathways have also been tested.They were at fi rst thought to be important players in the context of the insulin resistance of T2DM.Several of these genes are also expressed in pancreatic -cells, and several studies from knockout animals have demonstrated that they may also have an important role in the mechanisms of insulin secretion [23,24] .More than 50 different mutations have been found in the coding regions of the insulin receptor gene on chromosome 19p (see Chapter 15 ) [67] ; patients with these mutations seldom present with the common form of T2DM [68] , but rather with a syndrome of severe insulin resistance associated with leprechaunism, or with acanthosis nigricans, hirsutism and major hyperinsulinemia [69] .Missense variants in the gene encoding the fi rst substrate for the insulin receptor kinase ( IRS1 ) on chromosome 2q have been detected in several populations [70 -73] but an association of these variants with diabetes was not observed in all studies [74,75] .", + "\t\n\nFigure 2: Role of genes and the environment in development of obesity and type 2 diabetes Interaction of genes that aff ect body adiposity with environmental factors results in development of obesity and associated insulin resistance.However, only when genes for abnormal -cell function are present along with those for body adiposity does interaction with the environment result in development of type 2 diabetes.\t\n\nGlucose metabolism is normally regulated by a feedback loop including islet cells and insulin-sensitive tissues, in which tissue sensitivity to insulin aff ects magnitude of -cell response.If insulin resistance is present, cells maintain normal glucose tolerance by increasing insulin output.Only when cells cannot release suffi cient insulin in the presence of insulin resistance do glucose concentrations rise.Although -cell dysfunction has a clear genetic component, environmental changes play an essential part.Modern research approaches have helped to establish the important role that hexoses, aminoacids, and fatty acids have in insulin resistance and -cell dysfunction, and the potential role of changes in the microbiome.Several new approaches for treatment have been developed, but more eff ective therapies to slow progressive loss of -cell function are needed.Recent fi ndings from clinical trials provide important information about methods to prevent and treat type 2 diabetes and some of the adverse eff ects of these interventions.However, additional long-term studies of drugs and bariatric surgery are needed to identify new ways to prevent and treat type 2 diabetes and thereby reduce the harmful eff ects of this disease.", + "\t\n\nGenetic studies of IL6 and IL6R in type 2 diabetes and insulin resistance", + "\t\n\nInsulin resistance has a central role in the pathogenesis of several metabolic diseases, including type 2 diabetes, obesity, glucose intolerance, metabolic syndrome, atherosclerosis, and cardiovascular diseases.Insulin resistance and related traits are likely to be caused by abnormalities in the genes encoding for proteins involved in the composite network of insulin-signaling; in this review we have focused our attention on genetic variants of insulin-signaling inhibitor molecules.These proteins interfere with different steps in insulin-signaling: ENPP1/PC-1 and the phosphatases PTP1B and PTPRF/LAR inhibit the insulin receptor activation; INPPL1/SHIP-2 hydrolyzes PI3-kinase products, hampering the phosphoinositide-mediated downstream signaling; and TRIB3 binds the serine-threonine kinase Akt, reducing its phosphorylation levels.While several variants have been described over the years for all these genes, solid evidence of an association with type 2 diabetes and related diseases seems to exist only for rs1044498 of the ENPP1 gene and for rs2295490 of the TRIB3 gene.However, overall the data recapitulated in this Review article may supply useful elements to interpret the results of novel, more technically advanced genetic studies; indeed it is becoming increasingly evident that genetic information on metabolic diseases should be interpreted taking into account the complex biological pathways underlying their pathogenesis.\t\nInsulin resistance has a central role in the pathogenesis of several metabolic diseases, including type 2 diabetes, obesity, glucose intolerance, metabolic syndrome, atherosclerosis, and cardiovascular diseases.Insulin resistance and related traits are likely to be caused by abnormalities in the genes encoding for proteins involved in the composite network of insulin-signaling; in this review we have focused our attention on genetic variants of insulin-signaling inhibitor molecules.These proteins interfere with different steps in insulin-signaling: ENPP1/PC-1 and the phosphatases PTP1B and PTPRF/LAR inhibit the insulin receptor activation; INPPL1/SHIP-2 hydrolyzes PI3-kinase products, hampering the phosphoinositide-mediated downstream signaling; and TRIB3 binds the serine-threonine kinase Akt, reducing its phosphorylation levels.While several variants have been described over the years for all these genes, solid evidence of an association with type 2 diabetes and related diseases seems to exist only for rs1044498 of the ENPP1 gene and for rs2295490 of the TRIB3 gene.However, overall the data recapitulated in this Review article may supply useful elements to interpret the results of novel, more technically advanced genetic studies; indeed it is becoming increasingly evident that genetic information on metabolic diseases should be interpreted taking into account the complex biological pathways underlying their pathogenesis.", + "\tConclusion\n\nWe would propose that it is highly probable that more insulin resistance than b-cell dysfunction T2DM susceptibility genes remain undiscovered at the present time, most likely due to problems associated with study design and the complex nature of physiological responses to nutrients and insulin.In addition, it must be understood that even with 38 genes identified relevant to T2DM pathophysiology, the risk conferred by these combined genes accounts for only a small proportion of overall risk.It must be remembered that the rapid changes in T2DM incidence and prevalence observed in recent decades are a result of the interaction of a stable genetic background with a rapidlychanging environment.Future intervention at newly-discovered insulin secretion controlling loci should improve b-cell function allowing a more robust defence against environmental insult.Targeting oxidative stress, metabolic stress and low grade inflammation may provide fruitful avenues.However, novel therapeutic approaches, whether pharmacological or nonpharmacological, which can target the effects of diet-induced obesity on tissue-specific insulin resistance in the early pathogenesis of T2DM remain a central and invaluable goal of research aiming to halt the rapidly-increasing prevalence of T2DM and its complications worldwide.\tWhy the paucity of genes involved in insulin resistance\n\nrelative to b-cell function?\t\n\nHowever, these cases provide evidence for the existence of genetic mutations that can severely alter insulin sensitivity.It remains possible therefore that the paucity of insulin resistance genes found by GWAS may be at least in part explained by the relative difficulty of accurately measuring small variations in insulin sensitivity compared to measuring small changes in insulin secretion in large populations.", + "\t\n\nBaboons also show patterns similar to humans with respect to insulin resistance.Insulin resistance-related phenotypes were significantly heritable in baboons (Cai et al. 2004;Tejero, Freeland-Graves et al. 2004).We showed that one set of genes contributing to insulin resistance also appeared to influence adiposity-related phenotypes, which revealed a common genetic basis for development of insulin resistance and obesity (Cai et al. 2004).Variation in glucose transporter 4 (GLUT4) mRNAwas found to be under significant genetic influence and was genetically correlated with plasma insulin and body weight, supporting their regulation by a common set of genes (Tejero, Proffitt et al. 2004).", + "\t\n\nI nsulin resistance precedes and predicts the development of type 2 diabetes mellitus (DM) (1,2).Defects in insulin signal transduction, gene expression, and muscle glycogen synthesis, and accumulation of intramyocellular triglycerides have all been identified as potential mediators of insulin resistance in high-risk individuals (1,(3)(4)(5)(6)(7).However, the molecular pathogenesis of DM remains unknown.Mouse data highlight the importance of glucose uptake into muscle but suggest a role for novel mechanisms, distinct from insulin signaling pathways (8).The importance of genetic risk factors is exemplified by the high concordance of DM in identical twins, the strong influence of family history and ethnicity on risk, and the identification of DNA sequence alterations in both rare and common forms of DM (9).Environmental factors, including obesity, inactivity, and aging, also play critical roles in DM risk.Because both genotype and environment converge to influence cellular function via gene and protein expression, we hypothesize that alterations in expression define a phenotype that parallels the metabolic evolution of DM and provides potential clues to pathogenesis.We used high-density oligonucleotide arrays to identify genes differentially expressed in skeletal muscle from nondiabetic and type 2 diabetic subjects.Because hyperglycemia per se can modulate expression, we also evaluated gene expression in insulin-resistant subjects at high risk for DM (''prediabetes'') on the basis of family history of DM and Mexican-American ethnicity (10).We demonstrate that prediabetic and diabetic muscle is characterized by decreased expression of oxidative phosphorylation genes, many of which are regulated by nuclear respiratory factor (NRF)-dependent transcription.Further-more, expression of peroxisomal proliferator activator receptor coactivator (PGC1) and - (PPARGC1 and PERC), coactivators of both PPARG and NRF-dependent transcription, is significantly reduced in both prediabetic and diabetic subjects.Taken together, these data indicate that decreased PGC1 expression may be responsible for decreased expression of NRFdependent metabolic and mitochondrial genes and may contribute to the metabolic disturbances characteristic of insulin resistance and DM.", + "\t\n\nStudies carried out to identify genetic and nongenetic components participating in homeostatic regulation of glucose and in T2D physiopathology have identified insulin resistance as a postreceptor defect that ultimately affects translocation of the glucose transporter GLUT4 toward the cell surface [9,10].The transduction of insulin signals is mediated by a series of phosphorylation cascades linked to the initial activation of the tyrosine kinase receptor of insulin and its action on the substrates of the insulin receptors (insulin receptor substrate IRS1, IRS-2, IRS-3, and IRS-4) [11].Tyrosine phosphorylation of IRS1 and its binding to phosphatidylinositol 3-kinase are critical events in the insulin signaling cascade leading to insulin-stimulated glucose transport. [12].The importance of IRS1 in insulin signaling has been confirmed in studies showing that this gene plays a very important role not only in peripheral insulin sensitivity, but also in the regulation of insulin secretion by pancreatic -cells [12,13].In addition, IRS1 knockout mice adipocytes showed considerable decrease in glucose transport and in the translocation of GLUT4 to the plasma membrane as a response to insulin [14].Insulin receptor substrate-1, whose gene is located in chromosome 2q36, has 21 sites for tyrosine kinase phosphorylation, which are responsible for most of its enzymatic function." + ], + [ + "\tA. Genetic Screening\n\nWe have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is 10 -15 times greater.Although 70% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3).", + "\t\n\nPrevious reports show that in the Japanese population, the frequency of a positive family history of diabetes in Japanese diabetic patients is particularly higher in parents of young-onset type 2 diabetic patients and lower in patients who have maximal BMI 35 kg/m 2 compared with those with maximal BMI 30 kg/m 2 (17).Therefore, we considered BMI and age at onset as possible covariates and/or confounding factors.We designed two subset populations, the first one is the subset termed Young-Onset45, in which both siblings were 45 years of age at diagnosis, and which includes the younger-diagnosed 20% families.The other is the subset termed Lean30, in which both siblings have a maximal BMI 30 kg/m 2 (Table 3).Genotyping of microsatellite markers.Genomic DNA was isolated from whole blood using the phenol-chloroform method.Genotyping was performed using a fluorescence-labeled human linkage mapping set (PE-LMSV2) comprising 400 highly informative microsatellite markers with an average intermarker spacing of 9.7 cM.Multiplex PCR conditions were set up for each of the 28 panels to amplify the 400 markers in 87 PCRs.PCR (95C for 12 min, then 40 cycles at 94C for 15 min, 55C for 15 min, 72C for 30 min, and 72C for 10 min) was performed with a 384-well plate on a GeneAmp PCR system (9700 Biblock; Perkin-Elmer, Foster City, CA) using the following (in 10-l reactions): 20 -40 ng genomic DNA, 2.5 mmol/l MgCl 2 , 0.25 mmol/l dNTPs (Pharmacia), variable amounts (0.2-1.5 pmol) of 5 and 3 primers, and 0.4 units AmpliTaq Gold DNA polymerase (Perkin-Elmer) in 1 PCR buffer II (Perkin-Elmer). (Multiplex PCR conditions are available from the authors on request. )An automated 96-channel pipettor Multimek 96 (Beckman) was used for the pipetting steps.Pooled amplification products were electrophoresed through 5% polyacrylamide gels (Long Ranger Singel Pack; Perkin Elmer) for 1.5 h at 2,000 V on 24-cm plates on an ABI 377 DNA sequencer.Semiautomated fragment sizing was performed by using Genescan 3.0 software (ABI), followed by allele calling with Genotyper 2.1 software (ABI).Some panels were electrophoresed on a multicapillary ABI 3700 sequencer and analyzed by Genescan-2.1 software (Perkin-Elmer).Among 400 markers in PE-LMSV2, eight markers (D1S214, D1S252, D3S2338, D3S1285, D4S1534, D7S640, D15S153, and D19S221) were not included because of technical problems.", + "\t\nType 1 diabetes (T1D) tends to cluster in families, suggesting there may be a genetic component predisposing to disease.However, a recent large-scale genome-wide association study concluded that identified genetic factors, single nucleotide polymorphisms, do not account for overall familiality.Another class of genetic variation is the amplification or deletion of .1 kilobase segments of the genome, also termed copy number variations (CNVs).We performed genome-wide CNV analysis on a cohort of 20 unrelated adults with T1D and a control (Ctrl) cohort of 20 subjects using the Affymetrix SNP Array 6.0 in combination with the Birdsuite copy number calling software.We identified 39 CNVs as enriched or depleted in T1D versus Ctrl.Additionally, we performed CNV analysis in a group of 10 monozygotic twin pairs discordant for T1D.Eleven of these 39 CNVs were also respectively enriched or depleted in the Twin cohort, suggesting that these variants may be involved in the development of islet autoimmunity, as the presently unaffected twin is at high risk for developing islet autoimmunity and T1D in his or her lifetime.These CNVs include a deletion on chromosome 6p21, near an HLA-DQ allele.CNVs were found that were both enriched or depleted in patients with or at high risk for developing T1D.These regions may represent genetic variants contributing to development of islet autoimmunity in T1D.", + "\t\n\nmore frequently than by chance alone among siblings who share the phenotype of type 1 diabetes.Nuclear families, or even just the affected sibling pairs themselves, are genotyped with panels of markers spanning the genome at a modest density.Linkage between a marker and a susceptibility locus for type 1 diabetes is determined by accumulating evidence across families.Since affected sibling pairs are relatively rare in type 1 diabetes, data from linkage studies are collected from a rather unique subgroup of families with type 1 diabetes.In general, linkage studies are the method of choice when the risk factors being sought have large effect sizes but are relatively rare.As risk factors become more common and have smaller effect sizes, association methods emerge as a potentially more powerful approach (Fig. 1).Since the genetic basis of type 1 diabetes is probably a complex mixture of small, moderate, and large genetic effects, multiple strategies are needed and vary according to the population being studied and their exposure to unknown environmental factors.\tDise a se Pr edic tion\n\nCurrent approaches for the prediction of type 1 diabetes take advantage of the major genetic risk factors, genotyping for HLA-DR and HLA-DQ loci (which is then combined with family history), and screening for autoantibodies directed against islet-cell antigens. 43,44The individual distribution of specific risk alleles correlates with gradations in disease penetrance, enabling a tiered staging strategy for the prediction of type 1 diabetes.For example, children who carry both of the highestrisk HLA haplotypes (DR3-DQ2 and DR4-DQ8) have a risk of approximately 1 in 20 for a diagnosis of type 1 diabetes by the age of 15 years. 45If the child has a sibling who has diabetes and the same haplotypes, the risk is even higher (approximately 55%). 46Since this haplotype combination occurs in only 2.3% of the white population, it is possible to envision universal screening strategies that pinpoint this highest-risk group.Inclusion of additional moderate HLA risk haplotypes and screening for autoantibodies would add cost and complexity to a population-screening approach but have the potential to identify the majority of all children with diabetes before the onset of the disease.If this were possible, then tests of potential preventive strategies could be performed, as outlined later in this article.The large number of new risk loci for type 1 diabetes that were recently identified from genomewide association studies could be added to these prediction schemes.These genetic factors are relatively easy, inexpensive, and noninvasive to measure and can be detected well before other features, such as autoantibodies, would typically develop.\t\nIn 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention.Type 1 diabetes becomes clinically apparent after a preclinical period of varying length, during which autoimmune destruction reduces the mass of beta cells in the pancreatic islets to a level at which blood glucose levels can no longer be maintained in a physiologic range.The disease has two subtypes: 1A, which includes the common, immune-mediated forms of the disease; and 1B, which includes nonimmune forms.In this review, we focus on subtype 1A, which for simplicity will be referred to as type 1 diabetes.Although there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied. Gene tic S t udiesBecause of the uncertainty regarding the number and action of genes involved in type 1 diabetes, genetic studies have tended to focus on approaches that require few assumptions about the underlying model of disease risk.The two primary approaches have been linkage studies (using pairs of affected relatives, typically siblings) and association studies (using either case-control or family-based designs).Linkage studies using affected sibling pairs seek to identify regions of the genome that are shared", + "\t\n\nMore than 60 susceptibility loci have been identified (Table 1).The greatest genetic risk (50%) for T1D is conferred by alterations to immune genes, especially those encoding the classical HLAs (Ounissi-Benkalha and Polychronakos, 2008).Other genetic loci (Table 1) are believed to influence population-level risk for T1D, although it is poorly understood how these non-HLA loci contribute to disease susceptibility (Ram et al., 2016a).\tThe genetics of type 1 diabetes\n\nThere is a strong genetic risk to T1D.This is exemplified by (Redondo et al., 2001) who demonstrated a strong concordance of genetic inheritance (65%) and T1D susceptibility in monozygotic twin pairs.That is, when one sibling is afflicted, there is a high probability that the other twin will develop T1D by the age of 60 years.Additionally, autoantibody positivity and islet destruction was observed after a prospective long-term follow-up of monozygotic twins of patients with T1D, despite initial disease-discordance among the twins (Redondo et al., 2008).", + "\tType 1 diabetes is a genetic disease\n\nFamily studies have indicated that genetic factors are important determinants of type 1 diabetes risk.First, the risk to a sibling of an affected individual is approximately 6%, as compared with an average risk of 0.4% (depending on the population), or a relative increased risk of 15-fold (17).The increased risk to siblings is referred to as l s (18) and is one measure of the degree of familial clustering of the disease.\t\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.", + "\tCONCLUSION\n\nThe greatest genetic risk (both increased risk, susceptible, and decreased risk, protective) for type 1 diabetes is conferred by specific alleles, genotypes, and haplotypes of the HLA class II (and class I) genes.There are currently about 50 non-HLA region loci that also affect the type 1 diabetes risk.Many of the assumed functions of the non-HLA genes of interest suggest that variants at these loci act in concert on the adaptive and innate immune systems to initiate, magnify, and perpetuate -cell destruction.The clues that genetic studies provide will eventually help lead us to identify how -cell destruction is influenced by environmental factors.While there is extensive overlap between type 1 diabetes and other immune-mediated diseases, it appears that type 1 and type 2 diabetes are genetically distinct entities.These observations may suggest ways to help identify causal gene(s) and, ultimately, a set of disease-associated variants defined on specific haplotypes.Unlike other complex human diseases, relatively little familial clustering remains to be explained for type 1 diabetes.The remaining missing heritability for type 1 diabetes is likely to be explained by as yet unmapped common variants, rare variants, structural polymorphisms, and gene-gene and/or gene-environmental interactions, in which we can expect epigenetic effects to play a role.The examination of the type 1 diabetes genes and their pathways may reveal the earliest pathogenic mechanisms that result in the engagement of the innate and adaptive immune systems to produce massive -cell destruction and clinical disease.The resources established by the international T1DGC are available to the research community and provide a basis for future discovery of genes that regulate the earliest events in type 1 diabetes etiology-potential targets for intervention or biomarkers for monitoring the effects and outcomes of potential therapeutic agents.", + "\t\n\nGenome-wide search for genes affecting the age at diagnosis of type 1 diabetes.\t\nGenome-wide search for genes affecting the age at diagnosis of type 1 diabetes.\t\n\nGenes affecting type 1 diabetes diagnosis age / A. Syreeni et al.\tIntroduction\n\nOver 60 loci in the genome contribute to genetic predisposition to type 1 diabetes (T1D) [1][2][3][4][5] in which insulin deficiency results from an autoimmune attack against insulin-producing beta cells of the pancreatic islets.Heterogeneity in the disease aetiology is recently acknowledged and immunological processes leading to T1D in individuals diagnosed later in life appear different from the processes in individuals having disease onset in early childhood, in which B cells are involved in the pathological process in the pancreas [5].Different genes and genetic variants may thus affect disease course at varying ages, also suggested by the high diagnosis age correlation (r 2 = 0.95) in Finnish monozygotic twins concordant for T1D [6].Of the known T1D risk loci, however, only the HLA locus and a few non-HLA loci, have been associated with age at diagnosis [7][8][9][10].Genetic risk score combines risk-increasing alleles into a single score and the genetic risk score for T1D has already been suggested for clinical use for screening of infants at highest T1D risk [11].All disease-susceptibility variants are included in the score, but only a few known T1D variants have stronger effects in individuals with early-onset disease [10].", + "\tGenetics\n\nBoth type 1 and type 2 diabetes are polygenic diseases where many common variants, largely with small effect size, contribute to overall disease risk.Disease heritability (h 2 ), defined as sibling-relative risk, is 3 for type 2 diabetes and 15 for type 1 diabetes (17).The lifetime risk of developing type 2 diabetes is ;40% if one parent has type 2 diabetes and higher if the mother has the disease (18).The risk for type 1 diabetes is ;5% if a parent has type 1 diabetes and higher if the father has the disease (19).Maturity-onset diabetes of the young (MODY) is a monogenic disease and has a high h 2 of ;50 (20).Mutations in any 1 of 13 different individual genes have been identified to cause MODY (21), and a genetic diagnosis can be critical for selecting the most appropriate therapy.For example, children with mutations in KCJN11 causing MODY should be treated with sulfonylureas rather than insulin.\tType 1 Diabetes\n\nThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk.", + "\t\n\nType 1 diabetes as well as type 2 diabetes shows a genetic predisposition, although only type 1 diabetes is HLA dependent [32,33,36,40].", + "\t\n\nType 1 diabetes risk stratification by T1D family history and HLA genotyping", + "\t\n\nStudies [71][72][73][74] in Mexican and Asian populations have identified several mutations associated with type 2 diabetes in young people.The high prevalence of type 2 diabetes in the parents of young people diagnosed with type 2 diabetes could reflect a stronger genetic predisposition, even when monogenic diabetes is excluded.This hypothesis suggests that efforts to define genes that cause type 2 diabetes by linkage might be more powerful if focused on young adults with diabetes, raising the question of whether type 2 diabetes in older populations has a relatively smaller genetic contribution and a stronger environmental contribution. 66" + ], + [ + "\t\n\nType 2 diabetes mellitus affects 9.6% of the adults in the United States and more than 200 million people worldwide.Diabetes can be a devastating disease, but it can now be treated with nine classes of approved drugs (insulins, sulfonylureas, glinides, biguanides, -glucosidase inhibitors, thiazolidinediones, glucagon-like peptide 1 mimetics, amylin mimetics, and dipeptidyl peptidase 4 inhibitors), in addition to diet and exercise regimens.Choosing which drug to give a patient is based on efficacy and also availability, cost, safety, tolerability, and convenience.Personalized medicine promises a path for individually optimized treatment choices, but realizing this promise will require a more comprehensive characterization of disease and drug response.In this issue of the JCI, Shu et al. make significant progress by integrating diverse data supporting the hypothesis that genetic variation in organic cation transporter 1 (OCT1) affects the response to the widely used biguanide metformin (see the related article beginning on page 1422).We discuss metformin, OCT1, pharmacogenetics, and how the integrative genomics revolution is likely to change our understanding and treatment of diabetes.\t\n\nType 2 diabetes mellitus affects 9.6% of the adults in the United States and more than 200 million people worldwide.Diabetes can be a devastating disease, but it can now be treated with nine classes of approved drugs (insulins, sulfonylureas, glinides, biguanides, -glucosidase inhibitors, thiazolidinediones, glucagon-like peptide 1 mimetics, amylin mimetics, and dipeptidyl peptidase 4 inhibitors), in addition to diet and exercise regimens.Choosing which drug to give a patient is based on efficacy and also availability, cost, safety, tolerability, and convenience.Personalized medicine promises a path for individually optimized treatment choices, but realizing this promise will require a more comprehensive characterization of disease and drug response.In this issue of the JCI, Shu et al. make significant progress by integrating diverse data supporting the hypothesis that genetic variation in organic cation transporter 1 (OCT1) affects the response to the widely used biguanide metformin (see the related article beginning on page 1422).We discuss metformin, OCT1, pharmacogenetics, and how the integrative genomics revolution is likely to change our understanding and treatment of diabetes.\t\nType 2 diabetes mellitus affects 9.6% of the adults in the United States and more than 200 million people worldwide.Diabetes can be a devastating disease, but it can now be treated with nine classes of approved drugs (insulins, sulfonylureas, glinides, biguanides, -glucosidase inhibitors, thiazolidinediones, glucagon-like peptide 1 mimetics, amylin mimetics, and dipeptidyl peptidase 4 inhibitors), in addition to diet and exercise regimens.Choosing which drug to give a patient is based on efficacy and also availability, cost, safety, tolerability, and convenience.Personalized medicine promises a path for individually optimized treatment choices, but realizing this promise will require a more comprehensive characterization of disease and drug response.In this issue of the JCI, Shu et al. make significant progress by integrating diverse data supporting the hypothesis that genetic variation in organic cation transporter 1 (OCT1) affects the response to the widely used biguanide metformin (see the related article beginning on page 1422).We discuss metformin, OCT1, pharmacogenetics, and how the integrative genomics revolution is likely to change our understanding and treatment of diabetes.", + "\tA small number of medications\nthat are currently approved for the treatment of T2DM, including metformin, GLP1 receptor\nagonists and SGLT2 inhibitors, have been or are being evaluated as adjuncts to insulin\ntherapy in patients with T1DM275. For instance, the addition of metformin to insulin therapy\ndid not significantly improve glycaemic control in children276 or adults with T1DM277 but\nprovided a modest reduction in total daily insulin dose and body mass index.", + "\t\n\nThe best example of pharmacogenetics has been in the treatment of patients with PNDM resulting from mutations in the Kir6.2 and SUR1 subunits of the K ATP channel.These patients frequently present with ketoacidosis and no detectable endogenous insulin secretion, and therefore insulin injections are the only treatment option.Insulin treatment is difficult in a young child, and outstanding glycemic control is rarely achieved.Finding that one-third of the patients with PNDM had mutations in the Kir6.2 channel that reduced channel closure in response to ATP led to the possibility of treating these patients with sulfonylureas that close the channel by an ATP-independent route (4,42).It was then possible to replace insulin injections with high-dose oral sulfonylureas in 90% of patients and also to achieve improved glycemic control without an increase in hypoglycemia (43,44).Insulin secretion is regulated despite the -cell having a limited response to ATP; this is predominantly mediated through nonclassical pathways for insulin secretion, particularly GLP1 (43).Excellent glycemic control is also seen in the majority of patients with SUR1 mutations treated with sulfonylureas (45).Therefore, 50% of patients diagnosed before 6 months with permanent diabetes can benefit greatly from a molecular diagnosis.To date, patients with K ATP channel mutations have maintained near normoglycemia for over 4 years (A.T.H., unpublished data).Doses tend to reduce over time, suggesting that the effectiveness of this treatment will be long lasting.", + "\tDevelop innovative approaches to pharmacological and surgical management\n\nInnovative approaches to managing obesity may lower certain barriers undermining treatment of both obesity and type 2 diabetes.For example, modulating the incretin axis may benefit both energy balance and glycemia.Novel pharmacological development may depend on information gained from more efficient use of genomic, proteomic, and metabolomic approaches and from information learned from studying weight-loss mechanisms in bariatric surgery.In addition, co-opting less traditional organs such as the brain and gut into the core pathophysiology of type 2 diabetes may reveal new biomarkers and/or targets for therapeutic intervention.Finally, safe and effective centrally acting drugs that decrease appetite or increase satiety are urgently needed.However, as regulatory agencies increase the need for safety testing, fewer new and innovative approaches for weight loss are being developed because of the prolonged time and immense expense involved.", + "\t\n\nPharmacogenomic studies in case of newer therapies are few.Incretin-based therapies, which help control postmeal glucagon levels and hence blood sugar, involve the use of two types of medicine classes -DPP-4 inhibitors and GLP-1 receptor analogs.\t\n\nTable 2 summarizes some of the gene-drug interactions for a few important medicinal classes used in diabetes treatment.", + "\tFuture developments in mostly untested areas\n\nBecause available treatments at present do not easily achieve and maintain normal concentrations of glucose as -cell function progressively decreases, new approaches are being developed (table 1), which represent mostly untested mechanisms.\t\n\nFigure 3: Drugs to treat type 2 diabetes (A) The rate of introduction of new classes of drugs has accelerated during the past 20 years.Two classes (animal insulin and inhaled insulin; red) are essentially no longer available as therapeutics. (B) Diff erent classes of drugs act on diff erent organ systems.Insulin is a replacement for the natural product of islet cells.Classic organ systems that have been targeted for decades comprise the pancreatic islet, liver, muscle, and adipose tissue.Non-classic targets have been focused on recently, and include the intestine, kidneys, and brain.DPP4=dipeptidyl peptidase 4. SGLT2=sodium-glucose co-transporter 2. GLP-1=glucagon-like peptide 1.\t\n\nIn view of the fact that type 2 diabetes is a progressive disease due to advancing -cell dysfunction, can new drugs slow loss of -cell function to provide durable glucose control?In the ADOPT study, 161 recently diagnosed and previously untreated patients were given 4 years of monotherapy with glibenclamide, metformin, or rosiglitazone.Glibenclamide produced the largest initial reduction in glycaemia, but provided poorest maintenance of overall glucose control.Whereas the onset of glucose lowering with the other two drugs was slower than for glibenclamide, it was most sustained with rosiglitazone, with intermediate maintenance of glucose control with metformin, which was mostly related to eff ect on -cell function. 11,161Whether recently introduced drugs will maintain glucose control over the long term remains to be established.Limited data from a few patients suggest that incretin-based therapies, which are purported to improve -cell health, could have such a benefi t. 162 Strategies to slow disease progression have also focused on people with impaired glucose tolerance or impaired fasting glucose because of their high risk of development of type 2 diabetes.Several studies have examined the ability of lifestyle modifi cation and drugs to slow progression to diabetes (table 2). ][165][166][167][168][169][170][171][172][173][174][175] Findings from prolonged follow-up showed that in some instances the benefi t of treatment was retained for 10 years or more, [176][177][178] and could reduce risk of development of severe retinopathy. 179In the DPP study, 180 restoration of individuals to normal fasting and 2 h glucose concentrations only once during the intervention phase was associated with a reduced rate of subsequent diabetes, mostly as a result of improved -cell function.A question that has largely gone unanswered is whether the interventions actually alter the natural history of the disease, or simply mask the development of diabetes as a result of earlier commencement of treatment. 181Only reports of the eff ects of troglitazone in DPP 172 and insulin glargine in ORIGIN 146 suggest a residual benefi t after prolonged withdrawal of the intervention.However, despite good rationale for approval of interventions to delay the onset of diabetes, 182 no drug has yet received offi cial sanction as a preventive treatment.\tOral and injectable drugs: present knowledge, lessons learned, and implications for the future\n\nThe increasing prevalence of type 2 diabetes has stimulated development of many new approaches to safely treat hyperglycaemia (fi gure 3).The aim of these therapies is to reduce and maintain glucose concentrations as close to normal for as long as possible after diagnosis (panels 1, 2), and thereby prevent development of complications.Although some therapies have been unsuccessful because of adverse eff ects or negligible therapeutic effi cacy, several are very well accepted and are used worldwide.The mode of action for most of these drugs has been reported (fi gure 3).However, individual responses to these drugs can diff er greatly, probably as a result of the heterogeneous nature of the pathophysiology of type 2 diabetes.The appendix provides further discussion on drugs that have been widely available for more than a decade (eg, sulfonylurea antidiabetics, biguanide antidiabetics, -glucosidase inhibitors, and peroxisome proliferatoractivated receptor agonists).", + "\tPotential for treatment\n\nSuccessful glycaemic control of T2D patients often requires a combination of several of oral agents, together with subcutaneous insulin for more severe cases.The use of currently available therapeutics can often lead to side effects, including increase in body weight, risk of hypoglycaemia and gastrointestinal problems.In addition, the efficacy of these drugs is limited to the early stages of T2D, when fasting blood glucose levels are relatively low, with approximately 40% of T2D patients on oral anti-diabetics failing to control their blood glucose and having to supplement with insulin.And, of course, all T1D patients currently face a lifetime of injecting insulin.So there is room for more efficacious therapeutic agents.", + "\tNanotechnology and Diabetes\n\nThe interface of nanotechnology in the treatment of diabetes has introduced novel strategies for glucose measurement and insulin delivery.Researchers have demonstrated the advantages of glucose sensors and closed-loop insulin delivery approaches in facilitating the diabetes treatment to make it [34] beneficial in both type 1 and type 2 diabetes.\t\n\nFor the management of type 2 diabetes, a well monitored glycemic control is required.The need to control the progressive deterioration of cell function is essential since it can lead to a loss of glycemic control.Conventional drugs and insulin are effective but cannot repair the associated metabolic and glucoregulatory dysfunctions.The menace of diabetes is increasing day by day and aggressive and targeted combinational therapy is the need of the hour particularly incretin based therapy and peptide analogs.This may restore and preserve cell function and halt the progression of type 2 diabetes [87].In the present era, the effectiveness and the success of the new drug will depend on its ability to treat/relieve one or more of the metabolic disturbances whether increased production of insulin or enhancement in glucose uptake and utilization by the peripheral tissues particularly skeletal muscle.Besides new generations of therapeutics, several other classes have also been reported as alternative strategies alone or in combinations to provide an effective treatment for diabetes.", + "\tTherapeutics\n\nAside from insulin and insulin analogs, therapies for diabetes include those that enhance insulin secretion, those that stimulate insulin action, those that reduce hepatic and endogenous glucose production, and those that impact glycemia through other mechanisms.By better understanding the pathophysiology and natural history of various subtypes of diabetes and applying what we know about the modes of action and pharmacogenomics of existing therapies, we can better apply a personalized approach to diabetes management.There is a growing body of evidence regarding which phenotypic and genotypic subsets of patients with diabetes respond best, or are resistant to, specific therapies (113), including sulfonylureas (114,115), metformin (116,117), thiazolidinediones (118,119), incretin therapies (120), and inhibitors of sodium-glucose cotransporter 2 (SGLT2) (121,122).", + "\t\n\nA variety of treatment modalities exist for individuals with type 2 diabetes mellitus (T2D).In addition to dietary and physical activity interventions, T2D is also treated pharmacologically with nine major classes of approved drugs.These medications include insulin and its analogues, sulfonylureas, biguanides, thiazolidinediones (TZDs), meglitinides, -glucosidase inhibitors, amylin analogues, incretin hormone mimetics, and dipeptidyl peptidase 4 (DPP4) inhibitors.Pharmacological treatment strategies for T2D are typically based on efficacy, yet favorable responses to such therapeutics are oftentimes variable and difficult to predict.Characterization of drug response is expected to substantially enhance our ability to provide patients with the most effective treatment strategy given their individual backgrounds, yet pharmacogenetic study of diabetes medications is still in its infancy.To date, major pharmacogenetic studies have focused on response to sulfonylureas, biguanides, and TZDs.Here, we provide a comprehensive review of pharmacogenetics investigations of these specific anti-diabetes medications.We focus not only on the results of these studies, but also on how experimental design, study sample issues, and definition of 'response' can significantly impact our interpretation of findings.Understanding the pharmacogenetics of anti-diabetes medications will provide critical baseline information for the development and implementation of genetic screening into therapeutic decision making, and lay the foundation for \"individualized medicine\" for patients with T2D.\t\nA variety of treatment modalities exist for individuals with type 2 diabetes mellitus (T2D).In addition to dietary and physical activity interventions, T2D is also treated pharmacologically with nine major classes of approved drugs.These medications include insulin and its analogues, sulfonylureas, biguanides, thiazolidinediones (TZDs), meglitinides, -glucosidase inhibitors, amylin analogues, incretin hormone mimetics, and dipeptidyl peptidase 4 (DPP4) inhibitors.Pharmacological treatment strategies for T2D are typically based on efficacy, yet favorable responses to such therapeutics are oftentimes variable and difficult to predict.Characterization of drug response is expected to substantially enhance our ability to provide patients with the most effective treatment strategy given their individual backgrounds, yet pharmacogenetic study of diabetes medications is still in its infancy.To date, major pharmacogenetic studies have focused on response to sulfonylureas, biguanides, and TZDs.Here, we provide a comprehensive review of pharmacogenetics investigations of these specific anti-diabetes medications.We focus not only on the results of these studies, but also on how experimental design, study sample issues, and definition of 'response' can significantly impact our interpretation of findings.Understanding the pharmacogenetics of anti-diabetes medications will provide critical baseline information for the development and implementation of genetic screening into therapeutic decision making, and lay the foundation for \"individualized medicine\" for patients with T2D.", + "\t\n\ntherapeutic target for the development of agents to improve glucose regulation and to prevent or treat type 2 diabetes.", + "\t\n\nThe only existing therapy is insulin for T1D.Developments in long-acting and glucose-sensitive insulins are improving the health and well-being of people with T1D, as are technological advances in continuous glucose monitoring devices, insulin pumps, closed-loop systems, and the artificial pancreas." + ], + [ + "\tGenetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18).", + "\tLESSONS LEARNED FOR MULTIFACTORIAL DISEASE\n\nMonogenic and syndromic forms account for only a small, though highly informative, proportion of cases of nonautoimmune diabetes.The challenge for medical science lies in bringing equivalent mechanistic insights and translational benefits to the hundreds of millions of people already affected by, or at risk of, more common, typical forms of diabetes.For type 2 diabetes, there is abundant evidence that individual susceptibility is influenced by both the combination of genetic variation at multiple sites and a series of environmental exposures encountered during life (52).Tracking down the specific genetic variants involved has been tougher than for monogenic forms of disease, since the correlations between genotype and phenotype are far weaker (53,54).However, recent efforts have now identified at least 17 confirmed type 2 diabetessusceptibility variants ( (69), and development and exploitation of this methodology has had the greatest impact on susceptibility gene discovery.Even so, many of these discoveries have been hard-won.One reason for this is that the \"candidate\" gene-based approach has proved, with notable exceptions (55,56), to be an inefficient route to susceptibility gene discovery; it is only with the advent of functionally agnostic genome-wide approaches that the floodgates have opened (70).Another reason is that detection of the variants of modest effect that appear to be responsible for much of type 2 diabetes susceptibility (per-allele odds ratios [ORs] 1.10 -1.40, for risk-allele frequencies 10 -90%) has required association studies conducted in extremely large sample sizes (thousands of individuals) (54).Variants within TCF7L2 have the largest effects seen so far, with a per-allele OR of 1.4 (57): the 15% of Europeans carrying two copies of the risk allele are at approximately twice the lifetime risk of type 2 diabetes as the 40% who have none.", + "\tGenes and T2DM -from \"susceptibility\" to \"determination\"\n\nAs far as genetic bacground of T2DM is concerned, the disease may be divided into two large groups: monogenic and polygenic forms [71,73] (Tab.1).Monogenic forms are a consequence of rare mutations in a single gene [73].Mutations may affect the structure and subsequently the function of a protein or tRNA.In some cases they may be localised in regulatory parts of genes and alter gene expression.Monogenic forms are characterised by high phenotypic penetrance, which means that the presence of the mutation practically determines the development of the disease.They are also characterised by early age of diagnosis, and frequently, but not always, a severe clinical picture, and occasionally the presence of extra-pancreatic features.Genetic background plays a critical role in their pathogenesis, while the environment only slightly modifies the clinical picture.The known forms of monogenic T2DM are characterized either by severe defect in insulin secretion or profound decrease in insulin sensitivity.Like in other Mendelian traits, in spite of their huge influence on the health of some individuals and families, their role in entire populations is very limited.\t\nThe development of type 2 diabetes (T2DM) is determined by two factors: genetics and environment.The genetic background of T2DM is undoubtedly heterogeneous.Most patients with T2DM exhibit two different defects: the impairment of insulin secretion and decreased insulin sensitivity.This means that there are at least two pathophysiological pathways and at least two groups of genes that may be involved in the pathogenesis of T2DM.As far as genetic bacground of T2DM is concerned, the disease may be divided into two large groups: monogenic and polygenic forms.In this review, we present genes known to cause rare monogenic forms of diabetes with predominant insulin deficiency (MODY -maturity-onset diabetes of the young, MIDD -maternally inherited diabetes with deafness) and uncommon syndromes of severe insulin resistance.We also describe some of the main approaches used to identify genes involved in the more common forms of T2D and the reasons for the lack of spectacular success in this field.Although major genes for T2DM still await to be discovered, we have probably established a \"road map\" that we should follow.\t\n\nIn polygenic forms of T2DM, the susceptibility genetic variants have very modest consequence at the individual level, however, their population effects are significant [71,73,78].In case of polygenic diseases, we search for common variants that are present in the group of patients and in healthy controls.Those polymorphisms generate just a small increase in individual risk.For common diabetes forms caused by many genes and the environment the same strategies as described above were generally used however, with much less success.This fact is a result of fundamental differences in the character of the genetic background of both monogenic and complex forms.Many susceptibility genes for T2DM have been suggested but in majority of cases it is difficult to replicate the findings in other populations.One of the major problems in the search for genes responsible for common forms of diabetes is the genetic heterogeneity of the disease with different genes responsible for the development of T2DM in different populations.Furthermore, even within the same ethnic group, different genes may be responsible for different subtypes of diabetes (for instance with predominating failure in insulin secretion or insulin resistance).This is why several genome scans that have been completed so far are in general not fully reproducible [17,40,72].In addition to that, there are multiple methodological problems.Researchers were studying various populations differing in age of onset of diabetes, severity of clinical picture of the disease, and way of treatment of diabetes.In general, for the purpose of genome scans the researchers have to collect a large number of families (rather small in size-for example sibs) [47,71,73,76,78,89,119].In addition to that, analysis had different, often weak, statistical power and at the level of interpretation different criteria of significance were used.Some studies were based on the very strict criteria proposed by scientists from Massachusetts Institute of Technology while others were analysed with the usage of more liberal rules [57].This is why drawing more general conclusions based on these studies should be very careful.\t\n\nThe development of type 2 diabetes (T2DM) is determined by two factors: genetics and environment.The genetic background of T2DM is undoubtedly heterogeneous.Most patients with T2DM exhibit two different defects: the impairment of insulin secretion and decreased insulin sensitivity.This means that there are at least two pathophysiological pathways and at least two groups of genes that may be involved in the pathogenesis of T2DM.As far as genetic bacground of T2DM is concerned, the disease may be divided into two large groups: monogenic and polygenic forms.In this review, we present genes known to cause rare monogenic forms of diabetes with predominant insulin deficiency (MODY -maturity-onset diabetes of the young, MIDD -maternally inherited diabetes with deafness) and uncommon syndromes of severe insulin resistance.We also describe some of the main approaches used to identify genes involved in the more common forms of T2D and the reasons for the lack of spectacular success in this field.Although major genes for T2DM still await to be discovered, we have probably established a \"road map\" that we should follow.", + "\tII. Genetics of Type 2 Diabetes\n\nType 2 diabetes clearly represents a multifactorial disease, and several findings indicate that genetics is an important contributing factor.First, certain ethnic minorities and indigenous groups with low population admixture (e.g., Pima Indians, Micronesians and other Pacific Islanders, Australian Aborigines, and Mexican-Americans) show exceptionally high type 2 diabetes prevalence (up to 21% in Pima Indians) (10 -12).Second, type 2 diabetes clusters within families and first-degree relatives have, compared with the general population, an up to 3.5-fold higher risk to develop the disease (13,14).Finally, twin studies demonstrated a markedly higher concordance for type 2 diabetes in monozygotic compared with dizygotic twins (70 vs. 10%) (15).Type 2 diabetes does not follow simple Mendelian inheritance and, therefore, is considered a polygenic disease.According to the generally accepted common variant-common disease hypothesis (16), complex diseases, such as type 2 diabetes, are caused by the simultaneous occurrence of common DNA sequence variations (minor allele frequencies 5%) in many genes.Each of these DNA alterations is supposed to exert only moderate effects on the affected genes' function and/or expression, but in their sum, these variations confer an increased susceptibility toward the adverse environmental factors mentioned above.Single nucleotide polymorphisms (SNPs), exchanges of single base pairs, cover approximately 90% of the sequence variation within the human genome (SNP Fact Sheet of the Human Genome Project; available at http://www.ornl.gov/sci/techresources/Human_Genome/faq/snps.shtml) and are therefore regarded as the major determinants of the individual predisposition to complex diseases.Thus, strong efforts are currently ongoing to map and catalog these sequence variations (The International HapMap Project at http://www.hapmap.org/index.html.en).However, the less frequent copy number variations (due to deletion and/or duplication of DNA segments one kilobase to several megabases in size) and smaller DNA insertions, deletions, duplications, and inversions may also play a role.All of these findings initiated an intensive search for the genes, or better gene variants, responsible for the genetic predisposition to type 2 diabetes.", + "\tDISCUSSION\n\nType 2 diabetes is a highly polygenic trait, and hundreds of loci associated with the disease have been identified, mostly via large GWAS meta-analyses conducted under additive genetic models (2,3).This prior work has produced useful results, identifying potential therapeutic targets and also enabling the creation of polygenic scores capable of quantifying one's genetic risk (34).A sizeable fraction of the heritability of type 2 diabetes, however, remains unexplained by loci identified using additive models.Recessive modeling offers a way to identify new associations, creating opportunities for discovery and improved genetic risk stratification.", + "\tINTRODUCTION\n\nDiabetes is a common, chronic disease that profoundly impacts health and longevity.Susceptibility is influenced by inheritance, and there has been substantial progress in identifying genes which, when mutated, influence individual risk of disease.Through study of common and rare forms, both polygenic and monogenic, diabetes genetics encompasses many pressing issues in human genetic research.", + "\t\n\nThe different types of heterogeneity at the phenotypic level are mirrored by potential different types of genetic heterogeneity.Thus, type 2 diabetes could be 'polygenic' as illustrated in Figure 1C, or it could be 'oligogenic' as illustrated in Figure 1D.Although there is no way to be certain about which pattern is correct, the many reports of linkages with substantial LOD scores (the ratio of the odds favoring vs the odds against linkage) between various chromosomal regions and type 2 diabetes (to be discussed below) are encouraging and favor the oligogenic pattern (Figure 1D).The uncertainties surrounding the issue of phenotypic and genetic heterogeneity are highly salient, since the strategies for gene discovery, the likelihood of success, and the public health relevance of the search for type 2 diabetes susceptibility genes are all profoundly dependent upon which of these types of heterogeneity turns out to be correct.\tThe Search for Diabetes Genes 111\n\n'polygenic', but rather 'oligogenic', i.e. that at least some diabetes susceptibility genes had relatively large effects.", + "\tVariant classification\n\nKey to diagnosing monogenic diabetes and other genetic conditions is not only identifying the variant but also distinguishing The Journal of Clinical Investigation of occurrences leads to a higher level of evidence supporting pathogenicity.However, the uncommonness of monogenic diabetes often makes it difficult for individual laboratories to acquire enough cases.By pooling case data, expert panels can achieve levels of case-based evidence for pathogenicity not possible for any single laboratory or clinic.", + "\t\n\nIn the past decade, genome-wide association (GWAS) and sequencing studies have identified genetic loci that help explain the inherited basis of T2D and glycemic traits.These studies are providing insights into the genetic architecture of T2D, including the number, frequency and effect sizes of risk variants in populations around the world.The polygenic nature of T2D is now well established, and multiple risk variants are being identified at some loci, suggesting allelic heterogeneity.Concurrently, increasing numbers of genes and variants have been implicated in monogenic forms of diabetes, including maturity onset diabetes of the young (MODY) and neonatal diabetes (7), and at least five genes have been implicated in both monogenic and polygenic diabetes (8).A recent simulation study evaluated genetic architectures for consistency with results from T2D genetic studies and found that many different disease models were still possible with respect to the number of loci, allele frequencies and level of selective pressure (9).Ongoing studies should more substantially narrow the bounds on feasible architectures (9).", + "\t\n\nIn the case of relatively uncommon monogenic and syndromic forms of diabetes, such as maturity onset diabetes of the young (MODY) and neonatal diabetes, identification of rare causal mutations has delivered both knowledge and clinical translation [4,5].In contrast, progress in unravelling the genetic architecture of more typical, common, multifactorial type 2 diabetes has been painfully slow [6].The reasons have been well-rehearsed [7].The complex web of susceptibility factors-genetic, environmental, social-that contributes to individual risk of developing type 2 diabetes means that most predisposing genetic variants will have only a modest marginal impact on disease risk.The majority of genetic studies performed to date have simply had insufficient power to uncover these reliably [7].The few type 2 diabetes-susceptibility variants convincingly demonstrated-notably the P12A variant in PPARG and E23K in KCNJ11 [8,9]-have only modest effects on disease risk (odds ratios ~1.2), far too small to offer (either individually or in combination) clinically useful predictive testing.Since these variants lie within genes whose products are already known to be therapeutic targets, these particular discoveries have also had limited capacity to deliver novel pathophysiological insights.Among those working on the genetics of type 2 diabetes, there was growing apprehension that these two genes might be providing a representative view of the genetic architecture of type 2 diabetes.", + "\tA\n\nnumber of studies have implicated a genetic basis for type 2 diabetes (1).The discovery of monogenic forms of the disease underscored the phenotypic and genotypic heterogeneity, although monogenic forms account for only a few percent of the disease (1).Defining the genetic basis of the far more common polygenic form of the disease presents more difficulties (2,3).Nevertheless, some interesting results have recently emerged.A genome scan of Hispanic-American families (330 affected sib-pairs [ASPs]) found linkage to chromosome 2q37 (logarithm of odds [LOD] 4.15) (4), and the causative gene has been recently reported (5).A number of other genome scans in various racial groups have identified other putative susceptibility loci (6 -8).The largest genome-wide scan for type 2 diabetes loci reported to date studied 477 Finnish families (716 ASPs) and found evidence for linkage to chromosome 20q12-13.1(LOD 2.06 at D20S107) (9).Interestingly, similar results have been reported by at least three other groups (10 -12).", + "\t\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized.\t\n\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized.\t\n\nIn this review, however, we focus on a different route from human genetics to translation, one that derives estimates of an individual's predisposition to diabetes and its subtypes (in the form of polygenic scores) from the patterns of individual geneticvariation at sites known to influence diabetes predisposition.\t\n\n During the last decade, there have been major advances in our understanding of the genetic basis of the most common subtypes of type 1 (T1D) and type 2 diabetes (T2D), with .500robust associations identified Although individual variants typically have only a modest effect on risk, when combined into a polygenic score, they offer increasing power to capture information on individual patterns of disease predisposition with the potential to influence clinical management", + "\t\nType 2 diabetes (T2D) had long been referred to as the ''geneticist's nightmare. ''Genome-wide association studies have fully confirmed the polygenic nature of T2D, demonstrating the role of many genes in T2D risk.The increasingly busier picture of T2D genetics is quite difficult to understand for the diabetes research community, which can create misunderstandings with geneticists, and can eventually limit both basic research and translational outcomes of these genetic discoveries.The present review wishes to lift the fog around genetics of T2D with the hope that it will foster integrated diabetes modeling approaches from genetic defects to personalized medicine." + ], + [ + "\t\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today.", + "\t\n\nWith further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients.", + "\t\n\nIn addition, the mechanisms whereby a given DNA change leads to an increased risk of diabetes need to be reconstructed.In type 1 diabetes we need to understand how the susceptibility variants influence immune response and tolerance.In type 2, we need to know whether they influence disease predisposition through primary effects on beta cell function, through insulin action, or by some other mechanism.", + "\t\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.\t\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.", + "\t\n\nBy identifying key biological processes and genes involved in the pathogenesis of diabetes, novel drug targets for the disease and related metabolic disorders such as obesity and metabolic syndrome may be determined.", + "\t\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner.", + "\tGenomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes.", + "\t\n\nRegardless, one expects many of the important susceptibility genes for type 2 diabetes will be uncovered in the next 10 years.Once that occurs, intense effort will be focused on developing targeted therapies.Also, medical care will shift to genetic testing of persons with type 2 diabetes, followed by giving them the most effective proven therapy for that genetic form of the disease.Also, their family members will undergo genetic testing while still normally glucose tolerant to determine if they carry a genetic predisposition.If so, specific treatment plans will be developed for prevention of the disease, again based on proven efficacy for each genetic defect.", + "\t\n\nFailure to understand the pathophysiology of diseases such as type 2 diabetes and obesity frustrates efforts to develop improved therapeutic and preventive strategies.The identification of DNA variants influencing disease predisposition will, it is hoped, deliver clues to the processes involved in disease pathogenesis.This would not only spur translational innovation but also provide opportunities for personalized medicine through stratification according to an individual person's risk and more precise classification of the disease subtype.In this article, I consider the extent to which these objectives have been realized.", + "\t\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow.", + "\t\n\nThe availability of detailed information on gene environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them.", + "\t\n\nGreat strides have been made clinically in the prevention, development, and treatment of the disease but no therapeutic method have been completely successful till date.With new technologies revolutionizing the treatment possibilities, the search for an effective medication is not far ahead.The extensive research leading to the discovery of the pathway genes contributing to the development of the disease and the sequencing of complete genomes have revolutionized the diabetes research.The development of the techniques like the PCRs, DNA microarray, and gene knockouts with silencing has opened up a new area in the identification of the defective genes/mutations in the genome of the organism.The increasing prevalence of diabetes globally is creating a financial burden on the economy of the respective country.Unlike some other diseases, treatment exists for diabetes, and if managed correctly, it is very effective in reducing complications such as heart attacks, amputations, blindness, and kidney failure.With the ongoing research, a right therapeutic for the treatment of diabetes is not unachievable.", + "\t\n\nThe future will see intensified research and improvement in such methodologies to identify and characterise the multiple genes underlying complex diseases.One of the most important goals of genetic studies of diabetes is to determine which multilocus genotypes (across all susceptibility loci) create the highest risk for development of diabetes.Individuals with those genotypes would be targeted for treatment to prevent diabetes when safe and effective prophylactic therapies become available.It is possible that several prophylactic options could be available, with effectiveness depending on the exact set of predisposing genes carried by the at-risk person.Thus, the next generation of genetic studies of Type I diabetes (and other complex disorders) will involve dissection of gene-gene interactions in order to clarify which persons, by virtue of their multilocus genotype, are most susceptible to diabetes.This research will be accompanied by studies of gene-environment interaction, when the relevant non-genetic factors are more clearly understood (eg.do differences in diabetes susceptibility via antiviral defence genes relate primarily to certain types of virus? ).", + "\tConclusions\n\nHow will sequencing genomes influence the health of people at risk for or affected with diabetes?The more complete understanding of the biological mechanisms underlying diabetes derived from these studies may lead to identification of novel drug targets.Individuals with variants in genes responsible for MODY or neonatal diabetes respond better to specific drugs [50,51], and sequencing may identify small numbers of individuals with combinations of rarer, more highly penetrant variants that respond better to specific therapeutic options.Although sets of known variants for type 2 diabetes do not add substantially to prediction of type 2 diabetes development in the overall population [52,53], identification of individuals at greater or lower genetic risk for diabetes within the overall population or in specific subgroups, such as younger onset or leaner individuals [54,55], could lead to better targeted health information and also allow identification of higher risk individuals leading to more efficient design of clinical trials for disease prevention.\t\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "\t\n\nAll very well, you may say, that must be great for the geneticists, but what does all of this mean for our understanding of diabetes?And what difference will this make to the clinical management of this condition?In truth, it is far too early to offer an authoritative answer to such questions, but here are three immediate lessons.", + "\tFUTURE PROSPECTIVES\n\nRecent technological developments around CRISPR-Cas9 and its derivative technologies, combined with advances in human cellular models, should accelerate our understanding of the interplay between diabetes risk-associated genetic variants and their functional roles in disease pathogenesis.These approaches may also find use in clinical applications and in drug screens (Figure 2), enhancing the development of precision medicines for personalized treatment.", + "\t\n\nT2D human genetics has other potentials; for instance, NGS large screen for mutations in new putative drug targets can be very helpful in validating them (for example, if causing insulin secretion defects in beta cells) and in determining drugs side effect in humans carrying loss-or gain-of-function mutations.T2D genetics has shown biologists the results of the experiments of nature that have mutated genes and non-coding regions, with direct effects on T2D risk.It revealed in human the role of key pathways involved in glucose homeostasis, opening novel therapeutic avenues.As modern genomics is not biased by prior researchers' hypotheses, but aims to assess all genome influences comprehensively, it brings unique integrated information that deserves to be tested by experimental biologists in animal models and cell lines.Biologists ought not to fear the plethora of T2D genes.On the contrary, together with geneticists, they should formulate innovative strategies for exploiting genetic discoveries in order to answer the following question in humans: among the numerous described metabolic pathways, what is essential to glucose control?What is causing early diabetic abnormalities?And what should be targeted to prevent or even cure T2D?", + "\tTHE GENETICS OF TYPE 1 DIABETES\n\nThe study of the genome to map disease-susceptibility regions for T1D and other multifactorial diseases has been facilitated by recent advances in next generation DNA sequencing methods." + ] + ], + "task_id": [ + { + "task_id": "4EE84E22AA592E8150F75B2C3540A23C" + }, + { + "task_id": "0B617CD2F3796318CCFF8D220E037B5A" + }, + { + "task_id": "D616F7F573665700708E48DD48AA4683" + }, + { + "task_id": "D4DBF70BD6F78E8A5C515706F4892B1E" + }, + { + "task_id": "0EB12EE3B8B38A98C5E6ACA63D55A150" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_1.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_1.json new file mode 100644 index 0000000..540702f --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_1.json @@ -0,0 +1,61 @@ +{ + "question": [ + "What is GeneNetwork.org, and how does it help scientists understand genetics?", + "How do researchers use GeneNetwork.org to study diseases?", + "What can GeneNetwork.org tell us about how genes interact with each other?", + "How does GeneNetwork.org help in finding the genetic causes of common diseases?", + "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?" + ], + "answer": [ + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "GeneNetwork.org provides a platform for studying gene interactions and exploring genetic correlates to various phenotypes. It uses co-regulation patterns from tens of thousands of samples to construct networks that link DNA differences to traits. It also offers tools for assessing associations among multiple genes and QTLs. The platform can visualize the relative positions and numbers of possible interacting partners, their interaction type (positive or negative correlation), and in some cases, suggest the directionality of the interaction. It also allows users to study relationships between genes, pathways, and phenotypes in an easy-to-use format.", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback" + ], + "contexts": [ + [], + [], + [ + "GeneNetwork: A Toolbox for Systems GeneticsMegan K. Mulligan1, Khyobeni Mozhui2, Pjotr Prins1,2, Robert W. Williams11.Departmentof Genetics, Genomics, and Informatics, University of Tennessee Health ScienceCenter, Memphis, USA2.PreventiveMedicine, University of Tennessee Health Science Center, Memphis, USAAuthor ManuscriptAbstractThe goal of systems genetics is to understand the impact of genetic variation across all levels ofbiological organization, from mRNAs, proteins, and metabolites, to higher-order physiological andbehavioral traits.", + "Until highly accurate genomics networks can beconstructed from more complete data, inferences drawnfrom biological networks should be considered as hypothesis that need to be further tested with experimental method, where the results can then illuminatethe representation of the biological system. The predictive power of the genomics networks could be enhancedby more systematically integrating interactions of informational molecules, such as protein-protein interactions, protein-DNA interactions, protein-RNA interactions, RNA-RNA interactions, protein state information, methylation state, and interactions with metabolites, as these types of data have become available(Schadt et al. 2009).", + "GeneNetwork is an interactive software (Geisert et al. , 2009), which enables usersreadily to reconstruct genetic network based on microarraydata without being intimately involved in complicatedmathematical computation. Materials and methodsMiceOne pair of heterozygous (lew/ ) mice was purchasedfrom the Mouse Mutant Stock Resource colonies at TheJackson Laboratory (TJL). A breeding colony was thenestablished by mating them at the University of TennesseeHealth Science Center (UTHSC).", + "Until highly accurate genomics networks can beconstructed from more complete data, inferences drawnfrom biological networks should be considered as hypothesis that need to be further tested with experimental method, where the results can then illuminatethe representation of the biological system. The predictive power of the genomics networks could be enhancedby more systematically integrating interactions of informational molecules, such as protein-protein interactions, protein-DNA interactions, protein-RNA interactions, RNA-RNA interactions, protein state information, methylation state, and interactions with metabolites, as these types of data have become available(Schadt et al. 2009).", + "GeneNetwork can allow users to study relationships between genes, pathways, andphenotypes in an easy to use format. 28bioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.", + "GeneNetwork: A Toolbox for Systems GeneticsMegan K. Mulligan1, Khyobeni Mozhui2, Pjotr Prins1,2, Robert W. Williams11.Departmentof Genetics, Genomics, and Informatics, University of Tennessee Health ScienceCenter, Memphis, USA2.PreventiveMedicine, University of Tennessee Health Science Center, Memphis, USAAuthor ManuscriptAbstractThe goal of systems genetics is to understand the impact of genetic variation across all levels ofbiological organization, from mRNAs, proteins, and metabolites, to higher-order physiological andbehavioral traits.", + "Those prior knowledge driven geneticsystem-level approaches do not necessarily overlap withgene network analyses which are used to find modulesof highly co-expressed genes with a gene of interest. Thegenerally held view is that genes which are associated orinteracting are more likely to share function and therebybuild up a network. However, this view seems to be theexception rather than the rule in gene networks (Gillisand Pavlidis 2012) since functional information withingene networks is typically concentrated in only a very fewinteractions whose properties cannot be reliably relatedto the rest of the network.", + "Peidis et al. BMC Systems Biology 2010, 4:14http://www.biomedcentral.com/1752-0509/4/14In 2005, we published the first report documentingthe ability of the systems genetics tool GeneNetwork topredict interactions between molecules that could bethen confirmed by molecular analysis [3]. The P2P-Rgene, coding for a hnRNP-related protein [4] that bindsboth the p53 [5] and Rb1 [4] tumor suppressor proteinswas used as a test molecule. P2P-R was entered intoGeneNetwork to search for a co-variant that was mosthighly co-expressed in three tissues of the BXD mousegenetic reference panel, ie,, cerebellum, hematopoieticstem cells and whole brain specimens.", + "GeneNetwork can allow users to study relationships between genes, pathways, andphenotypes in an easy to use format. 28bioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.", + "Taking this approach a step further, GeneNetwork[99] is constructedfrom co-regulation patterns found within tens of thousands of samplesfor which gene expression was measured. GeneNetwork provides unprecedented resolution and predictive power across multiple cell typesand tissues. Analogous to discovering patterns in expression data, thenetwork of protein-protein interactions can also be computationally predicted using various methods[381]. The combined current knowledge of how cells control functionssuch as growth, movement, dierentiation, metabolism, communication, and response to stress or pathogens is captured in high-level pathway databases such as WikiPathways[188], Reactome[97] or KEGG[180].", + "GeneNetwork is an interactive software (Geisert et al. , 2009), which enables usersreadily to reconstruct genetic network based on microarraydata without being intimately involved in complicatedmathematical computation. Materials and methodsMiceOne pair of heterozygous (lew/ ) mice was purchasedfrom the Mouse Mutant Stock Resource colonies at TheJackson Laboratory (TJL). A breeding colony was thenestablished by mating them at the University of TennesseeHealth Science Center (UTHSC).", + "Network based methods of co-expression analysis haveproven useful in identifying evolutionarily conserved gene and protein interactions (Stuart,Segal, Koller, & Kim, 2003), revealing highly connected hub genes that are crucial forsurvival (Carter, Brechbuhler, Griffin, & Bond, 2004), and detecting cell-type specificnetworks, even amongst heterogeneous populations such as the nervous system (Oldham etal. , 2008).", + "Next to direct protein-protein interactions, geneticinteractions from model organisms, and interactions withinwww.frontiersin.orgNeuroinformatics of major neuropsychiatric disorderspathways can be valuable information for a functional relationbetween seemingly unrelated genes. Spatiotemporal analysis ofgene expression correlation in human brain (using BrainSpandevelopmental transcriptome data; Kang et al. , 2011) has identified three co-expression modules. Although GO enrichment ofthe whole list (180 genes) did not highlight any functional categories, analysis of the co-expressed genes resulted in enrichmentof the modules. This suggests that co-expression is a meaningful factor in exploring disease gene specificity.", + "A new functional gene network for human genesIn order to test the general ability of a gene network to prioritize disease genes, particularly in conjunction with GWAS studies, we constructed a genome-scale functional network of human genes, incorporating diverse expression, protein interaction, genetic interaction, sequence, literature, and comparative genomics data, including both data collected directly from human genes, as well as that from orthologous genes of yeast, worm, and fly.The resulting HumanNet gene network can be accessed through a web interface (http://www.functionalnet.org/humannet).Using this interface, researchers can easily search the network using a set of ''seed'' Network-guided genome-wide association mining genes of interest.The interface returns a list of genes ranked according to their connections to the seed genes, together with the evidence used to identify each coupling.The interactions and evidence can be downloaded, and a network visualization tool has been incorporated.All linkages can also be downloaded for independent analysis.", + "As mentioned previously, GeneNetwork(www.genenetwork.org) is a collaborative Web-based resource equipped with tools andfeatures for studying gene/gene and exploring genetic correlates to neurobehavioralphenotypes (Chesler et al. , 2003, 2004). The Web site is home to a growing collection ofgene expression and phenotypic data from a variety of species and brain regions, with a hostof links to external resources for tracing the interrelationships of a gene among multipleWeb-based resources. GeneNetwork also offers a number of correlation and mappingstrategies for assessing associations among multiple genes and QTLs.", + "It is possible for agene to play an important role in relevant networks, although geneticvariation, specifically, may not contribute to the genes association withthe network. Protein-protein interactions and expression correlationchanges might be more important drivers for inclusion of such a gene in agiven network. Furthermore, the genetic variation in other genesassociated with the same pathway may confer the relevance of the overallnetwork.", + "GeneNetwork has a function that constructs such association networks using either phenotype or transcript abundance, or indeed both simultaneously. It provides avisualization of the relative positions and numbers of possible interacting partners, how they interact (positive ornegative correlation) and in some situations, based onprior knowledge, it may suggest the directionality of theinteraction. An association network using principal component scorescalculated using a selected set of malting quality andyield-related trait data as variables provides an overview ofthe key barley traits that segregate in the St/Mx population(Figure 3, Additional File 3).", + "Network-Based ApproachesBased on these large-scale molecular interactions data, such as protein-protein interactions (PPIs), genetic interactions, TF-target interactions, and miRNA-target interactions, molecular networks can be used to visualize the relationships among a gene set, with genes represented as nodes and their molecular interactions as edges.Topological features of a network can often reveal the most critical regulators as hubs, or nodes with the most links, and the functional units/neighborhood among genes as the network modules, within which nodes are densely connected and in between which the nodes are relatively loosely connected.", + "GeneNetwork.org also offers a powerful statistical platform foronline network analyses and mapping, enabling numerous molecular questions to be probed in one centralized location(Chesler et al. , 2003, 2005; Li et al. , 2010; Mulligan et al. , 2012,2017, 2019). Most data are from groups of animals or humanswho have been fully genotyped or even sequenced. As a result, itcan be used to model causal networks that link DNA differencesto traits such as differences in expression, cell number, volumes,and behavior using real-time computation and graphing.", + "These different sources of interactiondata can be collated into network models (see Note 1) whichallow analysis using techniques borrowed from graph theory. Klaus Schughart and Robert W. Williams (eds. ), Systems Genetics: Methods and Protocols, Methods in Molecular Biology, vol. 1488,DOI 10.1007/978-1-4939-6427-7_10, Springer Science+Business Media New York 2017239240Rupert W. OverallAn important advantage of a network representation over a simplelisting of genes correlating to a phenotype is that the interactionsbetween the genes are also taken into account." + ], + [], + [] + ], + "task_id": [ + { + "task_id": "EE2897F8FCF3BE2C8C19B4F50FF98A0A" + }, + { + "task_id": "AB28308963A7CA23CC34A71540C39D6D" + }, + { + "task_id": "7BB057A9D03590E415C6BBB5067F5511" + }, + { + "task_id": "89A02CE1A9BC0B7E6604478BD9024B13" + }, + { + "task_id": "F869432C96A0C9D74FF98A7900FA0F05" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_2.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_2.json new file mode 100644 index 0000000..fecb6cc --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_2.json @@ -0,0 +1,40 @@ +{ + "question": [ + "How does GeneNetwork.org make use of data from different populations around the world?", + "What kinds of genetic data are available on GeneNetwork.org?", + "How do scientists use GeneNetwork.org to study differences in gene expression?", + "Can GeneNetwork.org be used to learn about genetic influences on behavior?", + "What role does GeneNetwork.org play in personalized medicine?" + ], + "answer": [ + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback" + ], + "contexts": [ + [], + [], + [], + [], + [] + ], + "task_id": [ + { + "task_id": "72D16BB14C6B8C59DB45C46B0D070E0E" + }, + { + "task_id": "422190722E532AC62CBC822E7822EA18" + }, + { + "task_id": "277F800382DCBE27854EA848370B5B3E" + }, + { + "task_id": "D1DCDB999C8056628C62B7EEB0412369" + }, + { + "task_id": "F920B6CB94883D4F1514C45CA524BDC3" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_3.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_3.json new file mode 100644 index 0000000..f7a35a2 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_3.json @@ -0,0 +1,88 @@ +{ + "question": [ + "How does the information on GeneNetwork.org help in developing new treatments for diseases?", + "What is a gene network, and why is it important for understanding genetics?", + "How do researchers identify which genes are important for certain traits using GeneNetwork.org?", + "How can GeneNetwork.org help in understanding complex traits like height or intelligence?", + "Are there any known genetic mutations that cause premature aging?" + ], + "answer": [ + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Researchers can identify important genes for certain traits using GeneNetwork.org by utilizing its various features. They can use the global search bar to search for genes, mRNAs, or proteins across all datasets. They can also use the Calculate Correlations tab to assess genetic correlations of the trait of interest with all other records in the database. The platform also allows for the construction of association networks using phenotype or transcript abundance. Additionally, GeneNetwork.org provides background information about genes of interest, including the trait identifier, gene symbol, chromosomal location, and megabase position of the gene. It also allows for data mining in genomic regions containing candidates for quantitative trait genes.", + "GeneNetwork.org can help in understanding complex traits like height or intelligence by using both routine and advanced statistical methods to explore and test relations between these phenotypes and underlying genetic variation. It enables complex queries in real time, including very fast QTL mapping. The platform allows for the correlation and comparison across traits, and the identification of common genetic determinants of correlated phenotypes. It also facilitates the construction of molecular networks that drive these traits, providing a comprehensive view of the trait and aiding in the identification of key genes underlying these processes.", + "Yes, there are several known genetic mutations that cause premature aging. These include mutations in genes involved in DNA metabolism or regulation, such as those seen in Werner syndrome (WS), Bloom syndrome (BLM), Cockayne syndrome (CS), ataxia-telangiectasia (AT), Hutchinson-Gilford progeria syndrome (HGPS), and restrictive dermopathy (RD). Other examples include mutations in the LMNA gene causing Hutchinson-Gilford progeria syndrome, and mutations in RecQ genes causing Werner syndrome, Bloom syndrome, and Rothmund-Thomson syndrome." + ], + "contexts": [ + [], + [], + [ + "The GeneNetwork is an open resource and consists of a set of linked resources for systemsgenetics. It has been designed for integration of networks of genes, transcripts, and traits suchas toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using theroo lines were identified in numerous other QTL mapping studies [46,47,60,69,75,89,114,115]. For sets of phenotypes, particularly those in Gene Network's databases (Drosophilaphenotypes are not yet in this database), a variety of correlation analyses can be performedwith the gene expression data.", + "Author ManuscriptGeneNetwork main search page and organization. Most analyses in GeneNetwork willfollow the steps shown in panels A through D. In this workfl ow, a data set is selected (A)and mined for traits of interest based on user search queries (B). Traits are then selectedfrom the search (C) and placed in a collection for further inspection and quantitative analysis(D). The banner menu contains additional search options and helpful resources under theSearch and Help tab, respectively (E)Author ManuscriptMethods Mol Biol. Author manuscript; available in PMC 2020 September 17. Mulligan et al.", + "GeneNetwork.org is a tool for quantitative genetics that started in 2001 as WebQTL [38].It evolved from analyses of forward genetics in the BXD mouse family, to phenome-wide association studies and reverse genetics in a variety of species.Although GeneNetwork.orgcontains data for many species and populations, it most prominently contains data for the BXD family.Over 10,000 \"classical\" phenotypes, measured under a variety of environmental conditions and over 100 'omics datasets, are available on GeneNetwork.orgfor the BXD family.GeneNetwork.organd the BXD RI population are therefore a powerful tool for systems genetics and experimental precision medicine.The great advantage of inbred lines, with stable genometypes that can be resampled is that data can be reused and reanalysed over time, as tools improve.From the very start of the genome sequencing revolution, when loci were first mapped to causative genes, new tools and a greater understanding of the genome have allowed us to go back to old data and gain new insight.", + "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In thismanuscript we will outline some simple use cases, and show how a small number of plausiblecandidate genes can be identified for an immune phenotype. 1. DataOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. Thefirst is to use the global search bar located at the top of the page (Figure 1). This is a newfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of thedatasets.Alternatively, with the handful of candidatesidentified, it is practical to move to wet lab assays, for example seeing if over- or underexpression of our candidate genes in vitro leads to changes in CCL5 levels. ConclusionGeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. Here we have used GeneNetwork to explore an inflammatory phenotype, and identified a smallnumber of plausible candidate genes. A similar workflow can be used for any trait onGeneNetwork, or for any phenotype collected by an investigator in a genetically diversepopulation.Similarly, by using the dropdown menu on the left (Figure 1), a user can switch to phenotypes,and search for any phenotype of interest in the same way. Figure 1: The global search bar, also called the Search All function, is a good area to start exploringgenes, mRNA, and proteins within GeneNetwork. To best use this new tool, use standard gene symbolscontaining more than two characters in the name. Another area to acquire data is the Select and search pull-down menus (Figure 2). To getstarted, the user has to choose a population of interest.", + "Author ManuscriptGeneNetwork main search page and organization. Most analyses in GeneNetwork willfollow the steps shown in panels A through D. In this workfl ow, a data set is selected (A)and mined for traits of interest based on user search queries (B). Traits are then selectedfrom the search (C) and placed in a collection for further inspection and quantitative analysis(D). The banner menu contains additional search options and helpful resources under theSearch and Help tab, respectively (E)Author ManuscriptMethods Mol Biol. Author manuscript; available in PMC 2020 September 17. Mulligan et al.", + "Using the GeneNetwork database, we performedthe analysis in a two-step fashion: (1) we ranked correlationsusing Spearman rank test with n-numbers larger than 15 overlapping strains, and with P-values < 0.01; and (2) we performeda trait overrepresentation test using key word searches, in whichsignificantly correlated traits should be overrepresented in theGeneNetwork database. This approach should prevent finding ofa correlation by pure chance, albeit that there still could be abias toward studies with more in depth phenotyping. In total,we selected 34 traits (Table 1, Figure 1).", + ", (Chesler et al. , 2005; Galperin and Cochrane,2009; Gentleman et al. , 2004; Mailman et al. , 2007; Saal et al. , 2002; Swertz et al. , 2010)). One relatively well-known database is GeneNetwork (www.genenetwork.org) (Chesler etal. , 2005). GeneNetwork is designed primarily as a web service for exploratory andstatistical analysis of large published phenotype and genome datasets, and includes datafrom several species (see Supplementary Discussion). GeneNetwork includes extensivephenotype data extracted from the literature and submitted by users, which makes itpractical to compare data on drug responses with gene expression patterns.", + "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In thismanuscript we will outline some simple use cases, and show how a small number of plausiblecandidate genes can be identified for an immune phenotype. 1. DataOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. Thefirst is to use the global search bar located at the top of the page (Figure 1). This is a newfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of thedatasets.Alternatively, with the handful of candidatesidentified, it is practical to move to wet lab assays, for example seeing if over- or underexpression of our candidate genes in vitro leads to changes in CCL5 levels. ConclusionGeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. Here we have used GeneNetwork to explore an inflammatory phenotype, and identified a smallnumber of plausible candidate genes. A similar workflow can be used for any trait onGeneNetwork, or for any phenotype collected by an investigator in a genetically diversepopulation.", + "GeneNetwork provides users withuseful background information regarding their gene or genes of interest including the traitidentifier, gene symbol, chromosomal location, and megabase position of the gene. Inaddition to this, GeneNetwork can be used to study correlations between traits and toperform data mining in genomic regions containing candidates for quantitative trait genes(Hoffman et al. , 2011). All datasets in GeneNetwork are linked to a materials and methodsinformation page that summarizes experimental details relating to the dataset.", + "As mentioned previously, GeneNetwork(www.genenetwork.org) is a collaborative Web-based resource equipped with tools andfeatures for studying gene/gene and exploring genetic correlates to neurobehavioralphenotypes (Chesler et al. , 2003, 2004). The Web site is home to a growing collection ofgene expression and phenotypic data from a variety of species and brain regions, with a hostof links to external resources for tracing the interrelationships of a gene among multipleWeb-based resources. GeneNetwork also offers a number of correlation and mappingstrategies for assessing associations among multiple genes and QTLs.", + "Here we provide open access and availability tothese data by integrating them into the GeneNetwork, aweb-based analytical tool that has been designed for multiscale integration of networks of genes, transcripts andtraits and optimized for on-line analysis of traits controlled by a combination of allelic variants and environmental factors. GeneNetwork with its central module WebQTLfacilitates the exploitation of permanent genetic referencepopulations that are accompanied by genotypic, phenotypic and mRNA abundance datasets.GeneNetwork has a function that constructs such association networks using either phenotype or transcript abundance, or indeed both simultaneously. It provides avisualization of the relative positions and numbers of possible interacting partners, how they interact (positive ornegative correlation) and in some situations, based onprior knowledge, it may suggest the directionality of theinteraction. An association network using principal component scorescalculated using a selected set of malting quality andyield-related trait data as variables provides an overview ofthe key barley traits that segregate in the St/Mx population(Figure 3, Additional File 3).DiscussionUsing GeneNetwork for barleyThe framework for analysis using GeneNetwork for barleyis shown in Figure 1A. Associations between transcriptabundance, phenotypic traits and genotype can be established either using correlation or genetic linkage mappingfunctions [29,30]. The main page of GeneNetwork athttp://www.genenetwork.org provides access to subsets ofdata through pull-down menus that allow specific datasets to be queried. The datasets can be further restrictedusing a single text box for specific database entries toquery probe set or trait ID, or annotations associated withthe database entries.", + "GeneNetwork.org also offers a powerful statistical platform foronline network analyses and mapping, enabling numerous molecular questions to be probed in one centralized location(Chesler et al. , 2003, 2005; Li et al. , 2010; Mulligan et al. , 2012,2017, 2019). Most data are from groups of animals or humanswho have been fully genotyped or even sequenced. As a result, itcan be used to model causal networks that link DNA differencesto traits such as differences in expression, cell number, volumes,and behavior using real-time computation and graphing.Forexample, given the intense current interest in opiate addiction, it is important toremap decade-old data using new linearmixed-model mapping algorithms available in GeneNetwork.org. There is agreat amount of amassed data on opiateinduced changes in locomotion, and hundreds of other drug-related traits (Philip etal. , 2010) for .60 strains of recombinantinbred mice that have all been fully genotyped. This analysis can identify thegene variants that influence responsesto these drugs-of-abuse. Figure 5. Example workflow in GeneNetwork.org.", + "Using GeneNetwork, click on the Calculate Correlations tab to assessgenetic correlations of the trait of interest with all other records in thedatabase, including BXD published phenotypes, BXD genotypes, andmRNA from various brain regions as well as other tissues. To begin,select BXD Published Phenotypes from the Database pull-down menuand click Compute. The default option returns the top 500 phenotypesassociated with the trait of interest, but the Return pull-down barallows researchers to choose how many results to display. Researcherscan also choose between selecting Pearson or Spearman Rankcorrelations.", + "However, prioritizingthe long lists of genes produced bycomparative microarray studies conducted in either species has provenexceedingly difficult. As the costs associated with validating a given genesrole in driving a complex trait are considerable, an effective strategy for prioritizing candidate genes is crucial. Investigators therefore have used moresystems-level approaches that combinegenetic, genomic, and pharmacologicalmethods to better delineate gene networks causally related to ethanolbehaviors. Networks allow us to inferrelationships between genes and determine which are most important." + ], + [ + "Using the relationships between genotype,gene expression, and behavior in three databases created in the same recombinant inbredstrain set, advances in genome analysis technology have been applied to the reanalysis oftraits that have been historically importantfoundations in neuroscientific research. Directly building on these early achievementsis possible by using bioinformatics approaches to pull together newly developed resourcesand tools with the wide body of previousresults in the field. As complete genomesequences in both of these strains become available, the exact locations of SNPs, which maybe responsible for these phenotypic differences,will be determined.", + "Forexample, while the structure learned for this example dataset showsthat the Genotype in the dataset directly impacts Gene1 andGene3, the network structure alone is not able to fully describe thisimpact (e.g. , Does having Genotype = 1 tend to increase or decreasethe value of Gene1 and Gene3?). To more fully investigate thequantitative relationships between variables, users can click on aparticular node of the network and enter a value for the variable aseither evidence or an intervention (see Subheading 2.3.3).", + "These lines have been used for three decades to map thegenetic basis of complex phenotypes, and allow detection ofcausative genetic loci even for traits with modest heritability(Belknap 1998). The population also serves as a geneticreference population, allowing correlation and comparisonacross traits, both within and among different laboratoriesto evaluate common genetic determinants of correlatedphenotypes (Crabbe et al . 1996). This approach has beenfacilitated through the development of GeneNetwork(www.genenetwork.org), an Internet resource for the multivariate genetic analysis of complex traits in genetic referencepopulations (Chesler et al .", + "These networks may be exploited to identifyrelationships among complex phenotypes, polymorphic and non-polymorphic therapeutic targets, and sources of genetic variability in drug response or disease. Understanding these networks will also allow us to understand how different individualscan use highly polymorphic networks to achieve very similar phenotypic states inmany cases, and highly variable phenotypic states in others. Such analyses will necessarily require special adaptations of QTL analysis for gene expression, though, inBioinformatics for Geneticists, Second Edition.", + "It is of great interest to unravel the inner workings of how genotypes influence molecular networks to affect a phenotype such as agility, seizures, and even drug addiction, toname a few. Geneticists have already achieved great success in associating a genotype andphenotype for a trait determined by one gene (i.e. monogenic traits), but much presentattention is now focused on traits that are determined by many genes (i.e. complex traits). These traits are continuously distributed random variables and thus referred to as quantitative traits. Linear modeling is used to identify genotypes that predict phenotype values.", + "This strategy required a considerable eort, but also expanded the range of studies and possibleforms of analysis. In many cases, however, per subject phenotype datawere not available. GeneNetwork uses both routine and advanced statistical methods to extract, explore, and test relations among phenotypes and underlying genetic variation. It enables complex queries inreal time, including very fast QTL mapping.", + "As an example, Figure 1figure supplement 1A illustrates a sample networkand Figure 1figure supplement 1B depicts a group of correlated traits in this network. Relyingentirely on trait information, however, makes it difficult to identify the shared mechanisms and todistinguish shared molecular mechanisms from shared environmental influences. Alternatively,a common way to improve predictions is by integrating relationships between genes and traits,using genetrait correlations, associations, or causal mutations (Rzhetsky et al. , 2007; Cotsapaset al. , 2011; Baker et al. , 2012; Hwang et al. , 2012; Gat-Viks et al. , 2013).", + "When applied to the field of neuroscience, this can revealbiologically relevant meaning and render novel insights into the molecular mechanisms thatgovern behavior. Focusing on these interactions and the gene networks that emergecapitalize on the unbiased investigational methods imparted in whole-genome analysis. Moreover, due to the complexity of neurobehavioral traits, it may be more relevant andinformative to correlate the function of a network of genes with a phenotype, rather than anindividual gene. NIH-PA Author Manuscript4.1.", + "Using the relationships between genotype,gene expression, and behavior in three databases created in the same recombinant inbredstrain set, advances in genome analysis technology have been applied to the reanalysis oftraits that have been historically importantfoundations in neuroscientific research. Directly building on these early achievementsis possible by using bioinformatics approaches to pull together newly developed resourcesand tools with the wide body of previousresults in the field. As complete genomesequences in both of these strains become available, the exact locations of SNPs, which maybe responsible for these phenotypic differences,will be determined.", + "The combinationof expression genetics with classical linkage analysis, however,allows the in silico identification of candidate genes controllingpolygenic phenotypes as complex as adult neurogenesis and, at thesame time, reveals insights into regulatory transcriptional networksunderlying such phenotypes (18). Genetic polymorphisms influence systems-level phenotypesthrough a network of genes. The small molecular variation is anaturally occurring perturbation of this network that can reveal thegenes that comprise it. Discovering this network and the consequences of this variation are facilitated by the use of geneticreference populations.", + "These networks may be exploited to identifyrelationships among complex phenotypes, polymorphic and non-polymorphic therapeutic targets, and sources of genetic variability in drug response or disease. Understanding these networks will also allow us to understand how different individualscan use highly polymorphic networks to achieve very similar phenotypic states inmany cases, and highly variable phenotypic states in others. Such analyses will necessarily require special adaptations of QTL analysis for gene expression, though, inBioinformatics for Geneticists, Second Edition.", + "Theinformation that defines how variations in DNA lead to variations in complex traitsof interest flows through molecular networks that actually define the complex traits. Therefore, characterizing the molecular networks that underlie complex traits likedisease can provide a more comprehensive view of disease, and this in turn can leadto the direct identification of key genes underlying disease processes, as well as providing a rich biological context within which to infer the functional roles played bythese key genes.An alternative to the forward genetics approach to dissecting complex traits likedisease is the construction of molecular networks that drive disease, where suchnetworks are constructed from molecular phenotype data scored in populations thatmanifest disease. The information that defines how variations in DNA lead to variations in complex traits of interest flows through molecular networks that actuallydefine the complex traits.Therefore, characterizing the molecular networks thatunderlie complex traits like disease can provide a more comprehensive view of disease, and this in turn can lead to the direct identification of key genes underlyingdisease processes, as well as providing a rich biological context within which toinfer the functional roles played by these key genes.", + "The great thing about having accessto the data in Table 1 in GeneNetwork is that we can let these numbers speak forthemselves. Do the traits map strongly to any chromosomal location? If so, what fraction ofthe variance in the trait can be causally linked to the location(s)? Does performance on thistask, whatever it may be measuring, covary with hippocampal size or body weight? To whatextent does the speed of finding the platform during the learning phase of the studycorrespond to the persistence with which the strains search for the missing platform?", + "Detection of putative genetic networks underlyingcomplex traitsComplementary epistasis may be of especially greatimportanceDetecting and characterizing genetic networks underlying acomplex trait involves determining the number, genetic relationships, and hierarchy of segregating FGUs (or loci) associated withthe trait in a biparental population. Two general approaches arereadily available - the quantitative genetics approach and thepopulation genetics approach. The power to detect a geneticnetwork is largely dependent on its complexity, which isdetermined largely by the number of segregating loci, r, withineach of the signaling pathways underlying the trait.While gene networks controlling biological processes presumably include the genetic determinants of complex trait variation,these two important areas of study have remained largelyindependent. For example, gene networks consisting of multiplehierarchical signaling pathways might explain high-order epistasis,but only digenic epistasis affecting complex traits has been possibleto map [24,25]. Recent modeling efforts have suggested thatepistasis might be better explained by functional relationships inIntroductionGreat progress has been made in genetic dissection of quantitativetrait variation during the past two decades, but a few puzzling resultshave recurred in many QTL mapping studies.", + "Using the relationships between genotype,gene expression, and behavior in three databases created in the same recombinant inbredstrain set, advances in genome analysis technology have been applied to the reanalysis oftraits that have been historically importantfoundations in neuroscientific research. Directly building on these early achievementsis possible by using bioinformatics approaches to pull together newly developed resourcesand tools with the wide body of previousresults in the field. As complete genomesequences in both of these strains become available, the exact locations of SNPs, which maybe responsible for these phenotypic differences,will be determined.", + "These networks may be exploited to identifyrelationships among complex phenotypes, polymorphic and non-polymorphic therapeutic targets, and sources of genetic variability in drug response or disease. Understanding these networks will also allow us to understand how different individualscan use highly polymorphic networks to achieve very similar phenotypic states inmany cases, and highly variable phenotypic states in others. Such analyses will necessarily require special adaptations of QTL analysis for gene expression, though, inBioinformatics for Geneticists, Second Edition.", + "These networks may be exploited to identifyrelationships among complex phenotypes, polymorphic and non-polymorphic therapeutic targets, and sources of genetic variability in drug response or disease. Understanding these networks will also allow us to understand how different individualscan use highly polymorphic networks to achieve very similar phenotypic states inmany cases, and highly variable phenotypic states in others. Such analyses will necessarily require special adaptations of QTL analysis for gene expression, though, inBioinformatics for Geneticists, Second Edition." + ], + [ + "Studies of genes and molecular processes that are associated with segmental progeroid disorders, such as Hutchinson-Gilford progeria syndrome (HGPS, progeria, OMIM#176670), could be of importance when studying the genetic mechanisms of aging (Martin, 2005;Baker et al., 1981).For example, most cases of HGPS are caused by a de novo point mutation in the LMNA gene (LMNA c.1824C>T; p.G608G).This mutation activates a cryptic splice site that results in aberrant splicing of the lamin A transcript (Eriksson et al., 2003).Interestingly, it has been shown that the products of this aberrant splicing, the truncated transcript and resultant protein (named progerin), increase in number with aging in HGPS (Goldman et al., 2004;Cao et al., 2007;Rodriguez et al., 2009).In addition, several reports have found progerin, and increasing levels of progerin, in normal cells over the course of normal aging (Scaffidi & Misteli, 2006;McClintock et al., 2007;Cao et al., 2007;Rodriguez et al., 2009), which suggests a similar genetic mechanism in HGPS and normal aging.Moreover, genome-scale expression profiling in cells from HGPS patients, as well as in physiological aging, has revealed widespread transcriptional misregulation in multiple mammalian tissues (Ly et al., 2000;Csoka et al., 2004;Zahn et al., 2007;Scaffidi & Misteli, 2008;Cao et al., 2011;McCord et al., 2013).", + "DNA Repair and Accelerated Aging SyndromesThe association of human syndromes of accelerated aging with inherited mutations in DNA repair genes strongly implicates DNA damage in the human aging process.These disorders, known as segmental progeroid syndromes, are characterized by accelerated onset of a subset of human aging phenotypes that frequently include neurodegeneration (50).Mutations in genes involved in singleor double-strand DNA break repair result in cerebellar degenerative syndromes known as ataxias, which are manifested by movement disorders.The continued proliferation of cerebellar granule cells during postnatal development may underlie the vulnerability of the cerebellum to inherited deficits in genome stability.In contrast, inherited mutations in DNA helicases, such as Werner and Rothmund-Thomson syndromes, give rise to features of accelerated aging that often do not include nervous system dysfunction.This may reflect the role of RecQ-like helicases in recombinant events in replicating cells.Inherited mutations in enzymes involved in nucleotide and base excision repair, including xeroderma pigmentosum and Cockayne syndrome, are characterized by accelerated aging phenotypes that include neurodegeneration, mental retardation, and delayed psychomotor development (50).A new human progeroid syndrome that is caused by a loss of function mutation in the XPF-ERCC1 endonuclease that repairs helix-distorting DNA lesions was recently described.Mice deficient in ERCC1 recapitulate the progeroid features and exhibit a gene expression profile in the liver that overlaps with that of normal aging mice (correlation coefficient 0.32), suggesting that this type of DNA damage may contribute to the aging process (51).Segmental progerias typically have a short life span of less than 20 years, which may account for the absence of Alzheimer-type neuropathological Double-strand break (DSB): a severe form of DNA damage involving scission of both DNA strands, usually induced by ionizing radiation or ROS NHEJ: nonhomologous end joining changes.However, individuals with Werner syndrome, a longer-lived progeroid syndrome, can have variable neuropathology, with one 57-year-old case reportedly showing unusually high levels of amyloid -protein deposition in the brain (52).", + "Hutchinson-Gilford progeria syndrome (HGPS) and Werner syndrome are rare human genetic disorders characterized by premature aging phenotypes with a shortened life span.This group of diseases resembles physiological aging to a certain extent, serving as excellent models to gain insight into the biology of aging in humans (24,25).These diseases are due to either a mutation in genes encoding the DNA repair machinery or the A-type lamin, leading to disorganized chromatin structures.The causative mutations behind these progeria syndromes indicate that genomic instability and chromatin deterioration are causes of human aging.Furthermore, the knowledge we gain from understanding the molecular pathology of these human premature aging diseases provides us with useful information to understand the complex aging process.Individuals with HGPS do not recapitulate all aging phenotypes because they usually show segmental progeria affecting multiple tissues.By recapitulating some molecular and cellular changes that are characteristics of the natural aging process, these models provide us with a unique opportunity to understand the aging process in a human model (24,25).", + "Researchers in recent studies have focused on gene mutations accompanying known progeroid syndromes, such as Hutchinson-Gilford progeria, Werner syndrome, Rothmund-Thomson syndrome, Cockayne syndrome, ataxia telangiectasia, and Down syndrome. 143The most common skin disorders of these syndromes, which are characterized by an acceleration of the aging phenotype, are alopecia, skin atrophy and sclerosis, telangiectasia, poikiloderma, thinning and graying of hair, and several malignancies.Most of these syndromes are inherited in an autosomal recessive way and mostly display defects in DNA replication, recombination, repair, and transcription.Expression gene patterns of skin cells derived from old and young donors with Werner syndrome, 144 show that 91% of the analyzed genes have similar expression changes in Werner syndrome and in normal aging, implying transcription alterations common to Werner syndrome and normal aging represent general events in the aging process.", + "DNA Repair-Related Progeroid SyndromesAs mentioned previously, premature aging syndromes are often caused by mutations in genes whose function is to preserve genomic integrity.In this respect, the RecQ family of DNA helicases has been found to function in DNA damage repair, including base excision repair and in DNA double-strand break (DBS) repair, as well as in DNA replication subjected to a normal or stressed state [36].Mutations in three RecQ genes (WRN, BLM, and RECQL4) give rise to the Werner syndrome (WS), Bloom syndrome (BS), and Rothmund-Thomson syndrome (RTS), respectively [37].Additional genetic defects in the DNA damage repair system also cause the following disorders: Cockayne syndrome (CS), xeroderma pigmentosum (XP), and trichothiodystrophy (TTD).An alternative strategy to the investigation of aging using the humans themselves is the study of progeroid syndromes, a group of very rare genetic disorders characterized by accelerated aging and the presence of clinical features that resemble physiological aging, including osteoarthritis and osteoporosis, loss of muscle mass, hair loss, short stature, skin tightness, and cardiovascular diseases [4].In addition to the genuine medical interest in improving the quality of life of these patients, the study of progeroid syndromes has attracted great interest in the past 10 years, in that they constitute an invaluable source of information for understanding the molecular basis of human aging.ConclusionsRecent advances in the study of progeroid syndromes, especially HGPS, have provided novel insights into our understanding of the aging process in humans.The main progeroid syndromes revised in this chapter are caused by mutations in genes encoding for DNA repair enzymes or the nuclear lamina protein lamin A, which reinforces the notion that genome instability is a critical determinant of aging.The study models that recapitulate progeroid syndromes have dramatically stimulated aging research; while cellular models have allowed the dissection of basic cellular and molecular processes linked to aging, mice models have facilitated screening of therapeutic drugs.It is expected that upcoming technologies and the design of novel optimized animal models will help to accomplish a translational medicine approach in aging research, with HGPS being the ideal model for such a goal.", + "Progeroid syndromesPatients suffering from progeroid syndromes, or accelerated aging phenotypes, display an array of physical and biological features that vary widely between tissues and diseases and among individuals.Some of the main characteristics for the specific disorders of interest to this review are cited below (for further review of molecules involved and clinical presentation, see Ref. 96).A general dilemma in studies on the role of telomeres in progeroid syndromes (and aging) is that telomere involvement could be direct as well as indirect.For example, the increased cell death resulting from defective DNA repair could result in telomere shortening via increased compensatory (stem) cell turnover or via direct effects on (repair of) telomeric DNA.For many segmental aging disorders, it has proven to be very difficult to distinguish between direct and indirect effects on telomere length.Perhaps phenotypically the most striking segmental aging genetic disorder in humans, Hutchinson-Gilford Progeria syndrome (HGPS), is caused by point mutations in lamin A, a key component of nuclear scaffolding (34,72).Lamin A deficiency results in absence of hair, craniofacial deformities (\"pinched\" facial features), emaciated and wrinkled appearance, as well as cardiovascular defects that eventually lead to stroke or heart attack at a very young age.The disease is characterized by specific defects in FIG. 8. Defects in human telomerase.The human telomerase complex is minimally composed of two proteins, telomerase reverse transcriptase (hTERT, green) and dyskerin (or DKC1, blue), that both bind specifically to a folded RNA molecule (or hTERC, black) containing a telomere repeat anchoring sequence and a template (red box).Known mutations in each component have now been linked to autosomal dominant dyskeratosis congenita (AD DC), bone marrow failure (BMF), and idiopathic pulmonary fibrosis (IPF) (6,63,127,134,151,217,231,234).The telomerase complex is thought to dimerize, bind to the single-strand G-rich telomere end, and catalyze the addition of new repeats (see also Figs. 3 and 4).The complex translocates along (newly added) telomere tracts for further elongation.Mutations affecting telomerase function lead to failure to assemble a functional complex.In the majority of cases, the level of telomerase activity is reduced by 50%.Such a reduction in telomerase activity compromises telomere length maintenance and increases apoptosis and senescence in proliferating cells (see Fig. 4).nuclear shape (183).Because expression of (defective) lamin A is limited to certain cell types, some cells and tissues are more affected than others.While there is evidence that DNA damage responses in cells expressing mutant lamin A are abnormal (133), the role of telomeres in this disorders (if any) remains to be clarified.A number of other segmental aging disorders have been more directly linked to telomere (dys)function.Among these, Fanconi anemia (FA) and ataxia telangiectasia (AT) are generally autosomal recessive diseases caused by mutations in, respectively, Fanconi genes (encoding any of 12 Fanconi anemia complementation group proteins) and the ataxia telangiectasia mutated gene (encoding the ATM protein).These proteins are implicated in DNA damage and repair pathways; in addition, ATM is known to phosphorylate FANCD2 (for reviews, see Refs.64,118,190).Both diseases are associated with accelerated telomere shortening (29,121,123,146), and abnormalities in telomere replication or repair are thought to play a role in the pathogenesis, particularly in the progression of the disease to immunodeficiency and bone marrow failure, as well as in the increased predisposition to malignancy in young adults.Other syndromes related to the Fanconi DNA damage response pathway include Nijmegen breakage syndrome (NBS) and Seckel syndrome.Other \"progeroid\" genes that have been implicated in DNA replication and repair are the family of genes encoding the RecQ DNA helicases.One of the functions of these enzymes is to assist in the resolution and repair of broken or stalled replication forks.Telomeric DNA is known to readily form higher order DNA structures such as G quadruplex structures in vitro (159), and it seems plausible, based on work in C. elegans (42), that specialized helicases are required to resolve structures of G-rich DNA arising sporadically during lagging strand DNA synthesis (62).Helicases that could be involved include RecQ protein-like 2 (RecQL2), RecQL3, and RecQL4 with known mutations that give rise to Werner (WRN), Bloom (BLM), and Rothmund Thompson syndromes, respectively.Accelerated telomere shortening is observed in Werner's syndrome (51), and pathology in animal model systems is accentuated in the context of telomerase deficiency (40,156).", + "The relationship between DNA damage accumulation and aging has gained maximum credibility through studies conducted on various human progeria syndromes, which are genetic disorders where patients precociously develop features resembling natural aging.Most of the reported progeria syndromes, including Werner syndrome (WS), Bloom's syndrome (BS), Rothmund-Thomson syndrome (RTS), Cockayne syndrome type A and type B (CSA and CSB), Xeroderma pigmentosum (XP), Trichothiodystrophy (TTD) and Hutchinson-Gilford progeria syndrome (HGPS) are caused by mutations of genes that are directly or indirectly involved in DNA repair.Of these, WS, BS and RTS are associated with defects in RecQ helicases, i.e.RECQL2 (WRN), RECQL3 (BLM) and RECQL4 respectively, whereas CS, XP and TTD shared similar defects in NER pathway.RecQ helicases are a group of highly conserved proteins from bacteria to humans.The roles of RecQ helicases in DNA metabolism, including DNA replication, transcription, repair and recombination, have been extensively investigated and are demonstrated to be the underlying pathological basis of WS, BS and RTS [139][140][141][142].Most recently, delayed DNA damage checkpoint response and defective DNA repair were found to contribute to the progeria phenotypes in HGPS as well [143].", + "They arise from mutations in one or several genes involved in DNA metabolism or in its regulation.Accelerated aging also may result from partial genome imbalances as seen in the chromosomal disorders of Down, Klinefelter and Turner syndromes.These defects result in part from accumulated damage to DNA.Such damage may result inability to maintain replicative fidelity of the genome [2][3][4].Thus, organisms with mutations to genes directly involved in basic genome structure, maintenance and replicative fidelity would understandably have an accelerated aging phenotype and/or shortened life spans.Individuals with a progeroid syndrome have a premature aging phenotype and, depending on the specific mutations involved, the effects on lifespan may range from moderate to severe.Examples include Werner syndrome (WS), Bloom syndrome (BLM), Cockayne syndrome (CS), ataxia-telangiectasia (AT), Hutchinson-Gilford progeria syndrome (HGPS), and restrictive dermopathy (RD).", + "The identification of these diseases spurred the creation of numerous animal models, and the characterization of engineered laboratory mutants led to the identification of many new human diseases of systemic and segmental accelerated aging.The animal models are useful for discovering how, when, and where (in what tissues) DNA damage contributes to aging, an area in which much work is still needed.The models, because of their accelerated aging, are useful for rapid hypothesis and drug testing.The models for the large part faithfully recapitulate the human genetic diseases; however, it is notable that mice tend to display a milder phenotype than humans.This might arise from the environmental contribution to human disease, which is not well reproduced in experimental model systems.Collectively, however, these human diseases and their conservation in multiple animal model systems strongly support the role of DNA damage as a proximal contributor to aging.", + "The number of identified genes associated with progeroid syndromes has increased in recent years, possibly shedding light as well on mechanisms underlying ageing in general.Several heritable premature aging syndromes have for a long time been linked to defects in genome maintenance, due to altered DNA repair mechanisms.These mainly include the following autosomal recessive syndromes: (i) Werner syndrome, due to mutations in RecQL2 DNA helicase; (ii) Cockayne syndrome (CS) type A and B, due to mutations in the genes encoding the group 8 or 6 excision-repair cross-complementing proteins (ERCC8 and ERCC6), respectively; (iii) Rothmund-Thomson syndrome (RTS), due to RecQL4 mutations; (iv) trichothiodystrophy (TTD), due to mutations in the genes ERCC2/XPD and ERCC3/XPB, encoding the two helicase subunits of the transcription/repair factor TFIIH, as well as in TFB5, encoding the tenth subunit of TFIIH (Giglia-Mari et al., 2004); (v) ataxia-telangiectasia, due to mutations in the ataxia-telangiectasia mutated gene (ATM); (vi) xeroderma pigmentosum (XP), a genetically heterogeneous autosomal recessive disorder in which can be distinguished at least seven complementation groups, due to mutations of different DNA excisionrepair proteins (Hasty et al., 2003;Kipling et al., 2004).All these progeroid diseases, involving heritable defects in DNA repair, suggest a central role of genome integrity maintenance in the aging process.ConclusionFrom a pathophysiological point of view, the known Progeroid syndromes are caused either by mutations in genes encoding DNA repair proteins, such as in WS, Bloom syndrome (BS), Rothmund-Thomson syndrome, Cockayne syndrome, xeroderma pigmentosum or trichothiodystrophy (Hasty et al., 2003;Wood et al., 2005), or by mutations in genes encoding Lamins A/C or partners involved in their biological pathway, such as HGPS or RD (De Sandre-Giovannoli et al., 2003;Eriksson et al., 2003;Navarro et al., 2004Navarro et al., , 2005)).Progeroid syndromes are heritable human disorders displaying features that recall premature ageing.In these syndromes, premature aging is defined as ''segmental'' since only some of its features are accelerated.A number of cellular biological pathways have been linked to aging, including regulation of the insulin/growth hormone axis, pathways involving ROS metabolism, caloric restriction, and DNA repair.Different animal models, ranging from yeast, to nematodes, to mice, have been instrumental in obtaining evidence for these connections (Hasty et al., 2003).Several heritable premature aging syndromes have for a long time been linked to defects in genome maintenance, due to altered DNA repair mechanisms.These mainly include the following autosomal recessive syndromes: (i) Werner syndrome, due to mutations in RecQL2 DNA helicase; (ii) Cockayne syndrome (CS) type A and B, due to mutations in the genes encoding the group 8 or 6 excision-repair cross-complementing proteins (ERCC8 and ERCC6), respectively; (iii) Rothmund-Thomson syndrome (RTS), due to RecQL4 mutations; (iv) trichothiodystrophy (TTD), due to mutations in the genes ERCC2/XPD and ERCC3/XPB, encoding the two helicase subunits of the transcription/repair factor TFIIH, as well as in TFB5, encoding the tenth subunit of TFIIH (Giglia-Mari et al., 2004); (v) ataxia-telangiectasia, due to mutations in the ataxia-telangiectasia mutated gene (ATM); (vi) xeroderma pigmentosum (XP), a genetically heterogeneous autosomal recessive disorder in which can be distinguished at least seven complementation groups, due to mutations of different DNA excisionrepair proteins (Hasty et al., 2003;Kipling et al., 2004).All these progeroid diseases, involving heritable defects in DNA repair, suggest a central role of genome integrity maintenance in the aging process.The number of identified genes associated with progeroid syndromes has increased in recent years, possibly shedding light as well on mechanisms underlying ageing in general.Among these, premature aging syndromes related to alterations of the LMNA gene have recently been identified.LMNA encodes Lamins A/C, ubiquitous nuclear proteins belonging to the intermediate filament superfamily.These premature aging disorders have thus been classified as ''Laminopathies'', the large group of diseases associated to Lamin A/C defects.This group of heterogeneous disorders includes three main subgroups: (1) neuromuscular disorders (Emery-Dreifuss muscular dystrophy, limb-girdle", + "However, only those genetic disorders that exhibit premature aging, neurodegeneration (mental defects), and some form of chromosomal/DNA damage all together will be empha-sized here.Perhaps the most appropriate disorder under this category is Down's syndrome.It has several features of premature aging and the genetic defect is trisomy of the distal part of the long arm of chromosome 21.The critical segment of chromosome 21 is shown to have three genes coding for copper-and zinc-dependent superoxide dismutase, oncogene ets-2, and cystathione ~-synthase (Delabar et al., 1987).Since elevated levels of superoxide dismutase are found in various tissues of these individuals, it is postulated that the accelerated aging of these patients may be caused by overproduction of superoxide dismutase, which is responsible for the production of H20 2 while scavenging the oxygen-free radicals.The brains of Down's syndrome individuals are particularly vulnerable to oxidative DNA damage because the high levels of superoxide dismutase found in this tissue are not accompanied by an elevation in the glutathione peroxidase and catalase (Balazs and Brookshank, 1985) that would have normally helped in removing the overproduced H202.Other genetic syndromes characterized by signs of nervous debility, premature aging, and DNA damage/ decreased DNA-repair capacity, are Ataxia Telangiectasia (AT) and Cockayne syndrome (CS).", + "Rare genetic disorders of agingProgeria, also known as Hutchinson-Gilford progeria syndrome, affects one in four million births worldwide with equal distribution between sex and race, causing a child's body to age more rapidly (Genetics Home Reference, 2019a).Symptoms typically occur within the first year of life, and most children do not live past 13 years.Mutation in the LMNA gene (not an adduct or telomere factor) contributes to abnormal lamin A protein, called progerin, causing cell instability and cells to easily breakdown (Genetics Home Reference, 2019a).There is no current cure for progeria but farnesyltransferase inhibitors, a cancer drug, has shown promise in reversing cell damage (Genetics Home Reference, 2019a).Other supportive treatments include cardiovascular diseaserelated issues, growth hormones, and bone/joint health.Adalia Rose has taken to social media, with multiple YouTube and Facebook postings, to help others understand her case of progeria.", + "Mitochondrial DNA (mtDNA) mutations are thought to have a causal role in many age-related pathologies.Here we identify mtDNA deletions as a driving force behind the premature aging phenotype of mitochondrial mutator mice, and provide evidence for a homology-directed DNA repair mechanism in mitochondria that is directly linked to the formation of mtDNA deletions.In addition, our results demonstrate that the rate at which mtDNA mutations reach phenotypic expression differs markedly among tissues, which may be an important factor in determining the tolerance of a tissue to random mitochondrial mutagenesis.", + "INTRODUCTIONIn genetics, identification of genotype-phenotype relationships relies on generated or selected mutants, which highlight underlying mechanisms.For the biology of aging, mutants that display delayed or accelerated aging have been invaluable.Rare heritable syndromes have been identified in the human population that exhibit multiple features of premature aging.A search in the Online Mendelian Inheritance in Man database (OMIM version February 25, 2015) using the keywords \"premature aging,\" \"progeria,\" or \"progeroid\" yielded 20 syndromes with at least one known mutated gene.Certainly this list is far from complete; for example, ataxia telangiectasia, fanconi anemia, and maternally transmitted mitochondrial syndromes such as maternally inherited diabetes and deafness and mitochondrial encephalomyopathy (MIDD/MELAS) are missing.Additionally, many more conditions await identification as unrecognized progeroid syndrome.The application of powerful exome and whole genome sequencing technologies will dramatically accelerate molecular resolution of genetic defects in rare patients with features of accelerated aging, and through this process, many new genes underlying these conditions will be identified.However, when we assign a primary function to each of the causally mutated genes in the known syndromes, it appears that the majority is linked to perturbed genome integrity, a second class represents metabolism, and one syndrome appears connected with cell adhesion (Figure 1).Recently, evidence has emerged for bidirectional interactions between the main aging-related processes: For instance, most DNA damage is derived from endogenous metabolic sources, and compromised genome function indirectly affects many cellular processes including metabolism (1, 2).This suggests the existence of a tightly interwoven network that underlies aging, which is the focus of this review.Progeria-associated syndromes classified by primary function of the causal genetic defect.These 20 human syndromes, listed outside of the circle, were selected from the OMIM database using the keywords \"premature aging,\" \"progeria,\" and \"progeroid. \"Related primary functions were combined in the categories genome integrity, metabolism, and adhesion (inner circle).Abbreviations: DSB, DNA double-strand break; MDPL, mandibular hypoplasia, deafness, progeroid features, and lipodystrophy; PI3K, phosphoinositide-3-kinase; PS, phosphatidylserine; XFE, XpF-Ercc1.GAPO indicates growth retardation, alopecia, pseudoanodontia, and optic atrophy.SHORT indicates short stature, hyperextensibility, hernia, ocular depression, Rieger anomaly, and teething delay." + ] + ], + "task_id": [ + { + "task_id": "25114E7E0872CA766015799208B593EF" + }, + { + "task_id": "E72B05930CFE17F0F8781A3F85380920" + }, + { + "task_id": "9296083F6746C5627B41C544450F0EF2" + }, + { + "task_id": "0EE082035FC7C6545DEDFD74266E8E43" + }, + { + "task_id": "DB6A2E2E3506A39B4CF3362C0B22A471" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_4.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_4.json new file mode 100644 index 0000000..74e5ce8 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_4.json @@ -0,0 +1,59 @@ +{ + "question": [ + "What are the practical applications of the research done through GeneNetwork.org?", + "How can I access and use the data available on GeneNetwork.org?", + "What are some recent discoveries made using GeneNetwork.org?", + "How do scientists ensure the accuracy of the data on GeneNetwork.org?", + "What\u2019s the difference between looking at one gene and studying a whole gene network?" + ], + "answer": [ + "GeneNetwork.org is used for exploratory and statistical analysis of large published phenotype and genome datasets. It allows researchers to model causal networks linking DNA differences to traits such as differences in expression, cell number, volumes, and behavior. It can also be used to identify plausible candidate genes for specific phenotypes, predict the behavior of a system following perturbations, and identify genes directly interacting with a compound of interest. Furthermore, it can be used to investigate the co-expression of genes with distinct functional sets, and generate networks of genes based on their connectivity.", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback" + ], + "contexts": [ + [ + "Nonetheless,influence networks do have practical utility for (1) identifyingfunctional modules, that is, identify the subset of genes thatregulate each other with multiple (indirect) interactions, buthave few regulations to other genes outside the subset;(2) predicting the behaviour of the system following perturbations, that is, gene network models can be used to predictthe response of a network to an external perturbation andto identify the genes directly hit by the perturbation (diBernardo et al, 2005), a situation often encountered in the drugdiscovery process, where one needs to identify the genes thatare directly interacting with a compound of interest; (3)identifying real physical interactions by integrating the genenetwork with additional information from sequence data andother experimental data (i.e.", + "These programs have the capability to integrate large datasets ofgenetic and expression data from humans and animalstudies. Notably, the GeneNetwork program (www.genenetwork.org) can combine expression data gathered fromdifferent brain regions and tissues and map these withdescribed traits as a way to build gene networks [65]. Evolving developments in bioinformatics promise muchmore; it is now feasible to adopt a new modular approach,known as systems biology. Systems biology is a mathematical modeling technique applied to complex biologicalorganizations or processes for the purposes of generatingpredictive models that are more representative of biological situations [66,67].", + "This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.", + "GeneNetwork is one ofeither generate or test ideas by reusing data that oftenan interlinked trio of sites built up by NIAAA (GeneWeaverhave been rescued from the classic literature. Below is a short list of both well-known and more esoteric and WebGestalt are the other two) to house extensiveresources, many of which have been supported by NIAAA, data for human, monkey, rat, mouse, and fruit fly.", + "Thus, a simple WebQTL session generates hypotheses that caninitiate investigations into genes previously unsuspected of havingany functional involvement with the original reference gene, letalone with thymocyte development. These examples give an indication of only some of the functionsavailable within GeneNetwork. Currently, systems genetics isa powerful technology for dening clusters of co-regulated genes. Its use is centred upon user-specied genes and can identify novelpotential master regulatory genes for further investigation. We areworking to increase the functionality and power of the GeneNetwork and systems genetics further in a number of areas.", + "Each data set containing gene identifierswas uploaded into the online application, and each gene was overlaid onto a molecularnetwork developed from information contained in the ingenuity pathways database. Networks of genes were then generated based on their connectivity, and we chose the top50 significant networks.", + "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In thismanuscript we will outline some simple use cases, and show how a small number of plausiblecandidate genes can be identified for an immune phenotype. 1. DataOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. Thefirst is to use the global search bar located at the top of the page (Figure 1). This is a newfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of thedatasets.Recent improvements toGeneNetwork have reinvigorated it, including the addition of data from 10 species, multi-omicsanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource forpredictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most commonfunctions on GeneNetwork, as a gateway to deeper analyses, demonstrating how a smallnumber of plausible candidate genes can be found for a typical immune phenotype.", + "This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.", + ", (Chesler et al. , 2005; Galperin and Cochrane,2009; Gentleman et al. , 2004; Mailman et al. , 2007; Saal et al. , 2002; Swertz et al. , 2010)). One relatively well-known database is GeneNetwork (www.genenetwork.org) (Chesler etal. , 2005). GeneNetwork is designed primarily as a web service for exploratory andstatistical analysis of large published phenotype and genome datasets, and includes datafrom several species (see Supplementary Discussion). GeneNetwork includes extensivephenotype data extracted from the literature and submitted by users, which makes itpractical to compare data on drug responses with gene expression patterns.", + "In the currentstudies, the online GeneNetwork system was employed to further probe P2P-R biological functions. Molecularstudies were then performed to confirm the GeneNetwork evaluations. Results: GeneNetwork and associated gene ontology links were used to investigate the coexpression of P2P-R withdistinct functional sets of genes in an adipocyte genetic reference panel of HXB/BXH recombinant strains of ratsand an eye genetic reference panel of BXD recombinant inbred strains of mice.", + "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In thismanuscript we will outline some simple use cases, and show how a small number of plausiblecandidate genes can be identified for an immune phenotype. 1. DataOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. Thefirst is to use the global search bar located at the top of the page (Figure 1). This is a newfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of thedatasets.Recent improvements toGeneNetwork have reinvigorated it, including the addition of data from 10 species, multi-omicsanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource forpredictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most commonfunctions on GeneNetwork, as a gateway to deeper analyses, demonstrating how a smallnumber of plausible candidate genes can be found for a typical immune phenotype.", + "Taking this approach a step further, GeneNetwork[99] is constructedfrom co-regulation patterns found within tens of thousands of samplesfor which gene expression was measured. GeneNetwork provides unprecedented resolution and predictive power across multiple cell typesand tissues. Analogous to discovering patterns in expression data, thenetwork of protein-protein interactions can also be computationally predicted using various methods[381]. The combined current knowledge of how cells control functionssuch as growth, movement, dierentiation, metabolism, communication, and response to stress or pathogens is captured in high-level pathway databases such as WikiPathways[188], Reactome[97] or KEGG[180].", + "However, the accurate derivation of such high-throughput data andtheir analysis in terms of biological function has been critical to truly leveraging the postgenomicrevolution. This chapter will describe an approach that focuses on the use of gene networks to bothorganize and interpret genomic expression data. Such networks, derived from statistical analysisof large genomic datasets and the application of multiple bioinformatics data resources, potentially allow the identification of key control elements for networks associated with human disease,and thus may lead to derivation of novel therapeutic approaches.", + "To test this hypothesis, we used the Web-basedGeneNetwork databases that have been recently introducedto the scientific community and proved to be a powerful toolfor hypothesis-driven investigations (Chesler et al. 2003,2004; Wang et al. 2003). Researchers can take advantageof genetic diversity in panels of recombinant inbred mousestrains to use these databases for studies of the regulation ofgene expression and genetic mechanisms of complex traits. Our in silico investigation provided evidence for potentialfunctional relationships among the 21 DAT-associated proteins detected by mass spectrometry in this study.", + "As mentioned previously, GeneNetwork(www.genenetwork.org) is a collaborative Web-based resource equipped with tools andfeatures for studying gene/gene and exploring genetic correlates to neurobehavioralphenotypes (Chesler et al. , 2003, 2004). The Web site is home to a growing collection ofgene expression and phenotypic data from a variety of species and brain regions, with a hostof links to external resources for tracing the interrelationships of a gene among multipleWeb-based resources. GeneNetwork also offers a number of correlation and mappingstrategies for assessing associations among multiple genes and QTLs.", + "Here we provide open access and availability tothese data by integrating them into the GeneNetwork, aweb-based analytical tool that has been designed for multiscale integration of networks of genes, transcripts andtraits and optimized for on-line analysis of traits controlled by a combination of allelic variants and environmental factors. GeneNetwork with its central module WebQTLfacilitates the exploitation of permanent genetic referencepopulations that are accompanied by genotypic, phenotypic and mRNA abundance datasets.", + "GeneNetwork.org also offers a powerful statistical platform foronline network analyses and mapping, enabling numerous molecular questions to be probed in one centralized location(Chesler et al. , 2003, 2005; Li et al. , 2010; Mulligan et al. , 2012,2017, 2019). Most data are from groups of animals or humanswho have been fully genotyped or even sequenced. As a result, itcan be used to model causal networks that link DNA differencesto traits such as differences in expression, cell number, volumes,and behavior using real-time computation and graphing.", + "While the improvement of gene rankings upon application of GeneRank is already significant in the examplespresented, it may become even more so once comprehensive high-quality biological network informationbecomes available. Of particular interest in that respectwill be transcriptional regulatory networks, such as arenow being generated by technologies like ChIP-chip (see[19-21] for early examples using yeast as a model organism). As discussed above, the information encoded insuch regulatory networks will be intuitively amenable toGeneRank analysis. It will also re-introduce an element ofdirectedness into the network, moving it even closer to theoriginal PageRank application." + ], + [], + [], + [], + [] + ], + "task_id": [ + { + "task_id": "1AE8A08B2F0E63DC504738485B576741" + }, + { + "task_id": "BB02D281C914C63292C0AE91D32CE476" + }, + { + "task_id": "852E48D775CF521A5BA7FFF4F42E87C2" + }, + { + "task_id": "FC617AC44D108DA97F5988E63DF6C0E6" + }, + { + "task_id": "D40628404D48DA90F3E2B0F93FB6640E" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_5.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_5.json new file mode 100644 index 0000000..7f19b57 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_5.json @@ -0,0 +1,16 @@ +{ + "question": [ + "How can GeneNetwork.org contribute to advancements in genetic engineering?" + ], + "answer": [ + "Please try to rephrase your question to receive feedback" + ], + "contexts": [ + [] + ], + "task_id": [ + { + "task_id": "3B79463FDBC2A8CA0EB1B2887A903D9A" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_aging_1.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_aging_1.json new file mode 100644 index 0000000..d24987a --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_aging_1.json @@ -0,0 +1,99 @@ +{ + "question": [ + "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?", + "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?", + "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?", + "How do changes in the gut microbiome composition correlate with aging and longevity?", + "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" + ], + "answer": [ + "Immunosenescence, the age-related decline in immune efficacy, affects both the innate and adaptive immune systems. This decline compromises the body's defense against pathogens, leading to increased susceptibility to infections and diseases. Additionally, aging is associated with 'inflamm-aging,' a state of chronic sterile inflammation that can promote age-related diseases and functional decline. Changes in the immune system also include a decrease in the expression of genes associated with key immune functions, such as phagocytosis in macrophages, which can lead to dysfunction of innate immune cells. Furthermore, the accumulation of apoptosis-resistant cells in the elderly can lead to dysfunctional immune responses.", + "Advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging by integrating and analyzing large and diverse datasets generated from genomic, functional, phenotypic, and lifestyle data. These technologies can predict age, forecast potential diseases in aging, and contribute to personalized medical treatment. Machine learning algorithms can also be used to assess the importance of specific factors in aging, predict future outcomes, and identify potential biomarkers for age-related diseases. Furthermore, they can help in the interpretation of complex omics data sets, which contain vast measurements of potential candidate markers.", + "Recent discoveries have shown that systemic factors, including circulating microvesicles, play a significant role in aging phenotypes. For instance, age-related alterations in vasoprotective endocrine factors, such as growth hormone, IGF-1, and estrogens, have been found to regulate multiple aspects of vascular aging processes. Studies using heterochronic parabiosis in mice have demonstrated the impact of circulating factors on aging phenotypes. Additionally, there is initial evidence that antigeronic factors present in young mice can rejuvenate microvascular network architecture in aged mice. However, the exact nature of these antigeronic circulating factors remains unknown. Progeronic circulating factors, which increase with age and impair tissue homeostasis, have also been identified. Further studies are needed to identify additional progeronic and antigeronic factors and their impact on aging.", + "Changes in the gut microbiome composition correlate with aging and longevity in several ways. Studies on centenarians and supercentenarians have shown that the microbiota adapts to the physiological changes of the long aging process, promoting health and survival. The concentration of certain bacteria, like Bacteroidetes, increases with age, while others like Actinobacteria decrease. Age-related decrease in microbiota diversity can lead to larger populations of certain microbial species, potentially increasing the chances for the evolution of novel, potentially pathogenic strains. These changes can contribute to increased frailty and development of diseases during the late stages of life. However, a healthy microbiota, characterized by the presence of bacterial compounds like Christensenellaceae, Akkermansia, and Bifidobacterium, has been linked to longevity.", + "The key molecular mechanisms through which caloric restriction extends lifespan across different species include signaling through the insulin-like growth factor pathway, chromatin regulation by sir2, and oxidative damage. Caloric restriction also increases the response to oxidative stress and reduces the shortening of telomeres in chromosomes, which directly intervenes in the repair of DNA damage. Additionally, it affects nutrient-sensing pathways such as insulin/insulin-like growth factor (IGF-1) signalling and target of rapamycin (TOR) signalling." + ], + "contexts": [ + [ + "\t\n\nOn the other hand, a direct relationship exists between physiological aging and increasing incidence of chronic inflammatory diseases.In its acute form, inflammation acts as a protective mechanism in response to pathogen invasion or tissue damage and helps to restore physiological integrity and function.However, in its chronic form, inflammation can exert detrimental effects on the cellular as well as the organismic level.Chronically inflamed tissue is characterized by infiltration of immune cells, neovascularization, fibrosis, and often tissue damage and necrosis [3].The innate immune system, especially the mononuclear phagocyte system, is the most important mediator of chronic inflammation.Monocytes originate from the myeloid hematopoietic cell lineage in bone marrow.In the blood stream, monocytes are recruited by specific stimuli into different tissues, where they differentiate into phagocytic Oxidative Medicine and Cellular Longevity macrophages.Macrophages participate in the killing of invading microorganisms and emerging tumor cells through the production of reactive oxygen or nitrogen species (ROS and RNS).In addition, macrophages secrete cytokines, which play a key role in the regulation of multiple immune functions, especially inflammatory responses [3].During aging, the continuous pressure on the immune system caused by repeated antigen stimulation, such as infections, food antigens, allergens, and self antigens, leads to an increase in activated cells and secretion of proinflammatory cytokines, such as TNF [4].These circulating proinflammatory factors may keep the immune system in a state of chronic lowlevel activation, a phenomenon described as \"inflammaging\" [5,6].Eventually, this causes \"immunosenescence,\" that is, an age-related decline in the capacity of adaptive immunity, consisting of more specific responses carried out by B and T cells [7].Thus, with advanced age, the immune system undergoes a gradual remodeling in the attempt to reestablish a new balance that assures survival, however, favoring the development of chronic inflammatory conditions [5,6,8,9].", + "\tThe Neuroimmune System Upon Aging\n\nThe age-associated synaptic dysfunction can also be a consequence of alterations in astrocytes and microglia, as the aging process has also been described as inflammaging, a status of chronic inflammation that contributes to the pathogenesis of neurodegenerative diseases [174].Recent data further suggest an important role of the immune system in regulating the progression of brain aging and neurodegenerative disease.This can be seen as a cause-or-consequence dilemma: do immune and inflammatory pathways become hyperactivated with age and promote degeneration or, instead, immune responses fail to cope with age-related stress and may contribute to disease [175]?", + "\tAging is one of the inevitably dominant risk associated with many diseases. Several biological factors contribute to this etiology which include loss of telomeres, stem cells activity and metabolism, escalation of environmental and biological stress, dysfunctioning of various micro-and macromolecules, and cell cycle and weakening of immune system (Franceschi et al., 2018).In case of cellular and molecular damage before elderly age, injury is healed to maintain the hemostasis.Nonetheless, with aging, repair mechanism is slowed or completely halted, leading to number of pathologies (Cortopassi, Gurung, & Pinto-Plata, 2017).", + "\t\n\nimmunity can become hyperactivated, exacerbating the age-related damage caused by innate immune responses [33].The risk of collateral damage by the adaptive immune system also potentially increases with age via autoimmunity factors, but this is believed to be counteracted by a parallel rise in self-protective mechanisms [42].Overall, the collateral damage inflicted by the innate immune system over the course of a long life is likely to be greater than that caused by adaptive immunity.\t\n\nThe damage caused by the ageing adaptive and innate immune systems gives us insights into how these different arms of the immune system may influence longevity.In general, adaptive immune function diminishes with age, whereas innate immune function is maintained [34,[43][44][45][46]. Whilst this may initially suggest that the innate immune system withstands the test of time better than the adaptive immune system, a chronic stimulation of innate immunity underpins this pattern [35].Innate immune cells become increasingly proinflammatory with age [46,47] and trained", + "\t\n\nThe increased expression of genes involved in immune response and inflammation observed in the colon of the 21-month-old mice points to an affected immune system in this part of the intestine of aging mice.This observation is in agreement with the fact that changes in the immune system are one of the hallmarks of the aging body.Immunosenescence is the functional decline of the adaptive immune system brought on by natural aging whereby protection against infection by pathogens and the effectiveness of vaccination decline [45,46].The second aging-induced change in the immune system is called inflammaging which is characterized by a lowgrade chronic inflammation process that contributes to the pathogenesis of many age-related diseases [47][48][49].A large variety of cells with a defense function are present especially in the lamina propria and the submucosa of the intestine accomplishing immune protection via the innate as well as by the adaptive immune response.Interestingly, our microarray and Q-PCR data clearly show that activity of both branches of the immune system is enhanced in response to aging exclusively in the colon but not in the small intestine of old mice.Expression levels of well-established pro-inflammatory cytokines like IFN, TNF, IL6 and IL1 turned out to be extremely low in the colon of both old and young mice and below the threshold of our microarray analysis.These low expression levels are probably due to the fact that these cytokines are predominantly produced by immune cells in the mucosa which is a rather low percentage of cells in relation to all cells present in the intestinal tissue.Q-PCR analysis confirmed the very low basal expression levels of these pro-inflammatory cytokines, yet a weak but significant induction of IFN TNF and IL-1 in the colon of aging mice was observed.This result suggests that low-grade inflammation might be present in the colon of the aging mice in our study, although it should be noted that no altered expression of a number of established inflammation markers like Tolllike receptors (TLRs), C-type lectin receptors (CLRs) and retinoic acid-inducible receptors (RLRs) [50] was detectable.", + "\tIntroduction\n\nAgeing of the immune system (immunosenescence) contributes to the increased susceptibility of the elderly to infectious disease and to the poor outcome of vaccination.Defence against pathogens is compromised mainly because of changes in adaptive immunity mediated by T and B lymphocytes; however, all components of the immune system are affected (Fig 1).Dissecting the crucial alterations responsible for dysfunctional immunity in old age will facilitate the development of rational interventions to reconstitute appropriate immune function.Given the increasing proportion of elderly people in most countries and their disproportionate consumption of health-care resources, this issue is rapidly gaining in importance.The meeting, which was dedicated solely to studies of immunosenescence, filled two days with the 'A to Z' of immunity, covering topics ranging from development to senescence, innate immunity to adaptive immunity, and genes to environments, in organisms ranging from mice to monkeys and humans.Understanding and eventually modulating immune dysfunction in the elderly now beckons.\tClinical implications of immunosenescence\n\nAs mentioned above, complications from acute infectious are likely to be more severe in the elderly owing to impaired innate immunity.However, questions remain concerning 'normal, healthy' ageing and the important clinical issue of responses to vaccinations in old age.In a mouse model of the highly relevant human pathogen influenza, the virus is cleared from the lungs more slowly in old animals, correlating with a delayed and decreased peak of cytotoxic T-cell production (D.Murasko, Philadelphia, PA, USA).Therefore, cellular responses are crucial for controlling the virus, but do not function adequately in old animals.Although there is an accumulation of memory cells (the clonal expansion referred to above), they are not solely responsible for this decrease in the virus-specific response.Both memory and naive T cells in old, but not young, mice are resistant to apoptosis, and do not 'make space' for new responses.In the mouse model, cell-transfer experiments showed that both the old environment and the old cells contributed to the problem-young cells did not deplete when transferred to an old environment and old cells did not deplete when transferred to a young environment.The factors inducing apoptosis resistance have not yet been identified; however, it is clearly important to do so and to search for them in humans.\tConclusions\n\nAll components of the immune system are altered as ageing proceeds (Fig 1 ); however, the T-cell and B-cell compartments seem to be particularly susceptible.The most severe clinical impact is probably a result of the loss of diversity in the TCR and B-cell-receptor repertoire, owing to the accumulation of dysfunctional cells, and decreased thymic and bone-marrow output.Several interventions discussed at the meeting could conceivably contribute to the restoration of appropriate immune function in the near future.\tLymphocyte development and ageing\n\nThe cells of the immune system turn over rapidly and therefore need constant replacement from the pool of haematopoietic stem cells (HSCs).If the HSCs themselves aged, it would compromise all downstream events that depend on their integrity, including production of immune cells and subsequent immune responsiveness (Rando, 2006).Evidence for age-associated alterations in the ability of HSCs to reconstitute the haematopoietic system of an animal derives from findings of increased self-renewal with age, resulting in an expansion of the HSC pool size even when transplanted into young animals (D.Rossi, Stanford, CA, USA).However, purified HSCs from old mice showed less activity on a per-cell basis and tended to generate more myeloid cells-for example, macrophages-than lymphocytes.Expression profiling of young and old HSCs revealed that genes mediating lymphoid fate and function were systematically downregulated, whereas myeloid-specification genes were upregulated, with age.The concerted nature of these changes suggests epigenetic involvement as a mechanism that contributes to HSC functional decline with age.There is also a gradual decline in the ability of murine HSCs to progress through the various stages of B-cell-differentiation (K.Dorshkind, Los Angeles, CA, USA).This reflects, in part, the microenvironmental changes involving altered production of interleukin 7 (IL-7) by stromal cells as they age (M.Cancro, Philadelphia, PA).B cells must also compete for the cytokine BLys (or B-cell activating factor (BAFF)), the receptor levels of which determine survival.Declining B-cell production in aged animals results in selective accumulation of marginal zone and memory B cells at the expense of the follicular pool of B cells.The follicular pool is responsible for producing protective immune responses to newly encountered pathogens, such as influenza H5N1.Loss of the declining stem-cell function, and the resultant decline of the follicular B-cell compartment, leads to enhanced infectious disease-related morbidity with ageing (J.Cambier, Denver, CO, USA).Hence, age affects both HSCs and the environment that determines their fate.\tInnate immunity\n\nSo, what are the age-associated changes that can be directly measured in macrophages, dendritic cells, neutrophils, natural killer (NK) cells and so on?These might be at least as important, if not more so, than the changes to adaptive immunity discussed above (Solana et al, 2006).The number and proliferation of a particular subset of 'natural' T cells with NK-cell and regulatory functions, bearing invariant V14J18 receptors (iNKT cells), is decreased in the elderly; however, whether these changes have any clinical impact is not yet known (R. Solana, Crdoba, Spain).Neutrophils from old people retain normal chemotaxis and superoxide-generation capacity, but are compromised in phagocytosis in the healthy elderly and more so in the traumatized elderly ( J. Lord, Birmingham, UK); these findings have important implications for infection in the elderly.Trauma, in the form of burn injury in mice, resulted in the death of old animals from infections that young animals were able to resist.This susceptibility of old mice correlated with higher levels of pro-inflammatory IL-6 and decreased T-cell function, and could be in part reversed by oestrogen treatment (E.J. Kovacs, Maywood, IL, USA).Dendritic cells-the essential bridge between innate and adaptive immunity-are similar in young and old people in terms of their response to cytokines (although those from the elderly secrete more IL-6 and tumour necrosis factor- (TNF)), surface phenotypes and morphology, whereas chemotaxis and, as with neutrophils, phagocytosis are impaired (S.Gupta, Irvine, CA, USA).Gene arrays indicate only a small number of differences between young and old dendritic cells, far fewer than in T cells.Nonetheless, functional impairment in antigen presentation was found, such that dendritic cells from young or old people stimulated naive CD8 cells equally well, but those from the elderly failed to stimulate CD4 cells appropriately.\t\n\nApoptosis-resistant cells that accumulate in old mice and humans-and fill the 'immunological space'-might be dysfunctional in several ways.In young mice, the number of T cells staining with soluble major histocompatibility complex (MHC)-peptide multimers carrying influenza epitopes was similar to the number of cells producing the antiviral and pro-inflammatory cytokine interferon- (IFN) on antigen stimulation.However, in old mice, the number of tetramer-positive cells exceeded the number of IFNproducers, indicating that some cells bearing antigen-specific receptors failed to respond appropriately to receptor ligation (H.Ertl, Philadelphia, PA, USA).This is similar to the situation in elderly humans, who have been found to accumulate large clonal expansions, primarily-and for unknown reasons-of cytomegalovirus (CMV)-specific CD8 cells (Pawelec et al, 2005).In the mice, this lack of reactivity was not due to poor antigen presentation by dendritic cells (Ertl).The reason for poor reactivity remains unknown; however, responses could be restored, in part, by vaccination using an adenovirus vector AdC68 that naturally infects chimpanzees rather than mice, as a way of improving immunizations by modifying the vaccine product.This might also be possible in humans by using better adjuvants for vaccination (E.Nagy, Vienna, Austria).Deciphering the mechanisms by which adjuvants enhance responses in order to design 'elderly-specific' vaccines will become increasingly important.This applies not only to infectious diseases but also possibly to vaccinating against cancer, as illustrated by differences in responses to anticancer immunizations in young and old mice.In a breast cancer model, preventive vaccination using DNA encoding certain cancer antigens was successful in protecting 90% of the young mice, but only 60% of the old mice, from developing metastases.This correlated with lower levels of IFN and IL-2 in old mice (C.Gravekamp, San Francisco, CA, USA).The production of IL-6, which is a potential inhibitor of vaccine-induced T-cell responses, was high in both young and old mice.Increasing IFN and IL-2, and depressing IL-6 production in the elderly, would therefore seem to be desirable.", + "\tAging and variability among immune cells\n\nHow and why the immune system becomes less effective with age are not well understood.Martinez-Jimenez et al. performed single-cell sequencing of CD4+ T cells in old and young mice of two species.In young mice, the gene expression program of early immune activation was tightly regulated and conserved between species.However, as mice aged, the expression of genes involved in pathways responding to immune cell stimulation was not as robust and exhibited increased cell-to-cell variability.", + "\t\nThe aging population is at a higher risk for age-related diseases and infections.This observation could be due to immunosenescence: the decline in immune efficacy of both the innate and the adaptive immune systems.Age-related immune decline also links to the concept of 'inf lamm-aging,' whereby aging is accompanied by sterile chronic inf lammation.Along with a decline in immune function, aging is accompanied by a widespread of 'omics' remodeling.Transcriptional landscape changes linked to key pathways of immune function have been identified across studies, such as macrophages having decreased expression of genes associated to phagocytosis, a major function of macrophages.Therefore, a key mechanism underlying innate immune cell dysfunction during aging may stem from dysregulation of youthful genomic networks.In this review, we discuss both molecular and cellular phenotypes of innate immune cells that contribute to age-related inf lammation.\t\n\nThe aging population is at a higher risk for age-related diseases and infections.This observation could be due to immunosenescence: the decline in immune efficacy of both the innate and the adaptive immune systems.Age-related immune decline also links to the concept of 'inf lamm-aging,' whereby aging is accompanied by sterile chronic inf lammation.Along with a decline in immune function, aging is accompanied by a widespread of 'omics' remodeling.Transcriptional landscape changes linked to key pathways of immune function have been identified across studies, such as macrophages having decreased expression of genes associated to phagocytosis, a major function of macrophages.Therefore, a key mechanism underlying innate immune cell dysfunction during aging may stem from dysregulation of youthful genomic networks.In this review, we discuss both molecular and cellular phenotypes of innate immune cells that contribute to age-related inf lammation.\tIntroduction\n\nThe human population is aging, which has led to the rise in prevalence of many so-called age-related diseases.Not only is the aging population much more susceptible to age-related diseases, they are also more susceptible to infections.For example, elderly individuals are at a higher risk of developing severe COVID-19 or complications from influenza infections [1,2].This increased chance of infection can be due to the decline of the function of the immune system, a phenomenon called 'immunosenescence' [3].Age-related changes in the function of the immune system are also accompanied by a chronic sterile inflammation, a mechanism dubbed 'inflamm-aging,' which is thought to promote age-related disease and functional decline [4].Inflamm-aging is associated with many different factors, most typically encompassing increases in pro-inflammatory cytokines tumor necrosis factor alpha [TNFa], interleukin 1 beta [IL1b] and interleukin 6 [IL6] [5].Although these cytokines may directly contribute to increased systemic inflammation.Age-related increase in genomic instability may itself also drive aspects of inflammaging.Indeed, re-activation of LINE-1 transposable elements during aging and in senescent cells has been proposed to drive an interferon response, thus contributing to sterile inflammation [20][21][22].In addition, chronic DNAdamage signaling itself, for instance in aged lymphocytes, may also render them more activation-prone through innate receptors even in the absence of infection [23].\t Immune decline is a hallmark of aging. Aging associates with a state of chronic sterile inflammation.\t Aging associates with a state of chronic sterile inflammation. Innate immune cells undergo widespread molecular and functional remodeling with aging.\t\n\nIn this review, we will focus on how innate immune cells act as key contributors to age-related inflammation (Figure 1).We will discuss both molecular and cellular phenotypes which have been described in the aging innate immune system, and how they could relate to the phenomenon of inflamm-aging and immunosenescence.\t\n\nImportantly, a key mechanism underlying innate immune cell dysfunction during aging may stem from dysregulation of youthful genomic networks.Indeed, aging is accompanied by widespread remodeling of transcriptional landscapes across tissues and cell types (reviewed in [33]).In addition, age-related inflammatory signatures at the transcriptional levels have been observed across species and tissues, suggesting that such 'omic' remodeling is a conserved aging response [34,35]." + ], + [ + "\t\n\nKnowledge of genetic interrelationship between the biomarkers of aging may lead to the discovery of a downstream common pathway that summarizes aging processes; the list of biomarkers should be as comprehensive as possible via incorporating other well-known systems involved in aging in addition to the musculoskeletal system.Further development of the pleiotropy-based approaches will be useful for other studies of multiple related phenotypes which employ genome-wide associations to decipher genetics in the absence of disease endophenotypes, which is the case of human aging.With the advent of these approaches, new candidate genes may emerge for further pursuit.In its turn, discovery of the \"phenome of aging\" may translate into innovative diagnostic and therapeutic interventions to improve the overall health of older men and women.", + "\t\n\nBiomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases.We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the transcriptome.We develop an ensemble machine learning method that predicts age to a median error of 4 years, outperforming previous methods used to predict age.The ensemble was further validated by testing it on ten progeria patients, and our method is the only one that predicts accelerated aging in these patients.", + "\t\n\nIt should be mentioned that although the objectives of those researchers sound encouraging and ambitious, the search for biomarkers of ageing for their application in the improvement of human health, and prevention of diseases related to ageing, will only increase the generation of data.The great part of the search for biomarkers has been as a result of the extensive studies of human cohorts, resulting in genomic, functional, phenotypic, and lifestyle data of the individuals studied (Table 13.1).Thus, due to the generation of these data and technological advances, possibly in the future, artificial intelligence programs will be able to reliably forecast the life of an individual, as well as the possible diseases that he may suffer in ageing; so these advances and discoveries will allow us to achieve a \"personalized medical treatment\" as a result of to the integration of biomarkers of ageing.\tMeg3\n\nDecrease in cell senescence [85] (continued) number of biomarkers that are candidates to determine human ageing.However, these biomarkers have considerable variability among different individuals because the ageing process has an intrinsic multicausal nature.So, a multisystemic integration of biomarkers to determine biological age is still reliably found.Currently, thanks to the different analyses performed using new technologies and new knowledge on the molecular basis, there are leading to the discovery of many Biomarkers classified according to their type and their modulation in ageing novel molecular markers.Some of these technologies are the omics techniques, such as metabolomics, proteomics or genomics, also induces data generation, offering an overview of new biomarkers of ageing.However, it remains to be clarified which markers can be an accurate, reliable predictor of ageing.Among the various studies carried out to solve these questions, the MARK-AGE study was a project supported by the European Commission.The main objective of this project was to carry out a population study of approximately 3200 subjects to identify a set of ageing biomarkers, which together with correctly established parameters, would measure the biology of an individual, compared to the result that would only have using a biomarker individually [72].\tIntegration of Biomarkers of Ageing\n\nBiomarkers of ageing allow estimating the biological age of an organism (Table 13.1) while providing information on their health status.Different studies are looking for the integrated use of multiple biomarkers, in order to make the estimation of health status more accurate.As we could see throughout this chapter, there are a large\t\n\nTo make significant progress in aging research, we urgently need molecular biomarkers for aging studies, particularly in humans.This chapter focuses on the inflammatory state, the markers of oxidative stress, and the hormonal profile which are the main functions that impact the development of aging and can be influenced by the gene and environmental variables in which human beings develop.", + "\tDiscussion\n\nMachine-learning can be applied as a systems biology approach, integrating multiple classes of biometric data to assess the importance of specific factors, while also predicting future outcomes.Whereas conventional assessments of disease identification exist, more detailed genomic and epigenomic testing is likely to reveal a comprehensive, systemic valuation of an etiology.To-date, studies have applied machine-learning algorithms in examining the physiological, biochemical, and/or genetic components of disease onset or progression [51].The advantage of our current study is through the assimilation of patient-matched data across a variety of critically impacted systems, providing an archetype for developing novel, descriptive, diagnostic measures through machine-learning algorithms that are specific for each disease type.By individually representing our datasets in Figs. 2, 3 and 4, we were able to reach more conclusive data in Fig. 5 by choosing the most predictive features for our final model.For the first time, a multi-omics, machine-learning approach was used to assess the progression and development of type 2 diabetes mellitus in a patient population, identifying potential biomarkers for cardiovascular risk and revealing the fundamental role of genetics in the pathology.\t\n\nIn the current study, machine-learning was used as a predictive tool to integrate cardiac physiological, biochemical, genomic, and epigenomic biomarker data in a patient-matched fashion and enable determination of type 2 diabetic status.In 50 patients, machine-learning algorithms revealed the interconnectedness between diabetic classification, mitochondrial function, and methylation status.Our study highlights how novel biomarkers can be used to augment existing diagnostic standards as well as provide new, and more precise, methods for identifying the development and severity of type 2 diabetes mellitus in potentially at-risk populations, such as those with prediabetes.While we examine physiological, biochemical, and molecular datasets using machine-learning algorithms, our goal was to understand which features possessed the best predictive accuracies and if these specific features could be used alone, or in conjunction, with HbA1c.The purpose for the inclusion of models that do not rise above 50% predictive accuracy was to contrast them against those models that do rise above 50% in the absence of HbA1c, to determine which biomarkers are the best overall predictors.\t\n\nThe quantity and diversity of omics-based approaches continues to expand.Convenience and increasingly inexpensive options for biometric-based valuations incite a growing demand for the incorporation and meaningful explanation of large and diverse patient datasets.The methodology outlined in this manuscript can serve as an archetype for the development and implementation of machine-learning to other disciplines seeking to evaluate disease progression.By using various health outcomes datasets, we were able to identify, and combine, the most prominent biomarkers into an accurate predictive algorithm engineered around 50 patients.While we have identified specific genetic features that are highly predictive in 50 patients, as a much larger patient population is applied to this model, the prioritization of other features is likely to occur, enhancing the diagnostic potential for the individual diabetic or prediabetic patient.Indeed, this is the advantage of using machine-learning models, in that they continue to learn and develop more accurate predictions as the number of features and sampled population grows.\tMolecular pathogenesis and machine-learning\n\nWhile clinical practice has recently experienced a surge in deep learning applications used for non-invasive imaging [52], implementing machine-learning algorithms to the fundamental biochemistry and cellular and molecular processes of the body is now only blossoming.Onset and progression of type 2 diabetes has been traditionally measured through blood glucose levels, but, the multifaceted aspects of the disease could create variability in prognosis between vastly different demographic and ethnic groups.Owusu Adjah et al. [14] recently identified BMI as a risk factor for determining ethnic group disposition to type 2 diabetes mellitus.Specifically, the relationship between BMI and increased incidence of diabetes mellitus is non-linear; some groups, such as South Asian populations, were more disposed to developing the disease even at lower BMIs.While the current Fig. 6 Overview of machine-learning pipeline implementing biological variables across a spectrum of gathered information.From the patient population undergoing coronary artery bypass graft surgery (CABG), physiological parameters (demographics, health reports, etc.) and atrial tissue were used for subsequent analyses.From cardiac tissue genomic (mitochondrial DNA), epigenomic (TFAM promoter CpG methylation), and biochemical (nuclear and mitochondrial function) were assessed.Cumulatively, the biological data was processed through tree ensembles in SHAP and validated through CART analysis with tenfold cross validation.Using these machine-learning algorithms, graphical depictions and biomarker feature importance are able to be derived, allowing for prediction of the onset and progression of diabetes.Ultimately, by using biological data at the genomic and epigenomic level, it allows for precision medicine approaches and more personalized diagnostics and prognostics.TFAM: transcription factor A, mitochondrial; mtDNA: mitochondrial DNA; CpG: cytosine nucleotide followed by a guanine nucleotide; CART: Classification and Regression Trees; SHAP: SHapley Additive exPlanations manuscript examines cardiovascular tissue, other less invasive approaches have been used to apply machinelearning algorithms.By retrieving blood from the basilica vein, circulating biomarkers were examined for their role in predicting early recurrence of atrial fibrillation following cryoballoon ablation [53].Support vector machines confirmed that decreased levels of creatine-kinase (CK-MB) and Troponin T (TnT) were associated with increased early recurrence of atrial fibrillation following cryoballoon ablation.Additionally, a unique, non-invasive approach for potentially diagnosing type 2 diabetes in patients was performed through the examination of toenails.Carter et al. [54], through a variety of machine learning algorithms, focused on 22 elements, including aluminum, cesium, nickel, vanadium, and zinc, and was able to get an AUC of 0.90 when predicting diabetic status using a random forest model.Similar to parts of the aims of this study, other groups have attempted to use machine learning to separate diabetic and non-diabetic patients without the inclusion of blood glucose or HbA1c [55].In a testing set of 13,700 patients from the Luzhou, China region, random forest machine-learning algorithms provided a 0.7225 accuracy when predicting diabetic status from physical examination data in the absence of blood glucose [55].Also using a random forest model, Tang et al. [56] revealed how CpG island methylation data, combined with microRNA expression profiles, can be instrumental in cancer pathogenesis; implementing this two-feature selection process, they were able to identify the best tissue specific features, ultimately allowing for the identification of the originating tissue where tumor progression began.In a similar fashion, the machine-learning algorithm HeteSim [57], which examines heterogeneous datasets and calculates their relatedness, was employed in ascribing how gene profiles can be related to phenotypic outcomes, specifically in the validation and prediction of genes classified within major diseases [58].", + "\tWhat do chemical biomarkers tell us about aging? Aging is not a homogeneous process\tThe nature of chemical biomarkers of aging\n\nCentral to the study of chemical theories of biogerontology is the definition of biomarkers of the aging process, chemical 'handles' that can be used to assess the progress of aging and the effectiveness of anti-aging strategies.As it turns out, most of the age-biomarkers measured today are products of non-enzymatic chemistry.Living organisms are complex mixtures of reactive chemicals, including dietary components, metabolic intermediates, side-products of metabolism, xenobiotics, drugs, etc.Reactions between the constituents of this mixture occur at random throughout the body, but evidence of the role of cumulative non-enzymatic chemistry in aging is most apparent in long-lived proteins, such as lens crystallins and tissue collagens.The increase in post-synthetic chemical modifications of crystallins with age results in an agedependent increase in brown color and fluorescence of lens proteins.These chemical modifications are associated with aggregation, crosslinking and insolubilization of lens proteins, leading gradually to the development of cataracts (Hoenders and Bloemendal 1983;Harding et al. 1989).Similar changes occur in collagens (Bailey et al. 1998) leading to decreased elasticity of the extracellular matrix, resulting, for example, in the age-dependent stiffening of tendons Dilysine crosslink", + "\t\n\nPeople of the same chronological age have different aging states, which can be monitored using various biomarkers (Belsky et al. 2015).These markers are usually measurable indicators of a particular outcome or source of aging, such as phenotypical measures like frailty and molecular measures like DNA methylation dynamics (Schumacher et al. 2021;Lpez-Otn et al. 2023).Although informative, they are not always quantitatively predictive of an individual's true biological age, nor are they easy to obtain.The advancement of high-throughput screening platforms and extensive longitudinal studies has greatly facilitated the search for new noninvasive and quantitative biomarkers of aging.For instance, highthroughput sequencing allows unbiased multiomics profiling of DNA, RNA, and epigenetic changes during aging, providing a comprehensive view of senescence at tissue and single-cell levels (Solovev et al. 2020;Aging Atlas Consortium 2021).These omics data sets contain vast and noisy measurements of potential candidate markers and, consequently, require carefully designed computational models to identify and extract predictive signals from the data.However, construction of such models is often highly degenerate, yielding little overlap of identified biomarkers between studies and thus making results difficult to interpret (Thompson et al. 2018;Galkin et al. 2020).\t\n\nMost of the existing omics-based aging clocks have been constructed using data from bulk tissues, which neglect the variations in cell compositions and cell-to-cell aging heterogeneity.To gain a more detailed and nuanced view of cell type-specific molecular changes during aging, several studies have applied machine-learning models to single-cell transcriptomics and DNA methylation data (Trapp et al. 2021;Buckley et al. 2023).Despite their success in predicting chronological age within specific training contexts, these clocks are constrained by their applicability to a limited number of cell types and tissues.Their generalizability to other cell types and disease data, particularly in cases with ambiguous cell type identities, remains uncertain.Additionally, problems like data sparsity and batch effects are more pronounced in single-cell omics data, further complicating the identification of consensus aging markers and the interpretation of model results.Furthermore, as chronological age is often the only available measure of biological age, it becomes critical to determine whether the features learned from single-cell omics data can capture other dimensions of biological aging.", + "\t\n\nEach of these criteria deserves some amplification.A biomarker validation program would start with a list of candidate biomarkers, each known to be age-sensitive (by cross-sectional and/or longitudinal analyses) in adults.By hypothesis, some of these traits would reflect interindividual differences in the aging process, but each would also be sensitive to genetic and nongenetic factors that also vary among individuals, statistical \"noise\" that would interfere with the extraction of the \"signal\" attributable to aging itself.A correlation between age-sensitive immune parametersfor example, T-cell proliferation and T-cell cytokine production-would be relatively unhelpful in evaluating each of these parameters as potential biomarkers of aging, because the two assays are closely related and likely to be influenced by many factors unrelated to aging (e.g., recent infection, vaccination history, polymorphisms in immune system genes).However, a correlation between T-cell proliferation and, for example, muscle strength, or reflex speed, or lens protein cross-linking, or age at menopause, would be difficult to attribute to any obvious metabolic or pathophysiological mechanism other than linkage to some fundamental aging rate that might by hypothesis retard or accelerate changes in a wide range of age-sensitive traits.", + "\tMultiomics technology\n\nThe broad diversity of omics biomarkers that have been used to assess biological responses provides new opportunities to understand the impact of the environment on the risk of age-related diseases.For example, the multiomics analysis and integration method produces a priority list of multiple sets of biomarkers, which together reflect the molecular responses of the exposome.Each of these data warrants integration into a biomarker panel to aid physicians in developing age-related disease diagnoses and prognoses [78].", + "\t\n\nOverall, demonstrating that a particular intervention is affecting human aging, as done in model organisms, is virtually impossible.Interventions, including drugs, emerging from basic research on aging will probably target specific age-related pathological conditions and/or dysfunction.Subsequent studies of health biomarkers and multiple age-related diseases may reveal broader effects.Success in animal models or short-term human studies may be sufficient to convince potential patients of the usefulness of particular dietary supplements or approaches, as exemplified by those voluntarily undergoing CR (http://www.crsociety.org/),which can serve as basis for further studies (Soare et al., 2011).\t\n\nTo facilitate target gene prioritization, a number of additional approaches may be employed.For example, in silico studies of transcriptional regulation can allow the identification of upstream regulators (for review, see de Magalha es et al., 2010).Furthermore, an emerging approach to study the complex interactions between the multiple components of biological systems is network biology (Baraba si et al., 2011).Given the complexity of aging, network approaches may be particularly suited to identify crucial regulators of its modulation by the environment.For instance, knowing the protein-protein interaction network of candidate proteins allows the identification of hubs, proteins with a large number of interactions, which tend to be more biologically relevant (Fig. 3).Together with other biological (e.g., kinases and receptors are often seen as promising drug targets), medical, and strategic considerations already used for target selection in drug discovery (for review, see Knowles and Gromo, 2003), the integrated knowledge of aging-related pathways can help identify suitable targets for drug discovery.In addition, the advent of largescale databases of compounds and drugs, such as Drug-Bank (Wishart et al., 2008), STITCH (Kuhn et al., 2008), and the Connectivity Map (Lamb et al., 2006), paves the way to cross-linking longevity/CR-associated genes with drug databases to identify candidate molecules for effects on aging.\t\n\nWe now know of hundreds of genes that regulate aging in model organisms, dozens associated with longevity in humans, and hundreds differentially expressed with age.This vast amount of information yields increased power for personalized and stratified medicine, for identifying biomarkers of aging, and for drug development to extend lifespan and ameliorate age-related diseases.Overall, it gives us a blueprint (albeit still imperfect) of how aging is controlled that we can use to potentially manipulate the basic aging process, whatever its underlying molecular mechanisms may be.Moreover, our knowledge of nutrient-sensing pathways that mediate the effects of CR has greatly increased in recent years, opening new opportunities for drug discovery and ultimately for perhaps developing an antiaging pill that retards aging with minimal side effects.", + "\tIntegrating genomics and biomarker research\n\nOnce the use of established biomarkers of biological age is standardized, the biomarker information can be integrated into studies aimed at finding causal determinants of aging and longevity.An example of an integrated approach to identify lifespan regulating loci is represented by testing whether genetic variants associated with potential biomarkers also associate with longevity.To date, GWAS have identified many genetic variants that associate with age-associated traits, such as leukocyte telomere length and features from glycome and metabolome profiles [84][85][86].The joint effect of the majority of these variants on aging and longevity still needs to be determined.One study identified a haplotype in the TERT gene that was associated with increased telomere length and longevity, which indicates that genetic variants associated with telomere length regulation might also play a role in longevity [87]." + ], + [ + "\tSeveral studies have shown\nthat the systemic milieu regulates stem cell decline during aging. Liang et al. showed\nthat HSCs have a reduced ability to home to the bone marrow and spleen after\ntransplantation into old versus young recipients (Liang et al. , 2005). Further experiments\ndemonstrated that the muscle stem cell niche adversely effects stem cell function as\nevidenced by the restoration of old stem cell regenerative potential upon exposure to a\nyoung systemic microenvironment (Conboy et al. , 2005; Conboy and Rando, 2005).\tHowever, studies do indicate that aged tissues have a diminished capacity to return to a\nhomeostatic state after exposure to stress or injury, therefore indicating a defect in stem\ncell function during the aging process. Since the HSC population provides an ideal\nmodel to study stem cell aging, it is necessary to elucidate the mechanisms of\nhematopoietic aging and expand the findings to other tissues and organ systems. Theories of Aging and Age Related Epigenomic Changes\nThere are two major theories of organismal aging: evolutionary and damage\nbased.\tHSCs as a Model for Stem Cell Aging\nWhen studying aging it is important to choose an appropriate model system. For\ninstance, cells (such as skin and blood) that undergo continuous turnover are removed\nfrom circulation long before they have time to feel the effects of aging, and certainly long\nbefore they could exert an effect on tissue function. The predominant substrates for\naging, thus it seems, would be long-lived cells in the organism, namely tissue specific\nstem cells, since this population is exposed to both intrinsic and extrinsic effectors of\naging throughout the lifespan of an individual.\tWith\nthis in mind, it has been hypothesized that the aging or functional failure of tissuespecific stem cells, which fulfill this job, may limit tissue repair and renewal, therefore\ncontributing to overall organismal aging (Krtolica, 2005; Van Zant and Liang, 2003). Because of the unprecedented experimental model systems that are available for the\nexploration of HSCs, stem cell aging research in the field of hematology has been the\nsubject of extensive studies. Indeed, the hematopoietic system has served as an important\nmodel for advancing our understanding of stem cell biology and its association with\naging.", + "\t\n\nThe several lines of evidence support the hypothesis that essential metabolic pathways interconnected with environmental factors and genetic background are involved in the appearance of different markers of cellular senescence.They have emerged as potential regulators of cellular senescence, particularly through those pathways involved in the maintenance and repair of stem cells and progenitor cells: mitochondrial integrity, mitotic competence, and eradication of senescent cells.The complexity of events that are under the control of the genetic programs induced in response to environmental challenges creates the need for further studies that must be performed to unravel the biological roles of the highly dynamic aging process through different tissues and different stages of cell life.The increasing research across different species has allowed the identification of conserved processes associated with the biology of aging.However, it is essential to consider that information from lower organisms cannot be generalized, since worms do not develop age-associated diseases such as osteoporosis, arthritis, or Alzheimer's disease.", + "\t\n\nThere is growing evidence that noncell-autonomous mechanisms play a critical role in orchestrating vascular aging processes (Figure 1).Aging-induced alterations in vasoprotective endocrine factors are of particular importance.Such changes include an age-related decline in circulating levels of growth hormone, 215 IGF-1, 216 and estrogens, all of which regulate multiple aspects of endothelium-dependent vasodilation, 217 autoregulation of blood flow, 218 vascular structural remodeling, atherogenesis, 219 and angiogenic processes. 220he impact of circulating factors on aging phenotypes was also demonstrated by studies using mice with heterochronic parabiosis, which involves surgically connecting the circulatory system of a young and an aged mouse. 221erebromicrovascular density typically declines with advanced age, 222 and there is initial evidence that circulating antigeronic factors (which reverse/prevent development of aging phenotypes) present in young mice can rejuvenate microvascular network architecture in aged heterochronic parabionts. 221he antigeronic circulating factors present in young mice are currently unknown, and the previously proposed role for GDF11 (growth differentiation factor 11) 221 remains controversial.Future studies should identify additional antigeronic factors that might be targeted by interventions to extend vascular health span.Progeronic circulating factors increase with age and impair tissue homeostasis in young animals.There is initial evidence that mediators secreted by senescent cells (eg, inflammatory cytokines, such as TNF- 35 ) may serve as progeronic circulating factors.Further studies are warranted to identify additional progeronic proteins and determine their impact on atherogenesis, endothelial function, blood-brain barrier integrity, and microvascular function in aging.\t\n\nAdditional evidence to support a central role of antigeronic circulating factors governing vascular aging processes is derived from studies on caloric restriction-a dietary regimen, which improves health and slow the aging process in evolutionarily distant organisms. 223Caloric restriction was shown to promote a youthful endothelial phenotype by upregulating and activating eNOS in aged animals [223][224][225] and perhaps humans. 226 critical role of antigeronic circulating factors in vasculoprotective phenotypic responses induced by caloric restriction was first indicated by the observations that in vitro treatment of cultured aged endothelial cells with sera derived from caloric restricted animals mimics phenotypic effects observed in vivo during caloric restriction, promoting anti-inflammatory and proangiogenic effects. 42,227Treatment with sera derived from caloric restricted animals upregulates SIRT1 228 ; however, the exact nature of the circulating factor responsible for this effect remains elusive. ][231] Human studies are needed to identify novel progeronic and antigeronic circulating factors and their cofactors, activators, or inhibitors/antagonists and to seek associations with vascular aging phenotypes.Future studies should also identify cellular origins of circulating progeronic and antigeronic factors that impact vascular aging and characterize pathological conditions that alter their levels in circulation with aging.Further, mechanistic studies describing the cellular effects of progeronic and antigeronic circulating factors in the vascular wall are warranted.", + "\t\n\nMitochondrial-derived peptides (MDPs) in aging-related phenotypes", + "\t\nBackground: Aging is believed to have a close association with cardiovascular diseases, resulting in various pathological alterations in blood vessels, including vascular cell phenotypic shifts.In aging vessels, the microRNA(miRNA)mediated mechanism regulating the vascular smooth muscle cell (VSMC) phenotype remains unclarified.MiRNA microarray was used to compare the expressions of miRNAs in VSMCs from old rats (oVSMCs) and young rats (yVSMCs).Quantitative reverse transcription real-time PCR (qRT-PCR) and small RNA transfection were used to explore the miR-542-3p expression in oVSMCs and yVSMCs in vitro.Calcification induction of yVSMCs was conducted by the treatment of -glycerophosphate (-GP).Alizarin red staining was used to detect calcium deposition.Western blot and qRT-PCR were used to investigate the expression of the smooth muscle markers, smooth muscle 22 (SM22) and calponin, and the osteogenic markers, osteopontin (OPN), and runt-related transcription factor 2 (Runx2).Lentivirus was used to overexpress miR-542-3p and bone morphogenetic protein 7 (BMP7) in yVMSCs.Luciferase reporter assay was conducted to identify the target of miR-542-3p.Results: Compared with yVSMCs, 28 downregulated and 34 upregulated miRNAs were identified in oVSMCs.It was confirmed by qRT-PCR that oVSMC expressed four times lower miR-542-3p than yVSMCs.Overexpressing miR-542-3p in yVSMCs suppressed the osteogenic differentiation induced by -GP.Moreover, miR-542-3p targets BMP7 and overexpressing BMP7 in miR-542-3p-expressing yVSMCs reverses miR-542-3p's inhibition of osteogenic differentiation.Conclusions: miR-542-3p regulates osteogenic differentiation of VSMCs through targeting BMP7, suggesting that the downregulation of miR-542-3p in oVSMCs plays a crucial role in osteogenic transition in the aging rat.\t\n\nBackground: Aging is believed to have a close association with cardiovascular diseases, resulting in various pathological alterations in blood vessels, including vascular cell phenotypic shifts.In aging vessels, the microRNA(miRNA)mediated mechanism regulating the vascular smooth muscle cell (VSMC) phenotype remains unclarified.MiRNA microarray was used to compare the expressions of miRNAs in VSMCs from old rats (oVSMCs) and young rats (yVSMCs).Quantitative reverse transcription real-time PCR (qRT-PCR) and small RNA transfection were used to explore the miR-542-3p expression in oVSMCs and yVSMCs in vitro.Calcification induction of yVSMCs was conducted by the treatment of -glycerophosphate (-GP).Alizarin red staining was used to detect calcium deposition.Western blot and qRT-PCR were used to investigate the expression of the smooth muscle markers, smooth muscle 22 (SM22) and calponin, and the osteogenic markers, osteopontin (OPN), and runt-related transcription factor 2 (Runx2).Lentivirus was used to overexpress miR-542-3p and bone morphogenetic protein 7 (BMP7) in yVMSCs.Luciferase reporter assay was conducted to identify the target of miR-542-3p.Results: Compared with yVSMCs, 28 downregulated and 34 upregulated miRNAs were identified in oVSMCs.It was confirmed by qRT-PCR that oVSMC expressed four times lower miR-542-3p than yVSMCs.Overexpressing miR-542-3p in yVSMCs suppressed the osteogenic differentiation induced by -GP.Moreover, miR-542-3p targets BMP7 and overexpressing BMP7 in miR-542-3p-expressing yVSMCs reverses miR-542-3p's inhibition of osteogenic differentiation.", + "\t\n\nThe major question is whether replicative senescence does play a role in human aging.Several studies have shown an inverse relationship between donor age and the replicative life span in vitro for fibroblasts or MSC [13,44,45].This effect is usually relatively small with a high variation between different donor samples [12,46].At least some of the variability was attributed to differences in donor health status, conditions for the biopsy and the initial CFU-F frequency in the bone marrow sample [47].Furthermore, the pace of senescence might be affected by the culture conditions [19,48].In MSC preparations used in this study we did not discern any age-associated effects on replicative senescence.If the number of cumulative population doublings was not significantly affected by aging it is all the more surprising, that there was a significant association between age-induced gene expression changes and replicative senescence.These results indicate that the molecular sequels of aging in vivo and replicative senescence in vitro are based on similar mechanisms.", + "\t\n\nFinally, we asked whether additional cellular components of the immune system also show increased transcriptional variability upon aging.", + "\tSystemic aging has been more formally proposed as the hormonal\n\n3\ncontrol of aging, where changes in humoral factors with age can cause system-wide\nchanges in the homeostatic condition (Wise, Krajnak et al. 1996). Support for this idea\nhas gained traction from studies of mice expressing a mutant form of the KLOTHO gene\nencoding a protein hormone that leads to phenotypic changes characteristic of accelerated\naging (Kuro-o, Matsumura et al. 1997). Conversely, when the wild-type KLOTHO gene\nis overexpressed in mice it leads to a modest yet significant increase in both male and\nfemale lifespan (Kurosu, Yamamoto et al. 2005).\tStudies of invertebrate systems such as C. elegans and D. melanogaster\nhave yielded keen insight into stem cell biology and mechanisms of aging, but it has\npredominantly been the study of the mammalian hematopoietic system that has led to the\ncurrent understanding of the physiology of hematopoiesis. The utilization of mouse\ngenetics has only recently been fully realized as a tool as it was this mammalian model\nthat yielded the breakthrough discoveries of Till and McCulloch (Till and McCulloch\n1961).", + "\t\n\nOur results indicate that cell identity influences multiple aspects of aging, highlighting the importance of aging studies at the single-cell level.However, it remains difficult to identify which age-related changes are causal and link molecular changes at the level of individual cell types to physiological aging phenotypes, like reduced glomerular filtration rate or decreased pulmonary regeneration.Future single-cell studies may focus on collecting additional time points and phenotypes throughout the aging process, allowing for time series-based causal inference methods (Granger 1969;Bar-Joseph et al. 2012;Finkle et al. 2018;Qiu et al. 2018;Lu et al. 2019) to reveal the relationships between the molecular players of aging.Functional challenges, such as the differentiation of stem cells during regeneration or the stimulation of immune cells during infection, would also help dissect how transcriptional aging magnitudes and differential gene expression influence tissue function.Single-cell measurements collected during functional challenges may also reveal the dynamics of perturbation and subsequent return to homeostasis necessary to evaluate \"resilience\" in a given cell type (Kirkland et al. 2016;Hadley et al. 2017).\t\n\nAt both the molecular and functional level, a host of aging phenotypes and associated mechanisms have been revealed in individual cell types (Shaw et al. 2010;Chakkalakal et al. 2012;Keyes et al. 2013;Liu et al. 2013;Flach et al. 2014;Blau et al. 2015;Brack and Muoz-Cnoves 2016;Keyes and Fuchs 2018).Although some of these studies present unique features of aging within individual cell identities, it is difficult to compare them systematically because of differences in experimental conditions and assay methodology.Using traditional molecular biology assays, it is difficult to measure high-dimensional molecular phenotypes across multiple cell identities, making large-scale comparisons of aging phenotypes across cell identities intractable.The recent development of single-cell RNA-sequencing (scRNA-seq) has ameliorated this limitation, allowing for measurement of transcriptional features across all prevalent cell identities in a tissue in a single experiment.\t\nAging is a pleiotropic process affecting many aspects of mammalian physiology.Mammals are composed of distinct cell type identities and tissue environments, but the influence of these cell identities and environments on the trajectory of aging in individual cells remains unclear.Here, we performed single-cell RNA-seq on >50,000 individual cells across three tissues in young and old mice to allow for direct comparison of aging phenotypes across cell types.We found transcriptional features of aging common across many cell types, as well as features of aging unique to each type.Leveraging matrix factorization and optimal transport methods, we found that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating.These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals.\t\n\nAging is a pleiotropic process affecting many aspects of mammalian physiology.Mammals are composed of distinct cell type identities and tissue environments, but the influence of these cell identities and environments on the trajectory of aging in individual cells remains unclear.Here, we performed single-cell RNA-seq on >50,000 individual cells across three tissues in young and old mice to allow for direct comparison of aging phenotypes across cell types.We found transcriptional features of aging common across many cell types, as well as features of aging unique to each type.Leveraging matrix factorization and optimal transport methods, we found that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating.These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals.", + "\tDiscussion\n\nConsequences of disease as well as age exert profound influences upon cells including alteration of gene expression, metabolism, functional competency, replicative potential, and more [10,18].Certain features of aged cells are exacerbated or mitigated by environmental conditions in host tissues such as oxidative stress, nutrient status, inflammatory / cytokine production, and pathological changes [5,7,40,42].Many of these conditions can be recapitulated in cell culture studies with treatments that mimic the aged tissue environment [6,37].Studies using established cell lines to study biological consequences of aging are of limited value for extrapolation to the complex in vivo mileau.In situ studies have provided significant insight regarding adaptations and distinct features of aged cells [9,46,55], but whether the characteristic phenotypic state of aged cells is retained following isolation and culture expansion is poorly understood.Moreover, conditions of culture expansion inherently favor cells with the highest proliferative and survival potential.Thus, it is unclear to what extent culture expansion allows hallmarks of aging to persist when harvesting cellular samples from aged tissue and subjecting them to multiple passages after initial isolation.", + "\tConcluding remarks and future perspectives\n\nAging research has rapidly expanded over the past two decades, with studies ranging from lifespan-extending [68,69,71].However, when their effect on cell death and senescence leads to stem cell loss and tissue degeneration, they might contribute to aging [66,67]." + ], + [ + "\t\n\nHowever, the simplest solution to restoring pathological disturbances in the composition of the gut microbiota may be a change in dietary habits.Diet has been shown to strongly affect the composition of the microbiome (73).When obese humans were put either on a fatrestricted or carbohydrate-restricted low-calorie diet, an increase in the abundance of Bacteroidetes and a decrease in Firmicutes was reported (12).In another study, diet-induced weight loss versus weight-stabilization interventions in obese humans increased intestinal microbial gene richness and was associated with a reduced systemic inflammation (74).These data corroborate with another controlled diet intervention study in 98 human subjects showing that certain dominant gut microbial communities, or \"enterotypes,\" correlated with specific kinds of diets (73).For example, Bacteroides was associated with a protein-rich diet, whereas Prevotella correlated with a fiber-rich diet; moreover, gut microbiota composition could be altered within 24 h whereas enterotype remained stable during the 10 days of the study.Based on this rapid and dramatic plasticity of intestinal microbiota composition, there is a specific need to determine intestinal microbiota composition in a standardized way (e.g., sequencing several fecal samples per person over a specific time point while taking dietary intake and medication use into account).", + "\t\n\nWe next performed partial correlation analysis to investigate whether exercise-induced compositional changes in microbiota were associated with improvements in clinical parameters independent of body weight, fat mass, and visceral fat.We found that after adjustment for body weight and adiposity, associations between alterations of microbial species and improvements in insulin sensitivity-related indexes and a cluster of other metabolic features remained significant (Figure 3).At the community level, alteration in the gut microbiota was significantly associated with the percentage reduction of HOMA-IR (p < 0.01, ADONIS).Among the 19 species significantly correlated with the improvements of glucose homeostasis and insulin sensitivity, Ruminococcus gnavus, Alistipes shahii, Streptococcus mitis group, Eubacterium hallii, and Escherichia coli showed the strongest associations (Figure 3).Consistently, most of these species were also found to be differentially altered between responders and non-responders (Figure 2E).Taken together, the above findings imply that distinct changes of these species may underlie the difference in the improvement of glycemic homeostasis in response to a standard exercise regimen.", + "\t\n\nOn the other hand, studies on centenarians and supercentenarians have evidenced the adaptation of the microbiota to the physiological changes of the long aging process.It has been demonstrated that the microbiota on this population maintains the health and promotes the survival.Additionally, a relationship between a healthy microbiota and longevity had been proposed [44].A possible pathway is an immunological and metabolic regulation linked to the increase of bacterial compounds like Christensenellaceae, Akkermansia, and Bifidobacterium [44,45].\t\n\nFigure 9.1 depicts a visual representation of the gut microbiota composition throughout the lifespan.Variations between individuals and within an individual throughout the lifespan can be seen.In this respect, it can be said that the concentration of Bacteroidetes grows as an individual does, from 12.6% for newborns to 57% for older adults.Conversely, Actinobacteria composition reduces with age until it reaches 0.4%, and the Firmicutes, Proteobacteria, and other microbial are maintained relatively stable throughout life in healthy adults and decay at old age [20][21][22].\t\n\nThe human holobiont (commensal microbes and their multicellular eukaryotic host) constitutes a highly integrated system, which undergoes dynamic changes through time as it integrates and responds to signals from the environment.Microbiome research and aging is flourishing as we better understand the bidirectional interactions, and its evolution with a life-course perspective for the gut microbiota undergoes dynamic changes during host aging.Changes in host intestinal cell Foreword vii composition and architecture occurring during aging are matched by a decrease in the microbiota taxonomic diversity.Age-related decrease in taxonomic diversity leads to larger population size for a few age-associated microbial species, increasing the chances for the evolution of novel potentially pathogenic microbial strains, which have been related both to neurodegeneration and frailty.This knowledge positions the microbiome as a promising element for translational research.\t\n\nAll the information given by the aging research allows knowing that the microbial composition has an essential role in the establishment of cellular and tissue homeostasis.Additionally, it is known that age-dependent changes in the microbial composition can contribute to increasing of frailty and development of diseases during the late stages of life [42,43].\t\n\nAlthough the causes that lead to changes in the composition and function of the microbiota during aging are still unknown, the evidence has established that the local microbiome plays an essential role in human health.\t\n\nTherefore, research in the field has demonstrated that aging is a potential modifier of the composition and function of the human microbiome.Figure 9.3 shows the local composition of the microbiome in an average older adult.It can be seen that Bacteroidetes and Firmicutes species are the most prevalent in this age.\tMicrobiome Research and Aging: A Clinical Perspective\n\nAging is characterized by the accumulation of damage at the molecular level (DNA and proteins) and dysfunction of the organelles [31][32][33].In addition to senescent cells and compositional changes in the extracellular compartment, these changes are determinants of the organic and systemic decline [34][35][36].The microbiota reacts dynamically to these environment changes by altering the metabolic function and composition of individual bacterial species.\tConclusions\n\nDuring the last years, significant advances in the field of microbiome and aging research have been carried out; new approaches for its study have allowed the understanding of the genomic nature of the microbiota.In this regard, the introduction of metagenomics had increased knowledge of the genes that potentially allow microbes to influence their hosts in unexpected ways.Thanks to these advances, it is well known that microbiota constitutes an essential determinant of the health and longevity of humans.\t\n\nFig. 9.1 Gut microbiota throughout lifespan\tMicrobiome and Age-Related Neurodegenerative Diseases\n\nDifferent microorganisms such as bacteria, fungi, archaea, and viruses compose the human intestinal microbiota that represents, in physiologic conditions, a perfect commensalism association with their host [51,52].In general, the human intestinal microbiota is shaped by the healthy microbiota (bacteria that normally colonize the intestine) and opportunistic bacteria (which are the agents responsible for infections).Among the billions of symbiotic microorganisms that compose the intestinal microbiome, four bacteria phyla are mainly reported in adults, i.e.Firmicutes (~51%), Bacteroidetes (~48%), Proteobacteria, and Actinobacteria, (1%) [53].Lactobacteria species stand out among the normal microbiome (Lactobacillus rhamnosus, Lactobacillus acidophilus, and Lactobacillus plantarum), Bifidobacterium (B.bifidum), Enterococci, Propionobacteria, and Peptostreptococci.In the same way, opportunistic bacteria include the Bacteriodes spp.Bacilli, Clostridia, Enterobacteria, Actinobacteria, Peptococci, Staphylococci, and Streptococcus [54].Several factors, such as diet, hygiene, antibiotic exposure, and modify the intestinal microbiota [55,56].Interestingly, age also contributes significantly to the microbiome modification; in fact a recent publication highlights the vital role that represents the host aging in the microbial evolution since as the host get aged the organism experiments molecular and functional changes that induce shifts to the microbial niche [57], nevertheless, for detailed information about changes in microbiome during aging, please refer to the Chap.9 in this book.In the following paragraphs, we discuss the recent data about the relationship between the pathogenesis of the two most prevalent ND and the microbiome, which represents a new field of research.\t\n\nDiet can be a potent gut microbiome modifier.For this reason, numerous studies have been conducted to demonstrate the impact of specific diet components on the diversity of the gut microbiota [8].The results of many of these studies have proved that probiotics and prebiotics consumption are a feasible alternative, especially for specific population groups such as older adults [59].\t\n\nMany areas of opportunity can be mentioned.However, modulation of the microbiome by extrinsic factors can be a way to apply the actual knowledge in the clinical setting.Nowadays, it is possible to ensure that lifestyle and diet play a significant role in determining the microbiome.In this respect, novel therapies, as fecal transplantation adds to the traditional dietary interventions, both demonstrated to be a potential therapeutic approach for the aging population.\t\n\nIt is well known that aging is a risk factor for neurodegeneration and dementia [58]; nevertheless, recent studies support the idea that gut microbiota may have an effect on the brain and the behaviour of patients, since the evidence suggests that some metabolites secreted by the intestinal microbiota can affect in a certain way, the cognitive capacity of patients diagnosed with ND [59][60][61][62][63].This hypothesis is not entirely new since several decades ago, the concept that bidirectional communication between the CNS and the intestinal organs plays a role in emotional regulation [64,65].Four decades later, the hypothesis that the brain has a regulation of the gastrointestinal tract arose and with the help of the murine model, the existence of the brain-gut axis was reported [66].This axis is carried out through the neuroendocrine and neuroimmune system, working together with the sympathetic and parasympathetic arms of the autonomic nervous system and the enteric nervous system.", + "\t\n\nChanges in the gut microbiota in terms of composition and functionality during the process of aging have previously been reported [19,20,51] and it has been postulated that these changes might contribute to the development of immunosenescence and inflammaging [18,52].To establish whether the enhanced expression of genes playing a role in the immune system are due to modifications in the microbiota we measured the total number of all bacteria and of the two most prominent phyla colonizing the colon, Bacteriodetes and Firmicutes, in the luminal content of the colon.We did not observe aging-related changes.More advanced techniques like pyrosequencing are required to determine whether total number of bacteria and changes in the composition of the microbiota might play a causal role in the observed changed expression of immune-related genes in the colon of our aging mice.Although it is difficult to assess the physiological consequences of the enhanced expression of genes involved in inflammation and immune response, it seems most likely that this effect is important for the health status of the aging colon.", + "\tSignatures of aging in gut\n\nFor gut or the digestive system, six clusters of age-associated genes had significant enrichment of functional annotations (Fig. 2C; Supplemental Table 10).Aging in gut was found to be associated with down-regulation of genes (Clusters 1, 2, 3, and 4) participating in oxidative phosphorylation, aromatic compound metabolism, muscle contraction, amino sugar metabolism, regulation of apoptosis, and vesicle transport.Aging was also associated with up-regulation of genes (Clusters 5 and 6) involved in regulating various physiological processes, amino acid metabolism, and regulation of transport.These results suggest that metabolic pathways, especially nutrient intake and energy production, are primarily affected during aging of gut, which are the fundamental function of the digestive system.", + "\t\n\nSequencing of bacteria species within our gut, collectively labeled the gut microbiome, explains individual differences in the metabolism of consumed food with potential associations with body weight (Karlsson, Tremaroli, Nielsen, & Backhed, 2013).Gut permeability to bacteria is further associated with obesity and obesity-related inflammation (Teixeira et al., 2012).Over time, these mechanisms will more fully be integrated into the overarching models of obesity.", + "\tThe microbiome and weight change\n\nThe human microbiome may play a significant role in the etiology of obesity in both humans and animal models (64).Hosted in the gastrointestinal tract, the gut microbiome is part of a large endocrine organ that regulates not only nutrient sensing and metabolism but also satiety and energy homeostasis.The millions of microorganisms comprising the complex intestinal \"superorganism\" perform a number of functions for host health, including food processing, breakdown and metabolism of indigestible nutrients, pathogen displacement, synthesis of vitamins, and regulation of body weight (65).They play such an important role that we now know that microbiota disruptions in early life can have long-lasting effects on body weight in adulthood (66).The host bacterial composition has been shown to adapt in response to dietary factors and in response to weight loss.Diet or surgically induced weight loss promote alterations in the gut that can impact the efficacy of the treatment strategies (67,68).Specific bacterial species can have influences by themselves.For example, the archaeon Methanobrevibacter smithii, has an enhanced ability to metabolize dietary substrates or end products of the metabolism of other bacteria, thereby increasing host energy intake and weight gain (69).", + "\tThis microbial\ncommunity is established early in life, influenced by maternal and environment factors and\nable to impact the health of the host [2]. For example, early studies provided evidence that\ndiet plays an important role in the composition of gastrointestinal microbiota. Specifically,\ntransition to a low-fat diet in overweight humans led to a gut microbial composition similar\nto that of healthy controls [3, 4]. Also, gnotobiotic animals displayed substantial weight gains\nfollowing exposure to a complex gastrointestinal microbiota from overweight individuals\n[5, 6]." + ], + [ + "\tCONCLUSIONS\n\nOur purpose in this review is to outline the prospects of unifying mechanism in the genetics of aging.In case after case, from mice to worms to flies to yeast, genetic variants that modify metabolism also modify life span.These effects, collectively, are as general as that of caloric restriction, which also increases longevity and resistance to stress in many situations.The evolutionary theory of aging proposes that the life span is indirectly selected on the basis of the reproductive schedule.In turn, the reproductive schedule is coordinated by neural and endocrine mechanisms in multicellular organisms.Therefore, to consider that genes determining the life span could be expressed in neuronal and endocrine cells in diverse animals is no longer far-fetched.Consistent with this hypothesis are experiments in Drosophila and C. elegans in which life span was manipulated by the expression of genes in specific neurons.Genetic approaches may, thus, be able to identify a set of circuits that regulate longevity that were established in ancestral metazoans.", + "\tGenetic Programs\n\nAs stated above, the universality of aging phenotypes within a species argues for an underlying genetic program.The redistribution of the Sir complex from telomeres to the nucleolus in yeast is a specific molecular While the effects of these hormones on specific orthan rats fed ad libitum, with a consequent decline in the incidence of hepatocellular carcinoma (Muskhelishvili gans are apparent, their relation to the aging process itself, if any, is not yet clear.To our knowledge, there has occurred in the past 200 years.However, slowing the aging process may increase vitality and quality of has been no animal study in which hormone supplementation extended maximum life span.However, the recent life over the entire life span of individuals.In this regard, it is noteworthy that calorically restricted rodents have findings in C. elegans provide a basis to believe that humoral factors may turn out to play an important role an extended life span that is relatively free of disease.For society, the implications of slowing the aging pro-in at least some aspects of human aging.cess are more complex.Of course, in an increasingly overpopulated world, it would be important to offset Perspective any significant effects on longevity with a compensatory Recent advances in the study of aging indicate that this reduction in birth rates.In fact, in many industrialized process is amenable to molecular analysis and may be countries, the current birth rate is sufficiently low to relatively simple.The potential of single gene mutations afford zero or negative growth.Most importantly, if the to greatly extend life span in model systems suggests slowing of aging is associated with improved health and that relatively few limiting cellular or organismal proproductivity of long-lived individuals, there may be a cesses control the rate of aging, at least in these species.", + "\t\n\nThen we have those pharmaceutical strategies that are www.impactaging.combased on emulating the pathways implicated in the response of lifespan to dietary restriction, particularly sirtuin-targeting agents like resveratrol [e.g.25].Again, like hormone manipulation, these pathways are heavily bound up with the regulation of reproduction, making the curtailment of the cost of reproduction the most likely mechanism by which the beneficial effects of emulating dietary restriction are achieved [cf. 26].This is a strategy in which longevity is increased by metabolic refrigeration, pseudo-hibernation, or curtailing functions [11].From the standpoint of evolutionary biology, this is, again, not an extension of the period of adaptation.It is instead trading one set of adaptations off against another.Most people do not regard curtailing their metabolism, cognition, affective stability or reproductive functions as a useful approach to the problem of aging.Nonetheless, some are willing to trade-off some of their adaptive functions for an increased lifespan, and for them this \"anti-aging\" strategy will have its attractions.", + "\tMetabolism\n\nStudies show that calorie restriction is the most consistent means to prolong life expectancy and health across several experimental models [55], ranging from yeasts to primates.It not only increases life expectancy, but it also delays the onset of many features and hallmarks of ageing, including age-related diseases.Transcriptional profiles are currently being applied and investigated.One of them is a caloric restriction (CR), which increases the response to oxidative stress and reduces the shortening of telomeres in chromosomes; this has a direct intervention in the repair of DNA damage.Data from human trials (such as CALERIE, Biosphere-2 and CRON) indicate that moderate CR accompanied by adequate nutrition has positive effects on health and dramatically reduces the multiple metabolic factors involved in the pathogenesis of disease chronicles, including type 2 diabetes, heart and cerebrovascular diseases, and cancer [56].", + "\t\n\nLimitations of translating the results of preclinical studies should be recognized.An important recent example is caloric restriction. 239Although caloric restriction confers significant life span extension and cardiovascular protection in laboratory rodents 5,18,42,97,223,240,241 and in certain cohorts of nonhuman primates, 227,242 its protective effects in nonhuman primates in other studies 243 and in patients with multiple cardiovascular risk factors are less evident. 244Additionally, in cross-sectional studies, the older groups may represent a selected long-lived subset of the younger population.There are existing longitudinal studies in humans (eg, InCHIANTI study) and nonhuman primates, and important information related to mechanisms of vascular aging could be derived from add-on studies to these existing cohorts.", + "\t\n\nOn the other hand, the beneficial effects of caloric restriction are associated with alterations in metabolism, particularly the insulin/insulin-like growth factor 1 (IGF-1) pathways, which could reflect an evolution mechanism to ensure survival of a species during period of food shortage [3].Many genetic manipulations affecting nutrient-sensing pathways including the insulin and mTOR (mammalian target of rapamycin) pathways mimic the effect of caloric restriction on lifespan in yeast, worm, flies and mice and support this hypothesis [3].This review will firstly discuss in general terms how trace elements affect ageing and then use Selenium (Se) as an example to illustrate how trace elements influence the ageing process.Furthermore, the review will also illustrate how the so-called \"Omics technologies\" can be used to unravel the modes of action of trace elements and to identify biomarkers to define the optimal intake for health at the molecular level.\t\n\nEvidence is building up showing that caloric restriction, without malnutrition, extends lifespan in species ranging from yeast to non-human primates [3], but it appears, on the contrary, that inadequate/sub-optimal intake of micronutrients contribute to the development of chronic diseases.In his \"Triage theory\", B. Ames suggested that this could reflect the need for an organism to re-allocate micronutrients according to triage priorities to favour short-term survival over long-term wellbeing [4,5].The consequences of this re-allocation may remain unnoticed in the day-to-day experience but are likely to show up late in life as cancers, Alzheimer's disease, Parkinson's disease, diabetes and cardiovascular diseases.", + "\t\n\nCaloric restriction (CR) is the only intervention shown to extend lifespan in mammals (5).It is also the most effective means known of reducing cancer incidence and increasing the mean age of onset of age-related diseases and tumors (6).Our studies made use of an experimental design that allowed us to clearly distinguish the effects of diet from those of age on genome-wide expression patterns.Another distinctive aspect of the study allowed us to resolve changes in gene expression induced directly by CR from those that arise over time as a consequence of the interaction between CR and aging.", + "\tGenDR-genomics of DR\n\nDR, of which caloric restriction is the most widely studied regimen, is the most robust non-genetic intervention shown to extend lifespan in a multitude of species, from yeast to mammals (12,14).However, the exact mechanisms of how DR extends lifespan remain unknown.To decipher the mechanisms of DR in a systematic fashion, we established GenDR (http://genomics.senescence.info/diet/), the first database of DR-associated genes.Because GenDR and related analysis of DR networks have been recently described elsewhere (15), they will only be briefly described herein.To create GenDR, we compiled from the literature a list of DR-essential genes from model organisms.DR-essential genes were defined as those which, if genetically modified, interfere with DR-mediated lifespan extension and, ideally, do not affect the lifespan of animals on an ad libitum diet (or at least do not appear to be merely causing disease).A subset of these genes act as genetic DR mimetics, as their manipulation leads to an increased lifespan for ad libitum fed animals, which is not further extended by DR.One such example is the growth hormone receptor gene in mice (16), in fact the only mouse gene currently in GenDR.In GenDR, the respective homologues of DR-essential genes are included for all the common model organisms, as well as for humans (15).A complementary data set in GenDR is a list of genes consistently differentially expressed in mammals under DR.In a recent meta-analysis, a common signature of genes differentially expressed in DR across different mammalian species, strains, tissues and experiments was derived.This signature provides a set of genes that are most robustly responding to DR (17).", + "\t\n\nIt seems that organisms from yeast to mammals have evolved genetic programs to cope with periods of starvation that can also postpone aging and age-related diseases, but how can we take advantage of those mechanisms to improve human health?Because assaying the longevity effects of CR in humans is practically impossible, studying its molecular mechanisms in lower life forms could be beneficial to humans through the identification of candidate genes, pathways and molecular mechanisms.Although CR will not be suitable for everyone, targeting its mechanisms and developing CR mimetics may lead to drug development for a number of age-related and metabolic diseases.\t\n\nBy far the most widely studied dietary manipulation of aging is caloric restriction (CR), also called dietary restriction.CR consists of restricting the food intake of organisms normally fed ad libitum without triggering malnutrition and is the only dietary intervention shown, to date, to increase longevity and modulate the process of aging in several model organisms (Bishop and Guarente, 2007;Fontana et al., 2010;Spindler, 2010).Even in mammals, such as mice and rats, CR can extend longevity by up to 50%, delay physiological aging, and postpone or diminish the morbidity of most age-related diseases (Masoro, 2005).Ongoing studies in rhesus monkeys suggest that CR can lower the incidence of aging-related deaths in primates (Colman et al., 2009).", + "\tINTRODUCTION\n\nGenomic studies into human longevity are inspired by the fact that, in animal models, healthy lifespan has proved to be remarkably plastic, and major pathways of lifespan regulation have been identified.Considerable lifespan extension has been induced in models as diverse as yeast, worms, fish, flies and rodents by applying genetic manipulation and dietary restriction (DR) (see [1] for review).Reduced activity of nutrient-sensing pathways such as insulin/insulin-like growth factor (IGF-1) signalling (IIS) and target of rapamycin (TOR) signalling mediated lifespan extension, and also the extension of lifespan by DR [2].An interesting observation from the perspective of human ageing is that, in rodents and monkeys, diets restricted in glucose, fat or protein uptake reduced or delayed the risk of cancer and metabolic disease, thus extending the healthspan of the animals [2].Following the discovery of genes and pathways involved in animal lifespan extension, human research has focused on the corresponding candidate human genes with genetic, genomic and epigenetic studies into ageing and longevity.The designs of these studies differ with respect to the selection of naturally occurring phenotypes and the study populations, which include population-based, patient-based, family-based and exposure-based cohorts.", + "\t\n\nThe 'hormesis' hypothesis of aging is based on the observation that caloric restriction or chronic low-level exposure to any of these stresses induces cross-resistance to other stresses at the same time that it extends life span (41).Hormesis effects on aging are observed in many eukaryotes in addition to budding yeast.Although the mechanistic details of these effects remain unclear, we have argued that they include a general response to environmental stresses that blocks entry into S phase under environmentally stressful conditions that are suboptimal for replicating DNA, thus protecting cells from replication stress (30).", + "\tINTRODUCTION\n\nMore than 70 years ago, McCay and his colleagues demonstrated that a reduction in total food intake after weaning significantly increased both mean and maximum life spans of laboratory rats (1).Over the last seven decades, numerous laboratories have successfully repeated McCay's findings using various strains of rats and mice as well as non-mammalian species, such as fish and flies (2)(3)(4)(5)(6).Thus, food restriction has been established as a powerful experimental tool, and the anti-aging action of food restriction has become one of the most active areas of research in the realm of biogerontology (6).While life span extension by food restriction appears to be due to alterations in aging processes, the underlying mechanism(s) by which food restriction exerts its anti-aging effects remain elusive.Identification of important antiaging and anti-tumor targets of food restriction and elucidating the molecular mechanisms by which food restriction exerts its beneficial effects could eventually provide targets for intervention in humans.", + "\tIn comparison, caloric\nrestriction, intermittent fasting, or a ketogenic diet generally improve lifespan and health\n811 These dietary effects are not solely dependent on patterns of caloric intake, but are\nmodulated by dietary macro- and micronutrient composition, the amount of time spent in\ndifferent metabolic states, age of onset, periodicity of access to food, sex, and of greatest\nimportance to us in this studydifferences in genometype (strain) and gene-by-dietary\ninteractions 12,13. While the effects of differences in dietary composition and caloric restriction on lifespan\nhave been studied extensively, key results remain controversial 1416.\tThis again indicates that that weight gain\naccounts for only 45% of the change in lifespan. Author Manuscript\n\nOur findings can be compared to strain variation and GXE effects in response to dietary\nrestriction. Dietary restriction without malnutrition is regarded as having an almost universal\nbenefit on longevity 5355. One exception is a pair of studies on the impact of moderately\nintense restrictiona 40% reduction in caloric intakeacross a large family of LXS strains\nof mice (n of up 44 strains with 1020 replicates per strain) 17,19.", + "\tNutrition, phenotype and longevity\n\nNo issue so 'vividly' illustrates the power of diet to alter health as the consistent observation of the effect of caloric restriction (CR) on longevity.To date, neither drug, gene nor environmental intervention have been successfully demonstrated to prolong longevity in animals; however, the simple reduction of food calories can increase life span by 30-40% across a number of model organisms, including yeast, Drosophilia, Caenorhabditis elegans, rodents and monkeys [5][6][7].This effect of CR raises one of the most intriguing questions facing life scientists today.Despite the demonstrated positive age-related benefits of a reduction in energy intake -including decreased insulin resistance [8], increased production of glucocorticoids [9] and increased production of heat-shock proteins [10] -the mechanisms by which CR contributes to increased longevity remain unknown.How CR leads to longer life span cannot be attributed to any single factor without considering the simultaneous effects of the others.CR could alter multiple age-related processes, from energy metabolism to oxidative stress and DNA repair.Unravelling the multiparametric links of CR and aging led to the seminal genomic experiment for nutrition: the gene expression analysis of young and old tissues in normal and CR animals [11 ] is a pioneering example of the use of DNA arrays to explore the effects of CR and aging on gene expression in mouse skeletal muscle.The experiment is compelling for its simplicity and its implications, that is, the gene expression profiles for a clear phenotypic difference were compared (young versus old versus CR old mice).The power of the technique was evident by the discovery of a wide range of affected genes, including those involved in protein and energy metabolism, biosynthesis (e.g. of fatty acids), and macromolecular damage, implying immediately that the effects of aging and CR are broad, yet interrelated.More detailed experiments are now being pursued around the world following the identification of the genes that are altered during aging and protected by CR.The publication of this experiment also followed the now routine approach of supplying the raw database through an accessible internet site.", + "\t\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways.", + "\t\n\nA key question still unresolved is to what extent the mechanisms of aging are conserved between species with vastly different lifespans.Some studies suggest that similar mechanisms are involved in aging in many species.For example, caloric restriction extends lifespan in yeast, worms, flies, mice, and primates (Weindruch 2003).Additionally, signaling through the insulin-like growth factor pathway, chromatin regulation by sir2, and oxidative damage have each been shown to affect lifespan in diverse model organisms (Tissenbaum and Guarente 2002).Other studies emphasize that changes occurring at the end of life are unlikely to be evolutionarily conserved (Kirkwood and Austad 2000).In the wild, very few animals (including humans) survive to their maximal biological lifespan.Thus, the changes in physiology that occur in very old animals have minimal effects on the fitness of individuals, and are unlikely to be evolutionarily conserved.Therefore, aging is likely to be species-specific, and studies of old age in model organisms are unlikely to be relevant to humans.", + "\t\n\nIn summary, we postulate that due to metabolic changes and the subsequent increase in stress response, physiological processes evoked by starvation show strong correlation with anti-aging processes (Table 2).These observations suggest that starvation may evoke the same stress response reaction as caloric restriction, which is the only treatment known to prolong lifespan in all organisms tested to date.The difference is that starvation results in a much stronger or prolonged induction.This accentuated response during starvation may facilitate the experimental identification of basic molecular mechanisms linking nutrition and health using a nutrigenomic approach." + ] + ], + "task_id": [ + { + "task_id": "A1F96E809E942FD33A6C0199E338F5A9" + }, + { + "task_id": "2075EEA93169E52BFF50C80C4ECDF0DC" + }, + { + "task_id": "B2CABF057C243D3BEA707C861220A769" + }, + { + "task_id": "8800709B5C665E09BB7A9FBD6E7E7359" + }, + { + "task_id": "BFACDAF6E27C8B3594B93785B26B0AC6" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_aging_2.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_aging_2.json new file mode 100644 index 0000000..dbaf184 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_aging_2.json @@ -0,0 +1,109 @@ +{ + "question": [ + "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?", + "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?", + "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?", + "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?", + "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + ], + "answer": [ + "Oxidative stress, primarily caused by reactive oxygen species (ROS) generated from cellular metabolism, leads to damage to various cellular components, including DNA, proteins, and lipids. This damage accumulates over time, leading to functional decline in cells. In particular, damage to DNA can lead to mutations and impaired cellular functions. The balance between DNA damage and its repair is a major determinant of cell longevity. Additionally, oxidative damage to mitochondria, a major source of ROS, can impair energy production and further contribute to aging. This process is supported by the oxidative stress theory of aging and the mitochondrial theory of aging.", + "Extracellular matrix (ECM) remodeling plays a significant role in the aging process. Aging results in changes in ECM biosynthesis, modifications of ECM components, and alterations of cell-matrix interactions, which contribute to age-related pathologies. The synthesis of many ECM components, such as elastin, declines with age, impairing the elasticity and resilience of tissues. Age-related ECM changes also alter vascular mechanotransduction, dysregulating cell responses to alterations in the environment. Additionally, aging and cellular senescence increase the secretion of matrix metalloproteinases (MMPs), which compromises the structural integrity of tissues and promotes pathological remodeling. This can lead to increased tissue stiffness, a common feature of aging tissues. Furthermore, dysregulated collagen synthesis contributes to vascular fibrosis and arterial stiffening.", + "Recent developments in autophagy research have shown that autophagy, a cellular degradation process, plays a crucial role in aging and longevity. Certain compounds like rapamycin, resveratrol, and polyamines can induce autophagy, with polyamines showing results in human clinical research. Autophagy is also linked to the regulation of various processes that contribute to aging, such as protein degradation, mitochondrial metabolism, and stress response. Studies have shown a decline in autophagy in aging mammals, and increased autophagy is required for lifespan extension in certain organisms. Furthermore, the up-regulation of autophagy by certain compounds has been associated with increased lifespan in various organisms. Dysfunctional autophagy is implicated in many age-related diseases, and the activation of autophagy has been linked with increasing lifespan in animal models.", + "Age-related shifts in stem cell niche composition and function can lead to a decrease in tissue regeneration capacity. This is because the stem cell's ability to self-renew and produce progeny to replenish worn-out and damaged cells in aged tissues may be compromised. This could result in a depletion of stem or progenitor cell pools, promoting age-related pathologies. Additionally, the induction of stem cell senescence may affect tissue renewal. Furthermore, the balance between stem cell proliferation and tissue regeneration, which is crucial for maximizing longevity, may be disrupted, leading to an aged phenotype.", + "Cross-links and AGEs accumulation can lead to several detrimental effects on aging tissues. They can cause structural changes in proteins, lipids, and nucleic acids, leading to altered function and potential damage. AGEs can mediate intracellular glycation of mitochondrial proteins, increasing ROS levels and triggering oxidative stress. They can also bind with RAGEs, activating signaling pathways that upregulate inflammatory cytokines and adhesion molecules. In the vascular system, AGEs can cause endothelial dysfunction, arterial stiffness, and increased capillary permeability. In the context of diabetes, AGEs can accelerate the death of certain cells, disrupt retinal vascular integrity, and induce neural cell dysfunction and death." + ], + "contexts": [ + [ + "\t\n\nCell senescence, telomere shortening, and oxidative stress Attempts at synthesizing two major areas of focus in aging research, cell senescence [287,288] and free radicals, have been made since the 1970s (for a recent review see [289,290]).Early results by Packer and Smith suggested that vitamin E treatment could completely prevent cell senescence [291]; however, this result proved to be irreproducible [292].Nevertheless, it was observed that decreasing oxygen tension, from the customary 21% O 2 to more physiological levels (3% O 2, as would be found in vivo) led to an increase in cell doublings before senescence (i.e., an increase in the Hayflick limit or replicative life span [293][294][295][296]).Similar effects were also reported using antioxidants [296][297][298].In the 1990s, von Zglinicki et al. reported that a mild increase in oxygen tension (40%) triggered senescence within 3 cell divisions in human fibroblasts [299].von Zglinicki and co-workers proposed that oxidative damage to telomeres was responsible for the rapid triggering of senescence [299][300][301] and recent studies show that telomeric DNA may be particularly sensitive to oxidative damage [302].Following von Zglinicki et al. 's report, other investigators, using different oxidative stressors and different cell types, have reported very similar results.Mild oxidative stress reduces clonal life span and conversely, reduction of oxidative stress extends clonal life span [303][304][305][306][307]. Guarente's lab has provided additional evidence in this general direction, with the demonstration that RNAi knockdown of Sod1 triggered early senescence in human fibroblasts [308].This result is consistent with the earlier report by Epstein's laboratory that fibroblasts derived from Sod1 / mice failed to grow at all in culture [188].A great breakthrough in this area occurred when Campisi's lab demonstrated that senescence could be prevented completely in primary mouse cells when the cells were grown at 3% oxygen, instead of the customary 21% [309].This also resulted in a dramatic reduction of oxidative damage-signature mutations [310].In other words, these investigators demonstrated that in vitro senescence in mice cells was directly related to oxygen toxicity, i.e., oxidative damage.", + "\t\n\nThe free radical theory of aging, first proposed by Harman in 1956 [21], has received a lot of attention over the years as indicated by the number of scientific reviews on antioxidant interventions in different animal models and human clinical trials.The mitochondrion has been identified as a major source of reactive oxygen species (ROS) and thus oxidative stress potentially contributing to the aging process, although several plasma membrane and cystosolic enzymes may also contribute to the increased intracellular pro-oxidant status observed during aging [22].In the mitochondrial respiratory chain, electrons entering complexes I and II are transferred to complex III, then IV where they are combined with molecular oxygen and hydrogen to form H 2 O. Redox reactions at respiratory complexes I, III, and IV are coupled to the extrusion of protons from the mitochondrial matrix into the intermembrane space.The re-entry of protons into the matrix is coupled to the synthesis of ATP from ADP and P i .This oxidative phosphorylation is responsible for the vast majority of ATP production and oxygen consumption in most types of animal cells [23].Up to 2% of oxygen used in this complex reaction undergoes monoelectronic reduction and results in the formation of superoxide anion and hydrogen peroxide, which can lead to the formation of the more toxic species hydroxyl radicals [24,25].Such reactive species can attack and modify genomic DNA.An important type of oxidative DNA lesion accumulating with age is 8-oxo-deoxyguanine [26].If unrepaired, this adduct in genomic DNA may lead to a point mutation upon DNA replication.During DNA replication, 8-oxo-deoxyguanines present on either strand of DNA can mispair with adenosines and lead to G:C T:A transversion mutations.A misincorporation of an 8-oxodeoxyguanine as a substrate nucleotide can also lead to the same type of mutational pattern [27].", + "\t\n\nOur results are consistent with the oxidative stress theory of aging originally proposed by Denham Harman [26], and the notion that a vicious cycle of ROS generation and oxidative damage is the ultimate driver of aging [27].Our data also indicate that endogenous nuclear DNA damage is able to trigger this cycle of escalating ROS abundance, oxidative damage, senescent cell accumulation and age-related pathology.\t\n\nTo determine if this oxidative stress is pathological, we suppressed it pharmacologically in Ercc1 -/ mice with the mitochondrial-targeted radical scavenger XJB-5-131.Chronic administration XJB-5-131 significantly reduced both oxidative DNA damage and senescence (Fig. 5).The reduced level of senescent cells corresponded to a reduction in agerelated morbidity.This is consistent with numerous recent studies demonstrating that genetic or pharmacologic elimination of senescent cells slows age-related decline [2,4,7,8,[84][85][86].The observation that suppressing oxidant production is sufficient to decreases senescence indicates that reactive species are required to ultimately cause or maintain senescence in response to genotoxic stress.", + "\t\n\nIntroduction as replication errors, spontaneous chemical changes to Although aging is nearly universally conserved among the DNA, programmed double-strand breaks (DSBs) (in eukaryotic organisms, the molecular mechanisms unlymphocyte development), and DNA damaging agents derlying aging are only beginning to be elucidated.A that are normally present in cells.The latter category useful conceptual framework for considering the probincludes reactive oxygen species (ROS), such as superlem of aging is the Disposable Soma model (Kirkwood oxide anion, hydroxyl radical, hydrogen peroxide, nitric and Holliday, 1979).This model proposes that organoxide, and others.Major sources of cellular ROS proisms only invest enough energy into maintenance of the duction are the mitochondria, peroxisomes, cytochrome soma to survive long enough to reproduce.Aging oc-p450 enzymes, and the antimicrobial oxidative burst of curs at least in part as a consequence of this imperfect phagocytic cells.ROS can cause lipid peroxidation, maintenance, rather than as a genetically programmed protein damage, and several types of DNA lesions: sinprocess.Although aging may involve damage to varigle-and double-strand breaks, adducts, and crossous cellular constituents, the imperfect maintenance of links.The situation in which ROS exceed cellular antinuclear DNA likely represents a critical contributor to oxidant defenses is termed oxidative stress.As normal aging.Unless precisely repaired, nuclear DNA damage byproducts of metabolism, ROS are a potential source can lead to mutation and/or other deleterious cellular of chronic, persistent DNA damage in all cells and may and organismal consequences.Damage to both nuclear contribute to aging (Sohal and Weindruch, 1996).The DNA, which encodes the vast majority of cellular RNA ROS theory of aging is discussed in depth in this issue and proteins, and mitochondrial DNA have been proof Cell by Balaban et al. (2005).In brief, longer-lived posed to contribute to aging (Karanjawala and Lieber, species generally show higher cellular oxidative stress 2004).The reader is referred to the review by Balaban resistance and lower levels of mitochondrial ROS proet al. in this issue of Cell concerning the potential role duction than shorter-lived species.Caloric restriction, of mitochondrial DNA damage in aging (Balaban et al.,", + "\t\n\nWe previously showed that superoxide plays a primary role in chronological age-dependent DNA damage and mutations.Our model is that the DNA damage caused by oxidative and other types of stress accumulated during aging in nondividing cells generates double-strand breaks during the fi rst round of replication after the exit from G 0 .Cells lacking SGS1 attempt to repair this damage by homologous recombination between sister chromatids but generate a large number of GCRs, especially at advanced age.", + "\t\n\nReactive oxygen species (ROS) have long been at the center of the debate on causes of aging and a central player in the free-radical theory of aging.One form of oxidative damage that is considered irreversible and has been correlated with age in various organisms, including replicative age in yeast, is protein carbonylation (Nystrom 2005).Protein carbonyls have been proposed as a yeast aging factor based on the observations that both protein carbonyls (Aguilaniu et al. 2003;Erjavec and Nystrom 2007) and aggregates containing heavily carbonylated proteins (Erjavec et al. 2007) are asymmetrically retained in mother cells during division.The proper asymmetric segregation of oxidatively damaged proteins appears to be dependent on a functioning actin cytoskeleton (Aguilaniu et al. 2003;Erjavec et al. 2007), which has independently been linked to ROS and life span through the actin bundling protein, Scp1 (Gourlay et al. 2004).", + "\t\n\nThere is some evidence that related processes occur in mammals.First, cells damaged by oxidative stress in vitro undergo stochastic transcriptional changes that parallel those in aged heart tissue (Bahar et al., 2006).Second, a deficiency in the DNA repair factor ERCC1 accelerates aging phenotypes and generates gene expression profiles reminiscent of aged animals (Niedernhofer et al., 2006).Third, cells that senesce because of replicative aging in vitro or in aged tissues in vivo exhibit alterations in heterochromatin (Herbig et al., 2006;Narita et al., 2006) and secrete growth factors that can drive tumorigenesis (Campisi, 2005).Finally, oxidative DNA damage at promoters correlates with gene repression in the aging human brain (Lu et al., 2004) and has been linked to both transcriptional and epigenetic changes that may contribute to Alzheimer's disease (Wu et al., 2008).", + "\t\n\nThere are many theoretical considerations on oxidative damage of mitochondria about aging.The \"free radical theory of aging,\" proposed by Harman in 1956 (138), that free radicals cause nonspecific damage to macromolecules, such as DNA, lipids, and proteins, has attracted much attention in recent years due to development in free radical biology.Harman (139) also proposed aging as consequences of mitochondrial aging that free radical reactions may contribute to changes in the mitochondrial inner membrane with age due to effects on both mtDNA and nDNA.Based on the observation of Drosophila, Miquel et al. (238) postulated that there is a distinct possibility of free radical-or lipid peroxide-induced inactivation of the mtDNA of fixed postmitotic cells with the passage of time.Fleming et al. (110) proposed that the site of irreversible injury is the mtDNA rather than the biomembranes.A two-step hypothesis on the mechanisms of in vitro cell aging, \"oxygen radical-mitochondrial injury hypothesis of cell aging,\" was proposed by Miquel and Fleming (239) that the fundamental cause of cell aging is an instability of the mitochondrial genome because of a lack of or balance between mitochondrial repair and the disorganizing effects of oxygen radicals.Thus, deprived of the ability to regenerate their mitochondrial populations, the cells will sustain an irreversible decline in their ability to synthesize ATP, with concomitant senescent degradation of physiological performance, and eventual death.Bandy and Davison (15) suggested that mitochondrial genome mutations may increase oxidative stress as implications for carcinogenesis and aging.", + "\tAging\n\nThe oxidative stress theory of aging proposes that accumulation of oxidative DNA damage over the life span of an organism leads to gradual decline of cellular functions and eventual death (Bohr, 2002).This model is supported by several circumstantial evidences including the observation that lower free radical production and/or antioxidant treatment protects against agerelated deterioration, and cognitive decline (Lemon et al., 2003).Further, deficit or decrease in the repair of oxidative DNA damage appears to correlate with premature aging and age-related diseases (Bohr et al., 2007).It appears likely that overall genome repair, specifically the balance between DNA damage and its repair is a major determinant of the longevity and cell viability.A specific defect in processing 5 0 dRP residue at the strand break in Sir2 (SIRT6 homolog)-deficient mice displayed age-related degenerative phenotype (Mostoslavsky et al., 2006).The activities of DGs OGG1, NTH1 and uracil DNA glycosylase (UNG) in brain mitochondria decrease significantly with age (Gredilla et al., 2010).", + "\tPrxs and the free radical hypothesis of aging\n\nThe evolved version of Harman's (Harman 2003) free radical theory of aging proposes that organisms age because the constituents of cells and tissues accumulate damage over time caused by reactive oxygen (and/or nitrogen) species originating from endogenous metabolism, including, among many other possible activities, mitochondrial respiration.At first glance, it appears that the data concerning Prxs and aging fit this theory like a glove, as Prxs become ''damaged'' (catalytically inactivated as a peroxidase) during aging due to a modification caused by a reactive oxygen species (ROS), specifically hydrogen peroxide (or organic hydroperoxides), and that counteracting this ''damage'' by elevating the levels of the ''repair'' enzyme Srx1 prolongs life span (Molin et al. 2011).Moreover, as the Prxs themselves act as enzymatic antioxidants and protect the genome against oxidative modifications (see below), it is possible that peroxidedependent inactivation of Prxs gives rise to a negative feedback loop with respect to the cell's capacity for ROS homeostasis.", + "\t\n\nAging is a dynamic and complex process defined as the time-dependent functional decline.With age, homeostasis declines and damage accumulates.One of prime candidates that induce macromolecular damage is oxidative stress from reactive oxygen species (ROS) generated from normal physiological activities.Indeed, many long-lived mutants are resistant to oxidative stress [53].Ferroptosis involves metabolic dysfunction that results in the production of both cytosolic and lipid ROS [36,38].Repression of SLC7A11 transcription by p53 results in reduction of cystine uptake.Because of less cystine uptake, the levels of intracellular glutathione (GSH) will be reduced and the cellular system for defending oxidative stress is abrogated.Thus, the sensitivity of ROS-induced ferroptosis is significantly increased in p53-activating cells.We showed that SLC7A11 is downregulated by p53 and that p53mediated ferroptosis is dramatically induced in the testis of p53 3KR/3KR Xrcc4 -/-mice.Thus, it is very likely that the combination of genomic instability and p53-mediated ferroptosis contributes significantly to the aging associated phenotypes observed in p53 3KR/3KR Xrcc4 -/-mice.", + "\tSources of Damage Increase with Age\n\nThe free radical theory of aging posits that aging is caused primarily by oxidative damage incurred by ROS that chemically modify critical cellular biomolecules (13).This theory has evolved over the years to become the oxidative stress theory of aging, but the principle is the same, in that the accumulation of oxidative damage drives aging.In support of this theory, a large body of literature indicates that oxidative damage to all cellular macromolecules increases with age.Furthermore, overexpression of antioxidant enzymes that detoxify ROS, such as copper-and zinc-containing superoxide dismutase (SOD), manganese-containing SOD, or catalase, increase the life span of Drosophila melanogaster by as much as 30% (14).Additionally, most long-lived mutants in D. melanogaster and Caenorhabditis elegans have increased resistance to oxidative stress.In mammals, the role of oxidative stress is less clear because overexpression of catalase, SOD1 (pancellular expression), or SOD2 (mitochondrial) does not extend the life span of mice (15).However, overexpression of catalase specifically targeted to the mitochondria does extend the life span of some mice up to 20% (16).Additionally, treatment with the antioxidant nordihydroguaiaretic acid (NDGA) and an activator of NRF2 (master regulator of antioxidant response) extends median life span in male mice (17).\t\n\nThe free radical theory of aging evolved to the mitochondrial theory of aging when mitochondria were implicated as the primary source of ROS.Electrons leaked from the electron transport chain at the inner mitochondrial membrane can react with molecular oxygen to produce a superoxide radical, which can be converted by SOD to yield hydrogen peroxide (H 2 O 2 ).In the presence of transition metal ions (e.g., Fe 2+ or Cu + ), H 2 O 2 can be further converted to the highly reactive hydroxyl radical via the Fenton-type reaction.These ROS react locally to damage genes or proteins necessary for oxidative phosphorylation, leading to further uncoupling of electron transport and increased ROS production in a feed-forward manner.Abundant evidence shows that ROS and oxidative damage increase as organisms age.But which cellular target of these damaging radicals and other reactive molecules is health and life limiting?If the answer is DNA, then one expects DNA damage to accumulate with age.", + "\tThe Free Radical Theory of Aging. The free radical theory of aging proposed by Denham Harman more than fifty years ago postulates that aging results from the accumulation of deleterious effects caused by free radicals, and the ability of an organism to cope with cellular damage induced by ROS plays an important role in determining organismal lifespan [3].In agreement with this theory, increased ROS production by mitochondria and increased 8-oxo-dG content in the mtDNA are frequently detected in aged tissues [40,[47][48][49][50], suggesting that progressive accumulation of oxidative DNA damage is a contributory factor to the aging process.Consistently, many studies have found that increased oxidative damage in cells is associated with aging [51][52][53].Furthermore, genetic studies in worm, fly, and mouse have linked enhanced stress resistance or reduced free radical production with increased lifespan [27].Mutant strains of C. elegans that are resistant to oxidative stress have extended lifespan, whereas those more susceptible to free radicals have shortened lifespan [54,55].Mice lacking the antioxidant enzyme superoxide dismutase 1 (SOD1) exhibit a 30% decrease in life expectancy [56].Conversely, simultaneous overexpression of SOD1 and catalase extends lifespan in Drosophila [57].Small synthetic mimetics of SOD/catalase increase lifespan in C. elegans [58], while treatment of antioxidant drugs in mice increases the median lifespan up to 25% [59,60].Further supporting this hypothesis, mice lacking Ogg1 and Myh, two enzymes of the base excision repair pathway that repairs oxidative DNA damage, show a 50% reduction in life expectancy [61].Collectively, these studies demonstrate that interplay between ROS and protective antioxidant responses is an important factor in determining aging and lifespan.\tMitochondria and Aging\n\n3.1.The Mitochondrial Theory of Aging.Because mitochondria are the major producer of ROS in mammalian cells, the close proximity to ROS places mitochondrial DNA (mtDNA) prone to oxidative damage [104].Consistently, many studies have shown that 8-oxo-dG, one of the common oxidative lesions, is detected at higher level in mtDNA than nuclear DNA, suggesting that mtDNA is more susceptible to oxidative damage [52,[105][106][107][108][109][110][111][112][113].As both the major producer and primary target of ROS, mitochondria are thought to play an important role in aging.The mitochondrial theory of aging, extended from the free radical theory, proposes that oxidative damage generated during oxidative phosphorylation of mitochondrial macromolecules such as mtDNA, proteins, or lipids is responsible for aging [114].As mtDNA encodes essential components of oxidative phosphorylation and protein synthesis machinery [115], oxidative damageinduced mtDNA mutations that impair either the assembly or the function of the respiratory chain will in turn trigger further accumulation of ROS, which results in a vicious cycle leading to energy depletion in the cell and ultimately cell death [104,114,[116][117][118].", + "\t\n\nThere is an emerging consensus that oxidative damage is of central importance to much of the age-related overall decline of animal cells, from yeast to humans [2][3][4][5][6][7] .Caloric restriction or environmental conditions that favour a decrease in oxidative metabolism also increase lifespan 8 , and transgenic or knockout animals with decreased oxidative metabolism have increased lifespans.For example, flies that consume oxygen at a high rate have a reduced lifespan, and low oxygen-consumption rates and cold temperatures favour a prolonged lifespan 9,10 .Lipids, proteins and DNA have all been argued to be Ageing, repetitive genomes and DNA damage Michael R. Lieber and Zarir E. Karanjawala www.nature.com/reviews/molcellbioP E R S P E C T I V E S to one another, thereby permitting a copying of information from one sister chromatid to the other.This typically restores the information content at the break site back to normal.", + "\t\n\nA key macromolecule at risk for ROS-mediated damage is nuclear DNA [1], which is evident from the wide range of oxidative DNA lesions that accumulate gradually in rodents and humans with advancing age [6,7].\tIntroduction\n\nA prevailing hypothesis to explain the molecular basis of ageing is Harman's ''free-radical theory of ageing'', which states that endogenous reactive oxygen species (ROS), which result from cellular metabolism, continually damage biomolecules [1].In line with this hypothesis, it has been shown that increased resistance to oxidative stress (e.g., by improved antioxidant defense) extends the lifespan of Caenorhabditis elegans, Drosophila, and rodents [2][3][4], whereas hypersensitivity to oxygen considerably reduces the lifespan of nematodes [5].", + "\tReplication stress, mitochondria and growth signaling\n\nIncreased oxidative damage to DNA and other cellular constituents by ROS produced in dysfunctional mitochondria is an important component of modern versions of the 'free radical theory' of aging (3,71).It is often assumed that the production of ROS in mitochondria is directly proportional to the rate of mitochondrial respiration, and that increased respiration promotes aging.A number of recent studies in budding yeast and mammals argue that these long-held assumptions are incorrect (72).For example, caloric restriction and other experimental manipulations that enhance respiration in budding yeast reduce, rather than increase levels of ROS at the same time that they enhance life span (73).Similarly, budding yeast cells cultured in medium containing glycerol or ethanol, which are metabolized via respiratory pathways, exhibit a longer chronological life span (22).Furthermore, deletion of TOR1 extends chronological life span of budding yeast by enhancing respiration, but reducing ROS (21).As might be expected based on these reports, experimental manipulations that increase the production of ROS in mitochondria shorten the chronological life span of this organism (73,74)." + ], + [ + "\tSenescence and apoptosis are thought to contribute\nto aging and age-related disorders by decreasing the proliferative potential of progenitor\nstem cells, altering tissue regenerative capacity, decreasing tissue function and by altered\ntissue architecture and microenvironment caused by altered gene expression and secretion of\ninflammatory cytokines, growth factors, and proteases (Campisi 2003; Coppe et al. 2008;\nGarfinkel et al. 1994; Krtolica and Campisi 2002; Kuilman et al. 2008; Novakova et al. 2010; Ohtani and Hara 2013).", + "\t\n\nThere exists a substantial body of research addressing the tissue, cellular and molecular changes that accompany or directly contribute to aging in a range of model organisms (reviewed in [7]).However, the majority of data, generated in model organisms or in vitro (cellular senescence), has yet to be validated in human aging.Moreover the relative contribution of putative gerontogenes to human pathological agerelated processes is unknown.Age-associated impaired healing correlates with increased inflammation, increased matrix proteolysis and delayed re-epithelialization leading to chronic wound states, processes modulated by exogenous estrogen treatment [8].In a recent study we characterized estrogen-regulated changes in gene expression using a model of delayed wound healing in young mice that have been rendered hypogonadal by ovariectomization (hence removing any effects of 'intrinsic aging') [9].Thus, using comparative analysis we are now in a position to address the relative contributions of estrogen and aging to healing in elderly humans.", + "\t\nAging alters gene expression of growth and remodeling factors in human skeletal muscle both at rest and in response to acute resistance exercise.\t\n\nAging alters gene expression of growth and remodeling factors in human skeletal muscle both at rest and in response to acute resistance exercise.", + "\t\n\nStructural integrity of skeletal muscle.Some noteworthy genes that were differentially expressed only in older subjects after RL support the concept that the muscles of older subjects may have experienced a degree of stress far exceeding that in young subjects despite being exposed to the exact same stressor.For example, gene expression of MyBPH was robustly elevated (4.1-fold) in the old only, as was myosin head domain containing 1 (MYOHD1; 1.4-fold).MyBPH is an integral myosin binding partner in the A band of myofibrils that interacts with the myosin rods and titin to provide structural integrity to the contractile apparatus.Reduced MyBPH expression is associated with muscle weakness in age-related disorders (30).Interestingly, localization of MyBPH to the contractile apparatus is directed by its C terminal domain consisting of two fibronectin type III motifs (24), and our microarray analysis also revealed a 1.6-fold increase among the old in the expression of fibronectin type III domain containing 3B (FNDC3B).As shown in mice, MyBPH is upregulated in the young after more intense eccentric loading (5), again suggesting age differences in the degree of mechanical stress required to activate many of these transcriptional responses (with young muscles requiring greater stress than old).MyBPH expres-sion is modulated by the transcription factor SMARCA4 (SWI/ SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 4), which was also significantly upregulated in the old only.Interestingly, SMARCA4 is activated by glucocorticoid receptor signaling and, in turn, regulates the expression of notable muscle-specific genes including myogenin, troponin T, and MyBPH.A strain on muscle integrity among the old was also suggested by significant downregulation (1.7-fold) of both type IV collagen 3 (COL4A3) and 4 (COL4A4) mRNA expression and 1.6-fold upregulation of TUBA8.Type IV collagen, a major constituent of basement membranes, is degraded by matrix metalloproteinases (MMP-2 and MMP-9) in response to muscle damage (49).These findings suggest that the muscles of the older subjects may have been attempting to launch a compensatory effort to maintain structural integrity-a response to this degree was apparently not sensed as necessary among the younger subjects.", + "\tRole of Extracellular Matrix Remodeling in Vascular Aging\n\nThe extracellular matrix (ECM) is an important contributor to health and longevity.This noncellular compartment, ubiquitous to all tissues and organs does not only provide essential mechanical scaffolding but mediates highly dynamic biomechanical and biochemical signals required for tissue homeostasis, morphogenesis, and cell differentiation.Studies on model organisms suggest that evolutionarily conserved pathways regulate ECM remodeling during aging and that promotion of ECM youthfulness by antiaging interventions is an essential signature of longevity assurance. 206Aging in mammals also results in significant changes in ECM biosynthesis, postsynthetic modifications of ECM components, and alterations of cell-matrix interactions, which contribute to the development of a spectrum of age-related pathologies. 207ge-related alterations of the ECM, including the subendothelial basement membrane, intima, media, adventitia, and interstitial matrix (which constitute more than half of the mass of the vascular tissue), play a fundamental role in impairment of both structural and regulatory homeostasis of the vasculature. 208With age, the expression of growth factors that regulate ECM biosynthesis is altered 45 and the synthesis of many ECM components (eg, elastin) declines, which impairs elasticity and resilience of the vascular wall to mechanical damage and rupture induced by bursts in wall tension because of pulsatile pressure waves. 208Age-related ECM changes also likely alter vascular mechanotransduction, dysregulating cell responses to alterations in the hemodynamic environment.Additionally, aging and cellular senescence alter the secretory phenotype of vascular endothelial and smooth muscle cells, increasing MMP secretion. 45This together with increased MMP activation 208 induced by high ROS levels compromises the structural integrity of the vasculature and promotes pathological remodeling (eg, in hypertension), resulting in increased likelihood of aneurysm formation and vessel rupture, including the development of cerebral microhemorrhages. 45The available evidence suggests that many of these age-related ECM alterations are governed by circulating factors and factors produced in the vascular wall, including the extended renin-angiotensin-aldosterone system (see above) and an age-related decline in circulating IGF-1. 209ollagen synthesis is also dysregulated with age in the vascular wall likely because of the effects of increased paracrine action of TGF- (transforming growth factor-), 123 which contributes to vascular fibrosis and arterial stiffening. 208Additional features that contribute to increased arterial stiffness include decreased elastin synthesis, elastin degradation and fragmentation, elastin calcification, alterations in cross-linking of extracellular matrix components (eg, by increased presence of advanced glycation end products). 208,210,211he pathophysiological consequences of age-related ECM remodeling and arterial stiffening have been the subject of a recent comprehensive review by AlGhatrif and Lakatta. 6In brief, as the large conduit arteries stiffen in aging, aortic pulse wave velocity, systolic pressure, and pulse pressure significantly increase, 212 whereas diastolic pressure decreases.Decreased diastolic pressure leads to a decline in coronary blood flow.Increased systolic pressure promotes left ventricular remodeling, diastolic dysfunction, and exacerbates atherogenesis.Because of the dilation of conduit arteries, wall tension significantly increases, contributing to the development of aneurysms.In addition to alterations in the biomechanical properties of large arteries, age-related ECM remodeling likely also affects microvascular transport and barrier functions. 213Age-related alteration of the ECM structure and composition are also manifested in the wall of veins, contributing to the pathogenesis of varicosities. 214\t\n\nFigure 4. Conceptual model for the pathogenic role of cellular senescence in vascular aging.The model predicts that increased presence of senescent endothelial or smooth muscle cells (SMCs) in the aged vasculature and their proinflammatory secretome (SASP [senescence-associated secretory phenotype]) contributes to impaired angiogenesis and microvascular rarefaction, pathological remodeling of the extracellular matrix (ECM), barrier disruption, chronic inflammation, and atherogenesis.MMP indicates matrix metalloproteinase.", + "\t\n\nAge-related transcriptional remodeling and mitochondria", + "\t\n\nChromatin remodeling in aging, J. G. Wood et al.", + "\tAging is only, in part, the result of crosslinking reactions\n\nWhile Bjorksten (1968) proposed that crosslinking was a major feature of the chemical aging of tissues, particularly of collagen, it has become apparent in recent years that many age-dependent chemical modifications of protein are monofunctional.These include oxidative modifications of phenylalanine, tyrosine and methionine residues (Table 1), carboxyalkylation of lysine (Table 4), and deamidation and racemization of amino acids.Extracellular matrix proteins accumulate higher levels of monofunctional chemical modifications, as well as crosslinks, not because they are uniquely sensitive to damage, but because they generally turnover more slowly.There are few quantitative studies on the age-dependent accumulation of biomarkers in intracellular proteins, even in proteins with long half-lives, such as contractile proteins in muscle or histones in post-mitotic cells.These proteins may be exposed to higher levels of reactive oxygen species generated in mitochondria or peroxisomes, or to higher levels of reactive carbonyl intermediates in glycolysis, but are also better protected by intracellular antioxidant and detoxification systems.", + "\t\n\nVarious extracellular matrix-related proteins were differentially regulated herein.Extracellular matrix proteins provide structural support, mechanical properties, and strength of tissues, including vocal folds, playing a pivotal role in phonation [62,71,72].Collagens XIV, XVIII, and Fibulin 5 were downregulated in older rabbit vocal folds compared to young tissue.To our knowledge, these specific collagen types have not been investigated in depth in vocal fold tissue; however, studies suggest that the changes in the collagen fiber density and arrangements within the lamina propria may affect phonation [73,74].Collagen type IV is exclusive to extracellular matrix basal membranes [75] and is present in the human vocal fold basal membrane providing support to epithelial and endothelial cells [76].Collagen type IV was upregulated in older rabbit vocal folds compared to young, an effect of aging observed in our study.The relationship between Collagen type IV and aging is not well established.Increased accumulation of Collagen type IV is reported in the basal lamina of cerebral microvessels in humans [77] but decreased in the skin of older adults [78].Conversely, several extracellular matrix proteins were upregulated, including Collagen type XVIII and Fibulin 5, in the presence of dehydration when observing the effect of hydration status alone.These protein changes may be related to the remodeling of the extracellular matrix [79] in response to dehydration.Moreover, the accumulation of collagens and the decrease of elastins may result in extracellular matrix stiffness in aging larynx and other organs [59,79].Finally, Lamin A was upregulated by dehydration, by a smaller magnitude, especially when observing the mean difference within the young groups.Previous data has identified that Lamin proteins A and C are important for imparting the nucleus with its stiffness, and their expression has been reported to scale with tissue stiffness [80].Thus, upregulation of this protein due to dehydration may be related to tissue stiffness in the vocal fold of rabbits.", + "\t\n\nRecently, collagen production and extracellular matrix remodeling were determined to be essential for longevity in C. elegans.Collagen may directly affect signaling processes associated with longevity in C. elegans, including signaling through SKN-1 [40,58].We note that HSF-1 was also recently shown to regulate cytoskeletal integrity in a process that can influence stress resistance and longevity in C. elegans [59].Thus, the linkage of both the extracellular matrix and the cytoskeleton to HSF-1 may provide a mechanism by which HSF-1 promotes longevity.\tHSF-1 regulates collagen genes which may affect the aging process\n\nIt is interesting that cuticle structure genes constitute the largest overlap with aging-related genes.In humans, mutations in collagens lead to a large number of heritable human diseases such as osteoporosis and musculoskeletal diseases [53].Collagens are long-lived proteins known to accumulate damage during aging, leading to a decline in tissue health [54].Also, type I collagens become resistant to proteolysis upon age [55,56], affecting their turnover.Interestingly, mice expressing cleavageresistant type I collagen go through an accelerated aging process [57].Thus, cellular aging can be affected by the state of the extracellular matrix in mammals.", + "\t\n\nAn observation that is specific for males is the global downregulation with aging of genes involved in the synthesis of the ECM and in particular of different forms of collagen (Table 2).In addition, aging males but not females showed a decrease in collagen type III.Interestingly, collagen type III decreases the size of collagen bundles and thereby increases vascular elasticity (11).Therefore, a decreased expression of collagen type III can participate in the increased stiffness that characterizes the aging aorta (23).An interesting observation from our study that directly relates to the mechanism of vascular remodeling is the upregulation in aging males of the transcript encoding collagen type VIII (Table 3).That specific collagen type, which is upregulated in response to vascular injury (24), promotes VSMC migration (1).The upregulation of this transcript together with the downregulation of other isoforms in aging males again supports the notion that this group is more susceptible to neointimal proliferation, VSMC migration, and potentially atherosclerosis.\t\n\nOur study shows that the genomic adaptation to vascular aging involves not only the genes involved in ECM composition and VSMC differentiation and migration, but also many other categories of genes participating in intracellular functions, such as cell signaling, DNA repair, metabolism, and protein synthesis.Our study also illustrates that most of the changes in gene expression with aging differ between males and females and correspond to different sets of transcription factors.Indeed, 5% of the 600 genes that were regulated by aging were observed in both old males and females.GO analysis also shows that specific subsets of genes are regulated differently between sexes, especially the genes participating in ECM composition and VSMC phenotype.We therefore propose that these transcriptional differences may underlie the different physiological properties of aging arteries between males and females, as well as their different susceptibility to vascular complications, such as hypertension or atherosclerosis.Furthermore, the analyses in young monkeys demonstrated major differences in genes regulating vascular structure, implying that the sex differences in vascular stiffness that develop with aging are programmed at an early age.", + "\tChronic liver diseases are characterized by aberrant matrix deposition, calling for our\nattention to the role of ECM in resolution of liver fibrosis. Tissue remodeling is regulated by MMPs,\ninvolved in the ECM degradation, and TIMPs, their endogenous inhibitors. Their subtle balance\nmaintains liver fibrogenesis. Tissue homeostasis is further regulated by proteolytic activity of the\nPLAU/PLAT/plasmin, responsible for the maintenance of the physiologic levels of ECM (40). PLAU promotes ECM degradation through activation of MMPs (MMP-2, -3 and -9; (41, 42),\nincreases the differentiation of hepatic stem cells, and HGF-dependent regeneration of hepatocytes\n(43).", + "\t\n\nMechanistically, the age-related increase in elastin degradation may result from augmented activity of proteases with elastinolytic activity, including certain MMPs and cysteinyl cathepsins, enzymes that, in turn, are regulated by inflammatory mediators (54,55).Collagen catabolism falls in aging arteries.\t\n\nAugmented transforming growth factor (TGF)-b activity favors the accumulation of collagen in the aortic wall.The activity of various elastases, including matrix metalloproteinases (MMPs), such as MMP-9 and MMP-12, as well as overexpression of the cysteine proteinases cathepsins S, K, and L, and the serine proteinase neutrophil elastase, elaborated by inflammatory cells, can all contribute to depletion of elastin (11).These alterations in the aorta's extracellular matrix contribute importantly to its loss of distensibility.This increased stiffness raises reflected waves and elevates systolic pressure.Yet diastolic pressure tends to decline with age.As aortic pulse wave velocity increases, pulse pressure rises (12).Indeed, pulse pressure is an independent risk factor for CV events (13).Isolated systolic hypertension accounts for the majority of uncontrolled hypertension in Americans over 50 years of age (14,15).substantially stroke and total mortality, with lesser benefit for ischemic cardiac events (16).Avoiding excessive sodium intake may provide an additional, nonpharmacological intervention for control of hypertension in older individuals (17,18).Some have raised concerns regarding the safety of aggressive lowering of blood pressure in elderly patients, particularly those with concomitant coronary artery disease (19).Indeed, a J-shaped curve relating CV outcomes to blood pressure may pertain to this In addition to reducing stroke, a major impediment to independent living and function in older patients, antihypertensive therapy may limit the development of dementing illnesses, as shown in the Syst-Eur trial (27).Decreased dementia and cognitive decline accrue with longer duration of antihypertensive treatment (28).An asymmetric loss to follow-up of individuals with impaired cognition may have biased the results of dementia in the SHEP study to the null (29).With regard to the former, vascular aging alters the function of the endothelium, the cells that line the lumen of blood vessels.Endothelial dysfunction includes reduced vasodilatory and antithrombotic properties, with an increase in oxidative stress and inflammatory cytokines (33)(34)(35) favoring atherogenesis and thrombosis, and predisposing to CVD (36).Human and experimental studies concur that diminished bioavailability of nitric oxide (NO), a key mediator of vasorelaxation and antiatherogenic processes, underlies age-dependent endothelial dysfunction (37,38).Reduced NO bioavailability can occur due to decreased synthesis or increased degradation of NO.Under normal conditions, endothelial nitric oxide synthase (eNOS) produces NO from L-arginine in the presence of the cofactor tetrahydrobiopterin (BH4) (39).Although studies differ regarding eNOS protein expression with age (34,40,41), recent data suggest an age-related alteration in eNOS function, referred to as eNOS uncoupling (42).", + "\tBackground\n\nTissue aging is caused by intrinsic and extrinsic factors that induce complex molecular changes and, in turn, a deterioration of cellular structures and function.These changes are major causes of age-related diseases like cancer or cardiovascular disorders [1,2].The main molecular adaptations occurring during aging are loss of genomic stability due to reduced DNA repair capacities [3], loss of proliferative potential caused by increased senescence [1,4], and age-related alterations in the DNA-methylation patterns that affect cellular plasticity [5,6].Metabolic adaptations are also considered to play a major role in aging [7][8][9][10].For instance, the metabolic function of mitochondria is progressively impaired during aging in different tissues [8,11].This can result in increased generation of reactive oxygen species that foster genomic instability [8,12].Moreover, several studies reported that caloric restrictions and diet adaptations, such as supplementation of food with branched chain amino acids [13,14], can significantly increase lifespan [15].This suggests that metabolic activity as well as nutrient sensing pathways are highly relevant for cellular aging processes (reviewed in [10]).Accordingly, interference with the insulin/IGF1 and the mammalian target of rapamycin (mTOR) pathways increased lifespan in different model organisms [7,[16][17][18].", + "\t\n\nWe examined the list of 447 age-regulated genes for functional groups showing a consistent change with age.One group includes genes involved in the formation of the extracellular matrix, which show a consistent increase in expression in old age.Seven age-regulated genes encode proteins known to play key roles in maintaining epithelial polarity (three types of claudins, two cadherins, occludin, and a cell adhesion molecule), all but one of which increase expression in old age (see Table S4).Forty-nine age-regulated genes encode protein components of the extracellular matrix, all but four of which increase expression in old age.In the kidney, the extracellular matrix could play a key role in governing the filtration of blood via the basement membrane, a capacity that declines with age.The observation that genes involved in forming the extracellular matrix increase expression in the kidney with age may be directly relevant to the age-related decline in glomerular filtration rate." + ], + [ + "\tStochastic damage\n\nFigure 2. Longevity assurance, ageing and disease.New studies of the biology of ageing are revealing processes that control when and how fast ageing occurs, such as insulin-IGF-1 signalling [6], cellular senescence [4], protein refolding [43][44][45], autophagy [41] and phase 1 and 2 detoxification [36,37,52].These represent major points of intervention against ageing-related disease.As shown here, lifespan pathways control improved cellular maintenance, which leads to slowed ageing (e.g.slowed normal cognitive ageing) and protection against diseases of ageing (e.g.neurodegenerative diseases of ageing, such as Alzheimer's and Parkinson's disease, and cancer).Ageing can evolve via selection to reduce investment in energetically costly somatic maintenance processes and instead to increase early fitness traits such as growth and reproduction [50,51].Arrows denote stimulation, and T bars inhibition, of the process indicated.Red and green denote changes leading to ageing and longevity, respectively.", + "\t\n\nFig. 4. Schematic showing how some external interventions trigger longevity, often at least partly through stimulating autophagy.The pink writing refers to dietary, chemical, or therapeutic interventions that can extend life span, in at least some organisms (described in the text).Arrows indicate stimulating effects, and blocked lines indicate inhibitory effects.This schematic is not meant to be exhaustive but highlights the pathways that alter the epigenetic information and autophagy.", + "\t\n\nTORC1 regulates several downstream processes that may contribute to its role in aging, including protein degradation via autophagy, mitochondrial metabolism, stress response, and mRNA translation (Stanfel et al. 2009).Autophagy, which literally means \"self eating\", is a degradative process through which cellular components are engulfed by cytoplasmic vesicles and transported to the lysosome/vacuole for degradation (Klionsky 2007).Autophagy is repressed by TOR signaling and is induced in response to starvation or treatment with TOR inhibitors, such as rapamycin (Noda and Ohsumi 1998).A decline in the autophagic response has been reported in aging mammals (Cuervo and Dice 2000), and increased autophagy is required for life span extension in long-lived C. elegans mutants with reduced insulin/IGF-1-like signaling (Melendez et al. 2003).Several recent studies have also uncovered an important role for autophagy in the response to DR. DR induces autophagy in yeast, worms, and flies (Juhasz et al. 2007;Morck and Pilon 2006;Takeshige et al. 1992) and is reported to be required for life span extension from DR or TOR-inhibition in both worms and flies (Hansen et al. 2008;Jia and Levine 2007;Juhasz et al. 2007).Recently, up-regulation of autophagy by spermidine has also been shown to be associated with increased life span in yeast, nematodes, and flies (Eisenberg et al. 2009).", + "\tInductors of Autophagy and its Impact on Aging\n\nAutophagy has a role in homeostasis, which plays an essential role in the maintenance of cellular physiology and the prevention of cellular damage.Among the inducers of autophagy have been described the already-mentioned rapamycin, resveratrol, and polyamines; however, only polyamines have demonstrated results in clinical research in humans [65].It is known that these compounds can induce the canonical autophagy pathway, which includes inactivation of the mammalian objective of the rapamycin complex 1 (mTORC1), allowing phosphorylation and activation of the Unc-51 complex (Ulk1/2), where the cascade of the other members of the complex is subsequently activated, ULK as FIP200 and ATG13 [65].\t\n\nOn the other hand, interventions using chemical inducers of macroautophagy, such as rapamycin, an mTOR inhibitor, can increase the life span of middle-aged mice like that induced by spermidine or polyamine-producing gut flora supplementation [87].In an unexpected finding, aged cells showed an increased accumulation of protein aggregates, suggesting a decline in lysosome functionality during aging even though the number of lysosomes increased [72,88].This disparity could be due to changes in the pH, as suggested by the fact that the vacuolar V-type ATPase complex, which is responsible for maintaining vacuolar pH, decreased during aging, suggesting a mechanistic link between altered protein complex composition and lysosome dysfunction [72,88].The stress-induced synthesis of cytosolic and organelle-specific chaperones was also impaired in aging.Mutant mice that were deficient in a co-chaperone of the heat-shock family exhibited accelerated aging phenotypes, whereas long-lived mouse strains showed a marked upregulation of some heat-shock proteins [89].\t\n\n2016;351:173-6.81.Koga H, Kaushik S, Cuervo AM.Protein homeostasis and aging: the importance of exquisite quality control.Ageing Res Rev. 2011;10:205-15.82.Labbadia J, Morimoto RI.The biology of proteostasis in aging and disease.Annu Rev Biochem.2015;84:435-64.83.Rubinsztein DC, Mario G, Kroemer G. Autophagy and aging.Cell.2011;146:682-95.84.Tomaru U, Takahashi S, Ishizu A, Miyatake Y, Gohda A, Suzuki S, et al.Decreased proteasomal activity causes age-related phenotypes and promotes the development of metabolic abnormalities.Am J Pathol.2012;180:963-72.85.Rodriguez KA, Edrey YH, Osmulski P, Gaczynska M, Buffenstein R. Altered composition of liver proteasome assemblies contributes to enhanced proteasome activity in the exceptionally long-lived naked mole-rat.Brodsky JL, editor.PLoS One.2012.https://doi.org/10.1371/journal.pone.0035890.86.Chondrogianni N, Georgila K, Kourtis N, Tavernarakis N, Gonos ES.Enhanced proteasome degradation extends Caenorhabditis elegans lifespan and alleviates aggregationrelated pathologies.Free Radic Biol Med.2014;75:S18.https://doi.org/10.1016/j.freeradbiomed.2014.10.632.87.91.Haigis MC, Yankner BA.The aging stress response.Mol Cell.2010;40:333-44.92.Johnson SC, Rabinovitch PS, Kaeberlein M. mTOR is a key modulator of ageing and agerelated disease.Nature.2013 Jan 16;493:338-45.93.Lamming DW, Ye L, Astle CM, Baur JA, Sabatini DM, Harrison DE.Young and old genetically heterogeneous HET3 mice on a rapamycin diet are glucose intolerant but insulin sensitive.Aging Cell.2013;12:712-8.\tConserved Metabolic Pathways Offer Clues to the Factors of Aging and Longevity\n\nEvolutionarily conserved pathways, from yeast to mammals, robustly correlate with aging and longevity, and their deregulation has been implied with the development of cellular aging and include the mechanistic target of rapamycin (mTOR), insulin/ insulin growth factor 1 signaling (IIS), AMPK sensing, and sirtuin (SIRT) pathways [90].The harmonized regulation of these metabolic pathways maintains cellular and organismal homeostasis, even in the presence of external perturbations like changes in nutrient availability, temperature, oxygen level, or internal alterations, including protein misfolding and DNA damage [91].", + "\t\n\npivotal in this aspect providing molecular insights and having huge conceptual contributions in the field.Characterising the contribution of individual mutants in ageing is a continuously active and informative activity in the field.On top of these studies, genome-wide screens have provided insights on the role of evolutionarily conserved processes and signalling pathways in ageing such as nutrient response [17,18], protein translation, oxidative damage [19,20], mitochondrial function [21,22] and autophagy [22,23] opening new avenues for biogerontology research.Yeasts have proved informative and helped in understanding mechanisms of highly conserved pathways (from yeast to human) in physiology, health and disease such as the Target of Rapamycin (TOR) [24], glucose sensing (PKA) and stress response pathways (Sty1/p38) [25].\t\n\nA competitive ageing assay was performed in budding yeast where samples from the ageing pool were collected at specific timepoints [58].Mutants were then detected using a microarray DNA hybridization technique that quantifies abundance of the barcode tags of each mutant.Using this approach multiple short-and long-lived mutants were identified with autophagy mutants being among the short-lived and mutants coding for proteins involved in de novo purine biosynthesis pathway, which ultimately produces IMP and AMP were among the long-lived ones [58].Validation experiments targeting autophagy or purine biosynthesis has the expected lifespan outcomes.In a similar approach, deletion of genes involved in protein sorting in vacuoles, autophagy and mitochondrial function shortened life span, confirming that respiration and degradation processes are essential for long-term survival.Among the genes whose deletion significantly extended life span were genes implicated in fatty acid transport and biosynthesis, cell signalling and transfer RNA (tRNA) methylation such as ACB1, CKA2 and TRM9, respectively [59].", + "\t\n\nWe have recently conducted a genome-wide screen using siRNA library to identify genes regulating autophagy in human cells under normal nutritional conditions (5).In this image-based screen we took advantage of the autophagy specific GFP-LC3 reporter whose translocation from the cytosol to autophagosomes can serve as a quantitative measure of autophagy.In this study, we specifically explore the mechanisms that regulate autophagy in neural cells using the hits identified in our screen.We demonstrate that reactive oxygen species (ROS) play a general function in mediation of autophagy upstream of the type III PI3 kinase and that this pathway is essential for the up-regulation of autophagy by A.Interestingly, our data show that genes regulating autophagy are differentially expressed in normal aging and in AD patient brains.Finally, we identify candidate molecular targets that may be safely manipulated to modulate autophagy to treat neurodegenerative diseases.\t\n\nConversely, expression of the key autophagy genes, such as Atg5 and Atg7, was down-regulated in aging.This is consistent with our previous data demonstrating transcriptional down-regulation of beclin 1, in normal human brain aging (11).Together, this suggests, that unlike AD, the normal aging process may lead to transcriptional down-regulation of autophagy.\t\n\nTo further define the biological processes affected by downregulation of autophagy in aging, we used gene ontology canonical pathway analysis.It revealed a significant enrichment in the \"Axon guidance\" (P = 0.0009) and \"Regulation of actin cytoskeleton\" (P = 0.038) pathways, suggesting a connection between regulation of autophagy, axon guidance and actin dynamics.Construction of protein-protein interaction networks anchored by the hit genes belonging to these pathways (12,13) revealed two related networks encompassing, respectively, 27 (11%) and 61 (26%) of the hit genes (Fig. S6 C and D).Importantly, both networks directly connect to the known autophagy machinery through the interaction of the RIP kinase (RIPK1) and PKC (PRKCZ) with p62/sequestrosome (SQSTM1).In addition, syndecan 2 (SDC2), a part of the \"Regulation of actin cytoskeleton\" network, interacts with syntenin, a binding partner of ULK1, the human ortholog of yeast Atg1 (14).ULK1 is known to play a role in the regulation of endocytic processes involved in axon guidance (15) and to promote synapse formation in Drosophila (16).These data suggest that some of the molecular networks involved in the regulation of autophagy are closely connected to those regulating endocytosis, actin dynamics, and neuronal axon guidance, and that autophagy may play a wider role in the development and maintenance of neuronal function.\t\n\nTranscriptional Regulation of Autophagy in Normal Brain Aging.To determine whether the regulation of autophagy may have wider implications in normal aging of the human brain, we analyzed expression of the autophagy screen hit genes in a set of younger versus older human brain samples (10).We observed differential expression of a large subset of genes, including a group of 32 genes significantly (P < 0.05) up-regulated and 46 down-regulated with age (Fig. 6A and Fig. S6 A and B and Table S9).Gene ontology biological process analysis revealed that the age up-regulated group was highly enriched in genes involved in mediation and regulation of the MAP kinase pathway (P = 1.6 10 4 ).An increase in the activity of MAP kinase pathway was predicted by our previous analysis to lead to the suppression of autophagy (5).\t\n\nDifferential Expression of Autophagy Regulators in Normal Aging and in AD.Our gene expression data suggest that autophagy is also differentially regulated at the transcriptional level in normal human brain aging versus in AD.Because autophagy is known to play a protective role against onset of neurodegeneration in animal models (2,3,20,21), its down-regulation in normal aging could contribute to the observed age-dependent predisposition to development of chronic neurodegenerative diseases.In addition, the extensive overlap of the autophagy screen hits with Fig. 6.Expression of autophagy screen hit genes in normal human aging.Clustering analysis (dChip) of mRNA expression levels of select autophagy hit genes in younger (40 y old) versus older (70 y old) human brain samples, based on (i) minimum 1.2-fold change between the average expression, and (ii) P value <0.05 using unpaired t test.\tDiscussion\n\nIn this study, we demonstrate that the type III PI3 kinase plays a fundamental role in the regulation of autophagy and that ROS function as general mediators of autophagy induction upstream of this kinase.This pathway has an essential function in the initiation of autophagy in response to mitochondrial damage following exposure to A, the main pathogen of AD.At the same time, A is able to slow down autophagic processing through ROS independent inhibition of lysosomal degradation.In addition, our analysis of expression of the autophagy screen hits suggests that autophagy is differentially regulated at the transcriptional level in normal human aging and in AD, with overall levels decreased in normal aging but elevated in AD.", + "\t\n\nAt least two aspects need to be addressed using a system biology approach in aging research.First, although many different pathways, compartments or processes are known to be closely related to aging, such as the IIS pathway, autophagy, mitochondria, oxidative stress response and so on, it remains unclear as to how they interact, are co-regulated and balanced during aging.To provide a glimpse of this problem, we visualized the network communities among the known aging regulators based on entries in the GenAge database [62,63] and controlling growth and proliferation (green nodes), DNA damage response for maintaining integrity of the genome (red nodes), mitochondria and oxidative stress response (yellow nodes), and ribosome and translation (blue nodes).It is obvious that the first two are intensively linked and closely entangled, while the latter two are relatively independent processes with only few links connected to the first two processes.Also, it is interesting to note that, by comparing the molecular interaction-based network with the co-citation network, the role of autophagy and protein transport in aging might be either overestimated due to study bias or under-estimated by the incompleteness of the molecular interactions among these genes.\tINTRODUCTION\n\nAging has fascinated researchers since ancient times.The hugely complicated process that has been revealed may be interpreted from different aspects, such as the accumulation of oxidative damage, shortening of telomeres, the costs of reproduction, metabolic rates, cellular senescence, etc., and these have in turn given rise to diverse theories of aging [1].However, thanks to forward and reverse genetic technologies, researchers in the recent decades have established that despite its complexity, a single or a few key genes in a few key pathways can modulate the aging rate.The most important players would appear to be those in nutrient sensing pathways or stress response pathways, such as DAF-2/IGF1R and DAF-16/FOXO in the Insulin/IGF like signaling pathway, AAK-2/AMPK in another nutrient sensing pathway, JNK in the stress response pathway, LET-363/mTOR as an inhibitor of autophagy and activator of translation and SIRT1/SIR2 in genome stability maintenance, to name a few [2,3].In addition to genetic perturbations, dietary perturbations, such as diet restriction (DR) are known to significantly extend lifespan in most organisms examined from yeasts to primates, although different pathways may act under different DR conditions, and alternative DR strategies also effect C.elegans lifespan in different ways [3,4].The main pathways revealed under different DR regimens are summarized in Fig. (1).In this small, convoluted DR response network, DAF-16 and ceTOR/LET-363 *Address correspondence to this author at the Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China; Tel: 86-21-54920458; Fax: 86-21-54920451; E-mail: jdhan@picb.ac.cn These authors contributed equally to this work.", + "\t\n\nIn vitro and animal studies have reported a decline in autophagy with age [26,36,[40][41][42][43]; however, to our knowledge, only one other publication has reported an age-associated decline in expression of autophagy genes, which was carried out in a small number of human brain tissue samples [44].Overall, these findings for major components of core autophagy machinery and upstream regulators provide evidence for a transcriptional decline in autophagy gene expression with age in human monocytes.The identification of key genes contributing to a decline in autophagy are of great interest, as pharmacologic activation of autophagy has been linked with increasing lifespan in animal models, including mice [45].Further, dysfunctional autophagy is now widely implicated in pathophysiological processes of many age-related diseases such as cancer, Alzheimer's, diabetes, and cardiovascular diseases [46].However, longitudinal studies are necessary to validate the age-related transcriptional decline of autophagy gene expression in human monocytes, and to investigate the relationship between these age-related patterns and the development of age-associated diseases.", + "\tThe cell-autonomous theory on the\nother hand posits that individual cells are the targets of the aging process, via a timedependent increase in homeostatic dysfunction. The potential mechanisms include\nincreases in the production of reactive oxygen species, telomere shortening and, not\nsurprisingly, genomic instability. An implication of this theory is that long-lived cells in\nthe organism, such as neurons, muscle, and importantly stem cells, would be the\npredominant substrates of aging, while those cells that undergo rapid and continuous\nturnover would be removed before they could exert an effect on tissue function.", + "\tConcluding remarks and future perspectives\n\nAging research has rapidly expanded over the past two decades, with studies ranging from lifespan-extending [68,69,71].However, when their effect on cell death and senescence leads to stem cell loss and tissue degeneration, they might contribute to aging [66,67]." + ], + [ + "\tFurther evidence of age-related changes in stem cells include the finding that a\nhigher proportion of Thy-1loSca-1+Lin-Mac-1-CD4-c-kit+ cells from old mice are in\nS/G2/M phases of the cell cycle (Morrison, 1996), and the results of Henckaerts\net al. , who showed that the proliferative response of Lin-Sca-1+c-kit+ marrow cells\nto the early-acting cytokines KL, Flt3L and TPO, decreased dramatically with age\n(Henckaerts et al. 2002). As mentioned previously, the bone marrow niche is the optimal\nmicroenvironment for the growth and functional maintenance of HSCs (Moore\n2004; Nilsson et al. 2001).\t17\nAging Effects on Hematopoietic Stem Cells and Bone Marrow Niche\nAs discussed above, HSC expansion and transplantation is clinically\nimportant to treat patients with hematological and non-hematological disorders. It\nis also well known that cancer risk increases in older people (Balducci and\nExtermann FEB 2000). Therefore, understanding aging effects on hematopoietic\nsystem, especially on HSCs and their bone marrow microenvironment (niche),\nmay not only help to prevent malignant transformation, but also to determine\nefficacy of aging stem cells for transplantation (Pinto et al. 2003; Van Zant and\nLiang 2003) .", + "\t\n\nMost mammalian tissues can be described as being comprised of two major cellular components: stem or progenitor cells, which are responsible for regenerative capacity or repair after injury, and differentiated somatic cells, responsible for adult stem cell support and specialized tissue/organ functions.Based on this classification, two major mechanisms can account for tissue degeneration associated with age: loss of stem cell pool division potential (loss of regenerative capacity) and loss of differentiated somatic cell function, which directly leads to loss of organ function.Loss of differentiated somatic cell function can additionally indirectly affect adult stem and progenitor cells by altering the tissue microenvironment that is essential for stem cell support (the stem cell niche).In general, loss of stem cell pool division potential can occur through multiple mechanisms including stem cell senescence, death or dysfunction of the niche.One specific mechanism that can account for the loss of both stem cell and differentiated somatic cell function is the gradual accumulation of persistent DNA damage.Persistent DNA damage and its erroneous resolution *To whom correspondence should be addressed.Tel: +1 415 209 2042; Fax: 415-209-22232; Email: dbhaumik@buckinstitute.org 2007 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/)which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.include telomeric dysfunction (9)(10)(11) and somatic mutations (12), both of which increase with age; both also have been proposed to contribute to the loss of stem and differentiated somatic cell function with age (13,14).DNA damage accumulation in stem cells has been detected in mice and clearly contributes to the attrition of stem cell division potential during aging (15).Thus, it is likely that DNA damage contributes to aging by limiting stem cell division potential and by also interfering with somatic tissue functions, including stem cell niches.", + "\t\n\nA diminished capacity to maintain tissue homeostasis is a central physiological characteristic of ageing.As stem cells regulate tissue homeostasis, depletion of stem cell reserves and/or diminished stem cell function have been postulated to contribute to ageing 1 .It has further been suggested that accumulated DNA damage could be a principal mechanism underlying age-dependent stem cell decline 2 .We have tested these hypotheses by examining haematopoietic stem cell reserves and function with age in mice deficient in several genomic maintenance pathways including nucleotide excision repair 3,4 , telomere maintenance 5,6 and non-homologous end-joining 7,8 .Here we show that although deficiencies in these pathways did not deplete stem cell reserves with age, stem cell functional capacity was severely affected under conditions of stress, leading to loss of reconstitution and proliferative potential, diminished self-renewal, increased apoptosis and, ultimately, functional exhaustion.Moreover, we provide evidence that endogenous DNA damage accumulates with age in wild-type stem cells.These data are consistent with DNA damage accrual being a physiological mechanism of stem cell ageing that may contribute to the diminished capacity of aged tissues to return to homeostasis after exposure to acute stress or injury.", + "\tSeveral studies have shown\nthat the systemic milieu regulates stem cell decline during aging. Liang et al. showed\nthat HSCs have a reduced ability to home to the bone marrow and spleen after\ntransplantation into old versus young recipients (Liang et al. , 2005). Further experiments\ndemonstrated that the muscle stem cell niche adversely effects stem cell function as\nevidenced by the restoration of old stem cell regenerative potential upon exposure to a\nyoung systemic microenvironment (Conboy et al. , 2005; Conboy and Rando, 2005).\tSince stem cells\nare capable of self-renewal and produce progeny to replenish worn-out and damaged cells\nin aged tissues, the induction of stem cell senescence may compromise tissue renewal by\ndepletion of stem or progenitor cell pools and thus promote age-related pathologies. 6\nIt is apparent that the HSC compartment undergoes considerable age-related\nchanges, however it is not yet clear whether theses changes are intrinsic to the cells\nthemselves or whether they occur due to alterations in the hematopoietic\nmicroenvironment, commonly referred to as the HSC niche.\tHowever, studies do indicate that aged tissues have a diminished capacity to return to a\nhomeostatic state after exposure to stress or injury, therefore indicating a defect in stem\ncell function during the aging process. Since the HSC population provides an ideal\nmodel to study stem cell aging, it is necessary to elucidate the mechanisms of\nhematopoietic aging and expand the findings to other tissues and organ systems. Theories of Aging and Age Related Epigenomic Changes\nThere are two major theories of organismal aging: evolutionary and damage\nbased.\tWith\nthis in mind, it has been hypothesized that the aging or functional failure of tissuespecific stem cells, which fulfill this job, may limit tissue repair and renewal, therefore\ncontributing to overall organismal aging (Krtolica, 2005; Van Zant and Liang, 2003). Because of the unprecedented experimental model systems that are available for the\nexploration of HSCs, stem cell aging research in the field of hematology has been the\nsubject of extensive studies. Indeed, the hematopoietic system has served as an important\nmodel for advancing our understanding of stem cell biology and its association with\naging.\tIn view of the importance of stem cells for maintaining\nimmune function and in a broader sense tissue homeostasis and longevity, there is a\ncritical need to better understand the mechanisms involved in HSC aging. 17\nFigure 1.1 The HSC hierarchy. The HSC compartment can be functionally divided into three populations; long-term\nHSCs, which have extensive self-renewal capacity, short-term HSCs, which have limited\nself-renewal capacity, and multipotent progenitor cells which cannot self-renew and give\nrise to common lymphoid progenitors (CLP) and common myeloid progenitors (CMP).", + "\tIn other words, lower HSC proliferation results in a\nmore youthful stem cell, but poorer tissue regeneration, and\nconsequently an aged phenotype; this indicates that stem cell\nproliferation and tissue regeneration are nely balanced to\nmaximize longevity, so that cell cycle disruption results in an\nuncoupling of tissue and organismal aging from the aging of\nthe resident stem cell. Finally, three lines of evidence in our work indicate broad\nchanges in epigenetic regulation with age.\tIf the rejuvenating effect of stem cells were perfect, senescing cells would be\nreplaced indenitely; but even in highly regenerative tissues\nsuch as the skin, the gut, and the hematopoietic system, agerelated decline in function is well established [1]. Still unclear\nare the effects of aging on the stem cells themselves, which\ncould contribute to inferior tissue repair. Hematopoietic stem cells (HSCs) continuously replenish\nthe blood and immune system throughout life. Data from\nmice support an age-related decline in stem cell function [1],\nsuggesting that older HSCs are inadequate to cope with the\ndemands of blood production.", + "\tFurthermore, the differentiation potential of the HSC compartment\nappears to become skewed toward the myeloid lineage with age\n(26 28). As HSC have been shown to cycle (29), replicative stress,\neven in the absence of detectable telomere erosion (30, 31), may\nunderlie at least some of the age-related changes in HSC (32). Many traits affecting the hemopoietic stem and progenitor cell\ncompartments also change with age in a mouse strain-dependent\nfashion (2123, 3234) and have been implicated in organismal\nlife span (21, 3234). The responsiveness of LSK cells to TGF-2\nshowed mouse strain-dependent variation in young mice.", + "\tFurther evidence of age-related changes in stem cells include the finding that a\nhigher proportion of Thy-1loSca-1+Lin-Mac-1-CD4-c-kit+ cells from old mice are in\nS/G2/M phases of the cell cycle (Morrison, 1996), and the results of Henckaerts\net al. , who showed that the proliferative response of Lin-Sca-1+c-kit+ marrow cells\nto the early-acting cytokines KL, Flt3L and TPO, decreased dramatically with age\n(Henckaerts et al. 2002). As mentioned previously, the bone marrow niche is the optimal\nmicroenvironment for the growth and functional maintenance of HSCs (Moore\n2004; Nilsson et al. 2001).\t17\nAging Effects on Hematopoietic Stem Cells and Bone Marrow Niche\nAs discussed above, HSC expansion and transplantation is clinically\nimportant to treat patients with hematological and non-hematological disorders. It\nis also well known that cancer risk increases in older people (Balducci and\nExtermann FEB 2000). Therefore, understanding aging effects on hematopoietic\nsystem, especially on HSCs and their bone marrow microenvironment (niche),\nmay not only help to prevent malignant transformation, but also to determine\nefficacy of aging stem cells for transplantation (Pinto et al. 2003; Van Zant and\nLiang 2003) .", + "\tIntroduction\n\nThe regenerative potential of our body decreases upon aging.Regenerative tissues depend on specialized adult stem cells, thus aging in these tissues can be interpreted as signs of aging in somatic stem cells [1].Adult stem cells are characterized by the dual function to differentiate into different cell lineages and to selfrenew for maintenance of the stem cell pool.It is, however, still controversial if this self-renewal also includes juvenation or if adult stem cells are doomed to undergo aging upon each cell division.It is unclear if adult stem cells undergo functional and molecular changes, if their number decreases because of aging, or if aging is due to extrinsic environmental factors without any effect on the stem cell pool [2,3].\t\n\nThere is emerging evidence that aging is not purely a cell intrinsic process, but rather regulated by interaction with the cellular microenvironment.For example, Ju and co-workers have demonstrated that telomere dysfunction induces alterations in the microenvironment that affect aging of the hematopoietic system [55].In general, adult stem cells have a slow turnover and reside in specialized niches, protected from the environment and only a few are activated at a time [33,56].By keeping adult stem cells in a quiescent state, the stem cell niche might also play a crucial role in regulating replicative senescence.Strong experimental data for this hypothesis derives form serial transplantation experiments of HSC in mice.The reconstituting ability declines continuously within 4 to 5 transfers [57,58] and this decline is thought to be telomereindependent [59], although it has been reported that telomere length decreases by serial transplantation [60].Recently, Wilson and co-workers have demonstrated that there is a dormantfraction of HSC that divides only five times during the lifetime of mice and especially these dormant HSC posses repopulating activity upon serial transplantation [61].The stem cell niche could therefore play a central role in maintaining a dormant pool of HSC to prevent replicative senescence over the lifetime of the organism [62].\t\nThe regenerative potential diminishes with age and this has been ascribed to functional impairments of adult stem cells.Cells in culture undergo senescence after a certain number of cell divisions whereby the cells enlarge and finally stop proliferation.This observation of replicative senescence has been extrapolated to somatic stem cells in vivo and might reflect the aging process of the whole organism.In this study we have analyzed the effect of aging on gene expression profiles of human mesenchymal stromal cells (MSC) and human hematopoietic progenitor cells (HPC).MSC were isolated from bone marrow of donors between 21 and 92 years old.67 genes were age-induced and 60 were age-repressed.HPC were isolated from cord blood or from mobilized peripheral blood of donors between 27 and 73 years and 432 genes were age-induced and 495 were age-repressed.The overlap of age-associated differential gene expression in HPC and MSC was moderate.However, it was striking that several age-related gene expression changes in both MSC and HPC were also differentially expressed upon replicative senescence of MSC in vitro.Especially genes involved in genomic integrity and regulation of transcription were age-repressed.Although telomerase activity and telomere length varied in HPC particularly from older donors, an age-dependent decline was not significant arguing against telomere exhaustion as being causal for the aging phenotype.These studies have demonstrated that aging causes gene expression changes in human MSC and HPC that vary between the two different cell types.Changes upon aging of MSC and HPC are related to those of replicative senescence of MSC in vitro and this indicates that our stem and progenitor cells undergo a similar process also in vivo.\t\n\nThe regenerative potential diminishes with age and this has been ascribed to functional impairments of adult stem cells.Cells in culture undergo senescence after a certain number of cell divisions whereby the cells enlarge and finally stop proliferation.This observation of replicative senescence has been extrapolated to somatic stem cells in vivo and might reflect the aging process of the whole organism.In this study we have analyzed the effect of aging on gene expression profiles of human mesenchymal stromal cells (MSC) and human hematopoietic progenitor cells (HPC).MSC were isolated from bone marrow of donors between 21 and 92 years old.67 genes were age-induced and 60 were age-repressed.HPC were isolated from cord blood or from mobilized peripheral blood of donors between 27 and 73 years and 432 genes were age-induced and 495 were age-repressed.The overlap of age-associated differential gene expression in HPC and MSC was moderate.However, it was striking that several age-related gene expression changes in both MSC and HPC were also differentially expressed upon replicative senescence of MSC in vitro.Especially genes involved in genomic integrity and regulation of transcription were age-repressed.Although telomerase activity and telomere length varied in HPC particularly from older donors, an age-dependent decline was not significant arguing against telomere exhaustion as being causal for the aging phenotype.These studies have demonstrated that aging causes gene expression changes in human MSC and HPC that vary between the two different cell types.Changes upon aging of MSC and HPC are related to those of replicative senescence of MSC in vitro and this indicates that our stem and progenitor cells undergo a similar process also in vivo.\tDiscussion\n\nThe deterioration of the regenerative potential upon aging might be due to functional changes in adult stem cells.To test this hypothesis we have investigated differential gene expression in primary, human MSC and HPC derived from different age groups.In this study, we demonstrate for the first time age-related gene expression changes in human MSC and HPC and that there is a moderate but significant concordance in the expression profiles upon aging in vivo and replicative senescence in vitro.It needs to be pointed out, that chronological age and biological age do not necessarily coincide.Multiparametric assessment of biological age might be valuable in this context.Furthermore, MSC and HPC preparations are heterogeneous and it is conceivable that they represent a mixture of different aged or senescent subsets.Further research will be necessary to address age-related changes on a single cell level to investigate the heterogeneity of aging within cell populations.activating complex, polypeptide 5 (SNAPC5) and peroxisome proliferator-activated receptor gamma (PPARG) were age-repressed.Furthermore, we have validated age associated changes in HPC for 9 genes (B): S100 calcium binding protein A10 (S100A10); vimentin (VIM); myeloid-associated differentiation marker (MYADM); pim-1 oncogene (PIM1) and annexin A2 (ANXA2) were age-induced.Timeless interacting protein (TIPIN); myosin regulatory light chain interacting protein (MYLIP); lymphocyte transmembrane adaptor 1 (LAX1) and Early growth response 1 (ERG1) were agerepressed.Protocadherin 9 (PCDH9) was not amplified in HPC from elderly donors whereas interleukine 7 receptor (IL7R) was not amplified in young samples (not presented in the figure).Differential gene expression was always calculated in relation to the mean of young samples.The mean foldratio (6SD) is demonstrated for median aged and old donor samples.RT-PCR results (red) were always in line with microarray data (blue) for all genes tested.doi:10.1371/journal.pone.0005846.g003", + "\tFor instance, mice null for the repair\nprotein Ercc1 show progressive marrow failure resulting in a pancytopenia, while the\nmice exhibit several symptoms of premature aging (Prasher, Lalai et al. 2005). However,\nno studies to date have demonstrated conclusively that diminished DNA repair capacity\nof HSCs with age results in their functional impairment, much less a decreased ability to\nrepair DNA lesions with age. 10\nGenetic regulation of stem cell proliferation\n\nThese many ramifications of the proliferative nature of hematopoietic stem cells\nbegs the question of what are the key molecules regulating this vital feature." + ], + [ + "\tHowever, under diabetic conditions, AGEs generated by the exposure of proteins and lipids\nto high glucose levels crosslink ECM proteins, impair ECM degradation by MMPs and\nincrease cardiac stiffness, which together manifest as early diastolic dysfunction33,5254. AGEs can also promote the differentiation of fibroblasts into myofibroblasts, which\nproliferate and induce ECM dyshomeostasis by secreting profibrotic cytokines and matrix\nproteins. Furthermore, the altered cardiac mechanics lead to the release of other stimuli\nincluding transforming growth factor- (TGF), tumour necrosis factor (TNF), angiotensin\nII and various interleukins, which activate profibrotic responses in fibroblasts and\nmyofibroblasts55.", + "\t\n\nMuch work has focused on molecular features often observed with advanced age-cellular senescence, autophagy, oxidative stress, and epigenetic changes.Vascular remodeling, as a consequence of these features, is well documented leading to endothelial dysfunction and arterial stiffness.Although such features are also invoked in other conditions such as heart failure with preserved ejection fraction and valvular calcification, disentangling the key causal features suitable for therapeutic modulation remains elusive.", + "\t\n\nNonenzymatic glycation of proteins and lipids occurs with aging, a process that is accelerated in the setting of glucose dysregulation, such as diabetes mellitus [7].Advanced glycation end products (AGEs) formation has been implicated in a number of pathological processes associated with micro-and macrovascular diabetic complications [8][9][10].It has been demonstrated that the effects of AGEs are partially mediated through their interactions with cell surface receptor, the receptor for advanced glycation end products (RAGE) [11].The soluble form of RAGE (sRAGE) is a proteolytic cleavage product of RAGE, which has AGE-binding property but lacks the signaling cascade [12].In Caucasians without T2DM, sRAGE has been associated with decreased renal function assessed by estimated glomerular filtration rate (eGFR) or serum creatinine level [13][14][15].In Caucasian T2DM patients, sRAGE has been associated with albuminuria [16], decreased eGFR [17] and new or worsening kidney diseases and mortality [18].However, to date, only two studies reported associations of sRAGE level with renal function in Asians with T2DM [19,20].Although sRAGE is increasingly gaining importance as a biomarker in diabetic complications, it is not clear how sRAGE level is regulated and why it varies among studies.In addition, genetic studies of sRAGE remain very limited.", + "\t\n\nAdvanced glycation end-products (AGE) are the result of nonenzymatic glycation, which produces heterogeneous bioactive molecules, such as lipids, proteins, and nucleic acids [59].The accumulation of AGEs in aged tissues leads to several processes, such as inflammation, obesity, apoptosis, and other adverse processes related to ageing [47].These AGEs are detected by various techniques, such as gas chromatography, high-performance liquid chromatography, spectrometry, and immunochemical technique [60], which make them robust biomarkers that can be analyzed by different methodologies.", + "\t\n\nCritical areas of vascular aging research include the role of senescence, epigenetics, stress resilience, inflammation, macromolecular damage, proteostasis, mitochondrial and metabolic dysfunction, and impaired stem cell biology.The specific roles for cell-autonomous and noncell-autonomous mechanisms contributing to vascular aging need to be elucidated further.The role of signal transduction pathways linked to regulation of cellular energetics in the vascular aging process should be better defined.Future studies should also lead to improved understanding of the role circadian clocks to vascular aging.New studies investigating cellular heterogeneity in vascular aging are warranted.Stochastic macromolecular damage leads to regional variability in the presence of senescent cells, cells with altered metabolism, mitochondrial dysfunction, and increased ROS production.Such regional variability likely contributes to the focal development of vascular pathologies, ranging from atherosclerotic plaques to microhemorrhages.Single-cell gene expression analysis should facilitate better understanding of the pathophysiological role of functional heterogeneity.Finally, how environmental factors and lifestyle choice impact the vascular aging processes should be better understood.", + "\t\n\nThe characteristics of the second pathway include the formation of advanced glycation end-products (AGEs) from excessive imbibing of glucose [7].The AGEs via interaction with their receptor, RAGE, transduce a complex series of signaling events that result in cellular dysfunctions, thus generating an inflammatory response and reactive oxygen species (ROS), which in turn cause oxidative stress [7].Both in vitro and in vivo studies support the relevance of this pathway in the pathogenesis of diabetic nephropathy [7].The fact that several inhibitors of AGEs, such as pyridoxamine, LR-90 and KIOM-79, have been demonstrated to be beneficial in various murine models of diabetes emphasizes the role of AGE:RAGE interactions [8][9][10].Although these inhibitors may be effective in murine models, their efficacy certainly needs to be evaluated in diabetic nephropathy in humans.", + "\tAging is only, in part, the result of oxidative, free radical chemistry\n\nThe free radical theory of aging (Harman 1992) proposes that reactive oxygen is the major culprit in aging, leading to age-dependent oxidative modification, crosslinking and denaturation of proteins, with resultant loss of protein and enzyme structure and function.This theory has been expanded in recent years to include not only direct oxidation of proteins by reactive oxygen, but also the modification of proteins by Maillard reaction products, AGEs and ALEs (Thorpe and Baynes 1996).The majority of AGEs that are known to accumulate with age in tissue proteins are glycoxidation products, formed by combined glycation and oxidation reactions of precursors, such as glucose or ascorbate (Baynes 1991).In non-diabetic patients, levels of the glycoxidation products CML and pentosidine correlate with levels of methionine sulfoxide and o-tyrosine in skin collagen, indicating that these products are formed in parallel with one another (Wells-Knecht et al. 1997).Although oxidation appears to be important in the formation of AGEs and crosslinking of protein by glucose and ascorbate (Fu et al. 1994), some AGEs, such as pyrraline and crosslines, are formed non-oxidatively from glucose.The crosslines increase in lens proteins with age (Obayashi et al. 1996), so that oxidation is not essential for an age-dependent increase in crosslinking of protein by carbohydrates.In contrast to AGEs, ALEs require oxidative conditions for their formation -the first intermediate in ALE formation is a lipid peroxide, formed from a polyunsaturated fatty acid (PUFA) by an enzymatic or non-enzymatic autoxidation reaction involving molecular oxygen.The EAGLEs, CEL and MOLD, increase with age in collagen and crystallins, but cannot be classified as oxidative or nonoxidative since they may be formed either oxidatively during peroxidation of PUFA (Fu et al. 1996) or non-oxidatively from glyceraldehyde 3-phosphate or dihydroxyacetone phosphate formed during anaerobic glycolysis (Ahmed et al. 1997).Other modifications of amino acids, including deamidation, racemization and formation of hydroxykynurenine adducts are also age-dependent, non-crosslinking modifications of proteins.\tAging may be accelerated by inflammation and disease\n\nThe relationship between aging and age-related, chronic disease is complex.Healthy aging generally leads to a longer life, while chronic disease and associated inflammatory processes generally accelerate the aging process, i.e. shorten life span.The relationship between aging and chronic disease may be illustrated by diabetes, a disease in which the accumulation of AGEs in tissue proteins is accelerated by hyperglycemia.CML and pentosidine are biomarkers of normal aging of tissue collagens, and their accelerated accumulation in collagen in diabetes is de facto evidence that diabetes is a disease characterized by accelerated aging of collagen (Dyer et al. 1993).The acceleration of protein aging in diabetes is apparent, not only by the increase in AGEs, but also by increases in browning and fluorescence of collagen, and decreased solubility, decreased elasticity and increased thickness of basement membranes in diabetes (Baynes and Thorpe 1999).Notably, the rates of accumulation of other biomarkers, such as o-tyrosine and methionine sulfoxide in skin collagen, do not change significantly in diabetes (Wells-Knecht et al. 1997).Thus, the acceleration of chemical aging of collagen in diabetes is unbalanced or 'pathologic' in nature, apparently driven by the increase in circulating levels of oxidizable substrates (carbohydrates and lipids) (Baynes 1991(Baynes , 1999;;Baynes and Thorpe 1999a, b), rather than an increase in oxidative stress.Diabetes also increases the risk for cardiovascular disease, the major cause of mortality in the western world, while the increased risk for cataracts in diabetes may result from increases in both glycation and oxidative stress in the lens (Stevens 1998).", + "\tMG is elevated in the diabetic state and is\nthought to contribute to the development of diabetic complications, particularly through the\nformation of AGEs (60). AGE modification of vascular extracellular matrix proteins causes\n\nW\n\ncross-linking, which alters elastic properties and traps low-density lipoprotein in the vessel wall\n(60). Upon ligating RAGE, AGEs cause endothelial dysfunction, activation of NF-B, release of\n\nIE\n\npro-inflammatory molecules, and formation of vessel-damaging ROS (60). Through detoxifying\nMG, GLO1 is thought to protect against diabetic complications.", + "\tIt is based on the tendency of glucose to\nundergo oxydation in the presence of traces of heavy metal\nions, thus creating reactive ketoaldehydes, hydrogen peroxyde, and free radicals. It is clear now that the rearrangement of Schiff bases, Amadori products and/or AGEs is\naccompanied by generation of reactive oxygen species that\ncause conformational changes and fragmentation of the\nglycated proteins (11, 12). The proteins modified by AGEs\nare shown to be toxic, immunogenic, and capable of triggering cellular injury responses after binding to specific\nreceptors (1315).\tTaking into consideration that glycation is a slow process, it has always been regarded as typical for the longliving organisms and as affecting the long-living proteins\n(haemoglobin, crystalline, etc.)only. Surprisingly, our\nrecent studies indicated that glycation takes place also in\nE. coli and affects both the host bacterial and recombinant\nproteins (16, 17). Once started in vivo, glycation can not\nbe stopped after isolation and purification of the protein. Accumulation of AGEs continues even when pure protein preparations are stored in deep frozen solutions. A\ngreat number of studies have been dedicated to the search\nfor inhibitors of glycation.\tMullarkey CJ, Edelstein D, Brownlee M (1990) Free radical generation by early glycation products: a mechanism for accelerated atherogenesis in diabetes. Biochem Biophys Res Commun\n173:932939. Sakurai T, Tsuchiya S (1988) Superoxide production from nonenzymatically glycated protein. FEBS Lett 236:406410\nWendt T, Tanji N, Guo J, Hudson BI, Bierhaus A, Ramasamy R,\nArnold B, Nawroth PP, Yan SF, DAgati V, Schmidt AM (2003)\nGlucose, glycation, and RAGE: implications for amplification of\ncellular dysfunction in diabetic nephropathy. J Am Soc Nephrol\n14:13831395. Wautier JL, Schmidt AM (2004) Protein glycation: a firm link to\nendothelial cell dysfunction. Circ Res 95:233238.", + "\t\n\nFigure 15: Aspects of hyperglycemia-related vascular cell dysfunction.Hyperglycemia-induces a range of pathways in cells such as endothelium, and these include the polyol pathway, reactive oxygen species (ROS) formation, and advanced glycation endproducts (AGEs) formation.Excess glucose in endothelial cells enters polyol pathway; the electron donors like reduced nicotinamide adenine dinucleotide (NADH) and Flavin adenine dinucleotide (FADH2) accumulate in the mitochondria, thus affecting the electron transport chain; the excess electrons increase ROS in mitochondria; ROS triggers accumulation of AGEs; ROS and AGEs create mitochondrial DNA damage and mitochondrial dysfunction; protein kinase C (PKC) and AGE mediated activation of nuclear factor kappa B (NFB) activate the expression of inflammation proteins, tumor suppressor p53, and inducible nitric oxide synthase (iNOS); increased nitric oxide (NO) by iNOS is highly reactive with superoxide anions; the peroxynitrite thus generated acts as a strong oxidant and completes the vicious cycle of oxidative stress by increasing ROS production; accumulation of AGEs also increases ROS production independent of glucose levels\tM A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 50\n\nglycation and lipoxidation end-products and upregulation of the receptor for AGEs (RAGE) has a key role in the hyperglycemia-induced activation of Mller glia and downstream cytokine production in the context of diabetic retinopathy (Berner et al., 2012;Curtis et al., 2011;Yong et al., 2010;Zong et al., 2010).Diabetes has also been reported to accelerate death of Mller glia (Feenstra et al., 2013;Hammes et al., 1995), an effect which has recently been linked to the disruption of retinal vascular integrity and the induction of neural cell dysfunction and death (Shen et al., 2012).A schematic diagram summarising how Mller glia changes are believed to contribute to the sight threatening complications of diabetic retinopathy is presented in Figure 11.Apart from the Mller cells, activated microglial cells adjacent to the vessels also appear to have a key role in vasoregression, the vascular hallmark of the early stages of diabetic retinopathy in both animal models (McVicar et al., 2015) and diabetic patients (Scott et al., 2014b).", + "\tTaking into consideration that glycation is a slow process, it has always been regarded as typical for the longliving organisms and as affecting the long-living proteins\n(haemoglobin, crystalline, etc.)only. Surprisingly, our\nrecent studies indicated that glycation takes place also in\nE. coli and affects both the host bacterial and recombinant\nproteins (16, 17). Once started in vivo, glycation can not\nbe stopped after isolation and purification of the protein. Accumulation of AGEs continues even when pure protein preparations are stored in deep frozen solutions. A\ngreat number of studies have been dedicated to the search\nfor inhibitors of glycation.\tMullarkey CJ, Edelstein D, Brownlee M (1990) Free radical generation by early glycation products: a mechanism for accelerated atherogenesis in diabetes. Biochem Biophys Res Commun\n173:932939. Sakurai T, Tsuchiya S (1988) Superoxide production from nonenzymatically glycated protein. FEBS Lett 236:406410\nWendt T, Tanji N, Guo J, Hudson BI, Bierhaus A, Ramasamy R,\nArnold B, Nawroth PP, Yan SF, DAgati V, Schmidt AM (2003)\nGlucose, glycation, and RAGE: implications for amplification of\ncellular dysfunction in diabetic nephropathy. J Am Soc Nephrol\n14:13831395. Wautier JL, Schmidt AM (2004) Protein glycation: a firm link to\nendothelial cell dysfunction. Circ Res 95:233238.\tIt is based on the tendency of glucose to\nundergo oxydation in the presence of traces of heavy metal\nions, thus creating reactive ketoaldehydes, hydrogen peroxyde, and free radicals. It is clear now that the rearrangement of Schiff bases, Amadori products and/or AGEs is\naccompanied by generation of reactive oxygen species that\ncause conformational changes and fragmentation of the\nglycated proteins (11, 12). The proteins modified by AGEs\nare shown to be toxic, immunogenic, and capable of triggering cellular injury responses after binding to specific\nreceptors (1315).", + "\tVascular endothelial dysfunction. In diabetes, endothelial dysfunction is linked to the accumulation of toxic lipids 90 , AGEs 91 and/or aggregated proteins 59 in the vasculature.Proteinaceous deposition on blood vessel walls damages endothelial cells 59,91 , increases the production of reactive oxygen species (ROS) 92,93 and impairs production of vasodilatory substances 92 , which results in a reduced cerebral blood flow.Stalled blood flow can lead to neurovascular uncoupling and hypoxic neuronal injury [92][93][94] .Elevated ROS production can further damage cellular structures and activate matrix metalloproteinases, inducing cytoskeletal reorganization and vascular remodelling 93 .Cytoskeletal reorganization affects the stability of tight junction proteins, resulting in increased capillary permeability, depletion of energy resources and altered neural viability 92,93 .", + "\t\n\nAdvanced glycation end products (AGEs) are a heterogeneous group of macromolecules that are formed by the nonenzymatic glycation of proteins, lipids, and nucleic acids.Overproduction of AGEs is considered the most important pathophysiological mechanism that induces diabetic complications (Semba et al. 2010).On one hand, AGEs mediate intracellular glycation of mitochondrial respiratory chain proteins and increase ROS levels, thus triggering oxidative stress (Coughlan et al. 2009) and endoplasmic reticulum stress (Piperi et al. 2012).On the other hand, binding of AGEs with receptors for advanced glycation end products (RAGEs) activates the AGE signalling axis to induce activation of NF-KB signalling and JAK/STAT signalling, which upregulate inflammatory cytokines and adhesion molecules (Basta 2008;Basta et al. 2004).The evidence indicates that exposure to AGEs is connected with the risk of adverse ageing-related outcomes.Akt1, Bsk, and P38b have been found to be crucial in the regulation of the AGE-RAGE-signalling pathway.Transforming growth factor beta (TGF-beta) is a major growth factor in joints that is crucial in maintaining chondrocyte homeostasis.However, the TGF-beta-signalling pathway changes with ageing, resulting in an age-related decline in the anabolic response that favours hypertrophy of chondrocytes and the development of osteoarthritis (Baug et al. 2014).In addition, Upadhyay et al. also reviewed the important role of TGF in the developmental processes of D. melanogaster and the role of TGF in regulating hormones, neurons and innate immunity (Upadhyay et al. 2017).Therefore, ageing-induced TGF-beta dysregulation is associated with deleterious effects on longevity and ageing itself.Dpp, Mad, and S6k are functionally crucial in the TGF-beta-signalling pathway.", + "\tIntroduction\n\nIn individuals with diabetes, nonenzymatic glycation of proteins leads to the formation of advanced glycation end products (AGE) and this process occurs at an accelerated rate in chronic hyperglycaemia 1 , and also the levels are found to be increased in complications of diabetes, such as diabetic retinopathy (DR). 2 AGE induces a variety of pathological changes, such as increased basement membrane thickening, arterial stiffness, and glomerular sclerosis. 3,4AGEs bind to a specific receptor known as receptor for advanced glycation end products (RAGE).RAGE is expressed in many of the cell types, such as the endothelial cells, monocytes, and lymphocytes, including the beta cells of the pancreas.RAGE-mediated signaling leads to the activation of transcription factors, such as NF-kB, AP-1, and STAT-1, 5,6 the adhesion molecules VCAM, ICAM, and tissue factor, 7,8 which promote a procoagulant state in the microcapillaries of the retina.This results in a hypoxic state that leads to the initiation of the angiogenic process in proliferative DR." + ] + ], + "task_id": [ + { + "task_id": "A05D259409652DBA4BBB171E44BC0E4A" + }, + { + "task_id": "92D5CE6EE0709DACC5A0B1DAFC050200" + }, + { + "task_id": "82159196857E23B681446BAEAD1E37B8" + }, + { + "task_id": "62833A83C24DBF2F02AB95C0D6E00814" + }, + { + "task_id": "0BF2D6A0BF2A7B5B35D42D578BF25E9E" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_aging_3.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_aging_3.json new file mode 100644 index 0000000..a9cecd0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_aging_3.json @@ -0,0 +1,99 @@ +{ + "question": [ + "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?", + "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?", + "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?", + "How do changes in the gut microbiome composition correlate with aging and longevity?", + "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" + ], + "answer": [ + "Immunosenescence, the age-related decline in immune efficacy, affects both the innate and adaptive immune systems. This decline compromises the body's defense against pathogens, leading to increased susceptibility to infections and diseases. Additionally, aging is associated with 'inflamm-aging,' a state of chronic sterile inflammation that can promote age-related diseases and functional decline. Changes in the immune system also include a decrease in the expression of genes associated with key immune functions, such as phagocytosis in macrophages, which can lead to dysfunction of innate immune cells. Furthermore, the accumulation of apoptosis-resistant cells in the elderly can lead to dysfunctional immune responses.", + "Advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging by integrating and analyzing large and diverse datasets generated from genomic, functional, phenotypic, and lifestyle data. These technologies can predict age, forecast potential diseases in aging, and contribute to personalized medical treatment. Machine learning algorithms can also be used to assess the importance of specific factors in aging, predict future outcomes, and identify potential biomarkers for age-related diseases. Furthermore, they can help in the interpretation of complex omics data sets, which contain vast measurements of potential candidate markers.", + "Recent discoveries have shown that systemic factors, including circulating microvesicles, play a significant role in aging phenotypes. For instance, age-related alterations in vasoprotective endocrine factors, such as growth hormone, IGF-1, and estrogens, have been found to regulate multiple aspects of vascular aging processes. Studies using heterochronic parabiosis in mice have demonstrated the impact of circulating factors on aging phenotypes. Additionally, there is initial evidence that antigeronic factors present in young mice can rejuvenate microvascular network architecture in aged mice. However, the exact nature of these antigeronic circulating factors remains unknown. Progeronic circulating factors, which increase with age and impair tissue homeostasis, have also been identified. Further studies are needed to identify additional progeronic and antigeronic factors and their impact on aging.", + "Changes in the gut microbiome composition correlate with aging and longevity in several ways. Studies on centenarians and supercentenarians have shown that the microbiota adapts to the physiological changes of the long aging process, promoting health and survival. The concentration of certain bacteria, like Bacteroidetes, increases with age, while others like Actinobacteria decrease. Age-related decrease in microbiota diversity can lead to larger populations of certain microbial species, potentially increasing the chances for the evolution of novel, potentially pathogenic strains. These changes can contribute to increased frailty and development of diseases during the late stages of life. However, a healthy microbiota, characterized by the presence of bacterial compounds like Christensenellaceae, Akkermansia, and Bifidobacterium, has been linked to longevity.", + "The key molecular mechanisms through which caloric restriction extends lifespan across different species include signaling through the insulin-like growth factor pathway, chromatin regulation by sir2, and oxidative damage. Caloric restriction also increases the response to oxidative stress and reduces the shortening of telomeres in chromosomes, which directly intervenes in the repair of DNA damage. Additionally, it affects nutrient-sensing pathways such as insulin/insulin-like growth factor (IGF-1) signalling and target of rapamycin (TOR) signalling." + ], + "contexts": [ + [ + "\t\n\nOn the other hand, a direct relationship exists between physiological aging and increasing incidence of chronic inflammatory diseases.In its acute form, inflammation acts as a protective mechanism in response to pathogen invasion or tissue damage and helps to restore physiological integrity and function.However, in its chronic form, inflammation can exert detrimental effects on the cellular as well as the organismic level.Chronically inflamed tissue is characterized by infiltration of immune cells, neovascularization, fibrosis, and often tissue damage and necrosis [3].The innate immune system, especially the mononuclear phagocyte system, is the most important mediator of chronic inflammation.Monocytes originate from the myeloid hematopoietic cell lineage in bone marrow.In the blood stream, monocytes are recruited by specific stimuli into different tissues, where they differentiate into phagocytic Oxidative Medicine and Cellular Longevity macrophages.Macrophages participate in the killing of invading microorganisms and emerging tumor cells through the production of reactive oxygen or nitrogen species (ROS and RNS).In addition, macrophages secrete cytokines, which play a key role in the regulation of multiple immune functions, especially inflammatory responses [3].During aging, the continuous pressure on the immune system caused by repeated antigen stimulation, such as infections, food antigens, allergens, and self antigens, leads to an increase in activated cells and secretion of proinflammatory cytokines, such as TNF [4].These circulating proinflammatory factors may keep the immune system in a state of chronic lowlevel activation, a phenomenon described as \"inflammaging\" [5,6].Eventually, this causes \"immunosenescence,\" that is, an age-related decline in the capacity of adaptive immunity, consisting of more specific responses carried out by B and T cells [7].Thus, with advanced age, the immune system undergoes a gradual remodeling in the attempt to reestablish a new balance that assures survival, however, favoring the development of chronic inflammatory conditions [5,6,8,9].", + "\tThe Neuroimmune System Upon Aging\n\nThe age-associated synaptic dysfunction can also be a consequence of alterations in astrocytes and microglia, as the aging process has also been described as inflammaging, a status of chronic inflammation that contributes to the pathogenesis of neurodegenerative diseases [174].Recent data further suggest an important role of the immune system in regulating the progression of brain aging and neurodegenerative disease.This can be seen as a cause-or-consequence dilemma: do immune and inflammatory pathways become hyperactivated with age and promote degeneration or, instead, immune responses fail to cope with age-related stress and may contribute to disease [175]?", + "\tAging is one of the inevitably dominant risk associated with many diseases. Several biological factors contribute to this etiology which include loss of telomeres, stem cells activity and metabolism, escalation of environmental and biological stress, dysfunctioning of various micro-and macromolecules, and cell cycle and weakening of immune system (Franceschi et al., 2018).In case of cellular and molecular damage before elderly age, injury is healed to maintain the hemostasis.Nonetheless, with aging, repair mechanism is slowed or completely halted, leading to number of pathologies (Cortopassi, Gurung, & Pinto-Plata, 2017).", + "\t\n\nimmunity can become hyperactivated, exacerbating the age-related damage caused by innate immune responses [33].The risk of collateral damage by the adaptive immune system also potentially increases with age via autoimmunity factors, but this is believed to be counteracted by a parallel rise in self-protective mechanisms [42].Overall, the collateral damage inflicted by the innate immune system over the course of a long life is likely to be greater than that caused by adaptive immunity.\t\n\nThe damage caused by the ageing adaptive and innate immune systems gives us insights into how these different arms of the immune system may influence longevity.In general, adaptive immune function diminishes with age, whereas innate immune function is maintained [34,[43][44][45][46]. Whilst this may initially suggest that the innate immune system withstands the test of time better than the adaptive immune system, a chronic stimulation of innate immunity underpins this pattern [35].Innate immune cells become increasingly proinflammatory with age [46,47] and trained", + "\t\n\nThe increased expression of genes involved in immune response and inflammation observed in the colon of the 21-month-old mice points to an affected immune system in this part of the intestine of aging mice.This observation is in agreement with the fact that changes in the immune system are one of the hallmarks of the aging body.Immunosenescence is the functional decline of the adaptive immune system brought on by natural aging whereby protection against infection by pathogens and the effectiveness of vaccination decline [45,46].The second aging-induced change in the immune system is called inflammaging which is characterized by a lowgrade chronic inflammation process that contributes to the pathogenesis of many age-related diseases [47][48][49].A large variety of cells with a defense function are present especially in the lamina propria and the submucosa of the intestine accomplishing immune protection via the innate as well as by the adaptive immune response.Interestingly, our microarray and Q-PCR data clearly show that activity of both branches of the immune system is enhanced in response to aging exclusively in the colon but not in the small intestine of old mice.Expression levels of well-established pro-inflammatory cytokines like IFN, TNF, IL6 and IL1 turned out to be extremely low in the colon of both old and young mice and below the threshold of our microarray analysis.These low expression levels are probably due to the fact that these cytokines are predominantly produced by immune cells in the mucosa which is a rather low percentage of cells in relation to all cells present in the intestinal tissue.Q-PCR analysis confirmed the very low basal expression levels of these pro-inflammatory cytokines, yet a weak but significant induction of IFN TNF and IL-1 in the colon of aging mice was observed.This result suggests that low-grade inflammation might be present in the colon of the aging mice in our study, although it should be noted that no altered expression of a number of established inflammation markers like Tolllike receptors (TLRs), C-type lectin receptors (CLRs) and retinoic acid-inducible receptors (RLRs) [50] was detectable.", + "\tIntroduction\n\nAgeing of the immune system (immunosenescence) contributes to the increased susceptibility of the elderly to infectious disease and to the poor outcome of vaccination.Defence against pathogens is compromised mainly because of changes in adaptive immunity mediated by T and B lymphocytes; however, all components of the immune system are affected (Fig 1).Dissecting the crucial alterations responsible for dysfunctional immunity in old age will facilitate the development of rational interventions to reconstitute appropriate immune function.Given the increasing proportion of elderly people in most countries and their disproportionate consumption of health-care resources, this issue is rapidly gaining in importance.The meeting, which was dedicated solely to studies of immunosenescence, filled two days with the 'A to Z' of immunity, covering topics ranging from development to senescence, innate immunity to adaptive immunity, and genes to environments, in organisms ranging from mice to monkeys and humans.Understanding and eventually modulating immune dysfunction in the elderly now beckons.\tClinical implications of immunosenescence\n\nAs mentioned above, complications from acute infectious are likely to be more severe in the elderly owing to impaired innate immunity.However, questions remain concerning 'normal, healthy' ageing and the important clinical issue of responses to vaccinations in old age.In a mouse model of the highly relevant human pathogen influenza, the virus is cleared from the lungs more slowly in old animals, correlating with a delayed and decreased peak of cytotoxic T-cell production (D.Murasko, Philadelphia, PA, USA).Therefore, cellular responses are crucial for controlling the virus, but do not function adequately in old animals.Although there is an accumulation of memory cells (the clonal expansion referred to above), they are not solely responsible for this decrease in the virus-specific response.Both memory and naive T cells in old, but not young, mice are resistant to apoptosis, and do not 'make space' for new responses.In the mouse model, cell-transfer experiments showed that both the old environment and the old cells contributed to the problem-young cells did not deplete when transferred to an old environment and old cells did not deplete when transferred to a young environment.The factors inducing apoptosis resistance have not yet been identified; however, it is clearly important to do so and to search for them in humans.\tConclusions\n\nAll components of the immune system are altered as ageing proceeds (Fig 1 ); however, the T-cell and B-cell compartments seem to be particularly susceptible.The most severe clinical impact is probably a result of the loss of diversity in the TCR and B-cell-receptor repertoire, owing to the accumulation of dysfunctional cells, and decreased thymic and bone-marrow output.Several interventions discussed at the meeting could conceivably contribute to the restoration of appropriate immune function in the near future.\tLymphocyte development and ageing\n\nThe cells of the immune system turn over rapidly and therefore need constant replacement from the pool of haematopoietic stem cells (HSCs).If the HSCs themselves aged, it would compromise all downstream events that depend on their integrity, including production of immune cells and subsequent immune responsiveness (Rando, 2006).Evidence for age-associated alterations in the ability of HSCs to reconstitute the haematopoietic system of an animal derives from findings of increased self-renewal with age, resulting in an expansion of the HSC pool size even when transplanted into young animals (D.Rossi, Stanford, CA, USA).However, purified HSCs from old mice showed less activity on a per-cell basis and tended to generate more myeloid cells-for example, macrophages-than lymphocytes.Expression profiling of young and old HSCs revealed that genes mediating lymphoid fate and function were systematically downregulated, whereas myeloid-specification genes were upregulated, with age.The concerted nature of these changes suggests epigenetic involvement as a mechanism that contributes to HSC functional decline with age.There is also a gradual decline in the ability of murine HSCs to progress through the various stages of B-cell-differentiation (K.Dorshkind, Los Angeles, CA, USA).This reflects, in part, the microenvironmental changes involving altered production of interleukin 7 (IL-7) by stromal cells as they age (M.Cancro, Philadelphia, PA).B cells must also compete for the cytokine BLys (or B-cell activating factor (BAFF)), the receptor levels of which determine survival.Declining B-cell production in aged animals results in selective accumulation of marginal zone and memory B cells at the expense of the follicular pool of B cells.The follicular pool is responsible for producing protective immune responses to newly encountered pathogens, such as influenza H5N1.Loss of the declining stem-cell function, and the resultant decline of the follicular B-cell compartment, leads to enhanced infectious disease-related morbidity with ageing (J.Cambier, Denver, CO, USA).Hence, age affects both HSCs and the environment that determines their fate.\tInnate immunity\n\nSo, what are the age-associated changes that can be directly measured in macrophages, dendritic cells, neutrophils, natural killer (NK) cells and so on?These might be at least as important, if not more so, than the changes to adaptive immunity discussed above (Solana et al, 2006).The number and proliferation of a particular subset of 'natural' T cells with NK-cell and regulatory functions, bearing invariant V14J18 receptors (iNKT cells), is decreased in the elderly; however, whether these changes have any clinical impact is not yet known (R. Solana, Crdoba, Spain).Neutrophils from old people retain normal chemotaxis and superoxide-generation capacity, but are compromised in phagocytosis in the healthy elderly and more so in the traumatized elderly ( J. Lord, Birmingham, UK); these findings have important implications for infection in the elderly.Trauma, in the form of burn injury in mice, resulted in the death of old animals from infections that young animals were able to resist.This susceptibility of old mice correlated with higher levels of pro-inflammatory IL-6 and decreased T-cell function, and could be in part reversed by oestrogen treatment (E.J. Kovacs, Maywood, IL, USA).Dendritic cells-the essential bridge between innate and adaptive immunity-are similar in young and old people in terms of their response to cytokines (although those from the elderly secrete more IL-6 and tumour necrosis factor- (TNF)), surface phenotypes and morphology, whereas chemotaxis and, as with neutrophils, phagocytosis are impaired (S.Gupta, Irvine, CA, USA).Gene arrays indicate only a small number of differences between young and old dendritic cells, far fewer than in T cells.Nonetheless, functional impairment in antigen presentation was found, such that dendritic cells from young or old people stimulated naive CD8 cells equally well, but those from the elderly failed to stimulate CD4 cells appropriately.\t\n\nApoptosis-resistant cells that accumulate in old mice and humans-and fill the 'immunological space'-might be dysfunctional in several ways.In young mice, the number of T cells staining with soluble major histocompatibility complex (MHC)-peptide multimers carrying influenza epitopes was similar to the number of cells producing the antiviral and pro-inflammatory cytokine interferon- (IFN) on antigen stimulation.However, in old mice, the number of tetramer-positive cells exceeded the number of IFNproducers, indicating that some cells bearing antigen-specific receptors failed to respond appropriately to receptor ligation (H.Ertl, Philadelphia, PA, USA).This is similar to the situation in elderly humans, who have been found to accumulate large clonal expansions, primarily-and for unknown reasons-of cytomegalovirus (CMV)-specific CD8 cells (Pawelec et al, 2005).In the mice, this lack of reactivity was not due to poor antigen presentation by dendritic cells (Ertl).The reason for poor reactivity remains unknown; however, responses could be restored, in part, by vaccination using an adenovirus vector AdC68 that naturally infects chimpanzees rather than mice, as a way of improving immunizations by modifying the vaccine product.This might also be possible in humans by using better adjuvants for vaccination (E.Nagy, Vienna, Austria).Deciphering the mechanisms by which adjuvants enhance responses in order to design 'elderly-specific' vaccines will become increasingly important.This applies not only to infectious diseases but also possibly to vaccinating against cancer, as illustrated by differences in responses to anticancer immunizations in young and old mice.In a breast cancer model, preventive vaccination using DNA encoding certain cancer antigens was successful in protecting 90% of the young mice, but only 60% of the old mice, from developing metastases.This correlated with lower levels of IFN and IL-2 in old mice (C.Gravekamp, San Francisco, CA, USA).The production of IL-6, which is a potential inhibitor of vaccine-induced T-cell responses, was high in both young and old mice.Increasing IFN and IL-2, and depressing IL-6 production in the elderly, would therefore seem to be desirable.", + "\tAging and variability among immune cells\n\nHow and why the immune system becomes less effective with age are not well understood.Martinez-Jimenez et al. performed single-cell sequencing of CD4+ T cells in old and young mice of two species.In young mice, the gene expression program of early immune activation was tightly regulated and conserved between species.However, as mice aged, the expression of genes involved in pathways responding to immune cell stimulation was not as robust and exhibited increased cell-to-cell variability.", + "\t\nThe aging population is at a higher risk for age-related diseases and infections.This observation could be due to immunosenescence: the decline in immune efficacy of both the innate and the adaptive immune systems.Age-related immune decline also links to the concept of 'inf lamm-aging,' whereby aging is accompanied by sterile chronic inf lammation.Along with a decline in immune function, aging is accompanied by a widespread of 'omics' remodeling.Transcriptional landscape changes linked to key pathways of immune function have been identified across studies, such as macrophages having decreased expression of genes associated to phagocytosis, a major function of macrophages.Therefore, a key mechanism underlying innate immune cell dysfunction during aging may stem from dysregulation of youthful genomic networks.In this review, we discuss both molecular and cellular phenotypes of innate immune cells that contribute to age-related inf lammation.\t\n\nThe aging population is at a higher risk for age-related diseases and infections.This observation could be due to immunosenescence: the decline in immune efficacy of both the innate and the adaptive immune systems.Age-related immune decline also links to the concept of 'inf lamm-aging,' whereby aging is accompanied by sterile chronic inf lammation.Along with a decline in immune function, aging is accompanied by a widespread of 'omics' remodeling.Transcriptional landscape changes linked to key pathways of immune function have been identified across studies, such as macrophages having decreased expression of genes associated to phagocytosis, a major function of macrophages.Therefore, a key mechanism underlying innate immune cell dysfunction during aging may stem from dysregulation of youthful genomic networks.In this review, we discuss both molecular and cellular phenotypes of innate immune cells that contribute to age-related inf lammation.\tIntroduction\n\nThe human population is aging, which has led to the rise in prevalence of many so-called age-related diseases.Not only is the aging population much more susceptible to age-related diseases, they are also more susceptible to infections.For example, elderly individuals are at a higher risk of developing severe COVID-19 or complications from influenza infections [1,2].This increased chance of infection can be due to the decline of the function of the immune system, a phenomenon called 'immunosenescence' [3].Age-related changes in the function of the immune system are also accompanied by a chronic sterile inflammation, a mechanism dubbed 'inflamm-aging,' which is thought to promote age-related disease and functional decline [4].Inflamm-aging is associated with many different factors, most typically encompassing increases in pro-inflammatory cytokines tumor necrosis factor alpha [TNFa], interleukin 1 beta [IL1b] and interleukin 6 [IL6] [5].Although these cytokines may directly contribute to increased systemic inflammation.Age-related increase in genomic instability may itself also drive aspects of inflammaging.Indeed, re-activation of LINE-1 transposable elements during aging and in senescent cells has been proposed to drive an interferon response, thus contributing to sterile inflammation [20][21][22].In addition, chronic DNAdamage signaling itself, for instance in aged lymphocytes, may also render them more activation-prone through innate receptors even in the absence of infection [23].\t Immune decline is a hallmark of aging. Aging associates with a state of chronic sterile inflammation.\t Aging associates with a state of chronic sterile inflammation. Innate immune cells undergo widespread molecular and functional remodeling with aging.\t\n\nIn this review, we will focus on how innate immune cells act as key contributors to age-related inflammation (Figure 1).We will discuss both molecular and cellular phenotypes which have been described in the aging innate immune system, and how they could relate to the phenomenon of inflamm-aging and immunosenescence.\t\n\nImportantly, a key mechanism underlying innate immune cell dysfunction during aging may stem from dysregulation of youthful genomic networks.Indeed, aging is accompanied by widespread remodeling of transcriptional landscapes across tissues and cell types (reviewed in [33]).In addition, age-related inflammatory signatures at the transcriptional levels have been observed across species and tissues, suggesting that such 'omic' remodeling is a conserved aging response [34,35]." + ], + [ + "\t\n\nKnowledge of genetic interrelationship between the biomarkers of aging may lead to the discovery of a downstream common pathway that summarizes aging processes; the list of biomarkers should be as comprehensive as possible via incorporating other well-known systems involved in aging in addition to the musculoskeletal system.Further development of the pleiotropy-based approaches will be useful for other studies of multiple related phenotypes which employ genome-wide associations to decipher genetics in the absence of disease endophenotypes, which is the case of human aging.With the advent of these approaches, new candidate genes may emerge for further pursuit.In its turn, discovery of the \"phenome of aging\" may translate into innovative diagnostic and therapeutic interventions to improve the overall health of older men and women.", + "\t\n\nBiomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases.We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the transcriptome.We develop an ensemble machine learning method that predicts age to a median error of 4 years, outperforming previous methods used to predict age.The ensemble was further validated by testing it on ten progeria patients, and our method is the only one that predicts accelerated aging in these patients.", + "\t\n\nIt should be mentioned that although the objectives of those researchers sound encouraging and ambitious, the search for biomarkers of ageing for their application in the improvement of human health, and prevention of diseases related to ageing, will only increase the generation of data.The great part of the search for biomarkers has been as a result of the extensive studies of human cohorts, resulting in genomic, functional, phenotypic, and lifestyle data of the individuals studied (Table 13.1).Thus, due to the generation of these data and technological advances, possibly in the future, artificial intelligence programs will be able to reliably forecast the life of an individual, as well as the possible diseases that he may suffer in ageing; so these advances and discoveries will allow us to achieve a \"personalized medical treatment\" as a result of to the integration of biomarkers of ageing.\tMeg3\n\nDecrease in cell senescence [85] (continued) number of biomarkers that are candidates to determine human ageing.However, these biomarkers have considerable variability among different individuals because the ageing process has an intrinsic multicausal nature.So, a multisystemic integration of biomarkers to determine biological age is still reliably found.Currently, thanks to the different analyses performed using new technologies and new knowledge on the molecular basis, there are leading to the discovery of many Biomarkers classified according to their type and their modulation in ageing novel molecular markers.Some of these technologies are the omics techniques, such as metabolomics, proteomics or genomics, also induces data generation, offering an overview of new biomarkers of ageing.However, it remains to be clarified which markers can be an accurate, reliable predictor of ageing.Among the various studies carried out to solve these questions, the MARK-AGE study was a project supported by the European Commission.The main objective of this project was to carry out a population study of approximately 3200 subjects to identify a set of ageing biomarkers, which together with correctly established parameters, would measure the biology of an individual, compared to the result that would only have using a biomarker individually [72].\tIntegration of Biomarkers of Ageing\n\nBiomarkers of ageing allow estimating the biological age of an organism (Table 13.1) while providing information on their health status.Different studies are looking for the integrated use of multiple biomarkers, in order to make the estimation of health status more accurate.As we could see throughout this chapter, there are a large\t\n\nTo make significant progress in aging research, we urgently need molecular biomarkers for aging studies, particularly in humans.This chapter focuses on the inflammatory state, the markers of oxidative stress, and the hormonal profile which are the main functions that impact the development of aging and can be influenced by the gene and environmental variables in which human beings develop.", + "\tDiscussion\n\nMachine-learning can be applied as a systems biology approach, integrating multiple classes of biometric data to assess the importance of specific factors, while also predicting future outcomes.Whereas conventional assessments of disease identification exist, more detailed genomic and epigenomic testing is likely to reveal a comprehensive, systemic valuation of an etiology.To-date, studies have applied machine-learning algorithms in examining the physiological, biochemical, and/or genetic components of disease onset or progression [51].The advantage of our current study is through the assimilation of patient-matched data across a variety of critically impacted systems, providing an archetype for developing novel, descriptive, diagnostic measures through machine-learning algorithms that are specific for each disease type.By individually representing our datasets in Figs. 2, 3 and 4, we were able to reach more conclusive data in Fig. 5 by choosing the most predictive features for our final model.For the first time, a multi-omics, machine-learning approach was used to assess the progression and development of type 2 diabetes mellitus in a patient population, identifying potential biomarkers for cardiovascular risk and revealing the fundamental role of genetics in the pathology.\t\n\nIn the current study, machine-learning was used as a predictive tool to integrate cardiac physiological, biochemical, genomic, and epigenomic biomarker data in a patient-matched fashion and enable determination of type 2 diabetic status.In 50 patients, machine-learning algorithms revealed the interconnectedness between diabetic classification, mitochondrial function, and methylation status.Our study highlights how novel biomarkers can be used to augment existing diagnostic standards as well as provide new, and more precise, methods for identifying the development and severity of type 2 diabetes mellitus in potentially at-risk populations, such as those with prediabetes.While we examine physiological, biochemical, and molecular datasets using machine-learning algorithms, our goal was to understand which features possessed the best predictive accuracies and if these specific features could be used alone, or in conjunction, with HbA1c.The purpose for the inclusion of models that do not rise above 50% predictive accuracy was to contrast them against those models that do rise above 50% in the absence of HbA1c, to determine which biomarkers are the best overall predictors.\t\n\nThe quantity and diversity of omics-based approaches continues to expand.Convenience and increasingly inexpensive options for biometric-based valuations incite a growing demand for the incorporation and meaningful explanation of large and diverse patient datasets.The methodology outlined in this manuscript can serve as an archetype for the development and implementation of machine-learning to other disciplines seeking to evaluate disease progression.By using various health outcomes datasets, we were able to identify, and combine, the most prominent biomarkers into an accurate predictive algorithm engineered around 50 patients.While we have identified specific genetic features that are highly predictive in 50 patients, as a much larger patient population is applied to this model, the prioritization of other features is likely to occur, enhancing the diagnostic potential for the individual diabetic or prediabetic patient.Indeed, this is the advantage of using machine-learning models, in that they continue to learn and develop more accurate predictions as the number of features and sampled population grows.\tMolecular pathogenesis and machine-learning\n\nWhile clinical practice has recently experienced a surge in deep learning applications used for non-invasive imaging [52], implementing machine-learning algorithms to the fundamental biochemistry and cellular and molecular processes of the body is now only blossoming.Onset and progression of type 2 diabetes has been traditionally measured through blood glucose levels, but, the multifaceted aspects of the disease could create variability in prognosis between vastly different demographic and ethnic groups.Owusu Adjah et al. [14] recently identified BMI as a risk factor for determining ethnic group disposition to type 2 diabetes mellitus.Specifically, the relationship between BMI and increased incidence of diabetes mellitus is non-linear; some groups, such as South Asian populations, were more disposed to developing the disease even at lower BMIs.While the current Fig. 6 Overview of machine-learning pipeline implementing biological variables across a spectrum of gathered information.From the patient population undergoing coronary artery bypass graft surgery (CABG), physiological parameters (demographics, health reports, etc.) and atrial tissue were used for subsequent analyses.From cardiac tissue genomic (mitochondrial DNA), epigenomic (TFAM promoter CpG methylation), and biochemical (nuclear and mitochondrial function) were assessed.Cumulatively, the biological data was processed through tree ensembles in SHAP and validated through CART analysis with tenfold cross validation.Using these machine-learning algorithms, graphical depictions and biomarker feature importance are able to be derived, allowing for prediction of the onset and progression of diabetes.Ultimately, by using biological data at the genomic and epigenomic level, it allows for precision medicine approaches and more personalized diagnostics and prognostics.TFAM: transcription factor A, mitochondrial; mtDNA: mitochondrial DNA; CpG: cytosine nucleotide followed by a guanine nucleotide; CART: Classification and Regression Trees; SHAP: SHapley Additive exPlanations manuscript examines cardiovascular tissue, other less invasive approaches have been used to apply machinelearning algorithms.By retrieving blood from the basilica vein, circulating biomarkers were examined for their role in predicting early recurrence of atrial fibrillation following cryoballoon ablation [53].Support vector machines confirmed that decreased levels of creatine-kinase (CK-MB) and Troponin T (TnT) were associated with increased early recurrence of atrial fibrillation following cryoballoon ablation.Additionally, a unique, non-invasive approach for potentially diagnosing type 2 diabetes in patients was performed through the examination of toenails.Carter et al. [54], through a variety of machine learning algorithms, focused on 22 elements, including aluminum, cesium, nickel, vanadium, and zinc, and was able to get an AUC of 0.90 when predicting diabetic status using a random forest model.Similar to parts of the aims of this study, other groups have attempted to use machine learning to separate diabetic and non-diabetic patients without the inclusion of blood glucose or HbA1c [55].In a testing set of 13,700 patients from the Luzhou, China region, random forest machine-learning algorithms provided a 0.7225 accuracy when predicting diabetic status from physical examination data in the absence of blood glucose [55].Also using a random forest model, Tang et al. [56] revealed how CpG island methylation data, combined with microRNA expression profiles, can be instrumental in cancer pathogenesis; implementing this two-feature selection process, they were able to identify the best tissue specific features, ultimately allowing for the identification of the originating tissue where tumor progression began.In a similar fashion, the machine-learning algorithm HeteSim [57], which examines heterogeneous datasets and calculates their relatedness, was employed in ascribing how gene profiles can be related to phenotypic outcomes, specifically in the validation and prediction of genes classified within major diseases [58].", + "\tWhat do chemical biomarkers tell us about aging? Aging is not a homogeneous process\tThe nature of chemical biomarkers of aging\n\nCentral to the study of chemical theories of biogerontology is the definition of biomarkers of the aging process, chemical 'handles' that can be used to assess the progress of aging and the effectiveness of anti-aging strategies.As it turns out, most of the age-biomarkers measured today are products of non-enzymatic chemistry.Living organisms are complex mixtures of reactive chemicals, including dietary components, metabolic intermediates, side-products of metabolism, xenobiotics, drugs, etc.Reactions between the constituents of this mixture occur at random throughout the body, but evidence of the role of cumulative non-enzymatic chemistry in aging is most apparent in long-lived proteins, such as lens crystallins and tissue collagens.The increase in post-synthetic chemical modifications of crystallins with age results in an agedependent increase in brown color and fluorescence of lens proteins.These chemical modifications are associated with aggregation, crosslinking and insolubilization of lens proteins, leading gradually to the development of cataracts (Hoenders and Bloemendal 1983;Harding et al. 1989).Similar changes occur in collagens (Bailey et al. 1998) leading to decreased elasticity of the extracellular matrix, resulting, for example, in the age-dependent stiffening of tendons Dilysine crosslink", + "\t\n\nPeople of the same chronological age have different aging states, which can be monitored using various biomarkers (Belsky et al. 2015).These markers are usually measurable indicators of a particular outcome or source of aging, such as phenotypical measures like frailty and molecular measures like DNA methylation dynamics (Schumacher et al. 2021;Lpez-Otn et al. 2023).Although informative, they are not always quantitatively predictive of an individual's true biological age, nor are they easy to obtain.The advancement of high-throughput screening platforms and extensive longitudinal studies has greatly facilitated the search for new noninvasive and quantitative biomarkers of aging.For instance, highthroughput sequencing allows unbiased multiomics profiling of DNA, RNA, and epigenetic changes during aging, providing a comprehensive view of senescence at tissue and single-cell levels (Solovev et al. 2020;Aging Atlas Consortium 2021).These omics data sets contain vast and noisy measurements of potential candidate markers and, consequently, require carefully designed computational models to identify and extract predictive signals from the data.However, construction of such models is often highly degenerate, yielding little overlap of identified biomarkers between studies and thus making results difficult to interpret (Thompson et al. 2018;Galkin et al. 2020).\t\n\nMost of the existing omics-based aging clocks have been constructed using data from bulk tissues, which neglect the variations in cell compositions and cell-to-cell aging heterogeneity.To gain a more detailed and nuanced view of cell type-specific molecular changes during aging, several studies have applied machine-learning models to single-cell transcriptomics and DNA methylation data (Trapp et al. 2021;Buckley et al. 2023).Despite their success in predicting chronological age within specific training contexts, these clocks are constrained by their applicability to a limited number of cell types and tissues.Their generalizability to other cell types and disease data, particularly in cases with ambiguous cell type identities, remains uncertain.Additionally, problems like data sparsity and batch effects are more pronounced in single-cell omics data, further complicating the identification of consensus aging markers and the interpretation of model results.Furthermore, as chronological age is often the only available measure of biological age, it becomes critical to determine whether the features learned from single-cell omics data can capture other dimensions of biological aging.", + "\t\n\nEach of these criteria deserves some amplification.A biomarker validation program would start with a list of candidate biomarkers, each known to be age-sensitive (by cross-sectional and/or longitudinal analyses) in adults.By hypothesis, some of these traits would reflect interindividual differences in the aging process, but each would also be sensitive to genetic and nongenetic factors that also vary among individuals, statistical \"noise\" that would interfere with the extraction of the \"signal\" attributable to aging itself.A correlation between age-sensitive immune parametersfor example, T-cell proliferation and T-cell cytokine production-would be relatively unhelpful in evaluating each of these parameters as potential biomarkers of aging, because the two assays are closely related and likely to be influenced by many factors unrelated to aging (e.g., recent infection, vaccination history, polymorphisms in immune system genes).However, a correlation between T-cell proliferation and, for example, muscle strength, or reflex speed, or lens protein cross-linking, or age at menopause, would be difficult to attribute to any obvious metabolic or pathophysiological mechanism other than linkage to some fundamental aging rate that might by hypothesis retard or accelerate changes in a wide range of age-sensitive traits.", + "\tMultiomics technology\n\nThe broad diversity of omics biomarkers that have been used to assess biological responses provides new opportunities to understand the impact of the environment on the risk of age-related diseases.For example, the multiomics analysis and integration method produces a priority list of multiple sets of biomarkers, which together reflect the molecular responses of the exposome.Each of these data warrants integration into a biomarker panel to aid physicians in developing age-related disease diagnoses and prognoses [78].", + "\t\n\nOverall, demonstrating that a particular intervention is affecting human aging, as done in model organisms, is virtually impossible.Interventions, including drugs, emerging from basic research on aging will probably target specific age-related pathological conditions and/or dysfunction.Subsequent studies of health biomarkers and multiple age-related diseases may reveal broader effects.Success in animal models or short-term human studies may be sufficient to convince potential patients of the usefulness of particular dietary supplements or approaches, as exemplified by those voluntarily undergoing CR (http://www.crsociety.org/),which can serve as basis for further studies (Soare et al., 2011).\t\n\nTo facilitate target gene prioritization, a number of additional approaches may be employed.For example, in silico studies of transcriptional regulation can allow the identification of upstream regulators (for review, see de Magalha es et al., 2010).Furthermore, an emerging approach to study the complex interactions between the multiple components of biological systems is network biology (Baraba si et al., 2011).Given the complexity of aging, network approaches may be particularly suited to identify crucial regulators of its modulation by the environment.For instance, knowing the protein-protein interaction network of candidate proteins allows the identification of hubs, proteins with a large number of interactions, which tend to be more biologically relevant (Fig. 3).Together with other biological (e.g., kinases and receptors are often seen as promising drug targets), medical, and strategic considerations already used for target selection in drug discovery (for review, see Knowles and Gromo, 2003), the integrated knowledge of aging-related pathways can help identify suitable targets for drug discovery.In addition, the advent of largescale databases of compounds and drugs, such as Drug-Bank (Wishart et al., 2008), STITCH (Kuhn et al., 2008), and the Connectivity Map (Lamb et al., 2006), paves the way to cross-linking longevity/CR-associated genes with drug databases to identify candidate molecules for effects on aging.\t\n\nWe now know of hundreds of genes that regulate aging in model organisms, dozens associated with longevity in humans, and hundreds differentially expressed with age.This vast amount of information yields increased power for personalized and stratified medicine, for identifying biomarkers of aging, and for drug development to extend lifespan and ameliorate age-related diseases.Overall, it gives us a blueprint (albeit still imperfect) of how aging is controlled that we can use to potentially manipulate the basic aging process, whatever its underlying molecular mechanisms may be.Moreover, our knowledge of nutrient-sensing pathways that mediate the effects of CR has greatly increased in recent years, opening new opportunities for drug discovery and ultimately for perhaps developing an antiaging pill that retards aging with minimal side effects.", + "\tIntegrating genomics and biomarker research\n\nOnce the use of established biomarkers of biological age is standardized, the biomarker information can be integrated into studies aimed at finding causal determinants of aging and longevity.An example of an integrated approach to identify lifespan regulating loci is represented by testing whether genetic variants associated with potential biomarkers also associate with longevity.To date, GWAS have identified many genetic variants that associate with age-associated traits, such as leukocyte telomere length and features from glycome and metabolome profiles [84][85][86].The joint effect of the majority of these variants on aging and longevity still needs to be determined.One study identified a haplotype in the TERT gene that was associated with increased telomere length and longevity, which indicates that genetic variants associated with telomere length regulation might also play a role in longevity [87]." + ], + [ + "Several studies have shown\nthat the systemic milieu regulates stem cell decline during aging. Liang et al. showed\nthat HSCs have a reduced ability to home to the bone marrow and spleen after\ntransplantation into old versus young recipients (Liang et al. , 2005). Further experiments\ndemonstrated that the muscle stem cell niche adversely effects stem cell function as\nevidenced by the restoration of old stem cell regenerative potential upon exposure to a\nyoung systemic microenvironment (Conboy et al. , 2005; Conboy and Rando, 2005).\tHowever, studies do indicate that aged tissues have a diminished capacity to return to a\nhomeostatic state after exposure to stress or injury, therefore indicating a defect in stem\ncell function during the aging process. Since the HSC population provides an ideal\nmodel to study stem cell aging, it is necessary to elucidate the mechanisms of\nhematopoietic aging and expand the findings to other tissues and organ systems. Theories of Aging and Age Related Epigenomic Changes\nThere are two major theories of organismal aging: evolutionary and damage\nbased.\tHSCs as a Model for Stem Cell Aging\nWhen studying aging it is important to choose an appropriate model system. For\ninstance, cells (such as skin and blood) that undergo continuous turnover are removed\nfrom circulation long before they have time to feel the effects of aging, and certainly long\nbefore they could exert an effect on tissue function. The predominant substrates for\naging, thus it seems, would be long-lived cells in the organism, namely tissue specific\nstem cells, since this population is exposed to both intrinsic and extrinsic effectors of\naging throughout the lifespan of an individual.\tWith\nthis in mind, it has been hypothesized that the aging or functional failure of tissuespecific stem cells, which fulfill this job, may limit tissue repair and renewal, therefore\ncontributing to overall organismal aging (Krtolica, 2005; Van Zant and Liang, 2003). Because of the unprecedented experimental model systems that are available for the\nexploration of HSCs, stem cell aging research in the field of hematology has been the\nsubject of extensive studies. Indeed, the hematopoietic system has served as an important\nmodel for advancing our understanding of stem cell biology and its association with\naging.", + "The several lines of evidence support the hypothesis that essential metabolic pathways interconnected with environmental factors and genetic background are involved in the appearance of different markers of cellular senescence.They have emerged as potential regulators of cellular senescence, particularly through those pathways involved in the maintenance and repair of stem cells and progenitor cells: mitochondrial integrity, mitotic competence, and eradication of senescent cells.The complexity of events that are under the control of the genetic programs induced in response to environmental challenges creates the need for further studies that must be performed to unravel the biological roles of the highly dynamic aging process through different tissues and different stages of cell life.The increasing research across different species has allowed the identification of conserved processes associated with the biology of aging.However, it is essential to consider that information from lower organisms cannot be generalized, since worms do not develop age-associated diseases such as osteoporosis, arthritis, or Alzheimer's disease.", + "There is growing evidence that noncell-autonomous mechanisms play a critical role in orchestrating vascular aging processes (Figure 1).Aging-induced alterations in vasoprotective endocrine factors are of particular importance.Such changes include an age-related decline in circulating levels of growth hormone, 215 IGF-1, 216 and estrogens, all of which regulate multiple aspects of endothelium-dependent vasodilation, 217 autoregulation of blood flow, 218 vascular structural remodeling, atherogenesis, 219 and angiogenic processes. 220he impact of circulating factors on aging phenotypes was also demonstrated by studies using mice with heterochronic parabiosis, which involves surgically connecting the circulatory system of a young and an aged mouse. 221erebromicrovascular density typically declines with advanced age, 222 and there is initial evidence that circulating antigeronic factors (which reverse/prevent development of aging phenotypes) present in young mice can rejuvenate microvascular network architecture in aged heterochronic parabionts. 221he antigeronic circulating factors present in young mice are currently unknown, and the previously proposed role for GDF11 (growth differentiation factor 11) 221 remains controversial.Future studies should identify additional antigeronic factors that might be targeted by interventions to extend vascular health span.Progeronic circulating factors increase with age and impair tissue homeostasis in young animals.There is initial evidence that mediators secreted by senescent cells (eg, inflammatory cytokines, such as TNF- 35 ) may serve as progeronic circulating factors.Further studies are warranted to identify additional progeronic proteins and determine their impact on atherogenesis, endothelial function, blood-brain barrier integrity, and microvascular function in aging.\t\n\nAdditional evidence to support a central role of antigeronic circulating factors governing vascular aging processes is derived from studies on caloric restriction-a dietary regimen, which improves health and slow the aging process in evolutionarily distant organisms. 223Caloric restriction was shown to promote a youthful endothelial phenotype by upregulating and activating eNOS in aged animals [223][224][225] and perhaps humans. 226 critical role of antigeronic circulating factors in vasculoprotective phenotypic responses induced by caloric restriction was first indicated by the observations that in vitro treatment of cultured aged endothelial cells with sera derived from caloric restricted animals mimics phenotypic effects observed in vivo during caloric restriction, promoting anti-inflammatory and proangiogenic effects. 42,227Treatment with sera derived from caloric restricted animals upregulates SIRT1 228 ; however, the exact nature of the circulating factor responsible for this effect remains elusive. ][231] Human studies are needed to identify novel progeronic and antigeronic circulating factors and their cofactors, activators, or inhibitors/antagonists and to seek associations with vascular aging phenotypes.Future studies should also identify cellular origins of circulating progeronic and antigeronic factors that impact vascular aging and characterize pathological conditions that alter their levels in circulation with aging.Further, mechanistic studies describing the cellular effects of progeronic and antigeronic circulating factors in the vascular wall are warranted.", + "Mitochondrial-derived peptides (MDPs) in aging-related phenotypes", + "Background: Aging is believed to have a close association with cardiovascular diseases, resulting in various pathological alterations in blood vessels, including vascular cell phenotypic shifts.In aging vessels, the microRNA(miRNA)mediated mechanism regulating the vascular smooth muscle cell (VSMC) phenotype remains unclarified.MiRNA microarray was used to compare the expressions of miRNAs in VSMCs from old rats (oVSMCs) and young rats (yVSMCs).Quantitative reverse transcription real-time PCR (qRT-PCR) and small RNA transfection were used to explore the miR-542-3p expression in oVSMCs and yVSMCs in vitro.Calcification induction of yVSMCs was conducted by the treatment of -glycerophosphate (-GP).Alizarin red staining was used to detect calcium deposition.Western blot and qRT-PCR were used to investigate the expression of the smooth muscle markers, smooth muscle 22 (SM22) and calponin, and the osteogenic markers, osteopontin (OPN), and runt-related transcription factor 2 (Runx2).Lentivirus was used to overexpress miR-542-3p and bone morphogenetic protein 7 (BMP7) in yVMSCs.Luciferase reporter assay was conducted to identify the target of miR-542-3p.Results: Compared with yVSMCs, 28 downregulated and 34 upregulated miRNAs were identified in oVSMCs.It was confirmed by qRT-PCR that oVSMC expressed four times lower miR-542-3p than yVSMCs.Overexpressing miR-542-3p in yVSMCs suppressed the osteogenic differentiation induced by -GP.Moreover, miR-542-3p targets BMP7 and overexpressing BMP7 in miR-542-3p-expressing yVSMCs reverses miR-542-3p's inhibition of osteogenic differentiation.Conclusions: miR-542-3p regulates osteogenic differentiation of VSMCs through targeting BMP7, suggesting that the downregulation of miR-542-3p in oVSMCs plays a crucial role in osteogenic transition in the aging rat.\t\n\nBackground: Aging is believed to have a close association with cardiovascular diseases, resulting in various pathological alterations in blood vessels, including vascular cell phenotypic shifts.In aging vessels, the microRNA(miRNA)mediated mechanism regulating the vascular smooth muscle cell (VSMC) phenotype remains unclarified.MiRNA microarray was used to compare the expressions of miRNAs in VSMCs from old rats (oVSMCs) and young rats (yVSMCs).Quantitative reverse transcription real-time PCR (qRT-PCR) and small RNA transfection were used to explore the miR-542-3p expression in oVSMCs and yVSMCs in vitro.Calcification induction of yVSMCs was conducted by the treatment of -glycerophosphate (-GP).Alizarin red staining was used to detect calcium deposition.Western blot and qRT-PCR were used to investigate the expression of the smooth muscle markers, smooth muscle 22 (SM22) and calponin, and the osteogenic markers, osteopontin (OPN), and runt-related transcription factor 2 (Runx2).Lentivirus was used to overexpress miR-542-3p and bone morphogenetic protein 7 (BMP7) in yVMSCs.Luciferase reporter assay was conducted to identify the target of miR-542-3p.Results: Compared with yVSMCs, 28 downregulated and 34 upregulated miRNAs were identified in oVSMCs.It was confirmed by qRT-PCR that oVSMC expressed four times lower miR-542-3p than yVSMCs.Overexpressing miR-542-3p in yVSMCs suppressed the osteogenic differentiation induced by -GP.Moreover, miR-542-3p targets BMP7 and overexpressing BMP7 in miR-542-3p-expressing yVSMCs reverses miR-542-3p's inhibition of osteogenic differentiation.", + "The major question is whether replicative senescence does play a role in human aging.Several studies have shown an inverse relationship between donor age and the replicative life span in vitro for fibroblasts or MSC [13,44,45].This effect is usually relatively small with a high variation between different donor samples [12,46].At least some of the variability was attributed to differences in donor health status, conditions for the biopsy and the initial CFU-F frequency in the bone marrow sample [47].Furthermore, the pace of senescence might be affected by the culture conditions [19,48].In MSC preparations used in this study we did not discern any age-associated effects on replicative senescence.If the number of cumulative population doublings was not significantly affected by aging it is all the more surprising, that there was a significant association between age-induced gene expression changes and replicative senescence.These results indicate that the molecular sequels of aging in vivo and replicative senescence in vitro are based on similar mechanisms.", + "Finally, we asked whether additional cellular components of the immune system also show increased transcriptional variability upon aging.", + "Systemic aging has been more formally proposed as the hormonal\n\n3\ncontrol of aging, where changes in humoral factors with age can cause system-wide\nchanges in the homeostatic condition (Wise, Krajnak et al. 1996). Support for this idea\nhas gained traction from studies of mice expressing a mutant form of the KLOTHO gene\nencoding a protein hormone that leads to phenotypic changes characteristic of accelerated\naging (Kuro-o, Matsumura et al. 1997). Conversely, when the wild-type KLOTHO gene\nis overexpressed in mice it leads to a modest yet significant increase in both male and\nfemale lifespan (Kurosu, Yamamoto et al. 2005).\tStudies of invertebrate systems such as C. elegans and D. melanogaster\nhave yielded keen insight into stem cell biology and mechanisms of aging, but it has\npredominantly been the study of the mammalian hematopoietic system that has led to the\ncurrent understanding of the physiology of hematopoiesis. The utilization of mouse\ngenetics has only recently been fully realized as a tool as it was this mammalian model\nthat yielded the breakthrough discoveries of Till and McCulloch (Till and McCulloch\n1961).", + "Our results indicate that cell identity influences multiple aspects of aging, highlighting the importance of aging studies at the single-cell level.However, it remains difficult to identify which age-related changes are causal and link molecular changes at the level of individual cell types to physiological aging phenotypes, like reduced glomerular filtration rate or decreased pulmonary regeneration.Future single-cell studies may focus on collecting additional time points and phenotypes throughout the aging process, allowing for time series-based causal inference methods (Granger 1969;Bar-Joseph et al. 2012;Finkle et al. 2018;Qiu et al. 2018;Lu et al. 2019) to reveal the relationships between the molecular players of aging.Functional challenges, such as the differentiation of stem cells during regeneration or the stimulation of immune cells during infection, would also help dissect how transcriptional aging magnitudes and differential gene expression influence tissue function.Single-cell measurements collected during functional challenges may also reveal the dynamics of perturbation and subsequent return to homeostasis necessary to evaluate \"resilience\" in a given cell type (Kirkland et al. 2016;Hadley et al. 2017).\t\n\nAt both the molecular and functional level, a host of aging phenotypes and associated mechanisms have been revealed in individual cell types (Shaw et al. 2010;Chakkalakal et al. 2012;Keyes et al. 2013;Liu et al. 2013;Flach et al. 2014;Blau et al. 2015;Brack and Muoz-Cnoves 2016;Keyes and Fuchs 2018).Although some of these studies present unique features of aging within individual cell identities, it is difficult to compare them systematically because of differences in experimental conditions and assay methodology.Using traditional molecular biology assays, it is difficult to measure high-dimensional molecular phenotypes across multiple cell identities, making large-scale comparisons of aging phenotypes across cell identities intractable.The recent development of single-cell RNA-sequencing (scRNA-seq) has ameliorated this limitation, allowing for measurement of transcriptional features across all prevalent cell identities in a tissue in a single experiment.\t\nAging is a pleiotropic process affecting many aspects of mammalian physiology.Mammals are composed of distinct cell type identities and tissue environments, but the influence of these cell identities and environments on the trajectory of aging in individual cells remains unclear.Here, we performed single-cell RNA-seq on >50,000 individual cells across three tissues in young and old mice to allow for direct comparison of aging phenotypes across cell types.We found transcriptional features of aging common across many cell types, as well as features of aging unique to each type.Leveraging matrix factorization and optimal transport methods, we found that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating.These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals.\t\n\nAging is a pleiotropic process affecting many aspects of mammalian physiology.Mammals are composed of distinct cell type identities and tissue environments, but the influence of these cell identities and environments on the trajectory of aging in individual cells remains unclear.Here, we performed single-cell RNA-seq on >50,000 individual cells across three tissues in young and old mice to allow for direct comparison of aging phenotypes across cell types.We found transcriptional features of aging common across many cell types, as well as features of aging unique to each type.Leveraging matrix factorization and optimal transport methods, we found that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating.These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals.", + "Discussion Consequences of disease as well as age exert profound influences upon cells including alteration of gene expression, metabolism, functional competency, replicative potential, and more [10,18].Certain features of aged cells are exacerbated or mitigated by environmental conditions in host tissues such as oxidative stress, nutrient status, inflammatory / cytokine production, and pathological changes [5,7,40,42].Many of these conditions can be recapitulated in cell culture studies with treatments that mimic the aged tissue environment [6,37].Studies using established cell lines to study biological consequences of aging are of limited value for extrapolation to the complex in vivo mileau.In situ studies have provided significant insight regarding adaptations and distinct features of aged cells [9,46,55], but whether the characteristic phenotypic state of aged cells is retained following isolation and culture expansion is poorly understood.Moreover, conditions of culture expansion inherently favor cells with the highest proliferative and survival potential.Thus, it is unclear to what extent culture expansion allows hallmarks of aging to persist when harvesting cellular samples from aged tissue and subjecting them to multiple passages after initial isolation.", + "Concluding remarks and future perspectives\n\nAging research has rapidly expanded over the past two decades, with studies ranging from lifespan-extending [68,69,71].However, when their effect on cell death and senescence leads to stem cell loss and tissue degeneration, they might contribute to aging [66,67]." + ], + [ + "\t\n\nHowever, the simplest solution to restoring pathological disturbances in the composition of the gut microbiota may be a change in dietary habits.Diet has been shown to strongly affect the composition of the microbiome (73).When obese humans were put either on a fatrestricted or carbohydrate-restricted low-calorie diet, an increase in the abundance of Bacteroidetes and a decrease in Firmicutes was reported (12).In another study, diet-induced weight loss versus weight-stabilization interventions in obese humans increased intestinal microbial gene richness and was associated with a reduced systemic inflammation (74).These data corroborate with another controlled diet intervention study in 98 human subjects showing that certain dominant gut microbial communities, or \"enterotypes,\" correlated with specific kinds of diets (73).For example, Bacteroides was associated with a protein-rich diet, whereas Prevotella correlated with a fiber-rich diet; moreover, gut microbiota composition could be altered within 24 h whereas enterotype remained stable during the 10 days of the study.Based on this rapid and dramatic plasticity of intestinal microbiota composition, there is a specific need to determine intestinal microbiota composition in a standardized way (e.g., sequencing several fecal samples per person over a specific time point while taking dietary intake and medication use into account).", + "\t\n\nWe next performed partial correlation analysis to investigate whether exercise-induced compositional changes in microbiota were associated with improvements in clinical parameters independent of body weight, fat mass, and visceral fat.We found that after adjustment for body weight and adiposity, associations between alterations of microbial species and improvements in insulin sensitivity-related indexes and a cluster of other metabolic features remained significant (Figure 3).At the community level, alteration in the gut microbiota was significantly associated with the percentage reduction of HOMA-IR (p < 0.01, ADONIS).Among the 19 species significantly correlated with the improvements of glucose homeostasis and insulin sensitivity, Ruminococcus gnavus, Alistipes shahii, Streptococcus mitis group, Eubacterium hallii, and Escherichia coli showed the strongest associations (Figure 3).Consistently, most of these species were also found to be differentially altered between responders and non-responders (Figure 2E).Taken together, the above findings imply that distinct changes of these species may underlie the difference in the improvement of glycemic homeostasis in response to a standard exercise regimen.", + "\t\n\nOn the other hand, studies on centenarians and supercentenarians have evidenced the adaptation of the microbiota to the physiological changes of the long aging process.It has been demonstrated that the microbiota on this population maintains the health and promotes the survival.Additionally, a relationship between a healthy microbiota and longevity had been proposed [44].A possible pathway is an immunological and metabolic regulation linked to the increase of bacterial compounds like Christensenellaceae, Akkermansia, and Bifidobacterium [44,45].\t\n\nFigure 9.1 depicts a visual representation of the gut microbiota composition throughout the lifespan.Variations between individuals and within an individual throughout the lifespan can be seen.In this respect, it can be said that the concentration of Bacteroidetes grows as an individual does, from 12.6% for newborns to 57% for older adults.Conversely, Actinobacteria composition reduces with age until it reaches 0.4%, and the Firmicutes, Proteobacteria, and other microbial are maintained relatively stable throughout life in healthy adults and decay at old age [20][21][22].\t\n\nThe human holobiont (commensal microbes and their multicellular eukaryotic host) constitutes a highly integrated system, which undergoes dynamic changes through time as it integrates and responds to signals from the environment.Microbiome research and aging is flourishing as we better understand the bidirectional interactions, and its evolution with a life-course perspective for the gut microbiota undergoes dynamic changes during host aging.Changes in host intestinal cell Foreword vii composition and architecture occurring during aging are matched by a decrease in the microbiota taxonomic diversity.Age-related decrease in taxonomic diversity leads to larger population size for a few age-associated microbial species, increasing the chances for the evolution of novel potentially pathogenic microbial strains, which have been related both to neurodegeneration and frailty.This knowledge positions the microbiome as a promising element for translational research.\t\n\nAll the information given by the aging research allows knowing that the microbial composition has an essential role in the establishment of cellular and tissue homeostasis.Additionally, it is known that age-dependent changes in the microbial composition can contribute to increasing of frailty and development of diseases during the late stages of life [42,43].\t\n\nAlthough the causes that lead to changes in the composition and function of the microbiota during aging are still unknown, the evidence has established that the local microbiome plays an essential role in human health.\t\n\nTherefore, research in the field has demonstrated that aging is a potential modifier of the composition and function of the human microbiome.Figure 9.3 shows the local composition of the microbiome in an average older adult.It can be seen that Bacteroidetes and Firmicutes species are the most prevalent in this age.\tMicrobiome Research and Aging: A Clinical Perspective\n\nAging is characterized by the accumulation of damage at the molecular level (DNA and proteins) and dysfunction of the organelles [31][32][33].In addition to senescent cells and compositional changes in the extracellular compartment, these changes are determinants of the organic and systemic decline [34][35][36].The microbiota reacts dynamically to these environment changes by altering the metabolic function and composition of individual bacterial species.\tConclusions\n\nDuring the last years, significant advances in the field of microbiome and aging research have been carried out; new approaches for its study have allowed the understanding of the genomic nature of the microbiota.In this regard, the introduction of metagenomics had increased knowledge of the genes that potentially allow microbes to influence their hosts in unexpected ways.Thanks to these advances, it is well known that microbiota constitutes an essential determinant of the health and longevity of humans.\t\n\nFig. 9.1 Gut microbiota throughout lifespan\tMicrobiome and Age-Related Neurodegenerative Diseases\n\nDifferent microorganisms such as bacteria, fungi, archaea, and viruses compose the human intestinal microbiota that represents, in physiologic conditions, a perfect commensalism association with their host [51,52].In general, the human intestinal microbiota is shaped by the healthy microbiota (bacteria that normally colonize the intestine) and opportunistic bacteria (which are the agents responsible for infections).Among the billions of symbiotic microorganisms that compose the intestinal microbiome, four bacteria phyla are mainly reported in adults, i.e.Firmicutes (~51%), Bacteroidetes (~48%), Proteobacteria, and Actinobacteria, (1%) [53].Lactobacteria species stand out among the normal microbiome (Lactobacillus rhamnosus, Lactobacillus acidophilus, and Lactobacillus plantarum), Bifidobacterium (B.bifidum), Enterococci, Propionobacteria, and Peptostreptococci.In the same way, opportunistic bacteria include the Bacteriodes spp.Bacilli, Clostridia, Enterobacteria, Actinobacteria, Peptococci, Staphylococci, and Streptococcus [54].Several factors, such as diet, hygiene, antibiotic exposure, and modify the intestinal microbiota [55,56].Interestingly, age also contributes significantly to the microbiome modification; in fact a recent publication highlights the vital role that represents the host aging in the microbial evolution since as the host get aged the organism experiments molecular and functional changes that induce shifts to the microbial niche [57], nevertheless, for detailed information about changes in microbiome during aging, please refer to the Chap.9 in this book.In the following paragraphs, we discuss the recent data about the relationship between the pathogenesis of the two most prevalent ND and the microbiome, which represents a new field of research.\t\n\nDiet can be a potent gut microbiome modifier.For this reason, numerous studies have been conducted to demonstrate the impact of specific diet components on the diversity of the gut microbiota [8].The results of many of these studies have proved that probiotics and prebiotics consumption are a feasible alternative, especially for specific population groups such as older adults [59].\t\n\nMany areas of opportunity can be mentioned.However, modulation of the microbiome by extrinsic factors can be a way to apply the actual knowledge in the clinical setting.Nowadays, it is possible to ensure that lifestyle and diet play a significant role in determining the microbiome.In this respect, novel therapies, as fecal transplantation adds to the traditional dietary interventions, both demonstrated to be a potential therapeutic approach for the aging population.\t\n\nIt is well known that aging is a risk factor for neurodegeneration and dementia [58]; nevertheless, recent studies support the idea that gut microbiota may have an effect on the brain and the behaviour of patients, since the evidence suggests that some metabolites secreted by the intestinal microbiota can affect in a certain way, the cognitive capacity of patients diagnosed with ND [59][60][61][62][63].This hypothesis is not entirely new since several decades ago, the concept that bidirectional communication between the CNS and the intestinal organs plays a role in emotional regulation [64,65].Four decades later, the hypothesis that the brain has a regulation of the gastrointestinal tract arose and with the help of the murine model, the existence of the brain-gut axis was reported [66].This axis is carried out through the neuroendocrine and neuroimmune system, working together with the sympathetic and parasympathetic arms of the autonomic nervous system and the enteric nervous system.", + "\t\n\nChanges in the gut microbiota in terms of composition and functionality during the process of aging have previously been reported [19,20,51] and it has been postulated that these changes might contribute to the development of immunosenescence and inflammaging [18,52].To establish whether the enhanced expression of genes playing a role in the immune system are due to modifications in the microbiota we measured the total number of all bacteria and of the two most prominent phyla colonizing the colon, Bacteriodetes and Firmicutes, in the luminal content of the colon.We did not observe aging-related changes.More advanced techniques like pyrosequencing are required to determine whether total number of bacteria and changes in the composition of the microbiota might play a causal role in the observed changed expression of immune-related genes in the colon of our aging mice.Although it is difficult to assess the physiological consequences of the enhanced expression of genes involved in inflammation and immune response, it seems most likely that this effect is important for the health status of the aging colon.", + "\tSignatures of aging in gut\n\nFor gut or the digestive system, six clusters of age-associated genes had significant enrichment of functional annotations (Fig. 2C; Supplemental Table 10).Aging in gut was found to be associated with down-regulation of genes (Clusters 1, 2, 3, and 4) participating in oxidative phosphorylation, aromatic compound metabolism, muscle contraction, amino sugar metabolism, regulation of apoptosis, and vesicle transport.Aging was also associated with up-regulation of genes (Clusters 5 and 6) involved in regulating various physiological processes, amino acid metabolism, and regulation of transport.These results suggest that metabolic pathways, especially nutrient intake and energy production, are primarily affected during aging of gut, which are the fundamental function of the digestive system.", + "\t\n\nSequencing of bacteria species within our gut, collectively labeled the gut microbiome, explains individual differences in the metabolism of consumed food with potential associations with body weight (Karlsson, Tremaroli, Nielsen, & Backhed, 2013).Gut permeability to bacteria is further associated with obesity and obesity-related inflammation (Teixeira et al., 2012).Over time, these mechanisms will more fully be integrated into the overarching models of obesity.", + "\tThe microbiome and weight change\n\nThe human microbiome may play a significant role in the etiology of obesity in both humans and animal models (64).Hosted in the gastrointestinal tract, the gut microbiome is part of a large endocrine organ that regulates not only nutrient sensing and metabolism but also satiety and energy homeostasis.The millions of microorganisms comprising the complex intestinal \"superorganism\" perform a number of functions for host health, including food processing, breakdown and metabolism of indigestible nutrients, pathogen displacement, synthesis of vitamins, and regulation of body weight (65).They play such an important role that we now know that microbiota disruptions in early life can have long-lasting effects on body weight in adulthood (66).The host bacterial composition has been shown to adapt in response to dietary factors and in response to weight loss.Diet or surgically induced weight loss promote alterations in the gut that can impact the efficacy of the treatment strategies (67,68).Specific bacterial species can have influences by themselves.For example, the archaeon Methanobrevibacter smithii, has an enhanced ability to metabolize dietary substrates or end products of the metabolism of other bacteria, thereby increasing host energy intake and weight gain (69).", + "\tThis microbial\ncommunity is established early in life, influenced by maternal and environment factors and\nable to impact the health of the host [2]. For example, early studies provided evidence that\ndiet plays an important role in the composition of gastrointestinal microbiota. Specifically,\ntransition to a low-fat diet in overweight humans led to a gut microbial composition similar\nto that of healthy controls [3, 4]. Also, gnotobiotic animals displayed substantial weight gains\nfollowing exposure to a complex gastrointestinal microbiota from overweight individuals\n[5, 6]." + ], + [ + "\tCONCLUSIONS\n\nOur purpose in this review is to outline the prospects of unifying mechanism in the genetics of aging.In case after case, from mice to worms to flies to yeast, genetic variants that modify metabolism also modify life span.These effects, collectively, are as general as that of caloric restriction, which also increases longevity and resistance to stress in many situations.The evolutionary theory of aging proposes that the life span is indirectly selected on the basis of the reproductive schedule.In turn, the reproductive schedule is coordinated by neural and endocrine mechanisms in multicellular organisms.Therefore, to consider that genes determining the life span could be expressed in neuronal and endocrine cells in diverse animals is no longer far-fetched.Consistent with this hypothesis are experiments in Drosophila and C. elegans in which life span was manipulated by the expression of genes in specific neurons.Genetic approaches may, thus, be able to identify a set of circuits that regulate longevity that were established in ancestral metazoans.", + "\tGenetic Programs\n\nAs stated above, the universality of aging phenotypes within a species argues for an underlying genetic program.The redistribution of the Sir complex from telomeres to the nucleolus in yeast is a specific molecular While the effects of these hormones on specific orthan rats fed ad libitum, with a consequent decline in the incidence of hepatocellular carcinoma (Muskhelishvili gans are apparent, their relation to the aging process itself, if any, is not yet clear.To our knowledge, there has occurred in the past 200 years.However, slowing the aging process may increase vitality and quality of has been no animal study in which hormone supplementation extended maximum life span.However, the recent life over the entire life span of individuals.In this regard, it is noteworthy that calorically restricted rodents have findings in C. elegans provide a basis to believe that humoral factors may turn out to play an important role an extended life span that is relatively free of disease.For society, the implications of slowing the aging pro-in at least some aspects of human aging.cess are more complex.Of course, in an increasingly overpopulated world, it would be important to offset Perspective any significant effects on longevity with a compensatory Recent advances in the study of aging indicate that this reduction in birth rates.In fact, in many industrialized process is amenable to molecular analysis and may be countries, the current birth rate is sufficiently low to relatively simple.The potential of single gene mutations afford zero or negative growth.Most importantly, if the to greatly extend life span in model systems suggests slowing of aging is associated with improved health and that relatively few limiting cellular or organismal proproductivity of long-lived individuals, there may be a cesses control the rate of aging, at least in these species.", + "\t\n\nThen we have those pharmaceutical strategies that are www.impactaging.combased on emulating the pathways implicated in the response of lifespan to dietary restriction, particularly sirtuin-targeting agents like resveratrol [e.g.25].Again, like hormone manipulation, these pathways are heavily bound up with the regulation of reproduction, making the curtailment of the cost of reproduction the most likely mechanism by which the beneficial effects of emulating dietary restriction are achieved [cf. 26].This is a strategy in which longevity is increased by metabolic refrigeration, pseudo-hibernation, or curtailing functions [11].From the standpoint of evolutionary biology, this is, again, not an extension of the period of adaptation.It is instead trading one set of adaptations off against another.Most people do not regard curtailing their metabolism, cognition, affective stability or reproductive functions as a useful approach to the problem of aging.Nonetheless, some are willing to trade-off some of their adaptive functions for an increased lifespan, and for them this \"anti-aging\" strategy will have its attractions.", + "\tMetabolism\n\nStudies show that calorie restriction is the most consistent means to prolong life expectancy and health across several experimental models [55], ranging from yeasts to primates.It not only increases life expectancy, but it also delays the onset of many features and hallmarks of ageing, including age-related diseases.Transcriptional profiles are currently being applied and investigated.One of them is a caloric restriction (CR), which increases the response to oxidative stress and reduces the shortening of telomeres in chromosomes; this has a direct intervention in the repair of DNA damage.Data from human trials (such as CALERIE, Biosphere-2 and CRON) indicate that moderate CR accompanied by adequate nutrition has positive effects on health and dramatically reduces the multiple metabolic factors involved in the pathogenesis of disease chronicles, including type 2 diabetes, heart and cerebrovascular diseases, and cancer [56].", + "\t\n\nLimitations of translating the results of preclinical studies should be recognized.An important recent example is caloric restriction. 239Although caloric restriction confers significant life span extension and cardiovascular protection in laboratory rodents 5,18,42,97,223,240,241 and in certain cohorts of nonhuman primates, 227,242 its protective effects in nonhuman primates in other studies 243 and in patients with multiple cardiovascular risk factors are less evident. 244Additionally, in cross-sectional studies, the older groups may represent a selected long-lived subset of the younger population.There are existing longitudinal studies in humans (eg, InCHIANTI study) and nonhuman primates, and important information related to mechanisms of vascular aging could be derived from add-on studies to these existing cohorts.", + "\t\n\nOn the other hand, the beneficial effects of caloric restriction are associated with alterations in metabolism, particularly the insulin/insulin-like growth factor 1 (IGF-1) pathways, which could reflect an evolution mechanism to ensure survival of a species during period of food shortage [3].Many genetic manipulations affecting nutrient-sensing pathways including the insulin and mTOR (mammalian target of rapamycin) pathways mimic the effect of caloric restriction on lifespan in yeast, worm, flies and mice and support this hypothesis [3].This review will firstly discuss in general terms how trace elements affect ageing and then use Selenium (Se) as an example to illustrate how trace elements influence the ageing process.Furthermore, the review will also illustrate how the so-called \"Omics technologies\" can be used to unravel the modes of action of trace elements and to identify biomarkers to define the optimal intake for health at the molecular level.\t\n\nEvidence is building up showing that caloric restriction, without malnutrition, extends lifespan in species ranging from yeast to non-human primates [3], but it appears, on the contrary, that inadequate/sub-optimal intake of micronutrients contribute to the development of chronic diseases.In his \"Triage theory\", B. Ames suggested that this could reflect the need for an organism to re-allocate micronutrients according to triage priorities to favour short-term survival over long-term wellbeing [4,5].The consequences of this re-allocation may remain unnoticed in the day-to-day experience but are likely to show up late in life as cancers, Alzheimer's disease, Parkinson's disease, diabetes and cardiovascular diseases.", + "\t\n\nCaloric restriction (CR) is the only intervention shown to extend lifespan in mammals (5).It is also the most effective means known of reducing cancer incidence and increasing the mean age of onset of age-related diseases and tumors (6).Our studies made use of an experimental design that allowed us to clearly distinguish the effects of diet from those of age on genome-wide expression patterns.Another distinctive aspect of the study allowed us to resolve changes in gene expression induced directly by CR from those that arise over time as a consequence of the interaction between CR and aging.", + "\tGenDR-genomics of DR\n\nDR, of which caloric restriction is the most widely studied regimen, is the most robust non-genetic intervention shown to extend lifespan in a multitude of species, from yeast to mammals (12,14).However, the exact mechanisms of how DR extends lifespan remain unknown.To decipher the mechanisms of DR in a systematic fashion, we established GenDR (http://genomics.senescence.info/diet/), the first database of DR-associated genes.Because GenDR and related analysis of DR networks have been recently described elsewhere (15), they will only be briefly described herein.To create GenDR, we compiled from the literature a list of DR-essential genes from model organisms.DR-essential genes were defined as those which, if genetically modified, interfere with DR-mediated lifespan extension and, ideally, do not affect the lifespan of animals on an ad libitum diet (or at least do not appear to be merely causing disease).A subset of these genes act as genetic DR mimetics, as their manipulation leads to an increased lifespan for ad libitum fed animals, which is not further extended by DR.One such example is the growth hormone receptor gene in mice (16), in fact the only mouse gene currently in GenDR.In GenDR, the respective homologues of DR-essential genes are included for all the common model organisms, as well as for humans (15).A complementary data set in GenDR is a list of genes consistently differentially expressed in mammals under DR.In a recent meta-analysis, a common signature of genes differentially expressed in DR across different mammalian species, strains, tissues and experiments was derived.This signature provides a set of genes that are most robustly responding to DR (17).", + "\t\n\nIt seems that organisms from yeast to mammals have evolved genetic programs to cope with periods of starvation that can also postpone aging and age-related diseases, but how can we take advantage of those mechanisms to improve human health?Because assaying the longevity effects of CR in humans is practically impossible, studying its molecular mechanisms in lower life forms could be beneficial to humans through the identification of candidate genes, pathways and molecular mechanisms.Although CR will not be suitable for everyone, targeting its mechanisms and developing CR mimetics may lead to drug development for a number of age-related and metabolic diseases.\t\n\nBy far the most widely studied dietary manipulation of aging is caloric restriction (CR), also called dietary restriction.CR consists of restricting the food intake of organisms normally fed ad libitum without triggering malnutrition and is the only dietary intervention shown, to date, to increase longevity and modulate the process of aging in several model organisms (Bishop and Guarente, 2007;Fontana et al., 2010;Spindler, 2010).Even in mammals, such as mice and rats, CR can extend longevity by up to 50%, delay physiological aging, and postpone or diminish the morbidity of most age-related diseases (Masoro, 2005).Ongoing studies in rhesus monkeys suggest that CR can lower the incidence of aging-related deaths in primates (Colman et al., 2009).", + "\tINTRODUCTION\n\nGenomic studies into human longevity are inspired by the fact that, in animal models, healthy lifespan has proved to be remarkably plastic, and major pathways of lifespan regulation have been identified.Considerable lifespan extension has been induced in models as diverse as yeast, worms, fish, flies and rodents by applying genetic manipulation and dietary restriction (DR) (see [1] for review).Reduced activity of nutrient-sensing pathways such as insulin/insulin-like growth factor (IGF-1) signalling (IIS) and target of rapamycin (TOR) signalling mediated lifespan extension, and also the extension of lifespan by DR [2].An interesting observation from the perspective of human ageing is that, in rodents and monkeys, diets restricted in glucose, fat or protein uptake reduced or delayed the risk of cancer and metabolic disease, thus extending the healthspan of the animals [2].Following the discovery of genes and pathways involved in animal lifespan extension, human research has focused on the corresponding candidate human genes with genetic, genomic and epigenetic studies into ageing and longevity.The designs of these studies differ with respect to the selection of naturally occurring phenotypes and the study populations, which include population-based, patient-based, family-based and exposure-based cohorts.", + "\t\n\nThe 'hormesis' hypothesis of aging is based on the observation that caloric restriction or chronic low-level exposure to any of these stresses induces cross-resistance to other stresses at the same time that it extends life span (41).Hormesis effects on aging are observed in many eukaryotes in addition to budding yeast.Although the mechanistic details of these effects remain unclear, we have argued that they include a general response to environmental stresses that blocks entry into S phase under environmentally stressful conditions that are suboptimal for replicating DNA, thus protecting cells from replication stress (30).", + "\tINTRODUCTION\n\nMore than 70 years ago, McCay and his colleagues demonstrated that a reduction in total food intake after weaning significantly increased both mean and maximum life spans of laboratory rats (1).Over the last seven decades, numerous laboratories have successfully repeated McCay's findings using various strains of rats and mice as well as non-mammalian species, such as fish and flies (2)(3)(4)(5)(6).Thus, food restriction has been established as a powerful experimental tool, and the anti-aging action of food restriction has become one of the most active areas of research in the realm of biogerontology (6).While life span extension by food restriction appears to be due to alterations in aging processes, the underlying mechanism(s) by which food restriction exerts its anti-aging effects remain elusive.Identification of important antiaging and anti-tumor targets of food restriction and elucidating the molecular mechanisms by which food restriction exerts its beneficial effects could eventually provide targets for intervention in humans.", + "\tIn comparison, caloric\nrestriction, intermittent fasting, or a ketogenic diet generally improve lifespan and health\n811 These dietary effects are not solely dependent on patterns of caloric intake, but are\nmodulated by dietary macro- and micronutrient composition, the amount of time spent in\ndifferent metabolic states, age of onset, periodicity of access to food, sex, and of greatest\nimportance to us in this studydifferences in genometype (strain) and gene-by-dietary\ninteractions 12,13. While the effects of differences in dietary composition and caloric restriction on lifespan\nhave been studied extensively, key results remain controversial 1416.\tThis again indicates that that weight gain\naccounts for only 45% of the change in lifespan. Author Manuscript\n\nOur findings can be compared to strain variation and GXE effects in response to dietary\nrestriction. Dietary restriction without malnutrition is regarded as having an almost universal\nbenefit on longevity 5355. One exception is a pair of studies on the impact of moderately\nintense restrictiona 40% reduction in caloric intakeacross a large family of LXS strains\nof mice (n of up 44 strains with 1020 replicates per strain) 17,19.", + "\tNutrition, phenotype and longevity\n\nNo issue so 'vividly' illustrates the power of diet to alter health as the consistent observation of the effect of caloric restriction (CR) on longevity.To date, neither drug, gene nor environmental intervention have been successfully demonstrated to prolong longevity in animals; however, the simple reduction of food calories can increase life span by 30-40% across a number of model organisms, including yeast, Drosophilia, Caenorhabditis elegans, rodents and monkeys [5][6][7].This effect of CR raises one of the most intriguing questions facing life scientists today.Despite the demonstrated positive age-related benefits of a reduction in energy intake -including decreased insulin resistance [8], increased production of glucocorticoids [9] and increased production of heat-shock proteins [10] -the mechanisms by which CR contributes to increased longevity remain unknown.How CR leads to longer life span cannot be attributed to any single factor without considering the simultaneous effects of the others.CR could alter multiple age-related processes, from energy metabolism to oxidative stress and DNA repair.Unravelling the multiparametric links of CR and aging led to the seminal genomic experiment for nutrition: the gene expression analysis of young and old tissues in normal and CR animals [11 ] is a pioneering example of the use of DNA arrays to explore the effects of CR and aging on gene expression in mouse skeletal muscle.The experiment is compelling for its simplicity and its implications, that is, the gene expression profiles for a clear phenotypic difference were compared (young versus old versus CR old mice).The power of the technique was evident by the discovery of a wide range of affected genes, including those involved in protein and energy metabolism, biosynthesis (e.g. of fatty acids), and macromolecular damage, implying immediately that the effects of aging and CR are broad, yet interrelated.More detailed experiments are now being pursued around the world following the identification of the genes that are altered during aging and protected by CR.The publication of this experiment also followed the now routine approach of supplying the raw database through an accessible internet site.", + "\t\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways.", + "\t\n\nA key question still unresolved is to what extent the mechanisms of aging are conserved between species with vastly different lifespans.Some studies suggest that similar mechanisms are involved in aging in many species.For example, caloric restriction extends lifespan in yeast, worms, flies, mice, and primates (Weindruch 2003).Additionally, signaling through the insulin-like growth factor pathway, chromatin regulation by sir2, and oxidative damage have each been shown to affect lifespan in diverse model organisms (Tissenbaum and Guarente 2002).Other studies emphasize that changes occurring at the end of life are unlikely to be evolutionarily conserved (Kirkwood and Austad 2000).In the wild, very few animals (including humans) survive to their maximal biological lifespan.Thus, the changes in physiology that occur in very old animals have minimal effects on the fitness of individuals, and are unlikely to be evolutionarily conserved.Therefore, aging is likely to be species-specific, and studies of old age in model organisms are unlikely to be relevant to humans.", + "\t\n\nIn summary, we postulate that due to metabolic changes and the subsequent increase in stress response, physiological processes evoked by starvation show strong correlation with anti-aging processes (Table 2).These observations suggest that starvation may evoke the same stress response reaction as caloric restriction, which is the only treatment known to prolong lifespan in all organisms tested to date.The difference is that starvation results in a much stronger or prolonged induction.This accentuated response during starvation may facilitate the experimental identification of basic molecular mechanisms linking nutrition and health using a nutrigenomic approach." + ] + ], + "task_id": [ + { + "task_id": "99D2FBCC88055B11FD6F2181CECBFB9C" + }, + { + "task_id": "1FBEC3B0E217E49DDB92D2633A8C8A7E" + }, + { + "task_id": "2F1DB635DB8670F444D7835B65E10E06" + }, + { + "task_id": "F1D310BC7601CB79594B450C7874881E" + }, + { + "task_id": "C56D0E0F1C9234ECD5058B1BAC023499" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_aging_4.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_aging_4.json new file mode 100644 index 0000000..9ee18d0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_aging_4.json @@ -0,0 +1,109 @@ +{ + "question": [ + "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?", + "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?", + "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?", + "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?", + "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + ], + "answer": [ + "Oxidative stress, primarily caused by reactive oxygen species (ROS) generated from cellular metabolism, leads to damage to various cellular components, including DNA, proteins, and lipids. This damage accumulates over time, leading to functional decline in cells. In particular, damage to DNA can lead to mutations and impaired cellular functions. The balance between DNA damage and its repair is a major determinant of cell longevity. Additionally, oxidative damage to mitochondria, a major source of ROS, can impair energy production and further contribute to aging. This process is supported by the oxidative stress theory of aging and the mitochondrial theory of aging.", + "Extracellular matrix (ECM) remodeling plays a significant role in the aging process. Aging results in changes in ECM biosynthesis, modifications of ECM components, and alterations of cell-matrix interactions, which contribute to age-related pathologies. The synthesis of many ECM components, such as elastin, declines with age, impairing the elasticity and resilience of tissues. Age-related ECM changes also alter vascular mechanotransduction, dysregulating cell responses to alterations in the environment. Additionally, aging and cellular senescence increase the secretion of matrix metalloproteinases (MMPs), which compromises the structural integrity of tissues and promotes pathological remodeling. This can lead to increased tissue stiffness, a common feature of aging tissues. Furthermore, dysregulated collagen synthesis contributes to vascular fibrosis and arterial stiffening.", + "Recent developments in autophagy research have shown that autophagy, a cellular degradation process, plays a crucial role in aging and longevity. Certain compounds like rapamycin, resveratrol, and polyamines can induce autophagy, with polyamines showing results in human clinical research. Autophagy is also linked to the regulation of various processes that contribute to aging, such as protein degradation, mitochondrial metabolism, and stress response. Studies have shown a decline in autophagy in aging mammals, and increased autophagy is required for lifespan extension in certain organisms. Furthermore, the up-regulation of autophagy by certain compounds has been associated with increased lifespan in various organisms. Dysfunctional autophagy is implicated in many age-related diseases, and the activation of autophagy has been linked with increasing lifespan in animal models.", + "Age-related shifts in stem cell niche composition and function can lead to a decrease in tissue regeneration capacity. This is because these shifts can result in a reduced ability of stem cells to self-renew and produce progeny to replenish worn-out and damaged cells in aged tissues. This can lead to a depletion of stem or progenitor cell pools, promoting age-related pathologies. Additionally, the induction of stem cell senescence may compromise tissue renewal. Therefore, understanding these shifts is crucial for developing therapies to counteract age-related decline in tissue regeneration.", + "Cross-links and AGEs accumulation in aging tissues can lead to several adverse effects. They can cause structural changes in proteins, lipids, and nucleic acids, leading to altered cellular functions. AGEs can mediate intracellular glycation of mitochondrial respiratory chain proteins, increasing ROS levels and triggering oxidative stress. They can also bind with RAGEs, activating signaling pathways that upregulate inflammatory cytokines and adhesion molecules. In the vascular system, AGEs can cause endothelial dysfunction, arterial stiffness, and vascular remodeling. In diabetes, AGEs can crosslink extracellular matrix proteins, impair degradation by MMPs, and increase cardiac stiffness. Overall, the accumulation of AGEs can lead to inflammation, apoptosis, and other processes related to aging." + ], + "contexts": [ + [ + "\t\n\nCell senescence, telomere shortening, and oxidative stress Attempts at synthesizing two major areas of focus in aging research, cell senescence [287,288] and free radicals, have been made since the 1970s (for a recent review see [289,290]).Early results by Packer and Smith suggested that vitamin E treatment could completely prevent cell senescence [291]; however, this result proved to be irreproducible [292].Nevertheless, it was observed that decreasing oxygen tension, from the customary 21% O 2 to more physiological levels (3% O 2, as would be found in vivo) led to an increase in cell doublings before senescence (i.e., an increase in the Hayflick limit or replicative life span [293][294][295][296]).Similar effects were also reported using antioxidants [296][297][298].In the 1990s, von Zglinicki et al. reported that a mild increase in oxygen tension (40%) triggered senescence within 3 cell divisions in human fibroblasts [299].von Zglinicki and co-workers proposed that oxidative damage to telomeres was responsible for the rapid triggering of senescence [299][300][301] and recent studies show that telomeric DNA may be particularly sensitive to oxidative damage [302].Following von Zglinicki et al. 's report, other investigators, using different oxidative stressors and different cell types, have reported very similar results.Mild oxidative stress reduces clonal life span and conversely, reduction of oxidative stress extends clonal life span [303][304][305][306][307]. Guarente's lab has provided additional evidence in this general direction, with the demonstration that RNAi knockdown of Sod1 triggered early senescence in human fibroblasts [308].This result is consistent with the earlier report by Epstein's laboratory that fibroblasts derived from Sod1 / mice failed to grow at all in culture [188].A great breakthrough in this area occurred when Campisi's lab demonstrated that senescence could be prevented completely in primary mouse cells when the cells were grown at 3% oxygen, instead of the customary 21% [309].This also resulted in a dramatic reduction of oxidative damage-signature mutations [310].In other words, these investigators demonstrated that in vitro senescence in mice cells was directly related to oxygen toxicity, i.e., oxidative damage.", + "\t\n\nThe free radical theory of aging, first proposed by Harman in 1956 [21], has received a lot of attention over the years as indicated by the number of scientific reviews on antioxidant interventions in different animal models and human clinical trials.The mitochondrion has been identified as a major source of reactive oxygen species (ROS) and thus oxidative stress potentially contributing to the aging process, although several plasma membrane and cystosolic enzymes may also contribute to the increased intracellular pro-oxidant status observed during aging [22].In the mitochondrial respiratory chain, electrons entering complexes I and II are transferred to complex III, then IV where they are combined with molecular oxygen and hydrogen to form H 2 O. Redox reactions at respiratory complexes I, III, and IV are coupled to the extrusion of protons from the mitochondrial matrix into the intermembrane space.The re-entry of protons into the matrix is coupled to the synthesis of ATP from ADP and P i .This oxidative phosphorylation is responsible for the vast majority of ATP production and oxygen consumption in most types of animal cells [23].Up to 2% of oxygen used in this complex reaction undergoes monoelectronic reduction and results in the formation of superoxide anion and hydrogen peroxide, which can lead to the formation of the more toxic species hydroxyl radicals [24,25].Such reactive species can attack and modify genomic DNA.An important type of oxidative DNA lesion accumulating with age is 8-oxo-deoxyguanine [26].If unrepaired, this adduct in genomic DNA may lead to a point mutation upon DNA replication.During DNA replication, 8-oxo-deoxyguanines present on either strand of DNA can mispair with adenosines and lead to G:C T:A transversion mutations.A misincorporation of an 8-oxodeoxyguanine as a substrate nucleotide can also lead to the same type of mutational pattern [27].", + "\t\n\nOur results are consistent with the oxidative stress theory of aging originally proposed by Denham Harman [26], and the notion that a vicious cycle of ROS generation and oxidative damage is the ultimate driver of aging [27].Our data also indicate that endogenous nuclear DNA damage is able to trigger this cycle of escalating ROS abundance, oxidative damage, senescent cell accumulation and age-related pathology.\t\n\nTo determine if this oxidative stress is pathological, we suppressed it pharmacologically in Ercc1 -/ mice with the mitochondrial-targeted radical scavenger XJB-5-131.Chronic administration XJB-5-131 significantly reduced both oxidative DNA damage and senescence (Fig. 5).The reduced level of senescent cells corresponded to a reduction in agerelated morbidity.This is consistent with numerous recent studies demonstrating that genetic or pharmacologic elimination of senescent cells slows age-related decline [2,4,7,8,[84][85][86].The observation that suppressing oxidant production is sufficient to decreases senescence indicates that reactive species are required to ultimately cause or maintain senescence in response to genotoxic stress.", + "\t\n\nIntroduction as replication errors, spontaneous chemical changes to Although aging is nearly universally conserved among the DNA, programmed double-strand breaks (DSBs) (in eukaryotic organisms, the molecular mechanisms unlymphocyte development), and DNA damaging agents derlying aging are only beginning to be elucidated.A that are normally present in cells.The latter category useful conceptual framework for considering the probincludes reactive oxygen species (ROS), such as superlem of aging is the Disposable Soma model (Kirkwood oxide anion, hydroxyl radical, hydrogen peroxide, nitric and Holliday, 1979).This model proposes that organoxide, and others.Major sources of cellular ROS proisms only invest enough energy into maintenance of the duction are the mitochondria, peroxisomes, cytochrome soma to survive long enough to reproduce.Aging oc-p450 enzymes, and the antimicrobial oxidative burst of curs at least in part as a consequence of this imperfect phagocytic cells.ROS can cause lipid peroxidation, maintenance, rather than as a genetically programmed protein damage, and several types of DNA lesions: sinprocess.Although aging may involve damage to varigle-and double-strand breaks, adducts, and crossous cellular constituents, the imperfect maintenance of links.The situation in which ROS exceed cellular antinuclear DNA likely represents a critical contributor to oxidant defenses is termed oxidative stress.As normal aging.Unless precisely repaired, nuclear DNA damage byproducts of metabolism, ROS are a potential source can lead to mutation and/or other deleterious cellular of chronic, persistent DNA damage in all cells and may and organismal consequences.Damage to both nuclear contribute to aging (Sohal and Weindruch, 1996).The DNA, which encodes the vast majority of cellular RNA ROS theory of aging is discussed in depth in this issue and proteins, and mitochondrial DNA have been proof Cell by Balaban et al. (2005).In brief, longer-lived posed to contribute to aging (Karanjawala and Lieber, species generally show higher cellular oxidative stress 2004).The reader is referred to the review by Balaban resistance and lower levels of mitochondrial ROS proet al. in this issue of Cell concerning the potential role duction than shorter-lived species.Caloric restriction, of mitochondrial DNA damage in aging (Balaban et al.,", + "\t\n\nWe previously showed that superoxide plays a primary role in chronological age-dependent DNA damage and mutations.Our model is that the DNA damage caused by oxidative and other types of stress accumulated during aging in nondividing cells generates double-strand breaks during the fi rst round of replication after the exit from G 0 .Cells lacking SGS1 attempt to repair this damage by homologous recombination between sister chromatids but generate a large number of GCRs, especially at advanced age.", + "\t\n\nReactive oxygen species (ROS) have long been at the center of the debate on causes of aging and a central player in the free-radical theory of aging.One form of oxidative damage that is considered irreversible and has been correlated with age in various organisms, including replicative age in yeast, is protein carbonylation (Nystrom 2005).Protein carbonyls have been proposed as a yeast aging factor based on the observations that both protein carbonyls (Aguilaniu et al. 2003;Erjavec and Nystrom 2007) and aggregates containing heavily carbonylated proteins (Erjavec et al. 2007) are asymmetrically retained in mother cells during division.The proper asymmetric segregation of oxidatively damaged proteins appears to be dependent on a functioning actin cytoskeleton (Aguilaniu et al. 2003;Erjavec et al. 2007), which has independently been linked to ROS and life span through the actin bundling protein, Scp1 (Gourlay et al. 2004).", + "\t\n\nThere is some evidence that related processes occur in mammals.First, cells damaged by oxidative stress in vitro undergo stochastic transcriptional changes that parallel those in aged heart tissue (Bahar et al., 2006).Second, a deficiency in the DNA repair factor ERCC1 accelerates aging phenotypes and generates gene expression profiles reminiscent of aged animals (Niedernhofer et al., 2006).Third, cells that senesce because of replicative aging in vitro or in aged tissues in vivo exhibit alterations in heterochromatin (Herbig et al., 2006;Narita et al., 2006) and secrete growth factors that can drive tumorigenesis (Campisi, 2005).Finally, oxidative DNA damage at promoters correlates with gene repression in the aging human brain (Lu et al., 2004) and has been linked to both transcriptional and epigenetic changes that may contribute to Alzheimer's disease (Wu et al., 2008).", + "\t\n\nThere are many theoretical considerations on oxidative damage of mitochondria about aging.The \"free radical theory of aging,\" proposed by Harman in 1956 (138), that free radicals cause nonspecific damage to macromolecules, such as DNA, lipids, and proteins, has attracted much attention in recent years due to development in free radical biology.Harman (139) also proposed aging as consequences of mitochondrial aging that free radical reactions may contribute to changes in the mitochondrial inner membrane with age due to effects on both mtDNA and nDNA.Based on the observation of Drosophila, Miquel et al. (238) postulated that there is a distinct possibility of free radical-or lipid peroxide-induced inactivation of the mtDNA of fixed postmitotic cells with the passage of time.Fleming et al. (110) proposed that the site of irreversible injury is the mtDNA rather than the biomembranes.A two-step hypothesis on the mechanisms of in vitro cell aging, \"oxygen radical-mitochondrial injury hypothesis of cell aging,\" was proposed by Miquel and Fleming (239) that the fundamental cause of cell aging is an instability of the mitochondrial genome because of a lack of or balance between mitochondrial repair and the disorganizing effects of oxygen radicals.Thus, deprived of the ability to regenerate their mitochondrial populations, the cells will sustain an irreversible decline in their ability to synthesize ATP, with concomitant senescent degradation of physiological performance, and eventual death.Bandy and Davison (15) suggested that mitochondrial genome mutations may increase oxidative stress as implications for carcinogenesis and aging.", + "\tAging\n\nThe oxidative stress theory of aging proposes that accumulation of oxidative DNA damage over the life span of an organism leads to gradual decline of cellular functions and eventual death (Bohr, 2002).This model is supported by several circumstantial evidences including the observation that lower free radical production and/or antioxidant treatment protects against agerelated deterioration, and cognitive decline (Lemon et al., 2003).Further, deficit or decrease in the repair of oxidative DNA damage appears to correlate with premature aging and age-related diseases (Bohr et al., 2007).It appears likely that overall genome repair, specifically the balance between DNA damage and its repair is a major determinant of the longevity and cell viability.A specific defect in processing 5 0 dRP residue at the strand break in Sir2 (SIRT6 homolog)-deficient mice displayed age-related degenerative phenotype (Mostoslavsky et al., 2006).The activities of DGs OGG1, NTH1 and uracil DNA glycosylase (UNG) in brain mitochondria decrease significantly with age (Gredilla et al., 2010).", + "\tPrxs and the free radical hypothesis of aging\n\nThe evolved version of Harman's (Harman 2003) free radical theory of aging proposes that organisms age because the constituents of cells and tissues accumulate damage over time caused by reactive oxygen (and/or nitrogen) species originating from endogenous metabolism, including, among many other possible activities, mitochondrial respiration.At first glance, it appears that the data concerning Prxs and aging fit this theory like a glove, as Prxs become ''damaged'' (catalytically inactivated as a peroxidase) during aging due to a modification caused by a reactive oxygen species (ROS), specifically hydrogen peroxide (or organic hydroperoxides), and that counteracting this ''damage'' by elevating the levels of the ''repair'' enzyme Srx1 prolongs life span (Molin et al. 2011).Moreover, as the Prxs themselves act as enzymatic antioxidants and protect the genome against oxidative modifications (see below), it is possible that peroxidedependent inactivation of Prxs gives rise to a negative feedback loop with respect to the cell's capacity for ROS homeostasis.", + "\t\n\nAging is a dynamic and complex process defined as the time-dependent functional decline.With age, homeostasis declines and damage accumulates.One of prime candidates that induce macromolecular damage is oxidative stress from reactive oxygen species (ROS) generated from normal physiological activities.Indeed, many long-lived mutants are resistant to oxidative stress [53].Ferroptosis involves metabolic dysfunction that results in the production of both cytosolic and lipid ROS [36,38].Repression of SLC7A11 transcription by p53 results in reduction of cystine uptake.Because of less cystine uptake, the levels of intracellular glutathione (GSH) will be reduced and the cellular system for defending oxidative stress is abrogated.Thus, the sensitivity of ROS-induced ferroptosis is significantly increased in p53-activating cells.We showed that SLC7A11 is downregulated by p53 and that p53mediated ferroptosis is dramatically induced in the testis of p53 3KR/3KR Xrcc4 -/-mice.Thus, it is very likely that the combination of genomic instability and p53-mediated ferroptosis contributes significantly to the aging associated phenotypes observed in p53 3KR/3KR Xrcc4 -/-mice.", + "\tSources of Damage Increase with Age\n\nThe free radical theory of aging posits that aging is caused primarily by oxidative damage incurred by ROS that chemically modify critical cellular biomolecules (13).This theory has evolved over the years to become the oxidative stress theory of aging, but the principle is the same, in that the accumulation of oxidative damage drives aging.In support of this theory, a large body of literature indicates that oxidative damage to all cellular macromolecules increases with age.Furthermore, overexpression of antioxidant enzymes that detoxify ROS, such as copper-and zinc-containing superoxide dismutase (SOD), manganese-containing SOD, or catalase, increase the life span of Drosophila melanogaster by as much as 30% (14).Additionally, most long-lived mutants in D. melanogaster and Caenorhabditis elegans have increased resistance to oxidative stress.In mammals, the role of oxidative stress is less clear because overexpression of catalase, SOD1 (pancellular expression), or SOD2 (mitochondrial) does not extend the life span of mice (15).However, overexpression of catalase specifically targeted to the mitochondria does extend the life span of some mice up to 20% (16).Additionally, treatment with the antioxidant nordihydroguaiaretic acid (NDGA) and an activator of NRF2 (master regulator of antioxidant response) extends median life span in male mice (17).\t\n\nThe free radical theory of aging evolved to the mitochondrial theory of aging when mitochondria were implicated as the primary source of ROS.Electrons leaked from the electron transport chain at the inner mitochondrial membrane can react with molecular oxygen to produce a superoxide radical, which can be converted by SOD to yield hydrogen peroxide (H 2 O 2 ).In the presence of transition metal ions (e.g., Fe 2+ or Cu + ), H 2 O 2 can be further converted to the highly reactive hydroxyl radical via the Fenton-type reaction.These ROS react locally to damage genes or proteins necessary for oxidative phosphorylation, leading to further uncoupling of electron transport and increased ROS production in a feed-forward manner.Abundant evidence shows that ROS and oxidative damage increase as organisms age.But which cellular target of these damaging radicals and other reactive molecules is health and life limiting?If the answer is DNA, then one expects DNA damage to accumulate with age.", + "\tThe Free Radical Theory of Aging. The free radical theory of aging proposed by Denham Harman more than fifty years ago postulates that aging results from the accumulation of deleterious effects caused by free radicals, and the ability of an organism to cope with cellular damage induced by ROS plays an important role in determining organismal lifespan [3].In agreement with this theory, increased ROS production by mitochondria and increased 8-oxo-dG content in the mtDNA are frequently detected in aged tissues [40,[47][48][49][50], suggesting that progressive accumulation of oxidative DNA damage is a contributory factor to the aging process.Consistently, many studies have found that increased oxidative damage in cells is associated with aging [51][52][53].Furthermore, genetic studies in worm, fly, and mouse have linked enhanced stress resistance or reduced free radical production with increased lifespan [27].Mutant strains of C. elegans that are resistant to oxidative stress have extended lifespan, whereas those more susceptible to free radicals have shortened lifespan [54,55].Mice lacking the antioxidant enzyme superoxide dismutase 1 (SOD1) exhibit a 30% decrease in life expectancy [56].Conversely, simultaneous overexpression of SOD1 and catalase extends lifespan in Drosophila [57].Small synthetic mimetics of SOD/catalase increase lifespan in C. elegans [58], while treatment of antioxidant drugs in mice increases the median lifespan up to 25% [59,60].Further supporting this hypothesis, mice lacking Ogg1 and Myh, two enzymes of the base excision repair pathway that repairs oxidative DNA damage, show a 50% reduction in life expectancy [61].Collectively, these studies demonstrate that interplay between ROS and protective antioxidant responses is an important factor in determining aging and lifespan.\tMitochondria and Aging\n\n3.1.The Mitochondrial Theory of Aging.Because mitochondria are the major producer of ROS in mammalian cells, the close proximity to ROS places mitochondrial DNA (mtDNA) prone to oxidative damage [104].Consistently, many studies have shown that 8-oxo-dG, one of the common oxidative lesions, is detected at higher level in mtDNA than nuclear DNA, suggesting that mtDNA is more susceptible to oxidative damage [52,[105][106][107][108][109][110][111][112][113].As both the major producer and primary target of ROS, mitochondria are thought to play an important role in aging.The mitochondrial theory of aging, extended from the free radical theory, proposes that oxidative damage generated during oxidative phosphorylation of mitochondrial macromolecules such as mtDNA, proteins, or lipids is responsible for aging [114].As mtDNA encodes essential components of oxidative phosphorylation and protein synthesis machinery [115], oxidative damageinduced mtDNA mutations that impair either the assembly or the function of the respiratory chain will in turn trigger further accumulation of ROS, which results in a vicious cycle leading to energy depletion in the cell and ultimately cell death [104,114,[116][117][118].", + "\t\n\nThere is an emerging consensus that oxidative damage is of central importance to much of the age-related overall decline of animal cells, from yeast to humans [2][3][4][5][6][7] .Caloric restriction or environmental conditions that favour a decrease in oxidative metabolism also increase lifespan 8 , and transgenic or knockout animals with decreased oxidative metabolism have increased lifespans.For example, flies that consume oxygen at a high rate have a reduced lifespan, and low oxygen-consumption rates and cold temperatures favour a prolonged lifespan 9,10 .Lipids, proteins and DNA have all been argued to be Ageing, repetitive genomes and DNA damage Michael R. Lieber and Zarir E. Karanjawala www.nature.com/reviews/molcellbioP E R S P E C T I V E S to one another, thereby permitting a copying of information from one sister chromatid to the other.This typically restores the information content at the break site back to normal.", + "\t\n\nA key macromolecule at risk for ROS-mediated damage is nuclear DNA [1], which is evident from the wide range of oxidative DNA lesions that accumulate gradually in rodents and humans with advancing age [6,7].\tIntroduction\n\nA prevailing hypothesis to explain the molecular basis of ageing is Harman's ''free-radical theory of ageing'', which states that endogenous reactive oxygen species (ROS), which result from cellular metabolism, continually damage biomolecules [1].In line with this hypothesis, it has been shown that increased resistance to oxidative stress (e.g., by improved antioxidant defense) extends the lifespan of Caenorhabditis elegans, Drosophila, and rodents [2][3][4], whereas hypersensitivity to oxygen considerably reduces the lifespan of nematodes [5].", + "\tReplication stress, mitochondria and growth signaling\n\nIncreased oxidative damage to DNA and other cellular constituents by ROS produced in dysfunctional mitochondria is an important component of modern versions of the 'free radical theory' of aging (3,71).It is often assumed that the production of ROS in mitochondria is directly proportional to the rate of mitochondrial respiration, and that increased respiration promotes aging.A number of recent studies in budding yeast and mammals argue that these long-held assumptions are incorrect (72).For example, caloric restriction and other experimental manipulations that enhance respiration in budding yeast reduce, rather than increase levels of ROS at the same time that they enhance life span (73).Similarly, budding yeast cells cultured in medium containing glycerol or ethanol, which are metabolized via respiratory pathways, exhibit a longer chronological life span (22).Furthermore, deletion of TOR1 extends chronological life span of budding yeast by enhancing respiration, but reducing ROS (21).As might be expected based on these reports, experimental manipulations that increase the production of ROS in mitochondria shorten the chronological life span of this organism (73,74)." + ], + [ + "\tSenescence and apoptosis are thought to contribute\nto aging and age-related disorders by decreasing the proliferative potential of progenitor\nstem cells, altering tissue regenerative capacity, decreasing tissue function and by altered\ntissue architecture and microenvironment caused by altered gene expression and secretion of\ninflammatory cytokines, growth factors, and proteases (Campisi 2003; Coppe et al. 2008;\nGarfinkel et al. 1994; Krtolica and Campisi 2002; Kuilman et al. 2008; Novakova et al. 2010; Ohtani and Hara 2013).", + "\t\n\nThere exists a substantial body of research addressing the tissue, cellular and molecular changes that accompany or directly contribute to aging in a range of model organisms (reviewed in [7]).However, the majority of data, generated in model organisms or in vitro (cellular senescence), has yet to be validated in human aging.Moreover the relative contribution of putative gerontogenes to human pathological agerelated processes is unknown.Age-associated impaired healing correlates with increased inflammation, increased matrix proteolysis and delayed re-epithelialization leading to chronic wound states, processes modulated by exogenous estrogen treatment [8].In a recent study we characterized estrogen-regulated changes in gene expression using a model of delayed wound healing in young mice that have been rendered hypogonadal by ovariectomization (hence removing any effects of 'intrinsic aging') [9].Thus, using comparative analysis we are now in a position to address the relative contributions of estrogen and aging to healing in elderly humans.", + "\t\nAging alters gene expression of growth and remodeling factors in human skeletal muscle both at rest and in response to acute resistance exercise.\t\n\nAging alters gene expression of growth and remodeling factors in human skeletal muscle both at rest and in response to acute resistance exercise.", + "\t\n\nStructural integrity of skeletal muscle.Some noteworthy genes that were differentially expressed only in older subjects after RL support the concept that the muscles of older subjects may have experienced a degree of stress far exceeding that in young subjects despite being exposed to the exact same stressor.For example, gene expression of MyBPH was robustly elevated (4.1-fold) in the old only, as was myosin head domain containing 1 (MYOHD1; 1.4-fold).MyBPH is an integral myosin binding partner in the A band of myofibrils that interacts with the myosin rods and titin to provide structural integrity to the contractile apparatus.Reduced MyBPH expression is associated with muscle weakness in age-related disorders (30).Interestingly, localization of MyBPH to the contractile apparatus is directed by its C terminal domain consisting of two fibronectin type III motifs (24), and our microarray analysis also revealed a 1.6-fold increase among the old in the expression of fibronectin type III domain containing 3B (FNDC3B).As shown in mice, MyBPH is upregulated in the young after more intense eccentric loading (5), again suggesting age differences in the degree of mechanical stress required to activate many of these transcriptional responses (with young muscles requiring greater stress than old).MyBPH expres-sion is modulated by the transcription factor SMARCA4 (SWI/ SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 4), which was also significantly upregulated in the old only.Interestingly, SMARCA4 is activated by glucocorticoid receptor signaling and, in turn, regulates the expression of notable muscle-specific genes including myogenin, troponin T, and MyBPH.A strain on muscle integrity among the old was also suggested by significant downregulation (1.7-fold) of both type IV collagen 3 (COL4A3) and 4 (COL4A4) mRNA expression and 1.6-fold upregulation of TUBA8.Type IV collagen, a major constituent of basement membranes, is degraded by matrix metalloproteinases (MMP-2 and MMP-9) in response to muscle damage (49).These findings suggest that the muscles of the older subjects may have been attempting to launch a compensatory effort to maintain structural integrity-a response to this degree was apparently not sensed as necessary among the younger subjects.", + "\tRole of Extracellular Matrix Remodeling in Vascular Aging\n\nThe extracellular matrix (ECM) is an important contributor to health and longevity.This noncellular compartment, ubiquitous to all tissues and organs does not only provide essential mechanical scaffolding but mediates highly dynamic biomechanical and biochemical signals required for tissue homeostasis, morphogenesis, and cell differentiation.Studies on model organisms suggest that evolutionarily conserved pathways regulate ECM remodeling during aging and that promotion of ECM youthfulness by antiaging interventions is an essential signature of longevity assurance. 206Aging in mammals also results in significant changes in ECM biosynthesis, postsynthetic modifications of ECM components, and alterations of cell-matrix interactions, which contribute to the development of a spectrum of age-related pathologies. 207ge-related alterations of the ECM, including the subendothelial basement membrane, intima, media, adventitia, and interstitial matrix (which constitute more than half of the mass of the vascular tissue), play a fundamental role in impairment of both structural and regulatory homeostasis of the vasculature. 208With age, the expression of growth factors that regulate ECM biosynthesis is altered 45 and the synthesis of many ECM components (eg, elastin) declines, which impairs elasticity and resilience of the vascular wall to mechanical damage and rupture induced by bursts in wall tension because of pulsatile pressure waves. 208Age-related ECM changes also likely alter vascular mechanotransduction, dysregulating cell responses to alterations in the hemodynamic environment.Additionally, aging and cellular senescence alter the secretory phenotype of vascular endothelial and smooth muscle cells, increasing MMP secretion. 45This together with increased MMP activation 208 induced by high ROS levels compromises the structural integrity of the vasculature and promotes pathological remodeling (eg, in hypertension), resulting in increased likelihood of aneurysm formation and vessel rupture, including the development of cerebral microhemorrhages. 45The available evidence suggests that many of these age-related ECM alterations are governed by circulating factors and factors produced in the vascular wall, including the extended renin-angiotensin-aldosterone system (see above) and an age-related decline in circulating IGF-1. 209ollagen synthesis is also dysregulated with age in the vascular wall likely because of the effects of increased paracrine action of TGF- (transforming growth factor-), 123 which contributes to vascular fibrosis and arterial stiffening. 208Additional features that contribute to increased arterial stiffness include decreased elastin synthesis, elastin degradation and fragmentation, elastin calcification, alterations in cross-linking of extracellular matrix components (eg, by increased presence of advanced glycation end products). 208,210,211he pathophysiological consequences of age-related ECM remodeling and arterial stiffening have been the subject of a recent comprehensive review by AlGhatrif and Lakatta. 6In brief, as the large conduit arteries stiffen in aging, aortic pulse wave velocity, systolic pressure, and pulse pressure significantly increase, 212 whereas diastolic pressure decreases.Decreased diastolic pressure leads to a decline in coronary blood flow.Increased systolic pressure promotes left ventricular remodeling, diastolic dysfunction, and exacerbates atherogenesis.Because of the dilation of conduit arteries, wall tension significantly increases, contributing to the development of aneurysms.In addition to alterations in the biomechanical properties of large arteries, age-related ECM remodeling likely also affects microvascular transport and barrier functions. 213Age-related alteration of the ECM structure and composition are also manifested in the wall of veins, contributing to the pathogenesis of varicosities. 214\t\n\nFigure 4. Conceptual model for the pathogenic role of cellular senescence in vascular aging.The model predicts that increased presence of senescent endothelial or smooth muscle cells (SMCs) in the aged vasculature and their proinflammatory secretome (SASP [senescence-associated secretory phenotype]) contributes to impaired angiogenesis and microvascular rarefaction, pathological remodeling of the extracellular matrix (ECM), barrier disruption, chronic inflammation, and atherogenesis.MMP indicates matrix metalloproteinase.", + "\t\n\nAge-related transcriptional remodeling and mitochondria", + "\t\n\nChromatin remodeling in aging, J. G. Wood et al.", + "\tAging is only, in part, the result of crosslinking reactions\n\nWhile Bjorksten (1968) proposed that crosslinking was a major feature of the chemical aging of tissues, particularly of collagen, it has become apparent in recent years that many age-dependent chemical modifications of protein are monofunctional.These include oxidative modifications of phenylalanine, tyrosine and methionine residues (Table 1), carboxyalkylation of lysine (Table 4), and deamidation and racemization of amino acids.Extracellular matrix proteins accumulate higher levels of monofunctional chemical modifications, as well as crosslinks, not because they are uniquely sensitive to damage, but because they generally turnover more slowly.There are few quantitative studies on the age-dependent accumulation of biomarkers in intracellular proteins, even in proteins with long half-lives, such as contractile proteins in muscle or histones in post-mitotic cells.These proteins may be exposed to higher levels of reactive oxygen species generated in mitochondria or peroxisomes, or to higher levels of reactive carbonyl intermediates in glycolysis, but are also better protected by intracellular antioxidant and detoxification systems.", + "\t\n\nVarious extracellular matrix-related proteins were differentially regulated herein.Extracellular matrix proteins provide structural support, mechanical properties, and strength of tissues, including vocal folds, playing a pivotal role in phonation [62,71,72].Collagens XIV, XVIII, and Fibulin 5 were downregulated in older rabbit vocal folds compared to young tissue.To our knowledge, these specific collagen types have not been investigated in depth in vocal fold tissue; however, studies suggest that the changes in the collagen fiber density and arrangements within the lamina propria may affect phonation [73,74].Collagen type IV is exclusive to extracellular matrix basal membranes [75] and is present in the human vocal fold basal membrane providing support to epithelial and endothelial cells [76].Collagen type IV was upregulated in older rabbit vocal folds compared to young, an effect of aging observed in our study.The relationship between Collagen type IV and aging is not well established.Increased accumulation of Collagen type IV is reported in the basal lamina of cerebral microvessels in humans [77] but decreased in the skin of older adults [78].Conversely, several extracellular matrix proteins were upregulated, including Collagen type XVIII and Fibulin 5, in the presence of dehydration when observing the effect of hydration status alone.These protein changes may be related to the remodeling of the extracellular matrix [79] in response to dehydration.Moreover, the accumulation of collagens and the decrease of elastins may result in extracellular matrix stiffness in aging larynx and other organs [59,79].Finally, Lamin A was upregulated by dehydration, by a smaller magnitude, especially when observing the mean difference within the young groups.Previous data has identified that Lamin proteins A and C are important for imparting the nucleus with its stiffness, and their expression has been reported to scale with tissue stiffness [80].Thus, upregulation of this protein due to dehydration may be related to tissue stiffness in the vocal fold of rabbits.", + "\t\n\nRecently, collagen production and extracellular matrix remodeling were determined to be essential for longevity in C. elegans.Collagen may directly affect signaling processes associated with longevity in C. elegans, including signaling through SKN-1 [40,58].We note that HSF-1 was also recently shown to regulate cytoskeletal integrity in a process that can influence stress resistance and longevity in C. elegans [59].Thus, the linkage of both the extracellular matrix and the cytoskeleton to HSF-1 may provide a mechanism by which HSF-1 promotes longevity.\tHSF-1 regulates collagen genes which may affect the aging process\n\nIt is interesting that cuticle structure genes constitute the largest overlap with aging-related genes.In humans, mutations in collagens lead to a large number of heritable human diseases such as osteoporosis and musculoskeletal diseases [53].Collagens are long-lived proteins known to accumulate damage during aging, leading to a decline in tissue health [54].Also, type I collagens become resistant to proteolysis upon age [55,56], affecting their turnover.Interestingly, mice expressing cleavageresistant type I collagen go through an accelerated aging process [57].Thus, cellular aging can be affected by the state of the extracellular matrix in mammals.", + "\t\n\nAn observation that is specific for males is the global downregulation with aging of genes involved in the synthesis of the ECM and in particular of different forms of collagen (Table 2).In addition, aging males but not females showed a decrease in collagen type III.Interestingly, collagen type III decreases the size of collagen bundles and thereby increases vascular elasticity (11).Therefore, a decreased expression of collagen type III can participate in the increased stiffness that characterizes the aging aorta (23).An interesting observation from our study that directly relates to the mechanism of vascular remodeling is the upregulation in aging males of the transcript encoding collagen type VIII (Table 3).That specific collagen type, which is upregulated in response to vascular injury (24), promotes VSMC migration (1).The upregulation of this transcript together with the downregulation of other isoforms in aging males again supports the notion that this group is more susceptible to neointimal proliferation, VSMC migration, and potentially atherosclerosis.\t\n\nOur study shows that the genomic adaptation to vascular aging involves not only the genes involved in ECM composition and VSMC differentiation and migration, but also many other categories of genes participating in intracellular functions, such as cell signaling, DNA repair, metabolism, and protein synthesis.Our study also illustrates that most of the changes in gene expression with aging differ between males and females and correspond to different sets of transcription factors.Indeed, 5% of the 600 genes that were regulated by aging were observed in both old males and females.GO analysis also shows that specific subsets of genes are regulated differently between sexes, especially the genes participating in ECM composition and VSMC phenotype.We therefore propose that these transcriptional differences may underlie the different physiological properties of aging arteries between males and females, as well as their different susceptibility to vascular complications, such as hypertension or atherosclerosis.Furthermore, the analyses in young monkeys demonstrated major differences in genes regulating vascular structure, implying that the sex differences in vascular stiffness that develop with aging are programmed at an early age.", + "\tChronic liver diseases are characterized by aberrant matrix deposition, calling for our\nattention to the role of ECM in resolution of liver fibrosis. Tissue remodeling is regulated by MMPs,\ninvolved in the ECM degradation, and TIMPs, their endogenous inhibitors. Their subtle balance\nmaintains liver fibrogenesis. Tissue homeostasis is further regulated by proteolytic activity of the\nPLAU/PLAT/plasmin, responsible for the maintenance of the physiologic levels of ECM (40). PLAU promotes ECM degradation through activation of MMPs (MMP-2, -3 and -9; (41, 42),\nincreases the differentiation of hepatic stem cells, and HGF-dependent regeneration of hepatocytes\n(43).", + "\t\n\nMechanistically, the age-related increase in elastin degradation may result from augmented activity of proteases with elastinolytic activity, including certain MMPs and cysteinyl cathepsins, enzymes that, in turn, are regulated by inflammatory mediators (54,55).Collagen catabolism falls in aging arteries.\t\n\nAugmented transforming growth factor (TGF)-b activity favors the accumulation of collagen in the aortic wall.The activity of various elastases, including matrix metalloproteinases (MMPs), such as MMP-9 and MMP-12, as well as overexpression of the cysteine proteinases cathepsins S, K, and L, and the serine proteinase neutrophil elastase, elaborated by inflammatory cells, can all contribute to depletion of elastin (11).These alterations in the aorta's extracellular matrix contribute importantly to its loss of distensibility.This increased stiffness raises reflected waves and elevates systolic pressure.Yet diastolic pressure tends to decline with age.As aortic pulse wave velocity increases, pulse pressure rises (12).Indeed, pulse pressure is an independent risk factor for CV events (13).Isolated systolic hypertension accounts for the majority of uncontrolled hypertension in Americans over 50 years of age (14,15).substantially stroke and total mortality, with lesser benefit for ischemic cardiac events (16).Avoiding excessive sodium intake may provide an additional, nonpharmacological intervention for control of hypertension in older individuals (17,18).Some have raised concerns regarding the safety of aggressive lowering of blood pressure in elderly patients, particularly those with concomitant coronary artery disease (19).Indeed, a J-shaped curve relating CV outcomes to blood pressure may pertain to this In addition to reducing stroke, a major impediment to independent living and function in older patients, antihypertensive therapy may limit the development of dementing illnesses, as shown in the Syst-Eur trial (27).Decreased dementia and cognitive decline accrue with longer duration of antihypertensive treatment (28).An asymmetric loss to follow-up of individuals with impaired cognition may have biased the results of dementia in the SHEP study to the null (29).With regard to the former, vascular aging alters the function of the endothelium, the cells that line the lumen of blood vessels.Endothelial dysfunction includes reduced vasodilatory and antithrombotic properties, with an increase in oxidative stress and inflammatory cytokines (33)(34)(35) favoring atherogenesis and thrombosis, and predisposing to CVD (36).Human and experimental studies concur that diminished bioavailability of nitric oxide (NO), a key mediator of vasorelaxation and antiatherogenic processes, underlies age-dependent endothelial dysfunction (37,38).Reduced NO bioavailability can occur due to decreased synthesis or increased degradation of NO.Under normal conditions, endothelial nitric oxide synthase (eNOS) produces NO from L-arginine in the presence of the cofactor tetrahydrobiopterin (BH4) (39).Although studies differ regarding eNOS protein expression with age (34,40,41), recent data suggest an age-related alteration in eNOS function, referred to as eNOS uncoupling (42).", + "\tBackground\n\nTissue aging is caused by intrinsic and extrinsic factors that induce complex molecular changes and, in turn, a deterioration of cellular structures and function.These changes are major causes of age-related diseases like cancer or cardiovascular disorders [1,2].The main molecular adaptations occurring during aging are loss of genomic stability due to reduced DNA repair capacities [3], loss of proliferative potential caused by increased senescence [1,4], and age-related alterations in the DNA-methylation patterns that affect cellular plasticity [5,6].Metabolic adaptations are also considered to play a major role in aging [7][8][9][10].For instance, the metabolic function of mitochondria is progressively impaired during aging in different tissues [8,11].This can result in increased generation of reactive oxygen species that foster genomic instability [8,12].Moreover, several studies reported that caloric restrictions and diet adaptations, such as supplementation of food with branched chain amino acids [13,14], can significantly increase lifespan [15].This suggests that metabolic activity as well as nutrient sensing pathways are highly relevant for cellular aging processes (reviewed in [10]).Accordingly, interference with the insulin/IGF1 and the mammalian target of rapamycin (mTOR) pathways increased lifespan in different model organisms [7,[16][17][18].", + "\t\n\nWe examined the list of 447 age-regulated genes for functional groups showing a consistent change with age.One group includes genes involved in the formation of the extracellular matrix, which show a consistent increase in expression in old age.Seven age-regulated genes encode proteins known to play key roles in maintaining epithelial polarity (three types of claudins, two cadherins, occludin, and a cell adhesion molecule), all but one of which increase expression in old age (see Table S4).Forty-nine age-regulated genes encode protein components of the extracellular matrix, all but four of which increase expression in old age.In the kidney, the extracellular matrix could play a key role in governing the filtration of blood via the basement membrane, a capacity that declines with age.The observation that genes involved in forming the extracellular matrix increase expression in the kidney with age may be directly relevant to the age-related decline in glomerular filtration rate." + ], + [ + "\tStochastic damage\n\nFigure 2. Longevity assurance, ageing and disease.New studies of the biology of ageing are revealing processes that control when and how fast ageing occurs, such as insulin-IGF-1 signalling [6], cellular senescence [4], protein refolding [43][44][45], autophagy [41] and phase 1 and 2 detoxification [36,37,52].These represent major points of intervention against ageing-related disease.As shown here, lifespan pathways control improved cellular maintenance, which leads to slowed ageing (e.g.slowed normal cognitive ageing) and protection against diseases of ageing (e.g.neurodegenerative diseases of ageing, such as Alzheimer's and Parkinson's disease, and cancer).Ageing can evolve via selection to reduce investment in energetically costly somatic maintenance processes and instead to increase early fitness traits such as growth and reproduction [50,51].Arrows denote stimulation, and T bars inhibition, of the process indicated.Red and green denote changes leading to ageing and longevity, respectively.", + "\t\n\nFig. 4. Schematic showing how some external interventions trigger longevity, often at least partly through stimulating autophagy.The pink writing refers to dietary, chemical, or therapeutic interventions that can extend life span, in at least some organisms (described in the text).Arrows indicate stimulating effects, and blocked lines indicate inhibitory effects.This schematic is not meant to be exhaustive but highlights the pathways that alter the epigenetic information and autophagy.", + "\t\n\nTORC1 regulates several downstream processes that may contribute to its role in aging, including protein degradation via autophagy, mitochondrial metabolism, stress response, and mRNA translation (Stanfel et al. 2009).Autophagy, which literally means \"self eating\", is a degradative process through which cellular components are engulfed by cytoplasmic vesicles and transported to the lysosome/vacuole for degradation (Klionsky 2007).Autophagy is repressed by TOR signaling and is induced in response to starvation or treatment with TOR inhibitors, such as rapamycin (Noda and Ohsumi 1998).A decline in the autophagic response has been reported in aging mammals (Cuervo and Dice 2000), and increased autophagy is required for life span extension in long-lived C. elegans mutants with reduced insulin/IGF-1-like signaling (Melendez et al. 2003).Several recent studies have also uncovered an important role for autophagy in the response to DR. DR induces autophagy in yeast, worms, and flies (Juhasz et al. 2007;Morck and Pilon 2006;Takeshige et al. 1992) and is reported to be required for life span extension from DR or TOR-inhibition in both worms and flies (Hansen et al. 2008;Jia and Levine 2007;Juhasz et al. 2007).Recently, up-regulation of autophagy by spermidine has also been shown to be associated with increased life span in yeast, nematodes, and flies (Eisenberg et al. 2009).", + "\tInductors of Autophagy and its Impact on Aging\n\nAutophagy has a role in homeostasis, which plays an essential role in the maintenance of cellular physiology and the prevention of cellular damage.Among the inducers of autophagy have been described the already-mentioned rapamycin, resveratrol, and polyamines; however, only polyamines have demonstrated results in clinical research in humans [65].It is known that these compounds can induce the canonical autophagy pathway, which includes inactivation of the mammalian objective of the rapamycin complex 1 (mTORC1), allowing phosphorylation and activation of the Unc-51 complex (Ulk1/2), where the cascade of the other members of the complex is subsequently activated, ULK as FIP200 and ATG13 [65].\t\n\nOn the other hand, interventions using chemical inducers of macroautophagy, such as rapamycin, an mTOR inhibitor, can increase the life span of middle-aged mice like that induced by spermidine or polyamine-producing gut flora supplementation [87].In an unexpected finding, aged cells showed an increased accumulation of protein aggregates, suggesting a decline in lysosome functionality during aging even though the number of lysosomes increased [72,88].This disparity could be due to changes in the pH, as suggested by the fact that the vacuolar V-type ATPase complex, which is responsible for maintaining vacuolar pH, decreased during aging, suggesting a mechanistic link between altered protein complex composition and lysosome dysfunction [72,88].The stress-induced synthesis of cytosolic and organelle-specific chaperones was also impaired in aging.Mutant mice that were deficient in a co-chaperone of the heat-shock family exhibited accelerated aging phenotypes, whereas long-lived mouse strains showed a marked upregulation of some heat-shock proteins [89].\t\n\n2016;351:173-6.81.Koga H, Kaushik S, Cuervo AM.Protein homeostasis and aging: the importance of exquisite quality control.Ageing Res Rev. 2011;10:205-15.82.Labbadia J, Morimoto RI.The biology of proteostasis in aging and disease.Annu Rev Biochem.2015;84:435-64.83.Rubinsztein DC, Mario G, Kroemer G. Autophagy and aging.Cell.2011;146:682-95.84.Tomaru U, Takahashi S, Ishizu A, Miyatake Y, Gohda A, Suzuki S, et al.Decreased proteasomal activity causes age-related phenotypes and promotes the development of metabolic abnormalities.Am J Pathol.2012;180:963-72.85.Rodriguez KA, Edrey YH, Osmulski P, Gaczynska M, Buffenstein R. Altered composition of liver proteasome assemblies contributes to enhanced proteasome activity in the exceptionally long-lived naked mole-rat.Brodsky JL, editor.PLoS One.2012.https://doi.org/10.1371/journal.pone.0035890.86.Chondrogianni N, Georgila K, Kourtis N, Tavernarakis N, Gonos ES.Enhanced proteasome degradation extends Caenorhabditis elegans lifespan and alleviates aggregationrelated pathologies.Free Radic Biol Med.2014;75:S18.https://doi.org/10.1016/j.freeradbiomed.2014.10.632.87.91.Haigis MC, Yankner BA.The aging stress response.Mol Cell.2010;40:333-44.92.Johnson SC, Rabinovitch PS, Kaeberlein M. mTOR is a key modulator of ageing and agerelated disease.Nature.2013 Jan 16;493:338-45.93.Lamming DW, Ye L, Astle CM, Baur JA, Sabatini DM, Harrison DE.Young and old genetically heterogeneous HET3 mice on a rapamycin diet are glucose intolerant but insulin sensitive.Aging Cell.2013;12:712-8.\tConserved Metabolic Pathways Offer Clues to the Factors of Aging and Longevity\n\nEvolutionarily conserved pathways, from yeast to mammals, robustly correlate with aging and longevity, and their deregulation has been implied with the development of cellular aging and include the mechanistic target of rapamycin (mTOR), insulin/ insulin growth factor 1 signaling (IIS), AMPK sensing, and sirtuin (SIRT) pathways [90].The harmonized regulation of these metabolic pathways maintains cellular and organismal homeostasis, even in the presence of external perturbations like changes in nutrient availability, temperature, oxygen level, or internal alterations, including protein misfolding and DNA damage [91].", + "\t\n\npivotal in this aspect providing molecular insights and having huge conceptual contributions in the field.Characterising the contribution of individual mutants in ageing is a continuously active and informative activity in the field.On top of these studies, genome-wide screens have provided insights on the role of evolutionarily conserved processes and signalling pathways in ageing such as nutrient response [17,18], protein translation, oxidative damage [19,20], mitochondrial function [21,22] and autophagy [22,23] opening new avenues for biogerontology research.Yeasts have proved informative and helped in understanding mechanisms of highly conserved pathways (from yeast to human) in physiology, health and disease such as the Target of Rapamycin (TOR) [24], glucose sensing (PKA) and stress response pathways (Sty1/p38) [25].\t\n\nA competitive ageing assay was performed in budding yeast where samples from the ageing pool were collected at specific timepoints [58].Mutants were then detected using a microarray DNA hybridization technique that quantifies abundance of the barcode tags of each mutant.Using this approach multiple short-and long-lived mutants were identified with autophagy mutants being among the short-lived and mutants coding for proteins involved in de novo purine biosynthesis pathway, which ultimately produces IMP and AMP were among the long-lived ones [58].Validation experiments targeting autophagy or purine biosynthesis has the expected lifespan outcomes.In a similar approach, deletion of genes involved in protein sorting in vacuoles, autophagy and mitochondrial function shortened life span, confirming that respiration and degradation processes are essential for long-term survival.Among the genes whose deletion significantly extended life span were genes implicated in fatty acid transport and biosynthesis, cell signalling and transfer RNA (tRNA) methylation such as ACB1, CKA2 and TRM9, respectively [59].", + "\t\n\nWe have recently conducted a genome-wide screen using siRNA library to identify genes regulating autophagy in human cells under normal nutritional conditions (5).In this image-based screen we took advantage of the autophagy specific GFP-LC3 reporter whose translocation from the cytosol to autophagosomes can serve as a quantitative measure of autophagy.In this study, we specifically explore the mechanisms that regulate autophagy in neural cells using the hits identified in our screen.We demonstrate that reactive oxygen species (ROS) play a general function in mediation of autophagy upstream of the type III PI3 kinase and that this pathway is essential for the up-regulation of autophagy by A.Interestingly, our data show that genes regulating autophagy are differentially expressed in normal aging and in AD patient brains.Finally, we identify candidate molecular targets that may be safely manipulated to modulate autophagy to treat neurodegenerative diseases.\t\n\nConversely, expression of the key autophagy genes, such as Atg5 and Atg7, was down-regulated in aging.This is consistent with our previous data demonstrating transcriptional down-regulation of beclin 1, in normal human brain aging (11).Together, this suggests, that unlike AD, the normal aging process may lead to transcriptional down-regulation of autophagy.\t\n\nTo further define the biological processes affected by downregulation of autophagy in aging, we used gene ontology canonical pathway analysis.It revealed a significant enrichment in the \"Axon guidance\" (P = 0.0009) and \"Regulation of actin cytoskeleton\" (P = 0.038) pathways, suggesting a connection between regulation of autophagy, axon guidance and actin dynamics.Construction of protein-protein interaction networks anchored by the hit genes belonging to these pathways (12,13) revealed two related networks encompassing, respectively, 27 (11%) and 61 (26%) of the hit genes (Fig. S6 C and D).Importantly, both networks directly connect to the known autophagy machinery through the interaction of the RIP kinase (RIPK1) and PKC (PRKCZ) with p62/sequestrosome (SQSTM1).In addition, syndecan 2 (SDC2), a part of the \"Regulation of actin cytoskeleton\" network, interacts with syntenin, a binding partner of ULK1, the human ortholog of yeast Atg1 (14).ULK1 is known to play a role in the regulation of endocytic processes involved in axon guidance (15) and to promote synapse formation in Drosophila (16).These data suggest that some of the molecular networks involved in the regulation of autophagy are closely connected to those regulating endocytosis, actin dynamics, and neuronal axon guidance, and that autophagy may play a wider role in the development and maintenance of neuronal function.\t\n\nTranscriptional Regulation of Autophagy in Normal Brain Aging.To determine whether the regulation of autophagy may have wider implications in normal aging of the human brain, we analyzed expression of the autophagy screen hit genes in a set of younger versus older human brain samples (10).We observed differential expression of a large subset of genes, including a group of 32 genes significantly (P < 0.05) up-regulated and 46 down-regulated with age (Fig. 6A and Fig. S6 A and B and Table S9).Gene ontology biological process analysis revealed that the age up-regulated group was highly enriched in genes involved in mediation and regulation of the MAP kinase pathway (P = 1.6 10 4 ).An increase in the activity of MAP kinase pathway was predicted by our previous analysis to lead to the suppression of autophagy (5).\t\n\nDifferential Expression of Autophagy Regulators in Normal Aging and in AD.Our gene expression data suggest that autophagy is also differentially regulated at the transcriptional level in normal human brain aging versus in AD.Because autophagy is known to play a protective role against onset of neurodegeneration in animal models (2,3,20,21), its down-regulation in normal aging could contribute to the observed age-dependent predisposition to development of chronic neurodegenerative diseases.In addition, the extensive overlap of the autophagy screen hits with Fig. 6.Expression of autophagy screen hit genes in normal human aging.Clustering analysis (dChip) of mRNA expression levels of select autophagy hit genes in younger (40 y old) versus older (70 y old) human brain samples, based on (i) minimum 1.2-fold change between the average expression, and (ii) P value <0.05 using unpaired t test.\tDiscussion\n\nIn this study, we demonstrate that the type III PI3 kinase plays a fundamental role in the regulation of autophagy and that ROS function as general mediators of autophagy induction upstream of this kinase.This pathway has an essential function in the initiation of autophagy in response to mitochondrial damage following exposure to A, the main pathogen of AD.At the same time, A is able to slow down autophagic processing through ROS independent inhibition of lysosomal degradation.In addition, our analysis of expression of the autophagy screen hits suggests that autophagy is differentially regulated at the transcriptional level in normal human aging and in AD, with overall levels decreased in normal aging but elevated in AD.", + "\t\n\nAt least two aspects need to be addressed using a system biology approach in aging research.First, although many different pathways, compartments or processes are known to be closely related to aging, such as the IIS pathway, autophagy, mitochondria, oxidative stress response and so on, it remains unclear as to how they interact, are co-regulated and balanced during aging.To provide a glimpse of this problem, we visualized the network communities among the known aging regulators based on entries in the GenAge database [62,63] and controlling growth and proliferation (green nodes), DNA damage response for maintaining integrity of the genome (red nodes), mitochondria and oxidative stress response (yellow nodes), and ribosome and translation (blue nodes).It is obvious that the first two are intensively linked and closely entangled, while the latter two are relatively independent processes with only few links connected to the first two processes.Also, it is interesting to note that, by comparing the molecular interaction-based network with the co-citation network, the role of autophagy and protein transport in aging might be either overestimated due to study bias or under-estimated by the incompleteness of the molecular interactions among these genes.\tINTRODUCTION\n\nAging has fascinated researchers since ancient times.The hugely complicated process that has been revealed may be interpreted from different aspects, such as the accumulation of oxidative damage, shortening of telomeres, the costs of reproduction, metabolic rates, cellular senescence, etc., and these have in turn given rise to diverse theories of aging [1].However, thanks to forward and reverse genetic technologies, researchers in the recent decades have established that despite its complexity, a single or a few key genes in a few key pathways can modulate the aging rate.The most important players would appear to be those in nutrient sensing pathways or stress response pathways, such as DAF-2/IGF1R and DAF-16/FOXO in the Insulin/IGF like signaling pathway, AAK-2/AMPK in another nutrient sensing pathway, JNK in the stress response pathway, LET-363/mTOR as an inhibitor of autophagy and activator of translation and SIRT1/SIR2 in genome stability maintenance, to name a few [2,3].In addition to genetic perturbations, dietary perturbations, such as diet restriction (DR) are known to significantly extend lifespan in most organisms examined from yeasts to primates, although different pathways may act under different DR conditions, and alternative DR strategies also effect C.elegans lifespan in different ways [3,4].The main pathways revealed under different DR regimens are summarized in Fig. (1).In this small, convoluted DR response network, DAF-16 and ceTOR/LET-363 *Address correspondence to this author at the Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China; Tel: 86-21-54920458; Fax: 86-21-54920451; E-mail: jdhan@picb.ac.cn These authors contributed equally to this work.", + "\t\n\nIn vitro and animal studies have reported a decline in autophagy with age [26,36,[40][41][42][43]; however, to our knowledge, only one other publication has reported an age-associated decline in expression of autophagy genes, which was carried out in a small number of human brain tissue samples [44].Overall, these findings for major components of core autophagy machinery and upstream regulators provide evidence for a transcriptional decline in autophagy gene expression with age in human monocytes.The identification of key genes contributing to a decline in autophagy are of great interest, as pharmacologic activation of autophagy has been linked with increasing lifespan in animal models, including mice [45].Further, dysfunctional autophagy is now widely implicated in pathophysiological processes of many age-related diseases such as cancer, Alzheimer's, diabetes, and cardiovascular diseases [46].However, longitudinal studies are necessary to validate the age-related transcriptional decline of autophagy gene expression in human monocytes, and to investigate the relationship between these age-related patterns and the development of age-associated diseases.", + "\tThe cell-autonomous theory on the\nother hand posits that individual cells are the targets of the aging process, via a timedependent increase in homeostatic dysfunction. The potential mechanisms include\nincreases in the production of reactive oxygen species, telomere shortening and, not\nsurprisingly, genomic instability. An implication of this theory is that long-lived cells in\nthe organism, such as neurons, muscle, and importantly stem cells, would be the\npredominant substrates of aging, while those cells that undergo rapid and continuous\nturnover would be removed before they could exert an effect on tissue function.", + "\tConcluding remarks and future perspectives\n\nAging research has rapidly expanded over the past two decades, with studies ranging from lifespan-extending [68,69,71].However, when their effect on cell death and senescence leads to stem cell loss and tissue degeneration, they might contribute to aging [66,67]." + ], + [ + "\tFurther evidence of age-related changes in stem cells include the finding that a\nhigher proportion of Thy-1loSca-1+Lin-Mac-1-CD4-c-kit+ cells from old mice are in\nS/G2/M phases of the cell cycle (Morrison, 1996), and the results of Henckaerts\net al. , who showed that the proliferative response of Lin-Sca-1+c-kit+ marrow cells\nto the early-acting cytokines KL, Flt3L and TPO, decreased dramatically with age\n(Henckaerts et al. 2002). As mentioned previously, the bone marrow niche is the optimal\nmicroenvironment for the growth and functional maintenance of HSCs (Moore\n2004; Nilsson et al. 2001).\t17\nAging Effects on Hematopoietic Stem Cells and Bone Marrow Niche\nAs discussed above, HSC expansion and transplantation is clinically\nimportant to treat patients with hematological and non-hematological disorders. It\nis also well known that cancer risk increases in older people (Balducci and\nExtermann FEB 2000). Therefore, understanding aging effects on hematopoietic\nsystem, especially on HSCs and their bone marrow microenvironment (niche),\nmay not only help to prevent malignant transformation, but also to determine\nefficacy of aging stem cells for transplantation (Pinto et al. 2003; Van Zant and\nLiang 2003) .", + "\t\n\nMost mammalian tissues can be described as being comprised of two major cellular components: stem or progenitor cells, which are responsible for regenerative capacity or repair after injury, and differentiated somatic cells, responsible for adult stem cell support and specialized tissue/organ functions.Based on this classification, two major mechanisms can account for tissue degeneration associated with age: loss of stem cell pool division potential (loss of regenerative capacity) and loss of differentiated somatic cell function, which directly leads to loss of organ function.Loss of differentiated somatic cell function can additionally indirectly affect adult stem and progenitor cells by altering the tissue microenvironment that is essential for stem cell support (the stem cell niche).In general, loss of stem cell pool division potential can occur through multiple mechanisms including stem cell senescence, death or dysfunction of the niche.One specific mechanism that can account for the loss of both stem cell and differentiated somatic cell function is the gradual accumulation of persistent DNA damage.Persistent DNA damage and its erroneous resolution *To whom correspondence should be addressed.Tel: +1 415 209 2042; Fax: 415-209-22232; Email: dbhaumik@buckinstitute.org 2007 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/)which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.include telomeric dysfunction (9)(10)(11) and somatic mutations (12), both of which increase with age; both also have been proposed to contribute to the loss of stem and differentiated somatic cell function with age (13,14).DNA damage accumulation in stem cells has been detected in mice and clearly contributes to the attrition of stem cell division potential during aging (15).Thus, it is likely that DNA damage contributes to aging by limiting stem cell division potential and by also interfering with somatic tissue functions, including stem cell niches.", + "\t\n\nA diminished capacity to maintain tissue homeostasis is a central physiological characteristic of ageing.As stem cells regulate tissue homeostasis, depletion of stem cell reserves and/or diminished stem cell function have been postulated to contribute to ageing 1 .It has further been suggested that accumulated DNA damage could be a principal mechanism underlying age-dependent stem cell decline 2 .We have tested these hypotheses by examining haematopoietic stem cell reserves and function with age in mice deficient in several genomic maintenance pathways including nucleotide excision repair 3,4 , telomere maintenance 5,6 and non-homologous end-joining 7,8 .Here we show that although deficiencies in these pathways did not deplete stem cell reserves with age, stem cell functional capacity was severely affected under conditions of stress, leading to loss of reconstitution and proliferative potential, diminished self-renewal, increased apoptosis and, ultimately, functional exhaustion.Moreover, we provide evidence that endogenous DNA damage accumulates with age in wild-type stem cells.These data are consistent with DNA damage accrual being a physiological mechanism of stem cell ageing that may contribute to the diminished capacity of aged tissues to return to homeostasis after exposure to acute stress or injury.", + "\tSeveral studies have shown\nthat the systemic milieu regulates stem cell decline during aging. Liang et al. showed\nthat HSCs have a reduced ability to home to the bone marrow and spleen after\ntransplantation into old versus young recipients (Liang et al. , 2005). Further experiments\ndemonstrated that the muscle stem cell niche adversely effects stem cell function as\nevidenced by the restoration of old stem cell regenerative potential upon exposure to a\nyoung systemic microenvironment (Conboy et al. , 2005; Conboy and Rando, 2005).\tSince stem cells\nare capable of self-renewal and produce progeny to replenish worn-out and damaged cells\nin aged tissues, the induction of stem cell senescence may compromise tissue renewal by\ndepletion of stem or progenitor cell pools and thus promote age-related pathologies. 6\nIt is apparent that the HSC compartment undergoes considerable age-related\nchanges, however it is not yet clear whether theses changes are intrinsic to the cells\nthemselves or whether they occur due to alterations in the hematopoietic\nmicroenvironment, commonly referred to as the HSC niche.\tHowever, studies do indicate that aged tissues have a diminished capacity to return to a\nhomeostatic state after exposure to stress or injury, therefore indicating a defect in stem\ncell function during the aging process. Since the HSC population provides an ideal\nmodel to study stem cell aging, it is necessary to elucidate the mechanisms of\nhematopoietic aging and expand the findings to other tissues and organ systems. Theories of Aging and Age Related Epigenomic Changes\nThere are two major theories of organismal aging: evolutionary and damage\nbased.\tWith\nthis in mind, it has been hypothesized that the aging or functional failure of tissuespecific stem cells, which fulfill this job, may limit tissue repair and renewal, therefore\ncontributing to overall organismal aging (Krtolica, 2005; Van Zant and Liang, 2003). Because of the unprecedented experimental model systems that are available for the\nexploration of HSCs, stem cell aging research in the field of hematology has been the\nsubject of extensive studies. Indeed, the hematopoietic system has served as an important\nmodel for advancing our understanding of stem cell biology and its association with\naging.\tIn view of the importance of stem cells for maintaining\nimmune function and in a broader sense tissue homeostasis and longevity, there is a\ncritical need to better understand the mechanisms involved in HSC aging. 17\nFigure 1.1 The HSC hierarchy. The HSC compartment can be functionally divided into three populations; long-term\nHSCs, which have extensive self-renewal capacity, short-term HSCs, which have limited\nself-renewal capacity, and multipotent progenitor cells which cannot self-renew and give\nrise to common lymphoid progenitors (CLP) and common myeloid progenitors (CMP).", + "\tIn other words, lower HSC proliferation results in a\nmore youthful stem cell, but poorer tissue regeneration, and\nconsequently an aged phenotype; this indicates that stem cell\nproliferation and tissue regeneration are nely balanced to\nmaximize longevity, so that cell cycle disruption results in an\nuncoupling of tissue and organismal aging from the aging of\nthe resident stem cell. Finally, three lines of evidence in our work indicate broad\nchanges in epigenetic regulation with age.\tIf the rejuvenating effect of stem cells were perfect, senescing cells would be\nreplaced indenitely; but even in highly regenerative tissues\nsuch as the skin, the gut, and the hematopoietic system, agerelated decline in function is well established [1]. Still unclear\nare the effects of aging on the stem cells themselves, which\ncould contribute to inferior tissue repair. Hematopoietic stem cells (HSCs) continuously replenish\nthe blood and immune system throughout life. Data from\nmice support an age-related decline in stem cell function [1],\nsuggesting that older HSCs are inadequate to cope with the\ndemands of blood production.", + "\tFurthermore, the differentiation potential of the HSC compartment\nappears to become skewed toward the myeloid lineage with age\n(26 28). As HSC have been shown to cycle (29), replicative stress,\neven in the absence of detectable telomere erosion (30, 31), may\nunderlie at least some of the age-related changes in HSC (32). Many traits affecting the hemopoietic stem and progenitor cell\ncompartments also change with age in a mouse strain-dependent\nfashion (2123, 3234) and have been implicated in organismal\nlife span (21, 3234). The responsiveness of LSK cells to TGF-2\nshowed mouse strain-dependent variation in young mice.", + "\tFurther evidence of age-related changes in stem cells include the finding that a\nhigher proportion of Thy-1loSca-1+Lin-Mac-1-CD4-c-kit+ cells from old mice are in\nS/G2/M phases of the cell cycle (Morrison, 1996), and the results of Henckaerts\net al. , who showed that the proliferative response of Lin-Sca-1+c-kit+ marrow cells\nto the early-acting cytokines KL, Flt3L and TPO, decreased dramatically with age\n(Henckaerts et al. 2002). As mentioned previously, the bone marrow niche is the optimal\nmicroenvironment for the growth and functional maintenance of HSCs (Moore\n2004; Nilsson et al. 2001).\t17\nAging Effects on Hematopoietic Stem Cells and Bone Marrow Niche\nAs discussed above, HSC expansion and transplantation is clinically\nimportant to treat patients with hematological and non-hematological disorders. It\nis also well known that cancer risk increases in older people (Balducci and\nExtermann FEB 2000). Therefore, understanding aging effects on hematopoietic\nsystem, especially on HSCs and their bone marrow microenvironment (niche),\nmay not only help to prevent malignant transformation, but also to determine\nefficacy of aging stem cells for transplantation (Pinto et al. 2003; Van Zant and\nLiang 2003) .", + "\tIntroduction\n\nThe regenerative potential of our body decreases upon aging.Regenerative tissues depend on specialized adult stem cells, thus aging in these tissues can be interpreted as signs of aging in somatic stem cells [1].Adult stem cells are characterized by the dual function to differentiate into different cell lineages and to selfrenew for maintenance of the stem cell pool.It is, however, still controversial if this self-renewal also includes juvenation or if adult stem cells are doomed to undergo aging upon each cell division.It is unclear if adult stem cells undergo functional and molecular changes, if their number decreases because of aging, or if aging is due to extrinsic environmental factors without any effect on the stem cell pool [2,3].\t\n\nThere is emerging evidence that aging is not purely a cell intrinsic process, but rather regulated by interaction with the cellular microenvironment.For example, Ju and co-workers have demonstrated that telomere dysfunction induces alterations in the microenvironment that affect aging of the hematopoietic system [55].In general, adult stem cells have a slow turnover and reside in specialized niches, protected from the environment and only a few are activated at a time [33,56].By keeping adult stem cells in a quiescent state, the stem cell niche might also play a crucial role in regulating replicative senescence.Strong experimental data for this hypothesis derives form serial transplantation experiments of HSC in mice.The reconstituting ability declines continuously within 4 to 5 transfers [57,58] and this decline is thought to be telomereindependent [59], although it has been reported that telomere length decreases by serial transplantation [60].Recently, Wilson and co-workers have demonstrated that there is a dormantfraction of HSC that divides only five times during the lifetime of mice and especially these dormant HSC posses repopulating activity upon serial transplantation [61].The stem cell niche could therefore play a central role in maintaining a dormant pool of HSC to prevent replicative senescence over the lifetime of the organism [62].\t\nThe regenerative potential diminishes with age and this has been ascribed to functional impairments of adult stem cells.Cells in culture undergo senescence after a certain number of cell divisions whereby the cells enlarge and finally stop proliferation.This observation of replicative senescence has been extrapolated to somatic stem cells in vivo and might reflect the aging process of the whole organism.In this study we have analyzed the effect of aging on gene expression profiles of human mesenchymal stromal cells (MSC) and human hematopoietic progenitor cells (HPC).MSC were isolated from bone marrow of donors between 21 and 92 years old.67 genes were age-induced and 60 were age-repressed.HPC were isolated from cord blood or from mobilized peripheral blood of donors between 27 and 73 years and 432 genes were age-induced and 495 were age-repressed.The overlap of age-associated differential gene expression in HPC and MSC was moderate.However, it was striking that several age-related gene expression changes in both MSC and HPC were also differentially expressed upon replicative senescence of MSC in vitro.Especially genes involved in genomic integrity and regulation of transcription were age-repressed.Although telomerase activity and telomere length varied in HPC particularly from older donors, an age-dependent decline was not significant arguing against telomere exhaustion as being causal for the aging phenotype.These studies have demonstrated that aging causes gene expression changes in human MSC and HPC that vary between the two different cell types.Changes upon aging of MSC and HPC are related to those of replicative senescence of MSC in vitro and this indicates that our stem and progenitor cells undergo a similar process also in vivo.\t\n\nThe regenerative potential diminishes with age and this has been ascribed to functional impairments of adult stem cells.Cells in culture undergo senescence after a certain number of cell divisions whereby the cells enlarge and finally stop proliferation.This observation of replicative senescence has been extrapolated to somatic stem cells in vivo and might reflect the aging process of the whole organism.In this study we have analyzed the effect of aging on gene expression profiles of human mesenchymal stromal cells (MSC) and human hematopoietic progenitor cells (HPC).MSC were isolated from bone marrow of donors between 21 and 92 years old.67 genes were age-induced and 60 were age-repressed.HPC were isolated from cord blood or from mobilized peripheral blood of donors between 27 and 73 years and 432 genes were age-induced and 495 were age-repressed.The overlap of age-associated differential gene expression in HPC and MSC was moderate.However, it was striking that several age-related gene expression changes in both MSC and HPC were also differentially expressed upon replicative senescence of MSC in vitro.Especially genes involved in genomic integrity and regulation of transcription were age-repressed.Although telomerase activity and telomere length varied in HPC particularly from older donors, an age-dependent decline was not significant arguing against telomere exhaustion as being causal for the aging phenotype.These studies have demonstrated that aging causes gene expression changes in human MSC and HPC that vary between the two different cell types.Changes upon aging of MSC and HPC are related to those of replicative senescence of MSC in vitro and this indicates that our stem and progenitor cells undergo a similar process also in vivo.\tDiscussion\n\nThe deterioration of the regenerative potential upon aging might be due to functional changes in adult stem cells.To test this hypothesis we have investigated differential gene expression in primary, human MSC and HPC derived from different age groups.In this study, we demonstrate for the first time age-related gene expression changes in human MSC and HPC and that there is a moderate but significant concordance in the expression profiles upon aging in vivo and replicative senescence in vitro.It needs to be pointed out, that chronological age and biological age do not necessarily coincide.Multiparametric assessment of biological age might be valuable in this context.Furthermore, MSC and HPC preparations are heterogeneous and it is conceivable that they represent a mixture of different aged or senescent subsets.Further research will be necessary to address age-related changes on a single cell level to investigate the heterogeneity of aging within cell populations.activating complex, polypeptide 5 (SNAPC5) and peroxisome proliferator-activated receptor gamma (PPARG) were age-repressed.Furthermore, we have validated age associated changes in HPC for 9 genes (B): S100 calcium binding protein A10 (S100A10); vimentin (VIM); myeloid-associated differentiation marker (MYADM); pim-1 oncogene (PIM1) and annexin A2 (ANXA2) were age-induced.Timeless interacting protein (TIPIN); myosin regulatory light chain interacting protein (MYLIP); lymphocyte transmembrane adaptor 1 (LAX1) and Early growth response 1 (ERG1) were agerepressed.Protocadherin 9 (PCDH9) was not amplified in HPC from elderly donors whereas interleukine 7 receptor (IL7R) was not amplified in young samples (not presented in the figure).Differential gene expression was always calculated in relation to the mean of young samples.The mean foldratio (6SD) is demonstrated for median aged and old donor samples.RT-PCR results (red) were always in line with microarray data (blue) for all genes tested.doi:10.1371/journal.pone.0005846.g003", + "\tFor instance, mice null for the repair\nprotein Ercc1 show progressive marrow failure resulting in a pancytopenia, while the\nmice exhibit several symptoms of premature aging (Prasher, Lalai et al. 2005). However,\nno studies to date have demonstrated conclusively that diminished DNA repair capacity\nof HSCs with age results in their functional impairment, much less a decreased ability to\nrepair DNA lesions with age. 10\nGenetic regulation of stem cell proliferation\n\nThese many ramifications of the proliferative nature of hematopoietic stem cells\nbegs the question of what are the key molecules regulating this vital feature." + ], + [ + "\tHowever, under diabetic conditions, AGEs generated by the exposure of proteins and lipids\nto high glucose levels crosslink ECM proteins, impair ECM degradation by MMPs and\nincrease cardiac stiffness, which together manifest as early diastolic dysfunction33,5254. AGEs can also promote the differentiation of fibroblasts into myofibroblasts, which\nproliferate and induce ECM dyshomeostasis by secreting profibrotic cytokines and matrix\nproteins. Furthermore, the altered cardiac mechanics lead to the release of other stimuli\nincluding transforming growth factor- (TGF), tumour necrosis factor (TNF), angiotensin\nII and various interleukins, which activate profibrotic responses in fibroblasts and\nmyofibroblasts55.", + "\t\n\nMuch work has focused on molecular features often observed with advanced age-cellular senescence, autophagy, oxidative stress, and epigenetic changes.Vascular remodeling, as a consequence of these features, is well documented leading to endothelial dysfunction and arterial stiffness.Although such features are also invoked in other conditions such as heart failure with preserved ejection fraction and valvular calcification, disentangling the key causal features suitable for therapeutic modulation remains elusive.", + "\t\n\nNonenzymatic glycation of proteins and lipids occurs with aging, a process that is accelerated in the setting of glucose dysregulation, such as diabetes mellitus [7].Advanced glycation end products (AGEs) formation has been implicated in a number of pathological processes associated with micro-and macrovascular diabetic complications [8][9][10].It has been demonstrated that the effects of AGEs are partially mediated through their interactions with cell surface receptor, the receptor for advanced glycation end products (RAGE) [11].The soluble form of RAGE (sRAGE) is a proteolytic cleavage product of RAGE, which has AGE-binding property but lacks the signaling cascade [12].In Caucasians without T2DM, sRAGE has been associated with decreased renal function assessed by estimated glomerular filtration rate (eGFR) or serum creatinine level [13][14][15].In Caucasian T2DM patients, sRAGE has been associated with albuminuria [16], decreased eGFR [17] and new or worsening kidney diseases and mortality [18].However, to date, only two studies reported associations of sRAGE level with renal function in Asians with T2DM [19,20].Although sRAGE is increasingly gaining importance as a biomarker in diabetic complications, it is not clear how sRAGE level is regulated and why it varies among studies.In addition, genetic studies of sRAGE remain very limited.", + "\t\n\nAdvanced glycation end-products (AGE) are the result of nonenzymatic glycation, which produces heterogeneous bioactive molecules, such as lipids, proteins, and nucleic acids [59].The accumulation of AGEs in aged tissues leads to several processes, such as inflammation, obesity, apoptosis, and other adverse processes related to ageing [47].These AGEs are detected by various techniques, such as gas chromatography, high-performance liquid chromatography, spectrometry, and immunochemical technique [60], which make them robust biomarkers that can be analyzed by different methodologies.", + "\t\n\nCritical areas of vascular aging research include the role of senescence, epigenetics, stress resilience, inflammation, macromolecular damage, proteostasis, mitochondrial and metabolic dysfunction, and impaired stem cell biology.The specific roles for cell-autonomous and noncell-autonomous mechanisms contributing to vascular aging need to be elucidated further.The role of signal transduction pathways linked to regulation of cellular energetics in the vascular aging process should be better defined.Future studies should also lead to improved understanding of the role circadian clocks to vascular aging.New studies investigating cellular heterogeneity in vascular aging are warranted.Stochastic macromolecular damage leads to regional variability in the presence of senescent cells, cells with altered metabolism, mitochondrial dysfunction, and increased ROS production.Such regional variability likely contributes to the focal development of vascular pathologies, ranging from atherosclerotic plaques to microhemorrhages.Single-cell gene expression analysis should facilitate better understanding of the pathophysiological role of functional heterogeneity.Finally, how environmental factors and lifestyle choice impact the vascular aging processes should be better understood.", + "\t\n\nThe characteristics of the second pathway include the formation of advanced glycation end-products (AGEs) from excessive imbibing of glucose [7].The AGEs via interaction with their receptor, RAGE, transduce a complex series of signaling events that result in cellular dysfunctions, thus generating an inflammatory response and reactive oxygen species (ROS), which in turn cause oxidative stress [7].Both in vitro and in vivo studies support the relevance of this pathway in the pathogenesis of diabetic nephropathy [7].The fact that several inhibitors of AGEs, such as pyridoxamine, LR-90 and KIOM-79, have been demonstrated to be beneficial in various murine models of diabetes emphasizes the role of AGE:RAGE interactions [8][9][10].Although these inhibitors may be effective in murine models, their efficacy certainly needs to be evaluated in diabetic nephropathy in humans.", + "\tAging is only, in part, the result of oxidative, free radical chemistry\n\nThe free radical theory of aging (Harman 1992) proposes that reactive oxygen is the major culprit in aging, leading to age-dependent oxidative modification, crosslinking and denaturation of proteins, with resultant loss of protein and enzyme structure and function.This theory has been expanded in recent years to include not only direct oxidation of proteins by reactive oxygen, but also the modification of proteins by Maillard reaction products, AGEs and ALEs (Thorpe and Baynes 1996).The majority of AGEs that are known to accumulate with age in tissue proteins are glycoxidation products, formed by combined glycation and oxidation reactions of precursors, such as glucose or ascorbate (Baynes 1991).In non-diabetic patients, levels of the glycoxidation products CML and pentosidine correlate with levels of methionine sulfoxide and o-tyrosine in skin collagen, indicating that these products are formed in parallel with one another (Wells-Knecht et al. 1997).Although oxidation appears to be important in the formation of AGEs and crosslinking of protein by glucose and ascorbate (Fu et al. 1994), some AGEs, such as pyrraline and crosslines, are formed non-oxidatively from glucose.The crosslines increase in lens proteins with age (Obayashi et al. 1996), so that oxidation is not essential for an age-dependent increase in crosslinking of protein by carbohydrates.In contrast to AGEs, ALEs require oxidative conditions for their formation -the first intermediate in ALE formation is a lipid peroxide, formed from a polyunsaturated fatty acid (PUFA) by an enzymatic or non-enzymatic autoxidation reaction involving molecular oxygen.The EAGLEs, CEL and MOLD, increase with age in collagen and crystallins, but cannot be classified as oxidative or nonoxidative since they may be formed either oxidatively during peroxidation of PUFA (Fu et al. 1996) or non-oxidatively from glyceraldehyde 3-phosphate or dihydroxyacetone phosphate formed during anaerobic glycolysis (Ahmed et al. 1997).Other modifications of amino acids, including deamidation, racemization and formation of hydroxykynurenine adducts are also age-dependent, non-crosslinking modifications of proteins.\tAging may be accelerated by inflammation and disease\n\nThe relationship between aging and age-related, chronic disease is complex.Healthy aging generally leads to a longer life, while chronic disease and associated inflammatory processes generally accelerate the aging process, i.e. shorten life span.The relationship between aging and chronic disease may be illustrated by diabetes, a disease in which the accumulation of AGEs in tissue proteins is accelerated by hyperglycemia.CML and pentosidine are biomarkers of normal aging of tissue collagens, and their accelerated accumulation in collagen in diabetes is de facto evidence that diabetes is a disease characterized by accelerated aging of collagen (Dyer et al. 1993).The acceleration of protein aging in diabetes is apparent, not only by the increase in AGEs, but also by increases in browning and fluorescence of collagen, and decreased solubility, decreased elasticity and increased thickness of basement membranes in diabetes (Baynes and Thorpe 1999).Notably, the rates of accumulation of other biomarkers, such as o-tyrosine and methionine sulfoxide in skin collagen, do not change significantly in diabetes (Wells-Knecht et al. 1997).Thus, the acceleration of chemical aging of collagen in diabetes is unbalanced or 'pathologic' in nature, apparently driven by the increase in circulating levels of oxidizable substrates (carbohydrates and lipids) (Baynes 1991(Baynes , 1999;;Baynes and Thorpe 1999a, b), rather than an increase in oxidative stress.Diabetes also increases the risk for cardiovascular disease, the major cause of mortality in the western world, while the increased risk for cataracts in diabetes may result from increases in both glycation and oxidative stress in the lens (Stevens 1998).", + "\tMG is elevated in the diabetic state and is\nthought to contribute to the development of diabetic complications, particularly through the\nformation of AGEs (60). AGE modification of vascular extracellular matrix proteins causes\n\nW\n\ncross-linking, which alters elastic properties and traps low-density lipoprotein in the vessel wall\n(60). Upon ligating RAGE, AGEs cause endothelial dysfunction, activation of NF-B, release of\n\nIE\n\npro-inflammatory molecules, and formation of vessel-damaging ROS (60). Through detoxifying\nMG, GLO1 is thought to protect against diabetic complications.", + "\tIt is based on the tendency of glucose to\nundergo oxydation in the presence of traces of heavy metal\nions, thus creating reactive ketoaldehydes, hydrogen peroxyde, and free radicals. It is clear now that the rearrangement of Schiff bases, Amadori products and/or AGEs is\naccompanied by generation of reactive oxygen species that\ncause conformational changes and fragmentation of the\nglycated proteins (11, 12). The proteins modified by AGEs\nare shown to be toxic, immunogenic, and capable of triggering cellular injury responses after binding to specific\nreceptors (1315).\tTaking into consideration that glycation is a slow process, it has always been regarded as typical for the longliving organisms and as affecting the long-living proteins\n(haemoglobin, crystalline, etc.)only. Surprisingly, our\nrecent studies indicated that glycation takes place also in\nE. coli and affects both the host bacterial and recombinant\nproteins (16, 17). Once started in vivo, glycation can not\nbe stopped after isolation and purification of the protein. Accumulation of AGEs continues even when pure protein preparations are stored in deep frozen solutions. A\ngreat number of studies have been dedicated to the search\nfor inhibitors of glycation.\tMullarkey CJ, Edelstein D, Brownlee M (1990) Free radical generation by early glycation products: a mechanism for accelerated atherogenesis in diabetes. Biochem Biophys Res Commun\n173:932939. Sakurai T, Tsuchiya S (1988) Superoxide production from nonenzymatically glycated protein. FEBS Lett 236:406410\nWendt T, Tanji N, Guo J, Hudson BI, Bierhaus A, Ramasamy R,\nArnold B, Nawroth PP, Yan SF, DAgati V, Schmidt AM (2003)\nGlucose, glycation, and RAGE: implications for amplification of\ncellular dysfunction in diabetic nephropathy. J Am Soc Nephrol\n14:13831395. Wautier JL, Schmidt AM (2004) Protein glycation: a firm link to\nendothelial cell dysfunction. Circ Res 95:233238.", + "\t\n\nFigure 15: Aspects of hyperglycemia-related vascular cell dysfunction.Hyperglycemia-induces a range of pathways in cells such as endothelium, and these include the polyol pathway, reactive oxygen species (ROS) formation, and advanced glycation endproducts (AGEs) formation.Excess glucose in endothelial cells enters polyol pathway; the electron donors like reduced nicotinamide adenine dinucleotide (NADH) and Flavin adenine dinucleotide (FADH2) accumulate in the mitochondria, thus affecting the electron transport chain; the excess electrons increase ROS in mitochondria; ROS triggers accumulation of AGEs; ROS and AGEs create mitochondrial DNA damage and mitochondrial dysfunction; protein kinase C (PKC) and AGE mediated activation of nuclear factor kappa B (NFB) activate the expression of inflammation proteins, tumor suppressor p53, and inducible nitric oxide synthase (iNOS); increased nitric oxide (NO) by iNOS is highly reactive with superoxide anions; the peroxynitrite thus generated acts as a strong oxidant and completes the vicious cycle of oxidative stress by increasing ROS production; accumulation of AGEs also increases ROS production independent of glucose levels\tM A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 50\n\nglycation and lipoxidation end-products and upregulation of the receptor for AGEs (RAGE) has a key role in the hyperglycemia-induced activation of Mller glia and downstream cytokine production in the context of diabetic retinopathy (Berner et al., 2012;Curtis et al., 2011;Yong et al., 2010;Zong et al., 2010).Diabetes has also been reported to accelerate death of Mller glia (Feenstra et al., 2013;Hammes et al., 1995), an effect which has recently been linked to the disruption of retinal vascular integrity and the induction of neural cell dysfunction and death (Shen et al., 2012).A schematic diagram summarising how Mller glia changes are believed to contribute to the sight threatening complications of diabetic retinopathy is presented in Figure 11.Apart from the Mller cells, activated microglial cells adjacent to the vessels also appear to have a key role in vasoregression, the vascular hallmark of the early stages of diabetic retinopathy in both animal models (McVicar et al., 2015) and diabetic patients (Scott et al., 2014b).", + "\tIt is based on the tendency of glucose to\nundergo oxydation in the presence of traces of heavy metal\nions, thus creating reactive ketoaldehydes, hydrogen peroxyde, and free radicals. It is clear now that the rearrangement of Schiff bases, Amadori products and/or AGEs is\naccompanied by generation of reactive oxygen species that\ncause conformational changes and fragmentation of the\nglycated proteins (11, 12). The proteins modified by AGEs\nare shown to be toxic, immunogenic, and capable of triggering cellular injury responses after binding to specific\nreceptors (1315).\tTaking into consideration that glycation is a slow process, it has always been regarded as typical for the longliving organisms and as affecting the long-living proteins\n(haemoglobin, crystalline, etc.)only. Surprisingly, our\nrecent studies indicated that glycation takes place also in\nE. coli and affects both the host bacterial and recombinant\nproteins (16, 17). Once started in vivo, glycation can not\nbe stopped after isolation and purification of the protein. Accumulation of AGEs continues even when pure protein preparations are stored in deep frozen solutions. A\ngreat number of studies have been dedicated to the search\nfor inhibitors of glycation.\tMullarkey CJ, Edelstein D, Brownlee M (1990) Free radical generation by early glycation products: a mechanism for accelerated atherogenesis in diabetes. Biochem Biophys Res Commun\n173:932939. Sakurai T, Tsuchiya S (1988) Superoxide production from nonenzymatically glycated protein. FEBS Lett 236:406410\nWendt T, Tanji N, Guo J, Hudson BI, Bierhaus A, Ramasamy R,\nArnold B, Nawroth PP, Yan SF, DAgati V, Schmidt AM (2003)\nGlucose, glycation, and RAGE: implications for amplification of\ncellular dysfunction in diabetic nephropathy. J Am Soc Nephrol\n14:13831395. Wautier JL, Schmidt AM (2004) Protein glycation: a firm link to\nendothelial cell dysfunction. Circ Res 95:233238.", + "\tVascular endothelial dysfunction. In diabetes, endothelial dysfunction is linked to the accumulation of toxic lipids 90 , AGEs 91 and/or aggregated proteins 59 in the vasculature.Proteinaceous deposition on blood vessel walls damages endothelial cells 59,91 , increases the production of reactive oxygen species (ROS) 92,93 and impairs production of vasodilatory substances 92 , which results in a reduced cerebral blood flow.Stalled blood flow can lead to neurovascular uncoupling and hypoxic neuronal injury [92][93][94] .Elevated ROS production can further damage cellular structures and activate matrix metalloproteinases, inducing cytoskeletal reorganization and vascular remodelling 93 .Cytoskeletal reorganization affects the stability of tight junction proteins, resulting in increased capillary permeability, depletion of energy resources and altered neural viability 92,93 .", + "\t\n\nAdvanced glycation end products (AGEs) are a heterogeneous group of macromolecules that are formed by the nonenzymatic glycation of proteins, lipids, and nucleic acids.Overproduction of AGEs is considered the most important pathophysiological mechanism that induces diabetic complications (Semba et al. 2010).On one hand, AGEs mediate intracellular glycation of mitochondrial respiratory chain proteins and increase ROS levels, thus triggering oxidative stress (Coughlan et al. 2009) and endoplasmic reticulum stress (Piperi et al. 2012).On the other hand, binding of AGEs with receptors for advanced glycation end products (RAGEs) activates the AGE signalling axis to induce activation of NF-KB signalling and JAK/STAT signalling, which upregulate inflammatory cytokines and adhesion molecules (Basta 2008;Basta et al. 2004).The evidence indicates that exposure to AGEs is connected with the risk of adverse ageing-related outcomes.Akt1, Bsk, and P38b have been found to be crucial in the regulation of the AGE-RAGE-signalling pathway.Transforming growth factor beta (TGF-beta) is a major growth factor in joints that is crucial in maintaining chondrocyte homeostasis.However, the TGF-beta-signalling pathway changes with ageing, resulting in an age-related decline in the anabolic response that favours hypertrophy of chondrocytes and the development of osteoarthritis (Baug et al. 2014).In addition, Upadhyay et al. also reviewed the important role of TGF in the developmental processes of D. melanogaster and the role of TGF in regulating hormones, neurons and innate immunity (Upadhyay et al. 2017).Therefore, ageing-induced TGF-beta dysregulation is associated with deleterious effects on longevity and ageing itself.Dpp, Mad, and S6k are functionally crucial in the TGF-beta-signalling pathway.", + "\tIntroduction\n\nIn individuals with diabetes, nonenzymatic glycation of proteins leads to the formation of advanced glycation end products (AGE) and this process occurs at an accelerated rate in chronic hyperglycaemia 1 , and also the levels are found to be increased in complications of diabetes, such as diabetic retinopathy (DR). 2 AGE induces a variety of pathological changes, such as increased basement membrane thickening, arterial stiffness, and glomerular sclerosis. 3,4AGEs bind to a specific receptor known as receptor for advanced glycation end products (RAGE).RAGE is expressed in many of the cell types, such as the endothelial cells, monocytes, and lymphocytes, including the beta cells of the pancreas.RAGE-mediated signaling leads to the activation of transcription factors, such as NF-kB, AP-1, and STAT-1, 5,6 the adhesion molecules VCAM, ICAM, and tissue factor, 7,8 which promote a procoagulant state in the microcapillaries of the retina.This results in a hypoxic state that leads to the initiation of the angiogenic process in proliferative DR." + ] + ], + "task_id": [ + { + "task_id": "F66E835853D76F6DE8C2DC6F22DACDA3" + }, + { + "task_id": "49C4A8D68287C56E8A7C9DB0F55E223E" + }, + { + "task_id": "46C0740EB9753AFA9AFB6BA4E90EB0CA" + }, + { + "task_id": "33D49E56B7C81F0F0E66C72E385F8594" + }, + { + "task_id": "A483880F1B854CB70D1389C423835FFA" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_diabetes_1.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_diabetes_1.json new file mode 100644 index 0000000..03f04f4 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_diabetes_1.json @@ -0,0 +1,106 @@ +{ + "question": [ + "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?", + "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?", + "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?", + "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?", + "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?" + ], + "answer": [ + "Recent advancements in multi-omics approaches, including proteomics and metabolomics, allow for a comprehensive characterization of metabolic changes associated with Type 2 diabetes. These tools can identify and measure thousands of metabolites in a given biological sample, providing insights into the disease's development and progression. They can also identify potential early biomarkers for the disease and its comorbidities. Furthermore, these approaches can help understand the body's responses to environmental and lifestyle factors, and the interactions between genetic and environmental factors that produce changes in the body's biochemical profile. This can aid in early diagnosis, therapy monitoring, and understanding the disease's pathogenesis.", + "The latest meta-analyses of large-scale genome-wide association studies (GWAS) have identified novel diabetic loci near several genes. These include JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2, UBASH3A, BACH2, AGMO, GDAP1, PTF1A, SIX3, ALDH2, NKX6-3, ANK1, and a microRNA cluster. Additionally, a locus near the LMO7 gene on 13q22 and another near the EFR3B gene on 2p23 were identified. A novel signal was also detected near AGMO.", + "Epigenetic modifications like DNA methylation and histone modification can alter the expression of diabetes-related genes without changing the underlying DNA sequence. DNA methylation involves the addition of a methyl group to a cytosine within cytosine-phosphate-guanine (CpG) dinucleotides, which can regulate gene expression. Histone modifications, on the other hand, involve changes to the proteins around which DNA is wound, affecting the accessibility of genes for transcription. These modifications can be influenced by factors such as hyperglycemia, inflammation, and oxidative stress, leading to changes in gene expression that contribute to diabetes and its complications.", + "The gut microbiome plays a significant role in modulating host genetic predispositions to diabetes. It has been observed that alterations in the gut microbiome can precede the onset of Type 1 Diabetes (T1D). Dysbiosis of gut microbiota, characterized by an imbalance in the microbial community, can contribute to insulin resistance and the pathogenesis of T2D. The gut microbiome can influence glucose metabolism and insulin sensitivity, and changes in its composition can affect the development and progression of diabetes. Certain gut microbiota can improve glucose homeostasis and leptin sensitivity, potentially offering therapeutic targets for diabetes prevention and management. However, the interactions between host genetics, metabolism, and the immune system in shaping the microbiome and predilection to disease are still being explored.", + "Machine learning algorithms have shown significant effectiveness in integrating genomic data to predict individual risk and progression of diabetes. The most commonly used algorithms are Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Decision Trees (DT), with SVM being the most successful. The prediction accuracy of these algorithms is often above 80%. Recurrent Neural Network (RNN) models have also been used to predict type 2 diabetes with promising results. However, the effectiveness can vary depending on the specific algorithm used, the quality of the data, and the number of features or attributes used in the model." + ], + "contexts": [ + [ + "\t\n\nMechanistic and translational studies that focus on the characterisation of archetypes are likely to be more tractable.For each of the component pathways, we should seek to deepen our understanding of the molecular and physiological machinery responsible for homeostatic control, and of the specific genetic and environmental factors that 'push' individuals towards diabetes.We should aim to identify biomarkers that serve as robust readouts for each of those processes.We already have some examples of these (e.g.islet antibodies, urinary C-peptide) but access to increasingly powerful 'omic' readouts (transcriptomics, proteomics, metabolomics) brings the promise of others [21].We should aim to determine the extent to which the various pharmacological and behavioural interventions that are available influence diabetes progression and management in the different archetype groups.In doing so, we will determine the extent to which we can expect to optimise prevention and therapy on the basis of this improved diagnostic precision.Alternatively, we may find that many treatments work fairly well irrespective of individual pathology, since, to reverse the diabetic phenotype, it may be sufficient to shift enough of the contributing pathways in a beneficial direction.\t\nThe current focus on delivery of personalised (or precision) medicine reflects the expectation that developments in genomics, imaging and other domains will extend our diagnostic and prognostic capabilities, and enable more effective targeting of current and future preventative and therapeutic options.The clinical benefits of this approach are already being realised in rare diseases and cancer but the impact on management of complex diseases, such as type 2 diabetes, remains limited.This may reflect reliance on inappropriate models of disease architecture, based around rare, highimpact genetic and environmental exposures that are poorly suited to our emerging understanding of type 2 diabetes.This review proposes an alternative 'palette' model, centred on a molecular taxonomy that focuses on positioning an individual with respect to the major pathophysiological processes that contribute to diabetes risk and progression.This model anticipates that many individuals with diabetes will have multiple parallel defects that affect several of these processes.One corollary of this model is that research efforts should, at least initially, be targeted towards identifying and characterising individuals whose adverse metabolic trajectory is dominated by perturbation in a restricted set of processes.", + "\t\n\nAs discussed earlier, these high-throughput approaches are already being implemented in diabetic complications research.They have been complemented with systems biology and systems genetics efforts to effectively identify new players in and drug targets for diabetic complications [105].There are also ongoing efforts to systematically profile epigenetic marks in tissues, cells and archived genomic DNA from various clinical trials.The major challenge, however, is expected to be in the analysis of the ensuing large datasets, the complexity of bioinformatics/biostatistics and in silico modelling.If these hurdles can be overcome, these efforts are likely to yield novel insights into epigenome variations linked with diabetic complications.", + "\t\n\nGriffin JL, Vidal-Puig A. Current challenges in metabolomics for diabetes research: a vital functional genomic tool or just a ploy for gaining funding?Physiol Genomics 34: 1-5, 2008.First published April 15, 2008; doi:10.1152/physiolgenomics.00009.2008.-Metabolomicsaims to profile all the small molecule metabolites found within a cell, tissue, organ, or organism and use this information to understand a biological manipulation such as a drug intervention or a gene knockout.While neither mass spectrometry or NMR spectroscopy, the two most commonly used analytical tools in metabolomics, can provide a complete coverage of the metabolome, compared with other functional genomic tools for profiling biological moieties the approach is cheap and high throughput.In diabetes and obesity research this has provided the opportunity to assess large human populations or investigate a range of different tissues in animal studies both rapidly and cheaply.However, the approach has a number of major challenges, particularly with the interpretation of the data obtained.For example, some key pathways are better represented by high concentration metabolites inside the cell, and thus, the coverage of the metabolome may become biased towards these pathways (e.g., the TCA cycle, amino acid metabolism).There is also the challenge of statistically modeling datasets with large numbers of variables but relatively small sample sizes.This perspective discusses our own experience of some of the benefits and pitfalls with using metabolomics to understand diseases associated with type 2 diabetes.NMR spectroscopy; mass spectrometry; obesity; functional genomics WHILE IT IS DIFFICULT TO DATE the start of any field this is particularly true of -omic technologies.The desire to profile a large number of entities involved in any tier of a biological system has been a common thread in biology.The field of metabolomics is no exception to this statement.While the term metabolomics (23) and the related term metabonomics (22) were coined in the late 90s, it is difficult to distinguish some of the work conducted now under the umbrella of metabolomics from much earlier studies involving largescale profiling of metabolites by mass spectrometry (for example Refs.16,30) and NMR spectroscopy (for example Refs.3,5).Indeed many of the basic processes that occur in current metabolomic laboratories would not be that dissimilar to work carried out by the pioneers of metabolic research who gave their names to the various pathways we study.In this brief article we discuss some of the benefits modern metabolomic approaches provide to functional genomics, with particular reference to diabetes and the metabolic syndrome, and outline some of the challenges the field faces if it is to develop into a mature technology.", + "\t\nClinical and epidemiological metabolomics provides a unique opportunity to look at genotypephenotype relationships as well as the body's responses to environmental and lifestyle factors.Fundamentally, it provides information on the universal outcome of influencing factors on disease states and has great potential in the early diagnosis, therapy monitoring, and understanding of the pathogenesis of disease.Diseases, such as diabetes, with a complex set of interactions between genetic and environmental factors, produce changes in the body's biochemical profile, thereby providing potential markers for diagnosis and initiation of therapies.There is clearly a need to discover new ways to aid diagnosis and assessment of glycemic status to help reduce diabetes complications and improve the quality of life.Many factors, including peptides, proteins, metabolites, nucleic acids, and polymorphisms, have been proposed as putative biomarkers for diabetes.Metabolomics is an approach used to identify and assess metabolic characteristics, changes, and phenotypes in response to influencing factors, such as environment, diet, lifestyle, and pathophysiological states.The specificity and sensitivity using metabolomics to identify biomarkers of disease have become increasingly feasible because of advances in analytical and information technologies.Likewise, the emergence of high-throughput genotyping technologies and genome-wide association studies has prompted the search for genetic markers of diabetes predisposition or susceptibility.In this review, we consider the application of key metabolomic and genomic methodologies in diabetes and summarize the established, new, and emerging metabolomic and genomic biomarkers for the disease.We conclude by summarizing future insights into the search for improved biomarkers for diabetes research and human diagnostics.\t\n\nClinical and epidemiological metabolomics provides a unique opportunity to look at genotypephenotype relationships as well as the body's responses to environmental and lifestyle factors.Fundamentally, it provides information on the universal outcome of influencing factors on disease states and has great potential in the early diagnosis, therapy monitoring, and understanding of the pathogenesis of disease.Diseases, such as diabetes, with a complex set of interactions between genetic and environmental factors, produce changes in the body's biochemical profile, thereby providing potential markers for diagnosis and initiation of therapies.There is clearly a need to discover new ways to aid diagnosis and assessment of glycemic status to help reduce diabetes complications and improve the quality of life.Many factors, including peptides, proteins, metabolites, nucleic acids, and polymorphisms, have been proposed as putative biomarkers for diabetes.Metabolomics is an approach used to identify and assess metabolic characteristics, changes, and phenotypes in response to influencing factors, such as environment, diet, lifestyle, and pathophysiological states.The specificity and sensitivity using metabolomics to identify biomarkers of disease have become increasingly feasible because of advances in analytical and information technologies.Likewise, the emergence of high-throughput genotyping technologies and genome-wide association studies has prompted the search for genetic markers of diabetes predisposition or susceptibility.In this review, we consider the application of key metabolomic and genomic methodologies in diabetes and summarize the established, new, and emerging metabolomic and genomic biomarkers for the disease.We conclude by summarizing future insights into the search for improved biomarkers for diabetes research and human diagnostics.\t\n\nIn this brief review, we consider recent applications of metabolomic and related technologies in diabetes together with their use in relation to clinical diagnostics.Technical details of the methodologies involved and their use in basic diabetes research have been covered in several excellent articles and reviews (1,3).", + "\tnovEl \"-omics\" TEcHnologiEs\n\nThe number of scientific articles on transcriptomics, proteomics, and metabolomics has been increasing substantively over the state art state art past 10-15 years.The accumulation of information from novel \"-omics\" technologies comes with substantial hope and expectations that these hypothesis-free approaches will yield novel insights into many disease processes and that these insights will eventually translate into clinical applications that will pave the way from current medical routine to the ideal of personalized medicine.With regard to T2D and CVD, the use of data from transcriptomics, proteomics, and metabolomics studies for their predictive potential is still at a very early stage.Here, we aim to provide an overview of studies that are representative of current developments in this research field.", + "\tOther 'omics' tools\n\n Given the current epidemic status of T2D, the need for the hour is a deeper understanding of associated pathological mechanisms, for timely intervention. To realize this objective, a range of novel tools and techniques need to be integrated in diabetes research, as no one technique is capable of providing the solution by itself.Epigenomics, transcriptomics, proteomics, metabolomics, and computational biology are some tools of the proposed 'omics' toolbox which may contribute to the field of T2D research.\tReview Siddiqui & Tyagi\n\nThe goal of personalized treatment and care for diabetes can be realized by integrating patient-specific knowledge with data from 'omics' technologies.Advances in genomics (including epigenomics), transcriptomics, proteomics and metabolomics may not only help in identifying, assessing and quantitating individual disease risk early on, but will also be beneficial in understanding the specific responses to drug therapy and lifestyle interventions.This can be further complemented with patient information on their economic status, ease or difficulty of access to healthcare (more of a challenge in developing countries), environment (e.g., exposure to high pollution levels, work culture, social structure among others) and lifestyle (e.g., smoking, physical activity, eating preferences among others).An evidence-based therapy, which is implemented timely and incorporates such personal values, circumstances and data, can be more effective in managing diabetes at an individual level.Although the 'omics' revolution has been more successful in providing insights into monogenic diseases than polygenic disorders, its potential in expanding knowledge of genetic determinants influencing diabetes susceptibility and treatment cannot be overlooked.In diabetes research, omics tools have proven their worth in identifying not only susceptibility genes but also biological markers of disease pathology, thereby adding to the understanding of the disease process.\t\n\nSince data from any one tool is insufficient in providing a comprehensive picture, data from all 'omics' tools (genomics, transcriptomics, proteomics, metabolomics among others) can be used in a systems biology approach for a better understanding at tissue or organ system level.Systems biology integrates the given information into interaction networks [74].These networks assess both functional interactions and mathematical correlations between given data in a biological setting and provide a broader picture.Jain et al. [75] have demonstrated the use of a systems biology approach for uncovering genome to phenome correlations in T2D by identifying pathways known to be associated with disease pathology.Although the field of systems biology holds promise, it is still in its nascent stage and requires extensive work to be able to map diseases in complex tissues and organ systems.", + "\tConclusions and Future Perspectives\n\nCurrent approaches such as transcriptome and proteome profiling, as well as molecular genetics, using various cell lines, animal models and human samples have greatly facilitated the understanding of the mechanism(s) relevant to the progression of diabetic nephropathy.Based on the data generated by using these techniques, the newly discovered biomarkers could serve as therapeutic targets for the amelioration of diabetic nephropathy, which certainly contribute to the reduction in mortality and morbidity in chronic kidney disease patients that progress to ESRD.In addition to transcriptome and proteome approaches, the future trends for the identification of the biomarkers and therapeutic target genes could include genome-scale DNA methylation profiling [75].The emerging role of epigenome control of the cancer cells, germ cells and pluripotent stem cells has been emphasized in the transcriptional regulation of various genes that receive sustained long-term injury for years and decades.Intensive long-term versus conventional short-interval symptomatic therapy seems to have remarkable beneficial effects on the risk of cardiovascular disease in patients with type 1 diabetes and this suggests that there may be alterations in the genomic DNA-or histonemethylation pattern which may be linked to the long-term 'metabolic memory' for the progression of vascular complications of diabetes [76].Such a methylation-related profiling would certainly advance the field, especially with respect to development of new biomarkers and various therapeutic strategies.In addition to the delineation of epigenome control of the genes, metabolic phenotyping using 1H spectroscopy [77] and lectin microarray [78] for the glycan profiling would also promote the identification of the new biomarkers of diabetic nephropathy.Finally, integration of the information from different sources using system biology approaches would be an important step in data-mining for the identification of relevant genes that are pertinent to the diagnosis and therapy for diabetic nephropathy.", + "\tNovel biomarkers from '-omics' technologies as potential components of risk models\n\nDespite moderate or even good model accuracy in some studies (Table 1, ESM Table 2), current prediction algorithms leave room for improvement and raise the question of whether novel biomarkers could be clinically useful, particularly if they could improve risk models that already contain measures of glycaemia.The range of molecules that could serve as potential biomarkers of diabetes risk includes genetic variants, RNA transcripts, peptides and proteins, lipids and small metabolites, cellular markers and metabolic waste products [39].Owing to current advances in '-omics' technologies, such as genomics, transcriptomics, proteomics and metabolomics, the number of candidate biomarkers keeps growing; however, only a small proportion of these has been investigated with reference to their potential to improve the prediction of type 2 diabetes.", + "\t\n\nThe so-called omics (eg, metabolomics, lipidomics, proteomics, genomics, and transcriptomics) are based on the study of constituents of the cell or body in a collective way.The fi ndings made with use of these approaches are being integrated to better understand the pathophysiology of type 2 diabetes and the heterogeneity of responses to diff erent glucose-lowering therapies.Findings from studies that used metabolomics and lipidomics showed that increases in branched-chain and aromatic aminoacids were associated with obesity and type 2 diabetes. 84,85Furthermore, patients with high concentrations of specifi c six-carbon sugars, aminoacids, and fatty acids, and low concentrations of other aminoacids and fatty acids, had an increased risk of developing type 2 diabetes over a 7 year follow-up. 86hether all or some of these substrate markers are associated with genetic determinants, dietary factors, or the actions of gut microbes has not been established.", + "\tMetabolomics and novel circulating biomarkers\n\nMetabolomics is a comprehensive characterization of metabolic changes connected to disease development and progression.High sensitivity and resolution of mass spectrometry achieved with liquid or gas chromatography allows the detection and quantification of thousands of metabolites.An alternative method to quantify metabolites is the high-throughput serum nuclear magnetic resonance platform, but the number of metabolites identified using this method is substantially lower compared with mass spectrometry [22].By using high throughput technologies, metabolomics allows the identification and measurement of metabolites recognizable in a given biological sample.Identification of small biomolecules (metabolites) makes it possible to find early biomarkers for a disease of interest, including T2D and its comorbidities.A recent systematic review and meta-analysis covering the years from 2008 to 2017 included 14 studies and 4,592 individuals with T2D and 11,492 without T2D [23].Their report noted a 1.89-, 1.63-, and 1.87-fold higher risk of T2D associated for leucine, alanine, and oleic acid, respectively, whereas lysophosphatidylcholine C18:0 and creatinine were associated with 20% and 37% decreased risk of T2D, respectively.Our 4.6-year follow-up study of the METSIM cohort included 5,181 participants having metabolomics data available for twenty amino acids at baseline.Five amino acids (tyrosine, alanine, isoleucine, aspartate and glutamate) were significantly associated with a decrease in insulin secretion and an increased risk of incident T2D after adjustment for confounding factors [24].All essential amino acids, and especially branch-chain amino acids, stimulate insulin secretion and GLP-1 release [25].The mechanisms of reduced insulin secretion of five amino acids in our study remains to be determined but could be explained, at least in part, by glucagon regulation [26,27].Interestingly, a recent study demonstrated a causal relationship between the gut microbiome, short-chain fatty acids and metabolic diseases.The host-genetic-driven increase in gut production of the fecal short-chain fatty acid butyrate was significantly associated with improved insulin response after an OGTT, and another short-chain fatty propionate, was causally related to an increased risk of T2D in the MR.These data provide evidence of a causal effect of the gut microbiome on metabolic traits [28].The metabolomics approach has limitations in the identification of metabolites for the risk of T2D.There is no consensus on how to standardize metabolomics results, making it difficult to compare the findings across different studies.Additionally, protocols and statistical approaches may differ, and instrumentation can yield varied sets of detectable metabolites [29].Despite these potential limitations, studies applying metabolomics have the potential to identify a unique set of metabolites predictive of T2D.", + "\tRecent advances in mass spectrometry have expanded the scope and reliability\nof proteomics and metabolomics measurements. These tools are now capable of identifying thousands of factors driving diverse\nmolecular pathways, their mechanisms, and\nconsequent phenotypes and thus substantially contribute toward the understanding of\ncomplex systems. RATIONALE: Genome-wide association stud-\n\nies (GWAS) have revealed many causal loci\nassociated with specific phenotypes, yet the\nidentification of such genetic variants has\nbeen generally insufficient to elucidate the\nmolecular mechanisms linking these genetic\nvariants with specific phenotypes. A multitude\nof control mechanisms differentially affect\nthe cellular concentrations of different classes of biomolecules.", + "\tConclusion\n\nOur study represents the first multi-platform approach to the metabolome-wide analyses of diabetes in a general population.The identification of biomarkers allowing prediction of disease progression and its complications from such studies would be certainly beneficial.However, for the caveats discussed above, we feel that this study should be considered as a pilot for future work.One major finding of our work is the identification of a series of known, and also some novel, deregulated metabolites that associate with diabetes under sub-clinical conditions in the general population.These metabolites have been discovered by integrative metabolomics applying different platforms including nuclear magnetic resonance (NMR) and mass spectrometry (MS).Out of the multitude of metabolites measured, a holistic view of differences reflecting global variations in pathophysiology emerges from our study.The coverage of the metabolome's diversity allows the detection of systemic metabolic imbalances, thereby providing a disease-specific picture of human physiology (Figure 3).A pronounced increase in the sample size in future studies will likely allow for further detection of other metabolites of unrecognized associations with diabetic pathways.Finally, our study shows how functional metabolomics can contribute to obtaining a more sophisticated classification of the disease as well as rational optimization of diagnostic and treatment options, as recently suggested by Bain et al. [4].\t\n\nThe principal concept of metabolomics being able to find some metabolites differing in a control and a type 2 diabetic group is established.It is not our goal here to show this once again.The questions we ask are rather ''How well are different approaches suited to attain this goal? ''and ''What are optimal settings under which such studies can be successful? ''.Others have already investigated these questions before [16,17,18].However, we believe that this topic is much too complex than to be answered fully in a single study.For instance, the work described in the recent paper in this journal by Lanza et al. [19] covers only a small patient group of 7 cases and 7 controls.Our study, in contrast is based on 40 cases and 60 controls from an epidemiological cohort.Work reviewed recently by Madsen et al. [20] overlaps to some extent with our study, but none of them address aspects related to sub-clinical signals in a general population.Our focus is on participants from epidemiological studies rather than on patients under clinical conditions.Herein, we identify a series of differentially ''expressed'' metabolites that associate with diabetes under sub-clinical conditions in the general population.This question has not been addressed to this extent by any published paper.In particular, we see our work as a pilot that bears the potential of being scaled up to much larger sample sizes, since population studies such as KORA eventually provide access to much larger sample sizes, taken under rigorous standardized blood sample collection conditions in dedicated study centers (e.g.overnight fasting, standard protocol for serum and plasma preparation, storage in liquid nitrogen until measurement).These kinds of samples generally have not been available from clinical studies until recently.It is in this light that we provide here a proof of concept that metabolomics can uncover key metabolites differing in a control and a type 2 diabetic group.", + "\t\n\nCurrent technologies, such as metabolomics, proteomics, and genomics contribute to the development of a plethora of new biomarkers.In the case of DM, biomarkers may reflect the presence and severity of hyperglycemia or presence and severity of the related complications in diabetes [23].", + "\t\n\nMetabolomics studies allow metabolites involved in disease mechanisms to be discovered by monitoring metabolite level changes in predisposed individuals compared with healthy ones (Shaham et al, 2008;Newgard et al, 2009;Zhao et al, 2010;Pietilainen et al, 2011;Rhee et al, 2011;Wang et al, 2011;Cheng et al, 2012;Goek et al, 2012).Altered metabolite levels may serve as diagnostic biomarkers and enable preventive action.Previous cross-sectional metabolomics studies of T2D were either based on small sample sizes (Shaham et al, 2008;Wopereis et al, 2009;Zhao et al, 2010;Pietilainen et al, 2011) or did not consider the influence of common risk factors of T2D (Newgard et al, 2009).Recently, based on prospective nested case-control studies with relative large samples (Rhee et al, 2011;Wang et al, 2011), five branched-chain and aromatic amino acids were identified as predictors of T2D (Wang et al, 2011).Here, using various comprehensive largescale approaches, we measured metabolite concentration profiles (Yu et al, 2012) in the population-based (Holle et al, 2005;Wichmann et al, 2005) Cooperative Health Research in the Region of Augsburg (KORA) baseline (survey 4 (S4)) and follow-up (F4) studies (Rathmann et al, 2009;Meisinger et al, 2010;Jourdan et al, 2012).The results of these crosssectional and prospective studies allowed us to (i) reliably identify candidate biomarkers of pre-diabetes and (ii) build metabolite-protein networks to understand diabetes-related metabolic pathways." + ], + [ + "\t\nAims/hypothesis Genome-wide association studies (GWAS) for type 2 diabetes have uncovered >400 risk loci, primarily in populations of European and Asian ancestry.Here, we aimed to discover additional type 2 diabetes risk loci (including Africanspecific variants) and fine-map association signals by performing genetic analysis in African populations.Methods We conducted two type 2 diabetes genome-wide association studies in 4347 Africans from South Africa, Nigeria, Ghana and Kenya and meta-analysed both studies together.Likely causal variants were identified using fine-mapping approaches.Results The most significantly associated variants mapped to the widely replicated type 2 diabetes risk locus near TCF7L2 (p = 5.3 10 13 ).Fine-mapping of the TCF7L2 locus suggested one type 2 diabetes association signal shared between Europeans and Africans (indexed by rs7903146) and a distinct African-specific signal (indexed by rs17746147).We also detected one novel signal, rs73284431, near AGMO (p = 5.2 10 9 , minor allele frequency [MAF] = 0.095; monomorphic in most non-African populations), distinct from previously reported signals in the region.In analyses focused on 100 published type 2 diabetes risk loci, we identified 21 with shared causal variants in African and non-African populations.Conclusions/interpretation These results demonstrate the value of performing GWAS in Africans, provide a resource to larger consortia for further discovery and fine-mapping and indicate that additional large-scale efforts in Africa are warranted to gain further insight in to the genetic architecture of type 2 diabetes.", + "\t\n\nIn 2008, to increase the power of identifying variants with modest effects, a meta-analysis of three GWAS, including Diabetes Genetics Initiative (DGI), Finland-United States Investigation of NIDDM Genetics (FUSION), and Wellcome Trust Case Control Consortium (WTCCC), were conducted.This study detected at least six previously unknown loci that reached genome-wide significance for association with T2D ( < 5 10 8 ), with the loci being JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2 [19].Genetic variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, and THADA have been reported to affect pancreatic -cell functions [59,60].", + "\t, for the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 9\n\nGenome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D) [1][2][3][4][5][6][7][8][9][10][11] .Established associations to common and rare variants explain only a small proportion of the heritability of T2D.As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and B2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975.We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P 5.0 10 -14 ), CDC123-CAMK1D (P 1.2 10 -10 ), TSPAN8-LGR5 (P 1.1 10 -9 ), THADA (P 1.1 10 -9 ), ADAMTS9 (P 1.2 10 -8 ) and NOTCH2 (P 4.1 10 -8 ) gene regions.Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.\t\n\nBy combining three GWA scans involving 10,128 samples (enhanced through imputation approaches) and undertaking largescale replication in up to 79,792 additional samples, we identified six additional loci that apparently harbor common genetic variants influencing susceptibility to T2D.These findings are consistent with a model in which the preponderance of loci detectable through the GWA approach (using current arrays and indirect LD mapping) have modest effects (ORs between 1.1 and 1.2).Given such a model, our study (in which we followed up only 69 signals out of over 2 million meta-analysed SNPs) would be expected to recover only a subset of the loci with similar characteristics (that is, those that managed to reach our stage 1 selection criteria).Further efforts to expand GWA metaanalyses and to extend the number of SNPs taken forward to largescale replication should confirm additional genomic loci, as should targeted analysis of copy number variation.However, the present data provide only crude estimates of the overall effect on susceptibility attributable to variants at these loci.The effect of the actual common causal variant responsible for the index association (once identified) will typically be larger, and many of these loci are likely to carry additional causal variants, including, on occasion, low-frequency variants of larger effect: three genes with common variants that influence risk of T2D were first identified on the basis of rare mendelian mutations (in KCNJ11, WFS1 and HNF1B).Regardless of effect size, these loci provide important clues to the processes involved in the maintenance of normal glucose homeostasis and in the pathogenesis of T2D.\t\n [3][4][5]7,10 , for the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 9Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D) [1][2][3][4][5][6][7][8][9][10][11] .Established associations to common and rare variants explain only a small proportion of the heritability of T2D.As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and B2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975.We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P 5.0 10 -14 ), CDC123-CAMK1D (P 1.2 10 -10 ), TSPAN8-LGR5 (P 1.1 10 -9 ), THADA (P 1.1 10 -9 ), ADAMTS9 (P 1.2 10 -8 ) and NOTCH2 (P 4.1 10 -8 ) gene regions.Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.", + "\t\nDiabetes impacts approximately 200 million people worldwide, of whom approximately 10% are affected by type 1 diabetes (T1D).The application of genome-wide association studies (GWAS) has robustly revealed dozens of genetic contributors to the pathogenesis of T1D, with the most recent meta-analysis identifying in excess of 40 loci.To identify additional genetic loci for T1D susceptibility, we examined associations in the largest meta-analysis to date between the disease and ,2.54 million SNPs in a combined cohort of 9,934 cases and 16,956 controls.Targeted follow-up of 53 SNPs in 1,120 affected trios uncovered three new loci associated with T1D that reached genome-wide significance.The most significantly associated SNP (rs539514, P = 5.66610 211 ) resides in an intronic region of the LMO7 (LIM domain only 7) gene on 13q22.The second most significantly associated SNP (rs478222, P = 3.50610 29 ) resides in an intronic region of the EFR3B (protein EFR3 homolog B) gene on 2p23; however, the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A.The third most significantly associated SNP (rs924043, P = 8.06610 29 ) lies in an intergenic region on 6q27, where the region of association is approximately 900 kb and harbors multiple genes including WDR27, C6orf120, PHF10, TCTE3, C6orf208, LOC154449, DLL1, FAM120B, PSMB1, TBP, and PCD2.These latest associated regions add to the growing repertoire of gene networks predisposing to T1D.", + "\t\nOBJECTIVE-Two recent genome-wide association (GWA) studies have revealed novel loci for type 1 diabetes, a common multifactorial disease with a strong genetic component.To fully utilize the GWA data that we had obtained by genotyping 563 type 1 diabetes probands and 1,146 control subjects, as well as 483 case subject-parent trios, using the Illumina HumanHap550 BeadChip, we designed a full stage 2 study to capture other possible association signals.RESEARCH DESIGN AND METHODS-From our existing datasets, we selected 982 markers with P 0.05 in both GWA cohorts.Genotyping these in an independent set of 636 nuclear families with 974 affected offspring revealed 75 markers that also had P 0.05 in this third cohort.Among these, six single nucleotide polymorphisms in five novel loci also had P 0.05 in the Wellcome Trust Case-Control Consortium dataset and were further tested in 1,303 type 1 diabetes probands from the Diabetes Control and Complications Trial/Epidemiology of Dia-betes Interventions and Complications (DCCT/EDIC) plus 1,673 control subjects.RESULTS-Two markers (rs9976767 and rs3757247) remained significant after adjusting for the number of tests in this last cohort; they reside in UBASH3A (OR 1.16; combined P 2.33 10 8 ) and BACH2 (1.13; combined P 1.25 10 6 ).CONCLUSIONS-Evaluation of a large number of statistical GWA candidates in several independent cohorts has revealed additional loci that are associated with type 1 diabetes.The two genes at these respective loci, UBASH3A and BACH2, are both biologically relevant to autoimmunity.", + "\t\n\nGenome-wide association studies (GWAS) have recently revealed many novel SNPs associated with type 2 diabetes.These include SNPs located in the regions near TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, and CDKN2A-CDKN2B [8][9][10][11][12][13].A second phase of studies identified many additional variants, including those near JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, and KCNQ1 [14,15].The two genes in which common variants were previously convincingly associated with type 2 diabetes, PPARG and KCNJ11, were also identified in these GWAS [12,16,17].More recently, numerous other SNPs have been identified in additional GWAS and meta-analyses [18].", + "\t\n\n. A genome-wide association study identifies novel risk Loci for Type 2 diabetes.Nature 445(7130), 881-885 (2007).31 The Wellcome Trust Case Control Consortium.Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.Nature 447, 661-678 (2007).Twelve Type 2 diabetes susceptibility loci identified through large-scale association analysis.Nat.Genet.42(7), 579-589 (2010).33 SIGMA Type 2 Diabetes Consortium, Williams AL, Jacobs SB, Moreno-Macas H, Huerta-Chagoya A et al.Sequence variants in SLC16A11 are a common risk factor for Type 2 diabetes in Mexico.Nature 506(7486), 97-101 (2014).34 Ma RC, Hu C, Tam CH et al.Genome-wide association study in a Chinese population identifies a susceptibility locus for Type 2 diabetes at 7q32 near PAX4.Diabetologia 56(6), 1291-1305 (2013).35 Hara K, Fujita H, Johnson TA et al.Genome-wide association study identifies three novel loci for Type 2 diabetes.Hum.Mol.Genet.23(1), 239-46 (2014).36 Palmer ND, McDonough CW, Hicks PJ et al.A genomewide association search for Type 2 diabetes genes in African Americans.PLoS ONE 7(1), e29202 (2012).37 Hanson RL, Muller YL, Kobes S et al.A genome-wide association study in American Indians implicates DNER as a susceptibility locus for Type 2 diabetes.Diabetes 63(1), 369-376 (2014).", + "\t\n\nFigure 1 illustrates the metaanalysis of risk estimates for six of the loci (CDKAL1, CDKN2A/B, HHEX, IGF2BP2, SLC30A8, and KCNQ1), using data from published studies in East Asia, including Chinese populations from China (9, 20 -23) and Hong Kong (10) as well as Korean (7,10,24) and Japanese (6,7,25,26) populations.In essence, the metaanalysis showed that these six diabetes susceptibility loci identified through GWAS are associated with T2DM in populations across Asia.", + "\t\n\nNovel T2D-associated loci driven by common variants.Beyond the detailed characterization of the known T2D-associated regions, we also identified seven novel loci, among which, five were driven by common variants with modest effect sizes (1.06 < OR < 1.12; Table 1, Fig. 2, Supplementary Fig. 6 and 7).", + "\t\n\nA meta -analysis of three GWA scans followed by a large -scale replication (Diagram consortium including more than 50 000 individuals in total) has identifi ed additional susceptibility loci for T2DM, with OR ranging from 1.09 to 1.15, near six genes: JAZF1 , CDC123 -CAMK1D , TSPAN8 -LGR5 , THADA , ADAMTS9 and NOTCH2 [174] .Variants at JAZF1 , CDC123 -CAMK1D and TSPAN8 -LGR5 are associated with small alterations in insulin secretion, whereas the mechanisms linking the other loci to T2DM remain to be clarifi ed [175] .In each GWA scan, other loci showed signifi cant associations with T2DM, but were not fol-with Mendelian forms of diabetes, such as MODY, which are caused by rare mutations in the coding sequence resulting in signifi cant amino acid substitutions or truncated proteins, leading to hyperglycemia even in the absence of other diabetogenic exposures.", + "\tZeggini, E., Scott, L.J. , Saxena, R., Voight, B.F., Marchini, J.L. , Hu, T., de\nBakker, P.I. , Abecasis, G.R. , Almgren, P., Andersen, G., et al. 2008. Metaanalysis of genome-wide association data and large-scale replication\nidentifies additional susceptibility loci for type 2 diabetes. Nat. Genet. 40: 638645. Zielenski, J., Corey, M., Rozmahel, R., Markiewicz, D., Aznarez, I., Casals, T.,\nLarriba, S., Mercier, B., Cutting, G.R. , Krebsova, A., et al. 1999. Detection\nof a cystic fibrosis modifier locus for meconium ileus on human\nchromosome 19q13. Nat. Genet. 22: 128129.", + "\t\n\nGenetic studies performed since 2012 have identified many additional T2D loci based on risk alleles common in one population but less common in others.Studies in African Americans identified RND3-RBM43 (28), HLA-B and INS-IGF2 (29).Studies in South Asians identified TMEM163 (30) and SGCG (31).One locus, SLC16A11-SLC16A13, was simultaneously identified in Japanese and Mexican Americans (32,33), and studies in East Asians identified ANK1 (34), GRK5 and RASGRP1 (35), LEP and GPSM1 (32), and CCDC63 and C12orf51 (36).A study of individuals from Greenland identified TBC1D4 (37), and a sequencing-based study of Danes with follow-up in other Europeans identified MACF1 (38).Finally, the largest GWAS to date in American Indians identified DNER at near genome-wide significance (P = 6.6 10 8 ) (39).Three of these studies imputed GWAS data using the 1000 Genomes Project sequence-based reference panels, providing better genome coverage (29,32,33,40).Taken together, these studies highlight the value of diverse populations, including founder and historically isolated populations, to detect risk loci.\t\n\nMeta-analyses across populations provide further opportunities to detect loci with shared risk alleles.Meta-analysis of 17 418 T2D cases and 70 298 controls from European, African-American, Hispanic-Latino, and Asian studies using a gene-based CardioChip array was first to identify the BCL2 locus for T2D (26).A recent genome-wide trans-ancestry meta-analysis of 26 488 T2D cases and 83 964 controls from European, East Asian, South Asian and Mexican ancestry, with follow-up in an additional 21 491 T2D cases and 55 647 controls of European ancestry, identified seven new T2D loci (48).The trans-ancestry part of this latter study was performed using variants imputed based on genotype data from the International HapMap Project (49), and follow-up was limited to variants available in Metabochip-typed datasets, suggesting that future trans-ancestry meta-analyses incorporating data imputed to denser reference panels will identify additional loci.", + "\t\n\nFinally, a recent study identified additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs. 21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds.Thus, by combining data involving 10,128 samples, the authors found in the initial stages of the analysis highly associated variants (they followed only 69 signals out of over 2 million metaanalyzed SNPs) with P values 10 4 in unknown loci, and 11 of these type 2 diabetes' associated SNPs were taken forward to further stages of analysis.Large stage replication testing allowed the detection of at least six previously unknown loci with robust evidence for association with type 2 diabetes.", + "\t\n\nTo identify common type 2 diabetes susceptibility variants, large-scale genome-wide association studies (GWAS) have been conducted in white individuals, yielding more than 60 genetic loci to date [5,6].Although many of these regions have been successfully replicated in Asian populations [7][8][9][10][11], discrepancies in allelic frequencies and effect sizes have demonstrated that interethnic differences exist.GWAS conducted in Japanese individuals [12,13], as well as meta-analyses of GWAS in South Asian [14] and East Asian [15] groups, have revealed additional variants not detected in GWAS with white individuals, with several signals, including KCNQ1, later replicated in many populations [12,13].Previous GWAS in Chinese suggested several loci but lacked large-scale replication [16][17][18].\tDiscussion\n\nThis study reports a meta-analysis of GWAS for type 2 diabetes in a Chinese population, and has identified a novel diabetes-associated locus.Furthermore, we replicated the association in additional East Asian samples, and found an association in samples of European descent.In addition to the multiethnic samples used in our study, our study also benefits from a detailed phenotyping of the Chinese samples, which allowed additional analyses of the effect of the risk variant on clinical traits and the course of disease to be carried out.", + "\tIdentification of type 2 diabetes loci in 433,540 East Asian individuals\n\nMeta-analyses of genome-wide association studies (GWAS) have identified more than 240 loci that are associated with type 2 diabetes (T2D) 1,2 ; however, most of these loci have been identified in analyses of individuals with European ancestry.Here, to examine T2D risk in East Asian individuals, we carried out a meta-analysis of GWAS data from 77,418 individuals with T2D and 356,122 healthy control individuals.In the main analysis, we identified 301 distinct association signals at 183 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 61 loci that are newly implicated in predisposition to T2D.Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes.Previously undescribed associations include signals in or near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect the differentiation of muscle and adipose cells 3 .At another locus, expression quantitative trait loci at two overlapping T2D signals affect two genes-NKX6-3 and ANK1-in different tissues [4][5][6] .Association studies in diverse populations identify additional loci and elucidate disease-associated genes, biology, and pathways.", + "\t\n\nTo contend with the stringent significance thresholds that account for the number of independent tests performed across the genome, identification of additional T2D susceptibility loci required larger population samples, which was achieved by combining existing GWA studies in meta-analyses.The Diabetes Genetics Replication And Meta-analysis (DIAGRAM, http://www.diagram-consortium.org/) consortium carried out the first meta-analysis for T2D (Zeggini et al. 2008) of three GWA studies of European-descent individuals, including ~4500 cases and 5500 controls.Differences in the genotyping platforms used for individual GWA studies were overcome by imputation using a common variant set based on haplotype structure of densely characterized reference samples in HapMap (Consortium IH 2005) and extended the analysis to ~2.2 million SNPs across the genome 2.1) for each locus listed on the y-axis.Loci are sorted by descending order of per-allele effect size within each year.Colors highlight the discovery study approach: red, candidate gene; yellow, large-scale association; blue, genome-wide association; dark blue, genome-wide association meta-analysis; sky blue, genome-wide meta-analysis with Metabochip follow-up; green, genome-wide meta-analysis of glycemic traits; pink, genome-wide sex-differentiated meta-analysis with larger effects in women; brown, genome-wide sex-differentiated meta-analysis with larger effects in men; hacky, genome-wide meta-analysis in lean/ obese; gray, whole-exome sequencing.For loci with sex differentiation, the effect size for the sex with larger effect is presented.X-axis lists loci names, labeled by the gene names within region.Yaxis shows odds ratio for T2D observed at a given locus.Loci are split by the year of discovery and are ordered from top to bottom by the decreasing OR on T2D risk within each year.Shadow is used for loci from studies with discovery including non-European individuals The DIAGRAM consortium published two further meta-analyses, each based on increasingly larger case-control samples from European populations.The first combined discovery data from 21 GWA studies in up to 8130 individuals with T2D and 38,987 controls all imputed to a HapMap 2 reference panel, followed by large-scale replication in 34,412 cases and 59,925 controls where 13 (11 novel) out of 23 autosomal signals were confirmed (Tables 2.1 and 2.2) (Voight et al. 2010).This meta-analysis was the first to examine T2D associations on chromosome X (taking X-inactivation into account) and identified an association at DUSP9 with a large effect on T2D risk (OR 1.27, Table 2.2; Fig. 2.1) (Voight et al. 2010).The second meta-analysis, in addition to dramatically increasing the sample size (34,840 cases and 114,981 controls), implemented a novel cost-effective strategy for large-scale replication based on the CardioMetabochip (Metabochip), an Illumina iSelect genotyping array.Metabochip, which was designed through collaboration between six GWA consortia studying metabolic and atherosclerotic/ cardiovascular diseases and traits (Voight et al. 2012), permitted follow-up of ~66,000 putative signals for cardiometabolic phenotypes (~5000 of which were selected for T2D) (Morris et al. 2012).The Metabochip array also contained approximately 120,000 SNP probes to fine map 257 established loci in an attempt to identify causal T2D susceptibility variants.The DIAGRAM meta-analysis with Metabochip follow-up established T2D associations at 10 loci (Tables 2.1 and 2.2), including two at CCND2 and GIPR with larger effects on T2D risk in males and females, respectively (Morris et al. 2012).Among previously established T2D loci, sex differentiation in effect size has been shown for KCNQ1, DGKB, and BCL11A (larger effects in males) and GRB14 (larger effects in females)." + ], + [ + "\t\n\nThe identification of affected methylation sites is important because it provides evidence that a particular gene is susceptible to being modified by exposure to maternal diabetes.The direction of change is also important because it suggests that the expression and therefore the function of this gene is likely being modified in an inverse manner if the methylation change occurs in promoters or enhancers; however, the epigenome can also be influenced by other factors (such as microRNA and histone modifications), and as such, the direction of DNA methylation change observed in the overlapping genes in our stud may not be as important relative to the fact that the epigenome of a particular gene is susceptible to being altered.", + "\t\n\nIn addition to changes following exposure to intra-uterine hyperglycaemia, epigenetic changes have also been noted in other experimental settings of hyperglycaemia.For example, increased DNA methylation has been described for the promoter region of the peroxisome proliferator-activated receptor-g (PPARg) coactivator-1a gene (PPARGC1A) in diabetic islets (Ling et al., 2008).Similar hypermethylation in the promoter region of the PPARGC1A gene has been noted in the skeletal muscle from diabetic patients, and correlated with mitochondrial content (Barr es et al., 2009).Epigenetic changes have also been suggested to be responsible for the \"legacy effect\" of reduced risk of vascular complications after a period of sustained tight glucose control, or \"metabolic memory\" of transient hyperglycaemia and increased risk of diabetic vascular injury (Pirola et al., 2010).Histone methylation variations have been noted in monocytes cultured in high glucose, as well as blood monocytes of diabetic patients (Miao et al., 2007).In a series of landmark experiments, it was shown that endothelial cells exposed to short-term hyperglycaemia had persistently increased expression of the NF-kB active subunit p65, and was associated with increased promoter H3K4me1 and occupancy by the histone monomethyltransferase SET7/9.In addition, transient hyperglycaemia was also associated with sustained reduction of H3K9 methylation on the NF-kB p65 promoter, as well as recruitment of lysine-specific demethylase (LSD1) (El-Osta et al., 2008;Brasacchio et al., 2009).LSD1 has also been found to regulate H3K4 methylation in vascular smooth muscle cells in hyperglycaemic conditions, and may mediate the vascular inflammation (Reddy et al., 2008).Other epigenetic mechanisms including microRNAs and long noncoding RNAs have also been implicated in the pathogenesis of diabetic complications (Kato et al., 2014).", + "\tEpigenetic histone modifications and diabetic complications\n\nExciting recent research has demonstrated a role for epigenetic histone modifications in diabetes and its complications.HATs and HDACs have been found to play important roles in the regulation of several key genes linked to diabetes as reviewed by Gray and De Meyts (46).\t\n\nFigure 3: Scheme for the role of epigenetic mechanisms downstream of hyperglycemia in leading to diabetic complications.Diabetic conditions or hyperglycemia can activate several signal transduction pathways and transcription factors that can lead to sustained expression of pathological genes in the nucleus by co-operating with epigenetic factors.This can occur via a loss of repression and a corresponding gain in activation pathways leading to long-lasting epigenetic changes through gene promoter histone lysine modifications near key transcription factor binding sites or other important chromatin regions.Depending on the specific lysineresidue that is methylated, histone lysine methylation is associated with either gene activation (H3K4me) or repression (H3K9me).Modifications at other lysine residues may also be involved.These associations are further complicated by the gene location modified, either promoter or coding region, and the degree of methylation, all of which can affect accessibility of chromatin and transcriptional outcomes.These epigenetic modifications can be maintained through cell division via mechanisms that are not yet clearly understood but may include DNA methylation as well as transmission of histone lysine methylation marks.The persistence of these epigenetic changes might explain the metabolic memory phenomenon responsible for the continued development of diabetic complication even after glucose control has been achieved.\t\n\nFigure 2: Model for epigenetic regulation of pathological gene expression in diabetes via changes in chromatin histone modifications.Post translational modifications on the Nterminal histone tails in chromatin play essential roles in gene regulation and are regulated by various chromatin modifiers.Histone lysine methyltransferases (HMTs) and lysine demethylases (KDMs) regulate histone lysine methylation (Kme), while histone acetyltransferases (HATs) and histone deacetylases (HDACs) control histone acetylation (Ac).In the proposed model shown, various chromatin modifiers maintain sufficient levels of repressive histone marks to maintain strict control of pathologic gene expression under normal conditions;these would include methylation of H3K9 and demethylation of H3K4 in addition to deacetylation by HDACs.However, under diabetic conditions, including hyperglycemia, the\t\n\nHowever, much less is known about DNA methylation in diabetes.A recent report has shown that the insulin promoter DNA was methylated in mouse embryonic stem cells and only becomes demethylated as the cells differentiate into insulin expressing cells, and both the human and mouse insulin promoters were specifically demethylated in pancreatic beta cells suggesting epigenetic regulation of insulin expression (81).In the agouti mouse, DNA methylation and expression of the agouti gene can affect the tendency to develop obesity and diabetes (103).\t\n\nmodifications have also been found to play an important role in altering gene expression patterns associated with various diseases(91).Clinical as well as experimental studies with animal and cells models have clearly demonstrated the deleterious effects of hyperglycemia and the importance of maintaining good glucose control to prevent the onset or severity of diabetic complications.In addition, evidence shows that hyperglycemia can induce epigenetic changes to the chromatin structure via activation of various factors and signaling pathways.This has implicated specific key HMTs and KDMs related to active and repressed chromatin states and has demonstated epigenetic regulation of key inflammatory genes in vascular cells.It is highly likely that other HMTs and KDMs, DNA methylation and related chromatin factors are also involved in epigenetic changes induced by elevated glucose in multiple target organs and cells Epigenetic Mechanisms in Diabetic Complications 25 and contribute to metabolic memory of several debilitating diabetic complications (Figure3).However, diabetes is much more complicated than a simple state of hyperglycemia.It is associated with several risk factors and, in particular T2D involves insulin resistance, obesity, dyslipidemia, environmental factors, nutrition, lifestyles and genetics, in addition to hyperglycemia.Each of these risk factors could in itself induce epigenetic changes to the chromatin structure ultimately altering gene expression patterns in conjunction with elevated glucose in various target tissues including kidney, heart, liver, retina, nervous system, muscle, blood vessels and blood cells.Alarming estimates indicate that the rates of diabetes, metabolic syndrome and associated complications are rapidly increasing and therefore additional strategies to curb these trends are needed.With respect to diabetic nephropathy, it is imperative to conduct further exploration into the epigenetic causes and related treatment options, given the widespread prevalence, and the rapid transition to ESRD despite the available therapies.Such information can complement the currently available and new genetic and molecular data to begin the development of personalized medicine for diabetic nephropathy(136) and other complications.Well defined cell and animal models with and without treatments with standard diabetes drugs, antioxidants and related interventions will further our understanding of diabetic complications and metabolic memory and how they might be prevented.Epigenetic drugs such as inhibitors of DNA methylation, HATs and HDACs, and some histone demethylases are already being evaluated for cancer and other diseases(2,129,131).Currently available drugs for diabetic complications(18) could be tested for their potential ability to alter epigenetic marks.In recent years, there has been significant progress in the fields of epigenetics and epigenomics mainly due to increased understanding of basic molecular mechanisms and Epigenetic Mechanisms in Diabetic Complications 26 remarkable advances in powerful genome-wide technologies, instrumentation and bioinformatics software.Thus massive parallel next generation sequencing and ChIP-sequencing have been used to simultaneously map several histone marks and DNA methylation in human adult and stem cells and have demonstrated associations with distinct cell and development states and gene", + "\tHISTONE PTMS AND DIABETES\n\nHistone PTMs regulate chromatin structure and gene expression by recruiting chromatin remodeling proteins, transcription co-activators, and co-repressors. 26Emerging evidence shows the involvement of key histone PTMs in the regulation of genes associated with the pathogenesis of diabetes.Regulation of insulin gene expression as well as its secretion from islets in response to changing glucose levels is a key process in glucose homeostasis, one that is dysregulated in diabetes.Studies show that the islet-specific TF Pdx-1 can modulate this process of insulin regulation through epigenetic mechanisms. 59In response to increased glucose conditions, Pdx1 recruits co-activator HATs p300 and CBP and a HMT SET7/9 (SET7), which increases activation marks H3/H4Kac and H3K4me2, respectively, at the insulin promoter to promote open chromatin formation accessible to transcription machinery and enhance insulin transcription. 59,60In contrast, under low glucose conditions, Pdx1 recruits corepressors HDAC1 and HDAC2, promoting chromatin compaction and inhibition of insulin expression. 59nterestingly, Pdx-1 also controls the islet-specific expression of SET7 by direct interaction with its promoter. 60Genome-wide mapping of HK4me1, H3K4me3, H3K79me2 in islets revealed several isletspecific promoters and enhancers.Furthermore, several regulatory elements located near diabetes-susceptible loci showed allele-specific differences in their activity. 61Another study also mapped open chromatin regions in islets and identified associations of allele-specific differences in enhancer activity with genetic variations near diabetes-susceptible loci, 62 further highlighting how genetic variations in noncoding regions might affect chromatin structure in diabetes.Histone PTMs along with DNAme also were found to play an important role in epigenetic regulation of Pdx1 and insulin expression in islets of diabetic offspring from intrauterine growth restriction rats, suggesting that histone PTMs can be affected by maternal malnutrition. 34dipogenesis plays an important role in the pathogenesis of metabolic abnormalities and is tightly controlled by the transcription factors CCAAT/ enhancer binding protein (C/EBP) and peroxisome proliferator activated receptor (PPAR).Dynamic changes in histone PTMs and recruitment of the corresponding modifiers can regulate C/EBP and PPAR-induced gene expression involved in adipocyte differentiation. 63,64Interestingly, epigenetic inactivation of PPAR has been shown in adipocytes from T2D animals, 65 further supporting a role for epigenetic processes in adipocyte dysfunction and T2D.Another study reported increased predisposition to obesity and metabolic syndrome in mice deficient in Jhdm2a, a H3K9me2 demethylase, showing that deficiency in key histone-modifying enzymes might contribute to metabolic abnormalities. 66Overall, these studies highlight how alterations in chromatin structure can contribute to diabetes development.This is clearly a research area likely to show increased activity in the upcoming years.It is possible that epigenetic changes that contribute to the pathology of diabetes also directly or indirectly can affect target organs leading to complications.", + "\tDNA or Histone Modifications\n\nNew research investigations have addressed the link between epigenetic factors, type 2 DM and CVD.Hyperglycemia, for example, can induce epigenetic changes that lead to the overexpression of genes implicated in vascular inflammation.In particular, hyperglycemia has been shown to activate the NF-kB signaling pathway in cultured THP-1 monocytes, leading to the production of MCP-1 and other inflammatory factors, and to the expression of adhesion molecules in endothelial cells, providing a plausible molecular mechanism for endothelial dysfunction and atherosclerosis (107).On the other hand, clinical studies have demonstrated that early intensive control of glycemia in diabetic patients is crucial to prevent chronic micro-and macrovascular complications, reinforcing the notion that glycemia may have a longstanding influence on clinical outcomes, a phenomenon called \"metabolic memory\" (108).", + "\t1.5) DNA or Histone Modifications\n\nWe discovered a connection between an epigenetic factor of T2DM and CVD in new research investigations.For instance, hyperglycemia can cause epigenetic alterations that result in the enhanced expression of genes that contribute to vascular inflammation.In particular, it has been demonstrated that hyperglycemia activates the NF-kB signalling pathway in cultured THP-1 monocytes, producing MCP-1 and other inflammatory factors as well as causing endothelial cells to express adhesion molecules.This finding suggests a possible molecular mechanism for endothelial dysfunction and atherosclerosis. [24]On the other hand, clinical investigations have supported the idea that glycemia may have a longlasting impact on clinical outcomes, a condition known as \"metabolic memory,\" by demonstrating that early intensive control of glycemia in diabetes patients is critical to avoid chronic micro-and macrovascular challenges.In aortic endothelial cells, it has been proven that exposure to hyperglycemia corresponds with the opposite acetylation of the histone H3K9/K14 and altered pattern of addition of methyl group to DNA, assisting an epigenetic role for hyperglycemia.Following the temporarily elevation of levels of glucose, numerous histone lysine alterations have also been reported.They could be in charge of the RELA gene's ongoing transcriptional activation, which produces the p65 subunit of NF-kB, even when endothelial cells were later exposed to regular glucose concentrations.Overall, this action caused some target genes associated to endothelial dysfunction to become transcriptionally active, while as a result, other target genes become transcriptionally repressed.ICAM, HMOX1, MCP-1, SLC7A11, MMP10, and MMP1 genes' enhanced expression may also be caused by acetylation or hyperacetylation. [25]However, besides glucose toxicity, plenty of other physiological and pathological mechanisms that might have been involved in hyperglycemia and caused epigenetic modifications to have also been reported.These include ROS, PKC stimulation, and AGEs.Therefore, hyperglycemia is not the only factor that can cause epigenetic modifications.Notably, the CpG decreased intensity of methylation of the p66Shc inducer and a rise in H3 histone acetylation can both be considerably induced by ROS production.So, elevated concentrations of p66Shc, a mitochondrial adaptor that regulates a balance of redox in the cells, and meaningful activation of PKC are related to ROS-induced epigenetic alterations, sustaining endothelial dysfunction and vascular impacts.Additional research has examined the relationships between epigenetic changes and the risk of CVD for cardio-metabolic phenotypes like unusual weight gain, imbalance of lipids, impaired insulin sensitivity, inflammation, and high blood pressure.In a new analysis, histone deacetylases (HDACs) behavior and expression in connection to serum glucose, inflammation, and impaired insulin sesitivity in patients with type 2 DM were measured using peripheral blood mononuclear cells.HDAC3 activity and expression were induced by low-grade long-term inflammation and insulin resistance, and they correlated favourably with circulating levels of TNF-, IL-6, and other proinflammatory markers and adversely with Sirt1 expression. [26]Numerous studies have shown a connection between the addition of methyl group to DNA and the probability of cardiovascular disease.Elevated concentrations of methylation were seen in the predisposing haplotype rs8050136 of the FTO gene, a well-known gene linked to a greater risk of becoming obese and cardiovascular diseases; a similar technique has been proposed for the rs9939609 diversity.IGF2 methylation and changes to the lipid profile were linked in an additional candidate gene analysis of obese individuals.An epigenetic marker of metabolic risk, IGF2 higher intensity of methylation was specifically related to greater triglyceride/HDL cholesterol ratios.Some other investigations that merged genome-wide transcriptome and addition of methyl group to CpG profiling by array observed that insulin-resistant patients' adipose tissue had many more differentially methylated predicted sites than controls, including genes associated in signal transduction and the interaction with principal receptors to bind to the extracellular matrix.been discovered to be heavily and impartially related with impaired insulin sensitivity, were also found to have modified methylation.Furthermore, it has been demonstrated that the addition of methyl group of the PPAR promoter contributes to the division of the adipose tissue macrophages in obese mice from an anti-inflammatory (M2) to a proinflammatory (M1) phenotypic expression.Ultimately, there is scientific proof that modifications in the antenatal environment's impacts on epigenetic modifications may affect the risk of Myocardial infarction. [27]", + "\tSummary\n\nIncreasing evidence shows that, besides the well-described biochemical mechanisms, epigenetic mechanisms might also participate by fine-tuning gene expression to modulate the aetiology of diabetic complications.Persistence of epigenetic modifications triggered by diabetic stimuli could be one of the key mechanisms underlying metabolic memory.However, the involvement of many epigenetic factors and mechanisms involved in the regulation of the modifications by upstream signal transduction pathways remains unknown.However, this is a rapidly expanding and dynamic field and it is likely that other epigenetic factors related to diabetic complications will soon be uncovered.Epigenomics may also aid in determining the functional roles of complications-associated genetic variants.It would be worthwhile to assess whether lifestyle modifications such as exercise and healthy diets can reduce diabetic complications by altering epigenetic marks.A recent study showed the beneficial effects of exercise on epigenetic marks related to diabetes [106].Because epigenetic changes are potentially reversible in nature, combination therapies with epigenetic drugs (epidrugs) [38] and antagomirs (miRNA inhibitors) [8] could be considered to complement the current treatments for complications.However, there are also key challenges.Since epigenetic patterns are cell specific, data from heterogeneous tissue samples and biopsies could be difficult to interpret.Furthermore, apart from hyperglycaemia, other factors associated with diabetes, including insulin resistance, obesity, dyslipidaemia, environment, lifestyles and genetics, can work independently or co-operatively to also promote epigenetic changes in various affected target tissues.", + "\t\n\nEmerging evidence suggests an epigenomic link to T2D development.Reversible epigenetic changes such as histone modifications and DNA methylation may occur during intrauterine development and are believed to have long-term effects on offspring health and survival, including manifestation of disease phenotypes such as obesity or diabetes later in life [59,60].Environmental and nutritional stimuli influence future science group Genetics, genomics & personalized medicine in Type 2 diabetes: a perspective on the Arab region Review [61].Epigenetic regulation of genes may be responsible for the observed difference in T2D risk and drug response between individuals [62,63].Epigenomics may not only shed light on the environmental (including diet and lifestyle) effect on T2D susceptibility in individuals but epigenetic markers may also help identify those at risk well before disease manifestation.Gene-expression analysis or transcriptomics is used for studying the expression profile of genes.A comparative analysis of expression states of genes between healthy and diseased cells can identify those implicated in disease pathology.The changes in expression of disease susceptibility genes can be monitored during different stages of a disease and help in disease prognosis.Similarly, a comparative expression profile for treated and untreated samples can help identify changes in gene-expression upon treatment with a particular drug.This makes geneexpression analysis an important tool for elucidating the role of genes in different biological states, for identifying potential targets for drug intervention and for biomarker selection to be used in disease diagnosis.In diabetes, gene-expression profiling has been used for establishing differential expression of inflammatory genes [64], for studying the effects of insulin treatment in skeletal muscle [65] and more recently for correlating insulin resistance and an altered lipid profile in peripheral blood [66].", + "\tDNA Methylation and Diabetic Kidney Disease\n\nEpigenetic imprinting is thought to be important for determining the predisposition for chronic and latent diseases, like DKD [5].We have previously shown that exposure of microvascular endothelial cells to hyperglycaemia is able to induce changes in DNA methylation on genome wide ChIP-Seq, Fig. 1 The histone code.The specific site, type, extent and diversity of post-translational modifications histone proteins leads to specific signalling effects, including the repression (red signal) or activation (green signal) of gene expression leading to changes in gene expression, including activation of pro-inflammatory pathways implicated in diabetic complications such as DKD [5, 12, 13].Studies in the zebrafish also demonstrate that hyperglycaemia-induced DNA methylation changes.Diabetes is also induces aberrant DNA methylation in the proximal tubules of the kidney, including key targets implicated in glucose metabolism and transport, leading to a resistance to the effects of pioglitazone [14].However, an elevated glucose level is not the only factor that leads to maladaptive epigenetic modifications in diabetes.DNA methylation can also be influenced by reactive oxygen species, both directly through oxidative modification DNA preventing methylation and indirectly through its effects on methylation writing/erasing enzymes [15].Many other factors including hypoxia, inflammation, cytokines and growth factors, drugs, nutrition and even physical activity can modify epigenetic profiles [16,17]; the sum of which and their interactions being the key determinant of the resulting phenotype.\tHistone Modifications and Diabetic Kidney Disease\n\nPost-translational modification of nucleosomal histones are among the best characterised of epigenetic modifications with respect to diabetes and are clearly implicated in the induction in the expression of genes implicated in DKD [8,24].For example, following exposure to glucose there is persistent transcriptional upregulation of expression of the proinflammatory mediator NF-B (p65; Rel (A)) in vitro and in vivo.This is specifically associated with monomethylation of H3K4 adjacent to the p65 proximal promoter, such that inhibition of Set7-dependent methylation at this site is able to prevent its induction without restoring euglycaemia [8,24].We have also recently reported the persistent induction of other pathogenic genes that may be mediated by H3K4m1 writing events, including the induction of IL-8 following exposure to transient hyperglycaemia [25].Exposure to hyperglycaemia also dynamically changes histone acetylation in cells exposed to hyperglycaemia [12, 13] and diabetic patients.More recently, genome-wide increases in monocyte H3 acetylation were associated with conventional treatment compared with intensive treatment group subjects of the Diabetes Control and Complications Trial (DCCT), indicating a possible mechanism of metabolic memory in humans [26].However, overall transcriptional activity is more likely to be dependent on the sum of multiple histone marks, and their interaction with other epigenetic modifications (e.g.DNA methylation) rather than any individual changes [27].For example, glomerulosclerosis in diabetic mice is associated with enrichment of H3 histones dimethylated at K4, acetylated at K9 and K27, and phosphorylated at S10.", + "\tEpigenetics, Micro RNAs (miRNAs) and Diet: Are They Involved in DM? Previous epigenetic studies have focused on the heritable alteration of DNA and proteins, linking the DNA and histones, which induces modifications in chromatin structure without changing the nucleotide sequence.Modulations in gene expression can be caused by epigenetic mechanisms such as DNA methylation, histone modifications, small and non-coding RNAs [139].Non-coding RNAs (ncRNAs) have been implicated in the epigenetic regulation of gene expression, and recent studies have shown that miRNAs can induce chromatin remodeling.miRNAs are single-stranded RNA molecules that range in size from 18 to 22 nucleotides.The mammalian genome encodes several hundred miRNAs that fine-tune gene expression through the modulation of target mRNAs [140].These findings suggest that DNA methylation, histone modification and miRNAs may function in concert to regulate gene expression [141].", + "\t\nThe global diabetes epidemic poses a major challenge.Epigenetic events contribute to the etiology of diabetes; however, the lack of epigenomic analysis has limited the elucidation of the mechanistic basis for this link.To determine the epigenetic architecture of human pancreatic islets we mapped the genome-wide locations of four histone marks: three associated with gene activation-H3K4me1, H3K4me2, and H3K4me3-and one associated with gene repression, H3K27me3.Interestingly, the promoters of the highly transcribed insulin and glucagon genes are occupied only sparsely by H3K4me2 and H3K4me3.Globally, we identified important relationships between promoter structure, histone modification, and gene expression.We demonstrated co-occurrences of histone modifications including bivalent marks in mature islets.Furthermore, we found a set of promoters that is differentially modified between islets and other cell types.We also use our histone marks to determine which of the known diabetes-associated single-nucleotide polymorphisms are likely to be part of regulatory elements.Our global map of histone marks will serve as an important resource for understanding the epigenetic basis of type 2 diabetes.", + "\t\n\nIn addition to genetic factors, epigenetic mechanisms, such as DNA methylation, histone modifications, chromatin remodeling, and RNA editing and biogenesis have recently emerged as a potential link between gene expression and environmental factors [21].DNA methylation refers to the reversible attachment of a methyl group to a cytosine within cytosine-phosphate-guanine (CpG) dinucleotides [22].In differentiated cells, DNA methylation contributes to the maintenance of normal DNA structure, chromosome stability, and gene regulation [23].DNA methylation regulates gene expression without altering the underlying DNA sequence and is of particular interest because of its emerging role in T2D and its complications [24][25][26][27].We recently showed that aberrant DNA methylation is involved in nerve degeneration in T2D and DPN in a small cohort of patients [24].Specifically, our results highlighted the role of DNA methylation in regulating pathways previously shown to be implicated in DPN pathogenesis, including axon guidance, glycerophospholipid metabolism, and MAPK signaling.However, much less is known about the impact of differential DNA methylation on gene expression in DPN and how the interaction between genetic and epigenetic mechanisms may affect biological pathways during DPN pathogenesis.", + "\t\n\nDNA methylation can be mitotically stable over time, producing long-term changes in gene expression.The present study suggests that changes in DNA methylation of genes involved in pancreatic development and insulin secretion may result in epigenetic dysregulation of these genes, which may mediate the increased risk of diabetes in individuals exposed to a diabetic intrauterine environment.", + "\t\n\nSeveral studies show that key histone post-translational modifications are involved in the regulation of genes associated with the pathogenesis of diabetes, such as insulin and islet-specific transcription factors. 48,60In addition, several groups are examining the role of histone post-translational modifications in adipocytes related to type 2 diabetes, obesity and the metabolic syndrome. 48,60hese endeavours highlight the increasing evidence that histone post-translational modifications can have key roles in the pathogenesis of diabetes.Logically, they can be expected to also affect chromatin structure of target genes in organs associated with complications, including the kidney.", + "\t\n\nEpigenetic mechanisms allow alteration of genome function without mutating the underlying sequence.They involve the interacting actions of DNA methylation (the addition of a methyl group to the 5th carbon position of cytosine), histone modifications and noncoding RNAs [18].A number of indirect lines of evidence point to the involvement of epigenetic changes in diabetic nephropathy.Murine models of disease progression displaying temporal variation in gene expression have indicated these supra-sequence devices may be involved in the pathogenesis [19].Gene expression changes reflect dynamic alterations in gene transcription and also messenger RNA stability, which may be influenced by the epigenetic modification of the genome in response to chronic hyperglycaemic stress.Altered DNA methylation has been additionally implicated in vascular disease [20,21].Furthermore, characteristics observed in diabetic nephropathy such as hyperhomocysteinaemia, dyslipidaemia, inflammation and oxidative stress can promote aberrant DNA methylation [22][23][24]." + ], + [ + "\t\nFew concepts in recent years have garnered more disease research attention than that of the intestinal (i.e. 'gut') microbiome.This emerging interest has included investigations of the microbiome's role in the pathogenesis of a variety of autoimmune disorders, including type 1 diabetes (T1D).Indeed, a growing number of recent studies of patients with T1D or at varying levels of risk for this disease, as well as in animal models of the disorder, lend increasing support to the notion that alterations in the microbiome precede T1D onset.Herein, we review these investigations, examining the mechanisms by which the microbiome may influence T1D development and explore how multi-disciplinary analysis of the microbiome and the host immune response may provide novel biomarkers and therapeutic options for prevention of T1D.\t\n\nFew concepts in recent years have garnered more disease research attention than that of the intestinal (i.e. 'gut') microbiome.This emerging interest has included investigations of the microbiome's role in the pathogenesis of a variety of autoimmune disorders, including type 1 diabetes (T1D).Indeed, a growing number of recent studies of patients with T1D or at varying levels of risk for this disease, as well as in animal models of the disorder, lend increasing support to the notion that alterations in the microbiome precede T1D onset.Herein, we review these investigations, examining the mechanisms by which the microbiome may influence T1D development and explore how multi-disciplinary analysis of the microbiome and the host immune response may provide novel biomarkers and therapeutic options for prevention of T1D.\tTherapeutic targeting of the gut microbiome to block T1D progression\n\nExperimental microbiome manipulation in young T1D prone rodents provides robust protection from isletautoimmunity and disease, providing proof of principle that microbial therapy could provide effective protection of individuals with high genetic risk [12].The gut microbiome is extensively remodelled during early postnatal development and throughout childhood and puberty [9,41,42].This natural fluctuation in microbial colonization provides a window of opportunity to modify this risk factor in children with risk markers of anti-islet autoimmunity.\t\n\nBased on the available body of literature, it is feasible to suggest that the well-described increased incidence in T1D over the past 50 years [15,16] arises, at least in part, from one of two primary mechanisms related to the intestinal microbiome.In the first notion (Fig. 1), defective development and/or alteration of healthy microbiota in an individual at genetic risk for T1D may result in abnormal immunoregulation that enables autoimmune destruction of insulin-producing cells.This notion is supported by evidence suggesting that immune education required for self/ non-self immunoregulation is, to a large degree, conferred early in life, through maturation and education of the immune system by microbiota that colonize the gastrointestinal tract, living symbiotically with the host [18,19].The second concept (Fig. 1), acting either independently of or co-incident with the first, is that enhanced leakiness of the gut epithelial barrier (observed in both human patients and animal models of T1D) either results from an altered microbiome or is a key determinant of an altered microbiome, or 'dysbiosis' [17,20].Either type of microbiome-mediated mechanism could underlie the observed combination of increasing disease incidence as well as the younger age of onset [21], resulting from less robust or delayed maturation of immunoregulation in early childhood.Understanding such mechanisms is an important consideration.Indeed, if a central role for the microbiome in T1D risk was confirmed, as will be discussed later, the disease might be preventable by augmenting or accelerating healthy microbiota-induced immunoregulation, as well as by attenuating intestinal leakiness.However, before undertaking such therapeutic efforts, it would appear critical to determine first whether and how an altered microbiome contributes to either defective immunoregulation and/or gut leakiness in T1D.\tUncovering a pathogenic role for the microbiome in T1D -a proposed pathway forward\n\nAs mentioned previously, interactions between susceptibility genes and environmental determinants of T1D remain poorly defined [16].The most pressing outstanding questions regarding the microbiome as an environmental determinant in T1D are (i): does the microbiome hold any additional clues into disease aetiology, including potential viral or bacterial antigens and metabolites; (ii) is there a microbiome-wide dysbiosis linked to pathogenesis (i.e.development of autoimmunity, progression of autoimmunity, onset of clinical disease); and (iii) is defective microbiome-induced immunoregulation contributing to pathogenesis of T1D?\t\n\n Does altered maturation or development of an adult microbiome or a dysbiotic state contribute to the pathogenesis of human type 1 diabetes, what is the mechanism(s), and when does it occur? Does an altered microbiome or dysbiosis act at the level of initiation of autoimmunity and/or progression of type 1 diabetes? What is the basis of healthy microbiome-induced immunoregulation and does the lack of such contribute to the pathogenesis of human type 1 diabetes? Is altered gut epithelial function and integrity important in the pathogenesis of type 1 diabetes, and if so, what is the mechanism(s) and relation to dysbiosis and how do we demonstrate impaired function in humans? How important are the interactions between host genetics, metabolism and the immune system in shaping the microbiome and predilection to disease? Are faecal samples an appropriate representation of the microbiome for type 1 diabetes studies? What are the most promising type 1 diabetes preventive/therapeutic opportunities targeting the microbiome, microbiome-induced immunoregulation, or microbiome-altered gut permeability?", + "\t\nAssessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide.To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals.We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses.MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance.An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes.\t\n\nAssessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide.To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals.We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses.MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance.An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes.", + "\t\n\nIn Brief Liu et al. identify the gut microbiota as an important determinant in the responsiveness of individuals with prediabetes to exercise for the improvement of glucose metabolism and insulin sensitivity.These findings may help in the implementation of a personalized lifestyle intervention for diabetes prevention.\t\n\nA growing body of evidence suggests that dysbiosis of gut microbiota plays an important role in the pathogenesis of insulin resistance and T2D (Bouter et al., 2017) through multiple mechanisms, including increased gut permeability and low-grade endotoxemia, changes in production of short-chain fatty acids (SCFAs) and branched-chain amino acids (BCAAs), and perturbation of bile acid metabolism (Utzschneider et al., 2016).Compositional and functional changes of gut microbiota have been observed in individuals with T2D and prediabetes (Allin et al., 2018;Qin et al., 2012), whereas fecal microbial transplantation from healthy donors into patients with metabolic syndrome results in increased microbial diversity and improved glycemic control, as well as insulin sensitivity (Kootte et al., 2017).\t\n\nIn conclusion, our study uncovers gut microbiota and its metabolism as key molecular transducers to the heterogeneous adaption to exercise intervention on glucose metabolism and insulin sensitivity.This finding, together with our demonstration of the predictive value of baseline microbial signatures for individualized responsiveness to exercise, may facilitate clinical implementation of personalized lifestyle intervention for diabetes management.\t\n\nConsidering the important role of the gut microbiota in regulating glucose homeostasis and insulin sensitivity, we next explored whether it was involved in the heterogeneous metabolic effects of exercise in our cohort.", + "\t\n\nHere, we unraveled novel mechanisms linking gut microbiota changes and metabolism in genetic obese mice and found that prebiotics improved leptin sensitivity in diet-induced leptin-resistant mice.Further work is required to understand the functional links between the metabolic/ catabolic activities of gut bacteria and their impact on host metabolism.For instance, it would be of interest to establish a causal relationship, instead of correlations as shown here, by using transfer of bacterial communities.An alternative experiment would be to analyze intestinal (fecal) microbiota in a time-series study in view of identifying the specific impact of prebiotics and the gut microbes on the onset of obesity and type 2 diabetes.\t\n\nCONCLUSIONS-We conclude that specific gut microbiota modulation improves glucose homeostasis, leptin sensitivity, and target enteroendocrine cell activity in obese and diabetic mice.By profiling the gut microbiota, we identified a catalog of putative bacterial targets that may affect host metabolism in obesity and diabetes.", + "\t\n\nThe intestinal microbiome also seems to be important to the pathophysiology of type 2 diabetes. 46The microbiome has about 100 times more genetic information than has the human genome, together comprising the human metagenome.Many products of the microbiome provide functions beyond that of the host genome, thereby serving an important role in human physiology.These gut communities are thought to play an important part in several conditions and disorders (eg, obesity and type 2 diabetes), although which bacterial species cause changes to human metabolism is not clear. 47Findings from two studies that used faecal samples suggested that functional changes in the gut microbiome might be directly linked to development of type 2 diabetes; 48,49 however, metagenomic markers diff er between populations, suggesting that their ability to predict development of diabetes will probably vary. 49Findings from a recent proof-of-concept study 50 showed improvements in insulin sensitivity in patients with metabolic syndrome 6 weeks after infusion of intestinal microbiota from lean individuals.Lastly, diff erent gut fl ora might aff ect nutrient absorption, because in human beings nutrient load can alter the faecal bacterial community in a short time. 51he nervous system is another important regulator of metabolic processes.Both sympathetic and parasympathetic nervous systems control glucose metabolism, directly through neuronal input, and indirectly through the circulation to aff ect release of insulin and glucagon 52 and production of hepatic glucose. 53In human beings, the vagus is important in regulation of islets, because severing of this nerve results in impaired insulin secretion. 54The hypothalamus is an important integrator, because its ablation in rats results in dysregulation of cells and development of hyperinsulinaemia. 557][58] Insulin action at this site is also essential in regulation of bodyweight, with decreased activity leading to obesity. 59Infl ammationinduced neuronal injury occurs rapidly in rodents fed a high-fat diet. 60Findings from imaging studies of obese and lean people suggest that structural changes occur in the hypothalamus, consistent with the occurrence of gliosis in obesity. 60Finally, clock genes expressed in the brain are important in establishment of circadian rhythmicity and, together with sleep, have become a focus of investigation because changes in diurnal patterns and quality of sleep can have important eff ects on metabolic processes. 61,62", + "\t\n\nOver the last five years, several studies have linked diet/nutrients (mainly dietary fiber), gut microbiota and the expression of genes involved in immune responses.It is well known that the diet has a profound effect on the gut microbiota.In mice and humans, microbes respond differently to dietary components, and long-term dietary habits have been linked to the abundance of certain microbial genera [23].The gut lumen contains large amounts of nutrients that strongly influence the composition of the microbiota, which affects gut immunity.These alterations in gut immunity can precipitate T1DM in individuals prone to T1DM.It has also been observed that diabetes-prone BioBreeding (BBdp) rats housed in specific germ-free (GF) conditions and weaned onto cereal diets displayed an upregulation of the interferon gamma (Ifng) and interleukin 15 (Il15) genes and a downregulation of the forkhead box P3 (Foxp3) gene [24].Both Ifng and IL-15 are proinflammatory cytokines that promote T1DM in non-obese diabetic (NOD) mice [25], whereas Foxp3 is a master transcription factor that directs the differentiation and function of regulatory T cells and plays a central role in the inhibition of autoimmunity and suppression of physiological immune responses [26].When BBdp rats were weaned onto cereal diets and housed in specific pathogen-free conditions (allowing gut microbiota growth), the rats also showed an upregulation of the lymphocyte-specific protein tyrosine kinase (Lck) gene [23].Lck encodes tyrosine kinase/p56, a lymphocyte-specific protein involved in the initiation of T cell activation [27].Finally, in this last condition, BBdp rats showed decreased expression of the cathelicidin antimicrobial peptide (Camp) gene.CAMP is a multifunctional antimicrobial effector and immunomodulatory host defense factor [28], which may alter the gut microbiota.", + "\t\n\nSpecific microbiome profiles render individuals prone to develop obesity and altered glucose metabolism 313 .The ability to identify protective microbiome profiles might provide a key to the development of obesity and diabetes interventions.It remains to be determined whether specific dietary components are involved in microbiome changes and induce unfavourable transitions.Probiotics or pharmacological manipulation of microbiome elements that favour more 'healthy' flora may prove to be useful in stemming the 'twin epidemics' of obesity and T2DM 313 .Surgical rearrangement of the gastrointestinal tract has shown remarkable efficacy in treating obese patients with T2DM 307,314 .Development of minimally invasive reversible procedures, such as the duodenal sleeve and temporary mucosal barriers, might replace surgery in the near future.", + "\t\n\nIn conclusion, our data suggest that the levels of glucose tolerance or severity of diabetes should be considered while linking microbiota with obesity and other metabolic diseases in humans.It is especially important for developing the strategies to modify the gut microbiota in order to control metabolic diseases, since obesity and diabetes might be associated with different bacterial populations.\t\n\nBackground: Recent evidence suggests that there is a link between metabolic diseases and bacterial populations in the gut.The aim of this study was to assess the differences between the composition of the intestinal microbiota in humans with type 2 diabetes and non-diabetic persons as control.", + "\t\n\nIn recent years, several associations between common chronic human disorders and altered gut microbiome composition and function have been reported 1,2 .In most of these reports, treatment regimens were not controlled for and conclusions could thus be confounded by the effects of various drugs on the microbiota, which may obscure microbial causes, protective factors or diagnostically relevant signals.Our study addresses disease and drug signatures in the human gut microbiome of type 2 diabetes mellitus (T2D).Two previous quantitative gut metagenomics studies of T2D patients that were unstratified for treatment yielded divergent conclusions regarding its associated gut microbial dysbiosis 3,4 .Here we show, using 784 available human gut metagenomes, how antidiabetic medication confounds these results, and analyse in detail the effects of the most widely used antidiabetic drug metformin.We provide support for microbial mediation of the therapeutic effects of metformin through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind known intestinal adverse effects in the form of a relative increase in abundance of Escherichia species.Controlling for metformin treatment, we report a unified signature of gut microbiome shifts in T2D with a depletion of butyrate-producing taxa 3,4 .These in turn cause functional microbiome shifts, in part alleviated by metformininduced changes.Overall, the present study emphasizes the need to disentangle gut microbiota signatures of specific human diseases from those of medication." + ], + [ + "\t\n\nIn this review, we limit our summary to data obtained from studies that compared clinical risk scores with scores derived from extended models containing multiple genetic markers for T2D or CVD; we also report the AUCs for the relevant risk models.To assess the issue of prediction, prospective studies are warranted.However, given the scarcity of appropriate studies, our overview includes studies with both prevalent and incident cases, as indicated in Tables 3 and 4.", + "\t\n\nIn this review, we limit our summary to data obtained from studies that compared clinical risk scores with scores derived from extended models containing multiple genetic markers for T2D or CVD; we also report the AUCs for the relevant risk models.To assess the issue of prediction, prospective studies are warranted.However, given the scarcity of appropriate studies, our overview includes studies with both prevalent and incident cases, as indicated in Tables 3 and 4.", + "\tSummary and outlook\n\nA lot of work has been performed to assess the incremental value of novel markers, beyond established risk factors, for the prediction of diabetes.Nevertheless, several questions remain to be answered.First, the addition of biomarkers to conventional diabetes risk scores has so far not or, at best, only slightly improved the predictive ability of the models.This raises the question, under which condition novel markers may have a larger incremental value.Often biomarkers are strongly correlated with conventional risk factors so that they do not provide additional predictive information [98,100].While in the near future many novel biomarkers are expected to be described as a result of technological progress, these will only improve diabetes prediction if they are at best weakly correlated with established risk factors.Moreover, it is conceivable that the slope of a biomarker trajectory (the change of the biomarker over time) captures incremental predictive information above the last measurement of the marker alone.However, the potential of trajectories has not yet been assessed for diabetes prediction.\t\n\nThird, beyond optimising the predictive ability of diabetes risk scores, there is a wide range of issues which have not been considered in this review.From a public health perspective, it has to be asked whether diabetes risk scores are accepted by physicians, and which barriers might prevent physicians from using them; how scores are best implemented in clinical practice; to what extent intuitive risk assessments made by physicians are concordant with score-based assessments; and how good is the effectiveness and efficiency of diabetes prediction models.All these questions have hardly been addressed so far.Another issue to consider regarding noneconomic costs relates to false positive test results (which could increase anxiety) and false negative risk estimates (which could lead to false reassurance).Finally, the successful implementation of any prognostic diabetes model will depend on a cost-effective intervention strategy for those persons for whom a high risk of developing type 2 diabetes is diagnosed.This list demonstrates that the assessment of the performance of novel biomarkers in risk models needs to be investigated in a substantially larger context than it is currently before recommendations for their widespread use can be given with certainty.", + "\tVelu in [12] employed the most emerged three techniques for classification of the\ndiabetic patients, i.e. , EM algorithms, H Means + clustering, and Genetic Algorithm\n(GA) [6]. From their result analysis, H Means + clustering techniques give a better\nresult as compared to other two techniques in case of diabetes disease. Ganji in\n[13] adopted fuzzy ant colony optimization techniques to find the set of rules for the\nadiabatic patient and their diagnosis. Now it is also used for the prima Indian diabetes\ndatasets. Jayalakshmi T. in [14] diagnoses the adiabatic patient through their new\napproachANN techniques.\t: Prediction of diabetes using classification algorithms. Proc. Comput. Sci. 132, 15781585 (2018)\n10. Aljumah, A.A., Ahamad, M.G. , Siddiqui, M.K. : Application of data mining: diabetes health\ncare in young and old patients. J. King Saud Univ. Comput. Inf. Sci. 25(2), 127136 (2013)\n11. Iyer, A., Jeyalatha, S., Sumbaly, R.: Diagnosis of diabetes using classification mining\ntechniques. arXiv preprint arXiv:1502.03774\n12. Velu, C.M. , Kashwan, K.R. : Visual data mining techniques for classification of diabetic patients. In: 2013 3rd IEEE International Advance Computing Conference (IACC), pp. 10701075. IEEE (2013)\n13. Ganji, M.F. , Abadeh, M.S.\tThe analytical process can be done by different machine learning\nalgorithms. This paper presents two sets of machine learning approach for prediction\nof diabetes. One of them is a classification-based algorithm, and the other one is a\nhybrid algorithm. In classification, we have taken the random forest algorithm. For\nhybrid approach, we have chosen XGBoost algorithm. These two algorithms were\nimplemented and compared in order to explore the prediction accuracy in diabetes\nfor two different machine learning approaches and got the mean score 74.10% which\nis better than the Random Forest algorithm.\tIn: International Conference on Remote\nEngineering and Virtual Instrumentation, pp. 306314 (2019)\n17. Aishwarya, R., Gayathri, P., Jaisankar, N.: A method for classification using machine learning\ntechnique for diabetes. Int. J. Eng. Technol. 5, 29032908 (2013)\n18. Rashid, T.A. , Abdulla, S.M. , Abdulla, R.M. : Decision support system for diabetes mellitus\nthrough machine learning techniques. Int. J. Adv. Comput. Sci. Appl. 7, 170178 (2016)\n19. Wang N, Kang G (2012) Monitoring system for type 2 diabetes mellitus. In: IEEE Conference\non E-health Networking, pp. 6267\n20.", + "\tComputational Insight into Diabetes Research\n\nWhen it comes to machine learning and data mining, significant conclusions are drawn through the present detailed account.It is worth mentioning that the vast majority of the reported articles enhanced classification accuracy, above 80%, in the prediction of DM.With regard to the prediction task itself, almost all of the common known classification algorithms have been employed.However, the most commonly used ones are SVM, ANN, and DT.It should be mentioned that SVM rises as the most successful algorithm in both biological and clinical datasets in DM.A great deal of articles (~85%) used the supervised learning approaches, i.e. in classification and regression tasks.In the remaining 15%, association rules were employed mainly to study associations between biomarkers.More specifically, concerning the part dealing with the evaluation task, in all reported research reports, the identified subsets of biomarkers (features) were evaluated through appropriate procedures, such as splitting the dataset into train and test set or via cross-validation.By analogy, the same approaches have been followed in DM prediction.\t\n\nIn the case of nephropathy, Huang et al. employed a Decision Tree-based prediction tool that combines both genetic and clinical features in order to identify diabetic nephropathy in patients with T2D [81].Leung et al. compared several machine learning methods that include partial least square regression, classification and regression tree, the C5.0 Decision Tree, Random Forest, naive Bayes, neural networks and support vector machines [82].The dataset used consists of both genetic (Single Nucleotide Polymorphisms -SNPs) and clinical data.Age, age of diagnosis, systolic blood pressure and genetic polymorphisms of uteroglobin and lipid metabolism arose as the most efficient predictors.", + "\tOverview of the risk assessment algorithms\n\nWe tested a machine-learning approach called Support Vector Machine (SVM, see Methods), as well as logistic regression (LR, see Methods) in order to assess individual disease risk for type 1 diabetes (T1D) using three GWAS datasets (Table 1).SVM is one of the most popular classifiers in the field of machine learning and achieves state-of-the-art accuracy in many computational biology applications [28].In essence, SVM is a supervised machinelearning algorithm that produces a linear boundary to achieve maximum separation between two classes of subjects (cases versus controls), by mathematical transformation (kernel function) of the input features (SNP genotypes) for each subject.Unlike most regression-based methods, SVM allows more input features (such as SNPs or genes) than samples, so it is particularly useful in classifying high-dimensional data, such as microarray gene expression data [29].We also applied LR as a control algorithm, since it is widely used in genetic studies to model the joint effects of multiple variants.Unlike previous disease assessment studies that typically use genotype data from a handful of validated susceptibility loci, we examined a large ensemble of SNP markers with suggestive evidence for association with T1D, using a few Pvalue cutoff thresholds ranging from 1610 23 to 1610 28 , as well as highly stringent quality control measures (see Methods).When more relaxed P-value criteria are being used, the contributing SNPs scatter across the genome; when more stringent criteria are used (P,1610 28 ), only a few independent loci contribute (assuming that all MHC markers represent a single locus).Furthermore, we included the 45 known T1D susceptibility markers [4] into the prediction models to ensure that their predictive values were accounted for.Although these SNP lists may contain some false positive loci that are not genuinely associated with T1D, recent advancements in machine-learning, such as regularization, have made classifiers more tolerant to irrelevant input features [30].Since we cannot completely eliminate falsely associated loci from the list of predictors, our goal is to include them in the prediction models (using various thresholds) and then assess their influence on performance.\tDiscussion\n\nIn this study, we tested the plausibility of building a classifier and using a large number of SNPs for disease risk assessment on three large T1D datasets.In general, the SVM algorithm achieved satisfactory performance when hundreds of SNPs were included in prediction models, with AUC scores of ,0.84 for predicting disease risk for T1D in several GWAS datasets.In contrast, the SVM or the LR algorithm achieved only an AUC score of 0.66-0.68when 45 known T1D susceptibility loci were used.This difference clearly indicates that the predictive value lies in utilizing a large number of SNPs in a sophisticated machine-learning algorithm.We note that another recent study also reported that using thousands of SNPs improve the performance of disease risk assessment compared to using fewer SNPs for diseases studied by WTCCC [39], although the study used a cross-validation design.On the other hand, we observed a decrease in the predictive accuracy when too many SNPs were used, suggesting an upper bound of the number of SNPs for T1D risk assessment before noises from falsely associated markers lead to degraded performance.However, we caution that this upper bound depends on the sample size and the power of the study to rank truly associated SNPs higher than background noises.\t\n\nFigure2.Performance of risk assessment models trained on the CHOP/Montreal-T1D dataset.For both the WTCCC-T1D and the GoKind-T1D datasets, the SVM (support vector machine) algorithm consistently outperforms LR (logistic regression), and the best performance is achieved when SNPs were selected using P-value cutoff of 1610 26 or 161025 .doi:10.1371/journal.pgen.1000678.g002", + "\tMethodology\n\nThis study is focused on predicting future illnesses such as type-2 diabetes from genomic and tabular data.Genomic data are analyzed for possible gene expression highly likely to be affected by type-2 diabetes.Tabular data from the PIMA dataset with various features are also explored through the proposed RNN model by identifying the feature vector's pivotal features.The proposed model relies on the Deep Neural Networks (DNN) framework for analyzing the genomic data, making the precise assessment of possible future illnesses with better Accuracy than the conventional pattern-matching techniques.DNN is a probabilistic measure that would summarize the possible illness outcome that would better assist in decision-making by the physicians.The working procedure and implementation details are discussed in the current section.The models are trained from the available gene base from scratch initially, and at the later stages, the model learns from the experimental outcomes.\t\n\nVarious studies have been presented to predict future illness through existing patient data using machine learning algorithms.Predicting future illness has become a demanding topic in healthcare [29].Several studies have used machine intelligence techniques to analyze the Pima Indian Diabetes Dataset.C. Yue [30] has investigated various hybrid approaches, including Neural Networks, integrated Quantum Particle Swarm Optimization (QPSO), and Weighted Least Square (WLS) Support Vector Machine (SVM) for diabetes prediction, with the WLS-SVM hybrid model showing a classification accuracy of 82.18%.However, the hybridization model needs considerable effort in the evaluation process.In addition, the SVM model is not suitable for working with larger data [31].Moreover, the SVM model underperforms if the number of attributes for every data point exceeds the training samples.The combinational models for diabetes prediction using Cross-validation and Self-Organizing Maps (SOM) have achieved an accuracy of 78.4% [32,33].SOM can rely on the associated weights of neurons for precise classification.Inappropriate assignment of initial weights may impact the model's performance.A C4.5 technique [34] has been used to analyze the PIMA dataset, attaining an Accuracy of 71.1%.The model works through the entropy value associated with the feature vector.The conventional classification models exhibit poor performance when working with distinct feature vectors [35].\tExperimental Outcome of Genomic Data\n\nThe performance of the proposed RNN model for predicting type 2 diabetes was analyzed using performance evaluation metrics such as sensitivity, specificity, F1 score, Mathews correlation Coefficient, and accuracy measures [76].The above-discussed metrics are assessed through true positive, true negative, false positive, and false negative values approximating experimental outcomes.The dataset is split into a training set and a validation set at a ratio of 70:30.In the following graph, as shown in Figure 7, it is clear that data values are skewed toward data instances, indicating that no diabetes exists.The percentage of available data records of non-diabetic patients (or those who do not have diabetes) is almost double that of diabetic patients.\t\n\nAll the mentioned models rely on tabular datasets such as PIMA and ECG signals [47] in classifying the records with possible diabetic illnesses.The current study considers that genomic data yields a better patient-centric outcome than tabular data.\tResults and Discussion\n\nThe proposed model has been evaluated on genomic data and the tabular data by using the same feature engineering mechanism and the layered approach for predicting the type-2 diabetes.The proposed RNN-based type-2 diabetes is evaluated against genomic and tabular data from the PIMA Indian dataset independently and the evaluations are presented independently in the current section.The model was evaluated against two datasets concerning various evaluation metrics such as sensitivity, specificity, Accuracy, and F1 score.The classification efficiency of the proposed model was assessed using true positive (TuP, the number of times that the model accurately predicted the gene with a high possibility of diabetes correctly), true negative (TuN, identifying the gene with less possibility of diabetes precisely), false positive (FsP, misinterpreting the gene with the high possibility of diabetes as low possibility of diabetes), and false negative (FsN, misinterpreting the low diabetes gene as a high possibility of illness).The sensitivity metric determines the ratio of how many were accurately recognized as positive samples out of how many were truly positive samples in the complete dataset.The specificity measure determines the ratio of how many were recognized as negative samples out of how many among the samples are truly negative from the complete dataset.The Accuracy measures the correctly predicted True positives and Negative samples against the overall sample in the complete dataset.The harmonic mean of sensitivity and specificity measures are determined as the F1 score.MCC is the best single-value classification score for summarizing the confusion matrix.The formulas for the aforementioned metrics are presented through Equations ( 27)-( 32) [75].\tRecurrent Neural Network Model for Type 2 Diabetes Forecasting Based on Genomic Data\n\nPredictions of future illness can be performed through Convolutional Neural Networks (CNN), as stated by Leevy J.L. et al. [51] and Yadav S.S. and Jadhav S. M. [52] using Recurrent Neural Network (RNN) module-based architecture described by SivaSai J.G. et al. [53].CNN model consists of many intermediate nodes connected.Each node is significant in delivering the output following the anticipated outcome.RNN is robust in handling variable-length input sequences with the help of internal auxiliary memory modules [54].The detailed architecture along with the implementation procedure for the proposed approach, is presented in this section.\t\n\nA fuzzy entropy approach for feature selection for a similarity classifier has been evaluated against various medical datasets, such as Pima-Indian diabetes, exhibiting an accuracy of 75.29% [36].A fuzzy model primarily depends on the membership evaluation that requires considerable effort.Non-linearity in evaluating the model will limit the model's performance [37].Genetic Algorithm (GA) with Radial Basis Function Neural Network (RBF NN) has been used in the evaluation process of diabetes data, exhibiting an accuracy of 77.39% over the testing dataset [38].Moreover, for artificial evolutionary algorithms such as GA, the most prohibitive and restricting element is frequently repeated fitness function assessment for complex gene patterns.Hybridization of models with GA would need more computational efforts than neural networks alone.Various cutting-edge technologies for the classification and prediction of type-2 diabetes are presented in Table 1." + ] + ], + "task_id": [ + { + "task_id": "034CBC335A5D1820E523A0A1CF93FCB8" + }, + { + "task_id": "4ADB4C703E90EC89A3B3F7605F9C00E8" + }, + { + "task_id": "62A20F96A8B7FE15B0B6D25C97A96794" + }, + { + "task_id": "C94269DE09DD00872EC23EC6D7093632" + }, + { + "task_id": "5E472FF7C262791E739F6BD6707642D5" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_diabetes_2.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_diabetes_2.json new file mode 100644 index 0000000..3dc276d --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_diabetes_2.json @@ -0,0 +1,119 @@ +{ + "question": [ + "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?", + "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?", + "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?", + "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?", + "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?" + ], + "answer": [ + "Recent findings suggest that long non-coding RNAs (lncRNAs) play a significant role in the regulation of insulin secretion and sensitivity. They are involved in the regulation of islet development and function, and their abnormal expression is associated with diabetes. For instance, lncRNAs like H19, HI-LNC25, LOC283177, TUG1, MEG3, and PLUTO have been shown to influence islet structure, insulin synthesis, secretion, and glucose homeostasis. However, the exact molecular mechanisms through which lncRNAs influence pancreatic biology and diabetes are still under investigation.", + "Post-translational modifications (PTMs) such as phosphorylation, acetylation, -Olinked N-acetylglucosamine (O-GlcNAc), SUMOylation, and ubiquitination contribute to the activation of PPAR, a key player in glucose homeostasis. These PTMs can influence ligand affinity, DNA binding, coactivator recruitment, and/or proteasomal degradation, thereby affecting the signaling pathways involved in glucose homeostasis. For instance, in the insulin signaling pathway, phosphorylation events play a crucial role in the regulation of glucose uptake and metabolism.", + "Studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstrm Syndrome, has led to the discovery of rare, but highly penetrant, alleles that could be tracked using family-based linkage approaches. This has provided valuable insights into the cellular and molecular processes operating in the pancreatic islet and other tissues that control glucose homeostasis. Furthermore, it has uncovered a whole class of diseases, the ciliopathies, that result from defects in the genetic control of ciliary development and function.", + "The genetic and epigenetic differences between monozygotic twins discordant for diabetes suggest that both genetic and environmental factors contribute to the disease's etiology. While monozygotic twins share 100% of their genetic material, the discordance in diabetes incidence (30-70%) indicates the influence of environmental factors. These may include diet, gestational infections, and viral infections. Epigenetic modifications, such as altered DNA methylation, also play a role in gene expression regulation and contribute to diabetes etiology. Therefore, the study of these twins helps to understand the complex interplay of genetic predisposition and environmental triggers in the development of diabetes.", + "Recent studies have identified several therapeutic targets through the interaction between genetic variants and environmental factors in diabetes development. These include the PPARG and thiazolidinediones, KCNJ11 and sulfonylurea therapy, and the glucagon-like peptide-1 receptor (GLP1R), which is the target of GLP1R agonists. Other potential targets include drug receptors or drug metabolizers such as OCT genes, ABCC8, and CYP2C9." + ], + "contexts": [ + [ + "\tUsing microarray analysis, we discovered that gene-targeting of the 7 nAChR\nresults in up-regulation of an insulin-signaling network in the NAc. A genetic correlation\nnetwork of insulin-related genes and Chrna7 was independently elucidated in the NAc\nacross the BXD panel, thus validating that our microarray results are likely not due to\ndevelopmental compensation in 7 KO mice. Insulin-degrading enzyme, Ide, mRNA\nwas significantly decreased and previous rodent studies have demonstrated that both\nknock-out of this gene (Farris, 2003), as well as a mutation decreasing its catabolic\nactivity (Fakhrai, 2000), results in hyperinsulinemia and glucose intolerance.", + "\t\n\nSome recently-identified miRNAs have been associated with insulin secretion, insulin resistance, and inflammation, and differences have emerged in some circulating miRNA levels between individuals with and without type 2 diabetes (40).Zhao and others (41) examined some miRNAs in pregnant women at 16-19 weeks of gestation (WG), finding a significantly lower expression of 3 miRNAs (miR-29a, miR-132 and miR222) in women who went on to develop GDM at 24-28 WG than in those who did not develop GDM.MiR-29 plays a part in glucose homeostasis: its overexpression inhibits insulinstimulated glucose uptake and downregulates gluconeogenesis (42).MiR-132 targets the insulin-mediated regulation of cytochrome P450 (which is involved in hepatic metabolism), and it has a role in trophoblast expansion (its reduced expression impairs normal trophoblast development) (42,43).MiR-222 is involved in regulating the cell cycle (controlling the cyclindependent kinase inhibitor).", + "\t\n\nA human islet cis-regulatory network has been generated by integrating chromatin accessibility data, RNA-sequencing data and chromatin immunoprecipitation-sequencing data for five key -cell transcription factors (FOXA2, MAFB, NKX2.2, NKX6.1 and PDX1) 25 .Using this regulatory network, loci associated with T2DM risk that influence fasting glycaemia were found to be enriched in active islet enhancers.Furthermore, these loci were predicted to alter enhancer activity by disrupting transcription factor binding sites 25 .For example, a T2DM risk variant (rs58692659) falls within the ZFAND3 locus and was demonstrated to disrupt a NEUROD1 binding site, which is an important islet transcription factor for islet cell development and function, thereby preventing NEUROD1 binding 25 .Furthermore, T2DM risk loci are enriched in and predicted to disrupt regulatory factor X (RFX) transcription factor binding sites 27 .These data provide compelling evidence that islet-specific regulatory regions have a central role in T2DM pathophysiology and suggest a direct link between genetic variation and changes in gene expression.", + "\t\n\nThe inability to detect insulin-signaling changes in both studies can be explained by a number of technical and biological hypotheses.First, perhaps the number of insulinsignaling genes that were transcriptionally deregulated was too few to be considered significant by statistical procedures.Second, perhaps the assembled insulin-signaling gene set used in our analysis did not accurately capture the transcriptional alterations in insulin signaling.Alternatively, it is plausible that the changes in a diabetic state were produced by phosphorylation-mediated signaling that was not detected by transcriptional profiling.", + "\t\n\nUpon further epigenetic regulatory elements in diabetes, micro-RNAs, such as miR-15a and miR-29b, were found to be downregulated in type 2 diabetes, whereas miR-27a and miR-320a were upregulated and might open the possibility for new diagnostic markers [187, [231][232][233].", + "\t\n\nAs ER stress markers were not activated to potentially explain reduced insulin secretion, genes related to insulin secretion pathway were investigated using real-time-PCR, which revealed downregulation of the glucose-stimulated insulin secretion (GSIS) pathway and the glucose uptake pathway in RIN-m -cells when compared to the control, indicating impairment of these pathways.mRNA levels by real-time PCR (Fig. 4c) showed a decrease in glucose transporter 2 (Glut2 [MIM: 138160]) to 54% compared to the control, p < 0.001.Pancreatic and duodenal homeobox 1 (Pdx1 [MIM: 600733]) was also suppressed to 85.7%, p = 0.01.On the other hand, the forkhead box protein A2 (Foxa2 [MIM: 600288]) mRNA level, which regulates PDX1, was unchanged, while the mRNA of glucokinase (Gck [MIM: 138079]), which phosphorylates glucose in the first step of the GSIS pathway in -cells, was slightly elevated (11.5%, p = 0.008).", + "\t\n\nIt has been hypothesized that most of the new genetic variants affect -cell function, development or survival but not insulin sensitivity [6].Consistent with this, we found all of the genes except Adam30 and Cdkn2a were expressed in pancreatic islets.These genes were expressed, however in the transformed -cell line, MIN6.The expression of all the genes except Lgr5 decreased following incubation of the islets in high glucose concentrations.It can thus be hypothesized that these genes may normally play a beneficial role in islet function, and a reduction in the expression of these genes could contribute to glucotoxic -cell dysfunction or survival.However, we also found evidence that most of the genes could have potential roles in other metabolically-relevant tissues.Genes affecting insulin sensitivity may be expected to be expressed in peripheral insulin sensitive tissues, such as liver and adipose tissue, and be responsive to metabolic status.Consumption of a high fat diet was associated with a tendency for the expression of several of these genes to be decreased.Similarly, many of the genes were regulated by feeding and fasting.Only the two splice isoforms of Cdkn2a had no evidence of metabolic regulation in any of the other tissues examined.", + "\t\nRecent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell.MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis.We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility.We used Illumina small RNA sequencing to profile the miRNA fraction in three preparations each of primary human islets and of enriched beta-cells generated by fluorescenceactivated cell sorting.In total, 366 miRNAs were found to be expressed (i.e..100cumulative reads) in islets and 346 in betacells; of the total of 384 unique miRNAs, 328 were shared.A comparison of the islet-cell miRNA profile with those of 15 other human tissues identified 40 miRNAs predominantly expressed (i.e..50% of all reads seen across the tissues) in islets.Several highly-expressed islet miRNAs, such as miR-375, have established roles in the regulation of islet function, but others (e.g.miR-27b-3p, miR-192-5p) have not previously been described in the context of islet biology.As a first step towards exploring the role of islet-expressed miRNAs and their predicted mRNA targets in T2D pathogenesis, we looked at published T2D association signals across these sites.We found evidence that predicted mRNA targets of islet-expressed miRNAs were globally enriched for signals of T2D association (p-values ,0.01, q-values ,0.1).At six loci with genome-wide evidence for T2D association (AP3S2, KCNK16, NOTCH2, SCL30A8, VPS26A, and WFS1) predicted mRNA target sites for islet-expressed miRNAs overlapped potentially causal variants.In conclusion, we have described the miRNA profile of human islets and beta-cells and provide evidence linking islet miRNAs to T2D pathogenesis.\t\n\nRecent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell.MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis.We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility.We used Illumina small RNA sequencing to profile the miRNA fraction in three preparations each of primary human islets and of enriched beta-cells generated by fluorescenceactivated cell sorting.In total, 366 miRNAs were found to be expressed (i.e..100cumulative reads) in islets and 346 in betacells; of the total of 384 unique miRNAs, 328 were shared.A comparison of the islet-cell miRNA profile with those of 15 other human tissues identified 40 miRNAs predominantly expressed (i.e..50% of all reads seen across the tissues) in islets.Several highly-expressed islet miRNAs, such as miR-375, have established roles in the regulation of islet function, but others (e.g.miR-27b-3p, miR-192-5p) have not previously been described in the context of islet biology.As a first step towards exploring the role of islet-expressed miRNAs and their predicted mRNA targets in T2D pathogenesis, we looked at published T2D association signals across these sites.We found evidence that predicted mRNA targets of islet-expressed miRNAs were globally enriched for signals of T2D association (p-values ,0.01, q-values ,0.1).At six loci with genome-wide evidence for T2D association (AP3S2, KCNK16, NOTCH2, SCL30A8, VPS26A, and WFS1) predicted mRNA target sites for islet-expressed miRNAs overlapped potentially causal variants.In conclusion, we have described the miRNA profile of human islets and beta-cells and provide evidence linking islet miRNAs to T2D pathogenesis.\tDiscussion\n\nUsing next-generation sequencing, we have established the first catalog of miRNAs in human pancreatic islets and beta-cells, and explored the overlap between these miRNAs and T2D genetic susceptibility.Our catalog not only serves as a valuable resource for those interested in the roles of specific miRNAs in normal islet physiology and beta-cell function, it also provides a reference for the study of miRNA mediated abnormalities in islets from type 2 diabetic donors.The abundance of miR-375 in the miRNA profile provides valuable support for a critical role in human pancreatic beta-cells, mirroring the well-established role in rodent islet biology.miR-375 null mice are hyperglycaemic and exhibit reduced beta-cell mass [40].In a clonal rodent beta-cell line (MIN6), knockdown or over-expression of this miRNA influences glucose-stimulated insulin secretion [7].Furthermore, knockdown of miR-375 in obese ob/ ob mice results in a more profound effect on glycaemia leading to a severe diabetic phenotype in these mice [40].Our study establishes that miR-375 is also abundantly expressed in human islets and warrants further studies to define the contribution of miR-375 to the pathogenesis of T2D.\t\n\nFew of the 10 most islet-specific miRNAs (Figure 2B; all with specificity scores .0.8) have previously been implicated in islet function.For miR-184, miR-182-5p and miR-127-3p, there is published evidence for a role in insulin biosynthesis and secretion, though for miR-184 and miR-127-3p this is restricted to a correlation between islet expression levels and glucose-stimulated insulin secretion [17,18].For other miRNA transcripts, such as miR-409-5p and miR-183-5p, the high degree of islet-specificity may point to novel roles in the development and maintenance of islet cellular phenotype.", + "\t\n\nIn sum, this work provides new information about how CDKN2A/B T2D SNPs impact islet biology, suggests that the ANRIL lncRNA may play a role in human islets, and uncovers a link between a T2D SNP and b-cell proliferation.Further studies into the CDKN2A/B locus to develop a mechanistic understanding of how these SNPs impact islet biology to influence T2D risk could one day open the door for using personalized genomic information to inform T2D subtype definitions and therapeutic choice.", + "\t\n\nThe following section will discuss the roles of lncRNAs in metabolic tissues and deregulation of which are implicated in varied metabolic phenotypes associated with diabetes.\tLncRNAs as regulators of islet function\n\nThe pancreatic islet is an important central node to researchers to understand the pathophysiology of diabetes [53].The possible regulation of islet development and function by lncRNAs was first demonstrated by Ding et al., where the lncRNA, H19 (Fig. 4), was shown to be involved in transgenerational transmission of gestational diabetes mellitus which leads to impaired islet structure and function [54].To understand the roles of lncRNAs in regulating pancreatic function, several research groups have profiled lncRNA expression in mouse and human pancreatic islets [55,56].Transcriptome analysis in pancreatic -cells of type 2 diabetes patients identified tissue-specific and dynamically regulated abnormally expressed lncR-NAs.These lncRNAs are often located near islet-specific chromatin domains containing islet-specific coding genes or mapped to diabetes susceptible genetic loci.Knockdown of HI-LNC25, a cell-specific lncRNA conserved between mouse and human resulted in decreased GLIS3, an important islet transcription factor, thereby suggesting its functional importance in pancreatic cells [56] (Fig. 4).A coexpression analysis has identified that the lncRNA, LOC283177, correlates with the expression of insulin synthesis and secretion [51] (Fig. 4).Yin et al. demonstrated that silencing of the lncRNA, TUG1 in vivo increased apoptosis in pancreatic cells and decreased insulin secretion leading to elevated fasting glucose levels (Fig. 4).Expression of TUG1 is decreased in a non-obese diabetic (NOD) mouse and is suppressed by glucose treatment in pancreatic Nit-1 cells, indicating its association with diabetes [57].Another lncRNA, MEG3 was reported to be downregulated in the pancreatic tissue of Type 1 Diabetic (T1D) and T2D mice models and its expression was dynamically modulated by glucose in Min6 and primary mouse islet cells (Fig. 4).In vivo silencing of MEG3 led to impaired glucose tolerance and decreased insulin secretion, as also evident by the reduced insulin-positive cells.There was a significant decrease in the Pdx-1 and MafA levels indicating MEG3 as a novel -cell regulator [58].Deletion of a conserved lncRNA, linc1 (-cell long intergenic non-coding RNA 1) in adult mice results in defective islet development and disruption of glucose homeostasis [59] (Fig. 4).Decreased levels of the lncRNA, PLUTO (Fig. 4) in islets of T2D or impaired glucose tolerant subjects affect the 3D chromatin structure and transcription of Pdx-1, a key cell transcription factor implicating its role in insulin synthesis and cell-specific regulatory network [60].In spite of these reports, the elucidation of lncRNAmediated molecular mechanisms in pancreatic biology still awaits further detailed investigations.", + "\t\n\nThe known tissue specificity of gene expression regulation means that the most informative studies will measure transcript levels in the specific tissue(s) relevant to the disease.In the case of type 2 diabetes, characterization of physiological responses (e.g., stimulus-induced insulin secretion, insulin sensitivity) suggests most loci are associated with defects in pancreatic b-cell function (2,3,7).Therefore there is a real need to measure gene expression in human b-cells (or whole islets, as these have been shown to be a suitable proxy [8]).There have, however, been very few reports linking type 2 diabetesassociated variation with islet gene expression using the classical eQTL approach (9,10).", + "\tInsulin secretion\n\nProgression from altered glucose metabolism to overt diabetes occurs as the reduction in -cell mass and function is further aggravated.Thus, an attractive intervention is one that will halt the progressive decline in -cell mass and function and prevent the need for exogenous insulin replacement that otherwise follows 1 .Agents that suppress inflammation, including IL-1 blockers and salsalate (a potent inhibitor of NF-B), have shown some promise in improving glycaemic control and -cell function 143,269,270 .MicroRNAs play a pivotal part in the physiological and pathological processes involved in glucose metabolism by post-transcriptional regulation of gene expression.Particular microRNAs can regulate -cell function 271 , exposing key regulatory signalling pathways involved in restoration of -cell mass, and provide a promising strategy for improving insulin secretion and -cell health in T2DM.Identification of novel insulin secretagogues that act directly on -cells and enteroendocrine K cells and L cells in the intestine are under investigation, and members of the G protein-coupled class of receptors have shown promise 272 .GLP1 receptor agonists induce -cell proliferation in rodents 273 , but studies in humans have not demonstrated a similar effect 237 .A series of novel signalling pathways have been reported to be strongly associated with -cell mass restoration.For example, the PI3K-PKC pathway has been shown to augment glucose-mediated -cell prolifer ation, and activation of PKC may provide a novel approach to increase human -cell proliferation 274 .", + "\t\nThe inheritance of variants that lead to coding changes in, or the mis-expression of, genes critical to pancreatic beta cell function can lead to alterations in insulin secretion and increase the risk of both type 1 and type 2 diabetes.Recently developed clustered regularly interspaced short palindromic repeats (CRISPR/Cas9) gene editing tools provide a powerful means of understanding the impact of identified variants on cell function, growth, and survival and might ultimately provide a means, most likely after the transplantation of genetically \"corrected\" cells, of treating the disease.Here, we review some of the disease-associated genes and variants whose roles have been probed up to now.Next, we survey recent exciting developments in CRISPR/Cas9 technology and their possible exploitation for b cell functional genomics.Finally, we will provide a perspective as to how CRISPR/Cas9 technology may find clinical application in patients with diabetes.\tGWAS-Identified Genes\n\nFollowing the successful identification of genetic loci by GWAS, several candidate genes within or surrounding genetic loci which are thought to play roles in b cell function, in particular, in proinsulin processing and secretion, have been examined in mechanistic studies.Gene editing tools have quickly replaced techniques such as shRNA-based silencing and HDR-mediated deletion to become a mainstream technique in studies of gene function.For example, the critical b cell-enriched NEUROD1 and SLC30A8 genes were deleted in EndoC-bH1 cells using these approaches in recent studies (243).Similarly, pancreatic duodenum homeobox-1 (PDX1), an important regulator of the INS gene, was also mutated by CRISPR-Cas9 resulting in a line with defective glucose-induced Ca 2+ influx and insulin secretion (244).Our laboratory has inactivated the type 2 diabetes-related STARD10 and FCHSD2 genes in EndoC-bH1 cells using a lentiviral approach and demonstrated effects on insulin secretion (and see above) (117).Furthermore, Fang et al. used CRISPR screening technology and identified several genes involved in insulin regulation in mouse MIN6 cells (172).\t\n\ninsulin secretion.We begin by providing examples of genes and loci associated with altered T2D risk.Finally, we review the CRISPR tools that may offer the potential to correct these variants in the human b cell.", + "\t\n\nT2D loci were also identified at clusters of noncoding RNAs with roles in islet cell function.One locus includes a set of microRNAs specifically expressed in islet cells, the maternally expressed noncoding RNA MEG3, and the paternally expressed gene DLK1.Targets of these microRNAs increase cell apoptosis 40 , and reduced Meg3 expression impairs insulin secretion 41 .DLK1 inhibits adipocyte differentiation, thereby protecting against obesity 3 , and promotes pancreatic ductal cell differentiation into cells, increasing insulin secretion 42,43 .Other variants near MEG3 have been associated with type 1 diabetes 44 (EAS and EUR LD r 2 = 0 with EAS lead variant).The other noncoding RNA locus is the MIR17HG cluster of miRNAs, which regulate glucose-stimulated insulin secretion and pancreatic cell proliferation stress 45 ; one of these microRNAs, miR-19a, affects hepatic gluconeogenesis 46 .Yet another T2D locus is located near TRAF3, which is a direct target of the MIR17HG microRNA cluster and promotes hyperglycaemia by increasing hepatic glucose production 47,48 .The T2D association results suggest that these noncoding RNAs influence disease susceptibility." + ], + [ + "\tThis\nphosphorylation triggers the activation of the docking protein IR substrate 1 (IRS1), which\nsubsequently activates phosphatidylinositol 3-kinase (PI3K) and RAC serine/threonineprotein kinase 2 (AKT2), which has a critical role in glucose metabolism. PI3K and AKT2\nactivation promotes the translocation of glucose transporter 4 (GLUT4) and the free fatty\nacid (FFA) transporter CD36 from intracellular stores to the plasma membrane, thereby\n\nNat Rev Cardiol. Author manuscript; available in PMC 2021 February 01. Tan et al. Page 48\n\nAuthor Manuscript\nAuthor Manuscript\n\nleading to increased glucose and FFA uptake.", + "\tProtein kinase B (c-Akt) in phosphatidylinositol-3-OH\nkinase\nsignal\ntransduction. Nature. 1995;376(6541):599-602.\ndoi:10.1038/376599a0\n\n53. Herzig S, Long F, Jhala US, et al. CREB regulates hepatic gluconeogenesis\nthrough\nthe\ncoactivator\nPGC-1. Nature. 2001;413(6852):179-183.\ndoi:10.1038/35093131\n\n54. Matsumoto M, Pocai A, Rossetti L, Depinho RA, Accili D. Impaired regulation of\nhepatic glucose production in mice lacking the forkhead transcription factor\nFoxo1 in liver. Cell Metab. 2007;6(3):208-216. doi:10.1016/j.cmet.2007.08.006\n\n55. Wang ND, Finegold MJ, Bradley A, et al. Impaired energy homeostasis in\nC/EBP alpha knockout mice. Science. 1995;269(5227):1108-1112.\ndoi:10.1126/science.7652557\n\n56.\tIt exerts its functions through\n\nactivating the phosphatidylinositol-3-kinase (PI3K)-AKT signaling pathway and\nphosphorylating a variety of substrates, including glycogen synthase kinase-3 (GSK3)\n51\n\n, the forkhead (FOXO) transcription factors, and cAMP regulatory element-binding\n\nprotein (CREB)\n\n52\n\ngluconeogenesis\n\n. CREB, FOXO1, and C/EBP are transcription factors involved in\n\n5355\n\n. The detailed mechanisms of how insulin maintains albumin\n\nexpression require further investigation. Insulin resistance occurs in patients with sepsis\n\n56\n\n, obesity and diabetes\n\n57\n\n, implying\n\na role for severe or persistent inflammation.", + "\t\n\n) including PABPC4, NRBP1, CALCRL, CTC-498M16.4,and FADS1.Shared TWAS associations suggested the involvement of glucose and energy homeostasis via PKB/AKT signaling or epigenetic modulator (methylation, acetylation, or lncRNA) in RHR and T2D/cardiometabolic, and provided potential biological shared pathways, mechanisms, or potential therapeutic targets to follow-up in the future.", + "\t\n\nThe above discussion remarkably converges on the TGF-beta signaling effector SMAD3.TGF-beta signaling is involved in the regulation of insulin gene transcription, pancreatic islets b cell function, and glucose tolerance and energy homeostasis [36,[59][60][61].SMAD3 is known to localize at insulin gene promoter and repress insulin gene transcription [61].SMAD3 knock-out mice are associated with improved glucose tolerance and insulin sensitivity [36].Exhibiting altered expression of genes related to adipogenesis, lipid accumulation, and fatty acid b oxidation, these mice show resistance to obesity and insulin resistance induced by high fat diet [36,59].Further, levels of TGF-beta1 have been found to positively correlate with adiposity in human subjects [59].Also, systemic blockade of TGF-beta signaling has been found to protect mice from obesity, diabetes and hepatic steatosis [59].Indeed, pharmacological manipulation of TGF-beta signaling is considered to offer a potential therapeutic strategy in obesity and diabetes [59,60].", + "\tSignal transduction\n\nMAPK1 is an important regulator of -cell function (Lawrence et al, 2008), for example contributing directly to short-versus long-term insulin response and regulation of pro-apoptotic CHOP10 (Lawrence et al, 2007).MAPK1 constitutes the center of a regulatory network implicated in elevated free fatty acid (FFA) levels (Sengupta et al, 2009) common in T2D patients.MAPK/ERK signalling is exacerbated by FFA that lead to dephosphorylation of cascade proteins by PP2A/PPP2R4 (Guo et al, 2010) pointing towards a certain level of interwovenness between the identified processes, in this case signal transduction (adaptation category) and ER stress (dysfunction/cell death category, cf. Figure 5B). (Figure 2C) CDK5R1 acts as an activator of CDK5 (Ubeda et al, 2004) whose expression is regulated by glucose and which inhibits insulin secretion (Wei et al, 2005).Hyperglycaemia-caused overactivation of CDK5 may contribute to -cell glucotoxicity (Ubeda et al, 2006). (Figure S4C)", + "\tThe binding of insulin with its ligand specific\nreceptor increases glucose metabolism, lipid synthesis\nand cellular proliferation via PKB/AKT signaling [27, 28]. In fact, dysregulation of PKB/AKT signaling provokes a\nbroad range of diseases such as cancer, diabetes and heart\ndisease [29, 30]. CTMP was first identified as a PKB/AKT\nbinding partner with tumor-suppressor function. PKB/\nAKT is negatively regulated by the binding of CTMP\nwith the C-terminal regulatory domain of pPKB/AKT\n[31, 32]. Together with CTMP, LETM1 is associated with\nmitochondrial morphology via optic atrophy 1 (OPA1)\nregulation [33].", + "\t\n\nWith T2D status, and with increases in fasting glucose, fasting insulin and BMI, we observed lower expression of genes involved in endoplasmic reticulum protein localization and translational elongation.For T2D, the most significant trends were for decreased expression of cellular respiration genes (q-value 1.4 10 35 ), consistent with previous observations in skeletal muscle samples from T2D and NGT individuals following hyperinsulinemic-euglycemic clamp 7 .Mitochondrial regulatory protein PGC-1alpha (PPARGC1A) was identified by Mootha et al. 7 as a potential master regulator of mitochondrial expression.We observed lower, non-significantly different expression levels of PPARGC1A (b 0.24, q-value 0.57) in individuals with T2D.Decreased mitochondrial function is a component of the mTOR pathway which is dysregulated in metabolic diseases; downregulation of the pathway shifts cells away from protein synthesis and cell growth and towards protein catabolism 8 .Consistent with this, for T2D, we observed lower expression of genes involved in generation of precursor metabolites, translational elongation and higher expression of genes involved in protein polyubiquitination (Fig. 1c).", + "\t\n\nTwo negative feedback loops in this insulin signaling pathway are of interest.Additionally to tyrosine phosphorylation, both the insulin receptor and IRS proteins are also phosphorylated on serine residues, which may attenuate ) inhibition under certain conditions described in the review; green: insulinomimetic effects of zinc; red: effects of zinc deficiency leading to insulin resistance.After binding of insulin to the subunits of the tetrameric insulin receptor, the kinase activity of the subunit is stimulated, which results in transphosphorylation of the subunit [35,130].This induces phosphorylation of members of the IRS family and subsequent interaction with signaling molecules like the p85 subunit of the PI3K [131].PI3K in turn triggers phosphorylation of PDK1, a serine kinase that activates Akt/PKB [132,133].Akt leads to stimulation of GLUT 4 translocation in adipocytes and to inhibition of GSK-3, thereby allowing activation of glycogen synthase in adipocytes, translocation of GLUT to the cell surface and induction of glucose metabolism [35,[134][135][136][137][138][139][140][141].In addition, inhibition of GSK-3 results in enhanced protein synthesis and gene expression [35,142].Zinc leads to tyrosine phosphorylation of the subunit of the insulin-receptor [143 a ] and to inhibition of PTP1B which dephosphorylates the insulin receptor, thus increasing phosphorylation of the receptor [144 b ].Akt is activated by zinc in a PI3K-dependent way [143 c ] and zinc inhibits GSK-3, just like insulin [145 d ].Moreover, zinc plays a role in glucose transport since it is part of IRAP, a molecule probably required for maintenance of normal GLUT levels [129 e ].Zn: zinc.\t\n\nsignaling by decreasing insulin-stimulated tyrosine phosphorylation.This is mediated by PI3K, Akt, GSK-3 and mammalian target of rapamycin [35].GSK-3 is capable of phosphorylating IRS-1, subsequently converting this molecule into an inhibitor of the insulin receptor tyrosine kinase activity in vitro and in insulin-resistant rat muscle after insulin stimulation [141,158].A second mechanism negatively influencing insulin signaling is the rapid dephosphorylation of the insulin receptor and its substrates by protein tyrosine phosphatase 1B (PTP1B) [35].", + "\tDiscussion\n\nThe G protein/cAMP/PKA mediated signal transduction pathway is of high importance for growth, cell differentiation and metabolism due to extracellular ligands.The a-subunit of stimulatory G proteins Gsa is crucial for mediating these effects.In the present study, we report the positive results of the largest mutation screening of the a subunit of stimulatory G proteins described so far, leading to the identification of two new hotspots and 33 mutations that have not been reported before.Furthermore, we demonstrate for the first time a connection between the severity of the mutation and the phenotypical signs of subcutaneous calcifications and brachymetacarpia in patients with PHPIa.", + "\t\n\nFigure 1: Schematic representation of the insulin-signaling pathway.Dashed light-blue line borders indicate insulin-signaling inhibitor proteins.PTPRF = protein tyrosine phosphatase receptor type F; ENPP1 = ectonucleotide pyrophosphatase/phosphodiesterase 1; PTPN1 = protein tyrosine phosphatase nonreceptor type 1; IRS = insulin receptor substrate; PI3K = phosphoinositides 3 kinase; nck = noncatalytic region of tyrosine kinase adaptor protein 1; INPPL1 = inositol polyphosphate phosphatase-like 1; TRIB3 = tribbles homolog 3; mTOR = mammalian target of rapamycin; Foxo = forkhead box protein O1; BAD = Bcl-2-associated death promoter; PHAS-I = phosphorylated heatand acid-stable protein regulated by insulin; and p70S6K = p70-ribosomal S6 kinase.", + "\t\n\nand although complex, occur largely in a canonical sequence resulting in a single outcome (Fig. 2) -hence perturbation at any stage in this sequence will almost inevitably result in decreased release of the hormone into the portal circulation.In contrast, variations in function of a single gene product involved in insulin signalling are unlikely to have an effect on all aspects of insulin action and hence would not present with major effects on glucose metabolisms (Fig. 2).", + "\t\n\nUnder normal conditions, the glucose regulation process commences when insulin binds to its corresponding insulin receptor (IR), which results in auto-phosphorylation of its tyrosine residues [171].This allows IR to phosphorylate insulin receptor substrate 1 (IRS-1) on tyrosine residues, which further triggers the phosphorylation of downstream molecules and induces the phosphatidylinositol 3-kinase (PI3K) signaling transduction cascade [171,172].PI3K, when activated, results in the conversion of phosphatidylinositol 4,5-bisphosphate (PIP2) to phosphatidylinositol (3,4,5)-triphosphate (PIP3).Consequently, downstream 3-phosphoinositide dependent protein kinase1 (PDK1) is activated, which subsequently activates, among other kinases, Akt, resulting in phosphorylation of its substrate (AS160), which regulates translocation of glucose transporter 4 (GLUT4) to the transmembrane and allows for glucose uptake and regulation of protein and lipid metabolism [171,172] (Figure 4).\t\n\nUnder normal conditions, the glucose regulation process commences when insulin binds to its corresponding insulin receptor (IR), which results in auto-phosphorylation of its tyrosine residues [171].This allows IR to phosphorylate insulin receptor substrate 1 (IRS-1) on tyrosine residues, which further triggers the phosphorylation of downstream molecules and induces the phosphatidylinositol 3-kinase (PI3K) signaling transduction cascade [171,172].PI3K, when activated, results in the conversion of phosphatidylinositol 4,5-bisphosphate (PIP2) to phosphatidylinositol (3,4,5)-triphosphate (PIP3).Consequently, downstream 3-phosphoinositide dependent protein kinase1 (PDK1) is activated, which subsequently activates, among other kinases, Akt, resulting in phosphorylation of its substrate (AS160), which regulates translocation of glucose transporter 4 (GLUT4) to the transmembrane and allows for glucose uptake and regulation of protein and lipid metabolism [171,172] (Figure 4).Insulin binds to the insulin receptor, causing autophosphorylation of its tyrosine residues.This causes phosphorylation of insulin receptor substrate-1 (IRS-1) on its tyrosine residues, which leads to the phosphorylation of the phosphatidylinositol 3-kinase (PI3K) signaling transduction cascade.PI3K catalyzes the phosphorylation of phosphatidylinositol 4,5-bisphosphate (PIP2) to phosphatidylinositol (3,4,5)-triphosphate (PIP3).PIP3 activates 3-phosphoinositide-dependent protein kinase-1 (PDK-1) as a result, which in turn, phosphorylates the downstream protein \"AKT\", which phosphorylates its substrate AS160.AS160 regulates glucose translocator 4 (GLUT4) and aids in its translocation to the plasma membrane, where it allows glucose to flow.\t\n\nFigure 4. PI3K/Akt signaling pathway.Insulin binds to the insulin receptor, causing autophosphorylation of its tyrosine residues.This causes phosphorylation of insulin receptor substrate-1 (IRS-1) on its tyrosine residues, which leads to the phosphorylation of the phosphatidylinositol 3-kinase (PI3K) signaling transduction cascade.PI3K catalyzes the phosphorylation of phosphatidylinositol 4,5-bisphosphate (PIP2) to phosphatidylinositol (3,4,5)-triphosphate (PIP3).PIP3 activates 3-phosphoinositide-dependent protein kinase-1 (PDK-1) as a result, which in turn, phosphorylates the downstream protein \"AKT\", which phosphorylates its substrate AS160.AS160 regulates glucose translocator 4 (GLUT4) and aids in its translocation to the plasma membrane, where it allows glucose to flow.\t\n\nIn GDM pregnancies, decreased expression levels of the following insulin signaling components: IRS1, PIP3, PIK3, and GLUT4, have been reported [173][174][175].Furthermore, alternative phosphorylation of IRS1 at serine residues was exhibited in GDM patients, which prevents the PI3K signaling cascade from taking place, and thus, inhibits insulin action [176].The exact underlying mechanism through which disrupted insulin signaling Insulin binds to the insulin receptor, causing autophosphorylation of its tyrosine residues.This causes phosphorylation of insulin receptor substrate-1 (IRS-1) on tyrosine residues, which leads to the phosphorylation of the phosphatidylinositol 3-kinase (PI3K) signaling transduction cascade.PI3K catalyzes the phosphorylation of phosphatidylinositol 4,5-bisphosphate (PIP2) to phosphatidylinositol (3,4,5)-triphosphate (PIP3).PIP3 activates 3-phosphoinositide-dependent protein kinase-1 (PDK-1) as a result, which in turn, phosphorylates the downstream protein \"AKT\", which phosphorylates its substrate AS160.AS160 regulates glucose translocator 4 (GLUT4) and aids in its translocation to the plasma membrane, where it allows glucose to flow.", + "\tIn conclusion, by employing a combination of pharmacological and genetic gain- and loss-of-function genetic approaches,\nour studies show that the activation of the TGR5 signaling\npathway counteracts the metabolic dysfunction associated\nwith diabesity. TGR5 activation results in a range of beneficial\nmetabolic effects that include resistance to weight gain and\nhepatic steatosis, preservation of liver and pancreatic function,\nand the maintenance of glucose homeostasis and insulin sensitivity. These effects are due to enhanced mitochondrial function\nin muscle, BAT, and enteroendocrine cells, resulting in an\nincrease in energy expenditure and incretin secretion (Figure 7).", + "\tInsulin and DHEA signaling\n\nIn addition to the changes in central metabolic pathways, we found significant regulation of hormonal pathways.We could reproduce the transcriptional regulation of IGFs (insulin-like growth factors) and IGFBPs (IGF binding proteins).IGF1 is a major growth signaling molecule that is transcriptionally activated by insulin and growth hormone (GH) under good nutrient conditions, thereby allowing cell growth and proliferation (Kelley et al., 1996) sion is strongly reduced, while its deactivating binding proteins IGFBP1 and IGFBP2 are up-regulated.", + "\tPost-Translational Modifications Control PPAR Signaling Affecting Drug Effectiveness\n\nDistinct biological networks converge into PPAR signaling and several molecular effectors directly or indirectly regulate its activation [19], resulting in finely regulated tissue-specific responses.A large number of endogenous/exogenous compounds, coactivators, and corepressors affect PPAR activity, inducing different signal transduction pathways and biological effects.Beyond epigenetic, transcriptional, and translational regulatory mechanisms, different post-translational modifications (PTMs), such as phosphorylation, acetylation, -Olinked N-acetylglucosamine (O-GlcNAc), SUMOylation, and ubiquitination, contribute to PPAR activation [120].Each PTM represents a separate feature to be exploited for cell-or tissue-specific modulation [17], allowing rapid responses to internal and external stimuli.Of note, PTMs control PPAR activity, potentially influencing ligand affinity, DNA binding, coactivator recruitment, and/or proteasomal degradation." + ], + [ + "\tA GLIMPSE INTO THE FUTURE\n\nGetting from the extremes to a comprehensive view of diabetes genetics.As described above, success in the identification of genes impacting on individual risk of diabetes has come from two distinct approaches to gene discovery.The first, linkage mapping within monogenic and syndromic families, has delivered causal variants that are rare but highly penetrant.The second, large-scale association mapping, is now yielding growing numbers of common variants: these have, at best, modest effect sizes and low penetrance.Several genes are featured in the lists generated by both approaches.For example, mutations in KCNJ11, PPARG, WFS1, and TCF2 (HNF1B) are causal for syndromic and/or monogenic forms of diabetes, while common variants in these same genes influence predisposition to typical type 2 diabetes (55,56,64 -66).While common variants in GCK (another gene causal for MODY) do not influence type 2 diabetes risk per se, they have a clear impact on fasting glucose levels within the population (88).\tLESSONS LEARNED FOR MULTIFACTORIAL DISEASE\n\nMonogenic and syndromic forms account for only a small, though highly informative, proportion of cases of nonautoimmune diabetes.The challenge for medical science lies in bringing equivalent mechanistic insights and translational benefits to the hundreds of millions of people already affected by, or at risk of, more common, typical forms of diabetes.For type 2 diabetes, there is abundant evidence that individual susceptibility is influenced by both the combination of genetic variation at multiple sites and a series of environmental exposures encountered during life (52).Tracking down the specific genetic variants involved has been tougher than for monogenic forms of disease, since the correlations between genotype and phenotype are far weaker (53,54).However, recent efforts have now identified at least 17 confirmed type 2 diabetessusceptibility variants ( (69), and development and exploitation of this methodology has had the greatest impact on susceptibility gene discovery.Even so, many of these discoveries have been hard-won.One reason for this is that the \"candidate\" gene-based approach has proved, with notable exceptions (55,56), to be an inefficient route to susceptibility gene discovery; it is only with the advent of functionally agnostic genome-wide approaches that the floodgates have opened (70).Another reason is that detection of the variants of modest effect that appear to be responsible for much of type 2 diabetes susceptibility (per-allele odds ratios [ORs] 1.10 -1.40, for risk-allele frequencies 10 -90%) has required association studies conducted in extremely large sample sizes (thousands of individuals) (54).Variants within TCF7L2 have the largest effects seen so far, with a per-allele OR of 1.4 (57): the 15% of Europeans carrying two copies of the risk allele are at approximately twice the lifetime risk of type 2 diabetes as the 40% who have none.", + "\tLessons from GWA studies\n\nThe most important lesson is the demonstration of the power of genetics to provide novel insights into disease aetiology.Of the 11 genes or regions now implicated in type 2 diabetes, only four were strong biological candidates (PPARG, KCNJ11, WFS1, TCF2) [8,9,[11][12][13][14].Three had some corroborating evidence (IGF2BP2, the HHEX-IDE gene region, SLC30A8) [2][3][4][5][6], but for the remainder, evidence of their link to diabetes came as a complete surprise.These studies provide the first evidence implicating Wnt-signalling pathways (TCF7L2) and cell cycle control (CDKAL1 and CDKN2A/2B) in the pathogenesis of type 2 diabetes [2,3,5,6].For type 1, the key new discoveries highlight the contribution to disease pathogenesis of the PTPN gene family and IL-2 signalling [1,7].", + "\t\n\nMajor consortia addressing the genetic basis of diabetes complications and associated traits", + "\t\n\nGenetic determinants of diabetes and metabolic syndromes.", + "\t\n\nUnfortunately, these questions are not yet answered.The early 1990s was the beginning of the era of molecular biol- ogy, and it was generally assumed that within a few years this powerful new technology would identify the genetic defects in type 2 diabetes.Indeed, the genetic basis for many monogenic forms of diabetes has been discovered such as mitochondrial genome defects and the association with diabetes and deafness, Wolfram's syndrome, several rare syndromes of extreme insulin resistance and obesity, and many of the MODY syndromes (maturity onset diabetes of youth).Still, these account for only a small proportion of diabetes.", + "\t\n\nGenome-wide association studies (GWAS) have made a significant contribution to our current knowledge of the role(s) of genetic variation in population-level susceptibility to T1D (Mychaleckyj et al., 2010).", + "\t\nIt has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes.Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups.This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders.\t\n\nIt has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes.Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups.This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders.", + "\t\nGenome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.", + "\t\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.", + "\t\n\nIn the past decade, genome-wide association (GWAS) and sequencing studies have identified genetic loci that help explain the inherited basis of T2D and glycemic traits.These studies are providing insights into the genetic architecture of T2D, including the number, frequency and effect sizes of risk variants in populations around the world.The polygenic nature of T2D is now well established, and multiple risk variants are being identified at some loci, suggesting allelic heterogeneity.Concurrently, increasing numbers of genes and variants have been implicated in monogenic forms of diabetes, including maturity onset diabetes of the young (MODY) and neonatal diabetes (7), and at least five genes have been implicated in both monogenic and polygenic diabetes (8).A recent simulation study evaluated genetic architectures for consistency with results from T2D genetic studies and found that many different disease models were still possible with respect to the number of loci, allele frequencies and level of selective pressure (9).Ongoing studies should more substantially narrow the bounds on feasible architectures (9).", + "\t\n\nIn the case of relatively uncommon monogenic and syndromic forms of diabetes, such as maturity onset diabetes of the young (MODY) and neonatal diabetes, identification of rare causal mutations has delivered both knowledge and clinical translation [4,5].In contrast, progress in unravelling the genetic architecture of more typical, common, multifactorial type 2 diabetes has been painfully slow [6].The reasons have been well-rehearsed [7].The complex web of susceptibility factors-genetic, environmental, social-that contributes to individual risk of developing type 2 diabetes means that most predisposing genetic variants will have only a modest marginal impact on disease risk.The majority of genetic studies performed to date have simply had insufficient power to uncover these reliably [7].The few type 2 diabetes-susceptibility variants convincingly demonstrated-notably the P12A variant in PPARG and E23K in KCNJ11 [8,9]-have only modest effects on disease risk (odds ratios ~1.2), far too small to offer (either individually or in combination) clinically useful predictive testing.Since these variants lie within genes whose products are already known to be therapeutic targets, these particular discoveries have also had limited capacity to deliver novel pathophysiological insights.Among those working on the genetics of type 2 diabetes, there was growing apprehension that these two genes might be providing a representative view of the genetic architecture of type 2 diabetes.", + "\t\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.", + "\t\nWhilst the heritable nature of Type 2 diabetes has been recognized for many years, only in the past two decades have linkage analyses in families and genome-wide association studies in large populations begun to reveal the genetic landscape of the disease in detail.Whilst the former have provided a powerful means of identifying the genes responsible for monogenic forms of the disease, the latter highlight relatively large genomic regions.These often harbour multiple genes, whose relative contribution to exaggerated disease risk is uncertain.In the present study, the approaches that have been used to dissect the role of just a few (TCF7L2, SLC30A8, ADCY5, MTNR1B and CDKAL1) of the ~500 genes identified at dozens of implicated loci are described.These are usually selected based on the strength of their effect on disease risk, and predictions as to their likely biological role.Direct determination of the effects of identified polymorphisms on gene expression in disease-relevant tissues, notably the pancreatic islet, are then performed to identify genes whose expression is affected by a particular polymorphism.Subsequent functional analyses then involve perturbing gene expression in vitro in b-cell lines or isolated islets and in vivo in animal models.Although the majority of polymorphisms affect insulin production rather than action, and mainly affect the b cell, effects via other tissues may also contribute, requiring careful consideration in the design and interpretation of experiments in model systems.These considerations illustrate the scale of the task needed to exploit genome-wide association study data for the development of new therapeutic strategies.\t\n\nWhilst the heritable nature of Type 2 diabetes has been recognized for many years, only in the past two decades have linkage analyses in families and genome-wide association studies in large populations begun to reveal the genetic landscape of the disease in detail.Whilst the former have provided a powerful means of identifying the genes responsible for monogenic forms of the disease, the latter highlight relatively large genomic regions.These often harbour multiple genes, whose relative contribution to exaggerated disease risk is uncertain.In the present study, the approaches that have been used to dissect the role of just a few (TCF7L2, SLC30A8, ADCY5, MTNR1B and CDKAL1) of the ~500 genes identified at dozens of implicated loci are described.These are usually selected based on the strength of their effect on disease risk, and predictions as to their likely biological role.Direct determination of the effects of identified polymorphisms on gene expression in disease-relevant tissues, notably the pancreatic islet, are then performed to identify genes whose expression is affected by a particular polymorphism.Subsequent functional analyses then involve perturbing gene expression in vitro in b-cell lines or isolated islets and in vivo in animal models.Although the majority of polymorphisms affect insulin production rather than action, and mainly affect the b cell, effects via other tissues may also contribute, requiring careful consideration in the design and interpretation of experiments in model systems.These considerations illustrate the scale of the task needed to exploit genome-wide association study data for the development of new therapeutic strategies.", + "\tA\n\nnumber of studies have implicated a genetic basis for type 2 diabetes (1).The discovery of monogenic forms of the disease underscored the phenotypic and genotypic heterogeneity, although monogenic forms account for only a few percent of the disease (1).Defining the genetic basis of the far more common polygenic form of the disease presents more difficulties (2,3).Nevertheless, some interesting results have recently emerged.A genome scan of Hispanic-American families (330 affected sib-pairs [ASPs]) found linkage to chromosome 2q37 (logarithm of odds [LOD] 4.15) (4), and the causative gene has been recently reported (5).A number of other genome scans in various racial groups have identified other putative susceptibility loci (6 -8).The largest genome-wide scan for type 2 diabetes loci reported to date studied 477 Finnish families (716 ASPs) and found evidence for linkage to chromosome 20q12-13.1(LOD 2.06 at D20S107) (9).Interestingly, similar results have been reported by at least three other groups (10 -12).", + "\t\n\nThe earliest successes for genetic discovery in diabetes and obesity arose from the study of monogenic and syndromic forms of disease, for which the segregation of rare, but highly penetrant, alleles could be tracked using family-based linkage approaches that are well suited to that setting.Maturity-onset diabetes of the young, for example, accounts for ~1-2% of cases of nonautoimmune diabetes presenting in early adulthood. 14ost cases of maturity-onset diabetes of the young are now known to result from rare coding mutations in either the hepatocyte nuclear factor-1A (HNF1A) or glucokinase (GCK) genes.In patients with these conditions, a precise molecular diagnosis brings important benefits in terms of individual prognostication and treatment optimization. 14These discoveries have also generated valuable insights into the cellular and molecular processes-operating in the pancreatic islet and other tissuesthat control glucose homeostasis. 15To give a further example, identification of the mutations underlying syndromic forms of obesity, including Bardet-Biedl, has uncovered a whole class of diseases, the ciliopathies, that result from defects in the genetic control of ciliary development and function. 16arly attempts to apply family-based linkage approaches to more common forms of diabetes and obesity proved to be unrewarding.In their seminal paper in 1996, Risch and Merikangas 17 highlighted the merits of association, as opposed to linkage, analysis for the detection of the low-penetrance alleles most likely to be relevant to common disease.It would take a decade before the density of available markers would allow genomewide screens for association to be implemented. 18In the interim, association analyses that focused attention on genetic variation within presumed biological candidates resulted in some successes in risk variant detection.For T2D, these included associations with variants in the genes encoding key therapeutic targets such as the peroxisome proliferator-activated receptor- (PPARG) and the islet K ATP channel (KCNJ11); 19,20 an equivalent example for obesity would relate to variants in the melanocortin 4 receptor (MC4R) gene. 21More often than not, however, these candidate gene studies were plagued by inadequate sample size and overly liberal significance thresholds, a lethal combination that led to a profusion of unreliable reports of association. 22][25][26] Given the content of the genotyping arrays employed, these studies have focused on the detection of signals attributable to common variants (typically of a minor allele frequency above 5%).9][30][31] In the case of T2D, the current count of risk loci, each confirmed to genome-wide significance, is around 65; [27][28][29] for BMI and obesity, the count is about half that number. 25Looking across these loci, several important features emerge.", + "\t\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.", + "\t\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + ], + [ + "\tGenes\n\n2][43][44][45][46][47] Twin studies need to be considered carefully, however, as the intrauterine environments of dizygotic-twin (separate placentas), monozygotic-twin (60-70% share one placenta), and singleton pregnancies (one placenta without competition for maternal nutrients) will all be diff erent, and this can be a confounder in the inter pretation of eff ects. 44A large study from Sweden on familial risk of type 2 diabetes showed that the relative risks were highest in individuals with at least two aff ected siblings, irrespective of parental diabetes status. 42This fi nding suggests that a recessive pattern of inheritance from uncommon genetic defects, the sharing of similar intrauterine, postnatal, or both environments by siblings (eg, breastfeeding or bottle feeding or childhood nutrition), or a combination of these factors is important.9][50] A greater number of these loci are associated with impaired -cell function (KCNJ11, TCF7L2, WFS1, HNF1B, SLC30A8, CDKAL1, IGF2BP2, CDKN2A, CDKN2B, NOTCH2, CAMK1D, THADA, KCNQ1, MTNR1B, GCKR, GCK, PROX1, SLC2A2, G6PC2, GLIS3, ADRA2A, and GIPR) than impaired insulin sensitivity (PPARG, IRS1, IGF1, FTO, and KLF14) or obesity (FTO). 38,48,50Of these, TCF7L2 is the strongest susceptibility locus for type 2 diabetes, being associated with -cell dysfunction. 48Most patients with monogenic forms of diabetes also have gene defects that aff ect islet -cell function. 51,52Nevertheless, only around 10% of the heritability of type 2 diabetes can be explained by susceptibility loci identifi ed so far, with each locus having a low eff ect size. 36The remaining heritability might be related to a large number of less common variants (allele frequency <5%) that are diffi cult to fi nd with current approaches of genome-wide association studies, and/or epigenetic phenomena.", + "\t\n\nAnother component of T1D that aids in our understanding of the disease and assessment of risk is genetic inheritance.A longterm (up to 40 year) study of twin pairs in Finland revealed a monozygotic (MZ) pairwise concordance for T1D of 27.3% while the concordance for dizygotic (DZ) twins was 3.8% [4].The impact of genetics was further made clear in this study because upon diagnosis of T1D in one twin, the length of time to diagnosis in the other twin in the concordant pairs was a maximum of 6.9 years in MZ twins and 23.6 years in DZ twins [4].In addition to measuring incidence of T1D in twin studies, islet antigen-specific autoimmunity can also be determined.As a precursor to T1D, autoimmunity is defined as the presence of antibodies to islet autoantigens in sera [5].In another study, 83 unaffected monozygotic twins were followed for nearly 44 years and incidence of autoimmunity or diagnosis of T1D was recorded.This study showed a 65% cumulative incidence of T1D by 60 years of age and more than 75% tested positive for an islet autoantibody during the course of the study.Once autoimmunity was established, the risk of diabetes was 89% within 16 years of the first positive autoantibody test.\t\n\nClearly genetics play an important role in the T1D disease process as both MZ and DZ twins have the same environmental exposures but different concordance rates and length to diagnosis of the second twin.Numerous genes have been associated with T1D, the most significant being the HLA region on chromosome 6 [6].More than 90% of type 1 diabetics carry HLA alleles DR3-DQ2 or DR4-DQ8 compared to no more than 40% of the general population [7].Alleles at HLA-DQB1 are known to be, in part, protective [8].Single nucleotide polymorphisms (SNPs) are also associated with T1D.A recent genome-wide association study of approximately 2,000 patients with each of 7 common, chronic diseases, including T1D, and 7,000 shared controls confirmed the association of SNPs in 5 previously identified regions with T1D and discovered 5 novel associations.However, the authors concluded that these regions, with the exception of the HLA on chromosome 6, confer only modest effects on T1D, and ''the association signals so far identified account for only a small proportion of overall familiality'' [9].These results suggest that additional genetic variants contribute to inheritance of T1D.", + "\t\n\nGenetic predisposition for the development of NIDDM has been strongly indicated by higher concor-dance rates in monozygotic than in dizygotic twins (Barnett et al., 1981;Newman et al., 1987), by clustering in families (Bennett, 1990), and by a strong correlation with the degree of population admixture (Zimmet et al., 1982;Chakraborty et al., 1986;Groop and Toumi, 1997).Although some rare monogenic forms of early onset NIDDM-like diseases in humans have been identified (reviewed in Froguel et al., 1997), genes responsible for the common forms of late-onset NIDDM remain unknown.Genome-wide scans for such genes have detected linkages of diabetes phenotypes with NIDDM1 on chromosome 2q in Mexican Americans (Hanis et al., 1996) and NIDDM2 on chromosome 12q in Finnish families (Mahtani et al., 1996).In a major effort, complex haplotypes in the Calpain 10 gene (CAPN10) at the NIDDM1 locus have recently been associated with increased risk for developing type II diabetes in Mexican Americans and Northern Europeans (Horikawa et al., 2000).CAPN10 is the first NIDDM gene cloned thus far.", + "\t\n\nAlthough there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied.\t\nIn 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention.Type 1 diabetes becomes clinically apparent after a preclinical period of varying length, during which autoimmune destruction reduces the mass of beta cells in the pancreatic islets to a level at which blood glucose levels can no longer be maintained in a physiologic range.The disease has two subtypes: 1A, which includes the common, immune-mediated forms of the disease; and 1B, which includes nonimmune forms.In this review, we focus on subtype 1A, which for simplicity will be referred to as type 1 diabetes.Although there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied. Gene tic S t udiesBecause of the uncertainty regarding the number and action of genes involved in type 1 diabetes, genetic studies have tended to focus on approaches that require few assumptions about the underlying model of disease risk.The two primary approaches have been linkage studies (using pairs of affected relatives, typically siblings) and association studies (using either case-control or family-based designs).Linkage studies using affected sibling pairs seek to identify regions of the genome that are shared", + "\t\n\nThe marked increase of T1D incidence cannot be solely attributed to genetic risk (Snouffer, 2018).In fact, disease discordance in monozygotic twins (30-70%) strongly suggests environmental factors contribute to the aetiology of T1D (Redondo et al., 2008).These contributions may manifest through epigenetic modification including altered DNA methylation (Cepek et al., 2016;Paul et al., 2016;Stefan et al., 2014), which has been reported to play a key role in the transcriptional regulation of gene expression, and in some part, contributes to the aetiology of T1D (Stefan et al., 2014).Other environmental exposures attributable to the rising prevalence of T1D include diet (Hansen et al., 2006), gestational infections (Rei Lindehammer et al., 2012), and viral infections (Lnnrot et al., 2000).As such, it is highly likely that these non-genetic triggers interact with susceptibility genes in genetically predisposed individuals to influence the development of T1D.", + "\t\n\nWhile these data indicate a major role for inborn susceptibility, they also underscore the role of environment and random chance.Secular trends in diet and physical activity are associated with a rising rate of T2D, demonstrating the impact of environment.Monozygotic twins are less than 100% concordant for both T1D and T2D, demonstrating that environment and/or random chance plays a major role in disease.Formal estimates of heritability (100) and long-term follow-up of monozygotic twins ascertained without disease bias (131) confirm the role of these nongenetic factors.", + "\t\n\nGenetic susceptibility to type 1 diabetes (T1D) is well supported by epidemiologic evidence; however, disease risk cannot be entirely explained by established genetic variants identified so far.This study addresses the question of whether epigenetic modification of the inherited DNA sequence may contribute to T1D susceptibility.Using the Infinium HumanMethylation450 BeadChip array (450k), a total of seven long-term disease-discordant monozygotic (MZ) twin pairs and five pairs of HLA-identical, disease-discordant non-twin siblings (NTS) were examined for associations between DNA methylation (DNAm) and T1D.Strong evidence for global hypomethylation of CpG sites within promoter regions in MZ twins with TID compared to twins without T1D was observed.DNA methylation data were then grouped into three categories of CpG sites for further analysis, including those within: 1) the major histocompatibility complex (MHC) region, 2) non-MHC genes with reported T1D association through genome wide association studies (GWAS), and 3) the epigenome, or remainder of sites that did not include MHC and T1D associated genes.Initial results showed modest methylation differences between discordant MZ twins for the MHC region and T1D-associated CpG sites, BACH2, INS-IGF2, and CLEC16A (DNAm difference range: 2.2%e5.0%).In the epigenome CpG set, the greatest methylation differences were observed in MAGI2, FANCC, and PCDHB16, (DNAm difference range: 6.9%e16.1%).These findings were not observed in the HLA-identical NTS pairs.Targeted pyrosequencing of five candidate CpG loci identified using the 450k array in the original discordant MZ twins produced similar results using control DNA samples, indicating strong agreement between the two DNA methylation profiling platforms.However, findings for the top five candidate CpG loci were not replicated in six additional T1Ddiscordant MZ twin pairs.Our results indicate global DNA hypomethylation within gene promoter regions may contribute to T1D; however, findings do not support the involvement of large DNAm differences at single CpG sites alone in T1D.", + "\tParticipants\n\nTwo cohorts of monozygotic (MZ) and dizygotic (DZ) twins discordant for type 1 diabetes were tested for TPOA to determine the relative influence of genetic and environmental factors.Initially, type 1 diabetes-discordant twin pairs were selected from the British Diabetic Twin Study [5] and a US twin cohort [4].The basic characteristics of the twins are shown in Table 1.These individuals fulfilled the following criteria: (1) twin pairs initially disease discordant; (2) both twins available for study; (3) neither twin receiving drugs other than human insulin; (4) all had normal plasma creatinine; and (5) diabetes initially excluded in the co-twin by OGTT and random whole-blood glucose <7.0 mmol/l.Monozygosity was established using both clinical data and DNA fingerprinting (data not shown) and type 1 diabetes was defined by standard criteria [9].\t\n\nChapter 3 evaluates the heritability of TPOA, which was estimated in type 1 diabetes discordant MZ and DZ twin pairs from UK and US twin cohorts.To address the problem of limited sample size and power, a meta-analysis was carried out using structural equation model fitting.We further investigated whether the same environmental factors that caused type 1 diabetes in discordant twin pairs also caused a higher risk of thyroid autoimmunity as defined by TPOA.", + "\t\n\nTwin studies provide further evidence for heritability of type 1 diabetes susceptibility.Monozygotic twins are 100% genetically identical (excepting epigenetic events such as the rearrangements of immunoglobulin and T-cell receptor genes, which occur differently in each individual).Dizygotic twins share only 50% of their genetic material.In contrast to the difference in the degree of genetic similarity, both twin pairs are exposed to environmental factors that are likely equally similar for monozygotic and dizygotic twins (especially samesex dizygotic twins).Thus, the degree to which monozygotic twins show greater concordance for disease susceptibility compared with dizygotic twins indicates the degree to which genetic factors contribute to disease susceptibility [see Boomsma et al. (19) for review].\t\n\nPerhaps the most informative twin studies for this purpose are those based on large twin registries, because they avoid ascertainment bias that can confound clinic-based studies.In clinic-based studies, where ascertainment of a twin pair depends on at least one twin being affected, concordant affected pairs have two chances to be identified, whereas discordant pairs, with only one affected sibling, have only one chance.Thus, concordance rates can be overestimated using diagnosis-based ascertainment strategies [reviewed in Redondo et al. (20)].Prospective studies of initially discordant pairs can also be used to avoid this bias, and, furthermore, provide information about the rate of concordance over time.For type 1 diabetes, the concordance rate for monozygotic twins from these studies has been estimated as 21-53%, with most estimates between 30-50% [see Redondo et al. (20) and references therein].One study (21) estimated a cumulative concordance rate as high as 70%, adjusted for age of onset of the affected twin and last observation of the unaffected twin.As expected, the concordance rate increases over time as new diagnoses of diabetes are made (22).Interestingly, much of the risk to a co-twin is within the first 3 yr after the index twin's diagnosis (23), perhaps consistent with a shared environmental exposure, but also consistent with a genetic role in determining age of diagnosis (24).From the excess concordance in monozygotic compared with dizygotic twins, it has been estimated that as much as 66-72% of the variation in type 1 diabetes risk is attributable to genetic factors (21, 24a), although other studies have yielded lower estimates (23).This fraction is also referred to as the heritability, or h 2 .The concordance rate is much higher for monozygotic twins when one twin is diagnosed at an early age (23,25), suggesting that heritability might be highest for very early onset type 1 diabetes.\t\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.\tType 1 diabetes is an environmental disease\n\nThere are convincing data that non-genetic factors, perhaps environmental factors in early childhood, also play a role in diabetes susceptibility.First, the heritability of type 1 diabetes is only 72% or less, implying that at least a quarter of the risk of type 1 diabetes is not determined by inherited sequence variation.Consistent with this concept, the concordance rate for type 1 diabetes in monozygotic twins is estimated at 70% or less.It is possible that genetic events that distinguish monozygotic twins, such as the rearrangement of the T-cell receptor genes, or other stochastic, random events play a role in type 1 diabetes susceptibility.However, shared environmental factors are implicated by comparing the concordance rates for siblings and for dizygotic twins.While both siblings and dizygotic twins share 50% of their genetic material in common, dizygotic twins are exposed to a more similar environment than are siblings.Thus, the fact that dizygotic twins exhibit a higher rate of concordance for type 1 diabetes than do siblings [13 vs. 7% in Denmark; see Kyvik et al. (21)] implies that shared environmental factors affect the risk of type 1 diabetes.These shared environmental influences could be prenatal (intrauterine), or related to diet, infectious exposure, or other factors.Additional epidemiologic evidence confirms the importance of environmental factors: there is seasonality both in the month of birth (generally lower in winter) and in the month of diagnosis (generally peaking in winter), although the degree of seasonality varies among populations (12,(31)(32)(33)(34).", + "\tMonogenic vs. polygenic diabetes\n\nMonogenic and polygenic diabetes are traditionally considered distinct, with monogenic diabetes resulting from one highly penetrant variant in one gene in a given individual, and polygenic diabetes resulting from the contribution of several variants with smaller effects in the context of environmental/lifestyle factors.In T1D, autoimmune dysfunction is the prominent mechanism, with variation in the major histocompatibility locus and other genomic factors combining with apparent environmental triggers to result in beta cell loss and diabetes.In monogenic diabetes, highly penetrant variants, mostly Finally, while lack of features of either autoimmunity or obesity/metabolic syndrome raises the likelihood of monogenic diabetes, these features can co-exist with monogenic diabetes, particularly obesity given its high prevalence especially in youth.In the Treatment Options for Diabetes in Adolescents and Youth (TODAY) clinical trial in which overweight or obesity was required for the newly diagnosed youth with T2D enrolled, at least 4.5% were identified as having MODY.Those with HNF4A-MODY had poor response to metformin, representing a previously missed opportunity for optimal treatment 9 .In summary, monogenic and polygenic forms of diabetes exist along more of a continuum than previously appreciated.Therefore, knowledge about P R E V I E W monogenic diabetes not only provides opportunities for etiology-based treatment of the minority of individuals with highly penetrant variants, but also informs broader understanding of diabetes etiology.", + "\t\n\nRecent GWAS have successfully identified more than 40 independent T1DM-associated tagging SNPs; however, the sum of these loci does not fully explain the heritability estimated from familial studies [16].For example, twin studies have shown that for di-zygotic twins, the pairwise T1DM concordance rate is 10%, whereas for mono-zygotic twins, the concordance rate is approximately 50% [17].Thus, dietary and other environmental factors also influence T1DM incidence and development.These factors primarily include the use of breast milk vs. infant formula [18], highly hydrolyzed infant formula vs. conventional infant formula [19], early/late exposure to gluten [20] and vitamin D [21].Interestingly, a newly diagnosed child fed a gluten-free diet was shown to remain healthy without insulin therapy for 20 months [22].", + "\t\n\nBecause close relatives of diabetic patients share common environmental factors, it could be argued that shared environment alone accounts for the increased risk of Type I diabetes among relatives of diabetic patients.More definitive evidence for a genetic basis is obtained by comparing the diabetes concordance rates in monozygotic (MZ, 100 % shared genes) and dizygotic twins (DZ, average 50 % shared genes), because twins experience similar environments both before and after birth.These studies have consistently shown a higher Type I diabetes concordance rate in MZ twins than DZ twins [1013], demonstrating a clear genetic basis for this disorder.The MZ twin concordance rate also provides a rough idea of the degree of genetic compared with non-genetic determination in specific environments.This concordance rate has been variously estimated as 34 % by age 30 [11], 43 % within 12 years of diagnosis of the index case [14], and 50 % within 40 years of index diagnosis [15], implying strong non-genetic factors (reflected as discordance) in the aetiology of Type I diabetes.", + "\tType 1 Diabetes\n\nDiscordance rates in twins, the rise in global incidence, variance in geographic prevalence, and assimilation of local disease incidence rates when individuals migrate from low-to high-incidence countries all support an environmental influence on risk for developing type 1 diabetes.Furthermore, many lines of evidence suggest that environmental factors interact with genetic factors in both the triggering of autoimmunity and the subsequent progression to type 1 diabetes.Supporting this gene-environment interaction is the fact that most subjects with the highest-risk HLA haplotypes do not develop type 1 diabetes.", + "\t\n\nIt is therefore intriguing that A1C levels are significantly correlated in monozygotic twins whether they are concordant for type 1 diabetes or not (4): in a discordant twin pair one twin is treated with insulin, whereas the other one isn't, and thus this degree of correlation suggests that genetic contributors to A1C may be detectable despite the superimposition of a strong environmental modifier.Rig-orous estimates of heritability of treated A1C, however, are not available." + ], + [ + "\tFuture directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4.", + "\t\n\nAn individual's risk of developing T2D is influenced by a combination of lifestyle, environmental, and genetic factors.Uncovering the genetic contributors to diabetes holds promise for clinical impact by revealing new therapeutic targets aimed at the molecular and cellular mechanisms that lead to disease.Genome-wide association studies performed during the past decade have uncovered more than 100 regions associated with T2D (5)(6)(7)(8)(9)(10)(11)(12).Although these studies have provided a better understanding of T2D genetics, the majority of identified variants fall outside protein-coding regions, leaving the molecular mechanism by which these variants confer altered disease risk obscure.Consequently, T2D genome-wide association studies have identified few loci with clear therapeutic potential.", + "\tGENETIC SUSCEPTIBILITY AND GENE-ENVIRONMENT INTERACTIONS-\n\nThe recent advent of genome-wide association studies (GWAS) has led to major advances in the identification of common genetic variants contributing to diabetes susceptibility (40).To date, at least 40 genetic loci have been convincingly associated with type 2 diabetes, but these loci confer only a modest effect size and do not add to the clinical prediction of diabetes beyond traditional risk factors, such as obesity, physical inactivity, unhealthy diet, and family history of diabetes.Many diabetes genes recently discovered through GWAS in Caucasian populations have been replicated in Asians; however, there were significant interethnic differences in the location and frequency of these risk alleles.For example, common variants of the TCF7L2 gene that are significantly associated with diabetes risk are present in 20-30% of Caucasian populations but only 3-5% of Asians (41,42).Conversely, a variant in the KCNQ1 gene associated with a 20-30% increased risk of diabetes in several Asian populations (43,44) is common in East Asians, but rare in Caucasians.It is intriguing that most diabetes susceptibility loci that have been identified are related to impaired b-cell function, whereas only a few (e.g., peroxisome proliferator-activated receptor-g, insulin receptor substrate 1, IGF-1, and GCKR) are associated with insulin resistance or fasting insulin, which points toward b-cell dysfunction as a primary defect for diabetes pathogenesis.It should be noted that most of the single nucleotide polymorphisms uncovered may not be the actual causal variants, which need to be pinpointed through fine-mapping, sequencing, and functional studies.", + "\t\n\nConclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations.", + "\tGenomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes.", + "\t\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484\t\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484\tPharmacogenetics in disease progression\n\nOver the recent years, more than 90 susceptibility genes have been identified by genome-wide association studies (GWAS) [55][56][57][58].However, the knowledge of the potential interactions between T2D predisposing genetic variants and the efficacy of treatment of T2D is sparse.Identification of gene-treatment interactions is challenging and requires large sample sizes and sophisticated analytical methods.Furthermore, detailed information on lifestyle and compliance to treatment as well as a long follow-up period are necessary for analysis of pharmacogenomics in T2D.\t\n\nTo date, a number of genetic variants have been identified to be associated with response to antidiabetic drugs.Of these, some variants are present in either drug receptors or drug metabolizers as for OCT genes, KCNJ11, ABCC8, and CYP2C9.Other variants are known T2D susceptibility variants such as TCF7L2.To identify variants of importance for antiglycemic drug response, GWAS in large cohorts of patients with diabetes with detailed measures of pharmacotherapy are lacking.The pharmacologic management of patients with diabetes often involves drug classes other than antidiabetics.Pharmacogenetic studies on statin and antihypertensive treatment have reported several genetic variants associated with treatment response and adverse drug reactions [101,102].It therefore seems natural to conclude that the future perspectives in pharmacogenetics is to conduct genetic studies in large cohorts with wellphenotyped individuals, thorough data collection on baseline treatment, concomitant treatment, adherence to therapy as well as data collection on comorbidity and additional disease diagnoses.These types of pharmacogenetic studies may provide unique opportunities for future genotype-based treatment standards and may help in delaying or changing the slope of disease progression among patients with T2D.", + "\t\n\nGenetic determinants of diabetes and metabolic syndromes.", + "\t\n\nOver the past two years, there has been a spectacular change in the capacity to identify common genetic variants that contribute to predisposition to complex multifactorial phenotypes such as type 2 diabetes (T2D).The principal advance has been the ability to undertake surveys of genome-wide association in large study samples.Through these and related efforts, $20 common variants are now robustly implicated in T2D susceptibility.Current developments, for example in high-throughput resequencing, should help to provide a more comprehensive view of T2D susceptibility in the near future.Although additional investigation is needed to define the causal variants within these novel T2Dsusceptibility regions, to understand disease mechanisms and to effect clinical translation, these findings are already highlighting the predominant contribution of defects in pancreatic b-cell function to the development of T2D.", + "\t\n\nThe availability of detailed information on gene environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them.", + "\t\n\nThere is strong evidence that novel T2D genes will be potentially exciting pharmaceutical targets.There is strong evidence in favour of this already, as the most established T2D susceptibility genes are also well-known drug targets, namely PPARG and thiazolidinediones [45] and KCNJ11 and sulfonylurea therapy [46,128].", + "\tFUTURE PERSPECTIVES\n\nContinued investment in studies of G E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications.", + "\t\n\nRegulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk.Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints.We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing and follow-up in 39,979 individuals by targeted genotyping, with additional in silico followup in consortia.We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials.We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents.A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies.The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk.Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials.Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.\t\nRegulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk.Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints.We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing and follow-up in 39,979 individuals by targeted genotyping, with additional in silico followup in consortia.We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials.We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents.A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies.The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk.Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials.Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.\tDISCUSSION\n\nAnticipating the side effects of drugs before phase 3 clinical trials could support drug discovery and development, reducing attrition rates and saving considerable time and money.The promise of human genetics in this endeavor (2, 3, 7, 27) depends on the availability of genetic variants that mimic pharmaceutical interventions.We undertook a systematic study to identify such genetic variants in the context of diabetes and obesity and identified an association between fasting glucose and T2D with a missense variant in GLP1R, the gene encoding the GLP-1 receptorthe target of the GLP1R agonist class of T2D therapies.Regulatory authorities require evidence that therapies for T2D are not associated with unacceptable increases in cardiovascular risk.The reduced risk associated with the glucose-lowering genetic variant in GLP1R provides evidence that not only will GLP1R agonists meet this regulatory hurdle but they may also reduce CHD events.Ongoing trials of GLP1R agonists are designed to resolve this uncertainty and will also augment the evidence on the broader validity of genetic approaches in drug target validation.", + "\tConclusions\n\nRecent large collaborative studies to clarify the genetics of T2DM have identified variants in nine gene areas that are associated with a moderately increased risk of developing the disease.Further studies may identify more of these variants and ultimately improve the possibility of predicting disease risk in healthy subjects.Search for the patho-physiological role of these variants has not been easy, although evidence is emerging for their involvement in either pancreatic development or in the control of insulin secretion.The elucidation of novel pathways involved to the etiology of T2DM may contribute to improved prevention and treatment of the disease.The influence of environmental factors such as lifestyle and diet must not be overlooked, and future studies should be especially focused on the interactions between dietary factors and the genetic variants involved in T2DM risk.In the light of the recent investigative efforts, the genetics of T2DM is probably no longer ''the geneticist's nightmare'', but it certainly remains an intriguing puzzle that is yet to be solved.", + "\t\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis.\t\n\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + ] + ], + "task_id": [ + { + "task_id": "267860332B035B03D684CFB2CBB42ECF" + }, + { + "task_id": "31595629DC7ABAE42ABEE4B01B1599DE" + }, + { + "task_id": "A3D28FFFE853D22D58BE99FFC84F2DC8" + }, + { + "task_id": "AF799975123DFCE3735C1802F530B8B0" + }, + { + "task_id": "4487D79862055ACAFD215A9D700626FC" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_diabetes_3.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_diabetes_3.json new file mode 100644 index 0000000..eac65b9 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_diabetes_3.json @@ -0,0 +1,104 @@ +{ + "question": [ + "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?", + "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?", + "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?", + "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?", + "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?" + ], + "answer": [ + "Rare variants identified through whole-genome sequencing can contribute to the heritability of Type 2 diabetes by revealing genetic determinants that are not detected by Genome-Wide Association Studies (GWAS). These rare variants, especially those with significant effects, can increase our understanding of Type 2 diabetes heritability and help identify individuals at risk early on. However, current studies suggest that these rare variants are likely scattered across many genes, rather than being clustered in a small number of genes.", + "Recent findings suggest that non-coding RNAs, specifically long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play significant roles in the pathogenesis of diabetes and its complications. They are involved in various cellular processes such as inflammation, fibrosis, and hypertrophy in renal cells, which contribute to the progression of diabetic nephropathy. They can also affect DNA methylation and histone modifications, resulting in increased chromatin accessibility to transcription factors near pathological genes. Furthermore, lncRNAs and miRNAs can increase the expression of pathological genes via post-transcriptional mechanisms. Some specific lncRNAs like MALAT1, MEG3, ANRIL, PVT1, MIAT, MGC, Gm4419, and TUG1 have been implicated in complications like diabetic retinopathy and nephropathy. Similarly, miRNAs have been found to regulate important pathogenic responses and hold potential as diagnostic biomarkers and therapeutic targets.", + "The interaction between multiple polygenic risk scores (PRS) improves the prediction of Type 1 and Type 2 diabetes by aggregating the genetic risk of individual alleles across the genome. This provides a comprehensive view of an individual's genetic predisposition to diabetes. The PRS can capture information on individual patterns of disease predisposition, which can help predict diabetes risk, support differential diagnosis, and understand phenotypic and clinical heterogeneity. However, the effectiveness of PRS can vary across different ethnic groups and populations.", + "Recent single-cell RNA-sequencing studies have revealed that multiple monogenic diabetes genes are highly expressed in beta cells. However, other non-beta cell types also express genes mutated in monogenic diabetes. Dysregulated glucagon secretion in type 1 diabetic islets is accompanied by decreased expression of important islet transcription factors and increased expression of stress response factors, suggesting changes in alpha cell identity may lead to their dysfunction. Transcriptomic heterogeneity in normal and T2D islets is associated with variability in alpha cell electrophysiological measures. These studies implicate the dysfunction of both alpha and beta cells in diabetes pathogenesis.", + "In diabetic individuals, there are significant differential DNA methylation profiles in pancreatic islets compared to non-diabetic individuals. This includes 276 CpG loci affiliated to promoters of 254 genes displaying significant differential DNA methylation in diabetic islets. These methylation changes were not present in blood cells from diabetic individuals nor were they experimentally induced in non-diabetic islets by exposure to high glucose. These changes can affect over 250 genes, some of which are also differentially expressed, and may be linked to b-cell functionality, cell death, and adaptation to metabolic stress. These epigenetic changes are not observed in other tissues, indicating tissue-specificity." + ], + "contexts": [ + [ + "\t\n\nIt should be noted that a great number of low frequency variants might not be identified by GWAS owing to the required genome-wide significance level.According to the existing studies, many important loci are also obscured as a result of borderline associations.The known variants account for only a small amount of the overall estimated genetic heritability; therefore, there is still a long way to go in terms of understanding the pathogenesis of type 2 diabetes.", + "\t\n\nIf common causal alleles explain a substantial component of T2D susceptibility, the contribution of rare and low-frequency risk variants may be less than is often assumed: resequencing studies will soon provide empirical data to address this hypothesis.In particular, it will be important to determine whether, as the number of susceptibility loci increases, there is evidence that the pathophysiological mechanisms implicated by human genetics coalesce around a limited set of core pathways and networks.Our data suggest that this may be the case, with a variety of analytical approaches pointing to cell cycle regulation, adipocytokine signaling and CREBBP-related transcription factor activity as key processes involved in T2D pathogenesis.", + "\tFuture perspective\n\nGiven the rapid pace of technological advancement in genetics, discovery of many more genetic determinants of T2D may be expected in future.At present, GWAS are limited in their ability to detect rare variants.Sequencing, which is expected to become much more economical, may benefit greatly in this respect by identifying rare genetic variants with significant effects on T2D risk in a given population.This would result in an increased understanding of T2D heritability so that at risk individuals may be detected early on.However, functional studies need to evolve at an equally rapid pace to be able to translate these discoveries into clinical practice.\tGenetics & genomics of T2D\n\n Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D.However, only close to 10% heritability is explained by these variants.Other genetic variants, particularly those which are rare but with significant effects need to be identified. Genetic variability is responsible for the difference in response to antidiabetic drugs seen across individuals.", + "\t\n\nOver the past two years, there has been a spectacular change in the capacity to identify common genetic variants that contribute to predisposition to complex multifactorial phenotypes such as type 2 diabetes (T2D).The principal advance has been the ability to undertake surveys of genome-wide association in large study samples.Through these and related efforts, $20 common variants are now robustly implicated in T2D susceptibility.Current developments, for example in high-throughput resequencing, should help to provide a more comprehensive view of T2D susceptibility in the near future.Although additional investigation is needed to define the causal variants within these novel T2Dsusceptibility regions, to understand disease mechanisms and to effect clinical translation, these findings are already highlighting the predominant contribution of defects in pancreatic b-cell function to the development of T2D.", + "\tGenetic variants\n\nThe heritability of glycaemic traits and type 2 diabetes is high [40], and the large genome-wide association studies published to date since the first in 2007, based on up to >10 5 study participants, has helped us to better understand the genetic architecture of this disease.Single nucleotide polymorphisms (SNPs) in more than 60 regions throughout the genome (so-called susceptibility loci containing multiple genes) were found to be associated with the risk of type 2 diabetes [39, 41-44].Most of these SNPs are common, with minor allele frequencies of 10-90%.Interestingly, loci associated with diabetes risk show only a partial overlap with loci that determine levels of fasting glucose, 2 h glucose and HbA 1c .Thus, some loci influence both disease risk and glycaemic traits, whereas others seem to mainly regulate glucose levels within the physiological range without affecting the development of overt type 2 diabetes, and vice versa [45,46].", + "\t\n\nFigure 3 displays results for three representative models: a 'purifying selection' model in which low-frequency and rare variants explain approximately 75% of T2D heritability; an intermediate model in which both common and lower-frequency variants contribute substantially; and a 'neutral' model in which common variants explain about 75% of T2D heritability.The predictions of the first two models differ markedly from the empirical data with respect to the numbers of low-frequency and rare risk variants that are associated with T2D.Specifically, these two models predict a larger number and greater effect size of low-frequency variants should be found in our whole-genome sequencing study as compared to those observed in the empirical data.By contrast, the empirical data are consistent with predictions under the 'neutral' commonvariant model.\t\nThere is compelling evidence that the individual risk of type 2 diabetes (T2D) is strongly influenced by genetic factors 1 .Progress in characterizing the specific T2D-risk alleles responsible has been catalysed by the ability to perform genome-wide association studies (GWAS).Over the past decade, successive waves of T2D GWAS-featuring ever larger samples, progressively denser genotyping arrays supplemented by imputation against more complete reference panels, and richer ethnic diversity-have delivered more than 80 robust association signals 2-8 .However, in these studies, the alleles interrogated for association were predominantly common (minor allele frequency (MAF) >5%), and with limited exceptions 7,9 , the variants driving known association signals were also common, with individually modest impacts on T2D risk [2][3][4][5][6][7][8]10 . Varation at known loci explains only a minority of observed T2D heritability 2,3,11 .Residual genetic variance is partly explained by a long tail of common variant signals of lesser effect 2 .However, the contribution to T2D risk that is attributable to lower-frequency variants remains a matter of considerable debate, not least because of the relevance of disease architecture to clinical application 11 .Next-generation sequencing enables direct evaluation of the role of lower-frequency variants to disease risk 7,12,13 .This paper describes the efforts of the coordinated, complementary strategies pursued by the Genetics of Type 2 Diabetes (GoT2D) and Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) consortia.GoT2D collected comprehensive genomewide sequence data from 2,657 T2D cases and controls; T2D-GENES focused on exome sequence variation, assembling data (after inclusion of GoT2D exomes) from a multiethnic sample of 12,940 individuals.Both consortia used genotype data to expand the sample size available for association testing for a subset of the variants exposed by sequencing.", + "\t\n\nRecent data (67) and ongoing investigations indicate that other types of common genetic variation (e.g., copy number or structural variants, such as deletions and duplications) may contribute little to the observed familial clustering of type 1 diabetes risk.However, rare loss-offunction structural gene variants could still make an important contribution to type 1 diabetes risk, through identification of which particular gene in a region of association could harbor a causal variant.With further advances in array and sequencing technologies, it is anticipated that such loss-of-function variants will be identified that influence susceptibility to type 1 diabetes (68).Inferences from genetic studies.Each newly identified association of a candidate locus with type 1 diabetes presents new challenges.Finding the causal genes and the causal variants, understanding how they affect disease pathophysiology, and dissecting their contribution to type 1 diabetes risk remain the major undertakings.For some genes, the effect sizes of risk alleles are such that larger collections of patients will be needed to identify the causal genes and limit the number of potential causal variants.Genotype-phenotype fine-mapping studies, however, can be performed with much smaller sample sizes while still achieving convincing statistical evidence (e.g., 42).Each confirmed gene, based on both statistical and functional evidence, provides a key piece of the etiology of type 1 diabetes, regardless of the magnitude of the odds ratio as a measure of the population association.\t\n\nCombinations of many alleles, possibly hundreds, combine with effects of environmental factors (probably numerous and ubiquitous) to establish the risk profile for type 1 diabetes.Each common variant in isolation has a subtle effect on disease risk, but each may alter a key function in the immune system and its interaction with pancreatic -cells.Recent discussion of \"missing heritability\" for complex human traits has considered the source of this variation and appropriate research strategies to detect these genetic effects (61).Studies in populations that are distinct from Europeans or European ancestry, such as populations of recent African ancestry or from Asian countries, are likely to narrow the large chromosomal regions of association identified in current studies and to increase the yield of rare variants (69).Future studies examining rare variants, structural variation, and polymorphisms not well imputed should be helpful in uncovering the remaining missing heritability in type 1 diabetes.", + "\t\n\nUntil recently, genome-wide linkage and candidate studies have been the main genetic epidemiological approaches to identifying the precise genetic variants underlying T2D heritability.These efforts confirmed only a few susceptibility variants, including those in PPARG, KCNJ11, WFS1, HNF1A, HNF1B, HNF4A, TCF7L2, and ADIPOQ (1,6,27,56,81,102).Recent genome-wide association studies (GWAS) have unveiled over 50 novel loci associated with T2D and more than 40 associated with T2D-related traits including fasting insulin, glucose, and proinsulin (16,48,57,82,87,97,105) (Table 1).Clinical investigations of some of the T2D loci, thus far, suggest that the genetic components of T2D risk act preferentially through -cell function (20).This pattern may only be a function of case diagnostic criteria, which weigh heavily on parameters reflecting advanced stages of the disease.This notion is supported by the incomplete overlap of single-nucleotide polymorphisms (SNPs) contributing to variation in quantitative traits with those associated with overt T2D (20).With the exception of TCF7L2, most variants contribute modestly to T2D risk and together explain only a small proportion of the familial clustering of T2D, suggesting that many more loci await discovery (10,12,97).", + "\tDiscussion\n\nIt has been hypothesized that rare genetic variants with moderate effects on disease risk could account for much of the missing heritability of complex traits. 6,9,10,62We have taken a first step toward testing this hypothesis for type 2 diabetes.We did not detect any significant associations between rare coding variants and common forms of diabetes.Our study was underpowered to detect weak genetic effects, but if much of the heritability of type 2 diabetes is explained by variants in a modest number of genes, we should have detected at least one associated locus at our Bonferroni significance threshold.Thus, our empirical results, combined with the statistical power simulations, suggest that when clustered in fewer than 20 genes, coding variants of moderate effect do not account for much of the missing heritability of a common polygenic disorder such as type 2 diabetes.\t\n\nOne common disease that has been subjected to intense genetic study is type 2 diabetes. 32The heritability of type 2 diabetes has been estimated to be around 30%. [33][34][35] Through GWASs, 63 loci have been reproducibly associated with type 2 diabetes. 36However, as for other complex traits, the associated SNPs can only account for <20% of the heritability estimated from family studies. 36ere, we seek to evaluate the role that rare coding variants play in the genetic basis of common forms of type 2 diabetes.We performed a deep whole-exome sequencing study of 2,000 Danish individuals.We applied both single-marker and gene-based association tests.Although we failed to detect any significant association after multiple test corrections, our simulations suggest that our results are informative about the genetic architecture of type 2 diabetes.In particular, our study suggests that when clustered in a small number of genes, rare coding variants of moderate to strong effect are unlikely to account for much of the missing heritability.Rather, if rare coding variants are an important factor in type 2 diabetes risk, they are most likely scattered across many genes.Our results have important implications for the design and interpretation of future medical resequencing studies.\t\n\nOur empirical and simulation results are compatible with a variety of different genetic architectures for type 2 diabetes.First, if rare coding variants are responsible for the majority of the heritability of the trait, the variants are most likely scattered across many (>20) different genes.Thus, genetic variants in no one gene can account for much of the heritability of the trait.Biologically, such a model would postulate that there are a large number of genes that can be mutated to cause type 2 diabetes in a given individual.Each individual would then carry a subset of genetic variants located in several of the many causal genes.Our finding that genes previously implicated in obesity risk through GWASs showed unusually low SKAT p values in our study supports a scenario in which low-frequency and rare variants in multiple genes could be responsible for risk of common metabolic diseases.It also suggests that genes carrying common variants associated with a trait could also carry additional low-frequency and rare coding variants that increase disease risk.\t\n\nAlthough our results argue that low-frequency and rare coding variants in a modest number of genes do not account for the majority of the heritability of common forms of type 2 diabetes, it is not clear how generalizable this result is to other complex traits.Several other exome sequencing studies have failed to detect any significant associations between low-frequency variants and schizophrenia, 77 epilepsy, 78 autism, 79 or autoimmune diseases. 80][83] Thus, the genetic architecture and the role of low-frequency and rare variants are likely to be trait dependent and will need to be addressed empirically.", + "\tType 2 Diabetes\n\nWhile a subset of genetic variants are linked to both type 1 and type 2 diabetes (42,43), the two diseases have a largely distinct genetic basis, which could be leveraged toward classification of diabetes (44).Genome-wide association studies have identified more than 130 genetic variants associated with type 2 diabetes, glucose levels, or insulin levels; however, these variants explain less than 15% of disease heritability (45)(46)(47).There are many possibilities for explaining the majority of type 2 diabetes heritability, including disease heterogeneity, gene-gene interactions, and epigenetics.Most type 2 variants are in noncoding genomic regions.Some variants, such as those in KCNQ1, show strong parent-of-origin effects (48).It is possible that children of mothers carrying KCNQ1 are born with a reduced functional b-cell mass and thereby are less able to increase their insulin secretion when exposed to insulin resistance (49).Another area of particular interest has been the search for rare variants protecting from type 2 diabetes, such as loss-of-function mutations in SLC30A8 (50), which could offer potential new drug targets for type 2 diabetes.", + "\t\n\nAt least three large exome and genome sequencing projects are ongoing to discover variants influencing type 2 diabetes and related traits.The Go-T2D study is performing lowcoverage whole-genome sequencing, deep exome sequencing, and 2.5 M SNP array genotyping of 1,425 type 2 diabetes cases and 1,425 controls from Northern Europe [41].The T2D-GENES Project 1 study is performing exome sequencing of 5,000 type 2 diabetes cases and 5,000 controls from five ancestral groups, and the T2D-GENES Project 2 study is performing deep whole-genome sequencing of >500 individuals from 20 large Mexican American pedigrees [42].These projects will detect many novel lowfrequency and rare variants that, when analyzed in sufficiently large numbers of subjects, can be expected to identify new insights into the genetic basis for disease.\tConclusions\n\nHow will sequencing genomes influence the health of people at risk for or affected with diabetes?The more complete understanding of the biological mechanisms underlying diabetes derived from these studies may lead to identification of novel drug targets.Individuals with variants in genes responsible for MODY or neonatal diabetes respond better to specific drugs [50,51], and sequencing may identify small numbers of individuals with combinations of rarer, more highly penetrant variants that respond better to specific therapeutic options.Although sets of known variants for type 2 diabetes do not add substantially to prediction of type 2 diabetes development in the overall population [52,53], identification of individuals at greater or lower genetic risk for diabetes within the overall population or in specific subgroups, such as younger onset or leaner individuals [54,55], could lead to better targeted health information and also allow identification of higher risk individuals leading to more efficient design of clinical trials for disease prevention.", + "\t\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 10 8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total 88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes.\t\n\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 10 8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total 88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes." + ], + [ + "\t\n\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop / mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression.\t\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop / mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression.", + "\t\n\nNumerous recent reports have demonstrated abnormal expression of various miRNAs in renal, vascular and retinal cells under diabetic conditions, and in vivo models of related diabetic complications [8,[87][88][89][90][91]. Notably, the functional relevance of these miRNAs has been highlighted by the fact they target key genes associated with the progression of, or protection against, these complications.In particular, the role of miRNAs in diabetic nephropathy has been extensively studied, including in the actions of TGF- related to fibrosis and other key renal outcomes in vitro and in vivo [8,[87][88][89][90].In diabetic retinopathy, several miRNAs have been reported to modulate the disease by targeting factors associated with angiogenesis, inflammation, and oxidant stress in RECs and in diabetic retinas [88,89].Reports have also implicated various miRNAs in the aberrant expression of genes associated with diabetic cardiomyopathy [88,91].In addition, effective in vivo targeting of miRNAs has now been demonstrated thanks to advances in nucleotide chemistry and the design of nuclease-resistant anti-miRNAs, which suggest future translational potential of miRNA-based therapies for human diabetic complications [8].Importantly, since miRNAs are stable in biological fluids such as urine and serum [8], they are being assessed in samples from various clinical cohorts as valuable biomarkers for the early detection of diabetic complications, for which there is a major unmet clinical need.It is clear that research in the field of miRNAs and diabetic complications will continue at a rapid pace.", + "\tIntroduction\n\nDiabetes-related complications represent one of the most important health problems worldwide with dire social and economic projections (Cooper, 2012).One of the most important medical concerns of the diabetes epidemic is diabetic nephropathy (DN).Diabetic nephropathy is regarded as a prototypical disease of gene and environmental interactions because not all diabetic subjects with traditional risk factors develop clinically evident nephropathy, indicating a role for individual susceptibility.The majority (>85%) of GWAS-identified single nucleotide polymorphisms (SNPs) are located in the non-coding regions of the genome and thus their functional implication lies in identifying the target genes, cell types, and the mode of dysregulation caused by these non-coding SNPs (Maurano et al., 2012).Recent studies indicate that complex trait-causing variants localize to cell-type-specific, functionally important gene regulatory regions where they can disrupt or create transcription factor binding sites to alter transcript levels only in disease-target cell types (Ko and Susztak, 2013;Susztak, 2014).Several elements of the immune system including cytokines and resident chemokines, macrophage recruitment, T lymphocytes, and immune complex deposition have recently been associated with DN (Navarro-Gonzlez and Mora-Fernndez, 2008;Gaballa and Farag, 2013).Since renal cells are also capable of synthesizing pro-inflammatory cytokines such as tumor necrotic factor-alpha (TNF-), interleukin-1 (IL-1) and interleukin-6 (IL-6), therefore, these cytokines acting in a paracrine or autocrine manner may induce significant effects leading to the development and progression of several renal disorders (Matoba et al., 2010;Pruijm et al., 2012;Shankar et al., 2011).The rationale of this study involved a concerted effort of genotyping, correlation and gene expression techniques involving three pro-inflammatory cytokine genes in the development and progression of DN as well as identification of high risk patients involving susceptibility or poor clinical outcome.", + "\t\n\nThese studies indicated limited detection of certain biological processes that are also relevant to the pathogenesis of diabetic nephropathy.These included genes pertinent to inflammation and angiogenesis.The limited detection was thought to be attributed to the apparent lack of sensitivity that was associated with the geneoriented averaging probe signals.This shortcoming was rectified by the use of ChipInspector, which is based on single probe analysis and de novo gene annotation that bypasses the probe set definition based on the out-of-date genomic data.In doing so, the single probe-based analysis yielded reduced background noise with enhanced sensitivity and fewer false positives.It also successfully identified the Wnt signaling pathway activated in diabetic nephropathy [63].\t\n\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3 isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies.\tNewly Identified Genes Relevant in the Progression of Diabetic Nephropathy\n\nThe cellular events such as increased flux of polyols and hexosamines; generation of AGEs; increased activity of PKC, transforming growth factor--Smad-MAPK (mitogen-activated protein kinase) pathway and GTP-binding proteins; G1 cell cycle arrest associated with altered expression of cyclin kinases and their inhibitors; and generation of ROS are responsible for a final outcome of increased synthesis and deposition of ECM.The ROS, whether mitochondrial or cell membrane-derived, are also responsible for the activation of the renin-angiotensin system that eventually contributes to glomerular hyperfiltration and subsequent renal fibrosis (fig. 1) [71].In addition to these macromolecules, newly identified genes, such as RSOR/MIOX, Tim44 and Rap1b, may also be an integral part of the hyperglycemia-induced cytosolic and mitochondrial processes that culminate in the development of diabetic nephropathy [48][49][50][51][52][53][54][55].\t\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3 isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies.", + "\t\n\nThe current study takes an important first step towards this goal by identifying specific sets of genes whose expression accurately classifies patient samples with regard to diabetic neuropathy progression and by analysing their interactions within known cellular pathways.Identifying common elements in these complex networks will yield novel insights into disease pathogenesis, provide new therapeutic targets and identify potential diabetic neuropathy biomarkers.The genes identified in the current study confirm data gathered from experimental models of diabetes and provide a comprehensive picture of the expression of multiple targets in a single human tissue sample.", + "\tM A N U S C R I P T A C C E P T E D\n\nIn relation to the regulation of gene expression, the role of microRNAs (miRNAs) in diabetic retinopathy has been gaining more emphasis.miRNAs are non-coding small RNAs which modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs.miRNAs can be detected in serum (free, associated with proteins or within membrane-bound particles) (Weiland et al., 2012), vitreous (Ragusa et al., 2013) and aqueous (Dunmire et al., 2013).As reviewed by Mastropasqua et al., miRNAs hold considerable interest for diabetic retinopathy since they can regulate important pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism (Mastropasqua et al., 2014).Although based on a small patient sample, it has been reported that three separate miRNAs (miR-21, miR-181c, and miR-1179) in serum of patients with diabetic retinopathy have potential to be used as biomarkers for early detection of disease (Li et al., 2014;Qing et al., 2014).While this is still a growing research area, miRNAs hold considerable clinical potential in the diabetic retinopathy field, both as possible drug-targets for regulation of dysfunctional cell responses and as diagnostic biomarkers.", + "\t\n\nAll these suggest towards important roles of various lncRNAs in complications associated with diabetes and, therefore, assume importance to be studied in detail.\tRoles of lncRNAs in diabetic complications\n\nApart from being involved in major metabolic tissues during diabetes as discussed above, lncRNAs are implicated in complications associated with diabetes.Diabetic retinopathy is one of the common complications in diabetic patients, which leads to impaired or loss of vision.Altered expression of lncRNAs, namely MALAT1 [82,83] and MEG3 [84], are reported to be associated with diabetic retinopathy.In STZ-induced diabetic rats, the expression of MALAT1 is elevated in the endothelial cells of the retina and knockdown of MALAT1 ameliorates retinopathy in STZ-induced rats [82].The lncRNA, MEG3, was also found to be downregulated in the retina of STZ-induced diabetic mice and its in vitro knockdown in retinal endothelial cells was found to regulate cell proliferation, viability, and migration [84].Hyperglycemia as in diabetes causes upregulation of ANRIL levels in endothelial cells [85,86], and this elevates the levels of the PRC2 subunit, EZH2 that consequently promotes the expression of VEGF, a key promoter of angiogenesis [85].Another major complication associated with diabetes is diabetic nephropathy, and this is considered a major cause of end-stage renal disease and disability in diabetic patients [87].Recent studies show that lncRNAs play important roles in the development of diabetic nephropathy and accumulation of extracellular matrix (ECM) proteins.There is higher expression of the lncRNA, PVT1, during diabetic nephropathy, and this increase leads to increased fibrosis due to accumulation of ECM proteins in renal cells [88]; downregulation of PVT1 reduces ECM accumulation [88].LncRNA PVT1 is also a host to miR-1207-5p and this miRNA is shown to regulate the expression of fibronectin1 (FN1), plasminogen activator inhibitor-1 (PAI1), and transforming growth factor beta 1 (TGF1) [89].In renal tube injury during diabetes, the lncRNA, MIAT, is under-expressed, and this negatively correlates with creatinine and BUN levels in the serum of these subjects.It has been shown to regulate cell viability of proximal convoluted renal tubules [90].In diabetic nephropathic mice, the lncRNA, MGC, is increased in renal mesangial cells.Interestingly, this lncRNA harbours a cluster of approximately 40 miRNAs, and is regulated by the ER stress marker C/EBP homologous protein (CHOP) [91].In CHOP -deficient mice, there is decreased expression of the lncRNA, MGC, and the clustered miRNAs, and these mice have shown an improvement in diabetic nephropathy [91].Diabetic nephropathy is also associated with increased levels of lincRNA, Gm4419, and this exerts its action by interacting with NF-.Knockdown of this lincRNA in renal mesangial cells lowers cellular proliferation and inhibits expression of NF- in hyperglycemic states [92].The lncRNA, TUG1, that is upregulated in diabetic nephropathy acts as sponge for miR-377 and regulates PPAR- expression which further modulates the expression of FN1, collagen type IV alpha 1 chain (COL4A1), PAI1, and TGF1 in renal mesangial cells [93].Diabetic cardiomyopathy is a critical end-stage complication associated with diabetes.Several such cardiovascular complications and myocardial dysfunction in diabetic patients lead to heart failure [94].Differential expression analysis in cardiac tissue from normal and diabetic rats shows that the lncRNA, MALAT1, is upregulated during cardiomyopathy and knockdown of this lncRNA improves left ventricular systolic function by reducing myocardial inflammation in diabetic rats [95,96].Decreased expression of the lncRNA, H19, is also reported during diabetes [68,70], and this often results in decreased expression of the exonic miRNA, miR-675 [97,98].mir-675 directly targets the voltage-dependent anion channel 1 (VDAC1) which is involved in mitochondria-mediated apoptosis in the cardiac tissue during diabetes.H19 overexpression in diabetic rats reduces oxidative stress, apoptosis, and inflammation, and improves ventricle function [98].LncRNAs NONRATT021972 and uc.48+ are reported to be associated with diabetic neuropathic pain [99,100], and inhibition of both have been shown to alleviate such neuropathic pain by activating the P2X3 receptor.Impaired wound closure is a notable complication associated with diabetes and a recent report shows decreased levels of the lncRNA, Lethe in such impaired dorsal wounds of diabetic mice.This was demonstrated to be associated with increased ROS production, possibly through regulation of NOX2 expression [101].", + "\t\n\nSkol et al. developed methods to study genomics and transcriptomics together to help discover genes that cause diabetic retinopathy.Genes involved in how cells respond to high blood sugar were first identified using cells grown in the lab.By comparing the activity of these genes in people with and without retinopathy the study identified genes associated with an increased risk of retinopathy in diabetes.In people with retinopathy, the activity of the folliculin gene (FLCN) increased more in response to high blood sugar.This was further verified with independent groups of people and using computer models to estimate the effect of different versions of the folliculin gene.", + "\t\n\nUnderstanding how these various pathways translate to cognitive dysfunction in humans with T2DM needs further investigation.", + "\t\nInsight into the molecular mechanisms that underlie the origin and progression of diabetic nephropathy remains limited in part because conventional research tools have restricted investigators to focus on single genes or isolated pathways.Microarray technologies provide opportunities for evaluating genetic factors and environmental effects at a genomic scale during the pathogenesis of diabetic nephropathy.Despite", + "\t\n\nDR. HARRINGTON: You mentioned Liu's data from China [abstract; Liu Z-H et al J Am Soc Nephrol 14:400A, 2003], which overwhelmed me.Apparently there are 182 genes whose expression is up-or down-regulated significantly in patients with diabetes.If I asked you to pick the \"top three\" genes other than the ACE polymorphisms, which three would you choose and why?DR.ADLER: Well, actually I didn't see all of their results nor did they report all 182.But I guess my favorite ones would be some that relate to the ROS pathway because this is an all-purpose pathway of cell injury fueled by a hyperglycemic environment; some that relate to podocyte structure to explain the development of proteinuria; and TGF-b, which is a master regulator of sclerosis and fibrosis.", + "\tIncRNAs and microRNAs\n\nFigure 1 | Emerging molecular mechanisms of diabetic nephropathy.Diabetic conditions induce the expression of growth factors such as transforming growth factor 1 and angiotensin II, cytokines and AGEs to promote inflammation, fibrosis and hypertrophy, which contribute to the progression of diabetic nephropathy.These factors stimulate various signal transduction mechanisms that activate downstream transcription factors.They can also affect DNA methylation and histone modifications, which result in increased chromatin accessibility to transcription factors near pathological genes in renal cells.Coordinated interactions between transcription factors and epigenetic mechanisms can increase the expression of not only coding RNAs, but also noncoding RNAs such as microRNAs and lncRNAs.Furthermore, microRNAs and lncRNAs can also increase the expression of pathological genes via post-transcriptional mechanisms.Notably, the induction of key coding genes and proteins, lncRNAs and microRNAs can also 'lock' open chromatin states to create persistent expression of genes, which could be one mechanism of metabolic memory.Abbreviations: AGE, advanced glycation end-product; lncRNA, long noncoding RNA.\tReview criteria\n\nA search for original published articles focusing on \"diabetic nephropathy\", \"signal transduction\", \"noncoding RNAs\", \"microRNAs\", \"long noncoding RNAs\", \"genetics\" and \"epigenetics\" was performed in MEDLINE and PubMed.All articles identified were English-language, full-text papers.We also searched the reference lists of identified articles for further relevant papers.\t\n\n| microRNAs relevant to the pathogenesis of diabetic nephropathy\tKey points\n\n Diabetic conditions induce inflammation, fibrosis and hypertrophy in renal cells through various cytokines and growth factors such as transforming growth factor 1, angiotensin II and platelet-derived growth factor The engagement of cytokines and growth factors with their receptors triggers signal transduction cascades that result in the activation of transcription factors to increase expression of inflammatory and fibrotic genes These signalling mechanisms affect epigenetic states-such as DNA methylation and chromatin histone modifications-to augment the expression of profibrotic and inflammatory genes, as well as noncoding RNAs Noncoding RNAs that are induced by diabetic conditions can also promote the expression of pathological genes via various post-transcriptional and post-translational mechanisms These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for 'metabolic memory' Key epigenetic regulators, microRNAs and long noncoding RNAs could serve as new therapeutic targets for diabetic nephropathy" + ], + [ + "\t\n\nGenetic risk scores (GRSs) that combine information from multiple genetic variants have been evaluated as a tool for the prediction of type 2 diabetes.Meigs et al. (23) found that a GRS with 18 variants was significantly associated with the risk of developing type 2 diabetes in the Framingham Heart Study (FHS) (odds ratio [OR] 1.12 per variant allele) and that persons in the highest out of three risk categories had an OR of 2.6 for developing type 2 diabetes compared with persons in the lowest risk category.However, this GRS did not improve the prediction of diabetes beyond traditional nongenetic risk factors (23), and the same was true for an updated GRS that included 65 variants (24).To put this into perspective, a prognostic marker with an OR of 3.0 that correctly identifies 80% of persons who will develop diabetes would incorrectly classify 60% of persons who will not develop diabetes (25); this degree of discrimination is not useful clinically (26).", + "\t\n\nDespite heterogeneity across populations in risk allele frequency or effect size in type 2 diabetes genes, the combined effects of multiple genetic variants using genetic scores based on the number of risk alleles appear to be similar across different ethnic groups.Typically, each risk allele increment is associated with a 10-20% increased risk of type 2 diabetes (41,42).These data suggest that the overall contribution of the identified genetic loci to type 2 diabetes is similar between Caucasians and other ethnic groups, and that these loci do not appear to explain ethnic differences in diabetes risk.In predicting future risk of diabetes, the clinical utility of these cumulative genetic risk scores appears to be limited in either high-or low-risk populations.", + "\t\n\nThe promise of genetic risk scoring for diabetes can be evaluated in the framework of three perspectives.First is the potential for robust prediction of diabetes risk.Second is the prospect of designing targeted preventive and therapeutic interventions (personalized medicine).Thirdly, increased knowledge could provide genomic clues to ethnic disparities in diabetes.Regarding robustness of prediction, results from the Framingham Offspring Study showed that clinical risk assessment (using age, sex, family history, BMI, fasting glucose level, systolic blood pressure, high-density lipoprotein cholesterol level, and triglyceride level) performed as well as cumulative genotype score at 18 loci in predicting incident type 2 diabetes during 28 years of follow-up of initially normoglycemic subjects (14).Also, cumulative genotype score at 34 loci did not add significantly to clinical risk factors in predicting progression from impaired glucose tolerance to type 2 diabetes among the multiethnic cohort enrolled in the Diabetes Prevention Program (15).One current limitation is the incomplete framework from which GRS is constructed.For example, the 17 SNPs studied in the present report (17) represent just about half of the .30diabe-toSNPs identified to date.Even the latter do not represent all possible risk loci, and important information on structural variants that might increase diabetes risk is often lacking.Thus, current experience renders the promise of robust genetic prediction and personalized diabetes intervention a distant hope.", + "\tDISCUSSION\n\nType 2 diabetes is a highly polygenic trait, and hundreds of loci associated with the disease have been identified, mostly via large GWAS meta-analyses conducted under additive genetic models (2,3).This prior work has produced useful results, identifying potential therapeutic targets and also enabling the creation of polygenic scores capable of quantifying one's genetic risk (34).A sizeable fraction of the heritability of type 2 diabetes, however, remains unexplained by loci identified using additive models.Recessive modeling offers a way to identify new associations, creating opportunities for discovery and improved genetic risk stratification.", + "\t\n\nTwo more recent population -based studies using a longitudinal design with prospectively investigated cohorts have examined the predictive value of a genotype score in addition to common risk factors for prediction of T2DM [194,195] .Meigs et al. [194] reported that a genotype score based on 18 risk alleles predicted new cases of diabetes in the community but provided only a slightly better prediction of risk than knowledge of common clinical risk factors alone [195] .A similar conclusion was drawn in the paper by Lyssenko et al. [196] , along with an improved value of genetic factors with an increasing duration of follow -up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured.They also showed that -cell function adjusted for insulin resistance (using the disposition index) was the strongest predictor of future diabetes, although subjects in the prediabetic stage presented with many features of insulin resistance.It is also noteworthy that many of the variants that were genotyped appear to infl uence -cell function.The addition of DNA data to the clinical model improved not only the discriminatory power, but also the reclassifi cation of the subjects into different risk strategies.Identifying subgroups of the population at substantially different risk of disease is important to target these subgroups of individuals with more effective preventative measures.As more genetic variants are now identifi ed, tests with better predictive performance should become available with a valuable addition to clinical practice.", + "\t\n\nRecent large-scale genome-wide association studies (GWAS) in diverse populations have identified hundreds of genetic loci associated with T2D [7][8][9].Polygenic risk scores (PRS), which aggregate the genetic risk of individual alleles across the genome, are thus promising to predict future T2D occurrence and improve early diagnosis, intervention, and prevention of T2D [10][11][12][13][14][15].However, to date, T2D PRS were most widely developed and validated in individuals of European descent.Given that the predictive performance of PRS often attenuates in non-European populations [16], and communities of color are experiencing continuing increased rates of T2D [2][3][4][5], it is critically important to assess and optimize the transferability of T2D PRS in diverse populations before they can be deployed in clinical settings.\t\n\nRecent studies have demonstrated in European individuals that T2D PRS can provide predictive power for incident T2D above and beyond established risk factors such as age, body mass index (BMI), smoking, physical activity levels, and history of high glucose and hypertension and can identify high-risk individuals and stratify lifetime risk trajectories of T2D patients [42,43], suggesting potential for clinical translation.However, most existing T2D scores were developed and validated in individuals of European descent.As the interest in the clinical implementation of PRS for common diseases like T2D continues to grow, a major challenge is the uncertainty about how best to combine multi-ethnic GWAS and estimate polygenic risk in diverse populations.\t\n\nBackground: Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color.Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D.However, to date, T2D PRS have been most widely developed and validated in individuals of European descent.Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.\t\nBackground: Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color.Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D.However, to date, T2D PRS have been most widely developed and validated in individuals of European descent.Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations. Methods:We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls).We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts. Results:The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined.The top 2% of the PRS distribution can identify individuals with an approximately 2.5-4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives.The post hoc ancestry adjustment", + "\t\n\nThe currently known risk variants have rather modest effect sizes; the presence of each risk variant or allele is only associated with increases in diabetes risk of between 5% and 40% (ORs 1.05-1.4).Therefore, these loci do not explain more than 10-15% of the estimated genetic heritability of type 2 diabetes [44,49].This estimate is in line with the observation that known risk variants explain only a small fraction of family history-associated diabetes risk [50].Combinations of up to 40 SNPs resulted in AROCs of 0.55-0.63,which is substantially lower than those achieved by age, sex and BMI alone.In some studies, the addition of genotype information to models based on established anthropometric and clinical It should be noted that the effect of genetic markers on risk prediction may be more pronounced in younger individuals, in leaner persons and in studies with long follow-up periods [53,54], but few studies on young populations, in which the assessment of future genetic risk may be most relevant, are currently available [55].The initial age of individuals is closely related to the time horizon for any model to predict type 2 diabetes.Several prospective studies have applied genetic risk scores for follow-up times of approximately 10 years.This time period corresponds to that in tools such as the Framingham Risk Score, which estimates an individual's 10-year risk for incident cardiovascular disease.It has been proposed that genetic risk scores might be more helpful in longer term prediction because, in contrast to variables used in clinical risk scores, genetic variants do not change over time [52,56].Eventually, the time horizon for risk models needs to correspond to the period before the onset of type 2 diabetes in which preventive efforts are most effective.", + "\t\n\nIn conclusion, the inclusion of common genetic variants that are associated with type 2 diabetes very slightly improved the prediction of future type 2 diabetes, as compared with the inclusion of clinical risk factors alone.Although this effect might be too small to allow for individual risk prediction, it could be useful in reducing the number of subjects who would need to be included in intervention studies aimed at the prevention of type 2 diabetes.Supported by grants from the Swedish Research Council (including Linn grant 31475113580), the Heart and Lung Foundation, the Swedish Diabetes Research Society, a Nordic Center of Excellence Grant in Disease Genetics, the Diabetes Program at the Lund University, the Finnish Diabetes Research Society, the Sigrid Juselius Foundation, the Phlsson Foundation, the Crafoord Foundation, the Folkhlsan Research Foundation, the Novo Nordisk Foundation, the European Network of Genomic and Genetic Epidemiology, the Wallenberg Foundation, and the European Foundation for the Study of Diabetes.", + "\t\n\nIdentification of individuals at increased genetic risk for T2D may enhance screening strategies and allow for targeted prevention.Previous attempts to deploy genetic data for disease prediction have shown limited utility 44,45 .We used a revised BMI-unadjusted meta-analysis, generated from all samples other than the UK Biobank samples, to develop genome-wide polygenic risk scores (PRSs) 46 , which we then applied to predict T2D status in the 18,197 cases and 423,697 controls from the UK Biobank (Europeans only; Methods) 46 .Maximal discrimination (area-under-the-curve C statistic of 66%, equivalent to that derived from BMI, age, and sex in the same sample) was obtained from a PRS of 136,795 variants (r 2 > 0.6, P < 0.076; Supplementary Fig. 10).Individuals in the top 2.5% of the PRS distribution were at 3.4-fold-increased risk (prevalence = 11.2%)compared with the median (prevalence = 3.3%), and at 9.4-fold-increased risk compared with the bottom 2.5% (prevalence = 1.2%).Low T2D prevalence in the UK Biobank reflected the age distribution of the cohort and preferential ascertainment of healthy individuals; however, similar prevalence ratios were observed in the subset of individuals > 55 years of age at recruitment (14.2% versus 1.6%).If applied to the general UK population, an equivalent performance would equate to lifetime T2D risks of ~59.7% and ~6.7% for individuals from those extremes, on the basis of current UK general-population prevalence rates for individuals > 55 years of age 47 .", + "\t\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized.\t\n\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized.\t\n\n During the last decade, there have been major advances in our understanding of the genetic basis of the most common subtypes of type 1 (T1D) and type 2 diabetes (T2D), with .500robust associations identified Although individual variants typically have only a modest effect on risk, when combined into a polygenic score, they offer increasing power to capture information on individual patterns of disease predisposition with the potential to influence clinical management\tSummary and Further Discussion\n\nAfter many years of frustration at the slow progress that had been made in the translation of recent discoveries in human genetics-notably the many risk variants for common, multifactorial forms of diabetes identified through GWAS and sequencing-there is now growing optimism that the use of polygenic scores will offer substantial clinical benefit and contribute to efforts to forestall the growing morbidity and mortality associated with these conditions.Some early clinical applications have emerged, mostly related to positive identification of those who have developed, or are at the highest imminent risk of developing, TD (, -).\tPolygenic Scores in Action\n\nPredicting T2D onset The slow onset of TD, coupled to evidence that the damaging consequences often predate the clinical diagnosis by some years (), emphasizes the clinical value of early diagnosis.The capacity for drugs and lifestyle interventions to lead to substantial reductions in the progression to diabetes (, ) motivates efforts to identify those at the greatest future risk of developing TD.As discussed above, genetic predictors have the particular advantage of offering predictive information that is stable throughout life.\t\n\nIn this review, however, we focus on a different route from human genetics to translation, one that derives estimates of an individual's predisposition to diabetes and its subtypes (in the form of polygenic scores) from the patterns of individual geneticvariation at sites known to influence diabetes predisposition.\t\n\n The generation of polygenic scores based on overall T2D predisposition can identify individuals with a high future risk of diabetes who may benefit from targeted interventions", + "\t\n\nThe discriminatory capacity of genetic variants for T2D risk prediction and patient stratification has been assessed in longitudinal studies by examining whether inclusion of genetic risk scores (GRS) in predictive models increases the area under the receiver-operating-characteristic curve compared to predictive models including only clinical parameters.Early studies suggested that inclusion of GRS provided little improvement in T2D risk prediction compared to clinical risk factors and family history alone (Lyssenko et al. 2008;Meigs et al. 2008;Balkau et al. 2008;Talmud et al. 2010;de Miguel-Yanes et al. 2011).More recent studies, incorporating increasing numbers of T2D risk variants into the GRS, have also had mixed results (Hivert et al. 2011;Muhlenbruch et al. 2013;Vaxillaire et al. 2014).For example, while a recent study incorporating 43 T2D associated variants showed little improvement in T2D prediction, inclusion of the GRS in predictive models improved the receiver-operating-characteristic curve for subgroups of subjects at increased risk of T2D, including obese subjects, older participants, and those with a family history of diabetes (Muhlenbruch et al. 2013).Similarly, Hivert et al. have shown that a GRS with 34 variants was significantly associated with increased risk of progression to T2D in high-risk individuals, as well as a reduced effect of lifestyle interventions on genetic risk (Hivert et al. 2011)." + ], + [ + "\tA measure of -cell exocytosis based on electrical current. the scalability of such studies.Moreover, a genome-wide CRISPR loss-of-function screen performed in 2019 identified 373 potential regulators of insulin production in the mouse insulinoma-derived Min6 -cell line 178 .Extending genome-wide screens to human -cell models and increasing the diversity of cellular read-outs will provide orthogonal data sets for integration with existing genetic and genomic resources, in order to elucidate downstream biology.As the current protocols for hiPSC differentiation are expensive, are time-consuming and have variability in differentiation efficiency, continued advancements in differentiation protocols will enable similar approaches in these cell models.", + "\t\nRecent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell.MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis.We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility.We used Illumina small RNA sequencing to profile the miRNA fraction in three preparations each of primary human islets and of enriched beta-cells generated by fluorescenceactivated cell sorting.In total, 366 miRNAs were found to be expressed (i.e..100cumulative reads) in islets and 346 in betacells; of the total of 384 unique miRNAs, 328 were shared.A comparison of the islet-cell miRNA profile with those of 15 other human tissues identified 40 miRNAs predominantly expressed (i.e..50% of all reads seen across the tissues) in islets.Several highly-expressed islet miRNAs, such as miR-375, have established roles in the regulation of islet function, but others (e.g.miR-27b-3p, miR-192-5p) have not previously been described in the context of islet biology.As a first step towards exploring the role of islet-expressed miRNAs and their predicted mRNA targets in T2D pathogenesis, we looked at published T2D association signals across these sites.We found evidence that predicted mRNA targets of islet-expressed miRNAs were globally enriched for signals of T2D association (p-values ,0.01, q-values ,0.1).At six loci with genome-wide evidence for T2D association (AP3S2, KCNK16, NOTCH2, SCL30A8, VPS26A, and WFS1) predicted mRNA target sites for islet-expressed miRNAs overlapped potentially causal variants.In conclusion, we have described the miRNA profile of human islets and beta-cells and provide evidence linking islet miRNAs to T2D pathogenesis.\t\n\nRecent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell.MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis.We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility.We used Illumina small RNA sequencing to profile the miRNA fraction in three preparations each of primary human islets and of enriched beta-cells generated by fluorescenceactivated cell sorting.In total, 366 miRNAs were found to be expressed (i.e..100cumulative reads) in islets and 346 in betacells; of the total of 384 unique miRNAs, 328 were shared.A comparison of the islet-cell miRNA profile with those of 15 other human tissues identified 40 miRNAs predominantly expressed (i.e..50% of all reads seen across the tissues) in islets.Several highly-expressed islet miRNAs, such as miR-375, have established roles in the regulation of islet function, but others (e.g.miR-27b-3p, miR-192-5p) have not previously been described in the context of islet biology.As a first step towards exploring the role of islet-expressed miRNAs and their predicted mRNA targets in T2D pathogenesis, we looked at published T2D association signals across these sites.We found evidence that predicted mRNA targets of islet-expressed miRNAs were globally enriched for signals of T2D association (p-values ,0.01, q-values ,0.1).At six loci with genome-wide evidence for T2D association (AP3S2, KCNK16, NOTCH2, SCL30A8, VPS26A, and WFS1) predicted mRNA target sites for islet-expressed miRNAs overlapped potentially causal variants.In conclusion, we have described the miRNA profile of human islets and beta-cells and provide evidence linking islet miRNAs to T2D pathogenesis.\tDiscussion\n\nUsing next-generation sequencing, we have established the first catalog of miRNAs in human pancreatic islets and beta-cells, and explored the overlap between these miRNAs and T2D genetic susceptibility.Our catalog not only serves as a valuable resource for those interested in the roles of specific miRNAs in normal islet physiology and beta-cell function, it also provides a reference for the study of miRNA mediated abnormalities in islets from type 2 diabetic donors.The abundance of miR-375 in the miRNA profile provides valuable support for a critical role in human pancreatic beta-cells, mirroring the well-established role in rodent islet biology.miR-375 null mice are hyperglycaemic and exhibit reduced beta-cell mass [40].In a clonal rodent beta-cell line (MIN6), knockdown or over-expression of this miRNA influences glucose-stimulated insulin secretion [7].Furthermore, knockdown of miR-375 in obese ob/ ob mice results in a more profound effect on glycaemia leading to a severe diabetic phenotype in these mice [40].Our study establishes that miR-375 is also abundantly expressed in human islets and warrants further studies to define the contribution of miR-375 to the pathogenesis of T2D.", + "\t\n\nOne strategy to study these monogenic syndromes would be to derive hiPSCs from these patients, differentiate them into pancreatic progenitors and then transplant these progenitors into immunocompromised (SCID-Beige or NSG) mice for in vivo maturation (Figure 2).This methodology has been recently used to successfully model MODY2, demonstrating that beta cells derived from hiPSCs with GCK mutation are indeed less sensitive to glucose levels [7].Endoplasmic reticulum (ER) stress-related diabetes in patients with Wolfram syndrome has also been modeled using hiPSC-derived beta cells, demonstrating that WFS1 protein maintains ER function in beta cells by acting upstream of the unfolded protein response (UPR) pathways [8].phenotypes occurring in humans.Likewise, the stepwise analysis of human pancreatic development with this strategy would likely provide mechanistic insights into the ability of a single gene mutation (PDX1, PTF1A, HNF1B, GATA6 and GATA4) to promote pancreatic agenesis/ atrophy.Further, studying mutations in KCNJ11 and ABCC8 using hiPSC-derived beta cells may elucidate the mechanistic differences between permanent and transient neonatal diabetes [64].Overall, insulin production and secretion could be compared between diseased and gene-corrected pancreatic cells to understand the underlying cause of each type of monogenic diabetes (Figure 2).", + "\tPRECISE CELLULAR GENOMICS\n\nElucidating the molecular mechanisms that lead to beta cell dysfunction and T2D pathogenesis has been a major focus of diabetes research for decades.However, advances in single cell genomic profiling techniques have led to greater understanding of non-beta cell type transcriptional regulation and suggest that they may play important roles in hallmark features of beta cell insufficiency and failure linked to T2D genetic risk and pathophysiology.Single cell transcriptome analysis of human islet cells indicate that multiple monogenic diabetes genes are highly expressed in beta cells (e.g., PDX1, PAX4, INS, HNF1A, and GCK) [27].However, other non-beta cell types express genes mutated in monogenic diabetes (such as PAX6 and RFX6), congenital hyperinsulinemia (HADH, UCP2) and those implicated as T2D GWAS target/effector genes [28].Recent study of type 1 diabetic (T1D) human islets has provided surprising insights into alpha cell biology.In T1D islets, the alpha cell proportions remain relatively unchanged despite abnormal glucagon secretion [29].This dysregulated glucagon secretion is instead accompanied by decreased expression of important islet transcription factors including ARX, MAFB, and RFX6 and increased expression of stress response factors such as ATF4, ERN1, and HSPA5 [29] suggesting that changes in alpha cell identity may ultimately lead to their dysfunction.Analysis of normal and T2D islet single cells with simultaneous RNA-seq and patch clamping (patch-seq) also revealed subpopulations of alpha cells with varying enrichment for ER stress response genes (e.g., DDIT3, XBP1, PPP1R15A) [30].Interestingly, this transcriptomic heterogeneity was consistent in normal and T2D islets and associated with variability in alpha cell electrophysiological measures; ER stressed alpha cells had lower cellular size and Na peak current.Prior single cell transcriptomic analyses have also noted subpopulations of ER-stressed beta cells [31,32] which implicates the dysfunction of both alpha and beta cells in diabetes pathogenesis.Similarly, the integrity of beta and alpha cell functions seem to be dependent on each other, as under hypoglycemic conditions, T2D islets show reduced insulin, C-peptide, and glucagon secretion [33].Additionally, during a glycemic clamp experiment, an increase in glucagon secretion was positively correlated with beta cell function suggesting that signaling between the two islet cell types is crucial for maintaining glucose homeostasis.Studies of delta cells in Sst-Cre transgenic mouse models [34e36] reveal that timely regulation of insulin secretion is controlled by various delta-cell specific pathways.Induction of the ghrelin receptor (Ghsr) in delta cells was correlated with enhanced somatostatin release and ultimately reduced insulin and glucagon secretion [35,36].Furthermore, the peptide hormone Ucn3 was shown to be co-released with insulin from beta cells to activate type 2 corticotropin-releasing hormone receptor (Crhr2) on delta cells in an alternate pathway that promotes somatostatin release and negatively regulates insulin levels [34].Delta cells are also notably enriched for G protein-coupled receptors (e.g., GLP1R, GIPR, GPR120) which exert careful control over metabolism [37].These receptors are also common therapeutic targets of T2D, suggesting that treatment and management of the disease should not neglect delta cell (dys)function and/or survival.Efforts to characterize the epigenomes of each islet cell type are emerging and revealing new insights of cellular fate and differentiation.Two groups have performed open chromatin profiling of purified beta and alpha cell fractions [10,12] and identified between 1850 and 3999 beta and 5316-27,000 alpha-specific peaks.These cell-specific regions were enriched for transcription factor motifs implicated in cell development and were enriched for diabetes-associated SNPs.Arda and colleagues also suggest that the beta cell epigenome is plastic and capable of being derived from other endocrine and exocrine precursor cells.Discrepancies in the numbers of cell-specific peaks determined by both groups are likely due to the cell surface markers used to enrich for each.CD26/DPP4, used by Arda et al., is a strong positive selector for alpha cells, which then enables negative selection for beta and other minor cell populations.However, this method of enrichment for beta cells will not remove contaminating delta and PP/gamma cells.Continued development of new tools and markers for islet cell enrichment, such as NTPDase3 [38] should continue to help us to understand changes elicited by genetic and environmental factors in each distinct cell type.Iterative proteomic screens in human islets are also proving useful for identifying putative cell-specific surface markers for isolation [39], wherein beta and delta cell populations were obtained by co-enrichment for CD9 and CD56.Challenges currently remain to exclusively enrich for the minor islet cell types (delta, gamma/PP), thus strategies that negatively select for these cells may be needed.Study of the rarer gamma/PP cells, which constitute roughly <1e5% of the total islet volume, remain limited due to the lack of known cell-surface markers for enrichment and purification (Figure 2).Whole islet analyses are unable to capture cell type-specific changes and therefore preclude analysis of their potential roles in T2D genetics and pathophysiology.Given the clear and extensive genotype effects on cis-RE usage [13,15] and gene expression [11,16,17] in islets, more extensive analysis of sorted cell types from multiple individuals is warranted to define a representative set of islet cell-specific REs and distinguish condition-specific from genotype-driven effects on their use and activity.\t\n\nunderstand each cell type's genomic architecture and better characterize their roles in islet resilience and failure.Experimental manipulation of the regulatory elements and/or the target genes identified by (epi)genomic approaches described above and modeling the putative pathways and processes they implicate in human islet cell lines (e.g., EndoC-bH1-H3) is essential to progress from correlation to causation.Similarly, transitioning from \"the\" mouse (C57BL/6) to multiple mouse models for insights into the effects of naturally occurring genetic variation on islet function and physiology [61] and for manipulation of key genomic elements should also help characterize the dynamic range of islet behavior and response.T2D is a heterogeneous, complex, and progressive disorder, as multiple subtypes have been identified and associated with different genetic risk and clinical outcome profiles.Future islet genomics studies that focus on identifying the distinct subgroups of individuals with distinct genes/pathways that are disrupted and/or contributing to islet (dys)function at basal and/or responsive states are needed.Furthermore, priority should be given to profiling more islets from pre-diabetic and T2D individuals to characterize the transition between basal to stressed to T2D state and determine if there are intermediate signatures for islet failure and T2D onset.Together, this multi-pronged approach toward studying T2D genetics and islet pathophysiology will help identify additional targets and opportunities for intervention that can be exploited for more precise and effective preventative, treatment, and management options for T2D.\t\n\nFigure2: Moving towards a more precise understanding of islet cellular genomics and responses.Proper elucidation of islet (dys)function and its association with T2D pathogenesis is confounded by individual genetic variation as well as islet cellular heterogeneity.To obtain a better understanding of both, future studies must prioritize strategies to obtain purified islet cell type populations (e.g., beta, alpha, delta, gamma/PP) via sorting with specific cell surface markers.Characterization of each cell type-specific genomic profile at baseline, stimulated, and diseased conditions will provide clearer understanding of key cellular and molecular processes that are altered and important in T2D development.Additionally, by sampling islets from multiple individuals and leveraging genotypes, it will be possible to identify cis-regulatory elements and genes that are influenced by genetics rather than disease state.SNP single nucleotide polymorphism; QTL quantitative trait locus; ER endoplasmic reticulum.\t\n\nFigure3: Challenges with identifying gene expression alterations in type 2 diabetes.Gene expression measurements from RNA-seq data typically represent only a snapshot of tissues' or cell types' transcriptome at a given point in time.In recent comparative analyses of islet intact and single cell transcriptomes from T2D and ND individuals, relatively few genes are significantly altered despite the clear phenotypic differences between them.This may suggest that the mechanisms that precede islet failure and T2D pathogenesis are post-transcriptional and cannot be detected in conventional RNA-seq analyses.However, it is also possible that the putative paths of these genes' alterations over the course of islet physiological decline and T2D development are simply being missed.Genes that are important for islet function and resilience (e.g., Gene A) and those whose expression directly induces or is the consequence of islet failure (e.g., Gene C) may be detected in a comparative analysis between islets at healthy and decompensated states.However, response genes that are temporarily induced by islet stress (e.g., Gene B) would not be detected in this comparison.", + "\tModels of beta cell function\n\nThe beta cell plays a central role in the development of both type 1 and type 2 diabetes as well as playing a key role in less common classifications of diabetes such as maturity onset diabetes of the young (MODY), gestational diabetes, neonatal diabetes and other beta cell syndromes such as hyperinsulinism.Therefore, models of beta cell function are highly relevant in understanding pathways that can lead to the inability of beta cells to secrete appropriate amounts of insulin.Such models are often genetically manipulated, such as mutations of Kir6.2 to study KATP channel function (Girard et al., 2009) or mutations in glucose kinase to understand the function of the glucose sensor in beta cells (Fenner et al., 2011).A role for serotonin in the expansion of islets in pregnancy has recently been elucidated by studying the islets of mice lacking the serotonin receptor Htr2b (Kim et al., 2010).Studies such as these can increase our knowledge of beta cell function and its role in a variety of conditions.However, it should be pointed out that the same mutation in humans can lead to different symptoms in mice as recently shown by Hugill et al., where a mutation in Kcnj11 (encoding a subunit of the KATP channel) caused hypersecretion of insulin and hypoglycaemia in their patient, but glucose intolerance and reduced insulin secretion in mice (Hugill et al., 2010).However, this may prove useful in understanding the transition from hyperinsulinism of infancy (HI) to diabetes in some patients (Hugill et al., 2010).", + "\t\nHuman genetic studies have emphasised the dominant contribution of pancreatic islet dysfunction to development of Type 2 Diabetes (T2D).However, limited annotation of the islet epigenome has constrained efforts to define the molecular mechanisms mediating the, largely regulatory, signals revealed by Genome-Wide Association Studies (GWAS).We characterised patterns of chromatin accessibility (ATAC-seq, n = 17) and DNA methylation (whole-genome bisulphite sequencing, n = 10) in human islets, generating high-resolution chromatin state maps through integration with established ChIP-seq marks.We found enrichment of GWAS signals for T2D and fasting glucose was concentrated in subsets of islet enhancers characterised by open chromatin and hypomethylation, with the former annotation predominant.At several loci (including CDC123, ADCY5, KLHDC5) the combination of fine-mapping genetic data and chromatin state enrichment maps, supplemented by allelic imbalance in chromatin accessibility pinpointed likely causal variants.The combination of increasingly-precise genetic and islet epigenomic information accelerates definition of causal mechanisms implicated in T2D pathogenesis.\t\n\nHuman genetic studies have emphasised the dominant contribution of pancreatic islet dysfunction to development of Type 2 Diabetes (T2D).However, limited annotation of the islet epigenome has constrained efforts to define the molecular mechanisms mediating the, largely regulatory, signals revealed by Genome-Wide Association Studies (GWAS).We characterised patterns of chromatin accessibility (ATAC-seq, n = 17) and DNA methylation (whole-genome bisulphite sequencing, n = 10) in human islets, generating high-resolution chromatin state maps through integration with established ChIP-seq marks.We found enrichment of GWAS signals for T2D and fasting glucose was concentrated in subsets of islet enhancers characterised by open chromatin and hypomethylation, with the former annotation predominant.At several loci (including CDC123, ADCY5, KLHDC5) the combination of fine-mapping genetic data and chromatin state enrichment maps, supplemented by allelic imbalance in chromatin accessibility pinpointed likely causal variants.The combination of increasingly-precise genetic and islet epigenomic information accelerates definition of causal mechanisms implicated in T2D pathogenesis.", + "\t\n\nA number of mechanisms could contribute to the reduced insulin secretion in vivo that has been associated with several T2D susceptibility variants.Dissection of the underlying cellular pathology requires 1) access to relevant human tissues from nonrisk and risk genotype carriers, which facilitates the correct translation of association signals compared with studying genetically modified animals, and 2) characterization of the effect of genotype on detailed cellular phenotypes.There are fundamental electrophysiological and secretory differences between human and rodent b-cells, making the study of human islets essential to investigate the influence of T2D susceptibility variants on b-cell function.The biophysical and ultrastructural examination of human b-cells in the current study identified four T2D variants that were associated with reduced exocytosis and enabled characterization of the mechanisms for the exocytotic impairment.The results shed new light on the pathophysiology linked with these risk variants, near TCF7L2, ADRA2A, KCNJ11, and KCNQ1, and demonstrate that defective b-cell exocytosis can be an important pathogenic mechanism in genetic subgroups of T2D.The data suggest that there may be considerable heterogeneity in the cellular pathways that lead to reduced insulin secretion, which may explain why the reduction of exocytosis is evident only in genetic subgroups and not in the entire T2D cohort.Stratification based on genetic variants may therefore be useful to better resolve the disease mechanisms.Similar approaches may therefore be valuable to study the T2D susceptibility variants that were not associated with defective b-cell exocytosis in the current study (Table 1) and may instead impair systemic insulin release through effects on b-cell mass and/or glucose sensing or indirectly via incretins and innervation.", + "\t\n\nNevertheless, for diseases such as diabetes and obesity, limited access to the tissues most obviously implicated in disease pathogenesis-the pancreatic b cell and hypothalamus, respectively-represents a serious obstacle to such studies.Advances in stem cell science offer the exciting prospect of overcoming this limitation through re-differentiation of patient-derived induced pluripotent stem (iPS) cells to generate authentic cellular models of key tissues.In parallel, ongoing large-scale sequencing studies are likely to reveal novel low frequency and rare risk alleles in coding sequence, some with larger effects than those encountered by existing GWAS.The expectation is that these will be inherently more amenable to experimental follow-up, accelerating the pace of functional discovery and delivering biological insights that will underpin the development of novel diagnostic and therapeutic options.", + "\t\n\nIt is worth mentioning that in [132], a meta-analysis study was conducted, where a collection of gene expression datasets of pancreatic beta-cells, conditioned in an environment resembling T1D induced apoptosis, such as exposure to proinflammatory cytokines, in order to identify relevant and differentially expressed genes.The specific genes were then characterized according to their function and prior literature-based information to build temporal regulatory networks.Moreover, biological experiments were carried out revealing that inhibition of two of the most relevant genes (RIPK2 and ELF3), previously unknown in T1D literature, have a certain impact on apoptosis.", + "\t\n\nNotably, single-cell transcriptome profiling has been utilized in the past few years to discern cellular heterogeneity within the islets of Langerhans (Fischer et al. 2019;Tritschler et al. 2019Tritschler et al. , 2017)), particularly for beta cells (Baron et al. 2016;Lawlor et al. 2017a;Segerstolpe et al. 2016;Teo et al. 2018;Xin et al. 2016).Segerstolpe et al. ( 2016) investigated cell-type specific gene expression in the pancreas of healthy and type 2 diabetic individuals and uncovered major gene expression differences (transcriptional signatures) between exocrine and endocrine cell types, including the less abundant cell types such as human delta, gamma and epsilon cells.Previously, these cells had been difficult to observe due to bulk characterization methods (Lawlor et al. 2017a), however, single-cell RNA sequencing has shed light on the novel roles for each rare cell type based on their activated signalling pathways and receptor proteins (Lawlor et al. 2017a;Segerstolpe et al. 2016).For example, insight into the transcriptome of the minority cell type, epsilon cells and its ghrelin-producing capability was provided (Segerstolpe et al. 2016), as well as the expression of the rare delta and gamma cell types that are prompted by hormonal cues from leptin, ghrelin and dopamine signalling pathways to facilitate metabolic signalling in the pancreas (Lawlor et al. 2017a).Further single-cell RNA investigations by Xin et al. (2016) showed a total of 245 genes to be affected by type 2 diabetes when compared to non-diabetic single-cell transcriptomes.Among the common transcript expression profiles found between the human islet cells, only 20 genes (for example, RBP4, DLK1, ADCYAP1, RGS16, SOX4, BMP5, TIMP2, TSPAN1, MAFB and TFF3) were specific to a certain cell type (Xin et al. 2016).Lastly, a few recent reviews have tracked the progress of genes linked to specific endocrine cell types in these studies (see Chiou et al. 2019;Tritschler et al. 2017), with some going as far as to re-analyse the single-cell transcriptome datasets using a machine learning approach (Ma and Zheng 2018).The in-depth analyses reported on oxidative stress being the perpetrator to enhance beta-cell dysfunction as a final result, together with the potential activation of pathways linked to beta-cell apoptosis that may be the resulting cause of an insulin gene expression deficit in type 2 diabetes (Ma and Zheng 2018).", + "\t\nThe inheritance of variants that lead to coding changes in, or the mis-expression of, genes critical to pancreatic beta cell function can lead to alterations in insulin secretion and increase the risk of both type 1 and type 2 diabetes.Recently developed clustered regularly interspaced short palindromic repeats (CRISPR/Cas9) gene editing tools provide a powerful means of understanding the impact of identified variants on cell function, growth, and survival and might ultimately provide a means, most likely after the transplantation of genetically \"corrected\" cells, of treating the disease.Here, we review some of the disease-associated genes and variants whose roles have been probed up to now.Next, we survey recent exciting developments in CRISPR/Cas9 technology and their possible exploitation for b cell functional genomics.Finally, we will provide a perspective as to how CRISPR/Cas9 technology may find clinical application in patients with diabetes.", + "\t\n\nOur primary intent was to employ an integrative genomics approach to identify mitogenic mechanisms with potential application for human beta cell expansion (Supplementary Fig. 1).This approach entails integrating whole-exome and RNAsequencing data into network analysis to computationally model insulinoma molecular events relative to normal adult and juvenile human beta cells.We reasoned that although some molecular events in insulinoma are likely relevant to the mechanisms of tumor formation, some may serve to uncover the genetic mechanisms that enforce beta cell quiescence, and are bypassed in such benign tumors.We further validated combinations of lead candidate genes derived from this approach as beta cell mitogenic mediators.Notably, we focused on insulinomas from subjects not known to be members of multiple endocrine neoplasia type 1 (MEN1) kindreds, as the MEN1 gene has been previously reported as one of the most frequently mutated genes in hereditary pancreatic neuroendocrine tumors (PNETs), although MEN1 mutations are uncommon in sporadic insulinomas [5][6][7] .Despite attempting to exclude MEN1 subjects, we nevertheless find widespread abnormalities in genes functionally related to MEN1, revealing a previously unsuspected unifying mechanism underlying insulinoma.", + "\t\n\nIn summary, we established an isogenic hESC platform to systematically evaluate the role of disease-associated loci in the survival and function of human pancreatic beta-like cells in vitro and in vivo.The platform can be used to study other disease-associated loci/variants with respect to beta-like cell function.It is worth noting that the glucose-responding cells derived using the current reported protocols are not equivalent to primary human beta cells.Ca 2+ flux assays suggested that approximately 30%-40% of the insulin-GFP + cells show increased cytosolic Ca 2+ concentrations in response to glucose stimulation (Figure S7Q), whereas robust glucose-induced signaling was observed in more than 70% of human beta cells based on the previous report (Rezania et al., 2014).The restricted functionality of pancreatic beta-like cells derived using current protocols might limit their application for evaluating subtle contributions of genes to glucose metabolism and Ca 2+ signaling.Thus, additional work is needed to further improve the protocol to derive mature pancreatic beta-like cells.In addition, the platform established here can also be applied to study the role of disease-associated loci/variants in other diabetes-related cell types, such as hepatocytes, adipocytes, muscles, and/or intestinal neuroendocrine cells.Finally, the system may be used as a highthroughput/content chemical screening platform to identify candidate drugs correcting allele-specific defects for precision therapy of metabolic diseases.\t\n\nWe built on recent work deriving glucose-responsive pancreatic beta-like cells from hESCs/iPSCs (Pagliuca et al., 2014;Rezania et al., 2014) and used isogenic hESC-derived glucose-responding cells to systematically examine the role of several GWAS-identified genes in pancreatic beta cell function and survival.Whereas the mutations do not affect the generation of insulin + cells, they impaired insulin secretion both in vitro and in vivo, coinciding with defective glucose homeostasis.CDKAL1 / insulin + cells also displayed hypersensitivity to glucolipotoxicity.A high-content chemical screen identified a candidate drug that rescued CDKAL1 / -specific defects by inhibiting the FOS/JUN pathway.These studies represent a proof of principle for the use of isogenic hESC-derived cells to define the precise role of genes associated with disease though GWASs in human pancreatic beta cells, as well as the leadcompound identification for pharmacological intervention of T2DM." + ], + [ + "\t\n\nAlthough these proof-of-concept studies provide exciting insights into possible epigenetic mechanisms that may underpin the developmental origins of obesity and metabolic disorders later in life, one has to bear in mind their limitations.The early studies in general investigated only a small sample, lacked independent replication, and the methylation changes detected through the hypothesis-free genome-wide approach often do not reach biological levels of significance.Additional considerations include the use of tissues that are not embryonic in origin (e.g.placental tissue), tissues that contain a mixture of different cell types (e.g.umbilical cord or cord blood) as well as tissue of mixed maternal or fetal origin (placenta again).Therefore, epigenetic changes in the tissues studied thus far may not represent the full spectrum, or the most relevant epigenetic changes associated with maternal hyperglycaemia and its metabolic consequences, given the difficulty of investigating relevant metabolic tissues such as the pancreatic islet, muscle, liver, adipose tissue and brain.It is expected that some of the changes present in accessible tissue such as cord blood may also be present in other tissues, though the relationship between epigenetic markers in different tissues remains to be clarified because epigenetic marks are likely to be tissue-and context-specific.Recent studies suggest there are some consistent changes in methylation that are observed in blood and other tissues such as brain, signifying that peripheral blood may be useful for identifying functionally relevant epigenetic pathways in disease-relevant tissues (Davies et al., 2012).Another important issue is the need for prospective studies to eliminate the effect of reverse causality.This has been more of a problem in epigenetic studies in other disciplines, but less so in the field of developmental origins of health and disease, where there are large numbers of well-characterized longitudinal birth cohorts with longterm follow-up and a variety of biological specimens collected.We recently conducted a genome-wide analysis of GDM methylation changes by comparing offspring of mothers with GDM or controls from our longitudinal follow-up study (Tam et al., 2008(Tam et al., , 2010)).We found several consistent differentially methylated regions between GDM-offspring and non-exposed offspring at 8 and 15 years, suggesting that, at least for some of these markers, once the epigenetic changes are set they may persist through adolescence and beyond (Luan et al., 2014).\t\n\nIn addition to changes following exposure to intra-uterine hyperglycaemia, epigenetic changes have also been noted in other experimental settings of hyperglycaemia.For example, increased DNA methylation has been described for the promoter region of the peroxisome proliferator-activated receptor-g (PPARg) coactivator-1a gene (PPARGC1A) in diabetic islets (Ling et al., 2008).Similar hypermethylation in the promoter region of the PPARGC1A gene has been noted in the skeletal muscle from diabetic patients, and correlated with mitochondrial content (Barr es et al., 2009).Epigenetic changes have also been suggested to be responsible for the \"legacy effect\" of reduced risk of vascular complications after a period of sustained tight glucose control, or \"metabolic memory\" of transient hyperglycaemia and increased risk of diabetic vascular injury (Pirola et al., 2010).Histone methylation variations have been noted in monocytes cultured in high glucose, as well as blood monocytes of diabetic patients (Miao et al., 2007).In a series of landmark experiments, it was shown that endothelial cells exposed to short-term hyperglycaemia had persistently increased expression of the NF-kB active subunit p65, and was associated with increased promoter H3K4me1 and occupancy by the histone monomethyltransferase SET7/9.In addition, transient hyperglycaemia was also associated with sustained reduction of H3K9 methylation on the NF-kB p65 promoter, as well as recruitment of lysine-specific demethylase (LSD1) (El-Osta et al., 2008;Brasacchio et al., 2009).LSD1 has also been found to regulate H3K4 methylation in vascular smooth muscle cells in hyperglycaemic conditions, and may mediate the vascular inflammation (Reddy et al., 2008).Other epigenetic mechanisms including microRNAs and long noncoding RNAs have also been implicated in the pathogenesis of diabetic complications (Kato et al., 2014).", + "\tEpigenetic Mechanisms in Diabetic Complications 22\n\nsupportive animal studies demonstrated that mice exposed to short-term hyperglycemia followed by glucose normalization displayed sustained increases in promoter H3K4me1 and p65 expression in aortic endothelial cells (35).It is likely that similar epigenetic changes also occur in cells such as retinal pericytes and endothelial cells, or renal mesangial cells, tubules and podoctyes that are involved in common diabetic complications, retinopathy and nephropathy.\t\n\nOverall, these results indicate that prior exposure to hyperglycemia and even periods of transient high glucose or metabolic control can lead to epigenetic changes in target cells altering chromatin structure and resulting in long lasting repercussions for gene expression levels associated with the pathology of diabetic micro-and macro-vascular complications (Figure 2).", + "\tSummary\n\nIncreasing evidence shows that, besides the well-described biochemical mechanisms, epigenetic mechanisms might also participate by fine-tuning gene expression to modulate the aetiology of diabetic complications.Persistence of epigenetic modifications triggered by diabetic stimuli could be one of the key mechanisms underlying metabolic memory.However, the involvement of many epigenetic factors and mechanisms involved in the regulation of the modifications by upstream signal transduction pathways remains unknown.However, this is a rapidly expanding and dynamic field and it is likely that other epigenetic factors related to diabetic complications will soon be uncovered.Epigenomics may also aid in determining the functional roles of complications-associated genetic variants.It would be worthwhile to assess whether lifestyle modifications such as exercise and healthy diets can reduce diabetic complications by altering epigenetic marks.A recent study showed the beneficial effects of exercise on epigenetic marks related to diabetes [106].Because epigenetic changes are potentially reversible in nature, combination therapies with epigenetic drugs (epidrugs) [38] and antagomirs (miRNA inhibitors) [8] could be considered to complement the current treatments for complications.However, there are also key challenges.Since epigenetic patterns are cell specific, data from heterogeneous tissue samples and biopsies could be difficult to interpret.Furthermore, apart from hyperglycaemia, other factors associated with diabetes, including insulin resistance, obesity, dyslipidaemia, environment, lifestyles and genetics, can work independently or co-operatively to also promote epigenetic changes in various affected target tissues.\tEpigenetics and the epigenome: rationale for study in diabetic complications\n\nEpigenetic control of gene regulation plays an important role in development, cell identity, stable inheritance of gene expression patterns in differentiated cells, genomic imprinting, X chromosome inactivation, stem cell plasticity, differential disease susceptibility between monozygotic twins, and cellular responses to environmental signals [34,35].", + "\t\nIn addition to genetic predisposition, environmental and lifestyle factors contribute to the pathogenesis of type 2 diabetes (T2D).Epigenetic changes may provide the link for translating environmental exposures into pathological mechanisms.In this study, we performed the first comprehensive DNA methylation profiling in pancreatic islets from T2D and non-diabetic donors.We uncovered 276 CpG loci affiliated to promoters of 254 genes displaying significant differential DNA methylation in diabetic islets.These methylation changes were not present in blood cells from T2D individuals nor were they experimentally induced in non-diabetic islets by exposure to high glucose.For a subgroup of the differentially methylated genes, concordant transcriptional changes were present.Functional annotation of the aberrantly methylated genes and RNAi experiments highlighted pathways implicated in b-cell survival and function; some are implicated in cellular dysfunction while others facilitate adaptation to stressors.Together, our findings offer new insights into the intricate mechanisms of T2D pathogenesis, underscore the important involvement of epigenetic dysregulation in diabetic islets and may advance our understanding of T2D aetiology.\t\n\nIn addition to genetic predisposition, environmental and lifestyle factors contribute to the pathogenesis of type 2 diabetes (T2D).Epigenetic changes may provide the link for translating environmental exposures into pathological mechanisms.In this study, we performed the first comprehensive DNA methylation profiling in pancreatic islets from T2D and non-diabetic donors.We uncovered 276 CpG loci affiliated to promoters of 254 genes displaying significant differential DNA methylation in diabetic islets.These methylation changes were not present in blood cells from T2D individuals nor were they experimentally induced in non-diabetic islets by exposure to high glucose.For a subgroup of the differentially methylated genes, concordant transcriptional changes were present.Functional annotation of the aberrantly methylated genes and RNAi experiments highlighted pathways implicated in b-cell survival and function; some are implicated in cellular dysfunction while others facilitate adaptation to stressors.Together, our findings offer new insights into the intricate mechanisms of T2D pathogenesis, underscore the important involvement of epigenetic dysregulation in diabetic islets and may advance our understanding of T2D aetiology.\t\n\nThe goal of the present work was to clarify the hitherto poorly understood connection between DNA methylation and T2D pathogenesis and to determine whether identified epigenetic changes translate into functional effects that impinge on pancreatic b-cell function.For this, we have explored DNA methylation landscapes in islets isolated from T2D patients and non-diabetic individuals.\t\n\nIn conclusion, we report the first comprehensive and detailed analysis of epigenetic changes in T2D, specifically an altered DNA methylation profile in the pancreatic islets of T2D patients with a major preponderance of hypomethylation in sequences outside CGIs.These aberrant methylation events affect over 250 genes, a subset of which is also differentially expressed.The dysregulation of these genes in T2D may notably be linked to b-cell functionality, cell death and adaptation to metabolic stress.Examination of two genes identified by methylation profiling, NIBAN and CHAC1, revealed their biological functions in distinct processes of the ER stress response.Furthermore, our data highlight genes belonging to biological processes whose involvement in T2D\t\n\nAn important question with regard to epigenetic changes is: are the observed DNA methylation changes reflected in gene activity?By comparing the obtained DNA methylation profiles with microarray gene expression data, we were able to determine that a high proportion of genes in whose promoter T2D-related differential DNA methylation occurs are actively transcribed in pancreatic islets.A comparison with expression data of islet cell types (Dorrell et al, 2011) showed that most of the differentially methylated genes are expressed in b-cells.This allowed us to conclude that T2Drelated aberrant DNA methylation partially happens in the promoters of active genes.One has to keep in mind though that the expression studies in islets as well as in the b-cells analysed non-diabetic material.We observed mostly DNA hypomethylation in diabetic islets, not infrequently accompanied by elevated gene expression.Therefore, it can be assumed that the T2D-related hypomethylation leads, in part, to the induction of formerly silent genes.", + "\t\n\nEmerging evidence suggests an epigenomic link to T2D development.Reversible epigenetic changes such as histone modifications and DNA methylation may occur during intrauterine development and are believed to have long-term effects on offspring health and survival, including manifestation of disease phenotypes such as obesity or diabetes later in life [59,60].Environmental and nutritional stimuli influence future science group Genetics, genomics & personalized medicine in Type 2 diabetes: a perspective on the Arab region Review [61].Epigenetic regulation of genes may be responsible for the observed difference in T2D risk and drug response between individuals [62,63].Epigenomics may not only shed light on the environmental (including diet and lifestyle) effect on T2D susceptibility in individuals but epigenetic markers may also help identify those at risk well before disease manifestation.Gene-expression analysis or transcriptomics is used for studying the expression profile of genes.A comparative analysis of expression states of genes between healthy and diseased cells can identify those implicated in disease pathology.The changes in expression of disease susceptibility genes can be monitored during different stages of a disease and help in disease prognosis.Similarly, a comparative expression profile for treated and untreated samples can help identify changes in gene-expression upon treatment with a particular drug.This makes geneexpression analysis an important tool for elucidating the role of genes in different biological states, for identifying potential targets for drug intervention and for biomarker selection to be used in disease diagnosis.In diabetes, gene-expression profiling has been used for establishing differential expression of inflammatory genes [64], for studying the effects of insulin treatment in skeletal muscle [65] and more recently for correlating insulin resistance and an altered lipid profile in peripheral blood [66].", + "\t\n\nWhether epigenetic changes pre-exist or are a consequence of T1D can only be established by long-term longitudinal studies of DNA methylation in subjects at risk for the disease.Since it will a priori remain almost impossible to investigate cells and mTEC in T1D patients, the question of tissue-specific methylation changes should have to be solved in animal models of T1D, like the NOD mouse.It is possible that the observed pattern of CpG methylation at the insulin locus may vary in other T1D and control populations as a reflect of gene-environment interactions proper to these populations.Until larger studies can be performed in such populations, the observed variations in DNA methylation should be considered restricted to the European people studied here.", + "\tISLET RESPONSES; MOVING BEYOND STEADY STATE MEASUREMENTS\n\nTo date, the overwhelming majority of studies including and assessing genetic variation have profiled the steady state patterns of epigenetic modifications and gene expression in islets or their constituent cell types.Others have compared how these steady state measures differ between T2D and non-diabetic (ND) individuals [13,16,40e44].Surprisingly, these studies, especially transcriptome analyses, have identified only modest alterations despite clear phenotypic differences in HbA1c and other metabolic traits in T2D vs. ND donors.This suggests that alterations in transcriptional regulation may not contribute to T2D pathogenesis, or that these (epi)genomic comparative studies are not effectively capturing the alterations associated with islet (patho) physiologic decline or T2D onset.Genomic assays such as RNA-seq provide only a snapshot of tissues' or cell types' transcriptomes at a given point in time.Genes that are important for islet function and resilience (e.g., Gene A) and genes whose expression induces islet failure (e.g., Gene C) would be detected in a comparative analysis between islets at healthy and T2D states (Figure 3).In contrast, genes that are temporarily induced by the initiation of islet stress or in the compensation or pre-diabetic stages (e.g., Gene B) before decline towards disease state would be missed.Furthermore, T2D is a complex disease with dynamic ranges of severity and secondary health complications across individuals.Thus, comparing single snapshots of gene expression in T2D individuals at different stages of islet health and disease progression may simply lead to obfuscation.Longitudinal studies of in vivo epigenetic and gene expression changes in islets of severe, early onset (db/db) or polygenic, late-onset (Tallyho, NZO) [45e47] diabetic mouse models may be the only practical solution to identify the temporal nature of these changes and identify the molecular features of islet dysfunction, compensation, and failure in T2D pathogenesis.Indeed, longitudinal analyses of aging islets in mice identified DNA methylation changes in key genomic regions associated with beta cell proliferation and metabolism [48].These findings suggest that changes in the islet (epi)genome and transcriptome may also be dynamic during the course of T2D development and progression.Alternatively, in vitro, it may be possible to subject human islets to diabetic-like conditions through the use of inflammatory cytokines and/ or oxidative and ER stress.Already, studies from a few groups have demonstrated clear differences in islet gene expression, including the modulation of putative T2D target genes, during stimulatory or stress responses, and certain epigenetic and gene expression features in islets are only revealed upon these in vitro or in vivo exposures, such as glucose-stimulated insulin secretion, palmitate, inflammatory cytokines or other response defects [49e53].Examining the transcriptomic and (epi)genomic changes of human islets under these various stressors over time may provide greater knowledge of the epigenetic and gene expression changes preceding islet stress, failure, and ultimately diabetes onset.", + "\t\n\nInteractions between environmental factors and genetic predisposition leading to epigenetic changes could provide a powerful risk association to diabetic complications, especially in relation to the metabolic memory phenomenon (Reddy et al., 2015).", + "\t\n\nRecent studies using vascular and inflammatory cells treated in vitro with high glucose (HG), or target cells and tissues derived from models of diabetes complications, provide strong evidence that alterations in epigenetic histone PTMs play key roles in diabetes-induced inflammation and vascular complications, and potentially in the metabolic memory phenomenon (17)(18)(19)(20)(21)(22)(23)(24)(25)(33)(34)(35)(36)(37)(38)(39)(40)(41).However, studies have not yet been performed directly in humans with diabetes and metabolic memory.To examine whether epigenetic mechanisms are related to glycemic history, the progression of complications and metabolic memory in human diabetes, we explored variations in the profiles of key histone PTMs at promoter regions in peripheral blood lymphocytes and monocytes obtained from selected EDIC cohort subjects.\t\n\nIn conclusion, we conducted comprehensive epigenomic profiling using cells from two selected subsets of DCCT/EDIC participants who experienced different rates of complications following a period with different levels of hyperglycemia to explore an epigenetic mechanism for metabolic memory in individuals with type 1 diabetes.Our results suggest that this metabolic memory phenomenon can in part be explained by increased epigenetic differences at key complication-related genes among individuals with higher HbA 1c levels that may contribute to further progression of complications during EDIC.", + "\tFuture research prospects\n\nalthough some of the fundamental mechanisms involved in generegulating epigenetic changes associ ated with hyperglycemia have now been identified, a number of funda mental challenges in this area remain to be addressed, such that the contribution of epigenetic changes to the etiology of diabetes mellitus can be under stood.From a clinical perspective, the continued follow up of participants in the DCCt-eDiC and uKPDs studies will enable investigators to determine the clinical effect of exposure to hyperglycemia, and whether tight glycemic control will appreciably lower the incidence of diabetic complications, further supporting the concept of metabolic memory.From a basic research perspective, the transfer of knowledge of epigenetic changes that drive gene expression will be critical to improved understand ing of the epigenome using highthroughput sequencing technologies.the development of more sensitive and sophisticated methodologies than those currently avail able and the advent of affordable, largescale, genome wide profiling and new bioinformatics tools will provide the means to determine the extent of specific epigenetic events that drive gene responses in patients with dia betes mellitus.Defining the molecular events that confer metabolic memory and its association with diabetic cell reviewS dysfunction will provide critical insights into the inter pretation of persistent epigenetic geneactivating events associated with Dna methylation and other histone modifications, as well as mirna expression patterns.\tCharting the epigenetic landscape\n\nthe studies discussed in this review have described important discoveries that mark the emergence of the epi genome and the tremendous influence of epi genetics on the etiology of diabetes mellitus.the identification of gene activating epigenetic changes mediated by hyper glycemia is of particular importance.the immunopurifica tion of chromatin and its associated protein determinants has profoundly influenced the investigation of chromatin structure and function. 79this investigation has resulted in a fundamental shift in our understanding of transcrip tional regulation and, specifically, the importance of struc tural and chemical variations of the chroma tinized Dna template in primary cellular models of hyper glycemia.the application of chromatin immuno purification can chart and distinguish gene sequences associated with histone modifications, transcription al coregulators and chromatin accessibility. 80,81he distinct patterns of gene expression associated with oxidative stress and the geneactivating changes in models of hyperglycemic variability have highlighted the contribution of cellular memory to the etiology of diabetes mellitus and inflammation of the vasculature.30 although the risk of persistent complications after return to normo glycemia is beginning to be appreciated, the molecular determinants that drive critical nuclear processes associ ated with metabolic memory are still not completely understood.82 emerging evidence suggests the patho genesis of diabetic complications could be influenced by gene-environment inter actions.although the nature of the epigenetic changes in models of glycemic vari ability have not been precisely mapped, regionalization of histone modifications is probably involved.24 to what extent does glucose regulate the transcriptional control afforded by structural and chemical modification of the chromatin template?Charting the epigenetic land scape is a major challenge and will probably reveal some surprising and unanticipated results.indeed, genomewide approaches to studying epigenetic determinants will add new levels of information that will help to establish an atlas of generegulatory events me diated by hyperglycemia.a profile of hyperacetylation events associated with geneactivating epigenetic changes has been developed to enable detailed study of the effects of hyperglycemia.this study used a novel approach to immunopurify the H3 acetylation moiety coupled with massive parallel sequencing approaches. 83Genomewide studies indi cate that human aortic cells are highly enriched with H3 acetylation in response to hyperglycemia and that such acetylation demonstrates specific regionalization in pro moter regions that often extend into transcribed areas of the gene sequence.Critical primary experiments to determine the hyperacetylation signature conferred by hyperglycemia will show the importance of genomewide epigenomic changes, such as those on human chromo somes 4q28.3,6q25.1, 12q23.3 and 22q12.3(Figure 5). intead of focusing on epigenetic changes at single loci, 28 which are often difficult to determine empirically, this discoverybased screening approach is unambiguous and indicates that histone acetylation has a widespread regu latory role that is correlated with geneactivating events.surprisingly, these studies distinguished major changes in genomewide hyperacetylation profiles as a result of physiologically relevant hyperglycemia that regulate specific histone code signatures precisely annotated to human gene expression patterns.with the identification of epigenetic changes, which will undoubtedly include other posttranslational modifications to histone and nonhistone proteins, we anticipate that understanding the genomic profiles will identify molecular pathways and signaling networks critical to the development of diabetes mellitus and its associated complications.", + "\t\n\nTo summarize: although there is ample evidence that epigenetic effects are likely to play a role in the pathogenesis of T2D and obesity, as well as to contribute to the observed familial aggregation, experimental studies that conclusively define the key loci responsible in disease-relevant tissues have yet to be performed (Table 1).It is also worth noting that existing studies have focused almost exclusively on DNA methylation, leaving other components of the epigenetic machinery (such as chromatin state) largely unexplored.Given limited access to diseaserelevant human tissues, progress will almost certainly depend on centralized community efforts such as the International Human Epigenome Consortium, which is generating reference epigenomes (including detailed methylome maps) for a panel state art state art of human tissues and cell types. 69This epigenetic counterpart of the Human HapMap effort will provide tissue-specific maps of methylation correlation structures, document the positions of variably methylated sites, and empower future epigenome-wide association study efforts." + ] + ], + "task_id": [ + { + "task_id": "251083E08BA7D70253243E7B75002E47" + }, + { + "task_id": "A6AC9E9314DDA1708A83D3B8F9D07EF6" + }, + { + "task_id": "DB518D0E92266848B81A1447F03D302C" + }, + { + "task_id": "8736099EC8F109703C73C7B7AE2046CA" + }, + { + "task_id": "E357FAAAD185EB55A4FB3E802EC9A57B" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_diabetes_4.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_diabetes_4.json new file mode 100644 index 0000000..fee1f31 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_diabetes_4.json @@ -0,0 +1,109 @@ +{ + "question": [ + "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?", + "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?", + "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?,", + "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?", + "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + ], + "answer": [ + "The text does not provide specific recent advancements in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo. However, it mentions the potential of using CRISPR/Cas9 gene editing tools to understand the impact of identified variants on cell function, growth, and survival, which might ultimately provide a means of treating the disease, likely after the transplantation of genetically", + "Genome-wide association studies (GWAS) integrate with multi-omics data by identifying genetic variants associated with diabetes and then examining these variants in the context of other biological data. This includes protein-protein interactions, gene expression data, and epigenomic data. The integration of this data can help to identify key pathways and mechanisms involved in the disease, such as insulin secretion and signaling. Additionally, the integration can help to identify potential drug targets and provide insights into the role of environmental factors and gene-environment interactions in disease development.", + "Genomic imprinting can impact the susceptibility and progression of diabetes by permanently programming the developing offspring's response to certain environmental factors. This can increase the risk of diabetes in subsequent generations, suggesting that these changes also affect the germ cell line. The imprinting can be influenced by factors such as maternal undernutrition, infant's low birthweight, and rapid postnatal child growth. Additionally, certain specific sncRNAs changes associated with paternal diets can impair glucose tolerance in offspring, potentially leading to type II diabetes. However, the exact mechanisms of this imprinting and its effects can vary between populations and are not fully understood.", + "Longitudinal genomics studies help in understanding gene-environment interactions in diabetes by tracking changes over time and identifying how genetic variants and environmental factors interact to influence diabetes risk. They can provide insights into the function of novel diabetes loci and the pathways through which environmental exposures act, thereby improving our understanding of diabetes etiology. These studies can also help identify high-risk individuals for diet and lifestyle interventions, and potentially inform pharmacological interventions if certain genotypes are found to be more or less responsive to specific medications. Furthermore, they can contribute to the development of personalized medicine in diabetes by identifying genetic variants that influence an individual's response to antidiabetic drugs.", + "Recent integrative genomics approaches have significantly advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions. Single-cell RNA sequencing has been used to discern cellular heterogeneity within the islets of Langerhans, particularly for beta cells. This has uncovered major gene expression differences between exocrine and endocrine cell types, including less abundant cell types. It has also shed light on the novel roles for each rare cell type based on their activated signalling pathways and receptor proteins. Epigenomic profiling, on the other hand, has provided detailed genome-wide patterns of methylation and chromatin accessibility in human islet material. This has led to a refined regulatory map which offers more granular annotation of functional impact. The combination of these techniques has helped to pinpoint likely causal variants in T2D pathogenesis and has accelerated the definition of causal mechanisms implicated in T2D pathogenesis." + ], + "contexts": [ + [ + "\t\n\nThe advancements in both differentiation protocols and genome-editing technologies make it now possible to study the effect of genetic perturbations on human -cell development.\tA measure of -cell exocytosis based on electrical current. the scalability of such studies.Moreover, a genome-wide CRISPR loss-of-function screen performed in 2019 identified 373 potential regulators of insulin production in the mouse insulinoma-derived Min6 -cell line 178 .Extending genome-wide screens to human -cell models and increasing the diversity of cellular read-outs will provide orthogonal data sets for integration with existing genetic and genomic resources, in order to elucidate downstream biology.As the current protocols for hiPSC differentiation are expensive, are time-consuming and have variability in differentiation efficiency, continued advancements in differentiation protocols will enable similar approaches in these cell models.\tGlucose\n\nPresently, there are several examples of patientderived hiPSC cell lines that have been used to determine the molecular mechanisms of diabetes mellitus.For example, hiPSC lines derived from patients with monogenic diabetes have been generated for several MODY genes, including GCK 79 , HNF1B 79 , HNF4A 79 , HNF1A 79,80 and CEL 79 .In one patient with NDM, an activating mutation located in the DNA-binding domain of STAT3 (Lys392Arg) was identified 81 .Of note, patient-derived STAT3 Lys392Arg hiPSC cells had premature endocrine cell differentiation owing to upregulation of NEUROG3 and INS that was rescued by correction of the mutation 82 , which is consistent with a pancreatic hypoplasia phenotype.In two other patients with NDM, heterozygous mutations in the INS gene (Cys96Arg and Cys109Tyr) were identified that were predicted to disrupt disulfide bonds in proinsulin 83 .Furthermore, hiPSC-derived -like cells derived from skin biopsies of affected individuals carrying these two mutations had increased endoplasmic reticulum stress and reduced proliferation 83 , suggesting a mechanistic cause of diabetes mellitus through decreased -cell mass.In addition, patient-specific hiPSCs carrying common heterozygous missense mutations found in the transactivation domain of the MODY gene PDX1 (Pro33Thr and Cys18Arg) had defects in the formation of pancreatic progenitors, which resulted in impaired glucose-stimulated insulin secretion 84 .", + "\tCONCLUSIONS\n\nhPSCs and the advancing genome editing tools appear to be a timely and potent combination for probing molecular mechanism(s) underlying diseases such as diabetes and metabolic syndromes.Studying monogenic forms of diabetes and syndromes of insulin resistance using these tools would be extremely useful given the lack of an autoimmune attack and confounding effects of insulin resistance and obesity.One caveat of this methodology at the moment is the \"low\" efficiency of deriving human beta cells in vitro [75,76], possibly due to our incomplete knowledge on human pancreatic development.Another explanation would be the lack of in vivo environmental cues emanating from proximal tissues such as the vasculature.Nonetheless, successful disease modeling of MODY2 [7] and Wolfram Syndrome [8] already suggests a high possibility of success.These technologies have the potential to elucidate the underlying pathophysiology that stem from defects in 1) beta cell development, metabolism or survival or 2) development of adipocyte.For instance in the case of MODY2, it is now clear that GCK mutation affects glucose-stimulated insulin secretion but not insulin synthesis or beta cell proliferation [7].With the latest advances in the derivation of mature and functional human pancreatic beta-like cells from hPSCs in vitro [75e77], eventually circumventing the requirement for in vivo maturation, disease modeling of diabetes is expected to progress exponentially.The knowledge gained from these hiPSC-based disease modeling studies can potentially be translated into the clinics by guiding clinicians on the appropriate type of medication to use for each condition based on the mechanism of action of the disease.Findings from these proposed studies could also offer clues to the pathophysiology of the \"garden variety\" of type 2 diabetes which is known to manifest defects in each of these tissues.hPSCs and genome editing tools may also provide an opportunity to better understand the relevance of gene variants identified from GWAS studies, in causing T1D, T2D, obesity and metabolic syndromes, given that they exhibit only modest effects and w85% of the variants map onto noncoding regions such as enhancers or regulatory elements [104].Investment into hPSCs and genome editing would allow a better mechanistic understanding of the pathophysiology of monogenic and complex diseases relevant for organismal homeostasis and therefore an improved approach to stratified personalized medicine.By identifying the impact of gene variants on disease predisposition, prophylactic measures in the form of lifestyle alterations or medication could be adopted early on in life to delay or even prevent the onset of diabetes and/or metabolic diseases.It is also likely that these hiPSCbased disease modeling studies would provide insights into approaches to predict the susceptibility of disease.Henceforth, the translational potential of studying human diabetes and metabolic syndrome disease mechanisms is huge, with opportunities for early prophylactic intervention that could have long-term implications for global health care and reduction of economic burden.While the derivation of hiPSCs from human tissues is relatively easier and gaining popularity compared to just a few years ago [2], it is likely that the modern technology of generating site-specific nucleases will also rapidly mature to make in vitro disease modeling a routine approach.\tEmploying hPSCs and genome editing tools to study diabetes and metabolic syndromes\n\nIn general, the strategy to carry out in vitro disease modeling of diabetes and related metabolic syndromes with hPSCs and genome editing tools would be to 1) derive hiPSCs from patients with these conditions, 2) generate \"repaired/corrected\" isogenic controls [53] and then 3) differentiate them into pancreatic cells or target cells of relevance, such as immune cells in the case of T1D or myocytes, adipocytes and hepatocytes in the case of T2D (Figure 2).If patient material is inaccessible, one could introduce (naturally occurring) mutations or gene variants into hESCs and differentiate them accordingly to study disease mechanisms.Since excellent reviews have been published recently, we will provide a brief overview to familiarize the reader with the classification of diabetes and metabolic disorders.\t\n\nBackground: Diabetes and metabolic syndromes are chronic, devastating diseases with increasing prevalence.Human pluripotent stem cells are gaining popularity in their usage for human in vitro disease modeling.With recent rapid advances in genome editing tools, these cells can now be genetically manipulated with relative ease to study how genes and gene variants contribute to diabetes and metabolic syndromes.Scope of review: We highlight the diabetes and metabolic genes and gene variants, which could potentially be studied, using two powerful technologies e human pluripotent stem cells (hPSCs) and genome editing tools e to aid the elucidation of yet elusive mechanisms underlying these complex diseases.Major conclusions: hPSCs and the advancing genome editing tools appear to be a timely and potent combination for probing molecular mechanism(s) underlying diseases such as diabetes and metabolic syndromes.The knowledge gained from these hiPSC-based disease modeling studies can potentially be translated into the clinics by guiding clinicians on the appropriate type of medication to use for each condition based on the mechanism of action of the disease.\t\n\nOne strategy to study these monogenic syndromes would be to derive hiPSCs from these patients, differentiate them into pancreatic progenitors and then transplant these progenitors into immunocompromised (SCID-Beige or NSG) mice for in vivo maturation (Figure 2).This methodology has been recently used to successfully model MODY2, demonstrating that beta cells derived from hiPSCs with GCK mutation are indeed less sensitive to glucose levels [7].Endoplasmic reticulum (ER) stress-related diabetes in patients with Wolfram syndrome has also been modeled using hiPSC-derived beta cells, demonstrating that WFS1 protein maintains ER function in beta cells by acting upstream of the unfolded protein response (UPR) pathways [8].phenotypes occurring in humans.Likewise, the stepwise analysis of human pancreatic development with this strategy would likely provide mechanistic insights into the ability of a single gene mutation (PDX1, PTF1A, HNF1B, GATA6 and GATA4) to promote pancreatic agenesis/ atrophy.Further, studying mutations in KCNJ11 and ABCC8 using hiPSC-derived beta cells may elucidate the mechanistic differences between permanent and transient neonatal diabetes [64].Overall, insulin production and secretion could be compared between diseased and gene-corrected pancreatic cells to understand the underlying cause of each type of monogenic diabetes (Figure 2).", + "\t\n\nMoving beyond cancer phenotypes, indirect in vivo screens are beginning to be used in other disease models.A genome-scale knockout screen in pancreatic beta-cells transplanted into a mouse model for Type 1 Diabetes identified genetic factors preventing autoimmune clearance of transplants.Inhibition of an identified gene hit, Rnls , with pargyline [101] prevented an autoimmune reaction and confirmed that the screen was able to identify candidates of therapeutic relevance [11] .", + "\t\n\nIn addition, knock-out and transgenic mice have become powerful tools in elucidating the influence of specific genes in glucose metabolism and the pathogenesis of diabetes.This includes understanding which transcription factors are involved in pancreas development (Habener et al., 2005) and elucidation of insulin signalling pathways (Kahn, 2003;Wang and Jin, 2009).Tissue-specific knockouts have proven to be particularly useful in studying insulin signalling (Neubauer and Kulkarni, 2006) as the global insulin receptor knock-out is non-viable (Accili et al., 1996).", + "\t\n\nThe use of gene-editing tools to create targeted monogenic animal models to explore the physio logical role of specific genes has burgeoned in the past 20 years.Although this endeavour has taught us a great deal about the cellular and molecular underpinnings of energy homeostasis, it is becoming increasingly clear that metabolic characterization of transgenic animal models might be less predictive of the physiological function of the gene of interest than is often assumed.For example, genetic manipulation could impose compensatory biological changes during development that in turn take over the function of an otherwise key gene.Exemplifying the predictive limitations of germline gene knockouts, mice deficient in glucagon-like peptide 1 receptor (GLP-1R) are protected from DIO and exhibit only mild defects in glucose tolerance.Taken at face value, these data imply that GLP-1R agonism would have little to no metabolic benefits 220,221 .Yet, GLP-1R agonists belong to the currently best-in-class therapeutics for treating obesity and T2DM, exemplifying the hazards of determining gene function on the basis of germline transgenic models and also illustrating the possibility of overlooking relevant therapeutic utility 222 .", + "\t\n\nIn the present study we devised a new strategy for predicting which proteins and biological pathways would be altered in vivo under such circumstances (Figure S1 in Additional file 1).Our approach was built on the in vitro molecular rules encompassed by the site-specific context score criteria, as these criteria can significantly enrich a gene list in genuine targets when a single miRNA is studied in a cell-based system [34].Using three to nine times the number of human subjects (n = 118) as previous studies [1][2][3][4] and a more comprehensive 'genomewide' RNA profiling strategy (>47,000 mRNA sequences, and >500 miRNA sequences), we aimed to identify the global molecular nature of skeletal muscle insulin resistance in human T2D and provide new bioinformatic and protein level validation for our conclusions.", + "\t\n\nIn this study, we set out to expand upon previous studies of the islet regulome in several ways.First, we explored the human islet methylome in unprecedented depth using Whole-Genome Bisulphite Sequencing (WGBS) applied to a set of 10 human islet preparations.Second, we explored both basal and genotype-dependent variation in chromatin accessibility through ATAC-seq in 17 human islet samples.Third, we integrated these genome-wide data with existing islet regulatory annotations to generate a high-resolution, epigenome map of this key tissue.Finally, we used this detailed map to interpret GWAS signals for T2D (and the related trait of fasting glucose) and deduce the molecular mechanisms through which some of these loci operate.", + "\t\n\nTargeted Allelic Expression Profiling in Human Islets Identifies cis-Regulatory Effects for Multiple Variants Identified by Type 2 Diabetes Genome-Wide Association Studies Diabetes 2015;64:1484-1491 | DOI: 10.2337/db14-0957", + "\t\n\nIt is increasingly clear that genetic variants may act through multiple tissues, e.g.liver, neurons and other islet cells, even though the major endpoint is often an alteration in circulating insulin levels [34,44,45]; thus, a purely 'b-cell-centric' approach to the unravelling of their actions is likely to have only partial success.Important challenges therefore remain to understand the underlying physiology and the tissue interactions through which variants may act.The deployment in animal models of new tools for genome editing optogenetics may help by providing a means to rapidly and selectively control gene expression in a single cell type, thus shining new light on the action of genes identified through genome-wide association study.", + "\t\nThe inheritance of variants that lead to coding changes in, or the mis-expression of, genes critical to pancreatic beta cell function can lead to alterations in insulin secretion and increase the risk of both type 1 and type 2 diabetes.Recently developed clustered regularly interspaced short palindromic repeats (CRISPR/Cas9) gene editing tools provide a powerful means of understanding the impact of identified variants on cell function, growth, and survival and might ultimately provide a means, most likely after the transplantation of genetically \"corrected\" cells, of treating the disease.Here, we review some of the disease-associated genes and variants whose roles have been probed up to now.Next, we survey recent exciting developments in CRISPR/Cas9 technology and their possible exploitation for b cell functional genomics.Finally, we will provide a perspective as to how CRISPR/Cas9 technology may find clinical application in patients with diabetes.\t\n\nThe inheritance of variants that lead to coding changes in, or the mis-expression of, genes critical to pancreatic beta cell function can lead to alterations in insulin secretion and increase the risk of both type 1 and type 2 diabetes.Recently developed clustered regularly interspaced short palindromic repeats (CRISPR/Cas9) gene editing tools provide a powerful means of understanding the impact of identified variants on cell function, growth, and survival and might ultimately provide a means, most likely after the transplantation of genetically \"corrected\" cells, of treating the disease.Here, we review some of the disease-associated genes and variants whose roles have been probed up to now.Next, we survey recent exciting developments in CRISPR/Cas9 technology and their possible exploitation for b cell functional genomics.Finally, we will provide a perspective as to how CRISPR/Cas9 technology may find clinical application in patients with diabetes.\t\n\nIn vivo delivery of CRISPR editing tools into pancreatic b cells in people with diabetes is likely to face enormous challenges for two main reasons: 1. b cells are postmitotic, thus disfavouring HDR-mediated CRISPR editing.2. Selective targeting to these cells will be required, likely involving cell type-tropic viruses (272), raising evident concerns over off-target effects and toxicity.Hence, the most likely and feasible way of CRISPR editing has to be an ex vivo system where b cells can first be engineered by CRISPR editing and then transplanted into patients (Figure 2).\t\n\ninsulin secretion.We begin by providing examples of genes and loci associated with altered T2D risk.Finally, we review the CRISPR tools that may offer the potential to correct these variants in the human b cell.\tIn Vitro and In Vivo b Cell Models for Studying Genetic Variants\n\nIn order to understand the pathogenic role of diabetes-associated genetic variants, tractable b cell models are essential.Mouse models, either transgenic or knock-out, are valuable for examining the roles of single genes, but their use is more limited in studies of intergenic regions given more substantial inter-species (mouse versus human) differences in these regions.As sources of human b cells, there are currently three possibilities.Firstly, primary islets isolated from organ donors: This source is, however, limited in terms of the availability and quality of islets (226).Secondly, clonal human b cells.Immortalized human EndoC-bH1 cells were developed in recent years after infection of foetal islets with large T antigen and further inoculation of islets in immunocompromised mice (227).Later generation EndoC-bH2 (228) and EndoC-bH3 (229) cell lines were subsequently established with more advanced features including regulated deletion of the immortalizing gene.The limitation of these cell lines, however, is their extremely slow growth rate which hampers their use.Given this slow growth rate -and the fact that these lines poorly tolerate expansion from a single cell-it is virtually impossible to modify them by HDR via CRISPR editing.A third possibility are therefore islet-like cells differentiated from human embryonic stem cells (hESC) or patient-derived induced pluripotent stem cells (iPSC).In light of the limitations of the above cellular models, laboratories are now focusing on hESC or iPSC in studies of gene function throughout b cell development by differentiating hESC/iPSC cells into mature b cells (230,231).Such directed differentiation protocols have recently been improved (21,159).", + "\t\n\ntreatment of T2DM.T5224 is able to strikingly rescue CDKAL1mutation-mediated pancreatic beta cell dysfunction in vivo, which is a proof of concept for a T2DM drug candidate rescuing a gene-specific defect in vivo." + ], + [ + "\t\n\nInsights from integration of genetic and epigenomic data.Using genome-wide association studies (GWAS) data for related glycaemic (for example, fasting glucose, 2-hour glucose), lipid (for example, triglycerides) and anthropometric traits (for example, BMI), a 2018 study proposed distinct clusters of T2DM GWAS loci, three of which showed clear evidence for islet cell dysfunction 21 (Fig. 1).Another study used soft clustering to group T2DM genetic loci on the basis of their associations with related traits, and similarly found loci that are associated with islet cell dysfunction 22 .Although for many of these loci neither the causal variants nor the effector transcripts are known, the most probable mechanism for modulation of the risk of T2DM is through islet cell dysfunction 21 .\tKey points\n\n Genome-wide association studies (GWAS) have identified >400 signals associated with the risk of type 2 diabetes mellitus (T2DM). The pancreatic islet has been identified as a key tissue involved in mediating GWAS signals in T2DM risk. Integrating genetic, epigenomic and cellular data can unlock the biology behind GWAS signals.", + "\t\n\nGenes identified in GWASs when evaluated in the context of complementary systems level data such as that related to proteinprotein interactions and to and gene expression can provide insights into the mechanisms underlying pathogenesis of complex traits [22][23][24].Here, we have combined these approaches toward deciphering genome to phenome correlation in T2D (Figure 1).Given that T2D GWAS genes do not directly relate to disease pathophysiology, our main aim was to examine if this genome to phenome correlation gap can be abridged by considering GWAS genes in conjunction with physical and genetic interaction, and gene expression data.\t\nGenome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic b cells and insulin signaling in target tissues.However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidates perturbation of insulin secretion.Also, the disease associated genes do not clearly converge on functional categories consistent with the known aspects of T2D pathophysiology.We used a systems biology approach to unravel genome to phenome correlation in T2D.We first examined enrichment of pathways in genes identified in T2D GWASs at genome-wide or lower levels of significance.Genes at lower significance threshold showed enrichment of insulin secretion related pathway.Notably, physical and genetic interaction network of these genes showed robust enrichment of insulin signaling and other T2D pathophysiology related pathways including insulin secretion.The network also overrepresented genes reported to interact with insulin secretion and insulin action targeting antidiabetic drugs.The drug interacting genes themselves showed overrepresentation of insulin signaling and other T2D relevant pathways.Next, we generated genomewide expression profiles of multiple insulin responsive tissues from nondiabetic and diabetic patients.Remarkably, the differentially expressed genes showed significant overlap with the network genes, with the intersection showing enrichment of insulin signaling and other pathways consistent with T2D pathophysiology.Literature search led our genomic, interactomic, transcriptomic and toxicogenomic evidence to converge on TGF-beta signaling, a pathway known to play a crucial role in pancreatic islets development and function, and insulin signaling.Cumulatively, we find that GWAS genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance.This seems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with genetically programmed b cell dysfunction to precipitate diabetes.\t\n\nGenome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic b cells and insulin signaling in target tissues.However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidates perturbation of insulin secretion.Also, the disease associated genes do not clearly converge on functional categories consistent with the known aspects of T2D pathophysiology.We used a systems biology approach to unravel genome to phenome correlation in T2D.We first examined enrichment of pathways in genes identified in T2D GWASs at genome-wide or lower levels of significance.Genes at lower significance threshold showed enrichment of insulin secretion related pathway.Notably, physical and genetic interaction network of these genes showed robust enrichment of insulin signaling and other T2D pathophysiology related pathways including insulin secretion.The network also overrepresented genes reported to interact with insulin secretion and insulin action targeting antidiabetic drugs.The drug interacting genes themselves showed overrepresentation of insulin signaling and other T2D relevant pathways.Next, we generated genomewide expression profiles of multiple insulin responsive tissues from nondiabetic and diabetic patients.Remarkably, the differentially expressed genes showed significant overlap with the network genes, with the intersection showing enrichment of insulin signaling and other pathways consistent with T2D pathophysiology.Literature search led our genomic, interactomic, transcriptomic and toxicogenomic evidence to converge on TGF-beta signaling, a pathway known to play a crucial role in pancreatic islets development and function, and insulin signaling.Cumulatively, we find that GWAS genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance.This seems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with genetically programmed b cell dysfunction to precipitate diabetes.", + "\t\nBackground: Genome-wide association studies (GWAS) have recently identified many new genetic variants associated with the development of type 2 diabetes.Many of these variants are in introns of known genes or between known genes, suggesting they affect the expression of these genes.The regulation of gene expression is often tissue and context dependent, for example occurring in response to dietary changes, hormone levels, or many other factors.Thus, to understand how these new genetic variants associated with diabetes risk may act, it is necessary to understand the regulation of their cognate genes.Results: We identified fourteen type 2 diabetes-associated genes discovered by the first waves of GWAS for which there was little prior evidence of their potential role in diabetes (Adam30,", + "\t\n\nGenomic variations and DNA profiling of those at risk for type 2 diabetes Despite many candidate gene studies and genome-wide linkage studies, very few susceptibility loci for type 2 diabetes have been identified until the recent emergence of genomic-wide association (GWA) data and large-scale replication studies (Table 2).Meta-analysis of GWA studies provides the unique opportunity to investigate the heterogeneity or consistency of genomic associations across diverse datasets and study populations.Recently, Voight et al. (32), using large-scale association analyses combining the data from eight GWA studies, identified 12 new susceptibility loci for type 2 diabetes.", + "\t\n\nGenome-wide association studies (GWAS) have made a significant contribution to our current knowledge of the role(s) of genetic variation in population-level susceptibility to T1D (Mychaleckyj et al., 2010).", + "\tIntroduction\n\nGenome-wide association studies (GWAS) have identified approximately 80 loci robustly associated with predisposition to type 2 diabetes (T2D) [1][2][3] and a further 70 influencing a range of continuous glycemic traits [4][5][6][7][8][9][10] in non-diabetic subjects.There is substantial, though far from complete, overlap between these two sets of loci.Physiological studies in non-diabetic individuals indicate that most of these loci primarily influence insulin secretion rather than insulin sensitivity, highlighting a key role for the pancreatic islets of Langerhans in the mechanistic underpinnings of these association signals [11,12].These findings have motivated efforts to catalogue the epigenomic and transcriptional landscape of human islets and to apply these findings to deliver biological insights into disease pathogenesis.Recently, it has been shown, for example, that GWAS signals for T2D and fasting glucose show significant co-localization with islet enhancers [13,14].", + "\tIntroduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4].", + "\t\n\nGenome-wide interaction studies have potential to identify gene variants that influence diabetes risk that might not be detected using hypothesis-driven approaches.However, the statistical power limitations of such studies when applying conventional tests of interaction, combined with the challenges of identifying large cohort collections with appropriately characterized environmental, genetic, and phenotypic data, pose challenges that conventional genetic association studies do not face.Several methods have been developed to mitigate these challenges; among the most promising is the joint meta-analysis approach, which is derived from the model with two degrees of freedom popularized by Kraft et al. (45) and developed further by Manning et al. (46).Manning et al. (47) went on to apply the joint meta-analysis approach in a genome-wide study of 52 cohorts in which they tested for SNP main effects and interactions (with BMI) on fasting glucose and insulin levels.The analysis yielded novel experiment-wide association signals for main effects, but none was discovered for interactions.", + "\t\nGenome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.\t\n\nGenome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.", + "\t\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.\t\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "\t\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 10 8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total 88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes.\t\n\nIn the past decade, genome-wide association (GWAS) and sequencing studies have identified genetic loci that help explain the inherited basis of T2D and glycemic traits.These studies are providing insights into the genetic architecture of T2D, including the number, frequency and effect sizes of risk variants in populations around the world.The polygenic nature of T2D is now well established, and multiple risk variants are being identified at some loci, suggesting allelic heterogeneity.Concurrently, increasing numbers of genes and variants have been implicated in monogenic forms of diabetes, including maturity onset diabetes of the young (MODY) and neonatal diabetes (7), and at least five genes have been implicated in both monogenic and polygenic diabetes (8).A recent simulation study evaluated genetic architectures for consistency with results from T2D genetic studies and found that many different disease models were still possible with respect to the number of loci, allele frequencies and level of selective pressure (9).Ongoing studies should more substantially narrow the bounds on feasible architectures (9).", + "\tIntroduction\n\nType 2 diabetes is a complex metabolic disease with a substantial heritable component [1].Over the past seven years, genome-wide association studies (GWAS) have successfully identified over 70 common risk variants associated with type 2 diabetes [2][3][4][5].Association signals at many of these loci localize to nonprotein-coding intronic and intergenic regions and likely harbor regulatory variants altering gene transcription.In recent years great advances have facilitated identification of regulatory elements genome-wide using techniques including DNase-seq and FAIRE-seq (formaldehyde-assisted isolation of regulatory elements), which identify regions of nucleosome depleted open chromatin, and ChIP-seq (chromatin immunoprecipitation), which identify histone modifications to nucleosomes and transcription factor binding sites.Several studies have successfully integrated trait-associated variants at GWAS loci with publicly available regulatory element datasets in disease-relevant cell types to guide identification of regulatory variants underlying disease susceptibility [6][7][8][9][10].", + "\tINTRODUCTION\n\nMultiple genome-wide association studies (GWASs) have correlated type 2 diabetes mellitus (T2DM) with genetic variants, yielding a large number of loci and associated gene products that are linked to the disease phenotype-often with little or no insight into the mechanism underlying that link (Hivert et al., 2014).The current challenge is to establish robust systems to systematically evaluate the role of these loci using disease-relevant cells.Previous studies have used patient samples, cell lines, or animal models to seek mechanistic insight but with significant limitations.Large variation is observed in primary patient samples, perhaps due to genetic heterogeneity, whereas animal models present major physiological and metabolic differences that hamper understanding of the precise function of human genes in T2DM.Therefore, a robust system to systematically evaluate the role of T2DM-associated genes using disease-relevant human cells will provide an important tool for diabetes research and spur the development of precision (allele-specific) therapies, exemplified by the use of sulfonylurea drugs to treat patients carrying certain KCNJ11 mutations (Gloyn et al., 2004).", + "\t\n\nBackground: Genome-wide association studies (GWAS) have identified several hundred susceptibility loci for type 2 diabetes (T2D).One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes.However, the causal variants identified by GWAS mostly fall into a non-coding sequence, complicating the task of defining the effector transcripts through which they operate.Methods: Here, we describe implementation of an analytical pipeline to address this question.First, we integrate multiple sources of genetic, genomic and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals.Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner tree approach) which uses external, experimentally confirmed protein-protein interaction (PPI) data to generate high-confidence sub-networks.Third, we use GWAS data to test the T2D association enrichment of the \"non-seed\" proteins introduced into the network, as a measure of the overall functional connectivity of the network." + ], + [ + "\tA. Genetic Screening\n\nWe have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is 10 -15 times greater.Although 70% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3).", + "\tIntrauterine environment and imprinting\n\nIntrauterine and postnatal environment can aff ect future risk of diabetes and cardiovascular disease via fetal programming. 117The thrifty genotype and thrifty phenotype hypotheses seem to apply to Asian populations.Maternal undernutrition, infant's low birthweight, and rapid postnatal child growth are all associated with increased risk of diabetes in off spring, and these factors might be especially relevant to developing countries such as India 88 and China. 1188][119] In view of the increase in childhood obesity and increasing number of women with young-onset diabetes in Asia, this link will further exacerbate the situation by creating a vicious cycle of diabetes begetting diabetes.", + "\tIf an environmental contributor is near ubiquitous and the genetic\npredisposition common as well, interventions are most sensibly weighted towards\nenvironmental risk factor modification. Even here, though, there is room for further research, since the etiopathogenesis\nof type 2 diabetes may not be as well understood as some suggest. Specifically,\nChaufan implies that dietary intervention to prevent prenatal programming\nleading to susceptibility to develop type 2 diabetes (the fetal origins of adult onset\ndisease hypothesis) is as evidence-based as dietary management of the adult diabetic state. However, many questions remain in this area.", + "\t\nIn 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention.Type 1 diabetes becomes clinically apparent after a preclinical period of varying length, during which autoimmune destruction reduces the mass of beta cells in the pancreatic islets to a level at which blood glucose levels can no longer be maintained in a physiologic range.The disease has two subtypes: 1A, which includes the common, immune-mediated forms of the disease; and 1B, which includes nonimmune forms.In this review, we focus on subtype 1A, which for simplicity will be referred to as type 1 diabetes.Although there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied. Gene tic S t udiesBecause of the uncertainty regarding the number and action of genes involved in type 1 diabetes, genetic studies have tended to focus on approaches that require few assumptions about the underlying model of disease risk.The two primary approaches have been linkage studies (using pairs of affected relatives, typically siblings) and association studies (using either case-control or family-based designs).Linkage studies using affected sibling pairs seek to identify regions of the genome that are shared", + "\t\n\nGenetic susceptibility to type 1 diabetes (T1D) is well supported by epidemiologic evidence; however, disease risk cannot be entirely explained by established genetic variants identified so far.This study addresses the question of whether epigenetic modification of the inherited DNA sequence may contribute to T1D susceptibility.Using the Infinium HumanMethylation450 BeadChip array (450k), a total of seven long-term disease-discordant monozygotic (MZ) twin pairs and five pairs of HLA-identical, disease-discordant non-twin siblings (NTS) were examined for associations between DNA methylation (DNAm) and T1D.Strong evidence for global hypomethylation of CpG sites within promoter regions in MZ twins with TID compared to twins without T1D was observed.DNA methylation data were then grouped into three categories of CpG sites for further analysis, including those within: 1) the major histocompatibility complex (MHC) region, 2) non-MHC genes with reported T1D association through genome wide association studies (GWAS), and 3) the epigenome, or remainder of sites that did not include MHC and T1D associated genes.Initial results showed modest methylation differences between discordant MZ twins for the MHC region and T1D-associated CpG sites, BACH2, INS-IGF2, and CLEC16A (DNAm difference range: 2.2%e5.0%).In the epigenome CpG set, the greatest methylation differences were observed in MAGI2, FANCC, and PCDHB16, (DNAm difference range: 6.9%e16.1%).These findings were not observed in the HLA-identical NTS pairs.Targeted pyrosequencing of five candidate CpG loci identified using the 450k array in the original discordant MZ twins produced similar results using control DNA samples, indicating strong agreement between the two DNA methylation profiling platforms.However, findings for the top five candidate CpG loci were not replicated in six additional T1Ddiscordant MZ twin pairs.Our results indicate global DNA hypomethylation within gene promoter regions may contribute to T1D; however, findings do not support the involvement of large DNAm differences at single CpG sites alone in T1D.", + "\t\n\nIt has been suggested that maternal imprinting of IDDM2 could contribute to the observation that type 1 diabetes appears to be transmitted less frequently to the offspring of diabetic mothers than to those of diabetic fathers [18,27].This, however, cannot be the explanation because there is no evidence for maternal imprinting in families from the UK, and yet in the 332 UK type 1 diabetes multiplex families studied here, there are 26 fathers with type 1 diabetes and only 7 affected mothers.The preferential transmission of predisposing IDDM2-VNTR alleles from fathers is consistent with a maternal imprinting effect in families from France [18], the USA [10,18,21] (Figure 2; Table 3) and Canada [27].However, in a large family data set from the UK, and in smaller data sets from Denmark and Sardinia, the transmission of VNTR susceptibility alleles is more pronounced from mothers than from fathers, and now significantly so in UK families (Figure 2; Table 3).Comparison of the results from the USA with those from the UK suggest that unexplained inter-population differences in this parent-of-origin effect may exist (P<0.025).However, it would appear that in some populations, and in particular the UK, maternal imprinting cannot provide a simple explanation for parent-of-origin effects in type 1 diabetes.Expression of insulin in the adult pancreas appears to be related to the length of the VNTR, with class III alleles associated with lower levels of INS mRNA.Our results are consistent with those of Lucassen et al. [24] and Owerbach and Gabbay [33] who have found a similar 1.5-to 3-fold lower expression associated with class III alleles in transiently transfected rodent pancreatic -cell lines in vitro.Also, in this issue, Vafiadis et al. [1] report that in 10 human foetal pancreas samples, the class III VNTR alleles are associated with significantly lower levels of INS mRNA than class I alleles.Kennedy et al. [25], on the other hand, also using transfected pancreatic -cell lines, found that the INS mRNA levels were higher in a class III VNTR compared with a class I VNTR transfectant.One explanation for this apparent discrepancy might lie in the type of VNTR alleles Kennedy et al. transfected into the cell line.We have found that there are over twenty class I VNTR alleles defined by length [10], and that alleles of a given length can vary in sequence composition [26] (S.T.B. & J.A.T., unpublished data).The transmission of some class I VNTR alleles (e.g.allele 814 mobility units; corresponding to 42 repeat units) from class I/III heterozygous parents to diabetic offspring does not reflect the overall significant positive transmission of the class I as a whole [10].", + "\t\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.", + "\t\n\nGenome-wide search for genes affecting the age at diagnosis of type 1 diabetes.\t\nGenome-wide search for genes affecting the age at diagnosis of type 1 diabetes.\t\n\nGenes affecting type 1 diabetes diagnosis age / A. Syreeni et al.", + "\tIntroduction\n\nGenetic susceptibility to the major autoimmune disor- ders, including insulin-dependent diabetes mellitus (IDDM), is complex.Much evidence suggests that IDDM is polygenic, explaining the paucity of ex- tended pedigrees and the high frequency of nonfamil- ial disease (Risch 1987; Thomson et al. 1988).To date, only a single genetic region, the major histocom- patibility complex (MHC) on chromosome 6, has Received May 29, 1990; revision received September 19, 1990.Address for correspondence and reprints: J. I. Bell, Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DU, England.", + "\t\n\nThus, the most likely scenario is that these genes are more poised for activation in the case group compared with the control group, contributing to various diabetes complications in the long term.This could be a consequence of the early exposure to hyperglycemia (measured by HbA 1c level), which is known to be associated with increased rates of long-term diabetes complications.", + "\tType 1 Diabetes\n\nThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk.", + "\tPerspectives\n\nThe studies described in this review systematically indicate an association between paternal diets, quantitative changes in specific sncRNAs, and impaired glucose tolerance in firstgeneration offspring.The potential importance of these findings for humans is obvious, considering that type II diabetes is one of the fastest growing health problems of our time and that impaired glucose tolerance defines a pre-diabetic state.If the described results are indeed applicable to humans, measures of specific sncRNAs (e.g.miRNA-let-7, tRF-Gly-GCC, and tRF modifications m 5 C and m 2 G) could potentially work as assayable quantitative biomarkers.Obesity biomarkers have significant potential to improve characterization of an obesity phenotype, which may prove relevant in assessing the risk of type II diabetes, thereby helping to counteract the global rise of obesity and diabetes.Identification of these epigenetic biomarkers could help identify younger individuals who are predisposed to the development of obesity and type II diabetes, allowing for personalized intervention.", + "\t\n\nThe diabetogenic effects of manipulating the intrauterine environment are probably mediated by a permanent programming of the developing offspring, e.g. by the mechanism of imprinting.Of interest, the increased risk of diabetes continues into subsequent generations, suggesting the changes also affect the germ cell line [143].", + "\tDiabetes 2004, 53:1905-1910. Page 8 of 9\n(page number not for citation purposes)\nBMC Medical Genetics 2006, 7:85\n\n17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. Schulze MB, Hu FB: Primary prevention of diabetes: what can\nbe done and how much can be prevented? Annu Rev Public\nHealth 2005, 26:445-467. King H, Aubert RE, Herman WH: Global burden of diabetes,\n19952025: prevalence, numerical estimates, and projections. Diabetes Care 1998, 21:1414-1431. Permutt MA, Wasson J, Cox N: Genetic epidemiology of diabetes. J Clin Invest 2005, 115:1431-1439.", + "\t\nExtensive epidemiological and experimental evidence have shown that exposure to an adverse intrauterine environment as observed in offspring of pregnancies complicated by obesity or diabetes, can program susceptibility to metabolic, endocrine and cardiovascular disorders later in life.Although most studies have concentrated on the maternal environment, it is also becoming evident that paternal exposure to obesity or diabetes can result in the later development of metabolic disorders in the offspring.Such programmed effects might not be limited to the first directly exposed generation, but could be transmitted to subsequent generations.This suggests the existence of mechanisms by which metabolic changes in parental phenotype are transmissible to offspring.The mechanisms which underpin the transmission of the programmed effects across generations are still unclear.However, epigenetic regulation of transcription has emerged as a strong candidate for mediating the heritability of metabolic diseases.Here, we review the most relevant evidence from human and animal studies showing transmission of programming effects of obesity or diabetes across generations, and the current mechanisms underlying either maternal or paternal influences on the metabolic status of offspring.\t\n\nAlthough most studies have concentrated on the maternal environment, it is also becoming evident that paternal exposure to obesity or diabetes can result in the later development of metabolic disorders in the offspring.Such programmed effects might not be limited to the first directly exposed generation, but could be transmitted to subsequent generations.This suggests the existence of mechanisms by which metabolic changes in parental phenotype are transmissible to offspring.The mechanisms which underpin the transmission of the programmed effects across generations are still unclear.However, epigenetic regulation of transcription has emerged as a strong candidate for mediating the heritability of metabolic diseases.Here, we review the most relevant evidence from human and animal studies showing transmission of programming effects of obesity or diabetes across generations, and the current mechanisms underlying either maternal or paternal influences on the metabolic status of offspring.", + "\t\n\nPresently, 48 other genomic regions, referred to as susceptibility regions, have been found to also confer susceptibility to T1D (Burren et al., 2011;Steck and Rewers, 2011;Yang et al., 2011;Bluestone et al. 2010;Poicot et al., 2010;Todd et al., 2010;Todd et al., 2007).But their contribution is minimal in comparison to the HLA locus (Gillespie, 2014).Also, research has shown that less than 10% of individuals with HLA-conferred diabetes susceptibility actually progress to clinical disease (Knip andSiljandera, 2008, Wenzlau et al., 2008).This implies that additional factors are needed to trigger and drive -cell destruction in genetically predisposed persons (Knip and Siljandera, 2008).Environmental factors are believed to influence the expression of T1D.The reason being that in the case of identical twins, if one twin has T1D, the other twin only has it 30%-50% of the time, despite having the same genome.This means that other factors contribute to the prevalence or onset of this disease (Knip et al., 2005).\tCONTRIBUTION TO KNOWLEDGE\n\nThrough this research, the following facts have been discovered about the genomics of T1D:" + ], + [ + "\t\n\nThe fact that all these technologies are relatively high throughput also means that large scale studies can be performed.This is particularly appropriate to type 2 diabetes, obesity, and their related disorders, which are on the whole polygenic disorders.This necessitates either the cross comparison of a number of monogenic animal models of the disease to produce an understanding of the extremes of the metabolic interactions associated with the diseases or the investigation of a disease process in a large population to investigate how a specific gene mutation or disease process interacts with the genetic diversity found in a normal population.", + "\t\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes.", + "\t\n\nSuccessful integration of genotyping data with longitudinal phenotypic information produced from several national health registries has provided strong support for 10 loci showing a genome-wide significance for the association with T2DM complications, some of them with already known importance to the comorbid conditions analyzed.We believe that these findings provide deeper insight into the pathogenesis of T2DM complications and suggest novel candidate genes for further functional studies, while our targeted approach highlights several susceptibility loci showing a directionally consistent impact on phenotype in multiple populations.", + "\tGenomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes.", + "\t\n\nThe availability of detailed information on gene environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them.", + "\t\n\nIn the Framingham Offspring Cohort, knowledge of common genetic variation aptly reclassifies younger people for T2D risk beyond clinical risk factors, but not older people (12).In the Whitehall II Cohort, common genetic variants for 2-h glucose contributed to the agerelated rise of 2-h glucose levels, whereas associations of variants for fasting glucose were constant over time (40).Although these two studies do not include G E interactions, they support the notion that genetic variants that modify T2D susceptibility to a particular exposure may do so only when the exposure occurs during specific developmental stages (34).It is therefore important to consider the dynamic or timedependent nature of G E interactions (2).\tFUTURE PERSPECTIVES\n\nContinued investment in studies of G E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications.\t\n\nCross-sectional studies of G E interactions for quantitative traits such as measures of insulin sensitivity, glucose tolerance, and adiposity have also been informative.Focusing on continuously distributed traits may produce insight to some causal pathway underlying disease that is not adequately captured by studies of overt T2D (24).", + "\t\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases.\t\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative.\tThe Rationale for Studying Gene-Environment Interactions\n\nIt is often said that T2D is the consequence of geneenvironment interactions [17].Indeed, both the environment and the genome are involved in diabetes etiology, and there are many genetic and environmental risk factors for which very robust evidence of association exists.But when epidemiologists and statisticians discuss gene-environment interactions, they are usually referring to the synergistic relationship between the two exposures, and there is limited empirical evidence for such effects in the etiology of cardiometabolic disease.Indeed, in non-monogenic human obesity, a condition widely believed to result from a genetic predisposition triggered by exposure to adverse lifestyle factors, of the >200 human gene-lifestyle interaction studies reported since 1995, only a few examples of gene-environment interactions have been adequately replicated [18], and because these results are derived primarily from cross-sectional studies with little or no experimental validation, even those that have been robustly replicated may not represent causal interaction effects.The evidence base for T2D is thinner still.Nevertheless, other data support the existence of gene-environment interactions in complex disease, thus motivating the search for empirically defined interactions in T2D.", + "\t\n\nGenome-wide interaction studies have potential to identify gene variants that influence diabetes risk that might not be detected using hypothesis-driven approaches.However, the statistical power limitations of such studies when applying conventional tests of interaction, combined with the challenges of identifying large cohort collections with appropriately characterized environmental, genetic, and phenotypic data, pose challenges that conventional genetic association studies do not face.Several methods have been developed to mitigate these challenges; among the most promising is the joint meta-analysis approach, which is derived from the model with two degrees of freedom popularized by Kraft et al. (45) and developed further by Manning et al. (46).Manning et al. (47) went on to apply the joint meta-analysis approach in a genome-wide study of 52 cohorts in which they tested for SNP main effects and interactions (with BMI) on fasting glucose and insulin levels.The analysis yielded novel experiment-wide association signals for main effects, but none was discovered for interactions.", + "\t\nGenome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.", + "\t\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.\t\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "\t\n\nIn summary, the present findings demonstrate that analysis of physiologically defined genome-wide interactions with variants strongly associated with insulin secretion is a potentially powerful approach for discovery of novel T2D loci and for expanding the knowledge base of disease etiology.A similar approach examining interactions with variants associated with key biomarkers may be of wider relevance in other complex human diseases.Results highlight the need for further study of genetic variation underlying T2D risk in African Americans as a means to improve our overall understanding of this disease.", + "\t\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.\t\n\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.", + "\tGenomics and gene-environment interactions\n\nEven though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mellitus are more susceptible to T2DM than others, which suggests that individual differences in response to lifestyle interventions exist 76 .Substantial evidence from twin and family studies has suggested a genetic basis of T2DM 77 .Over the past decade, successive waves of T2DM genome-wide association studies have identified >100 robust association signals, demonstrating the complex polygenic nature of T2DM 5 .Most of these loci affect T2DM risk through primary effects on insulin secretion, and a minority act through reducing insulin action 78 .Individually, the common variants (minor allele frequency >5%) identified in these studies have only a modest effect on T2DM risk and collectively explain only a small portion (~20%) of observed T2DM heritability 5 .It has been hypothesized that lower-frequency variants could explain much of the remaining heritability 79 .However, results of a large-scale sequencing study from the GoT2D and T2D-GENES consortia, published in 2016, do not support such a hypothesis 5 .Genetic variants might help reveal possible aetiological mechanisms underlying T2DM development; however, the variants identified thus far have not enabled clinical prediction beyond that achieved with common clinical measurements, including age, BMI, fasting levels of glucose and dyslipidaemia.A study published in 2014 linked susceptibility variants to quantitative glycaemic traits and grouped these variants on the basis of their potential intermediate mechanisms in T2DM pathophysiology: four variants fitted a clear insulin resistance pattern; two reduced insulin secretion with fasting hyperglycaemia; nine reduced insulin secretion with normal fasting glycaemia; and one altered insulin processing 80 .Considering such evidence, the genetic architecture of T2DM is highly polygenic, and thus, substantially larger association studies are needed to identify most T2DM loci, which typically have small to modest effect sizes 81 .", + "\t\n\nThe public health genomics approach to type 2 diabetes.So, while exciting gene discoveries are being made, what can we do?The answer may lie in the relatively new field of public health genomics, \"a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health\" (12).Researchers, policymakers, and practitioners in public health genomics use populationbased data on genetic variation and gene-environment interactions to develop, implement, and evaluate evidencebased tools for improving health and preventing disease.They also apply systematic evidence-based knowledge synthesis and appraisal of the clinical validity and utility of genomic applications in health practice.Validated genomic information is then integrated into disease control and prevention programs (13)." + ], + [ + "Recent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell.MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis.We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility.We used Illumina small RNA sequencing to profile the miRNA fraction in three preparations each of primary human islets and of enriched beta-cells generated by fluorescenceactivated cell sorting.In total, 366 miRNAs were found to be expressed (i.e..100cumulative reads) in islets and 346 in betacells; of the total of 384 unique miRNAs, 328 were shared.A comparison of the islet-cell miRNA profile with those of 15 other human tissues identified 40 miRNAs predominantly expressed (i.e..50% of all reads seen across the tissues) in islets.Several highly-expressed islet miRNAs, such as miR-375, have established roles in the regulation of islet function, but others (e.g.miR-27b-3p, miR-192-5p) have not previously been described in the context of islet biology.As a first step towards exploring the role of islet-expressed miRNAs and their predicted mRNA targets in T2D pathogenesis, we looked at published T2D association signals across these sites.We found evidence that predicted mRNA targets of islet-expressed miRNAs were globally enriched for signals of T2D association (p-values ,0.01, q-values ,0.1).At six loci with genome-wide evidence for T2D association (AP3S2, KCNK16, NOTCH2, SCL30A8, VPS26A, and WFS1) predicted mRNA target sites for islet-expressed miRNAs overlapped potentially causal variants.In conclusion, we have described the miRNA profile of human islets and beta-cells and provide evidence linking islet miRNAs to T2D pathogenesis.\t\n\nRecent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell.MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis.We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility.We used Illumina small RNA sequencing to profile the miRNA fraction in three preparations each of primary human islets and of enriched beta-cells generated by fluorescenceactivated cell sorting.In total, 366 miRNAs were found to be expressed (i.e..100cumulative reads) in islets and 346 in betacells; of the total of 384 unique miRNAs, 328 were shared.A comparison of the islet-cell miRNA profile with those of 15 other human tissues identified 40 miRNAs predominantly expressed (i.e..50% of all reads seen across the tissues) in islets.Several highly-expressed islet miRNAs, such as miR-375, have established roles in the regulation of islet function, but others (e.g.miR-27b-3p, miR-192-5p) have not previously been described in the context of islet biology.As a first step towards exploring the role of islet-expressed miRNAs and their predicted mRNA targets in T2D pathogenesis, we looked at published T2D association signals across these sites.We found evidence that predicted mRNA targets of islet-expressed miRNAs were globally enriched for signals of T2D association (p-values ,0.01, q-values ,0.1).At six loci with genome-wide evidence for T2D association (AP3S2, KCNK16, NOTCH2, SCL30A8, VPS26A, and WFS1) predicted mRNA target sites for islet-expressed miRNAs overlapped potentially causal variants.In conclusion, we have described the miRNA profile of human islets and beta-cells and provide evidence linking islet miRNAs to T2D pathogenesis.", + "PRECISE CELLULAR GENOMICS\n\nElucidating the molecular mechanisms that lead to beta cell dysfunction and T2D pathogenesis has been a major focus of diabetes research for decades.However, advances in single cell genomic profiling techniques have led to greater understanding of non-beta cell type transcriptional regulation and suggest that they may play important roles in hallmark features of beta cell insufficiency and failure linked to T2D genetic risk and pathophysiology.Single cell transcriptome analysis of human islet cells indicate that multiple monogenic diabetes genes are highly expressed in beta cells (e.g., PDX1, PAX4, INS, HNF1A, and GCK) [27].However, other non-beta cell types express genes mutated in monogenic diabetes (such as PAX6 and RFX6), congenital hyperinsulinemia (HADH, UCP2) and those implicated as T2D GWAS target/effector genes [28].Recent study of type 1 diabetic (T1D) human islets has provided surprising insights into alpha cell biology.In T1D islets, the alpha cell proportions remain relatively unchanged despite abnormal glucagon secretion [29].This dysregulated glucagon secretion is instead accompanied by decreased expression of important islet transcription factors including ARX, MAFB, and RFX6 and increased expression of stress response factors such as ATF4, ERN1, and HSPA5 [29] suggesting that changes in alpha cell identity may ultimately lead to their dysfunction.Analysis of normal and T2D islet single cells with simultaneous RNA-seq and patch clamping (patch-seq) also revealed subpopulations of alpha cells with varying enrichment for ER stress response genes (e.g., DDIT3, XBP1, PPP1R15A) [30].Interestingly, this transcriptomic heterogeneity was consistent in normal and T2D islets and associated with variability in alpha cell electrophysiological measures; ER stressed alpha cells had lower cellular size and Na peak current.Prior single cell transcriptomic analyses have also noted subpopulations of ER-stressed beta cells [31,32] which implicates the dysfunction of both alpha and beta cells in diabetes pathogenesis.Similarly, the integrity of beta and alpha cell functions seem to be dependent on each other, as under hypoglycemic conditions, T2D islets show reduced insulin, C-peptide, and glucagon secretion [33].Additionally, during a glycemic clamp experiment, an increase in glucagon secretion was positively correlated with beta cell function suggesting that signaling between the two islet cell types is crucial for maintaining glucose homeostasis.Studies of delta cells in Sst-Cre transgenic mouse models [34e36] reveal that timely regulation of insulin secretion is controlled by various delta-cell specific pathways.Induction of the ghrelin receptor (Ghsr) in delta cells was correlated with enhanced somatostatin release and ultimately reduced insulin and glucagon secretion [35,36].Furthermore, the peptide hormone Ucn3 was shown to be co-released with insulin from beta cells to activate type 2 corticotropin-releasing hormone receptor (Crhr2) on delta cells in an alternate pathway that promotes somatostatin release and negatively regulates insulin levels [34].Delta cells are also notably enriched for G protein-coupled receptors (e.g., GLP1R, GIPR, GPR120) which exert careful control over metabolism [37].These receptors are also common therapeutic targets of T2D, suggesting that treatment and management of the disease should not neglect delta cell (dys)function and/or survival.Efforts to characterize the epigenomes of each islet cell type are emerging and revealing new insights of cellular fate and differentiation.Two groups have performed open chromatin profiling of purified beta and alpha cell fractions [10,12] and identified between 1850 and 3999 beta and 5316-27,000 alpha-specific peaks.These cell-specific regions were enriched for transcription factor motifs implicated in cell development and were enriched for diabetes-associated SNPs.Arda and colleagues also suggest that the beta cell epigenome is plastic and capable of being derived from other endocrine and exocrine precursor cells.Discrepancies in the numbers of cell-specific peaks determined by both groups are likely due to the cell surface markers used to enrich for each.CD26/DPP4, used by Arda et al., is a strong positive selector for alpha cells, which then enables negative selection for beta and other minor cell populations.However, this method of enrichment for beta cells will not remove contaminating delta and PP/gamma cells.Continued development of new tools and markers for islet cell enrichment, such as NTPDase3 [38] should continue to help us to understand changes elicited by genetic and environmental factors in each distinct cell type.Iterative proteomic screens in human islets are also proving useful for identifying putative cell-specific surface markers for isolation [39], wherein beta and delta cell populations were obtained by co-enrichment for CD9 and CD56.Challenges currently remain to exclusively enrich for the minor islet cell types (delta, gamma/PP), thus strategies that negatively select for these cells may be needed.Study of the rarer gamma/PP cells, which constitute roughly <1e5% of the total islet volume, remain limited due to the lack of known cell-surface markers for enrichment and purification (Figure 2).Whole islet analyses are unable to capture cell type-specific changes and therefore preclude analysis of their potential roles in T2D genetics and pathophysiology.Given the clear and extensive genotype effects on cis-RE usage [13,15] and gene expression [11,16,17] in islets, more extensive analysis of sorted cell types from multiple individuals is warranted to define a representative set of islet cell-specific REs and distinguish condition-specific from genotype-driven effects on their use and activity.\t\n\nunderstand each cell type's genomic architecture and better characterize their roles in islet resilience and failure.Experimental manipulation of the regulatory elements and/or the target genes identified by (epi)genomic approaches described above and modeling the putative pathways and processes they implicate in human islet cell lines (e.g., EndoC-bH1-H3) is essential to progress from correlation to causation.Similarly, transitioning from \"the\" mouse (C57BL/6) to multiple mouse models for insights into the effects of naturally occurring genetic variation on islet function and physiology [61] and for manipulation of key genomic elements should also help characterize the dynamic range of islet behavior and response.T2D is a heterogeneous, complex, and progressive disorder, as multiple subtypes have been identified and associated with different genetic risk and clinical outcome profiles.Future islet genomics studies that focus on identifying the distinct subgroups of individuals with distinct genes/pathways that are disrupted and/or contributing to islet (dys)function at basal and/or responsive states are needed.Furthermore, priority should be given to profiling more islets from pre-diabetic and T2D individuals to characterize the transition between basal to stressed to T2D state and determine if there are intermediate signatures for islet failure and T2D onset.Together, this multi-pronged approach toward studying T2D genetics and islet pathophysiology will help identify additional targets and opportunities for intervention that can be exploited for more precise and effective preventative, treatment, and management options for T2D.\t\n\nFigure2: Moving towards a more precise understanding of islet cellular genomics and responses.Proper elucidation of islet (dys)function and its association with T2D pathogenesis is confounded by individual genetic variation as well as islet cellular heterogeneity.To obtain a better understanding of both, future studies must prioritize strategies to obtain purified islet cell type populations (e.g., beta, alpha, delta, gamma/PP) via sorting with specific cell surface markers.Characterization of each cell type-specific genomic profile at baseline, stimulated, and diseased conditions will provide clearer understanding of key cellular and molecular processes that are altered and important in T2D development.Additionally, by sampling islets from multiple individuals and leveraging genotypes, it will be possible to identify cis-regulatory elements and genes that are influenced by genetics rather than disease state.SNP single nucleotide polymorphism; QTL quantitative trait locus; ER endoplasmic reticulum.", + "Genetic and physiological studies have highlighted the singular importance of pancreatic islet dysfunction in type 2 diabetes, but epigenomic characterisation of this tissue has been limited in large-scale community projects such as ENCODE and GTEx.The present study seeks to address this deficit by describing, in unprecedented detail, genome-wide patterns of methylation and chromatin accessibility in human islet material.We have combined these data with existing islet epigenomic marks to generate a refined regulatory map which, based on the evidence of improved enrichment for T2D association signals, offers more granular annotation of functional impact.\t\n\nIn this study, we set out to expand upon previous studies of the islet regulome in several ways.First, we explored the human islet methylome in unprecedented depth using Whole-Genome Bisulphite Sequencing (WGBS) applied to a set of 10 human islet preparations.Second, we explored both basal and genotype-dependent variation in chromatin accessibility through ATAC-seq in 17 human islet samples.Third, we integrated these genome-wide data with existing islet regulatory annotations to generate a high-resolution, epigenome map of this key tissue.Finally, we used this detailed map to interpret GWAS signals for T2D (and the related trait of fasting glucose) and deduce the molecular mechanisms through which some of these loci operate.\t\n\nHuman genetic studies have emphasised the dominant contribution of pancreatic islet dysfunction to development of Type 2 Diabetes (T2D).However, limited annotation of the islet epigenome has constrained efforts to define the molecular mechanisms mediating the, largely regulatory, signals revealed by Genome-Wide Association Studies (GWAS).We characterised patterns of chromatin accessibility (ATAC-seq, n = 17) and DNA methylation (whole-genome bisulphite sequencing, n = 10) in human islets, generating high-resolution chromatin state maps through integration with established ChIP-seq marks.We found enrichment of GWAS signals for T2D and fasting glucose was concentrated in subsets of islet enhancers characterised by open chromatin and hypomethylation, with the former annotation predominant.At several loci (including CDC123, ADCY5, KLHDC5) the combination of fine-mapping genetic data and chromatin state enrichment maps, supplemented by allelic imbalance in chromatin accessibility pinpointed likely causal variants.The combination of increasingly-precise genetic and islet epigenomic information accelerates definition of causal mechanisms implicated in T2D pathogenesis.\t\nHuman genetic studies have emphasised the dominant contribution of pancreatic islet dysfunction to development of Type 2 Diabetes (T2D).However, limited annotation of the islet epigenome has constrained efforts to define the molecular mechanisms mediating the, largely regulatory, signals revealed by Genome-Wide Association Studies (GWAS).We characterised patterns of chromatin accessibility (ATAC-seq, n = 17) and DNA methylation (whole-genome bisulphite sequencing, n = 10) in human islets, generating high-resolution chromatin state maps through integration with established ChIP-seq marks.We found enrichment of GWAS signals for T2D and fasting glucose was concentrated in subsets of islet enhancers characterised by open chromatin and hypomethylation, with the former annotation predominant.At several loci (including CDC123, ADCY5, KLHDC5) the combination of fine-mapping genetic data and chromatin state enrichment maps, supplemented by allelic imbalance in chromatin accessibility pinpointed likely causal variants.The combination of increasingly-precise genetic and islet epigenomic information accelerates definition of causal mechanisms implicated in T2D pathogenesis.", + "It is worth mentioning that in [132], a meta-analysis study was conducted, where a collection of gene expression datasets of pancreatic beta-cells, conditioned in an environment resembling T1D induced apoptosis, such as exposure to proinflammatory cytokines, in order to identify relevant and differentially expressed genes.The specific genes were then characterized according to their function and prior literature-based information to build temporal regulatory networks.Moreover, biological experiments were carried out revealing that inhibition of two of the most relevant genes (RIPK2 and ELF3), previously unknown in T1D literature, have a certain impact on apoptosis.", + "Against this background, a genome-wide analysis to identify stable and well-expressed genes in human islets and beta cells represents an essential tool for accurate normalization.To achieve this goal, we used high-depth RNA-sequencing data from the human beta cell line EndoC-H1 and human islets exposed to pro-inflammatory cytokines or palmitate.Genes were validated as putative reference genes by qPCR in EndoC-H1 cells, human islets and induced pluripotent stem cell (iPSC)-derived islets.", + "Notably, single-cell transcriptome profiling has been utilized in the past few years to discern cellular heterogeneity within the islets of Langerhans (Fischer et al. 2019;Tritschler et al. 2019Tritschler et al. , 2017)), particularly for beta cells (Baron et al. 2016;Lawlor et al. 2017a;Segerstolpe et al. 2016;Teo et al. 2018;Xin et al. 2016).Segerstolpe et al. ( 2016) investigated cell-type specific gene expression in the pancreas of healthy and type 2 diabetic individuals and uncovered major gene expression differences (transcriptional signatures) between exocrine and endocrine cell types, including the less abundant cell types such as human delta, gamma and epsilon cells.Previously, these cells had been difficult to observe due to bulk characterization methods (Lawlor et al. 2017a), however, single-cell RNA sequencing has shed light on the novel roles for each rare cell type based on their activated signalling pathways and receptor proteins (Lawlor et al. 2017a;Segerstolpe et al. 2016).For example, insight into the transcriptome of the minority cell type, epsilon cells and its ghrelin-producing capability was provided (Segerstolpe et al. 2016), as well as the expression of the rare delta and gamma cell types that are prompted by hormonal cues from leptin, ghrelin and dopamine signalling pathways to facilitate metabolic signalling in the pancreas (Lawlor et al. 2017a).Further single-cell RNA investigations by Xin et al. (2016) showed a total of 245 genes to be affected by type 2 diabetes when compared to non-diabetic single-cell transcriptomes.Among the common transcript expression profiles found between the human islet cells, only 20 genes (for example, RBP4, DLK1, ADCYAP1, RGS16, SOX4, BMP5, TIMP2, TSPAN1, MAFB and TFF3) were specific to a certain cell type (Xin et al. 2016).Lastly, a few recent reviews have tracked the progress of genes linked to specific endocrine cell types in these studies (see Chiou et al. 2019;Tritschler et al. 2017), with some going as far as to re-analyse the single-cell transcriptome datasets using a machine learning approach (Ma and Zheng 2018).The in-depth analyses reported on oxidative stress being the perpetrator to enhance beta-cell dysfunction as a final result, together with the potential activation of pathways linked to beta-cell apoptosis that may be the resulting cause of an insulin gene expression deficit in type 2 diabetes (Ma and Zheng 2018).", + "The inheritance of variants that lead to coding changes in, or the mis-expression of, genes critical to pancreatic beta cell function can lead to alterations in insulin secretion and increase the risk of both type 1 and type 2 diabetes.Recently developed clustered regularly interspaced short palindromic repeats (CRISPR/Cas9) gene editing tools provide a powerful means of understanding the impact of identified variants on cell function, growth, and survival and might ultimately provide a means, most likely after the transplantation of genetically \"corrected\" cells, of treating the disease.Here, we review some of the disease-associated genes and variants whose roles have been probed up to now.Next, we survey recent exciting developments in CRISPR/Cas9 technology and their possible exploitation for b cell functional genomics.Finally, we will provide a perspective as to how CRISPR/Cas9 technology may find clinical application in patients with diabetes.\t\n\nThe inheritance of variants that lead to coding changes in, or the mis-expression of, genes critical to pancreatic beta cell function can lead to alterations in insulin secretion and increase the risk of both type 1 and type 2 diabetes.Recently developed clustered regularly interspaced short palindromic repeats (CRISPR/Cas9) gene editing tools provide a powerful means of understanding the impact of identified variants on cell function, growth, and survival and might ultimately provide a means, most likely after the transplantation of genetically \"corrected\" cells, of treating the disease.Here, we review some of the disease-associated genes and variants whose roles have been probed up to now.Next, we survey recent exciting developments in CRISPR/Cas9 technology and their possible exploitation for b cell functional genomics.Finally, we will provide a perspective as to how CRISPR/Cas9 technology may find clinical application in patients with diabetes.", + "Our primary intent was to employ an integrative genomics approach to identify mitogenic mechanisms with potential application for human beta cell expansion (Supplementary Fig. 1).This approach entails integrating whole-exome and RNAsequencing data into network analysis to computationally model insulinoma molecular events relative to normal adult and juvenile human beta cells.We reasoned that although some molecular events in insulinoma are likely relevant to the mechanisms of tumor formation, some may serve to uncover the genetic mechanisms that enforce beta cell quiescence, and are bypassed in such benign tumors.We further validated combinations of lead candidate genes derived from this approach as beta cell mitogenic mediators.Notably, we focused on insulinomas from subjects not known to be members of multiple endocrine neoplasia type 1 (MEN1) kindreds, as the MEN1 gene has been previously reported as one of the most frequently mutated genes in hereditary pancreatic neuroendocrine tumors (PNETs), although MEN1 mutations are uncommon in sporadic insulinomas [5][6][7] .Despite attempting to exclude MEN1 subjects, we nevertheless find widespread abnormalities in genes functionally related to MEN1, revealing a previously unsuspected unifying mechanism underlying insulinoma.\t\n\nAn integrative approach suggests proliferation drivers.Our integrative genomics approach led to the identification of ten coexpression modules (Fig. 2e) that may underlie insulinoma pathogenesis, many of which were linked to potential epigenetic dysregulation (Fig. 3b).Given our interest in beta cell regeneration, and seeking the mechanisms that may permit escape from beta cell quiescence in benign insulinomas, we elected to focus on the bisque4 co-expression module, the module in insulinomas that contained DEGs enriched for cell proliferation.Furthermore, the bisque4 cell cycle module was particularly enriched for betacell specific histone mark signature, including genes with bivalent marks, both H3K27me3 and H3K4me3 (Kaestner beta 28 : foldenrichment = 5.2, FDR = 4.0 10 6 ) (Fig. 3a,b).\t\nAlthough diabetes results in part from a deficiency of normal pancreatic beta cells, inducing human beta cells to regenerate is difficult.Reasoning that insulinomas hold the \"genomic recipe\" for beta cell expansion, we surveyed 38 human insulinomas to obtain insights into therapeutic pathways for beta cell regeneration.An integrative analysis of whole-exome and RNA-sequencing data was employed to extensively characterize the genomic and molecular landscape of insulinomas relative to normal beta cells.Here, we show at the pathway level that the majority of the insulinomas display mutations, copy number variants and/or dysregulation of epigenetic modifying genes, most prominently in the polycomb and trithorax families.Importantly, these processes are coupled to co-expression network modules associated with cell proliferation, revealing candidates for inducing beta cell regeneration.Validation of key computational predictions supports the concept that understanding the molecular complexity of insulinoma may be a valuable approach to diabetes drug discovery.", + "We built on recent work deriving glucose-responsive pancreatic beta-like cells from hESCs/iPSCs (Pagliuca et al., 2014;Rezania et al., 2014) and used isogenic hESC-derived glucose-responding cells to systematically examine the role of several GWAS-identified genes in pancreatic beta cell function and survival.Whereas the mutations do not affect the generation of insulin + cells, they impaired insulin secretion both in vitro and in vivo, coinciding with defective glucose homeostasis.CDKAL1 / insulin + cells also displayed hypersensitivity to glucolipotoxicity.A high-content chemical screen identified a candidate drug that rescued CDKAL1 / -specific defects by inhibiting the FOS/JUN pathway.These studies represent a proof of principle for the use of isogenic hESC-derived cells to define the precise role of genes associated with disease though GWASs in human pancreatic beta cells, as well as the leadcompound identification for pharmacological intervention of T2DM.\t\n\nIn summary, we established an isogenic hESC platform to systematically evaluate the role of disease-associated loci in the survival and function of human pancreatic beta-like cells in vitro and in vivo.The platform can be used to study other disease-associated loci/variants with respect to beta-like cell function.It is worth noting that the glucose-responding cells derived using the current reported protocols are not equivalent to primary human beta cells.Ca 2+ flux assays suggested that approximately 30%-40% of the insulin-GFP + cells show increased cytosolic Ca 2+ concentrations in response to glucose stimulation (Figure S7Q), whereas robust glucose-induced signaling was observed in more than 70% of human beta cells based on the previous report (Rezania et al., 2014).The restricted functionality of pancreatic beta-like cells derived using current protocols might limit their application for evaluating subtle contributions of genes to glucose metabolism and Ca 2+ signaling.Thus, additional work is needed to further improve the protocol to derive mature pancreatic beta-like cells.In addition, the platform established here can also be applied to study the role of disease-associated loci/variants in other diabetes-related cell types, such as hepatocytes, adipocytes, muscles, and/or intestinal neuroendocrine cells.Finally, the system may be used as a highthroughput/content chemical screening platform to identify candidate drugs correcting allele-specific defects for precision therapy of metabolic diseases.\tDISCUSSION\n\nWith more than 80 loci associated with T2DM identified by GWASs, a robust platform to evaluate the role of these loci using disease-relevant cells is urgently needed.Here, we report proof of principle for using isogenic hESC-derived glucose-responding cells to evaluate the role of these loci in the function and survival of human pancreatic beta cells under conditions mimicking both health and disease.The derived glucose-responding cells share the same genetic background, providing a unique resource to determine the precise role of genes or loci in human pancreatic beta cells independent of complications from genetic heterogeneity implied by other approaches, such as patient-derived iPSCs." + ] + ], + "task_id": [ + { + "task_id": "5683950D08F6BDAB60BEA8BAC610A10B" + }, + { + "task_id": "4261C4238D9943A6D54F8F140746169F" + }, + { + "task_id": "2F912BE55930B6A50194B230FDF18693" + }, + { + "task_id": "CC682B18ACF7B69EFDC2BE439BB2E059" + }, + { + "task_id": "662BE235286FA528E8B3E9610248FB71" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_gn_1.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_gn_1.json new file mode 100644 index 0000000..964fa74 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_gn_1.json @@ -0,0 +1,40 @@ +{ + "question": [ + "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?", + "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?", + "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?", + "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?", + "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + ], + "answer": [ + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback" + ], + "contexts": [ + [], + [], + [], + [], + [] + ], + "task_id": [ + { + "task_id": "8D1E141D648E5869E9231D1D816D3F0B" + }, + { + "task_id": "F1CDC70335589DDE69AB182D3B7F19EE" + }, + { + "task_id": "94563EE20767BE8C58226505DADC1A78" + }, + { + "task_id": "4113FAD4A3D16D0E317AB7BBA08B8DDF" + }, + { + "task_id": "00520BA174DCF1785E5A34048FEEB431" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_gn_2.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_gn_2.json new file mode 100644 index 0000000..6abf5b0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_gn_2.json @@ -0,0 +1,40 @@ +{ + "question": [ + "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?", + "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?", + "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?", + "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?", + "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + ], + "answer": [ + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback" + ], + "contexts": [ + [], + [], + [], + [], + [] + ], + "task_id": [ + { + "task_id": "50467BE398D77655C6E26E7FA4B0DD75" + }, + { + "task_id": "98A6AF486AC558307D3D62DCE3072801" + }, + { + "task_id": "C258FC7AE58EB75FA86735D5648F56EB" + }, + { + "task_id": "2854F2565410212047BF4E6FBB605344" + }, + { + "task_id": "8131F0E7B26E93866105CBEA618A6559" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_gn_3.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_gn_3.json new file mode 100644 index 0000000..fd79449 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_gn_3.json @@ -0,0 +1,76 @@ +{ + "question": [ + "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?", + "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?", + "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?", + "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?", + "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + ], + "answer": [ + "Multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets by systematically integrating interactions of informational molecules, such as protein-protein interactions, protein-DNA interactions, protein-RNA interactions, RNA-RNA interactions, protein state information, methylation state, and interactions with metabolites. This comprehensive integration of data allows for a more accurate construction of genomics networks, leading to more reliable predictions of phenotypic traits. Additionally, the integration of genotype and gene-expression data in a single model can drastically reduce QTL candidate genes and infer the structure of networks from expression-regulatory polymorphisms to variation.", + "The computational challenges in analyzing large-scale transcriptomic data within GeneNetwork.org include handling the massive data sets, deciding how much confidence to place in QTL extracted from noisy array and proteomic platforms, and integrating multiple types of data across the biological scale. Solutions to these challenges involve the use of statistical tools to extract relevant patterns of covariation and causal relations, Bayesian and combinatorial network-analysis methods, and the integration of additional information such as SNP distributions. The GeneNetwork web service also provides workflows and methods to navigate massive multiscalar data sets and an extensive systems genetics toolkit for analysis and synthesis. Furthermore, machine-learning approaches like GANs can be used to generate larger expression datasets for network inference.", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback" + ], + "contexts": [ + [ + "Until highly accurate genomics networks can beconstructed from more complete data, inferences drawnfrom biological networks should be considered as hypothesis that need to be further tested with experimental method, where the results can then illuminatethe representation of the biological system. The predictive power of the genomics networks could be enhancedby more systematically integrating interactions of informational molecules, such as protein-protein interactions, protein-DNA interactions, protein-RNA interactions, RNA-RNA interactions, protein state information, methylation state, and interactions with metabolites, as these types of data have become available(Schadt et al. 2009).", + "Until highly accurate genomics networks can beconstructed from more complete data, inferences drawnfrom biological networks should be considered as hypothesis that need to be further tested with experimental method, where the results can then illuminatethe representation of the biological system. The predictive power of the genomics networks could be enhancedby more systematically integrating interactions of informational molecules, such as protein-protein interactions, protein-DNA interactions, protein-RNA interactions, RNA-RNA interactions, protein state information, methylation state, and interactions with metabolites, as these types of data have become available(Schadt et al. 2009).", + "Tointegrate phenotypes in MKD with genomic datasets, wechose to include the Protein Family Database (Pfam) [17],Clusters of Orthologous Groups (COGs) [18,19], KyotoEncyclopedia of Genes and Genomes (KEGG) [20], andbiological concepts found in the Gene Ontology (GO)[21,22] which span multiple scales of biology. A key challenge of the post-genomic era is to conceive large-scalestudies of genomes and observable characteristics of organisms(phenotypes) and to interpret the data thus produced. The goal ofthis phenomic study is to improve our understanding of complexbiological systems in terms of their molecular underpinnings.Inaddition, the method has the potential to predict newcorrelations between phenotypes and biological systemsrepresented in GO as shown in the complete result datasetsat http://phenos.bsd.uchicago.edu/prok_phenotype. Whileprevious correlations studies had been completed on onlyfour phenotypes [5,6], we present an additional 38 phenotype-to-GO correlations. We propose that this methodpotentially enables a systems-biology approach to analyzegenomic datasets by providing a systematic view of themolecular mechanisms beneath phenotypes across differentclassications of genes (protein families, pathways, molecularfunctions, and biological processes).", + "Recent improvements toGeneNetwork have reinvigorated it, including the addition of data from 10 species, multi-omicsanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource forpredictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most commonfunctions on GeneNetwork, as a gateway to deeper analyses, demonstrating how a smallnumber of plausible candidate genes can be found for a typical immune phenotype.", + "Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).By integrating genotypeand gene-expression data in a single model, Kulp and Jagular (2006) have drastically reduced QTL candidate genes, and perhaps inferred the structure of networksfrom expression-regulatory polymorphisms to variation. Combinatorial algorithmsare applied to networks that are drawn by thresholding the gene-expression correlation matrix by high-pass filtering criteria. Edges in the graph represent highgenetic correlation, and the vertices or nodes represent traits. Maximal cliques, thelargest possible sets of completely connected (perfectly intercorrelated) traits, and16.5 SYSTEMS GENETIC ANALYSIS401other dense subgraphs are extracted and annotated from these gene sets (Baldwinet al. , 2005).", + "Other studies applied gene network modeling algorithms to identify thepotential regulators in complex diseases, for example cardiomyopathy [79], hepatic steatosis [80], as well ascoronary artery disease [81]. Finally, there are many other integrative approaches available for the analysis of multi-omics data, buthave not yet been applied in mouse systems genetics studies. Examples include the transcriptome-wideassociation study (TWAS) that integrates GWAS with expression datasets from other independent cohorts toprioritize candidate gene for phenotypic traits.We also developed a set of methods to analyze the different layers ofomics data that contribute to complex traits. In particular, intermediate phenotypes, including transcripts,proteins, and metabolites [22, 46, 73] were exploited to consolidate G2P and GXE connections. Despite theirpotential, transcriptome-/proteome-wide association studies (T/PWAS), which test the associations between aphenotype and all transcripts or proteins of a given tissue, have not been fully explored [74, 75], largelybecause of the limited availability of cohorts with such data (see above).", + "Recent improvements toGeneNetwork have reinvigorated it, including the addition of data from 10 species, multi-omicsanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource forpredictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most commonfunctions on GeneNetwork, as a gateway to deeper analyses, demonstrating how a smallnumber of plausible candidate genes can be found for a typical immune phenotype.", + "Theseresults do not rule out the possibility that for other tissues, conditions or organisms, utilizing thealternative methods as part of the third stage of the GEMOT algorithm may enhance its performance. We next aimed to characterize GEMOTs utility for a large biological network that included groupsof traits that share the same causal transcripts. Accordingly, each synthetic network included100 traits, 200 transcripts and 100 variants, featuring five co-mapped sub-networks. A singe datacollection consists of 100 networks, each containing five co-mapped sub-networks that carry the samenumber of traits (Materials and methods).", + "Here we provide open access and availability tothese data by integrating them into the GeneNetwork, aweb-based analytical tool that has been designed for multiscale integration of networks of genes, transcripts andtraits and optimized for on-line analysis of traits controlled by a combination of allelic variants and environmental factors. GeneNetwork with its central module WebQTLfacilitates the exploitation of permanent genetic referencepopulations that are accompanied by genotypic, phenotypic and mRNA abundance datasets.", + "Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).By integrating genotypeand gene-expression data in a single model, Kulp and Jagular (2006) have drastically reduced QTL candidate genes, and perhaps inferred the structure of networksfrom expression-regulatory polymorphisms to variation. Combinatorial algorithmsare applied to networks that are drawn by thresholding the gene-expression correlation matrix by high-pass filtering criteria. Edges in the graph represent highgenetic correlation, and the vertices or nodes represent traits. Maximal cliques, thelargest possible sets of completely connected (perfectly intercorrelated) traits, and16.5 SYSTEMS GENETIC ANALYSIS401other dense subgraphs are extracted and annotated from these gene sets (Baldwinet al. , 2005).", + "The integration of genotypic and expression and other data haverecently been shown, in a Bayesian network framework [76], to enhance the overallaccuracy of predictive networks [40, 5153]. We have also recently demonstratedhow this class of network can be used to inform associations identified in GWAstudies [40]. 9 SummaryThe significant challenge we face in the post-genome era is deciphering the biological function of individual genes, pathways, and networks that drive complexphenotypes like disease.", + "Data upload into open access databases (such asGeneNetwork) allows an integrated analysis of different resources,phenotypic, genomic, and proteomic traits as well as data derivedfrom different tissues and diseases. Fibrogenesis is a common pathway that is induced by injury in almost every tissue [34], whichimplies core fibrogenic pathways [35] and common systemic modifiers [2], but also tissue-specific mechanisms [36]. Hence, data integration enhances the chance to detect genuine modifiers acrossorgans. GeneNetwork is a valuable platform that can be used byresearchers without advanced skills of bioinformatics to performsystems genetics analyses.", + "Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).", + "Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).By integrating genotypeand gene-expression data in a single model, Kulp and Jagular (2006) have drastically reduced QTL candidate genes, and perhaps inferred the structure of networksfrom expression-regulatory polymorphisms to variation. Combinatorial algorithmsare applied to networks that are drawn by thresholding the gene-expression correlation matrix by high-pass filtering criteria. Edges in the graph represent highgenetic correlation, and the vertices or nodes represent traits. Maximal cliques, thelargest possible sets of completely connected (perfectly intercorrelated) traits, and16.5 SYSTEMS GENETIC ANALYSIS401other dense subgraphs are extracted and annotated from these gene sets (Baldwinet al. , 2005).", + "Integration ofthese data in a readily computable form is required for facileidentication of highly supported genephenotype, genegene and phenotypephenotype relations (Akil et al. 2011). Here we demonstrate the benet of data integration in theGeneWeaver software system that enabled us to identifya common biological basis for two phenotypic endpoints related to alcoholism. This convergent evidence enabled us toprioritize our search for a biological correlate of these behaviors for which we could then demonstrate functional andgenetic validity." + ], + [ + "These programs have the capability to integrate large datasets ofgenetic and expression data from humans and animalstudies. Notably, the GeneNetwork program (www.genenetwork.org) can combine expression data gathered fromdifferent brain regions and tissues and map these withdescribed traits as a way to build gene networks [65]. Evolving developments in bioinformatics promise muchmore; it is now feasible to adopt a new modular approach,known as systems biology. Systems biology is a mathematical modeling technique applied to complex biologicalorganizations or processes for the purposes of generatingpredictive models that are more representative of biological situations [66,67].", + "This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.", + "Our work has been largely motivated by the computational demands ofsystems like GeneWeaver [82, 86], a web-based software platform for the integrationof functional genomics data. GeneWeaver includes a database containing lists ofgenes from diverse sources, along with descriptive metadata associated with theselists. Through gene homology, the lists can be combined across species such thatgenes on the lists are translated to a common reference. This enables theconstruction of a bipartite graph, with vertices representing individual genes.", + "Thismachine-learning approach could be readily extended to theCell 173, June 14, 2018 1589multi-omics datasets that drive network biology. A simpleexample would be using GANs to generate dramatically largerexpression datasets that can be used in the context ofnetwork inference to generate predictive models of transcriptional regulation. The black box nature of most next-generation machinelearning models presents an additional challenge for biologicalapplications.", + "This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.", + "Readersmay refer [42] for a comprehensive review on various availablesoftware tools. GeneNetWeaver (GNW) [43] is a Java-based reverse engineering tool for generating synthetic benchmark expression datasetsfrom gold standard DREAM challenge network. E. coli and Yeasttranscriptional regulatory networks are integrated as test case forbenchmark. Comparative assessment of inference algorithmsagainst DREAM challenge data can also be performed with thehelp GNW. Cytoscape [44] is a powerful tool most suitable forlarge-scale network analysis.", + "Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).", + "A large number of bioinformatics tools have been developed to predict genefunction based on sequence homology [143-145], protein structure [144-146], phylogenetic profiles [147-149],protein-protein interactions [150-152], genetic interactions [153-155], and co-expression [156-162]. With the development of transcriptome profiling technologies, thousands of high-throughput studies havegenerated a wealth of genome-wide data that has become a valuable resource for systems genetics analyses. A few web resources, including GEO [163], ArrayExpress [164], GeneNetwork [165], and Bgee [166] amongstothers, have created repositories of such expression data for curation, reuse, and integration.", + "Network effectsWith the technological developments that allow for samplingof genomic, transcriptomic and proteomic data for multiple targets in the same set of samples, it is now possible to performnetworking analyses. These techniques involve assessing the coexpression of transcripts or proteins and building maps of modulesof closely related proles. Within these network maps, verticesrepresent transcripts or proteins and the edges represent thesimilarity between expression proles of pairs of transcripts or proteins. The principle of this analysis is that genes involved in thesame functional pathway are linked via short paths within the coexpression network.", + "However, the accurate derivation of such high-throughput data andtheir analysis in terms of biological function has been critical to truly leveraging the postgenomicrevolution. This chapter will describe an approach that focuses on the use of gene networks to bothorganize and interpret genomic expression data. Such networks, derived from statistical analysisof large genomic datasets and the application of multiple bioinformatics data resources, potentially allow the identification of key control elements for networks associated with human disease,and thus may lead to derivation of novel therapeutic approaches., 2011; Rosen et al. , 2003, 2007). GeneNetwork alsoprovides links to the following external databases: NCBI Entrez Gene, Summary from onMendelian Inheritance in Man (OMIM), GenBank, HomoloGene, UCSC Genome Browser(UCSC), BioGPS, STRING, PANTHER, Gemma, the brain synapse database, and the AllenBrain Atlas. Int Rev Neurobiol. Author manuscript; available in PMC 2014 July 21. O'Brien et al. Page 134. BIOINFORMATICS APPROACHES IN BEHAVIORAL NEUROSCIENCENIH-PA Author ManuscriptDespite the various high-throughput technologies employed and plat-forms available toperform expression analysis, a unifying consequence is the generation of large-scaleexpression datasets.", + "One challenge facing investigators in the interpretation of the massive data sets on GeneNetworkand elsewhere is deciding how much confidence toplace in QTL extracted from still noisy array andproteomic platforms after having conducted manythousands of statistical tests with poorly understooddegrees of dependency. Statistical approaches toaddress these types of challenges have beendeveloped using either theoretically derived (Landerand Kruglyak 1995) or empirical (Churchill and Doerge 1994) p values as well as false discovery rates(Benjamini and Hochberg 1995; Benjamini and Yekutieli 2005; Storey and Tibshirani 2003).", + "Bioinformatics Data SourcesThe ability to identify bona fide candidate genes in any genetic study is limited by one's knowledge of the function of the genes that are determined to harbor the most risk of that disease.With more than 30,000 full-length genes and tens of thousands of transcript variants, micro-RNAs, and noncanonical open reading frames (ORFs), the human genome annotation is undergoing constant development and refinement.Much of this information is now publicly accessible in major database interfaces, including the Gene Ontology, InterPro, pFam, KEGG, UCSC, and NCBI sites.Additional information, of course, can be found in actual published accounts.One major challenge is to develop tools that can cross-reference the results of genetic and functional genomic studies with these massive data sources, to develop knowledge networks for inference testing.Through free software (such as Cytoscape) and commercial software (such as Ingenuity and Pathway Assist), this goal is now within reach.In fact, these software applications can even use natural language-based text mining algorithms to directly integrate author statements with experimental data.", + "Here we provide open access and availability tothese data by integrating them into the GeneNetwork, aweb-based analytical tool that has been designed for multiscale integration of networks of genes, transcripts andtraits and optimized for on-line analysis of traits controlled by a combination of allelic variants and environmental factors. GeneNetwork with its central module WebQTLfacilitates the exploitation of permanent genetic referencepopulations that are accompanied by genotypic, phenotypic and mRNA abundance datasets.", + "Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).", + "The integration of genotypic and expression and other data haverecently been shown, in a Bayesian network framework [76], to enhance the overallaccuracy of predictive networks [40, 5153]. We have also recently demonstratedhow this class of network can be used to inform associations identified in GWAstudies [40]. 9 SummaryThe significant challenge we face in the post-genome era is deciphering the biological function of individual genes, pathways, and networks that drive complexphenotypes like disease.", + "Computations can be as simple as sets of correlations andAnderson et al. Open Source Web-Based ToolsJ. Neurosci. , February 3, 2021 41(5):927936 933volume and neuron number (Fig. 5D). This type of information is important, forexample, when extrapolating from MRIvolume differences in humans to potentialvariations in cell number (Hibar et al. ,2015). One exciting area of research enabledby GeneNetwork.org is the reanalysis ofphenotypes generated before 2010, whichwould greatly benefit from recent computational methods and datasets.", + "Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006).", + "Coupling a large multispecies repository curated and empirical functionalgenomics data to fast computational tools allows for the rapid integrative analysis of heterogeneous datafor interpreting and extrapolating systems genetics results. Key words IT-tools for systems genetics, GeneWeaver data base, Data mining, QTL candidate gene1IntroductionSystems genetics studies generate large volumes of gene expressionnetworks, and positional candidate genes. Resolving and prioritizing these results requires refinement of the causal variants, functional role of genes and gene products and relationships of genecoexpression networks to mechanistic biology.", + "Several approaches have been developed to integrate multiple data typesacross the biological scale, particularly gene expression and genotype information. Both Bayesian (Li et al. , 2005, 2006) and combinatorial network-analysis methods(Baldwin et al. , 2005; Chesler et al. , 2005; Chesler and Langston, 2005) are beingapplied to genetical genomic data sets. The Bayesian approaches to microarray datamay allow a causal interpretation (Friedman et al. , 2000; Peer et al. , 2001). Thisis particularly true when additional information, such as SNP distributions, is incorporated directly in the modeling (Li et al. , 2005, 2006)." + ], + [], + [], + [] + ], + "task_id": [ + { + "task_id": "0B24AD048D04D91C50DF2CF3D792F0A5" + }, + { + "task_id": "87C90485F82B8FE83E20A2BFE13113C5" + }, + { + "task_id": "607BFA90ED01542767EE86A202B27038" + }, + { + "task_id": "690C8150471F6DDA0D8329DC88D2C27D" + }, + { + "task_id": "1F35AC388AAF04FB9E8FB3806CE85510" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_gn_4.json b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_gn_4.json new file mode 100644 index 0000000..d605034 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/gpt4o/dataset_domainexpert_gn_4.json @@ -0,0 +1,40 @@ +{ + "question": [ + "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?", + "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?", + "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?", + "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?", + "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + ], + "answer": [ + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback", + "Please try to rephrase your question to receive feedback" + ], + "contexts": [ + [], + [], + [], + [], + [] + ], + "task_id": [ + { + "task_id": "90A7674779E3320F5F7ADC4419DC7624" + }, + { + "task_id": "CB7C63955ED2E239114B04F3EF36AADE" + }, + { + "task_id": "5A7498A61427D762BF080934BBB87FA3" + }, + { + "task_id": "139E439E73A0121BE95144BC656120D5" + }, + { + "task_id": "74730F9F07A359644CD63AC5C1683E38" + } + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_aging_1.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_aging_1.json new file mode 100644 index 0000000..e180eee --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_aging_1.json @@ -0,0 +1,106 @@ +{ + "question": [ + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "which genes are involved in the aging process", + "what causes the aging process" + ], + "answer": [ + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes." + ], + "contexts": [ + [ + "Intervention trials and cell-based monotherapy", + "Rapamycin is used clinically to prevent organ transplant rejection, for some forms of cancer, and to prevent restenosis in cardiac stents (Kaeberlein 2013b).Shortterm treatment with the rapamycin derivative RAD001 improves ageassociated decline in immune function, as measured by antibody response to an influenza vaccine, in healthy elderly people (Mannick et al. 2014).Rapamycin has been shown to be well tolerated in dogs, improves outcome in a glycogen storage disease model (Yi et al. 2014), and is currently being tested in veterinary clinical trials as a treatment for osteosarcoma (Paoloni et al. 2010).A veterinary clinical trial is underway to assess the side effect profile and effects on age-associated cardiac function following 10 weeks of rapamycin treatment in healthy elderly dogs (Kaeberlein 2015).Rapamycin is currently the most effective pharmacological intervention for extending lifespan and delaying a broad range of age-related functional declines in rodents (Johnson et al. 2013).However, the doses used clinically to prevent organ transplant rejection are associated with side effects, such as impaired wound healing, edema, elevated circulating triglycerides, impaired glucose homeostasis, gastrointestinal discomfort, and mouth ulcers (Augustine et al. 2007;de Oliveira et al. 2011).These adverse side effects would likely preclude long-term use of rapamycin at these levels in otherwise healthy people.With the possible exception of impaired glucose homeostasis (Lamming et al. 2012), these side effects have not been observed at doses that are associated with increased lifespan and healthspan in mice, however, raising the possibility that lower doses of this drug could promote healthy aging with minimal adverse effects.This trial is designed to determine whether treatment with the drug rapamycin (see Table 1) can significantly reduce age-related disease and disability as well as mortality in middle-aged large dogs.The initial phase of this trial, which is in progress at the time of this writing, is intended to enroll at least 32 dogs 6 years of age or older and 40 lb in weight or greater.Each animal receives an initial veterinary exam and comprehensive blood work along with a cardiac exam including echocardiography (Fig. 3).Those dogs that do not present with any abnormalities or significant pre-existing health conditions are randomized into either placebo or rapamycin treatment groups for a 10-week treatment period.Initial rapamycin dosing regimens were determined, in part, based on pharmacokinetic analysis of rapamycin treatment in healthy dogs (Larson et al. 2016).After 10 weeks in the study, each dog receives another full exam and blood chemistry panel as well as repeat cardiac exam.The primary goals of this first phase are to establish appropriate dosing of rapamycin in the absence of significant adverse events and to determine whether similar improvements in cardiac function are achieved in aged dogs after 10 weeks of rapamycin treatment, as has been observed in laboratory mice (Dai et al. 2014;Flynn et al. 2013).To date, only one study has been performed assessing the impact of a rapamycin derivative on healthy aging in people.In this trial, it was observed that 6 weeks of treatment with the rapamycin derivative RAD001 (everolimus) was sufficient to enhance function of the aged immune system, as assessed by response to an influenza vaccine (Mannick et al. 2014).This recapitulates what was observed in elderly mice (Chen et al. 2009), and suggests that at least some of the mechanisms by which rapamycin delays aging in mice work similarly in humans.Although both compounds have essentially identical biological activities, RAD001 was used in this study instead of rapamycin because the study was funded by Novartis, who holds the patent rights for RAD001 (rapamycin is now off patent and sold as a generic drug).The doses of RAD001 used in the human immune aging study were lower than those typically used to prevent organ transplant rejection and showed improved side effect profiles, although some adverse effects, including the presence of mouth sores in a subset of the patients, were noted.Pending the outcome of phase 1, we anticipate enrolling several hundred additional dogs with similar entry criteria into a longer-term, 3-5 year study, to carefully assess the extent to which rapamycin improves health and reduces mortality in middle-age companion dogs.In addition to cardiac function, assessments of multiple age-related phenotypes will be performed including measures of cognitive function, muscle function, kidney function, glucose homeostasis, and cancer incidence.Many of these parameters are beneficially impacted by rapamycin in aging mice (Johnson et al. 2015), and we predict that rapamycin will induce similar improvements in aging dogs.Fig. 3 Design of the current short-term rapamycin intervention trial.Dogs must weigh at least 40 pounds and be at least 6 years old at time of entry into the study.If no significant pre-existing health conditions are detected at the first exam, dogs are randomized into either placebo or one of the rapamycin treatment groups.Red indicates the 10-week period during which the dogs receive either rapamycin or placebo.Dogs receive the same generic rapamycin (sirolimus) pill that is provided to human patients.Asterisk Serum and feces are collected at each appointment for future metabolomic and microbiome analyses and for quantitation of circulating rapamycin levels", + "All patients included in the study were receiving medications such as anti-hypertensive drugs (mainly angiotensin-converting enzyme inhibitors), blood sugar lowering agents and diuretics.In addition, infusions of human recombinant erythropoietin and iron hydroxide were administered.Just one patient reported a history of gastric cancer.Patients with chronic viral diseases (hepatitis, HIV) were excluded from the study.", + "RapamycinRapamycin is a macrolide isolated from Streptomyces hygroscopicus, a bacteria from Pascua Island (Rapa Nui).It has functions as an antibiotic, an immune suppressant drug, and it is also proposed as a CRM.After the first studies, it was found that rapamycin could induce the extension of the replicative life of yeast through the inhibition of TOR signaling [57].This compound could extend the lifetime useful in 20-month-old mice in correlation with TOR activity [58].These studies were the basis of the research to determine the function of rapamycin as a CRM, due to its modulating properties over proteostasis.In addition, studies suggest that rapamycin can be combined with other compounds (metformin, losartan, statins, propranolol, and aspirin among others) to potentiate their anti-aging activity [59].", + "One out of the 25 FDA approved Breast cancerdrugs (Gemcitabine), was found in the top 20 drug list from LINCS from breast cancer stage I (dark magenta). As shown in Fig. 12, one drug out of 25 FDA approved Breast cancer drugs, Gemcitabine, was found asrepurposed drug from LINCS for breast cancer stage III. Letrozole (Breast cancer drug) has similar structure(greater than 60%) with Ruxolitinib (repurposed drug from LINCS) a drug for the treatment of intermediate orhigh-risk myelofibrosis (Fig. 13).One out of the 25 FDA approved Breastcancer drugs (Palbociclib), was found in the top 20 drug list from LINCS from breast cancer stage II (deep pink). Scientific Reports | 6:20518 | DOI: 10.1038/srep2051813www.nature.com/scientificreports/Figure 11. Highlighted target genes that physically interact with genes from the breast cancer stageII common network pattern and their corresponding repurposed drugs from LINCS, along with theirstructurally similar Breast cancer drugs. As shown in Figs 1617 two target genes (TOP2A and TYMS) are also involved in the Triple Negative pattern.Two of them (Gemcitabine and Palbociclib) are included in the list of the 25 knownFDA-approved Breast cancer therapeutic drugs. We performed a Hypergeometric distribution test in order tofind the statistical significance of this drug overlapping. More precisely, LINCS_L1000 database is comprisedfrom 20,413 chemical reagents. Twenty two out of twenty five breast cancer drugs are also included in LINCSdatabase. Finally, from the 105 drugs that were found from our analysis, the probability of finding two drugs tooverlap with the Breast Cancer drugs in LINCS is 0.005471157, pointing out that there is statistical significancein their selection.Two from the 25 FDAapproved Breast cancer drugs (Gemcitabine and Palbociclib), was found in the top 20 drug list from LINCSfrom Luminal A breast cancer (dark magenta and deep pink respectively).One out of the 25 FDAapproved Breast cancer drugs (Gemcitabine), was found in the top 20 drug list from LINCS from breast cancerstage III (dark magenta). that was found from the drug repurposing analysis of HER2 pattern. It has similar structure - 75% withWZ-4002 repurposed drug, which is a novel mutant-selective inhibitor of EGFR. Finally, both Palbocicliband WZ-4002 are structurally similar to Dasatinib (more than 60%), which is a cancer drug used to treatacute lymphoblastic leukemia.18 two drugs out of 25 FDA approved Breast cancer drugs Gemcitabine and Palbociclib were also found as repurposed drugs from LINCS for breast cancer Luminal A (Fig. 18). Two genes from theLuminal A network pattern physically interact with four genes that involved in Histone deacetylases class(HDAC1, HDAC2, HDAC3 and HDAC8), which are target genes of Vorinostat (repurposed drug from LINCS). Vorinostat is a member of a larger class of compounds that inhibit histone deacetylases (HDAC) and it is usedto treat cutaneous T cell lymphoma (CTCL).Network pattern for each breast cancer subtype and the common interactions across Luminal Aand Luminal B. As shown in Fig. 8, one drug out of 25 FDA approved Breast cancer drugs, Gemcitabine, was proposed asrepurposed drug by the LINCS for breast cancer stage I. Furthermore, Gemcitabine is quite similar (tanimoto31similarity greater than 80%) with Clofarabine and Kinetin-riboside (repurposed drugs from LINCS). Clofarabineis also an anti-cancer, antineoplastic chemotherapy drug and is classified as an antimetabolite.One from the 25 FDAapproved Breast cancer drugs (Gemcitabine), was found in the top 20 drug list from LINCS from breast cancerstage IV (dark magenta). at the G2/M phase. Coconut milk contains kinetin riboside and is thought to have the potential to inhibit the progression of many cancers, including prostate, colon and breast cancer. One study found that carcinogen-inducedmammary tumors in mice were reduced by coconut oil too (http://foodforbreastcancer.com/). Moreover, in StageI, Sepantronium bromide (repurposed drug from LINCS) has been found similar with Vinblastine Breast cancerdrug and Idarubicin with Doxorubicin and Epirubicin respectively.", + "Rapamycin has serious side effects, particularly as an immunosuppressor, and thus it is not suitable as an antiaging drug.As in sirtuins, however, these studies highlight the road from basic discovery on the biology of aging to antiaging interventions.Further studies of the TOR pathway and of repressors more specific of its downstream signaling pathway are ongoing.Whether rapamycin produces a change in another parameter related to energy uptake or utilization is unknown, and determining which of its effects modulate lifespan is an important unsolved question.Like resveratrol, TOR has attracted considerable attention from the pharmaceutical industry, particularly in the context of cancer (Meric-Bernstam and Gonzalez-Angulo, 2009).", + "Hayes DF, Stearns V, Rae J, Flockhart D; 32 Consortium on Breast Cancer Pharmacogenomics.A model citizen?Is tamoxifen more effective than aromatase inhibitors if we pick the right patients?J. Natl Cancer Inst.100(9), 610-613 (2008)." + ], + [ + "Recent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals.", + "FUTURE DIRECTIONS: HIGHER RESOLUTION DATA VIA HIGHER THROUGHPUT ASSAYSOne inescapable conclusion of the aggregate results of genome-wide studies of aging to date (see summary Table 1) is that we have not come close to saturating the number of potentially lifespan-altering genes in any organism.This is in no small part because directly generating survival curves is a relatively time-consuming process in most model organisms using current methods.There are several possible ways to address this.One way that has been tried is by attempting to find surrogate phenotypes [72,73,126] that can be screened more rapidly, or even scored under selection.Another is mining candidates from the many whole-genome expression profiles.Results to date with these have been very fruitful, but have not suggested that these methods alone will rapidly saturate our search for lifespan-and healthspan-altering genes in tractable model organisms.", + "Chromosome mapping of genes that were differentially expressed in mice of different ages and/or in response to CR revealed a wide distribution of genes with some physical clustering of responsive genes within the genome.The latter findings are consistent with the concept that aging is a complex process and that evolutionary adaptations to aging, if they exist, may or may not involve geographic clustering of functionally related genes.", + "Geneticlinkage studies of long-lived human families identified alongevity locus while candidate gene approaches have beenused to identify and confirm the association betweenspecific variants in the FOXO3A gene and humanlongevity [37]. Genome-wide association studies havealso been used to identify the association of APOE with life123Aging Clin Exp Resspan and have yielded insights into potential biologicalpathways and processes related to aging. Despite thesesuccesses, several problems are inherent in humanlongevity studies including potentially high degrees ofenvironmental heterogeneity, genetic diversity, and lack ofbirth matched controls, among others [8].", + "The aging process most certainly is under highly polygenic controls This should not discourage us from pursuing a search for those loci which may be of profound importance to human aging as it ordinarily occurs in most human beings.", + "In most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes.", + "1993), andgene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do notprovide very useful evidence with respect to the question of the number of loci thataffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations ofevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are nowamenable to the application of genomic methods.", + "Accepted Article 2013 The Authors Aging Cell 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland over 90 years and 1,955 controls between 55 and 80 years did not reveal genome-wide significant loci (Newman et al., 2010) and neither did the analyses of all-cause mortality and survival free of major disease in this cohort (Walter et al., 2011).A smaller Dutch study of 403 nonagenarians and 1,670 controls younger than 65 years identified the APOE gene as a mortality locus (Deelen et al., 2011), which was confirmed in a German study of 763 long-lived individuals and 1,085 younger controls (Nebel et al., 2011) and a longitudinal study of 1,606 Danes showed that the effect size of this association increases at the highest ages (Jacobsen et al., 2010).Apparently, the influence of the common genetic variation on longevity is small which requires large meta-GWA studies for identification.Alternatively, rare genetic variants may play a more important role in longevity.Since the previous linkage studies showed contradictory results potentially due to heterogeneity in the longevity phenotype, it is expected that longevity is influenced by many private rare variants.", + "Ageing is complex and takes a long time to study -a lifetime in fact.This makes it difficult to discern its causes, among the countless possibilities based on an individual's genes, behaviour or environment.While thousands of regions in an individual's genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle.Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average.", + "Several explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect.Second, the largely negative findings of this and other studies contrast with the intriguing animal studies of longevity.Very large effects of single genes on lifespan have indeed been observed in laboratory animals, but humans often have several homologues of these genes which might significantly differ in function or compensate for mutated genes through redundant mechanisms (Kuningas et al., 2008).This could explain why our top findings did not include genes in these pathways found in animal models.Animal models also represent genetically homogenous populations and are exposed to controlled environmental influences.The lack of replication of animal model findings in humans suggests that the use of knockout animals may not provide the optimal approach to understanding the variation in survival in humans as interactions with environmental factors may obscure the associations and prevent the identification of loci in humans.The lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010).", + "In addition to timing differences, a small proportion of genes (10%-15%) exhibit opposite trends of expression changes with age in humans and macaques (Supplemental Fig. S13).Interestingly, such differences are ;1.5 times more common in aging than in development, an observation consistent with the lower strength of purifying selection on the gene regulation at old age (discussed below).These differences could also reflect extreme shifts in developmental timing between species, as well as technical artifacts.Future studies, using additional species and alternative methodology, are needed to address this issue.", + "1993), andgene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do notprovide very useful evidence with respect to the question of the number of loci thataffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations ofevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are nowamenable to the application of genomic methods.", + "The remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design.", + "TRANSLATION OF LONGEVITY MODEL ORGANISMS AND CORE AGING PATHWAYSGenetic studies on lifespan have proven to be challenging.While longevity is a defining trait for a given species, the lifespan of individuals is of limited heritability, making analyses more difficult.Exceptional human life span, although a rare phenotype, is likely multifactorial; refined analyses are required to obtain statistically robust genomic signatures of longevity (Zhang et al., 2020) and these have proven elusive.Unlike laboratory models, the effect of environmental variance cannot be controlled in human studies, potentially masking purely biological aging mechanisms.Even laboratory models cannot replicate the complex \"environment\" of humans; it includes psychosocial, economic, and cultural factors, rather than strictly biological.These human-specific confounders are difficult or impossible to target in traditional model organisms.Despite these limitations, experimentally tractable model organisms have proven invaluable in deciphering the purely genetic contribution to lifespan, including genes and pathways conserved across the tree of life.ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITYHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE 2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans.", + "With modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms.", + "Most of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31].Conclusions and prospectsOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + ], + [ + "Apoptosis, or controlled cell death [62], is another major stressed-cell response, and was also represented in our results (Fig. 9e).A large body of direct evidence points to apoptosis as one of the main routes of RPE degeneration in AMD [63].Induction of apoptosis upon stress is dictated by the action of master regulator p53, and it was recently shown that aging increases the activity of p53 in RPE cells and the likelihood for apoptotic cell death [64].Consistent with this evidence, we found association with pathways in Transcriptional regulation by TP53 group (Fig. 9d).In particular, Regulation of TP53 activity through methylation was among the top pathway in our association analysis (Table 1), suggesting that p53 modification by methylation and the closely related histone modifications [Protein lysine methyltransferases (PKMTs) methylate histone lysine in Fig. 9e] play important roles in RPE apoptosis regulation.In the intrinsic apoptotic pathway induced by oxidative stress, cytochrome c is released from mitochondria into the cytosol, binding and activating caspases, the main proteases central to apoptotic action.We found association in pathways involving 'inhibitor of apoptosis' (IAP) and its negative regulator 'second mitochondrial activator of caspases' (SMAC) [65], which suggests that disruption to regulatory mechanisms preventing apoptosis in RPE cells may play roles in AMD.", + "ApoptosisPersistent DNA damage", + "42ABSTRACT 18A MODULARIZED MODEL OF APOPTOSISHA Harrington, KHo, Sk Ghosh, KC Tung , CY Kao, and B AgudaImperial College London, Courant Institute of Mathematical Sciences New YorkUniversity, University of Texas at Arlington, University of Texas SouthwesternMedical Center, Mathematical Biosciences Institute, and Department ofMathematics, The Ohio State University Columbus, OH, USABackground: One of the key physiological mechanisms employed by the cell(during development and for maintenance of homeostasis) in multi-cellularorganism is apoptosis, which is characterized by a sequence of well-definedevents resulting in cell destruction.", + "14Apoptosis is caused by the activation of the caspase cascade, which isinitiated by two signaling routes (stress-induced death and death-domainreceptor-induced death) (Domen 2001). This process can be prevented by antiapoptotic molecules, such as Bcl-2 (Domen and Weissman 2000). Directevidence for the involvement of apoptosis in HSC number regulation came fromthe findings that overexpression of the anti-apoptotic gene bcl-2 led to increasednumbers of Thy-1.1low, Sca-1+, c-kit+, Lin- cells, a population with long-termmulti-lineage repopulation potential (Domen et al. 2000).Several lines of evidence have indicated that apoptosis acts as animportant regulator of stem cells. First of all, expression of some apoptosisrelated genes were detected in human and/or murine HSCs (Domen 2001). Secondly, targeted disruption of some of these genes in null and dominantnegative mutant mice interfered with normal apoptotic processes in HSCs. Forexample, overexpression of Bcl-2, a negative regulator of apoptosis, increasednot only the numbers and competitive repopulation capabilities of HSCs, but alsothe resistance of HSCs to apoptosis induced by ionizing radiation (Domen andWeissman 2003).", + "ApoptosisCell suicide, or apoptosis, is a well-studied biological phenomenon in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism.The lack of an apparent evolutionary benefit for such a process in a single-celled organism initially caused controversy about the presence of an apoptotic pathway in yeast.Today, however, a number of yeast orthologues to mammalian apoptosis genes have been discovered and apoptotic-like cell death has been linked to mating, colony formation, and aging (Buttner et al. 2006;Eisenberg et al. 2007;Frohlich et al. 2007).With respect to aging, both replicatively and chronologically aged cells that die have increased ROS and display apoptotic phenotypes (Fabrizio et al. 2004a;Herker et al. 2004;Laun et al. 2001).The importance of apoptosis in yeast aging has yet to be fully characterized.At the very least, yeast apoptosis provides a useful pathway for studying genetic interactions for age-related diseases that affect humans, such as cancer.Readers interested in further information related to yeast apoptosis are referred to several in-depth reviews (Buttner et al. 2006;Eisenberg et al. 2007;Frohlich et al. 2007).", + "Early redistribution of plasma membrane phosphatidylserine is a generalfeature of apoptosis regardless of the initiating stimulus: inhibition by overexpression ofBcl-2 and Abl. J Exp Med 182: 1545-56. Mathew CG (2006). Fanconi anaemia genes and susceptibility to cancer. Oncogene 25:5875-84. McBride MW, Carr FJ, Graham D, Anderson NH, Clark JS, Lee WK et al (2003). Microarray analysis of rat chromosome 2 congenic strains. Hypertension 41: 847-53. Merino-Trigo A, Kerr MC, Houghton F, Lindberg A, Mitchell C, Teasdale RD et al(2004).", + "When a cell harbors such severe DNA damage that it is beyond repair, it is disposed of through apoptosis.Alternatively, DNA damage can induce cellular senescence, the irreversible cessation of mitosis.Both processes are critically dependent on p53, which is known as the guardian of the genome [3] .DNA damage may also trigger autophagy, a cellular catabolic process that maintains homeostasis [4] .It should be noted that under normal conditions cells are rarely exposed to very high doses of DNAdamaging agents, which may be the explanation why we do not age and die because we run out of cells.However, aging is associated with some atrophy [1] and it is conceivable that at older ages bursts of DNA damage, for example from free radical reactions associated with inflammation, do occur and give rise to an increasingly high rate of apoptosis or cellular senescence.While there is some evidence for increased apoptosis and cellular senescence at old age, it is doubtful that under normal conditions this would lead to a significant loss of functional cells.", + "Apoptosis, or programmed cell death, literally eliminates cells at risk for neoplastic transformation.Senescence, by contrast, permanently arrests their growth.Both processes are controlled by the p53 tumor suppressor protein (Amundson, Myers, & Fornace, 1998;Bringold & Serrano, 2000;Hickman, Moroni, & Helin, 2002;Itahana, Dimri, & Campisi, 2001).p53 is a transcriptional regulator that both transactivates and transrepresses target genes in response to stress (Prives & Hall, 1999;Ryan, Phillips, & Voudsen, 2001).These target genes, in turn, stimulate DNA repair, transient cell cycle arrest, permanent cell cycle arrest (senescence) or cell death (apoptosis), depending on cell type, degree and type of damage, and other variables.In contrast, cells that lack normal p53 regulation or function -for example, tumor cells -tend to die in response to telomere dysfunction.Some normal human cells, on the other hand, undergo a senescence growth arrest.In either case, when present, p53 is crucial for mediating the cellular response to telomere dysfunction (Yaswen & Stampfer, 2002) (Fig. 4).", + "Cell death, and in particularapoptosis, can be caused by a number of mechanisms includingloss of growth factors and excitotoxicity (e.g. , Bhutta and Anand,2002; Nikolic et al. , 2013). It is of interest therefore, that proximalto the region of the QTL there are several genes that are relatedto growth factors including the latent transforming growth factorprotein 2 (ltbp2), placental growth factor (pgf), and transforminggrowth factor beta (Tgf beta).", + "Apoptosis-related gene expression profiles", + "Apoptosis.Programmed death of cells during embryogenesis and metamorphosis or during cell turnover in adult tissues.", + "14Apoptosis is caused by the activation of the caspase cascade, which isinitiated by two signaling routes (stress-induced death and death-domainreceptor-induced death) (Domen 2001). This process can be prevented by antiapoptotic molecules, such as Bcl-2 (Domen and Weissman 2000). Directevidence for the involvement of apoptosis in HSC number regulation came fromthe findings that overexpression of the anti-apoptotic gene bcl-2 led to increasednumbers of Thy-1.1low, Sca-1+, c-kit+, Lin- cells, a population with long-termmulti-lineage repopulation potential (Domen et al. 2000).Several lines of evidence have indicated that apoptosis acts as animportant regulator of stem cells. First of all, expression of some apoptosisrelated genes were detected in human and/or murine HSCs (Domen 2001). Secondly, targeted disruption of some of these genes in null and dominantnegative mutant mice interfered with normal apoptotic processes in HSCs. Forexample, overexpression of Bcl-2, a negative regulator of apoptosis, increasednot only the numbers and competitive repopulation capabilities of HSCs, but alsothe resistance of HSCs to apoptosis induced by ionizing radiation (Domen andWeissman 2003).", + "Fraction of cells displaying apoptosis", + "It has been known that mitochondria play a central role in the life and death of cells (Kroemer & Reed, 2000).Apoptosis was observed in developmentally arrested embryos by 72 h, but not at 24 h after FCCP treatment, despite considerable telomere attrition at this early stage, suggesting that telomere attrition occurs prior to apoptosis and may serve as an intermediate step between mitochondrial dysfunction and apoptosis.These results also suggest that telomere shortening may signal apoptosis (Lee et al ., 1998;Karlseder et al ., 1999).", + "Cell DeathA form of programmed cell death, apoptosis is necessary for normal cell turnover and is essential to a plethora of other biological processes.Apoptosis can be executed via Bcl-2 activation of caspases, via signals from the death receptor on the plasma membrane, or via induction by granzyme B secreted from cytotoxic T cells (Tc cells) [35].Endonucleases and proteases are activated by active caspases, eventually leading to the death of the cell.With age, however, apoptotic activity changes.In heart [36], kidney [37], skeletal muscle [38], and Tc cells [39], increased apoptosis has been reported, perhaps contributing to loss of cellularity in these tissues.This escalation across various tissues may be attributed to the increased production of free radicals [40] and furthermore exacerbated by the accumulation of DNA damage in the aged cells [41].As the risk increases for cells to turn cancerous and dysfunctional with advancing age, increased apoptosis in aged cells is argued to be a defense strategy.In other tissues, such as the colon, apoptosis appears to decrease with age perhaps contributing to the accumulation of senescent cells and age-associated carcinogenesis [42].", + "The regulation and execution of apoptosis in endothelial cells is a complex process involving paracrine factors, membrane receptors, interaction of pro-and anti-apoptotic factors and cysteinyl aspartate-specific proteases (caspases).Recent studies suggest that in aging there is an imbalance in the expression of pro-and anti-apoptotic genes resulting in an enhanced apoptosis in the myocardium (19), central nervous system (24), skeletal muscle (10), lung (33), and liver (2,33).Yet, age-related alterations in the expression of pro-and anti-apoptotic genes in coronary arteries have not been elucidated.", + "Apoptosis modulating genesApopotosis or programmed cell death is associated with alterations in cell morphology, particularly the nucleus, with endonucleatytic cleavage of DNA into nucleosomal length fragments.Apoptosis may result from withdrawal of growth signals." + ], + [ + "Indicative biological pathways associated with the candidate aging genesFig. 2 Significant biological processes associated with the candidate aging genesFollowing are examples of the identified genes and experimental or GWAS link between these genes and aging.On the list of the 25 top genes, NAP1L4 encodes a member of the nucleosome assembly protein (NAP) family, which interacts with both core and linker histones, and shuttles between the cytoplasm and nucleus, suggesting a role as histone chaperone.Histone protein levels decline during aging, and dramatically affect chromatin structure.Remarkably, the lifespan can be extended by manipulations that reverse the age-dependent changes to chromatin structure, indicating the pivotal role of chromatin structure in aging [32].In another example, gene expression of NAP1L4 increases with age in the skin tissue [33].Findings of GWAS link a number of the identified genes to age-related disorders, such as GAB2 and late onset Alzheimer's disease [86], and QKI and coronary heart disease/myocardial infarction [79].Interestingly, GWAS reports also link QKI to successful aging [87].", + "Examples of biological candidate genes with pleiotropic functions, which are involved in aging in general and in musculoskeletal aging in particular, are numerous: (a) in addition to the IGF-1 and vitamin D genes, estrogen metabolism pathway genes, including estrogen receptors and aromatase (CYP19), are associated with fat-free mass (Walsh et al. 2005) and BMD (Shearman et al. 2004), prostate and breast cancer (Gallicchio et al. 2006), and cardiovascular disease risk (Shearman et al. 2003).", + "In-depth analysis of the age-regulated genes revealed that multiple genes in the DNA damage response pathway were upregulated with age including those that function in non-homologous end-joining repair (mre11, rad50, Ku80 and mus308) and in translesion DNA synthesis (mus205 and DNApol-eta) [44][45][46].Genes that encoded enzymes with antioxidant properties, such as the thioredoxin reductase Trxr-1, and antioxidant genes involved in glutamate metabolism, such as GlnRS, isoQC and QC, were also upregulated with age [47][48][49][50].We also observed increased age-associated expression of chaperone genes (Cct1, Cct4, Cct5, Cct6, Hsc70-4) and the unfolded protein response transcription factor Xbp1, consistent with an induction of the unfolded protein response [51][52][53].Under stress conditions, there is a translational switch that favors production of stressrelated proteins while decreasing translation of other proteins [54].Paralogs of canonical translation factors such as NAT1 and Rack1, which were both upregulated, promote this switch to cap-independent translation [55,56].Notably, Rheb, which is downregulated with age, positively regulates ribosome production and capdependent translation by activating the mechanistic target of rapamycin (mTOR) kinase pathway [57].Thus, decreased Rheb levels during aging could decrease mTOR pathway activity, which extends lifespan and is protective against age-related pathology [58].Together, these data suggest that multiple genes are induced in aging photoreceptors to mitigate the effects of oxidative stress, protein misfolding and DNA damage.", + "CellAge vs human orthologues of longevity-associated model organism genesTo understand how senescence is linked to the genetics of aging processes, we looked at the intersection of CellAge genes and the 869 genes in the human orthologues of model organisms' longevity-associated genes (LAGs) dataset, collected based on quantitative changes in lifespan [34].Like CellAge, where genes are classified based on whether their upregulation induces, inhibits, or has an unknown impact on CS, the longevity orthologues dataset also provides information on the effect of upregulation of its genes, namely whether it promotes (pro, 421) or inhibits (anti, 448) longevity (Additional file 1: Table S7; Additional file 2: Fig. S2).Using network biology, we implicated the CellAge genes in various processes, particularly cell division and immune system processes.We used network topology to identify potential regulators of CS and bottlenecks that could impact various downstream processes if deregulated.Indeed, we identified 11 genes that have already been shown to contribute towards CS, which will be added to future versions of CellAge.Finally, we experimentally verified 26 genes that induce CS morphology or biomarkers when knocked down in human mammary fibroblasts.Of these, 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) were strong hits in inducing a senescent phenotype.Results: We develop CellAge (http://genomics.senescence.info/cells),a manually curated database of 279 human genes driving cellular senescence, and perform various integrative analyses.Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes.Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates.We also build cellular senescence protein-protein interaction and co-expression networks.Clusters in the networks are enriched for cell cycle and immunological processes.Network topological parameters also reveal novel potential cellular senescence regulators.Using siRNAs, we observe that all 26 candidates tested induce at least one marker of senescence with 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) decreasing cell number, activating p16/p21, and undergoing morphological changes that resemble cellular senescence.Conclusions: Overall, our work provides a benchmark resource for researchers to study cellular senescence, and our systems biology analyses reveal new insights and gene regulators of cellular senescence.", + "Genomics-a fundamental basis for understanding skin agingIn the last decade, genomic tools such as gene chips have been widely developed.This accomplishment has provided us with deeper insights into the molecular events underlying skin aging. 137Gene expression profiling has led to identification of pathways affected by aging, and this information has led to the development of new strategies to enable better skin repair and antiaging benefits. 138ene expression patterns were examined in sun-protected (buttocks) and sun-exposed skin (extensor forearm) from 10 young (age 19 to 20 years) and 10 older women (age 63 to 67 years) to examine gene expression profiles associated with chronologic skin aging and photoaging.Chronologic and photoaging were both associated with downregulation of the biologic process of lipid synthesis.In particular, genes involved in cholesterol and fatty acid synthesis were downregulated, as were genes associated with epidermal differentiation, including keratin filaments and cornified envelope components.An upregulation of the biologic processes of inflammatory response and wound healing, the molecular functions of cytokine activity and protease activity and the cellular component theme of extracellular matrix was also observed in both skin aging types.Elastin gene expression was upregulated with aging only in the photodamaged arm and remained unchanged in the sunprotected buttock.This finding corresponds to the histopathologic findings that show typical elastotic changes, the \"solar elastosis,\" in photoaged skin. 139urther studies conducted to investigate changes in gene expression during skin aging have been performed on naturally aged human foreskin obtained from children and elderly men.Some of the mechanisms proposed to be involved in the induction of aging comprise disturbed lipid metabolism, altered insulin and STAT3 signalling, upregulation of apoptotic genes partly due to the deregulation of FOXO1, downregulation of members of the jun and fos family, differential expression of cytoskeletal proteins (eg, keratin 2A, 6A, and 16A), extracellular matrix components (eg, PI3, S100A2, A7, A9, SPRR2B), and proteins involved in cell-cycle control (eg, CDKs, GOS2). 140Similar results have been presented by a study related to aging of skeletal muscle. 141n a previous study, we proposed that one of the factors significantly involved in the initiation of aging might be the physiologic decline of hormones occurring with age.Human SZ95 sebocytes in vitro treated with hormone levels that can be found in 60 year-old women produce less lipids than sebocytes treated with a hormone mixture representing that found in the serum of 20 year-old women. 6A differential gene expression between SZ95 sebocytes under the 20 and 60 year-old hormone mixture detected differentially expressed genes that are involved in biologic processes such as DNA repair and stability, mitochondrial function, oxidative stress, cell cycle and apoptosis, ubiquitin-induced proteolysis, and transcriptional regulation. 139,140A comparison of these results with data obtained from the aged kidney 142 identified key genes that may be of great importance for global aging.The most significantly altered signalling pathway was that of TGF-.A disturbed function of this cascade has been also c-Fos, which heterodimerize to form the activator protein 1 (AP-1) complex.AP-1 is a key regulator of skin aging, because it induces the expression of the MMP family and inhibits type I procollagen gene expression through interference with TGF- signalling pathway.It has been postulated that MAP kinases may be activated by excess production of reactive oxygen species (ROS) that occurs with advanced age and may be superimposed by extrinsic factors such as ultraviolet irradiation.Excess ROS production also leads to accumulation of cellular damage, which includes oxidation of DNA resulting in mutations, oxidation of proteins leading to reduced function, and oxidation of membrane lipids resulting in reduced transport efficiency and altered transmembrane signalling.IL, interleukin; NF-B, nuclear factor-B; TGF-, transforming growth factor-; TSP-1, thrombospondin-1; TSP-2, thrombospondin-2; VEGF, vascular endothelial growth factor.associated with tumorigenesis, such as in pancreatic, prostate, intestine, breast, and uterine cancer.", + "Analysis of prior research (Online Resource 5) shows that the revealed genes can be explicitly involved in other key biological processes in an organism whose role is known to be changing with aging.Specifically, ten genes (BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, and ZKSCAN1) regulate transcription which is believed to be disrupted when an organism is getting older (Roy et al. 2002).The DBH, TPO, and LSS genes are involved in synthesis of catecholamine, thyroid, and vitamin D hormones, respectively.The GPER binds estrogen and HCRTR2 binds orexin-A and orexin-B neuropeptid hormones.Hormonal deregulation with aging is considered to be one of the major components of senescent processes in an organism (Barzilai and Gabriely 2010).Five genes (ATG2A, NEDD4L, PSMB1, UBXN4, and USP6) are involved in degradation of proteins through ubiquitin-proteasome and the lysosomal/autophagic system.Dysfunction of this system leads to accumulation of damaged proteins in an organism that is associated with aging (Koga et al. 2011).Protein degradation through ubiquitin-mediated proteolysis plays an important role in cell-cycle regulation (Reed 2003).The PSMB1, SIK1, TRIP13, and TTN genes in the revealed set coordinate cell cycle.Cell cycle is linked with the aging-related processes in humans through a gradual increase in cell division errors in all tissues in an organism (Ly et al. 2000).Five genes (EEF1A2, DBH, ITGB2, TUBB2C, and WRN) take part in regulation of apoptosis which plays an important role in the aging process and tumorigenesis (Salvioli et al. 2008).Seven genes (ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, and SOAT2) are involved in lipid metabolism which plays one of the key roles in human longevity and healthy aging (Barzilai et al. 2003).", + "Genes that are age-regulated in all tissues would reveal genes involved in core mechanisms that underlie cellular ageing.Zahn et al. [63] discovered genetic pathways that show common age regulation in human kidney, brain and muscle.They used microarrays to analyse expression in 81 skeletal muscle samples from patients aged 16 -86 years and found 250 age-regulated muscle genes [63].Similar to the ageing expression profile for the kidney, the overall expression behaviour of this set of age-regulated muscle genes correlated with the physiological as well as chronological age of the muscle sample.Next, they compared their muscle-ageing results to previously published data on kidney and brain ageing of similarly large sample size [56,60].Although most of the age-related changes were tissue specific, they found evidence for common age regulation of six genetic pathways in all three tissues.Specifically, there is an overall increase in expression of the extracellular matrix genes, the ribosomal genes, the cell growth genes and the complement activation genes in all three tissues.Increased overall expression of the extracellular matrix and complement activation gene sets with advancing age may contribute to widespread fibrosis and inflammation in the elderly.There is an overall decrease in expression of the chloride transport genes and the electron transport genes in all three tissues.Decreased overall expression of electron transport chain genes with age might support the mitochondrial free-radical theory of ageing [67], as free-radical generation by mitochondria would preferentially damage the electron transport chain protein complexes.Decreased expression of the electron transport genes (encoded in the nucleus) might be caused by feedback regulation from damage to the electron transport chain protein complexes [63].However, it is also possible that increased oxidative damage occurs as a consequence of the decreased expression of the electron transport chain genes.In addition, an increasing number of studies in model organisms have critically challenged the mitochondrial free-radical theory of ageing [68].", + "DiscussionAging studies from model organisms such as yeast, worms, and flies have repeatedly shown that changes in the expression of certain genes have an effect upon longevity.Although similar aging processes are likely to operate across multiple species [30], it has been much more difficult to identify longevity candidate genes in human studies [30].A key question in human aging is to what extent a signature of aging may be detectable across tissues.Until now there has been a lack of large transcriptional profiles from the same human individuals in multiple tissues.The MuTHER study provides insight into the human aging process by interrogating the largest multiple human tissue gene expression resource to identify genes in which expression was affected by chronological age.The analysis of the skin and adipose tissues samples identified several hundred genes responsive to changes in chronological age.However, the 43 shared genes in skin and adipose tissue showed a single common identifiable pathway related to the stress response.From over 1,800 transcripts that have altered expression with age in skin and adipose tissues, 14 also had age-related differential expression in brain.The limited overlap in these two experiments may partly reflect the smaller sample size of the brain expression dataset, the differences in age range between the studies (16 to 83 years for brain samples; 39 to 85 years for MUTHER samples), or the inclusion of males in the brain samples.But it may also imply, as other studies have suggested, that the effects of age on gene transcription are tissue specific [6,31,32].This hypothesis was supported by the comparison with known related aging genes from the GenAge database, which identified an overlap for a small number of aging-related genes with our data.The GenAge database was the result of a meta-analysis using age-related expression profiles from human brain, kidney, and skeletal muscle, and several expression profiles from mouse and rat; no adipose tissue or skin samples were included (Additional file, Table 1 in [7]).The limited overlap between these datasets supports the idea that molecular signatures of aging reflect predominantly a tissue-specific transcriptional response.The lack of age-related genes in transformed LCLs, suggest that the transformation to immortalize a cell line may mask or even remove the age-related signatures in gene expression.The transformation of primary B lymphocytes into LCLs requires infection by the Epstein-Barr virus which has the effect of disrupting the p53 signaling pathway in order to induce growth and survival [33].Joehanes et al. [15] identified only five genes with age-associated expression in LCLs, including p53 itself (TP53).Although the authors attribute the lack of age-affected genes to their small sample size (n=50) and narrow age range, our analysis with a much larger sample size found even fewer age-related changes, suggesting a lack of detectable aging signature in LCLs.The analysis in the subset of fresh lymphocytes suggested an age influence in fresh lymphocytes may potentially be detectable with a larger sample size.", + "Genes Whose Expression Decreased with Age.Of the 26 genes that decreased expression with age in control mice, 23% are involved in DNA replication and the cell cycle (Table 2).Most of these have a negative effect on cell growth and division.Among these, the product of phosphatase and tensin homolog (Pten) gene is a tumor suppressor that induces cell-cycle arrest through inhibition of the phosphoinositide 3-kinase pathway (28).B cell translocation gene 2 (Btg2) is a tumor suppressor that increases expression in response to DNA damage (29).The murine gene product of the amino-terminal enhancer of split (Aes) is a potent corepressor of gene expression and cellular proliferation (30).Calcium-binding protein A11 (S100a10) binds to and regulates the activity of annexin II, which is involved in the transduction of calcium-related mitogenic signals (31).Insulin-like growth factor (IGF) binding protein 1 (Igfbp1) plays an important role in the negative regulation of the IGF-1 system, a stimulator of mitogenesis (32).", + "daf-16 dependent genesAmong the 52 genes that we have tested, 29 genes act almost completely in a daf-16 dependent manner, to regulate lifespan (Table 2).One of the genes identified was daf-2 (Y55D5A_391.b).This serves as a proof of principle that our screen is effective in identification of aging genes.", + "Several of the genes we identify have previously been shown to influence lifespan in experiments on model organisms.For example, knockouts of the orthologs of APOE, LDLR, CDKN2B, and RBM38 in mice shortens their lifespan [24][25][26][27] , while knockout of IGF1R has the opposite effect 28 .Similarly, overexpression of the FOXO3 orthologue in Drosophila melanogaster 29 and the SNCA orthologue in Caenorhabditis elegans 30 have shown to extend their respective lifespans.Many of our genes are also enriched for pathways previously related to ageing in eukaryotic model organisms, including genomic stability, cellular senescence, and nutrient sensing 31 .For example, FOXO3 and IGF1R are well-known players modulating survival in response to dietary restriction 32 , but we also highlight genes involved in the response to DNA damage and apoptosis, such as CDKN2B, USP28, E2F2, and BCL3.In addition to hallmarks discovered in model organisms, our results suggest that haem metabolism may play a role in human ageing.This pathway includes genes involved in processing haem and differentiation of erythroblasts 33 .Although the enrichment is largely driven by genes linked to the LDLR locus, genes linked to other loci of interest (such as FOXO3, CDKN2B, LINC02513) are involved in similar biological pathways: myeloid differentiation, erythrocyte homeostasis, and chemical homeostasis.", + "Hundreds of genes in several pathways act as regulators of ageing (1,32).However, analysis of DrugAge and other HAGR databases has revealed that the overlap between the targets of lifespan-extending drugs and known ageing related genes is modest (31).This indicates that most ageing-related pathways have yet to be targeted pharmacologically; DrugAge may aid in guiding further assays.This was recently demonstrated in one study where machine learning was used to predict whether a compound would increase lifespan in worms using data from Dru-gAge.The best model had 80% prediction accuracy and the top hit compounds could broadly be divided into compounds affecting mitochondria, inflammation, cancer, and gonadotropin-releasing hormone (33).", + "Top 25genes co-expressed with aging related genesAging-related gene prediction and putative transcriptional mechanismsGeneFriends was used to identify genes related to aging.A seed list of genes known to be consistently overexpressed with age in mammals was used [18].In total, 1119 genes were co-expressed with the aging seed list at p <10 -6 ; Table 1 shows the top 25 genes.Many of these genes have been associated with age-related diseases.Several other genes that have been shown to play a role in aging such as lysosomal-associated membrane protein-2 Lamp2 [19] (p = 5.68 -30 ), Fas [20] (p = 2.70 -31 ) and growth hormone receptor Ghr [21] (p = 1.34 -19 ) also showed a significant co-expression.Anxa2, Anxa3 and Anxa4 also show a low p-value (p < 10 -25 ) as well as several S100 calcium binding proteins which have been shown to interact with annexins [22].", + "Genetic studies have shown that aging can be slowed in mutants that are defective in a wide range of cellular processes (such as mitochondrial function, chromatin regulation, insulin signaling, transcriptional regulation, and genome stability).This indicates that aging is a complex process driven by diverse molecular pathways and biochemical events.As such, a powerful approach to study aging is to use systems biology, which allows a multitude of factors affecting aging to be analyzed in parallel.For example, DNA microarrays and gene expression chips have been used to perform a genome-wide analysis of changes in gene expres-sion in old age.Extensive studies in Caenorhabditis elegans and Drosophila melanogaster have identified hundreds of ageregulated genes (Hill et al. 2000;Zou et al. 2000;Lund et al. 2002;Pletcher et al. 2002;Murphy et al. 2003).Several studies have described age-regulated genes in the muscle and brain of mice (Lee et al. 1999(Lee et al. , 2000) ) and the retina and muscle of humans (Yoshida et al. 2002;Welle et al. 2003Welle et al. , 2004).These age-regulated genes may serve as markers of aging, enabling one to assess physiological age independently of chronological age.Analysis of the functions of these age-regulated genes has identified specific biochemical mechanisms that change toward the end of life.", + "Age-Regulated Genes Involved in Reproductive Capacity.Decline in reproductive capacity is an age-related phenotype, and the reproductive system seems to play an important role in longevity (22).For example, signals from germ cells can affect lifespan in C. elegans (23).In our study, we observed decreased RNA levels for several genes involved in reproduction (Fig. 3).These include two genes that encode members of the Acp family.The Acp from male flies stimulates female egg-laying and facilitates storage of sperm in the female genital tract (24).In addition, two ESTs showing age-regulated decrease of transcript levels represent different genes with homology to Arabidopsis MALE STERIL- In Northern analysis, the ratios were calculated by dividing mRNA levels at 25-, 40-, and 50-day time points by those at 3-day time points after normalization with mRNA levels of the control gene rp49.Ratios in microarray analysis are provided from each of the duplicate experiments for comparison.ITY 2 (MS2; ref. 25), a gene involved in gametogenesis.Furthermore, an EST with homology to peanut, a member of the septin family (26), is down-regulated in older flies.This downregulation may reflect a decrease in spermatogenesis." + ], + [ + "There are multiple definitions of the aging process.Aging may be perceived as the random, systemic loss of molecular fidelity that, after reproductive maturity, accumulates to levels that eventually exceed tissue repair, turnover, or maintenance capacity (Hayflick 2004).The underlying molecular mechanisms of aging remain a subject of debates (de Magalhaes et al. 2009): tissue deterioration might not be programmed, being just a function of increase in entropy (Hayflick 2004).No genes are necessary to drive a stochastic process; however, there are genes that act to prevent an organism from destruction and disorganization.It may be due to the absence of specific disease-causing alleles or due to the presence of favorable alleles (Halaschek-Wiener et al. 2009).These genes may inhibit entropy, regulate inflammation, maintain DNA repair (such as telomere maintenance factors), or provide antioxidant functions (e.g., antagonists of reactive oxygen species).As healthy cells adapt to degeneration, differential expression of genes with age may indicate a transcriptional response to aging rather than a deleterious mechanism of aging per se (de Magalhaes et al. 2009).It might be postulated that there exist alleles that confer a pleiotropic effect on structure and function during aging (Lunetta et al. 2007).These alleles should regulate the ability of an organism to withstand challenging endogenous and exogenous influences.", + "Why does ageing evolve? The intrinsic decline in function that occurs during ageing appears to be caused by the accumulation of damage, particularly at the molecular level.As far as we know, no genes have evolved specifically because they cause damage to accumulate, and the evolution of ageing can therefore be understood only as a side-effect of other causes of evolutionary change.The mechanisms by which ageing can evolve were first elucidated by J.B.S. Haldane [14], P.B. Medawar [15] and G.C. Williams [16].Extrinsic hazards from disease, predation and accidents mean that even potentially immortal organisms will die.Genetic effects that become apparent only later in life encounter a reduced force of natural selection, because not all their bearers will survive to express them.Haldane pointed out that late-onset genetic diseases in humans, such as Huntington's disease, encounter only weak selection, because most reproduction is complete by the age of onset [14].Ageing could therefore result from the accumulation under mutation pressure of age-specific, deleterious mutations.In addition, if some mutations have pleiotropic effects, with beneficial effects in youth, such as high fecundity, but also with a higher subsequent rate of ageing, then they could be incorporated into the population by natural selection, which will act more strongly on the early, beneficial effect.Thus, variation in the rate of ageing would result from the readjustment of a tradeoff between youthful benefits and the subsequent rate of ageing.Both processes imply that faster ageing will evolve where the extrinsic hazard to adults is greatest, a hypothesis in general supported by the data [1,2,17].", + "A. TheoriesIn looking back at the development of aging studies, we can see that it did not follow a straight or logical course.On the contrary, it can be compared with the flow of several convergent streams winding in their course.To date, numerous proposals have been made for the paradigm of aging.These include Hayflick's contributions (153) on programmed cellular incapacitation derived from flbroblast studies, a decrease in immunologic response, deleterious endocrinological changes, nuclear somatic gene mutation, mitochondrial somatic gene mutation, oxygen free radical damage to proteins and nucleic acids, molecular instabilities, molecular cross-linking, glycation reactions, and so on.There is little doubt that many of these factors contribute to the overall aging, but what are primary causes, and what are secondary outcomes?", + "Ageing Is Adjusted by Genetic, Environmental, and Stochastic ProcessesEnough evidence suggests that ageing is the result of different events such as molecular damage, mutations, incomplete repair, genetic programs, and continued development, among others [16].These events, in turn, are caused by genetic factors, environmental conditions, and even stochastic factors, which are mentioned below in this chapter.Different stochastic theories of ageing focus on specific mechanisms that may lead to ageing.The catastrophic error theory poses that the accumulation of errors in protein synthesis causes damage in cell function.The theory of cross-linking holds this process between proteins and other macromolecules responsible for ageing, while the theory of free radicals suggests that ageing is the result of inadequate protection against cell and tissue damage by free radicals and oxidative stress throughout life.Finally, the wear-and-tear theory poses that the cumulative damage that eventually leads to ageing and death is, in fact, the result of the continuous functioning of vital processes, during which stochastic errors gradually arise.IntroductionAging is a natural and irreversible process characterized by a progressive decay in physiological, biochemical, and structural functions of individuals.Aging is a multifactorial process that can be affected by two main factors: environmental and genetic.Environmental factors are nutrition, pathologies, pollution exposure, physical activity, and microbiota, while genetic factors are issues that have been associated with antioxidant and DNA damage responses, the fidelity of genetic information transfer, the efficiency of protein degradation, the extent of cellular responsiveness to stress, the mechanisms of epigenetic regulation, and the ability to elongate telomeres.All of them can determine how fast we age.Traditionally, aging studies had used several model organisms, from yeast to mammals, especially rodents (rats and mice).Most of the studies are made under controlled conditions, where only a few variables are observed, and the subjects are members of the same strain with the same genetic backgrounds or the same mutations.The information that so far has been obtained about aging has helped us to describe different factors that influence this process and that are the fundamental concepts of the various theories of aging.However, these theories do not fully explain the aging process in the different models of aging study.This is the case of the study of aging in humans, where it is very difficult to control the environmental and genetic variables.That is why issues haven't been solved such as the following: How does time influence aging?When do we start to age?How do we know we are old?Is it possible to delay aging?Those and more questions are the cornerstones for aging studies.Biological aging has been associated with the decrease in the repair and regeneration capacity of tissues and organs; it is a time-dependent process.This reduction can be observed by an increase in the acquisition of diseases and functional and reproductive disability, which eventually lead to death.On the other hand, it has been observed that in humans, people with the same chronological age exhibit different trajectories in the decrease of physiological functions associated with biological aging and what complicates the understanding of the molecular and physiological phenomena that drive the complex and multifactorial processes that underlie biological aging in humans.", + "The underlying cause of aging remains one of the central mysteries of biology.Recent studies in several different systems suggest that not only may the rate of aging be modified by environmental and genetic factors, but also that the aging clock can be reversed, restoring characteristics of youthfulness to aged cells and tissues.This Review focuses on the emerging biology of rejuvenation through the lens of epigenetic reprogramming.By defining youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges.", + "Aging does not happen in a vacuum.Aging must be the result of changes that occur in molecules that have existed at one time with no age changes.It is the state of these pre-existing molecules that governs longevity determination.The pre-existing state is, as I have already described, maintained by repair and turnover systems that themselves eventually succumb to irreparable age changes.Longevity determination is the state of all molecules prior to succumbing to irreparable loss of molecular structure.Biological aging is more than simply the occurrence of random changes in molecules.It also includes the role of the many repair systems found within cells.Thus, a more complete, but less concise, explanation of the first causes of aging in biological systems is the following:", + "Understanding the deleterious processes that cause aging has been a human endeavor ever since we figured out that we grew old and that we didn't like it.Many hypotheses have been proposed to explain the root cause of aging (1).One broad-based hypothesis is that generalized homeostatic failure leads to age-related decline.Although notions of time-and use-related deterioration may be applicable to mechanical objects, they fall short as analogies to biological systems because energy input should theoretically maintain living systems indefinitely.Yet, despite the regenerative potential of biological organisms, progressive deterioration accompanies postmaturational aging.That the organism's repair capabilities cannot keep up with wear and tear is, according to evolutionary theory, explained by the inevitable declining force of natural selection with age.According to this reasoning, there is no selective advantage to maintaining somatic cells in perfect order much beyond reproductive maturation (1).Hence, a long life depends on the timing of maturation and the quality of somatic cell maintenance.Wear and tear on the DNA often has been touted as a possible basis for our progressive age-related decline.Supporting this notion is the work of de Boer et al. (2) reported on page 1276 of this week's issue.They reveal important evidence for imperfect genome maintenance of DNA damage as a possible causal factor in aging.Harman, with his \"free radical theory of aging\" (3), was the first to propose that metabolic by-products called reactive oxygen species (ROS) continually damage cellular macromolecules, including DNA.Incomplete repair of such damage would lead to its accumulation over time and eventually result in age-related deterioration.A number of observations support the free radical theory, including the discovery that dietary restriction delays aging and extends life-span in a wide range of rodents and other species, possibly by reducing free radical damage.The notion that genomic DNA could be a major target of continual free radical attack over time is supported by the recent observation that genetic lesions accumulate with age and that dietary restriction reduces this accumulation in rodents (4).In addition, deletion of p66 shc , a signaling protein that maintains oxidant levels, increases resistance to oxidative damage and extends the life-span of mice (5).", + "Instead, aging is expected tobe a pervasive failure of adaptation across most, if not all, of the physiological mechanismsthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute the cause ofaging to any one physiological mechanism or gene for aging or programmed death. Although common genetic pathways might be identified that contribute to aging among avariety of organisms (cf.", + "BackgroundAging is a complex process characterized by the progressive degeneration of a healthy phenotype and correlated with a decline in the ability to withstand cellular stress and damage.The subject of investigation for decades, the underlying molecular genetic causes of and responses to aging remain an area of active study.Research from model systems has characterized a range of physiological and molecular phenotypes associated with aging.These include genomic instability caused by accumulation of DNA damage, dysregulation of repair mechanisms, and telomere attrition; epigenetic alterations; dysregulation of transcription; loss of proteostasis; cellular senescence; and deregulated nutrient sensing, metabolic pathways, and energy use (reviewed in [1]).Separating causation from correlation between these phenotypes and aging remains a challenge, however.", + "IntroductionUnderstanding what actually causes ageing remains admittedly a fundamental and fascinating problem in biology [1].Experimental data accumulated in the last three decades have led to the identification of various environmental and genetic factors, as well as chemical substances that influence lifespan in divergent eukaryotic species [1,2].Organisms normally age faster and hence live shorter under stress conditions that can lead to the generation of DNA mutations and, often as a consequence of mutations, damaged cytoplasmic constituents (including injured proteins, lipids, carbohydrates and organelles).Such types of damage can interfere with cellular functioning; thereby, they should be eliminated by effective repair and self-cleaning mechanisms to maintain cellular homeostasis.These mechanisms include DNA repair pathways, molecular chaperons, as well as the proteasome-ubiquitin system and lysosome-mediated autophagy, the main forms of cellular self-degradation [3].This has led to the attractive model that the gradual, lifelong accumulation of unrepaired cellular damage drives the ageing process and determines the incidence of age-related fatal diseases [4,5].", + "In conclusion, aging may not be primarily due to damage accumulating from the basic biochemical reactions that make up life but rather the result of the developmental program or of changes brought about by it.Our hypothesis is that the timing of development regulates the rate of aging among mammals, with a subset of developmental mechanisms determining the pace and causing most agerelated changes.Maybe people change as they grow old due to the same mechanisms that drive changes throughout the earlier stages in life.", + "Instead, aging is expected tobe a pervasive failure of adaptation across most, if not all, of the physiological mechanismsthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute the cause ofaging to any one physiological mechanism or gene for aging or programmed death. Although common genetic pathways might be identified that contribute to aging among avariety of organisms (cf.", + "In 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13].", + "IntroductionThe fundamental manifestation of the aging process is a progressive decline in the functional maintenance of tissue homeostasis and an increasing propensity to degenerative diseases and death [1].It has attracted significant interest to study the underlying mechanisms of aging, and many theories have been put forward to explain the phenomenon of aging.There is an emerging consensus that aging is a multifactorial process, which is genetically determined and influenced epigenetically by environment [2].Most aging theories postulate a single physiological cause of aging, and likely these theories are correct to a certain degree and in certain aspects of aging.", + "Many factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics.", + "Trying to explain aging in terms of a singular process would be in conflict with evolutionary theory.Even if loss of genome sequence integrity was the most conserved cause of aging, already active in the first replicators (Vijg, 2007), natural selection would allow a multitude of mutations with late adverse effects to accumulate in the germline, many of which would be positively selected for because of their beneficial effects early in life (Williams, 1957), In this respect, somatic mutation accumulation could be a conserved, inevitable cause of aging but superposed on multiple other processes that usually cause the earlier demise of an individual." + ] + ], + "task_id": [ + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_aging_2.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_aging_2.json new file mode 100644 index 0000000..e2ed5ed --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_aging_2.json @@ -0,0 +1,110 @@ +{ + "question": [ + "which genes are involved in aging", + "what genes are involved in the aging process", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What genetic factors influence aging in humans? " + ], + "answer": [ + "Several genes are involved in aging, including BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, CDKN2B, USP28, E2F2, BCL3, NAP1L4, GAB2, QKI, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, and several S100 calcium binding proteins.", + "Several genes are involved in the aging process. These include BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, IGF-1, CYP19, NAP1L4, GAB2, QKI, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, USP28, E2F2, BCL3, mre11, rad50, Ku80, mus308, mus205, DNApol-eta, Trxr-1, GlnRS, isoQC, QC, Cct1, Cct4, Cct5, Cct6, Hsc70-4, Xbp1, NAT1, Rack1, Rheb, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, SIR2, DAF-2/IGF1R, DAF-16/FOXO, AAK-2/AMPK, LET-363/mTOR, SIRT1/SIR2, p53, Werner's syndrome gene, iff-1, SIR2, TERT, ZBTB16, CA9, HEY2, Smarca4, Amarcb1, Hdac1, -5, -6, Dnmt3b, and genes in the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans." + ], + "contexts": [ + [ + "Following are examples of the identified genes and experimental or GWAS link between these genes and aging.On the list of the 25 top genes, NAP1L4 encodes a member of the nucleosome assembly protein (NAP) family, which interacts with both core and linker histones, and shuttles between the cytoplasm and nucleus, suggesting a role as histone chaperone.Histone protein levels decline during aging, and dramatically affect chromatin structure.Remarkably, the lifespan can be extended by manipulations that reverse the age-dependent changes to chromatin structure, indicating the pivotal role of chromatin structure in aging [32].In another example, gene expression of NAP1L4 increases with age in the skin tissue [33].Findings of GWAS link a number of the identified genes to age-related disorders, such as GAB2 and late onset Alzheimer's disease [86], and QKI and coronary heart disease/myocardial infarction [79].Interestingly, GWAS reports also link QKI to successful aging [87].Indicative biological pathways associated with the candidate aging genes", + "Examples of biological candidate genes with pleiotropic functions, which are involved in aging in general and in musculoskeletal aging in particular, are numerous: (a) in addition to the IGF-1 and vitamin D genes, estrogen metabolism pathway genes, including estrogen receptors and aromatase (CYP19), are associated with fat-free mass (Walsh et al. 2005) and BMD (Shearman et al. 2004), prostate and breast cancer (Gallicchio et al. 2006), and cardiovascular disease risk (Shearman et al. 2003).", + "In-depth analysis of the age-regulated genes revealed that multiple genes in the DNA damage response pathway were upregulated with age including those that function in non-homologous end-joining repair (mre11, rad50, Ku80 and mus308) and in translesion DNA synthesis (mus205 and DNApol-eta) [44][45][46].Genes that encoded enzymes with antioxidant properties, such as the thioredoxin reductase Trxr-1, and antioxidant genes involved in glutamate metabolism, such as GlnRS, isoQC and QC, were also upregulated with age [47][48][49][50].We also observed increased age-associated expression of chaperone genes (Cct1, Cct4, Cct5, Cct6, Hsc70-4) and the unfolded protein response transcription factor Xbp1, consistent with an induction of the unfolded protein response [51][52][53].Under stress conditions, there is a translational switch that favors production of stressrelated proteins while decreasing translation of other proteins [54].Paralogs of canonical translation factors such as NAT1 and Rack1, which were both upregulated, promote this switch to cap-independent translation [55,56].Notably, Rheb, which is downregulated with age, positively regulates ribosome production and capdependent translation by activating the mechanistic target of rapamycin (mTOR) kinase pathway [57].Thus, decreased Rheb levels during aging could decrease mTOR pathway activity, which extends lifespan and is protective against age-related pathology [58].Together, these data suggest that multiple genes are induced in aging photoreceptors to mitigate the effects of oxidative stress, protein misfolding and DNA damage.", + "CellAge vs human orthologues of longevity-associated model organism genesTo understand how senescence is linked to the genetics of aging processes, we looked at the intersection of CellAge genes and the 869 genes in the human orthologues of model organisms' longevity-associated genes (LAGs) dataset, collected based on quantitative changes in lifespan [34].Like CellAge, where genes are classified based on whether their upregulation induces, inhibits, or has an unknown impact on CS, the longevity orthologues dataset also provides information on the effect of upregulation of its genes, namely whether it promotes (pro, 421) or inhibits (anti, 448) longevity (Additional file 1: Table S7; Additional file 2: Fig. S2).", + "Analysis of prior research (Online Resource 5) shows that the revealed genes can be explicitly involved in other key biological processes in an organism whose role is known to be changing with aging.Specifically, ten genes (BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, and ZKSCAN1) regulate transcription which is believed to be disrupted when an organism is getting older (Roy et al. 2002).The DBH, TPO, and LSS genes are involved in synthesis of catecholamine, thyroid, and vitamin D hormones, respectively.The GPER binds estrogen and HCRTR2 binds orexin-A and orexin-B neuropeptid hormones.Hormonal deregulation with aging is considered to be one of the major components of senescent processes in an organism (Barzilai and Gabriely 2010).Five genes (ATG2A, NEDD4L, PSMB1, UBXN4, and USP6) are involved in degradation of proteins through ubiquitin-proteasome and the lysosomal/autophagic system.Dysfunction of this system leads to accumulation of damaged proteins in an organism that is associated with aging (Koga et al. 2011).Protein degradation through ubiquitin-mediated proteolysis plays an important role in cell-cycle regulation (Reed 2003).The PSMB1, SIK1, TRIP13, and TTN genes in the revealed set coordinate cell cycle.Cell cycle is linked with the aging-related processes in humans through a gradual increase in cell division errors in all tissues in an organism (Ly et al. 2000).Five genes (EEF1A2, DBH, ITGB2, TUBB2C, and WRN) take part in regulation of apoptosis which plays an important role in the aging process and tumorigenesis (Salvioli et al. 2008).Seven genes (ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, and SOAT2) are involved in lipid metabolism which plays one of the key roles in human longevity and healthy aging (Barzilai et al. 2003).", + "In addition to testing genes known to be associated with age-related diseases and phenotypes for association with longevity, genes known to promote longevity in model organisms have been examined in human populations.Mutations in insulin or insulinlike signalling pathway genes have been shown to extend lifespan in Caenorhabditis elegans [20], Drosophila melanogaster [21,22] and mice [23,24].The insulin-signalling pathway negatively regulates the forkhead (FOXO) transcription factor [25].When insulin or insulin-like growth factor signalling is low, FOXO is activated and lifespan extension occurs [26].An overrepresentation of rare insulin-like growth factor I receptor (IGFIR) mutations has been observed in centenarians [27].These mutations are associated with reduced activity of IGFIR as measured in transformed lymphocytes [27].", + "Genes Whose Expression Decreased with Age.Of the 26 genes that decreased expression with age in control mice, 23% are involved in DNA replication and the cell cycle (Table 2).Most of these have a negative effect on cell growth and division.Among these, the product of phosphatase and tensin homolog (Pten) gene is a tumor suppressor that induces cell-cycle arrest through inhibition of the phosphoinositide 3-kinase pathway (28).B cell translocation gene 2 (Btg2) is a tumor suppressor that increases expression in response to DNA damage (29).The murine gene product of the amino-terminal enhancer of split (Aes) is a potent corepressor of gene expression and cellular proliferation (30).Calcium-binding protein A11 (S100a10) binds to and regulates the activity of annexin II, which is involved in the transduction of calcium-related mitogenic signals (31).Insulin-like growth factor (IGF) binding protein 1 (Igfbp1) plays an important role in the negative regulation of the IGF-1 system, a stimulator of mitogenesis (32).", + "daf-16 dependent genesAmong the 52 genes that we have tested, 29 genes act almost completely in a daf-16 dependent manner, to regulate lifespan (Table 2).One of the genes identified was daf-2 (Y55D5A_391.b).This serves as a proof of principle that our screen is effective in identification of aging genes.", + "Several of the genes we identify have previously been shown to influence lifespan in experiments on model organisms.For example, knockouts of the orthologs of APOE, LDLR, CDKN2B, and RBM38 in mice shortens their lifespan [24][25][26][27] , while knockout of IGF1R has the opposite effect 28 .Similarly, overexpression of the FOXO3 orthologue in Drosophila melanogaster 29 and the SNCA orthologue in Caenorhabditis elegans 30 have shown to extend their respective lifespans.Many of our genes are also enriched for pathways previously related to ageing in eukaryotic model organisms, including genomic stability, cellular senescence, and nutrient sensing 31 .For example, FOXO3 and IGF1R are well-known players modulating survival in response to dietary restriction 32 , but we also highlight genes involved in the response to DNA damage and apoptosis, such as CDKN2B, USP28, E2F2, and BCL3.In addition to hallmarks discovered in model organisms, our results suggest that haem metabolism may play a role in human ageing.This pathway includes genes involved in processing haem and differentiation of erythroblasts 33 .Although the enrichment is largely driven by genes linked to the LDLR locus, genes linked to other loci of interest (such as FOXO3, CDKN2B, LINC02513) are involved in similar biological pathways: myeloid differentiation, erythrocyte homeostasis, and chemical homeostasis.", + "In recent years, some of the aging-related genes identified in worms have been shown to have mammalian homologs that modulate longevity and delay age-related diseases in mice, in particular as part of the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway (Bartke, 2005), and variants in these genes have even been associated with human longevity, such as the daf-2 homolog IGF1R (Suh et al., 2008).Therefore, there is great potential for human homologs of genes shown to modulate aging in model organisms to represent pharmaceutical targets with human applications.", + "Hundreds of genes in several pathways act as regulators of ageing (1,32).However, analysis of DrugAge and other HAGR databases has revealed that the overlap between the targets of lifespan-extending drugs and known ageing related genes is modest (31).This indicates that most ageing-related pathways have yet to be targeted pharmacologically; DrugAge may aid in guiding further assays.This was recently demonstrated in one study where machine learning was used to predict whether a compound would increase lifespan in worms using data from Dru-gAge.The best model had 80% prediction accuracy and the top hit compounds could broadly be divided into compounds affecting mitochondria, inflammation, cancer, and gonadotropin-releasing hormone (33).", + "Aging-related gene prediction and putative transcriptional mechanismsGeneFriends was used to identify genes related to aging.A seed list of genes known to be consistently overexpressed with age in mammals was used [18].In total, 1119 genes were co-expressed with the aging seed list at p <10 -6 ; Table 1 shows the top 25 genes.Many of these genes have been associated with age-related diseases.Several other genes that have been shown to play a role in aging such as lysosomal-associated membrane protein-2 Lamp2 [19] (p = 5.68 -30 ), Fas [20] (p = 2.70 -31 ) and growth hormone receptor Ghr [21] (p = 1.34 -19 ) also showed a significant co-expression.Anxa2, Anxa3 and Anxa4 also show a low p-value (p < 10 -25 ) as well as several S100 calcium binding proteins which have been shown to interact with annexins [22].Top 25genes co-expressed with aging related genes", + "Fig. 7 Functional relationships of genes implicated in longevity.The genes in red/blue boxes represent genes with increased/decreased mRNA expression in ageing Drosophila (color figure online)", + "The genome-wide RNAi study conducted by the Ruvkun lab, authored by Hamilton et al. [88], identified a total of 89 additional aging genes with disparate functions including cell structure, cell surface proteins, cell signaling, cellular metabolism, and protein turnover.Of the 66 genes with previously known functions, 17 corresponded to various aspects of carbon metabolism, including citric acid cycle enzymes and subunits of complexes I, IV, and V of the ETC.Researchers also speculated that protein translation might play a role in lifespan regulation, based on the identification of iff-1 (T05G5.10),a gene that has homology to the translation initiation factor eIF5A.Other hits from this screen included two genes containing PH domains known to interact with phosphatidylinositol lipids, multiple G protein-coupled receptors, protein processing and degradation genes such as proteases and ubiquitin ligases/hydrolases, and chromatin modifying factors.", + "INTRODUCTIONAging has fascinated researchers since ancient times.The hugely complicated process that has been revealed may be interpreted from different aspects, such as the accumulation of oxidative damage, shortening of telomeres, the costs of reproduction, metabolic rates, cellular senescence, etc., and these have in turn given rise to diverse theories of aging [1].However, thanks to forward and reverse genetic technologies, researchers in the recent decades have established that despite its complexity, a single or a few key genes in a few key pathways can modulate the aging rate.The most important players would appear to be those in nutrient sensing pathways or stress response pathways, such as DAF-2/IGF1R and DAF-16/FOXO in the Insulin/IGF like signaling pathway, AAK-2/AMPK in another nutrient sensing pathway, JNK in the stress response pathway, LET-363/mTOR as an inhibitor of autophagy and activator of translation and SIRT1/SIR2 in genome stability maintenance, to name a few [2,3].In addition to genetic perturbations, dietary perturbations, such as diet restriction (DR) are known to significantly extend lifespan in most organisms examined from yeasts to primates, although different pathways may act under different DR conditions, and alternative DR strategies also effect C.elegans lifespan in different ways [3,4].The main pathways revealed under different DR regimens are summarized in Fig. (1).In this small, convoluted DR response network, DAF-16 and ceTOR/LET-363 *Address correspondence to this author at the Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China; Tel: 86-21-54920458; Fax: 86-21-54920451; E-mail: jdhan@picb.ac.cn These authors contributed equally to this work.", + "IntroductionAging affects nearly all organisms and is a major risk factor in most human diseases.Recent work has begun to uncover molecular mechanisms that specify lifespan and to identify alterations in cellular physiology that occur at the end of life (Tissenbaum and Guarente 2002).For example, oxidative damage caused by the generation of free radicals in the mitochondria has been found to hasten aging by causing an accumulation of damaged cellular components (Droge 2003).Telomere shortening may also play a role in aging by preventing DNA replication and cell division in later years (Hasty et al. 2003).Genetic studies have identified many genes that play a role in specifying lifespan.For example, mutations in yeast sir2 (chromatin regulator), worm daf-2 (insulin-like growth factor receptor), fly methuselah (tyrosine kinase receptor), mouse p53, and the human Werner's syndrome gene (DNA helicase) cause dramatic changes in lifespan (Guarente and Kenyon 2000).Several aging mechanisms alter longevity in multiple organisms.For example, mutations in the gene encoding insulin-like growth factor receptor alter lifespan in worms, flies, and mice, indicating that an endocrine signaling pathway has a conserved role in aging (Hekimi and Guarente 2003).Genetic studies have shown that aging can be slowed in mutants that are defective in a wide range of cellular processes (such as mitochondrial function, chromatin regulation, insulin signaling, transcriptional regulation, and genome stability).This indicates that aging is a complex process driven by diverse molecular pathways and biochemical events.As such, a powerful approach to study aging is to use systems biology, which allows a multitude of factors affecting aging to be analyzed in parallel.For example, DNA microarrays and gene expression chips have been used to perform a genome-wide analysis of changes in gene expres-sion in old age.Extensive studies in Caenorhabditis elegans and Drosophila melanogaster have identified hundreds of ageregulated genes (Hill et al. 2000;Zou et al. 2000;Lund et al. 2002;Pletcher et al. 2002;Murphy et al. 2003).Several studies have described age-regulated genes in the muscle and brain of mice (Lee et al. 1999(Lee et al. , 2000) ) and the retina and muscle of humans (Yoshida et al. 2002;Welle et al. 2003Welle et al. , 2004).These age-regulated genes may serve as markers of aging, enabling one to assess physiological age independently of chronological age.Analysis of the functions of these age-regulated genes has identified specific biochemical mechanisms that change toward the end of life.", + "In addition to testing genes known to be associated with age-related diseases and phenotypes for association with longevity, genes known to promote longevity in model organisms have been examined in human populations.Mutations in insulin or insulinlike signalling pathway genes have been shown to extend lifespan in Caenorhabditis elegans [20], Drosophila melanogaster [21,22] and mice [23,24].The insulin-signalling pathway negatively regulates the forkhead (FOXO) transcription factor [25].When insulin or insulin-like growth factor signalling is low, FOXO is activated and lifespan extension occurs [26].An overrepresentation of rare insulin-like growth factor I receptor (IGFIR) mutations has been observed in centenarians [27].These mutations are associated with reduced activity of IGFIR as measured in transformed lymphocytes [27]." + ], + [ + "Following are examples of the identified genes and experimental or GWAS link between these genes and aging.On the list of the 25 top genes, NAP1L4 encodes a member of the nucleosome assembly protein (NAP) family, which interacts with both core and linker histones, and shuttles between the cytoplasm and nucleus, suggesting a role as histone chaperone.Histone protein levels decline during aging, and dramatically affect chromatin structure.Remarkably, the lifespan can be extended by manipulations that reverse the age-dependent changes to chromatin structure, indicating the pivotal role of chromatin structure in aging [32].In another example, gene expression of NAP1L4 increases with age in the skin tissue [33].Findings of GWAS link a number of the identified genes to age-related disorders, such as GAB2 and late onset Alzheimer's disease [86], and QKI and coronary heart disease/myocardial infarction [79].Interestingly, GWAS reports also link QKI to successful aging [87].", + "Examples of biological candidate genes with pleiotropic functions, which are involved in aging in general and in musculoskeletal aging in particular, are numerous: (a) in addition to the IGF-1 and vitamin D genes, estrogen metabolism pathway genes, including estrogen receptors and aromatase (CYP19), are associated with fat-free mass (Walsh et al. 2005) and BMD (Shearman et al. 2004), prostate and breast cancer (Gallicchio et al. 2006), and cardiovascular disease risk (Shearman et al. 2003).", + "In-depth analysis of the age-regulated genes revealed that multiple genes in the DNA damage response pathway were upregulated with age including those that function in non-homologous end-joining repair (mre11, rad50, Ku80 and mus308) and in translesion DNA synthesis (mus205 and DNApol-eta) [44][45][46].Genes that encoded enzymes with antioxidant properties, such as the thioredoxin reductase Trxr-1, and antioxidant genes involved in glutamate metabolism, such as GlnRS, isoQC and QC, were also upregulated with age [47][48][49][50].We also observed increased age-associated expression of chaperone genes (Cct1, Cct4, Cct5, Cct6, Hsc70-4) and the unfolded protein response transcription factor Xbp1, consistent with an induction of the unfolded protein response [51][52][53].Under stress conditions, there is a translational switch that favors production of stressrelated proteins while decreasing translation of other proteins [54].Paralogs of canonical translation factors such as NAT1 and Rack1, which were both upregulated, promote this switch to cap-independent translation [55,56].Notably, Rheb, which is downregulated with age, positively regulates ribosome production and capdependent translation by activating the mechanistic target of rapamycin (mTOR) kinase pathway [57].Thus, decreased Rheb levels during aging could decrease mTOR pathway activity, which extends lifespan and is protective against age-related pathology [58].Together, these data suggest that multiple genes are induced in aging photoreceptors to mitigate the effects of oxidative stress, protein misfolding and DNA damage.", + "Results: We develop CellAge (http://genomics.senescence.info/cells),a manually curated database of 279 human genes driving cellular senescence, and perform various integrative analyses.Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes.Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates.We also build cellular senescence protein-protein interaction and co-expression networks.Clusters in the networks are enriched for cell cycle and immunological processes.Network topological parameters also reveal novel potential cellular senescence regulators.Using siRNAs, we observe that all 26 candidates tested induce at least one marker of senescence with 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) decreasing cell number, activating p16/p21, and undergoing morphological changes that resemble cellular senescence.Conclusions: Overall, our work provides a benchmark resource for researchers to study cellular senescence, and our systems biology analyses reveal new insights and gene regulators of cellular senescence.", + "Genes involved intranscriptional silencing via chromatin remodeling (Smarca4 and Amarcb1) as well ashistone deacetylases (Hdac1, -5, and -6) and a DNA methyltransferace (Dnmt3b) weredownregulated in aged cells. They also showed that several chromosomal regionschanged with age in a coordinated manner resulting in an overall increase intranscriptional activity. They propose that chromatin dysregulation and epigeneticchanges drive the loss of cellular function and ultimately drive the aging process inHSCs.", + "Analysis of prior research (Online Resource 5) shows that the revealed genes can be explicitly involved in other key biological processes in an organism whose role is known to be changing with aging.Specifically, ten genes (BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, and ZKSCAN1) regulate transcription which is believed to be disrupted when an organism is getting older (Roy et al. 2002).The DBH, TPO, and LSS genes are involved in synthesis of catecholamine, thyroid, and vitamin D hormones, respectively.The GPER binds estrogen and HCRTR2 binds orexin-A and orexin-B neuropeptid hormones.Hormonal deregulation with aging is considered to be one of the major components of senescent processes in an organism (Barzilai and Gabriely 2010).Five genes (ATG2A, NEDD4L, PSMB1, UBXN4, and USP6) are involved in degradation of proteins through ubiquitin-proteasome and the lysosomal/autophagic system.Dysfunction of this system leads to accumulation of damaged proteins in an organism that is associated with aging (Koga et al. 2011).Protein degradation through ubiquitin-mediated proteolysis plays an important role in cell-cycle regulation (Reed 2003).The PSMB1, SIK1, TRIP13, and TTN genes in the revealed set coordinate cell cycle.Cell cycle is linked with the aging-related processes in humans through a gradual increase in cell division errors in all tissues in an organism (Ly et al. 2000).Five genes (EEF1A2, DBH, ITGB2, TUBB2C, and WRN) take part in regulation of apoptosis which plays an important role in the aging process and tumorigenesis (Salvioli et al. 2008).Seven genes (ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, and SOAT2) are involved in lipid metabolism which plays one of the key roles in human longevity and healthy aging (Barzilai et al. 2003).", + "Genes that are age-regulated in all tissues would reveal genes involved in core mechanisms that underlie cellular ageing.Zahn et al. [63] discovered genetic pathways that show common age regulation in human kidney, brain and muscle.They used microarrays to analyse expression in 81 skeletal muscle samples from patients aged 16 -86 years and found 250 age-regulated muscle genes [63].Similar to the ageing expression profile for the kidney, the overall expression behaviour of this set of age-regulated muscle genes correlated with the physiological as well as chronological age of the muscle sample.Next, they compared their muscle-ageing results to previously published data on kidney and brain ageing of similarly large sample size [56,60].Although most of the age-related changes were tissue specific, they found evidence for common age regulation of six genetic pathways in all three tissues.Specifically, there is an overall increase in expression of the extracellular matrix genes, the ribosomal genes, the cell growth genes and the complement activation genes in all three tissues.Increased overall expression of the extracellular matrix and complement activation gene sets with advancing age may contribute to widespread fibrosis and inflammation in the elderly.There is an overall decrease in expression of the chloride transport genes and the electron transport genes in all three tissues.Decreased overall expression of electron transport chain genes with age might support the mitochondrial free-radical theory of ageing [67], as free-radical generation by mitochondria would preferentially damage the electron transport chain protein complexes.Decreased expression of the electron transport genes (encoded in the nucleus) might be caused by feedback regulation from damage to the electron transport chain protein complexes [63].However, it is also possible that increased oxidative damage occurs as a consequence of the decreased expression of the electron transport chain genes.In addition, an increasing number of studies in model organisms have critically challenged the mitochondrial free-radical theory of ageing [68].In addition to testing genes known to be associated with age-related diseases and phenotypes for association with longevity, genes known to promote longevity in model organisms have been examined in human populations.Mutations in insulin or insulinlike signalling pathway genes have been shown to extend lifespan in Caenorhabditis elegans [20], Drosophila melanogaster [21,22] and mice [23,24].The insulin-signalling pathway negatively regulates the forkhead (FOXO) transcription factor [25].When insulin or insulin-like growth factor signalling is low, FOXO is activated and lifespan extension occurs [26].An overrepresentation of rare insulin-like growth factor I receptor (IGFIR) mutations has been observed in centenarians [27].These mutations are associated with reduced activity of IGFIR as measured in transformed lymphocytes [27].", + "Aging can be viewed as a lethal by-product of activities, such as reproduction and food intake, that are controlled by genes [1].Since most of these genes are evolutionarily conserved, distant species may share common pathways of aging [2].The insulin/insulin-like growth factor 1 (IGF1) signaling pathway could be one such common pathway, as it modulates aging in many species, including Caenorhabditis elegans, Drosophila, mice [3], and possibly humans [4].An elegant study carried out in C. elegans by applying microarray techniques showed that a member of the SIR2like protein family is regulated downstream of DAF-16, a FOXO-family transcription factor that affects the rate of aging in response to the insulin/IGF1 pathway [5].SIR2 proteins constitute an evolutionarily conserved family of NAD-dependent deacetylases called sirtuins [6][7][8].In model organisms the expression levels of SIR2 modulate life span [9][10][11].Since sirtuins are NAD + dependent these proteins through different routes may link energy metabolism, genome maintenance, and aging [11,12].Thus SIR2 genes may play a crucial role in conserved pathways of aging and longevity.", + "Regarding cancer and aging, Serrano and Blasco (2007) suggested that an equilibrium between mechanisms diminishing cellular damage and mechanisms preventing excessive cellular proliferation is required between both processes [43].The authors argue that the p53 pathway may be seen as an anti-aging mechanism as it is a key defense mechanism against cellular damage protecting from both aging and cancer.One effect of aging at the cellular level is reduced telomerase activity and progressive shorter telomeres in somatic cells [45].Shortened telomeres are highly recombinogenic, leading to a genome-susceptible cancer development [46,47].Genomic instability driven by dysfunctional telomeres is also associated with the transition from benign to malignant tumors [48].Conversely, telomere dysfunction also acts to induce the p53 gene to suppress tumor development by initiating cell-cycle arrest, cellular senescence or, apoptosis.Our analysis has identified several genes involved in the regulation and activity of the p53 pathway as being affected by age.In skin, the telomerase reverse transcriptase (TERT) showed an age-related expression in association with a genetic variant (rs10866530).In addition p21, a gene directly regulated by p53 and also involved in telomere-driven aging, was shown to be differentially expressed with age [49].In brain, theZBTB16, CA9,and HEY2, genes associated to the p53 pathway directly or via SIRT1, all showed age-related expression.The activity of p53 has been shown to enhance the transcription of inhibitors of the insulin receptor pathway, preventing cell growth and division after stress signaling [50,51] and many genes from the insulin signaling pathway have been extensively associated with longevity in multiple studies and organisms.Our results suggest that the link between aging and cancer is evident in multiple tissues through differential expression of genes with age.", + "Several of the genes we identify have previously been shown to influence lifespan in experiments on model organisms.For example, knockouts of the orthologs of APOE, LDLR, CDKN2B, and RBM38 in mice shortens their lifespan [24][25][26][27] , while knockout of IGF1R has the opposite effect 28 .Similarly, overexpression of the FOXO3 orthologue in Drosophila melanogaster 29 and the SNCA orthologue in Caenorhabditis elegans 30 have shown to extend their respective lifespans.Many of our genes are also enriched for pathways previously related to ageing in eukaryotic model organisms, including genomic stability, cellular senescence, and nutrient sensing 31 .For example, FOXO3 and IGF1R are well-known players modulating survival in response to dietary restriction 32 , but we also highlight genes involved in the response to DNA damage and apoptosis, such as CDKN2B, USP28, E2F2, and BCL3.In addition to hallmarks discovered in model organisms, our results suggest that haem metabolism may play a role in human ageing.This pathway includes genes involved in processing haem and differentiation of erythroblasts 33 .Although the enrichment is largely driven by genes linked to the LDLR locus, genes linked to other loci of interest (such as FOXO3, CDKN2B, LINC02513) are involved in similar biological pathways: myeloid differentiation, erythrocyte homeostasis, and chemical homeostasis.", + "In recent years, some of the aging-related genes identified in worms have been shown to have mammalian homologs that modulate longevity and delay age-related diseases in mice, in particular as part of the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway (Bartke, 2005), and variants in these genes have even been associated with human longevity, such as the daf-2 homolog IGF1R (Suh et al., 2008).Therefore, there is great potential for human homologs of genes shown to modulate aging in model organisms to represent pharmaceutical targets with human applications.", + "Aging-related gene prediction and putative transcriptional mechanismsGeneFriends was used to identify genes related to aging.A seed list of genes known to be consistently overexpressed with age in mammals was used [18].In total, 1119 genes were co-expressed with the aging seed list at p <10 -6 ; Table 1 shows the top 25 genes.Many of these genes have been associated with age-related diseases.Several other genes that have been shown to play a role in aging such as lysosomal-associated membrane protein-2 Lamp2 [19] (p = 5.68 -30 ), Fas [20] (p = 2.70 -31 ) and growth hormone receptor Ghr [21] (p = 1.34 -19 ) also showed a significant co-expression.Anxa2, Anxa3 and Anxa4 also show a low p-value (p < 10 -25 ) as well as several S100 calcium binding proteins which have been shown to interact with annexins [22].", + "The genome-wide RNAi study conducted by the Ruvkun lab, authored by Hamilton et al. [88], identified a total of 89 additional aging genes with disparate functions including cell structure, cell surface proteins, cell signaling, cellular metabolism, and protein turnover.Of the 66 genes with previously known functions, 17 corresponded to various aspects of carbon metabolism, including citric acid cycle enzymes and subunits of complexes I, IV, and V of the ETC.Researchers also speculated that protein translation might play a role in lifespan regulation, based on the identification of iff-1 (T05G5.10),a gene that has homology to the translation initiation factor eIF5A.Other hits from this screen included two genes containing PH domains known to interact with phosphatidylinositol lipids, multiple G protein-coupled receptors, protein processing and degradation genes such as proteases and ubiquitin ligases/hydrolases, and chromatin modifying factors.", + "INTRODUCTIONAging has fascinated researchers since ancient times.The hugely complicated process that has been revealed may be interpreted from different aspects, such as the accumulation of oxidative damage, shortening of telomeres, the costs of reproduction, metabolic rates, cellular senescence, etc., and these have in turn given rise to diverse theories of aging [1].However, thanks to forward and reverse genetic technologies, researchers in the recent decades have established that despite its complexity, a single or a few key genes in a few key pathways can modulate the aging rate.The most important players would appear to be those in nutrient sensing pathways or stress response pathways, such as DAF-2/IGF1R and DAF-16/FOXO in the Insulin/IGF like signaling pathway, AAK-2/AMPK in another nutrient sensing pathway, JNK in the stress response pathway, LET-363/mTOR as an inhibitor of autophagy and activator of translation and SIRT1/SIR2 in genome stability maintenance, to name a few [2,3].In addition to genetic perturbations, dietary perturbations, such as diet restriction (DR) are known to significantly extend lifespan in most organisms examined from yeasts to primates, although different pathways may act under different DR conditions, and alternative DR strategies also effect C.elegans lifespan in different ways [3,4].The main pathways revealed under different DR regimens are summarized in Fig. (1).In this small, convoluted DR response network, DAF-16 and ceTOR/LET-363 *Address correspondence to this author at the Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China; Tel: 86-21-54920458; Fax: 86-21-54920451; E-mail: jdhan@picb.ac.cn These authors contributed equally to this work.", + "IntroductionAging affects nearly all organisms and is a major risk factor in most human diseases.Recent work has begun to uncover molecular mechanisms that specify lifespan and to identify alterations in cellular physiology that occur at the end of life (Tissenbaum and Guarente 2002).For example, oxidative damage caused by the generation of free radicals in the mitochondria has been found to hasten aging by causing an accumulation of damaged cellular components (Droge 2003).Telomere shortening may also play a role in aging by preventing DNA replication and cell division in later years (Hasty et al. 2003).Genetic studies have identified many genes that play a role in specifying lifespan.For example, mutations in yeast sir2 (chromatin regulator), worm daf-2 (insulin-like growth factor receptor), fly methuselah (tyrosine kinase receptor), mouse p53, and the human Werner's syndrome gene (DNA helicase) cause dramatic changes in lifespan (Guarente and Kenyon 2000).Several aging mechanisms alter longevity in multiple organisms.For example, mutations in the gene encoding insulin-like growth factor receptor alter lifespan in worms, flies, and mice, indicating that an endocrine signaling pathway has a conserved role in aging (Hekimi and Guarente 2003).Genetic studies have shown that aging can be slowed in mutants that are defective in a wide range of cellular processes (such as mitochondrial function, chromatin regulation, insulin signaling, transcriptional regulation, and genome stability).This indicates that aging is a complex process driven by diverse molecular pathways and biochemical events.As such, a powerful approach to study aging is to use systems biology, which allows a multitude of factors affecting aging to be analyzed in parallel.For example, DNA microarrays and gene expression chips have been used to perform a genome-wide analysis of changes in gene expres-sion in old age.Extensive studies in Caenorhabditis elegans and Drosophila melanogaster have identified hundreds of ageregulated genes (Hill et al. 2000;Zou et al. 2000;Lund et al. 2002;Pletcher et al. 2002;Murphy et al. 2003).Several studies have described age-regulated genes in the muscle and brain of mice (Lee et al. 1999(Lee et al. , 2000) ) and the retina and muscle of humans (Yoshida et al. 2002;Welle et al. 2003Welle et al. , 2004).These age-regulated genes may serve as markers of aging, enabling one to assess physiological age independently of chronological age.Analysis of the functions of these age-regulated genes has identified specific biochemical mechanisms that change toward the end of life.", + "Age-Regulated Genes Involved in Reproductive Capacity.Decline in reproductive capacity is an age-related phenotype, and the reproductive system seems to play an important role in longevity (22).For example, signals from germ cells can affect lifespan in C. elegans (23).In our study, we observed decreased RNA levels for several genes involved in reproduction (Fig. 3).These include two genes that encode members of the Acp family.The Acp from male flies stimulates female egg-laying and facilitates storage of sperm in the female genital tract (24).In addition, two ESTs showing age-regulated decrease of transcript levels represent different genes with homology to Arabidopsis MALE STERIL- In Northern analysis, the ratios were calculated by dividing mRNA levels at 25-, 40-, and 50-day time points by those at 3-day time points after normalization with mRNA levels of the control gene rp49.Ratios in microarray analysis are provided from each of the duplicate experiments for comparison.ITY 2 (MS2; ref. 25), a gene involved in gametogenesis.Furthermore, an EST with homology to peanut, a member of the septin family (26), is down-regulated in older flies.This downregulation may reflect a decrease in spermatogenesis.", + "Genes that are age-regulated in all tissues would reveal genes involved in core mechanisms that underlie cellular ageing.Zahn et al. [63] discovered genetic pathways that show common age regulation in human kidney, brain and muscle.They used microarrays to analyse expression in 81 skeletal muscle samples from patients aged 16 -86 years and found 250 age-regulated muscle genes [63].Similar to the ageing expression profile for the kidney, the overall expression behaviour of this set of age-regulated muscle genes correlated with the physiological as well as chronological age of the muscle sample.Next, they compared their muscle-ageing results to previously published data on kidney and brain ageing of similarly large sample size [56,60].Although most of the age-related changes were tissue specific, they found evidence for common age regulation of six genetic pathways in all three tissues.Specifically, there is an overall increase in expression of the extracellular matrix genes, the ribosomal genes, the cell growth genes and the complement activation genes in all three tissues.Increased overall expression of the extracellular matrix and complement activation gene sets with advancing age may contribute to widespread fibrosis and inflammation in the elderly.There is an overall decrease in expression of the chloride transport genes and the electron transport genes in all three tissues.Decreased overall expression of electron transport chain genes with age might support the mitochondrial free-radical theory of ageing [67], as free-radical generation by mitochondria would preferentially damage the electron transport chain protein complexes.Decreased expression of the electron transport genes (encoded in the nucleus) might be caused by feedback regulation from damage to the electron transport chain protein complexes [63].However, it is also possible that increased oxidative damage occurs as a consequence of the decreased expression of the electron transport chain genes.In addition, an increasing number of studies in model organisms have critically challenged the mitochondrial free-radical theory of ageing [68].In addition to testing genes known to be associated with age-related diseases and phenotypes for association with longevity, genes known to promote longevity in model organisms have been examined in human populations.Mutations in insulin or insulinlike signalling pathway genes have been shown to extend lifespan in Caenorhabditis elegans [20], Drosophila melanogaster [21,22] and mice [23,24].The insulin-signalling pathway negatively regulates the forkhead (FOXO) transcription factor [25].When insulin or insulin-like growth factor signalling is low, FOXO is activated and lifespan extension occurs [26].An overrepresentation of rare insulin-like growth factor I receptor (IGFIR) mutations has been observed in centenarians [27].These mutations are associated with reduced activity of IGFIR as measured in transformed lymphocytes [27]." + ], + [ + "We briefly comment on rare mutations that shorten life span through the early onset of diseases that are increasingly common during aging in the general population, e.g., familial forms of Alzheimer, breast cancer, coronary artery disease, type II diabetes, etc.The later onset forms of these diseases are associated with causes of death at later ages.A major question is what role the more common allelic variants of these same genes have in \"normal aging\".Although examination of this huge emerging topic goes beyond the present discussion, we may consider the example of Werner's syndrome, a rare autosomal recessive that causes adult onset progeria with a high incidence of cancer and atherosclerosis (70).The absence of Alzheimer-type dementia in Werner's syndrome illustrates the \"segmental\" nature of this and other progerias (70).Thus, heritable shortening of life span should not be considered as a simple acceleration of general aging processes.The Werner's lesion maps to a defective gene encoding a helicase and exonuclease, which also has several polymorphisms.In Japan, 1367Arg was associated with a lower risk of myocardial infarction (70), although it was not associated with longevity in Finland (14).In general, we know little of the genetic factors involved in frailty and morbidity at later ages, which are important to the geneenvironment interactions implied in the major longevity increase seen during the twentieth century.", + "Indicative diseases associated with the candidate aging genes", + "Dementia has an age-and sex-standardized prevalence of ~7.1% in Europeans 1 , with Alzheimer's disease (AD) being the most common form of dementia (50-70% of cases) 2 .AD is pathologically characterized by the presence of amyloid-beta plaques and tau neurofibrillary tangles in the brain 3 .Most patients are diagnosed with AD after the age of 65, termed late-onset AD (LOAD), while only 1% of AD cases have an early onset (before the age of 65) 3 .On the basis of twin studies, the heritability of LOAD is estimated to be ~60-80% (refs. 4,5 ), suggesting that a large proportion of individual differences in LOAD risk is driven by genetics.The heritability of LOAD is spread across many genetic variants; however, Zhang et al. 6 suggested that LOAD is more of an oligogenic than a polygenic disorder due to the large effects of APOE variants.Zhang et al. 6 and Holland et al. 7 predicted there to be ~100-10,000 causal variants contributing to LOAD; however, only a fraction have been identified.Increasing the sample size of genome-wide association studies (GWAS) will improve the statistical power to identify the missing causal variants and may highlight additional disease mechanisms.In combination with increasing the number of samples, it is beneficial to use different approaches to identify rare and private variation to help identify additional causal variants and increase understanding of disease mechanisms; however, we deem this to be out of the scope of the current analysis.", + "Alzheimer's disease (AD) is a devastating neurodegenerative disorder of late life with complex inheritance.Mutations in three known genes lead to the rare early-onset autosomal dominant form of AD, while a common polymorphism (e4) in the gene encoding apolipoprotein E (APOE ) is a risk factor for more typical late-onset (>60 years) AD.A recent study concluded that there are up to four additional genes with an equal or greater contribution to the disease.We performed a 9 cM genome screen of 437 families with AD, the full National Institute of Mental Health (NIMH) sample, which has been carefully ascertained, evaluated and followed by our group over the last decade.Performing standard parametric and non-parametric linkage analyses, we observed a 'highly significant' linkage peak by Lander and Kruglyak criteria on chromosome 19q13, which probably represents APOE.Twelve additional locations-on 1q23, 3p26, 4q32, 5p14, 6p21, 6q27, 9q22, 10q24, 11q25, 14q22, 15q26 and 21q22-met criteria for 'suggestive' linkage [i.e.two-point lod score (TLS) !1.9 and/ or multipoint lod score (MLS) !2.2] in at least one of our analyses.Although some of these will surely prove to be false positives, these linkage signals should provide a valuable framework for future studies aimed at identifying additional susceptibility genes for late-onset AD.Alzheimer's disease (AD) is a devastating neurodegenerative disorder of late life with complex inheritance.Mutations in three known genes lead to the rare early-onset autosomal dominant form of AD, while a common polymorphism (e4) in the gene encoding apolipoprotein E (APOE ) is a risk factor for more typical late-onset (>60 years) AD.A recent study concluded that there are up to four additional genes with an equal or greater contribution to the disease.We performed a 9 cM genome screen of 437 families with AD, the full National Institute of Mental Health (NIMH) sample, which has been carefully ascertained, evaluated and followed by our group over the last decade.Performing standard parametric and non-parametric linkage analyses, we observed a 'highly significant' linkage peak by Lander and Kruglyak criteria on chromosome 19q13, which probably represents APOE.Twelve additional locations-on 1q23, 3p26, 4q32, 5p14, 6p21, 6q27, 9q22, 10q24, 11q25, 14q22, 15q26 and 21q22-met criteria for 'suggestive' linkage [i.e.two-point lod score (TLS) !1.9 and/ or multipoint lod score (MLS) !2.2] in at least one of our analyses.Although some of these will surely prove to be false positives, these linkage signals should provide a valuable framework for future studies aimed at identifying additional susceptibility genes for late-onset AD.", + "IntroductionAlzheimer's disease (AD) is a complex disorder and is the most common form of dementia [1].After age, family history is the single greatest risk factor for AD.AD can be classified into early and late onset forms.Mutations in three genes: PSEN1/2 and APP are known to cause early onset AD in an autosomal dominant manner [2,3].The majority of AD cases, however, are late onset (LOAD) and the APOE e4 allele is the strongest known genetic risk factor.Many additional genetic polymorphisms have been identified, though with substantially lower risk estimates [1,4,5,6,7,8,9,10].LOAD appears to be inherited and/or sporadic and there is evidence of a maternal inheritance pattern [11].Current estimates suggest that more than 20% of inherited LOAD cases are maternally inherited [12].", + "INTRODUCTIONMany common noninfectious diseases exhibit a more severe clinical presentation in older individuals.These diseases often exhibit complex etiology and can affect different tissues and cell types, with a wide spectrum of clinical outcomes.Prominent aging-associated neurodegenerative diseases are Alzheimer's disease (AD), Parkinson's disease (PD), and age-related macular degeneration (AMD), all of which can severely compromise the quality of life and have serious repercussions on both the individual and society at large.These late-onset diseases generally result from the interplay between multiple genetic susceptibility factors and environmental components.Sequencing of the human genome, cataloging of millions of single nucleotide polymorphisms (SNPs) together with the development of a map of common haplotypes, and technological innovations in genotyping are among the major milestones that are facilitating exploration of the genetic basis of common diseases (1,7,50).In the field of AMD genetics, these advances have led to the identification of several genetic susceptibility factors and enabled us to start dissecting the relationship between environmental risk factors and the genetic constitution of each individual (66,118,148).As a result, new opportunities are emerging for improved understanding of disease pathogenesis that may lead to better management and treatment of AMD.Clinical aspects of AMD are discussed only briefly (for a more in-depth discussion, see Reference 79).Aging-associated neurodegenerative diseases significantly influence the quality of life of affected individuals.Genetic approaches, combined with genomic technology, have provided powerful insights into common late-onset diseases, such as age-related macular degeneration (AMD).Here, we discuss current findings on the genetics of AMD to highlight areas of rapid progress and new challenges.We also attempt to integrate available genetic and biochemical data with cellular pathways involved in aging to formulate an integrated model of AMD pathogenesis.Aging-associated neurodegenerative diseases significantly influence the quality of life of affected individuals.Genetic approaches, combined with genomic technology, have provided powerful insights into common late-onset diseases, such as age-related macular degeneration (AMD).Here, we discuss current findings on the genetics of AMD to highlight areas of rapid progress and new challenges.We also attempt to integrate available genetic and biochemical data with cellular pathways involved in aging to formulate an integrated model of AMD pathogenesis.", + "Alzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia.With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets.Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately $20 genes with common variants contributing to LOAD risk.In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses.Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple lowfrequency and rare variant contributors to AD, and discuss ongoing efforts with next-generation sequencing studies to develop statistically well-powered and comprehensive genomic studies of AD.Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD.Genetics of Alzheimer Disease: Early-Onset ADIn the early to mid-1990s, genetic studies of AD focused on extended families with high burden of disease (two or more cases among first-degree relatives), and used linkage analysis of highly polymorphic genetic markers called short tandem repeats (STRs, or microsattelites) in order to identify genomic regions co-transmitting with disease in affected family members.This strategy, followed by \"fine mapping\"-the positional cloning of candidate genes-was used to identify genes and genetic variants contributing to AD risk.The first three genes known to cause AD were identified among families with multiple early-onset cases (age-at-onset <60 years): APP, encoding amyloid precursor protein [Goate et al., 1991], and PS1 and PS2, encoding presenilins I and II respectively [Levy-Lahad et al., 1995;Rogaev et al., 1995;Sherrington et al., 1995], each transmitting disease-causing variants in the predicted autosomal-dominant fashion.Alzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia.With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets.Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately $20 genes with common variants contributing to LOAD risk.In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses.Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple lowfrequency and rare variant contributors to AD, and discuss ongoing efforts with next-generation sequencing studies to develop statistically well-powered and comprehensive genomic studies of AD.Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD.", + "The genetics of Alzheimer disease (AD) to date support an age-dependent dichotomous model whereby earlier age of disease onset (60 years) is explained by 3 fully penetrant genes (APP [NCBI Entrez gene 351], PSEN1 [NCBI Entrez gene 5663], and PSEN2 [NCBI Entrez gene 5664]), whereas later age of disease onset (65 years) representing most cases of AD has yet to be explained by a purely genetic model.The APOE gene (NCBI Entrez gene 348) is the strongest genetic risk factor for later onset, although it is neither sufficient nor necessary to explain all occurrences of disease.Numerous putative genetic risk alleles and genetic variants have been reported.Although all have relevance to biological mechanisms that may be associated with AD pathogenesis, they await replication in large representative populations.Genome-wide association studies have emerged as an increasingly effective tool for identifying genetic contributions to complex diseases and represent the next frontier for furthering our understanding of the underlying etiologic, biological, and pathologic mechanisms associated with chronic complex disorders.There have already been success stories for diseases such as macular degeneration and diabetes mellitus.Whether this will hold true for a genetically complex and heterogeneous disease such as AD is not known, although early reports are encouraging.This review considers recent publications from studies that have successfully applied genome-wide association methods to investigations of AD by taking advantage of the currently available high-throughput arrays, bioinformatics, and software advances.The inherent strengths, limitations, and challenges associated with study design issues in the context of AD are presented herein.Arch Neurol.2008;65(3): 329-334 Alzheimer disease (AD) is the most common cause of dementia and the most prevalent neurodegenerative disorder associated with aging. 1 Alzheimer disease is a heterogeneous disorder with a complex etiology owing to genetic and environmental influences as causal or risk modifiers.The neuropathologic hallmarks of disease are extracellular amyloid plaques and intracellular neurofibrillary tangles of hyperphosphorylated tau protein. 2 Only 10% of AD cases occurring before 60 years of age (early-onset AD) are due to rare, fully penetrant (autosomal dominant) mutations in 3 genes: A precursor protein (APP) on chromosome 21, 3 presenilin 1 (PSEN1) on chromosome 14, 4 and presenilin 2 (PSEN2) on chromosome 1. 5,6In contrast, most cases of AD are later in onset ( 65 years of age) (late-onset AD), are nonfamilial, and are likely the result of highly prevalent genetic variants with low penetrance. 7To date, the only genetic risk factor for lateonset AD remains the apolipoprotein E gene (APOE), specifically the 4 allele, which is moderately penetrant, accounting for up to 50% of cases. 8owever, a robust literature reports numerous putative genetic risk alleles and promising genetic variants.Recent reports from individual studies reveal significant associations with the sortilin-related receptor (SORL1 [NCBI Entrez gene 6653]) 9,10 and glycine-rich protein 2-associated binding protein 2 (GAB2 [NCBI Entrez gene 9846]) 11 on chromosome 11; death-associated protein kinase 1 (DAPK1 [NCBI Entrez gene 1612]), 12 ubiquilin 1 (UBQLN1 [NCBI Entrez gene 299798]), 13 and adenosine triphosphate-binding cassette transporter 1, subfamily A (ABCA1 [NCBI Entrez gene 19]), on chromosome 9 14 ; and low-density lipoprotein receptor-related protein 6 (LRP6 [NCBI Entrez gene 4040]) on chromosome 12. 15 All of these putative variants still lack replication in large representative populations but have relevance to neuropathologic mechanisms and pathways that may be associated with AD pathogenesis ( A large meta-analysis from the AlzGene database 16 17 All are associated with relevant biological mechanisms and pathways but await replication to further elucidate their utility as significant markers for AD.", + "BackgroundAlzheimer's disease (AD) is the most common neurodegenerative disorder and the leading cause of dementia in the elderly [1].Diagnosis of AD is based on the presence of neurofibrillary tangles and amyloid plaques [2], and symptoms typically include memory loss and impaired cognitive ability.Although the pathological hallmarks associated with dementia-related symptoms in AD appear largely similar between both the early-onset and late-onset forms of the disease, their underlying etiologies contrast [3].Whereas early-onset AD is a familial autosomal dominant disorder caused by rare, highly penetrant mutations in one of a small set of genes (APP, PSEN1, and PSEN2), the more common late-onset form of the disease (accounting for 90-95 % of cases) occurs sporadically, and risk is determined by complex underlying mechanisms [3][4][5][6].Estimates based on twin concordance rates suggest heritability of late-onset AD is as high as 70 %, implicating major roles for genetic as well as non-genetic factors [6].Indeed, through candidate gene studies, as well as more recent genome-wide association studies (GWASs) and whole-exome sequencing, both common and rare variants associated with the late-onset form of AD have been identified [7][8][9][10][11].Collectively, however, common GWAS variants account for only a modest proportion (~30 %) of the underlying variance in disease susceptibility [12].Several environmental factors are also thought to play a role [5,6], yet exactly how these contribute to risk, onset, and progression remains poorly defined.", + "Alzheimer's disease is the most common type of dementia, and it is characterized by a decline in memory or other thinking skills.The greatest risk factor for Alzheimer's disease is advanced age.A recent genome-wide study identified a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11 is probably responsible for the association.The association of a protective haplotype with a 10-year delay in the onset of Alzheimer's disease and the identification of a CCL11 variant with possible functional roles in this association might allow the future development of immunomodulators with the potential to halve disease incidence.Alzheimer's disease is the most common type of dementia, and it is characterized by a decline in memory or other thinking skills.The greatest risk factor for Alzheimer's disease is advanced age.A recent genome-wide study identified a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11 is probably responsible for the association.The association of a protective haplotype with a 10-year delay in the onset of Alzheimer's disease and the identification of a CCL11 variant with possible functional roles in this association might allow the future development of immunomodulators with the potential to halve disease incidence.", + "INTRODUCTIONAlzheimer's disease (AD) is a common debilitating disorder with a prevalence that rises steeply with age from below 1% at 65 years to as high as 40% after the age of 90 [Bachman et al., 1992].Genes are known to play a role in the development of AD.Twin studies show heritabilities of around 60% [Bergem et al., 1997;Gatz et al., 1997].Indeed, variation in four genes has already been shown to cause rare forms of early-onset AD [the Amyloid Precursor Protein Gene (APP); Goate et al., 1991; Presenilin 1 (PS1); Sherrington et al., 1995; Presenilin 2 (PS2); Levy Lahad et al., 1995, Rogaev et al., 1995] or increase the general risk of disease development [Apolipoprotein E (APOE), Corder et al., 1993].As well as increasing disease susceptibility, APOE e4 alleles are associated with reduced age at onset (AAO) and appear to show their strongest effect below 70 years [Farrer et al., 1997].There is also evidence from both twin [Pedersen et al., 2001] and family studies [Tunstall et al., 2000;Li et al., 2002] that AAO in AD is heritable.Daw et al. [2000] have estimated that in addition to APOE, there are at least four loci with similar effect sizes, which contribute to AAO in AD.", + "IntroductionAlzheimer's disease (AD), a devastating neurodegenerative disease, is the most common form of dementia among the elderly.Genetically, AD is a complex and multifactorial disease with the possible involvement of multiple genes.The rare early-onset form of the disease usually follows an autosomal-dominant inheritance pattern and to date three genes have been identified: amyloid precursor protein (APP) and presenilin 1 and 2 (PSEN1 and PSEN2).The common late-onset form of the disease is much more complex than the early-onset form and until recently the apolipoprotein E (APOE) gene was the only major genetic factor accounting for 20-29% of the risk for late-onset AD. 1,2 Recent large genome-wide association studies (GWAS) have identi-fied nine additional genes for late-onset AD, including CR1, BIN1, CLU (a.k.a.4][5][6][7] There is high heritability for AD risk (up to 80%), 8 but the total risk attributable to all confirmed loci is about 50%, indicating the presence of additional risk genes for late-onset AD.", + "NE OF EVERY 5 PERSONS AGED 65 years is predicted to develop Alzheimer disease (AD) in their lifetime, and genetic variants may play an important part in the development of the disease. 1 The apparent substantial heritability of late-onset AD 2 is inadequately explained by genetic variation within the well-replicated genes (apolipoprotein E [APOE; RefSeq NG_007084], presenilin-1 [PSEN1; RefSeq NG_007386], presenilin-2 [PSEN2; RefSeq NG_007381], and amyloid beta precursor protein [APP;" + ], + [ + "We briefly comment on rare mutations that shorten life span through the early onset of diseases that are increasingly common during aging in the general population, e.g., familial forms of Alzheimer, breast cancer, coronary artery disease, type II diabetes, etc.The later onset forms of these diseases are associated with causes of death at later ages.A major question is what role the more common allelic variants of these same genes have in \"normal aging\".Although examination of this huge emerging topic goes beyond the present discussion, we may consider the example of Werner's syndrome, a rare autosomal recessive that causes adult onset progeria with a high incidence of cancer and atherosclerosis (70).The absence of Alzheimer-type dementia in Werner's syndrome illustrates the \"segmental\" nature of this and other progerias (70).Thus, heritable shortening of life span should not be considered as a simple acceleration of general aging processes.The Werner's lesion maps to a defective gene encoding a helicase and exonuclease, which also has several polymorphisms.In Japan, 1367Arg was associated with a lower risk of myocardial infarction (70), although it was not associated with longevity in Finland (14).In general, we know little of the genetic factors involved in frailty and morbidity at later ages, which are important to the geneenvironment interactions implied in the major longevity increase seen during the twentieth century.", + "Indicative diseases associated with the candidate aging genes", + "Dementia has an age-and sex-standardized prevalence of ~7.1% in Europeans 1 , with Alzheimer's disease (AD) being the most common form of dementia (50-70% of cases) 2 .AD is pathologically characterized by the presence of amyloid-beta plaques and tau neurofibrillary tangles in the brain 3 .Most patients are diagnosed with AD after the age of 65, termed late-onset AD (LOAD), while only 1% of AD cases have an early onset (before the age of 65) 3 .On the basis of twin studies, the heritability of LOAD is estimated to be ~60-80% (refs. 4,5 ), suggesting that a large proportion of individual differences in LOAD risk is driven by genetics.The heritability of LOAD is spread across many genetic variants; however, Zhang et al. 6 suggested that LOAD is more of an oligogenic than a polygenic disorder due to the large effects of APOE variants.Zhang et al. 6 and Holland et al. 7 predicted there to be ~100-10,000 causal variants contributing to LOAD; however, only a fraction have been identified.Increasing the sample size of genome-wide association studies (GWAS) will improve the statistical power to identify the missing causal variants and may highlight additional disease mechanisms.In combination with increasing the number of samples, it is beneficial to use different approaches to identify rare and private variation to help identify additional causal variants and increase understanding of disease mechanisms; however, we deem this to be out of the scope of the current analysis.Dementia has an age-and sex-standardized prevalence of ~7.1% in Europeans 1 , with Alzheimer's disease (AD) being the most common form of dementia (50-70% of cases) 2 .AD is pathologically characterized by the presence of amyloid-beta plaques and tau neurofibrillary tangles in the brain 3 .Most patients are diagnosed with AD after the age of 65, termed late-onset AD (LOAD), while only 1% of AD cases have an early onset (before the age of 65) 3 .On the basis of twin studies, the heritability of LOAD is estimated to be ~60-80% (refs. 4,5 ), suggesting that a large proportion of individual differences in LOAD risk is driven by genetics.The heritability of LOAD is spread across many genetic variants; however, Zhang et al. 6 suggested that LOAD is more of an oligogenic than a polygenic disorder due to the large effects of APOE variants.Zhang et al. 6 and Holland et al. 7 predicted there to be ~100-10,000 causal variants contributing to LOAD; however, only a fraction have been identified.Increasing the sample size of genome-wide association studies (GWAS) will improve the statistical power to identify the missing causal variants and may highlight additional disease mechanisms.In combination with increasing the number of samples, it is beneficial to use different approaches to identify rare and private variation to help identify additional causal variants and increase understanding of disease mechanisms; however, we deem this to be out of the scope of the current analysis.The largest previous GWAS of LOAD, identified 29 risk loci from 71,880 (46,613 proxy) cases and 383,378 (318,246 proxy) controls 8 .Our current study expands this to include 90,338 (46,613 proxy) cases and 1,036,225 (318,246 proxy) controls.The recruitment of LOAD cases can be difficult due to the late age of onset, so proxy cases can allow for the inclusion of younger individuals by estimating their risk of LOAD using parental status.Proxy cases and controls were defined on the basis of known parental LOAD status weighted by parental age (Supplementary Information).In the current study, we identified 38 loci, including seven loci that have not been reported previously.Functional follow-up analyses implicated tissues, cell types and genes of interest through tissue and cell type enrichment, colocalization and statistical fine-mapping.This study highlights microglia, immune cells and protein catabolism as relevant to LOAD, while identifying previously unidentified genes of potential interest. ResultsGenome-wide inferences.We performed meta-analysis on data from 13 cohorts, totaling 1,126,563 individuals (Supplementary", + "Alzheimer's disease (AD) is a devastating neurodegenerative disorder of late life with complex inheritance.Mutations in three known genes lead to the rare early-onset autosomal dominant form of AD, while a common polymorphism (e4) in the gene encoding apolipoprotein E (APOE ) is a risk factor for more typical late-onset (>60 years) AD.A recent study concluded that there are up to four additional genes with an equal or greater contribution to the disease.We performed a 9 cM genome screen of 437 families with AD, the full National Institute of Mental Health (NIMH) sample, which has been carefully ascertained, evaluated and followed by our group over the last decade.Performing standard parametric and non-parametric linkage analyses, we observed a 'highly significant' linkage peak by Lander and Kruglyak criteria on chromosome 19q13, which probably represents APOE.Twelve additional locations-on 1q23, 3p26, 4q32, 5p14, 6p21, 6q27, 9q22, 10q24, 11q25, 14q22, 15q26 and 21q22-met criteria for 'suggestive' linkage [i.e.two-point lod score (TLS) !1.9 and/ or multipoint lod score (MLS) !2.2] in at least one of our analyses.Although some of these will surely prove to be false positives, these linkage signals should provide a valuable framework for future studies aimed at identifying additional susceptibility genes for late-onset AD.", + "IntroductionAlzheimer's disease (AD) is a complex disorder and is the most common form of dementia [1].After age, family history is the single greatest risk factor for AD.AD can be classified into early and late onset forms.Mutations in three genes: PSEN1/2 and APP are known to cause early onset AD in an autosomal dominant manner [2,3].The majority of AD cases, however, are late onset (LOAD) and the APOE e4 allele is the strongest known genetic risk factor.Many additional genetic polymorphisms have been identified, though with substantially lower risk estimates [1,4,5,6,7,8,9,10].LOAD appears to be inherited and/or sporadic and there is evidence of a maternal inheritance pattern [11].Current estimates suggest that more than 20% of inherited LOAD cases are maternally inherited [12].", + "INTRODUCTIONMany common noninfectious diseases exhibit a more severe clinical presentation in older individuals.These diseases often exhibit complex etiology and can affect different tissues and cell types, with a wide spectrum of clinical outcomes.Prominent aging-associated neurodegenerative diseases are Alzheimer's disease (AD), Parkinson's disease (PD), and age-related macular degeneration (AMD), all of which can severely compromise the quality of life and have serious repercussions on both the individual and society at large.These late-onset diseases generally result from the interplay between multiple genetic susceptibility factors and environmental components.Sequencing of the human genome, cataloging of millions of single nucleotide polymorphisms (SNPs) together with the development of a map of common haplotypes, and technological innovations in genotyping are among the major milestones that are facilitating exploration of the genetic basis of common diseases (1,7,50).In the field of AMD genetics, these advances have led to the identification of several genetic susceptibility factors and enabled us to start dissecting the relationship between environmental risk factors and the genetic constitution of each individual (66,118,148).As a result, new opportunities are emerging for improved understanding of disease pathogenesis that may lead to better management and treatment of AMD.Clinical aspects of AMD are discussed only briefly (for a more in-depth discussion, see Reference 79).Aging-associated neurodegenerative diseases significantly influence the quality of life of affected individuals.Genetic approaches, combined with genomic technology, have provided powerful insights into common late-onset diseases, such as age-related macular degeneration (AMD).Here, we discuss current findings on the genetics of AMD to highlight areas of rapid progress and new challenges.We also attempt to integrate available genetic and biochemical data with cellular pathways involved in aging to formulate an integrated model of AMD pathogenesis.Aging-associated neurodegenerative diseases significantly influence the quality of life of affected individuals.Genetic approaches, combined with genomic technology, have provided powerful insights into common late-onset diseases, such as age-related macular degeneration (AMD).Here, we discuss current findings on the genetics of AMD to highlight areas of rapid progress and new challenges.We also attempt to integrate available genetic and biochemical data with cellular pathways involved in aging to formulate an integrated model of AMD pathogenesis.", + "Alzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia.With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets.Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately $20 genes with common variants contributing to LOAD risk.In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses.Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple lowfrequency and rare variant contributors to AD, and discuss ongoing efforts with next-generation sequencing studies to develop statistically well-powered and comprehensive genomic studies of AD.Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD.Alzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia.With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets.Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately $20 genes with common variants contributing to LOAD risk.In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses.Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple lowfrequency and rare variant contributors to AD, and discuss ongoing efforts with next-generation sequencing studies to develop statistically well-powered and comprehensive genomic studies of AD.Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD.Genetics of Alzheimer Disease: Early-Onset ADIn the early to mid-1990s, genetic studies of AD focused on extended families with high burden of disease (two or more cases among first-degree relatives), and used linkage analysis of highly polymorphic genetic markers called short tandem repeats (STRs, or microsattelites) in order to identify genomic regions co-transmitting with disease in affected family members.This strategy, followed by \"fine mapping\"-the positional cloning of candidate genes-was used to identify genes and genetic variants contributing to AD risk.The first three genes known to cause AD were identified among families with multiple early-onset cases (age-at-onset <60 years): APP, encoding amyloid precursor protein [Goate et al., 1991], and PS1 and PS2, encoding presenilins I and II respectively [Levy-Lahad et al., 1995;Rogaev et al., 1995;Sherrington et al., 1995], each transmitting disease-causing variants in the predicted autosomal-dominant fashion.", + "Arch Neurol.2008;65(3): 329-334 Alzheimer disease (AD) is the most common cause of dementia and the most prevalent neurodegenerative disorder associated with aging. 1 Alzheimer disease is a heterogeneous disorder with a complex etiology owing to genetic and environmental influences as causal or risk modifiers.The neuropathologic hallmarks of disease are extracellular amyloid plaques and intracellular neurofibrillary tangles of hyperphosphorylated tau protein. 2 Only 10% of AD cases occurring before 60 years of age (early-onset AD) are due to rare, fully penetrant (autosomal dominant) mutations in 3 genes: A precursor protein (APP) on chromosome 21, 3 presenilin 1 (PSEN1) on chromosome 14, 4 and presenilin 2 (PSEN2) on chromosome 1. 5,6In contrast, most cases of AD are later in onset ( 65 years of age) (late-onset AD), are nonfamilial, and are likely the result of highly prevalent genetic variants with low penetrance. 7To date, the only genetic risk factor for lateonset AD remains the apolipoprotein E gene (APOE), specifically the 4 allele, which is moderately penetrant, accounting for up to 50% of cases. 8owever, a robust literature reports numerous putative genetic risk alleles and promising genetic variants.Recent reports from individual studies reveal significant associations with the sortilin-related receptor (SORL1 [NCBI Entrez gene 6653]) 9,10 and glycine-rich protein 2-associated binding protein 2 (GAB2 [NCBI Entrez gene 9846]) 11 on chromosome 11; death-associated protein kinase 1 (DAPK1 [NCBI Entrez gene 1612]), 12 ubiquilin 1 (UBQLN1 [NCBI Entrez gene 299798]), 13 and adenosine triphosphate-binding cassette transporter 1, subfamily A (ABCA1 [NCBI Entrez gene 19]), on chromosome 9 14 ; and low-density lipoprotein receptor-related protein 6 (LRP6 [NCBI Entrez gene 4040]) on chromosome 12. 15 All of these putative variants still lack replication in large representative populations but have relevance to neuropathologic mechanisms and pathways that may be associated with AD pathogenesis ( A large meta-analysis from the AlzGene database 16 17 All are associated with relevant biological mechanisms and pathways but await replication to further elucidate their utility as significant markers for AD.The genetics of Alzheimer disease (AD) to date support an age-dependent dichotomous model whereby earlier age of disease onset (60 years) is explained by 3 fully penetrant genes (APP [NCBI Entrez gene 351], PSEN1 [NCBI Entrez gene 5663], and PSEN2 [NCBI Entrez gene 5664]), whereas later age of disease onset (65 years) representing most cases of AD has yet to be explained by a purely genetic model.The APOE gene (NCBI Entrez gene 348) is the strongest genetic risk factor for later onset, although it is neither sufficient nor necessary to explain all occurrences of disease.Numerous putative genetic risk alleles and genetic variants have been reported.Although all have relevance to biological mechanisms that may be associated with AD pathogenesis, they await replication in large representative populations.Genome-wide association studies have emerged as an increasingly effective tool for identifying genetic contributions to complex diseases and represent the next frontier for furthering our understanding of the underlying etiologic, biological, and pathologic mechanisms associated with chronic complex disorders.There have already been success stories for diseases such as macular degeneration and diabetes mellitus.Whether this will hold true for a genetically complex and heterogeneous disease such as AD is not known, although early reports are encouraging.This review considers recent publications from studies that have successfully applied genome-wide association methods to investigations of AD by taking advantage of the currently available high-throughput arrays, bioinformatics, and software advances.The inherent strengths, limitations, and challenges associated with study design issues in the context of AD are presented herein.", + "BackgroundAlzheimer's disease (AD) is the most common neurodegenerative disorder and the leading cause of dementia in the elderly [1].Diagnosis of AD is based on the presence of neurofibrillary tangles and amyloid plaques [2], and symptoms typically include memory loss and impaired cognitive ability.Although the pathological hallmarks associated with dementia-related symptoms in AD appear largely similar between both the early-onset and late-onset forms of the disease, their underlying etiologies contrast [3].Whereas early-onset AD is a familial autosomal dominant disorder caused by rare, highly penetrant mutations in one of a small set of genes (APP, PSEN1, and PSEN2), the more common late-onset form of the disease (accounting for 90-95 % of cases) occurs sporadically, and risk is determined by complex underlying mechanisms [3][4][5][6].Estimates based on twin concordance rates suggest heritability of late-onset AD is as high as 70 %, implicating major roles for genetic as well as non-genetic factors [6].Indeed, through candidate gene studies, as well as more recent genome-wide association studies (GWASs) and whole-exome sequencing, both common and rare variants associated with the late-onset form of AD have been identified [7][8][9][10][11].Collectively, however, common GWAS variants account for only a modest proportion (~30 %) of the underlying variance in disease susceptibility [12].Several environmental factors are also thought to play a role [5,6], yet exactly how these contribute to risk, onset, and progression remains poorly defined.", + "Alzheimer's disease is the most common type of dementia, and it is characterized by a decline in memory or other thinking skills.The greatest risk factor for Alzheimer's disease is advanced age.A recent genome-wide study identified a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11 is probably responsible for the association.The association of a protective haplotype with a 10-year delay in the onset of Alzheimer's disease and the identification of a CCL11 variant with possible functional roles in this association might allow the future development of immunomodulators with the potential to halve disease incidence.Alzheimer's disease is the most common type of dementia, and it is characterized by a decline in memory or other thinking skills.The greatest risk factor for Alzheimer's disease is advanced age.A recent genome-wide study identified a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11 is probably responsible for the association.The association of a protective haplotype with a 10-year delay in the onset of Alzheimer's disease and the identification of a CCL11 variant with possible functional roles in this association might allow the future development of immunomodulators with the potential to halve disease incidence.", + "INTRODUCTIONAlzheimer's disease (AD) is a common debilitating disorder with a prevalence that rises steeply with age from below 1% at 65 years to as high as 40% after the age of 90 [Bachman et al., 1992].Genes are known to play a role in the development of AD.Twin studies show heritabilities of around 60% [Bergem et al., 1997;Gatz et al., 1997].Indeed, variation in four genes has already been shown to cause rare forms of early-onset AD [the Amyloid Precursor Protein Gene (APP); Goate et al., 1991; Presenilin 1 (PS1); Sherrington et al., 1995; Presenilin 2 (PS2); Levy Lahad et al., 1995, Rogaev et al., 1995] or increase the general risk of disease development [Apolipoprotein E (APOE), Corder et al., 1993].As well as increasing disease susceptibility, APOE e4 alleles are associated with reduced age at onset (AAO) and appear to show their strongest effect below 70 years [Farrer et al., 1997].There is also evidence from both twin [Pedersen et al., 2001] and family studies [Tunstall et al., 2000;Li et al., 2002] that AAO in AD is heritable.Daw et al. [2000] have estimated that in addition to APOE, there are at least four loci with similar effect sizes, which contribute to AAO in AD.", + "IntroductionAlzheimer's disease (AD), a devastating neurodegenerative disease, is the most common form of dementia among the elderly.Genetically, AD is a complex and multifactorial disease with the possible involvement of multiple genes.The rare early-onset form of the disease usually follows an autosomal-dominant inheritance pattern and to date three genes have been identified: amyloid precursor protein (APP) and presenilin 1 and 2 (PSEN1 and PSEN2).The common late-onset form of the disease is much more complex than the early-onset form and until recently the apolipoprotein E (APOE) gene was the only major genetic factor accounting for 20-29% of the risk for late-onset AD. 1,2 Recent large genome-wide association studies (GWAS) have identi-fied nine additional genes for late-onset AD, including CR1, BIN1, CLU (a.k.a.4][5][6][7] There is high heritability for AD risk (up to 80%), 8 but the total risk attributable to all confirmed loci is about 50%, indicating the presence of additional risk genes for late-onset AD.", + "NE OF EVERY 5 PERSONS AGED 65 years is predicted to develop Alzheimer disease (AD) in their lifetime, and genetic variants may play an important part in the development of the disease. 1 The apparent substantial heritability of late-onset AD 2 is inadequately explained by genetic variation within the well-replicated genes (apolipoprotein E [APOE; RefSeq NG_007084], presenilin-1 [PSEN1; RefSeq NG_007386], presenilin-2 [PSEN2; RefSeq NG_007381], and amyloid beta precursor protein [APP;" + ], + [ + "Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "Recent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals.", + "Background: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study.Background: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.", + "INTRODUCTIONHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining 20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging.", + "Somatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18].Influence of Genetic Factors in Ageing and LifespanAgeing is defined as the decline of physiological functions in several tissues and organs inducing an increasing probability of death [17].The understanding of genetic factors involved in ageing has been limited due to the complexity of this process and the heterogeneity among individuals and even among tissues [18][19][20].Tissue cells adopt a senescent phenotype as a consequence of multiple intrinsic, extrinsic, and stochastic factors [21].The combination of these genetic factors is related to longevity and healthy ageing [22].Although this decline is somewhat predictable, some individuals show a much slower decline and get to live past the age of 100.Studies in these individuals showed polymorphisms in some genes which are associated with long life, such as APOE and FOXO3.However, these associations have not been consistent across different populations, suggesting that ageing is rather polygenic [23].", + "On the other hand, the same evolutionary-motivated strategy suggesting to focus on more heterogeneous phenotypes (as opposite to more homogenous) can be highly beneficial for unraveling genetic predisposition to fundamental mechanisms of intrinsic biological aging and, consequently, to geriatric diseases.Indeed, aging is associated with systemic remodeling of an organism's functioning which increases chances of virtually all geriatric disorders (Franco et al. 2009;Franceschi et al. 2000;Martin et al. 2007;Cutler and Mattson 2006).Experiments with laboratory animals (Johnson 2006) and heritability estimates in humans (Christensen et al. 2006;Iachine et al. 1998) show that aging can be genetically regulated (Finch and Tanzi 1997;Martin et al. 2007;Vaupel 2010).Accordingly, yielding insights in genetic predisposition to aging-related processes in an organism could be a major breakthrough in preventing and/or ameliorating not one geriatric trait, but perhaps a major subset of such traits (Martin et al. 2007) that can greatly advance progress in solving the problem of extending healthy lifespan in humans.", + "M OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10).", + "IntroductionThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005).", + "TranslationalA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors.", + "Many factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics.", + "The genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity.IntroductionHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006).", + "IntroductionApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches.", + "GenAge: the aging gene database Philosophy and overview of resourcesIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhes, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging.", + "I NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity.", + "Studies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches.Conclusions and prospectsOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + ] + ], + "task_id": [ + "FE094A900BA5B3C48A3A67B18B2F12BD", + "8DCEF606839664C8B6C72CF1D181CEEA", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "B2F5CB7BCD9A827D3A6E0152C030C4B4" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_aging_3.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_aging_3.json new file mode 100644 index 0000000..3247692 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_aging_3.json @@ -0,0 +1,64 @@ +{ + "question": [ + "what genes are associated with aging?", + "Which genes are associated with aging in human ", + "What is GeneNetwork and how does it relate to aging research?" + ], + "answer": [ + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity." + ], + "contexts": [ + [ + "Following are examples of the identified genes and experimental or GWAS link between these genes and aging.On the list of the 25 top genes, NAP1L4 encodes a member of the nucleosome assembly protein (NAP) family, which interacts with both core and linker histones, and shuttles between the cytoplasm and nucleus, suggesting a role as histone chaperone.Histone protein levels decline during aging, and dramatically affect chromatin structure.Remarkably, the lifespan can be extended by manipulations that reverse the age-dependent changes to chromatin structure, indicating the pivotal role of chromatin structure in aging [32].In another example, gene expression of NAP1L4 increases with age in the skin tissue [33].Findings of GWAS link a number of the identified genes to age-related disorders, such as GAB2 and late onset Alzheimer's disease [86], and QKI and coronary heart disease/myocardial infarction [79].Interestingly, GWAS reports also link QKI to successful aging [87].Indicative biological pathways associated with the candidate aging genes", + "Examples of biological candidate genes with pleiotropic functions, which are involved in aging in general and in musculoskeletal aging in particular, are numerous: (a) in addition to the IGF-1 and vitamin D genes, estrogen metabolism pathway genes, including estrogen receptors and aromatase (CYP19), are associated with fat-free mass (Walsh et al. 2005) and BMD (Shearman et al. 2004), prostate and breast cancer (Gallicchio et al. 2006), and cardiovascular disease risk (Shearman et al. 2003).", + "In-depth analysis of the age-regulated genes revealed that multiple genes in the DNA damage response pathway were upregulated with age including those that function in non-homologous end-joining repair (mre11, rad50, Ku80 and mus308) and in translesion DNA synthesis (mus205 and DNApol-eta) [44][45][46].Genes that encoded enzymes with antioxidant properties, such as the thioredoxin reductase Trxr-1, and antioxidant genes involved in glutamate metabolism, such as GlnRS, isoQC and QC, were also upregulated with age [47][48][49][50].We also observed increased age-associated expression of chaperone genes (Cct1, Cct4, Cct5, Cct6, Hsc70-4) and the unfolded protein response transcription factor Xbp1, consistent with an induction of the unfolded protein response [51][52][53].Under stress conditions, there is a translational switch that favors production of stressrelated proteins while decreasing translation of other proteins [54].Paralogs of canonical translation factors such as NAT1 and Rack1, which were both upregulated, promote this switch to cap-independent translation [55,56].Notably, Rheb, which is downregulated with age, positively regulates ribosome production and capdependent translation by activating the mechanistic target of rapamycin (mTOR) kinase pathway [57].Thus, decreased Rheb levels during aging could decrease mTOR pathway activity, which extends lifespan and is protective against age-related pathology [58].Together, these data suggest that multiple genes are induced in aging photoreceptors to mitigate the effects of oxidative stress, protein misfolding and DNA damage.", + "Gene expression modules regulated by agingNearest-neighbor co-expression modules ranging in size from 2 to 40 genes were formed and the collective response of each module to aging across tissues was evaluated. ).The most significant 3-gene module included two proteasome subunit genes (Psmb8 and Psmb9), along with the MHC antigen H2-K1 (M = 10.0;P < 0.001; see Table 3).The three genes contained in this module exhibited highly correspondent patterns of differential expression, with decreased expression occurring in spleen with age, and an age-related up regulation of expression across 13 tissues (Additional File 11).A similar pattern was present with respect to other 3-gene co-expression modules, such as {Tyrobp, Mpeg1, Ctss} and {Sfi1, Pisd, 4933439C20Rik}, and with significant co-expression modules of larger size (Additional File 11).In each of these cases, genes belonging to the same module exhibited similar differential expression patterns in the same tissues, indicating that patterns of co-expression had considerable explanatory power in terms of age-related transcriptional effects.", + "Analysis of prior research (Online Resource 5) shows that the revealed genes can be explicitly involved in other key biological processes in an organism whose role is known to be changing with aging.Specifically, ten genes (BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, and ZKSCAN1) regulate transcription which is believed to be disrupted when an organism is getting older (Roy et al. 2002).The DBH, TPO, and LSS genes are involved in synthesis of catecholamine, thyroid, and vitamin D hormones, respectively.The GPER binds estrogen and HCRTR2 binds orexin-A and orexin-B neuropeptid hormones.Hormonal deregulation with aging is considered to be one of the major components of senescent processes in an organism (Barzilai and Gabriely 2010).Five genes (ATG2A, NEDD4L, PSMB1, UBXN4, and USP6) are involved in degradation of proteins through ubiquitin-proteasome and the lysosomal/autophagic system.Dysfunction of this system leads to accumulation of damaged proteins in an organism that is associated with aging (Koga et al. 2011).Protein degradation through ubiquitin-mediated proteolysis plays an important role in cell-cycle regulation (Reed 2003).The PSMB1, SIK1, TRIP13, and TTN genes in the revealed set coordinate cell cycle.Cell cycle is linked with the aging-related processes in humans through a gradual increase in cell division errors in all tissues in an organism (Ly et al. 2000).Five genes (EEF1A2, DBH, ITGB2, TUBB2C, and WRN) take part in regulation of apoptosis which plays an important role in the aging process and tumorigenesis (Salvioli et al. 2008).Seven genes (ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, and SOAT2) are involved in lipid metabolism which plays one of the key roles in human longevity and healthy aging (Barzilai et al. 2003).", + "In addition to testing genes known to be associated with age-related diseases and phenotypes for association with longevity, genes known to promote longevity in model organisms have been examined in human populations.Mutations in insulin or insulinlike signalling pathway genes have been shown to extend lifespan in Caenorhabditis elegans [20], Drosophila melanogaster [21,22] and mice [23,24].The insulin-signalling pathway negatively regulates the forkhead (FOXO) transcription factor [25].When insulin or insulin-like growth factor signalling is low, FOXO is activated and lifespan extension occurs [26].An overrepresentation of rare insulin-like growth factor I receptor (IGFIR) mutations has been observed in centenarians [27].These mutations are associated with reduced activity of IGFIR as measured in transformed lymphocytes [27].", + "Aging can be viewed as a lethal by-product of activities, such as reproduction and food intake, that are controlled by genes [1].Since most of these genes are evolutionarily conserved, distant species may share common pathways of aging [2].The insulin/insulin-like growth factor 1 (IGF1) signaling pathway could be one such common pathway, as it modulates aging in many species, including Caenorhabditis elegans, Drosophila, mice [3], and possibly humans [4].An elegant study carried out in C. elegans by applying microarray techniques showed that a member of the SIR2like protein family is regulated downstream of DAF-16, a FOXO-family transcription factor that affects the rate of aging in response to the insulin/IGF1 pathway [5].SIR2 proteins constitute an evolutionarily conserved family of NAD-dependent deacetylases called sirtuins [6][7][8].In model organisms the expression levels of SIR2 modulate life span [9][10][11].Since sirtuins are NAD + dependent these proteins through different routes may link energy metabolism, genome maintenance, and aging [11,12].Thus SIR2 genes may play a crucial role in conserved pathways of aging and longevity.", + "Cross-species translation of age-related processesTo identify convergent evidence across species for genes involved in aging, we integrated data from a total of 73 aging-associated gene sets (S4 Table ), derived from 31 publications across 6 species (yeast, worm, fly, rat, mouse, human), and from three web resources (GeneNetwork, GenAge [38], and GWAS Catalog (https://www.ebi.ac.uk/gwas/).Using the \"GeneSet Graph tool\" in GeneWeaver, we identified Cd63 as the most highly connected gene (i.e. it was present in the largest number of sets of genes) (Fig 3).Cd63 was present in 12 gene sets from seven publications across four species (fly, rat, mouse, and human; Table 3).The probability of finding at least one gene in a 12-way intersection, given the observed set sizes and species, is p < 0.0005 (permutations n = 2000).To validate Cd63 as an aging gene, we knocked down the C. elegans ortholog, tsp-7, by feeding RNAi and observed a 10.5% extension of mean lifespan (19.04.0,n = 312 for empty vector(RNAi) vs. 21.06.5 days, n = 317 for tsp-7(RNAi) at 25C; p = 4.8e-7 by the log-rank test) (Fig 4,S5 Table).Manipulating tsp-7 is thus sufficient to influence lifespan in at least one environmental context.", + "Genes Whose Expression Decreased with Age.Of the 26 genes that decreased expression with age in control mice, 23% are involved in DNA replication and the cell cycle (Table 2).Most of these have a negative effect on cell growth and division.Among these, the product of phosphatase and tensin homolog (Pten) gene is a tumor suppressor that induces cell-cycle arrest through inhibition of the phosphoinositide 3-kinase pathway (28).B cell translocation gene 2 (Btg2) is a tumor suppressor that increases expression in response to DNA damage (29).The murine gene product of the amino-terminal enhancer of split (Aes) is a potent corepressor of gene expression and cellular proliferation (30).Calcium-binding protein A11 (S100a10) binds to and regulates the activity of annexin II, which is involved in the transduction of calcium-related mitogenic signals (31).Insulin-like growth factor (IGF) binding protein 1 (Igfbp1) plays an important role in the negative regulation of the IGF-1 system, a stimulator of mitogenesis (32).", + "daf-16 dependent genesAmong the 52 genes that we have tested, 29 genes act almost completely in a daf-16 dependent manner, to regulate lifespan (Table 2).One of the genes identified was daf-2 (Y55D5A_391.b).This serves as a proof of principle that our screen is effective in identification of aging genes.", + "Signatures of aging in muscleFor the muscular system, six clusters of age-related genes with significant enrichment of functional annotation were identified (Fig. 2B; Supplemental Table 9).Aging in muscle was associated with an increase of transcript levels of genes (Clusters 1, 2, and 3) involved in a number of biological processes, including antimicrobial humoral response, ubiquitin-dependent protein catabolism, autophagic cell death, prosthetic group metabolism, protein membrane targeting, secretion pathway, transmembrane receptor protein tyrosine kinase signaling pathway, cell motility, and response to toxin as represented by glutathione S transferase.On the other hand, aging in muscle was found to be associated with decreased transcript levels of genes (Clusters 4-6) involved in generation of energy derived by oxidation of organic compounds as represented by succinate dehydrogenase B (SdhB), in oxidative phosphorylation as represented by ATPase coupling factor 6, in protein kinase cascade as represented by Jun-related antigen, and in metal ion transport as represented by ferritin 1 heavy chain homolog and I'm not dead yet (Indy).It has been shown that SdhB, ATP synthase, ferritin, and aconitase in C. elegans (Hamilton et al. 2005;Hansen et al. 2005) and Indy and SdhB in D. melanogaster (Rogina et al. 2000;Walker et al. 2006) modulate lifespan in these organisms, respectively.Overall, these findings suggest that a prominent feature of aging in muscle is the alteration of expression of genes involved in proteasomal and mitochondrial functions.", + "Several of the genes we identify have previously been shown to influence lifespan in experiments on model organisms.For example, knockouts of the orthologs of APOE, LDLR, CDKN2B, and RBM38 in mice shortens their lifespan [24][25][26][27] , while knockout of IGF1R has the opposite effect 28 .Similarly, overexpression of the FOXO3 orthologue in Drosophila melanogaster 29 and the SNCA orthologue in Caenorhabditis elegans 30 have shown to extend their respective lifespans.Many of our genes are also enriched for pathways previously related to ageing in eukaryotic model organisms, including genomic stability, cellular senescence, and nutrient sensing 31 .For example, FOXO3 and IGF1R are well-known players modulating survival in response to dietary restriction 32 , but we also highlight genes involved in the response to DNA damage and apoptosis, such as CDKN2B, USP28, E2F2, and BCL3.In addition to hallmarks discovered in model organisms, our results suggest that haem metabolism may play a role in human ageing.This pathway includes genes involved in processing haem and differentiation of erythroblasts 33 .Although the enrichment is largely driven by genes linked to the LDLR locus, genes linked to other loci of interest (such as FOXO3, CDKN2B, LINC02513) are involved in similar biological pathways: myeloid differentiation, erythrocyte homeostasis, and chemical homeostasis.To determine the age-related expression of the identified cisand trans-acting genes, we performed a look-up in the dataset of Peters et al. 14 .This large dataset contains the associations of genes with age in whole blood, so we limited ourselves to the cis-and trans-acting genes identified in the whole-blood datasets.We found that FOXO3 expression is increased with age in this dataset, which is in line with the life-extending variant decreasing expression (Supplementary Data 6).Moreover, one cis-(ILF3) and two trans-acting genes (E2F2 and PDZK1IP1) in the LDLR locus show a similar effect (i.e.increased or decreased expression with age combined with the life-extending variant decreasing or increasing expression, respectively).The most interesting, however, seems to be the LINC02513 locus, which showed multiple trans-acting genes to be strongly downregulated with age, while the lead life-extending variant increases expression.LEF1, CCR7, and ABLIM1 even belong to the most significantly affected genes in the whole transcriptomic dataset.This indicates that this long intergenic non-protein coding RNA may serve as a master regulator of age-related transcription in whole blood.", + "94DE MAGALHES ET AL. lar signatures of mammalian aging.Some of the genes overexpressed with age seem to be a response to aging, in that they have been previously found to have protective functions (de Magalha es et al., 2009b).As such, these genes may help organisms manage aging and could be targets for manipulation.Likewise, gene expression analysis of CR has been conducted to identify associated genes (Lee et al., 1999(Lee et al., , 2000)).A number of molecular signatures have emerged from such studies that could be useful to identify candidate processes and pathways that affect aging, biomarkers (see below), and candidate regulators (Anderson and Weindruch, 2010;Hong et al., 2010).", + "Aging-related gene prediction and putative transcriptional mechanismsGeneFriends was used to identify genes related to aging.A seed list of genes known to be consistently overexpressed with age in mammals was used [18].In total, 1119 genes were co-expressed with the aging seed list at p <10 -6 ; Table 1 shows the top 25 genes.Many of these genes have been associated with age-related diseases.Several other genes that have been shown to play a role in aging such as lysosomal-associated membrane protein-2 Lamp2 [19] (p = 5.68 -30 ), Fas [20] (p = 2.70 -31 ) and growth hormone receptor Ghr [21] (p = 1.34 -19 ) also showed a significant co-expression.Anxa2, Anxa3 and Anxa4 also show a low p-value (p < 10 -25 ) as well as several S100 calcium binding proteins which have been shown to interact with annexins [22].Top 25genes co-expressed with aging related genes", + "Fig. 7 Functional relationships of genes implicated in longevity.The genes in red/blue boxes represent genes with increased/decreased mRNA expression in ageing Drosophila (color figure online)", + "The genome-wide RNAi study conducted by the Ruvkun lab, authored by Hamilton et al. [88], identified a total of 89 additional aging genes with disparate functions including cell structure, cell surface proteins, cell signaling, cellular metabolism, and protein turnover.Of the 66 genes with previously known functions, 17 corresponded to various aspects of carbon metabolism, including citric acid cycle enzymes and subunits of complexes I, IV, and V of the ETC.Researchers also speculated that protein translation might play a role in lifespan regulation, based on the identification of iff-1 (T05G5.10),a gene that has homology to the translation initiation factor eIF5A.Other hits from this screen included two genes containing PH domains known to interact with phosphatidylinositol lipids, multiple G protein-coupled receptors, protein processing and degradation genes such as proteases and ubiquitin ligases/hydrolases, and chromatin modifying factors.", + "Genetic studies have shown that aging can be slowed in mutants that are defective in a wide range of cellular processes (such as mitochondrial function, chromatin regulation, insulin signaling, transcriptional regulation, and genome stability).This indicates that aging is a complex process driven by diverse molecular pathways and biochemical events.As such, a powerful approach to study aging is to use systems biology, which allows a multitude of factors affecting aging to be analyzed in parallel.For example, DNA microarrays and gene expression chips have been used to perform a genome-wide analysis of changes in gene expres-sion in old age.Extensive studies in Caenorhabditis elegans and Drosophila melanogaster have identified hundreds of ageregulated genes (Hill et al. 2000;Zou et al. 2000;Lund et al. 2002;Pletcher et al. 2002;Murphy et al. 2003).Several studies have described age-regulated genes in the muscle and brain of mice (Lee et al. 1999(Lee et al. , 2000) ) and the retina and muscle of humans (Yoshida et al. 2002;Welle et al. 2003Welle et al. , 2004).These age-regulated genes may serve as markers of aging, enabling one to assess physiological age independently of chronological age.Analysis of the functions of these age-regulated genes has identified specific biochemical mechanisms that change toward the end of life." + ], + [ + "Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "Background: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study.", + "Geneticlinkage studies of long-lived human families identified alongevity locus while candidate gene approaches have beenused to identify and confirm the association betweenspecific variants in the FOXO3A gene and humanlongevity [37]. Genome-wide association studies havealso been used to identify the association of APOE with life123Aging Clin Exp Resspan and have yielded insights into potential biologicalpathways and processes related to aging. Despite thesesuccesses, several problems are inherent in humanlongevity studies including potentially high degrees ofenvironmental heterogeneity, genetic diversity, and lack ofbirth matched controls, among others [8].", + "Additional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process.", + "Somatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18].", + "Ageing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.Ageing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.The only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained.", + "In most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes.", + "The lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010).Human longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p 5 10 8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p 10 5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.Human longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p 5 10 8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p 10 5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.", + "In addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations.", + "The genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity.", + "Although the models data set comprises all genes (to our knowledge) shown by the time of the latest update to statistically increase longevity or alter the aging process in a noticeable way, in the human data set we try to evaluate whether a given intervention is affecting the aging process itself or not.For example, many mutations may increase longevity by decreasing the incidence of specific diseases, rather than by altering the basic process of aging (de Magalhes et al ., 2005a(de Magalhes et al ., , 2005b)).Therefore, the human data set is not merely an extension of the work conducted in model organisms and of its bibliography, but a manually selected list of the most pertinent human aging candidate genes, each presented with a higher annotation level.We cite studies on whether the functions of aging-associated genes in model organisms are conserved in their human orthologues.Likewise, we cite flaws in previous studies based on new published observations, although we have a neutral stance on conflicting findings from different research groups.Our policy is to cite all conflicting reports and let visitors make their own decisions on how to interpret them.By contrast, each entry in GenAge model organisms has only one reference: the first publication reporting an association of the gene with longevity or aging.Moreover, one of the latest enhancements in the human data set was the inclusion of Gene Ontology annotation.Gene Ontology terms and annotation files were obtained from the Gene Ontology Consortium website (http://www.geneontology.org/ ) and provide an additional layer of description for the gene products in a cellular context (Ashburner et al ., 2000).", + "Ageing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.Ageing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.The only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained.", + "Most of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + ], + [ + "Our recent understanding of biological networks has led to new fields, like network medicine [29].Biological networks can be built using protein interaction and gene co-expression data.A previous paper used proteinprotein interactions to build genetic networks identifying potential longevity genes along with links between genes and aging-related diseases [30].Here, we present the network of proteins and genes co-expressed with the CellAge senescence genes.Assaying the networks, we find links between senescence and immune system functions and find genes highly connected to CellAge genes under the assumption that a guilt-by-association approach will reveal genes with similar functions [31].We next explored what information could be obtained by applying a network analysis to CellAge.From the list of CellAge genes, three networks of CS were generated: a PPI network and two co-expression networks, with the aim of identifying new senescence regulators based primarily on network centrality of the genes.We looked at the RNA-Seq co-expression network in detail, using the main connected component of 3198 genes to find highly central genes to the network as a whole, and those occupying subnetworks of interest.The RNA-Seq was a highly modular network, separated into some subnetworks of distinct functions (Fig. 4).The two largest and more central networks contained a number of known senescence genes.We expanded the analysis of these networks in particular, identifying a number of bottleneck nodes.Cluster 1 was enriched for cell cycle processes, which is not overly surprising given that senescence involves changes in cell cycle progression.However, cluster 2 comprised of enriched terms relating to immune system function.One of the aims in biogerontology is to understand and reverse the effects of aging on the immune system.Additional file 1: Table S38 highlights the genes in both clusters that are potential CS bottlenecks within the network and may warrant further study.Unweighted RNA-Seq co-expression networkWe used CellAge genes that induce and inhibit CS and their co-expressing partners to build a cellular senescence co-expression network.The network consists of a main connected network with 3198 nodes, and a number of smaller \"islands\" that are not connected to the main network (Fig. 4a).In this study, we look at the broad context of CS genes-their association with aging and aging-related diseases, functional enrichment, evolutionary conservation, and topological parameters within biological networks-to further our understanding of the impact of CS in aging and diseases.Using our networks, we generate a list of potential novel CS regulators and experimentally validate 26 genes using siRNAs, identifying 13 new senescence inhibitors.Network analysesThe CellAge genes form both protein-protein and gene co-expression networks.The formation of a proteinprotein interaction (PPI) network is significant in itself given that only ~4% of the genes in a randomly chosen gene dataset of similar size are interconnected [53].In order to have a more holistic view of CS, we were interested in the topological parameters of the networks that CS genes form.For this, several types of networks were constructed using the CellAge genes as seeds: the CS PPI network, along with two CS gene co-expression networks built using RNA-seq and microarray data.Biological networks generally have a scale-free topology in which the majority of genes (nodes) have few interactions (edges), while some have many more interactions, resulting in a power law distribution of the node degree (the number of interactions per node) [31,54].As expected, the node-degree distribution of the above networks does confirm a scale-free structure (Additional file 2: Fig. S9).Additional file 1: Table S32 presents the network summary statistics for the resulting networks.", + "Here we have curated studies from the aging literature and utilized integrative functional genomics in GeneWeaver to address four questions related to aging by analyzing these largescale, complex sets of data: 1) to identify molecular relations between cellular senescence and functional cognitive decline, 2) to examine the intersection between comorbid disease states, 3) to identify new druggable targets for longevity, and 4) to examine cross-species translation of age-related processes.GeneSet GraphTo identify the most highly connected gene within a group of gene sets related to aging, the \"GeneSet Graph\" tool was used.This tool presents a bipartite graph visualization of genes and gene sets.Genes are represented by elliptical nodes, and gene sets are represented by boxes.The least-connected genes are displayed on the left, followed by the gene sets, then the moreconnected genes in increasing order to the right.Genes and gene sets are connected by colored lines to show what genes are in which gene sets.A degree threshold is applied on the gene partite set to reduce the graph size.DiscussionThe growing number of studies and data in many fields, including ageing, requires the development of integrative and computational approaches to analyze the data for consensus and shared biological findings across conditions.Using GeneWeaver's database and analysis tools to address questions in aging research we were able to identify genes common to cellular senescence and functional cognitive decline; to examine gene products at the intersection between obesity and dementia, to identify several potential druggable targets for investigation in longevity, and to identify and validate a cross-species age-related gene from convergent evidence.Our identification of the role for CD63 in aging would not have been made without this use of this large genomic analysis tool.CD63 in C.elegans is member of the tertaspanin family of proteins [47].Tetraspanins are transmembrane scaffolding proteins involved in motility, cell adhesion, proliferation and activation.Recently we showed that knockdown of another tetraspanin in C.elegans, tsp-3, extends lifespan by >20% lifespan as well [48], suggesting that this protein family may be of broader interest in aging.", + "NIH-PA Author ManuscriptNIH-PA Author ManuscriptGeneNetwork (www.genenetwork.org), described in Chapter 6, is a suite of data sets andbioinformatics tools that stores, analyzes, and displays phenotypes as well as large geneexpression data sets for several species (human, monkey, mouse, rat, fly, barley, tomato, andArabidopsis) (Durrant et al. , 2012; Hoffman et al. , 2011; Rosen et al. , 2007). GeneNetworkusers can take advantage of a systems genetics approach (Rosen et al. , 2003, 2007).", + "Interaction network analysisThe increased accuracy and breadth of our RNA-seq data sets allowed us to generate networks of gene functional change in aging liver, above and beyond what was observed using DAVID or GOrilla.Using Ingenuity Pathway Analysis (IPA) we generated, from the differentially expressed protein-coding genes and ncRNAs, interaction networks of functional change.This resulted in multiple overlapping pro-aging networks from which we could distinguish three major molecular phenotypes: inflammation, proliferative homeostasis and lipid metabolism (Figs. 4, 5 and 6).", + "As mentioned previously, GeneNetwork(www.genenetwork.org) is a collaborative Web-based resource equipped with tools andfeatures for studying gene/gene and exploring genetic correlates to neurobehavioralphenotypes (Chesler et al. , 2003, 2004). The Web site is home to a growing collection ofgene expression and phenotypic data from a variety of species and brain regions, with a hostof links to external resources for tracing the interrelationships of a gene among multipleWeb-based resources. GeneNetwork also offers a number of correlation and mappingstrategies for assessing associations among multiple genes and QTLs.", + "The aim of this work was to construct an online tool that can be used to derive novel candidate genes for further studies in aging and complex diseases, in a quick and intuitive manner.Aging is not considered a disease, yet older individuals are more susceptible to several diseases such as Alzheimer's, Parkinson's and cancer.This is one of the reasons why research in this field is rapidly expanding and several hundreds of genes have been linked to aging [16].A major bottleneck in aging/ complex disease research is that it is difficult to determine the causality of transcriptional alterations.It is also unclear if the altered expression profile observed with aging/complex disease consists of one particular biological module or whether it consists of genes that act separately from each other.To this end, GeneFriends outputs transcription factors co-expressed with the genes supplied by the user.", + "Network analyses additionally revealed systems level relationships between age-related diseases and the aging regulators.Miller et al. [42] used a weighted gene coexpression network to identify transcriptional networks in Alzheimer's disease (AD) and found a significant association between gene expression changes during the progression of AD and those during normal aging.Wang et al. [43] constructed a human disease-aging network to study the relationships between aging genes and genetic disease genes.This study showed that disease genes located close to aging genes have central positions in the PPI network.Second, although high-throughput data on different layers of the living system Fig. (2) can now be easily obtained, it remains obscure as to how information flows or exchanges across these layers to arrive at the alternative \"old/aging\" state of the molecular network from the young state, what events cause the state transition and what are the network circuitry and epigenetic events locking the network in the aging state. [62,63].Clusters or communities in the networks were extracted by the MCL algorithm [64] and only top clusters with more than 10 genes for each network are shown, and different clusters with similar functional enrichment are merged. (A) The network based on a protein functional interaction network [65]. (B) The edges in the network represent cocitation of the two genes together in at least 2 PubMed abstracts under the context of aging, i.e. also co-cited with \"aging\", \"ageing\", \"lifespan\", \"life span\" as calculated by Cociter (http:// www.picb.ac.cn/ hanlab/cociter).In both graphs, the enriched functions within the gene clusters are coded by the colors of the nodes: green -signaling pathways, red -DNA damage response, yellow -mitochondria function and oxidative stress response, blue -ribosome and translation related genes, and purple -protein localization, transport and autophagy.Fig. (4).Network communities among known aging regulators in human and model organisms based on two different interactome datasets.Nodes include human aging regulators and human homologs of aging regulators in worm, fly and mouse from GenAge[62,63].Clusters or communities in the networks were extracted by the MCL algorithm[64] and only top clusters with more than 10 genes for each network are shown, and different clusters with similar functional enrichment are merged. (A) The network based on a protein functional interaction network[65]. (B) The edges in the network represent cocitation of the two genes together in at least 2 PubMed abstracts under the context of aging, i.e. also co-cited with \"aging\", \"ageing\", \"lifespan\", \"life span\" as calculated by Cociter (http:// www.picb.ac.cn/ hanlab/cociter).In both graphs, the enriched functions within the gene clusters are coded by the colors of the nodes: green -signaling pathways, red -DNA damage response, yellow -mitochondria function and oxidative stress response, blue -ribosome and translation related genes, and purple -protein localization, transport and autophagy.Network approaches are instrumental in discerning global properties of aging/lifespan regulators, making computational predictions and inferring the modularity and relationships of various aging regulators.However, they should be applied with great caution as to avoid bias introduced by the literature, the lack of spatial and temporal information, or the limited coverage of the network [44].", + "GeneNetwork.org also offers a powerful statistical platform foronline network analyses and mapping, enabling numerous molecular questions to be probed in one centralized location(Chesler et al. , 2003, 2005; Li et al. , 2010; Mulligan et al. , 2012,2017, 2019). Most data are from groups of animals or humanswho have been fully genotyped or even sequenced. As a result, itcan be used to model causal networks that link DNA differencesto traits such as differences in expression, cell number, volumes,and behavior using real-time computation and graphing.", + "Another use of GenAge is for researchers to associate genes already under investigation with other, little-known genes, which can lead to new experimental designs.To do this, protein-protein interactions are one possible approach, and GenAge's human data set features 673 interactions, most of which manually curated obtained from the Human Protein Reference Database (HPRD) (Peri et al ., 2003).In fact, one of our earliest applications of GenAge involved finding novel genes that may be linked to aging by way of an analysis of protein-protein interactions.The principle being that proteins not previously thought to be related to aging which interact with a large number of proteins directly linked to aging might too be involved in aging and are thus promising candidates for future studies (de Magalhes & Toussaint, 2004;Budovsky et al ., 2007).Similar works are made easy with GenAge.Protein-protein interactions with one or more genes as query can be visualized (Fig. 2), or they can be downloaded for use with more advanced biological pathway analysis software.By providing a list of candidate genes, the genes in GenAge can serve as basis for gene expression and genetic association longevity studies, including human studies, or even for clinical studies of interventions hypothesized to affect aging.In fact, recent gene expression studies have used GenAge to focus on aging-associated genes (Chen et al ., 2008;Hardman & Ashcroft, 2008).Because researchers may have disparate opinions regarding the relevance of different model systems to understand human aging, an important tool to investigate the human data set as a whole is GenAge's browser (http://genomics.senescence.info/genes/browser.php).The browser makes it possible to (among other things) retrieve only those entries that pass certain criteria related to the annotation in GenAge, such as selection process and gene function.For example, users can retrieve only genes associated with aging in non-mammalian model organisms.Several criteria can be set, all of which must be passed for genes to be retrieved.Afterwards, users can select all or a subgroup of the genes retrieved, which can then be further analysed using simple statistical tools (http://genomics.senescence.info/genes/analyse.php).Cross-links between the different sections of GenAge make it easy to navigate between the different tools and data sets." + ] + ], + "task_id": [ + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "3EC47C56606B02F00CF2449AB311365C", + "37A26345145679F7539EA8F512623F5E" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_1.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_1.json new file mode 100644 index 0000000..68b53cb --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_1.json @@ -0,0 +1,109 @@ +{ + "question": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "Explain Protective Genetic Factors Against Diabetes in Elderly Populations", + "Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk", + "Explain The Role of Longevity Genes in Protecting Against Diabetes", + "What are the types of diabetes" + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "Studies have shown that foreign genetic admixture can have a protective effect against diabetes. For instance, the prevalence of Type 2 Diabetes (T2D) in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with foreign genetic admixture. This suggests that foreign genotypes can reduce the risk of diabetes. Similar findings have been reported in Pima Indians and other Native American populations.", + "Lifestyle modifications, including increased physical activity, dietary changes, and weight reduction, have been shown to significantly reduce the risk of Type 2 Diabetes (T2D), which is often associated with aging. Regular physical activity improves insulin sensitivity, reducing the stress on insulin-producing cells. Dietary changes, particularly those reducing the intake of processed carbohydrates, energy-rich foods, and animal fats, can also lower T2D risk. Weight loss can directly improve insulin secretion and sensitivity. However, these lifestyle changes require not only individual efforts but also changes in social, built, and food environments.", + "The background text does not provide information on the role of longevity genes in protecting against diabetes.", + "The types of diabetes include Type 1 diabetes, Type 2 diabetes, gestational diabetes, and diabetes due to specific causes such as genetic defects causing deficient insulin secretion or action, diseases of the pancreas, and use of certain drugs. There are also subtypes of adult-onset diabetes identified as severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes." + ], + "contexts": [ + [ + "Genetics and pharmacogenomicsWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18).", + "With further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients.", + "Pharmacogenomics of Type 2 DiabetesWith the advent of GWAS, studies on the roles of inherited and acquired genetic variations in drug response have undergone an evolution from pharmacogenetics into pharmacogenomics, with a shift from the focus on individual candidate genes to GWAS [147].Clinically, it is often observed that even patients who receive similar antidiabetic regimens demonstrate large variability in drug disposition, glycemic response, tolerability, and incidence of adverse effects [148].This interindividual variability can be attributed to specific gene polymorphisms involved in the metabolism, transportation, and therapeutic mechanisms of oral antidiabetic drugs.Pharmacogenomics is on the agenda to explore feasible genetic testing to predict treatment outcome, so that appropriate steps could be taken to treat type 2 diabetes more efficiently.", + "Future directionsDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4.", + "Genomics of T2DDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes.Genetics & genomics of T2D Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D.However, only close to 10% heritability is explained by these variants.Other genetic variants, particularly those which are rare but with significant effects need to be identified. Genetic variability is responsible for the difference in response to antidiabetic drugs seen across individuals.", + "The aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484Diabetes progression is a multifactorial process; however, pharmacogenetics seems to play an important role in understanding the different phenotypes and progression rates among diabetic patients.Genetic variants associated with decreased effect of a certain drug might explain why some individuals are more likely to experience glycemic deterioration on a given treatment.In the following sections, different genetic variants and their impact on treatment efficacy and outcome will be addressed.The aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484To date, a number of genetic variants have been identified to be associated with response to antidiabetic drugs.Of these, some variants are present in either drug receptors or drug metabolizers as for OCT genes, KCNJ11, ABCC8, and CYP2C9.Other variants are known T2D susceptibility variants such as TCF7L2.To identify variants of importance for antiglycemic drug response, GWAS in large cohorts of patients with diabetes with detailed measures of pharmacotherapy are lacking.The pharmacologic management of patients with diabetes often involves drug classes other than antidiabetics.Pharmacogenetic studies on statin and antihypertensive treatment have reported several genetic variants associated with treatment response and adverse drug reactions [101,102].It therefore seems natural to conclude that the future perspectives in pharmacogenetics is to conduct genetic studies in large cohorts with wellphenotyped individuals, thorough data collection on baseline treatment, concomitant treatment, adherence to therapy as well as data collection on comorbidity and additional disease diagnoses.These types of pharmacogenetic studies may provide unique opportunities for future genotype-based treatment standards and may help in delaying or changing the slope of disease progression among patients with T2D.", + "Genetic determinants of diabetes and metabolic syndromes.", + "Thus, specific answers are lacking as to the genetic basis for type 2 diabetes.Still, speculations can be made about what eventually will be found.It is almost certain the genetic basis for type 2 diabetes and other common metabolic diseases will be extremely complex-that a predisposition for the disease will require several genetic hits as opposed to just one.Also, it is generally assumed there will be many susceptibility genes for type 2 diabetes, with enormous variability in different families and ethnic groups.Not known is whether there will be a common form of type 2 diabetes, with any one or even a few susceptibility genes accounting for a sizeable percentage of affected persons.As such, identifying diabetes genes will be slow and difficult.", + "Ta rge ted T r e atmen t a nd Pr e v en t ion4][75] In monogenic forms of diabetes, at least, genetic testing already drives the choice of therapy.For example, in patients who have maturity-onset diabetes of the young due to mutations in the gene encoding glucokinase (GCK), the hyperglycemia is mild and stable, the risk of complications is low, and dietary management is often sufficient.In contrast, in patients who have maturity-onset diabetes of the young due to mutations in HNF1A, the disease follows a more aggressive course, with a greater risk of severe complications, but is particularly responsive to the hypoglycemic effects of sulfonylureas. 62,73Most children with neonatal diabetes have mutations in KCNJ11 or ABCC8, adjacent genes that jointly encode the beta-cell ATP-sensitive potassium channel that mediates glucose-stimulated insulin secretion and is the target of sulfonylureas.In such children, treatment with sulfonylureas has proved more effective and convenient than the lifelong insulin therapy previously considered the default option. 74,75n children with severe obesity due to profound leptin deficiency, exogenous leptin therapy is lifesaving. 76s yet, there are insufficient genetic data to support management decisions for common forms of type 2 diabetes and obesity. 77Although the TCF7L2 genotype is associated with variation in the response to sulfonylurea treatment, 78 the effect is too modest to guide the care of individual patients.For the time being, the contribution of genetic information to therapy is most likely to come through the drug-discovery pipeline.Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physiological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32 and to characterize high-risk groups to enable more efficient clinical trials of agents designed to reduce the progression of type 2 diabetes or obesity or the risk of complications.", + "Type 2 DiabetesWhile a subset of genetic variants are linked to both type 1 and type 2 diabetes (42,43), the two diseases have a largely distinct genetic basis, which could be leveraged toward classification of diabetes (44).Genome-wide association studies have identified more than 130 genetic variants associated with type 2 diabetes, glucose levels, or insulin levels; however, these variants explain less than 15% of disease heritability (45)(46)(47).There are many possibilities for explaining the majority of type 2 diabetes heritability, including disease heterogeneity, gene-gene interactions, and epigenetics.Most type 2 variants are in noncoding genomic regions.Some variants, such as those in KCNQ1, show strong parent-of-origin effects (48).It is possible that children of mothers carrying KCNQ1 are born with a reduced functional b-cell mass and thereby are less able to increase their insulin secretion when exposed to insulin resistance (49).Another area of particular interest has been the search for rare variants protecting from type 2 diabetes, such as loss-of-function mutations in SLC30A8 (50), which could offer potential new drug targets for type 2 diabetes.Research GapsAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations.", + "Genome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 10 8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total 88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes.", + "Together, the findings from these studies were among the first to demonstrate that the genetic etiology of hyperglycemia may modulate response to hypoglycemia agents.Such results yielded strong implications for patient management and paved the way toward elucidating additional genetic factors that might influence drug response in the treatment of T2D.", + "Anumber of studies have implicated a genetic basis for type 2 diabetes (1).The discovery of monogenic forms of the disease underscored the phenotypic and genotypic heterogeneity, although monogenic forms account for only a few percent of the disease (1).Defining the genetic basis of the far more common polygenic form of the disease presents more difficulties (2,3).Nevertheless, some interesting results have recently emerged.A genome scan of Hispanic-American families (330 affected sib-pairs [ASPs]) found linkage to chromosome 2q37 (logarithm of odds [LOD] 4.15) (4), and the causative gene has been recently reported (5).A number of other genome scans in various racial groups have identified other putative susceptibility loci (6 -8).The largest genome-wide scan for type 2 diabetes loci reported to date studied 477 Finnish families (716 ASPs) and found evidence for linkage to chromosome 20q12-13.1(LOD 2.06 at D20S107) (9).Interestingly, similar results have been reported by at least three other groups (10 -12).", + "Because more than one genetic mutation contributes to T1D, the differences that occur between individuals of different backgrounds (for instance, race and locality) may need to be considered in the design of treatments.Personalized medicine is about the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or in their response to a specific treatment (Blau and Liakopoulou, 2013;Timmeman, 2013).This will allow for a more accurate diagnosis per individual, and design of specific treatment plans including gene therapy.", + "Genetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + ], + [ + "If an environmental contributor is near ubiquitous and the geneticpredisposition common as well, interventions are most sensibly weighted towardsenvironmental risk factor modification. Even here, though, there is room for further research, since the etiopathogenesisof type 2 diabetes may not be as well understood as some suggest. Specifically,Chaufan implies that dietary intervention to prevent prenatal programmingleading to susceptibility to develop type 2 diabetes (the fetal origins of adult onsetdisease hypothesis) is as evidence-based as dietary management of the adult diabetic state. However, many questions remain in this area.", + "In the bivariate analysis, there was a tendency of probands to be protected from diabetes when the father lived through a year or years of a surfeit of food during his SGP (OR 0.14, P=0.06).The same experience for the grandfather tended to be followed by a higher risk for the probands dying from diabetes, according to the bivariate analysis (OR 2.34, P=0.09).In the multivariable analysis, a father's exposure to a surfeit of food during his SGP tended to protect the proband from diabetes (OR 0.13, c.i. 0.02 -1.07, P=0.06).However, if the paternal grandfather was exposed to a surfeit of food during his SGP, then the proband had an over-mortality in diabetes (OR 4.1, c.i. 1.33 -12.93,P=0.01) when age at death and the effects of possible overeating among parents and grandparents during their respective SGP were taken into account.", + "EnvironmentThe second factor in Figure 1 is environmental aspects.An important concept is the diabetes genotype typically causes only a predisposition for glucose intolerance (note the terminology susceptibility gene was used in the preceding paragraphs).Whether one develops the diabetes phenotype depends on environmental factors, some obvious in how they act, others less so.For instance, the Nurses Health Survey showed positive associations between obesity and lack of physical activity in the development of type 2 diabetes (as expected), but also protection by not smoking and moderate alcohol intake (14).Already discussed, many studies have shown an association between TV watching, high calorie diets, and lack of physical activity with risk of diabetes, i.e., our modern lifestyle, so it is not surprising that there is an explosion in the incidence of diabetes worldwide.", + "Evidence from genetic admixture Some of the clearest data come from studies of genetic admixture.The prevalence of T2D in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with (unsuspected) foreign genetic admixture. 15Since there were no apparent cultural dierences between the groups, this indicated a protective eect of foreign genotypes on diabetes risk.Similar ndings have been reported in Pima Indians 16 and other Native American populations. 17", + "IntroductionClustering in families implicates a genetic component of diabetic nephropathy, but so far the specific genes underlying diabetic nephropathy remain largely unknown [1,2].Family studies have furthermore revealed that parental type 2 diabetes mellitus is associated with diabetic nephropathy in offspring with type 1 diabetes mellitus [3,4].A positive family history of type 2 diabetes mellitus has also been associated with cardiovascular disease [5] as well as markers of cardiovascular disease [6] in offspring with type 1 diabetes mellitus.Genetic variants or single-nucleotide polymorphisms (SNPs) predisposing to type 2 diabetes mellitus in the Finnish population have recently been identified in large-scale, genome-wide association studies [7,8].The question thus arises of whether these SNPs, which predispose to type 2 diabetes mellitus, also predispose to diabetic nephropathy and related complications in patients with type 1 diabetes mellitus.We therefore assessed the impact of a set of SNPs known to influence susceptibility to type 2 diabetes mellitus on diabetic nephropathy as well as diabetic retinopathy and cardiovascular disease in patients with type 1 diabetes mellitus.", + "Family and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.Type 1 diabetes has unusual epidemiological features related to genderType 1 diabetes also displays unusual patterns of inheritance that may yield insights into etiology and provide clues to the best methods for analyzing genetic studies.The risk to the offspring is generally greater from a mother or father who was diagnosed at an early age (again suggesting that early-onset cases are more heavily genetically 'loaded').However, the risk of diabetes is approximately two to four times higher for a child whose father has type 1 diabetes than one whose mother is affected [see (52,53) and references therein].This parental difference is largely due to a low risk for offspring of mothers who were diagnosed at a later age (53).The difference could be explained by at least three different factors.First, the risk alleles could only be active when transmitted by the father (such as is seen in imprinting, where only one of the parental alleles is expressed).Alternatively, a maternal environmental factor during pregnancy could be protective.However, it is difficult to see how this protective effect would be restricted to mothers diagnosed at a later age, especially since the protective effect was unrelated to the mother's duration of diabetes or even diabetic status at delivery (53).Finally, mothers who are diagnosed at a later age could represent more 'environmental' cases of diabetes, and thus be less likely to pass on risk genes to their offspring.Family and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.", + "CONCLUSIONThe greatest genetic risk (both increased risk, susceptible, and decreased risk, protective) for type 1 diabetes is conferred by specific alleles, genotypes, and haplotypes of the HLA class II (and class I) genes.There are currently about 50 non-HLA region loci that also affect the type 1 diabetes risk.Many of the assumed functions of the non-HLA genes of interest suggest that variants at these loci act in concert on the adaptive and innate immune systems to initiate, magnify, and perpetuate -cell destruction.The clues that genetic studies provide will eventually help lead us to identify how -cell destruction is influenced by environmental factors.While there is extensive overlap between type 1 diabetes and other immune-mediated diseases, it appears that type 1 and type 2 diabetes are genetically distinct entities.These observations may suggest ways to help identify causal gene(s) and, ultimately, a set of disease-associated variants defined on specific haplotypes.Unlike other complex human diseases, relatively little familial clustering remains to be explained for type 1 diabetes.The remaining missing heritability for type 1 diabetes is likely to be explained by as yet unmapped common variants, rare variants, structural polymorphisms, and gene-gene and/or gene-environmental interactions, in which we can expect epigenetic effects to play a role.The examination of the type 1 diabetes genes and their pathways may reveal the earliest pathogenic mechanisms that result in the engagement of the innate and adaptive immune systems to produce massive -cell destruction and clinical disease.The resources established by the international T1DGC are available to the research community and provide a basis for future discovery of genes that regulate the earliest events in type 1 diabetes etiology-potential targets for intervention or biomarkers for monitoring the effects and outcomes of potential therapeutic agents.", + "Genome-wide search for genes affecting the age at diagnosis of type 1 diabetes.Genes affecting type 1 diabetes diagnosis age / A. Syreeni et al.Genome-wide search for genes affecting the age at diagnosis of type 1 diabetes.", + "Type 1 DiabetesThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk.", + "Genetic factors have an important role in the development of diabetes, with some forms of the disease resulting from mutations in a single gene.Others are multifactorial in origin.The monogenic forms of diabetes account for approximately 5% of cases and are caused by mutations in genes encoding insulin 3 , the insulin receptor 4 , the glycolytic enzyme glucokinase 5 , and the transcription factors hepatocyte nuclear factor-1 (HNF-1), HNF-1, HNF-4, insulin promoter factor-1 and NeuroD1/BETA2 (refs 6-10).Mutations in maternally inherited mitochondrial genes can also cause diabetes, often in association with hearing loss 11 .", + "Genetics of Diabetic Complications in HumansEpidemiologic studies have clearly established that only a subgroup of individuals with diabetes are at risk of nephropathy (2).To identify genetic determinants and candidate genes that confer susceptibility or progression for DNP in individuals with type 1 and type 2 diabetes, the National Institutes of Health established the ongoing Family Investigation of Nephropathy and Diabetes study consortium.The Family Investigation of Nephropathy and Diabetes is using Mapping by Admixture Linkage Disequilibrium and traditional affected and discordant sibling pair and relative pair analyses.Previous linkage analysis studies led to the mapping of several susceptibility loci for DNP on specific regions on chromosomes 3, 7, 9, 12, and 20 (14,15).", + "However, these efforts to sift individuals into discrete subtypes of disease would appear to run counter to the evidence that points to a complex, graded, architecture of risk, one that is consistent with a multifactorial etiology, composed of genetic predisposition dominated by multiple common variants of modest effect, and pervasive exposures contributing to risk.In one recent study, Ahlqvist et al. () used basic clinical information from patients with newly diagnosed adult-onset diabetes to define five subtypes of late-onset diabetes: an autoimmune form (covering TD and other related clinical entities), two severe forms (one dominated by insulin deficiency, the other by insulin resistance), and two milder forms (termed \"obesityrelated\" and \"age-related\" diabetes).Whereas the genetic clusters that form the basis of pPS are defined at the level of the variants, these clinical subtypes are defined at the level of the individual and based on biomarkers and clinical data gathered at a specific point in the progression of an individual from health to disease.The latter is likely to limit their relevance to those who have not yet developed disease, and/or those who are on treatment.", + "Studies [71][72][73][74] in Mexican and Asian populations have identified several mutations associated with type 2 diabetes in young people.The high prevalence of type 2 diabetes in the parents of young people diagnosed with type 2 diabetes could reflect a stronger genetic predisposition, even when monogenic diabetes is excluded.This hypothesis suggests that efforts to define genes that cause type 2 diabetes by linkage might be more powerful if focused on young adults with diabetes, raising the question of whether type 2 diabetes in older populations has a relatively smaller genetic contribution and a stronger environmental contribution. 66", + "We found that the presence or absence of parental diabetes and the genotype score were independently associated with the risk of diabetes.This suggests that family history as a risk factor for diabetes conveys more than heritable genetic information; it probably includes nongenetic familial behaviors and norms.The lower relative risks for diabetes associated with observed parental diabetes as compared with those associated with self-reported family history (approximately 1.8 vs. approximately 2.2) support the contention that family history contains more risk information than is implied by inheritance of the diabetes phenotype alone.One of the limitations of our study is that the 18 SNPs we included are probably insufficient to account for the familial risk of diabetes.They account for a minority of diabetes heritability, and the SNP array platforms from which they were chosen capture only approximately 80% of common variants in Europeans.In addition, we have not considered structural variants that might confer a risk of diabetes.It is possible that the addition of rare risk alleles with large effects, or a much larger number of common risk alleles with small individual effects, could improve discrimination. 36Indeed, as many as 500 loci may underlie the genetic risk of type 2 diabetes. 16Also, we did not study interactions among genes or between genes and the environment that might alter the genetic risk in exposed persons.As more diabetes risk variants become known, their incorporation into the genotype score may explain more of the genetic risk implied by parental diabetes.Phenotypic Risk Factors and Definitions of DiabetesEach examination consisted of a medical history taking, physical examination, and collection of a fasting blood sample. 21In the sixth examination cycle (1995 through 1998), participants completed a self-administered questionnaire that asked about family history of disease.We defined a positive selfreported family history of diabetes as a report that one or both parents had diabetes; this definition is more than 56% sensitive and 97% specific for confirmed parental diabetes. 22Parental diabetes was confirmed by means of direct observation of the original cohort, over the course of 46 years of observation after their enrollment in the Framingham Heart Study, at the end of which time the mean age of surviving parents was 83 years.We considered diabetes to be present in a parent when medication was prescribed to control the diabetes or when the casual plasma glucose level was 11.1 mmol per liter or higher or 200.0 mg per deciliter or higher at any examination.We defined diabetes to be present in an offspring when treatment was prescribed to control the diabetes or when the fasting plasma glucose level was 7.0 mmol per liter or higher or 126.0 mg per deciliter or higher at any examination.More than 99% of the cases of diabetes among the participants in the Framingham Offspring Study are type 2 diabetes. 6", + "Genetics is one example of the 'other risk factors' involved in the pathogenesis of DR.Twin and epidemiological studies have strongly suggested a genetic component in the etiology of DR (6 -10), with heritability scores ranging from 27 to 52% in both type 1 and type 2 diabetes (7 -10).There is an increased risk of severe DR among family members of DR subjects (8,9) and in siblings of affected subjects (8,9).Furthermore, several studies have also shown a discrepant rate of the prevalence of DR among different racial ethnic groups in the US population, with a significantly higher prevalence observed among Hispanic, African-American and Chinese-American when compared with Caucasian populations (11).While these differences may partially be attributed to lifestyle factors, evidence from familial aggregation, ethnic differences and heritability clearly supports a genetic contribution in the etiology of DR." + ], + [ + "Together, these clinical trials demonstrate that diet and lifestyle modification is highly effective in preventing type 2 diabetes in different ethnic and racial groups.There is an urgent need to translate the findings from these trials into clinical and public health practice.Emphasis should be placed on early adoption of healthy habits in pediatric populations because these practices track through to adulthood.Physical activityNumerous epidemiologic studies show that increased physical activity reduces risk of diabetes, whereas sedentary behaviors increase risk.In the NHS (26), each 2-h/day increment of time spent watching television (TV) was associated with a 14% increase in diabetes risk.Each 2-h/day increment of standing or walking around at home was associated with a 12% reduction in risk.Each 1-h/day increment of brisk walking was associated with a 34% reduction in risk (Fig. 3).These results indicate a continuum in the relationship between physical activity levels and diabetes risk.Among sedentary behaviors (TV watching, sitting at work, and other sitting), prolonged TV watching was associated with the highest risk.Accumulating evidence strongly demonstrates that the majority of type 2 diabetes cases can be prevented through diet and lifestyle modification.However, the adoption of a healthy diet and lifestyle requires not only individual behavioral changes, but also changes in our food, built, and social environments.Public health strategies that target the obesogenic environment are critical.Translating clinical and epidemiologic findings into practice requires fundamental shifts in public policies and health systems.To curb the diabetes epidemic, primary prevention through the promotion of a healthy diet and lifestyle should be a global public policy priority.", + "An obvious conclusion is a manipulation of lifestyle provides an opportunity to reverse the diabetes trend.Stated another way, we cannot change our genetic make-up, but we can alter environmental factors.Indeed, many studies have shown that diet and exercise slow the onset of diabetes in persons with IGT (2,17,18).Also, low glycemic index diets have been shown to promote weight loss along with having metabolic benefits in persons with type 2 diabetes (19).The difficulty, of course, is trying to get people to change their habits.", + "In conclusion, evidence from epidemiological studies and RCTs demonstrate that lifestyle modifi cation comprising higher levels of PA and prudent food consumption may be eff ective in obesity and T2DM prevention.The positive eff ect of lifestyle on body weight seems somewhat transient, whereas the eff ect on T2DM is sustained for longer periods.Furthermore, lifestyle modifi cation appears to have an eff ect on diabetes risk independently of body weight and even of weight loss.As already pointed out in several of the T2DM prevention studies the reduction in diabetes risk has been paralleled by substantial weight loss and weight reduction has been considered to have major importance for diabetes prevention ( Knowler et 1998 ).Hence, lifestyle modifi cation seems to have an eff ect on T2DM not only through reduction in body weight, but also through improvement in insulin sensitivity, blood glucose control and lipid profi le.Whereas there is convincing evidence that lifestyle changes can prevent T2DM in randomized controlled studies, so far little is known whether a lifestyle intervention could also modify cardiovascular morbidity and mortality.The 20-year follow-up results from the Chinese Da Qing diabetes prevention study showed a non-signifi cant 17 % reduction in cardiovascular mortality in the combined (diet and/or PA) intervention group vs. controls ( Li et al., 2008 ).Similarly, lifestyle intervention in the Finnish DPS was not found to reduce signifi cantly cardiovascular mortality during the fi rst 10 years of follow-up ( Uusitupa et al., 2009 ).However, this study was not initially designed to examine the eff ect of lifestyle intervention on total mortality or cardiovascular morbidity, and therefore the statistical power may not have been suffi cient to detect small diff erences in cardiovascular events between the 2 groups.Besides, a longer follow-up period might be needed to answer this question.In the Malm Preventive trial with a 12-year follow-up of men with IGT total and cardiovascular mortality were lower among participants in the lifestyle intervention group, however, these results should be considered with caution due to the non-randomized design of the study ( Eriksson and Lindgarde, 1998 ).Recent fi ndings of bariatric surgery treatment of very obese subjects showed that weight loss indeed may reduce not only T2DM risk but also total mortality ( Sjstrm et al., 2007 ).Further investigations are needed to clarify whether prevention of T2DM by lifestyle modifi cation is associated with cardiovascular disease prevention; until then decisions have to be made on the basis of the best available information.Evidence from randomized controlled trailsThe effi cacy of lifestyle changes in obesity and T2DM prevention has been established in numerous randomized controlled trails (RCTs).Several of them may, however, be considered of major importance due to their large sample sizes (i.e., 458-3234 individuals) and long-term duration (i.e., 3-6 years).The Chinese Da Qing diabetes prevention study was the fi rst to investigate the eff ect of 6-year lifestyle change on body weight and diabetes incidence in individuals with impaired glucose tolerance (IGT) ( Pan et al., 1997 ).Pan and co-workers (1997) reported 42 % reduction in diabetes incidence, although no signifi cant diff erence in body weight was present.Similar results were found in the Finnish Diabetes Prevention Study (DPS) and the US Diabetes Prevention Program (DPP).DPS and DPP independently reported reduction in diabetes incidence of 58 % accompanied by significant reduction in body weight (5-7 %) as a result of the lifestyle modifi cation ( Knowler et al., 2002 ;Tuomilehto et al., 2001 ).These fi ndings were also confi rmed in Japanese and Indian populations, reporting 67.4 % and 28.5 % reduction in diabetes incidence, respectively ( Kosaka et 2011) reported signifi cant reduction in body weight and diabetes incidence at 1, as well as, at 3 years during a lifestyle modifi cation program carried out in a primary healthcare setting among subjects with IGT.All large-scale interventions have been successful in preventing T2DM during the active intervention period.Remarkably when the eff ectiveness of the lifestyle modifi cation programs was assessed on the long-term after discontinuation of the intervention, diabetes risk still remained substantially reduced.In the Finnish DPS, for instance, at extended follow-up 3 years after the 4-year intervention period a substantial reduction in body weight and T2DM incidence was still present ( Lindstrom et al., This document was downloaded for personal use only.Unauthorized distribution is strictly prohibited.", + "Because lifestyle changes to reduce bodyweight have always been an important therapy for type 2 diabetes, investigators of Look AHEAD trial 156 examined the eff ect of weight reduction (achieved by an intensive lifestyle intervention) on cardiovascular events.Despite diff erential weight loss for more than 10 years and improvements in many cardiovascular risk factors (including blood pressure and lipids), lifestyle change did not reduce cardiovascular events compared with diabetes support and education (control group).This fi nding might have been because large proportions of participants in both groups received medical treatment for these risk factors.However, participants in the group receiving Glucokinase Reduce hepatic production of glucoseTable 1: Selected therapeutic targets of largely untested mechanisms for type 2 diabetesintensive lifestyle intervention who had a history of a cardiovascular event at baseline had a tendency for an increased risk of a subsequent cardiovascular event; 156 a similar fi nding was reported in ACCORD. 144Several other fi ndings from Look AHEAD are worthy of comment.First, participants in the weight-loss group were more likely to achieve either partial or complete remission of diabetes, 157 had better glucose control needing fewer glucose-lowering drugs (including insulin), and were more likely to achieve a glycated haemoglobin A 1c measurement of less than 7% (53 mmol/mol) than were those in the control group. 158However, despite weight loss and addition of drugs, patients in the treatment group had similar progression of diabetes to that of the control group-ie, with continuous increases in glycated haemoglobin A 1c . 156Second, lifestyle change slowed progression of nephropathy.Third, other health outcomes associated with better quality of life-eg, sleep apnoea 159 and mobility 160 -improved.Thus, intensive lifestyle change in patients with type 2 diabetes has benefi ts, but unfortunately not for cardiovascular outcomes, which remain the major cause of premature mortality in type 2 diabetes.", + "INTRODUCTIONIntensive lifestyle interventions (eg, promoting increased physical activity and weight loss) can be effective in decreasing the incidence of type 2 diabetes mellitus (T2DM). 1 However, healthcare resources are limited, and participants in interventions to prevent diabetes should be prioritized.Identification of individuals at high risk of T2DM could facilitate the targeting of prevention efforts to those who could benefit from them and reduce the cost of preventing T2DM.", + "Epidemiological studies examining the associations between lifestyle behaviors and diabetes risk have reached similar conclusions as the clinical trials described above.For example, the 14-year follow-up University of Pennsylvania Alumni Health Study [52] (n = 5,990 men aged 39-68 years) showed PA (leisure time physical activity [LTPA] expressed in kcal expended per week through walking, stair climbing, and sports) was inversely associated with the incidence of T2D.Incidence rates declined as energy expenditure rose from 500 through 3,500 kcal/week.The age-adjusted relative risk ratio (RR) of T2D was reduced by about 6% for each 500 kcal increment increase in PA energy expenditure.There are two major factors that underlie these alarming projections.The first is T2D is associated with age, and Western populations are aging rapidly.The second major explanation is our lifestyles have changed dramatically in recent years.Epidemiological studies have identified strong T2D risk relationships for obesity, sedentary behavior [2][3][4], and diets rich in energy [5], processed carbohydrates [6], and animal fats [7].Collectively, these lifestyle factors impede the actions of insulin and raise hepatic glucose production, which can result in the diminution of endogenous insulin production and T2D.The strongest evidence for a causal relationship between adverse lifestyle behaviors and T2D comes from randomized controlled trials that show intensive lifestyle interventions involving structured exercise regimes which promote habitual physical activity (PA) and have a major beneficial impact on diabetes incidence in high-risk individuals [8,9].Practical ApplicationsAs we have described in this chapter, an abundance of evidence supports the protective effects of health lifestyle behaviors on type 2 diabetes risk.However, populationwide programs of intensive lifestyle interventions are probably unrealistic owing to the costs involved in running such trials and the difficulties in recruiting participants and motivating them to adhere to the interventions.It may be more feasible to identify individuals at high risk of diabetes who, because of their genetic characteristics, are likely to respond well to exercise interventions, as an example, and target these persons.This does not of course mean that healthy lifestyle behaviors would be discouraged in the remainder of the population, but one might prioritize other, more effective, preventive strategies in these individuals while continuing to promote the virtues of active lifestyles.The benefits to this approach might include reduced overall costs and greater preventive success.Moreover, because those who receive the intervention are likely to respond well and by consequence maintain motivation, attrition rates may diminish and adherence improve.The actualization of this perspective will first require robust empirical evidence, most likely emerging from the combination of epidemiology for hypothesis generation and clinical trials to test those hypotheses and provide evidence of causality.", + "Type 2 diabetes can be prevented or delayed by lifestyle modification, including increased physical activity, beneficial dietary changes, and weight reduction (22,44).However, only Model adjusted for age, gender, group, baseline value of moderate-to-vigorous physical activity, and baseline values and changes in body weight and in intakes of energy and energy-adjusted saturated fat and fiber. *The median (range) of each tertile of change in moderate-to-vigorous physical activity is shown.Adjusted interaction between moderate-to-vigorous physical activity (3 groups) and the polymorphism (2 groups) on the risk of developing type 2 diabetes.a few studies have investigated the effects of such lifestyle interventions on insulin sensitivity and insulin secretion in persons with IGT (21,46).On the basis of the 4-yr follow-up study of the DPS with repeated frequently sampled intravenous glucose tolerance test (FSIGT), insulin sensitivity improved along with lifestyle changes, while insulin secretion remained virtually unchanged (46).Most other data also indicate that physical activity, diet, and weight loss primarily increase insulin sensitivity.Insulin resistance and the associated glycemic stress may exhaust -cells and impair their function.Regular physical activity may diminish glycemic stress by improving insulin sensitivity of target tissues (18).While the mechanisms of improved -cell function in response to lifestyle interventions are still largely unknown, several studies suggest that physical activity (5,11), diet (19,26), weight loss (45), or their combination (21) may directly improve the first-phase insulin secretion that is an indicator of the -cell function.GENETIC FACTORS AND LIFESTYLE interact in the development of type 2 diabetes.Physical activity, favorable dietary changes, and weight reduction were essential components of a success-ful lifestyle intervention in two large randomized controlled trials on the prevention of type 2 diabetes in high-risk individuals with impaired glucose tolerance (IGT), including the Finnish Diabetes Prevention Study (DPS) (44) and the Diabetes Prevention Program (DPP) (22).In the DPS, increased physical activity was associated with a decreased risk of type 2 diabetes independently of changes in diet and body weight.The individuals who increased their physical activity most (i.e., were in the upper third of the change) were 66% less likely to develop type 2 diabetes than those in the lower third (24).", + "The worldwide explosion of the rates of diabetes and other metabolic diseases in the last few decades cannot be fully explained only by changes in the prevalence of classical lifestyle-related risk factors, such as physical inactivity and poor diet.For this reason, it has been recently proposed that other \"nontraditional\" risk factors could contribute to the diabetes epidemics.In particular, an increasing number of reports indicate that chronic exposure to and accumulation of a low concentration of environmental pollutants (especially the so-called persistent organic pollutants (POPs)) within the body might be associated with diabetogenesis.In this review, the epidemiological evidence suggesting a relationship between dioxin and other POPs exposure and diabetes incidence will be summarized, and some recent developments on the possible underlying mechanisms, with particular reference to dioxin, will be presented and discussed.The worldwide explosion of the rates of diabetes and other metabolic diseases in the last few decades cannot be fully explained only by changes in the prevalence of classical lifestyle-related risk factors, such as physical inactivity and poor diet.For this reason, it has been recently proposed that other \"nontraditional\" risk factors could contribute to the diabetes epidemics.In particular, an increasing number of reports indicate that chronic exposure to and accumulation of a low concentration of environmental pollutants (especially the so-called persistent organic pollutants (POPs)) within the body might be associated with diabetogenesis.In this review, the epidemiological evidence suggesting a relationship between dioxin and other POPs exposure and diabetes incidence will be summarized, and some recent developments on the possible underlying mechanisms, with particular reference to dioxin, will be presented and discussed.", + "Lifestyle modification including exercise, nutrition and behavioral changes is the cornerstone to prevent and treat type 2 diabetes.Oral antidiabetic medicationeither as single agent or combination therapyis frequently required to maintain metabolic control, as assessed by monitoring of glycated hemoglobin A 1C (HbA 1C ) levels.Eventually, a significant proportion of patients with type 2 diabetes require the exogenous administration of insulin [40].", + "Diet and lifestyle factorsDiet and lifestyle modification is an important aspect of T2DM prevention.Major clinical trials have demonstrated that intensive lifestyle interventions can lower the incidence of diabetes mellitus by 58% compared with control groups 55 .Trials have also shown that these interventions are more effective than pharmacological interventions 55 .Landmark clinical trials, such as the Diabetes Prevention Program in multi-ethnic Americans 55 , the Finnish Diabetes Prevention Study 56 and the Da Qing IGT and Diabetes Study in China 57 , have demonstrated that many cases of T2DM could be prevented through lifestyle interventions focused on increasing physical activity and adopting a healthy diet.Nevertheless, when lifestyle interventions are not feasible, pharmacological therapy can be considered as a strategy to prevent the development of T2DM.For example, metformin reduced the incidence of T2DM by 31% over an average follow-up period of 2.8 years among high-risk individuals from the USA who did not have diabetes mellitus 55 .Similarly, metformin reduced T2DM risk in clinical trials in India and China 58 .", + "Multiple interventions in adults with T2D have been evaluated for risk reduction and prevention, both in the short and the long term.A recent systematic review (69) reported that after active interventions lasting from 6 months to .6 years, relative risk reduction achieved from lifestyle interventions (39%) was similar to that attained from use of drugs (36%); however, only lifestyle interventions had a sustained reduction in risk once the intervention period had ended.Analysis of the postintervention follow-up period (;7 years) revealed a risk reduction of 28% with lifestyle modification compared with a nonsignificant risk reduction of 5% from drug interventions." + ], + [ + "Researchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today.", + "Recent gene discovery efforts have provided further evidence to support such assertions.Though, at this point, the identity of some of the genes mechanistically responsible for the association signals uncovered remains uncertain, it remains possible to determine, through studies of healthy populations, whether the type 2 diabetes-susceptibility variants themselves are mediating their effects through disruption of -cell function or insulin action.With the exception of FTO (known to influence type 2 diabetes risk through a primary effect on adiposity) and PPARG (long implicated in insulin action), all confirmed susceptibility alleles appear to exert their predominant effect on diabetes pathogenesis through abrogation of -cell function (or mass) (62,74 -77).It would be wrong to extrapolate too far: the known variants account for only a small proportion of overall genetic risk, and the focus on lean type 2 diabetes cases, which has characterized several of the genome-wide association (GWA) studies (58,59), may have generated a bias toward detection of variants detrimental to -cell performance.Nonetheless, the picture that emerges is one where alterations of -cell function seem to be playing the predominant role with respect to the inherited component of disease predisposition.", + "In briefGardner et al. queried the genomes of over 400,000 individuals and identified novel genes associated with type 2 diabetes risk.The biological function of these genes highlights potentially new therapeutic avenues for treatment of type 2 diabetes.", + "Ta rge ted T r e atmen t a nd Pr e v en t ion4][75] In monogenic forms of diabetes, at least, genetic testing already drives the choice of therapy.For example, in patients who have maturity-onset diabetes of the young due to mutations in the gene encoding glucokinase (GCK), the hyperglycemia is mild and stable, the risk of complications is low, and dietary management is often sufficient.In contrast, in patients who have maturity-onset diabetes of the young due to mutations in HNF1A, the disease follows a more aggressive course, with a greater risk of severe complications, but is particularly responsive to the hypoglycemic effects of sulfonylureas. 62,73Most children with neonatal diabetes have mutations in KCNJ11 or ABCC8, adjacent genes that jointly encode the beta-cell ATP-sensitive potassium channel that mediates glucose-stimulated insulin secretion and is the target of sulfonylureas.In such children, treatment with sulfonylureas has proved more effective and convenient than the lifelong insulin therapy previously considered the default option. 74,75n children with severe obesity due to profound leptin deficiency, exogenous leptin therapy is lifesaving. 76s yet, there are insufficient genetic data to support management decisions for common forms of type 2 diabetes and obesity. 77Although the TCF7L2 genotype is associated with variation in the response to sulfonylurea treatment, 78 the effect is too modest to guide the care of individual patients.For the time being, the contribution of genetic information to therapy is most likely to come through the drug-discovery pipeline.Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physiological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32 and to characterize high-risk groups to enable more efficient clinical trials of agents designed to reduce the progression of type 2 diabetes or obesity or the risk of complications.From Gene t ic s t o Biol o gyAn improved understanding of pathophysiology achieved through genetic discovery provides new opportunities for treatment, diagnosis, and monitoring.Studies of risk variants for type 2 diabetes in healthy populations have shown that most variants act through perturbation of insulin secretion rather than insulin action, establishing inherited abnormalities of beta-cell function or mass (or both) as critical components of the progression to type 2 diabetes (Fig. 3). 22,50 Type 2 diabetes results when pancreatic beta cells are unable to secrete sufficient insulin to maintain normoglycemia, typically in the context of increasing peripheral insulin resistance.The beta-cell abnormalities fundamental to type 2 diabetes are thought to include both reduced beta-cell mass and disruptions of beta-cell function.Insulin resistance can be the consequence of obesity or of obesity-independent abnormalities in the responses of muscle, fat, or liver to insulin.Examples of susceptibility variants that, given current evidence, are likely to influence predisposition to type 2 diabetes by means of each of these mechanisms are shown.For type 2 diabetes and obesity, the discovery of causal genes (Fig. 1 and 2) has followed three main waves.The first wave consisted of family-based linkage analyses (see the Glossary) and focused candidate-gene studies.These proved effective in identifying genes responsible for extreme forms of early-onset disease segregating as single-gene (mendelian) disorders.Genes underlying several distinct, familial forms of nonautoimmune diabetes -including maturity-onset diabetes of the young, mitochondrial diabetes with deafness, and neonatal diabetes -were characterized (see the review by Waterfield and Gloyn 3 ).Similar approaches revealed mutations in genes responsible for rare forms of severe childhood obesity, including the genes encoding leptin, the leptin receptor, and proopiomelanocortin (see the review by O'Rahilly 4 ).These discoveries have provided insights into processes critical for the maintenance of normal glucose homeostasis and energy balance and clues to the inner workings of the pancreatic beta cell and hypothalamus.For many families, this information has led to improved diagnostic and therapeutic options (described in more detail below).", + "Gene-lifestyle interaction studies supporting the protective role of diet, exercise or combined lifestyle interventions in individuals genetically susceptible to obesity and type 2 diabetes.This document was downloaded for personal use only.Unauthorized distribution is strictly prohibited.", + "INTRODUCTIONDiabetes is a common, chronic disease that profoundly impacts health and longevity.Susceptibility is influenced by inheritance, and there has been substantial progress in identifying genes which, when mutated, influence individual risk of disease.Through study of common and rare forms, both polygenic and monogenic, diabetes genetics encompasses many pressing issues in human genetic research.", + "Advances in technology and analytical approaches have identifi ed genes linked with type 2 diabetes.With use of candidate-gene approaches, PPARG was the fi rst gene identifi ed. 18Subsequently, mostly with use of genomewide association studies, more than 50 gene loci have been linked with type 2 diabetes. 19Furthermore, 53 loci have been linked with concentrations of insulin and glucose (however, not always with both fasting and 2 h concentrations of glucose), of which 33 are also associated with type 2 diabetes. 19,20Although some loci are associated with obesity and insulin resistance, most are linked with -cell function. 21Gene products for most of these loci have not been defi nitively identifi ed.Together, these genes do not explain much of the genetic basis of type 2 diabetes; the use of genotype risk scores only slightly improves prediction of subsequent diabetes compared with more frequently used clinical risk factors. 22,23side from obvious increases in caloric intake and decreased energy expenditure, other environmental factors seem to be important.Nutrient composition, specifi cally increased amounts of dietary fat (particularly saturated fat), are important to development of obesity, insulin resistance, -cell dysfunction, and glucose intolerance. 24Furthermore, an ageing-associated reduction in the responsiveness of cells to carbohydrate partly underlies the fall in glucose tolerance with ageing. 25he in-utero environment, established partly by the mother's body size, could produce epigenetic and geneexpression changes that aff ect the risk of development of obesity and type 2 diabetes for the off spring. 26Recent Figure 1: Feedback loop between islet cells and insulin-sensitive tissues (A) Insulin interacts in the liver to suppress glucose production, and in muscle and adipose tissue to stimulate uptake of glucose, aminoacids, and fatty acids.The amount of insulin released to maintain normal glucose homoeostasis is established by prevailing insulin sensitivity.This feedback is probably mediated through neuronal and humoral mechanisms, but exact mediators are still not known. (B) When insulin resistance develops in insulin-sensitive tissues, feedback to cells ensures that the cells increase insulin output to maintain normal glucose tolerance. (C) When cells are incapable of increasing insulin output in the presence of insulin resistance, the result is development of increased glucose concentrations, which initially manifests as impaired glucose tolerance.Because -cell dysfunction progresses, further elevations in glycaemia occur and diabetes is the eventual result.", + "The availability of detailed information on gene environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them.", + "Although the number of disease conditions for which the biomedical literature reports positive indications of genetic contributions increases weekly, diabetes has enjoyed a relatively long history of geneticized explanations.Medical geneticist James Neel's (1962) famous thrifty genotype hypothesis, for example, postulated that in the early stages of evolution those people who had a \"quick insulin trigger\" could rapidly convert sugar to fat in times of famine.Accordingly, peoples who have recently undergone a shift from hunter-gathering to a modern sedentary lifestyle (with concomitant energy dense food intake) are at increased risk of diabetes because they still carry genes that conferred this selective advantage. \"The Coca Colonization\" hypothesis (Zimmet 1997), as the thrifty genotype hypothesis is sometimes called, posits that recently \"primitive\" groups have undergone a \"domestication of lifestyle\" as they have moved to urban areas or lost their old way of life (Neel 1962(Neel , 1982;;Zimmet 1982).According to this hypothesis, these populations have, over time, evolved genetic traits that could metabolically compensate for periods of food scarcity.Because such scarcity is no longer the norm, the theory contends, the phenotypic consequence of thrifty genes in combination with the abundance of food and sedentary lifestyle typical of contemporary urban living make for impaired metabolic regulation of glucose.In other words, diabetes is thought to result from a genetic anachronism.", + "In support of our focus on developmental genes, pathway analysis of recent genome-wide association studies, which so far have yielded few T2D candidate genes, provided an integrated interpretation of the highest ranked risk genes for T2D [97].This analysis found that lipid metabolism and developmental genes were significantly over-represented in the upper ranked genes of the T2D genome-wide association studies, an observation based on thousands of samples, and one strongly consistent with the present independent analysis.Combined, we believe this presents strong evidence that developmental genes may play a role in setting or regulating the long-term responses of skeletal muscle to diabetes.", + "It has long been understood that genetics play a role in predisposition to type 2 diabetes (1).Mutations giving rise to several rare monogenic forms of this disorder have been cloned, including mutations in the insulin gene and in a number of genes conferring lean early-onset type 2 diabetes (maturity-onset diabetes of the young [MODY]); however, no gene predisposing to the common obese adultonset phenotype has been identified.One important reason for this is the substantial locus heterogeneity associated with diabetes risk.Despite identification of at least five MODY loci to date, there remain pedigrees that segregate autosomal dominant type 2 diabetes not attributable to detectable mutations in any of these genes (2).Similarly, it has been recognized that as many as 10% of patients diagnosed with type 2 diabetes may instead suffer from a disease etiologically (and presumably genetically) more akin to type 1 diabetes (3).", + "The future will see intensified research and improvement in such methodologies to identify and characterise the multiple genes underlying complex diseases.One of the most important goals of genetic studies of diabetes is to determine which multilocus genotypes (across all susceptibility loci) create the highest risk for development of diabetes.Individuals with those genotypes would be targeted for treatment to prevent diabetes when safe and effective prophylactic therapies become available.It is possible that several prophylactic options could be available, with effectiveness depending on the exact set of predisposing genes carried by the at-risk person.Thus, the next generation of genetic studies of Type I diabetes (and other complex disorders) will involve dissection of gene-gene interactions in order to clarify which persons, by virtue of their multilocus genotype, are most susceptible to diabetes.This research will be accompanied by studies of gene-environment interaction, when the relevant non-genetic factors are more clearly understood (eg.do differences in diabetes susceptibility via antiviral defence genes relate primarily to certain types of virus? ).", + "The molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.", + "The molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.", + "Genetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1", + "More than 65 loci, encoding up to 500 different genes, have been implicated by genome-wide association studies (GWAS) as conferring an increased risk of developing type 2 diabetes (T2D).Whilst mouse models have in the past been central to understanding the mechanisms through which more penetrant risk genes for T2D, for example, those responsible for neonatal or maturity-onset diabetes of the young, only a few of those identified by GWAS, notably TCF7L2 and ZnT8/SLC30A8, have to date been examined in mouse models.We discuss here the animal models available for the latter genes and provide perspectives for future, higher throughput approaches towards efficiently mining the information provided by human genetics.More than 65 loci, encoding up to 500 different genes, have been implicated by genome-wide association studies (GWAS) as conferring an increased risk of developing type 2 diabetes (T2D).Whilst mouse models have in the past been central to understanding the mechanisms through which more penetrant risk genes for T2D, for example, those responsible for neonatal or maturity-onset diabetes of the young, only a few of those identified by GWAS, notably TCF7L2 and ZnT8/SLC30A8, have to date been examined in mouse models.We discuss here the animal models available for the latter genes and provide perspectives for future, higher throughput approaches towards efficiently mining the information provided by human genetics.", + "BackgroundMultiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus.We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone." + ], + [ + "IntroductionDiabetes is one of the most common metabolic disorders.It is estimated that the number of diabetes patients worldwide has already exceeded 200 million [92].This creates a need to understand the etiology of the disease, genetic and enviromental factors influencing development of diabetes.Diabetes is a group of metabolic diseases that are characterized by elevated glucose level.Poorly controlled or undiagnosed disease may be associated with so called late complications of diabetes such as accelerated atherosclerosis, blindness, renal insufficiency, stroke, and amputation of extremities.Diabetes is also associated with a decrease in life expectancy.These facts make diabetes a major health problem.There are two main forms of diabetes: type 1 and type 2. It is type 2 diabetes (T2DM), previously known as non-insulin dependent, that is the much more prevalent form, responsible for 90% of the disease prevalence [92,125].In the majority of the industrialised world societies this disease affects a few percent of the entire population [125].Recent publications indicate an increase in the prevalence of diabetes world-wide, especially in younger people [80] affecting a substantial percentage of the pediatric age group in some populations [30].T2DM is characterised by the presence of two basic abnormalities: impairment of insulin secretion and decrease in insulin sensitivity [52].The disease creates a large pathophysiological spectrum from a predominantly secretory defect with moderate, if any, degree of insulin resistance to a predominantly insulin resistant disease with relative insulin deficiency.Whereas insulin resistance can be demonstrated early in life, many years before the diagnosis of diabetes, impairment of insulin secretion develops later in life, usually along with the onset of impaired glucose tolerance [52].", + "The ADA lists four subtypes of diabetes based on the clinical symptoms at time of presentation, [4] namely, Type 1 diabetes, Type 2 diabetes (T2D), gestational diabetes, and diabetes due to specific causes (genetic defects causing deficient insulin secretion or action, diseases of pancreas, use of certain drugs such as steroids, thiazides among others).Of these, T2D is the most prevalent (close to 90% of all cases) and is the major cause of morbidity and mortality in both developed and developing nations [1].At times it is difficult to assign a patient to a particular subtype due to the difference in conditions associated with hyperglycemia at the time of diagnosis [4,7].For example, a lady diagnosed with gestational diabetes mellitus during pregnancy is highly susceptible to develop T2D later.Therefore, other than proper treatment during and post pregnancy, a regular follow-up is required for stratifying disease risk, and for timely management before progression to another subtype.It is clear that the classification of diabetes may not be as simple as just categorizing it into any one of the four given subtypes due to its miscellaneous nature.Every case needs to be considered at the time of presentation, on the basis of the risk factors or underlying cause of hyperglycemia, the clinical symptoms, and disease prognosis.Table 1 lists the various subtypes of diabetes based on the classification suggested by the ADA [4].", + "Type 2 diabetes is the most common type of diabetes with prevalence in the United Kingdom of around 4%.It is most commonly diagnosed in middle-aged adults, although more recently the age of onset is decreasing with increasing levels of obesity (Pinhas-Hamiel and Zeitler, 2005).Indeed, although development of the disease shows high hereditability, the risk increases proportionally with body mass index (Lehtovirta et al., 2010).Type 2 diabetes is associated with insulin resistance, and a lack of appropriate compensation by the beta cells leads to a relative insulin deficiency.Insulin resistance can be improved by weight reduction and exercise (Solomon et al., 2008).If lifestyle intervention fails, there are a variety of drugs available to treat type 2 diabetes (Krentz et al., 2008), which can be divided into five main classes: drugs that stimulate insulin production from the beta cells (e.g.sulphonylureas), drugs that reduce hepatic glucose production (e.g.biguanides), drugs that delay carbohydrate uptake in the gut (e.g.a-glucosidase inhibitors), drugs that improve insulin action (e.g.thiazolidinediones) or drugs targeting the GLP-1 axis (e.g.GLP-1 receptor agonists or DPP-4 inhibitors).", + "RACIALIZED ETIOLOGIES OF DIABETESDiabetes is not one disease but many.More than 90 percent of all diabetics have type 2 diabetes, which is characterized by elevated blood glucose triggered by a combination of poor insulin production, insulin resistance in skeletal muscle and lipid tissue, or both.Type 2 diabetes is also known as Non-Insulin-Dependent Diabetes because, unlike the rarer form of the disease, people with type 2 diabetes produce insulin and therefore seldom need therapeutic insulin at the initial onset of disease.Type 2 diabetes (hereafter, \"diabetes\"), like heart disease, hypertension and asthma, is referred to as a complex disease because its putative determinants lay in both environmental and biological domains.That is, diabetes is caused by a still-unknown combination of factors that include lifestyle, diet, physical activity, and an array of physiological triggers.", + "IntroductionDiabetes mellitus (DM) is a group of metabolic diseases characterized by hyperglycemia, which results from defects in insulin secretion, insulin activity or both.DM is associated with the dysfunction and failure of different organs, such as the blood vessels, heart and kidneys [1], and this disease is considered a global burden [2].The International Diabetes Federation's most recent estimates indicate that 8.3% of adults (382 million individuals) have diabetes, and the number of individuals with this disease is expected to rise beyond 592 million in less than 25 years [2].The vast majority of cases of DM fall into two broad etiopathogenetic categories: type 1 and type 2 DM (T1DM and T2DM, respectively).T1DM, previously named insulin-dependent diabetes or juvenile-onset diabetes, results from cellular-mediated autoimmune destruction of pancreatic cells; therefore, patients are dependent on exogenous insulin.Individuals with T1DM are considered to have a genetic predisposition, although environmental factors, such as dietary components, also contribute to T1DM development [3].Thus, T1DM is the result of a complex interrelation among cells, the immune system and environmental factors in genetically susceptible individuals [3].T1DM appears predominately in children and young adults and affects 5%-10% of diabetic patients [2].T2DM is chronic disorder caused by insulin secretion deficiency and insulin resistance.T2DM is a complex trait that results from the contribution of many genes [4], many environmental factors, including diet [5], and the interactions among these genes and environmental factors.T2DM is more common among individuals aged 40 to 60 years and accounts for most cases of DM (more than 90%) [2].", + "ACCEPTED MANUSCRIPTmost common form of diabetes (90% of all diabetic patients), mainly characterized by insulin resistance.The main causes of T2D include lifestyle, physical activity, dietary habits and heredity, whereas T1D is thought to be due to autoimmunological destruction of the Langerhans islets hosting pancreatic- cells.T1D affects almost 10% of all diabetic patients worldwide, with 10% of them ultimately developing idiopathic diabetes.Other forms of DM, classified on the basis of insulin secretion profile and/or onset, include Gestational Diabetes, endocrinopathies, MODY (Maturity Onset Diabetes of the Young), neonatal, mitochondrial, and pregnancy diabetes.The symptoms of DM include polyurea, polydipsia, and significant weight loss among others.Diagnosis depends on blood glucose levels (Fasting plasma glucose = 7.0 mmol/L) [15].From the perspective of DM, although there are several types of diabetes, the overall results suggest that the articles reviewed refer to T1D and T2D, with T2D representing the majority of the articles.A few articles refer to prediabetes and only one pertains to the metabolic syndrome, which is a term for metabolism-related pathophysiology.The types of data used in each case of the present collection were either clinical, genetic, electrochemical, chemical or medical.Only a few articles used clinical data in combination with genetic data.In addition, it is worth mentioning that the vast majority of the articles reviewed handled only clinical datasets.When it comes to prediction, the main biomarkers used involve anthropometric parameters, demographic characteristics, known risk factors, medical and drug history data, laboratory measurements, and epidemiological data.The most", + "Classification of DiabetesOn the basis of insulin deficiency, diabetes can be classified into the following types as follows.", + "| INTRODUCTIONToday, more than 265 million people are affected across the world.It is estimated that by the year 2030 this number will reach 366 million people (about 4/4 percent of the world's population), and now the cause of death is more than 1.1 million per year (including 50% of the population under-70 years of age and 55% of women).On the other hand, given its negative effect on the economic growth of developing countries, it calls for universal mobilization to combat this disease (Bhattacharya, Dey, & Roy, 2007).Diabetes or diabetes mellitus is referred to as a heterogeneous group of metabolic disorders characterized by chronic hyperglycemia and carbohydrate, fat and protein metabolism disorders that result from a defect in the secretion of insulin, or impairment in its function, or both.Types of diabetes mellitus include type 1, type 2 diabetes and other kind of diabetes, but the two most common types of diabetes mellitus are type 1 and type 2, which are different in several aspects (Meshkani, Taghikhani, Mosapour et al., 2007).Type 1 diabetes has been identified with autoimmune destruction of pancreatic beta cells (insulin secreting cells) and accounts for about 5% of all diabetic people, while type 2 diabetes is a predominant disorder characterized by insulin resistance or a relative decline in insulin production, and accounts for about 90% of all types of diabetes mellitus (Meshkani, Taghikhani, Al-Kateb et al., 2007).Important factors that predispose a person to type 2 diabetes are multifactorial, including genetic factors and environments.However, its inheritance has certainly not been proven, but it is believed that first-degree relatives of diabetic patients have a higher chance to develop the disease.In this regard, recognizing gene polymorphisms of this disease seems to be necessary (Hring et al., 2014).Multiple genes have been studied in the pathogenesis of type 2 diabetes.", + "CONCLUSIONSDiabetes is currently broadly classified as type 1, type 2, gestational, and a group of \"other specific syndromes. \"However, increasing evidence suggests that there are populations of individuals within these broad categories that have subtypes of disease with a well-defined etiology that may be clinically characterized (e.g., LADA, MODY).These developments suggest that perhaps, with more focused research in critical areas, we are approaching a point where it would be possible to categorize diabetes in a more precise manner that can inform individual treatment decisions.Type 2 DiabetesIn the U.S., an estimated 95% of the nearly 30 million people living with diabetes have type 2 diabetes.An additional 86 million have prediabetes, putting them at high risk for developing type 2 diabetes (9).Among the demographic associations for type 2 diabetes are older age, race/ ethnicity, male sex, and socioeconomic status (9).", + "Type 2 diabetes mellitus (T2DM) is characterized by dysregulation of carbohydrate, lipid and protein metabolism, and results from impaired insulin secretion, insulin resistance or a combination of both.Of the three major types of diabetes, T2DM is far more common (accounting for more than 90% of all cases) than either type 1 diabetes mellitus (T1DM) or gestational diabetes.Over the past few decades, our understanding of the development and progression of T2DM has evolved rapidly.Its main cause is progressively impaired insulin secretion by pancreatic -cells, usually upon a background of pre-existing insulin resistance in skeletal muscle, liver and adipose tissue 1", + "Background Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous.A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis.", + "IntroductionIn 2018, a ground-breaking study identified five novel subtypes of adult-onset diabetes: severe autoimmune diabetes (SAID, including type 1 diabetes and latent autoimmune diabetes in adults [LADA]) and four subtypes of type 2 diabetes (severe insulin-deficient diabetes [SIDD], severe insulin-resistant diabetes [SIRD], mild obesity-related diabetes [MOD] and mild agerelated diabetes [MARD]) [1].These subtypes differ in their clinical characteristics, complications and genetic backgrounds [1,2].It is unclear if they also differ in modifiable risk factors.", + "Animal models of Type 2 diabetesType 2 diabetes represents a heterogeneous group of disorders characterized by insulin resistance and impaired insulin secretion and defined by a raised fasting or post-challenge blood glucose.Some subtypes of diabetes are now recognized as being because of specific single gene defects [e.g. the maturityonset diabetes of the young (MODY) syndromes [94], syndromes of severe insulin resistance [95] and mitochondrial diabetes [96]].However, for most patients with diabetes, several (if not many) genetic and environmental factors contribute to the causation and progression of the disease and also the late complications.", + "The disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition.The disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition.", + "IntroductionDiabetes mellitus, also known as simply diabetes, is the most prevalent disease in Westernized, developed countries, and the prevalence of this disease increases with age, accounting for 8.4% of all deaths worldwide [1].Diabetes is a well-recognized multifactorial endocrine metabolic disorder characterized by hyperglycemia (high blood sugar levels over a prolonged period) triggered by insulin secretion deficiencies, insulin action or both [2].The chronic hyperglycemia of diabetes is associated with dysfunction, long-term damage and failure of different organs, particularly the kidneys, heart, blood vessels, nerves and eyes.The development of diabetes involves various pathogenic processes including autoimmune destruction of the pancreatic -cells with subsequent insulin insufficiency which causes insulin resistance [3].The reason for the carbohydrate, fat and protein metabolism disorders in diabetes is insulin deficient activity on target tissues.Insulin deficient action results from insufficient insulin secretion and/or diminished tissue response [4].The great majority of diabetes cases fall into two broad categories of etiopathogenetics.Type 1 diabetes (T1D), falls in one category, is caused due to an absolute deficiency in insulin secretion from pancreatic beta cells.Genetic marker tests and serological evidences of an autoimmune pathological process in pancreatic islets can often be utilized for identification of individuals with increased risk of developing T1D [5].The more prevalent form of diabetes is type 2 diabetes mellitus (T2DM), which falls in the second category and is caused by a combination of insulin resistance and an inadequate compensatory insulin secretory response [6].Consequently, a degree of hyperglycemia occurs that might cause pathological and functional changes in different target tissues but without clinical symptoms and the condition may persist for a long time before T2DM is detected.There are other specific types of diabetes, such as exocrine pancreatic diseases, endocrinopathies, diabetes induced by drugs or chemicals, infection, uncommon forms of immune-mediated diabetes, other genetic syndromes, such as Down syndrome chromosomal abnormalities, Klinefelter syndrome, and sometimes diabetes-related Turner syndrome.Depending on the severity of the underlying disease, the degree of hyperglycemia can change over time [7].", + "TYPE 1 DIABETESDiabetes (or Diabetes mellitus) is a set of disorders characterized by either an absolute or a relative deficiency of insulin and/or insulin resistance.T1D accounts for about 10% of all diabetes cases (Maahs et al., 2010).It has been reported to be the second most prevalent chronic disease of childhood, with a peak onset at about twelve years (Imkampe and Gulliford, 2011)." + ] + ], + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "CB93CE86DA18F287DBEF22CB29C560CF", + "FEE16F5E4D12AF7E7B0DDBF6F047EB76", + "DF05AACA4A1466AC1753DE13631A6ACD", + "57CB850E74BC7A26A645CAAB823D35CD" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_2.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_2.json new file mode 100644 index 0000000..974e503 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_2.json @@ -0,0 +1,110 @@ +{ + "question": [ + "How many types of diabetes exist?", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? ", + "what genes are associated with diabetes?", + "What causes diabetes?" + ], + "answer": [ + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.", + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes." + ], + "contexts": [ + [ + "The prevalence of diabetes mellitus worldwide is sobering; the International Diabetes Federation estimates that 415 million people have diabetes mellitus, with 90% of these individuals having type 2 diabetes mellitus (T2DM) 1 .T2DM occurs when pancreatic -cells fail to release enough insulin to meet the demands of insulin-responsive tissues, which safely store and metabolize glucose.Driven by both genetic and environmental risk factors, T2DM is a complex, multifactorial disorder.Although the increasing prevalence of T2DM is driven by changes in our environment and mirrors the increase in obesity, the greater concordance for T2DM found in monozygotic compared with dizygotic twins has long provided evidence for a genetic component in T2DM risk 2 .", + "In the UK alone, nearly 1.8 million people are already recognized to have this disorder (consuming w5% of the total National Health Service budget), and the search is on to find the 'missing million' who are living with the condition but in whom the diagnosis has yet to be made. 3In the USA, the situation appears to be even more serious with some commentators predicting that one in every three Americans born in the year 2000 will go on to develop diabetes during their lifetime, bringing unprecedented costs in terms of healthcare dollars as well as human morbidity and mortality. 4The majority (w90%) of these cases will be type 2 in origin, reflecting a trend towards obesity and more sedentary lifestyles as the 'norm' rather than the exception in 'developed' societies.Indeed, the face of T2DM is changing, as a condition that was once considered the preserve of middle/old age is increasingly diagnosed in young adults and even children, reflecting the high rates of obesity (and, in particular, visceral adiposity) in these populations.", + "Table 1 lists the various subtypes of diabetes based on the classification suggested by the ADA [4].The ADA lists four subtypes of diabetes based on the clinical symptoms at time of presentation, [4] namely, Type 1 diabetes, Type 2 diabetes (T2D), gestational diabetes, and diabetes due to specific causes (genetic defects causing deficient insulin secretion or action, diseases of pancreas, use of certain drugs such as steroids, thiazides among others).Of these, T2D is the most prevalent (close to 90% of all cases) and is the major cause of morbidity and mortality in both developed and developing nations [1].At times it is difficult to assign a patient to a particular subtype due to the difference in conditions associated with hyperglycemia at the time of diagnosis [4,7].For example, a lady diagnosed with gestational diabetes mellitus during pregnancy is highly susceptible to develop T2D later.Therefore, other than proper treatment during and post pregnancy, a regular follow-up is required for stratifying disease risk, and for timely management before progression to another subtype.It is clear that the classification of diabetes may not be as simple as just categorizing it into any one of the four given subtypes due to its miscellaneous nature.Every case needs to be considered at the time of presentation, on the basis of the risk factors or underlying cause of hyperglycemia, the clinical symptoms, and disease prognosis.", + "IntroductionGlobally, diabetes affects more than 400 million people (World Health Organization, 2016), with Type 1 (insulin-dependent) diabetes (T1D) accounting for up to 10 percent of cases (American Diabetes Association, 2009).In the United States, T1D occurs at a rate of 15-30 cases per 100,000 children aged 0-14 years annually (International Diabetes Foundation, 2017;Maahs et al., 2010), with similar prevalence in Canada, Europe, Australia, and New Zealand (Fig. 1) (Derraik et al., 2012;International Diabetes Foundation, 2017;Maahs et al., 2010).By contrast, the estimated incidence rate of T1D among Asians, South Americans, and Africans is below 15 cases per 100,000 children (Fig. 1) (International Diabetes Foundation, 2017;Maahs et al., 2010).The global incidence of T1D has been rising by 3-5% per annum over the past two decades, with a notable increase in children below 10 years of age (Diamond Project, 2006;Patterson et al., 2009).", + "Animal Models9.2% in women and 9.8% in men, with approximately 347 million people suffering from the disease worldwide in 2008 (Danaei et al., 2011).There are several different classifications of diabetes, the most common being type 1 and type 2 diabetes.Type 2 diabetes is the most common type of diabetes with prevalence in the United Kingdom of around 4%.It is most commonly diagnosed in middle-aged adults, although more recently the age of onset is decreasing with increasing levels of obesity (Pinhas-Hamiel and Zeitler, 2005).Indeed, although development of the disease shows high hereditability, the risk increases proportionally with body mass index (Lehtovirta et al., 2010).Type 2 diabetes is associated with insulin resistance, and a lack of appropriate compensation by the beta cells leads to a relative insulin deficiency.Insulin resistance can be improved by weight reduction and exercise (Solomon et al., 2008).If lifestyle intervention fails, there are a variety of drugs available to treat type 2 diabetes (Krentz et al., 2008), which can be divided into five main classes: drugs that stimulate insulin production from the beta cells (e.g.sulphonylureas), drugs that reduce hepatic glucose production (e.g.biguanides), drugs that delay carbohydrate uptake in the gut (e.g.a-glucosidase inhibitors), drugs that improve insulin action (e.g.thiazolidinediones) or drugs targeting the GLP-1 axis (e.g.GLP-1 receptor agonists or DPP-4 inhibitors).", + "IntroductionDiabetes impacts the lives of approximately 200 million people worldwide [1], with chronic complications including accelerated development of cardiovascular disease.Over 90% of cases are of type 2 diabetes (T2D), with the bulk of the remainder presenting with type 1 diabetes (T1D).", + "Classification of DiabetesOn the basis of insulin deficiency, diabetes can be classified into the following types as follows.", + "| INTRODUCTIONToday, more than 265 million people are affected across the world.It is estimated that by the year 2030 this number will reach 366 million people (about 4/4 percent of the world's population), and now the cause of death is more than 1.1 million per year (including 50% of the population under-70 years of age and 55% of women).On the other hand, given its negative effect on the economic growth of developing countries, it calls for universal mobilization to combat this disease (Bhattacharya, Dey, & Roy, 2007).Diabetes or diabetes mellitus is referred to as a heterogeneous group of metabolic disorders characterized by chronic hyperglycemia and carbohydrate, fat and protein metabolism disorders that result from a defect in the secretion of insulin, or impairment in its function, or both.Types of diabetes mellitus include type 1, type 2 diabetes and other kind of diabetes, but the two most common types of diabetes mellitus are type 1 and type 2, which are different in several aspects (Meshkani, Taghikhani, Mosapour et al., 2007).Type 1 diabetes has been identified with autoimmune destruction of pancreatic beta cells (insulin secreting cells) and accounts for about 5% of all diabetic people, while type 2 diabetes is a predominant disorder characterized by insulin resistance or a relative decline in insulin production, and accounts for about 90% of all types of diabetes mellitus (Meshkani, Taghikhani, Al-Kateb et al., 2007).Important factors that predispose a person to type 2 diabetes are multifactorial, including genetic factors and environments.However, its inheritance has certainly not been proven, but it is believed that first-degree relatives of diabetic patients have a higher chance to develop the disease.In this regard, recognizing gene polymorphisms of this disease seems to be necessary (Hring et al., 2014).Multiple genes have been studied in the pathogenesis of type 2 diabetes.", + "CONCLUSIONSDiabetes is currently broadly classified as type 1, type 2, gestational, and a group of \"other specific syndromes. \"However, increasing evidence suggests that there are populations of individuals within these broad categories that have subtypes of disease with a well-defined etiology that may be clinically characterized (e.g., LADA, MODY).These developments suggest that perhaps, with more focused research in critical areas, we are approaching a point where it would be possible to categorize diabetes in a more precise manner that can inform individual treatment decisions.Type 2 DiabetesIn the U.S., an estimated 95% of the nearly 30 million people living with diabetes have type 2 diabetes.An additional 86 million have prediabetes, putting them at high risk for developing type 2 diabetes (9).Among the demographic associations for type 2 diabetes are older age, race/ ethnicity, male sex, and socioeconomic status (9).Type 1 DiabetesBetween 2001 and 2009, there was a 21% increase in the number of youth with type 1 diabetes in the U.S. (7).Its prevalence is increasing at a rate of ;3% per year globally (8).Though diagnosis of type 1 diabetes frequently occurs in childhood, 84% of people living with type 1 diabetes are adults (9).Type 1 diabetes affects males and females equally (10) and decreases life expectancy by an estimated 13 years (11).An estimated 5-15% of adults diagnosed with type 2 diabetes actually have type 1 diabetes or latent autoimmune diabetes of adults (LADA) (12).", + "Background Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous.A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis.", + "Diabetes mellitus now affects ~8% of the world's adult population [1], including ~3 000 000 individuals in the UK (with a further 600 000 people affected but presently undiagnosed) [2].Of these cases, > 90% have Type 2 diabetes.Treatments of the complications of the disease, which range from stroke, blindness and kidney failure to lower limb amputations and cancer, presently consume ~10% of the National Health Service budget, some 14 bn per year [3].These figures are anticipated to increase further in the next 10 years, driven by increasingly sedentary lifestyles and increases in obesity; the collision between these 'environmental' factors and genetic susceptibility (see below) being the key underlying driver.Whilst existing treatments ameliorate the symptoms of the disease, notably hyperglyca-emia, none target the underlying molecular aetiology.In particular, no available treatments tackle the progressive and largely irreversible loss of insulin production [4] which, in the face of insulin resistance, underlies the progressive deterioration in glucose control.Reductions in b-cell mass [5,6] and dysfunction [7] both contribute to this gradual impairment in insulin release.Recent years have seen an increase in the view that the former may play a less important role than the latter, with a 2008 study by Rahier et al. [6] reporting that b-cell mass (and insulin content) in people with Type 2 diabetes was on average ~35% lower than that of healthy control subjects.However, this difference was only ~24% within 5 years of diagnosis, far below levels likely to lead to the symptoms of diabetes.Indeed, given our present inability to monitor b-cell mass prospectively over the course of the disease, it is conceivable that the differences observed post mortem between healthy individuals and those with Type 2 diabetes [5,6] may reflect an increased predisposition to diabetes in those born with a lower than average b-cell mass.", + "INTRODUCTIONType 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 (1).The disease usually involves insulin resistance but is ultimately the result of pancreatic b cell failure, a sine qua non for disease development (2).In contrast, Type 1 diabetes (T1D) affects a smaller proportion of people with diabetes and is chiefly the result of pancreatic b cell destruction mediated by immune cells (3).", + "IntroductionDiabetes is a complex and heterogeneous disease with a staggering global impact and the most recent estimates indicate 346 million people worldwide suffer from this disease (WHO Diabetes Fact sheet No. 312, 2011).Type 2 diabetes mellitus (T2DM) is the most common form of diabetes, accounting for >90% of cases, and occurs when peripheral tissue insulin resistance accompanies insufficient b-cell insulin production.While >80% of diabetes deaths occur in low-and middle-income countries [1].India and China have the highest reported prevalence of diabetes with 65 and 98 million in 2013, respectively [2].", + "The disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition.The disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition.", + "Introduction: Is Type 2 Diabetes a Genetic Disorder?According to the World Health Organization (WHO), approximately 350 million people worldwide have diabetes, and this disorder is likely to be the seventh leading cause of death in 2030.Diabetes is an economic burden on healthcare systems, especially in developing countries (World Health Organization, 2013)." + ], + [ + "Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.", + "There are two major factors that underlie these alarming projections.The first is T2D is associated with age, and Western populations are aging rapidly.The second major explanation is our lifestyles have changed dramatically in recent years.Epidemiological studies have identified strong T2D risk relationships for obesity, sedentary behavior [2][3][4], and diets rich in energy [5], processed carbohydrates [6], and animal fats [7].Collectively, these lifestyle factors impede the actions of insulin and raise hepatic glucose production, which can result in the diminution of endogenous insulin production and T2D.The strongest evidence for a causal relationship between adverse lifestyle behaviors and T2D comes from randomized controlled trials that show intensive lifestyle interventions involving structured exercise regimes which promote habitual physical activity (PA) and have a major beneficial impact on diabetes incidence in high-risk individuals [8,9].Epidemiological studies examining the associations between lifestyle behaviors and diabetes risk have reached similar conclusions as the clinical trials described above.For example, the 14-year follow-up University of Pennsylvania Alumni Health Study [52] (n = 5,990 men aged 39-68 years) showed PA (leisure time physical activity [LTPA] expressed in kcal expended per week through walking, stair climbing, and sports) was inversely associated with the incidence of T2D.Incidence rates declined as energy expenditure rose from 500 through 3,500 kcal/week.The age-adjusted relative risk ratio (RR) of T2D was reduced by about 6% for each 500 kcal increment increase in PA energy expenditure.", + "Overall, results were similar in analyses restricted to diabetes mellitus identified at baseline only, although the confidence interval included 1.These results suggest that diabetes mellitus is related to risk of AD in old age.These findings are consistent with the results of 2 large longitudinal cohort studies. 5,6In one study, 5 diabetes mellitus doubled the risk of AD during 2 years of follow-up in a sample of more than 6000 older persons from a defined cohort.The other study, 6 using data from about 2500 Japanese American men, found a similar result: diabetes mellitus approximately doubled the risk of AD.In contrast, 2 other longitudinal studies 7,8 did not demonstrate a significant association between diabetes mellitus and incident AD, but in both, the results were in the direction of increased risk.Some, [9][10][11] but not all, 12 previous studies found that diabetes mellitus was related to change in cognitive function.One factor that may contribute to variability from study to study is that diabetes mellitus may be related to decline in some cognitive systems but not others.4][15] Although diabetes mellitus was related to level of global cognition and multiple cognitive domains at baseline, we found that diabetes mellitus was only related to decline in perceptual speed.The one study 12 that did not find a relation between diabetes mellitus and cognitive decline did not include a measure of perceptual speed.COMMENTIn a cohort of more than 800 older persons, we found that diabetes mellitus sometime in the study was associated with an increased risk of developing AD during a mean of 5.5 years of observation.The risk of incident AD was 65% higher in those with diabetes mellitus than in those without it.In summary, these findings suggest that diabetes mellitus is associated with AD and decline in cognitive function in older persons.December 12, 2003.DIABETES MELLITUS AND RISK OF ADDuring the follow-up evaluations, 151 persons developed AD, of whom 31 had diabetes mellitus.In a proportional hazards model adjusted for age, sex, and educational level, there was a 65% increase in the risk of developing AD in those with diabetes mellitus compared with those without diabetes mellitus (hazard ratio, 1.65; 95% confidence interval, 1.10-2.47).The cumulative hazard of AD over time, adjusted for age, sex, and educational level, is shown graphically in Figure 1 for typical participants with and without diabetes mellitus.Similar results were found in analyses with diabetes mellitus identified at baseline only (hazard ratio, 1.53; 95% confidence interval, 0.96-2.45).", + "Age. Age is another factor that has a considerable effect on outcomes in obesity and T2DM research.In humans, body weight increases with age and peaks at ~55 years in both men and women.Ageing per se is associated with a redistribution of both the fat-free mass and the fat mass, with the latter increase starting at ~30 years of age 129 .Intramuscular and intrahepatic fat are particularly increased in older persons, and this increase has been linked to insulin resistance 130 .Partially on the basis of these changes, ageing has been proposed to be an independent determinant of glucose tolerance, which progressively worsens with age 131,132 .", + "Age also plays a vital role in the onset of diabetes (Cowie & Eberhardt, 1995).In south-east Asia almost 97% diabetic patients are 40 years old or more (IDF Atlas, 2017).In Bangladesh, the reported age of diabetes is 40 years in 71% urban and 85% rural female, while in the case of male the proportion is 85.5% urban and 86.5% in rural population (IDF Atlas, 2017).The current study also pinpointed an exponential increase in the risk of onset of T2DM with the increase of age when 40 years was chosen as the reference (Table S4).Whether age and stress variables are risk factors for type 2 diabetes incidence was assessed by multivariate logistic regression (Table S4).Subjects in the age groups of (40-60) and >60 years had 1.78 (p = .005)and 3.19 (p = .006)greater risk for type 2 diabetes respectively than group of <40 years.Overall, patients under stressful condition are more likely to develop T2DM than that of nonstressed respondent (p = .000).Moreover, when stress is divided into two groups-low stress and high stress, we found that both males (p = .000)and females (p = .000)with high stress were at high risk of diabetes mellitus, whereas the association between low stress and T2DM incidence was significant only among males (Male: p = .002;Female: p = .115).The distribution and association of the genotypes, age, and stress with T2DM have been summarized in Table 3 and Figure 3.There was no difference in T2DM incidence between CT (p = .030)and TT/CC (p = .034)genotype containing people who were in age group of 40-60 years (Table 3).In contrast, people who were more than 60 years old with CT genotype (OR = 4.636, p = .029)were more prone to T2DM than that of TT/CC genotype (OR = 3.714, p = .007)subjects (Table 3).", + "Research GapsThere is a clear correlation of environmental influences to diabetes risk.Yet, the assembled experts agreed that hypothesis-driven research is needed to define direct causal relationships between specific environmental factors and pathophysiologies leading to diabetes.Research efforts need to address environmental etiologies of type 1 diabetes and determine their relative contribution to onset of autoimmunity and progression to symptomatic disease.Whether there is a direct causal role of the intestinal microbiota in pathogenesis of type 1 and type 2 diabetes and response to therapies needs to be determined.Public health interventions that successfully reduce the levels of consumption of energy-dense foods and/or reduce sedentary time and increase time spent in physical activity need to be evaluated to determine whether they can reduce type 2 diabetes incidence at a population level.", + "In sum, it is clear that multiple risk factors are involved in diabetes-associated cognitive decrements as well as in dementia in relation to diabetes 38 .On the basis of our assessment of the literature, it is also clear that there are still substantial knowledge gaps on how the risk factors interconnect, how the risk factors translate to potentially modifiable mechanisms and which genetic factors are involved.", + "The aim of this study was to investigate the association between age at natural menopause and risk of developing type 2 diabetes, and to assess whether this association is independent of potential intermediate risk factors for type 2 diabetes.Furthermore, we examined the role of endogenous sex hormone levels in the association between age at natural menopause and type 2 diabetes.Aims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens.Aims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens.Results During a median follow-up of 9.2 years, we identified 348 individuals with incident type 2 diabetes.After adjustment for confounders, HRs for type 2 diabetes were 3.7 (95% CI 1.8, 7.5), 2.4 (95% CI 1.3, 4.3) and 1.60 (95% CI 1.0, 2.8) for women with premature, early and normal menopause, respectively, relative to those with late menopause (ptrend <0.001).The HR for type 2 diabetes per 1 year older at menopause was 0.96 (95% CI 0.94, 0.98).Further adjustment for BMI, glycaemic traits, metabolic risk factors, C-reactive protein, endogenous sex hormone levels or shared genetic factors did not affect this association.Conclusions/interpretation Early onset of natural menopause is an independent marker for type 2 diabetes in postmenopausal women.association and explore whether the timing of natural menopause can add value to diabetes prediction and prevention.", + "Although drawing of definitive conclusions is difficult from these observational studies, their results suggest that young-onset type 2 diabetes is associated with a much more frequent occurrence of adverse macrovascular and microvascular outcomes and a more rapidly progressing severity of complications than is seen in type 1 diabetes or later-onset type 2 diabetes.In a study of the age-specific incidence of type 2 diabetes in the UK (a retrospective cohort study of patients with newly diagnosed type 2 diabetes between 1990 and 2010), the investigators reported a substantial increase in the proportion of people aged 40 years or younger at diagnosisThe prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults.", + "The biological processes linking aging and disease risk are poorly understood.Still, aging is considered to date as one of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes." + ], + [ + "A. Genetic ScreeningWe have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is 10 -15 times greater.Although 70% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3).II. THE GENETICS OF TYPE 1 DIABETESA comprehensive overview of genetic data in mouse and human is beyond the scope of this article.Instead, we will focus on how the various susceptibility genes and environmental triggers can fit in a mechanistic model for T1D etiology.", + "If an environmental contributor is near ubiquitous and the geneticpredisposition common as well, interventions are most sensibly weighted towardsenvironmental risk factor modification. Even here, though, there is room for further research, since the etiopathogenesisof type 2 diabetes may not be as well understood as some suggest. Specifically,Chaufan implies that dietary intervention to prevent prenatal programmingleading to susceptibility to develop type 2 diabetes (the fetal origins of adult onsetdisease hypothesis) is as evidence-based as dietary management of the adult diabetic state. However, many questions remain in this area.", + "In 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention.Type 1 diabetes becomes clinically apparent after a preclinical period of varying length, during which autoimmune destruction reduces the mass of beta cells in the pancreatic islets to a level at which blood glucose levels can no longer be maintained in a physiologic range.The disease has two subtypes: 1A, which includes the common, immune-mediated forms of the disease; and 1B, which includes nonimmune forms.In this review, we focus on subtype 1A, which for simplicity will be referred to as type 1 diabetes.Although there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied. Gene tic S t udiesBecause of the uncertainty regarding the number and action of genes involved in type 1 diabetes, genetic studies have tended to focus on approaches that require few assumptions about the underlying model of disease risk.The two primary approaches have been linkage studies (using pairs of affected relatives, typically siblings) and association studies (using either case-control or family-based designs).Linkage studies using affected sibling pairs seek to identify regions of the genome that are shared", + "EnvironmentThe second factor in Figure 1 is environmental aspects.An important concept is the diabetes genotype typically causes only a predisposition for glucose intolerance (note the terminology susceptibility gene was used in the preceding paragraphs).Whether one develops the diabetes phenotype depends on environmental factors, some obvious in how they act, others less so.For instance, the Nurses Health Survey showed positive associations between obesity and lack of physical activity in the development of type 2 diabetes (as expected), but also protection by not smoking and moderate alcohol intake (14).Already discussed, many studies have shown an association between TV watching, high calorie diets, and lack of physical activity with risk of diabetes, i.e., our modern lifestyle, so it is not surprising that there is an explosion in the incidence of diabetes worldwide.", + "The genetics of type 1 diabetesThere is a strong genetic risk to T1D.This is exemplified by (Redondo et al., 2001) who demonstrated a strong concordance of genetic inheritance (65%) and T1D susceptibility in monozygotic twin pairs.That is, when one sibling is afflicted, there is a high probability that the other twin will develop T1D by the age of 60 years.Additionally, autoantibody positivity and islet destruction was observed after a prospective long-term follow-up of monozygotic twins of patients with T1D, despite initial disease-discordance among the twins (Redondo et al., 2008).", + "Type 1 diabetes is a genetic diseaseFamily studies have indicated that genetic factors are important determinants of type 1 diabetes risk.First, the risk to a sibling of an affected individual is approximately 6%, as compared with an average risk of 0.4% (depending on the population), or a relative increased risk of 15-fold (17).The increased risk to siblings is referred to as l s (18) and is one measure of the degree of familial clustering of the disease.Family and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.Family and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.", + "Genes affecting type 1 diabetes diagnosis age / A. Syreeni et al.Genome-wide search for genes affecting the age at diagnosis of type 1 diabetes.", + "Thus, the most likely scenario is that these genes are more poised for activation in the case group compared with the control group, contributing to various diabetes complications in the long term.This could be a consequence of the early exposure to hyperglycemia (measured by HbA 1c level), which is known to be associated with increased rates of long-term diabetes complications.", + "Genetic Background and EnvironmentBoth type 1 and 2 diabetes as well as other rare forms of diabetes that are directly inherited, including MODY and diabetes due to mutations in mitochondrial DNA, are caused by a combination of genetic and environmental risk factors.Unlike some traits, diabetes does not seem to be inherited in a simple pattern.Undoubtedly, however, some people are born prone to developing diabetes more so than others.Several epidemiological patterns suggest that environmental factors contribute to the etiology of T1D.Interestingly, the recent elevated number of T1D incidents projects a changing global environment, which acts either as initiator and/or accelerator of beta cell autoimmunity rather than variation in the gene pool.Several genetic factors are involved in the development of the disease [127].There is evidence that more than twenty regions of the genome are involved in the genetic susceptibility to T1D.", + "Type 1 DiabetesThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk.GeneticsBoth type 1 and type 2 diabetes are polygenic diseases where many common variants, largely with small effect size, contribute to overall disease risk.Disease heritability (h 2 ), defined as sibling-relative risk, is 3 for type 2 diabetes and 15 for type 1 diabetes (17).The lifetime risk of developing type 2 diabetes is ;40% if one parent has type 2 diabetes and higher if the mother has the disease (18).The risk for type 1 diabetes is ;5% if a parent has type 1 diabetes and higher if the father has the disease (19).Maturity-onset diabetes of the young (MODY) is a monogenic disease and has a high h 2 of ;50 (20).Mutations in any 1 of 13 different individual genes have been identified to cause MODY (21), and a genetic diagnosis can be critical for selecting the most appropriate therapy.For example, children with mutations in KCJN11 causing MODY should be treated with sulfonylureas rather than insulin.", + "Type 1 diabetes as well as type 2 diabetes shows a genetic predisposition, although only type 1 diabetes is HLA dependent [32,33,36,40].", + "Genetic factors have an important role in the development of diabetes, with some forms of the disease resulting from mutations in a single gene.Others are multifactorial in origin.The monogenic forms of diabetes account for approximately 5% of cases and are caused by mutations in genes encoding insulin 3 , the insulin receptor 4 , the glycolytic enzyme glucokinase 5 , and the transcription factors hepatocyte nuclear factor-1 (HNF-1), HNF-1, HNF-4, insulin promoter factor-1 and NeuroD1/BETA2 (refs 6-10).Mutations in maternally inherited mitochondrial genes can also cause diabetes, often in association with hearing loss 11 .", + "The proportion of diabetics t h a t will result frommating between genetic types can be predicted withcertainty, since the inheritance is known to be underthe control of a recessive gene with complete penetrance. Offspring t h a t will exhibit the diabetic syndrome can be distinguished from those t h a t will not,as early as 3 weeks after birth. Some disadvantages are equally apparent. Diabetichomozygotes do not breed, and heterozygotes cannotbe distinguished from normals except b y progenytesting.", + "Studies [71][72][73][74] in Mexican and Asian populations have identified several mutations associated with type 2 diabetes in young people.The high prevalence of type 2 diabetes in the parents of young people diagnosed with type 2 diabetes could reflect a stronger genetic predisposition, even when monogenic diabetes is excluded.This hypothesis suggests that efforts to define genes that cause type 2 diabetes by linkage might be more powerful if focused on young adults with diabetes, raising the question of whether type 2 diabetes in older populations has a relatively smaller genetic contribution and a stronger environmental contribution. 66", + "Genetics is one example of the 'other risk factors' involved in the pathogenesis of DR.Twin and epidemiological studies have strongly suggested a genetic component in the etiology of DR (6 -10), with heritability scores ranging from 27 to 52% in both type 1 and type 2 diabetes (7 -10).There is an increased risk of severe DR among family members of DR subjects (8,9) and in siblings of affected subjects (8,9).Furthermore, several studies have also shown a discrepant rate of the prevalence of DR among different racial ethnic groups in the US population, with a significantly higher prevalence observed among Hispanic, African-American and Chinese-American when compared with Caucasian populations (11).While these differences may partially be attributed to lifestyle factors, evidence from familial aggregation, ethnic differences and heritability clearly supports a genetic contribution in the etiology of DR." + ], + [ + "To see which other significant genes were likely to have a role in diabetes we looked at all variant sets with a significant glucose, HbA1c, or T2D association and examined whether they had associations with additional diabetes traits (p 0.0016, correcting for 32 sets tested).Damaging missense variants in PDX1 and PFAS, which significantly associated with HbA1c levels in our primary analysis, associated with T2D diagnosis using this threshold (Table 3 and Supplementary Table 14).Identification of genes with a biological role in diabetes. Variants in two genes, GCK and GIGYF1, significantly associated with glucose, HbA1c and T2D diagnosis, strongly suggesting a biological role in diabetes; GCK is involved in Mendelian forms of diabetes while GIGYF1 has not previously been implicated by genetics in the disease.Both GCK and GIGYF1 are located on chromosome 7 but are 56 Mb apart, strongly suggesting that these signals are independent; this independence was confirmed by conditional analysis (Supplementary Table 13).Two additional variant sets, HNF1A pLOF and TNRC6B pLOF, had genome-wide associations with both T2D diagnosis and HbA1c levels while G6PC2 damaging missense variants associated with decreased levels of both glucose and HbA1c but not T2D diagnosis (Table 3).", + "One obvious locus to consider is TCF7L2 in the context of type 2 diabetes.Common genetic variation located within the gene encoding transcription factor 7 like 2 (TCF7L2) has been consistently reported to be strongly associated with the disease.Such reports range from 2006, when we first published the association [3], to the recent transethnic meta-analysis GWAS of type 2 diabetes [4].", + "Testing of these loci for association with T2D as a dichotomous trait in up to 40,655 cases and 87,022 nondiabetic controls demonstrated that the fasting glucose-raising alleles at seven loci (in or near ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 and the known T2D genes TCF7L2 and SLC30A8) are robustly associated (P < 5 10 8 ) with increased risk of T2D (Table 2).The association of a highly correlated SNP in ADCY5 with T2D in partially overlapping samples is reported by our companion manuscript 29 .We found less significant T2D associations (P < 5 10 3 ) for variants in or near CRY2, FADS1, GLIS3 and C2CD4B (Table 2).These data clearly show that loci with similar fasting glucose effect sizes may have very different T2D risk effects (see, for example, ADCY5 and MADD in Table 2).", + "Among the confirmed and potential type 2 diabetes risk genes described in Tables 1 and 2, eight genes influence whole-body or peripheral insulin sensitivity: ADIPOQ (47, 52, 250 -257), AHSG (75, 258), CAPN10 (259 -264), ENPP1 (265)(266)(267)(268)(269)(270)(271), PPARG (272)(273)(274)(275)(276)(277)(278)(279)(280)(281)(282)(283), PPARGC1A (284,285), SREBF1 (65), and TCF7L2 (133,151,286,287).", + "Despite identification of many putative causative genetic variants, few have generated credible susceptibility variants for type 2 diabetes.Indeed, the most important finding using linkage studies is the discovery that the alteration of TCF7L2 (TCF-4) gene expression or function (33) disrupts pancreatic islet function and results in enhanced risk of type 2 diabetes.Candidate gene studies have also reported many type 2 diabetes-associated loci and the coding variants in the nuclear receptor peroxisome proliferator-activated receptor-g (34), the potassium channel KCNJ11 (34), WFS1 (35), and HNF1B (TCF2) (36) are among the few that have been replicated (Table 2).Recently, there have been great advances in the analysis of associated variants in GWA and replication studies due to highthroughput genotyping technologies, the International HapMap Project, and the Human Genome Project.Type 2 susceptibility loci such as JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2, and ADCY5 (37,38) are among some of the established loci (Table 2).CDKN2A/B, CDKAL1, SLC30A8, IGF2BP2, HHEX/IDE, and FTO are other established susceptibility loci for diabetes (Table 2) (34,39,40).GWA studies have also identified the potassium voltage-gated channel KCNQ1 (32) as an associated gene variant for diabetes.A recent GWA study reporting a genetic variant with a strong association with insulin resistance, hyperinsulinemia, and type 2 diabetes, located adjacent to the insulin receptor substrate 1 (IRS1) gene, is the C allele of rs2943641 (41).Interestingly, the parental origin of the single nucleotide polymorphism is of importance because the allele that confers risk when paternally inherited is protected when maternally transmitted.GWA studies for glycemic traits have identified loci such as MTNR1B (42), GCK (glucokinase) (42), and GCKR (glucokinase receptor) (42); however, further investigation of genetic loci on glucose homeostasis and their impact on type 2 diabetes is needed.Indeed, a recent study by Soranzo et al. (42) using GWA studies identified ten genetic loci associated with HbA 1c .Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin may be associated with changes in levels of HbA 1c .", + "G enome-wide association studies (GWAS) have iden- tified several type 2 diabetes mellitus (T2DM) susceptibility loci including CDKAL1, CDKN2B, IGF2BP2, HHEX, SLC30A8, PKN2, LOC387761 (1)(2)(3)(4)(5), and KCNQ1, which was recently identified by similar GWAS approach in two independent Japanese samples (6,7).Although these associations have been well replicated in Japanese populations (8), the role of these loci in other East Asian populations remains less clear.For example, a study in China by Wu et al. (9) did not find significant associations between single-nucleotide polymorphisms (SNPs) in IGF2BP2 and SLC30A8 with T2DM, whereas an association between SNPs at the HHEX locus and T2DM was reported among Chinese living in Shanghai, but not among Chinese in Beijing.Another study in Hong Kong Chinese (10) also did not find an association with SNPs at the IGF2BP2 locus; however, they reported an association between T2DM with SNPs at the HHEX and SLC30A8 loci.", + "In studies where overt T2D has been the phenotype the majority of associated polymorphisms have encoded proteins known to be involved in -cell metabolism; for example TCF7L2, KCNJ11 and HHEX have shown robust association [170,171].This suggests that these genes could prove useful in predicting -cell preservation during the course of T2D.The glucokinase gene (GCK) coding for the initial glucose-sensing step in the -cell can have activating mutations causing hypoglycemia that might provide structural and functional models leading to drug targets for treating T2D [172].In the GoDARTs study, investigators examined the medication response of metformin and sulphonylurea based on the TCF7L2 variants mainly affecting the -cell.The carriers of the at risk 'T' allele responded less well to sulphonylurea therapy than metformin [173].Also it is of significant public health interest that in the Diabetes Prevention Program, lifestyle modifications were shown to reduce the risk of diabetes conferred by risk variants of TCF7L2 at rs7093146, and in placebo participants who carried the homozygous risk genotype (TT), there was 80% higher risk for developing diabetes compared to the lifestyle intervention group carrying the same risk genotypes [35].These findings could herald significant future progress in the field of T2D pharmacogenomics, possibly leading to the development and use of agents tailored on the basis of genotype.", + "Despite sharing only 9 loci (among 26 and 17 total in the two analyses, respectively), the separate analyses both identified genes involved in diabetes-related biological functions, including \"glucose homeostasis,\" \"pancreas development\" and \"insulin secretion\" (Supplementary Tables 3 and 5).Three of the top eleven scoring genes in our independent replication analysis have verified causal links to T2D, as annotated in the OMIM 41 .These include genes encoding transcription factors TCF7L2 (TCF4), which has extensive evidence of being causal in T2D 61,62 , and HNF1B, which is a known cause of maturity onset diabetes of the young 63 .Other high-ranking candidate genes have been identified as therapeutic targets in T2D (for example, CTBP1 (ref.64) and LEP 65 ), and the high-scoring gene HHEX has recently been shown to play a key role in islet function 66 .", + "Similar findings to AMD are now unfolding with type 2 DM.Grant et al. (24) first reported on a variant of the gene TCF7L2, which has been linked to reduced beta cell function and poor insulin response to oral glucose loads (51).Since its first discovery, this gene has been widely confirmed in independent studies as a pivotal susceptibility marker for type 2 DM (23,(25)(26)(27)(28)40).Recently, 6 genome-wide SNP association studies have identified and replicated in separate stages several additional novel genes conferring susceptibility to type 2 DM (23,(25)(26)(27)(28)40) (Table 2).Interestingly, these loci primarily include genes involved in pancreatic beta cell development and function as opposed to insulin resistance-the current accepted mechanism for type 2 DM.This development casts doubt on our traditional pathophysiological modeling of the type 2 diabetic patient and underscores the need for genomic studies to further define pathobiological processes of complex traits.", + "Of the 16 loci that have been associated with type 2 diabetes previously, [8][9][10][11][12][13][14][15] we showed that 11 -TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEXwere associated with an enhanced risk of future diabetes.Many of the variants that we genotyped appear to influence beta-cell function, possibly through effects on proliferation, regeneration, and apoptosis.There was a time-dependent increase in the BMI and a decrease in insulin sensitivity in the subjects from the Botnia study, an increase in insulin resistance that was reflected by an increase in insulin secretion.However, this increase was inadequate to compensate for the increase in insulin resistance in carriers with a high genetic risk, which resulted in a markedly impaired disposition index.Only variants in FTO were associated with an increased BMI.Both FTO and PPARG together with TCF7L2 and KCNJ11 predicted transition from impaired fasting glucose levels or impaired glucose tolerance to manifest diabetes, which suggests that a combination of increased obesity and insulin resistance with a deterioration in beta-cell function contribute to the manifestation of diabetes in these subjects.Collectively, our findings emphasize the critical role of inherited defects in beta-cell function for the development of type 2 diabetes.Type 2 DiabetesCommon variants in 11 genes were significantly associated with the risk of future type 2 diabetes in the MPP cohort, including TCF7L2 (odds ratio, 1.30; P = 9.510 13 ), PPARG (odds ratio, 1.20; P = 4.010 4 ), FTO (odds ratio, 1.14; P = 9.210 5 ), KCNJ11 (odds ratio, 1.13; P = 3.610 4 ), NOTCH2 (odds ratio, 1.13; P = 0.02), WFS1 (odds ratio, 1.12; P = 0.001), CDKAL1 (odds ratio, 1.11; P = 0.004), IGF2BP2 (odds ratio, 1.10; P = 0.008), SLC30A8 (odds ratio, 1.10; P = 0.008), JAZF1 (odds ratio, 1.08; P = 0.03), and HHEX (odds ratio, 1.07; P = 0.03) (Table 2).Although these findings could not be fully replicated in the smaller Botnia study, there was little heterogeneity between the studies with respect to the risk conferred by different genotypes.", + "To date, more than 70 genes have been identified as involved in T2DM, primarily by association analysis [34].In addition, via GWAS arrays, more than 100 SNPs have been identified for T2DM [35].From the 50 novel loci associated with T2DM previously identified, more than 40 loci have been associated with T2DM-related traits, including fasting proinsulin, insulin and glucose (Table 1) [36][37][38][39].However, for T2DM-related traits, such as the HOMA index or pancreatic cell function, there are virtually no published data examining the relationship between these traits or the genotype and environment interactions.Clinical investigations of some loci have suggested that the genetic components of T2DM risk act preferentially through cell function [40].Among all 40 loci associated with T2DM-related traits, only transcription factor-7-like 2 (TCF7L2) was shown to clearly contribute to T2DM risk [41].Several studies in white European [42], Indian [43], Japanese [44], Mexican American [45] and West African [46] individuals have shown a strong association between TCF7L2 and T2DM.It is also noteworthy that these populations represent the major racial groups with a high prevalence of T2DM.In all populations, TCF7L2 showed a strong association, with the odds of developing T2DM increased by 30%-50% for each allele inherited.This finding indicates an approximately double odds ratio compared to most other diabetes susceptibility polymorphisms.TCF7L2 is a transcription factor involved in the Wnt signaling pathway that is ubiquitously expressed, and it has been observed that TCF7L2 risk alleles result in the overexpression of TCF7L2 in pancreatic cells.This overexpression causes reduced nutrient-induced insulin secretion, which results in a direct predisposition to T2DM as well as an indirect predisposition via an increase in hepatic glucose production [47].Most Relevant T2DM Susceptibility GenesGene and environment interaction studies have shown a nice association between variants in peroxisome proliferator-activated receptor gamma (PPARG), TCF7L2 and fat mass and obesity-associated protein (FTO) genes, a Western dietary pattern and T2DM.", + "One of these genes associated with type 2 diabetes is the insulin receptor substrate 1 (IRS1, OMIM association number, 147545) (Alharbi, Khan, Abotalib, & Al-Hakeem, 2014;Alharbi, Khan, Munshi et al., 2014;Brender et al., 2013;Brunetti, Chiefari, & Foti, 2014) and another is the C-C motif chemokine receptor5(CCR5, OMIM association number, 601373) (Balistreri et al., 2007;Mokubo et al., 2006;Muntinghe et al., 2009).", + "Genes boosted in type 2 diabetesBefore the Wellcome Trust study, PPARG, KCNJ11, and TCF7L2 had all been identified as genes involved in type 2 diabetes through genome-wide association studies and replicated in follow-up studies (for review, see Bonnefond et al. 2010).The strongest candidate gene for type 2 diabetes, TCF7L2, was also the strongest signal seen in the Wellcome trust study, although the others were not so strong.However, the exact mechanism by which TCF7L2 acts was not entirely clear.In our analysis (Fig. 5), we find it directly connected to the b-catenin/WNT signaling pathway by its functional connection to CTNNB1, as well as to BACH2, a gene that has been repeatedly implicated in type 1 diabetes (e.g., Cooper et al. 2008;Madu et al. 2009), but which has not yet been linked to type 2 diabetes.BACH2 is among the genes most strongly boosted by network linkages, deriving additional signal from CREB5 and PARD3B, which both score highly in the GWAS data.PARD6G, PARD3B, and CDC42 are also emphasized by the method.Notably, these genes form a complex with PRKCZ (Koh et al. 2008), a variant of which correlates with type 2 diabetes in Han Chinese (Qin et al. 2008).EBF1, a known regulator of adipocyte differentiation (Akerblad et al. 2005) is also strongly boosted by the network, supporting a possible role in type 2 diabetes.", + "RESULTS-We confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 10 12 P unadjusted 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations.OBJECTIVE-Recent genome-wide association studies have identified six novel genes for type 2 diabetes and obesity and confirmed TCF7L2 as the major type 2 diabetes gene to date in Europeans.However, the implications of these genes in Asians are unclear.RESEARCH DESIGN AND METHODS-We studied 13 associated single nucleotide polymorphisms from these genes in 3,041 patients with type 2 diabetes and 3,678 control subjects of Asian ancestry from Hong Kong and Korea. RESULTS-We confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 10 12 P unadjusted 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations. CONCLUSIONS-Ourfindings support the important but differential contribution of these genetic variants to type 2 diabetes and obesity in Asians compared with Europeans.Diabetes 57: 2226-2233, 2008T ype 2 diabetes is a major health problem affecting more than 170 million people worldwide.In the next 20 years, Asia will be hit hardest, with the diabetic populations in India and China more than doubling (1).Type 2 diabetes is characterized by the presence of insulin resistance and pancreatic -cell dysfunction, resulting from the interaction of genetic and environmental factors.Until recently, few genes identified through linkage scans or the candidate gene approach have been confirmed to be associated with type 2 diabetes (e.g., PPARG, KCNJ11, CAPN10, and TCF7L2).Under the common variant-common disease hypothesis, several genome-wide association (GWA) studies on type 2 diabetes have been conducted in large-scale case-control samples.Six novel genes (SLC30A8, HHEX, CDKAL1, CDKN2A and CDKN2B, IGF2BP2, and FTO) with modest effect for type 2 diabetes (odds ratio [OR] 1.14 -1.20) had been reproducibly demonstrated in multiple populations of European ancestry.Moreover, TCF7L2 was shown to have the largest effect for type 2 diabetes (1.37) in the European populations to date (2-8).Although many of these genes may be implicated in the insulin production/secretion pathway (TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, and IGF2BP2) (6,9 -11), FTO is associated with type 2 diabetes through its regulation of adiposity (8,12,13).Moreover, two adjacent regions near CDKN2A/B are associated with type 2 diabetes and cardiovascular diseases risks, respectively (7,14 -16).Despite the consistent associations among Europeans, the contributions of these genetic variants in other ethnic groups are less clear.Given the differences in environmental factors (e.g., lifestyle), risk factor profiles (body composition and insulin secretion/resistance patterns), and genetic background (linkage disequilibrium pattern and risk allele frequencies) between Europeans and Asians, it is important to understand the role of these genes in Asians.A recent case-control study in 1,728 Japanese subjects revealed nominal association to type 2 diabetes for variants at the SLC30A8, HHEX, CDKAL1, CDKN2B, and FTO genes but not IGF2BP2 (17).In the present large-scale case-control replication study of 6,719 Asians, we aimed to test for the association of six novel genes from GWA studies and TCF7L2, which had the largest effect in Europeans, and their joint effects on type 2 diabetes risk and metabolic traits. RESEARCH DESIGN AND METHODSAll subjects were recruited from Hong Kong and Korea and of Asian ancestry.The subjects in the Hong Kong case-control study were of southern Han Chinese ancestry residing in Hong Kong.Participants for the case cohort consisting of 1,481 subjects with type 2 diabetes were selected from two", + "OBJECTIVE-Common variants in PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 genes have been shown to be associated with type 2 diabetes in European populations by genome-wide association studies.We have studied the association of common variants in these eight genes with type 2 diabetes and related traits in Indians by combining the data from two independent case-control studies.", + "IntroductionMany genes have been evaluated as candidates for T2D susceptibility.However, only variants in the TCF7L2, PPARG, KCNJ11 and HNFA4 genes have been extensively replicated in populations around the world, showing their indisputable association with T2D risk (Zeggini 2007).In the particular case of the HNF4A gene, it has been implicated in maturity-onset diabetes of the young type 1 (MODY 1) (Mitchell and Frayling 2002;Zhu et al. 2003).HNF4A is a member of the nuclear receptor super-family that plays a critical role in embryogenesis and metabolism, by regulating gene expression in pancreatic beta cells, liver and other tissues.The HNF4A gene is localized to chromosome 20q13, a region that has demonstrated evidence for linkage with T2D (Sladek et al. 1990;Ghosh et al. 1999).Several genetic studies, mainly in Caucasian and Asian populations, have provided evidence for the association of the variants in HNF4A with T2D (Ghosh et al. 1999;Silander et al. 2004;Winckler et al. 2005)." + ], + [ + "A wide array of other dietary compounds and environmental triggers have been shown to affect diabetes development in animal models, and for some of these such as omega-3 fatty acids (312), there is limited proof in human patients.", + "Type 2 diabetes is now a pandemic and shows no signs of abatement.In this Seminar we review the pathophysiology of this disorder, with particular attention to epidemiology, genetics, epigenetics, and molecular cell biology.Evidence is emerging that a substantial part of diabetes susceptibility is acquired early in life, probably owing to fetal or neonatal programming via epigenetic phenomena.Maternal and early childhood health might, therefore, be crucial to the development of eff ective prevention strategies.Diabetes develops because of inadequate islet -cell and adipose-tissue responses to chronic fuel excess, which results in so-called nutrient spillover, insulin resistance, and metabolic stress.The latter damages multiple organs.Insulin resistance, while forcing cells to work harder, might also have an important defensive role against nutrient-related toxic eff ects in tissues such as the heart.Reversal of overnutrition, healing of the cells, and lessening of adipose tissue defects should be treatment priorities.Type 2 diabetes is now a pandemic and shows no signs of abatement.In this Seminar we review the pathophysiology of this disorder, with particular attention to epidemiology, genetics, epigenetics, and molecular cell biology.Evidence is emerging that a substantial part of diabetes susceptibility is acquired early in life, probably owing to fetal or neonatal programming via epigenetic phenomena.Maternal and early childhood health might, therefore, be crucial to the development of eff ective prevention strategies.Diabetes develops because of inadequate islet -cell and adipose-tissue responses to chronic fuel excess, which results in so-called nutrient spillover, insulin resistance, and metabolic stress.The latter damages multiple organs.Insulin resistance, while forcing cells to work harder, might also have an important defensive role against nutrient-related toxic eff ects in tissues such as the heart.Reversal of overnutrition, healing of the cells, and lessening of adipose tissue defects should be treatment priorities.", + "Type 2 diabetes (T2D) is a result of complex gene-environment interactions, and several risk factors have been identified, including age, family history, diet, sedentary lifestyle and obesity.Statistical models that combine known risk factors for T2D can partly identify individuals at high risk of developing the disease.However, these studies have so far indicated that human genetics contributes little to the models, whereas socio-demographic and environmental factors have greater influence 1 .Recent evidence suggests the importance of the gut microbiota as an environmental factor, and an altered gut microbiota has been linked to metabolic diseases including obesity 2,3 , diabetes 4 and cardiovascular disease 5 .", + "The prevalence of diabetes mellitus worldwide is sobering; the International Diabetes Federation estimates that 415 million people have diabetes mellitus, with 90% of these individuals having type 2 diabetes mellitus (T2DM) 1 .T2DM occurs when pancreatic -cells fail to release enough insulin to meet the demands of insulin-responsive tissues, which safely store and metabolize glucose.Driven by both genetic and environmental risk factors, T2DM is a complex, multifactorial disorder.Although the increasing prevalence of T2DM is driven by changes in our environment and mirrors the increase in obesity, the greater concordance for T2DM found in monozygotic compared with dizygotic twins has long provided evidence for a genetic component in T2DM risk 2 .", + "DietExcessive caloric intake is a major driving force behind escalating obesity and type 2 diabetes epidemics worldwide, but diet quality also has independent effects.In the Nurses' Health Study (NHS), we found that the quality of fats and carbohydrates play an important role in the development of diabetes, independent of BMI and other risk factors (11).In particular, higher dietary glycemic load (GL) and trans fat are associated with increased diabetes risk, whereas greater consumption of cereal fiber and polyunsaturated fat is associated with decreased risk (Fig. 2).In a meta-analysis, we found that a 2 serving/day increment in whole-grain intake was associated with a 21% lower risk of diabetes (12).", + "IntroductionThe aetiology of type 2 diabetes is poorly defined: several studies indicate that the disease results from a combination of genetic susceptibility and external risk factors [1].According to this multifactorial model, genetically predisposed subjects will not necessarily develop overt disease unless they are also exposed to particular environmental factors [2].Important risk factors for the development of type 2 diabetes include a family history of diabetes, increased age, hypertension, lack of physical exercise, and obesity [1].", + "BackgroundNearly 350 million people world-wide are currently affected by diabetes, and the number of people with type 2 diabetes mellitus is increasing at an alarming rate [1].Type 2 diabetes results when the -cells of the pancreas are no longer capable of producing sufficient insulin to meet the body's demands.Thus -cell dysfunction is a key component of type 2 diabetes pathology.Although the increased prevalence of obesity and resulting insulin resistance is contributing to the increased prevalence of type 2 diabetes, many obese individuals are insulin resistant but do not develop diabetes [2].Genetic factors, many of which have been proposed to affect -cell function, play an important role in determining an individual's risk within this context [3][4][5][6].In a small number of individuals, type 2 diabetes is caused by rare single gene mutations, but for most individuals type 2 diabetes results from the combined effects of many common single-nucleotide polymorphisms (SNPs), each of which have a small effect on risk and likely interact with each other and with environmental and lifestyle factors [7].", + "Type I Diabetes is a disease that results when cells, such as fat and muscle cells, do not properly take up sugar from the blood.There are many symptoms of diabetes; however, one common symptom is a large increase of glucose levels in the blood, called hyperglycemia, because glucose cannot enter the other cells of the body.Hyperglycemia can cause blurred vision and can make one feel extremely hungry and very tired.In extreme cases it can 10 21 cause loss of consciousness.Type I diabetes is a genetic disease.", + "What these predisposing factors share is an ability to negatively impact the glucose homeostasis system through worsening of insulin resistance or to impair b-cell function.Superimposing these factors onto a genetically compromised glucose homeostasis system raises the risk of progressing to hyperglycemia.It is the rapid emergence of these disadvantageous environmental factors that is causing the worldwide diabetes epidemic.This concept of environmental changes promoting diabetes was highlighted many years ago by populations that rarely experienced type 2 diabetes, but then moved from a nomadic or farm existence to urban environments followed by an explosion of diabetes, typically with profound obesity: Pima Indians in the Southwest U.S., Saharan nomadic tribes, Australian Aborigines, and many others.Particularly dramatic were studies that showed reversal of the diabetes when they returned to their prior way of life (15).A recent example of this is the rapidly rising incidence of type 2 diabetes in China and India as people move from the country to cities-there is a 0.1-0.2%incidence of diabetes for rural farmers in China as opposed to well more than 5% for city dwellers.Perhaps the scariest example of this is children in the U.S. where the obesity statistics worsen yearly.As many as 20% of U.S. children are now obese, and they are developing all of the elements of the metabolic syndrome-insulin resistance, hypertension, hyperlipidemia, and glucose intolerance (16).", + "BackgroundType 2 diabetes (T2D) is a common, chronic disease caused by both genetic and environmental risk factors and their interactions [1], which has significantly increased prevalence in the past 20 years [2] and disproportionately afflicts communities of color [3][4][5].The current screening of T2D focuses on individuals with demographic and clinical risk factors, including overweight or obesity, age >35 years, and a family history of diabetes [6].However, despite preventative strategies and public health efforts to improve nutrition and physical activity, facilitate access to care, and limit tobacco and alcohol use, the morbidity and mortality associated with T2D remain unaltered [5], likely because most interventions are adopted too late in the course of disease trajectory.", + "BackgroundType 2 diabetes is a cause of poor health and early death that is spreading worldwide and exerting a fearsome human and economic toll [1,2].Prevention and control of diabetes requires a better understanding of its basic molecular causes.Type 2 diabetes is a heterogeneous disease arising from physiological dysfunction in the pancreas, skeletal muscle, liver, adipose and vascular tissue.Much of the heterogeneity of type 2 diabetes has a genetic basis.A full picture of the complex genetic architecture of diabetes has been elusive [3][4][5][6][7].", + "RACIALIZED ETIOLOGIES OF DIABETESDiabetes is not one disease but many.More than 90 percent of all diabetics have type 2 diabetes, which is characterized by elevated blood glucose triggered by a combination of poor insulin production, insulin resistance in skeletal muscle and lipid tissue, or both.Type 2 diabetes is also known as Non-Insulin-Dependent Diabetes because, unlike the rarer form of the disease, people with type 2 diabetes produce insulin and therefore seldom need therapeutic insulin at the initial onset of disease.Type 2 diabetes (hereafter, \"diabetes\"), like heart disease, hypertension and asthma, is referred to as a complex disease because its putative determinants lay in both environmental and biological domains.That is, diabetes is caused by a still-unknown combination of factors that include lifestyle, diet, physical activity, and an array of physiological triggers.", + "IntroductionType 2 diabetes (T2D) affects at least 6% of the world's population; the worldwide prevalence is expected to double by 2025 [1].T2D is a complex disorder that is characterized by hyperglycemia, which results from impaired pancreatic b cell function, decreased insulin action at target tissues, and increased glucose output by the liver [2].Both genetic and environmental factors contribute to the pathogenesis of T2D.The disease is considered to be a polygenic disorder in which each genetic variant confers a partial and additive effect.Only 5%-10% of T2D cases are due to single gene defects; these include maturity-onset diabetes of the young (MODY), insulin resistance syndromes, mitochondrial diabetes, and neonatal diabetes [3][4][5].Inherited variations have been identified from studies of monogenic diabetes, and have provided insights into b cell physiology, insulin release, and the action of insulin on target cells [6].", + "The worldwide explosion of the rates of diabetes and other metabolic diseases in the last few decades cannot be fully explained only by changes in the prevalence of classical lifestyle-related risk factors, such as physical inactivity and poor diet.For this reason, it has been recently proposed that other \"nontraditional\" risk factors could contribute to the diabetes epidemics.In particular, an increasing number of reports indicate that chronic exposure to and accumulation of a low concentration of environmental pollutants (especially the so-called persistent organic pollutants (POPs)) within the body might be associated with diabetogenesis.In this review, the epidemiological evidence suggesting a relationship between dioxin and other POPs exposure and diabetes incidence will be summarized, and some recent developments on the possible underlying mechanisms, with particular reference to dioxin, will be presented and discussed.", + "| DISCUSSIONThe rapidly increasing number of diabetic patients becomes a global burden especially for health sector in low-and middleincome countries including Bangladesh (Bleich, Koehlmoos, Rashid, Peters, & Anderson, 2011).Many reasons such as obesity, lack of physical activity, food habit, sedentary job nature and genetic makeup are factors accounting for developing diabetes (Lyssenko & Laakso, 2013;Vilchis-Gil, Galvn-Portillo, Klnder-Klnder, Cruz, & Flores-Huerta, 2015).Another cause is stress, which plays important role in the etiology of T2DM (S.J. Kelly & Ismail, 2015;Pouwer et al., 2010).Type 2 diabetic patients not only have to cope with this chronic disease, they are also at increased risk for several diseases like coronary heart disease, peripheral vascular disease, retinopathy, nephropathy, and neuropathy (Pouwer et al., 2010).To fight and control T2DM we have to seek out an alternative way of diagnosis and treatment based on patient's genetic information.This requires a deep insight into the etiology of this disease including associated single nucleotide polymorphism (SNP).", + "BackgroundType 2 Diabetes (T2D) is a complex metabolic disease that affects 25.8 million Americans in 2011, according to statistics reported by Centers for Disease Control and Prevention (CDC).T2D occurs when the body develops resistance to insulin due to the malfunction of insulin producing -cells.The developmental process of T2D involves a complex interplay between genetic and environmental factors.However, it is not clear how the underlying genetic defects give rise to T2D pathogenesis over time.Recent T2D genetic study results, particularly those from genome-wide association studies (GWAS), have yielded insights to the molecular mechanisms and underlying genetic risk factors of T2D [1].Among the many risk genes identified are: transcription factor 7-like 2 (TCF7L2) [2][3][4], peroxisome proliferator-activated receptor gamma (PPARG) [5][6][7], and potassium inwardlyrectifying channel, subfamily J, member 11 (KCNJ11) [5,6].", + "Aetiological factorsProspective studies suggest that the main pathophysiological defects leading to type 2 diabetes are insulin resistance and a relative insulin secretory defect.The main aetiological risk factors are age, obesity, family history, and physical inactivity.Dietary risk factors have recently emerged: risk is increased by high consumption of red and processed meat 13 and sugar-sweetened beverages, 14 and reduced by intake of fruit and vegetables, 15 some types of dairy products, 16 and some overall dietary patterns. 17Novel strategies to use quantifiable nutritional biomarkers are paving the way for more detailed understanding of the association between diet and diabetes.Although the heritability of type 2 diabetes is high (30e70%) and more than 60 genetic variants related with diabetes risk have now been identified, 18 even when combined into a genetic score, known genes contribute little to the prediction of diabetes.Phenotype-based risk models provide greater discrimination for diabetes, and the addition of genotypic information adds no more than 5e10% improvement in prediction.The current conclusion is that genetic variants provide insights into biological pathways and pathogenesis of diabetes, but not its prediction.It is likely that interactions between the environment/lifestyle and genetic factors provide the explanation for the risk of type 2 diabetes, but demonstrating such interaction is challenging.Encouraging research findings have recently shown higher absolute risk of diabetes associated with obesity at any level of genetic risk. 19evention and screening", + "IntroductionType 2 diabetes (T2D) is caused by the inability to regulate glucose levels as a result of insufficient insulin production or the incapability of the body to use bioavailable insulin (Asif 2014;Wong and Tabet 2015).Because of its complications such as retinopathy, nephropathy and heart disease, T2D is a critical disorder threatening adult health and life in humans.The number of T2D patients has steadily increased in recent decades and will continue to increase in the future, and is projected to reach 592 million patients worldwide by 2035 (Guariguata et al. 2014).In particular, Asian countries account for more than 60% of the world's T2D patients, and the T2D population is growing rapidly (Ramachandran et al. 2012).", + "IntroductionDiabetes mellitus, also known as simply diabetes, is the most prevalent disease in Westernized, developed countries, and the prevalence of this disease increases with age, accounting for 8.4% of all deaths worldwide [1].Diabetes is a well-recognized multifactorial endocrine metabolic disorder characterized by hyperglycemia (high blood sugar levels over a prolonged period) triggered by insulin secretion deficiencies, insulin action or both [2].The chronic hyperglycemia of diabetes is associated with dysfunction, long-term damage and failure of different organs, particularly the kidneys, heart, blood vessels, nerves and eyes.The development of diabetes involves various pathogenic processes including autoimmune destruction of the pancreatic -cells with subsequent insulin insufficiency which causes insulin resistance [3].The reason for the carbohydrate, fat and protein metabolism disorders in diabetes is insulin deficient activity on target tissues.Insulin deficient action results from insufficient insulin secretion and/or diminished tissue response [4].The great majority of diabetes cases fall into two broad categories of etiopathogenetics.Type 1 diabetes (T1D), falls in one category, is caused due to an absolute deficiency in insulin secretion from pancreatic beta cells.Genetic marker tests and serological evidences of an autoimmune pathological process in pancreatic islets can often be utilized for identification of individuals with increased risk of developing T1D [5].The more prevalent form of diabetes is type 2 diabetes mellitus (T2DM), which falls in the second category and is caused by a combination of insulin resistance and an inadequate compensatory insulin secretory response [6].Consequently, a degree of hyperglycemia occurs that might cause pathological and functional changes in different target tissues but without clinical symptoms and the condition may persist for a long time before T2DM is detected.There are other specific types of diabetes, such as exocrine pancreatic diseases, endocrinopathies, diabetes induced by drugs or chemicals, infection, uncommon forms of immune-mediated diabetes, other genetic syndromes, such as Down syndrome chromosomal abnormalities, Klinefelter syndrome, and sometimes diabetes-related Turner syndrome.Depending on the severity of the underlying disease, the degree of hyperglycemia can change over time [7]." + ] + ], + "task_id": [ + "9309F248E5933718BFB625E4EF2D3E42", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F", + "2272C482CC247E746D15C9F55EDD8BCE", + "2AE18C9AAFB4E3A103F03C86BBEB2DD1" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_3.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_3.json new file mode 100644 index 0000000..35f4f6f --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_3.json @@ -0,0 +1,104 @@ +{ + "question": [ + "Does cycling reduce risk of diabetes?", + "How can GeneNetwork assist in identifying genetic factors involved in diabetes?", + "What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?", + "What role does insulin play in the regulation of blood glucose levels?", + " How does aging affect the risk of developing type 2 diabetes?" + ], + "answer": [ + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network", + "GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.", + "Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present, cells increase insulin output to maintain normal glucose tolerance. However, if cells are incapable of this task, plasma concentrations of glucose increase.", + "Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes." + ], + "contexts": [ + [ + "Physical ActivityIn the Da Qing Impaired Glucose Tolerance and Diabetes Study, incident diabetes decreased by 46% in the exercise group [103].In the nonrandomized Malm Feasibility Study in 260 middle-aged men with impaired glucose tolerance, the incidence of diabetes was 50% lower in the intervention group after 5 years [104].In the Finnish Diabetes Prevention Study, subjects with a change in moderate-to-vigorous leisure-time physical activity (LTPA) in the highest tertile were 49% to 65% less likely to develop diabetes than those in the lowest tertile [105].In the Coronary Artery Risk Development in Young Adults study (CARDIA) with over 15 years of follow-up, there was a significant 17% reduction of risk of incident hypertension for every 300-exercise unit increment in average physical activity [106].In the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity (primarily cycling and walking) had a 34% lower odds of developing hypertension over 6 years compared to the least active [107].Thus, physical activity reduces the risk of developing diabetes and hypertension.The mechanism involves changes in body weight and glucose tolerance, as well as other factors [107].", + "Conclusion:In this cohort of men with diabetes, low fitness level was associated with increased risk of CVD mortality within normal weight, overweight, and class 1 obese weight categories.", + "In aggregate, these findings from the FHS make several important points.First, the incidence rate of diabetes is increasing.Second, because the relative risk of diabetes as a CVD risk factor has remained constant over time, the relative importance of diabetes with respect to CVD has increased.Finally, individuals with diabetes remain inadequately managed with regard to CVD risk factor levels.These findings highlight the importance of early identification of diabetes and a means to identify diabetes early in the life course to promote the early aggressive management of CVD risk factors.Another major remaining question is why the relative risk for diabetes as a CVD risk factor has failed to decrease over time.As described earlier, the rates of CVD among participants in the FHS have decreased; but this reduction has been outpaced by those without diabetes (Fox et al. 2004a).In terms of primary prevention, we can aim to reduce the burden of uncontrolled CVD risk factors, including incompletely treated hypertension, dyslipidemia, and participants with diabetes who continue to smoke (Preis et al. 2009a).Observational studies such as the FHS can help to explore rates of treatment and control for known modifiable risk factors.", + "Physical activityNumerous epidemiologic studies show that increased physical activity reduces risk of diabetes, whereas sedentary behaviors increase risk.In the NHS (26), each 2-h/day increment of time spent watching television (TV) was associated with a 14% increase in diabetes risk.Each 2-h/day increment of standing or walking around at home was associated with a 12% reduction in risk.Each 1-h/day increment of brisk walking was associated with a 34% reduction in risk (Fig. 3).These results indicate a continuum in the relationship between physical activity levels and diabetes risk.Among sedentary behaviors (TV watching, sitting at work, and other sitting), prolonged TV watching was associated with the highest risk.PREVENTABILITY OF TYPESeveral randomized clinical trials have demonstrated that diabetes is preventable.One of the first diabetes prevention trials was conducted in Daqing, China (58).After 6 years of active intervention, risk was reduced by 31, 46, and 42% in the diet-only, exercise-only, and diet-plus-exercise groups, respectively, compared with the control group.In a subsequent 14-year follow-up study, the intervention groups were combined and compared with control subjects to assess how long the benefits of lifestyle change can extend beyond the period of active intervention (59).Compared with control subjects, individuals in the combined lifestyle intervention group had a 51% lower risk of diabetes during the active intervention period, and a 43% lower risk over a 20-year follow-up.DietExcessive caloric intake is a major driving force behind escalating obesity and type 2 diabetes epidemics worldwide, but diet quality also has independent effects.In the Nurses' Health Study (NHS), we found that the quality of fats and carbohydrates play an important role in the development of diabetes, independent of BMI and other risk factors (11).In particular, higher dietary glycemic load (GL) and trans fat are associated with increased diabetes risk, whereas greater consumption of cereal fiber and polyunsaturated fat is associated with decreased risk (Fig. 2).In a meta-analysis, we found that a 2 serving/day increment in whole-grain intake was associated with a 21% lower risk of diabetes (12).", + "Evidence from randomized controlled trailsThe effi cacy of lifestyle changes in obesity and T2DM prevention has been established in numerous randomized controlled trails (RCTs).Several of them may, however, be considered of major importance due to their large sample sizes (i.e., 458-3234 individuals) and long-term duration (i.e., 3-6 years).The Chinese Da Qing diabetes prevention study was the fi rst to investigate the eff ect of 6-year lifestyle change on body weight and diabetes incidence in individuals with impaired glucose tolerance (IGT) ( Pan et al., 1997 ).Pan and co-workers (1997) reported 42 % reduction in diabetes incidence, although no signifi cant diff erence in body weight was present.Similar results were found in the Finnish Diabetes Prevention Study (DPS) and the US Diabetes Prevention Program (DPP).DPS and DPP independently reported reduction in diabetes incidence of 58 % accompanied by significant reduction in body weight (5-7 %) as a result of the lifestyle modifi cation ( Knowler et al., 2002 ;Tuomilehto et al., 2001 ).These fi ndings were also confi rmed in Japanese and Indian populations, reporting 67.4 % and 28.5 % reduction in diabetes incidence, respectively ( Kosaka et 2011) reported signifi cant reduction in body weight and diabetes incidence at 1, as well as, at 3 years during a lifestyle modifi cation program carried out in a primary healthcare setting among subjects with IGT.All large-scale interventions have been successful in preventing T2DM during the active intervention period.Remarkably when the eff ectiveness of the lifestyle modifi cation programs was assessed on the long-term after discontinuation of the intervention, diabetes risk still remained substantially reduced.In the Finnish DPS, for instance, at extended follow-up 3 years after the 4-year intervention period a substantial reduction in body weight and T2DM incidence was still present ( Lindstrom et al., This document was downloaded for personal use only.Unauthorized distribution is strictly prohibited.al., 2002 ;Kosaka et al., 2005 ;Lindstrom et al., 2003 ;Tuomilehto et al., 2001 ).In some studies although no or just minor weight loss was achieved, diabetes incidence was also reduced( Pan etal., 1997 ; Ramachandran et al., 2006 ).In addition, on the long term weight was partially or totally regained in all of the studies ( Knowler et al., 2009 ; Li et al., 2008 ; Lindstrom et al., 2006 ; Lindstrom et al., 2003 ).Despite this regain T2DM risk remained low or decreased further, thus the eff ect of lifestyle is unlikely to be solely due to body weight reduction.In support of this notion Pan et al. (1997) reported comparable decrease in T2DM incidence in the intervention group of Da Qing among overweight and lean individuals.", + "Epidemiological studies examining the associations between lifestyle behaviors and diabetes risk have reached similar conclusions as the clinical trials described above.For example, the 14-year follow-up University of Pennsylvania Alumni Health Study [52] (n = 5,990 men aged 39-68 years) showed PA (leisure time physical activity [LTPA] expressed in kcal expended per week through walking, stair climbing, and sports) was inversely associated with the incidence of T2D.Incidence rates declined as energy expenditure rose from 500 through 3,500 kcal/week.The age-adjusted relative risk ratio (RR) of T2D was reduced by about 6% for each 500 kcal increment increase in PA energy expenditure.Physical Activity and T2D RiskTraining studies show aerobic exercise enhances insulin action [43] and glucose metabolism [44] in healthy individuals and those at high risk of T2D.Exercise often normalizes plasma glucose levels by improving insulin sensitivity and glucose transportation [45].Exercise can also improve endothelial function, reduce inflammation, and beneficially affect the autonomic nervous system [46].Even in the absence of weight loss, exercise can enhance insulin sensitivity [9] and glycemic control [47].These findings are particularly relevant as they show regular exercise can be used effectively as a treatment for preventing T2D from developing in individuals with IFG/IGT and for improving insulin action in people with manifest diabetes.", + "Previous studies of physical activity and risk of diabetes have been predom inantly cross-sectional investigations in high-risk populations.Indirect evidence from descriptive comparisons of NIDDM prevalence in rural vs urban populations in Western Samoa1112 and the South Pa cific12 have supported the hypothesis that higher levels of physical activity may be protective against NIDDM.However, other aspects of urban living, including differences in diet, could have accounted for the variation in diabetes risk.Crosssectional studies among Polynesians,13 Melanesian and Indian Fijians,1415 Mi- cronesians,15 Swedes,16 and Mauritians17 have also proposed an association of physical activity with reduced preva lence of NIDDM.The absence of an as sociation between physical activity and glucose intolerance, however, also has been observed.3334In one retrospective study, a reduced risk of diabetes was observed among women who engaged in regular sports in college compared with those who did not, but obesity was not controlled in the analysis.18To our knowledge, only two previous prospec tive studies of physical activity and in cidence of NIDDM have been reported, both supporting a protective effect of exercise.1920Our results in male physi cians are similar to our earlier findings in female nurses,20 suggesting that gen der does not appreciably modify the re lation between physical activity and NIDDM incidence.Objective.\\p=m-\\Toexamine prospectively the association between regular exercise and the subsequent development of non\\p=m-\\insulin-dependent diabetes mellitus (NIDDM).Design.\\p=m-\\Prospective cohort study including 5 years of follow-up.Participants.\\p=m-\\21 271US male physicians participating in the Physicians' Health Study, aged 40 to 84 years and free of diagnosed diabetes mellitus, myo- cardial infarction, cerebrovascular disease, and cancer at baseline.Morbidity follow-up was 99.7% complete.Main Outcome Measure.\\p=m-\\IncidenceofNIDDM.Results.\\p=m-\\Atbaseline, information was obtained about frequency of vigorous exercise and other risk indicators.During 105141 person-years of follow-up, 285 new cases of NIDDM were reported.The age-adjusted incidence of NIDDM ranged from 369 cases per 100 000 person-years in men who engaged in vigorous exer- cise less than once weekly to 214 cases per 100000 person-years in those exer- cising at least five times per week (P, trend, <.001).Men who exercised at least once per week had an age-adjusted relative risk (RR) of NIDDM of 0.64 (95% Cl, 0.51 to 0.82; P=.0003) compared with those who exercised less frequently.The age-adjusted RR of NIDDM decreased with increasing frequency of exercise: 0.77 for once weekly, 0.62 for two to four times per week, and 0.58 for five or more times per week (P, trend, .0002).A significant reduction in risk of NIDDM persisted after adjustment for both age and body-mass index: RR, 0.71 (95% Cl, 0.56 to 0.91; P=.006) for at least once per week compared with less than once weekly, and P, trend, .009,for increasing frequency of exercise.Further control for smoking, hypertension, and other coronary risk factors did not materially alter these associa- tions.The inverse relation of exercise to risk of NIDDM was particularly pronounced among overweight men.Conclusions.\\p=m-\\Exerciseappears to reduce the development of NIDDM even after adjusting for body-mass index.Increased physical activity may be a promising approach to the primary prevention of NIDDM.", + "Type 2 diabetes can be prevented or delayed by lifestyle modification, including increased physical activity, beneficial dietary changes, and weight reduction (22,44).However, only Model adjusted for age, gender, group, baseline value of moderate-to-vigorous physical activity, and baseline values and changes in body weight and in intakes of energy and energy-adjusted saturated fat and fiber. *The median (range) of each tertile of change in moderate-to-vigorous physical activity is shown.Adjusted interaction between moderate-to-vigorous physical activity (3 groups) and the polymorphism (2 groups) on the risk of developing type 2 diabetes.a few studies have investigated the effects of such lifestyle interventions on insulin sensitivity and insulin secretion in persons with IGT (21,46).On the basis of the 4-yr follow-up study of the DPS with repeated frequently sampled intravenous glucose tolerance test (FSIGT), insulin sensitivity improved along with lifestyle changes, while insulin secretion remained virtually unchanged (46).Most other data also indicate that physical activity, diet, and weight loss primarily increase insulin sensitivity.Insulin resistance and the associated glycemic stress may exhaust -cells and impair their function.Regular physical activity may diminish glycemic stress by improving insulin sensitivity of target tissues (18).While the mechanisms of improved -cell function in response to lifestyle interventions are still largely unknown, several studies suggest that physical activity (5,11), diet (19,26), weight loss (45), or their combination (21) may directly improve the first-phase insulin secretion that is an indicator of the -cell function.GENETIC FACTORS AND LIFESTYLE interact in the development of type 2 diabetes.Physical activity, favorable dietary changes, and weight reduction were essential components of a success-ful lifestyle intervention in two large randomized controlled trials on the prevention of type 2 diabetes in high-risk individuals with impaired glucose tolerance (IGT), including the Finnish Diabetes Prevention Study (DPS) (44) and the Diabetes Prevention Program (DPP) (22).In the DPS, increased physical activity was associated with a decreased risk of type 2 diabetes independently of changes in diet and body weight.The individuals who increased their physical activity most (i.e., were in the upper third of the change) were 66% less likely to develop type 2 diabetes than those in the lower third (24).", + "Aerobic activity, alone or in combination with diet, can reduce systolic blood pressure, reduce total cholesterol, raise HDL cholesterol, and improve endothelial function in overweight patients with young-onset type 2 diabetes. 47owever, any potential benefits to the cardiovascular disease risk profile are lost within 3-6 months after cessation of exercise training, and do not confer protection against later cardiovascular events. 47,121Additionally, reviews 49,121,122 of the limited number of studies done to date have not identified substantial or lasting benefits of doing aerobic exercise on glucose homoeostasis for patients who are obese with young-onset type 2 diabetes, unless accompanied by dietary intervention.", + "Weight change is a complex outcome, as both the degree and pattern of weight change impact health.For example, in the Diabetes Prevention Program (DPP; described in more detail later), both short-and intermediate-term weight loss were associated with reduced diabetes risk and intermediate cardiometabolic risk factor levels, whereas weight cycling (defined as number of 5 lb [2.25 kg] weight cycles) raised diabetes risk, fasting glucose levels, insulin resistance, and systolic blood pressure.Initial (baseline to 1 month) and late (last 6 months of the 2-year intervention period) weight loss had no discernable impact of diabetes risk (26).Similar results have been reported in people with pre-existing diabetes who underwent lifestyle intervention as part of the Look AHEAD (Action for Health in Diabetes) trial (27).These studies point to alternative phenotypes that may be informative for genetics studies of weight loss/ maintenance/regain.", + "Physical activity. Increased physical activity is an essential component of all effective lifestyle-based trials for the prevention of T2DM.Prospective evidence has shown that both aerobic exercise and resistance training independently have beneficial effects on preventing T2DM 64 .One study has shown that spending more time on moderateintensity and vigorous-intensity physical activity is beneficial for preventing insulin resistance, independent of time spent sedentary 65 .By contrast, another study found that time spent sedentary was associated with an increased risk of T2DM, regardless of physical activity 66 .", + "Multiple interventions in adults with T2D have been evaluated for risk reduction and prevention, both in the short and the long term.A recent systematic review (69) reported that after active interventions lasting from 6 months to .6 years, relative risk reduction achieved from lifestyle interventions (39%) was similar to that attained from use of drugs (36%); however, only lifestyle interventions had a sustained reduction in risk once the intervention period had ended.Analysis of the postintervention follow-up period (;7 years) revealed a risk reduction of 28% with lifestyle modification compared with a nonsignificant risk reduction of 5% from drug interventions.", + "Engagement in regular physical activity and increased physical fitness are recommended for the prevention and treatment of diabetes and other pathological conditions 5,18,19 .We recently demonstrated that four months of moderate physical training, besides being beneficial to glycemic control, was also effective in improving the redox homeostasis in diabetic patients, lowering the oxidant species production and/or increasing the endogenous antioxidant defenses 20 .In the present study, we aimed to analyse the effect of regular engagement in moderate physical training on telomere length, spontaneous and H 2 O 2 -induced DNA damage, and apoptosis in purified blood leukocytes derived from untrained and trained T2D subjects, compared to age-matched untrained and trained controls.In addition, we examined whether exercise training affected the transcriptional level of a set of genes involved in DNA repairs systems, cell cycle control, as well as antioxidants and defence systems, by comparing untrained and trained T2D patients." + ], + [ + "IntroductionComplex diseases, such as diabetes and obesity, result from the interaction of genetic and environmental factors [1][2][3].Approximately 170 gene loci have been robustly implicated in diabetes through genome-wide association studies [4].Studies with knockout mouse models have identified hundreds of genes that can act autonomously to regulate insulin levels (MP:0001560) [5].However, it is still elusive to understand the underlying mechanisms of how these loci or genes contribute to diseases.Network modeling methods have been developed based on the premise that complex diseases are often caused by perturbation to a sub-network of genes [1,[6][7][8][9][10][11][12][13][14].We have applied these methods to identify causal genes for diabetes-related traits in multiple experimental mouse crosses [13][14] and human populations [1].These analyses suggest that potentially many thousands of genes, under the right circumstances, can affect metabolic states.", + "Genetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner.", + "GENE DISCOVERY IN T2DWhy?", + "Candidate g ene a pproachThe molecular screening of candidate genes to search for genetic variants (either rare when the allele frequency is < 0.01, or common in the population tested) potentially associated with diabetes status (i.e. more frequent in individuals with T2DM) has so far been the most frequently used approach to tackle the genetic determinants of T2DM [61] .There are many reasons why specifi c genes may be candidates: A gene may have a known or presumed biologic function in glucose homeostasis or energy balance in humans.", + "Interactions in diabetes <p>An integrative analysis combining genetic interactions and protein interactions can be used to identify candidate genes/proteins for type 1 diabetes and other complex diseases.</p>", + "Received: 7 May 2009 Accepted: 25 February 2010Published: 25 February 2010References1. Sieberts SK, Schadt EE: Moving toward a system genetics view of disease. Mamm Genome 2007, 18:389-401. 2. Keller MP, Choi Y, Wang P, Davis DB, Rabaglia ME, Oler AT, Stapleton DS,Argmann C, Schueler KL, Edwards S, Steinberg HA, Chaibub Neto E,Kleinhanz R, Turner S, Hellerstein MK, Schadt EE, Yandell BS, Kendziorski C,Attie AD: A gene expression network model of type 2 diabetes links cellcycle regulation in islets with diabetes susceptibility. Genome Res 2008,18:706-716. 3.", + "Genome-wide interaction studies have potential to identify gene variants that influence diabetes risk that might not be detected using hypothesis-driven approaches.However, the statistical power limitations of such studies when applying conventional tests of interaction, combined with the challenges of identifying large cohort collections with appropriately characterized environmental, genetic, and phenotypic data, pose challenges that conventional genetic association studies do not face.Several methods have been developed to mitigate these challenges; among the most promising is the joint meta-analysis approach, which is derived from the model with two degrees of freedom popularized by Kraft et al. (45) and developed further by Manning et al. (46).Manning et al. (47) went on to apply the joint meta-analysis approach in a genome-wide study of 52 cohorts in which they tested for SNP main effects and interactions (with BMI) on fasting glucose and insulin levels.The analysis yielded novel experiment-wide association signals for main effects, but none was discovered for interactions.", + "Genome-wide association studies (GWAS) have discovered germline genetic variation associated with type 2 diabetes risk (1)(2)(3)(4).One of the largest GWAS, involving DNA taken from individuals of European descent and conducted by the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium, identified 65 loci associated with type 2 diabetes risk (1).However, for most of these loci, the precise identity of the affected gene and the molecular mechanisms underpinning the altered risk are not known.", + "Genome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.", + "Figure5.Consideration of the human gene network boosts recovery of validated type 2 diabetes genes from GWAS analysis of 2000 patients and 3000 controls. (A,B) Plotted using the same conventions as in Figure4, analyzing WTCCC GWAS data (Wellcome Trust Case Control Consortium 2007) for type 2 diabetes alone and in combination with HumanNet and measuring performance as AUC (<5% FPR) for recovering the top 20 genes from a type 2 diabetes meta-analysis of 4549 cases and 5579 controls(Zeggini et al. 2008).As for Crohn's disease, consideration of the network boosts performance across a wide range of parameter values.Notably, consideration of the network strongly implicates the genes CTNNB1 and BACH2 in type 2 diabetes; CTNNB1 is well studied in connection with type 2 diabetes and BACH2 has been previously implicated in type 1 diabetes and celiac disease (e.g.,Cooper et al. 2008;Madu et al. 2009), but not type 2 diabetes.", + "GenomicsDuring the past few decades, candidate gene approach with case-control study design has been most successful in understanding the genetic etiology of any complex disease.The method begins with selection of putative candidate gene based on its functional role in disease related metabolic pathway, followed by prioritizing single nucleotide polymorphisms (SNPs) that have functional consequences either by affecting the gene regulation or its product.Finally, the prioritized SNPs/variants are genotyped in a random sample of cases and controls and tested for their association with the trait.So far, a total of 1874 unique markers that belong to 421 genes were identified as associated with type 2 diabetes through this approach (Lim et al. 2010).However, an overwhelming inconsistency is observed in the patterns of their association with the disease, with exception to the polymorphisms that belong to TCF7L2, CAPN10, PPARG, KCNJ11, ABCC8, HNF1A, HNF4A, GCK, PC-1/ENPPI, IRS, PTPN1, and LMNA genes which showed much greater degree of consistency (Kommoju and Reddy 2011;Ali 2013).Not being satisfied with this approach, researchers shifted the focus to genome wide association studies (GWAS), which is an agnostic method of testing for association of all the SNPs identified in human genome project with a particular disease through chip based microarray technologies such as Illumina and Affymetrics.A large number of cases and controls are screened through this method and the SNPs with strong signal/high significance (pB10 -08 ) are considered to be disease susceptible/causing.Only these SNPs are further evaluated for their functional consequences.Through this approach, numerous polymorphisms have been identified as associated with type 2 diabetes and the SNPs of TCF7L2, HHEX, CDKN2A/2B, IGF2BP2, SLC30A8, CDKAL1, HMGA2, KCNQ11, and NOTCHADAM30 genes being the most replicated ones (www.genome.gov/gwastudies).The search results for type 2 diabetes associated genetic variants yielded 388 significant SNPs from 58 GWAS studies.However, many of these type 2 diabetes associated variants need to be functionally validated before attempting to understand their prospective clinical benefits.The TCF7L2 is the only gene which is hitherto functionally characterized as key transcription factor coding gene and involved in regulating the glucose homeostasis (Savic et al. 2011;Boj et al. 2012).As a key component of WNT signaling pathway, it is involved in pancreatic b-cell proliferation and in turn insulin secretion and action (Gupta et al. 2008).It was initially identified as associated with the disease through a genetic linkage study on the Icelandic population (Grant et al. 2006) and subsequently replicated in Danish (Grant et al. 2006), European (Scott et al. 2006) and US cohorts (Zhang et al. 2006) and currently known to be associated across the ethnic groups worldwide (Kommoju and Reddy 2011).Additionally, a 4kb haplotype block at 9p21.3 chromosomal region was found specific to and associated with type 2 diabetes (Silander et al. 2009).Harboring CDKN2A/CDKN2B genes with functional implications in cell proliferation pathway, this chromosomal region was observed to be associated with multiple complex diseases and needs detailed exploration for its potential as a therapeutic target in general and particularly with type 2 diabetes.However, the variants identified by GWAS were found to explain only 10% of variation in type 2 diabetes and most of those (more than 90%) are located in the non-coding region (Grarup et al. 2014;Scott et al. 2016).The search for rare variants with larger penetrance and functional significance is on through next generation and exome sequencing strategies (Jenkinson et al. 2016).", + "One attractive methodology to circumvent the puzzle of choosing either a hypothesis-driven or an exploratory research may be the strategy of gene prioritization offered by the new bioinformatics tools based on the biological plausibility of a gene-disease association and on knowledge of the protein function. 6e propose an approach for expanding the selection of genes or loci of interest and prioritizing associations over GWAs related with genetic susceptibility to type 2 diabetes.The proposal profits from the recent initiatives of data sharing of the genome scan results that make the information publicly available as soon as they are generated and checked for quality.Both the DGI and the WTCCC are committed to embracing these principles as they made available all the phenotype-genotype data for type 2 diabetes.", + "Background: Many genetic studies, including single gene studies and Genome-wide association studies (GWAS), aim to identify risk alleles for genetic diseases such as Type II Diabetes (T2D).However, in T2D studies, there is a significant amount of the hereditary risk that cannot be simply explained by individual risk genes.There is a need for developing systems biology approaches to integrate comprehensive genetic information and provide new insight on T2D biology.Methods: We performed comprehensive integrative analysis of Single Nucleotide Polymorphisms (SNP's) individually curated from T2D GWAS results and mapped them to T2D candidate risk genes.Using protein-protein interaction data, we constructed a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners.We then studied the relationship between these T2D genes and curated gene sets.Results: We determined that T2D candidate risk genes are concentrated in certain parts of the genome, specifically in chromosome 20.Using the T2D genetic network, we identified highly-interconnected network \"hub\" genes.By incorporating T2D GWAS results, T2D pathways, and T2D genes' functional category information, we further ranked T2D risk genes, T2D-related pathways, and T2D-related functional categories.We found that highlyinterconnected T2D disease network \"hub\" genes most highly associated to T2D genetic risks to be PI3KR1, ESR1, and ENPP1.The well-characterized TCF7L2, contractor to our expectation, was not among the highest-ranked T2D gene list.Many interacted pathways play a role in T2D genetic risks, which includes insulin signalling pathway, type II diabetes pathway, maturity onset diabetes of the young, adipocytokine signalling pathway, and pathways in cancer.We also observed significant crosstalk among T2D gene subnetworks which include insulin secretion, regulation of insulin secretion, response to peptide hormone stimulus, response to insulin stimulus, peptide secretion, glucose homeostasis, and hormone transport.Overview maps involving T2D genes, gene sets, pathways, and their interactions are all reported.Conclusions: Large-scale systems biology meta-analyses of GWAS results can improve interpretations of genetic variations and genetic risk factors.T2D genetic risks can be attributable to the summative genetic effects of many genes involved in a broad range of signalling pathways and functional networks.The framework developed for T2D studies may serve as a guide for studying other complex diseases.ConclusionsLarge-scale systems biology meta-analyses of GWAS results can improve interpretations of genetic variations and genetic risk factors.In this work, we determined that T2D candidate risk genes are located in higher concentration in certain parts of the genome, specifically in chromosome 20.Using the T2D genetic network, we identified highly interconnected network \"hub\" genes.By incorporat-T2D GWAS results, T2D pathways, and T2D genes' functional category information, we further ranked T2D risk genes, T2D-related pathways, and T2D-related functional categories.Overview maps involving T2D genes, gene sets, pathways, and their interactions are all reported.Moreover, we demonstrate a computational framework built upon disease-specific data integration, Figure 2 T2D risk gene pathway interaction network.Here, an edge will be created between two pathways, if and only if the pathways involved three of more risk genes.Figure 3 T2D risk gene functional category crosstalk network.For this figure an edge will be created between two functional categories for all significant Gene Ontology catagories.To confirm the presence of molecular systems structures that may better explain missing heritability problems for T2D, we adopted a Systems Biology approach to studying T2D genetic risk gene networks as a whole rather than the risk genes individually.Prior to this study, several reports [10,11] examined genes implicated T2D differential expressions in affected tissues.In this study, we used T2Dassociated SNP information curated from the Type 2 Diabetes Genetic Association Database (T2DGADB), which integrated comprehensively reported SNPs, their odds ratios, population description, and all related metadata from various T2D GWAS performed worldwide [12].We further annotated individual SNPs collected from T2DGADB with information from the DbSNP database [13], including information such as nearby genes, Chromosomal location, gene functional class, and base changes.To create a model for T2D genetic risk gene molecular systems structure, we built a gene interaction network seeded by T2D risk genes collected from T2DGADB and expanded with high-confidence protein interaction data collected from the Human Annotated and Predicted Protein Interaction database (HAPPI) [14].We also ranked risk genes in the network according to these high confidence interactions.Results: We determined that T2D candidate risk genes are concentrated in certain parts of the genome, specifically in chromosome 20.Using the T2D genetic network, we identified highly-interconnected network \"hub\" genes.By incorporating T2D GWAS results, T2D pathways, and T2D genes' functional category information, we further ranked T2D risk genes, T2D-related pathways, and T2D-related functional categories.We found that highlyinterconnected T2D disease network \"hub\" genes most highly associated to T2D genetic risks to be PI3KR1, ESR1, and ENPP1.The well-characterized TCF7L2, contractor to our expectation, was not among the highest-ranked T2D gene list.Many interacted pathways play a role in T2D genetic risks, which includes insulin signalling pathway, type II diabetes pathway, maturity onset diabetes of the young, adipocytokine signalling pathway, and pathways in cancer.We also observed significant crosstalk among T2D gene subnetworks which include insulin secretion, regulation of insulin secretion, response to peptide hormone stimulus, response to insulin stimulus, peptide secretion, glucose homeostasis, and hormone transport.Overview maps involving T2D genes, gene sets, pathways, and their interactions are all reported.Conclusions: Large-scale systems biology meta-analyses of GWAS results can improve interpretations of genetic variations and genetic risk factors.T2D genetic risks can be attributable to the summative genetic effects of many genes involved in a broad range of signalling pathways and functional networks.The framework developed for T2D studies may serve as a guide for studying other complex diseases.", + "Genetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1", + "To gain insights into how the linking nodes of our final network contribute to T2D biology, we used the DisGeNET database [37], which collates gene-disease information from public data as well as from literature via natural language processing tools.We focused on the 274 linking nodes included in our model to avoid circularity arising from using the seeds, and identified 92 (~33%) with known links to T2D (Additional file 1: Table S2).Examples include as follows: (a) NEUROD1 which encodes a transcription factor that is involved in the development of the endocrine cell lineage and has been implicated in monogenic diabetes [38], (b) PRKCB involved in insulin resistance [39] and (c) GNAS, implicated in beta-cell proliferation [40].For this last gene, mouse knockouts have been shown to produce phenotypes concordant with diabetes [41].These examples demonstrate the potential of these analyses to draw in \"linking\" nodes as related to T2D even when they are not located within genome-wide association signals.Background: Genome-wide association studies (GWAS) have identified several hundred susceptibility loci for type 2 diabetes (T2D).One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes.However, the causal variants identified by GWAS mostly fall into a non-coding sequence, complicating the task of defining the effector transcripts through which they operate.Methods: Here, we describe implementation of an analytical pipeline to address this question.First, we integrate multiple sources of genetic, genomic and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals.Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner tree approach) which uses external, experimentally confirmed protein-protein interaction (PPI) data to generate high-confidence sub-networks.Third, we use GWAS data to test the T2D association enrichment of the \"non-seed\" proteins introduced into the network, as a measure of the overall functional connectivity of the network. Results:We find (a) non-seed proteins in the T2D protein-interaction network so generated (comprising 705 nodes) are enriched for association to T2D (p = 0.0014) but not control traits, (b) stronger T2D-enrichment for islets than other tissues when we use RNA expression data to generate tissue-specific PPI networks and (c) enhanced enrichment (p = 3.9 10 5 ) when we combine the analysis of the islet-specific PPI network with a focus on the subset of T2D GWAS loci which act through defective insulin secretion.Conclusions: These analyses reveal a pattern of non-random functional connectivity between candidate causal genes at T2D GWAS loci and highlight the products of genes including YWHAG, SMAD4 or CDK2 as potential contributors to T2D-relevant islet dysfunction.The approach we describe can be applied to other complex genetic and genomic datasets, facilitating integration of diverse data types into disease-associated networks.Background: Genome-wide association studies (GWAS) have identified several hundred susceptibility loci for type 2 diabetes (T2D).One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes.However, the causal variants identified by GWAS mostly fall into a non-coding sequence, complicating the task of defining the effector transcripts through which they operate.Methods: Here, we describe implementation of an analytical pipeline to address this question.First, we integrate multiple sources of genetic, genomic and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals.Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner tree approach) which uses external, experimentally confirmed protein-protein interaction (PPI) data to generate high-confidence sub-networks.Third, we use GWAS data to test the T2D association enrichment of the \"non-seed\" proteins introduced into the network, as a measure of the overall functional connectivity of the network." + ], + [ + "Data generated by these experiments are iteratively subjected to novelinformatics approaches, network analysis, and modeling to find important regulatory nodes, discover the emergent property of the system,and predict the systems behavior under various conditions. GEO, Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/); BIND,Biomolecular Interaction Network Database (http://www.unleashedinformatics.com/index.php?pg=products&refer=bind). GENETICSTHE TIDE HAS TURNEDTO RIGOROUS PHENOTYPINGThe classical forward genetic screen has been thesingle most powerful tool to conclusively identifycritical components of the circadian oscillator, and itscontribution in advancing the field of chronobiology cannot be overstated.", + "This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.GeneNetwork: A Toolbox for Systems GeneticsMegan K. Mulligan1, Khyobeni Mozhui2, Pjotr Prins1,2, Robert W. Williams11.Departmentof Genetics, Genomics, and Informatics, University of Tennessee Health ScienceCenter, Memphis, USA2.PreventiveMedicine, University of Tennessee Health Science Center, Memphis, USAAuthor ManuscriptAbstractThe goal of systems genetics is to understand the impact of genetic variation across all levels ofbiological organization, from mRNAs, proteins, and metabolites, to higher-order physiological andbehavioral traits.", + "GeneNetwork is one ofeither generate or test ideas by reusing data that oftenan interlinked trio of sites built up by NIAAA (GeneWeaverhave been rescued from the classic literature. Below is a short list of both well-known and more esoteric and WebGestalt are the other two) to house extensiveresources, many of which have been supported by NIAAA, data for human, monkey, rat, mouse, and fruit fly.", + "In the second part of this work the computed T2DM gene set has been used to identify biological networks on different layers of cellular information such as signaling and metabolic pathways, a comprehensive gene regulatory network and protein-protein interactions.Background: Multiple functional genomics data for complex human diseases have been published and made available by researchers worldwide.The main goal of these studies is the detailed analysis of a particular aspect of the disease.Complementary, meta-analysis approaches try to extract supersets of disease genes and interaction networks by integrating and combining these individual studies using statistical approaches.Results: Here we report on a meta-analysis approach that integrates data of heterogeneous origin in the domain of type-2 diabetes mellitus (T2DM).Different data sources such as DNA microarrays and, complementing, qualitative data covering several human and mouse tissues are integrated and analyzed with a Bootstrap scoring approach in order to extract disease relevance of the genes.The purpose of the meta-analysis is two-fold: on the one hand it identifies a group of genes with overall disease relevance indicating common, tissue-independent processes related to the disease; on the other hand it identifies genes showing specific alterations with respect to a single study.Using a random sampling approach we computed a core set of 213 T2DM genes across multiple tissues in human and mouse, including well-known genes such as Pdk4, Adipoq, Scd, Pik3r1, Socs2 that monitor important hallmarks of T2DM, for example the strong relationship between obesity and insulin resistance, as well as a large fraction ( 128) of yet barely characterized novel candidate genes.Furthermore, we explored functional information and identified cellular networks associated with this core set of genes such as pathway information, protein-protein interactions and gene regulatory networks.Additionally, we set up a web interface in order to allow users to screen T2DM relevance for any -yet non-associated -gene. Conclusion:In our paper we have identified a core set of 213 T2DM candidate genes by a metaanalysis of existing data sources.We have explored the relation of these genes to disease relevant information and -using enrichment analysis -we have identified biological networks on different layers of cellular information such as signaling and metabolic pathways, gene regulatory networks and protein-protein interactions.The web interface is accessible via http://t2dmgeneminer.molgen.mpg.de.", + "We decided to pursue the first hypothesis and adapted a systems biology perspective.Rather than looking for significant aberrations in expression of individual insulin-signaling genes, we looked for significant aberrations in the collective expression of a set of insulin-signaling genes whose protein products form a connected protein-protein interaction network.This was accomplished using a simple methodology referred to as gene network enrichment analysis (GNEA).", + "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In thismanuscript we will outline some simple use cases, and show how a small number of plausiblecandidate genes can be identified for an immune phenotype. 1. DataOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. Thefirst is to use the global search bar located at the top of the page (Figure 1). This is a newfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of thedatasets.Recent improvements toGeneNetwork have reinvigorated it, including the addition of data from 10 species, multi-omicsanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource forpredictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most commonfunctions on GeneNetwork, as a gateway to deeper analyses, demonstrating how a smallnumber of plausible candidate genes can be found for a typical immune phenotype.", + "This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.", + "Readersmay refer [42] for a comprehensive review on various availablesoftware tools. GeneNetWeaver (GNW) [43] is a Java-based reverse engineering tool for generating synthetic benchmark expression datasetsfrom gold standard DREAM challenge network. E. coli and Yeasttranscriptional regulatory networks are integrated as test case forbenchmark. Comparative assessment of inference algorithmsagainst DREAM challenge data can also be performed with thehelp GNW. Cytoscape [44] is a powerful tool most suitable forlarge-scale network analysis.", + "Researchers, however, have thepossibility to fully explore the results by altering the thresholds on the open web resource. Although onlyprotein-coding genes were included in our analysis, the same approach can be applied to non-coding genes63to reveal their potential functions. Similarly, GeneBridge can also be utilized to identify novel gene-diseaseassociations based on known disease-associated genes from databases, such as the Human DiseaseOntology (DO) [207] or DisGeNET [208]. The GeneBridge toolkit could also be applied to large-scaleproteomics datasets after correcting for the background of all measured proteins.", + "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In thismanuscript we will outline some simple use cases, and show how a small number of plausiblecandidate genes can be identified for an immune phenotype. 1. DataOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. Thefirst is to use the global search bar located at the top of the page (Figure 1). This is a newfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of thedatasets.Recent improvements toGeneNetwork have reinvigorated it, including the addition of data from 10 species, multi-omicsanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource forpredictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most commonfunctions on GeneNetwork, as a gateway to deeper analyses, demonstrating how a smallnumber of plausible candidate genes can be found for a typical immune phenotype.", + "Genome Biol 8(2):R25Hubner N, Wallace CA, Zimdahl H, Petretto E, Schulz H et al (2005)Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 37(3):243253Ihaka R, Gentleman RC (1996) R: a language for data analysis andgraphics. J Comput Graph Stat 5:299314Keller MP, Choi Y, Wang P, Davis DB, Rabaglia ME et al (2008) Agene expression network model of type 2 diabetes links cellcycle regulation in islets with diabetes susceptibility.", + "We next constructed protein-protein interaction networks.To do this, we selected 76 genes known from monogenic forms of diabetes, obesity, and hypertension or GWAS hits (type 2 diabetes, obesity, and hypertension) for which the lead association lies within the protein-coding part of the gene (Table S3).", + "To test this hypothesis, we used the Web-basedGeneNetwork databases that have been recently introducedto the scientific community and proved to be a powerful toolfor hypothesis-driven investigations (Chesler et al. 2003,2004; Wang et al. 2003). Researchers can take advantageof genetic diversity in panels of recombinant inbred mousestrains to use these databases for studies of the regulation ofgene expression and genetic mechanisms of complex traits. Our in silico investigation provided evidence for potentialfunctional relationships among the 21 DAT-associated proteins detected by mass spectrometry in this study.", + "Construction and analysis of the T2D risk genes networkTo further sift the results and explore functional connections, we also mapped genes onto known gene sets.For this purpose, we used DAVID [22,23] to search for enriched KEGG [24] pathways.We also used GARNET [25] to identify enriched Gene Ontology categories and their relationships.", + "Thereby such networks have the potential to beof importance in the emergence of precision medicine (Curtis, 2015; Desautels et al. , 2014;Glade Bender et al. , 2015; Jorgensen, 2015; Kummar et al. , 2015; Marquet et al. , 2015;Rubin, 2014) wherein therapeutic strategies need to be aligned with specific properties oftumors. Author ManuscriptMethodsGeneNetwork and WebGestaltGeneNetwork is an open access, online data analysis resource for systems biology andsystems genetics.", + "GeneNetwork.org also offers a powerful statistical platform foronline network analyses and mapping, enabling numerous molecular questions to be probed in one centralized location(Chesler et al. , 2003, 2005; Li et al. , 2010; Mulligan et al. , 2012,2017, 2019). Most data are from groups of animals or humanswho have been fully genotyped or even sequenced. As a result, itcan be used to model causal networks that link DNA differencesto traits such as differences in expression, cell number, volumes,and behavior using real-time computation and graphing." + ], + [ + "Insulin ResistanceInsulin is a pleiotropic hormone that plays a pivotal role in the development of hypertension, diabetes, and the metabolic syndrome.The main metabolic actions of insulin are to stimulate glucose uptake in skeletal muscle and heart and to suppress the production of glucose and very low-density lipoprotein (VLDL) in the liver [66].Under fasting conditions, insulin secretion is suppressed, leading to increased glucose synthesis in the liver and kidneys (gluconeogenesis) and increased conversion of glycogen to glucose in the liver (glycogenolysis) [67].After a meal, insulin is released from pancreatic -cells and inhibits gluconeogenesis and glycogenolysis [67].Insulin stimulates the sympathetic nervous system (SNS) to increase cardiac output and the delivery and utilization of glucose in the peripheral tissues [68].Other metabolic effects of insulin include inhibition of glucose release from the liver, inhibition of the release of free fatty acids (FFAs) from adipose tissue, and stimulation of the process by which amino acids are incorporated into protein [67].", + "Insulin Resistance in Type 2 DiabetesInsulin resistance is defined as impaired insulin-mediated glucose clearance into target tissues.Physiology studies many years ago showed most of the insulin-mediated clearance of a glucose load goes into skeletal muscle, plus the insulin response to the meal shuts down hepatic glucose production.We now know that the defect with insulin resistance is at both sites.In the fasting state, the degree of hyperglycemia is directly determined by the rate of glucose overproduction by the liver.With eating, failure of adequate insulin-mediated nutrient clearance into skeletal muscle combined with an attenuated halting of hepatic glucose production cause the raised postprandial glycemia.Reference ( 84) is an excellent review of the known pathophysiology from an investigator who performed many of the key studies.", + "The present: the crucial role of cells to glucose homoeostasis by feedback regulationThe importance of insulin resistance and -cell dysfunction to the pathogenesis of type 2 diabetes was debated for a long time; many thought that insulin resistance was the main abnormality in type 2 diabetes, and that inability to secrete insulin was a late manifestation. 5This notion changed with the fi nding that, as with most endocrine systems in human beings, a feedback loop operates to ensure integration of glucose homoeo stasis and maintenance of glucose concentration in a narrow range. 7his feedback loop relies on crosstalk between cells and insulin-sensitive tissues (fi gure 1).Insulin released in response to -cell stimu lation mediates uptake of glucose, aminoacids, and fatty acids by insulin-sensitive tissues.In turn, these tissues feed back information to islet cells about their need for insulin.The mediator of this process has not been identifi ed, but probably includes integration between the brain and humoral system.If insulin resistance is present, as often happens in people with obesity, cells increase insulin output to maintain normal glucose tolerance.However, if cells are incapable of this task, plasma concentrations of glucose increase.Glucose metabolism is normally regulated by a feedback loop including islet cells and insulin-sensitive tissues, in which tissue sensitivity to insulin aff ects magnitude of -cell response.If insulin resistance is present, cells maintain normal glucose tolerance by increasing insulin output.Only when cells cannot release suffi cient insulin in the presence of insulin resistance do glucose concentrations rise.Although -cell dysfunction has a clear genetic component, environmental changes play an essential part.Modern research approaches have helped to establish the important role that hexoses, aminoacids, and fatty acids have in insulin resistance and -cell dysfunction, and the potential role of changes in the microbiome.Several new approaches for treatment have been developed, but more eff ective therapies to slow progressive loss of -cell function are needed.Recent fi ndings from clinical trials provide important information about methods to prevent and treat type 2 diabetes and some of the adverse eff ects of these interventions.However, additional long-term studies of drugs and bariatric surgery are needed to identify new ways to prevent and treat type 2 diabetes and thereby reduce the harmful eff ects of this disease.", + "Molecular mechanisms of insulin resistance. Binding of insulin to its receptor activates insulin receptor tyrosine kinase and phosphorylation of a family of insulin receptor substrates (IRSs), especially IRS1 and IRS2 (REF.105) (FIG.6).These phosphorylated IRS proteins bind to and activate intracellular signalling molecules, most important of which is phosphatidylinositol 3-kinase (PI3K).PI3K promotes glucose transporter type 4 (GLUT4) translocation to the plasma membrane, resulting in glucose uptake into skeletal muscle, and phosphorylates and inactivates the transcription factor forkhead box protein O1 (FOXO1), altering transcription of downstream genes.Insulin also stimulates the RAS-mitogen-activated p rotein kinase (MAPK) pathway.Figure 4 | Insulin secretion in response to glucose.a | Characteristic insulin secretory response (reconstructed by deconvolution of plasma C-peptide levels) to oral glucose in patients with type 2 diabetes mellitus (T2DM) and in body mass index (BMI)-matched non-diabetic individuals.Note the higher fasting secretion rate, the initial blunted secretory response and the later catch-up phase (due to higher glycaemia).b | The insulin secretion rates of panel a are here plotted against the concomitant plasma glucose concentrations to show the deficit in glucose sensing in patients versus normal glucose-tolerant (NGT) controls.Actual experimental data have been averaged and interpolated to produce these graphs.Box 1 | Glucose homeostasisFollowing a meal, insulin secretion is stimulated and glucagon secretion is inhibited by the combined actions of hyperinsulinaemia and hyperglycaemia.Approximately 60-70% of insulin secretion is dependent on the release of the incretin hormones, including glucagon-like peptide 1 (GLP1) and gastric inhibitory polypeptide (GIP) by the L cells and the K cells in the gut, respectively.Collectively, the changes in glucose, insulin and glucagon levels suppress hepatic glucose production, stimulate muscle glucose uptake and inhibit lipolysis; the latter results in a reduction in the free fatty acid concentration in blood, which further enhances the effect of insulin on the liver and muscle.Type 2 diabetes mellitus is associated with major disturbances in all of the preceding physiological responses: insulin secretion is impaired; fasting plasma glucagon levels are increased and fail to suppress normally after a meal; basal hepatic glucose production is increased and fails to suppress normally after a meal; muscle glucose uptake is impaired; fasting plasma free fatty acid levels are increased and fail to suppress normally following a meal; and the post-meal rise in GLP1 and GIP is normal or modestly decreased.However, there is severe -cell resistance to the stimulatory effect of both GLP1 and GIP on insulin secretion.Insulin secretion.-cells integrate inputs from substrates (such as glucose, FFAs, arginine, fructose and amino acids), hormones and nerve endings to adjust insulin release in response to changing demands (for example, fasting-feeding cycles, exercise and stress) on a minuteto-minute basis in order to maintain normal blood glucose levels, and inter-individual differences affect this adjustment.For example, a lean, insulin-sensitive adult might need as little as 0.5 U of insulin to dispose of an oral load of 75 g of glucose over 2 hours, whereas an obese, insulin-resistant, glucose-intolerant person might require 45 U to perform the same task (~90-fold inter-individual difference).In vivo tests in humans using intravenous or oral glucose, arginine, sulfonylureas (antidiabetic drugs) or mixed meals have demonstrated impaired -cell function in overt T2DM.However, reliable quantitation of in vivo -cell dysfunction requires some form of modelling 78 .Absolute insulin secretion in response to an oral glucose challenge can be normal or even increased in T2DM (FIG.4a), except in long-standing, poorly controlled disease, in which absolute insulin secretion is reduced.However, when insulin secretion rates are plotted against the concomitant plasma glucose concentrations, patients with T2DM secrete substantially less insulin than non-diabetic controls (FIG.4b).This decline in -cell glucose sensing occurs along a continuum extending from normo glycaemia through prediabetes to decompensated diabetes in adults 79 and children 80 , and is a potent predictor of progression to diabetes independently of insulin resistance and classic phenotypic predictors 79 .Absolute insulin secretion is a positive antecedent of deteriorating glucose tolerance.Furthermore, the ability of -cells to respond to the rate of increase in plasma glucose concentration (rate sensitivity) is impaired in individuals with T2DM 79 .Antecedent hyperglycaemia and high levels of incretin hormones (GLP1 and GIP) potentiate glucosestimulated insulin release in healthy individuals.In patients with T2DM, glucose-mediated potentiation of insulin release is increased compared with normal glucose-tolerant individuals (owing to the hyperglycaemia); incretin potentiation, however, is severely compromised 81 .The incretin defect is not reversed by reducing the plasma glucose concentration 82 .", + "The effect of insulin has also been investigated both in vivo and in vitro. In vivo, contradictory results were obtained depending onthe way of administration and the quantity ofinsulin used. For instance, the intraperitonealadministration of a pharmacological dose of insulin decreased expression of FBPase (PlagnesJuan et al. , 2008), but similar acute treatmentwith physiological dose exhibited opposite effect (Polakof et al. , 2010d). Inhibitory actionof insulin can nevertheless be observed afterlong-term infusion of physiological quantity ofinsulin (Polakof et al. , 2010d).", + "However, a suggestion thatinsulin exerts partial control over gluconeogenesis isobserved since the activity of phosphoenolpyruvatecarboxyldnase in liver from younger diabetic mice isnot as greatly increased as it is in liver from olderdiabetics with blood sugar concentrations greater than250 mg/100 ml. P l a s m a insulin assay.The reasons for the ineffectiveness of this excesscirculating insulin in maintaining normal blood sugarconcentration and in regulating the rate of gluconeogenesis are obscure. A possibility, which cannot beexcluded, is the presence of insulin antagonists [23]. However, their presence seems unlikely in view of thepotent action of insulin in sustaining lipogenesis andin increasing glycolysis in these mice.", + "The pathophysiological processes leading to type 2 diabetesGlucose, a monosaccharide, is the key carbohydrate of energy metabolism.The three major sources of circulating glucose in the human body are intestinal absorption, gluconeogenesis and glycogenolysis.Blood glucose homeostasis is regulated by gluco-regulatory hormones such as insulin, glucagon, amylin, glucagon-like peptide 1, glucose-dependent insulinotropic peptide, epinephrine, cortisol and growth hormone (Stephen et al. 2004).Insulin is the key regulatory hormone of blood glucose homeostasis with its excitatory action of stimulating glucose uptake and inhibitory actions on gluconeogenesis, glycogenolysis, proteolysis, lipolysis and ketogenesis (Sonksen and Sonksen 2000).Ever since the role of insulin in glucose homeostasis is understood, it has been the primary therapeutic target in type 2 diabetes patients (Tibaldi 2013).The major pathological mechanisms of type 2 diabetes are the defective insulin secretion due to dysfunctional pancreatic b-cells and impaired insulin action through insulin resistance (Lin and Sun 2010; Ashcroft and Rorsman 2012).", + "Impaired b-cell function is considered a key factor in the pathogenesis of type 2 diabetes (T2D) driven by insulin resistance (1).Insulin secretion in response to an intravenous glucose stimulus is a two-phase process: the first peak of insulin secretion occurs rapidly within 5-10 min after the glucose infusion, followed by a second peak depending on the degree and duration of glucose stimulus (1).Although the insulin response to ingested glucose (e.g., from a meal) does not exhibit a clear biphasic shape under physiological conditions, an early insulin response with rapid elevations of portal and peripheral insulin concentrations has been observed (2,3).A previous study found that the plasma insulin response at 30 min after an oral glucose load was inversely associated with the 2-h plasma glucose concentrations in patients with impaired glucose tolerance (4).This implies that the early-phase insulin secretion is a marker for postprandial glucose homeostasis and plays a role in the development of T2D.", + "IntroductionType 2 diabetes is characterised by an elevation in blood glucose in the fasting state and/or following a glucose challenge resulting from insulin resistance and insufficient compensatory insulin secretion by pancreatic beta islet cells.Insulin action, as the insulin sensitivity index (S I ), can be estimated from the frequently sampled IVGTT with minimal model.Other indices include the acute insulin response to glucose (AIR g , reflecting insulin secretion) and the disposition index (DI=S I AIR g , measuring overall glucose homeostasis and taking account of the hyperbolic relationship between S I and insulin secretion).Glucose effectiveness (S G ) represents an insulin-independent effect whereby glucose mediates its own disposal from plasma.Impairments in these insulin action and glucose metabolism indices are recognised as prediabetic phenotypes involving pathogenic development and pathogenetic processes of type 2 diabetes.Exercise training improves peripheral S I and S G in healthy human subjects [1], and significant improvements in S I , AIR g , DI and S G in response to 20 weeks of endurance exercise training have been observed and reported in the HERITAGE Family Study [2].Recent investigations in HERITAGE provide further evidence that physiological training responses vary appreciably from person to person, and these individual differences are influenced by genetic factors [3].", + "(i) Removal of glucose from the blood is primarily achieved by insulin induction of glucose uptake into muscle.This involves insulin sensing and signalling within individual muscle cells, mobilisation of GLUT4 transporters to the cell membrane and conversion of glucose to glycogen for storage [31].Each of these processes has strict regulatory mechanisms that respond to more than just the amount of insulin the cells are exposed to (e.g.glycogen content, exercise, adrenaline, hypoxia, lipids, etc.). (ii) Glucose can be removed from the blood by adipose tissue and is also a fuel source for most cells in the body.At the same time endogenous glucose production in the liver is suppressed by insulin [32], but also by other nutrients (including glucose), and the liver is the primary site of insulin removal from the blood.Therefore there are at least three major organs that contribute directly to the level of glucose and insulin in the blood, and which work in concert to cope with variations in nutrient load or requirement, as well as to induce counterregulatory pathways to limit rebound in any given response.It is now known that many of the proteins involved in these actions work in a tissue-specific fashion, and that most of the intracellular molecular pathways involved have inherent redundancy (Fig. 2), with the ability to mask minor changes in the activity of the proteins involved [33,34]. (iii) Whole-body insulin resistance could arise from hepatic, muscle or adipose insulin resistance or combinations thereof.Glucose homeostasis depends in large part on production of appropriate quantities of insulin by pancreatic b-cells correctly timed around nutrient ingestion.In the evolution of an individual case of T2DM, it is generally considered that sensitivity to insulinmediated glucose disposal and insulin suppression of hepatic glucose production diminishes over time (e.g. as a result of increasing adiposity), with an initial compensatory increase in insulin secretion from b-cells to achieve glucose homeostasis.At this stage, which may be asymptomatic and prolonged, absolute insulin concentrations measured in plasma may be higher than the reference range.For an individual developing T2DM, a plot against time of total insulin secretion across a standard oral glucose tolerance test (OGTT) is therefore an inverted ''U''-shape as b-cells (teleologically) fail to maintain compensation [15].As compensation becomes less effective (''b-cell exhaustion''), even in the absence of a further deterioration of insulin sensitivity, either impaired glucose tolerance or impaired fasting glucose will develop before finally, the threshold is crossed for a diagnosis of T2DM (as defined by current WHO/ ADA glucose criteria).This trajectory of increase in insulin resistance, b-cell compensation and subsequent failure is nicely demonstrated in the Whitehall II study, a prospective follow up of London civil servants (Fig. 1) [16].In this model, insulin resistance plays an early (pre-diabetic) and important part in the development of T2DM, possibly even inducing b-cell failure due to the strain of prolonged compensation.Complex processes involved in insulin actionAs detailed earlier, clinical assessment of insulin sensitivity primarily relies on measurement of blood glucose and insulin, either in the fasted condition or under hormonal or nutrient ''clamp'' conditions.While the secretion of insulin is almost exclusively controlled by the functional state of the b-cell there are a large number of other tissues involved in maintaining proper response to changes in nutrients such as glucose.In addition there are multiple counter-regulatory mechanisms in the body to cope with changes in hormonal and nutrient exposure.In other words, mammals have evolved to keep a very tight control on blood glucose concentration and it is highly likely that multiple molecular problems would have to occur simultaneously to alter whole body insulin sensitivity significantly.", + "Pathophysiology and major risk factorsWhen the feedback loops between insulin action and insulin secretion do not function properly, the action of insulin in insulin-sensitive tissues such as liver, muscle and adipose tissue (insulin resistance in T2DM) and insulin secretion by pancreatic islet -cells (-cell dysfunction in T2DM) are affected, which results in abnormal blood levels of glucose 37 (FIG.2).In T2DM, insulin resistance contributes to increased glucose production in the liver and decreased glucose uptake in muscle and adipose tissue at a set insulin level.In addition, -cell dysfunction results in reduced insulin release, which is insufficient for maintaining normal glucose levels 38 .Both insulin resistance and -cell dysfunction occur early in the pathogenesis of T2DM, and their critical importance has been verified longitudinally in Pima Indian people progressing from normal glucose tolerance to impaired glucose tolerance to T2DM 39 .Figure 2 | Pathophysiology of hyperglycaemia in T2DM.Insulin secretion from the -cells in the pancreas normally reduces glucose output by the liver and increases glucose uptake by skeletal muscle and adipose tissue.Once -cell dysfunction in the pancreas and/or insulin resistance in the liver, skeletal muscle or adipose tissue occur, hyperglycaemia develops, leading to an excessive amount of glucose circulating in the blood.The various factors listed at the top affect insulin secretion and insulin action.T2DM, type 2 diabetes mellitus.", + "The role for pro-inflammatory cytokines in regulating insulin action and glucose homeostasis and their function in T2DM has been suggested by several lines of evidence." + ], + [ + "Type 2 diabetes (T2D) is a result of complex gene-environment interactions, and several risk factors have been identified, including age, family history, diet, sedentary lifestyle and obesity.Statistical models that combine known risk factors for T2D can partly identify individuals at high risk of developing the disease.However, these studies have so far indicated that human genetics contributes little to the models, whereas socio-demographic and environmental factors have greater influence 1 .Recent evidence suggests the importance of the gut microbiota as an environmental factor, and an altered gut microbiota has been linked to metabolic diseases including obesity 2,3 , diabetes 4 and cardiovascular disease 5 .", + "Diet, Nutrition, and Type 2 DiabetesObesity is pathophysiologically associated with the development of type II diabetes [199,200].Oxidative stress and inflammation, metabolic impairment and accelerated aging on both the micro-and macrocellular level contribute to the pathogenesis of metabolic diseases [201,202].", + "Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.", + "IntroductionThe aetiology of type 2 diabetes is poorly defined: several studies indicate that the disease results from a combination of genetic susceptibility and external risk factors [1].According to this multifactorial model, genetically predisposed subjects will not necessarily develop overt disease unless they are also exposed to particular environmental factors [2].Important risk factors for the development of type 2 diabetes include a family history of diabetes, increased age, hypertension, lack of physical exercise, and obesity [1].", + "T ype 2 diabetes, though poorly understood, is known to be a disease characterized by an inadequate beta-cell response to the progressive insulin resistance that typically accompanies advancing age, inactivity, and weight gain. 1 The disease accounts for substantial morbidity and mortality from adverse effects on cardiovascular risk and disease-specific complications such as blindness and renal failure. 2 The increasing global prevalence of type 2 diabetes is tied to rising rates of obesity 2 -in part a consequence of social trends toward higher energy intake and reduced energy expenditure.However, the mechanisms that underlie individual differences in the predisposition to obesity remain obscure.T ype 2 diabetes, though poorly understood, is known to be a disease characterized by an inadequate beta-cell response to the progressive insulin resistance that typically accompanies advancing age, inactivity, and weight gain. 1 The disease accounts for substantial morbidity and mortality from adverse effects on cardiovascular risk and disease-specific complications such as blindness and renal failure. 2 The increasing global prevalence of type 2 diabetes is tied to rising rates of obesity 2 -in part a consequence of social trends toward higher energy intake and reduced energy expenditure.However, the mechanisms that underlie individual differences in the predisposition to obesity remain obscure.Failure to understand the pathophysiology of diseases such as type 2 diabetes and obesity frustrates efforts to develop improved therapeutic and preventive strategies.The identification of DNA variants influencing disease predisposition will, it is hoped, deliver clues to the processes involved in disease pathogenesis.This would not only spur translational innovation but also provide opportunities for personalized medicine through stratification according to an individual person's risk and more precise classification of the disease subtype.In this article, I consider the extent to which these objectives have been realized.", + "Although the etiology of T2D has not been fully established, a number of risk factors are well defined.According to the ADA [22], the risk of developing T2D is associated with age (increased risk at 45 years), overweight/obesity, and lack of PA.T2D is more common in individuals with a family history of the disease, in certain ethnic groups (e.g., African-Americans, Hispanic-Americans, Native Americans, Asian-Americans, and Pacific Islanders), and in individuals with hypertension (140/90 mmHg in adults), dyslipidemia (high density lipoprotein cholesterol [HDL-C] 35 mg/dL (0.90 mmol/L) and/or a triglyceride level 250 mg/dL (2.82 mmol/L)), IFG, IGT, a history of vascular disease or gestational diabetes, or polycystic ovary syndrome.In addition, a range of common genetic variants are also known to raise the risk of T2D [23][24][25], of which some may interact with lifestyle factors to modify the risk of the disease [26].Several examples are provided below.", + "Background: Type 2 diabetes mellitus is an important risk factor for Alzheimer disease and is more prevalent in elderly minority persons compared with non-Hispanic white persons.", + "Age. Age is another factor that has a considerable effect on outcomes in obesity and T2DM research.In humans, body weight increases with age and peaks at ~55 years in both men and women.Ageing per se is associated with a redistribution of both the fat-free mass and the fat mass, with the latter increase starting at ~30 years of age 129 .Intramuscular and intrahepatic fat are particularly increased in older persons, and this increase has been linked to insulin resistance 130 .Partially on the basis of these changes, ageing has been proposed to be an independent determinant of glucose tolerance, which progressively worsens with age 131,132 .", + "Age also plays a vital role in the onset of diabetes (Cowie & Eberhardt, 1995).In south-east Asia almost 97% diabetic patients are 40 years old or more (IDF Atlas, 2017).In Bangladesh, the reported age of diabetes is 40 years in 71% urban and 85% rural female, while in the case of male the proportion is 85.5% urban and 86.5% in rural population (IDF Atlas, 2017).The current study also pinpointed an exponential increase in the risk of onset of T2DM with the increase of age when 40 years was chosen as the reference (Table S4).", + "Type 2 diabetes incidence is increasing in youth, especially among the racial and ethnic groups with disproportionately high risk for developing type 2 diabetes and its complications: American Indians, African Americans, Hispanics/Latinos, Asians, and Pacific Islanders (9).Older age is very closely correlated to risk for developing type 2 diabetes.More than one in four Americans over the age of 65 years have diabetes, and more than half in this agegroup have prediabetes (9).The prevalence of type 2 diabetes in the U.S. is higher for males (6.9%) than for females (5.9%) (15).Independent of geography, the risk of developing type 2 diabetes is associated with low socioeconomic status.Low educational level increases risk by 41%, low occupation level by 31%, and low income level by 40% (16).", + "The aim of this study was to investigate the association between age at natural menopause and risk of developing type 2 diabetes, and to assess whether this association is independent of potential intermediate risk factors for type 2 diabetes.Furthermore, we examined the role of endogenous sex hormone levels in the association between age at natural menopause and type 2 diabetes.", + "The prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults.The prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults.Although drawing of definitive conclusions is difficult from these observational studies, their results suggest that young-onset type 2 diabetes is associated with a much more frequent occurrence of adverse macrovascular and microvascular outcomes and a more rapidly progressing severity of complications than is seen in type 1 diabetes or later-onset type 2 diabetes.ComplicationsEarlier onset of type 2 diabetes is associated with a greater lifetime risk of diabetes-associated complications. 98vidence from several cross-sectional studies [99][100][101][102] has suggested that the burden of diabetes complications is greater for people with young-onset type 2 diabetes than for people with type 1 diabetes or later-onset type 2 diabetes.Based on a modelling study of a hypothetical cohort of adolescents and young adults in the USA, 99 overall life expectancy among patients diagnosed with type 2 diabetes Review at 20-40 years is reduced by 14 years in men and 16 years in women compared with people without diabetes.Summary and future research directionsAlthough it is tempting to extrapolate the disease course of type 2 diabetes in young people as just an earlier and more rapid form of type 2 diabetes in older adults, distinctive differences are evident.The young-onset phenotype has a stronger family history, a greater association with obesity, early loss of both first and second phases of insulin secretion alongside often severe insulin resistance, early onset and rapid progression of microvascular and macrovascular complications, and poor sustainability of responsiveness to oral glucose-lowering therapies, frequently neces sitating early introduction of insulin.In a study of the age-specific incidence of type 2 diabetes in the UK (a retrospective cohort study of patients with newly diagnosed type 2 diabetes between 1990 and 2010), the investigators reported a substantial increase in the proportion of people aged 40 years or younger at diagnosis", + "T ype 2 diabetes is a major risk factor for cardiovascular disease (CVD) and other age-related ailments and affects 200 million people worldwide (1).The prevalence of type 2 diabetes differs across regions and ethnicities, being higher in African-American, Asian, Native-American, and Hispanic populations.In addition to the classical disease biomarkers, type 2 diabetes patients exhibit significantly elevated oxidative DNA damage, as measured by concentrations of 8-hydroxydeoxyguanosine (8-OHdG) or 8-hydroxyguanosine (8-OHG) in leukocytes (2) or urine (3)such that their use as biomarkers in the diagnosis of the disease has been considered (3).Mitochondria control both energy metabolism and reactive oxygen species (ROS) production (4 -6).Thus, mitochondrial dysfunction may contribute to the development of type 2 diabetes (4).Furthermore, diabetic hamsters treated with inhibitors of advanced glycation end products (AGEs) showed reduced oxidative stress and restored pancreatic -cell function (7).However, the mechanism underlying the development of type 2 diabetes, how that mechanism relates to DNA damage, and how type 2 diabetes increases the risk of CVD are not well understood." + ] + ], + "task_id": [ + "2A2860BB54BC0D36A929838ED41243A7", + "F35BF9C40081CE0521E562CD95BA4C2F", + "9DD88454267DEF2106A3EA7E6E8B5443", + "732D340E5C8F09381CEFA440AD2A7AB6", + "CE5922BDA6B949A17665AB4E1A8138D5" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_4.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_4.json new file mode 100644 index 0000000..145a6ba --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_4.json @@ -0,0 +1,26 @@ +{ + "question": [ + "Can lifestyle changes reverse type 2 diabetes?" + ], + "answer": [ + "Lifestyle changes, including diet modification and increased physical activity, can help prevent and manage type 2 diabetes. They can improve insulin sensitivity, control blood glucose levels, and promote weight loss, which are all beneficial in managing the disease. However, they may not completely reverse the disease, especially in advanced stages." + ], + "contexts": [ + [ + "Accumulating evidence strongly demonstrates that the majority of type 2 diabetes cases can be prevented through diet and lifestyle modification.However, the adoption of a healthy diet and lifestyle requires not only individual behavioral changes, but also changes in our food, built, and social environments.Public health strategies that target the obesogenic environment are critical.Translating clinical and epidemiologic findings into practice requires fundamental shifts in public policies and health systems.To curb the diabetes epidemic, primary prevention through the promotion of a healthy diet and lifestyle should be a global public policy priority.Together, these clinical trials demonstrate that diet and lifestyle modification is highly effective in preventing type 2 diabetes in different ethnic and racial groups.There is an urgent need to translate the findings from these trials into clinical and public health practice.Emphasis should be placed on early adoption of healthy habits in pediatric populations because these practices track through to adulthood.PREVENTABILITY OF TYPESeveral randomized clinical trials have demonstrated that diabetes is preventable.One of the first diabetes prevention trials was conducted in Daqing, China (58).After 6 years of active intervention, risk was reduced by 31, 46, and 42% in the diet-only, exercise-only, and diet-plus-exercise groups, respectively, compared with the control group.In a subsequent 14-year follow-up study, the intervention groups were combined and compared with control subjects to assess how long the benefits of lifestyle change can extend beyond the period of active intervention (59).Compared with control subjects, individuals in the combined lifestyle intervention group had a 51% lower risk of diabetes during the active intervention period, and a 43% lower risk over a 20-year follow-up.", + "An obvious conclusion is a manipulation of lifestyle provides an opportunity to reverse the diabetes trend.Stated another way, we cannot change our genetic make-up, but we can alter environmental factors.Indeed, many studies have shown that diet and exercise slow the onset of diabetes in persons with IGT (2,17,18).Also, low glycemic index diets have been shown to promote weight loss along with having metabolic benefits in persons with type 2 diabetes (19).The difficulty, of course, is trying to get people to change their habits.One can predict that returning to healthy lifestyles would reverse the rising incidence of type 2 diabetes.Unfortunately, that is not a practical solution.Instead, the current approach is to better understand the pathogenesis of type 2 diabetes, hopefully followed by the development of pharmaceuticals that reverse the key pathogenic elements.We entered the 1990s knowing that type 2 diabetes was characterized by the classic triad of b-cell dysfunction, excess glucose production from the liver, and insulin resistance defined as impaired insulin-mediated glucose clearance into skeletal muscle (8).However, knowledge at that time provided no physiological connection between these organs.Another conundrum was how excess adiposity, i.e., being fat, caused insulin resistance, which again is a defect in skeletal muscle physiology.", + "Evidence from randomized controlled trailsThe effi cacy of lifestyle changes in obesity and T2DM prevention has been established in numerous randomized controlled trails (RCTs).Several of them may, however, be considered of major importance due to their large sample sizes (i.e., 458-3234 individuals) and long-term duration (i.e., 3-6 years).The Chinese Da Qing diabetes prevention study was the fi rst to investigate the eff ect of 6-year lifestyle change on body weight and diabetes incidence in individuals with impaired glucose tolerance (IGT) ( Pan et al., 1997 ).Pan and co-workers (1997) reported 42 % reduction in diabetes incidence, although no signifi cant diff erence in body weight was present.Similar results were found in the Finnish Diabetes Prevention Study (DPS) and the US Diabetes Prevention Program (DPP).DPS and DPP independently reported reduction in diabetes incidence of 58 % accompanied by significant reduction in body weight (5-7 %) as a result of the lifestyle modifi cation ( Knowler et al., 2002 ;Tuomilehto et al., 2001 ).These fi ndings were also confi rmed in Japanese and Indian populations, reporting 67.4 % and 28.5 % reduction in diabetes incidence, respectively ( Kosaka et 2011) reported signifi cant reduction in body weight and diabetes incidence at 1, as well as, at 3 years during a lifestyle modifi cation program carried out in a primary healthcare setting among subjects with IGT.All large-scale interventions have been successful in preventing T2DM during the active intervention period.Remarkably when the eff ectiveness of the lifestyle modifi cation programs was assessed on the long-term after discontinuation of the intervention, diabetes risk still remained substantially reduced.In the Finnish DPS, for instance, at extended follow-up 3 years after the 4-year intervention period a substantial reduction in body weight and T2DM incidence was still present ( Lindstrom et al., This document was downloaded for personal use only.Unauthorized distribution is strictly prohibited.As already pointed out in several of the T2DM prevention studies the reduction in diabetes risk has been paralleled by substantial weight loss and weight reduction has been considered to have major importance for diabetes prevention ( Knowler et 1998 ).Hence, lifestyle modifi cation seems to have an eff ect on T2DM not only through reduction in body weight, but also through improvement in insulin sensitivity, blood glucose control and lipid profi le.Whereas there is convincing evidence that lifestyle changes can prevent T2DM in randomized controlled studies, so far little is known whether a lifestyle intervention could also modify cardiovascular morbidity and mortality.The 20-year follow-up results from the Chinese Da Qing diabetes prevention study showed a non-signifi cant 17 % reduction in cardiovascular mortality in the combined (diet and/or PA) intervention group vs. controls ( Li et al., 2008 ).Similarly, lifestyle intervention in the Finnish DPS was not found to reduce signifi cantly cardiovascular mortality during the fi rst 10 years of follow-up ( Uusitupa et al., 2009 ).However, this study was not initially designed to examine the eff ect of lifestyle intervention on total mortality or cardiovascular morbidity, and therefore the statistical power may not have been suffi cient to detect small diff erences in cardiovascular events between the 2 groups.Besides, a longer follow-up period might be needed to answer this question.In the Malm Preventive trial with a 12-year follow-up of men with IGT total and cardiovascular mortality were lower among participants in the lifestyle intervention group, however, these results should be considered with caution due to the non-randomized design of the study ( Eriksson and Lindgarde, 1998 ).Recent fi ndings of bariatric surgery treatment of very obese subjects showed that weight loss indeed may reduce not only T2DM risk but also total mortality ( Sjstrm et al., 2007 ).Further investigations are needed to clarify whether prevention of T2DM by lifestyle modifi cation is associated with cardiovascular disease prevention; until then decisions have to be made on the basis of the best available information.al., 2002 ;Kosaka et al., 2005 ;Lindstrom et al., 2003 ;Tuomilehto et al., 2001 ).In some studies although no or just minor weight loss was achieved, diabetes incidence was also reduced( Pan etal., 1997 ; Ramachandran et al., 2006 ).In addition, on the long term weight was partially or totally regained in all of the studies ( Knowler et al., 2009 ; Li et al., 2008 ; Lindstrom et al., 2006 ; Lindstrom et al., 2003 ).Despite this regain T2DM risk remained low or decreased further, thus the eff ect of lifestyle is unlikely to be solely due to body weight reduction.In support of this notion Pan et al. (1997) reported comparable decrease in T2DM incidence in the intervention group of Da Qing among overweight and lean individuals.In conclusion, evidence from epidemiological studies and RCTs demonstrate that lifestyle modifi cation comprising higher levels of PA and prudent food consumption may be eff ective in obesity and T2DM prevention.The positive eff ect of lifestyle on body weight seems somewhat transient, whereas the eff ect on T2DM is sustained for longer periods.Furthermore, lifestyle modifi cation appears to have an eff ect on diabetes risk independently of body weight and even of weight loss.", + "Because lifestyle changes to reduce bodyweight have always been an important therapy for type 2 diabetes, investigators of Look AHEAD trial 156 examined the eff ect of weight reduction (achieved by an intensive lifestyle intervention) on cardiovascular events.Despite diff erential weight loss for more than 10 years and improvements in many cardiovascular risk factors (including blood pressure and lipids), lifestyle change did not reduce cardiovascular events compared with diabetes support and education (control group).This fi nding might have been because large proportions of participants in both groups received medical treatment for these risk factors.However, participants in the group receiving Glucokinase Reduce hepatic production of glucoseTable 1: Selected therapeutic targets of largely untested mechanisms for type 2 diabetesintensive lifestyle intervention who had a history of a cardiovascular event at baseline had a tendency for an increased risk of a subsequent cardiovascular event; 156 a similar fi nding was reported in ACCORD. 144Several other fi ndings from Look AHEAD are worthy of comment.First, participants in the weight-loss group were more likely to achieve either partial or complete remission of diabetes, 157 had better glucose control needing fewer glucose-lowering drugs (including insulin), and were more likely to achieve a glycated haemoglobin A 1c measurement of less than 7% (53 mmol/mol) than were those in the control group. 158However, despite weight loss and addition of drugs, patients in the treatment group had similar progression of diabetes to that of the control group-ie, with continuous increases in glycated haemoglobin A 1c . 156Second, lifestyle change slowed progression of nephropathy.Third, other health outcomes associated with better quality of life-eg, sleep apnoea 159 and mobility 160 -improved.Thus, intensive lifestyle change in patients with type 2 diabetes has benefi ts, but unfortunately not for cardiovascular outcomes, which remain the major cause of premature mortality in type 2 diabetes.", + "INTRODUCTIONIntensive lifestyle interventions (eg, promoting increased physical activity and weight loss) can be effective in decreasing the incidence of type 2 diabetes mellitus (T2DM). 1 However, healthcare resources are limited, and participants in interventions to prevent diabetes should be prioritized.Identification of individuals at high risk of T2DM could facilitate the targeting of prevention efforts to those who could benefit from them and reduce the cost of preventing T2DM.", + "GENETIC FACTORS AND LIFESTYLE interact in the development of type 2 diabetes.Physical activity, favorable dietary changes, and weight reduction were essential components of a success-ful lifestyle intervention in two large randomized controlled trials on the prevention of type 2 diabetes in high-risk individuals with impaired glucose tolerance (IGT), including the Finnish Diabetes Prevention Study (DPS) (44) and the Diabetes Prevention Program (DPP) (22).In the DPS, increased physical activity was associated with a decreased risk of type 2 diabetes independently of changes in diet and body weight.The individuals who increased their physical activity most (i.e., were in the upper third of the change) were 66% less likely to develop type 2 diabetes than those in the lower third (24).Type 2 diabetes can be prevented or delayed by lifestyle modification, including increased physical activity, beneficial dietary changes, and weight reduction (22,44).However, only Model adjusted for age, gender, group, baseline value of moderate-to-vigorous physical activity, and baseline values and changes in body weight and in intakes of energy and energy-adjusted saturated fat and fiber. *The median (range) of each tertile of change in moderate-to-vigorous physical activity is shown.Adjusted interaction between moderate-to-vigorous physical activity (3 groups) and the polymorphism (2 groups) on the risk of developing type 2 diabetes.a few studies have investigated the effects of such lifestyle interventions on insulin sensitivity and insulin secretion in persons with IGT (21,46).On the basis of the 4-yr follow-up study of the DPS with repeated frequently sampled intravenous glucose tolerance test (FSIGT), insulin sensitivity improved along with lifestyle changes, while insulin secretion remained virtually unchanged (46).Most other data also indicate that physical activity, diet, and weight loss primarily increase insulin sensitivity.Insulin resistance and the associated glycemic stress may exhaust -cells and impair their function.Regular physical activity may diminish glycemic stress by improving insulin sensitivity of target tissues (18).While the mechanisms of improved -cell function in response to lifestyle interventions are still largely unknown, several studies suggest that physical activity (5,11), diet (19,26), weight loss (45), or their combination (21) may directly improve the first-phase insulin secretion that is an indicator of the -cell function.", + "Several studies have shown that treatment with lifestyle change or medication can reduce the progression from prediabetes to diabetes (98,99).Furthermore, a clinical benefit of early therapy has been demonstrated (100,101), with reductions in retinopathy and cardiovascular and allcause mortality (102).This evidence suggests that identifying prediabetes at an early stage and keeping glucose levels close to normal could change the natural history of the disease (93).", + "Lifestyle modification including exercise, nutrition and behavioral changes is the cornerstone to prevent and treat type 2 diabetes.Oral antidiabetic medicationeither as single agent or combination therapyis frequently required to maintain metabolic control, as assessed by monitoring of glycated hemoglobin A 1C (HbA 1C ) levels.Eventually, a significant proportion of patients with type 2 diabetes require the exogenous administration of insulin [40].", + "Lifestyle changes9][120][121] Intervention studies involving diet alone to treat patients with youngonset type 2 diabetes have been limited.In a study 120 of 20 obese children and adolescents (mean age 145 years) with type 2 diabetes, improvements in weight (BMI was reduced from 435 to 393 kg/m), insulin sensitivity, and HbA 1c concentration were seen after following a very low calorie diet (<800 kcal per day) for a 2 month period.These improvements were, however, not maintained after cessation of the diet.", + "Diet and lifestyle factorsDiet and lifestyle modification is an important aspect of T2DM prevention.Major clinical trials have demonstrated that intensive lifestyle interventions can lower the incidence of diabetes mellitus by 58% compared with control groups 55 .Trials have also shown that these interventions are more effective than pharmacological interventions 55 .Landmark clinical trials, such as the Diabetes Prevention Program in multi-ethnic Americans 55 , the Finnish Diabetes Prevention Study 56 and the Da Qing IGT and Diabetes Study in China 57 , have demonstrated that many cases of T2DM could be prevented through lifestyle interventions focused on increasing physical activity and adopting a healthy diet.Nevertheless, when lifestyle interventions are not feasible, pharmacological therapy can be considered as a strategy to prevent the development of T2DM.For example, metformin reduced the incidence of T2DM by 31% over an average follow-up period of 2.8 years among high-risk individuals from the USA who did not have diabetes mellitus 55 .Similarly, metformin reduced T2DM risk in clinical trials in India and China 58 .T2DM managementModification of lifestyle, including weight loss, increasing physical activity and adopting a healthy diet, remains one of the first-line strategies for the management of T2DM.In the Look AHEAD (Action for Health in Diabetes) trial in the USA 133 , a 4-year intensive lifestyle intervention through caloric restriction and increased physical activity achieved increased weight loss, improved cardiometabolic risk profiles and a reduced requirement for medication to control CVD risk factors compared with the control group (who had diabetes mellitus and received support and education about lifestyle modifications) 134 .However, after a median follow-up period of 9.6 years, the trial was terminated because the intervention did not reduce the rate of CVD events 134 .", + "Multiple interventions in adults with T2D have been evaluated for risk reduction and prevention, both in the short and the long term.A recent systematic review (69) reported that after active interventions lasting from 6 months to .6 years, relative risk reduction achieved from lifestyle interventions (39%) was similar to that attained from use of drugs (36%); however, only lifestyle interventions had a sustained reduction in risk once the intervention period had ended.Analysis of the postintervention follow-up period (;7 years) revealed a risk reduction of 28% with lifestyle modification compared with a nonsignificant risk reduction of 5% from drug interventions." + ] + ], + "task_id": [ + "F0CC742EA104CB2C8B8BCA9CB6EB78F0" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_1.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_1.json new file mode 100644 index 0000000..861a366 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_1.json @@ -0,0 +1,113 @@ +{ + "question": [ + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?", + "genetics", + "what is bioinformatics", + "Explain the process of finding a genetic marker followed by a quantitative trait loci." + ], + "answer": [ + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.", + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait." + ], + "contexts": [ + [ + "INTRODUCTIONAsthma is a chronic disease of the airways defined by its symptoms, which include reversible airflow obstruction, inflammation, and bronchial hyperresponsiveness.The ancient Egyptians, Greeks, and Romans made reference to the symptoms of asthma, and today the disease is estimated to affect 235-334 million people worldwide (44,53).The atopic triad.Perhaps the most widely recognized pattern of co-occurrence is the one of asthma, atopic dermatitis (eczema), and allergic rhinitis (hay fever), which together are referred to as the atopic triad and characteristically present clinically in a temporal sequence known as the atopic march.Within this sequence, atopic dermatitis is typically the first component to manifest, with approximately 20-30% of individuals with mild disease and 70% of those with severe disease going on to develop asthma.Individuals who undergo this distinctive sequence of disease progression frequently exhibit a more severe and persistent phenotype, with increased risk of allergen sensitization.", + "Clinically, asthma is characterized by episodes of coughing, chest tightness, wheezing, dyspnea, or sputum production.Often, asthma sufferers experience a combination of these symptoms, or some symptoms more than others.Pulmonary breathing tests typically demonstrate variable airway obstruction and hyperreactivity, but may be normal, even in patients with severe and uncontrolled disease [8].Thus, the diagnosis of asthma, which is based on general clinical symptoms and variable lung function testing, is non-specific and heavily dependent on clinical history.Within the \"umbrella\" diagnosis of asthma there exists a diverse array of differing clinical phenotypes [9].For example, childhood asthma is often associated with personal and parental atopic diseases (i.e., atopic dermatitis, food allergy, eosinophilic esophagitis, allergic rhinitis), viral infections, and tobacco smoke exposure [10].Alternatively, adult-onset asthma is less associated with atopic disease [11,12], but more associated with female sex [13], sinus disease [14], and preceding respiratory infections such as pneumonia [15].In addition, adult-onset disease is often of higher severity [12,16] with a faster and more persistent decline in lung function [17].Moreover, although severe patients are found in every demographic and age group, the most common phenotype is an adult female that is older and obese [18].IntroductionAn estimated 9% of children and 6% of adults in the United States have asthma [1].The total number of asthma sufferers worldwide is estimated to be over 300 million, with an additional 100 million expected to develop asthma by 2025 [2][3][4][5].Developed countries are the most affected, with some of the highest rates found in the United Kingdom, Australia, New Zealand and the Republic of Ireland [3].Asthma prevalence is rising significantly in developing countries in transition to a more Western lifestyle [3].In 2007, the cost of disease in the United States was estimated to be $56 billion in relation to medical expenses, missed days of work, and early deaths [1].The rate of asthma deaths has likely plateaued, but is still as high as 250,000 per year worldwide [6].Morbidity and mortality are particularly high in ethnic minorities living below or near the poverty line, and African American children had a death rate 10 times that of non-Hispanic white children in 2015 [7].Thus, asthma is a costly, growing health problem associated with high morbidity and mortality.", + "Getting accurate estimatesof exposures is difficult, whether this is air pollution or toxins in our food anddrink, but these are important questions. Rutter: That is an important point. From the twin study data it is clear thatenvironmental effects account for quite a lot of the variance on all the multifactorial disorders. Yet the kinds of measures that are used arent terribly solid. Theyinclude broad thing such as socio-economic status (SES). Even where there aregood measures the care taken in testing for environmental mediation is usuallypoor.Bronchiolitis, a diseasethat happens in the first year of life in many infants, is strongly associated withsubsequent asthma. We ascertained it in the first years of life and have been following these people to age 25 now. For the people who had bronchiolitis and nowhave asthma, their parents recall much better that they had bronchiolitis than thosewho dont have asthma now. It is at least twice more. Extraordinarily, some ofthese latter parents dont recall that they took their child to the doctor in the fi rstyear of life.If you arrive in the USA whenyou are young you have almost the same prevalence of asthma as an adult as thosewho are born in the USA and who are not Mexican. But if you arrive at older agesyou have less asthma. If you arrive at the age of 20 you have the same asthma riskas those born in Mexico (Eldeirawi et al 2005). Kotb: This is extremely interesting. There is a relationship between depressionand the immune system. This especially applies to natural killer (NK) cells, whichare the main cells that fight cancers.A more constructive approach is the use of refined measures of environment: an interviewthat quantifies the level of independence of stressful life events (Brown & Harris1978) or objectively recorded events in natural experiments (Kilpatrick et al2007). Factors that are considered as environmental, e.g. smoking, are strongly determined by personality and genetic factors. Personality-related factors and stressfullife events also influence detection of physical health outcomes including abdominal pain, appendectomy, peptic ulcer or diabetes control (Creed 2000).", + "; Guffey, S.E. Investigation into pedestrian exposure to near-vehicle exhaust emissions. Environ. Health2009, 8, 13. [CrossRef] [PubMed]Our World in Data.org. 2017. Available online: https://ourworldindata.org/data-review-air-pollution-deaths (accessed on10 January 2022). Pope, C.A. , III. Respiratory disease associated with community air pollution and a steel mill, Utah Valley. Am. J. Public Health1989, 79, 623628. [CrossRef] [PubMed]Pope, C.A. , III. What do epidemiologic findings tell us about the health effects of environmental aerosols? J. Aerosol. Med. 2000,13, 335354. [CrossRef] [PubMed]Pope, C.A. , III.", + "Case for Support BBSRC Grant Application September 2005Integrative Analysis of the Genetic Factors behind Asthma and Atopic DermatitisPart I: Research ProposalBackgroundAIntroduction of topic of research and its academic and wider contextAsthma is the most common disease of childhood, and affects one child in seven in the UnitedKingdom. Atopic Dermatitis (AD, eczema) affects similar numbers of children. About 60% of children withsevere AD will have concomitant asthma. Treatments for both diseases are unsatisfactory. Abandonment oforthodox medical therapy for AD is common in many families who have children with the disease.", + "This is most common during the rainyseason when aerosols are created, which results in repeated inhalation of Bp [43, 44]. Environmental sampling studies reveal there is a positive association between theprevalence of disease and the degree of environmental contamination [7]. In addition toenvironmental factors, data suggests that host factors play an important role in mountingan immune response against infectious diseases [45] such as melioidosis. While healthypersons can contract melioidosis, most patients in endemic regions have an underlyingpredisposition [28], which suggests that the immunological status of the patient caninfluence disease initiation and progression [15].", + "Sensitivity analysisWe did two sets of post-hoc sensitivity analyses to assess the effects of potential poor recall of age of onset among individuals with adult-onset asthma, and the effects of misclassification of COPD as asthma among the adultonset cases, even with exclusion of cases with a reported diagnosis of COPD, emphysema, or chronic bronchitis.First, to assure that the adult-onset cases did not include a significant proportion of childhood-onset asthma in which symptoms remitted in early life but then relapsed in adulthood, we replaced adult-onset cases with increasing proportions of randomly selected childhood-onset cases, and then tested for association at the two most significant childhood onset-specific loci.This procedure was repeated 20 times for each proportion to quantify the sampling variability (appendix pp 7-8).Second, we did two analyses in which we removed either individuals with ages of asthma onset between 46 and 65 years or adult-onset cases and controls with FEV/FVC <070.For each, we compared p values and ORs with the GWAS including all adult-onset cases (appendix pp 8-9).We used data for British white individuals from UK Biobank data release July 19, 2017. 8We extracted disease status (asthma, allergic rhinitis, atopic dermatitis, food allergy, chronic obstructive pulmonary disease (COPD), emphysema, and chronic bronchitis), age of on set of asthma, and sex from self-reported question naires and hospital records (International Classification of Diseases 10th revision [ICD-10] codes) by querying our in-house protected UK Biobank database server. 9For our main case analysis, we included individuals who self-reported that they had doctor-diagnosed asthma.Further details of our research approach are provided in the appendix (pp 4-7).", + "; Guffey, S.E. Investigation into pedestrian exposure to near-vehicle exhaust emissions. Environ. Health2009, 8, 13. [CrossRef] [PubMed]Our World in Data.org. 2017. Available online: https://ourworldindata.org/data-review-air-pollution-deaths (accessed on10 January 2022). Pope, C.A. , III. Respiratory disease associated with community air pollution and a steel mill, Utah Valley. Am. J. Public Health1989, 79, 623628. [CrossRef] [PubMed]Pope, C.A. , III. What do epidemiologic findings tell us about the health effects of environmental aerosols? J. Aerosol. Med. 2000,13, 335354. [CrossRef] [PubMed]Pope, C.A. , III.", + "8 Thesocio-ecologic framework posits that various aspects of a childs environment directly and indirectly impact thechilds health and development.9 Drawing on this framework, Beck and colleagues10 examined several biologic,social and ecologic variables to provide a greater understanding of factors influencing asthma-related hospitalreadmissions for black children compared to their white counterparts. The study revealed that black childrenwere over two times as likely to be readmitted for an asthma-related illness compared to white children; thisresulted from significant differences in almost every socio-ecologic variable measured, including diseasemanagement practices and access to primary care.Specific AimsAsthma is the most common chronic pediatric medical condition in the United States, with a prevalenceover 9.6% in children under 18 years of age.1, 2 Low-income, urban children incur a disproportionate share ofasthma prevalence and morbidity;2-4 13% of children living below the poverty threshold are diagnosed withasthma compared to 8% of non-poor (>200% poverty),3 and poverty is associated with higher rates of asthmaattacks.1 Living in an urban area confers additional risk for asthma and increased ED utilization.4, 5Implementation of the National Asthma Education and Prevention Programs (NAEPP) Guidelines hascontributed to reductions in asthma morbidity and mortality rates, and these guidelines emphasize establishinga partnership between healthcare providers and patients/families to promote effective asthma management.6The NAEPP expert panel states, building a partnership requires that clinicians promote opencommunication and ensure that patients have a basic and accurate foundation of knowledge about asthma(p.124),6 yet care partnerships also require that the patient/parent effectively communicate issues such asemerging symptoms or response to medications.Vital & health statistics Series 3, Analytical and epidemiological studies. 2012(35):1-58. CDC. Current Asthma Prevalence. https://www.cdc.gov/asthma/most_recent_data.htm. 2015. UpdatedJune 2017. Accessed March 9, 2018. Northridge J, Ramirez OF, Stingone JA, Claudio L. The role of housing type and housing quality inurban children with asthma. Journal of urban health : bulletin of the New York Academy of Medicine. 2010;87(2):211-224. Flores G, Snowden-Bridon C, Torres S, et al. Urban minority children with asthma: substantialmorbidity, compromised quality and access to specialists, and the importance of poverty and specialtycare.Asthma Prevalence and DisparitiesAsthma is the most common chronic pediatric medical condition in the United States,1 affecting anestimated 6.2 million children annually.2 Poorly controlled pediatric asthma contributes to over 700,000 visits ayear to emergency departments (ED).1 Children living in impoverished, urban settings are disproportionatelyaffected by asthma,3 and the disparate impact of asthma is even worse among black and Latino children, andchildren whose parents have limited English proficiency (LEP) in these urban low-income areas.4-6 A 2017longitudinal study revealed that black race and Latino ethnicity are significantly associated with worse asthmaoutcomes including 1) asthma knowledge, 2) asthma-related quality of life, 3) asthma severity, and4) asthma control.The Journal of asthma : official journal of the Association for the Care of Asthma. 2017:16. Inkelas M, Garro N, McQuaid EL, Ortega AN. Race/ethnicity, language, and asthma care: findings froma 4-state survey. Annals of allergy, asthma & immunology : official publication of the American Collegeof Allergy, Asthma, & Immunology. 2008;100(2):120-127. National Asthma Education and Prevention Program. Expert Panel Report 3: Guidelines for theDiagnosis and Management of Asthma Bethesda, MD: National Institutes of Health, National Heart,Lung, and Blood Institute; 2007. Publication no. 08-045.1. NIH Consensus Group. Video report: What is mHealth?Contact PD/PI: Coker, Tumaini RuckerINTRODUCTION TO APPLICATIONResearch Plan OverviewChildhood asthma is the most common pediatric medical condition in the United States, anddisproportionately affects children living in low-income, urban settings. Many low-income, urban families rely onemergency department (ED) services as their source for sick care for their child. This is often due to not havinga primary care provider or sufficient access to their primary care provider for asthma management." + ], + [ + "Taxon Sampling and DNA ExtractionsWe extracted DNA from 72 pinned specimens from the National Museum of Natural History (NMNH) Entomology collection for this study.We plucked middle legs from the pinned bees using a pair of sterilized forceps and washed the tissue in 95% ethanol to remove dust, pollen, and other forms of accumulated debris on the bee legs.After evaporation of the ethanol (by drying the tissue on a clean Kimwipe ), the samples were placed in a freezer for several hours.DNA was then extracted destructively by grinding the frozen tissue with a sterile pestle, using a DNeasy Blood and TissueKit (Qiagen, Valencia, CA, USA) and following the manufacturer's protocol, except the DNA was eluted in 130L ddH 2 O instead of the supplied buffer.We ran 10L of each extract for 60 min at 100 volt on 1.5% agarose SB (sodium borate) gels, to estimate size of the genomic DNA.", + "Extraction of biomolecular fractions from faecal samples.Biomolecular fractions were extracted from unthawed, frozen faecal subsamples (150 mg) after pretreatment of the weighed subsamples with 1.5 ml RNAlater ICE (LifeTechnologies) overnight.The faeces-RNAlater ICE mixture was homogenized by bead-beating, as previously described 53 .Differential centrifugation and extraction using the All-In-One kit (Norgen Biotek) to recover DNA and proteins were carried out as previously described 53 .DNA fractions were supplemented with DNA extracted from 200 mg subsamples using the MOBIO Power Soil Kit.", + "Bulk DNA Extraction.Total DNA was collected from the cell pellets remaining after Ficoll density centrifugation for B lymphocyte isolation using the DNeasy Blood & Tissue Kit (Qiagen) following the manufacturer's specifications.The concentrations of DNA were quantified using the Qubit High-Sensitivity dsDNA Kit, and the qualities of DNA were evaluated with 1% agarose gel electrophoresis.", + "MethodsLaboratory procedures.We initially screened 107 ancient samples (Supplementary Data 1) in dedicated clean facilities at the ancient DNA lab of Jilin University, China, following published protocols for DNA extraction and library preparation 36,37 .Prior to sampling, we wiped all skeletal elements with 5% bleach and irradiated with UV-light for 30 min from each side.We drilled teeth to obtain fine powder using a dental drill (Dremel, USA).We sampled the dense part of petrous bones around the cochlea by first removing the outer part using the sandblaster (Renfert, Germany), and then grinding the clean inner part into fine powder with the mixer mill (Retsch, Germany).We digested the powder (50-100 mg) in 900 l 0.5 M EDTA (Sigma-Aldrich), 16.7 l of Proteinase K (Sigma-Aldrich), and 83.3 l ddH 2 O (Thermo Fisher, USA) at 37 C for 18 h.Then we transferred the supernatant to a MinElute silica spin column (QIAGEN, Germany) after fully mixed with the 13 ml custom binding buffer [5 M guanidine hydrochloride (MW 95.53), 40% Isopropanol, 90 mM Sodium Acetate (3 M), and 0.05% Tween-20] followed by two washes with PE buffer (80% ethanol).Then we eluted the DNA with 100 l TET buffer (QIAGEN, Germany).", + "Genomic DNA extractionLeukocytes were isolated from 5-ml peripheral blood samples.DNA was prepared by phenol extraction and chloroform extraction followed by isopropanol precipitation, washed with ethanol, and air-dried.Tris-EDTA buffer pH 8.0 was used to dissolve the final genomic DNA product.", + "The pulled down DNA fragments were extracted and purified using phenolchloroform extraction/ethanol precipitation.The samples were stored at -20 C until use.", + "DNA and RNA extractionFor nucleic acid extraction, pellets containing 2,000 to 5,000 nematodes were ground into fine powder with a liquid nitrogen-cooled mortar and pestle [88] and then extracted using either an RNeasy kit (Qiagen, Valencia, CA, USA) or a Genomic Tips kit (Qiagen; following the protocol for extraction of genomic DNA from cells in culture).Alternatives to the liquid nitrogen grinding procedure were attempted for DNA extraction (including homogenization, bead beating, three rounds of freeze-thaw, and simple incubation with the Genomic Tips digestion buffer from Qiagen, proteinase K and RNase A), but all resulted in the extraction of degraded genomic DNA.The integrity of genomic DNA after different extraction methods was evaluated by examination of highmolecular-weight genomic DNA using agarose gel electrophoresis and comparison of amplification of long PCR products from equal amounts of template (QPCR; described below).RNA was quantified with a NanoDrop Fluorospectrometer (NanoDrop Technologies, Wilmington, DE, USA) and analyzed for integrity with a BioAnalyzer (Agilent Technologies, Santa Clara, CA, USA).DNA quantity was measured before QPCR using PicoGreen dye (Invitrogen Corporation, Carlsbad, CA, USA), as described previously [21].", + "Bacterial DNA extractionDNA was extracted from the freeze-dried luminal content of the 4 sections of the intestine using the method described by Salonen et al. [28].In short, approximately 0.1 g was used for mechanical and chemical lysis using 0.5 ml buffer (500 mM NaCl, 50 mM Tris-HCl (pH 8), 50 mM EDTA, 4% SDS) and 0.25 g of 0.1 mm zirconia beads and 3 mm glass beads.Nucleic acids were precipitated by addition of 130 l, 10 M ammonium acetate, using one volume of isopropanol.Subsequently, DNA pellets were washed with 70% ethanol.Further purification of DNA was performed using the QiaAmp DNA Mini Stool Kit (Qiagen, Hilden, Germany).Finally, DNA was dissolved in 200 l Tris/EDTA buffer and its purity and quantity were checked spectrophotometrically (ND-1000, nanoDrop technologies, Wilmington, USA).DNA isolation from scrapings of the small intestine and the colon Genomic DNA was isolated from the crushed scraping by using DNeasy W Blood and Tissue Kit (Qiagen, Venlo, the Netherlands) according to the manufacturer's instructions.The DNA was treated with RNase and eluted in Tris/EDTA buffer (pH 9.0).DNA purity and quantity were checked spectrophotometricaly (ND-1000, nanoDrop technologies, Wilmington, USA).", + "DNA extractionIn a strictly controlled, separate and sterile workplace, approximately 0.2 mL saliva and 50 mL PBS containing the plaque sample were mixed with Qiagen's AL buffer by pulse vortexing for 30 s (Qiagen, Valencia, CA).Total DNA was extracted from the suspension of each sample using a QIAamp DNA Mini Kit (Qiagen, Valencia, CA).Isolated DNA was eluted in 50 mL distilled water.", + "Most typically, DNA is extracted from blood samples, dried blood spots, buccal swabs, saliva, tissue and even urine and stool samples.In forensic science, other sources have been validated e.g.bone, tooth pulp, dandruff and others.", + "Blood samples were collected by jugular venipuncture from each animal into 6-ml EDTA vacutainer tubes (Greiner Bio-One, GmbH).The collected blood samples were kept in iceboxes until refrigerated at 4 C.Genomic DNA was extracted using the DNeasy Blood and Tissue Kit (Qiagen), as per the manufacturer's instructions with a slight modification of increased lysis time to 90 min.DNA quality and quantity were determined using 1% agarose gel electrophoresis (Merck) and Qubit 3.0 Fluorometer (Life Technologies) respectively.", + "Genomic DNA extractionDNA from MEF cultures or mouse liver was isolated by phenol/chloroform extraction, as described [11].", + "DNA isusually recovered from cells by methods that include cell rupture but thatprevent the DNA from fragmenting by mechanical shearing. This is generally undertaken in the presence of EDTA, which chelates the magnesium ionsneeded as cofactors for enzymes that degrade DNA, termed DNase. Ideally,cell walls, if present, should be digested enzymatically (e.g. , lysozyme in thebacteria or bacterial cell). In addition the cell membrane should be solubilizedusing detergent.In specific cases, such as insects,contamination can be reduced by hypochlorite treatment before extractionto avoid contact with foreign DNA (15). DNA preparation includes thedigestion of samples using different lysis buffers, which contain proteinaseK at several concentrations. DNA purification has been performed bythe classical phenol-chloroform extraction and ethanol precipitation (16). Further treatment with RNAse and a further round of extraction and precipitation has been recommended (5,17). Negative controls using distilled waterinstead of a DNA sample can detect possible environmental or reagentcontaminants.DNA solutions can be stored frozen,although repeated freezing and thawing tends to damage long DNA moleculesby shearing. A flow diagram summarizing the extraction of DNA is given inFig. 1.2. The above-described procedure is suitable for total cellular DNA. If the DNA from a specific organelle or viral particle is needed, it is best toisolate the organelle or virus before extracting its DNA, because the recoveryof a particular type of DNA from a mixture is usually rather difficult.", + "Isolation of Total DNA from Tissues.Total DNA was isolated as described (19) with slight modifications.Briefly, 0.1-g samples of tissue were frozen in liquid nitrogen, and DNA was extracted from the frozen tissues by the proteinase KSDSphenol method.", + "Genomic DNA extractionGenomic DNA was extracted by the mixed alkyl trimethyl ammonium bromide (MATAB) procedure.Briefly, 250 mg of plant material was ground in liquid nitrogen and immediately incubated in 2 ml of pre-warmed extraction buffer (100 mM Tris-HCl, pH 8, containing 20 mM EDTA, 1.4 M NaCl, 2% (w/v) MATAB, 1% (w/v) PEG6000 (polyethylene glycol), 0.5% (w/v) sodium sulfite, 20% (w/v) Igepal CA630, 20% (w/v) lithium dodecyl sulfate, and 20% (w/v) sodium deoxycholate) at 74 C for 20 min.After purification with 2 ml of chloroform:isoamylalcohol (24:1, v/v), DNA extracts were precipitated with 1.6 ml of isopropanol then resuspended in 1 ml of buffer (50 mM Tris-HCl, pH 8, containing 10 mM EDTA and 0.7 M NaCl).The extracts were purified on anion exchange columns (QIAGEN-tip 20) following the manufacturer's instructions (QIAGEN, Valencia, CA).", + "After three washes withice-cold phosphate buffer saline (PBS), DNA was extracted from 100-150mg of cecal contentsusing the QIAmp DNA stool Mini Kit (Qiagen) following mechanical cell lysis as describedpreviously [10]. The supernatant from the first wash, which was 10 times volume per weight ofcecal contents, was stored at -80C for sIgA measurements. Extracted DNA was initially amplified using universal primers for the V5-V6 region of the 16S rRNA gene and containing barcoded adapters. The forward primer used was 784F (5-RGGATTAGATACCC-3) and thereverse primer was 1064R (5-CGACRRCCATGCANCACCT-3).", + "The conventional DNA extraction procedure involved the homogenization of single D. magna in 400 l of sperm lysis buffer (100 mM Tris-HCl, pH 8; 500 mM NaCl; 10 mM ethylenediaminetetraacetic acid [EDTA], pH 8; 1% SDS; 2% mercaptoethanol) followed by RNase treatment (40 g, 37C for 1.5 h).The DNA was then extracted in phenol (pH 8) and chloroform:isoamyl alcohol (1:1).The DNA was finally precipitated by two volumes of ice-cold ethanol in the presence of 3 M sodium acetate (1/10 of the DNA volume) and was incubated at 80C overnight.Precipitated DNA was harvested by centrifugation, dried in air, and the final pellet dissolved in sterile analytic grade water." + ], + [ + "Recent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals.", + "In orderto accomplish this task, we looked for possible novel genetic factors that regulatephysical activity levels. We used behavioral genetics methodology combined with atranslational genetics approach in order to propose genetic candidate regions as wellas candidate genes for this complex phenotype in humans (Chapter 2 and 3) andmice (Chapters 2, 3, and 4).", + "Since that time, observations across species have shown that life span can be extended by genetic factors.One of the first demonstrations of this entailed the study of recombinant inbred populations of the nematode worm Caenorhabditis elegans by Thomas E. Johnson.Then a postdoc in William (Bill) Wood's lab at the University of Colorado Boulder, Tom and Bill demonstrated that crosses of C. elegans strains did not display the heterosis effect that interfered with many other studies, \"As predicted, we found significant genetic effects on life span as well as other life history traits. \"This finding established a method for evaluating genetic factors that influenced life-span variation.In fact, their measurements of life span of the recombinant inbred strains demonstrated the heritability of life span to be 19%-51% (1).Consistent with theories of the 1970s and 1980s, it was concluded that these genetic factors were a collection of small influences across many genes.This finding was one of the first steps in demonstrating that genetic factors influence aging.As genetic analysis was making great progress in understanding other biological processes, such as developmental programming, the realization that aging could be investigated using the same tools was highly significant.GeneticsAging is influenced by genetic factors.It may be surprising to know that as recently as the 1970s and 1980s, the concept of modulating", + "Previous unbiased systemsgenetics approaches relying on the use of mouse genetic reference populations (GRPs) have been successful in identifying theunderlying mechanisms in complex metabolic traits, such asmitochondrial function (Chella Krishnan et al. , 2018; Norheimet al. , 2019; Williams et al. , 2016), lipid metabolism (Jha et al. ,2018a, 2018b; Linke et al. , 2020; Parker et al. , 2019), atherosclerosis (Bennett et al. , 2015; Smallwood et al. , 2014), and liver diseases (Chella Krishnan et al. , 2018; Hui et al. , 2018).", + "This population geneticmechanism also can maintain genetic variability for aging, like antagonistic pleiotropy. LARGE-EFFECT MUTANTS AND THE GENETICS OF AGINGOne approach that has become increasingly common in the characterization of the genetics of aging is to isolate aging mutants, usually from mutagenesis experiments, andthen to determine the mechanistic basis for the unusual life span in the mutants. Thisapproach has led to the discovery of genes that can enhance (e.g. , Maynard Smith 1958;Lin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span(e.g. , Pearl and Parker 1922).Research with animal modelshas established that genetic factors explain a significant amount of variation in both exercise capacity in an untrained state (Koch and Britton 2001) and in the physiological responses to training regimens (Troxell et al. 2003). Bunger et al. (1994) reported the results of sixty generations of selecting laboratorymice for an index combining high body weight and high stress resistance, where the308L E V E L S O F O B S E R VAT I O Nlatter denoted the distance to exhaustion on a treadmill.", + "The DNA of over 500,000 people was read to reveal the specific 'genetic fingerprints' of each participant.Then, after asking each of the participants how long both of their parents had lived, Timmers et al. pinpointed 12 DNA regions that affect lifespan.Five of these regions were new and had not been linked to lifespan before.Across the twelve as a whole several were known to be involved in Alzheimer's disease, smoking-related cancer or heart disease.Looking at the entire genome, Timmers et al. could then predict a lifespan score for each individual, and when they sorted participants into ten groups based on these scores they found that top group lived five years longer than the bottom, on average.", + "NATurE GENETicSadjustments, using a matched meta-analysis conducted on the same subset of 28 studies:", + "GENETIC ANALYSIS OF LONGEVITY, OF AGING, AND OF AGE-SENSITIVE TRAITS IN MICEBiogerontology has just begun to benefit from the attention and skills of professional geneticists.Geneticists can attack problems of aging from several related but fundamentally distinct directions.Studies of rare mutations at individual loci, such as the Werner's syndrome locus WRN, whose mutant form produces, in middle-aged people, several of the diseases typically not seen until old age, can give attractive points of entry into the pathophysiology of age-related diseases.In mice there are now four reports of mutations-two naturally occurring and two artificially produced-that lead to impressive increases in mean and maximal longevity (Miskin and Masos, 1997;Brown-Borg et al., 1996;Miller, 1999;Migliaccio et al., 1999), and thus provide extremely valuable models for testing mechanistic ideas and the control of aging.Some of these, such as the dw/dw and df/df dwarfing mutations that affect levels of growth hormone and thyroid hormone, provide clues to endocrine-dependent pathways that could regulate age effects in multiple cells and tissues.The recent report (Migliaccio et al., 1999) that mouse life span can be extended by an induced mutation that diminishes cell susceptibility to apoptotic death after injury should stimulate new inquiries into the effects of altered cell turnover on age-dependent changes.Each of these mutations, however, is exceptionally rare in natural populations; despite their effect on longevity, perhaps mediated by a direct effect on aging, each of the mutations is likely to have, overall, a negative effect on reproductive success and thus fail to become fixed in natural mouse populations.", + "Genetics had a strong impact on femoral traits (eg, bone volume fraction [BV/TV] basal Ca, h2 = 0.60) as well as their RCR (eg, BV/TV,h2 = 0.32). Quantitative trait locus (QTL) mapping identied up to six loci affecting each bone trait. A subset of loci was detected inboth diet groups, providing replication of environmentally robust genetic effects. Several loci control multiple bone phenotypes suggesting the existence of genetic pleiotropy. QTL controlling the bone RCR did not overlap with basal diet QTL, demonstrating geneticindependence of those traits.", + "This population geneticmechanism also can maintain genetic variability for aging, like antagonistic pleiotropy. LARGE-EFFECT MUTANTS AND THE GENETICS OF AGINGOne approach that has become increasingly common in the characterization of the genetics of aging is to isolate aging mutants, usually from mutagenesis experiments, andthen to determine the mechanistic basis for the unusual life span in the mutants. Thisapproach has led to the discovery of genes that can enhance (e.g. , Maynard Smith 1958;Lin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span(e.g. , Pearl and Parker 1922).", + "(17) The role ofgenetics in bone was first suggested by early twin studies(18,19) and family studies. (20-23) Forexample, Krall and Dawson-Hughes(22) measured familial resemblance of bone density of femaleand male members of 40 families. They reported that 46-62% of variance in bone density wasattributable to heredity. However, the fact that genetics does not explain all of the variation in bone18mass suggests that bone mass is also influenced by other environmental factors as well as theinteraction between genetics and extrinsic factors.", + "when examining the role that genetics may play in howchildren form attachments, as other studies have observedthat parenting particularly affected children with variouspolymorphisms of genes that regulate the DA system (i.e. , DAT19- and 10-repeat and Dopamine Receptor D4 7-repeat) andreward sensitivity (Bakermans-Kranenburg et al. , 2008; Bosmanset al. , 2020). Our findings further support the notion thatmultiple genes may make a child more or less susceptibleto their caregiving environment (Belsky and Beaver, 2011;Roisman et al.", + "when examining the role that genetics may play in howchildren form attachments, as other studies have observedthat parenting particularly affected children with variouspolymorphisms of genes that regulate the DA system (i.e. , DAT19- and 10-repeat and Dopamine Receptor D4 7-repeat) andreward sensitivity (Bakermans-Kranenburg et al. , 2008; Bosmanset al. , 2020). Our findings further support the notion thatmultiple genes may make a child more or less susceptibleto their caregiving environment (Belsky and Beaver, 2011;Roisman et al.", + "Previous unbiased systemsgenetics approaches relying on the use of mouse genetic reference populations (GRPs) have been successful in identifying theunderlying mechanisms in complex metabolic traits, such asmitochondrial function (Chella Krishnan et al. , 2018; Norheimet al. , 2019; Williams et al. , 2016), lipid metabolism (Jha et al. ,2018a, 2018b; Linke et al. , 2020; Parker et al. , 2019), atherosclerosis (Bennett et al. , 2015; Smallwood et al. , 2014), and liver diseases (Chella Krishnan et al. , 2018; Hui et al. , 2018).", + "TranslationalA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors.", + "Genetics of weight loss.A necessary condition for tailoring weight loss protocols to genetics or genomics is identifying reliable and meaningful genetic or genomic predictors.The heritability, or genetic variance, of weight loss first was documented in a careful laboratory study of identical twins.Bouchard and colleagues (C. Bouchard et al., 1994) induced weight loss in identical twin pairs through supervised exercise designed to produce of daily energy balance deficits of 500 kcals.Strong similarity between co-twins as compared to non-related individuals provided some of the first evidence of genetic involvement in magnitude of weight loss with intervention.", + "lifestyle and changes in diet, a significant proportion of heritable factors also contribute to individual susceptibility (Hu 2011).", + "Genetics had a strong impact on femoral traits (eg, bone volume fraction [BV/TV] basal Ca, h2 = 0.60) as well as their RCR (eg, BV/TV,h2 = 0.32). Quantitative trait locus (QTL) mapping identied up to six loci affecting each bone trait. A subset of loci was detected inboth diet groups, providing replication of environmentally robust genetic effects. Several loci control multiple bone phenotypes suggesting the existence of genetic pleiotropy. QTL controlling the bone RCR did not overlap with basal diet QTL, demonstrating geneticindependence of those traits." + ], + [ + "At a high level, the Research and Development Space of Bioinformatics canbe viewed as a set of non-orthogonal vectors (Figure 1) that describeBioinformatic ActivitiesBiological Data TypesBiological SpeciesComputing InfrastructureDevelopment EffortBioinformatic activities (acquisition, storage, retrieval, integration, analysis,visualization, modeling) need to be developed for multiple biological data typesArchitectures for Integration of Data and Applications33(nucleic and amino acid sequences, physical and linkage maps, RNA, protein andmetabolite expression arrays and clinical and eld assays) derived from multiple biological species using multiple biotechnology platforms.As Bioinformaticsemerges as a discipline, however, it is likely that both research and developmentcan and will be accommodated in large programmatic grants. 7. REFERENCESBenton, D., 2000, Standards to Enable Bioinformatics Data and Information Integration, In BarnettInternationals 2nd Annual Bioinformatics and Data Integration Conference, Philadelphia, PA.Boyle, J., 1998, Building Component Software for the Biological Sciences, CCP11 Newsletter, 4:2214. Dowell, R., Jokerst, A., Day, S., Eddy, L., and Stein, L., 2001, The distributed annotation system, BMCBioinformatics 2(7). This article is available at http://www.biomedcentral.com/1471-2105/2/7.3132William D. BeavisClinical AssaysBiologicalData TypesCellular NetworksMolecular NetworksProtein ExpressionInfrastructureRNA ExpressionMapsDNA SequenceBioinformaticActivitiesFlyAcquire DataStoreIntegrateQueryAnalyzeVisualizeModelYeast A.thalianaH.sapiensCow Pig corn soyBiologicalSpeciesCDevelopmentEffortFIGURE 1. Representation of the research and development space spanned by various aspects ofBioinformatics. to understanding the structure and evolution of whole genomes. Even the morefocused and applied bioinformatics goals, e.g.", + "The Bioinformatics (Modeling core) analyzed biological data (responseto infection by a pathogen) from projects using Bayesian network analysis and created aBayesian Network Webserver (BNW - http://compbio.uthsc.edu/BNW). We have obtained significant results for all projects supported by this grant funding. We aretherefore very enthusiastic to follow up on the data we have obtained. We are applying forfunding from different sources to continue these studies either as separate projects for thedifferent DoD priority pathogens, or as a big program project that will involve pathogens andsupporting cores to do omics studies.", + "Ball Department ofBiochemistry, Stanford University MedicalSchool, Stanford, CA, USAJames R. Brown Bioinformatics,GlaxoSmithKline Pharmaceuticals, UpperProvidence, PA, USAAruna Bansal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKElissa J. Chesler Oak Ridge NationalLaboratory, Biosciences Division, OakRidge, TN, USAMichael R. Barnes Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKBryan J. Barratt Research andDevelopment Genetics, AstraZeneca,Alderley Park, Macclesfield, Cheshire, UKMatthew J. Betts Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyDiana Blaydon Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London, UKKarl W. Broman Department ofBiostatistics, Johns Hopkins University,Baltimore, MD, USAEllen M. Brown Discovery Informatics,AstraZeneca, Alderley Park, Macclesfield,Cheshire, UKRichard R. Copley Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKBarry Dancis Bioinformatics,GlaxoSmithKline Pharmaceuticals UpperProvidence, PA, USASteve Deharo Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKPaul S. Derwent Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKIan C. Gray Paradigm Therapeutics (S) PteLtd, 10 Biopolis Way, Singapore 138670Joel Greshock Translational Medicine,Clinical Pharmacology Division,GlaxoSmithKline Pharmaceuticals, UpperMerion, PA, USASimon C. Heath Centre National deGenotypage, Evry Cedex, FrancexviiiCONTRIBUTORSDavid P. Kelsell Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London,UKRalph McGinnis Wellcome Trust SangerInstitute, Hinxton, Cambridge, UKCharles A. Mein Genome Centre, QueenMarys School of Medicine and Dentistry,Charterhouse Square, London, UKMary Plumpton Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKRobert B. Russell Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyPhilippe Sanseau Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKColin A. M. Semple Bioinformatics, MRCHuman Genetics Unit, Edinburgh EH4 2XU,UKGavin Sherlock Department of Genetics,Stanford University Medical School,Stanford, CA, USAChristopher Southan Global CompoundSciences, AstraZeneca R&D, Molndal,SwedenMartin S. Taylor Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKMagnus Ulvsback MolecularPharmacology, AstraZeneca R&D, Molndal,SwedenCharlotte Vignal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKChaolin Zhang Department of BiomedicalEngineering, State University of New Yorkat Stony Brook, NY, USAMichael Q. Zhang Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAXiaoyue Zhao Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAGlossary of BioinformaticsBLAST (Basic Local Alignment Search Tool) A tool for identifying sequences in adatabase that match a given query sequence.", + "TheNCBI creates automated systems for storing and analyzing knowledge about molecular biology, biochemistry, andgenetics; facilitating the use of such databases and software by the research and medical community; coordinatingefforts to gather biotechnology information both nationallyand internationally; and performing research into advancedmethods of computer-based information processing for analyzing the structure and function of biologically importantmolecules. NCBI bioinformatics-related resources may beaccessed through its home page at: www.ncbi.nlm.nih.gov. The NCBI has three principal branches:1. Computational Biology Branch (http://www.ncbi.nlm. nih.gov/CBBresearch/)2. Information Engineering Branch (http://www.ncbi.nlm. nih.gov/IEB/)3.", + "Bioinformatics 18(Suppl 1):S136S144. doi: 10.1093/bioinformatics/18.suppl_1.S136.", + "CBELife Sciences EducationVol. 9, 98 107, Summer 2010ArticleTeaching Bioinformatics and Neuroinformatics by UsingFree Web-based ToolsWilliam Grisham,* Natalie A. Schottler,* Joanne Valli-Marill, Lisa Beck,and Jackson Beatty**Department of Psychology and Office of Instructional Development, University of California, Los Angeles,Los Angeles, CA 90095; and Department of Psychology, Bryn Mawr College, Bryn Mawr, PA 19010Submitted November 9, 2009; Revised February 25, 2010; Accepted March 2, 2010Monitoring Editor: Mary Lee LedbetterThis completely computer-based modules purpose is to introduce students to bioinformaticsresources.We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. Students integrate information gathered from websites dealing with anatomy (Mouse BrainLibrary), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and geneexpression analyses (University of California, Santa Cruz Genome Browser, National Center forBiotechnology Informations Entrez Gene, and the Allen Brain Atlas), and information resources(PubMed).", + "TheNCBI creates automated systems for storing and analyzing knowledge about molecular biology, biochemistry, andgenetics; facilitating the use of such databases and software by the research and medical community; coordinatingefforts to gather biotechnology information both nationallyand internationally; and performing research into advancedmethods of computer-based information processing for analyzing the structure and function of biologically importantmolecules. NCBI bioinformatics-related resources may beaccessed through its home page at: www.ncbi.nlm.nih.gov. The NCBI has three principal branches:1. Computational Biology Branch (http://www.ncbi.nlm. nih.gov/CBBresearch/)2. Information Engineering Branch (http://www.ncbi.nlm. nih.gov/IEB/)3.", + "CONCLUSIONNIH-PA Author ManuscriptBioinformatics is fundamentally about the information of biology. Information, in turn, isburied within a cacophony of data produced by a wide swath of molecular techniques. Inneuroscience, the breadth of data is exceptionally large as it spans genomics, proteomics,metabolomics, image analysis, and behavioral science, among other protocols, and requiresresearchers to store data with due diligence based on the data types, data scope and depth,and underlying querying requirements.", + "As David Searls, director of bioinformatics at SmithKline Beecham (King of Prussia, Pennsylvania), points out, bioinformatics is supported by theory; an increasing number of journals and scientific meetings are devoted to it; and it now has its own society, the International Society for Computational Biology (associated with the conference series Intelligent Systems for Molecular Biology), whose president is Larry Hunter of the National Library of Medicine.A case in point is Structural Bioinformatics (San Diego, California), a start-up company that, as its name suggests, is particularly interested in structural information about gene products.The company has been look-ing for a vice-president of bioinformatics since December -someone who takes a systems approach to structure-function issues, has a strong grounding in biology, cell biology and biochemistry and who knows how to use computational systems to solve these problems, but who is not necessarily a computational scientist.", + "Ball Department ofBiochemistry, Stanford University MedicalSchool, Stanford, CA, USAJames R. Brown Bioinformatics,GlaxoSmithKline Pharmaceuticals, UpperProvidence, PA, USAAruna Bansal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKElissa J. Chesler Oak Ridge NationalLaboratory, Biosciences Division, OakRidge, TN, USAMichael R. Barnes Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKBryan J. Barratt Research andDevelopment Genetics, AstraZeneca,Alderley Park, Macclesfield, Cheshire, UKMatthew J. Betts Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyDiana Blaydon Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London, UKKarl W. Broman Department ofBiostatistics, Johns Hopkins University,Baltimore, MD, USAEllen M. Brown Discovery Informatics,AstraZeneca, Alderley Park, Macclesfield,Cheshire, UKRichard R. Copley Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKBarry Dancis Bioinformatics,GlaxoSmithKline Pharmaceuticals UpperProvidence, PA, USASteve Deharo Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKPaul S. Derwent Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKIan C. Gray Paradigm Therapeutics (S) PteLtd, 10 Biopolis Way, Singapore 138670Joel Greshock Translational Medicine,Clinical Pharmacology Division,GlaxoSmithKline Pharmaceuticals, UpperMerion, PA, USASimon C. Heath Centre National deGenotypage, Evry Cedex, FrancexviiiCONTRIBUTORSDavid P. Kelsell Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London,UKRalph McGinnis Wellcome Trust SangerInstitute, Hinxton, Cambridge, UKCharles A. Mein Genome Centre, QueenMarys School of Medicine and Dentistry,Charterhouse Square, London, UKMary Plumpton Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKRobert B. Russell Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyPhilippe Sanseau Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKColin A. M. Semple Bioinformatics, MRCHuman Genetics Unit, Edinburgh EH4 2XU,UKGavin Sherlock Department of Genetics,Stanford University Medical School,Stanford, CA, USAChristopher Southan Global CompoundSciences, AstraZeneca R&D, Molndal,SwedenMartin S. Taylor Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKMagnus Ulvsback MolecularPharmacology, AstraZeneca R&D, Molndal,SwedenCharlotte Vignal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKChaolin Zhang Department of BiomedicalEngineering, State University of New Yorkat Stony Brook, NY, USAMichael Q. Zhang Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAXiaoyue Zhao Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAGlossary of BioinformaticsBLAST (Basic Local Alignment Search Tool) A tool for identifying sequences in adatabase that match a given query sequence.", + "The large number of bioinformatic tools that have beenmade available to scientists during the last few years has presented theproblem of which to use and how best to obtain scientifically valid answers(3). In this chapter, we will provide a guide for the most efficient way toanalyze a given sequence or to collect information regarding a gene, protein,structure, or interaction of interest by applying current publicly available software and databases that mainly use the World Wide Web.", + "At a high level, the Research and Development Space of Bioinformatics canbe viewed as a set of non-orthogonal vectors (Figure 1) that describeBioinformatic ActivitiesBiological Data TypesBiological SpeciesComputing InfrastructureDevelopment EffortBioinformatic activities (acquisition, storage, retrieval, integration, analysis,visualization, modeling) need to be developed for multiple biological data typesArchitectures for Integration of Data and Applications33(nucleic and amino acid sequences, physical and linkage maps, RNA, protein andmetabolite expression arrays and clinical and eld assays) derived from multiple biological species using multiple biotechnology platforms.As Bioinformaticsemerges as a discipline, however, it is likely that both research and developmentcan and will be accommodated in large programmatic grants. 7. REFERENCESBenton, D., 2000, Standards to Enable Bioinformatics Data and Information Integration, In BarnettInternationals 2nd Annual Bioinformatics and Data Integration Conference, Philadelphia, PA.Boyle, J., 1998, Building Component Software for the Biological Sciences, CCP11 Newsletter, 4:2214. Dowell, R., Jokerst, A., Day, S., Eddy, L., and Stein, L., 2001, The distributed annotation system, BMCBioinformatics 2(7). This article is available at http://www.biomedcentral.com/1471-2105/2/7.", + "Ball Department ofBiochemistry, Stanford University MedicalSchool, Stanford, CA, USAJames R. Brown Bioinformatics,GlaxoSmithKline Pharmaceuticals, UpperProvidence, PA, USAAruna Bansal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKElissa J. Chesler Oak Ridge NationalLaboratory, Biosciences Division, OakRidge, TN, USAMichael R. Barnes Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKBryan J. Barratt Research andDevelopment Genetics, AstraZeneca,Alderley Park, Macclesfield, Cheshire, UKMatthew J. Betts Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyDiana Blaydon Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London, UKKarl W. Broman Department ofBiostatistics, Johns Hopkins University,Baltimore, MD, USAEllen M. Brown Discovery Informatics,AstraZeneca, Alderley Park, Macclesfield,Cheshire, UKRichard R. Copley Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKBarry Dancis Bioinformatics,GlaxoSmithKline Pharmaceuticals UpperProvidence, PA, USASteve Deharo Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKPaul S. Derwent Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKIan C. Gray Paradigm Therapeutics (S) PteLtd, 10 Biopolis Way, Singapore 138670Joel Greshock Translational Medicine,Clinical Pharmacology Division,GlaxoSmithKline Pharmaceuticals, UpperMerion, PA, USASimon C. Heath Centre National deGenotypage, Evry Cedex, FrancexviiiCONTRIBUTORSDavid P. Kelsell Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London,UKRalph McGinnis Wellcome Trust SangerInstitute, Hinxton, Cambridge, UKCharles A. Mein Genome Centre, QueenMarys School of Medicine and Dentistry,Charterhouse Square, London, UKMary Plumpton Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKRobert B. Russell Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyPhilippe Sanseau Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKColin A. M. Semple Bioinformatics, MRCHuman Genetics Unit, Edinburgh EH4 2XU,UKGavin Sherlock Department of Genetics,Stanford University Medical School,Stanford, CA, USAChristopher Southan Global CompoundSciences, AstraZeneca R&D, Molndal,SwedenMartin S. Taylor Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKMagnus Ulvsback MolecularPharmacology, AstraZeneca R&D, Molndal,SwedenCharlotte Vignal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKChaolin Zhang Department of BiomedicalEngineering, State University of New Yorkat Stony Brook, NY, USAMichael Q. Zhang Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAXiaoyue Zhao Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAGlossary of BioinformaticsBLAST (Basic Local Alignment Search Tool) A tool for identifying sequences in adatabase that match a given query sequence.", + "There are online bioinformatics resources from which this type of information may be sourced.", + "Ball Department ofBiochemistry, Stanford University MedicalSchool, Stanford, CA, USAJames R. Brown Bioinformatics,GlaxoSmithKline Pharmaceuticals, UpperProvidence, PA, USAAruna Bansal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKElissa J. Chesler Oak Ridge NationalLaboratory, Biosciences Division, OakRidge, TN, USAMichael R. Barnes Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKBryan J. Barratt Research andDevelopment Genetics, AstraZeneca,Alderley Park, Macclesfield, Cheshire, UKMatthew J. Betts Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyDiana Blaydon Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London, UKKarl W. Broman Department ofBiostatistics, Johns Hopkins University,Baltimore, MD, USAEllen M. Brown Discovery Informatics,AstraZeneca, Alderley Park, Macclesfield,Cheshire, UKRichard R. Copley Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKBarry Dancis Bioinformatics,GlaxoSmithKline Pharmaceuticals UpperProvidence, PA, USASteve Deharo Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKPaul S. Derwent Bioinformatics,GlaxoSmithKline Pharmaceuticals, ThirdAvenue, Harlow, Essex, UKIan C. Gray Paradigm Therapeutics (S) PteLtd, 10 Biopolis Way, Singapore 138670Joel Greshock Translational Medicine,Clinical Pharmacology Division,GlaxoSmithKline Pharmaceuticals, UpperMerion, PA, USASimon C. Heath Centre National deGenotypage, Evry Cedex, FrancexviiiCONTRIBUTORSDavid P. Kelsell Centre for CutaneousResearch, Institute of Cell and MolecularScience, Queen Marys School of Medicineand Dentistry, Whitechapel, London,UKRalph McGinnis Wellcome Trust SangerInstitute, Hinxton, Cambridge, UKCharles A. Mein Genome Centre, QueenMarys School of Medicine and Dentistry,Charterhouse Square, London, UKMary Plumpton Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKRobert B. Russell Structural andComputational Biology Programme, EMBL,Meyerhofstrasse 1, 69117 Heidelberg,GermanyPhilippe Sanseau Bioinformatics,GlaxoSmithKline Pharmaceuticals,Stevenage, Hertfordshire, UKColin A. M. Semple Bioinformatics, MRCHuman Genetics Unit, Edinburgh EH4 2XU,UKGavin Sherlock Department of Genetics,Stanford University Medical School,Stanford, CA, USAChristopher Southan Global CompoundSciences, AstraZeneca R&D, Molndal,SwedenMartin S. Taylor Wellcome Trust Centrefor Human Genetics, University of Oxford,Oxford, UKMagnus Ulvsback MolecularPharmacology, AstraZeneca R&D, Molndal,SwedenCharlotte Vignal Discovery and PipelineGenetics, GlaxoSmithKlinePharmaceuticals, Third Avenue, Harlow,Essex, UKChaolin Zhang Department of BiomedicalEngineering, State University of New Yorkat Stony Brook, NY, USAMichael Q. Zhang Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAXiaoyue Zhao Cold Spring HarborLaboratory, Cold Spring Harbor, NY, USAGlossary of BioinformaticsBLAST (Basic Local Alignment Search Tool) A tool for identifying sequences in adatabase that match a given query sequence." + ], + [ + "This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. IntroductionThe association between a complex phenotypic trait andgenetic markers on the chromosomes can be detectedthrough statistical analysis, leading to the identification ofquantitative trait loci (QTL)regions of the chromosomesthat appear to be associated with the phenotype. Quantitativetrait loci (QTL) are expected to be associated with the genescontrolling some aspects of the phenotype.", + "Nowadays manydifferent cost-efficient genotyping solutions (including sequencing and SingleNucleotide Polymorphisms arrays) have opened the way to systematic genome-widefine mapping of quantitative traits (Quantitative Trait Locus or QTL mapping). The process of QTL mapping (Figure 1) consists in searching for genome regions that influence the value of a given trait. For example, identifying a QTL forplant height means finding a DNA region at which the plants that carry a certainallele tend to be significantly higher or lower than those carrying another allele.", + "QTLs are regions within thegenome whose genetic variation modulates quantitatively a phenotype characteristic ofthe particular trait under study (Lynch and Walsh, 1998). Determining the associationbetween variations in specific disease phenotypes or a trait, with variations in genotypesof a reference population can be used to locate a QTL. One of the methods used formapping QTLs associated with complex traits is genetic markers-trait association. Genetic markers associated with certain loci can be inherited in linkage disequilibrium. Generating populations with linked loci in disequilibrium is achieved though eithercrosses between inbred lines, or use of the out-bred populations.", + "Often, the first step in analysis of new traitdata is single-marker regression across all chromosomes. A hypothetical QTL is evaluated atthe location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott,1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchilland Doerge, 1994).", + "One possible approach to facilitate this endeavor is to identify quantitative trait loci(QTL) that contribute to the phenotype and consequently unravel the candidategenes within these loci. Each proposed candidate locus contains multiple genes and,therefore, further analysis is required to choose plausible candidate genes. One ofsuch methods is to use comparative genomics in order to narrow down the QTL to aregion containing only a few genes. We illustrate this strategy by applying it togenetic findings regarding physical activity (PA) in mice and human.", + "Elucidation of the molecular basis of these traits has provendifficult as they are under the control of multiple genes andgenetic loci. The standard approach to gene identificationinvolves mapping by linkage analysis in experimental crosses,and this has led to the localization in the rat genome ofhundreds of quantitative trait loci (QTLs) underlying traitvariation (68). We refer to these loci as physiological quantitative trait loci (pQTLs).", + "Often, the first step in analysis of new trait data is single-marker regression across all chromosomes.A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992).For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).By default, it returns a list of marker loci that show greater than sugges-tive association with the trait according to standard criteria (Lander and Kruglyak, 1995), but it will also accept user-defined criteria.Local maxima in the LRS in this list identify loci that are most likely to be near QTLs.WebQTL provides this list within a few seconds.", + "QTLs can be identified through their geneticlinkage to visible marker loci with genotypes that can be readily classified [94, 97]. Assuch, markers that are genetically linked quantitative trait will segregate more often withtrait values, whereas unlinked markers will lack an association with the phenotype [94,98]. The principal goal of a QTL analysis is to identify all QTLs linked to a trait anddiscern whether phenotypic differences are mainly due to a few loci with large effects, ormany loci with small effects [98].", + "This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. IntroductionThe association between a complex phenotypic trait andgenetic markers on the chromosomes can be detectedthrough statistical analysis, leading to the identification ofquantitative trait loci (QTL)regions of the chromosomesthat appear to be associated with the phenotype. Quantitativetrait loci (QTL) are expected to be associated with the genescontrolling some aspects of the phenotype.", + "The basic principle of classic QTL is trait segregation along with themarkers and necessitated the availability of two or more genetically differentlines corresponding with the phenotypic trait. Markers like single nucleotidepolymorphisms (SNPs) and microsatellites are used for genotypic distinctions(Vignal et al. , 2002). QTL mapping is achieved in four basic steps; the first one is the measurementof variation for a trait in the individuals. It is a prerequisite to have the traitsthat show phenotypic variability among the individuals (inbred strains).", + "Often, the first step in analysis of new trait data is single-marker regression across all chromosomes.A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992).For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).By default, it returns a list of marker loci that show greater than sugges-tive association with the trait according to standard criteria (Lander and Kruglyak, 1995), but it will also accept user-defined criteria.Local maxima in the LRS in this list identify loci that are most likely to be near QTLs.WebQTL provides this list within a few seconds.", + "Often, the first step in analysis of new traitdata is single-marker regression across all chromosomes. A hypothetical QTL is evaluated atthe location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott,1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchilland Doerge, 1994).", + "Quantitative Trait Locus (QTL) mappingTo map QTL, we used 934 AXB/BXA genetic informative markers obtained from http://www. genenetwork.org. For all the in vitro measurements and gene expression linkage analysis, agenome-wide scan was performed using R/qtl [57]. Significance of QTL logarithm-of-odds(LOD) scores was assessed using 1000 permutations of the phenotype data [114] and the corresponding p-values reported. For the cellular phenotypes, QTL significance was reported at agenome-wide threshold corresponding to p < 0.05.", + "Typically one may obtain a location known to derive from only one of the twoparent strains that contains a chromosomal region that correlates with a trait of interest. Since the actual gene and gene product will frequently remain unknown, the region isreferred to as quantitative trait locus (QTL), and is simply named for the trait itself(Alberts & Schughart, 2010). Growing sets of strain-dependent marker locations inestablished RI strains are continually updated in online repositories.", + "By definition, aquantitative trait locus is a chromosomal region that contains a gene, or genes, thatregulate a portion of the genetic variation for a particular phenotype (Wehner et al. 2001). The goal of QTL mapping is to identify regions of the genome that harbourgenes relevant to a specified trait. QTL map locations are commonly determined byinitial screening of mice with specific genetic characteristics, such as recombinantinbred strains, the F2 of two inbred strains, or recombinant congenic strains (Flint2003).", + "Often, the first step in analysis of new traitdata is single-marker regression across all chromosomes. A hypothetical QTL is evaluated atthe location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott,1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchilland Doerge, 1994).", + "QTL linkage studies are conducted in order to map a region or regions of the genome whichaffect a continuous or quantitative trait. In agriculture, as soon as markers linked to QTL arefound for economically important traits, these markers can be used for selecting individualsin breeding programmes. In human studies, the aim is often to identify markers indicatingdisease susceptibility. Current techniques for measuring markers are usually relatively slowand laborious. Newer DNA technology, such as SNP or single nucleotide polymorphisms(Kwok, 2001b; Patil et al.", + "Genomic regions linked to complex traits can be identified by genetic mappingand quantitative trait locus (QTL) analysis (Shehzad and Okuno 2014). 7QTL mappingQTL mapping with molecular markers is the first strategy in genetic studies. In plantbreeding, QTL mapping is an essential step required for marker-assisted selection(Mohan et al. 1997; Shehzad and Okuno 2014). The fundamental idea underlying QTLanalysis is to associate genotype and phenotype in a population exhibiting a geneticvariation (Broman and Sen 2009).Four steps of QTL mapping are (1) development aWpopulation, (2) genotyping the population using molecular markers, (3) phenotyping thepopulation for an interested trait, and (4) QTL analysis using statistical procedures to findIEmarkers linked to the QTL (Bernardo 2002). PREVPopulations used for genetic mapping can be a segregating population (F2 andbackcross) or a permanent population (double haploids or recombinant inbred lines). Recombinant inbred lines (RILs) are developed by selfing of individual progenies of theF2 plants until homozygosity is achieved (F7-F8).", + "Thistool allows systems genetic analysis of single genes or small sets of genes using a bottom-upapproach. relations define quantitative trait loci (QTLs). Because the marker is not typically theactual site of the polymorphism, interpolative methods have been developed to estimatethe distance of the QTL from the marker and the strength of the association. Usingmultiple-regression and model-fitting methods, the true complexity of the phenotypicvariation can be modeled through the consideration of multiple loci and environmentalfactors as predictors [13]." + ] + ], + "task_id": [ + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C", + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "02A94D1056FDA77BDA9AC6CFDE0D5FC6" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_2.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_2.json new file mode 100644 index 0000000..c8cde59 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_2.json @@ -0,0 +1,92 @@ +{ + "question": [ + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "What about recombination in the human genome?", + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? " + ], + "answer": [ + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome.", + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging." + ], + "contexts": [ + [ + "Primate and mouse centromeric DNA contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B.In humans, the CENP-B box is present on every chromosome except for the Y chromosome [20].It is paradoxically necessary for formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres, and mice lacking CENP-B are viable and fertile [21,22].", + "Box 3 Mechanism of homologous recombination and end joiningThe severe phenotype of the mouse mutants and the highly cancer-prone human syndromes highlight the importance of homologous recombination.Mouse KU mutants display sensitivity to agents that lead to breaks in DNA, and have immunological problems because the KU proteins are involved in V(D)J recombination of antibody gene sequences.In addition, these mutants display poor development, several features of premature ageing and increased apoptosis of postmitotic neurons in the developing brain.Mice with defects in DNA-PK cs (SCID mice) display a similar but generally milder phenotype.In contrast, XRCC4-and ligase IV-knockout mice seem more severe, with late embryonic lethality resulting from massive ATM-and p53-dependent neuronal apoptosis 33,38 .Cells in G1 have only the homologous chromosome for recombination repair.However, this may be difficult to find in the complex genome.Moreover, it is potentially dangerous as a template for repair as it may lead to homozygosity for recessive mutations.As an alternative, the end-joining reaction simply links ends of a DSB together, without any template, using the end-binding KU70/80 complex and DNA-PK cs , followed by ligation by XRCC4-ligase4 (reviewed by 27,33; see the right panel of the figure, stages V-VII).The function of KU70/80 might involve end protection and approximating the ends, in addition to a signalling function by DNA-PK cs .End joining may be further facilitated when the ends are still held together through nucleosomes or other structures.End joining is sometimes associated with gain or loss of a few nucleotides if internal microhomologies are used for annealing before sealing.This implies the involvement of DNA polymerases and/or nucleases.Note that the KU complex is also involved in telomere metabolism 27,62 .found to be lethal 34 .Inactivation of ATR by itself is inviable already at the blastocyst stage.Inactivation of BRCA1 and BRCA2 in mice is also embryonically lethal; cell lines display defects in homologous recombination [35][36][37] .371A tentative scenario for the homologousrecombination reaction is depicted in the left panel of the figure.To promote strand invasion into homologous sequences, the 5-3 exonuclease activity of the RAD50/MRE11/NBS1 complex (also a substrate for ATM phosphorylation) exposes both 3 ends 30 (I).RPA facilitates assembly of a RAD51 nucleoprotein filament that probably includes RAD51-related proteins XRCC2, XRCC3, RAD51B, C and D. RAD52 stimulates filament assembly (II).RAD51 has, like its Escherichia coli RecA counterpart, the ability to exchange the single strand with the same sequence from a double-stranded DNA molecule.Correct positioning of the sister chromatids by cohesins probably facilitates the identification of a homologous sequence.A candidate for the complex chromatin transactions associated with these DNA gymnastics is RAD54, a member of the SWI/SNF family of DNA-dependent ATPases.After identification of the identical sister chromatid sequence, the intact double-stranded copy is used as a template to properly heal the broken ends by DNA synthesis (III).Finally, the so-called Hollidayjunctions are resolved by resolvases 27,33,60 (IV).Homologous recombination involves the simultaneous action of large numbers of the same molecules, which are found to be concentrated in radiation-induced nuclear foci.These depend on, and also include, the BRCA1 and BRCA2 proteins 36 .Recent evidence implicates BRCA2 directly or indirectly in nuclear translocation of RAD51 (ref.61).", + "This picture poses more questions than it seeks to answer.Is the grouping of the regions by product rather than by type of region correct?Given that the recombina- tion fraction between HLA-A and HLA-B is of the order of .08%,and that this is likely to represent a distance of at least hundreds of thousands of nucleotides, how are the pieces put together over such relatively long distances?Is it possible that regions of the DNA loop out, so that transcripts can be made directly from noncon- tiguous DNA sequences, the loops being held in place by small RNAs as suggested for the control of splicing by Steitz, and her colleagues [24] and by others [25]?If these small RNAs are coded for well outside the HLA region, does this provide a mechanism for control of expression of products by unlinked genes, as may be the case for one of the constituent polypeptides of the HLA-DR product?What might be the nature of the signals that control which of a multiple set of alternative regions is expressed by any given chromosome?", + "Mamm Genome. 2006; 17:220229. [PubMed: 16518689]72. Romanoski CE, et al. Systems genetics analysis of gene-by-environment interactions in humancells. Am J Hum Genet. 2010; 86:399410. [PubMed: 20170901]73. Myers S, Freeman C, Auton A, Donnelly P, McVean G. A common sequence motif associatedwith recombination hot spots and genome instability in humans. Nature Genet. 2008; 40:11241129. [PubMed: 19165926]74. Myers S, et al. Drive against hotspot motifs in primates implicates the PRDM9 gene in meioticrecombination. Science. 2010; 327:876879. [PubMed: 20044541]75. Cordell HJ. Detecting gene-gene interactions that underlie human diseases. Nature Rev Genet. 2009; 10:392404.", + "Classification of common conserved sequences in mammalianintergenic regions. Hum. Mol. Genet. 2002, 11, 669674. 25. Zhu, L.; Swergold, G.D.; Seldin, M.F. Examination of sequence homology between humanchromosome 20 and the mouse genome: Intense conservation of many genomic elements. Hum. Genet. 2003, 113, 6070. 26. Pevzner, P.; Tesler, G. Human and mouse genomic sequences reveal extensive breakpoint reuse inmammalian evolution. Proc. Natl. Acad. Sci. USA 2003, 100, 76727677. 27. Christmann, R.B. ; Sampaio-Barros, P.; Stifano, G.; Borges, C.L. ; de Carvalho, C.R. ; Kairalla, R.;Parra, E.R. ; Spira, A.; Simms, R.; Capellozzi, V.L. ; et al.", + "a The table lists proteins in which mutations have been shown to increase homologous recombination (HR), gross chromosomal rearrangements (GCRs), chromosomal instability (CIN), sister chromatid exchanges (SCEs), tri-nucleotide repeat expansions and contractions (TNR), telomere fusions (Tel fusion), or fragile telomeres (Tel fragility).A phenotype inside brackets ([ ]) indicates that it is caused by overexpression of the protein.For further details and references see Supplementary Table1.Abbreviations: DSB, double-strand break; PCNA, proliferating cell nuclear antigen; RFC, replication factor C complex; SCF, Skp1-Cdc53/Cullin-F-box.Figure 3 Intermediates and chromosome structural alterations, as observed by different techniques. (a) Replication fork stalling, as monitored by 2D-gel electrophoresis and Southern analysis in yeast (for details about the technique, see Reference 161). (b) Slower human replication forks covering shorter DNA synthesis tracks, as determined by incorporation of IdU and CldU via DNA combing (52), which permits visualization of the process of replication on DNA fibers. (c) Accumulation of double-strand breaks (DSBs) or replicative stress, as inferred by H2AX foci or by H2AX pan staining, respectively, in human cells. (d ) DSBs or ssDNA (single-stranded DNA) gaps as seen directly by nuclear \"comet tails\" via single-cell electrophoresis assays in human cells (52). (e) Sister-chromatid exchanges (SCEs), as determined by Giemsa staining in human cells (207). ( f ) Hyper-recombination, as determined by colony sectoring in yeast (5). ( g) Gross chromosomal rearrangements (GCRs), as determined by spectral karyotyping in mouse cells (118). (h) Translocations, as visualized by pulse-field gel electrophoresis in yeast (168). (i ) Fragile sites, as detected by mitotic spreads in human cells (109). ( j) Telomere fusions, as determined by CO-FISH (chromosome-orientation fluorescent in situ hybridization) in mouse cells (124). (k) Anaphase bridges, presumably resulting from unfinished replication, dicentric chromosomes, and sister-chromatid nondisjunction, as detected by fluorescence microscopy in mouse cells.Arrows indicate the specific structural alterations referred to in each panel; in panel h, closed and open arrows indicate the position where the translocated or missing parental chromosome migrate or should migrate, respectively.When necessary, a normal control is shown on top of the panel, with the exception of panel a, which is shown on the left.Detailed description of each technique can be found in the references provided.Photos are from the laboratories of A. Nussenzweig ( g), A. Losada (k), M. Blasco ( j), L. Tora (i ), and ours (all others).Abbreviations: HR, homologous recombination; NHEJ, nonhomologous end-joining.", + "In humans, the pericentromeric region of chromosome 9 is densely packed with segmental genomic duplications (segdups) and is prone to microdeletions and microduplications. 5In order to evaluate this region for microdeletions and microduplications in family T, we screened genomic DNA from affected individual II-7 by arrayCGH with the Nimblegen HD2 platform with the previously described CHP-SKN sample 6 as the reference.Data were normalized and CNVs were called by identifying regions where Z-scores consistently deviated from the diploid mean.At 9q21.11, a genomic duplication of ~270 kb was apparent in the genomic DNA of II-7 (Figure 1D).The Genomic duplications may or may not be in tandem with their parent segment and may be either in the same or inverted orientation. 7We developed primers that would uniquely amplify genomic DNA with the duplication under each of these conditions.Forward (5 0 -CCCAGCAGA AGCAATGGTGGTAGCC-3 0 ) and reverse (5 0 -GGTGGTGAA TCCAAAAACACAAGAACAAAGTC-3 0 ) primers diagnostic for a tandem inverted duplication (Figure 2A) yielded products of expected size in family T relatives with hearing loss, but yielded no product in unaffected family T relatives (Figure 2B).Genotypes of all 58 participating relatives in family T indicated that the tandem inverted duplication was coinherited with hearing loss.The duplication spans approximately positions 71,705,804 to 71,974,823 (hg19) on chromosome 9 for a size of ~269,023 bp.The duplication includes the entire locus for the tight junction protein TJP2, which spans positions 71,788,971 to 71,870,124 (hg19).", + "Chromosomal context of human NORsHuman NORs are positioned on the short arms of the acrocentric chromosomes that still remain unsequenced and thus missing from the current human genome draft, GRCh38.p7.Seeking an understanding of the chromosomal context of human NORs and to identify potential NOR regulatory elements, my laboratory has begun to characterize the sequences on both proximal (centromeric) and distal (telomeric) sides of the rDNA arrays (Fig. 3A; Floutsakou et al. 2013).Building on earlier reports of sequences distal and proximal to the rDNA array on HSA21 and HSA22, respectively (Worton et al. 1988;Sakai et al. 1995;Gonzalez and Sylvester 1997), 207 kb of sequence immediately proximal and 379 kb distal to rDNA arrays have been reported recently (Floutsakou et al. 2013).Consensus proximal junction (PJ) and distal junction (DJ) sequences were constructed mostly from chromosome 21 BACs (bacterial artificial chromosomes).Comparison of these sequences with BACs and cosmids derived from the other acrocentrics revealed that the PJ and DJ sequences are, respectively, 95% and 99% identical between all five acrocentric chromosomes.Conservation of DJ sequences among the acrocentrics is consistent with frequent recombination between the rDNA arrays on each of the acrocentric chromosomes (Worton et al. 1988).However, conservation of PJ sequences suggests that there must also be frequent recombination events in the interval between the centromere and rDNA arrays.Proximal sequences are almost entirely segmentally duplicated, similar to the regions bordering centromeres.Consequently, they are unlikely to contain any specific elements that would regulate the activity of the linked NOR.In contrast, the distal sequence is predominantly unique to the acrocentric short arms and is dominated by a very large inverted repeat.Each arm of the inverted repeat is >100 kb, and they share an average sequence identity of 80%.There is a large (40-kb) block of a 48base-pair (bp) satellite repeat, CER, at the distal end of the DJ (Fig. 3A).CER blocks are found distal to the rDNA on all acrocentric chromosomes, with additional pericentromeric blocks on chromosomes 14 and 22. Finally, there are two blocks of a novel 138-bp tandem repeat, ACRO138, present within the DJ.The conservation of DJ sequence between the five human acrocentric chromosomes provides a unique opportunity to visualize NORs by FISH.Whereas the rDNA content of NORs can vary greatly, probing of human metaphase chromosome spreads with a DJ BAC results in signal that is consistent between NORs (Floutsakou et al. 2013).Using this probing scheme, it was observed that in most human cell lines analyzed, including multiple primary lines, at least one and sometimes as many as four of the NORs present have very little or no detectable rDNA (C van Vuuren and B McStay, unpubl. ).Many studies have used silver staining of metaphase spreads prepared from stimulated human peripheral blood lymphocytes to determine how many NORs are active in normal human cells.The number of active NORs ranges from seven to 10, with an average of eight (Heliot et al. 2000).Possibly, NORs with low rDNA content are active but fall below a detection threshold in silver staining.At this point, it is worth considering the distribution of active versus silent rDNA repeats in humans and other mammals.If 50% of rDNA repeats are truly repressed, there are insufficient \"silent\" NORs to house them.We must conclude that active NORs are a mosaic of active and silent repeats.", + "However, excluding some cases, recombinationsuppression occurs in a small genomic tractwhere these genes are located, and it doesnot extend over most of the sex chromosomepair, as occurs in mammals and birds (Bergeroand Charlesworth, 2009). It is not clear if thissuppression occurs by the presence of inversions or as a modulation of the recombinationmechanism itself, but both could be involved(Bergero and Charlesworth, 2009). Evidence ofrecombination in the SD region in sex reversalindividuals supports the second hypothesis.", + "Orthologous chromosomes between baboon and human", + "Lichter P, Cremer T, Borden J, Manuelidis L, Ward DC (1988) Delineation ofindividual human chromosomes in metaphase and interphase cells by in situ suppression hybridization using recombinant DNA libraries. Hum Genet 80:2242343. Jang W, Yonescu R, Knutsen T, Brown T, Reppert T, Sirotkin K, Schuler GD, RiedT, Kirsch IR (2006) Linking the human cytogenetic map with nucleotide sequence:the CCAP clone set. Cancer Genet Cytogenet 168:89974.NatureGenet 1:22222555. Foote S, Vollrath D, Hilton A, Page DC (1992) The human Y chromosome: overlapping DNA clones spanning the euchromatic region. Science 258:606656. Chumakov IM, Rigault P, Le Gall I et al (1995) A YAC contig map of the humangenome. Nature 377:17529757. Hudson TJ, Stein LD, Gerety SS et al (1995) An STS-based map of the humangenome. Science 270:1945195458. Coffey AJ, Roberts RG, Green ED et al (1992) Construction of a 2.6-Mb contig inyeast artificial chromosomes spanning the human dystrophin gene using an STSbased approach. Genomics 12:47448459.", + "Figure 4 Schematic depiction of proposed mechanisms for observed intrachromosomal rearrangements.The blue and red arrows indicate the orientation of the integrated plasmid loci and the recovered mouse sequences, respectively, on the original non-rearranged chromosome (left column).All four combinations are given for an arbitrarily orientated chromosome (green line).The middle column shows how two breakpoints (lightning signs) could lead to the inversion or deletion of the encompassed chromosomal sequence (yellow-orange dual tone line) and result in a recoverable mutation in the right column.The last row indicates the two options for a transposition, in which either the transgene locus or the recovered mouse sequence is copied or excised (as indicated by the pink and light blue arrows) and integrates in the breakpoint at the other location.As mentioned above, by taking into account that for a genome rearrangement to be detected, the 5 plasmid sequence of the breakpoint in lacZ must remain intact and end immediately in front of the recovered mouse sequence, the simplest intrachromosomal mutation that could have taken place was inferred (Fig. 4).Rearrangements with breakpoints in the mouse genome on either site of the integrated plasmid concatamer, but with reversely orientated sequences, could be inversions (Fig. 4).Rearrangements in the direction of the integrated plasmids, proximal for chromosome 3 and distal for chromosome 4 (Fig. 3), with similarly orientated breakpoints in the mouse genome, could be deletions (Fig. 4).Rearrangements in the reverse direction of the integrated plasmids, with reversely orientated mouse sequences, are more complicated and might be owing to transpositions (Fig. 4).According to these schemes, half of the intrachromosomal rearrangements would have been inversions, whereas deletions and transpositions each made up one fourth (Fig. 3).Alternatively, these rearrangements could be explained by translocations involving the transgene clusters integrated on either the homolog or the other chromosome.", + "FIGURE 3. Telomere arrays of chicken and human chromosomes: the chicken genome contains more telomere sequence than the human genome.Chicken (a) and human (b) metaphase chromosomes and interphase cells hybridized with a telomeric sequence-peptide nucleic acid (PNA)-fluorescein probe.Human and chicken slide preparations were processed, and images were captured using the same parameters.Qualitatively, the telomere-positive fluorescent signals (white spots) from chicken cells and chromosomes have greater intensity than those of human (4,6 diamidino-2-phenylindole, DAPI counterstain).", + "In a previous study on the accumulation of spontaneous genome rearrangements in normal mice with aging, we discovered that 50% of the events were intrachromosomal, i.e., large deletions or inversions [22].In contrast, in this present study most of the rearrangements resulted from inter-chromosomal recombination, in both the Ercc1-mutant and control animals (Table 3).Previously, we used lacZ-plasmid line 60 mice with integration sites on Chromosomes 3 and 4, while in the present study line 30 mice were used with a single integration site on Chromosome 11.This indicates that the relative frequency of translocations is founder line specific and could be due to the position of the lacZ-plasmid cluster on the chromosome.Indeed, the chromosomal integration sites in line 60 mice are in the E1 region of Chromosome 3 (half way along the chromosome) and the C5 region of Chromosome 4 (two-thirds of the way along the chromosome) [22], while the integration site of founder line 30 (used in this study) is on the centromeric tip of Chromosome 11 (region A1-A2; not shown).The proximal location on Chromosome 11 prevents the detection of all but relatively small intra-chromosomal recombinations; larger events would lead to loss of the centromere and, therefore, the entire chromosome.If the orientation of the integration site in line 30, which is currently unknown, is towards the centromere, transpositions and inversions towards the distal end are the only detectable large intra-chromosomal rearrangements (for a detailed explanation of the different chromosomal events that can occur at the lacZ locus, see [22])." + ], + [ + "Primate and mouse centromeric DNA contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B.In humans, the CENP-B box is present on every chromosome except for the Y chromosome [20].It is paradoxically necessary for formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres, and mice lacking CENP-B are viable and fertile [21,22].", + "Box 3 Mechanism of homologous recombination and end joiningThe severe phenotype of the mouse mutants and the highly cancer-prone human syndromes highlight the importance of homologous recombination.Mouse KU mutants display sensitivity to agents that lead to breaks in DNA, and have immunological problems because the KU proteins are involved in V(D)J recombination of antibody gene sequences.In addition, these mutants display poor development, several features of premature ageing and increased apoptosis of postmitotic neurons in the developing brain.Mice with defects in DNA-PK cs (SCID mice) display a similar but generally milder phenotype.In contrast, XRCC4-and ligase IV-knockout mice seem more severe, with late embryonic lethality resulting from massive ATM-and p53-dependent neuronal apoptosis 33,38 .Cells in G1 have only the homologous chromosome for recombination repair.However, this may be difficult to find in the complex genome.Moreover, it is potentially dangerous as a template for repair as it may lead to homozygosity for recessive mutations.As an alternative, the end-joining reaction simply links ends of a DSB together, without any template, using the end-binding KU70/80 complex and DNA-PK cs , followed by ligation by XRCC4-ligase4 (reviewed by 27,33; see the right panel of the figure, stages V-VII).The function of KU70/80 might involve end protection and approximating the ends, in addition to a signalling function by DNA-PK cs .End joining may be further facilitated when the ends are still held together through nucleosomes or other structures.End joining is sometimes associated with gain or loss of a few nucleotides if internal microhomologies are used for annealing before sealing.This implies the involvement of DNA polymerases and/or nucleases.Note that the KU complex is also involved in telomere metabolism 27,62 .found to be lethal 34 .Inactivation of ATR by itself is inviable already at the blastocyst stage.Inactivation of BRCA1 and BRCA2 in mice is also embryonically lethal; cell lines display defects in homologous recombination [35][36][37] .371A tentative scenario for the homologousrecombination reaction is depicted in the left panel of the figure.To promote strand invasion into homologous sequences, the 5-3 exonuclease activity of the RAD50/MRE11/NBS1 complex (also a substrate for ATM phosphorylation) exposes both 3 ends 30 (I).RPA facilitates assembly of a RAD51 nucleoprotein filament that probably includes RAD51-related proteins XRCC2, XRCC3, RAD51B, C and D. RAD52 stimulates filament assembly (II).RAD51 has, like its Escherichia coli RecA counterpart, the ability to exchange the single strand with the same sequence from a double-stranded DNA molecule.Correct positioning of the sister chromatids by cohesins probably facilitates the identification of a homologous sequence.A candidate for the complex chromatin transactions associated with these DNA gymnastics is RAD54, a member of the SWI/SNF family of DNA-dependent ATPases.After identification of the identical sister chromatid sequence, the intact double-stranded copy is used as a template to properly heal the broken ends by DNA synthesis (III).Finally, the so-called Hollidayjunctions are resolved by resolvases 27,33,60 (IV).Homologous recombination involves the simultaneous action of large numbers of the same molecules, which are found to be concentrated in radiation-induced nuclear foci.These depend on, and also include, the BRCA1 and BRCA2 proteins 36 .Recent evidence implicates BRCA2 directly or indirectly in nuclear translocation of RAD51 (ref.61).", + "Homologous Recombination RepairDuring HR, broken DNA ends are first recognized by the Mre11-Rad50-Xrs2 (MRX) complex (Mre11-Rad50-Nbs1 (MRN) complex in mammals) and are processed by MRE11 to 3' single-strand DNA (ssDNA) tails through a series of 5' 3' strand resection activities [6,9,45].The C-terminus of NBS1 interacts with ATM and recruits it to DSBs [46].ATM belongs to the phosphatidylinositol-3-like kinaserelated kinase (PIKK) family and plays an important role in the propagation of the initial DSB lesion by phosphorylating a number of downstream substrates.In undamaged cells, ATM forms inactive dimers or multimers.Upon induction of DSBs, ATM is autophosphorylated at serine 1981, leading to its dissociation into activated monomers [47].Activated ATM rapidly phosphorylates and activates downstream repair factors to directly promote their recruitment to sites of DNA damage.Perhaps, the most important event is the ATM-dependent phosphorylation of the histone variant H2AX at the C-terminal of the protein, corresponding to Ser139 ( -H2AX) [48].Other substrates for activated ATM include the proteins SMC1, NBS1, CHK2, p53, BRCA1 and MDC1 [49].Key amongst these substrates are the Chk2 kinase and p53 which act to reduce cyclin-dependent kinase (CDK) activity and arrest cells in the various stages of cell cycle to allow time of the completion of DNA repair.Following recruitment and activation of ATM, BRCA1, BRCA2 and RAD52 epistasis group proteins including XRCC2, XRCC3, RAD51B, RAD51C and RAD51D [50] are also recruited to DSBs to further transmit signals to downstream processing enzymes.The single-strand overhangs are then rapidly bound by ssDNA-binding protein replication protein A (RPA), and recruit Rad51 and Rad52 to the damaged sites [45].Loading of Rad51 onto the ssDNA tail subsequently results in the formation of ssDNA-Rad51 nucleoprotein filament, which then searches for its homologous counterpart in the corresponding intact sister chromatid.If the specific region of duplex DNA is found, strand invasion is initiated in the presence of another set of HR facilitating proteins (Rad54, Rad55, and Rad57) followed by strand exchange and joint molecule formation [45,51].Once the Holliday junctions are resolved, distal broken ends are sealed through DNA synthesis by DNA polymerase, resulting in an errorfree repair event and preserving genetic contents [44].In response to antigen or humoral stimulation, class switch recombination (CSR) further diversifies antibodies by switching their isotypes [63].CSR occurs between two switch (S) regions located upstream of C H (constant regions of immunoglobulin heavy chain).Similar to V(D)J recombination, CSR also involves DSB generation and NHEJ.Upon humoral stimulation, activation-induced cytidine deaminase (AID) deaminates deoxycytidine (dC) resulting in deoxyuracil (dU) bases on both strands of two transcriptionally active S regions [64].The dU is excised by the uracil DNA glycosylases (UNG) and the resultant abasic site is further cut by apurinic/apyrimidinic endonuclease 1/2 (APE-1/2), generating single strand breaks (SSBs).Either two adjacent SSBs on opposite strands spontaneously lead to one DSB, or the MMR machinery is triggered to convert SSB to DSB [65].Deficiency of AID, UNG, APE or any of the MMR components, including Msh2, Msh6, Mlh1, Pms2 and Exo1, leads to loss or reduction of CSR in B cells [63].After DSB formation, the NHEJ pathway is activated.The Ku70-Ku80 heterodimers bind to the DNA ends and recruit necessary proteins to process the DNA ends to facilitate the ligation mediated by Xrcc4-DNA ligase IV complex [66].CSR in Ku70 / and Ku80 / B cells is nearly ablated [67,68].Either Xrcc4 or DNA ligase IV deficiency causes significant reduction in CSR [69,70].While compatible ends are joined rapidly by canonical NHEJ components, complex lesions need substantial processing and are re-ligated slowly.In the later case, ATM, 53BP1 and MRM complex cooperate with canonical NHEJ components to mediate end-joining recombination.Disruption of ATM, 53BP1 or MRN complex in mice leads to defects in either V(D)J recombination or CSR or both [71][72][73][74].Recent studies in mouse models deficient in NHEJ core components revealed a robust alt-NHEJ pathway that utilizes microhomology to mediate the end joining in CSR [69,70].Alt-NHEJ leads to Ig locus deletion and translocation.However, the molecular mechanisms underlying alt-NHEJ are not well elucidated so far.", + "Action of RecQ helicases on DNA recombination intermediatesTwo key intermediates of HR (homologous recombination) are the four-stranded HJ and the three-stranded D-loop.An early important observation was made that BLM and WRN selectively bind HJ structures in vitro and are capable of efficiently promoting ATP-dependent HJ branch migration through greater than 2 kb of DNA [54,55], suggesting they may act upon such four-stranded structures at blocked or collapsed replication forks to allow processing into mature recombinants.Other RecQ helicases that have been shown to efficiently unwind HJ structures include E. coli RecQ, Sgs1, RECQ1 and RECQ5 [15,16,56,57].The bacterial HJ core recognition protein RuvA inhibits HJ branch migration by BLM, WRN, RECQ1 or RECQ5 [15,16,55,58], suggesting that these RecQ helicases specifically recognize the HJ core where they initiate unwinding.", + "This picture poses more questions than it seeks to answer.Is the grouping of the regions by product rather than by type of region correct?Given that the recombina- tion fraction between HLA-A and HLA-B is of the order of .08%,and that this is likely to represent a distance of at least hundreds of thousands of nucleotides, how are the pieces put together over such relatively long distances?Is it possible that regions of the DNA loop out, so that transcripts can be made directly from noncon- tiguous DNA sequences, the loops being held in place by small RNAs as suggested for the control of splicing by Steitz, and her colleagues [24] and by others [25]?If these small RNAs are coded for well outside the HLA region, does this provide a mechanism for control of expression of products by unlinked genes, as may be the case for one of the constituent polypeptides of the HLA-DR product?What might be the nature of the signals that control which of a multiple set of alternative regions is expressed by any given chromosome?", + "Mamm Genome. 2006; 17:220229. [PubMed: 16518689]72. Romanoski CE, et al. Systems genetics analysis of gene-by-environment interactions in humancells. Am J Hum Genet. 2010; 86:399410. [PubMed: 20170901]73. Myers S, Freeman C, Auton A, Donnelly P, McVean G. A common sequence motif associatedwith recombination hot spots and genome instability in humans. Nature Genet. 2008; 40:11241129. [PubMed: 19165926]74. Myers S, et al. Drive against hotspot motifs in primates implicates the PRDM9 gene in meioticrecombination. Science. 2010; 327:876879. [PubMed: 20044541]75. Cordell HJ. Detecting gene-gene interactions that underlie human diseases. Nature Rev Genet. 2009; 10:392404.", + "a The table lists proteins in which mutations have been shown to increase homologous recombination (HR), gross chromosomal rearrangements (GCRs), chromosomal instability (CIN), sister chromatid exchanges (SCEs), tri-nucleotide repeat expansions and contractions (TNR), telomere fusions (Tel fusion), or fragile telomeres (Tel fragility).A phenotype inside brackets ([ ]) indicates that it is caused by overexpression of the protein.For further details and references see Supplementary Table1.Abbreviations: DSB, double-strand break; PCNA, proliferating cell nuclear antigen; RFC, replication factor C complex; SCF, Skp1-Cdc53/Cullin-F-box.Figure 3 Intermediates and chromosome structural alterations, as observed by different techniques. (a) Replication fork stalling, as monitored by 2D-gel electrophoresis and Southern analysis in yeast (for details about the technique, see Reference 161). (b) Slower human replication forks covering shorter DNA synthesis tracks, as determined by incorporation of IdU and CldU via DNA combing (52), which permits visualization of the process of replication on DNA fibers. (c) Accumulation of double-strand breaks (DSBs) or replicative stress, as inferred by H2AX foci or by H2AX pan staining, respectively, in human cells. (d ) DSBs or ssDNA (single-stranded DNA) gaps as seen directly by nuclear \"comet tails\" via single-cell electrophoresis assays in human cells (52). (e) Sister-chromatid exchanges (SCEs), as determined by Giemsa staining in human cells (207). ( f ) Hyper-recombination, as determined by colony sectoring in yeast (5). ( g) Gross chromosomal rearrangements (GCRs), as determined by spectral karyotyping in mouse cells (118). (h) Translocations, as visualized by pulse-field gel electrophoresis in yeast (168). (i ) Fragile sites, as detected by mitotic spreads in human cells (109). ( j) Telomere fusions, as determined by CO-FISH (chromosome-orientation fluorescent in situ hybridization) in mouse cells (124). (k) Anaphase bridges, presumably resulting from unfinished replication, dicentric chromosomes, and sister-chromatid nondisjunction, as detected by fluorescence microscopy in mouse cells.Arrows indicate the specific structural alterations referred to in each panel; in panel h, closed and open arrows indicate the position where the translocated or missing parental chromosome migrate or should migrate, respectively.When necessary, a normal control is shown on top of the panel, with the exception of panel a, which is shown on the left.Detailed description of each technique can be found in the references provided.Photos are from the laboratories of A. Nussenzweig ( g), A. Losada (k), M. Blasco ( j), L. Tora (i ), and ours (all others).Abbreviations: HR, homologous recombination; NHEJ, nonhomologous end-joining.", + "Chromosomal context of human NORsHuman NORs are positioned on the short arms of the acrocentric chromosomes that still remain unsequenced and thus missing from the current human genome draft, GRCh38.p7.Seeking an understanding of the chromosomal context of human NORs and to identify potential NOR regulatory elements, my laboratory has begun to characterize the sequences on both proximal (centromeric) and distal (telomeric) sides of the rDNA arrays (Fig. 3A; Floutsakou et al. 2013).Building on earlier reports of sequences distal and proximal to the rDNA array on HSA21 and HSA22, respectively (Worton et al. 1988;Sakai et al. 1995;Gonzalez and Sylvester 1997), 207 kb of sequence immediately proximal and 379 kb distal to rDNA arrays have been reported recently (Floutsakou et al. 2013).Consensus proximal junction (PJ) and distal junction (DJ) sequences were constructed mostly from chromosome 21 BACs (bacterial artificial chromosomes).Comparison of these sequences with BACs and cosmids derived from the other acrocentrics revealed that the PJ and DJ sequences are, respectively, 95% and 99% identical between all five acrocentric chromosomes.Conservation of DJ sequences among the acrocentrics is consistent with frequent recombination between the rDNA arrays on each of the acrocentric chromosomes (Worton et al. 1988).However, conservation of PJ sequences suggests that there must also be frequent recombination events in the interval between the centromere and rDNA arrays.Proximal sequences are almost entirely segmentally duplicated, similar to the regions bordering centromeres.Consequently, they are unlikely to contain any specific elements that would regulate the activity of the linked NOR.In contrast, the distal sequence is predominantly unique to the acrocentric short arms and is dominated by a very large inverted repeat.Each arm of the inverted repeat is >100 kb, and they share an average sequence identity of 80%.There is a large (40-kb) block of a 48base-pair (bp) satellite repeat, CER, at the distal end of the DJ (Fig. 3A).CER blocks are found distal to the rDNA on all acrocentric chromosomes, with additional pericentromeric blocks on chromosomes 14 and 22. Finally, there are two blocks of a novel 138-bp tandem repeat, ACRO138, present within the DJ.", + "However, excluding some cases, recombinationsuppression occurs in a small genomic tractwhere these genes are located, and it doesnot extend over most of the sex chromosomepair, as occurs in mammals and birds (Bergeroand Charlesworth, 2009). It is not clear if thissuppression occurs by the presence of inversions or as a modulation of the recombinationmechanism itself, but both could be involved(Bergero and Charlesworth, 2009). Evidence ofrecombination in the SD region in sex reversalindividuals supports the second hypothesis.", + "Figure2| The homologous and nonhomologous DNA end-joining pathways.a | Homologous recombination requires that the cell be diploid for the DNA region involved.A break on one chromosome can invade the homologous region on the homologous chromosome with the aid of radiation-sensitivity protein 51 (RAD51) and the single-stranded-binding protein, replication protein A (RPA).Subsequent steps involve DNA synthesis by DNA polymerase to copy the information from the intact chromosome, before ligating the newly synthesized region back to the chromosome that is undergoing repair (lower portion of diagram).The DNA crossovers are resolved to generate the two resulting intact duplexes (not shown).If the chromosome being copied is wild type, then the information content of the broken chromosome is restored to wild type.Other proteins that participate in this pathway include RAD54, RAD55, RAD57, breast cancer protein 1 (BRCA1) and BRCA2, and the Fanconi anaemia gene products76 .b | The NHEJ pathway starts with the binding of the Ku heterodimer (Ku70-Ku86) to the DNA ends32 .Ku is thought to then recruit the Artemis-DNA-PK cs complex, which functions as an endonuclease to trim 5 and 3 overhangs.After fill-in synthesis, Ku recruits the XRCC4-DNA-ligase-IV complex to carry out the ligation.When the Artemis-DNA-PK cs complex trims the DNA ends, nucleotides at the DNA ends are permanently lost.", + "Though the pathway is not fullyknown it employs a number of proteins including XRCC1, PARP1 (Poly ADP Ribose22polymerase 1, DNA ligase III, Polynucleotide kinase (PNK) , Flap endonuclease 1(Fen1), Mre11, Rad50 and Nbs1 [111-113]. Homologous recombination: In homologous recombination the broken end of a DSB isjoined to its correct partner by using the information in the sister chromatid (in G2phase), homologous chromosome or a similar repeat in the DNA. This pathway isstarted by recognition of the DSB by MRN complex.", + "Lichter P, Cremer T, Borden J, Manuelidis L, Ward DC (1988) Delineation ofindividual human chromosomes in metaphase and interphase cells by in situ suppression hybridization using recombinant DNA libraries. Hum Genet 80:2242343. Jang W, Yonescu R, Knutsen T, Brown T, Reppert T, Sirotkin K, Schuler GD, RiedT, Kirsch IR (2006) Linking the human cytogenetic map with nucleotide sequence:the CCAP clone set. Cancer Genet Cytogenet 168:89974.", + "Figure 4 Schematic depiction of proposed mechanisms for observed intrachromosomal rearrangements.The blue and red arrows indicate the orientation of the integrated plasmid loci and the recovered mouse sequences, respectively, on the original non-rearranged chromosome (left column).All four combinations are given for an arbitrarily orientated chromosome (green line).The middle column shows how two breakpoints (lightning signs) could lead to the inversion or deletion of the encompassed chromosomal sequence (yellow-orange dual tone line) and result in a recoverable mutation in the right column.The last row indicates the two options for a transposition, in which either the transgene locus or the recovered mouse sequence is copied or excised (as indicated by the pink and light blue arrows) and integrates in the breakpoint at the other location.As mentioned above, by taking into account that for a genome rearrangement to be detected, the 5 plasmid sequence of the breakpoint in lacZ must remain intact and end immediately in front of the recovered mouse sequence, the simplest intrachromosomal mutation that could have taken place was inferred (Fig. 4).Rearrangements with breakpoints in the mouse genome on either site of the integrated plasmid concatamer, but with reversely orientated sequences, could be inversions (Fig. 4).Rearrangements in the direction of the integrated plasmids, proximal for chromosome 3 and distal for chromosome 4 (Fig. 3), with similarly orientated breakpoints in the mouse genome, could be deletions (Fig. 4).Rearrangements in the reverse direction of the integrated plasmids, with reversely orientated mouse sequences, are more complicated and might be owing to transpositions (Fig. 4).According to these schemes, half of the intrachromosomal rearrangements would have been inversions, whereas deletions and transpositions each made up one fourth (Fig. 3).Alternatively, these rearrangements could be explained by translocations involving the transgene clusters integrated on either the homolog or the other chromosome.", + "FIGURE 3. Telomere arrays of chicken and human chromosomes: the chicken genome contains more telomere sequence than the human genome.Chicken (a) and human (b) metaphase chromosomes and interphase cells hybridized with a telomeric sequence-peptide nucleic acid (PNA)-fluorescein probe.Human and chicken slide preparations were processed, and images were captured using the same parameters.Qualitatively, the telomere-positive fluorescent signals (white spots) from chicken cells and chromosomes have greater intensity than those of human (4,6 diamidino-2-phenylindole, DAPI counterstain).", + "In a previous study on the accumulation of spontaneous genome rearrangements in normal mice with aging, we discovered that 50% of the events were intrachromosomal, i.e., large deletions or inversions [22].In contrast, in this present study most of the rearrangements resulted from inter-chromosomal recombination, in both the Ercc1-mutant and control animals (Table 3).Previously, we used lacZ-plasmid line 60 mice with integration sites on Chromosomes 3 and 4, while in the present study line 30 mice were used with a single integration site on Chromosome 11.This indicates that the relative frequency of translocations is founder line specific and could be due to the position of the lacZ-plasmid cluster on the chromosome.Indeed, the chromosomal integration sites in line 60 mice are in the E1 region of Chromosome 3 (half way along the chromosome) and the C5 region of Chromosome 4 (two-thirds of the way along the chromosome) [22], while the integration site of founder line 30 (used in this study) is on the centromeric tip of Chromosome 11 (region A1-A2; not shown).The proximal location on Chromosome 11 prevents the detection of all but relatively small intra-chromosomal recombinations; larger events would lead to loss of the centromere and, therefore, the entire chromosome.If the orientation of the integration site in line 30, which is currently unknown, is towards the centromere, transpositions and inversions towards the distal end are the only detectable large intra-chromosomal rearrangements (for a detailed explanation of the different chromosomal events that can occur at the lacZ locus, see [22])." + ], + [ + "Genome Res, 2011, 21: 17691776Mattick JS, Dinger ME. The extent of functionality in the humangenome. HUGO J, 2013, 7, doi:10.1186/1877-6566-1187-1182ENCODE Project Consortium, Bernstein BE, Birney E, Dunham I,Green ED, Gunter C, Snyder M. An integrated encyclopedia of DNAelements in the human genome. Nature, 2012, 489: 5774Pheasant M, Mattick JS. Raising the estimate of functional humansequences. Genome Res, 2007, 17: 12451253Hu T, Long M, Yuan D, Zhu Z, Huang Y, Huang S. The geneticequidistance result, misreading by the molecular clock and neutraltheory and reinterpretation nearly half of a century later.", + "This approach enables, on the one hand, studying the process ofmammalian evolution and, on the other hand, translational studies using modelorganisms of complex human phenotypes. Detection of regions conserved betweendistant species points to high functional importance of these fragments of the DNAsequence. Human and mouse developmental lines diverged about 75 million years ago, andever since evolutionary forces shaped the two genotypes in a different manner(Waterston et al. , 2002). Nevertheless, the extent of the changes is, however, smallenough for conservation of local gene order (Waterston et al. , 2002).", + "First, the human and mouse genome projectselucidated the sequences of over 20,000 genes [Lander et al. ,2001; Venter et al. , 2001], and most are expressed in the CNS. The availability of gene sequences has allowed rapid analysis ofcandidate human disease and disorder genes and the isolation ofthe mouse homologues. Second, the application of site-specicrecombinase technology provides investigators with the opportunity to engineer genes in the mouse that will allow for thedeletion, insertion, inversion, or exchange of chromosomalDNA with high delity (for review see Branda and Dymechi,2004].", + "In some cases, structural variations, such as copy number polymorphisms,exist (Feuk et al. , 2006); however, because of the nature of the genome assemblyprocess, these will invariably be collapsed into a single contig that does not reflectthe natural sequence. To address the technical challenges of whole-genome assembly,the human genome is released as defined builds on a quarterly basis (Lander et al. ,2001; reviewed in Chapter 4). The increasing complexity of processes that mapdata to the genome implicitly involves some lag in availability of the most currentsequence assembly.Inpractical terms, this has meant that we acquire many fragments, from a few hundredbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the humangenome, two unfinished draft sequences were produced by different methods, oneby the International Human Genome Sequencing Consortium (IHGSC) and one byCelera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001).4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.", + "THE HUMAN GENOME PROJECT IS generating vast amounts of new information at breakneck speed and causing a fundamental shift in disease research.Now with the availability of a nearly complete, high-accuracy sequence of the mouse genome (7), a new and powerful paradigm for biomedical research is established.The remarkable similarity of mouse and human genomes, in both synteny and sequence, unconditionally validates the mouse as an exceptional model organism for understanding human biology.The discovery among inbred mouse strains of defined regions of high and low genomic variation inherited primarily from two ancestral Mus subspecies (6) holds great promise to make mapping and positional cloning more rapid and feasible.Haplotype maps of inbred mouse strains combined with sophisticated delineation of their phenotypic variation and gene expression patterns will enable complex trait analysis on an unprecedented scale.This issue of Journal of Applied Physiology highlights inbred strain surveys exploring phenotypic variation in drug responses [see Crabbe et al. (1) and Watters et al. (8) in this issue].These mouse initiatives demonstrate a viable, cost-effective alternative to human research requiring family studies, population linkage analysis, or genome-wide genotyping on a multitude of individuals for association mapping.", + "How Many Genes are There in the Human Genome?", + "The Landscape of Human Genome Variation", + "In some cases, structural variations, such as copy number polymorphisms,exist (Feuk et al. , 2006); however, because of the nature of the genome assemblyprocess, these will invariably be collapsed into a single contig that does not reflectthe natural sequence. To address the technical challenges of whole-genome assembly,the human genome is released as defined builds on a quarterly basis (Lander et al. ,2001; reviewed in Chapter 4). The increasing complexity of processes that mapdata to the genome implicitly involves some lag in availability of the most currentsequence assembly.Inpractical terms, this has meant that we acquire many fragments, from a few hundredbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the humangenome, two unfinished draft sequences were produced by different methods, oneby the International Human Genome Sequencing Consortium (IHGSC) and one byCelera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001).4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.", + "Science 291:130413513. Lander ES et al (2001) Initial sequencing and analysis of the human genome. Nature 409:8609214. Engle LJ, Simpson CL, Landers JE (2006) Using high-throughput SNP technologies to study cancer. Oncogene 25:159416015. Elston RC, Anne Spence M (2006) Advances in statistical human genetics over thelast 25 years. Stat Med 25:304930806. Larson GP et al (2005) Genetic linkage of prostate cancer risk to the chromosome3 region bearing FHIT. Cancer Res 65:8058147. Botstein D, Risch N (2003) Discovering genotypes underlying human phenotypes:past successes for mendelian disease, future approaches for complex disease.McPherson JD, Marra M, Hillier L et al (2001) A physical map of the humangenome. Nature 409:93494113. Burke DT, Carle GF, Olson MV. (1987) Cloning of large segments of exogenousDNA into yeast by means of artificial chromosome vectors. Science 236:80681214. Fleischmann RD, Adams MD, White O et al (1995) Whole-genome randomsequencing and assembly of Haemophilus influenzae Rd Science 269:49651215. Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of theflowering plant Arabidopsis thaliana. Nature 408:79681516.", + "T he human genome has been cracked wide open in recent years and is spilling many of its secrets.More than 100 genome wide association studies have been conducted for scores of hu man diseases, identifying hun dreds of polymorphisms that are widely seen to influence disease risk.After many years in which the study of complex human traits was mired in false claims and methodologic inconsistencies, ge nomics has brought not only com prehensive representation of com mon variation but also welcome rigor in the interpretation of sta tistical evidence.Researchers now know how to properly account for most of the multiple hypothesis testing involved in mining the ge nome for associations, and most reported associations reflect real biologic causation.But do they matter?", + "In some cases, structural variations, such as copy number polymorphisms,exist (Feuk et al. , 2006); however, because of the nature of the genome assemblyprocess, these will invariably be collapsed into a single contig that does not reflectthe natural sequence. To address the technical challenges of whole-genome assembly,the human genome is released as defined builds on a quarterly basis (Lander et al. ,2001; reviewed in Chapter 4). The increasing complexity of processes that mapdata to the genome implicitly involves some lag in availability of the most currentsequence assembly.Inpractical terms, this has meant that we acquire many fragments, from a few hundredbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the humangenome, two unfinished draft sequences were produced by different methods, oneby the International Human Genome Sequencing Consortium (IHGSC) and one byCelera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001).", + "In some cases, structural variations, such as copy number polymorphisms,exist (Feuk et al. , 2006); however, because of the nature of the genome assemblyprocess, these will invariably be collapsed into a single contig that does not reflectthe natural sequence. To address the technical challenges of whole-genome assembly,the human genome is released as defined builds on a quarterly basis (Lander et al. ,2001; reviewed in Chapter 4). The increasing complexity of processes that mapdata to the genome implicitly involves some lag in availability of the most currentsequence assembly.Inpractical terms, this has meant that we acquire many fragments, from a few hundredbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the humangenome, two unfinished draft sequences were produced by different methods, oneby the International Human Genome Sequencing Consortium (IHGSC) and one byCelera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001).4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + ], + [ + "We (Hein, Schierup and Wiuf) have published a300 page book on molecular population genetics titled Gene Genealogies, Sequence Variation and Evolution OxfordUniversity Press, and are presently developing a tutorial in association mapping that we hope to publish as a booklet in2006 and are also involved in a very large EU collaboration (Holland, Denmark, Iceland and UK) to find susceptibilitygenes for breast and prostate cancer. In comparative genomics, the most fundamental investigation is to find genes in a pair of aligned genomes.", + "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.This fully indexed but semi-intelligibleBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C4CH 1 BIOINFORMATICS CHALLENGES FOR THE GENETICISTbook of life immediately began to serve as a valuable framework for integration ofgenetic and biological data. However, knowledge of the genome sequence did notimmediately clarify the nature and structure of human genetic variation.", + "2011) human genome reference sequence provides a basis foranalyzing short-read, whole genome, DNA sequences andFunct Integr Genomics (2012) 12:19sequencing of individuals from families segregating for aparticular phenotype (Roach et al. 2010; Dewey et al. 2011;Tian et al. 2012). The comparative analysis of the genomesequences from members of a family enabled the noise inDNA sequencing, namely, sequencing errors and genetic heterozygosity in DNA from a diploid organism, to be defined(Roach et al. 2010; Dewey et al. 2011). Through the process oftracking sequence changes/inconsistencies in inheritance fromparent to offspring, Roach et al.", + "Characteristics of genotyping and sequencing technologies", + "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.This fully indexed but semi-intelligibleBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C4CH 1 BIOINFORMATICS CHALLENGES FOR THE GENETICISTbook of life immediately began to serve as a valuable framework for integration ofgenetic and biological data. However, knowledge of the genome sequence did notimmediately clarify the nature and structure of human genetic variation.", + "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.This fully indexed but semi-intelligibleBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C4CH 1 BIOINFORMATICS CHALLENGES FOR THE GENETICISTbook of life immediately began to serve as a valuable framework for integration ofgenetic and biological data. However, knowledge of the genome sequence did notimmediately clarify the nature and structure of human genetic variation.", + "Ample time was allotted to answer questions and a copy of \"A Guide to Your Genome\" (National Human Genome Research Institute 2007) was provided to further assist participants' understanding and ability to communicate results with family members or others.", + "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.This fully indexed but semi-intelligibleBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C4CH 1 BIOINFORMATICS CHALLENGES FOR THE GENETICISTbook of life immediately began to serve as a valuable framework for integration ofgenetic and biological data. However, knowledge of the genome sequence did notimmediately clarify the nature and structure of human genetic variation." + ], + [ + "In some organisms, there is no clear relationship between telomere length and lifespan.Age-related telomere attrition could not be detected in Daphnia pulex (57) or sea urchin species (Strongylocentrotus franciscanus and Lytechinus variegatus) (58).Studies in C. elegans examining natural variation in telomere length and experimentally manipulated telomere length detect no correlation with lifespan (59,60), and in Drosophila, which uses a telomerase-independent mechanism for telomere maintenance, there is a similar lack of correlation between longevity and telomere length (61).Similarly, data on sex differences in age-related telomere shortening are mixed.For example, in the ant species Lasius niger, the rate of telomere shortening is more rapid in short-lived males compared to longer-lived females.But, mean telomere length does not differ between the two types of females, queens and workers, despite the fact that queens live much longer than workers (up to 28 years vs 2-3 months) (62).These findings suggest that the question of how telomere shortening affects aging across species and how sex affects telomere attrition rates are complex.", + "With new methodologies to assess relative telomere length by Q-PCR, studies were designed to address the impact of telomere length on aging, aging associated pathologies, and mortality.One such study has correlated shorter leukocyte telomere lengths at age 60 with a three times higher risk of heart disease and an eightfold increase in risk of infection-related death (36), thereby associating measured relative cellular aging with disease and life expectancy.In a similar way, chronic stress was shown to correlate with short leukocyte telomere length, a phenomenon attributed to higher levels of oxidative stress at the cellular level (70).More recent studies have linked telomere length in smooth muscle cells with senescence and disease severity in patients with atherosclerosis (141,150).Leukocyte telomere length was also short in a cohort of similar patients and associated with a higher risk of developing occult cardiovascular disease (71).More data are needed to understand and validate the use of leukocyte telomere length as a biomarker for cardiovascular and other diseases.", + "Shortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19].", + "New research has indicated how social factors, such as subordination, may translate into biological effects (epel et al. 2004;Chae et al. 2014).In a now classic study, epel et al. ( 2004) examined the telomere lengths of fifty-eight healthy premenopausal women who either had a healthy child (n = 19) or were giving care to a chronically ill child (n = 39. )They measured perceived stress, years of caregiving, telomere length, and oxidative stress.They found highly statistically significant differences in telomere length between women taking care of chronically ill children and those who had healthy children.They found highly statistically significant negative correlations between telomere length and perceived stress and years of caregiving.Telomerase activity had highly statistically significant negative correlations with perceived stress and years of caregiving.Oxidative stress was highly positively correlated with perceived stress and years of caregiving.They concluded that the telomere length shortening was equivalent to 9 to 17 years of aging in the high stress group.Telomere length is considered a biomarker of aging (Finch and Kirkwood 2000).Thus, this study showed that caregiver stress had essentially aged these women 9 to 17 years compared to women who had healthy children.", + "Adult studies have also found a negative correlation with baseline telomere length, suggesting a negative feedback regulation of leukocyte telomere length (Farzaneh-Far et al. 2010;Aviv et al. 2009;Epel et al. 2008;Nordfjall et al. 2009).It is possible that while our follow-up period was shorter than Shalev et al. 2013 and adult studies, which had a minimum of 5 year intervals with the exception of Puterman et al. (2015) who followed for a one-year time period, there may be biological regulation of telomere length at 4 and 5 years of age such that shorter telomeres are more robustly maintained, whereas longer telomeres have greater rates of decline, over a short period of one year.It is unlikely that this relationship is due to assay error or regression to the mean given the consistency of our findings across studies.We have had similar findings of longer telomeres having greater rates of decline and shorter telomeres being maintained in our different studies (Farzaneh-Far et al. 2010;Epel et al. 2008;Puterman et al. 2015).The single, consistent predictor of the rate of telomere attrition shown in multiple adult and the few child longitudinal studies is the baseline measurement of telomere length at the start of each study.This suggests the importance of understanding predictors of telomere length prior to adulthood, as it determines in part the rate of change (Revesz et al. 2014a, b;Nordfjall et al. 2009).Moreover, longitudinal studies in adults have had found that telomere attrition rate is dependent on baseline telomere length independent of any phenotypic predictors of shortening, such as disease or demographic variables (Nordfjall et al. 2009), attesting to the importance of studies to evaluate risk factors for shortening prior to adulthood.Rates of decline in childhood may be particularly relevant for later chronic disease risk as shorter telomere length has been implicated in disease progression through exposure to cellular senescence, inflammatory cytokines and adipocyte hypertrophy (Raschenberger et al. 2015;Willeit et al. 2014;Monickaraj et al. 2012;Fyhrquist et al. 2013).Telomere attrition in adultsAdditional early childhood telomere studies are also needed so as to better interpret disease across the lifespan.Specifically, retrospective adult studies have pointed to the importance of early life exposures, finding associations of shorter telomere length in adulthood with perinatal complications, and stressful and deprived early childhood environments, including factors associated with lower socioeconomic status and those of physical or social neglect (Drury et al. 2012;Shalev et al. 2014;Tyrka et al. 2010, Kananen et al. 2010).Some adult studies have found that predictors of shorter telomere length involving inflammation and oxidative stress exposures such as depression are only observed in younger adults compared with middle aged and older ones (Philips et al. 2013), suggesting it may be harder to tease out risk factors for accelerated attrition as, firstly, these processes happen early in life, and, secondly, repeat exposures to inflammation and oxidative stress may statistically plateau out across older age groups.While a strong maternal heritability to telomere length is widely reported (Broer et al. 2013), with overall heritability of telomere length estimated to be 64 % (95 % CI 39-83 %) (Hjelmborg et al. 2015a, b), the heritability of attrition rate is poorly understood.Hjelmborg et al. recently published data on studies of monozygotic and dizygotic twins and calculated heritability of attrition rate to be 28 % (95 % CI 16-44 %), less than the heritability of telomere length, suggesting a sizable environmental component.However, this twin study emphasized the importance of understanding environmental factors at birth and in the first years of life to better understand telomere dynamics in adulthood, as environmental exposures in adulthood played a small role in explaining adult rate of telomere loss.Additionally, studies suggest that the genetic variants associated with leucocyte telomere length in adults do not determine leukocyte telomere length in children.Different genetic determinants impact child compared with adult telomere length (Stathopoulou et al. 2015).Stathopoulou et al. suggest that single nucleotide polymorphisms (SNPs), associated with generally minor effects on telomere length in adulthood, may determine adult telomere maintenance processes versus different SNPs which are responsible for regulating telomere attrition in childhood.Abstract Telomeres are the protective complexes at the end of chromosomes, required for genomic stability.Little is known about predictors of attrition in young children or the relationship between parental and child patterns of telomere change.Telomere length was assessed twice over one year, at 4 and at 5 years of age, in Latino preschool children (n = 77) and their mothers (n = 70) in whole blood leukocytes.Maternal and child rates of attrition during the same time period were compared in 70 mother-child pairs.More children showed lengthened telomeres over one year compared to their mothers and very few children showed attrition (2.6 %).Approximately 31 % of children and 16 % of mothers displayed lengthening over one year while 66 % of children showed maintenance in contrast with 74 % of mothers.The strongest predictor for child telomere length change was child's baseline telomere length (r = 0.61,p < 0.01).Maternal rate of change was associated with child rate of change (r = 0.33, p < 0.01).After controlling for child baseline telomere length, the relationship between child and maternal rate of change trended towards significance (Coeff = 0.20, 95 % CI 0.03 to 0.43; p = 0.08).", + "Blackburn and Epel, a health psychologist who did original research on how specific lifestyle and psychological habits can protect telomeres, published The Telomere Effect (Blackburn & Epel, 2017), in which they suggested that individuals with shorter telomeres developed diseases earlier in life (a shorter \"disease span\").What follows is the evidence from these authors, their colleagues, and other researchers describing how length of telomeres contributes to mind-body connection and healthy longevity.", + "As early as at the time of birth, each of the 92 telomeres of the human genome has its own characteristic length.Additionally, each telomere shortens by its individual attrition rate.In general, longer telomeres at birth are associated with higher age-dependent attrition rates and vice versa.Overall, telomere shortening appears more dynamic in males.It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo.In individuals, short telomeres are considered to be a sign of advanced age.Cawthon and coworkers (2003) showed that telomere shortening in humans likely contributes to mortality, supporting the hypothesis that they might act as a mitotic clock (Allsopp et al., 1992).Telomere length dynamics, however, does not seem to Abstract.During aging, telomeres are gradually shortened, eventually leading to cellular senescence.By T/C-FISH (telomere/centromere-FISH), we investigated human telomere length differences on single chromosome arms of 205 individuals in different age groups and sexes.For all chromosome arms, we found a linear correlation between telomere length and donor age.Generally, males had shorter telomeres and higher attrition rates.Every chromosome arm had its individual age-specifi c telomere length and erosion pattern, resulting in an unexpected heterogeneity in chromosomespecifi c regression lines.This differential erosion pattern, however, does not seem to be accidental, since we found a correlation between average telomere length of single chromosome arms in newborns and their annual attrition rate.Apart from the above-mentioned sex-specifi c discrepancies, chromosome arm-specifi c telomere lengths were strikingly similar in men and women.This implies a mechanism that arm specifi cally regulates the telomere length independent of gender, thus leading to interchromosomal telomere variations.In conclusion, these data suggest that with increasing physical and genetic length of whole chromosomes, the corresponding telomeres also tend to be longer and that recombination rate and telomere length are inversely proportional.In conclusion, a combination of overall and chromosomespecifi c shorter telomeres and more pronounced age-dependent telomere erosion could be observed in males.There is a prospective clinical study strongly suggesting that longer telomeres decrease the risk of dying (Cawthon et al., 2003).With this in mind, the telomere length discrepancies between the sexes may indeed be a factor infl uencing the differences in their life expectancy.In every chromosome a linear decline of telomere length with age was observed, being more pronounced in men independent of the examined chromosome arm.This might suggest that telomere length on single chromosome arms may be infl uenced by the same factors which determine overall telomere length.S. Mayer a S. Brderlein a S. Perner a I. Waibel a A. Holdenried a N. Ciloglu a C. Hasel a T. Mattfeldt a K.V. Nielsen b P. Mller a a Institute of Pathology, University of Ulm, Ulm (Germany); b DakoCytomation A/S, Glostrup (Denmark) follow uniformity.In previous studies, sex-specifi c differences in telomere length and attrition rate of men and women were found (Benetos et al., 2001;Cawthon et al., 2003;Nawrot et al., 2004), suggesting gender differences in behavior of telomeres.In individual chromosome arms, telomere length was also shown not to be homogeneous (Lansdorp et al., 1996;Benn, 1997;Martens et al., 1998;Surralles et al., 1999;Hao and Tan, 2001;Londono-Vallejo et al., 2001;Graakjaer et al., 2003), some telomeres being signifi cantly shorter, others longer than the average length.To date, these characteristics in telomere lengths could not be set in a biological context, as only a few groups have provided detailed information about chromosome-specifi c patterns of telomere distribution (Lansdorp et al., 1996;Graakjaer et al., 2003).Whether accumulation of short telomeres (Martens et al., 2000;Londono-Vallejo et al., 2001) or rather the shortest telomere of one specifi c chromosome arm (Hemann et al., 2001) elicits senescence, remains an open question so far.In recent literature, there are hints that the average telomere length may be higher in women and that their annual shortening rate may be somewhat lower (Vaziri et al., 1993;Rufer et al., 1998;Jeanclos et al., 2000), but these reported differences failed to reach statistical signifi cance except for one study (Jeanclos et al., 2000).Here, we provide compelling evidence that this is indeed the case.", + "Shortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19]." + ] + ], + "task_id": [ + "2F8796A8C3DC633F00DB901C9BA396DA", + "DEE6D385D1B01B4155AA4ABE59515893", + "10ABD2210053119B18D94F1FE266E73E", + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_3.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_3.json new file mode 100644 index 0000000..07fa4b8 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_3.json @@ -0,0 +1,116 @@ +{ + "question": [ + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "what are the bioinformatics tools for QTLs analysis?" + ], + "answer": [ + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching." + ], + "contexts": [ + [ + "Selection could occur at multiple levels, from germ cell generation and propagation to fertilization and early embryonic growth.Chromosomal abnormalities, including aneuploidy, were found in 10-20% of spermatozoa and oocytes (20) and in the cleaved embryo, with a 21% rate of abnormalities in preimplantation embryos (21).These findings led to a model for natural selection against chromosome abnormalities (21).Selection extends to the end of gestation: Only approximately 30% of all conceptions result in a live birth, with more than half of aborted fetuses containing chromosomal abnormalities (22), a number likely to be an underestimate because of technological limitations in measuring all possible mutations.But even in the very small fraction of germ cell duos that survive this withering genome attack and result in a live birth, a number of severe de novo mutations will still be found (23).The data on gross chromosomal alterations suggest that overall, mutation frequency early in life is very high.The functional consequence, however, is limited because of selection.Somewhat surprisingly, this picture points toward an initial decline in genomic alterations, allowing the adult individual to acquire a somatic genome optimally equipped to provide function.", + "In most plants and animals, sexis a necessary component of reproduction, and the question for evolutionary biologistsis why reproductive mechanisms have evolved that way. In one of the experimentsdescribed next, evolutionary geneticists have nevertheless devised a way to compareevolution with and without recombination in the obligately sexual fruit fly.Sex brings harmful alleles together into thesame genetic background, allowing selection to more efficiently purge them fromthe population and potentially producing some offspring that are fitter than eitherparent. However, the benefit of recombining deleterious mutations may depend on thenature of the epistatic interactions between them. The mutational deterministic hypothesis(Kondrashov 1988) depends partly on this epistasis.This disparity in investment is the basis for the twofold cost: asexualfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. Butsex usually also involves the basic process of physical recombination: the breakage andreunion of two different DNA or RNA molecules. Of these two processes, recombinationis clearly the more widespread feature of sexual reproduction. A variety of reproductivesystems, such as selfing and automixis, involve recombination but not outcrossing. Incontrast, relatively few reproductive systems have outcrossing without recombination.Longago, Wright (1931) noted that sex may destroy adaptation because a successful combination of characteristics is attained in individuals only to be broken up in the next generation by the mechanisms of meiosis itself. Similarly, if alleles at different loci werejointly responsible for the production of phenotypes, sex has the potential to break apartcoadapted gene complexes, as it moves alleles away from genetic backgrounds wherebeneficial epistatic interactions have evolved through natural selection. Why should sex therefore be so common, given the obvious costs?", + "Crossing over-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes cross over one another.Crossing over results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.This process is also known as meiotic recombination.The reason for the rarity of these mutations is natural selection: If the mutations result in disorders that decrease health and reproductive fitness, they will eventually be eliminated from a population.In exceptional cases, mutations may cause both beneficial and detrimental consequences, resulting in opposing forces of positive selection and negative selection that may cause the mutations to be preserved at nonrare frequencies in a population.For example, the HbS mutation in the HBB gene (which produces the subunit of hemoglobin) causes sickle cell disease when present in both alleles, a detrimental consequence, but protects against malaria when present in 1 allele, a beneficial consequence, ensuring that the mutation persists in populations in areas of the world where malaria is endemic.Genes are passed from parents to offspring via the process of meiosis by which gametes, the egg cells in the mother and the sperm cells in the father, are generated.Ordinarily, each cell has 23 pairs of chromosomes; the gametes have 23 unpaired chromosomes.In meiosis, the 23 pairs are split so that each gamete receives 1 chromosome from each pair (Figures 8 and 9).Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full complement of 23 chromosome pairs restored.If all goes well, the zygote gives rise to a live offspring.Recombination (meiotic recombination)-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes recombine with one another.Recombination results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.Also known as crossing over.", + "In the generation of gametes, crossing over regularly occurs, and genetic information is swapped between members of a chromosome pair.That doesn't matter within inbred animals, because the swapped parts are identical.In an F 1 animal, however, the chromosomes of a particular pair are genetically different, one each having come from each parent.Each gamete produced will be unique, as will be each F 2 zygote formed by uniting of the gametes from two F 1 parents.An F 2 group thus provides for expression of some genetic variability.This variability is limited to the allelic differences existing between the parent strains of the F 1 s, so that another F 2 , derived from different inbred strains, will express different genetic differences.", + "In most plants and animals, sexis a necessary component of reproduction, and the question for evolutionary biologistsis why reproductive mechanisms have evolved that way. In one of the experimentsdescribed next, evolutionary geneticists have nevertheless devised a way to compareevolution with and without recombination in the obligately sexual fruit fly.Sex brings harmful alleles together into thesame genetic background, allowing selection to more efficiently purge them fromthe population and potentially producing some offspring that are fitter than eitherparent. However, the benefit of recombining deleterious mutations may depend on thenature of the epistatic interactions between them. The mutational deterministic hypothesis(Kondrashov 1988) depends partly on this epistasis.This disparity in investment is the basis for the twofold cost: asexualfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. Butsex usually also involves the basic process of physical recombination: the breakage andreunion of two different DNA or RNA molecules. Of these two processes, recombinationis clearly the more widespread feature of sexual reproduction. A variety of reproductivesystems, such as selfing and automixis, involve recombination but not outcrossing. Incontrast, relatively few reproductive systems have outcrossing without recombination.Longago, Wright (1931) noted that sex may destroy adaptation because a successful combination of characteristics is attained in individuals only to be broken up in the next generation by the mechanisms of meiosis itself. Similarly, if alleles at different loci werejointly responsible for the production of phenotypes, sex has the potential to break apartcoadapted gene complexes, as it moves alleles away from genetic backgrounds wherebeneficial epistatic interactions have evolved through natural selection. Why should sex therefore be so common, given the obvious costs?", + "Traditionally, it has been agreed that thenal sex of an individual (phenotypic sex)depends on two sequential processes: the sexdetermination system of the species and thegonad differentiation process (Valenzuela,2008). However, recently, these two seeminglydistinct processes are viewed as part of a general process leading to gonad formation andsex ratios (Sarre et al. , 2004; Quinn et al. , 2011;Uller and Helantera, 2011).However, we expect thatonly at this level, the most signicant contributions brought by integrating epigenetics will bemade. Concluding Remarks and FutureProspectsFish sex ratios are the result of a complex interaction between genetic, biochemical, and environmental interactions. The ultimate resultof these interactions at the individual level isgender: male or female. However, at the population level, the combination of sex determination and differentiation sets the sex ratio. Inturn, sex ratios dene the reproductive capacityof populations and, if sex growth dimorphismexists, also the growth characteristics, something very important in an aquaculture context.The inheritance of sex based on major sexfactors, also known as chromosomal sex determination, includes monofactorial and multifactorial SD mechanisms, with the presence of aFunctional Genomic Analysis of Sex Determination and Differentiation in Teleost Fish(A)ZygoteSex determinationEmbryosSex differentiationLarvaeJuvenilesSex changeAdultsTime(B)Majorsex factorsMinorsex factorsMonofactorialaPolyfactorialdbcEnvironmentalEnvironmentaldifferencesFigure 8.2 Sex determination and differentiationin sh. (A) The processes of sex determination,sex differentiation, and sex change are representedalong the timeline of development.", + "Obehav is, in turn, influenced by offspring genesand environment (Ogene and Oenvir respectively). Hence, indirect genetic effects (blue arrows)and direct genetic effects (red arrow) are important influencers of behaviour. B) Parentoffspring conflict theory predicts that parental resource investment and offspring solicitationbehaviours are influenced by the fitness benefit to a focal individual (O), cost to a socialpartner such as a sibling (S1 and S2) or parent (P), and by their coefficient of relatedness(black arrows). 42Figure 2: Genomic imprinting can result in divergent phenotypes from the samegenotype. A) A paternally imprinted gene, i.e. maternally expressed.", + "Because of the small contribution, through the sperm, ofthe paternal transcriptome to the fertilized zygote, and because of the stronger maternal contributionto child rearing in most model organisms, parental effects are typically thought of as synonymous withmaternal effects, although true paternal effects are known to exist (Rando, 2012). Maternal effects have been shown to be important during embryonic development, leading todifferences in the birth weight of mice depending on the genotype of the mother (Cowley et al. ,1989; Wolf et al. , 2011).Therefore, the resulting phenotypic patterns lag a generationbehind the genetic transmission of the causal variants. The most well-studied parental genetic effectsare caused by deposition of maternal transcripts into the egg prior to fertilization, resulting indifferences in early embryonic development depending on the genotype of the mother. Certain geneshave also been shown to respond to maternal influence after birth through genetically definedmaternal behaviors (Weaver et al. , 2004).", + "It was believed by many that for each trait variant we should expect to find acorresponding genetic change, or gene for that trait. Through historical happenstance therelationship between genes and traits was set up and treated as if it were one-to-one. But theproduction of a trait involves not only genes, but also their interactions with each other and theenvironment, and chance." + ], + [ + "distinguishing prenatalfrom postnatal maternal effects, see below). Maternal effects canaccount for a large proportion of phenotypic variance, especiallyduring early life, and for some traits explain more variation thandirect genetic effects [33, 97, 99, 100, 102115]. However, maternal and offspring genotype are correlated (i.e. half their genes areshared), and in inbred lines they are fully confounded, thus separating the effects of their respective genotypes is difficult. To removethis confounding effect cross-fostering has been used, both in thelaboratory and in the field [119, 131].", + "Using genetic markers, the pattern of inheritance can be tracked throughfamilies. For example, by analyzing a marker linked to the eye color genein several generations, it is possible to determine from which grandparents achild has inherited its eye color alleles. More importantly, nding a markerlinked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.", + "Although autosomal SNPs are commonly used as genetic markers to infer ancestry or race/ethnicity membership, haploid such as mitochondria, Y-DNA, and X-lined markers are also important to provide separate stories of ancestry of individuals from paternal and maternal sides [42,43].Therefore, genetic structure created due to autosomal markers could be different from those of lineage markers (often influenced by political, social, and migration history of individuals/populations).mitochondrial DNA or mtDNA haploid is the maternally inherited mitochondrial genome (mtDNA) [44].All children inherit mtDNA from their mother, with no admixture from the father.Like Y-line DNA, mtDNA is passed intact from one generation to the next but through maternal line.a) Autosomal DNA (testing both sexes) markers: autosomal DNA tests utilize DNA from the 22 pairs of autosomal chromosomes.Autosomal DNA is inherited from both parents.Autosomal testing provides percentages of ethnicity using autosomal DNA SNP test (i.e., ancestry informative markers), and it is the most commonly used test to infer ancestry across diploid genome.b) Y-DNA or Y-SNPs (paternal line testing) markers: a haploid Y-DNA is the paternally inherited non-recombining portion of the Y chromosome, and it tests only for males.The Y-DNA testing tests the Y chromosome which is passed intact from father to son with no DNA from the mother.Y-DNA testing can then be used to trace direct paternal line.Y-DNA remains the same in each generation, allowing us to compare surname from different regions to see if we are from the same family.Y-line testing does not indicate anything about the contributions of the other ancestors in a family tree.In other words, you could be 3/4th Native American, with only the direct paternal line being European, and this test would tell you nothing at all about those other three Native lines.When testing the Y-chromosome, there are two types of tests, short tandem repeat (STR) and SNP markers.STR tests are best for recent ancestry while SNP tests tell about more ancient ancestry.c) Mitochondrial DNA (maternal line testing) markers:", + "Additional information about past breeding practices can be gleaned by quantifying the number of reproductive males and females in a population.This can be achieved by comparing levels of genetic diversity between sex chromosomes, autosomes and mtDNA 99 .In cattle, for example, gene flow from aurochs is evident in the autosomes but is absent in mtDNA 41 .This has been interpreted as a management strategy that may have involved allowing insemination of domesticated females by wild bulls 41,100 .In horses, a comparison of the levels of diversity of the Y chromosome and the autosomal chromosomes demonstrated that some cultures allowed fewer males to breed and instead selected specific stallion bloodlines 55 .This male-oriented breeding strategy was not practised by the Romans and only became increasingly prominent in the past 1,000 years as a result of the growing influence of Oriental stallions (Arabian, Persian and Turkmen) 101 .", + "Dr Ring: What makes the maternal gene so peculiar compared to the paternal?Dr Cookson: If you look in the epidemiologic sense, many studies show that there is increased risk of allergic disease if the mother is affected.However, very few studies have actually set out to test that formally and most of them might suffer from some sort of selection bias because the mother is more likely to be aware of her symptoms and feel guilty, and so on.It is very difficult to explain.Is it genomic imprinting, where the gene is only active when transmitted through the mother?I do not think all of these genes would be imprinted, though it is possible.It also seems that there are effects of the maternal phenotype.The maternal phenotype, if the mother is affected or unaffected, determines the strength of the maternal effect.Again, if a gene was imprinted, you would not expect maternal phenotype to be important.So, I think that this has something to do with maternal/fetal interaction, either through the placenta or shortly after birth.There is the issue of immune conflict between mother and child.At the same time, the mother is trying to prime the infant's immune system.", + "Genetic and Genomic Discovery Using Family StudiesIngrid B. Borecki, PhD; Michael A. Province, PhD G enetic studies traditionally have been performed on sets of related individuals, that is, families.Mendel's early studies in sweet peas (Pisum sativum) on the inheritance patterns of discrete traits from parents with specific mating types to offspring has shed light on the basic mechanisms of inheritance, including the fundamental laws of segregation of discrete factors (genes) from parents to offspring and the cosegregation of genes that are closely located on a chromosome (linkage).The distribution of traits within families exhibited mathematical segregation ratios in offspring from known mating types.These expected segregation ratios have been used as an important discovery tool in the study of human diseases in pedigrees, providing evidence for a multitude of single-gene disorders.Furthermore, in some cases, trait cosegregation with genetic markers with known positions provides mapping information that enables localization and, ultimately, identification of the relevant causative gene.", + "In fact, this idea has been pursued before in thecontext of signatures of reproductive isolation and shown to revealpatterns consistent with epistatic gene interactions that arise in theshape of Dobzhansky-Muller incompatibilities [10,11]. In contrast to the mouse data, the available human genotypeswere derived from outbred, ethnically distinct populations. In thiscase pairs of functionally interacting genes can be detectedfollowing a slightly different approach.", + "Fig. 3. Illustrations of the three CEU pedigrees (black) showing how genetic information from distant patrilineal relatives (arrow; red, patrilineal lines) can identify individuals.Filled squares represent sequenced individuals.To respect the privacy of these families, only abbreviated versions are presented.The sex of the CEU grandchildren was randomized.The numbers of grandchildren are not given.", + "DiscussionKinship and genetic driftAuthor ManuscriptThe expanded family of BXDs is a well powered resource for both forward and reversegenetic analyses of genome-to-phenome linkage. As this family has grown, relations amongindividual strains have become complex, requiring the use of linear mixed models (Arends etal. , 2010; Sul et al. , 2016; Zhou and Stephens, 2014) or nonparametric equivalents such asmixed random forests (Stephan et al. , 2015) that account for kinship, epoch, and othercofactors. The family has kinship at several levels.", + "When I was in high school, I remember often trying to match my friends to their parents at various school functions and being surprised at how easy this was.As human geneticists, in spite of the enormous advances being made in our field, we still cannot answer many of the everyday questions that we are asked, such as: \"Why does he look just like his mother? \"Max Perutz [1], in a recent editorial comment in the New Scientist entitled \"The Molecular Biology of the Future,\" suggested some questions, for, as he put it, \"an examination in some future century. \"Here are two of them: (1) \"The time has come\" the Walrus said, \"To talk of many things ...And why the sea is boiling hot And whether pigs have wings. \"Calculate the amount of genetic information this would require in megacricks.", + "Using genetic markers, the pattern of inheritance can be tracked throughfamilies. For example, by analyzing a marker linked to the eye color genein several generations, it is possible to determine from which grandparents achild has inherited its eye color alleles. More importantly, nding a markerlinked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.", + "Another way of avoiding stratification is to use family-based samples.This approach has several theoretical advantages: as well as being immune to stratification 114 , these samples can be used to determine whether an allele has different effects on disease when it is inherited maternally or paternally 115 , and DISCORDANT SIB designs [116][117][118] can control for the effects of shared environment.Furthermore, more complex family-based designs are possible 119 that might allow combined association and linkage analysis 120 , and family-based association tests have also been developed for quantitative traits [94][95][96][97][98] .However, pure sibship-based association studies are underpowered relative to case-control studies 107,116,117 , and the requirement for living parents might introduce an age-of-onset bias towards younger patients for diseases that usually arise late in life.Furthermore, family-based samples are often much more difficult to collect, particularly if larger pedigrees are sought.Finally, the most commonly used family-based design, the TRANSMISSION DISEQUILIBIRIUM TEST (TDT; see REF. 114) is susceptible to technical artefacts (see below).", + "There are also a number of companies that utilize ancestry informative markers (AIMs) and claim that they can provide accurate determinations of a person's ancestry.The problem with these services is their assumption that for all populations reliable genetic markers of high ancestry informative value exist.There is also a second assumption that the frequency of these markers has not changed through time.This may be true for persons of european descent, in areas that have not seen large population disruptions.however, it is doubtful that a reliable genetic marker panel can be produced for German or Lithuanian Jews, just as such a panel for Western or Central African regions that were impacted by the slave trade is less likely.To understand this sophistication requires training in evolutionary and population genetics.Unfortunately, many of the scientists working with these companies do not have adequate background in these disciplines.In general, American universities are not providing the majority of biology students training in these disciplines.Marocco (2000) reported that only 46 percent of the phD-granting public universities and 15 percent of the phD-granting private universities required evolution as a core course.Genetics is widely required as a core at the undergraduate level, but the topics of population and quantitative genetics are at the back of the major texts and the genetics courses are usually taught by molecular geneticists.Neither is evolution well covered in anthropology texts (White et al. 2009. )White and colleagues' ( 2009) study showed that these texts did not give a single accurate definition when the topic was present.Additionally, the definitions often changed when books were written for cultural versus physical anthropology and often changed within the same text.This means that even at the undergraduate level, the tools required to critically approach molecular reductionist thinking are not widely provided to students.Graduate curricula tend to be narrower than undergraduate training.Thus, the vast majority of scientists who go into human genetics, bioinformatics, computational biology, and genomics are not well prepared to address the complex interactions that account for the phenotypes we observe in modern societies.", + "To scrutinize the polygenic networks underlying complex diseases, however, mouse resourcesthat are optimized to study the actions of isolated genetic loci ona fixed background will be insufficient on their own. For example, predisposition to the metabolic syndrome is inherited ina non-Mendelian fashion stressing genetic heterogeneity andmultigenetic pathogenesis (Nandi et al. , 2004). With the reawakening as to the extraordinary genetic resources and phenotypicdiversity archived in extant inbred strains, however, a foundationis in place for tracking down these complex traits and quantitative trait loci (QTL).", + "Otherwise, tens of thousands or markers will appear significant inthe genome-wise association studies using up to one million geneticmarkers. Approaches to control for stratification include using ofself report of ancestry or genetically derived principle componentsin the analysis. For studies using inbred mouse lines, a cladogramwhich is a hierarchical grouping based on phylogenetic analysis ofstrain relatedness can be created to subdivide inbred strains intomore genetically homogenous subgroups.", + "These haplotype mosaics form the basis of geneticanalysis and data integration in the CC and DO. In contrastto natural or commercial outbred populations, the founderhaplotypes of these multiparental populations (and similarpopulations in other model organisms) are known and wellcharacterized by sequencing. This presents a tremendousadvantage in the search for causal variants of complextraits: provided a genomic segment in an experimentalanimal can be assigned to a founder haplotype using a fewtagging markers, the remaining known variants can beimputed with essentially complete certainty.", + "Although bilateral descent is the norm in Western societies, it is not universal and there is variation with cultural practices around lineage.In certain societies, individuals place greater importance on (and have greater knowledge about) one side of the family than another (unilineal descent).Thus, individuals in patrilineal groups trace relationships through males only so that your father's brother's children are members of your family, but not your father's sisters (Kottak, 2007).They are members of their husband's group or family.Efforts to create a family pedigree may be hampered if the participant is not familiar with her mother's relatives, but her mother's brother's children (her cousins) may be able to supplement her overall family history.Knowledge about the cultural system of unilineal descent avoids assuming the universality of bilateral descent.Cultural beliefs such as these also have implications in the conduct of genetic research in terms of confidentiality and autonomy (Benkendorf et al., 1997;Wertz, 1997).One cannot assume that the named proband is in a position to speak for the extended family in agreeing to participate in any genetic research (DudokdeWit et al., 1997).", + "In particular in polygynous species, a femalesoffspring may have different fathers and are thus more closely related through the maternalthan the paternal line. Therefore, any fitness cost to mothers, such as increased provisioningand care, affect maternally derived genes more strongly than paternally derived genes,leading to the silencing of the maternal copy (i.e. paternal expression) of genes that increaseresource transfer. 5. Coadaptation between offspring and maternal traitsThe genetics of the co-evolution of parental and offspring traits has been investigated usingquantitative genetics models and in several empirical studies (Agrawal et al.", + "Because of the small contribution, through the sperm, ofthe paternal transcriptome to the fertilized zygote, and because of the stronger maternal contributionto child rearing in most model organisms, parental effects are typically thought of as synonymous withmaternal effects, although true paternal effects are known to exist (Rando, 2012). Maternal effects have been shown to be important during embryonic development, leading todifferences in the birth weight of mice depending on the genotype of the mother (Cowley et al. ,1989; Wolf et al. , 2011)." + ], + [ + "Genetic mapping inmouse strains enhances the power of detecting modifier genes and identifying complexgenetic interactions. Genomewide quantitative trait locus (QTL) analysis, as described inmore detail below, represents a promising approach to detect genetic variants that areassociated with specific phenotypes and interact with each other. 16ACCEPTED MANUSCRIPTIn experimental crosses of two (inbred) strains the first generation (F1) ofoffsprings is genetically heterozygous but equal. Then in the next generation (F2) thePTstrain-specific genetic information is distributed across the genomes of their progeny andRIeach offspring is genetically unique.", + "Second, and perhaps moreimportant, is the difference in the size and types of thegenetic reference populations. In our previous study, wemapped the QTL with 36 F2 mice that were genotyped at82 markers. In the current study, by comparison, we wereable to map QTLs after examining 342 mice from 55 strainsthat were genotyped at approximately 4000 markers.", + "This contrast can be exploited to identify subregions that underlie the trans-QTLs [67]. SNPs were counted for all four pairs of parental haplotypesBvs D, B vs H, B vs C, and L vs Sand SNP profiles for the fourcrosses were compared (figure 6). Qrr1 is a highly polymorphicPLoS Genetics | www.plosgenetics.org8November 2008 | Volume 4 | Issue 11 | e1000260QTL Hotspot on Mouse Distal Chromosome 1Figure 5. QTL for aminoacyl-tRNA synthetases in distal Qrr1.", + "The traditional approach to QTL mapping is to usetwo strains that differ maximally in the phenotype asparental strains for genetic crosses, with the followingcaveats. QTL analysis based on a single cross will mostlikely reflect only a small portion of the net geneticvariation, and QTL detection will be limited to regionswhere the two progenitor strains have functional polymorphisms. Data from multiple crosses, or from an HS,will overcome this limitation and can also be used toreduce QTL intervals [5,30].", + "These candidate genes are then sequenced in the two parental inbredstrains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausiblecandidate polymorphisms, the major challenge remains \u0001 proving denitivelywhich nucleotide polymorphism underlies the QTL. The most direct proofwould be replacing one strains allele with another strains allele (creating aFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mousegenerations are depicted.", + "Furthermore, splicing QTLs(sQTLs) rather than eQTLs could comprise the molecular mechanism linking DNA variants with YFP53; thus, sQTL analysis could uncover genes that would not normally bedetected at the level of differential gene expression (DGE),53 and thus, a differentially181182Molecular-Genetic and Statistical Techniques for Behavioral and Neural ResearchFigure 8.5 Schematic for immediate, rapid ne mapping in select F2 recombinants of the RCC-F2cross. Top panel: Genome-wide signicant QTL (green trace; red dashed line signicance threshold;blue vertical lines Bayes credible interval).", + "Interval-specific haplotype analysisApproximately 97% of the genetic variation betweeninbred mouse strains is ancestral [22], so regions ofidentity by descent (IBD) between two strains used todetect a QTL are highly unlikely to contain the causalgenetic polymorphism underlying the QTL [28]. Forexample, a cross between C57BL/6J and A/J mice detectedwww.sciencedirect.coma blood pressure QTL on Chr 1 [7].", + "Interval-specific haplotype analysisApproximately 97% of the genetic variation betweeninbred mouse strains is ancestral [22], so regions ofidentity by descent (IBD) between two strains used todetect a QTL are highly unlikely to contain the causalgenetic polymorphism underlying the QTL [28]. Forexample, a cross between C57BL/6J and A/J mice detectedwww.sciencedirect.coma blood pressure QTL on Chr 1 [7].", + "At present, the BXD panel is composed of 80 different strains that all have beenfully genotyped.26 Variation in any quantifiable trait can be associated with thesegregation of parental alleles, and linkage genetics can map this variation toquantitative trait loci (QTLs), thereby identifying the genomic region(s) affectingthat trait. An overview of the QTL mapping approach is depicted in Figure 2. Classical QTL analysis has permitted the identification of loci that areassociated with variation in HSC traits.", + "In general,linking genetic variation with trait variation identifies QTL and a significant linkage ofphenotype and genotype suggest that the DNA status helps to determine trait expression. As stated above, mouse QTL studies provide distinct advantages over human studiesin the examination of genetic causes of a quantitative trait (e.g. alcoholism), even in theabsence of specific hypotheses regarding its aetiology or candidate genes.The progenitor mouse strainsshould have sufficient variation for the traits of interest and they should be genetically diverseenough to enable genetic mapping (BENNETT et al. 2006; FLINT 2003; GRISEL 2000). Thesample size required for the identification of QTL depends largely on the effect size that aQTL contributes to phenotypes on interest. Inference about QTL can be made if one or moregenetic markers are over- or underrepresented in the analysed individuals. Genotyping isoften done by means of microsatellite markers, which contains mono, di-, tri-, ortetranucleotide tandem repeats flanked by specific sequences (Figure 4a).This comparison gives information about the reliability of the observed genotypeinformation: The more the marker locations differ between the two maps (which signifiesvariation in marker positions), the higher the possibility of genotyping errors. QTL mapping was done in several stages to identify loci acting individually and QTL thatinteracted, either additively or epistatically. To determine individually-acting QTL, a singleQTL genome scan was conducted with the function scanone.", + "Importantly, whereasthese studies required substantial labor, time, and resources, X-QTL is a quick and easyapproach to achieve a comparable level of genetic dissection. The levels of complexityobserved here (e.g. 14 loci explaining 70% of the genetic variance for 4-NQO resistance) arestill dramatically lower than those seen in for some human traits in GWAS (e.g. 40 lociexplaining 5% of the variance for height 2,5). One obvious explanation is the difference inexperimental designs (line crosses vs. population association studies), but differences ingenetic architectures among species and traits may also contribute.", + "The method uses two pieces of information: mapping data from crosses thatinvolve more than two inbred strains and sequence variants in the progenitor strains within the intervalcontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that anysequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals exceptat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.", + "The method uses two pieces of information: mapping data from crosses thatinvolve more than two inbred strains and sequence variants in the progenitor strains within the intervalcontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that anysequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals exceptat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.", + "Genotyping all the individual progeny formarkers that show allelic variation between the parental strains (either single nucleotide polymorphisms or simple sequence repeats) will allow the detection of associations between trait values and marker genotype, and in this way demonstrate to whichset of markers a QTL is linked. To reduce the genotyping effort, selective genotypingof the individuals at the extremes of the phenotypic spectrum can be performed (20,23). Although these three approaches are in general considered to be the best to detect andmap QTL, they have several disadvantages for quantitative traits involving HSC.", + "So, how do you go about planning and performing a QTL study, and howdo you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbredstrains that have a markedly different trait. One can now look up many differenttraits of inbred mice online at the Mouse Phenome Database (http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). However, the trait you maywant to study may not be present in wild type mice, so you may want to crossa mutant (or genetically engineered) strain onto several inbred strains.QTL Theory and PlanningThe theory behind the most basic form of QTL mapping is based upon intercrossing two inbred strains. The mouse genome consists of 19 pairs of autosomes (non sex-determining chromosome) and the X and Y chromosomes. Inthe example shown in Fig. 18.1, we are intercrossing stain A (shown with ablack chromosome pair) with strain B (shown with a white chromosome pair). The initial F1 (filial generation 1) mice are true hybrids, with each individualFrom: Molecular Biomethods Handbook, 2nd Edition.", + "These candidate genes are then sequenced in the two parental inbredstrains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausiblecandidate polymorphisms, the major challenge remains \u0001 proving denitivelywhich nucleotide polymorphism underlies the QTL. The most direct proofwould be replacing one strains allele with another strains allele (creating aFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mousegenerations are depicted.", + "QTL mapping studies thenseek to detect the polymorphisms underlying the complex traits of interest byscanning for alleles that co-vary withthe traits. Similar experiments also can be conducted with special derivatives of inbredstrains known as recombinant inbred(RI) mice. These animals are derivedby cross-breeding two or more distinctparental strains (which often divergewidely for the trait of interest), followedby inbreeding of the offspring for severalgenerations (Bailey 1971). Given thecorrect breeding strategy, this method1This is an issue faced by GWASs researchers when classifyingsamples as cases or controls." + ], + [ + "The project also provides online analysis tools to allowidentification of correlations within its data set. GeneNetwork (http://www.genenetwork.org), encompassing WebQTL, is a database ofgenotypes and complex phenotypes ranging from gene expression to behaviour in standardinbred strains, and six panels of mouse recombinant inbred strains including the two largestsets (BXD and LXS) of approximately 80 strains each. Rat and Arabidopsis populations arealso represented. Approximately 1500 phenotypes spanning the 25 year history of thesestrains are incorporated in this public resource, many of which were retrieved from theliterature.", + "BioinformaticsAll of the genetic analyses were carried out in GeneNetwork, whichis an open source bioinformatics resource for systems genetics thatexists as both a repository for genetic, genomic and phenotypicdata together with a suite of statistical programs for data analysis that includes mapping and evaluating QTLs, examining phenotype/genotype correlations and building interaction networks. QTL mappingThe QTL mapping module of GeneNetwork was used to identifyQTLs for hippocampal morphometry and radial maze trait data. Thismodule enables interval mapping, composite interval mapping anda pairwise scan option to identify epistatic effects.", + "Thereare four options for QTL mapping on the GeneNetwork website: intervalmapping, marker regression analysis, composite interval mapping, and pairscan analysis. In this case, interval mapping was used to compute linkagemaps for the entire genome. The log of odds (LOD) score was used toassert that a causal relation exists between a chromosomal location and aphenotypic variant, such as Gsto1 expression variation.", + "Webqtl is an online database [110] of linked datasets, including genotype and expressiondata, covering multiple species including mouse, macaque monkey, rat, drosophila,arabidopsis, plants and humans [60]. While this tool cannot be used to calculate eQTLs, itcan be used to find and visualize eQTLs in different species, strains and tissues. It canperform single- and multiple-interval QTL mapping of up to 100 selected traits. Users canalso upload their own trait data for populations included in the database. It can also calculateand display trait-correlation matrices and network graphs (also for up to 100 traits).", + "Once the data is normalized appropriately (in our case, no normalization was required), the QTLcan be mapped. To do this, select the mapping tools drop down window (Figure 6). There arethree methods to choose from, GEMMA, Haley-Knott Regression, and R/qtl (Figure 6). Genomewide Efficient Mixed Model Analysis (GEMMA; github.com/genetics-statistics/GEMMA; (Zhouand Stephens, 2012) is a multivariate linear mixed model mapping tool that is used to mapphenotypes with SNPs with a correction for kinship or any other covariate of interest. Thisability to account for covariates is highly useful, but also this increases the time taken forcomputations.", + "WebQTL is the primary module in the GeneNetwork online resource (www.genenetwork.org),and provides a powerful environment to analyzetraits controlled by genetic variants (Chesler et al. 2004; Wang et al. 2003). It includes data from many485Fig. 2. Complexity of eQTL data. The graph shows a threedimensional schematic view of the high dimensionality ofthe eQTL data set generated from the BXH/HXB RI strainpanel (Hubner et al 2005; unpublished).", + "QTL MAPPING AND QTG DISCOVERY IN THE RCCA variety of statistical methods and tools have been developed for QTL mapping andimplemented in free software for public use. These methods are well suited for simplebackcross and F2 RCC populations. R/qtl9,39 was developed for identication ofQTLs and higher order modeling. Another Web-based tool, GeneNetwork orWebQTL (GeneNetwork.org),40 was developed for QTL mapping and to exploreassociations between variants, molecular traits (e.g. , gene expression), and higher orderphenotypes (e.g. , behavior) and facilitate QTG identication.", + "This enables gene expressioncorrelation and interval mapping, candidate gene searches and multitrait analyses. Each exported dataset was subject to an interval mapping analysis,which uses GeneNetworks embedded MapManager software(Manly et al . 2001) to perform HaleyKnott regression. Empirical P values were derived using 1000 permutations using the incorporatedpermutation feature of WebQTL. The peak of each statisticallysignificant (P -value <0.05) or suggestive (P -value <0.63) (Lander& Kruglyak 1995) QTL was determined based on empirical P values (Doerge & Churchill 1996). A one-LOD drop-off was usedto determine the QTL confidence interval about each peak.", + "The peak linkage valueand position was databased in GeneNetwork and userscan rapidly retrieve and view these mapping results forany probe set. Any of the QTL maps can also be rapidlyregenerated using the same Haley-Knott methods, againusing functions imbedded in GeneNetwork. GeneNetwork also enable a search for epistatic interactions (pairscanning function) and composite interval mapping withcontrol for a single marker. Data quality controlWe used two simple but effective methods to confirmcorrect sample identification of all data entered intoGeneNetwork.", + "QTL analysisAll QTL mapping for phenotypes was performed using the WebQTL software module of the170GeneNetwork (www.genenetwork.org) [34]. Interval mapping to evaluate potential QTLs wascalculated from the likelihood ratio statistics (LRS) as the softwares default measurement ofthe association between differences in traits and differences in particular genotype markers. Another common measure score, the log of the odds (LOD) ratio, can be converted from theLRS (LRS/4.61). Suggestive and significant LRS values were determined by applying 1000175permutations.", + "Once the data is normalized appropriately (in our case, no normalization was required), the QTLcan be mapped. To do this, select the mapping tools drop down window (Figure 6). There arethree methods to choose from, GEMMA, Haley-Knott Regression, and R/qtl (Figure 6). Genomewide Efficient Mixed Model Analysis (GEMMA; github.com/genetics-statistics/GEMMA; (Zhouand Stephens, 2012) is a multivariate linear mixed model mapping tool that is used to mapphenotypes with SNPs with a correction for kinship or any other covariate of interest. Thisability to account for covariates is highly useful, but also this increases the time taken forcomputations.", + "Unlike interval-specific haplotype analysis, which is most useful for narrowing a QTL shared bymultiple crosses, genome-wide haplotype analysisrequires only phenotype information from many inbredstrains and can effectively narrow a QTL identified inonly one experimental cross [36]. After narrowing the QTL to an interval that is !5 Mbusing these bioinformatics techniques or classical experimental methods, strain-specific sequence and geneexpression comparisons are effective for focusing on afew strong candidate genes (Figure 7).", + "Unlike interval-specific haplotype analysis, which is most useful for narrowing a QTL shared bymultiple crosses, genome-wide haplotype analysisrequires only phenotype information from many inbredstrains and can effectively narrow a QTL identified inonly one experimental cross [36]. After narrowing the QTL to an interval that is !5 Mbusing these bioinformatics techniques or classical experimental methods, strain-specific sequence and geneexpression comparisons are effective for focusing on afew strong candidate genes (Figure 7).", + "We considered QTL intervals that achieved genome-widesignificance for one phenotype, and genome-wide suggestive forothers, as highest priority for candidate gene analysis. The January 2017 BXD genotype file was used4 . Updated linear mixed model mapping algorithms are nowavailable on GeneNetwork 25 (Sloan et al. , 2016), that account forkinship among strains. These new algorithms include GEMMA(Zhou and Stephens, 2012), pyLMM6 (Sul et al. , 2016), andR/qtl27 .", + "GeneNetwork and WebQTL are our groups first attempts to embrace thesenew opportunities (Wang et al. 2003) and to generatean appropriate research environment that combinesdata sets, statistical resources, and summaries offindingsa knowledgebase (www.genenetwork.org). Mapping traits will become far easier; cloning allelicvariants for molecular and cellular phenotypes willprogress from difficult to trivial as it already has formost cis-QTL with high LOD scores.", + "Next, we used GeneNetwork2, an online analysis tool and data repository containinglegacy SNP and transcriptome datasets to explore gene regulatory networks (Chesler et al. 2004; Mulligan et al. 2017). We conducted both eQTL and PheQTL-eQTL network analysis using several BXD RI gene expressiondatasets from multiple brain regions (datasets documented in Supplementary Information) and using theentirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2 [BXDPublish; GN602].", + "Thereare four options for QTL mapping on the GeneNetwork website: intervalmapping, marker regression analysis, composite interval mapping, and pairscan analysis. In this case, interval mapping was used to compute linkagemaps for the entire genome. The log of odds (LOD) score was used toassert that a causal relation exists between a chromosomal location and aphenotypic variant, such as Gsto1 expression variation.", + "Genetic MappingIn this study we utilize GeneNetwork, a database containing phenotypes and genotypes,and also serves as an analysis engine for quantitative trait locus (QTL) mapping, geneticcorrelations, and phenome-wide association studies (PheWAS) (Sloan et al. , 2016; Mulligan etal. , 2017; Watson and Ashbrook, 2020). QTL analysis involves connecting phenotype data withgenotype data to examine genetic variation in traits controlled by multiple genes and theirinteraction with the environment (also called complex traits)(Lynch et al. , 1998; Myles andWayne, 2008; Goddard et al. , 2016).", + "Once the resulting record set of thequery is returned, it can be further restricted by selectingrelevant records based on attached annotations before forwarding it for further analysis. To map genetic loci associated with mRNA abundance ortrait phenotypes, any one of the three QTL mapping functions currently employed by GeneNetwork's WebQTLmodule can be used. These are 1. interval mapping, 2. single-marker regression, or 3. composite mapping [29,30].", + "genenetwork.org/) a set of 3795 markers. Linkage is reported withgenome-wide significance levels based on 2000 permutation tests. Two types of QTL mapping analysessimple mapping using the HaleyKnott regression equation, and composite interval mappingwereutilized in this study. Simple interval mapping was performed toillustrate the significance of any QTLs that regulate the TID. As asecondary analysis, composite interval mapping which controlled forthe influence of Tyrp1 was also performed with the goal of identifyingany secondary QTLs that may have been masked by the major QTL onChr 4." + ], + [ + "BioinformaticsAll of the genetic analyses were carried out in GeneNetwork, whichis an open source bioinformatics resource for systems genetics thatexists as both a repository for genetic, genomic and phenotypicdata together with a suite of statistical programs for data analysis that includes mapping and evaluating QTLs, examining phenotype/genotype correlations and building interaction networks. QTL mappingThe QTL mapping module of GeneNetwork was used to identifyQTLs for hippocampal morphometry and radial maze trait data. Thismodule enables interval mapping, composite interval mapping anda pairwise scan option to identify epistatic effects.", + "Below we detail several software tools thathave been used for eQTL analysis and provide a short description of the advantages andlimitations of each package, with a focus on the ease of use for the wider scientificcommunity (Table 1). Several of the packages were not specifically developed for eQTLanalysis, and thus our speed comparisons should be viewed in that context. However, suchpackages may already be used in individual laboratories, and therefore we include those that,in our opinion, may be scaled for eQTL analysis on data from genome-wide array platforms.", + "Other Useful QTL Mapping SoftwaresQTL Cartographer is a highly capable mapping programone that may beparticularly suitable for those with a background in UNIX and who are comfortable with advanced statistical analysis. The program is available for threeoperating systems at http://statgen.ncsu.edu/qtlcart/cartographer.html. Windows QTL Cartographer at http://statgen.ncsu.edu/qtlcart/ WQTLCart.htmis a command-line sibling and a relatively more user friendly version of QTLCartographer. This program includes a powerful graphic tool for presenting mapping results and can import and export data in a variety of formats and provide agraphical interface to QTL Cartographers features.", + "Built in to this resource are tools such as QTL Analyst to identify candidate genes and polymorphisms; literature correlation analysis by the latent semanticindexing tool, Semantic Gene Organizer (Homayouni et al. , 2005); and integratedanalysis with many large SNP sets, Gene Ontology overrepresentation and pathwaymatching (Zhang et al. , 2004), and annotation resources. By coupling flexible analytic tools with both molecular endophenotypes and higher-order phenotypic data,users can pursue a wealth of integrative systems genetics queries.", + "These relative phenotypevalues are then analyzed in the context of the mouse genotypeusing WebQTL tools available on www.genenetwork.com,which provides the QTL mapping for phenotypes of interest. The bioinformatics tools allow us to inspect the single nucleotide polymorphism density within the mapped loci and toexamine the genes within the loci in order to narrow down thenumber of candidate genes that should be further interrogated. The tools also allow us to identify interactive loci, throughwhich we can discover interactive pathways modulating themeasured phenotype.", + "ReviewTRENDS in Genetics Vol.21 No.12 December 2005Bioinformatics toolbox for narrowingrodent quantitative trait lociKeith DiPetrillo, Xiaosong Wang, Ioannis M. Stylianou and Beverly PaigenThe Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, USAQuantitative trait locus (QTL) analysis is a powerfulmethod for localizing disease genes, but identifying thecausal gene remains difficult. Rodent models of diseasefacilitate QTL gene identification, and causal genesunderlying rodent QTL are often associated with thecorresponding human diseases.Recently developedbioinformatics methods, including comparativegenomics, combined cross analysis, interval-specificand genome-wide haplotype analysis, followed bysequence and expression analysis, each facilitated bypublic databases, provide new tools for narrowingrodent QTLs. Here we discuss each tool, illustrate itsapplication and generate a bioinformatics strategy fornarrowing QTLs. Combining these bioinformatics toolswith classical experimental methods should accelerateQTL gene identification. IntroductionQuantitative trait locus (QTL) analysis is a method tolocalize chromosomal regions harboring genetic variantsthat affect a continuously distributed, polygenic phenotype(including many common diseases) [1].Summary of bioinformatics tools for dissecting rodent QTLsBioinformatics toolComparative genomicsCombined cross analysisInterval-specific haplotypeanalysisGenome-wide haplotypeanalysisSequence comparisonExpression comparisonSummaryIdentifies regions of chromosomal synteny in QTLs that are concordant acrossspeciesRecodes genotype information from multiple crosses detecting a shared QTL intoone susceptibility and one resistance genotype to combine the crosses in a singleQTL analysisDetects regions of IBD within QTLs shared in multiple crossesAssociates conserved haplotype patterns across the genome with a phenotype ininbred strainsSearches strain-specific sequence databases for regulatory or coding polymorphisms within the QTL intervalSearches EST or microarray databases to identify genes expressed in an organ ofinterest or genes exhibiting differential expression between the strains of interestthe homologous regions in humans, which complicatesthis approach.", + "Recently developedbioinformatics methods, including comparativegenomics, combined cross analysis, interval-specificand genome-wide haplotype analysis, followed bysequence and expression analysis, each facilitated bypublic databases, provide new tools for narrowingrodent QTLs. Here we discuss each tool, illustrate itsapplication and generate a bioinformatics strategy fornarrowing QTLs. Combining these bioinformatics toolswith classical experimental methods should accelerateQTL gene identification. IntroductionQuantitative trait locus (QTL) analysis is a method tolocalize chromosomal regions harboring genetic variantsthat affect a continuously distributed, polygenic phenotype(including many common diseases) [1].ReviewTRENDS in Genetics Vol.21 No.12 December 2005Bioinformatics toolbox for narrowingrodent quantitative trait lociKeith DiPetrillo, Xiaosong Wang, Ioannis M. Stylianou and Beverly PaigenThe Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, USAQuantitative trait locus (QTL) analysis is a powerfulmethod for localizing disease genes, but identifying thecausal gene remains difficult. Rodent models of diseasefacilitate QTL gene identification, and causal genesunderlying rodent QTL are often associated with thecorresponding human diseases.Summary of bioinformatics tools for dissecting rodent QTLsBioinformatics toolComparative genomicsCombined cross analysisInterval-specific haplotypeanalysisGenome-wide haplotypeanalysisSequence comparisonExpression comparisonSummaryIdentifies regions of chromosomal synteny in QTLs that are concordant acrossspeciesRecodes genotype information from multiple crosses detecting a shared QTL intoone susceptibility and one resistance genotype to combine the crosses in a singleQTL analysisDetects regions of IBD within QTLs shared in multiple crossesAssociates conserved haplotype patterns across the genome with a phenotype ininbred strainsSearches strain-specific sequence databases for regulatory or coding polymorphisms within the QTL intervalSearches EST or microarray databases to identify genes expressed in an organ ofinterest or genes exhibiting differential expression between the strains of interestthe homologous regions in humans, which complicatesthis approach.", + "1 The234IntroductionModern high-throughput technologies generate large amounts of genomic, transcriptomic, proteomic and metabolomic data. However, existing open source web-based tools for QTL analysis, such as webQTL[358] and QTLNetwork [377], are not easily extendable to dierent settings and computationally scalable for whole genome analyses. xQTLworkbench makes it easy to analyse large and complex datasets usingstate-of-the-art QTL mapping tools and to apply these methods to millions of phenotypes using parallelized Big Data solutions [342].", + "Software developed towards facilitating mining ofgenetic expression and variant associations includeeQTL Explorer, eQTL Viewer, FastMap and Lirnet. Bioinformatics concepts relating to eQTL have beenreviewed in [116]. eQTL Explorer (http://web. bioinformatics.ic.ac.uk/eqtlexplorer/) [117] as anaddition to resources provided by previous softwareslike WebQTL [118] and QTL Express [119], enablesintegrated visualization using a Java graphicalinterfaces; extracts eQTL results from externalsources (multiple microarray experiments) andpresents them such that they can be compared amongeach other, and with the pQTL (protein expression)mapped to the genome. eQTL Viewer (http://statgen.", + "These relative phenotypevalues are then analyzed in the context of the mouse genotypeusing WebQTL tools available on www.genenetwork.com,which provides the QTL mapping for phenotypes of interest. The bioinformatics tools allow us to inspect the single nucleotide polymorphism density within the mapped loci and toexamine the genes within the loci in order to narrow down thenumber of candidate genes that should be further interrogated. The tools also allow us to identify interactive loci, throughwhich we can discover interactive pathways modulating themeasured phenotype.", + "Author ManuscriptPrevious studies have used bioinformatics analyses in conjunction with a specific set ofcriteria to narrow down the set of genes into those most likely to underlie the differentialresponse (Baker et al. , 2017, Cook et al. , 2015). In the present study, genes within thesignificant QTLs were identified using the online tools available at GeneNetwork.org. Thegene lists include expressed sequence tags and Riken clones.", + "Built in to this resource are tools such as QTL Analyst to identify candidate genes and polymorphisms; literature correlation analysis by the latent semanticindexing tool, Semantic Gene Organizer (Homayouni et al. , 2005); and integratedanalysis with many large SNP sets, Gene Ontology overrepresentation and pathwaymatching (Zhang et al. , 2004), and annotation resources. By coupling flexible analytic tools with both molecular endophenotypes and higher-order phenotypic data,users can pursue a wealth of integrative systems genetics queries.", + "Another database, WebQTL, provides multiple tools that, when used incombination, provide valuable insight into candidate gene lists (11). WebQTL isan online database with built in statistical tools that take advantage of the isogenicnature of recombinant inbred (RI) animals. It combines a large database ofcomplex traits collected using RI animals with built in software to perform QTLanalysis and produce correlations of traits (11). WebQTL has genotypicinformation on five different RI lines including those derived from the C57/B6and DBA inbred strains (BxDs) (11).", + "Tools for QTL analysis have been developed and released for researchers such asR/qtl, QTL cartographer, MapQTL, and WebQTL. Recently, Wang et al. (2012)developed a free software for QTL mapping called QTL IciMapping which constructsgenetic linkage maps and QTL analysis by simple interval mapping and inclusivecomposite interval mapping. QTL IciMapping is available for segregating and inbred9populations and nested association mapping populations. Unlike R/qtl, QTL IciMappingis not available for Unix/Linux. Running QTL IciMapping using a basic computer for thenumbers of SNPs identified from genotype-by-sequencing (GBS) is time-consuming.", + "Built in to this resource are tools such as QTL Analyst to identify candidate genes and polymorphisms; literature correlation analysis by the latent semanticindexing tool, Semantic Gene Organizer (Homayouni et al. , 2005); and integratedanalysis with many large SNP sets, Gene Ontology overrepresentation and pathwaymatching (Zhang et al. , 2004), and annotation resources. By coupling flexible analytic tools with both molecular endophenotypes and higher-order phenotypic data,users can pursue a wealth of integrative systems genetics queries.", + "Built in to this resource are tools such as QTL Analyst to identify candidate genes and polymorphisms; literature correlation analysis by the latent semanticindexing tool, Semantic Gene Organizer (Homayouni et al. , 2005); and integratedanalysis with many large SNP sets, Gene Ontology overrepresentation and pathwaymatching (Zhang et al. , 2004), and annotation resources. By coupling flexible analytic tools with both molecular endophenotypes and higher-order phenotypic data,users can pursue a wealth of integrative systems genetics queries." + ] + ], + "task_id": [ + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B4BB83EB5D5C5C042E07173119046A13" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_4.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_4.json new file mode 100644 index 0000000..171450e --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_4.json @@ -0,0 +1,104 @@ +{ + "question": [ + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?", + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ], + "answer": [ + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.", + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "contexts": [ + [ + "For the observed difference in total variance of SS and LL, 0.93 and1.17 to be significant, the standard errors of the estimates need to be at most 0.06,a 2.5-fold reduction, implying a required sample size of 2.5 2.5 = 6.25 times oursample size or 5150 MZT. Plate 1 presents graphically the individual estimates of the items ai and bi foreach item i (using the model which includes known PLE). The x-axis representsthe normally distributed trait, liability to depression and the y-axis is the probability of endorsement of an item.", + "False-positive rate and correct null distributionTo investigate the properties of the LT statistic under the null we computed the mean value in the simulations above when c = 0.0.As seen in Table 2 this has the correct value of 1.00.In addition it has the correct median, with l GC ~1.00, 5.00% of tests with P-value,0.05and 1.00% of tests with P-value,0.01.We applied Kolmogorov-Smirnov test [31] to determine if the LT statistic differed significantly from a x 2 (1 dof) distribution.The two-tailed K-S test of the full distribution was not significant (Pvalue = 0.34), nor was the K-S test restricted to the tail where the LT statistic had x 2 .3.84 (P-value = 0.21).In order to further investigate the extreme tail of the distribution we ran 10 8 tests under the null and verified that 98 of the 10 8 tests (10 26 ) had a Pvalue,10 26 .The LT statistic is a score test when the parameters are estimated correctly and will therefore have the correct null distribution.We investigated the properties of the LT statistic when the parameters were severely mis-estimated and found no inflation (see Text S1 in File S1).Furthermore, since the LT statistic is an ATT test between g and the posterior mean of the residual of the liability E(eDz,t), it will not have an inflated false- positive rate provided that E(eDz,t) does not have heavy tails or extreme heteroscedasticity [32].E(eDz,t) is the area under the tail of a normal distribution and will therefore not have these properties provided that the clinical covariate does not.", + "Statistical AnalysesCategorical variables and continuous variables were tested and compared for significant differences using ttests.Quantitative trait association for SNPs was performed using a series of analysis of variance (ANOVA) tests.Hardy-Weinberg equilibrium (HWE) was assessed using the 2 goodness-of-fit statistic at a significance level of <0.05.To test all SNPs for genotypic association, adjusted odds ratios (ORs) and 95% confidence intervals (CI) were computed using Vassar statistic (http://vassarstats.net/) and SNPStats (http://bioinfo.iconcologia.net/SNPstats)softwares.All analyses were adjusted using linear regression.", + "statistical analysis after QC procedures (Supplementary data, Table S2).", + "Climate Correlations and Q X statistics for all six phenotypes in the global analysis.", + "3.2).Quantitative data are presented as mean standard error mean (SEM).Differences between two groups were analyzed by the student's t-test or Wilcoxon rank sum test.Differences among multiple groups were analyzed by the ANOVA.P values of multiple tests were adjusted by the Bonferroni method in the meta-analysis and were adjusted by the false discovery rate (FDR) in the differential Prior pairwise F ST values.D: Posterior pairwise F ST values.E: Blood eQTL analysis of rs3743121 (n = 30 in the TT group; n = 59 in the CT group; n = 62 in the CC group; expression levels were calculated as log 2 (fold of change, compared with TT group), *P < 0.05).Differential expression analysis of AQR in the skeletal muscle of rhesus macaques with metabolic syndrome (CTR: normal controls, MS: rhesus macaques with metabolic syndrome; n = 3 per group, data are presented as mean SEM, *P < 0.05; expression levels were calculated as fold of change compared with CTR group).", + "Data analysis and QTL mapping analysisDescriptive statistics like mean, standard deviation (SD), maximum and minimum trait value, coefficient of variation (CV%), analysis of variance (ANOVA) and heritability for each seed germination-related trait, and correlations among pairs of traits were calculated using the SPSS17.0software (http://www.spss.com).", + "Statistical AnalysisThe impact of delivery methods on knowledge and interpretation of their genomic risk and associations to categorical participant characteristics was assessed using 2 tests.These tests were used to analyze associations between categorical variables.Logistic regression was used to model the association between comprehension and the covariates of interest.Since only one participant was found to be of below adequate health literacy, this variable was not included in any further analysis.Change in agreement between patient responses to questions of risk was assessed using Kappa statistics and McNemar's test.Change in perceived risk after testing (1-week follow-up) was evaluated using Wilcoxon signed rank test and Spearman correlation coefficient.Two-sided p values are reported for all tests using a Type I error level of 0.05.", + "Empirical threshold significance values for pairwise interactions were determined using 100 permutationsof the data set. Statistical analyses. Weighted least-squares analysis (WLS) was performed toanalyze the TTD data of 14 inbred strains of 167/169 male and female mice(approximately 99% mortality). Preliminary investigations showed that the distribution of TTDs approximated normality, based on the Shapiro Wilk test. Within-strain variances were found to be heterogeneous, based on Levenes test. The WLS methodology, combined with a normality assumption, has the optimum power to detect differences between means of strains and susceptibilitygroups, compared to nonparametric methods.The same methodology (WLS) was used toanalyze organ CFU values, after a loge transformation was applied to approximate normality. Geometric means were calculated to estimate the medians ofeach strain and of sexes within each strain. Sex-specific analyses of TTDs and logeCFU determinations were also performed using WLS, with ANOVA models tocompare strain means by sex. Comparisons of between-strain to within-strainvariability were obtained by calculating the ratios of the variance between strainsto the residual variance, or the average variance within strains. These werecalculated for all TTD data and for each sex.", + "After applying the ShapiroWilk test to assess the normality of our data, an unpaired t-test was applied to analyse the THmeasurement in different areas. Quantitative trait locus mappingThe QTL mapping was done with http://gn2.genenetwork.org/. The dataset containingdopamine measurements of dorsal striata of 32 CC strains were located with search terms(Species: Mouse (mm10); Group: CC Family; Type: Phenotypes; Dataset: CC Phenotypes)and navigated to Record CCF_10001 and CCF_10002. The QTL mapping was done withGEMMA on all chromosomes, MAF >= 0.05 with LOCO method.", + "Fig. 1. (a) Quantile-quantile (Q-Q) plot after meta-analysis for time to death. (b) Quantile-quantile (Q-Q) plot after meta-analysis for time to event.", + "Yet another approach 34 relies on combining univariate test statistics 35 either by forming an omnibus test or a linear combination of test statistics.With such approaches, however, only part of the information contained in the actual profiles is utilized when the data is reduced to univariate statistics and their covariance.", + "A correlationcoefficient between the two measurements was highly significant (r = 0.99) indicating that technical error at thislevel of the analysis contributes little to case variation orstrain variation. A paired t-test confirmed that the difference between the first and second estimations was not significant (t < 1, NS). http://www.biomedcentral.com/1471-2202/10/44AnalysisData were analyzed using standard ANOVA and multipleregression techniques (JMP, SAS Institute, Cary, NC). QTLanalysis was performed using the WebQTL module ofGeneNetwork (GN, http://www.genenetwork.org).", + "STATISTICAL ANALYSISQuantitative variables were expressed as median with first and third quartiles into brackets.Raw observation counts in taxa summary plots were normalized by calculating relative abundance.Qualitative variables were compared using the Fisher's exact test and quantitative variable using Mann-Whitney's test.A P value less than 0.05 was considered statistically significant.Statistical analyses were performed using SPSS software (version 18.0; IBM Corp., Armonk, NY).", + "It is readily apparent from Figure 1that the values for norm reported here differ between thetwo analysis methods, resulting in different suggestive loci inthe subsequent QTL analysis. Such a difference between thetwo calculation methods raises the question of whether onemethod is indeed more accurate, or whether the two methodscapture different aspects of the mechanical response of thecorneoscleral shell in response to changes in IOP. We furtherinvestigated this difference by performing Bland-Altman analysis(Supplementary Figure 7).", + "Scheme 2: Schematic illustration of the beam-walking test (author: Anna Zemanov;reproduced with permission)4.3 Data analysis and statisticsStatistical tests include two-way ANOVA followed by Student-Newman-Keulsmethod, regression analysis (linear regression), permutation tests, and Pearson'scorrelation. To ensure that the desired traits are mapped, caution hat to be taken because a traitmay be tightly correlated with other traits. It is not desirable to unintentionally map genesthat control motility or anxiety. Therefore there is a possibility of mapping wrong QTL orno QTL at all.", + "QUANTIFICATION AND STATISTICAL ANALYSISStatistical details, including sample size (n), what n represents, and statistical test used can be found in the figure legends.In most cases, sample size was great enough to assume normality based on the central limit theorem, and parametric statistical tests were used.Two-sided tests were employed for more conservative calculations of significance.The threshold for statistical significance was set at p < 0.05.Unless otherwise stated, data in bar graphs are expressed as mean standard deviation.A combination of MAT-LAB_R2019b, R (v3.6), and Prism GraphPad were used for statistical analysis.", + "Other statistical methodsWe used Mann-Whitney tests to estimate the significance of non-normally distributed continuous variables across two groups and analysis of variance to estimate the significance of normally distributed variables across three or more groups.To ensure that low variation in methylation is not fallaciously responsible for observed associations, we ran the top two highly significant T2D CpG sites against 1000 simulated phenotypes, incorporating the same transformations and using the same covariates as for other polygenic analyses.To quantify the agreement between microarray and pyrosequencing techniques, we used the Bland-Altman procedure and estimated the regression-based limits of agreement.These analyses were done using the Stata 12.0 software package.", + "Statistical analysis of T/C dataStatistical tests were performed using MATLAB 6.1 software (The MathWorks, Inc.).Comparative statistics was done with Wilcoxon rank sum test.Values of P !0.05 were considered statistically signifi cant.The gender-specifi c relationship between telomere size and age was estimated by linear regression, the correlations among variables were assessed by Pearson's correlation coeffi cient." + ], + [ + "DiscussionWe proposed in this paper an integrative approach, iGWAS, that is able to analyze multiplatform genomic data under the family-based design.The model can be presented as a causal diagram (Fig. 1), which was set up based on the central dogma of molecular biology that DNA can be transcribed to mRNA expression and mRNA can then be translated to be protein to affect the phenotypic trait such as disease risk.The mediation diagram provides an intuitive illustration of our hypothesis.The iGWAS approach is integrative in different aspects.The model not only integrates different types of genomic data, i.e., SNP and gene expression data, but also incorporates different types of genetic/genomic association studies to delineate clinical outcome rather than perform a GWAS, an expression microarray study, and an eQTL study separately.Moreover, the iGWAS approach integrates biological knowledge into the computational model, as illustrated in the causal mediation diagram.", + "Further work is needed to determine how best to modify the tests proposed herein to deal with GWAS performed in structured populations.", + "GWAS and meta-analysisAt the discovery stage, genotyping data of the in-house Beijing dataset were obtained using the Affymetrix Genome-Wide Human SNP Array 5.0 (ThermoFisher, USA).Genome wide association study was performed using the PLINK v1.07 software (Purcell et al., 2007).", + "However, given that much ofthe variance is driven by societal, lifestyle and behavioralinfluences - and in addition there are also problemsrelated to DSM-based diagnostic criteria (Miller 2010) larger sample sizes for GWA analysis, inclusion of endophenotypes and CFG approaches are warranted. Havingalready massive transcriptomic, genetic and phenotypicdatasets available a Bayesian-like integration strategy canbe applied where multiple independent lines of geneticand genomic evidence is used, each by itself lackingsufficient discriminatory power, but combined leads tothe identification of high probability candidate genes orgene clusters.", + "Statistical analysisThe general statistical methods for linkage and GWA analyses are described in the Overview Methods [19].For diabetes-related quantitative traits we used additive GEE and FBAT models, testing associations between SNP genotypes and age-age 2 -sex-adjusted residual trait values.We kept 70,987 SNPs in the analyses that were on autosomes, had genotypic call rates 80%, HWE p 0.001 and MAF 10%.", + "In this manuscript, we summarize the strategies that we pursued to conduct the 100K genome-wide study, providing an overview for a series of 17 companion manuscripts (Table 1 of the Overview) describing associations with specific collections of traits [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42].The primary purpose of this project was to generate hypotheses regarding genetic factors that may contribute to the wide spectrum of phenotypic variables collected in the FHS through a genome-wide approach.More specifically, we primarily hypothesized that common genetic variants contributing to phenotypic variation can be detected through a genome-wide association study (GWAS) and that genetic loci contributing to phenotypic variation can be detected through linkage.Each manuscript also examines whether the 100K analyses replicated previously reported associations with consistent evidence from the literature for some specific traits.The main purpose of this series of publications is to describe the association results made available for investigators and to direct readers to their free availability in the database of Genotype and Phenotype (dbGaP) public repository http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 at the National Center for Biotechnology Information (NCBI), where these comprehensive results are posted and may be browsed in the context of multiple genomic tracks includ-ing Entrez Gene, RefSeq, dbSNP, genetic markers, and OMIM.The deposition of these data in a public repository is consistent with the long tradition of publishing preliminary results from the FHS to benefit the wider scientific community.", + "NIH-PA Author ManuscriptNIH-PA Author ManuscriptNat Rev Genet. Author manuscript; available in PMC 2013 November 01. Flint and EskinPage 21NIH-PA Author ManuscriptNIH-PA Author ManuscriptFigure 2. Overview of mouse GWASsNIH-PA Author ManuscriptMouse genome-wide association studies (GWASs) follow a common general approach. a |Mice in the study population are phenotyped for the traits of interest. Deciding which miceand their corresponding genetic structure to include in the study population is a key designconsideration in a mouse GWAS.", + "Prioritizing GWAS results: a review of statisticalmethods and recommendations for their application. Am J Hum Genet 2010;86:6e22. [76] Leiserson MDM, Eldridge JV, Ramachandran S, Raphael BJ. Network analysis of GWASdata. Curr Opin Genet Dev 2013;23:602e10. [77] Jia P, Zhao Z. Network.assisted analysis to prioritize GWAS results: principles,methods and perspectives. Hum Genet 2014;133:125e38. [78] Jensen MK, Pers TH, Dworzynski P, Girman CJ, Brunak S, Rimm EB. Proteininteraction-based genome-wide analysis of incident coronary heart disease. Circ Cardiovasc Genet 2011;4:549e56. [79] Schadt EE, Sachs A, Friend S. Embracing complexity, inching closer to reality. Sci STKE2005;2005:pe40.", + ", 2015) or GWAS summary statistics (Gusev et al. , 2016)could be used to facilitate the applications of our tools, especially ePheWAS, in such human cohorts. Altogether, this integrated systems genetics toolkit, which isfreely accessible on systems-genetics.org, can expedite in silicohypothesis generation and testing, facilitating the identificationand validation of new gene functions and gene networks inpopulations, which generally are robust and translate well acrossCell Systems 6, 113, January 24, 2018 11Please cite this article in press as: Li et al.", + "It is worth mentioning that other reports shared the concern about deciding the SNPs in GWAs that merit follow-up and further replication analysis.Chen et al. 19 recently proposed an approach for selecting SNPs based on a hierarchical model.This approach, which is not strictly based on biological plausibility of candidate's genes, allows the users to incorporate existing information about the SNPs into the analysis.For instance, the algorithm ranks P values assuming a weighting function that incorporates prior information about linkage or association evidence.", + "We performed a Bayesian gene-based GWAS analysis which is composed of 2 steps: We first conducted SNP-level association tests for the trio data using the likelihood ratio test (LRT) and obtained SNP level summary statistics and then conducted a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNPgene hierarchical structure.", + "Even on an individual GWA study scale, there are numerous benefits toinstituting the approaches discussed herein. Active capture of the complete processwill not only aid in the accurate interpretation of the individual study results but willalso permit the interpretation of results in a more comprehensive fashion through theintegration multiple data sets and results. 7 Constructing Gene Networks to Enhance GWASand GOGE ResultsAs discussed, generating a GOGE data set and performing a first-pass analysis onthis scale of data is a major undertaking.6 Further Recommendations for Efficiency Gainsin GOGE StudiesRecent large-scale GWAS initiatives have made gains by employing economies ofscale in instituting centralized SNP genotyping, data coordination and control centers (http://www.hapmap.org, http://www.wtccc.org.uk/) [48], providing data setsthat have undergone common quality control checks and standardized annotation tomultiple researchers for individual analysis [31, 49]. Additionally, one of the mostrecent operational advances is the use of a single large common control populationfor multiple casecontrol GWAS studies [31].", + "Step 6: Report summarization and visualization. of GWAS in disease prediction.There are many steps during a gene-set analysis.They are shown below as Steps 1 through Step 6:", + "ConclusionAuthor ManuscriptWe propose a shift from cataloging statistical genetic associations to using post-GWAS toolsto make biological sense of them. Incorporating the strategies outlined here should helpprioritize individual gene targets amenable to functional and mechanistic validation acrossspecies, which can create opportunities to better characterize polygenic risk for AUD, testthe prognostic utility of these loci and scores, and identify therapeutic starting points. AcknowledgmentsThis study was supported by National Institutes of Health grants R01AA020634 and P50AA022537 (MM);R01AA022994 (SH); K02DA032573 and U01MH109532 (AA); and K01AA024152 (JES).Author ManuscriptThe principal challenge that we are confronted with is the lack of a well- or even adequatelypowered GWAS of AUD. Even though the approaches outlined here more efficiently harnessall existing GWAS data, reliability of the results from these GWAS hinge on their samplesize. One of the largest efforts that is currently under way is being led by the PsychiatricGenomics Consortiums Substance Use Disorders group and includes 15,000 cases withDSM-IV alcohol dependence and >37, 000 controls that are largely alcohol exposed(Agrawal et al. 2016).", + "The ultimate objectives full descriptions ofthe susceptibility architecture of major biomedical traitsand translation of the findings into clinical practice remain distant. With completion of the initial wave of GWA scans, itis timely to consider the status of the field. This reviewconsiders each major step in the implementation of aGWA scan, highlighting areas where there is an emerging consensus over the ingredients for success, and thoseaspects for which considerable challenges remain.Joint (meta) analysis of data364 | May 2008 | volume 9from comparable GWA scans9,34,35,38,103 provides a lowcost approach to enhance power for both main andjoint (genegene and geneenvironment) effects, obtainin silico replication, inform SNP selection for subsequentreplication efforts and explore potential sources of heterogeneity.", + "Methodological aspectsThe success of GWAS in detecting new associations and potential risk factors for any particular disease or condition depends greatly on the experimental design, on careful selection of the populations, on large number of cases and on collaborative analytical approaches.Meta-analysis is a method that combines the results of a number of surveys and of replication studies on the most promising variants.It investigates the underlying processes and has become standard practice for publications of GWAS that search for common genetic variants regulating complex traits and disease risk.", + "To date, a growing body of comprehensive methods has been developed for downstream analyses of GWAS.Sharing of summary statistics can help enable these analyses, for example, by providing researchers with a more convenient way to look-up genetic association effect estimates to conduct causal inference analyses using methods such as two-sample Mendelian Randomization which assumes samples are non-overlapping 3,4 .In addition, sharing GWAS results can help researchers to further their understanding of the shared genetic basis of T2D with other traits of interest, to perform fine-mapping to pinpoint the causal genetic variants or identify genetic loci shared with other risk factors and disease outcomes.Therefore, the aim of this current work was to provide a reference dataset for researchers to utilize in order to conduct further genetic analyses, generate hypotheses and improve understanding of the aetiology, the biological pathways and mechanisms of T2D and related metabolic and cardiovascular diseases." + ], + [ + "2009;25:175460. 82. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, et al. Arobust, simple genotyping-by-sequencing (GBS) approach for high diversityspecies. PLoS One. 2011;6, e19379. 83. Ensemble Genomes. Available at: ftp://ftp.ensemblgenomes.org/. 84. Leinonen R, Sugawara H, Shumway M. The sequence read archive. NucleicAcids Res. 2011;39(Database issue):D1921. 85. Martin M. Cutadapt removes adapter sequences from high-throughputsequencing reads. EMBnet J. 2011;17:102. 86. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. Thesequence alignment/map format and SAMtools. Bioinformatics. 2009;25:20789. 87.", + "Biesecker, L., Mullikin, J., Facio, F., Turner,C., Cherukuri, P., Blakesley, R., Bouffard, G.,Chines, P., Cruz, P., Hansen, N., Teer, J.,Maskeri, B., Young, A., Manolio, T., Wilson,A., Finkel, T., Hwang, P., Arai, A., Remaley,A., Sachdev, V., Shamburek, R., Cannon, R.,and Green, E. (2009) The ClinSeq Project:Piloting large-scale genome sequencing forresearch in genomic medicine. Genome Res. 19, 16651674. 32. Wang, Z., Gerstein, M., and Snyder, M. (2009)RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 10, 5763. 33. Mortazavi, A., Williams, B., McCue, K.,Schaeffer, L., and Wold, B.Ng, S., Turner, E., Robertson, P., Flygare, S.,Bigham, A., Lee, C., Shaffer, T., Wong, M.,Bhattacharjee, A., Eichler, E., Bamshad, M.,Nickerson, D., and Shendure, J. (2009)Targeted capture and massively parallelsequencing of 12 human exomes. Nature. 461, 272276. 31.", + "Extensive documentation canbe downloaded in either pdf or Hypertext formats. The Tutorial is especially helpful, but readers should be aware that its files are somewhat inconspicuously tuckedin with Sample Data files, rather than being included in the Map Manager QTXmanual. For the current example, genotype data were downloaded from the Mouse GenomeDatabase (2001) (http://www.informatics.jax.org/). Specifically, it consists of mousechromosome 1 genotypes from the CopelandJenkins backcross, and a selected subset of 10 markers spanning the entire 100 cM length of the chromosome.", + "Resequencing of genomic regions of interest will also be necessary (see Electronic-DatabaseInformation for current examples).", + ", 2012;Chesler et al. , 2003; Jha et al. , 2018b, 2018a; Li et al. , 2018; Williams et al. , 2016). We havetherefore assembled deep companion resources, including full sequence for both parents(Baker et al. , 2019; Keane et al. , 2011; McKnite et al. , 2012; Wang et al. , 2016b; Wu et al. ,2014). Access to data and statistical tools are available from open-source web services(GeneNetwork.org and Systems-Genetics.org) (Li et al. , 2018; Sloan et al. , 2016; Williamsand Williams, 2017).", + "Best practice guidelines for the use of next-generation sequencing applications in genome diagnostics: A national collaborativestudy of dutch genome diagnostic laboratories. Human Mutation,34(10):13131321, Aug 2013. [363] D. Welter, J. MacArthur, J. Morales, T. Burdett, P. Hall,H. Junkins, A. Klemm, P. Flicek, T. Manolio, L. Hindor, and290BIBLIOGRAPHYet al. The nhgri gwas catalog, a curated resource of snp-trait associations. Nucleic Acids Research, 42(D1):D1001D1006, Dec2013. [364] Harm-Jan Westra, Marjolein J Peters, Tonu Esko, HaniehYaghootkar, Claudia Schurmann, Johannes Kettunen, Mark WChristiansen, Benjamin P Fairfax, Katharina Schramm, Joseph EPowell, and et al.", + "Zweig, A. S., Karolchik, D., Kuhn, R. M., Haussler, D., and Kent,W. J. (2008). UCSC genome browser tutorial. Genomics 92, 75 84. Vol. 9, Summer 2010107Downloaded from http://www.lifescied.org/ by guest on May 12, 2015", + "Useful Online Genomics Resources.", + "Bioinformatics tools for pathogen whole-genome sequencing", + "Genomic databases UCSC Human Genome Browser: visualize and browse genomes [4] https://genome.ucsc.edu/Ensembl: genomes and species tree [45] https://ensembl.org/GenBank: open access sequence database [44] https://www.ncbi.nlm.nih.gov/genbank/Multiple sequence alignment software MUSCLE: tool to align multiple sequences [52] http://www.ebi.ac.uk/Tools/msa/muscle/MAFFT: tool to align multiple genomic sequences [53] http://mafft.cbrc.jp/alignment/software/PRANK: tool to align multiple genomic sequences [54] http://www.ebi.ac.uk/goldman-srv/prank/Phylogenetic information TimeTree: database of divergence times [55] http://www.timetree.org/Mammalian supertrees: evolutionary trees [39] Fritz et al. [39] Analysis tools BLAST-basic local alignment search tool [46] https://blast.ncbi.nlm.nih.gov/Codeml-test for positive selection on phylogenies using multiple sequence alignments [56] http://abacus.gene.ucl.ac.uk/software/paml.html", + "The '1000 genomes' project and related individual sequencing projects", + "Sequencing a draft reference genomefor rainbow trout has been recently initiated; aconsortium involves collaborators from USDANational Cold and Cool Water Aquaculture(USDA-NCCCWA), Washington State University, University of Oregon, University ofCalifornia Davis, and West Virginia University (Palti et al. , 2011). The project will producea genome map and a draft reference genomesequence using the Illumina platform by sequencing the bacterial articial chromosomes(BACs) from the physical map minimal tilingpath (Palti, 2010; Miller et al. , 2011).", + "Extensive documentation canbe downloaded in either pdf or Hypertext formats. The Tutorial is especially helpful, but readers should be aware that its files are somewhat inconspicuously tuckedin with Sample Data files, rather than being included in the Map Manager QTXmanual. For the current example, genotype data were downloaded from the Mouse GenomeDatabase (2001) (http://www.informatics.jax.org/). Specifically, it consists of mousechromosome 1 genotypes from the CopelandJenkins backcross, and a selected subset of 10 markers spanning the entire 100 cM length of the chromosome.", + "The large number of bioinformatic tools that have beenmade available to scientists during the last few years has presented theproblem of which to use and how best to obtain scientifically valid answers(3). In this chapter, we will provide a guide for the most efficient way toanalyze a given sequence or to collect information regarding a gene, protein,structure, or interaction of interest by applying current publicly available software and databases that mainly use the World Wide Web.Hum Hered 63:678432122Gene/Protein Sequence AnalysisA Compilation of Bioinformatic ToolsBernd H. A. Rehm and Frank Reinecke1. IntroductionThe advent of automated high throughput DNA sequencing methods hasstrongly enabled genome sequencing strategies, culminating in determination ofthe entire human genome (1,2). An enormous amount of DNA sequence dataare available and databases still grow exponentially (see Fig. 22.1). Analysisof this overwhelming amount of data, including hundreds of genomes fromboth prokaryotes and eukaryotes, has given rise to the field of bioinformatics.", + "2014) (https://github.com/jaxcs/Seqnature) developed in the Churchill group at the Jackson Laboratory is similar, andis tailored to RNA-seq in the DO. Author ManuscriptConstruction of an individualized pseudogenome for a sample requires prior knowledge ofvariant sites in that samples genome. In, for instance, an F1 cross between strains for whichwhole-genome sequencing data are available, imputing the pseudogenome is trivial. Genomes of recombinant individuals (e.g. , CC or DO) can be expressed as mosaics offounder haplotypes on the basis of genotyping (discussed previously), and a pseudogenomestitched together accordingly.", + "In the following section, we provide an overview of the finished genome sequencing projects and report them in chronological order of their publication.", + "Extensive documentation canbe downloaded in either pdf or Hypertext formats. The Tutorial is especially helpful, but readers should be aware that its files are somewhat inconspicuously tuckedin with Sample Data files, rather than being included in the Map Manager QTXmanual. For the current example, genotype data were downloaded from the Mouse GenomeDatabase (2001) (http://www.informatics.jax.org/). Specifically, it consists of mousechromosome 1 genotypes from the CopelandJenkins backcross, and a selected subset of 10 markers spanning the entire 100 cM length of the chromosome.", + "Extensive documentation canbe downloaded in either pdf or Hypertext formats. The Tutorial is especially helpful, but readers should be aware that its files are somewhat inconspicuously tuckedin with Sample Data files, rather than being included in the Map Manager QTXmanual. For the current example, genotype data were downloaded from the Mouse GenomeDatabase (2001) (http://www.informatics.jax.org/). Specifically, it consists of mousechromosome 1 genotypes from the CopelandJenkins backcross, and a selected subset of 10 markers spanning the entire 100 cM length of the chromosome." + ], + [ + "We (Hein, Schierup and Wiuf) have published a300 page book on molecular population genetics titled Gene Genealogies, Sequence Variation and Evolution OxfordUniversity Press, and are presently developing a tutorial in association mapping that we hope to publish as a booklet in2006 and are also involved in a very large EU collaboration (Holland, Denmark, Iceland and UK) to find susceptibilitygenes for breast and prostate cancer. In comparative genomics, the most fundamental investigation is to find genes in a pair of aligned genomes.", + "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.2. von Heijne, G. Sequence analysis in molecular biology: Treasure trove or trivial pursuit(Academic Press, London, 1987). 3. Wolfsberg, T. G., Wetterstrand, K. A., Guyer, M. S., Collins, F. S. & Baxevanis, A. D. A usersguide to the human genome. Nature Genetics 32 (suppl) (2002). PrefaceI say locuslocus instead of genegene because if you work in human genetics longenough, you realize that you may never have a gene. But you learn not to let thatput you off. Peter A. HolmansMaking sense of the results of a genetic experiment is a challenge on any level.", + "Methods for DNA sequencing are constantly being improved, with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000, an end that appears to be in sight (Hayden, 2014).In the very near future, whole-genome sequencing will be routinely available for clinical purposes, perhaps even beginning at birth.The major challenge ahead is the interpretation of this information.How do our genes interact with each other, and how does the environment contribute to the development of health and disease?What are the individual and societal implications of knowing our genome sequence?The answers to these and other important questions will unfold in the years ahead.Thus, we are truly in an era where precision medicine may soon become a reality.", + "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.2. von Heijne, G. Sequence analysis in molecular biology: Treasure trove or trivial pursuit(Academic Press, London, 1987). 3. Wolfsberg, T. G., Wetterstrand, K. A., Guyer, M. S., Collins, F. S. & Baxevanis, A. D. A usersguide to the human genome. Nature Genetics 32 (suppl) (2002). PrefaceI say locuslocus instead of genegene because if you work in human genetics longenough, you realize that you may never have a gene. But you learn not to let thatput you off. Peter A. HolmansMaking sense of the results of a genetic experiment is a challenge on any level.", + "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.2. von Heijne, G. Sequence analysis in molecular biology: Treasure trove or trivial pursuit(Academic Press, London, 1987). 3. Wolfsberg, T. G., Wetterstrand, K. A., Guyer, M. S., Collins, F. S. & Baxevanis, A. D. A usersguide to the human genome. Nature Genetics 32 (suppl) (2002). PrefaceI say locuslocus instead of genegene because if you work in human genetics longenough, you realize that you may never have a gene. But you learn not to let thatput you off. Peter A. HolmansMaking sense of the results of a genetic experiment is a challenge on any level.", + "Ample time was allotted to answer questions and a copy of \"A Guide to Your Genome\" (National Human Genome Research Institute 2007) was provided to further assist participants' understanding and ability to communicate results with family members or others.", + "Whether within 10 or 12 (or 8) years, such inexpensive sequencing will change both research and clinical care, and progress does not need to wait even that long.The National Human Genome Research Institute (NHGRI) plans to focus a significant portion of the sequencing capacity that it supports on medical sequencing.For instance, the NHGRI and the National Cancer Institute are actively considering a Human Cancer Genome Project, 22 which would use DNA sequencing and a host of other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of cancer.Medical sequencing should also provide important insight into many other diseases.For example, sequencing all exons in X-linked mental retardation syndromes may reveal much about their etiology.Sequencing candidate genes in the extremes of the distribution of quantitative traits should also reveal much of importance about common diseases, such as coronary atherosclerosis. 23With further technological advances, other previously unimaginable research approaches will become real.", + "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.2. von Heijne, G. Sequence analysis in molecular biology: Treasure trove or trivial pursuit(Academic Press, London, 1987). 3. Wolfsberg, T. G., Wetterstrand, K. A., Guyer, M. S., Collins, F. S. & Baxevanis, A. D. A usersguide to the human genome. Nature Genetics 32 (suppl) (2002). PrefaceI say locuslocus instead of genegene because if you work in human genetics longenough, you realize that you may never have a gene. But you learn not to let thatput you off. Peter A. HolmansMaking sense of the results of a genetic experiment is a challenge on any level." + ], + [ + "In birds, where erythrocyte telomere length (ETL) is measured, the majority of species sampled have shown no sex difference (36).Nonetheless, bird telomere dynamics are complex and, as with humans, may be affected by environment and stress.For example, a longitudinal study of black-tailed gulls (Larus crassitostris) over 2-5 years found no correlation between ETL and age or sex.Rather, ETL attrition was correlated with reduced food availability and environmental stressors (55).In a captive zebra finch (Taeniopygia guttata) population, male and female mean telomere length decreased with increasing age of the animals, but did differ between sexes (56).As these examples illustrate, the relationship between telomere length, lifespan, and sex is likely to be complex in other vertebrates.Telomere attrition rates in humans are not constant, and when sex differences in telomere length first appear is unclear.Telomere attrition occurs rapidly from birth, slowing around 4 years of age, and the subsequent trajectory of telomere attrition continues to change in an age-and sex-specific fashion (43).In some studies, neonates show no sex differences, regardless of tissue used [eg, (44)].But in others, female newborns are reported to have longer telomeres than males [eg, (45)].Interestingly, a twin study comparing adults reported that women had longer mean LTL than men when samesex twin pairs (mono-and dizygotic) were compared.In contrast, men and women from opposite-sex twin pairs had similar telomere lengths, a difference that the authors attributed to antenatal influences of opposite-sex twins on one another (46).Results from studies of LTL in prepubescent children are mixed, reporting mean LTL either greater in females than in males (47) or not different (48).In two studies of adolescents (ages 13-18 years old), mean LTL was greater in females than in males (49,50), suggesting that sex differences in telomere length may arise during sexual maturation.A longitudinal study of Danish twins found that women had longer LTLs at baseline and displayed decelerated LTL attrition following menopause (51).Crucially, while LTL in women declined with age, the relationship between LTL attrition and age was no longer significant if menopausal status was included as a covariate.These examples illustrate that while many studies find greater telomere length in females, this trend is not universal.In some organisms, there is no clear relationship between telomere length and lifespan.Age-related telomere attrition could not be detected in Daphnia pulex (57) or sea urchin species (Strongylocentrotus franciscanus and Lytechinus variegatus) (58).Studies in C. elegans examining natural variation in telomere length and experimentally manipulated telomere length detect no correlation with lifespan (59,60), and in Drosophila, which uses a telomerase-independent mechanism for telomere maintenance, there is a similar lack of correlation between longevity and telomere length (61).Similarly, data on sex differences in age-related telomere shortening are mixed.For example, in the ant species Lasius niger, the rate of telomere shortening is more rapid in short-lived males compared to longer-lived females.But, mean telomere length does not differ between the two types of females, queens and workers, despite the fact that queens live much longer than workers (up to 28 years vs 2-3 months) (62).These findings suggest that the question of how telomere shortening affects aging across species and how sex affects telomere attrition rates are complex.In sum, as adults men have shorter telomeres than women in most populations sampled (39).Whether the sex difference in telomere length appears shortly after conception or later in life is unclear.Similarly, whether the sex difference in telomere lengths observed in adult humans results from slower attrition rates, differential telomere length at earlier ages, sex differences in the effects of telomere length on survival, sex differences in telomere maintenance, or other factors in not clear.Additional, carefully controlled longitudinal studies on the dynamics of telomere length and attrition rates in multiple tissues using standardized methods are needed to better evaluate the mechanisms creating sex differences in human telomere attrition during aging.", + "With new methodologies to assess relative telomere length by Q-PCR, studies were designed to address the impact of telomere length on aging, aging associated pathologies, and mortality.One such study has correlated shorter leukocyte telomere lengths at age 60 with a three times higher risk of heart disease and an eightfold increase in risk of infection-related death (36), thereby associating measured relative cellular aging with disease and life expectancy.In a similar way, chronic stress was shown to correlate with short leukocyte telomere length, a phenomenon attributed to higher levels of oxidative stress at the cellular level (70).More recent studies have linked telomere length in smooth muscle cells with senescence and disease severity in patients with atherosclerosis (141,150).Leukocyte telomere length was also short in a cohort of similar patients and associated with a higher risk of developing occult cardiovascular disease (71).More data are needed to understand and validate the use of leukocyte telomere length as a biomarker for cardiovascular and other diseases.", + "Shortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19].", + "Adult studies have also found a negative correlation with baseline telomere length, suggesting a negative feedback regulation of leukocyte telomere length (Farzaneh-Far et al. 2010;Aviv et al. 2009;Epel et al. 2008;Nordfjall et al. 2009).It is possible that while our follow-up period was shorter than Shalev et al. 2013 and adult studies, which had a minimum of 5 year intervals with the exception of Puterman et al. (2015) who followed for a one-year time period, there may be biological regulation of telomere length at 4 and 5 years of age such that shorter telomeres are more robustly maintained, whereas longer telomeres have greater rates of decline, over a short period of one year.It is unlikely that this relationship is due to assay error or regression to the mean given the consistency of our findings across studies.We have had similar findings of longer telomeres having greater rates of decline and shorter telomeres being maintained in our different studies (Farzaneh-Far et al. 2010;Epel et al. 2008;Puterman et al. 2015).The single, consistent predictor of the rate of telomere attrition shown in multiple adult and the few child longitudinal studies is the baseline measurement of telomere length at the start of each study.This suggests the importance of understanding predictors of telomere length prior to adulthood, as it determines in part the rate of change (Revesz et al. 2014a, b;Nordfjall et al. 2009).Moreover, longitudinal studies in adults have had found that telomere attrition rate is dependent on baseline telomere length independent of any phenotypic predictors of shortening, such as disease or demographic variables (Nordfjall et al. 2009), attesting to the importance of studies to evaluate risk factors for shortening prior to adulthood.Rates of decline in childhood may be particularly relevant for later chronic disease risk as shorter telomere length has been implicated in disease progression through exposure to cellular senescence, inflammatory cytokines and adipocyte hypertrophy (Raschenberger et al. 2015;Willeit et al. 2014;Monickaraj et al. 2012;Fyhrquist et al. 2013).Telomere attrition in adultsAdditional early childhood telomere studies are also needed so as to better interpret disease across the lifespan.Specifically, retrospective adult studies have pointed to the importance of early life exposures, finding associations of shorter telomere length in adulthood with perinatal complications, and stressful and deprived early childhood environments, including factors associated with lower socioeconomic status and those of physical or social neglect (Drury et al. 2012;Shalev et al. 2014;Tyrka et al. 2010, Kananen et al. 2010).Some adult studies have found that predictors of shorter telomere length involving inflammation and oxidative stress exposures such as depression are only observed in younger adults compared with middle aged and older ones (Philips et al. 2013), suggesting it may be harder to tease out risk factors for accelerated attrition as, firstly, these processes happen early in life, and, secondly, repeat exposures to inflammation and oxidative stress may statistically plateau out across older age groups.", + "Blackburn and Epel, a health psychologist who did original research on how specific lifestyle and psychological habits can protect telomeres, published The Telomere Effect (Blackburn & Epel, 2017), in which they suggested that individuals with shorter telomeres developed diseases earlier in life (a shorter \"disease span\").What follows is the evidence from these authors, their colleagues, and other researchers describing how length of telomeres contributes to mind-body connection and healthy longevity.", + "As early as at the time of birth, each of the 92 telomeres of the human genome has its own characteristic length.Additionally, each telomere shortens by its individual attrition rate.In general, longer telomeres at birth are associated with higher age-dependent attrition rates and vice versa.Overall, telomere shortening appears more dynamic in males.In recent literature, there are hints that the average telomere length may be higher in women and that their annual shortening rate may be somewhat lower (Vaziri et al., 1993;Rufer et al., 1998;Jeanclos et al., 2000), but these reported differences failed to reach statistical signifi cance except for one study (Jeanclos et al., 2000).Here, we provide compelling evidence that this is indeed the case.It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo.In individuals, short telomeres are considered to be a sign of advanced age.Cawthon and coworkers (2003) showed that telomere shortening in humans likely contributes to mortality, supporting the hypothesis that they might act as a mitotic clock (Allsopp et al., 1992).Telomere length dynamics, however, does not seem to Abstract.During aging, telomeres are gradually shortened, eventually leading to cellular senescence.By T/C-FISH (telomere/centromere-FISH), we investigated human telomere length differences on single chromosome arms of 205 individuals in different age groups and sexes.For all chromosome arms, we found a linear correlation between telomere length and donor age.Generally, males had shorter telomeres and higher attrition rates.Every chromosome arm had its individual age-specifi c telomere length and erosion pattern, resulting in an unexpected heterogeneity in chromosomespecifi c regression lines.This differential erosion pattern, however, does not seem to be accidental, since we found a correlation between average telomere length of single chromosome arms in newborns and their annual attrition rate.Apart from the above-mentioned sex-specifi c discrepancies, chromosome arm-specifi c telomere lengths were strikingly similar in men and women.This implies a mechanism that arm specifi cally regulates the telomere length independent of gender, thus leading to interchromosomal telomere variations.In conclusion, these data suggest that with increasing physical and genetic length of whole chromosomes, the corresponding telomeres also tend to be longer and that recombination rate and telomere length are inversely proportional.In conclusion, a combination of overall and chromosomespecifi c shorter telomeres and more pronounced age-dependent telomere erosion could be observed in males.There is a prospective clinical study strongly suggesting that longer telomeres decrease the risk of dying (Cawthon et al., 2003).With this in mind, the telomere length discrepancies between the sexes may indeed be a factor infl uencing the differences in their life expectancy.In every chromosome a linear decline of telomere length with age was observed, being more pronounced in men independent of the examined chromosome arm.This might suggest that telomere length on single chromosome arms may be infl uenced by the same factors which determine overall telomere length.S. Mayer a S. Brderlein a S. Perner a I. Waibel a A. Holdenried a N. Ciloglu a C. Hasel a T. Mattfeldt a K.V. Nielsen b P. Mller a a Institute of Pathology, University of Ulm, Ulm (Germany); b DakoCytomation A/S, Glostrup (Denmark) follow uniformity.In previous studies, sex-specifi c differences in telomere length and attrition rate of men and women were found (Benetos et al., 2001;Cawthon et al., 2003;Nawrot et al., 2004), suggesting gender differences in behavior of telomeres.In individual chromosome arms, telomere length was also shown not to be homogeneous (Lansdorp et al., 1996;Benn, 1997;Martens et al., 1998;Surralles et al., 1999;Hao and Tan, 2001;Londono-Vallejo et al., 2001;Graakjaer et al., 2003), some telomeres being signifi cantly shorter, others longer than the average length.To date, these characteristics in telomere lengths could not be set in a biological context, as only a few groups have provided detailed information about chromosome-specifi c patterns of telomere distribution (Lansdorp et al., 1996;Graakjaer et al., 2003).Whether accumulation of short telomeres (Martens et al., 2000;Londono-Vallejo et al., 2001) or rather the shortest telomere of one specifi c chromosome arm (Hemann et al., 2001) elicits senescence, remains an open question so far.", + "Shortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19]." + ] + ], + "task_id": [ + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C", + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_5.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_5.json new file mode 100644 index 0000000..1fe6931 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_5.json @@ -0,0 +1,112 @@ +{ + "question": [ + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing", + "Define dyslipidemia.", + "What is cytochrome?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?" + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence.", + "The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text." + ], + "contexts": [ + [ + "Biesecker, L., Mullikin, J., Facio, F., Turner,C., Cherukuri, P., Blakesley, R., Bouffard, G.,Chines, P., Cruz, P., Hansen, N., Teer, J.,Maskeri, B., Young, A., Manolio, T., Wilson,A., Finkel, T., Hwang, P., Arai, A., Remaley,A., Sachdev, V., Shamburek, R., Cannon, R.,and Green, E. (2009) The ClinSeq Project:Piloting large-scale genome sequencing forresearch in genomic medicine. Genome Res. 19, 16651674. 32. Wang, Z., Gerstein, M., and Snyder, M. (2009)RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 10, 5763. 33. Mortazavi, A., Williams, B., McCue, K.,Schaeffer, L., and Wold, B.", + "Extensive documentation canbe downloaded in either pdf or Hypertext formats. The Tutorial is especially helpful, but readers should be aware that its files are somewhat inconspicuously tuckedin with Sample Data files, rather than being included in the Map Manager QTXmanual. For the current example, genotype data were downloaded from the Mouse GenomeDatabase (2001) (http://www.informatics.jax.org/). Specifically, it consists of mousechromosome 1 genotypes from the CopelandJenkins backcross, and a selected subset of 10 markers spanning the entire 100 cM length of the chromosome.The full listof organisms, target sequence coverage and progress in sequencing can be monitored online (http://www.genome.gov/10002154). Based on the equations of Eddy( 2005) and simulations of Margulies et al. (2005), these genome sequences shouldprovide resolution of selective constraint down to a segment length of eight nucleotides, approaching the same scale as individual transcription factor-bindingsites.", + "Resequencing of genomic regions of interest will also be necessary (see Electronic-DatabaseInformation for current examples).", + "Best practice guidelines for the use of next-generation sequencing applications in genome diagnostics: A national collaborativestudy of dutch genome diagnostic laboratories. Human Mutation,34(10):13131321, Aug 2013. [363] D. Welter, J. MacArthur, J. Morales, T. Burdett, P. Hall,H. Junkins, A. Klemm, P. Flicek, T. Manolio, L. Hindor, and290BIBLIOGRAPHYet al. The nhgri gwas catalog, a curated resource of snp-trait associations. Nucleic Acids Research, 42(D1):D1001D1006, Dec2013. [364] Harm-Jan Westra, Marjolein J Peters, Tonu Esko, HaniehYaghootkar, Claudia Schurmann, Johannes Kettunen, Mark WChristiansen, Benjamin P Fairfax, Katharina Schramm, Joseph EPowell, and et al.", + "Zweig, A. S., Karolchik, D., Kuhn, R. M., Haussler, D., and Kent,W. J. (2008). UCSC genome browser tutorial. Genomics 92, 75 84. Vol. 9, Summer 2010107Downloaded from http://www.lifescied.org/ by guest on May 12, 2015", + "Useful Online Genomics Resources.", + "Bioinformatics tools for pathogen whole-genome sequencing", + "The '1000 genomes' project and related individual sequencing projects", + "Sequencing a draft reference genomefor rainbow trout has been recently initiated; aconsortium involves collaborators from USDANational Cold and Cool Water Aquaculture(USDA-NCCCWA), Washington State University, University of Oregon, University ofCalifornia Davis, and West Virginia University (Palti et al. , 2011). The project will producea genome map and a draft reference genomesequence using the Illumina platform by sequencing the bacterial articial chromosomes(BACs) from the physical map minimal tilingpath (Palti, 2010; Miller et al. , 2011).", + "Extensive documentation canbe downloaded in either pdf or Hypertext formats. The Tutorial is especially helpful, but readers should be aware that its files are somewhat inconspicuously tuckedin with Sample Data files, rather than being included in the Map Manager QTXmanual. For the current example, genotype data were downloaded from the Mouse GenomeDatabase (2001) (http://www.informatics.jax.org/). Specifically, it consists of mousechromosome 1 genotypes from the CopelandJenkins backcross, and a selected subset of 10 markers spanning the entire 100 cM length of the chromosome.The full listof organisms, target sequence coverage and progress in sequencing can be monitored online (http://www.genome.gov/10002154). Based on the equations of Eddy( 2005) and simulations of Margulies et al. (2005), these genome sequences shouldprovide resolution of selective constraint down to a segment length of eight nucleotides, approaching the same scale as individual transcription factor-bindingsites.", + "The large number of bioinformatic tools that have beenmade available to scientists during the last few years has presented theproblem of which to use and how best to obtain scientifically valid answers(3). In this chapter, we will provide a guide for the most efficient way toanalyze a given sequence or to collect information regarding a gene, protein,structure, or interaction of interest by applying current publicly available software and databases that mainly use the World Wide Web.Hum Hered 63:678432122Gene/Protein Sequence AnalysisA Compilation of Bioinformatic ToolsBernd H. A. Rehm and Frank Reinecke1. IntroductionThe advent of automated high throughput DNA sequencing methods hasstrongly enabled genome sequencing strategies, culminating in determination ofthe entire human genome (1,2). An enormous amount of DNA sequence dataare available and databases still grow exponentially (see Fig. 22.1). Analysisof this overwhelming amount of data, including hundreds of genomes fromboth prokaryotes and eukaryotes, has given rise to the field of bioinformatics.", + "2014) (https://github.com/jaxcs/Seqnature) developed in the Churchill group at the Jackson Laboratory is similar, andis tailored to RNA-seq in the DO. Author ManuscriptConstruction of an individualized pseudogenome for a sample requires prior knowledge ofvariant sites in that samples genome. In, for instance, an F1 cross between strains for whichwhole-genome sequencing data are available, imputing the pseudogenome is trivial. Genomes of recombinant individuals (e.g. , CC or DO) can be expressed as mosaics offounder haplotypes on the basis of genotyping (discussed previously), and a pseudogenomestitched together accordingly.", + "Geschwind and KonopkaPage 9Box 3NIH-PA Author ManuscriptThe challenges of next-generation sequencingNext-generation sequencing will be revolutionary in the amount and content of datagenerated, but there are many obstacles to surmount. Extensive comparisons ofsequencing data have not been published demonstrating whether there are batch effects indata due to sample preparation, library generation, flow cell preparation or machine run. Few studies have compared the commercial platforms for either gene expression or generegulation81,82. Data storage and analysis are currently a much larger challenge than datageneration.", + "In the following section, we provide an overview of the finished genome sequencing projects and report them in chronological order of their publication.", + "Extensive documentation canbe downloaded in either pdf or Hypertext formats. The Tutorial is especially helpful, but readers should be aware that its files are somewhat inconspicuously tuckedin with Sample Data files, rather than being included in the Map Manager QTXmanual. For the current example, genotype data were downloaded from the Mouse GenomeDatabase (2001) (http://www.informatics.jax.org/). Specifically, it consists of mousechromosome 1 genotypes from the CopelandJenkins backcross, and a selected subset of 10 markers spanning the entire 100 cM length of the chromosome.The full listof organisms, target sequence coverage and progress in sequencing can be monitored online (http://www.genome.gov/10002154). Based on the equations of Eddy( 2005) and simulations of Margulies et al. (2005), these genome sequences shouldprovide resolution of selective constraint down to a segment length of eight nucleotides, approaching the same scale as individual transcription factor-bindingsites.", + "Extensive documentation canbe downloaded in either pdf or Hypertext formats. The Tutorial is especially helpful, but readers should be aware that its files are somewhat inconspicuously tuckedin with Sample Data files, rather than being included in the Map Manager QTXmanual. For the current example, genotype data were downloaded from the Mouse GenomeDatabase (2001) (http://www.informatics.jax.org/). Specifically, it consists of mousechromosome 1 genotypes from the CopelandJenkins backcross, and a selected subset of 10 markers spanning the entire 100 cM length of the chromosome." + ], + [ + "gov/mapview/) evolved to allow graphical depictions of, and comparisons between,a wide range of genetic and physical maps in parallel with NCBI draft and finished sequence contigs. The locations of genes, markers, and SNPs are indicatedon the assembled sequences. As with Ensembl, there is a NCBI analysis protocolwhich aims to predict gene structures based upon EST and mRNA alignments withthe draft genome. This is carried out by a program called Acembly (unpublished;http://www.ncbi.nlm.nih.gov/IEB/Research/Acembly/index.html), which aims toderive gene structure from these alignments alone.There are now three well-designed websites (Table 4.1) offering users the chance tobrowse annotation of the draft human genome. All three sites offer a graphical interface to display the results of various analyses, such as gene predictions and similaritysearches, for draft and finished genomic sequence. These interfaces are indispensable for rapid, intuitive comparisons between the features predicted by differentprograms. For instance, one can see at once where an exon prediction overlaps withinterspersed repeats or a SNP.", + "Resequencing of genomic regions of interest will also be necessary (see Electronic-DatabaseInformation for current examples).", + "Useful Online Genomics Resources.", + "Bioinformatics tools for pathogen whole-genome sequencing", + "The '1000 genomes' project and related individual sequencing projects", + "Sequencing a draft reference genomefor rainbow trout has been recently initiated; aconsortium involves collaborators from USDANational Cold and Cool Water Aquaculture(USDA-NCCCWA), Washington State University, University of Oregon, University ofCalifornia Davis, and West Virginia University (Palti et al. , 2011). The project will producea genome map and a draft reference genomesequence using the Illumina platform by sequencing the bacterial articial chromosomes(BACs) from the physical map minimal tilingpath (Palti, 2010; Miller et al. , 2011).", + "gov/mapview/) evolved to allow graphical depictions of, and comparisons between,a wide range of genetic and physical maps in parallel with NCBI draft and finished sequence contigs. The locations of genes, markers, and SNPs are indicatedon the assembled sequences. As with Ensembl, there is a NCBI analysis protocolwhich aims to predict gene structures based upon EST and mRNA alignments withthe draft genome. This is carried out by a program called Acembly (unpublished;http://www.ncbi.nlm.nih.gov/IEB/Research/Acembly/index.html), which aims toderive gene structure from these alignments alone.There are now three well-designed websites (Table 4.1) offering users the chance tobrowse annotation of the draft human genome. All three sites offer a graphical interface to display the results of various analyses, such as gene predictions and similaritysearches, for draft and finished genomic sequence. These interfaces are indispensable for rapid, intuitive comparisons between the features predicted by differentprograms. For instance, one can see at once where an exon prediction overlaps withinterspersed repeats or a SNP.The full listof organisms, target sequence coverage and progress in sequencing can be monitored online (http://www.genome.gov/10002154). Based on the equations of Eddy( 2005) and simulations of Margulies et al. (2005), these genome sequences shouldprovide resolution of selective constraint down to a segment length of eight nucleotides, approaching the same scale as individual transcription factor-bindingsites.", + "The large number of bioinformatic tools that have beenmade available to scientists during the last few years has presented theproblem of which to use and how best to obtain scientifically valid answers(3). In this chapter, we will provide a guide for the most efficient way toanalyze a given sequence or to collect information regarding a gene, protein,structure, or interaction of interest by applying current publicly available software and databases that mainly use the World Wide Web.", + "2014) (https://github.com/jaxcs/Seqnature) developed in the Churchill group at the Jackson Laboratory is similar, andis tailored to RNA-seq in the DO. Author ManuscriptConstruction of an individualized pseudogenome for a sample requires prior knowledge ofvariant sites in that samples genome. In, for instance, an F1 cross between strains for whichwhole-genome sequencing data are available, imputing the pseudogenome is trivial. Genomes of recombinant individuals (e.g. , CC or DO) can be expressed as mosaics offounder haplotypes on the basis of genotyping (discussed previously), and a pseudogenomestitched together accordingly.", + "In the following section, we provide an overview of the finished genome sequencing projects and report them in chronological order of their publication.", + "gov/mapview/) evolved to allow graphical depictions of, and comparisons between,a wide range of genetic and physical maps in parallel with NCBI draft and finished sequence contigs. The locations of genes, markers, and SNPs are indicatedon the assembled sequences. As with Ensembl, there is a NCBI analysis protocolwhich aims to predict gene structures based upon EST and mRNA alignments withthe draft genome. This is carried out by a program called Acembly (unpublished;http://www.ncbi.nlm.nih.gov/IEB/Research/Acembly/index.html), which aims toderive gene structure from these alignments alone.There are now three well-designed websites (Table 4.1) offering users the chance tobrowse annotation of the draft human genome. All three sites offer a graphical interface to display the results of various analyses, such as gene predictions and similaritysearches, for draft and finished genomic sequence. These interfaces are indispensable for rapid, intuitive comparisons between the features predicted by differentprograms. For instance, one can see at once where an exon prediction overlaps withinterspersed repeats or a SNP.The full listof organisms, target sequence coverage and progress in sequencing can be monitored online (http://www.genome.gov/10002154). Based on the equations of Eddy( 2005) and simulations of Margulies et al. (2005), these genome sequences shouldprovide resolution of selective constraint down to a segment length of eight nucleotides, approaching the same scale as individual transcription factor-bindingsites.", + "gov/mapview/) evolved to allow graphical depictions of, and comparisons between,a wide range of genetic and physical maps in parallel with NCBI draft and finished sequence contigs. The locations of genes, markers, and SNPs are indicatedon the assembled sequences. As with Ensembl, there is a NCBI analysis protocolwhich aims to predict gene structures based upon EST and mRNA alignments withthe draft genome. This is carried out by a program called Acembly (unpublished;http://www.ncbi.nlm.nih.gov/IEB/Research/Acembly/index.html), which aims toderive gene structure from these alignments alone.There are now three well-designed websites (Table 4.1) offering users the chance tobrowse annotation of the draft human genome. All three sites offer a graphical interface to display the results of various analyses, such as gene predictions and similaritysearches, for draft and finished genomic sequence. These interfaces are indispensable for rapid, intuitive comparisons between the features predicted by differentprograms. For instance, one can see at once where an exon prediction overlaps withinterspersed repeats or a SNP.The full listof organisms, target sequence coverage and progress in sequencing can be monitored online (http://www.genome.gov/10002154). Based on the equations of Eddy( 2005) and simulations of Margulies et al. (2005), these genome sequences shouldprovide resolution of selective constraint down to a segment length of eight nucleotides, approaching the same scale as individual transcription factor-bindingsites.", + "LETTER RESEARCHthe sequence information available from the assembled scaftigs), resulting in the final reference gene catalogue used in this study." + ], + [ + "The dyslipidaemia in patients with Type 2 diabetes is characterised by hypertriglyceridaemia, low High Density Lipoprotein (HDL) and relatively normal Low Density Lipoprotein (LDL) and total cholesterol (Owen, et al., 2002).The lipid profiles of HNF1A MODY patients are similar to non-diabetic controls, but have higher HDL cholesterol and lower triglyceride levels compared to Type 2 diabetes patients (McDonald, et al., 2012;Owen, et al.,", + "Dyslipidaemia Atherogenic dyslipidemia is the overall term for blood fatdisordershigh triglycerides, low HDL cholesterol and high LDL cholesterolthat foster plaque buildups in artery walls. Ecogenetics The interaction of genetics with the environment. Eicosanoid Any of a family of naturally occurring substances derived from20-carbon polyunsaturated fatty acids; they include prostaglandins,thromboxanes, leukotrienes and epoxyeicosatrienoic acids, and function ashormones. Endophenotype Measurable biological characteristics thought to lie along thepathway from gene to disorder, but that are closer to the gene and are thoughtto have a simpler relationship with a given gene.", + "M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPTAbnormal lipid profiles have been known to be associated with the metabolic syndrome and AD for over a decade (Kuo et al., 1998;Roher et al., 1999;Merched et al., 2000).Plasma lipidomics allows the detection of sphingolipids and glycerophospholipids such as Cer, PI and PE that are present in very small amounts in the plasma.Changes in lipids disturb plasma membrane asymmetry (Axelsen et al., 2011), and this is likely to disturb peripheral liver A endosomal metabolism that is essential for mediating the clearance of A via ApoE-or another apolipoprotein-mediated pathway.", + "Serum lipids are important determinants of cardiovascular diseases and are related to morbidity [187] .The high heritability of circulating lipid levels is well established, and earlier studies of individuals with extreme lipid values or families with Mendelian forms of dyslipidemias have reported the involvement of numerous genes and respective proteins in lipid metabolism [188] .Recent GWA studies mostly carried out in samples enriched for T2DM cases have implicated a total of 19 loci controlling serum high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL) cholesterol and triglycerides (TG).The loci include the genes encoding ABCA1 , APOB , CELSR2 , CETP , DOCK7 , GALNT2 , GCKR , HMGCR , LDLR , LIPC , LIPG , LPL , MLXIPL , shown according to the number of at -risk alleles in each class (for a total of 15 single nucleotide polymorphisms genotyped in each individual, from the best replicated variants following the results of genome -wide association studies).The study was performed in 4232 patients with diabetes and 4595 normoglycemic adult subjects.Adapted from Cauchi et al. [193] .", + "Familial HypercholesterolemiaFamilial hypercholesterolemia is an inherited condition in which patients have extremely high blood levels of low-density lipoprotein (LDL) cholesterol, which results in abnormal deposition of cholesterol in various parts of the body and a dramatically increased risk of cardiovascular disease, which often manifests at an early age.Several genes have been implicated in this disorder.Mutations in LDLR, which encodes the LDL receptor, can affect the synthesis, structure, and function of the LDL receptor in a variety of ways, 6 resulting in the impaired ability of cells to remove cholesterol-carrying LDL particles from the bloodstream and thus the accumulation of LDL cholesterol in the blood.Although familial hypercholesterolemia is often regarded as an autosomal dominant disorder, LDLR mutations have an additive (codominant) effect such that patients who have 2 LDLR mutations have higher blood LDL cholesterol levels and experience earlier cardiovascular disease (as early as childhood) compared with patients with 1 LDLR mutation.Mutations in the APOB gene, which encodes the apolipoprotein B protein, which is a core protein of LDL particles and facilitates their removal from the bloodstream, can mimic the effects of LDLR mutations and result in familial hypercholesterolemia. 7 Finally, mutations in 2 other genes that encode proteins that affect the function of the LDL receptor, PCSK9 and LDLRAP1, can also result in familial hypercholesterolemia. 8,9 Unlike the other 3 genes, LDLRAP1 mutations are recessive and thus are required to affect both copies of the gene for patients to manifest disease.", + "Dyslipidemia was defined according to ATP III.High TC was defined as serum level of TC equal to or greater than 6.21 mmol/L (!240 mg/dL).Low HDL-C was defined as serum level of HDL-C less than 1.03 mmol/L (<40 mg/dL).High LDL-C was defined as serum level of LDL-C equal to or greater than 4.16 mmol/L (!160 mg/dL).High TG was defined as serum level of triglyceride equal to or greater than 2.26 mmol/L (!200 mg/dL).The ratio of TC/HDL-C equal to or greater than five was defined as abnormal.All the participants were free of lipid-lowering medicines.Studies reported that dyslipidemia was associated with hypertension [27e30].However, in our current study, this association was not observed in the Yi people by multivariate logistic regression analysis.Significant association between ever alcohol drinking, dyslipidemia, low HDL-C and high ratio of TC/HDL-C was observed (OR<1).Our findings showed an association between increasing BMI and increasing prevalence of dyslipidemia.The association between ever smoking and dyslipidemia was not observed in the current research by multivariate logistic regression analysis.Compared with females aged below 45 years, females at menopausal stage had higher risk for dyslipidemia, adjusted for family income, educational level, physical activity, ever smoking, ever alcohol drinking, BMI, and history of hypertension and T2DM.In exploring associated factors for prevalence of dyslipidemia, we used multivariate logistic regression analysis with high TC, TG and LDL-C, low HDL-C and abnormal ratio of TC/HDL-C as dependent variables, respectively.Independent variables included age, gender, family income, educational level, physical activity, ever smoking, ever alcohol drinking, BMI, and history of hypertension and T2DM, each of them had a p-value less than 0.05.No factor associated with high LDL-C was observed.Overweight or obesity, and history of T2DM were independent factors related to dyslipidemia.Male Yi people had significantly higher risk for dyslipidemia, as compared with that in females.Ever alcohol drinking was associated with dyslipidemia, i.e., low HDL-C and higher ratio of TC/ HDL-C.Compared with light physical activity, people engaged in moderate or heavy labor had lower risk for high TG Fig. 1.Prevalence of dyslipidemia by time before and after emigration in the Yi migrants.There are some limitations in this study that require consideration in interpretation of our findings.One is limited sample size.Another one is that no dietary survey was conducted in the current study, so no dietary factors associated with dyslipidemia could be estimated.", + "Perturbation of lipid metabolismIt is known that the characteristic features of dyslipidemia in humans with type 2 diabetes are high plasma triglyceride concentration, low HDL cholesterol concentrations and increased concentration of small dense LDL-cholesterol particles, while total cholesterol is not increased in patients with diabetes.These lipid changes in these individuals may be due to an increased free fatty acid flux secondary to insulin resistance [49].However, the perturbations observed here in lipid metabolism reflect the state of already treated diabetes.Association studies with blood lipid parameters [Adamski et al., unpublished data, [50]] show that many PC species associate with HDL and total cholesterol levels while PE species associate with triglyceride levels.In this study, we observed lower phosphatidylcholine (PC) and higher phosphatidylethanolamine (PE) concentration in the diabetes group matches the lower HDL and total cholesterol levels and higher triglyceride levels in this group, indicating that these glycerophospholipids may provide a more differentiated view of the shifted lipid homeostasis in patients with diabetes as what can be obtained from the bulk blood cholesterol and triglyceride parameters alone.Consistent with this finding, Gall et al. [34] observed reduced levels of multiple acylglycerophosphocholine species that were highly correlated with insulin resistance as measured by the euglycemic clamp.", + "INTRODUCTIONCardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide (He et al., 2005;Lozano et al., 2013;Murray et al., 2013).Suboptimal lipid levels contribute to the atherosclerotic process, with clinical trials and observational studies demonstrating a strong relation between blood lipid concentrations and CVD (Hokanson and Austin, 1996;LaRosa et al., 1999;Di Angelantonio et al., 2009;Huxley et al., 2011).The heritabilities of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride concentrations have long been established (Friedlander et al., 1997;Malhotra and Wolford, 2005;Luo et al., 2010;Zhang et al., 2010).More recently, genome-wide association studies (GWASs) have made important strides in identifying single nucleotide polymorphisms (SNPs) that contribute to the inter-individual variability in these complex phenotypes (Saxena et al., 2007;Kathiresan et al., 2008;Kooner et al., 2008;Wallace et al., 2008;Willer et al., 2008;Aulchenko et al., 2009;Kathiresan et al., 2009;Teslovich et al., 2010;Waterworth et al., 2010;Kim et al., 2011;Tan et al., 2012).Despite such progress, up to 75% of the variance in lipid levels due to genetic factors remains unexplained (Teslovich et al., 2010).Further research is needed to identify novel variants, genes, and biological pathways with important influences on lipid phenotypes.", + "AACE, Association of Clinical Endocrinologists; ADA, American Diabetes Association; HDL, high-density lipoprotein; IDF, International Diabetes Federation; LDL, low-density lipoprotein; NR, no recommendation; T2DM, type 2 diabetes mellitus; WDF, World Diabetes Foundation. *Individualized goals. High-risk or established cardiovascular disease.", + "Although hyperlipidemia is traditionally considered a risk factor for type 2 diabetes (T2D), evidence has emerged from statin trials and candidate gene investigations suggesting that lower LDL cholesterol (LDL-C) increases T2D risk.We thus sought to more comprehensively examine the phenotypic and genotypic relationships of LDL-C with T2D.Using data from the UK Biobank, we found that levels of circulating LDL-C were negatively associated with T2D prevalence (odds ratio 0.41 [95% CI 0.39, 0.43] per mmol/L unit of LDL-C), despite positive associations of circulating LDL-C with HbA 1c and BMI.We then performed the first genome-wide exploration of variants simultaneously associated with lower circulating LDL-C and increased T2D risk, using data on LDL-C from the UK Biobank (n 5 431,167) and the Global Lipids Genetics Consortium (n 5 188,577), and data on T2D from the Diabetes Genetics Replication and Meta-Analysis consortium (n 5 898,130).We identified 31 loci associated with lower circulating LDL-C and increased T2D, capturing several potential mechanisms.Seven of these loci have previously been identified for this dual phenotype, and nine have previously been implicated in nonalcoholic fatty liver disease.These findings extend our current understanding of the higher T2D risk among individuals with low circulating LDL-C and of the underlying mechanisms, including those responsible for the diabetogenic effect of LDL-C-lowering medications.Lipid-lowering medications, in particular from the statin drug class, are effective at lowering levels of circulating LDL-C and rates of adverse cardiovascular events (4) but convey an increased T2D risk (odds ratio [OR] 1.09) (5,6) in a dose-dependent manner (7).This increased risk, however, is outweighed at a population level by the cardiovascular event rate reduction.An increased T2D risk has also been reported in observational studies.Individuals with low levels of circulating LDL-C (e.g., ,60 mg/dL) exhibit a higher risk of prevalent and incident T2D (8,9), and among individuals with coronary disease, LDL-C and T2D are inversely related (10).In addition, individuals with familial hypercholesterolemia exhibit a decreased risk of T2D as well as lower BMI and triglyceride (TG) levels (11).", + "HypercholesterolemiaHyperlipidemia in the form of elevated cholesterol is among the most common medical disorder seen in individuals above the age of 40 years.Statins are the most widely obvserved drug class directed at lowering serum cholesterol.Statin drugs act by inhibiting HGM-CoA reductase activity and are among the most effective cholesterol-lowering agents available [156][157][158].However, there is a degree of variability in the response to statins among patients.Genetic variation at the APOE locus has been associated with plasma lipoprotein concentrations in both fasting and postprandial states [159].In this regard, APOE E2 carriers have been reported to be more responsive to lipid-lowering therapies [160].Differential response to statin medications has also been reported in relation to the patient's genotype status of the cholesteryl ester transfer gene [161], -fibrinogen gene [162] and lipoprotein lipase gene [163].Likewise, the ApoE protein has been associated with late-onset and sporadic Alzheimer's disease (AD) [164,165].However, in a study comparing influence of APOE genotype with clinical response to tacrine (acetylcholinesterase inhibitor), approximately two-thirds of the APOE E4 carriers presented ADAS scores that were worse compared with baseline levels [165].Whether ApoE will become clinically relevant in predicting response to AD therapy in the treatment of AD remains to be seen.", + "The Genetics of Hypercholesterolemia and Related Lipid PhenotypesHypercholesterolemia, Lipid Levels, and Their Familial Nature Population-based, long-term prospective studies and large clinical trials of the late 20th century incontrovertibly demonstrated that elevated LDL cholesterol (LDL-C) and reduced high-density lipoprotein cholesterol (HDL-C) were CVD risk factors. 88Clinical trials have demonstrated that lowering LDL-C and raising HDL-C can ameliorate risk. 89As this knowledge has been incorporated into clinical practice, mean serum total cholesterol concentrations have dropped in the United States in recent decades; however, 50% of US adults still have total cholesterol concentrations of at least 5.2 mmol/L (200 mg/dL), 90 the level that the National Cholesterol Education Program Expert Panel considers \"borderline-high risk.\" 91ecause at least half of the variation in serum cholesterol and other lipids can be explained by genetic variation, 91,92 unraveling the genetic pathogenesis of hypercholesterolemia and other lipid abnormalities could reap significant public health benefits.For example, identifying the common variants in genes that contribute to LDL-C and HDL-C could provide a knowledge base for the development of novel treatments and/or screening tests to determine who would most benefit from lifestyle modification or treatment for dyslipidemias.Important strides to this end have, in fact, already been made.", + "LipidsPopulation-based, long-term prospective studies and large clinical trials of the late 20th century incontrovertibly demonstrated that elevated LDL-C and reduced high-density lipoprotein cholesterol are CVD risk factors. 111Because at least half of the variation in serum cholesterol and other lipids can be explained by genetic variation, 112,113 unraveling the genetic pathogenesis of hypercholesterolemia and other lipid abnormalities could reap significant public health benefits by providing a knowledge base for the development of novel treatments or screening tests to determine who would most benefit from lifestyle modification or treatment for dyslipidemias.Important strides to this end have, in fact, already been made.", + "Obesityn=7255 in 2 population studies; genome metabolome integrated network analysis; serum Valcrcel et al 39 Fatty acids and lipoprotein subclasses n=1269 individual twins, including 561 complete pairs; genetic and environmental cause of the associations of serum fatty acids with lipoprotein profile; serum Jelenkovic et al 41 Biomarkers and risk assessment Subclinical atherosclerosis n=1595 young adults; circulating biomarkers for 6-year high carotid intima media thickness, new systemic biomarkers with improved risk stratification for subclinical atherosclerosis in comparison with conventional lipids; serum Wrtz et al 19 Type 1 diabetes mellitus and kidney disease Up to n=3544 patients with type 1 diabetes mellitus; cross-sectional and prospective associations of various systemic metabolites and lipoprotein subclass measures with the severity of diabetic kidney disease and mortality; introducing multiparametric risk assessment of diabetic nephropathy; serum Mkinen et al 36 Mkinen et al 26 All-cause mortality n=17 345 from 2 general population cohorts; 4 circulating biomarkers for 5-year risk of death; biomarker associations with multiple causes of death suggest novel systemic connectivities across seemingly disparate morbidities; improved prediction of the short-term risk of death from all causes above established risk factors; serum and plasma Fischer et al 32", + "IntroductionCoronary artery disease (CAD) is the leading cause of morbidity and mortality worldwide.Although our understanding of cardiovascular disease is improving, the underlying mechanisms and the comprehensive and detailed pathogenesis of CAD remain unclear.Disequilibrium of lipid metabolism is a causative factor of predisposition to CAD (Weber and Noels 2011).As a key structural component of functioning lipoproteins such as chylomicrons, very low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), and low-density lipoprotein (LDL), apolipoprotein B (APOB) is a crucial protein involved in the metabolism and maintenance of serum cholesterol homeostasis.These functioning lipoproteins participate in the process of transporting cholesterol and triglycerides throughout the circulation.Additionally, APOB binds to the LDL receptor, which mediates LDL degradation.Furthermore, a recent review indicated that subendothelial retention of APOB-containing lipoproteins was responsible for the initiation of atherogenesis (Benn 2009).Therefore, APOB plays an important role in the development of CAD." + ], + [ + "Recent genetic analysis suggests that free radical production can be increased by decoupling electron transport of ubiquinone to O 2 .A missense mutation in C. elegans cytochrome b560 causes a decrease in life span and oxygen hypersensitivity (as well as radiation hypersensitivity, a reasonable pleiotrophy because radiation induces free radicals, which damage DNA).Paradoxically, this is the opposite phenotype from the clk-1 defect in coenzyme Q biosynthesis.A model that explains this is that the cytochrome missense mutation causes a toxic build up of ubisemiquinone (a free radical that can generate superoxide) because the normal pathway for further reduction of singly reduced coenzyme Q is compromised (50).", + "Keywords: ethanol, Coenzyme Q, oxidative stress, hippocampus, mouse models, genetics, genomicsINTRODUCTIONCoenzyme Q (CoQ or ubiquinol) is a lipophilic molecule present in every cell membrane in thebody (Crane, 2001; Turunen et al. , 2004). It is best known for its roles as a mitochondrial electrontransporter and a potent membrane anti-oxidant (Ernster and Dallner, 1995; Bentinger et al. , 2007). CoQ is made up of a benzoquinone ring with an isoprenoid side chain (containing 610 units)conserved across species from yeast (as CoQ6 ), to mice (as CoQ7 ), to humans (as CoQ10 ) (Lenaz,1985).", + "The MT-CYB, a polypeptide with approximately 400 amino acid residues is one of the integral subunit of complex III of electron transport chain.Previously, mutations in MT-CYB gene have been described in uterine tumors (Shaik et al. 2011), cardiomyopathy (Feigenbaum et al. 2006), exercise intolerance (Massie et al. 2010) and histiocytoid cardiomyopathy (andreu et al. 2000).The MT-CYB T15062C, C15238a, T15378G and C15491G variants identified in the present study were specific to right atrial appendage tissues.These were previously not found to be reported in any of the human diseases in Mitomap database.", + "Mitochondria are indispensable organelles as they are responsible for the production of the majority of ATP in the cell.Most cellular ATP is generated by oxidative phosphorylation (OxPhos), a process through which electrons are extracted from reducing equivalents and transferred through four different respiratory complexes (RCs) present in the mitochondria inner membrane (CI-CIV).Electron transfer is coupled with the generation of a proton gradient through the mitochondrial membrane that drives the phosphorylation of ADP to ATP by the ATP-synthase complex (also known as complex V).", + "Second, the protein product of the CYP24A1 (cytochrome P450, family 24, subfamilyA, polypeptide 1; HGNC:2602) gene is responsible for degradation of vitamin D intoa physiologically inactive form. Vitamin D was shown to be essential for propermuscle functioning (Endo et al. , 2003; Pfeifer et al. , 2002) and polymorphisms in thevitamin D receptor are associated in humans with changed muscle strength in bothgenders (Windelinckx et al. , 2007); these changes are likely to influence the levels ofphysical activity.", + "The product of the UQCR gene is the ubiquinol-cytochrome c reductase complex, also called mitochondrial complex III.It functions to form a part of the mitochondrial respiratory chain.It may also act as a binding factor for the iron-sulfur protein.Mitochondrial Complex III is composed of one mitochondrial-encoded subunit (MT-CYB) and ten nuclear-encoded subunits.The complex is located within the mitochondrial inner membrane and plays an important role in biochemical synthesis of ATP.It functions to catalyze electrons to transfer from succinate and nicotinamide adenine dinucleotide linked dehydrogenases to mitochondrially encoded cytochrome b.It also functions to utilize the energy to translocate protons across the membrane 27 .Deficiency of isolated complex III has been detected in patients of neuromuscular and nonneuromuscular disorders in both children and adults 28 .", + "Figure 3. Mito-nuclear co-adaptation from functional assays. (a) Activities of oxidative phosphorylation (OXPHOS) enzymes in cultured cells with a Mus musculus (Mm) nucleus and mitochondria from other species (i.e.cytonuclear hybrid, or 'cybrid' cells).Species names, abbreviations and divergence times in millions of years (my) are as follows: Mus spretus (Ms) w2 my, Mus caroli (Mc) w3 my, Mus dunni (Md) w4 my, Mus pahari (Mp) w6 my, Rattus norvegicus (Rn) and Otomys irroratus (Oi) w12 my.OXPHOS enzyme activities were normalized to control activity (Mm, 100%Gs.d. ), and show normal levels with mitochondria from Ms, Mc and Md.The Mp cybrids showed normal complex I, II and III activity, but a deficiency in complex IV activity.The Rn cybrid showed deficiencies of complex I and III and a partial defect of complex IV, whereas the Oi cybrid showed a marked complex I and IV defect, and a severe complex III defect. (*P!0.05; **P!0.005; reproduced, with permission, from[49]. )(b) Cytochrome oxidase complex (COX) activities in the copepod Tigriopus californicus using mitochondria isolated from Santa Cruz (SC) or San Diego (SD) individuals and cytochrome c isolated from either SC (light-green bars) or SD (dark-green bars).Assays at 188C (bi) or 258C (bii). (Error barsZs.e. ; P!0.0001 for COX-by-CYTC, COX-by-Temperature, and CYTC-by Temperature interactions.Reproduced, with permission, from[54].)", + "Mitochondria are indispensable organelles as they are responsiblefor the production of the majority of ATP in the cell. Most cellularATP is generated by oxidative phosphorylation (OxPhos), a processthrough which electrons are extracted from reducing equivalentsand transferred through four different respiratory complexes(RCs) present in the mitochondria inner membrane (CICIV). Electron transfer is coupled with the generation of a protongradient through the mitochondrial membrane that drives the phosphorylation of ADP to ATP by the ATP-synthase complex (also knownas complex V). A full list of affiliations appears at the end of the paper.", + "One is a conversion interaction; its inputsare citratecytosol + CoAcytosol + ATPcytosol and its outputs are acetyl-CoAcytosol +oxaloacetic acidcytosol + ADPcytosol + P04cytosol . The second is a catalytic interaction; its input is ATP citrate lyasecytosol . In another example, to represent thetranslocation of citrate from the mitochondrion to the cytosol, two entities and asingle conversion interaction are used: citratemitochondrion goes to citratecytosol . Theformation or modication of a protein complex can be represented. For example,ACLA and ACLB are the subunits that compose the enzyme ACL.", + "In 1925, Keilin (188) discovered cytochromes in aerobic cells.He concluded that there are three distinct pigments, which he called cytochromes a, b, and c, and that they underwent oxidation-reduction changes in a determined sequence, which bridges dehydrogenase discovered by Wieland (414) and oxygenase by Warburg (407), leading to the concept of the respiratory chain.NADH + Flavoprotein -+ Cytochromes b +c+a+a3+OzDuring 1940 -1950s extensive studies identified mitochondria as centers of energy metabolism.In 1950-1960s studies on isolated mitochondria had clarified gross structure and bioenergetics of the respiratory chain that produces most of bioenergy in a cell.Mitochondria carry out the tricarboxylic acid cycle and the P-oxidation pathway for fatty acids.These degradative sequences essentially remove hydrogen from metabolic fuels with the release of CO2 and transfer it through coenzymic carrier to the respiratory chain in the mitochondrial inner membrane.The chain passes the electrons sequentially through complex I (NADH dehydrogenase) or complex II (succinate dehydrogenase), coenzyme Q (CoQ), complex III (ubiquinol: cytochrome c oxidoreductase), cytochrome c, and complex IV (cytochrome oxidase) to oxygen to give water, as schematically illustrated in Figure 1.The released energy is used to pump protons out of the mitochondrial inner membrane, creating an electrochemical gradient.The energy stored in this gradient is the driving force for complex V (ATP synthetase), which is also associated with the inner membrane, to condense ADP and Pi to make ATP.From the above mechanism of oxygen reduction, a large quantity of ROS is expected to generate from the genetically defected active sites of cytochrome oxidase and/or cytochrome b, or with too much oxygen supply over enzymic capability to dispose ROS.For example, the cytochrome oxidase subunit II is assessed as the binding site of cytochrome c.Hence, lack of the subunit II, which is commonly detected in the patients with mitochondrial myopathy (379), with KSS (241), or with MERRF (218), inevitably results in the decreased binding capacity cytochrome c to the oxidase, namely, the increased Michaelis constant (K,) of cytochrome c, leading to enhanced reduction of the respiratory chain at cytochrome b region and complex I, from where ROS production will explode similar to the ischemia-reperfusion episode of heart.All the patients harboring severe point mutations in the cytochrome oxidase subunit genes or in the cytochrome b gene expressed most severe clinical phenotype (280); for instance, a recipient of heart transplantation at age 7 (283) or a case of fatal infantile cardiomyopathy died at age 1 (291).A greater magnitude of ROS is produced by isolated mitochondria exposed to hyperbaric oxygen (36) or in vivo reperfusion of ischemic heart (204,267).The histochemical examination of biopsied hepatic tron transfer carriers and coupling ATPase, with a direct conversion of promitochondria to respiratory functional organelles (398).Promitochondria look clearly identifiable as normal mitochondria, having a normal outer membrane and an inner membrane with poorly developed cristae.Hence, a correlation between mitochondrial morphology and human aging would be expected not in the gross structure, but in histochemical demonstration of mtDNA encoded enzymes.cells (386) revealed no distinct difference with age in the activity of succinate dehydrogenase that is encoded bY nuclear gene.In contrast, mtDNA-encoded cytochrome oxidase (complex IV) in 1 40 autopsied hearts revealed randomly distributed cardiomyocytes without enzyme activity (254).The expression of the defect was independent of an underlying heart disease, but age was a discriminating factor.The defects occurred sporadically in the second decad .e but were regularly present from the sixth decade on.The results indicate that cytochrom .eoxidasedeficient heart muscle cells represent a degenerative lesion associated with cellular aging and may be involved Concerning number and size of mitochondria with aging, Tauchi and Sato (386) carried out an extensive survey of area and circumference of mitochondria of the in the reduction of myocardial contractile ability in senescence.Similarly, histochemical activity of cytochrome oxidase in limb muscle and in diaphragm (255) revealed randomly distributed muscle fibers without the enzyme activity, in contrast to normal histochemical reactivity for succinate dehydrogenase.A histochemical analysis of in diaphragmatic muscles from 49 subjects of different ages (54) demonstrated respiratory failure (cytochrome oxidase negativity) in occasional fibers from the fourth decade on with an -lo-fold increase between the fourth and ninth decade (from 0.16 to 2.85%).It thus had been a matter of great interest to discover why cytochrome oxidase has not been shown to generate significant amounts of such intermediates.On the basis of optical studies of oxy-and peroxy-cytochrome oxidase by Chance et al. ( 63), it became clear that the intermediates of oxygen reduction remain within the active site of cytochrome oxidase until the final reaction stage of water is achieved, probably for protection against cellular intoxication.From general properties of the mitochondrial generation of HzOz and effect of hyperbaric oxygen, it was postulated (36) that besides the well-known flavin reaction, formation of HZOZ may be due to interaction with an energy-dependent component of the respiratory chain at the cytochrome b level.These findings clearly indicated that the active sites of the complex IV and III, consisting of cytochromes a and b, respectively, play a crucial role not only for the cellular energy production, but also for protection against cellular oxidative damage.Hence, attenuation of the active sites, even low absolute level, could result in serious outcome in cellular viability.This point, however, has been not well recognized by the researchers until recently.", + "Second, the proteinproduct of the CYP24A1 (cytochrome P450, family 24, subfamilyA, polypeptide 1; HGNC:2602) gene is responsible for degradationof vitamin D into a physiologically inactive form. Vitamin D wasshown to be essential for proper muscle functioning [48,49] andpolymorphisms in the vitamin D receptor are associated in humanswith changed muscle strength in both genders [50]; these changesare likely to inuence the levels of physical activity. However, neither Mc3r nor Cyp24a1 contain a known non-synonymous codingSNP between the progenitor strains, A and B6.", + "The cytochrome P450s (CYPs) causes the site-specific oxidization of the cyclic skeleton synthesized by OSCs.", + "Second, the proteinproduct of the CYP24A1 (cytochrome P450, family 24, subfamilyA, polypeptide 1; HGNC:2602) gene is responsible for degradationof vitamin D into a physiologically inactive form. Vitamin D wasshown to be essential for proper muscle functioning [48,49] andpolymorphisms in the vitamin D receptor are associated in humanswith changed muscle strength in both genders [50]; these changesare likely to inuence the levels of physical activity. However, neither Mc3r nor Cyp24a1 contain a known non-synonymous codingSNP between the progenitor strains, A and B6.", + "IntroductionThe mitochondrion of the modern human cell is the product of an ancient symbiosis in which an oxidative bacterium took up residence in the proto-nucleated cell that had developed motility and endocytosis.Following this initial symbiotic event, most of the genes of the mitochondrion were transferred to the nuclear DNA (nDNA) where they now reside, are replicated and transcribed.The resulting nDNA-encoded mitochondrial mRNAs are then translated on cytosolic ribosomes into proteins which are selectively imported into the mitochondrion.This mitochondrial protein import is frequently mediated by an amino terminal targeting peptide which is removed on entrance of the polypeptide into the mitochondrial matrix.", + "The ITCHY library gave rise to variants with improved kcat with the substrateused for selection compared to either of the parental enzymes and additionallyshowed activity on ethacrynic acid, a compound recognized by neither parental enzyme. This combination of a human with nonhuman enzymes to formactive chimeras shows that this method could be used for the humanization ofproteins with therapeutic values that show no conserved framework allowingfor rational grafting. Chapter 36 Directed Protein Evolution3.1.9. SHIPRECCytochromes are proteins that contain heme groups and are responsible forthe transport of electrons.", + "One is a conversion interaction; its inputsare citratecytosol + CoAcytosol + ATPcytosol and its outputs are acetyl-CoAcytosol +oxaloacetic acidcytosol + ADPcytosol + P04cytosol . The second is a catalytic interaction; its input is ATP citrate lyasecytosol . In another example, to represent thetranslocation of citrate from the mitochondrion to the cytosol, two entities and asingle conversion interaction are used: citratemitochondrion goes to citratecytosol . Theformation or modication of a protein complex can be represented. For example,ACLA and ACLB are the subunits that compose the enzyme ACL." + ], + [ + "Genetic mapping inmouse strains enhances the power of detecting modifier genes and identifying complexgenetic interactions. Genomewide quantitative trait locus (QTL) analysis, as described inmore detail below, represents a promising approach to detect genetic variants that areassociated with specific phenotypes and interact with each other. 16ACCEPTED MANUSCRIPTIn experimental crosses of two (inbred) strains the first generation (F1) ofoffsprings is genetically heterozygous but equal. Then in the next generation (F2) thePTstrain-specific genetic information is distributed across the genomes of their progeny andRIeach offspring is genetically unique.", + "This contrast can be exploited to identify subregions that underlie the trans-QTLs [67]. SNPs were counted for all four pairs of parental haplotypesBvs D, B vs H, B vs C, and L vs Sand SNP profiles for the fourcrosses were compared (figure 6). Qrr1 is a highly polymorphicPLoS Genetics | www.plosgenetics.org8November 2008 | Volume 4 | Issue 11 | e1000260QTL Hotspot on Mouse Distal Chromosome 1Figure 5. QTL for aminoacyl-tRNA synthetases in distal Qrr1.", + "The traditional approach to QTL mapping is to usetwo strains that differ maximally in the phenotype asparental strains for genetic crosses, with the followingcaveats. QTL analysis based on a single cross will mostlikely reflect only a small portion of the net geneticvariation, and QTL detection will be limited to regionswhere the two progenitor strains have functional polymorphisms. Data from multiple crosses, or from an HS,will overcome this limitation and can also be used toreduce QTL intervals [5,30].", + "These candidate genes are then sequenced in the two parental inbredstrains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausiblecandidate polymorphisms, the major challenge remains \u0001 proving denitivelywhich nucleotide polymorphism underlies the QTL. The most direct proofwould be replacing one strains allele with another strains allele (creating aFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mousegenerations are depicted.", + "One key advantage of lookingat transcripts with expression levels linking to behavioral QTL is the potential tomake inferences about the causal DNA variants underlying behavioral traits andtheir mechanisms. In analysis of behavioral traits alone, since linkagedisequilibrium is far-ranging in the BXD cross and each marker represents a widestretch of genomic sequence across which inheritance is correlated, it is difficultto narrow down which gene and DNA sequence variation at a given mappedlocus influences the behavioral trait.", + "Interval-specific haplotype analysisApproximately 97% of the genetic variation betweeninbred mouse strains is ancestral [22], so regions ofidentity by descent (IBD) between two strains used todetect a QTL are highly unlikely to contain the causalgenetic polymorphism underlying the QTL [28]. Forexample, a cross between C57BL/6J and A/J mice detectedwww.sciencedirect.coma blood pressure QTL on Chr 1 [7].", + "Interval-specific haplotype analysisApproximately 97% of the genetic variation betweeninbred mouse strains is ancestral [22], so regions ofidentity by descent (IBD) between two strains used todetect a QTL are highly unlikely to contain the causalgenetic polymorphism underlying the QTL [28]. Forexample, a cross between C57BL/6J and A/J mice detectedwww.sciencedirect.coma blood pressure QTL on Chr 1 [7].", + "At present, the BXD panel is composed of 80 different strains that all have beenfully genotyped.26 Variation in any quantifiable trait can be associated with thesegregation of parental alleles, and linkage genetics can map this variation toquantitative trait loci (QTLs), thereby identifying the genomic region(s) affectingthat trait. An overview of the QTL mapping approach is depicted in Figure 2. Classical QTL analysis has permitted the identification of loci that areassociated with variation in HSC traits.", + "The progenitor mouse strainsshould have sufficient variation for the traits of interest and they should be genetically diverseenough to enable genetic mapping (BENNETT et al. 2006; FLINT 2003; GRISEL 2000). Thesample size required for the identification of QTL depends largely on the effect size that aQTL contributes to phenotypes on interest. Inference about QTL can be made if one or moregenetic markers are over- or underrepresented in the analysed individuals. Genotyping isoften done by means of microsatellite markers, which contains mono, di-, tri-, ortetranucleotide tandem repeats flanked by specific sequences (Figure 4a).In general,linking genetic variation with trait variation identifies QTL and a significant linkage ofphenotype and genotype suggest that the DNA status helps to determine trait expression. As stated above, mouse QTL studies provide distinct advantages over human studiesin the examination of genetic causes of a quantitative trait (e.g. alcoholism), even in theabsence of specific hypotheses regarding its aetiology or candidate genes.This comparison gives information about the reliability of the observed genotypeinformation: The more the marker locations differ between the two maps (which signifiesvariation in marker positions), the higher the possibility of genotyping errors. QTL mapping was done in several stages to identify loci acting individually and QTL thatinteracted, either additively or epistatically. To determine individually-acting QTL, a singleQTL genome scan was conducted with the function scanone.", + "Importantly, whereasthese studies required substantial labor, time, and resources, X-QTL is a quick and easyapproach to achieve a comparable level of genetic dissection. The levels of complexityobserved here (e.g. 14 loci explaining 70% of the genetic variance for 4-NQO resistance) arestill dramatically lower than those seen in for some human traits in GWAS (e.g. 40 lociexplaining 5% of the variance for height 2,5). One obvious explanation is the difference inexperimental designs (line crosses vs. population association studies), but differences ingenetic architectures among species and traits may also contribute.", + "The method uses two pieces of information: mapping data from crosses thatinvolve more than two inbred strains and sequence variants in the progenitor strains within the intervalcontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that anysequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals exceptat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.", + "The method uses two pieces of information: mapping data from crosses thatinvolve more than two inbred strains and sequence variants in the progenitor strains within the intervalcontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that anysequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals exceptat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.", + "which allows the the estimation of the degree of dominance of detected QTLs.but point out that various designs or linkage analysis methods have advantages and disadvantages and need to be chosen for the question at hand. In general, there are two common statistical approaches for detecting and/or locating QTL. Both approaches involve moving along the chromosome and considering data for one or several markers at a time and relating these to the traits of interest.", + "Genotyping all the individual progeny formarkers that show allelic variation between the parental strains (either single nucleotide polymorphisms or simple sequence repeats) will allow the detection of associations between trait values and marker genotype, and in this way demonstrate to whichset of markers a QTL is linked. To reduce the genotyping effort, selective genotypingof the individuals at the extremes of the phenotypic spectrum can be performed (20,23). Although these three approaches are in general considered to be the best to detect andmap QTL, they have several disadvantages for quantitative traits involving HSC.", + "So, how do you go about planning and performing a QTL study, and howdo you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbredstrains that have a markedly different trait. One can now look up many differenttraits of inbred mice online at the Mouse Phenome Database (http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). However, the trait you maywant to study may not be present in wild type mice, so you may want to crossa mutant (or genetically engineered) strain onto several inbred strains.QTL Theory and PlanningThe theory behind the most basic form of QTL mapping is based upon intercrossing two inbred strains. The mouse genome consists of 19 pairs of autosomes (non sex-determining chromosome) and the X and Y chromosomes. Inthe example shown in Fig. 18.1, we are intercrossing stain A (shown with ablack chromosome pair) with strain B (shown with a white chromosome pair). The initial F1 (filial generation 1) mice are true hybrids, with each individualFrom: Molecular Biomethods Handbook, 2nd Edition.", + "These candidate genes are then sequenced in the two parental inbredstrains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausiblecandidate polymorphisms, the major challenge remains \u0001 proving denitivelywhich nucleotide polymorphism underlies the QTL. The most direct proofwould be replacing one strains allele with another strains allele (creating aFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mousegenerations are depicted.", + "QTL mapping studies thenseek to detect the polymorphisms underlying the complex traits of interest byscanning for alleles that co-vary withthe traits. Similar experiments also can be conducted with special derivatives of inbredstrains known as recombinant inbred(RI) mice. These animals are derivedby cross-breeding two or more distinctparental strains (which often divergewidely for the trait of interest), followedby inbreeding of the offspring for severalgenerations (Bailey 1971). Given thecorrect breeding strategy, this method1This is an issue faced by GWASs researchers when classifyingsamples as cases or controls." + ] + ], + "task_id": [ + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1", + "58D6F365917926445960756A26B3FDC8", + "A5DEAEAC441B3BDC65B58EA6923FAE73", + "0CDD1C9219114BB2770C28D541F1060A" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_6.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_6.json new file mode 100644 index 0000000..250aed7 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_6.json @@ -0,0 +1,104 @@ +{ + "question": [ + "how does environment influence fertilisation", + "how does diet impact someone's height", + "which animal has the same number of chromosomes as human", + "what's ensures brains work", + "how do our brains maintain emotions" + ], + "answer": [ + "Environmental factors can influence fertilisation in several ways. Factors such as temperature, pressure, and chemical activity can affect the physiological processes involved in fertilisation. Lifestyle factors like smoking, irradiation, oral contraceptives, and low socioeconomic status can also impact natural conception. Moreover, environmental conditions can affect the quality of maternal care and offspring development. For instance, crowded larval environments can lead to a decline in quality over time, affecting the genetic polymorphism in populations. Additionally, environmental stressors can influence the responsiveness to prenatal stress, potentially affecting fertility.", + "The text does not provide information on how diet impacts someone's height.", + "The Bama miniature pig has the same number of chromosomes as humans.", + "The functioning of the brain is ensured by the complex interplay of neuronal networks, synaptic connections, and the precise regulation of molecular and cellular events. This includes the spatiotemporal regulation of the transcriptome, the signaling dynamics of integrated circuitry, and the balance between activating and inhibiting systems. Additionally, the maintenance of genomic integrity in neuronal cells is crucial for the brain's high metabolic activity.", + "Our brains maintain emotions through complex neural systems and processes. The reward-motivation system, which includes the striatum, amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex, plays a key role in processing emotional and motivational information. The amygdala, a principal component of the limbic system, controls emotionality. Stress, anxiety, and other emotion-related traits are influenced by multiple genetic, environmental, experiential, and epigenetic factors. Additionally, hormones released by stressful experiences can enhance memory consolidation, further influencing emotional responses." + ], + "contexts": [ + [ + "This may be due to the cost of increased solicitation (reflected in reduced bodyweight for the effort expended) for which we found evidence in our study. Bodyweight is indeednegatively correlated with the level of offspring solicitation (GLM, F1,66 = 20.57, P < 0.001 e.g. day10, r = -0.39, and day 14, r = -0.44; Figure 6 and Supplementary file 2, e). DiscussionOur study of the genetics underlying family interactions has revealed that genes expressed in offspring can indirectly influence the quality of maternal behaviour and thus offspring fitness.", + "UNINTENDED SELECTION IN LABORATORY ENVIRONMENTSIn nature, environmental variables are often highly correlated. For example, high-pressuredeep-sea habitats are generally cold, hydrothermal vents being an extremely rare (butextremely interesting) exception. The solubility of oxygen in water is negatively related totemperature; thus, even oxygen-saturated aquatic environments can have less availableoxygen than colder, subsaturated regions. In terrestrial environments, the saturatingvapor pressure of water increases dramatically with temperature, so that a parcel of aircontaining the same absolute quantity of water vapor will have a lower relative humidityas it warms.It hasalso been observed that, over the span of one generation, crowded larval environmentsshow a temporal decline in quality (Borash et al. 1998). Ammonia levels increase overtime, while food and ethanol levels decrease. This complexity appears to be responsiblefor a genetic polymorphism in crowded populations. Very early-developing genotypeshave high feeding rates but low tolerance to ammonia, while late-developing genotypesfeed more slowly and can tolerate higher ammonia levels. There may be many naturalenvironments that exhibit similar patterns of temporal decay (Borash et al. 1998).Temperature is the mostimportant and common physical variable affecting the distribution and abundance oforganisms in nature, as a 10C increase in temperature causes most biochemical reactions to increase in rate two- to threefold. Typical physiological temperatures span040C, although more extreme limits are well known (e.g. , overwintering plants andinsects, hot springs bacteria). Thus, selection experiments using temperature may behighly relevant to the real world. For aquatic organisms, the osmotic strength of the surrounding medium is an important environmental variable.In nature, thermodynamic variables such as temperature, pressure, and chemicalactivity (i.e. , the concentration of salts, hydrogen ions, etc. )differ across habitats. Lifeitself requires input of raw materials from the environment (nutrients, water, ions, etc. )that can then be used to drive physiological processes and make more organisms. We consider here two categories of environmental variables that have been used asselective agents in laboratory natural selection experiments.In ahumid environment, higher temperature will increase metabolism but wont increaseevaporative water loss; but in a dry environment, higher temperatures will increasemetabolism and water loss. A call for greater ecological realism is not without precedent. Ecologists have developed sophisticated laboratory facilities that can mimic simple terrestrial ecosystems. AtSilwood Park, for instance, the Ecotron consists of fifteen environmental chambers ableto control and manipulate photoperiod, illumination (balanced spectrum, dawn/dusksimulation), temperature, humidity, rainfall, and even CO2 (Lawton 1996). The chambers house multispecies ecosystems, allowing for complex ecological interactions ofplants and animals.", + "Alternatively, the \"limited oocyte pool\" hypothesis (Warburton, 1989) suggests a more direct effect of antral oocyte pool size on the risk of aneuploidy.The limited number of antral follicles available in older women could lead to the selection of a suboptimal oocyte for ovulation, for example one that is either immature or postmature.Some experimental evidence in other mammals supports the idea that such Sample described in Warburton et al. (1986).", + "In the most general terms, three types of environmental factors can influence human health during aging: physical, chemical, and biological.Physical factors include temperature and solar radiation.Chemical factors from natural and biological sources include trace toxins (asbestos, lead, tobacco smoke), but also trace morphogens that can cause subtle abnormalities in development.Biological factors include diet and infectious organisms, but also stress from social interactions.We know little about the concentrations of a vast number of bioactive substances that may be present sporadically in the environment.It seems fair to say that our concept of the environment will evolve rapidly with new technical developments and may come to include multigenerational effects.For example, in the case of diabetes, the maternal physiological state existing before pregnancy can influence fetal growth.Moreover, the ovary acquires its full stock of eggs in the fetus: thus, the egg cell from which all of our cells stem was exposed to the environment of our maternal grandmother (Finch and Loehlin, 1998).The depth of the transgenerational environment is a completely obscure aspect of human experience.", + "Low human fertilityAnother area of interest is that of changing fertility patterns in the developed world.Currently ESRC investment is focused upon both the economic and social trajectories of demographic change.There are a number of significant questions that need to be addressed in relation to involuntary infertility however.For example, infertility rates, which appear to be rising, and also the considerable variation that exists in the timing of the ending of the human reproductive span.Researchers need to know whether these factors are genetic, gene/environment interactions, or entirely environmentally induced, and why there is a need for heterogeneity of fecundity in biometric models of fertility, coital frequency or genetics (Hobcraft, 2003).", + "UNINTENDED SELECTION IN LABORATORY ENVIRONMENTSIn nature, environmental variables are often highly correlated. For example, high-pressuredeep-sea habitats are generally cold, hydrothermal vents being an extremely rare (butextremely interesting) exception. The solubility of oxygen in water is negatively related totemperature; thus, even oxygen-saturated aquatic environments can have less availableoxygen than colder, subsaturated regions. In terrestrial environments, the saturatingvapor pressure of water increases dramatically with temperature, so that a parcel of aircontaining the same absolute quantity of water vapor will have a lower relative humidityas it warms.It hasalso been observed that, over the span of one generation, crowded larval environmentsshow a temporal decline in quality (Borash et al. 1998). Ammonia levels increase overtime, while food and ethanol levels decrease. This complexity appears to be responsiblefor a genetic polymorphism in crowded populations. Very early-developing genotypeshave high feeding rates but low tolerance to ammonia, while late-developing genotypesfeed more slowly and can tolerate higher ammonia levels. There may be many naturalenvironments that exhibit similar patterns of temporal decay (Borash et al. 1998).Temperature is the mostimportant and common physical variable affecting the distribution and abundance oforganisms in nature, as a 10C increase in temperature causes most biochemical reactions to increase in rate two- to threefold. Typical physiological temperatures span040C, although more extreme limits are well known (e.g. , overwintering plants andinsects, hot springs bacteria). Thus, selection experiments using temperature may behighly relevant to the real world. For aquatic organisms, the osmotic strength of the surrounding medium is an important environmental variable.In nature, thermodynamic variables such as temperature, pressure, and chemicalactivity (i.e. , the concentration of salts, hydrogen ions, etc. )differ across habitats. Lifeitself requires input of raw materials from the environment (nutrients, water, ions, etc. )that can then be used to drive physiological processes and make more organisms. We consider here two categories of environmental variables that have been used asselective agents in laboratory natural selection experiments.In ahumid environment, higher temperature will increase metabolism but wont increaseevaporative water loss; but in a dry environment, higher temperatures will increasemetabolism and water loss. A call for greater ecological realism is not without precedent. Ecologists have developed sophisticated laboratory facilities that can mimic simple terrestrial ecosystems. AtSilwood Park, for instance, the Ecotron consists of fifteen environmental chambers ableto control and manipulate photoperiod, illumination (balanced spectrum, dawn/dusksimulation), temperature, humidity, rainfall, and even CO2 (Lawton 1996). The chambers house multispecies ecosystems, allowing for complex ecological interactions ofplants and animals.", + "How do we improve reproductive success and reduce the effects of maternal aging in the natural population as well as in the clinic?Identification of lifestyle factors that affect natural conception is important.Several factors including smoking, irradiation, oral contraceptives and low socioeconomic status (Christianson et al., 2004;Hunter et al., 2013) have been implicated but their molecular basis has yet to be elucidated.Disentangling the factors that influence aneuploidy may provide us with lifestyle interventions to reduce miscarriage rates and may move the J curve to the right and prevent the early truncation of reproductive lifespan caused by aneuploidy.", + "Several lines of evidence further suggest that fetal genetic effects may influence birth timing.First, fetal genes that are paternally imprinted mainly control placental and fetal membrane growth [16] .Because the placenta and fetal membranes likely play a role in preterm birth, fetal genes controlling these tissues may also contribute.Additionally, a study comparing the correlation in gestational age between full and half siblings suggests that preterm birth is influenced in part by fetal genetic factors [15] .Lastly, several studies suggest that paternity affects risk for the disorder.For example, several studies indicate that partner changes between pregnancies reduced risk of preterm birth [17,18] ; however, changes in paternity may reflect association with long interpregnancy intervals rather than paternity effects per se.Paternal race also has been associated with preterm birth risk.Previous studies observed that preterm birth rates are highest when both parents are Black and remain higher when one parent is Black, whether that parent is the mother or father [19,20] , suggesting that fetal race also influences birth timing.However, father's family history of preterm birth has been shown to have only a weak association with risk.While an early study of a Norwegian birth registry demonstrated a correlation between father and children's gestational ages [21] , a more recent and extensive study of this registry suggested fathers contributed little to no risk to preterm delivery [22] .Similarly, a recent study [14] suggested that paternal genetics contributed little to gestational age, but could not refute the possible role of maternally-inherited genes expressed in the fetus.Hence, while paternally-in-herited genes may contribute little to preterm birth or other disorders, maternally-inherited genes expressed in the fetus may still be important.Together, these data suggests that the fetal genome may contribute to birth timing, motivating further study defining the infant as the proband.", + "Young maternal age at conception may play a role in longer child telomere length but again the biology of these relationships including environmental versus genetic factors need to be better studied (Prescott et al. 2012).Furthermore, as rate of change reflects both genetic and environmental influences, it is important to determine whether parental rate of change might covary with child rate of change.", + "6.2 Mechanisms of indirect genetic effects on maternal care6.2.1 Prenatal effectsOffspring effects on maternal investment and postnatal behaviour begin in utero. Theplacenta is vital for the development of offspring in eutherian mammals (John and Surani,2000) by regulating transfer of nutrients from mother to offspring (Constncia et al. , 2002),which in turn increases maternal food intake (Newbern and Freemark, 2011) and alsoprimes the maternal brain for parenting behaviour (Bridges et al. , 1990, 1997).", + "DISCUSSIONDespite the fact that genetic factors that reduce the ability of an individual to reproduce are expected to be under intensive negative selection, reduced fertility is a common health condition in humans (de Kretser, 1997;Agarwal et al., 2015) and an important economic trait in dairy cattle.Previous studies that included measurements of progesterone and pregnancy-specific protein B levels suggested that a large portion of recorded non-conceptions in human and cattle are apparently the result of unrecognized EA (Edmonds et al., 1982;Humblot, 2001;Santos et al., 2004;Carthy et al., 2015).Markers are sorted in descending order of the probability to reject the null hypothesis of no effect on putative early abortion rate.The substitution effects and coefficients of determination are given for each marker for putative early abortion and conception status.", + "by the gross limitations of forward genetic approaches in humans,including limited ability to dissect environmental factors and gene XWhat this study addsenvironment interactions, particularly the contribution of environmen- Identifies candidate genes that may moderate the effectstal factors in utero (Burmeister, McInnis, & Zllner, 2008; Henriksen,of prenatal stress on cocaine responsiveness. Nordgaard, & Jansson, 2017), and these limitations in turn hinder the Demonstrates sex as a factor that moderates the effectsdevelopment of a mechanistic understanding of aetiology. Here, weof early life stress on cocaine responsiveness." + ], + [ + "Year Period, lbsNote.Weight changes are shown per increase in daily serving of the food or beverage.All weight changes were adjusted simultaneously for age, baseline body mass index, sleep duration, smoking status, physical activity, television watching, alcohol use, and all the dietary factors shown.Source.Adapted from Mozaffarian et al.6Women who increased their adherence to a Western pattern (high intakes of red and processed meats, refined grains, sweets or desserts, SSBs, and potatoes) gained the most weight across 8 years of follow-up.In parallel, women who increased their adherence to a prudent pattern (high intakes of fruits, vegetables, whole grains, fish, poultry, and salad dressing) gained the least weight. 14n a recent study of dietary quality characterized by established healthy diet indices (i.e., a Mediterranean-style diet, the Alternate Healthy Eating Index, and the Dietary Approaches to Stop Hypertension diet), higher or increasing adherence to any of these indices was associated with less weight gain in a given 4-year interval through midlife, with greater benefits observed in overweight women.15 (For additional information on dietary assessments in the NHS, please see Hu et al. in", + "In onestudy, vitamin D levels were inversely correlated with BMI(r = -0.22, p = 0.025), suggesting some potential benefitsfor individuals living with obesity, although this remainsto be investigated in a prospective study.14 For individuals living with obesity, an eight-week low-calorie dietprogramme supplemented with vitamin D led to a significant decrease in inflammatory markers, compared withthe same diet with a placebo supplement.15 Obesity isassociated with low plasma levels of 25-hydroxy-vitaminD, which can result from vitamin D deficiency.1619 Therefore, we compared body weights of the mice from eachgroup before and after treatment.", + "In all study cohorts, height and weight were measured wearing light clothing and no shoes, and BMI was calculated as weight divided by the square of the height (kg/m 2 ).Written informed consent was obtained from all participants and the research protocol was approved by the local human research ethics committees.", + "age-adjusted height residuals, cm.", + "explained by genes predisposing to obesity.The National Academy of Sciences-National Research Council (NAS-NRC) World War (WW) II Veteran Twin Registry of White male twin pairs 5 had their height, weight, and BP measured at the induction physical examination, which offers a unique opportunity to investigate the following questions: (1) the relative influence of genetic and environmental factors on height, weight, BMI, and BP (SBP and DBP); (2) the extent to which genetic and environmental influences on SBP and DBP are shared with those influencing BMI; (3) whether BMI has any modifying effect on genetic and environmental influences on SBP and DBP.What Is New? In the largest twin cohort with measured (rather than self-reported) weight, height, and blood pressure (BP), we investigated (1) the relative influence of genetic and environmental factors on height, weight, body mass index (BMI), and BP (systolic BP [SBP] and diastolic BP [DBP]); (2) the extent to which genetic and environmental influences on SBP and DBP are shared with those influencing BMI; (3) whether BMI has any modifying effect on genetic and environmental influences on SBP and DBP.", + "Over the last few decades, the adoption in Asian populations of western-style diets of increased fats and carbohydrates and of more sedentary habits has led to a marked increase in obesity (23,24).In particular, a cohort of women from the ongoing Cebu Longitudinal Health and Nutrition Survey (CLHNS) based in the Philippines showed a sixfold increase in prevalence of overweight and obesity associated with nearly two decades of substantial and continuing socioeconomic modernization (also illustrated by an increase in mean weight of 6.8 7.1 kg) (24).The portion of increased prevalence due to the changes in environment vs. increased age of these women is unclear.", + "In onestudy, vitamin D levels were inversely correlated with BMI(r = -0.22, p = 0.025), suggesting some potential benefitsfor individuals living with obesity, although this remainsto be investigated in a prospective study.14 For individuals living with obesity, an eight-week low-calorie dietprogramme supplemented with vitamin D led to a significant decrease in inflammatory markers, compared withthe same diet with a placebo supplement.15 Obesity isassociated with low plasma levels of 25-hydroxy-vitaminD, which can result from vitamin D deficiency.1619 Therefore, we compared body weights of the mice from eachgroup before and after treatment.", + "ResultsTable 2 displays anthropometric, biochemical and dietary characteristics of the study sample.The sample included 288 men and 383 women, with a mean age of 40.59 8 14.79 years.The individuals were on average overweight (BMI = 27.75 8 7.63) and their dietary fat intake represented 34.3% of daily energy intake.The results of significant interactions (p value ^ 0.01) are presented in table 3 and suggest that the majority of the SNPs that have been initially associated with T2DM at high levels of statistical significance in GWAS reports did not interact with dietary fat intake to influence either adiposity-or glucose homeostasis-related phenotypes.", + "Child weight parameters and accelerated shortening in childhoodObesity at different points in early childhood did not correlate with the rate of telomere attrition from 4 to 5 years of age (Table 4).Similarly, abdominal obesity did not correlate with the rate of telomere attrition (p = 0.65) (Table 4).", + "Diet significantly alters lifespan, not weight gain per seAuthor ManuscriptWe chose to focus on two time points for body weight analyses100 days on diet as a pointto evaluate early weight gain on HFD, and 400 days on diet, a stage that is close to themaximal weight on both diets. The mean weight of the population plateaus around 500 daysof age and declines thereafter on both diets.The consensus model highlighted a potential causal effect of diet on peak bodyweight measured relatively late in life (500 days), acting through circulating levels of totalNat Metab. Author manuscript; available in PMC 2022 March 22. Roy et al. Page 7Author Manuscriptand high-density lipoprotein cholesterol measured in the old-aged group (Extended Figure2). The Bayesian network analysis, as we structured it, failed to show any causality betweenserum metabolites and variability in lifespan.Early body weight gain associated with reduction in lifespanBody weight measured after 100 days on both diets also correlates negatively with lifespan,after adjusting for strain differences (Figure 3C), a one-gram increase now corresponding toa decrease of 4 days (p<0.0001, r = 0.22). Looking at change in body weight after 100 dayson diet, early body weight gain in response to HFD, but not CD, trended to be negativelycorrelated with lifespan, with a one-gram gain corresponding to a decrease of ~1.5 days (p =0.08, r = 0.06) (Figure 3D).(E) After 400 days on diet (~500 days age), body weight does not predictvariance in lifespan (see line labeled d in Panel A) (p = 0.63, r = 0.01) (n = 447 on CDand HFD). (F) Substantial weight change after prolonged HFD feedingdifference frombaseline to 400 days on diet (blue line)does not predict lifespan (p = 0.26, r = 0.02). (G)Strain-wise changes in median weight after 100 days on diets. Red points represent lifespansof cases on CD and blue points those on HFD. Lines represent median body weight (lefty axis).(C) Body weight after 100 dayson both diets (~260 days age) correlates negatively with lifespan (4 days/g, p <0.001, r =0.3, see line labeled c in Panel A) (n = 626 on CD, 665 on HFD). (D) Early weight changein response to HFD (blue line)the difference from baseline after 100 days on dietwasnegatively related with lifespan (4 days/g, p = 0.004, r = 0.1), but this is not true of casesremaining on CD.", + "Her father was 170 cm in height and grew significantly at the age of 14 years.Her mother was 153 cm in height with menarche at the age of 13 years.Her younger brother was 6 years old and maintained a height above the 50th percentile of the population with the same age and gender.There was no family history of diabetes mellitus or short stature.Her father is 172 cm, grew significantly at the age 15 years.Her mother is 158 cm and had menarche at age 14 years.Her elder brother is 22 years old and his height is 180 cm.There is no family history of diabetes mellitus or short stature.", + "Adult height is the result of both growth throughout childhood and loss of height during the aging process.We therefore assessed the influence of age on the 20 robust associations.We did not find any evidence that the effects on height were different in individuals o50 years compared to those aged 450 years (all P 4 0.01; similar results were obtained when we used a cut-off of 40 years of age), or when adjusting for age decade (see Supplementary Table 4 online).This suggests that the effects are predominantly on developmental and childhood growth rather than on processes involved in loss of height, although studies of more young adults and children are needed to confirm this.", + "IntroductionHeight, fat mass, and fat distribution differ substantially between men and women, and these differences may, in part, explain the sex-specific susceptibilities to certain diseases [1,2].A subtle sexual dimorphism in body composition is already apparent during childhood, and emerges more prominently during adolescence as boys start exceeding girls with regard to height and muscle mass, while girls accumulate more fat mass [3][4][5].These considerable differences in anthropometry may reflect sex-specific differences in steroid hormone regulation, adipogenesis, lipid storage, muscle metabolism, composition, and contractile speed, skeletal growth and maturation, or lipolysis, and suggest a genetic underpinning [1,2,[6][7][8][9][10]." + ], + [ + "To facilitate comparative research, the Alliance of GenomeResources provides an interface that allows users to searchfor and view genes, functional data, and disease associations from databases of the fly, mouse, rat, yeast, nematode,and zebra fish (http://www.alliancegenome.org, last access:3 January 2018). 3The mouse as a model animal for livestockresearchMice are mammals, sharing 92 to 95 % of protein coding genes with humans and other mammalian livestockspecies, such as cattle (Elsik et al. , 2009), pigs (Humphrayet al. , 2007), sheep (Iannuzzi et al. , 1999), and goats(Schibler et al. , 1998).", + "Sex ChromosomesSeveral studies have revealed high degrees of homology among autosomal chromosomes of bovids with similar banding patterns and gene order among the chromosome arms of cattle, river buffalo, sheep, and goats [14,15].Bovid sex chromosomes, unlike the highly similar autosomal chromosomes, share a slightly more complex rearrangement of sequences [5].Chromosome banding comparisons show that while large portions of these chromosomes are conserved, BBU-X has large blocks of constitutive heterochromatin that BTA-X lacks.Cytogenetic studies representing loci order on these sex chromosomes show complex rearrangements that may have occurred during the karyotype evolution of river buffalo and cattle.BBU-X and BTA-X share the same gene order but a different centromere position, indicating a centromere translocation event with the loss of constitutive heterochromatin in BTA-X, which differentiates it from BBU-X [5].Comparative FISH mapping shows the existence of a similar situation in river buffalo and cattle Y-chromosomes.BTA-Y and BBU-Y differ in an inversion including the centromere and breakage points in both arms (pericentric inversion) where BBU-Y is larger than BTA-Y and gains heterochromatin [5].Figure 1: At the cytogenetic level, water buffalo chromosomes can be matched to bovine chromosomes arm for arm.Each biarmed water buffalo chromosome is derived from the fusion of two bovine acrocentrics. (a) This shows the similar banding patterns for bovine chromosomes 29 and 16 to water buffalo chromosome 5 [22], (b) This shows similar banding patterns for bovine chromosome 12 and water buffalo chromosome 13 [22].", + "Second,it is possible to replicate experiments in reference cohorts (also known as referencepanels or reference populations), which is impossible in humans except for in cases ofmonozygotic twins. Third, it is easy to control the environment and model geneenvironment (GXE) interactions in mice [75]. Fourth, despite strong functional effects,the minor allele frequencies are often too low in the human population to attain sufficientstatistical power and significance in large association studies. In contrast, most of murinecrosses have been derived from two inbred strains, and as a result allele frequencies areclose to 0.5.", + "Figure S3.Chromosome karyotype of Bama miniature pig, Related to Figure 3. (A) Male and (B) Female.The examination of karyotype of Bama miniature pig by means of peripheral blood lymphocytes culture showed that the diploid chromosomes number was 38, 18 pairs of autosomes and one pair of sex chromosomes in both males (XY) and females (XX).The chromosomes were divided into four groups of a, b, c and d according to the standard of Reading Congress.The karyotype of the autosomes was 10sm+4st+10m+12t.The X chromosome was a metacentric chromosome whose length was between the 8 th and 9 th chromosome, while the Y chromosome was the smallest metacentric chromosome.Comparison of the BM genome with the human, and three common experimental animal (macaque, mouse, and dog), genomes unveiled three gene families, including ARF1 and IGHD, shared between the BM and human genomes but absent in macaque, mouse, and dog genomes (Figure S10).These genes may play roles in Alzheimer disease, pituitary dwarfism, and growth failure (from database ''Dis-GeNET'').The presence of these genes in the BM potentially facilitates research on the abovementioned diseases using this animal model.Moreover, BM has fewer unique genes compared with the Duroc (1,303 versus 1,531) (Figure S10), and the genes specific to BM were significantly enriched in the ''steroid hormone biosynthesis'' Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway (p = 0.00908), which is associated with sex hormone secretion, male testicles development, and rapid maturation of sperm.", + "Mice are evolutionarily relatively close to humans, and their size and short generationtime allows experiments to be set up and run with large enough numbers for statistical signicance. However, other types of model organisms such as zebrash[206] and worm[176] can oer unique advantagesover using rodents. While these organisms have a larger evolutionarydistance to humans, they are cheaper, faster and easier to breed and281.4. BIOINFORMATIC OPPORTUNITIEShave transparent bodies that are easy to dissect.[226]have shown that the conservation level between C. elegans and manis sucient to infer gene-gene interactions in man from worm data. Even though the global disease phenotypes may not be at all comparable, the molecular basis may be common (e.g. breast cancer andhigh male incidence of progeny). For example, research on stress response in C. elegans has provided detailed insight into the genetic andmolecular mechanisms underlying complex human diseases [294].", + "Even within mammals, where SrYis the gene responsible for testis determination,monotremata show a different multichromosomal sex determination mechanism (involving5X + 5Y chromosomes), and recently, threespecies in the rodent line have demonstrateda different switching gene to SrY (Graves andPeichel, 2010). The high conservation of sexdetermination within birds and mammals hasprobably to do with their high developmentFunctional Genomic Analysis of Sex Determination and Differentiation in Teleost Fishhomeostasis, including constant body temperature (Barske and Capel, 2008).", + "Drosophila melanogaster 240Xenopus laevis 600", + "Based on the branch-length valuesin Figure 6.1, a comparison of man and mouse has D = 0.63, but adding rat as athird species increases total D to 0.72. When calculating total D for an analysis, eachunique section of branch is counted only once, so rat adds only D = 0.086 to thetotal analysis; considerably more power could be added by using dog instead of, or inaddition to rat, as it would contribute D = 0.244 of unique branch length.", + "5C), being shared with ve other vertebrates,including dog (XM_848628), horse (XM_001916545), cow(NM_001099130), chimpanzee (XM_001150577), and human(NM_002202). This is also true of wild-derived subspecies andspecies of Mus, including Mus musculus musculus, Mus musculuscastaneus, and Mus spretus, which have also been sequenced aspart of the Mouse Genome Project. In marked contrast, 14 otherstrains of mice have the less conserved B allele. This suggeststhat the E-box is a regulatory element now widely propagated ina subset of laboratory mice, including B6/J.", + "Animal models have been widely used to study topicsthat could not be easily studied using human populations. In particular, rodent models such as those in mice havecontributed tremendously to our understanding of humangenetics and genomics. We will examine the sex similarityand dierence using data of whole genome gene expressionproles from a well-known mouse population of recombinant inbred (RI) strains derived from C57BL/6J andDBA/2J (BXD), which is the largest RI mouse populationand with remarkable data on whole genome expressionproles and phenotypes [1618].", + "They arenot more complex than mice or more deeply thoughtful than dogsor pigs, but they are incontrovertibly more similar biologically tohumans. This in itself is a reason for using these species in researchdespite the cost and emotional qualms their use engenders. A vocal wing of the legal community, led by Steven Wise,Laurence Tribe, and Alan Dershowitz, is exploring the idea thathumanity quotients can be assigned to life forms as diverse ashoneybees and chimpanzees, arguing that each species should beprovided with scaled legal protection.This scaling applies evento an organ such as brain that is considered unusually large inhumans; the brains of both mice and humans comprise roughly2% of total body mass, and in terms of neuron numbers mice areactually proportionally brainier than humans (approx 75 millionvs 100 billion neurons). Like humans, mice have significant bodysize sexual dimorphism; males typically weigh 3050% more thanfemales. The main advantage of small size is that a set of 810 animalscan be maintained in good health in a shoebox-sized cage.", + "Marsupial Sex Chromosomes and Sex DeterminationComparative sequencing, gene mapping, and chromosome painting between marsupials and eutherians, along with comparison with a chicken out-group, have revealed that the human X chromosome is made up of two ancient gene blocks, both of which are autosomal in chickens (Figure 6).One block, representing the marsupial X, is shared with approximately two-thirds of the eutherian X.A second block is also autosomal in marsupials and so must represent a region added to the X in a eutherian ancestor (44).Mapping the same genes in elephants shows that the fusion point of the ancient and added region corresponds to the centromere (109), suggesting an original Robertsonian fusion 160-105 Mya followed by a centric shift in the ancestor of non-afrotherian lineages.The lack of homology between the mammal XY and bird ZW sex chromosomes (81) and between the mammal XY and the varied systems of reptiles, frogs, and fish implies that the mammal XY system (and the SRY gene) arose later than 310 Mya (46).A much later date emerged from the surprising findings that the two gene blocks that make up the human XY pair are both autosomal in monotremes (platypus and echidna) and that the monotreme XY complex has homology instead to the bird ZW (133).This dates the emergence of the therian sex chromosomes and SRY at 166-160 Mya.Marsupials, like eutherians, normally have an XX female:XY male chromosomal sex determination or some simple variant [X 1 X 2 Y and XY 1 Y 2 systems, in which an autosome has become fused to the X or Y chromosome, are quite common in marsupials (49)].The X chromosome is smaller than the highly conserved 5% of the eutherian genome, and the basic Y chromosome is minute.The X and Y chromosomes do not undergo homologous pairing over a pseudoautosomal region in marsupials, which is a requirement for fertility in mice and humans.Instead, pairing makes use of a proteinaceous basal plate to which the X and Y are attached during meiosis and from which they segregate (33).", + "Based on the branch-length valuesin Figure 6.1, a comparison of man and mouse has D = 0.63, but adding rat as athird species increases total D to 0.72. When calculating total D for an analysis, eachunique section of branch is counted only once, so rat adds only D = 0.086 to thetotal analysis; considerably more power could be added by using dog instead of, or inaddition to rat, as it would contribute D = 0.244 of unique branch length.", + "Taking the most conservative estimate, Comparison of genome wide studies in vertebrates and flies" + ], + [ + "The neuronal networks formed by this largenumber of massively interconnected neurons generate complex spatiotemporal patterns ofneuronal activity that require coordinated activity across large populations of neurons usingboth short- and long-range synaptic connections. On an even larger scale, the mammalianbrain is composed of many structurally diverse networks, including the neocortex, thalamus,basal ganglia etc. Healthy brains are characterized by the continuous generation of behaviorrelated spatiotemporal activity patterns that propagate across multiple brain areas.", + "To retaingenes that are more active when the brain is still undergoing corematurational processes in humans, we used BrainSpan to select autosomaltranscripts expressed at least 1.5-fold more during the early postnataldevelopment (018 months after birth) than in adulthood (2040 years ofage), with the nal networks consisting of 154 genes in the PFC (seeTable S4) and 72 genes in the NAcc (see Table S5).", + "Heath: Do you have a hypothesized mechanism by which you get from earlyseparation to altered response when you are genetically vulnerable? Battaglia: Yes, one mechanism might have to do with the cholinergic system:intense stress causes some alternative splicing of acetylcholine esterase (Kaufer etal 1998). This has been found to be protective for the brain. One of our hypotheseshowever is that the same mechanism which may be protective for the higher braincan be a risk factor for the lower brain, for instance the medulla, because it mayenhance sensitivity to suffocatory stimuli (Battaglia & Ogliari 2005).", + "The rapidly expanding set of inference engines currently has 5 interrelated modules: BrainParts (gray matter regions, major fiber tracts, and ventricles),Cell Types, Molecules, Connections (between regions and celltypes), and Relations (between parts identified different neuroanatomical atlases). Nature Precedings : doi:10.1038/npre.2009.4000.1 : Posted 23 Nov 2009A genome-wide, 3-dimensional map of gene expression inthe adult mouse brain, the ABA reveals the expression patterns of approximately 20 000 genes throughout the adultmouse brain to the cellular level.", + "Furthermore, it was suggested that thebrain is prone to hyperactivity, and this hyperactive tone is down-regulated by brainregions and neurotransmitter systems that decrease the PA (Rowland 1998; Viggiano2008). It was proposed that the reticular activating system is responsible for arousalwhile cerebral cortex is mostly inhibitory (Rowland 1998). All in all, the interplay ofthe activating and inhibiting systems serves the purpose of maintaining so calledsensoristasis (term created by Schultz in 1965) which is an optimal level of sensorystimulation of the nervous system for each individual (Rowland 1998).", + "In the brain, more than any other organ, function followsform, he says. Cellular resolution of expression patterns will provenecessary to uncover as yet unknown relationships betweencircuitry, cell type, and gene expression in the brain, saysArthur Toga, a neuroscientist at the University of California,Los Angeles, and Allen Brain Atlas advisor. Ed Lein, aneuroscientist at the Allen Brain Institute, thinks thatmapping at the cellular scale will also redene anatomy. Traditionally, neuroanatomists have delineated brain regionspretty much by eye, identifying clusters of cells and patternsof connections that look the same.", + "Sensory, motor, and cognitive functionsrely on the signaling dynamics of integrated circuitry that isestablished during brain development. The CNS develops in asequence of events characterized by an initial stage of neurogenesis and migration prenatally, followed by an extended lateprenatal and postnatal period of neuronal and glial differentiation, establishment of synaptic connections, and renement ofthe integrated circuits [Levitt, 2003]. In humans, neurogenesisstarts at 8 weeks of gestation and, in some areas of the brain, suchas the frontal lobe, maturation of CNS circuitry continues wellinto adolescence.", + "To retaingenes that are more active when the brain is still undergoing corematurational processes in humans, we used BrainSpan to select autosomaltranscripts expressed at least 1.5-fold more during the early postnataldevelopment (018 months after birth) than in adulthood (2040 years ofage), with the nal networks consisting of 154 genes in the PFC (seeTable S4) and 72 genes in the NAcc (see Table S5).", + "Because brain tissue from individuals in these cohorts is not accessible during their lives, many studies use postmortem tissue to identify molecules (e.g. , RNA, protein) that are associatedwith performance on cognitive tests prior to death. However, in these studies, brain regions used to examine the molecularmediators of resilience are typically selected based on already-known involvement in disease risk (e.g. , hippocampus andprefrontal cortex). It is possible that molecular changes that confer resilience originate in brain regions outside those classically affected in AD and are thus not typically selected for analyses.In addition, mechanisms and molecules important forresilience are likely expressed and act well before the time at which these tissues can be accessed. This lack of access tobrain tissue early in the disease course is a signicant barrier to understanding the molecules most closely associated withthe onset of resilience (and/or dementia). In addition, the ability to test mechanistic hypotheses is generally limited in humanpopulations, as the identication of molecules associated with cognitive outcomes is largely correlative [81]. Other potentialcaveats and considerations, such as the limitations associated with retrospective group assignments, have been highlighted elsewhere [81].", + "It is a remarkablycomplex organ that integrates electrochemical signals, it receives and coordinatesactivities throughout the entire body. Despite the nonreplicative properties of thenervous system cells, it is proposed that through evolutionary pressures, thiscompartment has acquired distinct processes and mechanisms to minimizeneurodegeneration. One potential source of damage comes from our immunesystem, which has the capacity to scan the CNS and periphery for the presence offoreign antigens. The immune system is equipped with numerous effectorsmechanisms and can greatly alter the homeostasis and function of the CNS.", + "In the brain, more than any other organ, function followsform, he says. Cellular resolution of expression patterns will provenecessary to uncover as yet unknown relationships betweencircuitry, cell type, and gene expression in the brain, saysArthur Toga, a neuroscientist at the University of California,Los Angeles, and Allen Brain Atlas advisor. Ed Lein, aneuroscientist at the Allen Brain Institute, thinks thatmapping at the cellular scale will also redene anatomy. Traditionally, neuroanatomists have delineated brain regionspretty much by eye, identifying clusters of cells and patternsof connections that look the same.", + "The brain is the master organ of the body.It controls all other functions either directly or indirectly.The brain has two major types of cells, the neurons and glial cells.It is known that neurons, once differentiated are nondividing, and even in glial cells only a small fraction of them are dividing in adult and old ages (Korr, 1980).Thus it can be considered that most of the cells in an adult brain are postmitotic.Further, in the majority of the species the final number of differentiated neurons is reached very early in life (Dobbing, 1971) and therefore a neuron's life-span is almost equal to that of the whole animal.Considering the high metabolic activity in a neuronal cell, it must be of great necessity and importance to maintain the genomic integrity over a long period of time in order to keep up the fidelity of the cellular processes.Thus the processes of genomic damage and its repair assume special significance in nervous tissue.", + "Because brain tissue from individuals in these cohorts is not accessible during their lives, many studies use postmortem tissue to identify molecules (e.g. , RNA, protein) that are associatedwith performance on cognitive tests prior to death. However, in these studies, brain regions used to examine the molecularmediators of resilience are typically selected based on already-known involvement in disease risk (e.g. , hippocampus andprefrontal cortex). It is possible that molecular changes that confer resilience originate in brain regions outside those classically affected in AD and are thus not typically selected for analyses.", + "The brain is responsible for cognition, behavior, and much of what makes us uniquely human.The development of the brain is a highly complex process, and this process is reliant on precise regulation of molecular and cellular events grounded in the spatiotemporal regulation of the transcriptome.Disruption of this regulation can lead to neuropsychiatric disorders.RATIONALE: The regulatory, epigenomic, and transcriptomic features of the human brain have not been comprehensively compiled across time, regions, or cell types.Understanding the etiology of neuropsychiatric disorders requires sights into human development and disease.The brain is responsible for cognition, behavior, and much of what makes us uniquely human.The development of the brain is a highly complex process, and this process is reliant on precise regulation of molecular and cellular events grounded in the spatiotemporal regulation of the transcriptome.Disruption of this regulation can lead to neuropsychiatric disorders.RATIONALE: The regulatory, epigenomic, and transcriptomic features of the human brain have not been comprehensively compiled across time, regions, or cell types.Understanding the etiology of neuropsychiatric disorders requires sights into human development and disease.", + "Nonetheless, several CNS measures,including behavioral phenotypes, correlated with both brainand blood 5-HT traits. One conspicuous correlation with 5-HTturnover was found for brain weight, hippocampal weightand hippocampal stem cell number. Studies that manipulate5-HT turnover independent of CNS 5-HT turnover are neededto determine whether these correlations reflect a result of 5HT homeostasis per se vs. a shared determinant that may inearly life impact the trajectory of brain development (Bonninet al. 2007; Janusonis et al. 2004; Mazer et al. 1997).", + "In vitro,for example, blocking extrasynaptic NMDAtype glutamate receptors prevents amyloid-induced DSBs in neuronal cultures. In theaggregate, these data are all well controlledand very convincing. And yet, we find ourselves asking, How canthis possibly be? Do the neurons of our brainreally do serious damage to their genome everytime we execute a mental task? If 2 h of thinkingis enough to trigger DSBs in even a small percentage of our nerve cells, then each cell mustput its genome in jeopardy many times over thecourse of a year.", + "Nonetheless, several CNS measures,including behavioral phenotypes, correlated with both brainand blood 5-HT traits. One conspicuous correlation with 5-HTturnover was found for brain weight, hippocampal weightand hippocampal stem cell number. Studies that manipulate5-HT turnover independent of CNS 5-HT turnover are neededto determine whether these correlations reflect a result of 5HT homeostasis per se vs. a shared determinant that may inearly life impact the trajectory of brain development (Bonninet al. 2007; Janusonis et al. 2004; Mazer et al. 1997).", + "Although neuroimaging techniques unveil certain facets of CNSstructure and function, the human brains molecular profile is only attainable throughexamination of postmortem tissue. Many of the characteristics of the human brain may notbe conserved across species, emphasizing the inherent value of postmortem human tissue forinterrogating neuropsychiatric disorders (Hynd et al. 2003; Sutherland et al. 2016). Further,high-resolution maps for gene expression of the human brain across developmental periods,combined with separate genetic and proteomic datasets, can reveal potential neurobiologicalpathways and circuits underlying disease (Parikshak et al. 2013; Willsey et al. 2013). Addict Biol." + ], + [ + "Interestingly, fMRI studies in which personalized stressful imagery relating to pastexperiences was shown to healthy subjects, revealed significant increases in activationof the medial prefrontal cortex (mPFC), anterior cingulate, striatum, substantia nigra,thalamus, caudate, putamen, and hippocampus (Sinha, 2004), indicating a role for thestriatal-limbic-prefrontal circuits in response to emotional distress. In support of this,13eQTL mapping studies in the Miles laboratory implicate genes within the nucleusaccumbens (NAc) as potential modulators of anxiolytic-like phenotypes following acuteethanol administration (Putman, 2008; Wolen, 2012).", + "Proc Natl Acad Sci USA103:780785123242LeDoux JE (2000) Emotion circuits in the brain. Annu RevNeurosci 23:155184Lee GP, Meador KJ, Loring DW, Allison JD, Brown WS, PaulLK, Pillai JJ, Lavin TB (2004) Neural substrates of emotionas revealed by functional magnetic resonance imaging. CogBehav Neurol 17:917Li CX, Wei X, Lu L, Peirce JL Williams RW, Waters RS (2005)Genetic analysis of barrel field size in the first somatosensory area (S1) in inbred and recombinant inbred strains ofmice.J Neurosci 21:35033514McGaugh JL (2004) The amygdala modulates the consolidationof memories of emotionally arousing experiences. AnnuRev Neurosci 27:128McIntyre CK, Power AE, Roozendaal B, McGaugh JL (2003)Role of the basolateral amygdala in memory consolidation. Ann NY Acad Sci 985:273293Meyer-Lindenberg A, Buckholtz JW, Kolachana B, Hariri AR,Pezawas L, Blasi G, Wabnitz A, Honea R, Verchinski B,Callicott JH, Egan M, Mattay V, Weinberger DR (2006)Neural mechanisms of genetic risk for impulsivity andviolence in humans.Somatosens Mol Res 22:141150Lin CH, Hansen S, Wang Z, Storm DR, Tapscott SJ, Olson JM(2005) The dosage of the neuroD2 transcription factorregulates amygdala development and emotional learning. Proc Natl Acad Sci USA 102:1487714882Ling EA, Paterson JA, Privat A, Mori S, Leblond CP (1973)Investigation of glial cells in semithin sections. I. Identification of glial cells in the brain of young rats. J CompNeurol 149:4371Lu L, Airey DC, Williams RW (2001) Complex trait analysis ofthe hippocampus: mapping and biometric analysis of twonovel gene loci with specific effects on hippocampal structure in mice.", + "When attachments form in early infancy, activation and closer links are observed among neurobiological brain systems underpinning affiliation, reward, and stress management (Ulmer-Yaniv et al., 2016).Functional magnetic resonance imaging (fMRI) has been used to investigate the brain activity associated with humans' various social attachments (Feldman, 2017).These fMRIs provide evidence for three main inter-connected neural systems that integrate to establish, maintain, and enhance our attachments to others, including the rewardmotivation system (Berridge and Robinson, 1998), the embodied simulation/empathy network (Gallese, 2014), and mentalizing processes (Frith and Frith, 2006).The reward-motivation system comprises the striatum (nucleus accumbens, caudate, and putamen), amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex (ACC).The existence of convergent projections from the cortex to the striatum, along with hippocampal and amygdala-striatal projections, places the striatum as a central entry port for processing emotional/motivational information supporting human attachment (Haber and Knutson, 2010;Robinson et al., 2012;Pauli et al., 2016).The reward-motivation system employs DA and oxytocin rich pathways (Schultz, 2000;Berridge et al., 2009;Haber and Knutson, 2010) and supports multiple attachment-related motivational behaviors, such as social orienting, social seeking, and maintaining contact (Acevedo et al., 2012;Chevallier et al., 2012).Attachments have an intrinsic motivational value that combine immediate hedonic responses with approach motivation, goal-directed behavior, and learning (Berridge and Robinson, 1998).", + "Genetics of emotional regulation: therole of the serotonin transporter in neural function. Trends CognSci 10: 182191. Hefner K, Holmes A (2007). Ontogeny of fear-, anxiety- anddepression-related behavior across adolescence in C57BL/6Jmice. Behav Brain Res 176: 210215. Herman JP, Ostrander MM, Mueller NK, Figueiredo H (2005). Limbic system mechanisms of stress regulation: hypothalamopituitaryadrenocortical axis. Prog Neuropsychopharmacol BiolPsychiatry 29: 12011213. Herry C, Bach DR, Esposito F, Di Salle F, Perrig WJ, Scheffler Ket al (2007). Processing of temporal unpredictability in humanand animal amygdala. J Neurosci 27: 59585966.Nat Neurosci 8: 828834. Phelps EA, LeDoux JE (2005). Contributions of the amygdala toemotion processing: from animal models to human behavior. Neuron 48: 175187. Porsolt RD, Bertin A, Jalfre M (1978). Behavioural despair in ratsand mice: strain differences and the effects of imipramine. Eur JPharmacol 51: 291294. Quirk GJ, Mueller D (2007). Neural mechanisms of extinctionlearning and retrieval. Neuropsychopharmacology 33: 5672. Radley JJ, Rocher AB, Miller M, Janssen WG, Liston C, Hof PR et al(2006). Repeated stress induces dendritic spine loss in the ratmedial prefrontal cortex. Cereb Cortex 16: 313320.", + "Like most, if not all, neural phenotypes, stress,anxiety, and other emotion-related traits are extremely complex and are defined by theinterplay of multiple genetic, environmental, experiential, and epigenetic factors. The work presented in this dissertation is a multi-scalar, integrative analysis of themolecular and neuroanatomic substrates that underlie emotion-related behavior. Theamygdala is a principle component of the limbic system that controls emotionality. UsingBXD recombinant inbred (RI) mice as model organisms, the anatomy and cellulararchitecture of the amygdalaspecifically, the basolateral amygdala (BLA)wasexamined to assess the level of structural variation in this brain region.To this end, weemployed a repeated restraint stress protocol that has been shown to cause significantalterations in the morphology of principal neurons in three key corticolimbic regionsknown to mediate the effects of stress: medial prefrontal cortex (mPFC), amygdala andhippocampus (e.g. , Vyas et al. , 2002; Vyas et al. , 2003; Govindarajan et al. , 2006;Bennur et al. , 2007; Shansky et al. , 2009).Sensory inputs with emotional components aretransmitted to the amygdala where they are processed and further relayed to other regionsto modulate autonomic and behavioral responses, and to form emotional memories(LeDoux, 2000; Rosen, 2004). As a neural substrate of emotionality, manyneuropsychiatric disorders have been associated with structural changes in the amygdala. Individuals with genetically predisposed susceptibility to anxiety and depression havebeen shown to have higher amygdala reactivity and smaller amygdala volumes (Pezawaset al. , 2005). Structural changes in the amygdala have also been associated with traumaticstress disorder, bipolar disorder, and aggressive behavior (Hayek et al.These studies have uncovered complex geneticsunderlying behavior with multiple loci modulating stress responsiveness, fear, andanxiety levels in mice (Willis-Owen & Flint, 2006). Significant concordance betweenhuman and mouse traits also exists, e.g. , the gene Rgs2 was shown to modulate anxiety inmice, and was subsequently found to be associated with anxiety in human (Yalcin et al. ,2004; Smoller et al. , 2008). 1.3Neuroanatomy of Stress and Emotion-Related BehaviorThe amygdala is a neural structure central to the experience of emotions andexpression of emotional behavior.", + "Alterations in BDNF expression werealso found in response to emotions such asanxiety or fear in rodents (Rasmusson et al. ,2002), and it has been shown that BDNF affects emotional preferences in humans (Gasicet al. , 2009). It remains to be determined howthe stress itself or the associated behavioral responses contribute to mediating these changes. From this perspective, sh, whose brain organization is very similar to that of higher vertebrates, but is generally considered free ofemotional reactions, is interesting as an animalmodel of stress.", + "enhance our attachments to others, including the rewardmotivation system (Berridge and Robinson, 1998), the embodiedsimulation/empathy network (Gallese, 2014), and mentalizingprocesses (Frith and Frith, 2006). The reward-motivationsystem comprises the striatum (nucleus accumbens, caudate,and putamen), amygdala, ventral tegmental area, orbitofrontalcortex, ventromedial prefrontal cortex, and anterior cingulatecortex (ACC). The existence of convergent projections fromthe cortex to the striatum, along with hippocampal andamygdala-striatal projections, places the striatum as a centralentry port for processing emotional/motivational informationsupporting human attachment (Haber and Knutson, 2010;Robinson et al. , 2012; Pauli et al. , 2016).We focused on theprefrontal cortex due to its association with cognitive, emotionalfunctions, impulse control, and adaptive behaviors (Morecraftand Yeterian, 2002; Bechara and Van Der Linden, 2005), andthe striatum for its involvement in the reward motivation systemand potential to relate to attachment formation in infancyspecifically (Feldman, 2017). Convergent projections from thecortex to the striatum, along with hippocampal and amygdalastriatal projections, places the striatum as a central entry portfor processing emotional/motivational information supportinghuman attachments (Haber and Knutson, 2010; Robinson et al. ,2012; Pauli et al. , 2016; Feldman, 2017).", + "Central nervous system regions that regulate mood,emotion, feeding and reward are prominent sites of 5-HTsynthesis and release (Steinbusch 1981). As such, a numberof disorders have been reported to display alterations in CNS5-HT homeostasis, including anxiety, depression, obsessivecompulsive disorder and addiction (Barondes 1994). Althougha subject of significant debate (Risch et al. 2009), biochemicaland genetic evidence continues to drive consideration thatrisk for depression in some individuals may be linked to alimited capacity for normal brain 5-HT signaling.", + "enhance our attachments to others, including the rewardmotivation system (Berridge and Robinson, 1998), the embodiedsimulation/empathy network (Gallese, 2014), and mentalizingprocesses (Frith and Frith, 2006). The reward-motivationsystem comprises the striatum (nucleus accumbens, caudate,and putamen), amygdala, ventral tegmental area, orbitofrontalcortex, ventromedial prefrontal cortex, and anterior cingulatecortex (ACC). The existence of convergent projections fromthe cortex to the striatum, along with hippocampal andamygdala-striatal projections, places the striatum as a centralentry port for processing emotional/motivational informationsupporting human attachment (Haber and Knutson, 2010;Robinson et al. , 2012; Pauli et al. , 2016).We focused on theprefrontal cortex due to its association with cognitive, emotionalfunctions, impulse control, and adaptive behaviors (Morecraftand Yeterian, 2002; Bechara and Van Der Linden, 2005), andthe striatum for its involvement in the reward motivation systemand potential to relate to attachment formation in infancyspecifically (Feldman, 2017). Convergent projections from thecortex to the striatum, along with hippocampal and amygdalastriatal projections, places the striatum as a central entry portfor processing emotional/motivational information supportinghuman attachments (Haber and Knutson, 2010; Robinson et al. ,2012; Pauli et al. , 2016; Feldman, 2017).", + "(2010)Genetic control over the resting brain. Proc Natl Acad Sci U S A 107, 12231228. Glasser, M.F. , Smith, S.M. , Marcus, D.S. , Andersson, J.L.R. , Auerbach, E.J. , Behrens, T.E.J. ,Coalson, T.S. , Harms, M.P. , Jenkinson, M., Moeller, S., Robinson, E.C. , Sotiropoulos, S.N. ,Xu, J., Yacoub, E., Ugurbil, K. & Van Essen, D.C. (2016) The Human Connectome Projectsneuroimaging approach. Nat Neurosci 19, 11751187. Gracia-Rubio, I., Moscoso-Castro, M., Pozo, O.J. , Marcos, J., Nadal, R. & Valverde, O. (2016)Maternal separation induces neuroinflammation and long-lasting emotional alterations inmice.", + "He and othersconsidered that hormones released by stressful experiences couldenhance memory consolidation, indicating particularly the hormonesepinephrine and glucocorticoids as memory modulators (McGaugh &Roozendaal, 2009). It was suggested that several brain regions work insynergy to assure that emotionally significant experiences are wellremembered, and this could prepare the organism for future experiences by inducing long-term behavioral changes (Bahtiyar, Karaca,Henckens, & Roozendaal, 2020; McGaugh, 2013).", + "Central nervous system regions that regulate mood,emotion, feeding and reward are prominent sites of 5-HTsynthesis and release (Steinbusch 1981). As such, a numberof disorders have been reported to display alterations in CNS5-HT homeostasis, including anxiety, depression, obsessivecompulsive disorder and addiction (Barondes 1994). Althougha subject of significant debate (Risch et al. 2009), biochemicaland genetic evidence continues to drive consideration thatrisk for depression in some individuals may be linked to alimited capacity for normal brain 5-HT signaling." + ] + ], + "task_id": [ + "6D733CABEB70E4DBF150EAAFFED6C973", + "6267E2FEFF0332F88C2294C8F32C1FC1", + "3FFA45D7124495B37B6F7F2B7B780AF3", + "499C63633BB95DE93DC3A89615496443", + "405240F6F75C3927C1088287E19920AD" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_7.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_7.json new file mode 100644 index 0000000..53a2a37 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_general_7.json @@ -0,0 +1,40 @@ +{ + "question": [ + "what hormones do our brains release during stressful experiences?", + "what is the use of corticosterone?" + ], + "answer": [ + "During stressful experiences, our brains release hormones such as epinephrine, glucocorticoids, and corticosterone. The hypothalamic-pituitary-adrenal (HPA) axis also plays a significant role in this process, stimulating the release of these hormones.", + "Corticosterone, a type of glucocorticoid, is used in the body's response to stress. It initiates metabolic and physiological responses that help the body cope with stressors. It also provides negative feedback to inhibit activity in the hypothalamic-pituitary-adrenal (HPA) axis, which helps to dampen stress-activated defense reactions, including immune responses. Additionally, corticosterone can influence the expression of certain cytokines, potentially having anti-inflammatory effects." + ], + "contexts": [ + [ + "40. Roozendaal B, McEwen BS, Chattarji S. Stress, memory and theamygdala. Nat Rev Neurosci 10: 426 433, 2009. 41. Sala M, Perez J, Soloff P, Ucelli di Nemi S, Caverzasi E, Soares JC,Brambilla P. Stress and hippocampal abnormalities in psychiatric disorders. Eur Neuropsychopharmacol 14: 393405, 2004. 42. Shiba T, Kakuda S, Ishiguro M, Morita I, Oka S, Kawaski T,Wakatsuki S, Kato R. Crystal structure of GlcAT-S, a human glucuronyltransferase, involved in the biosynthesis of the HNK-1 carbohydrateepitope. Proteins 65: 499 508, 2006. 43.", + "Sci. 132.ter Heegde, F., De Rijk, R.H., Vinkers, C.H. , 2015. The brain mineralocorticoid receptorand stress resilience. Psychoneuroendocrinology 52, 92110. ter Horst, J.P., van der Mark, M.H. , Arp, M., Berger, S., de Kloet, E.R. , Oitzl, M.S. , 2012. Stress or no stress: mineralocorticoid receptors in the forebrain regulate behavioraladaptation. Neurobiol. Learn. Mem. 98, 3340. van Leeuwen, N., Bellingrath, S., de Kloet, E.R. , Zitman, F.G., DeRijk, R.H., Kudielka,B.M. , Wust, S., 2011. Human mineralocorticoid receptor (MR) gene haplotypesmodulate MR expression and transactivation: implication for the stress response. Psychoneuroendocrinology 36, 699709. Waterham, H.R.", + "Acute stress and dexamethasone rapidly increase hippocampal somatostatin synthesis and release fromthe dentate gyrus hilus. Hippocampus 11, 469477. Aubry, J.-M., Bartanusz, V., Jezova, D., Belin, D., Kiss, J.Z. , 1999. Single stress induceslong-lasting elevations in vasopressin mRNA levels in CRF hypophysiotrophic neurones, but repeated stress is required to modify AVP immunoreactivity. J.Neuroendocrinol. 11, 377384. Baker, J.A. , Li, J., Zhou, D., Yang, M., Cook, M.N. , Jones, B.C. , Mulligan, M.K. , Hamre,K.M. , Lu, L., 2017. Analyses of dierentially expressed genes after exposure to acutestress, acute ethanol, or a combination of both in mice.", + "Chronic unpredictable stress beforepregnancy reduce the expression of brain-derived neurotrophic factor and N-methyl-D-aspartate receptor inhippocampus of offspring rats associated with impairment of memory. Neurochem Res 35, 1038-49. Konig, P., Dedio, J., Oess, S., Papadakis, T., Fischer, A., Muller-Esterl, W. and Kummer, W., 2005. NOSIP and itsTinteracting protein, eNOS, in the rat trachea and lung. J Histochem Cytochem 53, 155-64. Longo, A., Oberto, A., Mele, P., Mattiello, L., Pisu, M.G. , Palanza, P., Serra, M. and Eva, C., 2015. NPY-Y1IPcoexpressed with NPY-Y5 receptors modulate anxiety but not mild social stress response in mice.Gynecol Endocrinol 12,61-7. MASajdyk, T.J., Schober, D.A. and Gehlert, D.R. , 2002. Neuropeptide Y receptor subtypes in the basolateral nucleusof the amygdala modulate anxiogenic responses in rats. Neuropharmacology 43, 1165-72. Shan, T., Ma, J., Ma, Q., Guo, K., Guo, J., Li, X., Li, W., Liu, J., Huang, C., Wang, F. and Wu, E., 2013.beta2-AR-HIF-1alpha: a novel regulatory axis for stress-induced pancreatic tumor growth and angiogenesis. DCurr Mol Med 13, 1023-34. TEShen, Q., Wang, X., Chen, Y., Xu, L., Wang, X. and Lu, L., 2009.", + "CRH-expressing neurons located in the PVN of the hypothalamus play a pivotal role in orchestrating thecentral stress response. CRH stimulates the release of ACTH from the anterior pituitary gland. In turn, ACTH acts on the adrenal cortex to increase the production andrelease of GC hormones. Proper functioning of all of these neurons is essential formaintaining a homeostatic state following a stressful event. Several neuronal pathways modulate HPA axis activity. For example, the hippocampus and prefrontalcortex inhibit the HPA axis, and the amygdala and monoaminergic input from thebrainstem stimulate CRH production by PVN neurons.Norepinephrinergic activation leadsto afferent catecholaminergic signaling from the NTS and ventrolateral medulla tothe periventricular nucleus (PVN) [52], which in turn activates the hypothalamicpituitaryadrenal (HPA) axis. Thus, activation of stress response systems is initiated, which assists in directing mood and behavior. Blood-borne cytokines are also known to produce a central response by acting oncytokine receptors within the brain. However, given their size and hydrophilicnature, trafcking to the brain was once considered to be isolated to circumventricular organs. In 1983, Blatteis et al.They havedetermined that psychological stress results in decreased hippocampal neurogenesisalongside signicant increases in hippocampal IL-1b protein, depressive-likebehavior, and ACTH and corticosterone release [169, 170]. The importance of IL-1in producing these behavioral and HPA axis outcomes was conrmed both genetically using IL-1r knockout mice and pharmacologically using IL-1ra. In IL-1R1knockout mice stress-induced brain, behavioral and endocrine perturbationsobserved in the wild-type mice were all attenuated [169, 170]. More importantly,these perturbations were abolished if wild-type mice were adrenalectomized [170].Koo JW, Duman RS (2008) IL-1beta is an essential mediator of the antineurogenic and anhedonic effects of stress. Proc Natl Acad Sci USA 105:751756118. Tanapat P, Hastings NB, Rydel TA, Galea LA, Gould E (2001) Exposure to fox odor inhibitscell proliferation in the hippocampus of adult rats via an adrenal hormone-dependent mechanism. J Comp Neurol 437:496504119. Malberg JE, Duman RS (2003) Cell proliferation in adult hippocampus is decreased by inescapable stress: reversal by uoxetine treatment. Neuropsychopharmacology 28:15621571120.RecentProg Horm Res 43:113173143. Rivier CL, Plotsky PM (1986) Mediation by corticotropin releasing factor (CRF) of adenohypophysial hormone secretion. Annu Rev Physiol 48:475494144. Jones MT, Gillham B (1988) Factors involved in the regulation of adrenocorticotropic hormone/beta-lipotropic hormone. Physiol Rev 68:743818145. Sapolsky RM, Romero LM, Munck AU (2000) How do glucocorticoids inuence stressresponses? Integrating permissive, suppressive, stimulatory, and preparative actions. EndocrRev 21:5589146. De Kloet ER, Vreugdenhil E, Oitzl MS, Joels M (1998) Brain corticosteroid receptor balancein health and disease. Endocr Rev 19:269301147. McEwen BS, Stellar E (1993) Stress and the individual.The HPA axis mediates theneuroendocrine response to stressors, both systemic stressors that threaten homeostasis and/or survival and perceived threats or psychogenic stressors [141]. Inputs tothe HPA axis provided by stressors and the endogenous circadian rhythm [142] actthrough central neural pathways to the paraventricular nuclei (PVN) of the hypothalamus, where CRH is synthesized. CRH (potentiated by arginine vasopressin[AVP]) [143, 144] stimulates the release of adrenocorticotropin (ACTH) from theanterior pituitary, which in turn stimulates synthesis and secretion of glucocorticoids from the adrenal cortex.", + "(2007). Corticotropin releasing hormonereceptor alterations elicited byacute and chronic unpredictablestressor challenges in stressorsusceptible and resilient strainswww.frontiersin.orgof mice. Behav. Brain Res. 181,180190. Bartels, A., and Zeki, S. (2004). The neural correlates of maternaland romantic love. Neuroimage 21,11551166. Bennett, H. A., Einarson, A., Taddio, A., Koren, G., and Einarson, T. R. (2004). Prevalence ofdepression during pregnancy: systematic review. Obstet. Gynecol. 103,698709. Beuzen, A., and Belzung, C. (1995). Link between emotional memoryand anxiety states: a study by principal component analysis. Physiol. Behav. 58, 111118.", + "Yun SJ, Park HJ, Yeom MJ, Hahm DH, Lee HJ, et al. (2002) Effect ofelectroacupuncture on the stress-induced changes in brain-derived neurotrophicfactor expression in rat hippocampus. Neurosci Lett 318: 8588. 31. Bousios S, Karandrea D, Kittas C, Kitraki E (2001) Effects of gender and stresson the regulation of steroid receptor coactivator-1 expression in the rat brain andpituitary. J Steroid Biochem Mol Biol 78: 401407. 32. Thome J, Pesold B, Baader M, Hu M, Gewirtz JC, et al. (2001) Stressdifferentially regulates synaptophysin and synaptotagmin expression in hippocampus. Biol Psychiatry 50: 809812. 33.", + "Vyas A, Jadhav S, Chattarji S (2006) Prolonged behavioral stress enhances synaptic connectivity in the basolateral amygdala. Neuroscience 143:387393. Wellman CL, Izquierdo A, Garrett JE, Martin KP, Carroll J, Millstein R, LeschKP, Murphy DL, Holmes A (2007) Impaired stress-coping and fear extinction and abnormal corticolimbic morphology in serotonin transporter knock-out mice. J Neurosci 27:684 691. Wiedholz LM, Owens WA, Horton RE, Feyder M, Karlsson RM, Hefner K,Sprengel R, Celikel T, Daws LC, Holmes A (2008) Mice lacking the AMPAGluR1 receptor exhibit striatal hyperdopaminergia and schizophreniarelated behaviors. Mol Psychiatry 13:631 640.", + "Crhbp regulates the activity of CRH (corticotropin releasinghormone), a stress hormone in the HPA axis (Westphal andSeasholtz, 2006), and is upregulated following stress (McClennenet al. , 1998). The HPA axis has a complex relationship with learning and memory- transient activation results in enhancement oflearning and memory (de Kloet et al. , 1999), while persistent activation levels results in cognitive deficit (de Kloet et al. , 2005). Enoch et al. reported that dense whole genome linkage scan ofhippocampal activation assessed by EEG resulted in a linkagepeak containing crhbp (Enoch et al. , 2008).", + "First, the CRH or corticotrophin releasing factor(CRF) is the principal regulator of the stress response, whichhas receptor genes expressed in several organs including brainsand heart (here we used crhr1, which is the type 1 CRHreceptor). UCN I (urocortin I precursor), UCN II (urocortin Iprecursor, which is a stresscopin-related petide), and UCN III(stresscopin) were among the 10 genes. It has been shown thatthis gene group is a member of the CRH peptide family and isfound in many discrete brain regions and that it influencesfeeding, anxiety, and auditory processing behaviors (28).", + "It has beendemonstrated that stress and corticosterone release are important regulators of hippocampal 5-HT1A receptors [294], and thatprolonged corticosterone treatment alters the responsiveness of5-HT1A receptors to 8-OH-DPAT in rat CA1 hippocampal neurons [295]. Interestingly, changes in the hippocampally mediated5-HT1A receptor responses depend on whether stress or corticosterone predominantly activate the mineralocorticoid receptoror the glucocorticoid receptor [296]. Depression is characterized by a dysregulated response to stress, which may result inchanges in hippocampal 5-HT1A receptors in humans.", + "Herman JP & Cullinan WE Neurocircuitry of stress: central control of the hypothalamopituitaryadrenocortical axis. Trends in Neurosciences 20, 7884 (1997). [PubMed: 9023876]10. Barbazanges A, Piazza PV, Le Moal M & Maccari S Maternal glucocorticoid secretion mediateslong-term effects of prenatal stress. J. Neurosci 16, 39433949 (1996). [PubMed: 8656288]11. Montano MM, Wang MH, Even MD & vom Saal FS Serum corticosterone in fetal mice: sexdifferences, circadian changes, and effect of maternal stress. Physiol. Behav 50, 323329 (1991). [PubMed: 1745676]12.", + "He and othersconsidered that hormones released by stressful experiences couldenhance memory consolidation, indicating particularly the hormonesepinephrine and glucocorticoids as memory modulators (McGaugh &Roozendaal, 2009). It was suggested that several brain regions work insynergy to assure that emotionally significant experiences are wellremembered, and this could prepare the organism for future experiences by inducing long-term behavioral changes (Bahtiyar, Karaca,Henckens, & Roozendaal, 2020; McGaugh, 2013).", + "Keywords: stress, hippocampus, microarray, C57BL/6J, DBA/2J, C57BL/6NJINTRODUCTIONStress is a generic term used to describe physiological and behavioral responses to realor perceived challenges (Wang et al. , 2013). These responses are integrated over multiplesystems (e.g. , autonomic, behavioral, endocrine, and immune) with the hypothalamic-pituitaryadrenal (HPA) axis being a main pillar of the neuroendocrine response to stress. Ultimately,stimulation of the HPA axis results in glucocorticoid hormone [corticosterone (CORT) in miceand cortisol in humans] release." + ], + [ + "Cortisol has widespread effects on the body, but its major roleis to counteract the stress response and attempt to return the body to homeostasis. Once this is achieved, cortisol can negatively regulate its own production throughinhibitory feedback loops at the level of secretion of both CRH and ACTH. Thisbiochemical pathway is known as the hypothalamic-pituitary-adrenal (HPA) axis.", + "Gulf War and Health: Volume 10: Update of HealthEffects of Serving in the Gulf War, 2016. National Academies Press. O'Callaghan, J.P., Kelly, K.A. , Locker, A.R. , Miller, D.B. , Lasley, S.M. , 2015a. Corticosterone primes theneuroinflammatory response to DFP in mice: potential animal model of Gulf War Illness. Journal ofneurochemistry 133, 708-721. O'Callaghan, J.P., Kelly, K.A. , Locker, A.R. , Miller, D.B. , Lasley, S.M. , 2015b. Corticosterone primes theneuroinflammatory response to DFP in mice: potential animal model of Gulf War Illness. J Neurochem133, 708-721. O'Callaghan, J.P., Michalovicz, L.T. , Kelly, K.A. , 2016.", + "The glucocorticoids, cortisol in humans and corticosterone in most rodents, have numerous metabolic and physiological effects andprovide negative feedback to inhibit HPA activity at the level of the pituitary, PVN,hippocampus, prefrontal cortex, and other brain areas [142144]. Glucocorticoids,acting on their receptors (mineralocorticoid receptors, MR; glucocorticoid receptors, GR), initiate metabolic and physiological responses that facilitate response toand coping with the stressor and, ultimately, dampen stress-activated defense reactions, including immune responses, to prevent them from overshooting and themselves causing harm [145, 146].In the short term, the metabolic and physiologicalchanges induced by the glucocorticoids promote survival (increased gluconeogenesis and blood pressure, suppressed immune and reproductive function). However,prolonged exposure to glucocorticoids can result in metabolic, cognitive, andimmune dysfunction [147]. Thus, it is important that the HPA axis be tightlycontrolled through efcient feedback and efcient termination of the stress response;the ability to turn off the stress response is as important as the ability to respondinitially [145].[45, 54] or by injection of glucocorticoid synthesis inhibitors [58] enhances theexpression of IL-1 and other cytokines in brain as a result of stress exposure. Thesendings parallel earlier studies showing that ADX enhanced the plasma IL-6response evoked by exposure to a novel environment [78] and t with the canonicalviewpoint that glucocorticoids have powerful anti-inammatory properties. Whilewe do not wish to dispute this viewpoint, it is interesting to note that antiinammatory actions of CORT are most pronounced at high and supraphysiologicalconcentrations, whereas lower concentrations of CORT appear to have someimmune-potentiating effects (e.g. , [6]).As such, an immunological stressor, such as infection or a mimetic such asLPS which works to activate the HPA axis, will result in downstream glucocorticoidrelease. This secretion of glucocorticoids should, in turn, attenuate the originatinginammation. However, this is often not the case when studying for patients ofdepression showing enhanced immune activation, and hence, additional processesmust also be at play such as the proinammatory nature of cytokines and glucocorticoid resistance (discussed below). It is important to recognize that cytokine regulation of HPA axis function is complex and requires a multifaceted approach to studying such complexity.CORT, on the other hand, appears to negatively inuence the expression of IL-1 and other cytokines throughinteraction with glucocorticoid responsive elements (GREs) in the promoter region of cytokine genes. There are also reciprocal interactions between NE andCORT, whereby NE is a key driver in central regulation of the HPA axis (via a1-adrenergic receptors) and CORT tempers the release of NE.[45] also demonstrated the profound impact of endogenous CORT to inhibit brain cytokine responses evoked by stress, showing that boththe magnitude and spatial distribution of IL-1 changes provoked by stress wereaugmented in ADX rats. Since then, our lab and others have gone on to successfullyexamine cytokine expression using gross dissections and ELISA detection (e.g. ,[22, 5557]) or at the level of gene expression using RT-PCR [5861], whichtogether further support the view that stress challenges have the ability to drivecytokine changes in the uninjured brain.The HPA axis has been shown to regulate immune responses. The main hormones involved in the HPA axis are corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), andglucocorticoids (GC), which are also called stress hormones. These hormones contribute to the regulation of immune responses and can also affect neuronal survival,neurogenesis, synaptic plasticity, and behavioral responses [1, 2]. The HPA axis isa three-tiered biological system that begins at the highest level with the release ofCRH from the hypothalamic paraventricular nucleus (PVN).Whether these low-dose facilitation effectsrelate more directly to the timing of CORT injection relative to cytokine measurements, or represent differential tissue sensitivity to glucocorticoids, remains to bedetermined [79, 80]. Moreover, ambient levels of CORT taken during or immediately after stress exposure do not appear to be predictive of central IL-1 expression[63, 65]. Regardless, the point here is that the long-held assumption that glucocorticoids exert unilateral, anti-inammatory actions does not broadly account for theliterature writ large.During theresolution phase of disease, hormone levels did not differ among the three prenatalgroups, and if anything, corticosterone and ACTH levels were somewhat lower inPAE compared to control animals with clinical signs of arthritis. It has been suggested [227] that low levels of glucocorticoid hormones in the context of high levelsof inammation may reect a disconnect between the immune and endocrine systems. As noted above, we have evidence for such a disconnect in our previous work.As such, CORT can interfere with IL-1 gene expression (and expression of othercytokines) through numerous pathways. When taken together, a simple framework for understanding the interactionbetween the major stress-responsive systems (SNS and HPA axis) and inammatoryconsequences of stress is that neuroinammatory consequences of stress are mechanistically intertwined between the stimulatory actions of the SNS and the inhibitoryactions of CORT (see [84] for a review), though much work remains to be done inthis area. This concept is depicted in Fig.The HPA response is slower, and the secretion of the glucocorticoid hormones initiates numerous metabolic and behavioral effects that mediateeffective coping with a stressor in the longer term. The HPA and autonomic systemsappear to be regulated by similar neurotransmitters (e.g. , acetylcholine, serotonin,norepinephrine, GABA). In addition, there is reciprocal stimulation of HPA andautonomic activity by CRH and norepinephrine and reciprocal actions of the glucocorticoids and catecholamines. The glucocorticoids are thought to feed back torestrain activity of both systems. Further, the activity and sensitivity of both systemsare modulated by stress and circadian inuences [140].Regardless, the evidence todate suggests that NE release is a key driver of inammatory processes in at leastsome brain structures during times of stress. In contrast to the actions of NE, which appear to stimulate the expression of IL-1and other cytokines, CORT appears to constrain cytokine gene and protein expression during times of stress, at least in broad strokes. Indeed, there is a plethora ofstudies demonstrating that removal of endogenous glucocorticoids through ADX142T. Deak et al.With that said, there are a variety of cellular mechanisms by which glucocorticoids impact gene expression for cytokines during times of stress, particularly in thecase of IL-1 gene expression. Many of these effects are produced through interaction of the CORT-receptor complex with repressor sites in the promoter region ofthe IL-1 gene, including the nGRE repressor site and interference with nuclear factor k-light chain-enhancer of activated B cells (NFkB) signaling. In addition, CORThas been shown to block cAMP response element-binding (CREB) phosphorylation, prevent NFkB binding to the DNA, and destabilize mRNA for IL-1 [8183].Similarly,paradigms involving neonatal immune activation have demonstrated increased propensities to develop stress-related behaviors alongside hypersecretion of corticosterone, elevated hippocampal cytokines, and altered GR abundance in adulthood[184186]. Such animal studies have been useful in dissociating the relationshipbetween the actions of cytokines and the HPA axis in the brain to provide a moredirect account of how these factors may inuence behavior. Raz Yirmiyas laboratory has been particularly inuential in this regard, reporting brain IL-1b to beresponsible for depressive-like behavior in relation to stress physiology.", + "Corticosterone primes theneuroinflammatory response to DFP in mice: Potential animal model of Gulf War Illness. J. Neurochem. 2015,133, 708721. OCallaghan, J.P.; Miller, D.B. Neuroinflammation disorders exacerbated by environmental stressors. Metabolism 2019, 100, 153951. [CrossRef] [PubMed]Ashbrook, D.G. ; Arends, D.; Prins, P.; Mulligan, M.K. ; Roy, S.; Williams, E.G. ; Lutz, C.M. ; Valenzuela, A.;Bohl, C.J. ; Ingels, J.F. ; et al. The expanded BXD family of mice: A cohort for experimental systems geneticsand precision medicine. BioRxiv 2019. [CrossRef]Peirce, J.L. ; Lu, L.; Gu, J.; Silver, L.M. ; Williams, R.W.", + "For example, the measurement of plasma cortisol, the main glucocor-ticoid secreted by the interrenal tissue of theteleosts in response to stress and the most usedindication of a stress status, may not be sufcient to assess physiological conditions under chronic stress, in particular due to theacclimation of the interrenal gland and theinuence of negative feedback mechanismson the hypothalamuspituitaryinterrenal axis(Rotllant et al. , 2000).Otherworks have assessed the changes in gene expression by microarray using an in vitro approach with cultured cells treated with animmune stressor (LPS) and compared the transcriptomic response when adding cortisol. Theresults showed that cortisol is able to counteract the immune activation, but other responseswere taking place anyway, such as the recoveryof cell activity, increase of protein synthesis, andenergetic metabolism (Mackenzie et al. , 2006).", + "Pruett SB, Fan R, Myers LP, Wu WJ, Collier S. Quantitative analysis of the neuroendocrine-immune axis: linearmodeling of the effects of exogenous corticosterone and restraint stress on lymphocyte subpopulations in thespleen and thymus in female B6C3F1 mice. Brain Behav Immun 2000 Dec;14(4):270-287. 56. Pruett SB, Fan R. Quantitative modeling of suppression of IgG1, IgG2a, IL-2, and IL-4 responses to antigen inmice treated with exogenous corticosterone or restraint stress. J Toxicol Environ Health A 2001 Feb9;62(3):175-189. 57. Munck A, Guyre PM, Holbrook NJ. Physiological functions of glucocorticoids in stress and their relation topharmacological actions." + ] + ], + "task_id": [ + "DA2C5FBAA7806455F89E896E641DD642", + "7B0629638DF00DF1183B67EE3BF39B1C" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_aging_1.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_aging_1.json new file mode 100644 index 0000000..fc034c8 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_aging_1.json @@ -0,0 +1,103 @@ +{ + "question": [ + "What is the significance of the length of telomeres?", + "Which mouse genes have been associated with longevity?", + "what genetic factor are associated with aging", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?" + ], + "answer": [ + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis." + ], + "contexts": [ + [ + "In birds, where erythrocyte telomere length (ETL) is measured, the majority of species sampled have shown no sex difference (36).Nonetheless, bird telomere dynamics are complex and, as with humans, may be affected by environment and stress.For example, a longitudinal study of black-tailed gulls (Larus crassitostris) over 2-5 years found no correlation between ETL and age or sex.Rather, ETL attrition was correlated with reduced food availability and environmental stressors (55).In a captive zebra finch (Taeniopygia guttata) population, male and female mean telomere length decreased with increasing age of the animals, but did differ between sexes (56).As these examples illustrate, the relationship between telomere length, lifespan, and sex is likely to be complex in other vertebrates.Comparative studies of age-related telomere attrition in other species also reveal a variety of patterns.Barrett and Richardson (36) recently summarized the comparative data available on sex differences in telomere length.They found a strong correlation between male-biased mortality and either shorter telomeres or greater telomere attrition in males across bird and mammal taxa.However, telomere length did not differ between males and females in species where females are shorter-lived than males (36), suggesting that telomere shortening is not associated with species-specific longevity in a simple linear fashion.These studies generally suffer from relatively small sample sizes and are largely cross-sectional.Further, the use of diverse assays, different tissues (eg, leukocytes in mammals vs erythrocytes in birds), and lack of standardized benchmarks for accuracy makes comparisons between studies difficult.In some organisms, there is no clear relationship between telomere length and lifespan.Age-related telomere attrition could not be detected in Daphnia pulex (57) or sea urchin species (Strongylocentrotus franciscanus and Lytechinus variegatus) (58).Studies in C. elegans examining natural variation in telomere length and experimentally manipulated telomere length detect no correlation with lifespan (59,60), and in Drosophila, which uses a telomerase-independent mechanism for telomere maintenance, there is a similar lack of correlation between longevity and telomere length (61).Similarly, data on sex differences in age-related telomere shortening are mixed.For example, in the ant species Lasius niger, the rate of telomere shortening is more rapid in short-lived males compared to longer-lived females.But, mean telomere length does not differ between the two types of females, queens and workers, despite the fact that queens live much longer than workers (up to 28 years vs 2-3 months) (62).These findings suggest that the question of how telomere shortening affects aging across species and how sex affects telomere attrition rates are complex.", + "With new methodologies to assess relative telomere length by Q-PCR, studies were designed to address the impact of telomere length on aging, aging associated pathologies, and mortality.One such study has correlated shorter leukocyte telomere lengths at age 60 with a three times higher risk of heart disease and an eightfold increase in risk of infection-related death (36), thereby associating measured relative cellular aging with disease and life expectancy.In a similar way, chronic stress was shown to correlate with short leukocyte telomere length, a phenomenon attributed to higher levels of oxidative stress at the cellular level (70).More recent studies have linked telomere length in smooth muscle cells with senescence and disease severity in patients with atherosclerosis (141,150).Leukocyte telomere length was also short in a cohort of similar patients and associated with a higher risk of developing occult cardiovascular disease (71).More data are needed to understand and validate the use of leukocyte telomere length as a biomarker for cardiovascular and other diseases.", + "Shortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19].", + "New research has indicated how social factors, such as subordination, may translate into biological effects (epel et al. 2004;Chae et al. 2014).In a now classic study, epel et al. ( 2004) examined the telomere lengths of fifty-eight healthy premenopausal women who either had a healthy child (n = 19) or were giving care to a chronically ill child (n = 39. )They measured perceived stress, years of caregiving, telomere length, and oxidative stress.They found highly statistically significant differences in telomere length between women taking care of chronically ill children and those who had healthy children.They found highly statistically significant negative correlations between telomere length and perceived stress and years of caregiving.Telomerase activity had highly statistically significant negative correlations with perceived stress and years of caregiving.Oxidative stress was highly positively correlated with perceived stress and years of caregiving.They concluded that the telomere length shortening was equivalent to 9 to 17 years of aging in the high stress group.Telomere length is considered a biomarker of aging (Finch and Kirkwood 2000).Thus, this study showed that caregiver stress had essentially aged these women 9 to 17 years compared to women who had healthy children.", + "The single, consistent predictor of the rate of telomere attrition shown in multiple adult and the few child longitudinal studies is the baseline measurement of telomere length at the start of each study.This suggests the importance of understanding predictors of telomere length prior to adulthood, as it determines in part the rate of change (Revesz et al. 2014a, b;Nordfjall et al. 2009).Moreover, longitudinal studies in adults have had found that telomere attrition rate is dependent on baseline telomere length independent of any phenotypic predictors of shortening, such as disease or demographic variables (Nordfjall et al. 2009), attesting to the importance of studies to evaluate risk factors for shortening prior to adulthood.Rates of decline in childhood may be particularly relevant for later chronic disease risk as shorter telomere length has been implicated in disease progression through exposure to cellular senescence, inflammatory cytokines and adipocyte hypertrophy (Raschenberger et al. 2015;Willeit et al. 2014;Monickaraj et al. 2012;Fyhrquist et al. 2013).Adult studies have also found a negative correlation with baseline telomere length, suggesting a negative feedback regulation of leukocyte telomere length (Farzaneh-Far et al. 2010;Aviv et al. 2009;Epel et al. 2008;Nordfjall et al. 2009).It is possible that while our follow-up period was shorter than Shalev et al. 2013 and adult studies, which had a minimum of 5 year intervals with the exception of Puterman et al. (2015) who followed for a one-year time period, there may be biological regulation of telomere length at 4 and 5 years of age such that shorter telomeres are more robustly maintained, whereas longer telomeres have greater rates of decline, over a short period of one year.It is unlikely that this relationship is due to assay error or regression to the mean given the consistency of our findings across studies.We have had similar findings of longer telomeres having greater rates of decline and shorter telomeres being maintained in our different studies (Farzaneh-Far et al. 2010;Epel et al. 2008;Puterman et al. 2015).We found primarily maintenance and lengthening from 4 to 5 years of age in children, with minimal telomere attrition, indicating that most of the telomere loss happens in the first 4 years, plateauing by age 4. Lastly, we found close to 10 % of the variance in rate of change in children shared by mothers.While some of this shared variance is genetic, there are likely environmental factors that need to be further identified that impact rate of telomere length change.Abstract Telomeres are the protective complexes at the end of chromosomes, required for genomic stability.Little is known about predictors of attrition in young children or the relationship between parental and child patterns of telomere change.Telomere length was assessed twice over one year, at 4 and at 5 years of age, in Latino preschool children (n = 77) and their mothers (n = 70) in whole blood leukocytes.Maternal and child rates of attrition during the same time period were compared in 70 mother-child pairs.More children showed lengthened telomeres over one year compared to their mothers and very few children showed attrition (2.6 %).Approximately 31 % of children and 16 % of mothers displayed lengthening over one year while 66 % of children showed maintenance in contrast with 74 % of mothers.The strongest predictor for child telomere length change was child's baseline telomere length (r = 0.61,p < 0.01).Maternal rate of change was associated with child rate of change (r = 0.33, p < 0.01).After controlling for child baseline telomere length, the relationship between child and maternal rate of change trended towards significance (Coeff = 0.20, 95 % CI 0.03 to 0.43; p = 0.08).", + "Blackburn and Epel, a health psychologist who did original research on how specific lifestyle and psychological habits can protect telomeres, published The Telomere Effect (Blackburn & Epel, 2017), in which they suggested that individuals with shorter telomeres developed diseases earlier in life (a shorter \"disease span\").What follows is the evidence from these authors, their colleagues, and other researchers describing how length of telomeres contributes to mind-body connection and healthy longevity.", + "As early as at the time of birth, each of the 92 telomeres of the human genome has its own characteristic length.Additionally, each telomere shortens by its individual attrition rate.In general, longer telomeres at birth are associated with higher age-dependent attrition rates and vice versa.Overall, telomere shortening appears more dynamic in males.In conclusion, a combination of overall and chromosomespecifi c shorter telomeres and more pronounced age-dependent telomere erosion could be observed in males.There is a prospective clinical study strongly suggesting that longer telomeres decrease the risk of dying (Cawthon et al., 2003).With this in mind, the telomere length discrepancies between the sexes may indeed be a factor infl uencing the differences in their life expectancy.In every chromosome a linear decline of telomere length with age was observed, being more pronounced in men independent of the examined chromosome arm.This might suggest that telomere length on single chromosome arms may be infl uenced by the same factors which determine overall telomere length.S. Mayer a S. Brderlein a S. Perner a I. Waibel a A. Holdenried a N. Ciloglu a C. Hasel a T. Mattfeldt a K.V. Nielsen b P. Mller a a Institute of Pathology, University of Ulm, Ulm (Germany); b DakoCytomation A/S, Glostrup (Denmark) follow uniformity.In previous studies, sex-specifi c differences in telomere length and attrition rate of men and women were found (Benetos et al., 2001;Cawthon et al., 2003;Nawrot et al., 2004), suggesting gender differences in behavior of telomeres.In individual chromosome arms, telomere length was also shown not to be homogeneous (Lansdorp et al., 1996;Benn, 1997;Martens et al., 1998;Surralles et al., 1999;Hao and Tan, 2001;Londono-Vallejo et al., 2001;Graakjaer et al., 2003), some telomeres being signifi cantly shorter, others longer than the average length.To date, these characteristics in telomere lengths could not be set in a biological context, as only a few groups have provided detailed information about chromosome-specifi c patterns of telomere distribution (Lansdorp et al., 1996;Graakjaer et al., 2003).Whether accumulation of short telomeres (Martens et al., 2000;Londono-Vallejo et al., 2001) or rather the shortest telomere of one specifi c chromosome arm (Hemann et al., 2001) elicits senescence, remains an open question so far.In recent literature, there are hints that the average telomere length may be higher in women and that their annual shortening rate may be somewhat lower (Vaziri et al., 1993;Rufer et al., 1998;Jeanclos et al., 2000), but these reported differences failed to reach statistical signifi cance except for one study (Jeanclos et al., 2000).Here, we provide compelling evidence that this is indeed the case.It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo.In individuals, short telomeres are considered to be a sign of advanced age.Cawthon and coworkers (2003) showed that telomere shortening in humans likely contributes to mortality, supporting the hypothesis that they might act as a mitotic clock (Allsopp et al., 1992).Telomere length dynamics, however, does not seem to Abstract.During aging, telomeres are gradually shortened, eventually leading to cellular senescence.By T/C-FISH (telomere/centromere-FISH), we investigated human telomere length differences on single chromosome arms of 205 individuals in different age groups and sexes.For all chromosome arms, we found a linear correlation between telomere length and donor age.Generally, males had shorter telomeres and higher attrition rates.Every chromosome arm had its individual age-specifi c telomere length and erosion pattern, resulting in an unexpected heterogeneity in chromosomespecifi c regression lines.This differential erosion pattern, however, does not seem to be accidental, since we found a correlation between average telomere length of single chromosome arms in newborns and their annual attrition rate.Apart from the above-mentioned sex-specifi c discrepancies, chromosome arm-specifi c telomere lengths were strikingly similar in men and women.This implies a mechanism that arm specifi cally regulates the telomere length independent of gender, thus leading to interchromosomal telomere variations.", + "Shortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19]." + ], + [ + "DOI: https://doi.org/10.7554/eLife.75244\b24 of 30Chromosomes and Gene Expression | Genetics and GenomicsResearch articleContinuedAuthor(s)YearDataset titleDataset URLDatabase and IdentifierLongevityteam2021Genetics of longevity inBXD micehttp://www.BDL_10006, 10006genenetwork.org/show_trait?trait_id=10006&dataset=BXD-LongevityPublishLongevityteam2021Genetics of longevity inBXD micehttp://www.BDL_10010, 10010genenetwork.org/show_trait?trait_id=10010&dataset=BXD-LongevityPublishLongevityteam2021Genetics of longevity inBXD micehttp://www.BDL_10011, 10011genenetwork.org/show_trait?trait_id=10011&dataset=BXD-LongevityPublishLongevityteam2020Genetics of longevity inBXD micehttp://www.BDL_10021, 10021genenetwork.org/show_trait?trait_id=10021&dataset=BXD-LongevityPublishLongevityteam2020Genetics of longevity inBXD micehttp://www.BDL_10022, 10022genenetwork.org/show_trait?trait_id=10022&dataset=BXD-LongevityPublishLongevityteam2020Genetics of longevity inBXD micehttp://www.BDL_10025, 10025genenetwork.org/show_trait?trait_id=10025&dataset=BXD-LongevityPublishLongevityteam2021Genetics and epigeneticsof aging and longevity inBXD micehttp://www.BDL_10066, 10066genenetwork.org/show_trait?trait_id=10066&dataset=BXD-LongevityPublishReferencesAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,Carlson M. 1994.", + "DOI: https://doi.org/10.7554/eLife.75244\b24 of 30Chromosomes and Gene Expression | Genetics and GenomicsResearch articleContinuedAuthor(s)YearDataset titleDataset URLDatabase and IdentifierLongevityteam2021Genetics of longevity inBXD micehttp://www.BDL_10006, 10006genenetwork.org/show_trait?trait_id=10006&dataset=BXD-LongevityPublishLongevityteam2021Genetics of longevity inBXD micehttp://www.BDL_10010, 10010genenetwork.org/show_trait?trait_id=10010&dataset=BXD-LongevityPublishLongevityteam2021Genetics of longevity inBXD micehttp://www.BDL_10011, 10011genenetwork.org/show_trait?trait_id=10011&dataset=BXD-LongevityPublishLongevityteam2020Genetics of longevity inBXD micehttp://www.BDL_10021, 10021genenetwork.org/show_trait?trait_id=10021&dataset=BXD-LongevityPublishLongevityteam2020Genetics of longevity inBXD micehttp://www.BDL_10022, 10022genenetwork.org/show_trait?trait_id=10022&dataset=BXD-LongevityPublishLongevityteam2020Genetics of longevity inBXD micehttp://www.BDL_10025, 10025genenetwork.org/show_trait?trait_id=10025&dataset=BXD-LongevityPublishLongevityteam2021Genetics and epigeneticsof aging and longevity inBXD micehttp://www.BDL_10066, 10066genenetwork.org/show_trait?trait_id=10066&dataset=BXD-LongevityPublishReferencesAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,Carlson M. 1994.", + "Leduc MS, Hageman RS, Meng Q et al (2010) Identification ofgenetic determinants of IGF-1 levels and longevity among mouseinbred strains. Aging Cell 9(5):823836. doi:10.1111/j.14749726.2010.00612.x10. Lang DH, Gerhard GS, Griffith JW et al (2010) Quantitative traitloci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)recombinant inbred mice. Aging Clin Exp Res 22(1):81911. Gelman R, Watson A, Bronson R et al (1988) Murine chromosomalregionscorrelatedwithlongevity. Genetics118(4):69370412. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse lociassociated with life span exhibit sex-specific and epistatic effects.Conclusions These results suggest a novel locus influencing survival in the B6/D2 genetic background, perhapsvia a metabolic disorder that emerges by 200 days of age inmale animals. KeywordsPathologyLongevity \u0001 Lifespan \u0001 Mouse \u0001 Linkage \u0001IntroductionLongevity, the quintessential complex trait, likely reflectsall aspects of an organisms life history. In humans, theestimated heritability of age at death is estimated at2533 % [1]. Genetic contributions to mortality rates arethus of great interest and may aid in the understanding ofdisease etiology and the process of aging itself [2].Here, we have extended this analysis to search forgenotypes related to survival to the age of 800 days in apopulation of a reciprocal F2 cross between (B6) and (D2)mice. Since QTL for longevity in mice have shown strongsex specificity [10, 12], we conducted sex-specific analyses. In addition, we also determined whether there wereany change in pathology changes associated with the locithat showed frequency distortions with aging. To confirmthe associations of the loci of interest with longevity andpathology, we performed replication analyses on a panel ofBXD recombinant inbred strains.", + "352(6291): p. aad0189. Liao, C.Y. , et al. , Genetic variation in the murine lifespan response to dietary restriction: from life extension to lifeshortening. Aging Cell, 2010. 9(1): p. 92-5. Johnson, M., Laboratory Mice and Rats. Mater. Methods, 2012. 2: p. 113. Fontaine, D.A. and D.B. Davis, Attention to Background Strain Is Essential for Metabolic Research: C57BL/6 andthe International Knockout Mouse Consortium. Diabetes, 2016. 65(1): p. 25-33. Simon, M.M. , et al. , A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol, 2013. 14(7): p. R82. Lilue, J., et al.", + "Mamm Genome 2001;12: 9302. 21 Gelman R, Watson A, Bronson R, Yunis E. Murine chromosomalregions correlated with longevity. Genetics 1988;118:693704. 22 Peirce JL, Lu L, Gu J, Silver LM, Williams RW. A new set of BXDrecombinant inbred lines from advanced intercross populations inmice. BMC Genet 2004;5:7. 23 Rahman ZS, Tin SK, Buenaventura PN et al. A novel susceptibilitylocus on chromosome 2 in the (New Zealand Black \u0004 New ZealandWhite) F1 hybrid mouse model of systemic lupus erythematosus. J Immunol 2002;168:30429. 24 Kono DH, Burlingame RW, Owens DG et al.", + "Conversely, the BXD strain with the shortest life span(BXD14) has the lowest responsiveness to the stimulatory effect ofTGF-2 when old (48). The region on chromosome 2 where asuggestive QTL regulating the responsiveness to TGF-2 in oldmice is located also contains two QTL for longevity (32). Finally,the strongest support for this hypothesis is the correlation betweenlongevity and the age-related increase in the serum-dependent effect of TGF-2 on LSK cells, the extent of which may determinestem cell function in aged mice.", + "FIGURE 8-5 Genetic regulation of longevity in mice stratified by cause of death.Female mice that inherit the C3H allele at D2Mit58 plus the BALB allele at D16Mit182 (light gray bars) have significantly higher longevity than their sisters (dark gray bars) with the C57BL/6 plus DBA/2 allele combination (\"all causes\" of death combined).Subsets of mice that died either of cancer or of a nonneoplastic (\"benign\") illness both show the association between genotype and longevity.Among the mice dying of neoplasia, subsets dying of lymphoma or of fibrosarcoma show equivalent, and significant, genotypic effects.Bars indicate means plus standard error of the mean.SOURCE:Miller et al. (unpublished results).The available dataset also provides examples in which genetic variants seem to influence the risk of specific late-life diseases.Figure 8-6, for example, shows longevity results for mice stratified by their inheritance at the 12th chromosome locus D12Mit167.This is a locus associated with differential longevity in both male and female mice, with the strongest effect (adjusted p < 0.01) seen in those mice living more than 657 days (Jackson et al., unpublished results).The longest-lived mice are those that inherit both the C57BL/6 allele from their mother and the C3H allele from their father; on average, they survive 93 days longer than siblings with the BALB plus C3H combination.Figure 8-6 shows that the D12Mit167, like the pair of loci illustrated in Figure 8-5, has significant and similar effects in mice dying of cancer (85 days) and in mice dying of non-neoplastic diseases (126 days).A more detailed analysis of the cancers, however, suggests that while lymphoma and hepatoma victims are equally protected by the favorable alleles (effect sizes of 93 and 167 days, respec- mice of two subgroups: those dying of the urinary syndrome MUS, and those dying of all other causes.The genetic analysis contrasts mice with both the C57BL/6 allele at D4Mit84 and the C3H allele at D9Mit110 to mice with any of the three other allele combinations.In the males dying of causes other than MUS, this allele pair is associated with a 170-day increment in longevity (post-hoc p < 0.00003).But for males that do die of MUS, the same allele combination is associated with a 187-day decline in mean life span (post-hoc p < 0.03).This effect is thus pleiotropic, in that these alleles accelerate death in mice susceptible to MUS, while postponing death for all other males in the population.Although these loci are associated with differential longevity in mice that do develop MUS, they do not have a significant effect on the chances that MUS will indeed occur (not shown).The risk of developing MUS seems to be under control of a separate locus on chromosome 6.As shown in the bottom panel of Figure 8-7, males that inherit the C3H allele at D6Mit268 are far more likely to develop MUS (28 percent risk) than are their brothers who receive the DBA/2 allele at this locus (7 percent risk; p = 0.012 by two-tailed Fisher's exact test).High levels of CD8M cells are associated with diminished longevity in mated females (left panel; p < 0.001), but not in virgin females (center panel).Among virgin males, those dying of diseases other than the urinary syndrome MUS show no association between CD8M and longevity (open circles, upper line), but those dying because of MUS show a nonsignificant trend (filled circles, lower line, R = -0.27,p = 0.13) similar to the relationship observed in mated females.SOURCE : Miller et al. (unpublished results).Male or female mice that inherit the C57BL/6 (maternal) and C3H (paternal) alleles at D12Mit167 (light gray bars) are longer lived than their siblings that inherit the BALB plus C3H combination.The \"effect size\" shown at the right represents that difference in mean longevity between mice in the two genetically different groups, with (**) = p < 0.01 and (*) = p < 0.05 by t-test.Similar effect sizes are seen for mice dying of cancer or of non-neoplastic illnesses (\"benign\"), and among the cancer deaths the genetic effect is similar for deaths due to lymphoma and hepatoma.The genetic effect on longevity seems to be minimal, however, for mice dying of fibrosarcoma.Bars show means plus standard errors.SOURCE : Miller et al. (unpublished results).Our own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice.", + ", Vogler, G.P. , Vandenbergh,D.J. , Blizard, D.A. , Stout, J.T. & McClearn, G.E. Quantitative TraitLocus (QTL) Analysis of Longevity in C57BL/6J byDBA/2J (BXD)Recombinant Inbred Mice. Aging Clin Exp Res (in press). Lionikas, A., Blizard, D.A. , Vandenbergh, D.J. , Glover, M.G. ,Stout, J.T. , Vogler, G.P. , McClearn, G.E. & Larsson, L. (2003)Genetic architecture of fast- and slow-twitch skeletal muscleweight in 200-day-old mice of the C57BL/6J and DBA/2J lineage. Physiol Genomics 16, 141152. Lionikas A., Blizard D.A. , Gerhard G.S. , Vandenbergh D.J. , Stout J.T. ,Vogler G.P. , McClearn G.E.", + "Deficiency mapping of quantitative trait loci affecting longevityin Drosophila melanogaster. Genetics 2000;156:11291146. [PubMed: 11063689]33. Ma RZ, et al. Identification of Bphs, an autoimmune disease locus, as histamine receptor H1. Science2002;297:620623. [PubMed: 12142541]Nat Rev Genet. Author manuscript; available in PMC 2007 November 5. Page 12NIH-PA Author Manuscript34. Vivian JL, Chen Y, Yee D, Schneider E, Magnuson T. An allelic series of mutations in Smad2 andSmad4 identified in a genotype-based screen of N-ethyl-N-nitrosourea-mutagenized mouseembryonic stem cells. Proc. Natl Acad. Sci. USA 2002;99:1554215547. [PubMed: 12432092]35. Vogel G. Scientists dream of 1001 complex mice.", + "34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated withlongevity. Genetics 118, 693704 (1988). [PubMed: 3163317]35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011). 36. Houtkooper RHet al.Mitonuclear protein imbalance as a conserved longevity mechanism. Nature497, 451457 (2013). [PubMed: 23698443]37. Williams EGet al.An Evolutionarily conserved role for the aryl hydrocarbon receptor in theregulation of movement. PLOS Genet. 10, e1004673 (2014). [PubMed: 25255223]38. Lang DHet al.Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)recombinant inbred mice. Aging Clin. Exp. Res. 22, 819 (2010).", + "In addition,the B6 mouse strain is one of the longest-lived mouse strains with a mean lifespan of 3years versus other mouse strains with mean lifespan from 1.5-2 years. Therefore, it isevident that the genetic background of a particular mouse strain can have a profoundeffect on the biology of the HSC population as well as organismal longevity. Indeed, it isfor this reason that it is difficult to compare findings from various laboratories wheredifferent mouse strains are used.", + "NIH-PA Author ManuscriptThis study indicated a large amount of genetic variation for mouse longevity; heritabilitywas 34% for AL and 36% for DR (60% of AL food intake). There was no significantcorrelation between mean longevity under these two conditions, although maximumlifespans of the AL and DR mice were significantly correlated. Similar observations weremade at the UTHSCSA on the ILSXISS RI mice (Liao et al. , 2010a, b; Mattson 2010),where they also observed similar heritability (28% AL males, 36% AL females, 55% DRmales, 53% DR females).For females, hairs of the congenic mice grew 31% faster, also highly significant (P =0.0006, 1-tailed). These results validated the presence of a gene in the differential regionaffecting FE. DiscussionWe report the outcomes of a quantitative genetic study on aging and longevity in the mouse. We studied an extant series of recombinant inbred strains (ILSXISS) that have been usedboth in DR aging studies as well as to study alcohol sensitivity (Williams et al. , 2004).(2007) is a separate issue from the analyses conducted in thisstudy (the AL efficiency model will be tested in future studies). Exp Gerontol. Author manuscript; available in PMC 2011 September 1. Rikke et al. Page 8NIH-PA Author ManuscriptOther studies have also reported that individual mice that maintained the highest BW werelikely to be the longest-lived individuals among cohorts of genetically identical mice(Weindruch et al. , 1986; Harper et al. , 2006).", + "Age-associated changes are conserved between mouse strainsLife span and aging vary between mouse strains.For example, C57BL/6 mice are long-lived compared to the short-lived DBA/2 mice (Turturro et al. 1999).To test the generality of our observations, we also examined LT-HSCs, ST-HSC and MPPs in young and old mice from the DBA/2 strain, which originates from a distinct breeding lineage (Fox 1997)." + ], + [ + "Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "Recent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals.", + "Background: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study.", + "Background: Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality.Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.Results: Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively.We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function.Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs.A polygenic score for GrimAge acceleration showed strong associations with adiposityrelated traits, educational attainment, parental longevity, and C-reactive protein levels.Conclusion: This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.", + "INTRODUCTIONHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining 20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging.", + "Before the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26].", + "Even more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010).", + "In conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies.", + "This population geneticmechanism also can maintain genetic variability for aging, like antagonistic pleiotropy. LARGE-EFFECT MUTANTS AND THE GENETICS OF AGINGOne approach that has become increasingly common in the characterization of the genetics of aging is to isolate aging mutants, usually from mutagenesis experiments, andthen to determine the mechanistic basis for the unusual life span in the mutants. Thisapproach has led to the discovery of genes that can enhance (e.g. , Maynard Smith 1958;Lin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span(e.g. , Pearl and Parker 1922).", + "M OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10).", + "The lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010).Human longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p 5 10 8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p 10 5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.IntroductionThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005).Human longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p 5 10 8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p 10 5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.", + "Many factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics.", + "The genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity.", + "IntroductionApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches.", + "GenAge: the aging gene database Philosophy and overview of resourcesIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhes, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging.", + "I NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + ], + [ + "Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "Studies revealed from 300 to 750 genes related to longevity that are critically involved in a variety of life activities, such as growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [5,6].Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability, and the endocrine-related pathway [7][8][9].In addition, the candidates for longevity encompass genes related to drug metabolism, the ones involved in protein folding, stabilization, and degradation, as well those related to coagulation and regulation of circulation [10], etc.In most cases, these genes or their polymorphic sites were examined in multiple population replication studies, which discovered certain longevity-associated genes or pathways [4][5][6][7][8][9][10].", + "Additional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process.", + "Before the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26].", + "In conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies.", + "The only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained.Gene associations with age-related traits found using longitudinal study data.", + "In most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes.", + "The lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010).Human longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p 5 10 8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p 10 5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.", + "Thus, substantially more work is needed in this area to establish whether longevity is driven by nuclear genomic stability.Diverse and unexpected bits of evidence support a relationship.For example, a disproportionate number of genes identified in unbiased and targeted genome-wide association studies (GWASs) as associated with longevity are involved in genome maintenance (75).One study involved age of natural menopause in 70,000 women and led to the identification of 44 genetic variants associated with early or late menopause, a strong biomarker of healthy TIFs (telomere dysfunction-induced foci): co-localization of multiple DNA damage response factors and repair proteins on uncapped telomeric DNA aging (76).Approximately two-thirds of these are associated with genome maintenance genes.Seven of ten significantly associated pathways are involved in DNA repair.The highly significant overrepresentation of DNA repair pathways indicates an intimate connection between genome maintenance and aging phenotypes.From unrelated studies, we know that reduced expression of the repair endonuclease ERCC1-XPF causes accelerated aging (3), whereas ERCC1 is one of the top genes under positive selective pressure in the longest-lived mammalian species, the bowhead whale (77).Intriguingly, hepatocytes from old rats have impaired NER, whereas caloric restriction, which extends longevity, restored the NER capacity of old rats to that of youthful levels (42).In a human interventional study, brief caloric restriction increased NER capacity in PBMCs of individuals who had low NER prior to dietary intervention (78).Therefore, increased DNA repair capacity could promote longevity and may even prove amenable to improvement.", + "The antagonistic pleiotropy and hyperfunction theories of ageing predict the presence of genetic variants important for growth and development in early life with deleterious effects towards the end of the reproductive window 19,20 .While we are unable to directly capture the genetic effects on individuals before age 40 due to the study design of our datasets, we found that the life-extending variant near FOXO3 is associated with a delay in the age at menarche and a decrease in intracranial volume and cognitive abilities.It thus appears that there are loci exhibiting antagonistic effects, although we are unable to discern whether this is due to true pleiotropy or due to linkage of causal variants within a region Genes which showed a significant effect (FDR < 5%) of gene expression on ageing traits are displayed here.Gene names are annotated with the direction of effect, where + andindicate whether the life-extending association of the locus is linked with higher or lower gene expression, respectively.Locus: nearest gene to lead variant in the multivariate analysis, Chr: chromosome, Position: base-pair position of lead variant (GRCh37), Cis-genes: genes in physical proximity (<500 kb) to the lead variant of the locus which colocalise with the multivariate signal, Trans-genes: genes located more than 500 kb from the lead variant of the locus.", + "In addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations.", + "Genes/loci identified by genome-wide association studies of longevity and lifespan traits.", + "The genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity.", + "The only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained.Gene associations with age-related traits found using longitudinal study data.", + "Candidate gene studies identified APOE and FOXO3A as human longevity genesThe first genetic longevity studies mainly focused on lifespan regulating loci that emerged from animal models [22].Lifespan Prospects & Overviews .... extension in animal models was obtained by applying caloric restriction or by modifying gene functions (mutagenesis) using RNA interference, knock-out or overexpression of single genes (GenAge; http://genomics.senescence.info/genes/)[23].The most interesting pathways identified using these models are the growth hormone (GH)/insulin/insulin-like growth factor 1 (IGF-1) signaling and mammalian target of rapamycin (mTOR) signaling pathways [24].Thus far, lifespan has been the main phenotype investigated in animal models.In order to make these models more translatable to human studies research should focus on defining the parameters that reflect the physiology and pathology of aging in both animals and humans [25,26].Most of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + ], + [ + "Mutation Rate in the Liver of Mice with AgeThe mouse is a good biological tool that allows the analyses of different tissues with little limitation on the amount of biological materials available.Mice are economical compared to larger mammals, and there is a huge volume of literature on the physiology, behavior, and biochemistry of such rodents.Importantly, it is possible to modify the diet of mice or treat them with drugs to mimic specific diseases and/or to improve their health status.Finally, their genomics and genetics have been extensively studied to such a point that now there is a battery of transgenic and knockout mice which, to some extent, phenocopy important age-related diseases.Many mice with mutations in different DNA repair proteins are available.Importantly, at least four transgenic lines with the lacI and/or LacZ reporter genes have been intensively used to estimate the mutation frequency or rate in the genome of different tissues with age.One such transgenic line bears a lambda shuttle vector that carries a lacI target and an alpha lacZ reporter gene [9,10].Genomic DNA is isolated from the tissue under study, and the shuttle vector is recovered by exposing the DNA to lambda phage packaging extracts in vitro.Mutations in the lacI target gene that inactivate the repressor gene allow expression of the alpha lacZ reporter gene, resulting in blue mutant plaques.Sequencing of the DNA from these plaques not only allows the estimation of the mutation frequency, but it also points to the type of mutation providing insights into potential mechanisms [9,10].The lacI gene is highly sensitive to base substitution and frame shift mutations, as well as small deletions and insertions, making the transgene an ideal choice for recovery of spontaneous and induced mutations [11,12].The Big Blue mouse contains approximately 40 copies of the lambda shuttle vector stably integrated as a tandem array at a single position in chromosome 4 [12].The MutaMouse contains the sequence of a phage carrying the lacZ gene integrated in a head-to-tail arrangement of approximately 40 copies located at a single insertion site in chromosome 3 [13,14].The technical difference in identifying mutations in these two mouse systems is that the Big Blue mouse model is based on forward mutations in the lacI reporter sequence derepressing the lacZ gene thereby yielding blue plaques as mutants.Thousands of plaques need to be examined.The Muta mouse is based on forward mutations in the lacZ reporter gene that can be easily selected because only mutants will generate plaques.Finally, the lacZ transgenic mice lines 30 and 60 bear a plasmid carrying the lacZ gene.Line 60 was found to have two integration sites, which were mapped to chromosomes 3 and 4. The plasmid integration site of line 30 is on chromosome 11.Each integration site in both transgenic lines has about ten to twenty plasmids per haploid genome [15].Plasmids are rescued by excision with the restriction enzyme HindIII, followed by separation from mouse genomic DNA by the use of magnetic beads coated with the lacI repressor protein, which will bind the lacI sequence.The recuperated DNA is then self-ligated to obtain circular plasmids that are finally transferred into Escherichia coli C bacteria (harboring a deletion of its own lacZ gene) for sequence analyses [15,16].Mice of line 60 are appropriate transgenic animals for the study genome rearrangements in the aging liver [15,17], and chromosomal translocations and deletions up to 66 megabases have been observed in the tissues of such mice [17].Such chromosomal rearrangements cannot be detected using the phage-based reporter models (the MutaMouse and the Big Blue models).", + "The availability of deep genome sequence data, and unrivaledmulti-omic and phenomic data make the BXDs a powerful tool with which to evaluate the causallinkage between genome, epigenome, and aging rates. In our previous work, we used an enrichment-based sequencing to assay the methylome in a modestnumber of BXD mice and reported rapid age-dependent methylation changes in mice on high-fat diet(HFD) and mice with higher body weight (Sandoval-Sierra et al. , 2020).", + "Byusing bioinformatics tools, data from various studies will be clustered and analyzed to find therelationship between myelin and myelin-related genes and see if any patterns can be found thathint at a common molecular mechanism. METHODSData will be pooled from various studies of alcohol on different strains of mice. Included in thisstudy will be DBA/2J (D2) and C57BL/6J (B6), two inbred mouse strains that exhibit contrastingdrinking behaviors. Other data will come from studies on ISS (inbred short sleep) and ILS(inbred long sleep) mice.", + "The availability of deep genome sequence data, and unrivaledmulti-omic and phenomic data make the BXDs a powerful tool with which to evaluate the causallinkage between genome, epigenome, and aging rates. In our previous work, we used an enrichment-based sequencing to assay the methylome in a modestnumber of BXD mice and reported rapid age-dependent methylation changes in mice on high-fat diet(HFD) and mice with higher body weight (Sandoval-Sierra et al. , 2020).", + "Here, we have extended this analysis to search forgenotypes related to survival to the age of 800 days in apopulation of a reciprocal F2 cross between (B6) and (D2)mice. Since QTL for longevity in mice have shown strongsex specificity [10, 12], we conducted sex-specific analyses. In addition, we also determined whether there wereany change in pathology changes associated with the locithat showed frequency distortions with aging. To confirmthe associations of the loci of interest with longevity andpathology, we performed replication analyses on a panel ofBXD recombinant inbred strains.J Gerontol A Biol Sci Med Sci 57(1):B9B1513. Foreman JE, Lionikas A, Lang DH et al (2009) Genetic architecture for hole-board behaviors across substantial time intervalsin young, middle-aged and old mice. Genes Brain Behav8(7):714727. doi:10.1111/j.1601-183X.2009.00516.x14. Lang DH, Conroy DE, Lionikas A et al (2009) Bone, muscle, andphysical activity: structural equation modeling of relationshipsand genetic influence with age. J Bone Miner Res24(9):16081617. doi:10.1359/jbmr.09041815. Blizard DA, Lionikas A, Vandenbergh DJ et al (2009) Bloodpressure and heart rate QTL in mice of the B6/D2 lineage: sexdifferences and environmental influences. Physiol Genomics36(3):158166.", + "Assessing epigenetic age in long-lived miceThe epigenetic-aging model was applied to the methylation profiles of long-lived mice and the age-matched controls not used for training (Additional file 2: Datasets used summary).Reductions in age were calculated by subtracting the epigenetic ages of the untreated, wild-type mice from those of the treated mice of the same genetic background.To assess the significance, we used an ANOVA for all 22-month-old mice or only 22-month-old UM-HET3 mice.We also compared the epigenetic ages between treatments with their agematched controls from the same genetic background using a t-test (Additional file 4: Treatment vs wild type stats).", + "Experimental Goals and SignificanceThe experimental goal of the work performed in this dissertation was to identifyspecific gene(s) and molecular pathways underlying HSC aging in two commonly usedstrains of inbred mice using a forward genetic approach.In order to understand the geneticbasis for the variation of HSC numbers in old B6 and D2 mice, we used theaforementioned forward genetic approach and performed genetic linkage analysis inBXD RI strains of mice. Using this approach, we identified a locus on murinechromosome 2 that is significantly linked to the variation in frequency of HSCs in agedB6 and D2 animals.Thus, in vitro and invivo results confirmed the linkage analysis, and demonstrated that the influence exertedby the D2 allele caused a significant reduction in HSC frequency and activity with age(Geiger et al. , 2005). Aging phenotypes in mice are difficult to study, partly because test subjects mustage ~2 years before they can be used in an experiment.In mice, the effect of aging onstem cells is highly strain-specific, thus suggesting genetic regulation plays a role in HSCaging. In C57BL/6 (B6) mice, the HSC population steadily increases with age, whereas inDBA/2 (D2) mice, this population declines. Our lab has previously mapped aquantitative trait locus (QTL) to murine chromosome 2 that is associated with thevariation in frequency of HSCs between aged B6 and D2 mice. In these dissertationstudies, I first aim to characterize the congenic mouse model which was generated byintrogressing D2 alleles in the QTL onto a B6 background.", + "We further demonstrated the effectiveness of the combineduse of genome-wide association mapping with correlation analysesagainst existing phenotypic and expression data sets to identifycandidate genes that may be involved in the age-related decline inadult neurogenesis. 18-month-old mice (at least 2 males and 2 females for C57BL/6J,A/J, CBA/J, DBA/2J, 129S1/SvImJ, and 129X1/SvJ; females only for theBALB/cByJ, C3H/HeJ, and FVB/NJ) were examined and comparedamong the 9 strains.", + "Accessing data resources in the mousephenome database for genetic analysis of murine life span and health span. J.Gerontol. A Biol. Sci. Med. Sci. 71 (2), 170177. Brown, R.E. , Stanford, L., Schellinck, H.M., 2000. Developing standardized behavioraltests for knockout and mutant mice. ILAR J. 41 (3), 163174. Bubier, J.A. , Jay, J.J., Baker, C.L. , Bergeson, S.E. , Ohno, H., Metten, P., Crabbe, J.C.,Chesler, E.J. , 2014. Identication of a QTL in Mus musculus for alcohol preference,withdrawal, and Ap3m2 expression using integrative functional genomics and precision genetics. Genetics 197 (4), 13771393. Burn, C.C. , 2008.", + "Breeding schemes for mouse genome-wide association study populationsa | In the classic F2 generation cross, two parental strains are mated to generate F1 strains. The F1 strains are then either mated to each other (intercross) or to one of the parentalstrains (backcross; not shown) to generate F2 offspring. These offspring are then genotypedand phenotyped. b | Recombinant inbred strains are generated by sibling mating F2intercross animals until the resulting progeny, at least 20 generations later, is fully inbred. These inbred lines are maintained in breeding colonies and can be purchased fromcommercial vendors.", + "Our own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice.", + "Future studies may involve examination of aging mice from the CxB sRI strains todetermine whether the differences in EP and marginal cell density persist throughout thelifespan. The relationship between the cochlear lateral wall metrics (strial thickness,marginal cell density, and spiral ligament thickness) may become more obvious in oldermice with more pronounced age-related strial pathology. Additionally, due in part to therelatively small set of CxB RI strains, the suggested QTLs must be confirmed usingadditional methods.", + "To identify genes and molecularpathways regulating memory capabilities during aging, here weperform a forward systems genetic analysis on an aged cohort ofstrains from the BXD GRP. 2. Methods2.1. AnimalsMale and female mice were group housed (2e5 per cage) andmaintained in colony housing (12-hour light/dark cycle) with adlibitum access to food and water.", + "If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.Validation of the aging signature in mice\" subsection.A total of 110 male and virgin female C57BL/6JN mice were used.Mouse groups are summarized in ST9.In the aging cohort, 6 1 months old (mo), 10 3mo, 6 6mo, 6 9mo, 10 12mo, 6 15mo, 10 18mo, 10 21 mo, 5 24 mo, 6 27mo and 6 30mo were used.In the parabiosis cohort, 11 4mo and 18 19mo were used.", + "The DNA methylation results at these sites can be integrated into a multivariablemodel for epigenetic age predictions in B6 mice, which clearly correlate with the chronological age.10MethodsStatistical analysisMiceLaboratory C57BL/6J (B6), DBA/2J (D2) and BXD inbred micewere obtained from Janvier Labs (France). All mice were fed acidified water and food ad libitum, and housed under pathogen-freeconditions at the University of Kentucky, Division of LaboratoryAnimal Resource, the animal facility at CCHMC.", + "34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated withlongevity. Genetics 118, 693704 (1988). [PubMed: 3163317]35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011). 36. Houtkooper RHet al.Mitonuclear protein imbalance as a conserved longevity mechanism. Nature497, 451457 (2013). [PubMed: 23698443]37. Williams EGet al.An Evolutionarily conserved role for the aryl hydrocarbon receptor in theregulation of movement. PLOS Genet. 10, e1004673 (2014). [PubMed: 25255223]38. Lang DHet al.Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)recombinant inbred mice. Aging Clin. Exp. Res. 22, 819 (2010)." + ] + ], + "task_id": [ + "42847DE50D50E6A9B26ED0B03CFD160E", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_aging_2.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_aging_2.json new file mode 100644 index 0000000..21d6de3 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_aging_2.json @@ -0,0 +1,28 @@ +{ + "question": [ + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ], + "answer": [ + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "contexts": [ + [ + "Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "DIFFERENT GENETIC MODELS OF EXCEPTIONAL LONGEVITYWe used the whole genome sequences of these two subjects to test different hypotheses about the genetics of exceptional longevity.These non-exclusive hypotheses and the results of the analyses are described in the sections that follow.We also used the genome sequences of these two subjects to test different genetic models of exceptional longevity.The insulin pathway, caloric restriction, and lipid metabolism significantly influence lifespan in other organisms including the mouse, fly, and worm (Christensen et al., 2006) and have provided natural candidates for the genetics of human exceptional longevity.Our analysis shows that while the man had several of the noted longevity variants in metabolic genes, particularly FOXO3A, the woman was homozygous only for one variant in HSP70 that is also common in the population.No additional novel coding SNPs in these putative genes were discovered, and the different genetic profiles of these candidate genes in the two supercentenarians suggest that not all of the genetic variants associated with exceptional longevity to-date are necessary to achieve such survival, and even if some of these variants may have a role in longevity there are likely many more yet to be discovered.This suggests that the metabolic hypothesis may be just one of the many paths to exceptional lifespan.It is also likely that environmental factors and possibly the genetic ancestry may influence the likelihood of an individual to live long ages directly or by interacting with the genetic background.The NECS has shown that the chance of male and female siblings of centenarians to live past 100 can be 8 and 17 times higher than the risk in the general population (Perls et al., 2002).Consistent with this observation, our data suggest that the genetic contribution increases with older and older ages as the limit of lifespan is approached (Sebastiani et al., 2012).The male supercentenarian included in this study had strong longevity in his family.Although we do not have information about the family history of the female supercentenarian, she has living offspring who are approaching their nineties in good health and are currently enrolled in the NECS.The heterogeneity of the results herein suggest that sequencing additional exceptionally old individuals of different genetic ancestry and possibly their family members will provide the critical information to understand roles of common and rare genetic determinants of exceptional longevity and healthspan.The nature and contribution of genetic variation to exceptional longevity remains unclear, particularly the role for undiscovered rare genetic variants with large effects and/or the presence of many common genetic variants with small effects (Bloss et al., 2010).Exceptional longevity is typically characterized by strong familiality (Perls et al., 2000(Perls et al., , 2002;;Atzmon et al., 2005;Schoenmaker et al., 2006) as well as a marked delay in disability (Terry et al., 2008) and, as human lifespan is approached at about age 110 years, many such individuals compress not only disability but also age-related diseases (Andersen et al., 2011).Studies of centenarians have provided strong evidence to support the hypothesis that a genetic contribution to human exceptional longevity is decisive, although only a small number of genetic variants with modest effects have been irrefutably linked to this phenotype (Schachter et al., 1994;Barzilai et al., 2003;Christensen et al., 2006;Wheeler and Kim, 2011).The technology of next generation sequencing provides a tool to generate data that may eventually provide an answer (Metzker, 2009).", + "Genetics of Interspecies Variation in Genome Instability and LongevityThe influence of genetics in longevity is most obvious when we consider the dramatic life span differences among species.Whereas a nematode worm can live no longer than approximately 30 days, a human can live to 100 years.It is generally assumed that such species-specific differences, which are far larger than the also-not-inconsiderable intraspecies variations in life span, reflect major", + "IntroductionWorldwide human populations have shown an increase in mean life expectancy in the past two centuries (Oeppen & Vaupel, 2002).This is mainly because of environmental factors such as improved hygiene, nutrition, and health care.The large variation in healthy lifespan among the elderly has prompted research into the determinants of aging and lifespan regulation.The genetic contribution to human lifespan variation was estimated at 25-30% in twin studies (Gudmundsson et al., 2000;Skytthe et al., 2003;Hjelmborg et al., 2006).The most prominent genetic influence is observed in families in which the capacity to attain a long lifespan clusters (Perls et al., 2000;Schoenmaker et al., 2006).Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al., 2008;Terry et al., 2008), raising the question whether protective mechanisms against disease exist in long-lived subjects.In most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes.", + "Clear evidence exists for heritability of human longevity, and much interest is focused on identifying genes associated with longer lives.To identify such longevity alleles, we performed the largest genomewide linkage scan thus far reported.Linkage analyses included 2118 nonagenarian Caucasian sibling pairs that have been enrolled in fifteen study centers of eleven European countries as part of the Genetics of Healthy Ageing (GEHA) project.In the joint linkage analyses we observed four regions that", + "Living to a late age without suffering any major health problems is a genetically influenced trait.To identify the genes contributing to this important phenotype, a 10 cM genome screen was performed in 95 pairs of male fraternal twins concordant for healthy aging.Individuals meeting these criteria were defined as those attaining the age of 70 free of cardiovascular disease (coronary surgery, diabetes, heart attack, and stroke) and prostate cancer.Six chromosomal regions were identified with logarithm of odds (LOD) scores greater than 1.2 ( p , .01).A region on chromosome 4 at marker D4S1564 produced a LOD score of 1.67; this was the same marker previously linked to extreme longevity segregating as an autosomal dominant trait in centenarian families.Our results provide independent evidence that a locus on the long arm of chromosome 4 is associated with better physical aging and/or longevity.Living to a late age without suffering any major health problems is a genetically influenced trait.To identify the genes contributing to this important phenotype, a 10 cM genome screen was performed in 95 pairs of male fraternal twins concordant for healthy aging.Individuals meeting these criteria were defined as those attaining the age of 70 free of cardiovascular disease (coronary surgery, diabetes, heart attack, and stroke) and prostate cancer.Six chromosomal regions were identified with logarithm of odds (LOD) scores greater than 1.2 ( p , .01).A region on chromosome 4 at marker D4S1564 produced a LOD score of 1.67; this was the same marker previously linked to extreme longevity segregating as an autosomal dominant trait in centenarian families.Our results provide independent evidence that a locus on the long arm of chromosome 4 is associated with better physical aging and/or longevity.", + "The DNA of over 500,000 people was read to reveal the specific 'genetic fingerprints' of each participant.Then, after asking each of the participants how long both of their parents had lived, Timmers et al. pinpointed 12 DNA regions that affect lifespan.Five of these regions were new and had not been linked to lifespan before.Across the twelve as a whole several were known to be involved in Alzheimer's disease, smoking-related cancer or heart disease.Looking at the entire genome, Timmers et al. could then predict a lifespan score for each individual, and when they sorted participants into ten groups based on these scores they found that top group lived five years longer than the bottom, on average.", + "The search for the genetic determinants of extreme human longevity has been challenged by the phenotype's rarity and its nonspecific definition by investigators.To address these issues, we established a consortium of four studies of extreme longevity that contributed 2,070 individuals who survived to the oldest one percentile of survival for the 1900 U.S. birth year cohort.We conducted various analyses to discover longevity-associated variants (LAV) and characterized those LAVs that differentiate survival to extreme age at death (eSAVs) from those LAVs that become more frequent in centenarians because of mortality selection (eg, survival to younger years).The analyses identified new rare variants in chromosomes 4 and 7 associated with extreme survival and with reduced risk for cardiovascular disease and Alzheimer's disease.The results confirm the importance of studying truly rare survival to discover those combinations of common and rare variants associated with extreme longevity and longer health span.The search for the genetic determinants of extreme human longevity has been challenged by the phenotype's rarity and its nonspecific definition by investigators.To address these issues, we established a consortium of four studies of extreme longevity that contributed 2,070 individuals who survived to the oldest one percentile of survival for the 1900 U.S. birth year cohort.We conducted various analyses to discover longevity-associated variants (LAV) and characterized those LAVs that differentiate survival to extreme age at death (eSAVs) from those LAVs that become more frequent in centenarians because of mortality selection (eg, survival to younger years).The analyses identified new rare variants in chromosomes 4 and 7 associated with extreme survival and with reduced risk for cardiovascular disease and Alzheimer's disease.The results confirm the importance of studying truly rare survival to discover those combinations of common and rare variants associated with extreme longevity and longer health span.", + "Longevity Genes-A Special CaseDemographers are fascinated by the possibility that one or more genes might determine the rate of decline in multiple organ systems.Several such genes have been identified in other species (Vaupel et al., 1998).These genes are sometimes called gerontogenes or longevity genes.The discovery of one or more genes that act as aging \"clocks\" in humans would be a major breakthrough for genetics.However, the mere existence of such genes would not have a major effect on demographic research.For example, a mutation in a longevity gene that was present in 0.1 percent of the population would still be rare (probably less than 1 percent) among centenarians. 19Such a genotype would not explain much about survival to the oldest ages.Therefore, in order to be important for demographic research, there would have to be common polymorphisms associated with large differences in survival.Vaupel has estimated that there could be hundreds of genotypes with frequencies of 5-10 percent that lower death rates by 5-10 percent (Vaupel, personal communication).", + "Here, we review advances in genomic analysis within and across species to help refine the genetic foundations of age-associated diseases and longevity.As such, independent evolutionary occurrences of this species-specific lifespan change can empower comparative approaches to refine the shared mechanisms associating with longevity phenotypes.These evolutionary-refined gene sets can then be leveraged to focus statistical analysis within human cases of extreme longevity to discover core mechanisms of regulation.", + "IntroductionHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006).", + "IntroductionApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches.", + "Unraveling the heritability of human longevity was one of the first problems faced by geneticists.Just over a century ago, Mary Beeton and Karl Pearson [1] described a resemblance among relatives for the duration of life.A short time later, Yule [2] and Fisher [3] proved that the correlation is to be expected if lifespan is influenced by what had recently been termed 'genes' [4].Indeed, a century of correlation studies have established that something on the order of 30-50% of the total variation in human life span is attributable to genetic variation [5].Despite the wealth of diversity, specific genes contributing to this variation have proven notoriously difficult to identify.Sample size and issues of shared environment limit family-based methods such as linkage analysis, where rough genomic positions of important genetic variants are identified by comparing a small number of exceptionally long-lived people in defined pedigrees." + ] + ], + "task_id": [ + "D53462CE61F52F7D31BB627998F4D75A" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_diabetes_1.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_diabetes_1.json new file mode 100644 index 0000000..f5a64b3 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_diabetes_1.json @@ -0,0 +1,106 @@ +{ + "question": [ + "How is gene expression in the liver affected by diabetes?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets", + "Is the gene TCF7L2 involved in diabetes?" + ], + "answer": [ + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D)." + ], + "contexts": [ + [ + "Studies have reported that SLC2A9 is expressed in both kidney and liver of human and mice and is upregulated in diabetes mice 25 .The SLC2A9 expression was found to be governed by p53 gene and is mediated by oxidative stress 26 .Oxidative stress play major and deterministic role in patho-physiology of T2DM and has been observed to be higher in T2DM patients than healthy controls 27 .The higher expression of SLC2A9 in diabetic condition may be governed by higher oxidative stress in diabetics.In a recent study, Hurba et al. observed that there is no significant difference in transport activity of coding rs16890979 (Val253Ile) variant containing protein and wild type protein in Xenopus oocyte expression system 28 .The higher activity of SLC2A9 in T2DM subjects compared to normoglycemics may be attributed to higher expression of total SLC2A9 protein in T2DM condition.", + "Multiple studies on the transcriptome level have been performed that emphasize the diversity of the disease and the complex pathophysiological interactions between different tissues, including fat, muscle, liver, pancreatic beta cells and brain [1].In several human studies, tissue biopsies from diabetic and normoglycaemic individuals have been profiled [12,13].In mouse studies differences in diet or mouse strains have been used to identify distinct expression profiles [14][15][16].Complementary ChIP-on-Chip studies reveal the associated gene regulatory network of important transcription factors (TFs) active in the rele-vant tissues [17,18].In the context of the onset of diabetes, several studies on the proteomic level have revealed differential expression of intracellular proteins as well as of secretory proteins in adipose tissue [19].Despite the availability of these large amounts of data, their common content as well as their specific differences, in particular in gene sets between human and rodent studies, has not yet been systematically evaluated.On the other side Slc2a2 is also changed in liver.Ptpn1 is expressed in all tissues showing only small fold-changes.Several genes from OMIM or KO-mice do not change at all on the expression level.This indicates that only the complete loss of the associated protein alters the system whereas the gene's expression is not altered in T2DM.For KO-mice we also see a strong tendency to genes only expressed in mice.", + "The activities of several key gluconeogenic enzymes are increased in both young and adult diabetes mice as compared with controls [4,7] in spite of the higher than normal circulating levels of plasma insulin.In contrast the activities of the insulin dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme and acetyl-CoA synthetase are increased indicating a normal response to elevated concentrations of plasma insulin [7].As in the obese mouse, insulin resistance coupled with a disappearance of receptor sites has been a consistent finding in most tissues studied [26].", + "Regulation of GWAS diabetes genes by glucose in pancreatic isletsMany of the recently discovered type 2 diabetes genes have been suggested to affect the development and/or function of pancreatic islets [6].The function, growth and survival of -cells can be regulated acutely and chronically by glucose [34].Thus, we examined whether the new type 2 diabetes susceptibility genes are regulated by overnight incubation in low (5 mM) or high (25 mM) glucose (Figure 5).Most genes were significantly or tended to be downregulated under conditions of high glucose.Cdkal1, Cdkn2a (Arf, P = 0.07), Ide, Jazf1, Camk1d, and Tspan8 (P = 0.06) expression levels were decreased ~50-60%.Meanwhile, the expression of Cdkn2b, Hhex (P = 0.10), Cdc123, Adamts9 (P = 0.09), and Thada were reduced 30-40%.To ensure the islets incubated in high glucose did not have globally decreased expression, we examined the expression of Txnip, which has been shown to be highly upregulated by glucose [35] and found that its expression was still significantly elevated in the islets cultured in high glucose (Figure 5).Mouse islets consist of -cells and other cell types.Thus, the MIN6 -cell line was also examined.We found that all the genes were expressed in this cell line (not shown), although this does not preclude that they also are expressed in other cell types within the islet.Figure 5 Regulation of new diabetes genes by glucose levels in pancreatic islets.Data are shown as fold-change, (2 Ct ) 2 CtSE[87], relative to those observed in the islets incubated in low (5 mM) glucose.Each group is the average of three replicates, each of which was comprised of pooled islets from two mice. * P < 0.05, *** P < 0.001.It has been hypothesized that most of the new genetic variants affect -cell function, development or survival but not insulin sensitivity [6].Consistent with this, we found all of the genes except Adam30 and Cdkn2a were expressed in pancreatic islets.These genes were expressed, however in the transformed -cell line, MIN6.The expression of all the genes except Lgr5 decreased following incubation of the islets in high glucose concentrations.It can thus be hypothesized that these genes may normally play a beneficial role in islet function, and a reduction in the expression of these genes could contribute to glucotoxic -cell dysfunction or survival.However, we also found evidence that most of the genes could have potential roles in other metabolically-relevant tissues.Genes affecting insulin sensitivity may be expected to be expressed in peripheral insulin sensitive tissues, such as liver and adipose tissue, and be responsive to metabolic status.Consumption of a high fat diet was associated with a tendency for the expression of several of these genes to be decreased.Similarly, many of the genes were regulated by feeding and fasting.Only the two splice isoforms of Cdkn2a had no evidence of metabolic regulation in any of the other tissues examined.", + "A recent study assessed gene expression in different islet cell types including the insulin-producing b-cells (Dorrell et al, 2011).A comparison showed that 240 of our 254 genes are covered by the microarray used by these authors.In all, 170 of these genes have a positive presence call in b-cells.This indicates that the majority of the genes we detected as differentially methylated in T2D islets are expressed in non-diabetic b-cells to a sufficient amount to be reliably detected by microarrays, that is, these are genes actively transcribed in b-cells.", + "Figure3: Challenges with identifying gene expression alterations in type 2 diabetes.Gene expression measurements from RNA-seq data typically represent only a snapshot of tissues' or cell types' transcriptome at a given point in time.In recent comparative analyses of islet intact and single cell transcriptomes from T2D and ND individuals, relatively few genes are significantly altered despite the clear phenotypic differences between them.This may suggest that the mechanisms that precede islet failure and T2D pathogenesis are post-transcriptional and cannot be detected in conventional RNA-seq analyses.However, it is also possible that the putative paths of these genes' alterations over the course of islet physiological decline and T2D development are simply being missed.Genes that are important for islet function and resilience (e.g., Gene A) and those whose expression directly induces or is the consequence of islet failure (e.g., Gene C) may be detected in a comparative analysis between islets at healthy and decompensated states.However, response genes that are temporarily induced by islet stress (e.g., Gene B) would not be detected in this comparison.", + "Figure 2. Diabetes increases the variability of gene expression levels in other experimental paradigms. (A) Microarray data from gene expression profiling in placentas from normal compared to diabetic pregnancies (Salbaum and Kappen, unpublished data) were processed as shown in Figure1B: the coefficient of variation was determined for each gene probe, and a histogram was obtained after logarithmic transformation.The curve representing the diabetic placenta samples was shifted to higher values, similar to the results obtained in embryos from diabetic pregnancies. (B) Publicly available microarray data from diabetic versus normal human kidney (GEO record GSE1009) were treated in the same fashion as described for embryonic or placental gene expression data.Similar to our own datasets, the curve representing the coefficients of variation for the diabetic samples is shifted toward higher values, again implying that the variability of gene expression levels is higher in diabetic samples compared to control samples.Our analysis of various expression profiling data sets suggests that, in the respective paradigms (mouse embryo, mouse placenta, and human kidney), diabetes leads to an increase in the variability of gene expression, possibly by affecting the precision of gene regulation in general.Although this would be consistent with our model for maternal diabetes-elicited NTD etiology, it is important to note that the currently available gene-profiling surveys were never designed to capture variability of gene expression as an explicit experimental parameter.In fact, microarray experiments are typically structured to eliminate variability as a confounding element as much as possible, such as through the use of pooled samples.To directly measure the extent of variability of gene expression brought about by maternal diabetes, it would be necessary to conduct expression-profiling experiments with individual embryo samples, and with a higher number of samples for each side of the experimental paradigm.In this way, it would be possible to not only classify genes according to their change in expression, but also according to their change in variability of gene expression.Such experiments would define which genes exhibit increased variability in expression levels.According to our model, these would be candidate genes to trigger birth defect pathogenesis.Functional assays will then be required to test which genes of this ''highly variable'' group are able to interact with the ''susceptibility'' component-NTD genes with consistent change of expression in all exposed individuals.", + "All these studies show that gene expression, in pancreatic islets, is very sensitive to nutrients and bioactive compounds present in food.The altered expression of genes involved in cell nutrient sensing, insulin synthesis, cell cycle, survival/apoptosis and cell maintenance can impair cell function and at the end facilitates cell failure (Figure 2).Figure 2. Effects of nutrients on cell gene expression.Pancreatic cells are able to sense dietary nutrients and respond to them releasing insulin.Different nutrients and their metabolites affect transcription of genes very important for maintenance of cell function and integrity.Flavonoids upregulate the expression of genes involved in insulin synthesis, nutrient-induced insulin release and cell proliferation and downregulate genes implicated in cell apoptosis.Proteins positively regulate insulin synthesis, insulin release, cell proliferation and growth upregulating the expression of mTOR, calcineurin and Pdx1.Fats upregulate OXPHOS genes leading to the generation of metabolic coupling factors critical for insulin exocytosis.On the other hand, a chronic exposure of -cells to high levels of fats (mainly saturated fatty acids) induces excessive levels of ROS and pro-inflammatory cytokines, leading to an increased apoptosis.The upregulation of the expression of cytokine genes and genes involved in pro-inflammatory signaling pathways, together with the downregulation of genes implicated in the antioxidant defenses of cells, contribute to cell apoptosis.Moreover, chronic exposure to fats and their byproducts downregulate the expression of genes necessary for insulin synthesis, nutrient-induced insulin release, cell integrity, maintenance and survival (Pdx1 and MafA).Impairment of -cell function is a hallmark of pancreatic -cell failure and may lead to development of DM.", + "It is worth mentioning that in [132], a meta-analysis study was conducted, where a collection of gene expression datasets of pancreatic beta-cells, conditioned in an environment resembling T1D induced apoptosis, such as exposure to proinflammatory cytokines, in order to identify relevant and differentially expressed genes.The specific genes were then characterized according to their function and prior literature-based information to build temporal regulatory networks.Moreover, biological experiments were carried out revealing that inhibition of two of the most relevant genes (RIPK2 and ELF3), previously unknown in T1D literature, have a certain impact on apoptosis.", + "The known tissue specificity of gene expression regulation means that the most informative studies will measure transcript levels in the specific tissue(s) relevant to the disease.In the case of type 2 diabetes, characterization of physiological responses (e.g., stimulus-induced insulin secretion, insulin sensitivity) suggests most loci are associated with defects in pancreatic b-cell function (2,3,7).Therefore there is a real need to measure gene expression in human b-cells (or whole islets, as these have been shown to be a suitable proxy [8]).There have, however, been very few reports linking type 2 diabetesassociated variation with islet gene expression using the classical eQTL approach (9,10).", + "Young diabetic mice, at the stage whenthey still have an increased capacity to utilize glucose,had increased hepatic activities of glueokinase, citratelyase and acetyl-CoA synthetase (Table 3). However,glueose-6-phosphate dehydrogenaseactivity in the livers of micein early diabetic stages was notquite as great as in normal livers. This enzyme may be the most sensitive to the action of insulin of thefour enzymes mentioned since thelivers of some diabetic mice inthe group had glucose-6-phosphatedehydrogenase activity equal tothat from normal mice.Thus theoverall decrease in activity in liversfrom the group of 12 diabetic miceprobably includes data from a fewmice in the transitional stage whenthe ability to metabolize glucosewas rapidly declining. Activities of allfour enzymes in liver from older diabetic mice with blood sugar concentrations approaching 600 mg / 100 mlwere greatly reduced. Enzyme activities in adiposetissue showed the same generalpatterns as those in liver with the exception that glucose-6-phosphate dehydrogenase was clearly elevated inadipose tissue from the youngerdiabetic mice over that seen in adipose tissue from normal controls.Many of the liver cells of the diabetic mouse arehypertrophied and filled with fat droplets, especiallyin areas surrounding the hepatic veins (Fig. 5). Theincrease in glycogen content seen in Table 1 is notvisible histologically as PAS-positivc, diastase-digestible material, but a striking difference in glycogendistribution in livers from normals and from diabeticsis apparent. I n normal liver (Fig. 4), glycogen isdistributed fairly uniformly throughout, whereas int h a t from the diabetic (Fig.", + "To evaluate the effects of hyperglycemia or other metabolic consequences of DM per se on expression, we identified 12 genes altered in DM as compared with both nondiabetic groups but not as a function of family history (Table 4, which is published as supporting information on the PNAS web site).This included a 70-kDa heat-shock protein (HSP701A), which was decreased by 42% in DM and whose expression correlated inversely with fasting glucose for all subjects (r 0.77).Expression of a related HSP70 gene was previously found to be reduced in Caucasian diabetic subjects (20).Genes differentially expressed between control and diabetic subjects may reflect either the pathophysiology of insulin resistance (primary alterations) or secondary effects of hyperglycemia, hyperlipidemia, and other metabolic factors.To identify potentially primary expression changes associated with insulin resistance, we compared gene expression in FH (nondiabetic but insulin resistant) and FH controls.One hundred sixty-six genes were differentially expressed between FH and FH (P 0.05) (Table 3, which is published as supporting information on the PNAS web site); 55 were common to both [FH vs. DM] and [FH vs. FH] comparisons.No single gene remained differentially expressed after Benjamini-Hochberg multiple comparison testing.However, ontology classification analysis (17) revealed that 20S and 26S proteasome complexes were the top-ranked cellular component terms (Z 7.7 and 7.3); mitochondrion-linked genes were also overrepresented (Z 3.2).Cell structure (P 0.004), protein degradation (P 3.7 10 4 ), and energy generation (P 0.003) groups were represented to a greater extent than expected for random distribution; with multiple comparison testing, the protein degradation26S proteasome (P 1 10 5 ) group remained significant." + ], + [ + "Figure 8 Molecular changes in the islets of patients with T2D mirror the processes altered in NOD mice.mRNA expression in human pancreatic islets from healthy individuals (n = 105) and those diagnosed with T2D (n = 14) was assessed through RNA-seq analysis. (a) Relationship between GLIS3 and MANF expression in healthy individuals (Spearman correlation P value = 0.043), individuals with T2D (Spearman correlation P value = 0.075) and all individuals (Spearman correlation P value = 0.028). (b-e) Expression of XRCC4 (b), LIG4 (c), H2AFX (d) and CDKN1A (e) in healthy islets as compared to i slets from patients withT2D (P values shown after multiple-testing correction).The median and interquartile range (IQR; box) are shown, with error bars indicating 1.5 times the IQR.Individual values are shown if beyond 1.5 times the IQR. (f) Relationship between H2AFX and LIG4 expression in human islets (Spearman correlation P value = 5 10 9 ).", + "All the genes involved in these pathways, as well as the genes involved in b-cells development and turnover, may be considered candidate genes for T2DM with predominant insulin deficiency.", + "One method of searching for the cause of NIDDM is via the candidate gene approach.Possible candidates for NIDDM include genes involved in specifying pancreatic islet (3-cell phenotype and in directing fj-cell development and (3-cell responses of glucose-mediated insulin synthesis and secretion.The transcription factor islet-1 (Isl-1) has been shown to be a unique protein that binds to the mini-enhancer or Far-FLAT region (nucleotide -247 to -198) of the rat insulin I gene (7).Isl-1, a protein comprised of 349 residues (38 kD), is a member of the LIM/homeodomain family of proteins, named for the first three members described: lin-11, isl-1, and mec-3 (8,9).These proteins are comprised of three putative regulatory regions, two LIM domains (cysteine-rich motifs) in the amino terminus of the protein, a homeobox domain near the middle, and a glutamine-rich transcriptional activation domain at the carboxyl end (7,9).With the use of an antibody to Isl-1, expression was shown to be restricted to a subset of endocrine cells, including islets, neurons involved in autonomic and endocrine control, and selected other tissues in the adult rat (10)(11)(12).", + "ResultsImpairment or alteration of the insulin-signaling pathway is a commonly recognized feature of type 2 diabetes.It is therefore notable that the IS-HD gene set (Dataset S4) was not detected to be significantly transcriptionally altered by application of either hypergeometric enrichmentt test, DEA or GSEA.In particular, applying GSEA to the transcriptional profile dataset of diabetic and normal glucose-tolerant skeletal muscle described in Mootha et al. [10] did not identify a significant level of alteration in the IS-HD gene set (p 0.536), while DEA produced a comparably weak enrichment score (p 0.607).The failure to detect a significant transcriptional alteration in IS-HD may be explained by a number of factors.The enrichment results depended on the specific choice of the IS-HD gene set, and it is possible that an alternatively defined insulin-signaling gene set would be determined as significantly enriched.Additionally, expression changes in a few critical genes in IS-HD may be sufficient to substantially alter insulin signaling, and running DEA on the large IS-HD set may miss the contributions from these few genes.", + "35ABSTRACT 11A GENE EXPRESSION NETWORK MODEL OF TYPE 2 DIABETESESTABLISHES A RELATIONSHIP BETWEEN CELL CYCLEREGULATION IN ISLETS AND DIABETES SUSCEPTIBILITYMP Keller, YJ Choi, P Wang, DB Davis, ME Rabaglia, AT Oler, DS Stapleton,C Argmann, KL Schueler, S Edwards, HA Steinberg, EC Neto, R Klienhanz, STurner, MK Hellerstein, EE Schadt, BS Yandell, C Kendziorski, and AD AttieDepts.", + "Second, we performed an extensive manual curation according to a previously described b-cell-targeted annotation (Kutlu et al, 2003;Ortis et al, 2010).In partial agreement with the IPA, we found these genes to fall into three broad categories: (1) genes related to b-cell dysfunction and death, (2) genes potentially facilitating the adaptation of the pancreatic islets to the altered metabolic situation in T2D and (3) genes whose role in disease pathogenesis remains to be unearthed (Figure 6B).The adaptation-related gene category contains few metabolism-associated genes (e.g., HK1, FBP2; Figure 6B, right part, Figure 7) and many more genes involved in signal transduction or encoding hormones, growth factors (e.g., EGF, FGF1, IGF2/IGF2AS; Figure 7), or transcription factors involved in important regulatory networks (for instance, FOXA2/HNF3B, PAX4 and SOX6) (Figure 6B, right part, Figure 7).In the b-cell dysfunction and death category, there were hypomethylated genes related to DNA damage and oxidative stress (e.g., GSTP1, ALDH3B1; Figure 7), the endoplasmic reticulum (ER) stress response (NIBAN, PPP2R4, CHAC1), and apoptosis (CASP10, NR4A1, MADD; Figure 6B, left part, Figure 7).Some genes of interest from the highlighted categories are depicted in Figure 7. Their annotated functions provide possible explanations of how the epigenetic dysregulation of these genes in diabetic islets is connected to T2D pathogenesis.Numerous genes that were identified by our methylation profiling approach have been functionally implicated in insulin secretion.Examination of the available literature on the function of these genes revealed three aspects of insulin secretion with which they interfere: some of these genes influence the expression of the insulin gene, like MAPK1 and SOX6, or its post-translational maturation, like PPP2R4 (cf. Figure 7 and references therein).Others can deregulate the process of insulin secretion itself (SLC25A5, Ahuja et al, 2007;RALGDS, Ljubicic et al, 2009) or influence synthesis as well as secretion (vitronectin, Kaido et al, 2006).A third group of differentially methylated genes affects (i) signalling processes in the b-cell leading to insulin secretion or (ii) glucose homeostasis in b-cells, thereby modulating insulin response upon stimulation.GRB10 (Yamamoto et al, 2008), FBP2 and HK1 (Figure 7) are examples for these genes.Additional genes found in our study have been implicated in the b-cells' capability to secrete insulin, though the mechanisms have not yet been fully established.The putative functions of these genes indicate a potential epigenetic impact on insulin secretion at multiple levels, namely signalling, expression/synthesis and secretion.", + "In summary, we have associated mutations in the SLC29A3 gene with diabetes mellitus in humans and the insulin signaling pathway in Drosophila.The mechanistic basis of these findings remains to be determined.This is strong evidence supporting the investment of resources to further investigate the role of SLC29A3 and its orthologs in diabetes and glucose metabolism in model systems.DISCUSSIONWe have identified mutations in the equilibrative nucleoside transporter 3 protein that are associated with an inherited syndrome of insulin-dependent DM, and provide prima facie evidence that the Drosophila ortholog of this protein interacts with the insulin signaling pathway.This is the first evidence that mutations in the human SLC29A3 gene can be associated with a diabetic phenotype.", + "These observations taken together suggest that molecules involved in innate immunity could serve as candidate genes that determine the susceptibility of sensitive strains of mice to virusinduced diabetes.Interestingly, deficiency of the Tyk2 gene results in a reduced antiviral response 24 .In addition, the human TYK2 gene was mapped to the possible type 1 diabetes susceptibility locus 25 .", + "A recent sequencing study provides an example of detection of rare variants in type 1 diabetes.Targeted sequencing in a series of candidate coding regions resulted in IFIH1 being identified as the causal gene in a region associated with type 1 diabetes by GWA studies (58).IFIH1 encodes a cytoplasmic helicase that mediates induction of the interferon response to viral RNA.The discovery of IFIH1 as a contributor to susceptibility to type 1 diabetes has strengthened the hypothesis (70) about a mechanism of disease pathogenesis involving virusgenetic interplay and raised type 1 interferon levels as a cofactor in -cell destruction.Nonetheless, it should be recognized that a component of the missing heritability (familial aggregation) in type 1 diabetes could well be due to unrecognized intra-familial environmental factors.Disease pathogenesis.Contemporary models of pathogenesis of type 1 diabetes support the involvement of two primary dramatis personae: the immune system and the -cell.The known and newly identified genetic risk factors for type 1 diabetes present exciting opportunities to build on to the current cast of disease mechanisms and networks.Most of the listed genes of interest (Table 2) and those in extended regions are assumed to regulate immune function.Some of these genes, however, may also have roles in the -cell (insulin being the most obvious example).Another gene, PTPN2, encoding a protein tyrosine phosphatase, was identified as affecting the risk for type 1 diabetes as well as for Crohn disease (47,71).PTPN2 is expressed in immune cells, and its expression is highly regulated by cytokines.However, PTPN2 is expressed also in -cells, where it modulates interferon (IFN)- signal transduction and has been shown to regulate cytokineinduced apoptosis (72).Other candidate genes, such as NOS2A, IL1B, reactive oxygen species scavengers, and candidate genes, identified in large GWA studies of type 2 diabetes, have not been found to be significant contributors to the susceptibility of type 1 diabetes (73).", + "Differential Expression Analyses of Type 1 Diabetes Mellitus Associated GenesFor the aforementioned 171 'novel' genes, we used t-test to compare ribonucleic acid expression signals in PBMCs or monocytes between type 1 diabetes mellitus patients and healthy controls.We found that 37 genes, including 21 non-HLA genes (e.g.FAM46B, OLFML3 and HIPK1), were differentially expressed between type 1 diabetes mellitus patients and controls (Table 2).For the differential expression study, the significance level of P < 5.0E-02 was used.", + "In this study, we have correlated the function and genotype of human islets obtained from diabetic and nondiabetic (ND) donors.We have analyzed a panel of 14 gene variants robustly associated with T2D susceptibility identified by recent genetic association studies.We have identified four genetic variants that confer reduced b-cell exocytosis and six variants that interfere with insulin granule distribution.Based on these observations, we calculate a genetic risk score for islet dysfunction leading to T2D that involves decreased docking of insulin-containing secretory granules, impaired insulin exocytosis, and reduced insulin secretion.", + "At present, insulin [15], glucokinase [16], amylin [17], mitochondrial DNA [18], and several transcriptional factors [19][20][21][22] are recognized as diabetogenic genes in pancreatic b-cells.In the present study we used the candidate gene approach in the examination of genomic variation in the a 1D and Kir6.2 channel genes in type 2 diabetic patients.", + "In summary, we report AEIs that are consistent with type 2 diabetes-associated variation regulating the expression of cis-linked genes in human islets.For some of the genes where significant AEI was identified (e.g., SLC30A8, WFS1), there is strong evidence from human genetics that small changes in gene dosage may have significant consequences for the pancreatic b-cell.For other genes with significant AEI (e.g., ANPEP, HMG20A), their role is less well defined, and hence this study should provide a platform for further work examining the effects of carefully manipulating the expression of these genes in human islets.", + "Results.Pathway analysis of genes with differentially methylated promoters identified the top 3 enriched pathways as maturity onset diabetes of the young (MODY), type 2 diabetes, and Notch signaling.Several genes in these pathways are known to affect pancreatic development and insulin secretion.", + "The authors then used mouse liver and adipose expressiondata from several mouse crosses to construct causal expression networks for the ERBB3 andRPS26 orthologs in the mouse. They then showed that ERBB3 is not associated with anyknown Type I diabetes genes whereas RPS26 is associated a network of several genes thatare part of the KEGG Type I diabetes pathway (Schadt et al. 2008). This type of analysisdemonstrates the power of combining human and mouse data with a network basedapproach that has been proposed for use in drug discovery (Schadt et al.", + "In conclusion, GWAS studies focusing on the causes of T2D have implicated islet dysfunction as a major contributing factor (18,71).By examining isolated islets for stress responses and cross-referencing gene hits with genes associated with glucose-stimulated insulin release in human populations with T2D, we identified 7 genes that may play a role in promoting or preventing islet decline in T2D.By further examining stress-induced expression changes in each of these genes, we identified 5 genes that stood out: F13a1 as a novel stress-inhibited gene in islets, Klhl6 and Pamr1 as induced genes specific to ER stress, Ripk2 as a broadly stress-induced gene, and Steap4 as an exceptionally cytokine-sensitive gene.These genes provide promising leads in elucidating islet stress responses and islet dysfunction during the development of T2D.Genome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of 2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D.Genome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of 2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D.", + "Finally, several of the linking nodes introduced into this islet network through their PPI connections represent interesting candidates for a role in T2D pathogenesis, and there are several examples where external data provides validation of those assignments.An interesting example involves the gene GINS4 which maps at the ANK1 locus.Though this gene generated a low PCS [0.03] and was not included in the set of seed genes for this locus, GINS4 knock-down has an impact in a human beta-cell line [14].In addition, cyclin-dependent kinase 2 (CDK2) has been shown to influence beta-cell mass in a compensatory mechanism related to age-and diet-induced stress, connecting beta-cell dysfunction and progressive beta-cell mass deterioration [54].YHWAG is a member of the 14-3-3 family, known to be signalling hubs for beta-cell survival [55], and disruption of SMAD4 drives islet hypertrophy [56]." + ], + [ + "Researchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today.", + "Genomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.Genomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.", + "In conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes.", + "Genomics of T2DDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes.", + "It is possible that there are genes that because of their known metabolic involvement are likely to interact with specific nutrients.For example, SLC30A8 which encodes a zinc transporter localized in secretory granules, interacted with dietary zinc to effect fasting insulin levels [132].However, the majority of GWAS variants have not shown interaction with environmental factors for effect on diabetes or related traits.Therefore, it is likely that prospective future studies will utilize improved assessment methods to increase power and avoid false interpretation [133,134].This could be enhanced by prioritizing variants that are most likely to have effects [135] or selective sampling according to extremes of the environmental factor could reduce the requirement for sample size [136].These and other strategies such as meta-analysis, nested case control and genotype-based studies have been recently reviewed [123,133] and the difficulties in measuring environmental exposures have been emphasized, including the application of analyses based on logistic regression [124] and problems with instruments such as physical activity questionnaires [137].Validated food frequency questionnaires are popular instruments for evaluation diabetes risk and are often used in conjunction with food analysis software [138,139].Similar methodology has been adapted to assess two predominant food consumption patterns by Prudent and Western [140], and demonstrated synergistic interaction with genotype and a less healthy Western dietary pattern in determining male risk for T2D by showing that the gene-diet interaction was higher in men with a high genetic risk score determined by a gene counting method [141].Also the effects of diet may predominate at specific developmental periods [142] suggesting that age and associated physiological changes are important as well as differences between genders.It has also been observed that homogeneity of an environmental factor such as physical activity in an Asian Indian study, may reduce ability to detect interaction, but could be solved by subgrouping by the level of activity [143], but increased recruitment would be needed to maintain power.", + "To date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow.", + "In recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management.", + "Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression,Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression,It is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8].The aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM.Nutrient-or dietary pattern-gene interactions in the development of DM.", + "A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "In a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases.In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.", + "The public health genomics approach to type 2 diabetes.So, while exciting gene discoveries are being made, what can we do?The answer may lie in the relatively new field of public health genomics, \"a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health\" (12).Researchers, policymakers, and practitioners in public health genomics use populationbased data on genetic variation and gene-environment interactions to develop, implement, and evaluate evidencebased tools for improving health and preventing disease.They also apply systematic evidence-based knowledge synthesis and appraisal of the clinical validity and utility of genomic applications in health practice.Validated genomic information is then integrated into disease control and prevention programs (13).", + "Genomics for Type 2 DiabetesMany research studies have been carried out on genetic-based illness prediction.Incorporating machine learning approaches with genetic-based illness prediction could result in an accurate outcome.This has intensified the role of Artificial Intelligence (AI) in healthcare.It has been estimated that approximately $36 billion will be invested in AI by 2025 [48].Deep genomics through machine learning approaches has outperformed accuracy in predicting and diagnosing illnesses such as cancer with minimal inclusion of radiologists.It is desired to have sufficient biological knowledge to understand how genetics can help us predict various conditions and analyze each chromosome to identify the disease-causing gene.Pre-existing research studies have focused on genomics and gene interaction patterns of various persistent illnesses such as Alzheimer's, multiple cancers, and Parkinson's." + ], + [ + "Researchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today.", + "enetic factors for many decades have been known to play a critical role in the etiology of diabetes, but it has been only recently that the specific genes have been identified.The identification of the underlying molecular genetics opens the possibility for understanding the genetic architecture of clinically defined categories of diabetes, new biological insights, new clinical insights, and new clinical applications.This article examines the new insights that have arisen from defining the etiological genes in monogenic diabetes and the predisposing polymorphisms in type 2 diabetes.", + "Genomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.Genomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.", + "Diabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes.", + "In conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes.", + "To date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow.", + "In recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management.", + "Nonetheless, \"evidence\" for the genetics of diabetes risk is mounting, often at the expense of understanding the social context and determinants of the disease.Biogenetic views tend to trump sociological views in the diabetes research imaginary of consortium members.However, the genetic epidemiologists who make up part of the diabetes consortium are not ignorant of the effects of proper diet and adequate exercise. \"Take away the television and the automobile and diabetes would all but disappear,\" quipped the head of one lab.Neither are researchers unsympathetic to those who suffer from social inequality in the United States.Their career and intellectual interests lie in genetic explanations of diabetes, which, as I aim to show in this discussion, involves folding political and economic social relationships into biomedical discourse.In fact, the case of diabetes genetic epidemiology illustrates how, in spite of the sympathies of diabetes scientists, arrangements of racial inequality in the United States find their way into diabetes research publications and drug company promotional campaigns.To illustrate this phenomenon further, I present two tales from the field, one dealing with the naming of a publication article, the other with the marketing of a diabetes drug.", + "DiscussionOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured.", + "Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression,Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression,The aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM.It is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8].", + "A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.In a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases.", + "Genetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + ], + [ + "In 2006, a large-scale association study identified TCF7L2 as an important genetic factor for T2D in Icelandic individuals [10].This discovery was a significant breakthrough as this association was then widely confirmed in populations of European origin and other ethnic groups, such as Japanese and American individuals [50][51][52][53][54][55][56][57].Therefore, TCF7L2 was regarded as the most significant T2D susceptibility gene identified to date.3.1.Impact of TCF7L2 on the Risk of T2D.TCF7L2 is the most intensively studied locus for T2D risk so far.The risk alleles of TCF7L2 were associated with enhanced expression of this gene in human islets as well as impaired insulin secretion both in vitro and in vivo.The authors also observed an impaired incretin effect in subjects carrying risk alleles of TCF7L2 and proposed the engagement of the enteroinsular axis in T2D [119].Dennis and colleagues then verified this result and indicated that TCF7L2 variant rs7903146 affected risk of T2D, at least in part, through modifying the effect of incretins on insulin secretion.This was not due to reduced secretion of glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide 1 (GLP-1), which exhibit an important physiological role in boosting insulin secretion following meals, but rather due to the effect of TCF7L2 on the sensitivity of -cells to incretins [120].TCF7L2 has also been linked to altered pancreatic islet morphology as exemplified by increased individual islet size and altered alpha and beta cell ratio/distribution within human islets [121].This phenomenon is also observed in other in vivo or in vitro studies [122][123][124].This further strengthened the evidence for the role of TCF7L2-associated alteration of cell types in islets in the pathogenesis of T2D.TCF7L2 encodes the transcription factor TCF4 which is related to Wnt signaling pathway and which plays a critical role in the pathogenesis of T2D.The major effector of the canonical Wnt signaling pathway is known as catenin/TCF.This bipartite transcription factor is formed by free -catenin (-cat) and a member of the TCF protein family, including TCF7L2 (previously known as TCF-4) [125].GWAS have revealed the involvement of a Wnt ligand (Wnt-5b), Wnt coreceptor (LRP-5), and the Wnt pathway effector TCF7L2 in the development of diabetes [126].Several previous studies also provide evidence that the -catenin/TCF axis participates in pancreatic cell proliferation and differentiation [127][128][129][130][131]. Treatment of -cells with purified Wnt protein or activated -catenin augmented the proliferation of these cells [132].Intriguingly, deletion of -catenin within the pancreatic epithelium resulted in an almost complete lack of acinar cells, whereas deletion of -catenin specifically in differentiated acinar cells had no such effect [128], suggesting that the TCF7L2-related Wnt signaling mainly perturbs pancreatic growth but not pancreatic function.However, deletion of islet TCF7L2 expression from -cells did not show any demonstrable effects on glucose-stimulated insulin secretion (GSIS) in adult mice, whereas manipulating TCF7L2 levels in the liver caused hypoglycemia and reduced hepatic glucose production [133].In concordance with these results, risk alleles in TCF7L2 were associated with hepatic but not peripheral insulin resistance and enhanced rate of hepatic glucose production in human [119].Therefore, TCF7L2-related disruption of -cell function is probably the indirect consequence of primary events in liver or other organs/systems.", + "Variant of transcription factor 7like 2(TCF7L2) gene confers risk of type 2 diabetes. Nat. Genet. 38: 320323. doi: 10.1038/ng1732GuhaThakurta D., Xie T., Anand M., Edwards S.W. , Li G., WangS.S. & Schadt E.E. 2006. Cis-regulatory variations: A study ofSNPs around genes showing cis-linkage in segregating mousepopulations. BMC Genomics 7: 235. doi: 10.1186/1471-21647-235Gunter C. 2008. Quantitative genetics. Nature 456: 719. doi:10.1038/456719aHaines J.L. , Hauser M.A. , Schnidt S., Scott W.K. , OlsonL.M. , Gallins P., Spencer K.L. , Kwan S.Y. , Noureddine M.,Gilbert J.R., Schnetz-Boutaud N., Agarwal A., Postel E.A.", + "One obvious locus to consider is TCF7L2 in the context of type 2 diabetes.Common genetic variation located within the gene encoding transcription factor 7 like 2 (TCF7L2) has been consistently reported to be strongly associated with the disease.Such reports range from 2006, when we first published the association [3], to the recent transethnic meta-analysis GWAS of type 2 diabetes [4].Our data also lead us to conclude that TCF7L2 could also play a role in the pathogenesis of type 2 diabetes.Note that although TCF7L2 is known to have multiple isoforms, our expression data revealed no significant differences in these splice variants (ESM Table 6).", + "In conclusion, our study confirms the involvement of TCF7L2 gene in the T2DM susceptibility.Moreover, as shown also by the logistic regression analysis results, we describe a significant contribution of the TCF7L2 genetic variability to the emerging diabetic complications such as retinopathy and CAN.DiscussionThis study examined the relationships between genetic variants of TCF7L2 gene and T2DM in an Italian population.Although the disease progression results from an interplay of environmental factors and genetic predisposition, in recent years TCF7L2 gene has been considered the strongest genetic determinant for the risk of developing T2DM [2-4, 19, 20].The gene encodes a transcription factor of the canonical Wnt signaling pathway, expressed in several tissues, known to have developmental roles in determining cell fate, survival, proliferation and movement [9].Wnt signaling plays an important role also in B-cell proliferation and insulin secretion and influences synthesis of glucagon-like peptide 1 (GLP-1) in intestinal L-cells [21].In our study, besides the confirmation of the role of TCF7L2 gene in the susceptibility to T2DM, we investigated whether variants of this gene could also be associated with diabetic complications in our diabetic population.", + "Recently, two moderately linked intronic SNPs (rs7903146 and rs12255372; r 2 0.7) in the confirmed diabetes risk gene TCF7L2 [transcription factor 7-like 2 (T-cell-specific, HMG-box); OMIM entry no.602228] were shown to affect GLP-1 responsiveness of -cells, as evidenced by a hyperglycemic clamp combined with GLP-1 infusion (199).This was confirmed by comparison of the effect of the representative SNP rs7903146 on insulin secretion upon an oral vs. an iv glucose load (200).Plasma GLP-1 levels were not different between the genotypes (199,200).TCF7L2 encodes a component of the bipartite transcription factor complex -catenin/transcription factor 7-like 2 that is involved in the Wnt signaling pathway (236).Using knockdown by RNA interference and overexpression by transfection, it was demonstrated, in human and murine islets, that TCF7L2 is required for -cell survival and -cell proliferation as well as for glucose-and incretin-stimulated insulin secretion (237).Furthermore, expression of the insulin gene was found to strongly correlate with TCF7L2 expression (200) and was decreased after TCF7L2 knockdown, suggesting that the insulin gene represents a direct target gene of transcription factor 7-like 2 (238).Importantly, novel results of Maedler's group (239) revealed that the expression of GLP-1 and GIP receptors in human islets likewise depends on the presence of transcription factor 7-like 2 providing a plausible explanation for this gene's involvement in incretin responsiveness of -cells.", + "In studies where overt T2D has been the phenotype the majority of associated polymorphisms have encoded proteins known to be involved in -cell metabolism; for example TCF7L2, KCNJ11 and HHEX have shown robust association [170,171].This suggests that these genes could prove useful in predicting -cell preservation during the course of T2D.The glucokinase gene (GCK) coding for the initial glucose-sensing step in the -cell can have activating mutations causing hypoglycemia that might provide structural and functional models leading to drug targets for treating T2D [172].In the GoDARTs study, investigators examined the medication response of metformin and sulphonylurea based on the TCF7L2 variants mainly affecting the -cell.The carriers of the at risk 'T' allele responded less well to sulphonylurea therapy than metformin [173].Also it is of significant public health interest that in the Diabetes Prevention Program, lifestyle modifications were shown to reduce the risk of diabetes conferred by risk variants of TCF7L2 at rs7093146, and in placebo participants who carried the homozygous risk genotype (TT), there was 80% higher risk for developing diabetes compared to the lifestyle intervention group carrying the same risk genotypes [35].These findings could herald significant future progress in the field of T2D pharmacogenomics, possibly leading to the development and use of agents tailored on the basis of genotype.", + "The first moves towards large-scale association mappingThe earliest indication that the 'hypothesis-free' association approach to gene identification might succeed for T2D came from the discovery that variants within the transcription factor 7-like 2 (TCF7L2) gene had a substantial effect on T2D susceptibility [15].TCF7L2 encodes a transcription factor that is active in the Wnt-signalling pathway and that had no 'track-record' as a candidate for T2D; indeed, this susceptibility effect was detected through a search for microsatellite associations across a large region of chromosome 10 that had been previously implicated in T2D susceptibility by linkage [16].Subsequent fine-mapping efforts localized the likely causal variant(s) to an intron within TCF7L2 [15,17].The fact that this signal was found within a region of apparent T2D linkage seems to have been serendipitous, because none of these variants within TCF7L2 are capable of explaining the linkage effect [15,17].Across a swathe of replication studies [3][4][5][6][7]18], it has become clear that TCF7L2 variants have a substantially stronger effect on T2D risk than those in PPARG and KCNJ11, with a per-allele odds ratio of $1.4 (Table 1; Figure 2).As a result, the 10% of Europeans that are homozygous for the risk allele have approximately twice the odds of developing T2D as those carrying no copies [15,18].The evidence implicating variants within TCF7L2 in T2D susceptibility has naturally prompted efforts to understand the mechanisms involved.Current evidence indicates that alteration of TCF7L2 expression or function disrupts pancreatic islet function, possibly through dysregulation of proglucagon gene expression, LGR5, leucine-rich repeat-containing G-protein coupled; NOTCH2, Notch homologue 2 (Drosophila); PPARG, peroxisome proliferator-activated receptor gamma; SLC30A8, solute carrier family 30 (zinc transporter), member 8; TCF7L2, transcription factor 7 like 2; THADA, thyroid adenoma associated; TSPAN8, tetraspanin 8; WFS1, Wolfram syndrome1.b Estimates of effect size (given as per-allele odds ratios, i.e. the increase in odds of diabetes per copy of the risk allele) and risk-allele frequencies are all reported for Europeandescent populations based on available data (Figure 2).", + "The genetic association between T2D and variants in transcription factor 7-like 2 (TCF7L2) was first discovered in a 2).It is interesting that the T allele of rs7903146 increases T2D risk while decreasing BMI, opposing the idea that increased BMI leads to insulin resistance and T2D.In comparison to FTO and MC4R variants, TCF7L2 variants have a much larger effect on T2D risk and a smaller effect on BMI, which might indicate that the TCF7L2 variants act via T2D to affect BMI (Fig. 2).TCF7L2 is a transcription factor functioning in WNT signaling, which is crucial for cell proliferation, motility, normal embryogenesis, and regulation of myogenesis and adipogenesis (reviewed in [96]).Although the causal variant is still unclear, the T2D risk allele appears to act via lowering the levels of insulin secretion and influencing beta-cell function (reviewed in [51,96,97]).", + "To date, more than 70 genes have been identified as involved in T2DM, primarily by association analysis [34].In addition, via GWAS arrays, more than 100 SNPs have been identified for T2DM [35].From the 50 novel loci associated with T2DM previously identified, more than 40 loci have been associated with T2DM-related traits, including fasting proinsulin, insulin and glucose (Table 1) [36][37][38][39].However, for T2DM-related traits, such as the HOMA index or pancreatic cell function, there are virtually no published data examining the relationship between these traits or the genotype and environment interactions.Clinical investigations of some loci have suggested that the genetic components of T2DM risk act preferentially through cell function [40].Among all 40 loci associated with T2DM-related traits, only transcription factor-7-like 2 (TCF7L2) was shown to clearly contribute to T2DM risk [41].Several studies in white European [42], Indian [43], Japanese [44], Mexican American [45] and West African [46] individuals have shown a strong association between TCF7L2 and T2DM.It is also noteworthy that these populations represent the major racial groups with a high prevalence of T2DM.In all populations, TCF7L2 showed a strong association, with the odds of developing T2DM increased by 30%-50% for each allele inherited.This finding indicates an approximately double odds ratio compared to most other diabetes susceptibility polymorphisms.TCF7L2 is a transcription factor involved in the Wnt signaling pathway that is ubiquitously expressed, and it has been observed that TCF7L2 risk alleles result in the overexpression of TCF7L2 in pancreatic cells.This overexpression causes reduced nutrient-induced insulin secretion, which results in a direct predisposition to T2DM as well as an indirect predisposition via an increase in hepatic glucose production [47].", + "From the first GWA study of T2D, published recently in Nature [141], the strongest association observed was with a gene that was already established as having a role in the disease, namely the Wnt-signaling pathway member, transcription factor 7-like 2 (TCF7L2) [142], which has already been extensively independently replicated [143][144][145][146][147][148][149][150][151][152].This association has now been refined utilizing a West African patient cohort [153]; this is due to the fact that, in this cohort, the associated SNP is contained in a smaller LD block due to higher haplotype diversity in populations of African ancestry and thus the region most likely to contain the functional variant was narrowed down.The precise mechanism of action for this variant and its influence on the susceptibility to T2D is still to be elucidated; but it is speculated that it could operate through the alteration of levels of the insulinotropic hormone, GLP-1, one of the peptides encoded by the proglucagon gene whose expression in enteroendocrine cells is transcriptionally regulated by TCF7L2 [118].In tandem with insulin, GLP-1 has a strong influence on blood glucose homeostasis [118].Indeed, GLP-1 analogs and inhibitors of dipeptidyl peptidase IV are currently in clinical development.It has been noted that individuals with both impaired glucose tolerance and the at-risk TCF7L2 variant are more likely to go on to develop T2D, with the effect reported to be stronger in a placebo group than in metformin and lifestyle-intervention groups [143].The variant is also associated with decreased insulin secretion, but not increased insulin resistance at baseline [143].The risk-conferring genotypes in TCF7L2 are thus associated with impaired -cell function, but not with insulin resistance and may, therefore, give some indication on optimal therapeutic intervention for the one in five T2D cases this variant impacts.", + "TCF7L2Transcription factor 7-like 2 was first implicated when a signal associated with Type 2 diabetes on chromosome 10q was shown in Icelandic populations to host a microsatellite DG10748, containing single nucleotide polymorphisms rs7903146 and rs12255372 in intron 3 of the TCF7L2 gene [20], associated with a ~45% increase in Type 2 diabetes risk per allele.As such, the TCF7L2 locus presently represents the strongest known genetic determinant of Type 2 diabetes.Risk allele carriers show impaired insulin production [21] and b-cell dysfunction in vitro [22].", + "Among all the loci, TCF7L2 so far has shown the strongest association with the largest effect size for type 2 diabetes in Europeans (5,(7)(8)(9)(10)(11)(12), Amish (25), and Indians (22,26,27), but not in Chinese (28) and Japanese (29) subjects.The present study confirms the association of TCF7L2 with type 2 diabetes with the largest effect size.The TCF7L2 gene product has been implicated in blood glucose homeostasis (5,30), and the variant rs7903146 is reported to be associated with measures of glucose metabolism (25).Consistent with these observations, we also found a strong association of TCF7L2 with HOMA-B and a nominal association with FPG and 2-h PPG, confirming the physiological role of TCF7L2 in glucose homeostasis.", + "In summary, we have identified a variant in a previously unknown candidate gene for type 2 diabetes, TCF7L2, within a previously reported linkage region on 10q 1,8 .We have observed association of a composite at-risk allele of microsatellite DG10S478 within intron 3 of the TCF7L2 gene to type 2 diabetes in Iceland, which was subsequently replicated in Denmark and the US with similar frequency and relative risks.These data from three populations constitute strong evidence in support of the notion that variants of the TCF7L2 gene contribute to the risk of type 2 diabetes.", + "TCF7L22.1.Background.The gene-encoding Transcription 7 Like-2 (TCF7L2, previously called TCF4) is the most important T2D susceptibility gene identified to date, with genetic variants strongly associated with diabetes in all major racial groups [27][28][29].Signals in this locus are the most consistently identified across various GWAS and are associated with the highest elevation of risk of developing adult-onset T2D.Each copy of the risk T-allele at rs7903146 has an increased odds ratio for T2D of 1.4-1.5 [60].Inheritance of the risk allele is also a useful predictor for the likelihood of conversion from a state of prediabetes to T2D [61,62].Additionally, results from a small number of studies also indicate that TCF7L2 variation may play an important role in cases of early onset T2D [63,64].", + "One of the strongest T2DM risk-association in all the GWAS studies was found for common variants in TCF7L2, a gene coding for a transcription factor that is part of the WNT signaling pathway involved in the regulation of myogenesis and angiogenesis, but also critical for the embryonic development of pancreatic islets [19].Recently, it has been shown that the variant allele results in overexpression of TCF7L2 in pancreatic beta-cells, reducing insulin secretion in response to a variety of stimuli [6,8].The odd ratios (OR, is an estimate of the relative risk, with values [1.0 indicating a positive and \\1.0 a negative association, conferred by each additional risk allele carried at each locus) calculated in the pooled studies for the T allele in the snp7903146 of TCF/L2 was 1.37 (1.31-1.43)[13].This variant resides in an intron of the gene.Other variants at this locus also confer increased risk for T2DM, although the specific genetic defect that results in impaired insulin secretion in carriers has not been identified yet.Alternatively, other genes in the region may contribute to T2DM susceptibility.Associations between the T variant of TCF7L2 and T2DM have been consistently confirmed in geographically, ethnically, and environmentally diverse populations (references in [19], without evidence of heterogeneity across ethnic groups [2].", + "The C to T (genomic position: 114748339) substitution at SNP rs7903146 of the intron 3 (IVS3C>T) is associated with T2DM and may function through impaired glucagon-like peptide 1 secretion, which is stimulated more by fat than by carbohydrate ingestion [25,26].TCF7L2 is present on chromosome 10q25, spanning 215.9 kb.It considered the most influential gene in determining the genetic susceptibility for T2DM today [27].TCF7L2 is the key transcriptional factor regulating glucose metabolism through the Wnt signaling pathway and has been reported to be critical for the development of the pancreas and islets during embryonic growth [3].Genetic variants in this gene are associated with increased risk of T2DM in a variety of study populations [28,29].", + "The variants in TCF7L2, MC4R, CDC123, KCNQ1, IGF2BP2, and SLC16A11 have all been previously associated with T2D in adults (20,25).In addition, a prior study in SEARCH reported that genetic variation in TCF7L2 is associated with an increased risk of T2D in African American youth, with the OR for diabetes stronger in African American than in non-Hispanic White youth (12).The divergent ethnicity-based results did not replicate in ProDiGY, suggesting that the earlier findings in SEARCH might be due to statistical fluctuations in the context of smaller sample sizes." + ] + ], + "task_id": [ + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C319861B08978CF5F7E6F0CD3A517A81", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027", + "CD1F7EAE0FDC758A8167118927ADFE71" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_diabetes_1_two.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_diabetes_1_two.json new file mode 100644 index 0000000..3758538 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_diabetes_1_two.json @@ -0,0 +1,76 @@ +{ + "question": [ + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?", + "How can I use genenetwork to find genes related with diabetes in humans?", + "How can I use the GeneNetwork tool to find genes related with diabetes in humans?" + ], + "answer": [ + "Yes, the gene IFIH1 is identified as a contributor to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.", + "GeneNetwork can be used to find genes related to diabetes in humans by analyzing Genome-Wide Association Study (GWAS) data. This involves integrating this data with the human gene network, which can boost the performance of recovering validated type 2 diabetes genes. The network can also strongly implicate certain genes in type 2 diabetes. Additionally, the DisGeNET database can be used to collate gene-disease information, which can contribute to understanding the biology of type 2 diabetes. This approach can identify", + "You can use the GeneNetwork tool to find genes related to diabetes in humans by navigating to genenetwork.org and using the global search bar at the top of the page. You can search for genes, mRNAs, or proteins across all of the datasets. Use standard gene symbols containing more than two characters in the name for best results. You can also switch to phenotypes and search for any phenotype of interest. Additionally, you can use the Select and search pull-down menus to choose a population of interest." + ], + "contexts": [ + [ + "Figure 8 Molecular changes in the islets of patients with T2D mirror the processes altered in NOD mice.mRNA expression in human pancreatic islets from healthy individuals (n = 105) and those diagnosed with T2D (n = 14) was assessed through RNA-seq analysis. (a) Relationship between GLIS3 and MANF expression in healthy individuals (Spearman correlation P value = 0.043), individuals with T2D (Spearman correlation P value = 0.075) and all individuals (Spearman correlation P value = 0.028). (b-e) Expression of XRCC4 (b), LIG4 (c), H2AFX (d) and CDKN1A (e) in healthy islets as compared to i slets from patients withT2D (P values shown after multiple-testing correction).The median and interquartile range (IQR; box) are shown, with error bars indicating 1.5 times the IQR.Individual values are shown if beyond 1.5 times the IQR. (f) Relationship between H2AFX and LIG4 expression in human islets (Spearman correlation P value = 5 10 9 ).Parallel transcriptional regulation in human isletsTo determine whether the findings observed in mice were applicable to humans, we investigated whether the pathway identified in NOD mice also demonstrated genetic linkage to diabetes or glucose regulation traits in humans.GLIS3 polymorphisms have previously been associated with altered glucose regulation; we additionally identified nominally significant associations for MANF, XRCC4 and LIG4 polymorphisms (Supplementary Table 2).In an independent approach that takes into account environmental effects, we analyzed RNA-seq data from human pancreatic islets isolated from 119 donors, including 14 diagnosed with T2D 28 .To assess the validity of the Glis3-Manf relationship observed in mice, we investigated the relationship of these two genes in human islets.A trend toward reduced GLIS3 expression was observed in T2D islets, whereas MANF expression appeared unchanged (Supplementary Fig. 13).Critically, a significant positive relationship was observed between GLIS3 and MANF levels in human islets (Fig. 8a).Next, we investigated whether patients with T2D might exhibit reduced XRCC4 expression, analogous to the NOD polymorphisms.We found no change in XRCC4 expression in T2D islets (Fig. 8b); however, the levels of the obligate binding partner encoded by LIG4 were significantly reduced (Fig. 8c).In mice, Xrcc4 polymorphisms were associated with increased senescence; likewise, in patients with T2D, the levels of the senescence markers H2AFX (Fig. 8d) and CDKN1A (Fig. 8e) were increased.Finally, a direct relationship was observed between reduced LIG4 and increased H2AFX levels (Fig. 8f).Although the cause of coregulation cannot be assessed in ex vivo human islets, the parallel with NOD mice strongly supports a conservation of diabetes susceptibility mechanisms across species.3,500,000 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 Fluorescence", + "All the genes involved in these pathways, as well as the genes involved in b-cells development and turnover, may be considered candidate genes for T2DM with predominant insulin deficiency.", + "One method of searching for the cause of NIDDM is via the candidate gene approach.Possible candidates for NIDDM include genes involved in specifying pancreatic islet (3-cell phenotype and in directing fj-cell development and (3-cell responses of glucose-mediated insulin synthesis and secretion.The transcription factor islet-1 (Isl-1) has been shown to be a unique protein that binds to the mini-enhancer or Far-FLAT region (nucleotide -247 to -198) of the rat insulin I gene (7).Isl-1, a protein comprised of 349 residues (38 kD), is a member of the LIM/homeodomain family of proteins, named for the first three members described: lin-11, isl-1, and mec-3 (8,9).These proteins are comprised of three putative regulatory regions, two LIM domains (cysteine-rich motifs) in the amino terminus of the protein, a homeobox domain near the middle, and a glutamine-rich transcriptional activation domain at the carboxyl end (7,9).With the use of an antibody to Isl-1, expression was shown to be restricted to a subset of endocrine cells, including islets, neurons involved in autonomic and endocrine control, and selected other tissues in the adult rat (10)(11)(12).", + "ResultsImpairment or alteration of the insulin-signaling pathway is a commonly recognized feature of type 2 diabetes.It is therefore notable that the IS-HD gene set (Dataset S4) was not detected to be significantly transcriptionally altered by application of either hypergeometric enrichmentt test, DEA or GSEA.In particular, applying GSEA to the transcriptional profile dataset of diabetic and normal glucose-tolerant skeletal muscle described in Mootha et al. [10] did not identify a significant level of alteration in the IS-HD gene set (p 0.536), while DEA produced a comparably weak enrichment score (p 0.607).The failure to detect a significant transcriptional alteration in IS-HD may be explained by a number of factors.The enrichment results depended on the specific choice of the IS-HD gene set, and it is possible that an alternatively defined insulin-signaling gene set would be determined as significantly enriched.Additionally, expression changes in a few critical genes in IS-HD may be sufficient to substantially alter insulin signaling, and running DEA on the large IS-HD set may miss the contributions from these few genes.", + "35ABSTRACT 11A GENE EXPRESSION NETWORK MODEL OF TYPE 2 DIABETESESTABLISHES A RELATIONSHIP BETWEEN CELL CYCLEREGULATION IN ISLETS AND DIABETES SUSCEPTIBILITYMP Keller, YJ Choi, P Wang, DB Davis, ME Rabaglia, AT Oler, DS Stapleton,C Argmann, KL Schueler, S Edwards, HA Steinberg, EC Neto, R Klienhanz, STurner, MK Hellerstein, EE Schadt, BS Yandell, C Kendziorski, and AD AttieDepts.", + "Second, we performed an extensive manual curation according to a previously described b-cell-targeted annotation (Kutlu et al, 2003;Ortis et al, 2010).In partial agreement with the IPA, we found these genes to fall into three broad categories: (1) genes related to b-cell dysfunction and death, (2) genes potentially facilitating the adaptation of the pancreatic islets to the altered metabolic situation in T2D and (3) genes whose role in disease pathogenesis remains to be unearthed (Figure 6B).The adaptation-related gene category contains few metabolism-associated genes (e.g., HK1, FBP2; Figure 6B, right part, Figure 7) and many more genes involved in signal transduction or encoding hormones, growth factors (e.g., EGF, FGF1, IGF2/IGF2AS; Figure 7), or transcription factors involved in important regulatory networks (for instance, FOXA2/HNF3B, PAX4 and SOX6) (Figure 6B, right part, Figure 7).In the b-cell dysfunction and death category, there were hypomethylated genes related to DNA damage and oxidative stress (e.g., GSTP1, ALDH3B1; Figure 7), the endoplasmic reticulum (ER) stress response (NIBAN, PPP2R4, CHAC1), and apoptosis (CASP10, NR4A1, MADD; Figure 6B, left part, Figure 7).Some genes of interest from the highlighted categories are depicted in Figure 7. Their annotated functions provide possible explanations of how the epigenetic dysregulation of these genes in diabetic islets is connected to T2D pathogenesis.Numerous genes that were identified by our methylation profiling approach have been functionally implicated in insulin secretion.Examination of the available literature on the function of these genes revealed three aspects of insulin secretion with which they interfere: some of these genes influence the expression of the insulin gene, like MAPK1 and SOX6, or its post-translational maturation, like PPP2R4 (cf. Figure 7 and references therein).Others can deregulate the process of insulin secretion itself (SLC25A5, Ahuja et al, 2007;RALGDS, Ljubicic et al, 2009) or influence synthesis as well as secretion (vitronectin, Kaido et al, 2006).A third group of differentially methylated genes affects (i) signalling processes in the b-cell leading to insulin secretion or (ii) glucose homeostasis in b-cells, thereby modulating insulin response upon stimulation.GRB10 (Yamamoto et al, 2008), FBP2 and HK1 (Figure 7) are examples for these genes.Additional genes found in our study have been implicated in the b-cells' capability to secrete insulin, though the mechanisms have not yet been fully established.The putative functions of these genes indicate a potential epigenetic impact on insulin secretion at multiple levels, namely signalling, expression/synthesis and secretion.", + "In summary, we have associated mutations in the SLC29A3 gene with diabetes mellitus in humans and the insulin signaling pathway in Drosophila.The mechanistic basis of these findings remains to be determined.This is strong evidence supporting the investment of resources to further investigate the role of SLC29A3 and its orthologs in diabetes and glucose metabolism in model systems.DISCUSSIONWe have identified mutations in the equilibrative nucleoside transporter 3 protein that are associated with an inherited syndrome of insulin-dependent DM, and provide prima facie evidence that the Drosophila ortholog of this protein interacts with the insulin signaling pathway.This is the first evidence that mutations in the human SLC29A3 gene can be associated with a diabetic phenotype.", + "These observations taken together suggest that molecules involved in innate immunity could serve as candidate genes that determine the susceptibility of sensitive strains of mice to virusinduced diabetes.Interestingly, deficiency of the Tyk2 gene results in a reduced antiviral response 24 .In addition, the human TYK2 gene was mapped to the possible type 1 diabetes susceptibility locus 25 .", + "A recent sequencing study provides an example of detection of rare variants in type 1 diabetes.Targeted sequencing in a series of candidate coding regions resulted in IFIH1 being identified as the causal gene in a region associated with type 1 diabetes by GWA studies (58).IFIH1 encodes a cytoplasmic helicase that mediates induction of the interferon response to viral RNA.The discovery of IFIH1 as a contributor to susceptibility to type 1 diabetes has strengthened the hypothesis (70) about a mechanism of disease pathogenesis involving virusgenetic interplay and raised type 1 interferon levels as a cofactor in -cell destruction.Nonetheless, it should be recognized that a component of the missing heritability (familial aggregation) in type 1 diabetes could well be due to unrecognized intra-familial environmental factors.Disease pathogenesis.Contemporary models of pathogenesis of type 1 diabetes support the involvement of two primary dramatis personae: the immune system and the -cell.The known and newly identified genetic risk factors for type 1 diabetes present exciting opportunities to build on to the current cast of disease mechanisms and networks.Most of the listed genes of interest (Table 2) and those in extended regions are assumed to regulate immune function.Some of these genes, however, may also have roles in the -cell (insulin being the most obvious example).Another gene, PTPN2, encoding a protein tyrosine phosphatase, was identified as affecting the risk for type 1 diabetes as well as for Crohn disease (47,71).PTPN2 is expressed in immune cells, and its expression is highly regulated by cytokines.However, PTPN2 is expressed also in -cells, where it modulates interferon (IFN)- signal transduction and has been shown to regulate cytokineinduced apoptosis (72).Other candidate genes, such as NOS2A, IL1B, reactive oxygen species scavengers, and candidate genes, identified in large GWA studies of type 2 diabetes, have not been found to be significant contributors to the susceptibility of type 1 diabetes (73).", + "Differential Expression Analyses of Type 1 Diabetes Mellitus Associated GenesFor the aforementioned 171 'novel' genes, we used t-test to compare ribonucleic acid expression signals in PBMCs or monocytes between type 1 diabetes mellitus patients and healthy controls.We found that 37 genes, including 21 non-HLA genes (e.g.FAM46B, OLFML3 and HIPK1), were differentially expressed between type 1 diabetes mellitus patients and controls (Table 2).For the differential expression study, the significance level of P < 5.0E-02 was used.", + "In this study, we have correlated the function and genotype of human islets obtained from diabetic and nondiabetic (ND) donors.We have analyzed a panel of 14 gene variants robustly associated with T2D susceptibility identified by recent genetic association studies.We have identified four genetic variants that confer reduced b-cell exocytosis and six variants that interfere with insulin granule distribution.Based on these observations, we calculate a genetic risk score for islet dysfunction leading to T2D that involves decreased docking of insulin-containing secretory granules, impaired insulin exocytosis, and reduced insulin secretion.", + "At present, insulin [15], glucokinase [16], amylin [17], mitochondrial DNA [18], and several transcriptional factors [19][20][21][22] are recognized as diabetogenic genes in pancreatic b-cells.In the present study we used the candidate gene approach in the examination of genomic variation in the a 1D and Kir6.2 channel genes in type 2 diabetic patients.", + "In summary, we report AEIs that are consistent with type 2 diabetes-associated variation regulating the expression of cis-linked genes in human islets.For some of the genes where significant AEI was identified (e.g., SLC30A8, WFS1), there is strong evidence from human genetics that small changes in gene dosage may have significant consequences for the pancreatic b-cell.For other genes with significant AEI (e.g., ANPEP, HMG20A), their role is less well defined, and hence this study should provide a platform for further work examining the effects of carefully manipulating the expression of these genes in human islets.", + "The authors then used mouse liver and adipose expressiondata from several mouse crosses to construct causal expression networks for the ERBB3 andRPS26 orthologs in the mouse. They then showed that ERBB3 is not associated with anyknown Type I diabetes genes whereas RPS26 is associated a network of several genes thatare part of the KEGG Type I diabetes pathway (Schadt et al. 2008). This type of analysisdemonstrates the power of combining human and mouse data with a network basedapproach that has been proposed for use in drug discovery (Schadt et al.", + "Genome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of 2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D.Genome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of 2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D.In conclusion, GWAS studies focusing on the causes of T2D have implicated islet dysfunction as a major contributing factor (18,71).By examining isolated islets for stress responses and cross-referencing gene hits with genes associated with glucose-stimulated insulin release in human populations with T2D, we identified 7 genes that may play a role in promoting or preventing islet decline in T2D.By further examining stress-induced expression changes in each of these genes, we identified 5 genes that stood out: F13a1 as a novel stress-inhibited gene in islets, Klhl6 and Pamr1 as induced genes specific to ER stress, Ripk2 as a broadly stress-induced gene, and Steap4 as an exceptionally cytokine-sensitive gene.These genes provide promising leads in elucidating islet stress responses and islet dysfunction during the development of T2D.", + "Finally, several of the linking nodes introduced into this islet network through their PPI connections represent interesting candidates for a role in T2D pathogenesis, and there are several examples where external data provides validation of those assignments.An interesting example involves the gene GINS4 which maps at the ANK1 locus.Though this gene generated a low PCS [0.03] and was not included in the set of seed genes for this locus, GINS4 knock-down has an impact in a human beta-cell line [14].In addition, cyclin-dependent kinase 2 (CDK2) has been shown to influence beta-cell mass in a compensatory mechanism related to age-and diet-induced stress, connecting beta-cell dysfunction and progressive beta-cell mass deterioration [54].YHWAG is a member of the 14-3-3 family, known to be signalling hubs for beta-cell survival [55], and disruption of SMAD4 drives islet hypertrophy [56]." + ], + [ + "Beyond new gene discovery in the field of research, an important challenge in the next coming years is how to set up a more open population-level and high-quality genetic screening strategy aiming to improve etiological diagnosis in almost all of cases with early-onset diabetes.", + "In briefGardner et al. queried the genomes of over 400,000 individuals and identified novel genes associated with type 2 diabetes risk.The biological function of these genes highlights potentially new therapeutic avenues for treatment of type 2 diabetes.", + "Results: Here we report on a meta-analysis approach that integrates data of heterogeneous origin in the domain of type-2 diabetes mellitus (T2DM).Different data sources such as DNA microarrays and, complementing, qualitative data covering several human and mouse tissues are integrated and analyzed with a Bootstrap scoring approach in order to extract disease relevance of the genes.The purpose of the meta-analysis is two-fold: on the one hand it identifies a group of genes with overall disease relevance indicating common, tissue-independent processes related to the disease; on the other hand it identifies genes showing specific alterations with respect to a single study.Using a random sampling approach we computed a core set of 213 T2DM genes across multiple tissues in human and mouse, including well-known genes such as Pdk4, Adipoq, Scd, Pik3r1, Socs2 that monitor important hallmarks of T2DM, for example the strong relationship between obesity and insulin resistance, as well as a large fraction ( 128) of yet barely characterized novel candidate genes.Furthermore, we explored functional information and identified cellular networks associated with this core set of genes such as pathway information, protein-protein interactions and gene regulatory networks.Additionally, we set up a web interface in order to allow users to screen T2DM relevance for any -yet non-associated -gene.DiscussionThe first part of our study was devoted to the identification of genes related to T2DM using different heterogeneous data sources in different organisms.Genes have been scored in each individual study according to their disease relevance and an overall score across the different studies has been computed that reflects their total disease relevance.By this approach we were able to identify 213 genes that have a general disease relevance showing high scores in many different studies as well as genes that have a specific disease relevance expressing high scores in only a few studies.", + "GENE DISCOVERY IN T2DWhy?", + "Genetic approaches to studying type 1 diabetesTwo approaches have been used to identify diabetes susceptibility genes: genome-wide linkage studies and candidate gene association studies [see also Field (57) for a discussion of these approaches as applied to type 1 diabetes].These approaches have definitively shown that the major histocompatibility complex (MHC) locus, also called human leukocyte antigen or HLA, contains the major inherited factor(s) that determines diabetes risk.At least two other genes contain variants that almost certainly affect risk: the insulin gene (INS) and CTLA4.We will review the merits of these two genetic approaches used to identify diabetes susceptibility genes and the results obtained thus far.We also discuss the possible impact of genetic and genomic advances on future genetic studies.", + "Received: 7 May 2009 Accepted: 25 February 2010Published: 25 February 2010References1. Sieberts SK, Schadt EE: Moving toward a system genetics view of disease. Mamm Genome 2007, 18:389-401. 2. Keller MP, Choi Y, Wang P, Davis DB, Rabaglia ME, Oler AT, Stapleton DS,Argmann C, Schueler KL, Edwards S, Steinberg HA, Chaibub Neto E,Kleinhanz R, Turner S, Hellerstein MK, Schadt EE, Yandell BS, Kendziorski C,Attie AD: A gene expression network model of type 2 diabetes links cellcycle regulation in islets with diabetes susceptibility. Genome Res 2008,18:706-716. 3.", + "In conclusion, the findings presented in our study suggest high power for gene-based association analyses in detecting disease-susceptibility genes across the human genome.Our findings point to the involvement of new pathways in the pathogenesis of type 1 diabetes mellitus, and provide more insights into the genetic basis of type 1 diabetes mellitus.", + "A systematic genomewide search for type 2 diabetes-susceptibility genes was performed on a subset of 440 participants in the 27 most informative extended families.Of the 440 individuals, 116 are diabetics (including probands), giving a prevalence of 26.4%.There are 3,745 relative pairs, with varying degrees of genetic", + "Genome-wide association studies (GWAS) have discovered germline genetic variation associated with type 2 diabetes risk (1)(2)(3)(4).One of the largest GWAS, involving DNA taken from individuals of European descent and conducted by the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium, identified 65 loci associated with type 2 diabetes risk (1).However, for most of these loci, the precise identity of the affected gene and the molecular mechanisms underpinning the altered risk are not known.", + "Figure5.Consideration of the human gene network boosts recovery of validated type 2 diabetes genes from GWAS analysis of 2000 patients and 3000 controls. (A,B) Plotted using the same conventions as in Figure4, analyzing WTCCC GWAS data (Wellcome Trust Case Control Consortium 2007) for type 2 diabetes alone and in combination with HumanNet and measuring performance as AUC (<5% FPR) for recovering the top 20 genes from a type 2 diabetes meta-analysis of 4549 cases and 5579 controls(Zeggini et al. 2008).As for Crohn's disease, consideration of the network boosts performance across a wide range of parameter values.Notably, consideration of the network strongly implicates the genes CTNNB1 and BACH2 in type 2 diabetes; CTNNB1 is well studied in connection with type 2 diabetes and BACH2 has been previously implicated in type 1 diabetes and celiac disease (e.g.,Cooper et al. 2008;Madu et al. 2009), but not type 2 diabetes.", + "A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "One attractive methodology to circumvent the puzzle of choosing either a hypothesis-driven or an exploratory research may be the strategy of gene prioritization offered by the new bioinformatics tools based on the biological plausibility of a gene-disease association and on knowledge of the protein function. 6e propose an approach for expanding the selection of genes or loci of interest and prioritizing associations over GWAs related with genetic susceptibility to type 2 diabetes.The proposal profits from the recent initiatives of data sharing of the genome scan results that make the information publicly available as soon as they are generated and checked for quality.Both the DGI and the WTCCC are committed to embracing these principles as they made available all the phenotype-genotype data for type 2 diabetes.", + "In this review, however, we focus on a different route from human genetics to translation, one that derives estimates of an individual's predisposition to diabetes and its subtypes (in the form of polygenic scores) from the patterns of individual geneticvariation at sites known to influence diabetes predisposition.", + "Family-based studies of the genetic determinants of type 2 diabetes and related precursor quantitative traits (QTs, e.g.plasma insulin and glucose levels) and GWA studies have now provided an abundance of evidence for potentially causative genes.These results have been drawn together onto a single map of the human genome sequence [86].The goal is to look for genomic locations where the presence of a potential underlying type 2 diabetes gene has been attested to repeatedly-diabetes genetic 'hot spots'.Such replication increases our confidence of the presence of an underlying gene.While GWA studies look for diabetes genes using a different approach to linkage analysis, the ultimate goal is the same-to find the genetic determinants of the disease.Therefore, the results of linkage and association must eventually match each other.The current analysis identifies multiple linkage locations that differ from those found in the recent GWA studies [87-89], and suggests the location of additional major type 2 diabetes susceptibility genes.", + "INTRODUCTIONMultiple genome-wide association studies (GWASs) have correlated type 2 diabetes mellitus (T2DM) with genetic variants, yielding a large number of loci and associated gene products that are linked to the disease phenotype-often with little or no insight into the mechanism underlying that link (Hivert et al., 2014).The current challenge is to establish robust systems to systematically evaluate the role of these loci using disease-relevant cells.Previous studies have used patient samples, cell lines, or animal models to seek mechanistic insight but with significant limitations.Large variation is observed in primary patient samples, perhaps due to genetic heterogeneity, whereas animal models present major physiological and metabolic differences that hamper understanding of the precise function of human genes in T2DM.Therefore, a robust system to systematically evaluate the role of T2DM-associated genes using disease-relevant human cells will provide an important tool for diabetes research and spur the development of precision (allele-specific) therapies, exemplified by the use of sulfonylurea drugs to treat patients carrying certain KCNJ11 mutations (Gloyn et al., 2004).", + "Genomic information associated with Type 2 diabetes.", + "To gain insights into how the linking nodes of our final network contribute to T2D biology, we used the DisGeNET database [37], which collates gene-disease information from public data as well as from literature via natural language processing tools.We focused on the 274 linking nodes included in our model to avoid circularity arising from using the seeds, and identified 92 (~33%) with known links to T2D (Additional file 1: Table S2).Examples include as follows: (a) NEUROD1 which encodes a transcription factor that is involved in the development of the endocrine cell lineage and has been implicated in monogenic diabetes [38], (b) PRKCB involved in insulin resistance [39] and (c) GNAS, implicated in beta-cell proliferation [40].For this last gene, mouse knockouts have been shown to produce phenotypes concordant with diabetes [41].These examples demonstrate the potential of these analyses to draw in \"linking\" nodes as related to T2D even when they are not located within genome-wide association signals.", + " Human Genome Project -its Implications in Diabetes GeneticsThe USA coordinator of the Human Genome Project at the National Institute of Health (NIH), Francis Collins (Bethesda, MD), expects the entire human genome to be sequenced by 2002, the complete sequence of chromosomes 22 and 7 already being available in 1999.The NIH will invest US$ 75 million to identify another 500 000 SNPs genome wide.The USA SNP mapping will be based on 500 cell lines and would have to be followed by linkage mapping in all major populations.The other global players of the Human Genome Project, including the SNP consortium and several private companies, are also putting major efforts into the identification of genes encoding type 2 diabetes.Extensive international collaborations will be crucial in order to carry the enormous financial and manpower burden needed to achieve these goals.Therefore, the data generated must be freely accessible throughout the scientific community.As diabetes will become a WHO priority in 2000, this might foster more investment into the research of the genetics of diabetes.", + "Genetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + ], + [ + "This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.", + "GeneNetwork is an interactive software (Geisert et al. , 2009), which enables usersreadily to reconstruct genetic network based on microarraydata without being intimately involved in complicatedmathematical computation. Materials and methodsMiceOne pair of heterozygous (lew/ ) mice was purchasedfrom the Mouse Mutant Stock Resource colonies at TheJackson Laboratory (TJL). A breeding colony was thenestablished by mating them at the University of TennesseeHealth Science Center (UTHSC).", + "T2DM-GeneMiner web toolIn order to allow users to screen the disease potential of any given gene of interest we developed T2DM-GeneMiner, a web interface summarizing the results of our work (Figure 1, [35]).The user interface is shown for the wellknown Adipoq and the resulting bar plots for two other genes, Pdk4 and Cfd, with lower content of available infor-mation.The resource is searchable by gene or protein IDs (for example Ensembl ID or gene symbol).The score distribution is shown as a bar plot and, where available, functional information is displayed.The two rightmost bars show the entropy, indicating uniform or specific score distribution, and the score.The red line at the score bar indicates the cut-off.Background: Multiple functional genomics data for complex human diseases have been published and made available by researchers worldwide.The main goal of these studies is the detailed analysis of a particular aspect of the disease.Complementary, meta-analysis approaches try to extract supersets of disease genes and interaction networks by integrating and combining these individual studies using statistical approaches.Results: Here we report on a meta-analysis approach that integrates data of heterogeneous origin in the domain of type-2 diabetes mellitus (T2DM).Different data sources such as DNA microarrays and, complementing, qualitative data covering several human and mouse tissues are integrated and analyzed with a Bootstrap scoring approach in order to extract disease relevance of the genes.The purpose of the meta-analysis is two-fold: on the one hand it identifies a group of genes with overall disease relevance indicating common, tissue-independent processes related to the disease; on the other hand it identifies genes showing specific alterations with respect to a single study.Using a random sampling approach we computed a core set of 213 T2DM genes across multiple tissues in human and mouse, including well-known genes such as Pdk4, Adipoq, Scd, Pik3r1, Socs2 that monitor important hallmarks of T2DM, for example the strong relationship between obesity and insulin resistance, as well as a large fraction ( 128) of yet barely characterized novel candidate genes.Furthermore, we explored functional information and identified cellular networks associated with this core set of genes such as pathway information, protein-protein interactions and gene regulatory networks.Additionally, we set up a web interface in order to allow users to screen T2DM relevance for any -yet non-associated -gene. Conclusion:In our paper we have identified a core set of 213 T2DM candidate genes by a metaanalysis of existing data sources.We have explored the relation of these genes to disease relevant information and -using enrichment analysis -we have identified biological networks on different layers of cellular information such as signaling and metabolic pathways, gene regulatory networks and protein-protein interactions.The web interface is accessible via http://t2dmgeneminer.molgen.mpg.de.", + "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In thismanuscript we will outline some simple use cases, and show how a small number of plausiblecandidate genes can be identified for an immune phenotype. 1. DataOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. Thefirst is to use the global search bar located at the top of the page (Figure 1). This is a newfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of thedatasets.Similarly, by using the dropdown menu on the left (Figure 1), a user can switch to phenotypes,and search for any phenotype of interest in the same way. Figure 1: The global search bar, also called the Search All function, is a good area to start exploringgenes, mRNA, and proteins within GeneNetwork. To best use this new tool, use standard gene symbolscontaining more than two characters in the name. Another area to acquire data is the Select and search pull-down menus (Figure 2). To getstarted, the user has to choose a population of interest.", + "This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.", + "The Web tool G2D (Genes to Diseases) prioritizesgenes across a user-entered chromosomal region according to their possible relationto an inherited disease by a combination of data mining of OMIM, PubMed MESH9.6 IDENTIFICATION OF POTENTIALLY FUNCTIONAL POLYMORPHISMS211terms and Gene Ontology (GO) classification. The tool allows users to inspect anyregion of the human genome to find candidate genes related to a genetic disease orphenotype defined in OMIM. It does this by identifying GO terms that match MESHterms for an OMIM record.", + "Researchers, however, have thepossibility to fully explore the results by altering the thresholds on the open web resource. Although onlyprotein-coding genes were included in our analysis, the same approach can be applied to non-coding genes63to reveal their potential functions. Similarly, GeneBridge can also be utilized to identify novel gene-diseaseassociations based on known disease-associated genes from databases, such as the Human DiseaseOntology (DO) [207] or DisGeNET [208]. The GeneBridge toolkit could also be applied to large-scaleproteomics datasets after correcting for the background of all measured proteins.", + "Protein interaction networksWe searched for protein networks spanning the regions shown to interact genetically (P values < 0.05; Table 2).This was performed using a high-confidence human protein inter- Markers of predictive value for T1D identified by decision tree analysis on T1D genome scan data from 1321 affected sib pair families.Markers identified in the total data set are ranked according to significance level (P < 0.05).Markers from data subsets are 'selected markers' and were selected on basis of whether they confirm loci from the latest T1D genome scan [25] or other references [26; 27].D.f. = degrees of freedom.", + "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In thismanuscript we will outline some simple use cases, and show how a small number of plausiblecandidate genes can be identified for an immune phenotype. 1. DataOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. Thefirst is to use the global search bar located at the top of the page (Figure 1). This is a newfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of thedatasets.Similarly, by using the dropdown menu on the left (Figure 1), a user can switch to phenotypes,and search for any phenotype of interest in the same way. Figure 1: The global search bar, also called the Search All function, is a good area to start exploringgenes, mRNA, and proteins within GeneNetwork. To best use this new tool, use standard gene symbolscontaining more than two characters in the name. Another area to acquire data is the Select and search pull-down menus (Figure 2). To getstarted, the user has to choose a population of interest.", + "Users begin by selecting one or more human diseases andclicking on Compare. The genes associated with the selected diseaseare tested for enrichment against all sets of known associated genes forworm phenotypes. The result reveals functionally coherent, evolutionarily conserved gene networks. Alternatively, users can also start by selecting worm phenotypes,which are tested against human diseases. In addition to cross-speciestesting, results of within-species disease enrichment are also available(e.g. to nd the closest related human disease for another input humandisease).", + "GeneNetwork is an interactive software (Geisert et al. , 2009), which enables usersreadily to reconstruct genetic network based on microarraydata without being intimately involved in complicatedmathematical computation. Materials and methodsMiceOne pair of heterozygous (lew/ ) mice was purchasedfrom the Mouse Mutant Stock Resource colonies at TheJackson Laboratory (TJL). A breeding colony was thenestablished by mating them at the University of TennesseeHealth Science Center (UTHSC).", + "Genome Biol 8(2):R25Hubner N, Wallace CA, Zimdahl H, Petretto E, Schulz H et al (2005)Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 37(3):243253Ihaka R, Gentleman RC (1996) R: a language for data analysis andgraphics. J Comput Graph Stat 5:299314Keller MP, Choi Y, Wang P, Davis DB, Rabaglia ME et al (2008) Agene expression network model of type 2 diabetes links cellcycle regulation in islets with diabetes susceptibility.", + "We next constructed protein-protein interaction networks.To do this, we selected 76 genes known from monogenic forms of diabetes, obesity, and hypertension or GWAS hits (type 2 diabetes, obesity, and hypertension) for which the lead association lies within the protein-coding part of the gene (Table S3).", + "First, we describe the construction of a functional network for human genes.This network spans 87% of validated protein coding genes, and provides strong predictive power for a majority of currently known genetic diseases.We evaluate six alternate approaches for prioritizing candidate disease genes using this network, and demonstrate the strongest overall performance with algorithms related to Google's PageRank.We then show that this network, in conjunction with genome-wide association data for Type 2 diabetes and Crohn's disease, boosts the identification of disease-associated genes that were discovered in later meta-analyses.This work suggests both a specific strategy and a general path to future improvements for the interpretation of GWAS data.Taken together, our work demonstrates that a high-quality functional network for human genes can provide a powerful resource for identifying causal genes in human disease.A new functional gene network for human genesIn order to test the general ability of a gene network to prioritize disease genes, particularly in conjunction with GWAS studies, we constructed a genome-scale functional network of human genes, incorporating diverse expression, protein interaction, genetic interaction, sequence, literature, and comparative genomics data, including both data collected directly from human genes, as well as that from orthologous genes of yeast, worm, and fly.The resulting HumanNet gene network can be accessed through a web interface (http://www.functionalnet.org/humannet).Using this interface, researchers can easily search the network using a set of ''seed'' Network-guided genome-wide association mining genes of interest.The interface returns a list of genes ranked according to their connections to the seed genes, together with the evidence used to identify each coupling.The interactions and evidence can be downloaded, and a network visualization tool has been incorporated.All linkages can also be downloaded for independent analysis.", + "Construction and analysis of the T2D risk genes networkTo further sift the results and explore functional connections, we also mapped genes onto known gene sets.For this purpose, we used DAVID [22,23] to search for enriched KEGG [24] pathways.We also used GARNET [25] to identify enriched Gene Ontology categories and their relationships.", + "GeneNetwork.org also offers a powerful statistical platform foronline network analyses and mapping, enabling numerous molecular questions to be probed in one centralized location(Chesler et al. , 2003, 2005; Li et al. , 2010; Mulligan et al. , 2012,2017, 2019). Most data are from groups of animals or humanswho have been fully genotyped or even sequenced. As a result, itcan be used to model causal networks that link DNA differencesto traits such as differences in expression, cell number, volumes,and behavior using real-time computation and graphing." + ] + ], + "task_id": [ + "173BE1F01E4A6074A0EBB7CC6137AC8D", + "F55160D302C899B2131AA8502A640684", + "B52AF52D46499DE2B98933F1786EC9E0" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_diabetes_2.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_diabetes_2.json new file mode 100644 index 0000000..4a847fa --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_diabetes_2.json @@ -0,0 +1,113 @@ +{ + "question": [ + "what are confounding factors in diabetes?", + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ], + "answer": [ + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors.", + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "contexts": [ + [ + "A wide array of other dietary compounds and environmental triggers have been shown to affect diabetes development in animal models, and for some of these such as omega-3 fatty acids (312), there is limited proof in human patients.", + "Type 2 diabetes (T2D) is a result of complex gene-environment interactions, and several risk factors have been identified, including age, family history, diet, sedentary lifestyle and obesity.Statistical models that combine known risk factors for T2D can partly identify individuals at high risk of developing the disease.However, these studies have so far indicated that human genetics contributes little to the models, whereas socio-demographic and environmental factors have greater influence 1 .Recent evidence suggests the importance of the gut microbiota as an environmental factor, and an altered gut microbiota has been linked to metabolic diseases including obesity 2,3 , diabetes 4 and cardiovascular disease 5 .", + "Dietary factors [source]Reduced risk Mediterranean diet pattern [130] Fruit and vegetable intake [131] Fermented dairy products [132] Fatty fish intake [133] Tea intake [134] Elevated risk Red and processed meat intake [135] Sweetened beverages [136] Null association Total dairy products or milk intake [132] Total fish intake [133] Dietary energy density [137] Carbohydrate intake [138] a Further information about the InterAct project can be found at www.inter-act.eu.There are also other forthcoming publications on dietary factors and the risk of diabetes.cohort studies also found an increased diabetes incidence among passive smokers [142].Finally, in-utero exposure to maternal smoking is associated with overweight and obesity which may predispose to diabetes and other metabolic disturbances in the offspring [143].Psychosocial factors encompass two broad areas which are more closely related to socioeconomic status or to psychological/psychiatric factors.Within the InterAct study, people who had a lower educational level had a 70% higher relative risk for diabetes, which remained at around 40% even after adjustment for differences in obesity [144].The association between emotional stress, job strain, anxiety and depressive disorders and increased incidence of type 2 diabetes is less well-established, but recent data [145][146][147] strongly indicate that this area merits further study to better understand the relationship between these potential risk factors.", + "It isplausible that such factors may also operate at the very beginning of the humanlifecourse but their identity, and the environmental factors they synergize with,remain unknown (Bloomfield et al 2006), awaiting discovery. Chaufan also makes a strong case that inequalities in the provision of healthcare and education are compounding the growing problem of type 2 diabetes inthe developed (and increasingly, less developed) nations today (Chaufan 2007). This is an important point, and one with which we agree, but it is concerned primarily with issues about resource allocation and distributive justice.Type 2 diabetes mellitus as an illustrative exampleThe persuasiveness of Chaufans argument comes from her dependence on type 2diabetes as her main illustrative example. It is true that environmental factors canaccount for up to 8090% of the population attributable risk for this condition(Cooper & Psaty 2003), and it may be that in a profoundly diabetogenic environment such as exists in many 21st century developed countries, knowing about G E interactions adds little per se to the management of an overweight and inactivepopulation.", + "Understanding risk factors for diabetes is therefore critical to its early diagnosis.Key risk factors for diabetes include obesity (Mokdad et al. 2001;Must et al. 1999) and prediabetes.A fasting blood sugar well into the \"reference range\" has been shown to be a risk factor for diabetes (Tirosh et al. 2005).Indeed, we have shown that the 4-year risk of diabetes among participants in the FHS with prediabetes ranges from a 12.7-fold increase (in men) to a 22.3fold increase (in women) (Levitzky et al. 2008).The metabolic syndrome, a constellation of metabolic risk factors that have been observed to cluster with each other more than would be expected by chance (Meigs et al. 1997), was formally acknowledged as a syndrome involving the fulfillment of at least 3 criteria, including elevated waist circumference, impaired fasting glucose, elevated blood sugar, elevated triglycerides, or low high-density lipoprotein cholesterol (Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults 2001).The presence of the metabolic syndrome is a strong risk factor for the subsequent development of diabetes, conferring a nearly 7-fold increased risk among those with as compared with those without the metabolic syndrome (Wilson et al. 2005).As a means of better trying to identify who is at early risk for diabetes, a prediction equation for incident diabetes was developed in the FHS (Wilson et al. 2007).A \"simple clinical model\" was derived, which includes parental history of diabetes, obesity, hypertension, low high-density lipoprotein cholesterol, elevated triglyceride levels, and impaired fasting glucose; the c-statistic for this model was robust at 0.85.Importantly, more complex models with variables such as waist circumference, insulin resistance, 2-hour postprandial glucose derived from an oral glucose tolerance test, and C-reactive protein were not independent predictors of diabetes.This prediction model highlights how simple clinical variables that are readily available can be used to identify individuals at high risk for developing diabetes even before they have evidence of the disease.In aggregate, these findings from the FHS make several important points.First, the incidence rate of diabetes is increasing.Second, because the relative risk of diabetes as a CVD risk factor has remained constant over time, the relative importance of diabetes with respect to CVD has increased.Finally, individuals with diabetes remain inadequately managed with regard to CVD risk factor levels.These findings highlight the importance of early identification of diabetes and a means to identify diabetes early in the life course to promote the early aggressive management of CVD risk factors.Another major remaining question is why the relative risk for diabetes as a CVD risk factor has failed to decrease over time.As described earlier, the rates of CVD among participants in the FHS have decreased; but this reduction has been outpaced by those without diabetes (Fox et al. 2004a).In terms of primary prevention, we can aim to reduce the burden of uncontrolled CVD risk factors, including incompletely treated hypertension, dyslipidemia, and participants with diabetes who continue to smoke (Preis et al. 2009a).Observational studies such as the FHS can help to explore rates of treatment and control for known modifiable risk factors.", + "DietExcessive caloric intake is a major driving force behind escalating obesity and type 2 diabetes epidemics worldwide, but diet quality also has independent effects.In the Nurses' Health Study (NHS), we found that the quality of fats and carbohydrates play an important role in the development of diabetes, independent of BMI and other risk factors (11).In particular, higher dietary glycemic load (GL) and trans fat are associated with increased diabetes risk, whereas greater consumption of cereal fiber and polyunsaturated fat is associated with decreased risk (Fig. 2).In a meta-analysis, we found that a 2 serving/day increment in whole-grain intake was associated with a 21% lower risk of diabetes (12).", + "IntroductionThe aetiology of type 2 diabetes is poorly defined: several studies indicate that the disease results from a combination of genetic susceptibility and external risk factors [1].According to this multifactorial model, genetically predisposed subjects will not necessarily develop overt disease unless they are also exposed to particular environmental factors [2].Important risk factors for the development of type 2 diabetes include a family history of diabetes, increased age, hypertension, lack of physical exercise, and obesity [1].", + "Environmental factors such as age, weight gain, excessive energy intake, physical inactivity and inheritance of genes predisposing to insulin resistance are major risk factors for development of T2D.Nutrient imbalances such as deficiency of vitamin D [19] and increased iron absorption and storage in the body [20,21], changes in gut microbiota [22] and exposure to pollutants [23] may confer risk for development of T2D.Early-life or intrauterine environment [24] and epigenetics [25] also play a role in conferring susceptibility to diabetes.Obstructive sleep apnea, which is associated with obesity, insulin resistance and glucose intolerance, also contributes to the pathology of T2D [26].", + "What these predisposing factors share is an ability to negatively impact the glucose homeostasis system through worsening of insulin resistance or to impair b-cell function.Superimposing these factors onto a genetically compromised glucose homeostasis system raises the risk of progressing to hyperglycemia.It is the rapid emergence of these disadvantageous environmental factors that is causing the worldwide diabetes epidemic.This concept of environmental changes promoting diabetes was highlighted many years ago by populations that rarely experienced type 2 diabetes, but then moved from a nomadic or farm existence to urban environments followed by an explosion of diabetes, typically with profound obesity: Pima Indians in the Southwest U.S., Saharan nomadic tribes, Australian Aborigines, and many others.Particularly dramatic were studies that showed reversal of the diabetes when they returned to their prior way of life (15).A recent example of this is the rapidly rising incidence of type 2 diabetes in China and India as people move from the country to cities-there is a 0.1-0.2%incidence of diabetes for rural farmers in China as opposed to well more than 5% for city dwellers.Perhaps the scariest example of this is children in the U.S. where the obesity statistics worsen yearly.As many as 20% of U.S. children are now obese, and they are developing all of the elements of the metabolic syndrome-insulin resistance, hypertension, hyperlipidemia, and glucose intolerance (16).", + "Taken together, non-invasive risk factors including age, sex, BMI, waist circumference, family history, smoking or hypertension form the basis of all diabetes risk scores.Routine clinical biomarkers, such as glucose, HbA 1c , lipids and uric acid, have the potential to improve the predictive ability of these basic risk factors, but AROCs rarely exceed 0.85.This argues in favour of a search for novel risk factors to further improve the accuracy of diabetes risk models.", + "There are two major factors that underlie these alarming projections.The first is T2D is associated with age, and Western populations are aging rapidly.The second major explanation is our lifestyles have changed dramatically in recent years.Epidemiological studies have identified strong T2D risk relationships for obesity, sedentary behavior [2][3][4], and diets rich in energy [5], processed carbohydrates [6], and animal fats [7].Collectively, these lifestyle factors impede the actions of insulin and raise hepatic glucose production, which can result in the diminution of endogenous insulin production and T2D.The strongest evidence for a causal relationship between adverse lifestyle behaviors and T2D comes from randomized controlled trials that show intensive lifestyle interventions involving structured exercise regimes which promote habitual physical activity (PA) and have a major beneficial impact on diabetes incidence in high-risk individuals [8,9].", + "In multivariate analyses (Table 3), diabetes was related to a higher risk of all-cause MCI even after adjusting for age, sex, ethnic group, years of education, APOE 4, hypertension, low-density lipoprotein level, heart disease, stroke, and current smoking (HR, 1.4; 95% confidence interval [CI], 1.1-1.8).", + "Clinical Factors Predicting Incidence of DiabetesIn both the MPP and Botnia studies, a family history of diabetes, an increased BMI, and increased levels of blood pressure and serum levels of triglycerides, apolipoprotein A-I, and liver enzymes were independent predictors of future type 2 diabetes (Table 1).In the MPP study, current smoking was also associated with a marked increase in the risk of diabetes.Impaired insulin secretion and action, particularly insulin secretion adjusted for insulin resistance (disposition index), were strong predictors of future diabetes.The presence of a first-degree family history of diabetes doubled the risk of the disease that was seen with an increased BMI (Fig. 2A) and a low disposition index (Fig. 2B).", + "The worldwide explosion of the rates of diabetes and other metabolic diseases in the last few decades cannot be fully explained only by changes in the prevalence of classical lifestyle-related risk factors, such as physical inactivity and poor diet.For this reason, it has been recently proposed that other \"nontraditional\" risk factors could contribute to the diabetes epidemics.In particular, an increasing number of reports indicate that chronic exposure to and accumulation of a low concentration of environmental pollutants (especially the so-called persistent organic pollutants (POPs)) within the body might be associated with diabetogenesis.In this review, the epidemiological evidence suggesting a relationship between dioxin and other POPs exposure and diabetes incidence will be summarized, and some recent developments on the possible underlying mechanisms, with particular reference to dioxin, will be presented and discussed.The worldwide explosion of the rates of diabetes and other metabolic diseases in the last few decades cannot be fully explained only by changes in the prevalence of classical lifestyle-related risk factors, such as physical inactivity and poor diet.For this reason, it has been recently proposed that other \"nontraditional\" risk factors could contribute to the diabetes epidemics.In particular, an increasing number of reports indicate that chronic exposure to and accumulation of a low concentration of environmental pollutants (especially the so-called persistent organic pollutants (POPs)) within the body might be associated with diabetogenesis.In this review, the epidemiological evidence suggesting a relationship between dioxin and other POPs exposure and diabetes incidence will be summarized, and some recent developments on the possible underlying mechanisms, with particular reference to dioxin, will be presented and discussed.", + "In sum, it is clear that multiple risk factors are involved in diabetes-associated cognitive decrements as well as in dementia in relation to diabetes 38 .On the basis of our assessment of the literature, it is also clear that there are still substantial knowledge gaps on how the risk factors interconnect, how the risk factors translate to potentially modifiable mechanisms and which genetic factors are involved.", + "Aetiological factorsProspective studies suggest that the main pathophysiological defects leading to type 2 diabetes are insulin resistance and a relative insulin secretory defect.The main aetiological risk factors are age, obesity, family history, and physical inactivity.Dietary risk factors have recently emerged: risk is increased by high consumption of red and processed meat 13 and sugar-sweetened beverages, 14 and reduced by intake of fruit and vegetables, 15 some types of dairy products, 16 and some overall dietary patterns. 17Novel strategies to use quantifiable nutritional biomarkers are paving the way for more detailed understanding of the association between diet and diabetes.Although the heritability of type 2 diabetes is high (30e70%) and more than 60 genetic variants related with diabetes risk have now been identified, 18 even when combined into a genetic score, known genes contribute little to the prediction of diabetes.Phenotype-based risk models provide greater discrimination for diabetes, and the addition of genotypic information adds no more than 5e10% improvement in prediction.The current conclusion is that genetic variants provide insights into biological pathways and pathogenesis of diabetes, but not its prediction.It is likely that interactions between the environment/lifestyle and genetic factors provide the explanation for the risk of type 2 diabetes, but demonstrating such interaction is challenging.Encouraging research findings have recently shown higher absolute risk of diabetes associated with obesity at any level of genetic risk. 19evention and screening" + ], + [ + "V. IMMUNE EVENTS IN TYPE 1 DIABETESSeveral silent immune events occur before the clinical symptoms of type 1 diabetes become apparent.Most importantly, autoantibodies are produced and self-reactive lymphocytes become activated and infiltrate the pancreas to destroy the insulin-producing beta-cells in the islets of Langerhans (56).This persistent, targeted destruction may go undetected for many years, and the first clinical symptoms only become apparent after a majority of the beta-cells have been destroyed or rendered dysfunctional, making the individual dependent on insulin for survival (Fig. 2).Therefore, high priority is given to the search for \"biomarkers\" as whistleblowers of an ongoing autoimmune response.We will highlight some important immunological events here.Additional information on immune cell cross-talk in T1D can be found elsewhere (243).", + "IntroductionType 1 diabetes (T1D) results from immune-mediated selective destruction of pancreatic islet cells resulting in insulin deficiency and hyperglycemia [1,2].Symptoms of polydipsia, polyuria, polyphagia and weight loss manifest when significant numbers of islet cells have been destroyed.However, antibodies to islet autoantigens can be detected in peripheral blood prior to clinical disease [1,3].With early diagnosis of disease or assessment of risk, immune therapy may impede islet destruction and preserve insulin production, delaying onset of clinical manifestations [2].", + "Background: The immune system matures mainly during the postnatal period through breastfeeding, and is partly modified by nutritive factors.The manner by which early feeding practices influence the development of type 1 diabetes mellitus (TID) is not clear.Also the use of genetics in prognostic evaluation of the disease has not be studied intensely.", + "Figure 1-Schematic of the pathogenesis of diabetes.Genetic and environmental factors, acting via complex immunological mechanisms, result in b-cell destruction that leads to type 1 diabetes.Gene-environment interactions also underlie susceptibility to type 2 diabetes, the pathophysiological hallmarks of which include insulin resistance and b-cell dysfunction.", + "The results revealed that a major type of immune actors known as T cells are under the control of genetic factors associated with type 1 diabetes susceptibility.For instance, a specific type of T cells showed shared genetic control with type 1 diabetes.In addition, 15 loci were identified that influenced immune responses in the patients.Among those, 12 have never been reported to be involved in immune responses in healthy people, implying that these regions might only regulate the immune system of individuals with type 1 diabetes and other similar disorders.Finally, Chu, Janssen, Koenen et al. propose 11 genes within the identified loci as potential targets for new diabetes medication.These results represent an important resource for researchers exploring the genetic and immune basis of type 1 diabetes, and they could open new avenues for drug development.Many studies have highlighted the role of environmental, genetical, and immunological factors in the pathogenesis of T1D (Pociot and Lernmark, 2016;Rewers and Ludvigsson, 2016).Environmental factors such as being overweight, infections, microbiome composition, and dietary deficiencies have been reported as risk factors for T1D (Rewers and Ludvigsson, 2016).In turn, the immunological pathogenesis (Cabrera et al., 2016) of T1D includes innate inflammation and adaptive immunity, such as enhanced T cell responses (Hundhausen et al., 2016).In the last two decades, large genome-wide association studies (GWAS) performed have underscored the contribution of genetic polymorphisms to T1D for the susceptibility, with ~60 genomic loci associated with T1D risk identified (Barrett et al., 2009;Bradfield et al., 2011;Cooper et al., 2008;Grant et al., 2009;Huang et al., 2012;Onengut-Gumuscu et al., 2015;Ram et al., 2016).While these loci show significant enrichment in specific immune-related biological pathways, such as cytokine signaling and T cell activation (Barrett et al., 2009;Cooper et al., 2008), the functional consequences of many of these loci and genetic variants are still unknown.We thus lack information that could link the genetic susceptibility factors to the immunological pathways potentially important for T1D pathogenesis.The genetically regulated inflammatory response signature in T1D may also be relevant for the inflammatory response in general and may become modified by the chronic hyperglycemic state.The composition and activity of the human immune system is under genetic control, and people with certain changes in their genes are more susceptible than others to develop type 1 diabetes.Previous studies have identified around 60 locations in the human DNA (known as loci) associated with the condition, but it remains unclear how these loci influence the immune system and whether diabetes will emerge.Interrelationship between immune-cell counts and cytokine production in T1DWe collected blood samples from 243 T1D patients (300DM cohort), following a previously described methodology (Aguirre-Gamboa et al., 2016;Ter Horst et al., 2016;Li et al., 2016).The baseline characteristics of the 300DM and a cohort of healthy individuals (500FG) are shown in Supplementary file 1B.Their median age was 53.5 years (range 20-85), and they had a median diabetes duration of 28 years (range 1-71 years).Hence, the cohort generally consisted of middle-aged people with long-standing T1D.We measured 72 types of immune cells covering both lymphocytes and monocyte lineages and 10/6 (300DM/500FG) different cytokines released in response to stimulation with four types of human pathogens in both cohorts (Figure 1A).Background: The large inter-individual variability in immune-cell composition and function determines immune responses in general and susceptibility o immune-mediated diseases in particular.While much has been learned about the genetic variants relevant for type 1 diabetes (T1D), the pathophysiological mechanisms through which these variations exert their effects remain unknown.Methods: Blood samples were collected from 243 patients with T1D of Dutch descent.We applied genetic association analysis on >200 immune-cell traits and >100 cytokine production profiles in response to stimuli measured to identify genetic determinants of immune function, and compared the results obtained in T1D to healthy controls.Results: Genetic variants that determine susceptibility to T1D significantly affect T cell composition.Specifically, the CCR5+ regulatory T cells associate with T1D through the CCR region, suggesting a shared genetic regulation.Genome-wide quantitative trait loci (QTLs) mapping analysis of immune traits revealed 15 genetic loci that influence immune responses in T1D, including 12 that have never been reported in healthy population studies, implying a disease-specific genetic regulation.Conclusions: This study provides new insights into the genetic factors that affect immunological responses in T1D.Background: The large inter-individual variability in immune-cell composition and function determines immune responses in general and susceptibility o immune-mediated diseases in particular.While much has been learned about the genetic variants relevant for type 1 diabetes (T1D), the pathophysiological mechanisms through which these variations exert their effects remain unknown.Methods: Blood samples were collected from 243 patients with T1D of Dutch descent.We applied genetic association analysis on >200 immune-cell traits and >100 cytokine production profiles in response to stimuli measured to identify genetic determinants of immune function, and compared the results obtained in T1D to healthy controls.Results: Genetic variants that determine susceptibility to T1D significantly affect T cell composition.Specifically, the CCR5+ regulatory T cells associate with T1D through the CCR region, suggesting a shared genetic regulation.Genome-wide quantitative trait loci (QTLs) mapping analysis of immune traits revealed 15 genetic loci that influence immune responses in T1D, including 12 that have never been reported in healthy population studies, implying a disease-specific genetic regulation.Conclusions: This study provides new insights into the genetic factors that affect immunological responses in T1D.", + "Type 2 diabetes is characterized by the failure of the -cells to compensate for peripheral insulin resistance (6).Within the last decade, an increasing body of evidence has accumulated in favor of a putative role of immuno-related mechanisms and factors in the pathogenesis of type 2 diabetes, both with regard to the progressive -cell failure and destruction and to the peripheral insulin resistance (2,3).", + "T1DM pathogenesis involves innate and adaptive immune activity (13) coupled with failures in central and peripheral tolerance mechanisms that enable expansion of disease-mediating autoreactive T cells (14).Other immune cells are also involved, including B cells, as evidenced by the development of autoantibodies that precede clinical onset in almost all patients (15).Chemokines and cytokines are involved in T1DM pathogenesis by influencing immune activity, impairing -cell function, and inducing -cell death (16,17).", + "If the pathogenesis of diabetes begins in very early life (perhaps even prenatally), then the immune status of the mother during pregnancy could be as relevant as the immune status of her diabetes-at-risk offspring.If so, then elucidating the genetic basis of Type I diabetes will also require analysis of maternal genotype and maternal-fetal genotype interactions.Very few studies of this nature have been conducted.Furthermore, if viral infection is involved in the initiation of the autoimmune process, then genetic differences between individuals in immune response towards viruses could alter their predisposition to Type I diabetes.", + "Figure 1-Genetic and environmental risk factors impact inflammation, autoimmunity, and metabolic stress.These states affect b-cell mass and/or function such that insulin levels are eventually unable to respond sufficiently to insulin demands, leading to hyperglycemia levels sufficient to diagnose diabetes.In some cases, genetic and environmental risk factors and gene-environment interactions can directly impact b-cell mass and/or function.Regardless of the pathophysiology of diabetes, chronic high blood glucose levels are associated with microvascular and macrovascular complications that increase morbidity and mortality for people with diabetes.This model positions b-cell destruction and/or dysfunction as the necessary common factor to all forms of diabetes.Among the environmental associations linked to type 1 diabetes are enteroviral and other infections (51,52) and altered intestinal microbiome composition (53).The timing of exposure to foods including cereal (54) and nutrients such as gluten ( 55) may influence b-cell autoimmunity.Low serum concentrations of vitamin D have been linked to type 1 diabetes.Perinatal risk factors and toxic doses of nitrosamine compounds have been implicated in the genesis of diabetes.", + "In type 1 diabetes, the autoimmune destruction of cells by the cellular and humoral immune system in the pancreatic islets of Langerhans leads to impaired insulin secretion and subsequently to hyperglycemia.This type of diabetes is characterized by the appearance of antigen-specific T cells and antibodies in peripheral blood which are directed against a variety of -cell antigens including glutamic acid decarboxylase, tyrosine phosphatase IA-2, a zinc transporter and insulin.The onset of type 1 diabetes frequently occurs before 20 years of age, but disease manifestation is also common in adult patients.Exogenous administration of insulin is necessary to maintain glucose homeostasis and to prevent early and late diabetic complications [32,36].In type 2 diabetes, comprising approximately 90% of the cases of diabetes mellitus, hyperglycemia is the consequence of a relative insulin deficiency and insulin resistance of various tissues including muscle and adipose tissue.While in early type 2 diabetes, insulin resistance and the resulting increased metabolic demand may be overcome by increased pancreatic insulin secretion, failure of cells to maintain adequate insulin production and a decrease in -cell mass are common in progressive disease, resulting in chronic hyperglycemia and loss of metabolic control [33,37,38].Hyperinsulinemia is associated with down-regulation of insulin receptors, thus further contributing to the exhaustion of insulin production in cells [39].Overweight and obesity are significant risk factors for type 2 diabetes, which is increasing as a consequence of the Western lifestyle.Hence, diabetes is expected to become be an even greater health problem in the future deserving further attention [33,37].", + "Brief Genetics ReportT ype 1 diabetes results from an immune-mediated destruction of insulin-producing -cells in the pancreatic islets of Langerhans.The activation of autoreactive lymphocytes and the cytokineinduced apoptosis of pancreatic -cells play a major role in the etiology of type 1 diabetes.1,25-Dihydroxyvitamin D 3 [ 1 , 2 5 ( O H ) 2 D 3 ] inhibits lymphocyte activation and affects other elements of the immune system, such as cytokine and immunoglobulin production, as well as major histocompatibility complex (MHC) class II and cluster of differentiation (CD)-4 expression (1).In NOD mice, the development of diabetes can be prevented by administration of 1,25(OH) 2 D 3 ( 2 ) , which inhibits lymphocyte activation and restores the altered ratio of CD4/CD8 cells.", + "Type 1 diabetes is an autoimmune disorder afflicting millions of people worldwide.Once diagnosed, patients require lifelong insulin treatment and can experience numerous disease-associated complications.The last decade has seen tremendous advances in elucidating the causes and treatment of the disease based on extensive research both in rodent models of spontaneous diabetes and in humans.Integrating these advances has led to the recognition that the balance between regulatory and effector T cells determines disease risk, timing of disease activation, and disease tempo.Here we describe current progress, the challenges ahead and the new interventions that are being tested to address the unmet need for preventative or curative therapies.", + "The immune system of some genetically susceptible children can be triggered by certain environmental factors to produce islet autoantibodies (IA) against pancreatic cells, which greatly increases their risk for Type-1 diabetes.An environmental factor under active investigation is the gut microbiome due to its important role in immune system education.", + "At clinical onset (stage 3), celltargeted auto immunity is likely to have occurred for a prolonged period, as indicated by the presence of CD4 + and CD8 + T cells, dendritic cells, macrophages and B cells in and around the islets of Langerhans in many, but not all, patients with newly diagnosed T1DM 2,104 .These data are based on observations from samples obtained at disease onset by fineneedle biopsy 105 or by highrisk minimal pancreatic tail resection 106 , and they have con firmed previous data from pancreatic tissue samples from individ uals who have succumbed to diabetic keto acidosis (that is, acidosis due to the breakdown of lipids to ketones as an alternative source of glucose) 2,107,108 .In this setting, the inflammatory lesion does not affect all islets, and the insulitis process is patchy.Importantly, the volume or mass of islet cells producing gluca gon, somato statin or pancreatic polypeptide remains unaffected at the clinical onset of T1DM 2,104 .At present, there is no explan ation of why the cells and not the cells that produce glucagon, somatostatin or pancreatic polypeptide are attacked by the immune system.Separate auto antibodies that target human pancreatic cells prod ucing glucagon and those that produce somatostatin have been found in some patients, but further studies of these potentially unique patients are needed 109 ." + ], + [ + "In 2008, to increase the power of identifying variants with modest effects, a meta-analysis of three GWAS, including Diabetes Genetics Initiative (DGI), Finland-United States Investigation of NIDDM Genetics (FUSION), and Wellcome Trust Case Control Consortium (WTCCC), were conducted.This study detected at least six previously unknown loci that reached genome-wide significance for association with T2D ( < 5 10 8 ), with the loci being JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2 [19].Genetic variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, and THADA have been reported to affect pancreatic -cell functions [59,60].", + ", for the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 9Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D) [1][2][3][4][5][6][7][8][9][10][11] .Established associations to common and rare variants explain only a small proportion of the heritability of T2D.As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and B2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975.We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P 5.0 10 -14 ), CDC123-CAMK1D (P 1.2 10 -10 ), TSPAN8-LGR5 (P 1.1 10 -9 ), THADA (P 1.1 10 -9 ), ADAMTS9 (P 1.2 10 -8 ) and NOTCH2 (P 4.1 10 -8 ) gene regions.Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.", + "Results from genome-wide association studies (GWAS) of type 1 diabetes (T1D) (Barrett et al., 2009), T2D (reviewed in Prokopenko et al., 2008), and related metabolic traits (Dupuis et al., 2010;Ingelsson et al., 2010;Prokopenko et al., 2009) suggest that genetic variation in cis-regulatory elements may play an important role in b cell (dys)function and diabetes susceptibility (De Silva and Frayling, 2010).Of the 18 most strongly associated single-nucleotide polymorphisms (SNPs) in each of the T2D-associated loci, only 3 are missense variants; the remaining are noncoding (Prokopenko et al., 2008).Furthermore, there is evidence for allele-specific effects of two T2Dassociated SNPs on the islet expression level of nearby genes (TCF7L2 [Lyssenko et al., 2007] and MTNR1B [Lyssenko et al., 2009]).However, the dearth of annotation of functional regulatory elements has limited the capacity to investigate the role of regulatory variation in complex diseases such as T2D.", + "Genetic studies of type diabetes (TD) have identified 50 susceptibility regions ,2 , finding major pathways contributing to risk 3 , with some loci shared across immune disorders 4-6 .To make genetic comparisons across autoimmune disorders as informative as possible, a dense genotyping array, the Immunochip, was developed, from which we identified four new TD-associated regions (P < 5 0 8 ).A comparative analysis with 5 immune diseases showed that TD is more similar genetically to other autoantibody-positive diseases, significantly most similar to juvenile idiopathic arthritis and significantly least similar to ulcerative colitis, and provided support for three additional new TD risk loci.Using a Bayesian approach, we defined credible sets for the TD-associated SNPs.The associated SNPs localized to enhancer sequences active in thymus, T and B cells, and CD34 + stem cells.Enhancer-promoter interactions can now be analyzed in these cell types to identify which particular genes and regulatory sequences are causal.T1D results from the autoimmune destruction of pancreatic cells, leading to absolute dependence on exogenous insulin to regulate blood glucose levels 7 .In the present study, we designed and used the Immunochip, a custom Illumina Infinium high-density genotyping array, to (i) identify additional risk loci for T1D, (ii) refine mapping of T1D risk loci to their sets of most associated credible SNPs in order to (iii) analyze the locations of the credible SNPs with respect to regulatory sequences in tissues and cell types, and (iv) assemble summary genome-wide association study (GWAS) and Immunochip results from multiple immune diseases to allow comparisons of the genetic risk profiles of these diseases.The T1D SNP and indel content selected for inclusion on the Immunochip was chosen on the basis of the 41 T1D-associated regions known at the time (February 2010) 1 and 3,000 'wildcard' SNPs that tagged candidate genes or other SNPs with suggestive evidence of association (5 10 8 < P < 1 10 5 ) from GWAS of T1D.In parallel, we collected and curated all available association results for immune diseases for which the Immunochip was designed.To allow efficient comparison and downstream analysis by the research community, we created a publicly available, integrated, web-based portal (ImmunoBase; see URLs) containing complete association summary statistics that are available for querying, browsing or bulk download.", + "Impact of T1D GWAS SNPs on immune phenotypes in T1D patientsConsidering that T1D is a multifactorial disease with a genetic component, we tested whether the known risk variants of T1D affect immune phenotypes and function.We first checked SNPs within the HLA locus in our association studies on cell proportion and cytokine production level.Consistent with our previous findings in 500FG, we did not observe any significant associations of HLA allelic variants in 300DM.We then acquired non-HLA genetic loci from published GWAS of European background were acquired from the GWAS-catalog (November 2019) (Buniello et al., 2019).Among these, genetic variants in 63 independent T1D loci were present in our data, and we found that 13 of these 63 were indeed associated with susceptibility to T1D with nominal significance (p-value < 0.05) (Supplementary file 1C).Figure 2. Impact of type 1 diabetes (T1D) genome-wide association studies (GWAS) single-nucleotide polymorphisms (SNPs) on immune phenotypes. (A) Quantile-quantile (Q-Q) plots of quantitative trait locus (QTL) profiles of 62 T1D GWAS loci grouped by cell populations.The distribution of p-values of associations with T cells traits (blue) shows a significant deviation from an expected uniform distribution (dashed line). (B) Histogram showing number of associations observed (red line) and those in permutations (blue bars). (C) Heatmap of QTL profiles of cell proportion carrying certain chemokine receptors across 62 T1D GWAS loci, colored by log10(p-values) and effect direction of the T1D risk allele.Arrowhead indicates a T1D risk allele rs11574435-T.The online version of this article includes the following figure supplement(s) for figure 2: Figure supplement 1. Qqplots of QTL profiles of 62 T1D GWAS loci grouped by cytokine types.We next investigated whether these genetic risk loci for T1D affect immune parameters and function.The quantile-quantile plot of the association of the 63 T1D GWAS loci with different cell types and cytokines illustrates an inflated deviation from an expected uniform distribution (Figure 2A, Figure 2-figure supplement 1).We further tested whether this deviation can be explained by chance by comparing the association of immune traits with T1D GWAS SNPs with that of 1000 randomly selected independent SNPs (Figure 2B, Materials and methods).The p-value shows that the T1D GWAS SNPs are enriched in association with T cell traits in the T1D cohort (p-value = 0.007).", + "Table 1Polymorphisms in the human genome associated with type 1 diabetes (Adapted from (Ram et al., 2016b)).The genetic polymorphism data (i.e.SNPs) has been associated with T1D using genome-wide association studies and meta-analyses (references as noted).SNP, single nucleotide polymorphism.", + "Recent large genome-wide association studies (GWAS) have identified multiple loci which harbor genetic variants associated with type 2 diabetes mellitus (T2D), many of which encode proteins not previously suspected to be involved in the pathogenesis of T2D.Most GWAS for T2D have focused on populations of European descent, and GWAS conducted in other populations with different ancestry offer a unique opportunity to study the genetic architecture of T2D.We performed genome-wide association scans for T2D in 3,955 Chinese (2,010 cases, 1,945 controls), 2,034 Malays (794 cases, 1,240 controls), and 2,146 Asian Indians (977 cases, 1,169 controls).In addition to the search for novel variants implicated in T2D, these multi-ethnic cohorts serve to assess the transferability and relevance of the previous findings from European descent populations in the three major ethnic populations of Asia, comprising half of the world's population.Of the SNPs associated with T2D in previous GWAS, only variants at CDKAL1 and HHEX/IDE/KIF11 showed the strongest association with T2D in the meta-analysis including all three ethnic groups.However, consistent direction of effect was observed for many of the other SNPs in our study and in those carried out in European populations.Close examination of the associations at both the CDKAL1 and HHEX/IDE/KIF11 loci provided some evidence of locus and allelic heterogeneity in relation to the associations with T2D.We also detected variation in linkage disequilibrium between populations for most of these loci that have been previously identified.These factors, combined with limited statistical power, may contribute to the failure to detect associations across populations of diverse ethnicity.These findings highlight the value of surveying across diverse racial/ethnic groups towards the fine-mapping efforts for the casual variants and also of the search for variants, which may be population-specific.Recent large genome-wide association studies (GWAS) have identified multiple loci which harbor genetic variants associated with type 2 diabetes mellitus (T2D), many of which encode proteins not previously suspected to be involved in the pathogenesis of T2D.Most GWAS for T2D have focused on populations of European descent, and GWAS conducted in other populations with different ancestry offer a unique opportunity to study the genetic architecture of T2D.We performed genome-wide association scans for T2D in 3,955 Chinese (2,010 cases, 1,945 controls), 2,034 Malays (794 cases, 1,240 controls), and 2,146 Asian Indians (977 cases, 1,169 controls).In addition to the search for novel variants implicated in T2D, these multi-ethnic cohorts serve to assess the transferability and relevance of the previous findings from European descent populations in the three major ethnic populations of Asia, comprising half of the world's population.Of the SNPs associated with T2D in previous GWAS, only variants at CDKAL1 and HHEX/IDE/KIF11 showed the strongest association with T2D in the meta-analysis including all three ethnic groups.However, consistent direction of effect was observed for many of the other SNPs in our study and in those carried out in European populations.Close examination of the associations at both the CDKAL1 and HHEX/IDE/KIF11 loci provided some evidence of locus and allelic heterogeneity in relation to the associations with T2D.We also detected variation in linkage disequilibrium between populations for most of these loci that have been previously identified.These factors, combined with limited statistical power, may contribute to the failure to detect associations across populations of diverse ethnicity.These findings highlight the value of surveying across diverse racial/ethnic groups towards the fine-mapping efforts for the casual variants and also of the search for variants, which may be population-specific.", + "The T1DGC, using the same samples as in the MHC and candidate gene investigations, reevaluated 382 SNPs from 21 recently reported candidate genes, assembling nearly 4,000 ASP families and fully characterizing (through tagging SNPs and reported variants) the genetic contributions to type 1 diabetes risk.These results suggest that, aside from the MHC, 11p15 (INS), 2q33 (CTLA and other genes), 10p15.1 (IL2RA), and 1p13 (PTPN22), few of these published candidate genes can be replicated.In addition, a total of 1,715 SNPs were selected from the Wellcome Trust Case Control Consortium (WTCCC) GWA study of type 1 diabetes, and 581 SNPs were selected that exhibited association with autoimmune disease and type 2 diabetes loci (45,46).These studies confirmed established loci (above) (47,48) and suggested additional risk conferred by loci on chromosomes 5q31 (TCF7 [P19T], transcription factor 7, T-cell specific, HMG-box), 18q12 (FHOD3, formin homology two domain containing 3), and Xp22 (TLR8/ TLR7 toll-like receptor 8/toll-like receptor 7).Type 1 diabetes has many susceptibility loci and therefore pathways in common with autoimmune diseases.With the recent exception of GLIS3 (49), no genetic overlap was found between type 1 diabetes and type 2 diabetes loci (45,46,50).The dataset established by the T1DGC from its Candidate Gene Workshops is available from the NIDDK Central Repository.Genome-wide linkage.A number of genome-wide scans for linkage to type 1 diabetes have been reported (4,(51)(52)(53)(54)(55).All these studies consistently demonstrated linkage of type 1 diabetes to the MHC and specifically to the HLA genes on human chromosome 6p21.3.Additional regions with evidence of linkage have been identified, but many of these regions have not been reproduced in independent studies.", + "The latest and largest meta-analyses for T1D [4] and T1D diagnosis age [9] have been performed with variants from the ImmunoChip, a large scale but targeted genotyping platform which covers only loci previously associated with immunological diseases.We now took a genome-wide approach by performing a large genome-wide association study (GWAS) meta-analysis in 12,539 individuals with T1D from the Finnish Diabetic Nephropathy (Finn-Diane) Study, the UK Genetic Resource Investigating Diabetes (UK GRID), and Sardinia cohorts.Our aim was to identify variants affecting T1D diagnosis age and thereafter, utilizing the genome-wide coverage of our analysis, we aimed to link the variants to open chromatin indicating active gene expression in different cell types and finally, we performed transcriptome-wide association analyses in disease-relevant tissues.", + "Genome-wide association studies (GWAS) have identified >100 independent SNPs that modulate the risk of type 2 diabetes (T2D) and related traits.However, the pathogenic mechanisms of most of these SNPs remain elusive.Here, we examined genomic, epigenomic, and transcriptomic profiles in human pancreatic islets to understand the links between genetic variation, chromatin landscape, and gene expression in the context of T2D.We first integrated genome and transcriptome variation across 112 islet samples to produce dense cis-expression quantitative trait loci (cis-eQTL) maps.Additional integration with chromatin-state maps for islets and other diverse tissue types revealed that cis-eQTLs for islet-specific genes are specifically and significantly enriched in islet stretch enhancers.High-resolution chromatin accessibility profiling using assay for transposase-accessible chromatin sequencing (ATACseq) in two islet samples enabled us to identify specific transcription factor (TF) footprints embedded in active regulatory elements, which are highly enriched for islet cis-eQTL.Aggregate allelic bias signatures in TF footprints enabled us de novo to reconstruct TF binding affinities genetically, which support the high-quality nature of the TF footprint predictions.Interestingly, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprints.Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions.Together, these results suggest that common regulatory variations have shaped islet TF footprints and the transcriptome and that a confluent RFX regulatory grammar plays a significant role in the genetic component of T2D predisposition.", + "Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge.Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose-tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs.We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls.We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the region of CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk.This brings the number of T2D loci now confidently identified to at least 10.", + "A Genome-Wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants Laura J. Scott, 1 Karen L. Mohlke, 2 Lori L. Bonnycastle, 3 Cristen J. Willer, 1 Yun Li, 1 William L. Duren, 1 Michael R. Erdos, 3 Heather M. Stringham, 1 Peter S. Chines, 3 Anne U. Jackson, 1 Ludmila Prokunina-Olsson, 3 Chia-Jen Ding, 1 Amy J. Swift, 3 Narisu Narisu, 3 Tianle Hu, 1 Randall Pruim, 4 Rui Xiao, 1 Xiao-Yi Li, 1 Karen N. Conneely, 1 Nancy L. Riebow, 3 Andrew G. Sprau, 3 Maurine Tong, 3 Peggy P. White, 1 Kurt N. Hetrick, 5 Michael W. Barnhart, 5 Craig W. Bark, 5 Janet L. Goldstein, 5 Lee Watkins, 5 Fang Xiang, 1 Jouko Saramies, 6 Thomas A. Buchanan, 7 Richard M. Watanabe, 8,9 Timo T. Valle, 10 Leena Kinnunen, 10,11 Gonalo R. Abecasis, 1 Elizabeth W. Pugh, 5 Kimberly F. Doheny, 5 Richard N. Bergman, 9 Jaakko Tuomilehto, 10,11,12 Francis S. Collins, 3 * Michael Boehnke 1 * Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge.Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs.We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls.We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the region of CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk.This brings the number of T2D loci now confidently identified to at least 10.", + "A Genome-Wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants Laura J. Scott, 1 Karen L. Mohlke, 2 Lori L. Bonnycastle, 3 Cristen J. Willer, 1 Yun Li, 1 William L. Duren, 1 Michael R. Erdos, 3 Heather M. Stringham, 1 Peter S. Chines, 3 Anne U. Jackson, 1 Ludmila Prokunina-Olsson, 3 Chia-Jen Ding, 1 Amy J. Swift, 3 Narisu Narisu, 3 Tianle Hu, 1 Randall Pruim, 4 Rui Xiao, 1 Xiao-Yi Li, 1 Karen N. Conneely, 1 Nancy L. Riebow, 3 Andrew G. Sprau, 3 Maurine Tong, 3 Peggy P. White, 1 Kurt N. Hetrick, 5 Michael W. Barnhart, 5 Craig W. Bark, 5 Janet L. Goldstein, 5 Lee Watkins, 5 Fang Xiang, 1 Jouko Saramies, 6 Thomas A. Buchanan, 7 Richard M. Watanabe, 8,9 Timo T. Valle, 10 Leena Kinnunen, 10,11 Gonalo R. Abecasis, 1 Elizabeth W. Pugh, 5 Kimberly F. Doheny, 5 Richard N. Bergman, 9 Jaakko Tuomilehto, 10,11,12 Francis S. Collins, 3 * Michael Boehnke 1 * Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge.Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs.We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls.We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the region of CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk.This brings the number of T2D loci now confidently identified to at least 10.", + "GWAS-Identified Variants in Protein-Coding RegionsGWAS-identified variants associated with T2D risk include single nucleotide polymorphisms (SNP), deletions, insertions and short sequence repeats (6,92).Although the majority of the variants reside in intergenic or intragenic regions, a few (less than 5%) are in protein-coding regions.As potential drug targets, these variant-containing genes have been subjected to investigation in b cells in recent years (5) using cellular and mouse knockout systems, as described in the examples below:", + "A systematic search for the variants associatedwith Type 2 diabetes mellitus, a common complex disease was recently done317318N. Shahby testing 392,935 single-nucleotide polymorphisms in a French casecontrol cohort (13). They used Illumina Infinium Human1 BeadArrays, whichassay 109,365 SNPs chosen using a gene-centred design; and Human Hap300BeadArrays, which assay 317,503 SNPs chosen to tag haplotype blocks identified by the Phase I HapMap. There were 59 SNPs, showing significant association with the disease in genome-wide study, which were tested on a largercohort using the Sequenom iPlex assay.They identified four SNPs containingvariants that confer type 2 diabetes risk. These loci include a nonsynonymouspolymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing -cells, and two linkage disequilibrium blocksthat contain genes potentially involved in -cell development or function(IDEKIF11HHEX and EXT2ALX4). Even when genome-wide studies are possible, there are statistical difficulties arising due to multiple hypotheses testing. A good review of this issue andpossible solutions are presented in (14). 3.2.3. Pool-Based Genome-Wide Association StudiesGenotyping of individual samples for genome-wide association (GWA) studies may be cost-prohibitive.", + "Association of genetic variants in genes encoding T2D and obesity drug targetsThe study design consisted of initial discovery of variants with suggestive associations to targeted genotyping and in silico follow-up analyses (Fig. 1).We investigated the association of 121 variants in six genes encoding therapeutic targets in use or in development for T2D or obesity (CNR2, DPP4, GLP1R, SLC5A1, HTR2C, and MCHR1)-drawn from a recent targeted exome sequencing study of 202 genes encoding drug targets (8)-with variation in the following traits: T2D, obesity, body mass index (BMI), waist circumference, fasting glucose, fasting insulin, and 2-hour glucose (Fig. 1).In the \"discovery analysis,\" we identified seven variants potentially associated with T2D-or obesity-related traits (where P < 0.001 or which were in a target of interest to GSK and P < 0.05) (Table 1).For these seven variants, \"follow-up analysis\" was performed by targeted genotyping in up to 39,979 additional individuals of European ancestry.Where possible, in silico follow-up analysis was performed for traits and variants available in large-scale genetic consortia data." + ], + [ + "Elucidate the pathogenesis linking obesity and type 2 diabetesA better understanding of mechanisms linking obesity, insulin resistance, and type 2 diabetes may ultimately facilitate more individualized treatment.One future research priority is to clarifty how identified gene variants affect glucose, fatty acid, and energy metabolism at both cellular and whole-body levels.Rather than searching for a single factor or theory explaining the predisposition to -cell decompensation in obese individuals, a multifactorial, synergistic explanation seems more compatible with current knowledge.Multiple mechanisms may link -cell dysfunction to systemic insulin resistance, including differing cellular responses to nutrient excess and impaired brain neurocircuits governing energy homeostasis.One way to approach this complex pathophysiology is to examine glucose-tolerant obese patients and study the association with and progression to -cell decompensation.", + "The framework described in this paper is aimed to address two key questions: (1) Can biological processes be identified that are consistently deregulated in different models of insulin resistance and diabetes and that may be manifested in a tissue-dependent or independent manner? (2) On a higher level, can tissue or condition-specific interaction networks be identified that more precisely characterize different insulinresistance models and suggest causal mechanisms?Author SummaryType 2 diabetes mellitus currently affects millions of people.It is clinically characterized by insulin resistance in addition to an impaired glucose response and associated with numerous complications including heart disease, stroke, neuropathy, and kidney failure, among others.Accurate identification of the underlying molecular mechanisms of the disease or its complications is an important research problem that could lead to novel diagnostics and therapy.The main challenge stems from the fact that insulin resistance is a complex disorder and affects a multitude of biological processes, metabolic networks, and signaling pathways.In this report, the authors develop a network-based methodology that appears to be more sensitive than previous approaches in detecting deregulated molecular processes in a disease state.The methodology revealed that both insulin signaling and nuclear receptor networks are consistently and differentially expressed in many models of insulin resistance.The positive results suggest such network-based diagnostic technologies hold promise as potentially useful clinical and research tools in the future.affected in the disease state. (3) Evaluate the hypothesis that genes in a given gene set are observed in a higher proportion (i.e., enriched) than expected by chance in the HSN and repeat for each gene set in the assembly.Repeat (2) and (3) for every insulin resistant or diabetic condition compared to normal in the dataset. (4) Order the gene sets of interest based on the number of different HSNs where they appear enriched. (5) For each gene set, assign a p-value to the number of conditions where it is enriched.The gene sets with a significant p-value are taken as transcriptionally affected across a broad set of diabetes-related models.Consistent with the stated goal of GNEA, gene sets enriched in a few conditions, while potentially interesting in their own right, will not generally be assigned a significant p-value (Figure 1).", + "of Biochemistry, Biostatistics & Medical Informatics, University ofWisconsin, Madison, WI; Rosetta Inpharmatics, Seattle, WA; KineMed,Emeryville, CA; Dept Nutritional Sciences & Toxicology, University ofCalifornia, Berkeley, CA, USAInsulin resistance is necessary but not sufficient for the development of type 2diabetes. Diabetes results when pancreatic -cells fail to compensate for insulinresistance by increasing insulin production through an expansion of -cell massor increased insulin secretion. Communication between insulin target tissues and-cells may initiate this compensatory response. Correlated changes in geneexpression between tissues can provide evidence for such intercellularcommunication.", + "The origin of chronic inflammatory processes observed in metabolic disorders is still a matter of debate. 9The recent obesity epidemic is a driving force for the worldwide increasing incidence of type 2 diabetes (T2D) as more than 80% of patients with T2D are overweight.Obesity-induced insulin resistance is the dominant underlying pathophysiological factor. 10As insulin resistance and metabolic inflammation are frequently observed in parallel, research in the past decade has tried to connect these two phenomena.It is widely accepted that the aetiology of insulin resistance is complex and involves various pathways. 11It is, however, also increasingly established that inflammatory pathways are critically involved in the evolution of insulin resistance. 12Overnutrition and certain diets could represent major starting points as they might alter the gut microbiota, lead to changes in lipid metabolism, hepatic steatosis and finally systemic inflammation. 13 14It remains, however, unclear at which sites inflammatory processes are initiated and the GI tract with its significantly altered microbiota could reflect one of the early events in these disorders.", + "Type 2 diabetes mellitus (T2D) is a common complex disease whose pathogenic mechanisms are known to a considerable extent [8,9].Several organs including pancreatic islets, liver, skeletal muscle, adipose tissues, gut, hypothalamus and the immune system play a role in its pathogenesis [10].Numerous multifactorial mechanisms that include genetic and environmental factors related to obesity are involved in the development of insulin resistance and impaired insulin secretion [8,9].Insulin resistance is associated with inactivity, obesity and ageing [8].The insulin secreting pancreatic islet b cells respond to insulin resistance by enhancing their mass and metabolic function.T2D however develops when increase in insulin secretion by b cells is not able to keep pace with the increase in insulin resistance [8,11].The latter thus characterizes both prediabetic condition and T2D.Prediabetic insulin resistance state however does not always lead to diabetes; enhanced secretion of insulin by b cells compensates for deficient insulin action in a considerable proportion of prediabetic individuals who do not develop T2D.Though the inability of b cells to secrete enough insulin primarily typifies T2D, the dysfunction can also be demonstrated in normoglycemic subjects [12].Therefore, derangements in both insulin secretion and Figure 1.Schematic representation of the workflow.T2D GWAS genes do not directly relate (indicated by 'X' on the left side) to pathways associated with disease pathophysiology.Conspicuously, effect of identified risk variants on continuous glycemic measures in nondiabetic subjects chiefly explains only perturbation of insulin secretion, not insulin resistance.Further, the genes found as associated with the disease do not clearly relate to processes and pathways consistent with the known aspects of T2D pathophysiology.The main aim of the present study was to ask the question (indicated by '?' on the right side) if GWAS data when considered in conjunction with interactome, toxicogenome and disease transcriptome data reveal genome to phenome correlation in T2D.Data available in public domain for GWAS, interactome and toxicogenome was used in the analysis.For disease transcriptome, new experimental data was generated.We specifically examined if interaction network of genes reported in T2D GWAS, genes showing altered expression after treatment with various antidiabetic drugs, and genes that are differentially expressed in insulin responsive tissues in male and female T2D patients do converge on insulin secretion, insulin resistance and other T2D associated pathophysiological pathways.doi:10.1371/journal.pone.0053522.g001", + "This underlying -cell decompensation manifests clinically as elevated fasting andPREVpostprandial blood glucose levels, diagnostic criteria for diabetes [4,5]. In humans, diabetes is often correlated with obesity, leading to a long-standinghypothesis that insulin resistance is a consequence of overnutrition and elevated dietaryfatty acids [6]. Chronic metabolic overload has a detrimental effect on whole bodymetabolism, and there is increasing evidence that the liver and adipose play a causalrole to drive this metabolic disequilibrium (Figure 1).", + "Increasing evidence from more recent studies also suggested that infl ammatory processes may have a pivotal role in metabolic diseases: prospective studies have shown that high plasma interleukin 6 (IL -6) levels increased T2DM risk [116] , but confl icting associations were found between a promoter polymorphism (G -174C) in IL6 and T2DM [117,118] .In a large joint analysis of 21 case -control studies, representing > 20 000 participants in one of the largest association studies addressing the role of a candidate gene in T2DM susceptibility, the IL6 promoter variant was found to be associated with a lower risk (OR 0.91, P = 0.037) [119] .In addition, association between T2DM and IL6R -D358A was reported in Danish white people [120] , and with TNF G -308A promoter SNP in the Finnish Diabetes Prevention Study [118] .The effects of both IL6 and IL6R variants on developing T2DM risk in interaction with age have been reported in a prospective study of a general French population [46] .", + "In the long term, these new approaches should identify additional genes and metabolic markers; profi les obtained through these assessments could provide the level of detail needed to establish the mediator (or mediators) of the feedback loop that interconnects cells with insulin-sensitive tissues, and help to unravel the heterogeneity of the disease.Furthermore, these assessments should complement and advance present understanding of the best approaches to treat the dysregulated metabolic milieu in type 2 diabetes, which includes not only glucose but also fatty acids and aminoacids.Glucose metabolism is normally regulated by a feedback loop including islet cells and insulin-sensitive tissues, in which tissue sensitivity to insulin aff ects magnitude of -cell response.If insulin resistance is present, cells maintain normal glucose tolerance by increasing insulin output.Only when cells cannot release suffi cient insulin in the presence of insulin resistance do glucose concentrations rise.Although -cell dysfunction has a clear genetic component, environmental changes play an essential part.Modern research approaches have helped to establish the important role that hexoses, aminoacids, and fatty acids have in insulin resistance and -cell dysfunction, and the potential role of changes in the microbiome.Several new approaches for treatment have been developed, but more eff ective therapies to slow progressive loss of -cell function are needed.Recent fi ndings from clinical trials provide important information about methods to prevent and treat type 2 diabetes and some of the adverse eff ects of these interventions.However, additional long-term studies of drugs and bariatric surgery are needed to identify new ways to prevent and treat type 2 diabetes and thereby reduce the harmful eff ects of this disease. The epidemic of type 2 diabetesThe worldwide explosion of obesity has resulted in an ever-increasing prevalence of type 2 diabetes-a noncommunicable disease that aff ects more than 370 million people. 1 Without concerted eff orts to address the pathogenesis and treatment of this syndrome, the harmful macrovascular and microvascular outcomes of type 2 diabetes will remain a major burden for decades to come.In this Review we examine aspects of the pathogenesis and treatment of type 2 diabetes, and discuss future needs if the most damaging result of obesity is to be reversed.Glucose metabolism is normally regulated by a feedback loop including islet cells and insulin-sensitive tissues, in which tissue sensitivity to insulin aff ects magnitude of -cell response.If insulin resistance is present, cells maintain normal glucose tolerance by increasing insulin output.Only when cells cannot release suffi cient insulin in the presence of insulin resistance do glucose concentrations rise.Although -cell dysfunction has a clear genetic component, environmental changes play an essential part.Modern research approaches have helped to establish the important role that hexoses, aminoacids, and fatty acids have in insulin resistance and -cell dysfunction, and the potential role of changes in the microbiome.Several new approaches for treatment have been developed, but more eff ective therapies to slow progressive loss of -cell function are needed.Recent fi ndings from clinical trials provide important information about methods to prevent and treat type 2 diabetes and some of the adverse eff ects of these interventions.However, additional long-term studies of drugs and bariatric surgery are needed to identify new ways to prevent and treat type 2 diabetes and thereby reduce the harmful eff ects of this disease.", + "MetabolomicsA Metabolomics approach has been applied to diabetes in several population-based studies in recent years, summarized in [68].Metabolomics profiling was previously performed typically in a small subset of large populations, and the number of metabolites was limited.In recent studies MR analysis has been combined in metabolomics in order to claim causality of the metabolites found to be associated with the risk of diabetes.Nowak and collaborators investigated the effects of insulin resistance and insulin secretion on fatty acid levels [69].The original cohort included 910 elderly men (ULSAM cohort).Insulin sensitivity was determined with gold standard measurement, the hyperinsulinemic euglycemic clamp, and beta-cell function with a Disposition Index during an oral glucose tolerance test.A total of 192 metabolites were measured using untargeted plasma metabolomics by liquid chromatography/mass spectrometry.MR analysis was based on two separate cohorts (PIVUS and TwinGene, n 2,613) followed by replication in three independent studies profiled on different metabolomics platforms (KORA/TwinsUK, n 7,824; CHARGE consortium, n 8,961; and Finnish consortium, n 8,330).In the observational part of the study the authors reported that bile acid, glycerophospholipid and caffeine metabolism were associated with insulin resistance, and fatty acids biosynthesis markers with impaired insulin secretion.In MR analysis the authors discovered and replicated causal effects of insulin resistance on lower levels of monosaturated fatty acids, palmitoleic acid and oleic acid.Beta-cell function did not have causal effects on any metabolites measured.The limitation of this study is a relatively small size of the ULSAM cohort, and the limited number of metabolites measured.", + "Our understanding of the pathophysiology of T2DM has been aided by the discovery of novel disease biomarkers.High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6) and tumour necrosis factor (TNF), are associated with an increased risk of T2DM 30 , whereas a high concentration of adiponectin, which has anti-inflammatory effects, is associated with a reduced risk 31 .Lower levels of sex hormone-binding globulin are associated with increased risk 32 , as are higher blood concentrations of branched-chain and aromatic amino acids 33 .Gut flora metabolites might predict future risk of T2DM because the gut microbiota is involved in energy extraction from the diet, modification of host gene expression, and increasing metabolic endotoxaemia (the level of e ndotoxins in blood) and chronic inflammation 34 .", + "Several lines of evidence suggest that T2D is an inflammatory disease (Donath and Shoelson 2011).Recent results from clinical trials with anti-inflammatory drugs have supported this hypothesis, and immunomodulatory strategies for the treatment of T2D to lower blood glucose levels in patients have been proposed (Barry et al. 2016).Cellular oxidative stress is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D (Evans et al. 2003) by inducing an inflammatory response.", + "In this mini-review, we discuss this question in the context of recent advances in the understanding of the physiology of glucose metabolism in order to determine whether the classical under-standing of T2DM pathophysiology should be revised and more focus placed on the b-cell in the development of therapies for T2DM.In particular, we consider the extent to which the difficulty in identifying insulin resistance genes to date reflects limitations of study design, inadequate physiological assessment of insulin resistance or the complex underlying pathophysiology of insulin resistance (i.e.multiple parallel compensatory pathways).ConclusionWe would propose that it is highly probable that more insulin resistance than b-cell dysfunction T2DM susceptibility genes remain undiscovered at the present time, most likely due to problems associated with study design and the complex nature of physiological responses to nutrients and insulin.In addition, it must be understood that even with 38 genes identified relevant to T2DM pathophysiology, the risk conferred by these combined genes accounts for only a small proportion of overall risk.It must be remembered that the rapid changes in T2DM incidence and prevalence observed in recent decades are a result of the interaction of a stable genetic background with a rapidlychanging environment.Future intervention at newly-discovered insulin secretion controlling loci should improve b-cell function allowing a more robust defence against environmental insult.Targeting oxidative stress, metabolic stress and low grade inflammation may provide fruitful avenues.However, novel therapeutic approaches, whether pharmacological or nonpharmacological, which can target the effects of diet-induced obesity on tissue-specific insulin resistance in the early pathogenesis of T2DM remain a central and invaluable goal of research aiming to halt the rapidly-increasing prevalence of T2DM and its complications worldwide.", + "| INTRODUCTIONChronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance, and -cell dysfunction in type 2 diabetes mellitus (T2DM) (Ehses, Perren, Eppler, Ribaux, & Pospisilik, 2007;Pickup, 2004).Recent studies have reported that the infiltration of the macrophages to pancreatic islets accelerates the -cell dysfunction.These macrophages secrete chemokines and stimulate the immune cell migration, as well as release of pro-inflammatory cytokines.In addition, the elevated glucose and palmitate concentrations increase chemokines release that induce granulocyte colony-stimulating factor and macrophage inflammatory protein-1 from human and mouse pancreatic islets both in vitro and in vivo (Ehses et al., 2007;Inoue et al., 2018).", + "To date, systematic review of the effects of disease risk variants on processes contributing to the diabetic state has mostly been restricted to the examination of basal indices of b-cell (BC) function or insulin sensitivity (2,3).These studies have demonstrated that most, but not all, of these loci exert their primary effects on disease risk through deficient insulin secretion rather than insulin resistance (IR) (2,(4)(5)(6).", + "The role for pro-inflammatory cytokines in regulating insulin action and glucose homeostasis and their function in T2DM has been suggested by several lines of evidence.Obesity, T2DM, and inflammation: Molecular mechanism(s) of associationIn obese people, insulin resistance is linked to the increased release of adipocyte-derived bioactive metabolites (ADBMs) such as lipids, free fatty acids, monocyte chemoattractant protein-1 (MCP-1), and pro-inflammatory cytokines. 30It should be emphasized, however, that although obesity is viewed as a predisposing factor to insulin resistance, other factors may also contribute.A study of young, insulin-resistant, lean offspring of patients with T2DM and insulin-sensitive controls of similar body mass index (BMI) showed similar plasma concentrations of TNF-, IL-6, and adiponectin between the insulin-resistant and insulin-sensitive groups. 34his suggests that in lean people, systemic inflammation may not play a significant role in the development of insulin resistance.In this case, proposed mechanisms for insulin resistance might then be attributed to a dysregulation of intramyocellular fatty acid metabolism. 14In the liver this would also include an altered expression of transcription factor 6- (ATF6) which controls expression of gluconeogenic genes. 35enetic predisposition also may contribute to the development of T2DM.Genome-wide association (GWA) and candidate gene studies over the past few years have so far uncovered 19 genes associated with T2DM. 36The disease-related genetic variants identified have high frequencies in the populations assessed although their individual contributions to increases in risk of T2DM are modest.Ongoing GWAs that target lowfrequency genetic variants and assess copy number variants (CNVs) in addition to single nucleotide polymorphisms (SNPs) are likely to identify additional loci associated with T2DM risk, and some of these may play a significant role in the risk of disease development. 36In lean subjects with T2DM, the dysregulation of fatty acid metabolism, the abnormal expression of gluconeogenic genes and the genetic predisposition necessitate the development of an additional set of biomarkers that target this subpopulation and relate to these risk factors." + ], + [ + "Key points Genome-wide association studies (GWAS) have identified >400 signals associated with the risk of type 2 diabetes mellitus (T2DM). The pancreatic islet has been identified as a key tissue involved in mediating GWAS signals in T2DM risk. Integrating genetic, epigenomic and cellular data can unlock the biology behind GWAS signals.", + "DISCUSSIONGenome-wide linkage scans aimed at identifying QTLs for type 2 diabetes and its associated traits are accumulating.However, findings seldom replicate across studies.Because type 2 diabetes represents a complex disorder with substantial clinical and genetic heterogeneity, efforts to define and identify genetically homogeneous subsamples", + "DiscussionThe present study applied a high-throughput functional genomics approach to identify the associations between genetic factors and inflammatory phenotype in patients with T1D.The results confirm a correlation between baseline immune-cell populations and ex vivo cytokine production in response to bacterial, fungal, non-microbial, and TLR ligand stimulations.We provide evidence for a direct link between T1D GWAS loci and immune functionality, particularly through circulating T cell subpopulations.We show that T cell alteration is largely driven by T1D genetics, while B cells do not show a significant association with T1D GWAS loci.The association between the proportion of CCR5+ Tregs and T1D susceptibility through CCR genes suggests that T1D-associated genetic variants contribute to alteration of immune function through a cumulative effect.Finally, out of 28 genome-wide significant genetic loci regulating immune-cell proportions and cytokine production, we identified 12 immune phenotype QTLs specific to 300DM.We also found 11 druggable genes as candidates for therapeutic intervention.Altogether, this study provides several novel insights into the genetic variability of immune traits in T1D.In the present study we aimed to comprehensively describe the immunopathological consequences of the genetic variants linked to T1D susceptibility, using a high-throughput functional genomics approach.As a part of the Human Functional Genomics Project (HFGP) (Netea et al., 2016), we carried out deep immunophenotyping in peripheral blood samples from a cohort of 243 T1D patients (300DM) using cell subpopulation composition and cytokine production upon stimulations as proxies of immunological function.Part of the results were then compared to those obtained in a populationbased cohort of 500 healthy individuals (500FG) that successfully characterized the impact of genetic factors (Aguirre-Gamboa et al., 2016;Li et al., 2016) on immune responses in healthy individuals.Here, we systematically evaluate the genetic regulation of the immune phenotypes in T1D and show how genetic variations affect immune-cell traits and cytokine production in response to stimulations.In total, we identify 15 genome-wide significant genomic loci (p-value < 5 10 -8 ) associated with immune phenotypes in the 300DM cohort, including 12 novel loci that have never been reported in any healthy population study.These data provide a deeper understanding of the immune mechanisms involved in the pathophysiology of T1D and affecting the general inflammatory response and may open avenues toward the development of novel diagnostics and potentially immunotherapies.", + "These GWA studies, as well as detecting new loci, provided the first 'genome-wide' perspective of the landscape of T2D susceptibility and thereby enabled clearer 'bench-marking' of other claimed T2D-susceptibility effects for which the accumulated evidence from candidate-gene studies remained somewhat equivocal [40].Examples include variants in the genes encoding calpain-10 (CAPN10; thought to be involved in b-cell function), insulin (INS; an obvious candidate) and PC-1 (ENPP1; the product of which is known to modulate insulin-receptor function).None of these genes has featured prominently in GWA analyses to date and, although this does not necessarily exclude a contribution to T2D predisposition, it indicates that the main effects attributable to these variants are small and/or subject to substantial modification by genetic background or environmental exposures.Either way, it seems likely that exhorbitantly large sample sets will be required before such signals can attain the standard of proof now available for the loci described in Table 1.", + "Genome-wide association studies (GWAS) have made a significant contribution to our current knowledge of the role(s) of genetic variation in population-level susceptibility to T1D (Mychaleckyj et al., 2010).", + "IntroductionGenome-wide association studies (GWAS) have identified approximately 80 loci robustly associated with predisposition to type 2 diabetes (T2D) [1][2][3] and a further 70 influencing a range of continuous glycemic traits [4][5][6][7][8][9][10] in non-diabetic subjects.There is substantial, though far from complete, overlap between these two sets of loci.Physiological studies in non-diabetic individuals indicate that most of these loci primarily influence insulin secretion rather than insulin sensitivity, highlighting a key role for the pancreatic islets of Langerhans in the mechanistic underpinnings of these association signals [11,12].These findings have motivated efforts to catalogue the epigenomic and transcriptional landscape of human islets and to apply these findings to deliver biological insights into disease pathogenesis.Recently, it has been shown, for example, that GWAS signals for T2D and fasting glucose show significant co-localization with islet enhancers [13,14].", + "It has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes.Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups.This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders.", + "Functional pathway and network analyses of GWAS data combined with proteomic/transcriptome data, i.e. expression data, have also highlighted how candidate genes interact and may be involved in immune-related mechanisms (6)(7)(8).This has added significantly to our understanding of T1DM etiology.Finally, T1DM susceptibility variants may affect both development Pociot et al. (9) and persistence (10)(11)(12) of autoimmunity and thus might serve as potential intervention targets in clinical studies aiming at diminishing autoimmunity.ConclusionsA major challenge is to translate GWAS findings into causal variants and target genes.The Immunochip effort has greatly contributed to our understanding of disease mechanisms by identifying pathways, which could not be linked to diabetes by existing hypothetical models.Diabetes is probably a much more diverse disease than the current subdivision into T1DM and T2D implies and a more precise subdivision into subgroups may also pave the way for a more individualized medicine.A holistic systems biology approach will also be required to obtain a complete picture of how genetic variation alters a protein function leading to diabetes.The rapid technology development during the past years holds promises that this will be possible in a not too distant future.", + "IntroductionGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4].", + "Genome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.Genome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.", + "More recently, GWA studies have become feasible in large cohorts of patients and controls.Using this approach compelling evidence for genetic variants involved in type 1 diabetes [31][32][33], type 2 diabetes [31,[34][35][36][37], age-related macular degeneration [38], inflammatory bowel disease [39], heart disease [40,41] and breast cancer [42] have already been described.", + "Molecular Biology Reports, 37: 501505. Lyssenko V, Groop L (2009) Genome-wide association study for type 2 diabetes: clinical applications. Current Opinion in Lipidology, 20: 8791. Maltecca C, Weigel KA, Khatib H, Cowan M, Bagnato A (2009) Whole-genome scan for quantitative trait loci associated with birth weight, gestation length and passive immune transfer in aHolstein Jersey crossbred population. AnimalGenetics, 40: 2734. Mardis ER (2008a) The impact of next-generationsequencing technology on genetics. Trends in Genetics, 24: 133141. Mardis ER (2008b) Next-generation DNA sequencing methods. Annual Review of Genomics and Human Genetics, 9: 387402.", + "How do we identify the major 'culprits' at the implicated genome-wide association study loci? If population-based genetics, including genome-wide association studies, have allowed progress in the identification of Type 2 diabetes loci to be rapid over the past few years, progress towards determining which of the gene variants close to the implicated loci confer altered disease risk and how (at the molecular, cellular and whole body level) has lagged some way behind.Indeed, given the number of possible single nucleotide polymorphisms and genes, unravelling these questions represents a monumental challenge, requiring multiple, complementary approaches.Nonetheless, the rewards of success, in terms of new understanding of disease mechanisms and even the identification of new targets for therapeutic intervention, are likely to be great, potentially allowing the treatment of underlying disease aetiology in a personalized (stratified) manner.", + "Background: Many genetic studies, including single gene studies and Genome-wide association studies (GWAS), aim to identify risk alleles for genetic diseases such as Type II Diabetes (T2D).However, in T2D studies, there is a significant amount of the hereditary risk that cannot be simply explained by individual risk genes.There is a need for developing systems biology approaches to integrate comprehensive genetic information and provide new insight on T2D biology.", + "INTRODUCTIONMultiple genome-wide association studies (GWASs) have correlated type 2 diabetes mellitus (T2DM) with genetic variants, yielding a large number of loci and associated gene products that are linked to the disease phenotype-often with little or no insight into the mechanism underlying that link (Hivert et al., 2014).The current challenge is to establish robust systems to systematically evaluate the role of these loci using disease-relevant cells.Previous studies have used patient samples, cell lines, or animal models to seek mechanistic insight but with significant limitations.Large variation is observed in primary patient samples, perhaps due to genetic heterogeneity, whereas animal models present major physiological and metabolic differences that hamper understanding of the precise function of human genes in T2DM.Therefore, a robust system to systematically evaluate the role of T2DM-associated genes using disease-relevant human cells will provide an important tool for diabetes research and spur the development of precision (allele-specific) therapies, exemplified by the use of sulfonylurea drugs to treat patients carrying certain KCNJ11 mutations (Gloyn et al., 2004).", + "Background: Genome-wide association studies (GWAS) have identified several hundred susceptibility loci for type 2 diabetes (T2D).One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes.However, the causal variants identified by GWAS mostly fall into a non-coding sequence, complicating the task of defining the effector transcripts through which they operate.Methods: Here, we describe implementation of an analytical pipeline to address this question.First, we integrate multiple sources of genetic, genomic and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals.Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner tree approach) which uses external, experimentally confirmed protein-protein interaction (PPI) data to generate high-confidence sub-networks.Third, we use GWAS data to test the T2D association enrichment of the \"non-seed\" proteins introduced into the network, as a measure of the overall functional connectivity of the network.", + "Genetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + ] + ], + "task_id": [ + "00BE70B5D71A5926E56942909C8B2A92", + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "055110B765AA502F9AAECE68CEC0DD24" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_1.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_1.json new file mode 100644 index 0000000..02296c7 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_1.json @@ -0,0 +1,101 @@ +{ + "question": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing." + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real." + ], + "contexts": [ + [ + "Gene editing has gained considerable interest with the identification of the CRISPR-Cas9 system, 27 which allows for a targeted modification in the DNA sequence of an organism.Researchers can utilize their knowledge of the basic biology of the gene and its protein function to precisely change the DNA sequence, thus altering the protein function of the gene and allowing for edits to stay within the species.Researchers at the University of Missouri used the CRISPR-Cas9 system to modify the CD163 gene such that the PRRS virus is not able to replicate inside the pig. 28This slight modification of the swine genome through gene editing keeps the pigs from succumbing to PRRS which has an annual estimated loss to the United States swine industry of over $660 million per year.Despite this benefit, given the public's concerns over food safety, it is likely that approval for such technology is years away in the US, Canada and Europe.However, in some cultures, there is a wide range of non-livestock species that are consumed.Therefore, it is conceivable that these countries and cultures may be open to transgenic/gene edited livestock.They may see the importance of useful gene editing which may lead to approval and consumption of reasonable genetically edited animal products such as those with modifications that are already found in nature or those that offer a substantial welfare benefit to society.", + "As a researcher who has devoted an entire career since 1994 to the development of genome editing tools and methods, I have been amazed by the rapid progress in the field over the last few years.Considering the widespread use of the tools, I am sure that the pace will continue to accelerate.Indeed, programmable nucleases, may eventually enable humans-products of evolution-to become masters of evolution.delivered preassembled recombinant Cas9-guide RNA ribonucleoproteins (RNPs) into animal embryos 6,9 and plant 11 and mammalian cells [73][74][75] .Indeed, Cas9 RNPs were rapidly turned over in cells 73 , reducing off-target effects and mosaicism in gene-edited organisms 11 .Cas9 RNPs can be delivered into cells by various methods, including microinjection 6,9 , electroporation 73 , lipofection 74 and protein transduction 75 .Importantly-and unlike in conventional gene therapy, where therapeutic genes are delivered via plasmids or viral vectors-Cas9 RNP delivery does not involve the use of exogenous DNA; host innate immune responses against foreign DNA are not elicited, and undesired integration of foreign DNA into the host genome is avoided.The ability to genetically modify living cells and organisms is a fundamental tool for biological research, but achieving highly specific targeted changes has been technically demanding.Genome editing has been recently democratized by the development of RGENs (see Glossary in Box 1), repurposed from the type II CRISPR-Cas9 prokaryotic adaptive immune system 1 .Unlike other programmable nucleases, namely ZFNs and TALENs, whose target specificities are determined by modifying their DNA-binding domains, CRISPR-Cas9 can be customized by replacing guide RNAs, making the system much more affordable and scalable.Cas9 nucleases have been successfully used for modifying genomes in human cells [2][3][4][5] , animals [6][7][8][9] and plants 10,11 , heralding the age of genome editing.Furthermore, Cas9 or guide RNAs have been linked to various effector proteins to enable targeted gene regulation 12,13 and epigenome modifications 14,15 .It is worth noting, however, that many of these feats had been demonstrated previously using other nucleases or DNA-binding proteins 1,16 .In this Perspective, I shed light on early genome editing platforms that laid the groundwork for the widespread use of CRISPR-Cas9 in research and medicine (Fig. 1).", + "In comparison to a transgenic approach, a gene editing technique such as CRISPR-Cas9 offers the advantage that gene-edited crops are not considered genetically modified organism (GMO) in some countries, such as the US, where the demand for natural food colorants such as anthocyanins is high.Indeed, the use of GMO crops as a source of natural pigments may be inconsistent with consumer interests.However, carrot cultivars engineered with either the transgenic or gene editing approach have not been reported so far, but their development is possible.", + "Caveats and Ethical Concerns of CRISPR-Cas ApplicationsDespite the presence of both a PAM sequence and a specific gRNA, the CRISPR-Cas9 system is not infallible.In fact, DSBs can occur at different sites in the genome, potentially causing so-called \"off-target\" effects.This eventuality remains to date the biggest concern in the field, as possible undesirable modifications must be properly identified and followed in order to guarantee safety for medical purposes.Nevertheless, there is still little evidence of the biological consequence of Cas9 off-target effects.Two recent studies describe new methods to investigate potential off-target effects in both mammals and plants [33,34].In both cases, whole-genome sequencing revealed that selective nucleotide changes, such as conversion of an adenine to a guanine, caused off-target occurrence very rarely, with a frequency comparable to the one of spontaneous mutations.However, substitution of a cytosine with a thymidine was linked to a sizable number of off-target mutations.This newly acquired information adds to the plethora of studies conducted on the safety of CRISPR, which altogether highlight the need for the establishment of clinical standards for the future use of genome-editing techniques in the clinic.Despite this and other technical challenges still ahead for CRISPR genome editing, the pace at which this technology has developed in recent years suggests many of these concerns could be addressed soon, as long as proper ethical guidelines and regulatory mechanisms are established.The notable accuracy and versatility of CRISPR-Cas for genome editing also opened the door to its use in preclinical and translational settings.In the latter case, CRISPR in vivo gene editing has led to several proof-of-concept studies that would have been unachievable without it, as in the first ever correction of inherited pathogenic mutations linked to degenerative disease in a living organism [22] and even shown to be possible in human embryos [23,24].It also has great potential in the field of precision medicine as large-scale population DNA sequencing studies have provided vast amounts of information linking particular diseases with specific genetic mutations which could, in theory, be targeted through CRISPR [25,26].This could be used during the identification and validation of potential DNA targets during the development of personalised drug or cell therapies, which will require the generation of engineered cell lines and/or animal models.Techniques such as HDR-mediated gene targeting are too labour intensive, with low targeting efficiencies and long times necessary for their establishment, and consequently are not ideally suited for drug discovery purposes.Conversely, CRISPR-Cas has been proven to be efficient for editing virtually any kind of cell line, from primary immune cells to induced pluripotent stem cells (iPSCs) [27,28].Additionally, CRISPR can also be used for functional screening in the development of combined inhibitory therapy aimed at strengthening the efficiency of targeted therapeutics.An example of the latter is shown in a study where a variation of the technology known as CRISPR interference (CRISPRi) was used in genome-wide scale to identify different survival pathways used by cancer cells after oncogene inactivation and allowing the identification of successful combination therapies [29].In terms of translational applications, the overall safety of CRISPR genome editing in humans will require long-term scrutiny before its adoption in the clinic.Nonetheless, a number of CRISPR-based clinical trials are currently in progress, including studies focused on targeting patients' own T cells in order to improve the immune response towards some forms of malignant cancer [30,31], and others aimed at correcting pathogenic mutations in the hematopoietic cells of patients with beta-thalassemia and sickle cell disease [32].ConclusionsThere is no reason to doubt that the development of CRISPR-Cas genome editing represents an unprecedented breakthrough in modern science, as it has potential applications in a wide array of disciplines ranging from agriculture, zoology and renewable energy to biomedicine and synthetic biology.This powerful tool holds promise for further elucidating the molecular causes of ageing by allowing scientists to probe genetic and epigenetic pathways with a level of sophistication that was unattainable just a few years ago.It will allow so in traditional animal and cell models of ageing, but it will also drastically accelerate the generation of refined versions of those models or even allow the development of new research approaches in non-model organisms.Moreover, CRISPR-based genome editing is already having a significant impact in research aiming to understand the cellular and molecular origins of age-related diseases, as well as developing potential treatments against them.The application of CRISPR-Cas gene editing for the treatment of age-related diseases is not over the horizon yet, as it will require the identification of causative genes and their role under a variety of contexts that could be as diverse as the ageing process is across individuals.However, CRISPR-Cas might also hold the key for solving such conundrum, as it has opened the way for achieving true personalised medicine by providing both the precision and scalability required for conducting genome-wide functional screens during the refinement of drug-and cell-based therapies for age-related diseases.Since its discovery, CRISPR-Cas technology has ignited a biological revolution by providing a highly versatile platform that allows fast and efficient genome editing in an ever-growing list of organisms.In this chapter we will first describe the most recent advances in the development and application of the CRISPR-Cas platform in biomedical research.Then we will discuss the most recent and notable basic research applications of this technology in the study of the molecular causes of ageing.Finally, we will review how CRISPR-Cas has been used for creating new models for the study of age-related diseases, as well as for manipulating diseaseassociated gene pathways.", + "CRISPR screening technologiesThe discovery of CRISPR-Cas9 as a sequence-specific programmable nuclease democratized gene editing and fueled progress in forward genetic screening [20 , 66] .Genetic screens using Cas9 with a pooled singleguide RNA (sgRNA) library allow the interrogation of seemingly all genes in a genome in a single experiment [96 , 97] [null] .Engineered Cas9 variants further extend the versatility of forward genetic screening.Catalytically inactive Cas9 (dCas9) fused with chromatin effector domains permit specific activation (CRISPRa) or inhibition (CRISPRi) of gene expression [37 , 54] .Recently developed and emerging technologies -base editors, prime editors, and Cas transposases -are beginning to enable new types of genetic screens with directed, controlled, and on demand mutations by allowing the creation of user specified modifications, such as single base conversion, deletions, and insertions [4 , 42 , 58] .", + "Coming on the heels of engineered nucleases, CRISPR-Cas9 tools have accelerated the pace of genomic research by permitting highly efficient knockouts or edits of virtually any gene in cells or model organisms.Multiple CRISPR-Cas9-based clinical trials are in progress or are expected to begin soon.Although Cas9engineered cells haven't yet demonstrated efficacy at scale, early trial results suggest that such cells are stable and don't cause acute adverse reactions in humans.Long-term safety is yet to be determined.Current applications largely focus on single-gene disorders for which gene editing can be carried out ex vivo on appropriate cells, such as bone marrow hematopoietic stem cells in the case of sickle cell anemia.Exploration is under way to develop delivery systems that can target the gene-editing apparatus to the appropriate tissue in vivo.Over the past 8 years, CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 (CRISPR-associated protein 9) technologies have emerged as accessible and adaptable tools for studying and altering genomes. 5RISPR-Cas9 can be used to induce genome edits by creating targeted DNA breaks that trigger site-specific DNA repair.In nextgeneration formats, it can also control the transcriptional output of genes or alter genome sequences using a process of nucleotide base editing that does not require repair of DNA breaks.As these technologies continue to mature, it will become increasingly possible to alter cellular genomes efficiently and accurately.", + "The type II CRISPR-Cas9 systems, repurposed from prokaryotic adaptive immune responses, are now widely used for targeted genome modifications in plants, animals, and human cells (Kim et al. 2014;Woo et al. 2015;Zuris et al. 2015).In particular, Cas9 nucleases have shown promise for gene and cell therapy (Maeder and Gersbach 2016).Typically, these nucleases are expressed or delivered in vivo using plasmid DNA or viruses (Yin et al. 2014;Ran et al. 2015).However, plasmid DNA delivery is often inefficient, especially in vivo, and can cause integration of small plasmid fragments degraded by endogenous nucleases at on-target and offtarget sites in the genome (Kim et al. 2014).Viral delivery of Cas9 can be highly efficient in vivo (Ran et al. 2015;Long et al. 2016;Nelson et al. 2016;Tabebordbar et al. 2016), but may be hampered by antibodies or T cells induced against the protein (Shankar et al. 2007;Calcedo et al. 2015;Chew et al. 2016).We and others have shown that preassembled Cas9 ribonucleoproteins (RNPs) can be delivered to human primary and stem cells and mice to modify target genes (Kim et al. 2014;Schumann et al. 2015;Zuris et al. 2015).Cas9 RNPs are rapidly turned over in cells, reducing off-target effects.Furthermore, Cas9 RNPs are unlikely to be limited by host immune systems because they function and disappear before the generation of antibodies and T cells directed against them.Currently, despite these advantages of RNPs, the difficult delivery of Cas9 RNPs in vivo limits its utility for therapeutic applications (Zuris et al. 2015).Here, we show that in vivo genome editing of an wild-type gene, whose up-regulation is responsible for pathogenesis, could be a new therapeutic modality for the treatment of nongenetic degenerative diseases.Our ultimate goal is to harness Cas9 RNPs for a clinical application of therapeutic genome surgery in patients with AMD.", + "Clustered regularly interspaced short palindromic repeat (CRISPR)-Cas nucleases have revolutionized the field of gene editing and have tremendous application in the field of molecular medicine [98][99][100][101][102].Despite a significant surge in CRISPR/Cas9mediated genome editing in various disease models, the progress in the field of AD has lagged behind substantially.We believe that genome editing can significantly improve the development of AD models and also create novel opportunities for the development of the next generation precision targeted AD gene and stem cell therapies.Since there are several excellent review articles on CRISPR/Cas9-mediated genome editing, here we will limit our focus on select recent articles that are noteworthy.CRISPR/Cas9 system can be engineered to either activate transcription (gain-of-function) or achieve gene silencing (Loss-of-function).Dahlman et al. have developed a CRISPR-based system that uses catalytically active Cas9 and distinct single guide (sgRNA) constructs to activate and knockout different genes in the same cell [103].Konermann et al. have used structure-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci [104].Using crystallographic studies, they have engineered a combination of sgRNA2.0,NLS-dCas9-VP64 and MS2-p65-HSF1 to develop one of the most effective transcription activation system.", + "Limitations of CRISPR-Cas9CRISPR provides a simple and easy tool not only for in vitro use but potentially also for in vivo genome editing.However, there are limitations and downsides to this approach.First, and despite considerable improvements in the technology, the risk of the offtarget effect remains and must be considered carefully.Second, DSB may lead to wide-ranging deletions or recombination events involving the on-target site (204).Third, in cycling cells, DNA double strand breaks caused by Cas9 cleavage may trigger a P53 response leading to apoptosis and enrichment for potentially oncogenic P53-deficient cells (205,206).Fourth, subjects may generate antibodies to Cas9, potentially limiting gene therapies (207,208).Genome editing tools that target the desired genomic region and allow for variants to be altered (e.g. from risk to protective), or for more substantial changes to be made (e.g. the deletion of a longer stretch of DNA harbouring a number of variants) and can help to answer each of these questions.These technologies are evolving rapidly (Figure 1 and Table 2).The most recently developed of these, Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) technology, originally developed by Doudna, Charpentier and their colleagues (72,73) and Zhang and his colleagues (50) has become a widely used tool for this purpose.Engineered CRISPR/Cas9 technology uses a guide RNA (gRNA) to direct CRISPR-associated endonuclease (Cas) to the target DNA and generate a double strand DNA break.Correction of a mutation or variant in the target DNA sequence can then be carried out by homology-directed DNA repair (HDR) with a donor template.Since its discovery eight years ago, CRISPR technology has evolved quickly to be a critical part of the molecular biologist's toolbox.", + "INTRODUCTIONGenome editing technologies based on the clustered regularly interspaced short palindromic repeats (CRISPR)-associated endonuclease Cas9 enable rapid and efficient modification of endogenous genes in a variety of cell types, allowing for analysis of gene function in many organs in vivo.CRISPR-Cas9 induces DNA double strand breaks (DSBs) at single-guide RNA (sgRNA)-specific loci in the genome, which are repaired through either non-homologous end-joining (NHEJ) or homology-directed repair (HDR) pathways.While NHEJ introduces unpredictable pattern of insertion or deletion (indel) mutations, HDR directs a precise recombination event between a homologous DNA donor template and the damaged DNA site (Cong et al., 2013;Cox et al., 2015;Doudna and Charpentier, 2014;Heidenreich and Zhang, 2016;Jinek et al., 2012;Mali et al., 2013;Sander and Joung, 2014;Wang et al., 2013;Yang et al., 2013).Thus, HDR can be used to precisely introduce sequence insertions, deletions or mutations by encoding the desired changes in the donor template DNA.", + "CRISPR technology has rapidly changed the face of biological research, such that precise genome editing has now become routine for many labs within several years of its initial development.What makes CRISPR/Cas9 so revolutionary is the ability to target a protein (Cas9) to an exact genomic locus, through designing a specific short complementary nucleotide sequence, that together with a common scaffold sequence, constitute the guide RNA bridging the protein and the DNA.Wild-type Cas9 cleaves both DNA strands at its target sequence, but this protein can also be modified to exert many other functions.For instance, by attaching an activation domain to catalytically inactive Cas9 and targeting a promoter region, it is possible to stimulate the expression of a specific endogenous gene.In principle, any genomic region can be targeted, and recent efforts have successfully generated pooled guide RNA libraries for coding and regulatory regions of human, mouse and Drosophila genomes with high coverage, thus facilitating functional phenotypic screening.In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening.CRISPR technology has rapidly changed the face of biological research, such that precise genome editing has now become routine for many labs within several years of its initial development.What makes CRISPR/Cas9 so revolutionary is the ability to target a protein (Cas9) to an exact genomic locus, through designing a specific short complementary nucleotide sequence, that together with a common scaffold sequence, constitute the guide RNA bridging the protein and the DNA.Wild-type Cas9 cleaves both DNA strands at its target sequence, but this protein can also be modified to exert many other functions.For instance, by attaching an activation domain to catalytically inactive Cas9 and targeting a promoter region, it is possible to stimulate the expression of a specific endogenous gene.In principle, any genomic region can be targeted, and recent efforts have successfully generated pooled guide RNA libraries for coding and regulatory regions of human, mouse and Drosophila genomes with high coverage, thus facilitating functional phenotypic screening.In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening.The recent development of clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 for experimental purposes has dismantled the perception that genome editing technology is off-limits for screening in mammalian systems (Heintze et al., 2013).Since this system employs the basic principle of Watson-Crick base pairing for gene targeting, generation of libraries with whole-genome target coverage is relatively easy and cost-effective.For instance, simple protocols are available to synthesize pooled lentiviral libraries by in silico design of oligonucleotides, which can then be cloned, packaged and delivered to cells by viral transduction (Paddison et al., 2004;LeProust et al., 2010).Similarly, the generation of arrayed libraries can be achieved by following protocols originally developed for arrayed shRNA library production that have been in use for a number of years (Moffat et al., 2006).All in all, the stage is set for CRISPR to make an enormous impact on genomic screening and thus scientific discovery in the coming years, and recent demonstrations of this system have shown great promise (Shalem et al., 2015).However, a number of technical challenges must be addressed in order to maximize the benefit of this technology.In this review, we will discuss current applications of CRISPR in functional genomics and provide a perspective on future developments in this area.", + "Genome editing for crop improvementReports of CRISPR-Cas9-based genome editing first appeared in 2013 (Cong et al., 2013;Feng et al., 2013;Mao et al., 2013).Since then, genome editing technologies have proven to be powerful and efficient tools for the improvement of many crop species.At present, genome editing has been widely used to introduce/modify agronomically important traits, such as increased yield, improved nutritional quality, and resistance to biotic and abiotic stresses, in multiple crops, including rice, wheat, maize, tomato, and potato (Lu et al., 2017;Soyk et al., 2017;Tang et al., 2017;D'Ambrosio et al., 2018;Ye et al., 2018;Miao et al., 2019;Zhang et al., 2019;Zhong et al., 2019;Butt et al., 2020;Zhang et al., 2020c;Li et al., 2021b;Zhan et al., 2021).CRISPR-Cas-based genome editing has been extended to targeted mutagenesis, base editing, and precisely targeted gene/allele replacement or tagging in plants.mportantly, using CRISPR-Cas9 technology, transgenes present in the genomes of genome-edited plants can be removed by chromosomal segregation via a simple self-pollination or hybridization step.Gene editing technologies continue to be developed and utilized (Mao et al., 2013;Lu and Zhu, 2017;Lu et al., 2020)." + ], + [ + "Epigenetics was originally thought of as the inheritance of traits not solely based on DNA sequence and has evolved substantially since its inception roughly fifty years ago.DNA methylation, which generally occurs at CpG islands, is the best characterized epigenetic modification that regulates gene expression and is inheritable.Recently, the term epigenetics has broadened rather than focusing so much on heredity, with a more all-encompassing and unifying definition as \"the structural adaptation of chromosomal regions so as to register, signal or perpetuate altered activity states\" (12).Histone modifications are now widely accepted to play a role in epigenetics; however, there are questions as to what role they specifically play.Histone modifications could precede or succeed DNA methylation, and whether they initiate the transcriptional memory or simply maintain it is still debated (10).In recent years, our understanding of these epigenetic mechanisms governing gene expression patterns without changes in the basic gene coding sequence has increased dramatically.However, the relationships to pathological and disease states such as diabetes and its complications are less clear and of much current interest.", + "EPIGENETIC STUDIESAn epigenetic mechanism is a biochemical alteration to the DNA molecule that does not change the sequence of the DNA but does infl uence gene expression.Epigenetics is often defi ned as the \"study of mitotically and/or meiotically heritable changes in gene function that cannot be explained by changes in DNA sequence\" (Russo, Martienssen, & Riggs, 1996, p. 1).", + "Epigenetics refers to reversible heritable mechanisms, which can affect gene expression without underlying changes in DNA sequences, but rather via chromatin modifications.Eukaryotic chromatin is a highly condensed structure containing repeating structural subunits, the nucleosomes.Each nucleosome consists of a histone octamer assembled of two copies of each histone (H2A, H2B, H3, and H4, as well as histone variants, such as macroH2A, H3.3 and H2A.Z), wrapped around by 147 base pairs of DNA [3,4].Each core histone possesses histone-fold domains serving for the interaction of the histones and N-terminal histone-tails.These tails can be subjected to post-translational modifications, which frequently affect gene expression.These modifications include, for instance, histone acetylation, methylation, phosphorylation and ubiquitination [5].", + "IntroductionEpigenetics is used to denote the regulation of gene transcription that cannot be attributed to sequence variation in the DNA.Although the term epigenetics includes a number of different mechanisms, DNA methylation and histone modification are most commonly discussed.DNA methylation in mammals appears to be specific to cytosine, predominantly to CpG (cytosine-phosphateguanine) dinucleotides.In promoter regions, CpG sites are often clustered in CpG islands (CGIs) were methylation is believed to repress gene expression [1].Even small changes in the methylation of a promoter region can introduce stable changes in gene expression, leading to silencing of a gene [2,3].Recently, different array-and sequence-based techniques for measuring of the genome-wide DNA methylation pattern have been developed, and the different techniques have yielded concordant results [4,5].", + "Epigenetic modifications reflect transmissible changes in gene expression that are not caused by changes in DNA sequence, but rather by methylation of cytosine residues in DNA and modifications of DNA-associated proteins such as histones.Epigenetic changes are influenced by environmental and lifestyle factors ranging from exposures in utero to adulthood.These changes are flexible over time in individuals and differ among tissue types in various sites in the body, thereby making their use as biomarkers for the prediction of disease challenging (see Supplementary Data online for full references).Currently, bead array-based platforms allow the genome-wide measurement of DNA methylation at more than 400,000 sites throughout the genome.", + "Epigenetic modifications reflect transmissible changes in gene expression that are not caused by changes in DNA sequence, but rather by methylation of cytosine residues in DNA and modifications of DNA-associated proteins such as histones.Epigenetic changes are influenced by environmental and lifestyle factors ranging from exposures in utero to adulthood.These changes are flexible over time in individuals and differ among tissue types in various sites in the body, thereby making their use as biomarkers for the prediction of disease challenging (see Supplementary Data online for full references).Currently, bead array-based platforms allow the genome-wide measurement of DNA methylation at more than 400,000 sites throughout the genome.", + "Epigenetics-Same Genes, But Different OutcomesEpigenetic modifications refer to dynamic changes written on and erased in and around our genes by specialised enzymes, which do not alter the DNA nucleotide sequence itself, but instead modify how it is transcribed.DNA does not exist naked within a eukaryotic cell, but is dynamically packaged as a DNA-protein complex called chromatin, that facilitates the packaging of extraordinary lengths of DNA into the tight confines of the cell nucleus.When a gene product is needed, chromatin is selectively unwound and made 'open' to allow access to transcription factors (known as euchromatin).Potentially more so than the DNA sequence itself, changes to the structure and accessibility of chromatin significantly influence the regulation of gene expression, both between different cells and within an individual cell over its lifetime.These changes are partly determined by epigenetics.This means that the same genes can result in different phenotypes without changes in the DNA sequence.For example, every cell in the body is genetically identical and has a same gene for insulin, but only the -cells of the pancreas have permissive epigenetic changes allowing open chromatin and insulin gene transcription.Elsewhere insulin expression is silenced by repressive epigenetic changes leading to chromatin condensation (known as heterochromatin).In the same way, genetically identical twins can become progressively more different as they age through accumulating epigenetic changes, even though their genetic similarity never changes.", + "Although the current ecological epigenetics literature is primarily focused on DNA methylation, other epigenetic modifications can alter gene expression.Histone modifications alter the way DNA is packaged and change the accessibility of the packaged DNA for transcription.These modifications can also interact with DNA methylation (Richards and Elgin 2002;Rapp and Wendel 2005).The activity of transposable elements, regions of DNA that have the ability to move within the genome and integrate into new sites, are regulated primarily by small interfering RNAs or by DNA methylation (Kazazian 2004;Kejnovsky et al. 2012;Richards et al. 2012a;Slotkin et al. 2012).Transposable elements have the potential to alter gene expression and function when inserted within coding regions, so regulation of these areas of the genome is highly important (Kazazian 2004;Feschotte 2008).Small interfering RNAs are active in DNA methylation pathways and histone methylation pathways.Similarities between these pathways in animals and plants suggest evolutionary conservation in these epigenetic processes (Saze et al. 2012).", + "Epigenetics is concerned with the study of heritable changes otherthan those in the DNA sequence and encompasses two major modifications of DNA orchromatin: DNA methylation and post-translational modification of histones (Callinan and Feinberg, 2006). These modifications are critical regulatory cues, makingDNA more or less accessible to DNA-binding proteins. Preliminary evidence suggeststhat epigenetics is something that geneticists must think about in their genetic analysis. Flanagan et al. (2006) demonstrated evidence of significant epigenetic variabilityin human sperm cells, suggesting that epigenetic patterns can be efficiently transmitted across generations, possibly influencing phenotypic outcomes in health and disease.", + "Epigenetics refers to mechanisms that can modify gene expression and phenotype without changes in the underlying DNA sequence (26,27).Although different cell types in a person are assumed to have identical DNA sequences, they possess distinct differences in their epigenetic information, such as DNA methylation and posttranslational modifications (PTMs) of histone proteins contained in the chromatin.Epigenetic modifications can occur when individuals are exposed to environmental factors, such as infections and nutritional changes, and can predispose them to diseases such as diabetes (28).Nucleosomes, the basic subunits of chromatin, consist of octamers of histones H2A, H2B, H3, and H4, wrapped by DNA.PTMs of histones (e.g., acetylation, methylation, phosphorylation, and ubiquitylation) form an epigenetic layer together with DNA methylation (29)(30)(31), which affects gene transcription.Acetylation of histones at lysine residues is generally associated with transcriptionally active genes, whereas lysine methylation leads to gene activation or repression, depending on the specific site and level of methylation (29,30,32).Alterations in histone PTMs and their interactions with other nuclear proteins at gene promoters or other regulatory regions can lead to relatively stable epigenetic changes that alter chromatin structure.In turn, this can lead to long-term dysregulated gene expression and disease progression.", + "The third epigenetic mechanism that influences gene expression is DNA methylation, which involves the addition of a methyl group to cytosine bases that are adjacent to a guanine base in a sequence of DNA.This combination of cytosine bases that are adjacent to nucleotides containing guanine (i.e., CpG dinucleotides) are often located near promoter sequences of many genes.Unlike chromatin remodeling and histone modifications, DNA methylation is heritable.That is, when DNA is replicated, the CpG dinucleotides on the newly synthesized strand are methylated before the cell divides.Thus, the two daughter cells that are produced by mitosis have the same DNA methylation pattern as the parent cell.When CpG dinucleotides are unmethylated, the genes are transcriptionally active.Methylated CpG dinucleotides, however, are transcriptionally inactive.", + "Things are made worse by the fact that we now know that exposure to toxic materials and stress can alter gene expression epigenetically.epigenetic change refers to changes in gene expression that do not involve any change in nucleotide sequence (Strachan and read 2011).These effects can be transient within the life span of an individual or they can be passed on to future generations.We know of at least three mechanisms that can accomplish these changes: DNA methylation, chromatin remodeling and histone modification, and microrNAs (Strachan and read 2011;Babenko, Kovalchuk, and Metz 2012).DNA methylation involves the addition of a methyl (Ch 3 -) group to the 5th carbon of a pyrimidine ring.The majority of these are found in CpG dinucleotides.human DNA is associated with histone proteins.These proteins are rich in the amino acids lysine and arginine.Usually 147 base pairs are wrapped around a complex of eight core histone molecules (called a nucleosome).histones are subject to different modifications that impact gene expression, these include acetylation (adding Ch 3 Ch 2 -group), and up to three methyl groups to the amino acid lysine, or phosphorylation of serines.This is accomplished by a large family of enzymes, histone acetyltransferases (hATs), histone methyltransferases (hMTs), and histone kinases.Generally these enzymes are associated with the expression of genes; while histone deacetylases, histone demethylases, and histone phosphatases reverse these effects.Small rNAs can act to alter gene expression either at the level of transcription (DNA -rNA) or of translation (mrNA -amino acid).", + "Epigenetic factors influence the regulation of gene expression without altering the DNA sequence and act as the bridge that links the intrinsic and extrinsic signals [6].The most common epigenetic modifications include DNA methylation, histone modification and RNA-based mechanisms [7].In particular, DNA methylation is one of the best-studied epigenetic modifications in recent decades, and plays a crucial role in many biological processes, such as development, differentiation, genomic imprinting and X chromosome inactivation (XCI) [8,9].With the accumulation of findings, the biological importance of DNA methylation attracts more and more attention.For example, several studies suggest that DNA methylation participates in the process involving the formation and stabilization of memories [10,11].A recent study demonstrated that demethylation by silencing DNA methyltransferase enzymes (DNMTs) affects cell survival [12].More interestingly, growing evidence is revealing that variations in DNA methylation caused by environmental stimuli can be transmitted from parents to offspring [13].", + "The field of \"epigenetics\" identifies the instructions (mechanisms) of gene expression (changes in the chromosome) caused by mechanisms other than changes in the DNA sequences.These instructions are important for normal functionality while their malfunction may lead to ageing, cancer, diseases, and maladaptive behavioral traits (Plomin, 2003).", + "Epigenetics refers toheritable gene expression changes that occur without DNAsequence alterations, and includes DNA methylation andhistone modifications such as acetylation, methylation, andubiquitylation. These modifications can result in either geneactivation or gene repression. That epigenetic gene repres-Immunogenetics (2008) 60:411422sion appears to be involved in the maintenance of stemness became apparent when a number of developmentalregulators were found to be epigenetically silenced inmurine ESCs and activated upon induction of ESCdifferentiation (Bernstein et al. 2006; Boyer et al. 2006).", + "Epigenetics is concerned with the study of heritable changes otherthan those in the DNA sequence and encompasses two major modifications of DNA orchromatin: DNA methylation and post-translational modification of histones (Callinan and Feinberg, 2006). These modifications are critical regulatory cues, makingDNA more or less accessible to DNA-binding proteins. Preliminary evidence suggeststhat epigenetics is something that geneticists must think about in their genetic analysis. Flanagan et al. (2006) demonstrated evidence of significant epigenetic variabilityin human sperm cells, suggesting that epigenetic patterns can be efficiently transmitted across generations, possibly influencing phenotypic outcomes in health and disease.", + "Epigenetic changes, such as DNA methylation and histone modifications, are also heritable and affect mRNA expression (14).These changes to the structure of DNA do not affect the sequence and can change with advancing age (15).It appears that genetic and epigenetic variations exert their effects by altering either the amount of RNA transcribed from a gene or the relative proportion of alternatively expressed isoforms produced by the alternative splicing mechanisms.These ultimately affect other downstream elements of the pathway, such as binding partners or inhibitors, resulting in a change in phenotype.It will therefore be necessary for future research programs to integrate genetic variation, epigenetics, and associated gene expression profiles to understand the origins of heritable traits and diseases.Such mechanistic understanding may contribute to the discovery of new therapeutic targets for aging pathologies (16).", + "EpigeneticsChanges arising from alterations in gene expression levels that are caused by reversible chemical modification of DNA, but not changes to the DNA sequence passed on from parents to offspring.", + "Epigenetics is concerned with the study of heritable changes otherthan those in the DNA sequence and encompasses two major modifications of DNA orchromatin: DNA methylation and post-translational modification of histones (Callinan and Feinberg, 2006). These modifications are critical regulatory cues, makingDNA more or less accessible to DNA-binding proteins. Preliminary evidence suggeststhat epigenetics is something that geneticists must think about in their genetic analysis. Flanagan et al. (2006) demonstrated evidence of significant epigenetic variabilityin human sperm cells, suggesting that epigenetic patterns can be efficiently transmitted across generations, possibly influencing phenotypic outcomes in health and disease.", + "Epigenetics is concerned with the study of heritable changes otherthan those in the DNA sequence and encompasses two major modifications of DNA orchromatin: DNA methylation and post-translational modification of histones (Callinan and Feinberg, 2006). These modifications are critical regulatory cues, makingDNA more or less accessible to DNA-binding proteins. Preliminary evidence suggeststhat epigenetics is something that geneticists must think about in their genetic analysis. Flanagan et al. (2006) demonstrated evidence of significant epigenetic variabilityin human sperm cells, suggesting that epigenetic patterns can be efficiently transmitted across generations, possibly influencing phenotypic outcomes in health and disease." + ], + [ + "Oxidative stress and mitochondrial DNANot long after it was discovered that mitochondria have their own genetic apparatus, Harman proposed that mitochondria play a central role in the free radical theory of aging [16].This idea was developed further by Miquel et al. [330], and the notion that mtDNA mutagenesis played a role in aging took hold.The phenotypical importance of mutations in mtDNA was demonstrated by Wallace et al. [331] and Holt et al. [332], who first showed that Leber's hereditary optic neuropathy and mitochondrial myopathies were caused by mtDNA mutations (reviewed in [333]).Because mtDNA is so close to the site of mitochondrial ROS production, it is exposed to considerably higher oxidative stress, resulting in 3-fold higher levels of DNA oxidative damage (the previously quoted 20-fold figure is apparently due to an isolation artifact [334,335]).In the 1990s a series of papers reported that the frequency of mitochondrial DNA deletions increases dramatically with age, being essentially undetectable in young individuals and reaching levels as high as 2% of mtDNA in old individuals.This age-related increase in mtDNA deletions was found in organisms as diverse as worms, mice, and humans (reviewed in [24,336]).The same is also true with mtDNA point mutations [337,338].Certain mtDNA polymorphisms have been found in increased frequency in centenarians, implying a protective effect during aging [339][340][341].Similar protective effects of mtDNA polymorphisms have been reported for the age-related neurodegenerative condition, Parkinson's disease [342].", + "Variation in the structure and function of mitochondria underlies variation in organismal energetics broadly (Seebacher et al., 2010) and evidence for the importance of mitochondrial function in the evolution of natural populations continues to accumulate (Ballard and Melvin, 2010;Glanville et al., 2012;Hicks et al., 2012;Kurbalija Novii et al., 2015).For example, variation in mitochondrial DNA sequences (mtDNA) can determine whole-organism metabolism, i.e., the rate at which organisms process energy from their environment, a phenomenon widespread across animal taxa (Arnqvist et al., 2010;Ballard et al., 2007;Ballard and Pichaud, 2014;Havird et al., 2019;Hood et al., 2018;James et al., 2016;Wolff et al., 2014).Specifically, mtDNA sequence variants are linked to functional metabolic differences in fish (Chapdelaine et al., 2020;Flight et al., 2011;Healy et al., 2019), birds (Scott et al., 2011), and mammals (Fontanillas et al., 2005), including humans (Amo and Brand, 2007;Dato et al., 2004;Niemi et al., 2003;Tranah et al., 2011).These mtDNA variants are often correlated with environmental factors such as temperature and altitude (Storz et al., 2010).However, other studies attempting to link mitochondrial function to mitochondrial DNA (mtDNA) sequence variation or environmental factors have offered mixed reports (Amo and Brand, 2007;Flight et al., 2011;Fontanillas et al., 2005;Hicks et al., 2012).The results here point to several potentially fruitful research directions.We have identified how nonsynonymous mutations in the mitochondrial genome associate with variation in whole-organism metabolism (including CytB, ND1, ND5 and ND6).A next step will be to characterize the molecular details of how these changes affect molecular function.It would also be beneficial to describe how variation in cellular oxygen consumption rate scales up to determine whole-organism metabolic rate across a range of temperatures, thus identifying potential mismatches across levels of organization that may impact organismal performance (Gangloff and Telemeco, 2018).While the interconnected processes that shape organismal and population-level responses to environmental variation do not lend themselves to simple narratives, and many molecular processes interact to produce the emergent ecotypic divergences at the phenotypic level, it is clear that the mitochondria play a central role even as that role may change across populations and ecological contexts (Fig. 1).Research within well-characterized natural systems, such as these garter snake populations, can offer illustrative case studies of how mitochondria respond to their environments, and thus impact physiological pathways and evolutionary patterns, creating variation in life histories and aging.Despite the complexities underlying observed variation in mitochondrial function, recent work has demonstrated examples of how evolution and plasticity in mitochondrial function across populations within a species can shape life histories.For example, evidence from Drosophila has demonstrated the effect of temperature on components of the ETC and has linked mtDNA variants to metabolic thermosensitivity (Pichaud et al., 2012), to differences in whole-organism metabolic rates (Kurbalija Novii et al., 2015), and to fitness-related traits (Ballard et al., 2007;Pichaud et al., 2011;Pichaud et al., 2010).In general, studies in birds and mammals demonstrate that mitochondria of longer-lived species are more efficient in ATP production, produce less reactive oxygen species, and demonstrate increased antioxidant capacities (Barja and Herrero, 2000;Ku et al., 1993;Lambert et al., 2007).While some studies in lizards and snakes demonstrate a similar pattern (Olsson et al., 2008;Robert et al., 2007), the extent to which these results are generalizable across vertebrate taxa is not yet known.The diversity of life-history traits and immense variation in longevity demonstrated by reptiles, both within and among species, make these taxa ideal candidates for understanding how variation in mitochondrial physiology drives this variation in whole-organism traits (reviewed in Hoekstra et al., 2019).Such work has moved to the forefront with a recent focus on the ecological and evolutionary significance of aging processes in wild populations (reviewed in Nussey et al., 2013;Fletcher and Selman, 2015;Gaillard and Lematre, 2020).Over evolutionary time, differential mortality rates are a selective force in shaping genetic structure.This results in divergence of a variety of physiological networks that shape, ultimately, patterns of aging and longevity in different habitats (Monaghan et al., 2008;Stojkovi et al., 2017).Such selective pressures can have differential effects on the nuclear and mitochondrial genomes (McKenzie et al., 2019;Wolff et al., 2014).Genetic variation in the mitochondrial genome is known to drive mitochondrial function in many species (Ballard and Melvin, 2010;McKenzie et al., 2019;Novelletto et al., 2016) and we find this in our system as well.Whole organism metabolic rate varies with the mitochondrial genome haplogroups we identified in this study.T. elegans individuals with the introgressed T. sirtalis mitochondrial genome had the lowest metabolic rate and had 68 amino acid changes in the ETC genes relative to the T. elegans mitochondrial genomes.As species divergence are a continuation of population divergence, this introgression provides additional insight into how genetic variation can alter mitochondrial function.Whether the lower metabolic rate in our snakes with the introgressed mitochondrial genome is due to the fixed amino acid changes between the species or a mismatch between the coadapted nuclear and mitochondrially-encoded ETC proteins that could alter function of the mitochondria (Burton et al., 2013;Haenel, 2017;Rawson and Burton, 2002;Toews et al., 2014;Wolff et al., 2014) will require further comparisons to T. sirtalis individuals.Building on previous work in this system, the current study tests three primary hypotheses about how variation in mtDNA and mitochondrial function relate to variation in life-history traits and aging within this system (Fig. 1): (1) First, we test whether rates of cellular oxygen consumption in isolated immune cells exhibit patterns that are consistent with the hypothesis that cellular processes drive whole-organism senescence and aging, and if these patterns differ between the SA and FA ecotypes and between sexes.By measuring basal, ATP-production associated, and maximal rates of cellular oxygen consumption, we further test for evidence that phenotypic divergence is dependent on a specific aspect of oxidative phosphorylation within immune cells.The energetics of these cells are particularly important given their essential role in modulating disease and infection, important factors contributing to senescence (Metcalf et al., 2019).We predict that SA snakes will maintain levels of cellular oxygen consumption across age, whereas the FA snakes will show a decline with age, especially in ATP-associated rates, possibly due to continual degradation of electron transport chain functionality from accumulating oxidative damage and reduced DNA repair mechanisms (Robert and Bronikowski, 2010;Schwartz and Bronikowski, 2013). ( 2) Second, we expand our mitochondrial genomics dataset to quantify mtDNA genetic structure across the landscape and test whether mtDNA haplotypes, and alleles at a nonsynonymous SNP in the Cytochrome B (CytB) gene correlate with aging ecotypes. (3) Third, we test the hypothesis that variation in mtDNA correlates with whole-organism variation in metabolic rates, suggesting a pathway linking mitochondrial genetic variation in mtDNA to whole-organism energetics.We first test whether different haplotypes differ in resting metabolic rate.Then, we test the effects of the nonsynonymous SNP in CytB on resting metabolic rate.The CytB gene encodes a component of complex III of the ETC, and was previously found to segregate between these life-history ecotypes (Schwartz et al., 2015).This SNP results in an amino acid substitution from isoleucine (aliphatic, hydrophobic) to threonine (hydrophilic) on a region that comes into close contact with a nuclear-encoded subunit (Schwartz et al., 2015).We combine previously published and new data on whole-organism resting metabolic rates (oxygen consumption) to test for the effects of this nonsynonymous mutation in three populations where we find heterogeneity at this nucleotide, thus allowing us to disentangle the effects of shared environment (population) from sequence variation (SNP).We predict that this SNP will correlate with variation in whole-organism metabolic rate, demonstrating a putatively adaptive difference between the derived and ancestral sequence.By utilizing this integrative data setfrom genes to organelles to whole organisms to populationsin a known life-history context, we are able to test hypotheses across levels of organization to provide a more complete picture of the complicated story of mitochondria and life history (Havird et al., 2019).", + "mtDNA DiversityUnlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage.It is unclear what advantage a uniparental mtDNA transmission confers, but one possibility is to minimize the number of distinct genomes to maximize the efficiency of a multi-genomic system (Hill et al. 2019).In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca and O'Farrell 2012; Rojansky et al. 2016).Paternal mitochondria from sperms that enter into the egg during fertilization are actively and selectively eliminated via mitophagy through two E3 ligases, PARKIN, and MUL1 (Rojansky et al. 2016).PARKIN and MUL1 serve redundant purposes, and mitophagy becomes insufficient to eliminate paternal mtDNA only in the absence of both (Rojansky et al. 2016).Even though oocytes have at least a thousand-fold more mitochondria than a sperm cell (Rojansky et al. 2016) and heteroplasmy levels would be very low if paternal mtDNA were to contaminate the embryo, the results can still be non-trivial.However, challenging this notion, a recent study provides evidence of potential paternal transmission (Luo et al. 2018), but awaits further corroborating studies (Lutz-Bonengel and Parson 2019).MtDNA exhibit a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms (van Oven and Kayser 2009; Wallace 1999; Wallace and Chalkia 2013).In fact, the co-evolution of the mitonuclear genomes has been proposed to be driven by mtDNA mutations that select for compensatory changes in the nuclear genome (Havird and Sloan 2016).Populations that share similar mtDNA polymorphisms can be clustered into distinct haplogroups that are designated using all letters of the alphabet (i.e., A through Z).The mtDNA haplogroups represent major branch points on the mitochondrial phylogenetic tree that have strong regional ties around the globe, thus supporting the concept of a 'mitochondrial eve' (Wallace 1999).Haplogroups present inherently different mitonuclear interactions (Zaidi and Makova 2019), which eventually affect the aging process (Wolff et al. 2016).For example, one haplogroup commonly found in Ashkenazi Jews can interact with a specific enrichment of an amino acid sequence in complex I, and result in altered susceptibility to type 2 diabetes mellitus (Gershoni et al. 2014).The effect of mitonuclear compatibility on lifespan is influenced by environmental cues in flies (Drummond et al. 2019).It is unclear if mitonuclear compatibility is invariable throughout an organism's life, or antagonistically pleiotropic during aging, making it a difficult moving target to understand.", + "Background: The accumulation of mitochondrial DNA (mtDNA) mutations, and the reduction of mtDNA copy number, both disrupt mitochondrial energetics, and may contribute to aging and age-associated phenotypes.However, there are few genetic and epidemiological studies on the spectra of blood mtDNA heteroplasmies, and the distribution of mtDNA copy numbers in different age groups and their impact on age-related phenotypes.In this work, we used whole-genome sequencing data of isolated peripheral blood mononuclear cells (PBMCs) from the UK10K project to investigate in parallel mtDNA heteroplasmy and copy number in 1511 women, between 17 and 85 years old, recruited in the TwinsUK cohorts.", + "DiscussionTwo significant questions are raised by the findings that mitochondrial DNA can integrate into the nucleus.Firstly, is this an extraordinarily rare event or is it occurring continually and at high frequency?Secondly, can such an event have pathological consequences to the organism?", + "PhylogenyThe mtDNA is maternally inherited (120) by offspring through the oocyte cytoplasm; namely, the mother transmits her mtDNAs to all of her offspring, and her daughters transmit their mtDNAs to the next generation.This is the consequence of the fact that the mature oocyte such as mouse (304) or bovine (144) contains lOO-1,000 times more mtDNA than is found in somatic cells.Hence, the few sperm mtDNAs that enter the egg (130) have little effect on the genotype.The maternal inheritance results in sequentially diverged mtDNA polymorphism of modern human, as shown in Figure 2. The polymorphism derives from the combinations of small deletions and additions of <14 bp in noncoding region and base substitutions including some point mutations in coding region.There have been few reports on distinct correlation between mitochondrial morphology and human aging, except changes in number and size of mitochondria associated with age.Concerning the gross structure of mitochondria, the overwhelming importance of the cell nucleus in mitochondrial biogenesis should be noted, because the major parts of mitochondrial proteins are encoded by nuclear genes that are stable during life with the efficient repair mechanism for nDNA.Early data on DNA polymorphism detected by restriction endonuclease (263) have suggested that the evolutionary change of mtDNA in higher animals occurs mainly by nucleotide substitution rather than by deletion and insertion.The mtDNA nucleotide sequence evolves 6-17 times faster than comparable nuclear DNA gene sequences (51,52,405).Rapid evolution of mtDNA of higher primates including human, 0.02 base substitutions per site per million years, was calculated from the restriction map of mtDNA (51).Because orthodox recombination mechanism appears to be absent in mtDNA (128), germline mutation seems to go down to posterity as maternal inheritance from our common ancestor (57).", + "A number of conclusions may be drawn from these results.Firstly, the data begin to answer the question of how closely mtDNA replication is kept in synchrony with nuclear DNA replication: it would appear to be regulated not by direct coupling to the nuclear DNA replication, but rather by the cell mass to be serviced by mitochondria.", + "It may be that high mtDNA levels are indeed indicative of compromised mitochondria, but that the underlying defects are unrelated to alterations in the DNA sequence.Alternatively, elevated quantities of mtDNA might be associated with increased metabolic requirements of the embryo, rather than organelles of suboptimal function.It is possible that embryos produced by older oocytes are under some form of stress and therefore have larger energy requirements.Functional experiments will be required to address these questions.Whatever the underlying basis, the current study has unequivocally demonstrated that female reproductive aging is associated with changes in the mtDNA content at the blastocyst stage.", + "Age-associated alterations of the mitochondrial genome occur in several different species; however, their physiological relevance remains unclear.The age-associated changes of mitochondrial DNA (mtDNA) include nucleotide point mutations and modifications, as well as deletions.In this review, we summarize the current literature on age-associated mtDNA mutations and deletions and comment on their abundance.A clear need exists for a more thorough evaluation of the total damage to the mitochondrial genome that accumulates in aged tissues. 1997 Elsevier Science Inc.", + "Mitochondrial geneticsOne underexplored avenue for determining maternal risk for preterm birth involves the influence of the mitochondrial genome.The high mutation rate of mito chondrial DNA (mtDNA), together with the fact that most of its encoded proteins are evolutionarily con served, allowing for the selection of neutral or beneficial variants, has generated interest in defining human mtDNA variations and their roles in human biology [58].", + "Clearly, as mitochondrial metabolic and genetic therapies advance for treating mitochondrial disease, they will also be available to enhance the personal lives of others.However, mitochondrial genetic variation appears to have been one of the primary factors that permitted our ancestors to adapt to new environments, survive adverse conditions, and multiple throughout the globe.Is it possible that by taking over control of individual mtDNA variation, we might also be setting our species on the road to functional decline and ultimately extinction?Mitochondrial therapeutics and performance enhancementIt is now clear that not all mtDNA variation is deleterious.Indeed, about 25% of all ancient mtDNA variation appears to have caused functional mitochondrial changes and thus been adaptive.Those mtDNA variants that are adapted to warm climates have mtDNA variants that result in tightly coupled OXPHOS, thus maximizing ATP output and minimizing heat production.The presence of these mtDNAs permits maximum muscle performance but also predispose sedentary individuals that consume excess calories to multiple problems.They would be prone to be overweight and their mitochondria would generate excessive ROS, thus making them susceptible to a variety of degenerative diseases, cancer and premature aging.Partially uncoupled mitochondria generate more heat, but at the expense of ATP production.Individual's with these variants are better able to tolerate the cold, and are less prone to obesity.They also generate less ROS making then resistant to degenerative diseases and aging.Finally, the mitochondria are why we breathe.Hence, mitochondrial variation might be an important factor in individual predisposition to altitude sickness.", + "Human mtDNA codes for 13 essential polypeptide components of the mitochondrial oxidative phosphorylation (OXPHOS) system.mtDNA undergoes strict maternal inheritance, resulting in the absence of bi-parental recombination (Elson et al., 2001) and has a high mutation rate (Tuppen et al., 2010).As such, the evolution of mtDNA is characterised by the emergence of distinct lineages (or haplogroups) (Hernstadt et al., 2002).This results in high levels of mtDNA variation at the population level despite its rather small size, which is also illustrated by the large number of sub-haplogroups (van Oven and Kayser, 2009).Africa" + ], + [ + "A number of additional ethical implications must be considered.Associating financial investments with the prevention of disease, especially where reproductive decisions are involved, requires sensitivity, caution, and ethical rigor.Funding decisions based on imputed cost-savings must not result in implicit pressure on individuals to violate personal ethics to reduce financial burden on society.As discussions regarding prenatal testing have demonstrated, 35 is risk that \"routinization\" of testing may lead to social or medical expectations of testing in all eligible individuals.These expectations, if linked with financial incentives for the health system, could risk applying implicit pressure on serious, and potentially irreversible, personal decisions.Such expectations, if applied at the population level, could risk becoming normalized, compromising the values of informed consent and individual autonomy.With regard to pregnancies affected by a genetic condition identified through population carrier screening, we modeled the decision to terminate affected pregnancies conservatively (0.50).This is despite the literature suggesting rates above 0.90 for elective TOP for conditions such as Down syndrome 33 and SMA. 34We recognize this issue is controversial, and that laws and ethical positions vary considerably between countries/ jurisdictions.Variations in population attitudes based on age, religion, and other factors, as well as the criticality of preserving individual choice, were acknowledged in adopting this highly conservative estimate.", + "The use of genetic testing from pre-conception through adulthood is expanding rapidly.As a result of this expansion, new ethical issues are emerging related to genetic testing and informed consent.These new issues create ethical challenges for nurses and all healthcare providers.Currently expanding areas include newborn screening and genetic testing of children.These new ethical challenges will be described below.The use of genetic testing from pre-conception through adulthood is expanding rapidly. Psychological risks for parents who are carriers may include parental guilt.", + "Ethnic and cultural backgrounds may also play a role in the decisions that families make regarding prenatal testing.Moyer et al. (1999) concluded that Caucasian women more often undergo prenatal diagnoses than African American or Asian women, or Latinas.Furthermore, Awwad et al. (2008) found American couples less inclined to involve extended relatives in the prenatal decision-making process than Native Palestinian couples.Both of these examples clearly indicate that cultural differences can impact the ways in which families negotiate prenatal decisions.Further research needs to investigate how different families engage in such discussions and decision-making processes, especially as prenatal testing becomes more common and better able to predict or prevent a wider range of genetic conditions.Tightly closed ethnic groups remain at high risk of serving as carriers for genetic mutations, but the management of this possibility varies greatly.For example, some Ashkenazi Jewish groups use screening for mutations for Tay-Sachs disease (TSD) as the basis for rabbinical marriage advice; whereas, children born to Amish families in Pennsylvania more often present with glutaric aciduria type 1 (GA1) but, given their beliefs, parents tend not to accept prenatal testing because of the implication of abortion (McKusick, 2000).Researchers studying factors that contribute toward a couple's choice to undergo prenatal testing have determined that partners base their decision upon several factors, including, but not limited to: parental beliefs about abortion, attitudes regarding disability and their \"perceptions of the usefulness of having the information revealed by genetic tests\" (Moyer et al., 1999, p. 522).Abortion beliefs constitute a key issue in the decision-making process.Even though a majority of parents receiving abnormal prenatal test results terminate their pregnancies (Redlinger-Grosse, Bernhardt, Berg, Muenke, & Biesecker, 2002), Moyer et al. noted that, when asked, more families reported that they would make use of prenatal testing than would be willing to terminate a pregnancy.The decision to continue or terminate a pregnancy after prenatal testing Downloaded by [University of the Sunshine Coast] at 10:32 05 August 2017 comprises a joint decision between both parents (e.g., Awwad et al., 2008;Beeson & Golbus, 1985); however, the nature of the conversations leading to the decision and the involvement of extended family members in the decisionmaking process remains highly understudied.The Genetic Divide(s) and CommunicationThe ability of scientists to \"map\" disease through several generations (Collins, 1999) raises practical and ethical issues of access to resulting opportunities and creates family communication challenges.Currently, prenatal testing for chromosomal diseases has become increasingly common (Moyer et al., 1999).Options such as pre-implantation genetic diagnosis (PGD) can identify over 1,250 disease-related mutations creating an opportunity for parents to select unaffected embryos for implantation in the womb (R. M. Green, 2008).Test results provide potential parents with information that may lead to decisions involving intervention in the genetic makeup of future children.Although some families welcome such options, others may be unable or unwilling to consider such procedures, due to fi nancial concerns or moral/ethical/religious beliefs.", + "Privacy IssuesFinally, privacy issues should be seriously considered when the use of genetic testing is contemplated, especially with respect to whole-genome sequencing of healthy people.It is an unanswered question under what circumstances, to what extent, and by what means genetic data should be incorporated into the medical record.Although easy access to such data could be helpful to providers in improving patient care, it remains to be seen how other parties (eg, insurance companies) might act on the data in ways that do not benefit patients.The US Congress acted to prohibit discrimination by employers and health insurers on the basis of genetic testing with the Genetic Information Nondiscrimination Act in 2008, but further safeguards will undoubtedly be needed as the health implications of genetic data become clearer.", + "The ethical evaluation of genetic testing in children is traditionally based on the balance of clinical benefits and risks (American Society of Human Genetics Board of Directors and the American College of Medical Genetics All correspondence concerning this article should be addressed to Benjamin Wilfond, MD, Treuman Katz Center for Pediatric Bioethics, Seattle Children's Hospital, Metropolitan Park West M/S: MPW 8-2, 1100 Olive Way, Room 876, Seattle WA 98101, USA.E-mail: benjamin.wilfond@seattlechildrens.org Board of Directors, 1995;Andrews, Fullerton, Holtzman, & Motolsky, 1994;Clarke, 1994;Wertz, Fanos, & Reilly, 1994).In the early 1990s, when there were only scant data about children who had received genetic tests results, the presumption was to give greater weight to the potential risks and to restrict testing.However, this criterion is not necessarily consistent with the general practice of respecting broad parental discretion in health care decisionmaking for and on behalf of their children.In general, parents are the presumed decision makers for their children and their decisions are respected unless they are abusive or neglectful (Buchanan & Brock, 1989;Goldstein, Freud, & Solnit, 1979;Ross, 1998).The tension between assessments of benefits and risks made by health care providers and policy makers, and the procedural respect owed to parental authority will be clearly tested as the ability to conduct and interpret whole-genome sequencing and related technologies gain in momentum.Ethical Considerations in Developing Policy for ''Comprehensive'' Genomic TestingIn the near future, genomic testing is likely to become more accessible and will provide both information about the risks of common conditions such as heart disease, diabetes, and hypertension as well as predictions about individual responses to specific pharmaceuticals and other medical therapies (Aspinall & Hamermesh, 2007).Over time, the number and range of conditions for which such testing is available is likely to expand to include more behavioral traits, ranging from information about anxiety and depression, to attention and addiction (Rothstein, 2005).Objective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical benefits and risks.However, this focus can be inconsistent with the general practice of respecting parental decision-making about their children's health care.We argue that respect for parental decision-making should play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions to use emerging genomic technologies.Methods Genomic testing involves the examination of thousands of DNA markers spanning genes throughout the genome and their interrelationships, yielding virtually limitless interpretations.We presume that parents and providers should proceed cautiously in applying genomic testing in children, as we explore how genomic testing will stress the fault lines of the traditional ethical analysis.Results Empirical data about the psychosocial risks and benefits of genetic testing of children do not reveal serious harms, yet virtually no such data exist yet about genomic testing.Unless empirical social and behavioral data indicate that genomic testing is highly likely to cause serious harms to the children, parental decisions to obtain comprehensive genomic testing in their children should be respected.Once comprehensive genomic testing of children becomes routine, resultant information may be more easily integrated by families than anticipated.Conclusions Research on the social and behavioral impact of comprehensive genomic testing on children and their families is needed to further inform parents, clinicians, and policy makers.Objective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical benefits and risks.However, this focus can be inconsistent with the general practice of respecting parental decision-making about their children's health care.We argue that respect for parental decision-making should play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions to use emerging genomic technologies.Methods Genomic testing involves the examination of thousands of DNA markers spanning genes throughout the genome and their interrelationships, yielding virtually limitless interpretations.We presume that parents and providers should proceed cautiously in applying genomic testing in children, as we explore how genomic testing will stress the fault lines of the traditional ethical analysis.Results Empirical data about the psychosocial risks and benefits of genetic testing of children do not reveal serious harms, yet virtually no such data exist yet about genomic testing.Unless empirical social and behavioral data indicate that genomic testing is highly likely to cause serious harms to the children, parental decisions to obtain comprehensive genomic testing in their children should be respected.Once comprehensive genomic testing of children becomes routine, resultant information may be more easily integrated by families than anticipated.Conclusions Research on the social and behavioral impact of comprehensive genomic testing on children and their families is needed to further inform parents, clinicians, and policy makers.To the extent that ''personal meaning'' gains wider acceptance as a legitimate criterion for expanding the availability of new tests and applications of genomic technology, the current policies and practices of restricting some genetic testing of children and mandating other tests will need to be reevaluated.There will be some parents who will find the information that becomes available through new technologies and data useful in shaping their parenting practices, while others will be more skeptical of their value.These disparate parental judgments may be independent of professional assessments of clinical validity and utility.Extrapolating from the empirical data about predictive genetic testing of children in at-risk families discussed earlier, we speculate that once comprehensive genomic testing of children becomes routine, the information may be more easily integrated by families than might be predicted.This is not meant to imply that whatever information parents want about their children should be provided carte blanche.Clearly, education and counseling will be crucial to ensure that families understand the limitations of the information.However, restrictions and mandates should be based on a criterion of risk of serious harm (Diekema, 2004).Given the lack of data confirming harm and the related data that indicate children may fare better than anticipated, such restrictions and mandates cannot be justified.Policies and practices will also need to clarify the role of the older adolescent in the decision-making process, although the issues related to balancing and assessing parental and adolescent interests and preferences goes beyond the focus of this article.This is also not meant to ignore the professional and moral obligation to educate parents and to help parents make good decisions on behalf of their children.It is morally appropriate for providers to strongly recommend particular tests in infancy and young childhood (i.e., PKU testing), and to strongly discourage other tests (e.g., ApoE testing of children for adult onset Alzheimer disease and heart disease because ApoE is not predictive but only provides an increased relative risk and has limited sensitivity and specificity) (Roberts, Cupples, Relkin, Whitehouse, & Green, 2005).Selective and directive recommendations are a routine aspect of pediatric practice.However, it will become increasingly important for professional organizations to begin to reconcile their support for mandatory genetic testing for some conditions and their support for restrictions for other conditions with the broad discretion that parents have and need in the health care arena in order to promote their children's well-being.What limits should be imposed, if any, need to be determined prior to commercial feasibility.In this article, we consider how genetic testing decisions for children have been made traditionally and how the anticipation of comprehensive genomic testing in the near future will stress the fault lines of traditional approaches.The potential for comprehensive genomic testing in children could shift the equilibrium towards expanding or reducing parental discretion, and forces us to reexamine the evidence for our genetic testing policies and practices.We will highlight specific domains where further empirical social and behavioral research is necessary to inform policy and practice.", + "Prenatal genetics is largely practiced by maternal-fetal medicine specialists due to severe deficiency in the number of qualified clinical geneticists.Recent years have witnessed a tremendous growth in the demand for chorionic villous sampling and amniocentesis for the diagnosis of single gene disorders.At KFSHRC alone, the number of prenatal samples that are tested for single gene disorders has increased from 5 in 2004 to 250 in 2013.Therapeutic abortion is permitted by law if performed within 120 days from the time of fertilization in order to comply with the Islamic view of the timing of ensoulment (Alkuraya and Kilani 2001).However, the approved indication for the procedure, which is \"severe malformation\", must be authorized by three attending-level physicians.The definition of \"severe\" is left to the discretion of the medical team after consulting with the family.For example, intellectual disability is a common indication for many therapeutic abortion procedures.Contrary to commonly held views, we have shown that early prenatal diagnosis is the method of choice for couples who had one or more children with single gene disorders, as long as they are provided with a culturally sensitive genetic counseling that addresses their religious and cultural concerns (Alkuraya and Kilani 2001).Nearly 45% of these couples opt for early prenatal diagnosis compared to 35% who choose preimplantation genetic diagnosis (PGD) (Alkuraya 2013a).PGD is available freely at KFSHRC but is also provided by the private sector.Noninvasive prenatal screening using cell-free fetal DNA in maternal blood is quickly becoming integrated in prenatal care.KFSHRC offers this test routinely to all pregnant women regardless of their perceived risk and the MOH is considering making this test available throughout its vast network of hospitals and medical centers.", + "Social and psychological implications of accessing genetic services and information.", + "A corollary of the predictive power of genetic information is the limited ability to prevent or treat many conditions with significant genetic factors involved.Indeed, virtually all of the complex ethical and legal issues relevant to genetic testing would disappear if there were effective preventions or treatments available for genetic conditions.The ability to predict future disease in conjunction with a limited ability to do much about it has important social and psychological implications that must be addressed in conducting genetic research.", + "Interpretations of the literature will likely mirror the priorities and evaluative tendencies of the reader.Are you willing to accept the overall trends in genetic and genomic testing evaluation and to trust that the existing clinical approaches will apply informed consent appropriately while identifying and supporting the rare individual who has a serious adverse response to the testing?If so, you might advocate that attention be turned more toward other issues relevant to the effective implementation of genetic and genomic testing.Or do you feel a strong need to understand in more detail the possible psychosocial harms of the testing, particularly the subtler impacts or responses of individuals who do not fit the norm?In that case, you would likely encourage renewed and innovative efforts to study the psychosocial consequences of the receipt of risk information from genetic and genomic testing.", + "Other social issues require our attention if genomic medicine is to benefit our patients.How should genetic tests be regulated?What, if any, are the appropriate uses of direct-to-consumer marketing of genetic tests?The Internet has recently had a proliferation of genetic-testing sites that feature claims grounded in greed and pseudoscience, rather than in data or reality.How will health care providers and the public distinguish between these and responsible testing services, whether they are available through the Internet or in the hospital?", + "Environmental FactorsAs widespread use of genetic testing increases, it is the responsibility of the medical community to ensure its equitable use across socioeconomic and cultural spectrums." + ], + [ + "To overcome the lack of phenotypic information in the 1000 Genomes Project, The ClinSeq Project was developed to pilot large-scale genome sequencing for research in genomic medicine at the National Institutes of Health Clinical Research Center in Bethesda, MD. 40 The study seeks to enroll 1000 individuals who will be evaluated for personal health status and family history.The project aims to:", + "We (Hein, Schierup and Wiuf) have published a300 page book on molecular population genetics titled Gene Genealogies, Sequence Variation and Evolution OxfordUniversity Press, and are presently developing a tutorial in association mapping that we hope to publish as a booklet in2006 and are also involved in a very large EU collaboration (Holland, Denmark, Iceland and UK) to find susceptibilitygenes for breast and prostate cancer. In comparative genomics, the most fundamental investigation is to find genes in a pair of aligned genomes.", + "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.This fully indexed but semi-intelligibleBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C4CH 1 BIOINFORMATICS CHALLENGES FOR THE GENETICISTbook of life immediately began to serve as a valuable framework for integration ofgenetic and biological data. However, knowledge of the genome sequence did notimmediately clarify the nature and structure of human genetic variation.", + "Methods for DNA sequencing are constantly being improved, with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000, an end that appears to be in sight (Hayden, 2014).In the very near future, whole-genome sequencing will be routinely available for clinical purposes, perhaps even beginning at birth.The major challenge ahead is the interpretation of this information.How do our genes interact with each other, and how does the environment contribute to the development of health and disease?What are the individual and societal implications of knowing our genome sequence?The answers to these and other important questions will unfold in the years ahead.Thus, we are truly in an era where precision medicine may soon become a reality.", + "Characteristics of genotyping and sequencing technologies", + "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.", + "IntroductionSince the first human genome was sequenced at an estimated cost of $150 million,several advanced high-throughput techniques some with lower costs - have come up. Atthe same time, this resulted in a data deluge and a critical need to connect theheterogeneous sequencing data and associated annotations structural and functional with the basic tenets of biology or molecular basis of development and disease.", + "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.", + "Ample time was allotted to answer questions and a copy of \"A Guide to Your Genome\" (National Human Genome Research Institute 2007) was provided to further assist participants' understanding and ability to communicate results with family members or others.", + "Whether within 10 or 12 (or 8) years, such inexpensive sequencing will change both research and clinical care, and progress does not need to wait even that long.The National Human Genome Research Institute (NHGRI) plans to focus a significant portion of the sequencing capacity that it supports on medical sequencing.For instance, the NHGRI and the National Cancer Institute are actively considering a Human Cancer Genome Project, 22 which would use DNA sequencing and a host of other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of cancer.Medical sequencing should also provide important insight into many other diseases.For example, sequencing all exons in X-linked mental retardation syndromes may reveal much about their etiology.Sequencing candidate genes in the extremes of the distribution of quantitative traits should also reveal much of importance about common diseases, such as coronary atherosclerosis. 23With further technological advances, other previously unimaginable research approaches will become real.", + "Key bioinformatic steps totake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stageregions in man (see Chapter 5). Similar issues also exist in the establishment oftrue orthology between genes in different species, where one is identified to play arole in a disease model. If two genes are truly orthologous, their evolution closelyfollows patterns of speciation (Fitch, 2000).In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily onBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\u0002C106CH 6 COMPARATIVE GENOMICSquestions of relevance to human genetics. We begin, in Section 6.2 by presenting anoverview of genome structure and content, providing a context for the subsequentdiscussions.4Assembling a View of theHuman GenomeColin A. M. SempleBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK4.1 IntroductionThe miraculous birth of the draft human genome sequence took place againstthe odds. It was only made possible by parallel revolutions in the technologiesused to produce, store and analyse the sequence data, and by the development ofnew, large-scale consortia to organize and obtain funding for the work (Watson,1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + ] + ], + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_1_two.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_1_two.json new file mode 100644 index 0000000..e40ab00 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_1_two.json @@ -0,0 +1,109 @@ +{ + "question": [ + "Which genes give a predisposition to developing T1D?", + "What is ensembl", + "Which database can I use for genetic, genomics, phenotype, and disease-related data generated from rat research?", + "What is RGD?", + "What resources can I use to do pathway analyses?" + ], + "answer": [ + "The genes that give a predisposition to developing T1D include the HLA class II genes, the insulin locus (INS) VNTR on 11p15, the cytotoxic T-lymphocyte-associated protein 4 (CTLA4) locus on 2q31, the PTPN22 gene on 1p13, and the interleukin 2 receptor alpha (IL2RA) on 10p15. Other genes associated with T1D include those in the HLA region on chromosome 6, specifically HLA alleles DR3-DQ2 or DR4-DQ8, and alleles at HLA-DQB1.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute. It is a publicly available web resource that contains automatically annotated genomes and integrates data from a wide range of biological research sources. The Ensembl database, launched in 1999, was the first to provide a window on the draft genome, curating the results of a series of computational analyses. It provides detailed information about the human genome including variants like SNPs, insertions, deletions and somatic mutations for several species. Ensembl also provides a DAS reference server giving access to a wide range of specialist annotations of the human genome.", + "You can use the Rat Genome Database (RGD) for genetic, genomic, phenotype, and disease-related data generated from rat research.", + "The background text does not provide information on what RGD is.", + "You can use various resources for pathway analyses such as Ingenuity Pathway Analysis (IPA), Disease Association Protein-Protein Link Evaluator (DAPPLE), Pathway Studio, MetaCore, REACTOME, LitInspector, NetPath, Predictive Networks, GeneGo, Database for Annotation, Visualization and Integrated Discovery (DAVID), PATHWAYASSIST, E! Ensemble, Protonet, Pandora, Pubmatrix, KEGG, Reactome, g:Profiler, Gene Ontology, Biocarta, GenMAPP, WebGestalt, Gene Set Enrichment Analysis (GSEA), ClueGo, CluePedia, Cytoscape, RegulonDB, WIT.UM-BBD, EcoCyc, MetaCyc, Enzyme and metabolic pathways database, and Gene-Set Enrichment Analysis (GSEA)." + ], + "contexts": [ + [ + "A. Genetic ScreeningWe have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is 10 -15 times greater.Although 70% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3).II. THE GENETICS OF TYPE 1 DIABETESA comprehensive overview of genetic data in mouse and human is beyond the scope of this article.Instead, we will focus on how the various susceptibility genes and environmental triggers can fit in a mechanistic model for T1D etiology.", + "T1D risk is strongly influenced by multiple genetic loci and as yet poorly understood environmental factors.The disease is highly heritable, with first-degree relatives of cases displaying approximately 15 times greater risk than the general population.Concordance in monozygotic twins is also as high as 50%.A number of genetic determinants of T1D had already been established before the era of genome-wide association studies.The strongest genetic factors include the HLA class II genes, encoding highly polymorphic antigen-presenting proteins that account for almost 50% of the genetic risk for T1D.Other established loci confer more modest, but substantial effects, such as the insulin locus (INS) VNTR on 11p15 [44][45][46][47], the cytotoxic T-lymphocyte-associated protein 4 (CTLA4) locus on 2q31 [48][49][50][51] and the PTPN22 gene on 1p13 [52,53].More recently, convincing statistical support for an additional T1D susceptibility locus on 10p15 harboring the interleukin 2 receptor alpha (IL2RA) was uncovered, utilizing non-coding SNPs [54][55][56].However, the majority of other associations in the pre-GWA era remain controversial [57][58][59], and linkage studies have established the fact that there are no other loci with an effect size approaching that of HLA.", + "Clearly genetics play an important role in the T1D disease process as both MZ and DZ twins have the same environmental exposures but different concordance rates and length to diagnosis of the second twin.Numerous genes have been associated with T1D, the most significant being the HLA region on chromosome 6 [6].More than 90% of type 1 diabetics carry HLA alleles DR3-DQ2 or DR4-DQ8 compared to no more than 40% of the general population [7].Alleles at HLA-DQB1 are known to be, in part, protective [8].Single nucleotide polymorphisms (SNPs) are also associated with T1D.A recent genome-wide association study of approximately 2,000 patients with each of 7 common, chronic diseases, including T1D, and 7,000 shared controls confirmed the association of SNPs in 5 previously identified regions with T1D and discovered 5 novel associations.However, the authors concluded that these regions, with the exception of the HLA on chromosome 6, confer only modest effects on T1D, and ''the association signals so far identified account for only a small proportion of overall familiality'' [9].These results suggest that additional genetic variants contribute to inheritance of T1D.Type 1 diabetes (T1D) tends to cluster in families, suggesting there may be a genetic component predisposing to disease.However, a recent large-scale genome-wide association study concluded that identified genetic factors, single nucleotide polymorphisms, do not account for overall familiality.Another class of genetic variation is the amplification or deletion of .1 kilobase segments of the genome, also termed copy number variations (CNVs).We performed genome-wide CNV analysis on a cohort of 20 unrelated adults with T1D and a control (Ctrl) cohort of 20 subjects using the Affymetrix SNP Array 6.0 in combination with the Birdsuite copy number calling software.We identified 39 CNVs as enriched or depleted in T1D versus Ctrl.Additionally, we performed CNV analysis in a group of 10 monozygotic twin pairs discordant for T1D.Eleven of these 39 CNVs were also respectively enriched or depleted in the Twin cohort, suggesting that these variants may be involved in the development of islet autoimmunity, as the presently unaffected twin is at high risk for developing islet autoimmunity and T1D in his or her lifetime.These CNVs include a deletion on chromosome 6p21, near an HLA-DQ allele.CNVs were found that were both enriched or depleted in patients with or at high risk for developing T1D.These regions may represent genetic variants contributing to development of islet autoimmunity in T1D.Type 1 diabetes (T1D) tends to cluster in families, suggesting there may be a genetic component predisposing to disease.However, a recent large-scale genome-wide association study concluded that identified genetic factors, single nucleotide polymorphisms, do not account for overall familiality.Another class of genetic variation is the amplification or deletion of .1 kilobase segments of the genome, also termed copy number variations (CNVs).We performed genome-wide CNV analysis on a cohort of 20 unrelated adults with T1D and a control (Ctrl) cohort of 20 subjects using the Affymetrix SNP Array 6.0 in combination with the Birdsuite copy number calling software.We identified 39 CNVs as enriched or depleted in T1D versus Ctrl.Additionally, we performed CNV analysis in a group of 10 monozygotic twin pairs discordant for T1D.Eleven of these 39 CNVs were also respectively enriched or depleted in the Twin cohort, suggesting that these variants may be involved in the development of islet autoimmunity, as the presently unaffected twin is at high risk for developing islet autoimmunity and T1D in his or her lifetime.These CNVs include a deletion on chromosome 6p21, near an HLA-DQ allele.CNVs were found that were both enriched or depleted in patients with or at high risk for developing T1D.These regions may represent genetic variants contributing to development of islet autoimmunity in T1D.", + "Background: The immune system matures mainly during the postnatal period through breastfeeding, and is partly modified by nutritive factors.The manner by which early feeding practices influence the development of type 1 diabetes mellitus (TID) is not clear.Also the use of genetics in prognostic evaluation of the disease has not be studied intensely. Aim:To study the relationship between early infant feeding patterns and susceptibility to TID through the HLA-DRB1 and DQ allelic polymorphism and identify the genes of high predictive value in the prognostic model. Methods:The study included 24 diabetic children with TID matched with 21 controls.All the children were exposed to detailed history of the disease process and anthropometry for weight, height and body mass index.Blood samples were collected from all 45 cases for measuring HLA-DRB1and HLA-DQB1allelic polymorphism for the susceptible genes of HLA-DRB1 0301, 0302, 0401 and 0402 and HLA-DQB1*02 and for the protective genes HLA-DRB1 07,*13 by polymerase chain reaction sequence specific primer (PCR-SSP) done by genomic DNA extraction using Genomic DNA purification kits.Results: Allelic polymorphism for the susceptible genes of HLA-DRB1 were shown to be higher in the diabetic group compared to the control group especially for the 0302 and 0401 alleles at P<0.05, but was not significant for HLA-DRB1-0301 and 0402 at P>0.05.HLADRB1*07 and HLADRB1*13 were significantly higher in the breastfed healthy but not in the diseased or the formula fed groups (p<0.001)(p<0.05).The detection of HLADRB1 0401 allele was more with retinopathy and HLADRB1 0301 allele with microalbuminuria. Conclusions:The absence of protective genes is a strong predictor of TID.Susceptibility genes are influenced by early feeding patterns and in turn affect the clinical course of the disease that could be of prognostic value in TID.", + "More than 60 susceptibility loci have been identified (Table 1).The greatest genetic risk (50%) for T1D is conferred by alterations to immune genes, especially those encoding the classical HLAs (Ounissi-Benkalha and Polychronakos, 2008).Other genetic loci (Table 1) are believed to influence population-level risk for T1D, although it is poorly understood how these non-HLA loci contribute to disease susceptibility (Ram et al., 2016a).The genetics of type 1 diabetesThere is a strong genetic risk to T1D.This is exemplified by (Redondo et al., 2001) who demonstrated a strong concordance of genetic inheritance (65%) and T1D susceptibility in monozygotic twin pairs.That is, when one sibling is afflicted, there is a high probability that the other twin will develop T1D by the age of 60 years.Additionally, autoantibody positivity and islet destruction was observed after a prospective long-term follow-up of monozygotic twins of patients with T1D, despite initial disease-discordance among the twins (Redondo et al., 2008).", + "Family and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.Family and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.", + "CONCLUSIONThe greatest genetic risk (both increased risk, susceptible, and decreased risk, protective) for type 1 diabetes is conferred by specific alleles, genotypes, and haplotypes of the HLA class II (and class I) genes.There are currently about 50 non-HLA region loci that also affect the type 1 diabetes risk.Many of the assumed functions of the non-HLA genes of interest suggest that variants at these loci act in concert on the adaptive and innate immune systems to initiate, magnify, and perpetuate -cell destruction.The clues that genetic studies provide will eventually help lead us to identify how -cell destruction is influenced by environmental factors.While there is extensive overlap between type 1 diabetes and other immune-mediated diseases, it appears that type 1 and type 2 diabetes are genetically distinct entities.These observations may suggest ways to help identify causal gene(s) and, ultimately, a set of disease-associated variants defined on specific haplotypes.Unlike other complex human diseases, relatively little familial clustering remains to be explained for type 1 diabetes.The remaining missing heritability for type 1 diabetes is likely to be explained by as yet unmapped common variants, rare variants, structural polymorphisms, and gene-gene and/or gene-environmental interactions, in which we can expect epigenetic effects to play a role.The examination of the type 1 diabetes genes and their pathways may reveal the earliest pathogenic mechanisms that result in the engagement of the innate and adaptive immune systems to produce massive -cell destruction and clinical disease.The resources established by the international T1DGC are available to the research community and provide a basis for future discovery of genes that regulate the earliest events in type 1 diabetes etiology-potential targets for intervention or biomarkers for monitoring the effects and outcomes of potential therapeutic agents.", + "IntroductionOver 60 loci in the genome contribute to genetic predisposition to type 1 diabetes (T1D) [1][2][3][4][5] in which insulin deficiency results from an autoimmune attack against insulin-producing beta cells of the pancreatic islets.Heterogeneity in the disease aetiology is recently acknowledged and immunological processes leading to T1D in individuals diagnosed later in life appear different from the processes in individuals having disease onset in early childhood, in which B cells are involved in the pathological process in the pancreas [5].Different genes and genetic variants may thus affect disease course at varying ages, also suggested by the high diagnosis age correlation (r 2 = 0.95) in Finnish monozygotic twins concordant for T1D [6].Of the known T1D risk loci, however, only the HLA locus and a few non-HLA loci, have been associated with age at diagnosis [7][8][9][10].Genetic risk score combines risk-increasing alleles into a single score and the genetic risk score for T1D has already been suggested for clinical use for screening of infants at highest T1D risk [11].All disease-susceptibility variants are included in the score, but only a few known T1D variants have stronger effects in individuals with early-onset disease [10].Genes affecting type 1 diabetes diagnosis age / A. Syreeni et al.Genome-wide search for genes affecting the age at diagnosis of type 1 diabetes.", + "The risk for T1D is strongly influenced by multiple genetic loci and environmental factors.The disease is heritable, with first-degree relatives of patients with T1D being at 15-fold greater risk for developing the condition than the general population.", + "Type 1 DiabetesThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk.", + "Type 1 diabetes risk stratification by T1D family history and HLA genotyping", + "Genetics. T1DM is a polygenic disease that is influ enced by environmental factors.Genetic risk factors are necessary but not sufficient for disease, as their pene trance is low.The concordance rate of T1DM among monozygotic twins is reported to be only 30%, although a recent study that involved longterm followup suggested that this percentage might be higher 47,48 .", + "Presently, 48 other genomic regions, referred to as susceptibility regions, have been found to also confer susceptibility to T1D (Burren et al., 2011;Steck and Rewers, 2011;Yang et al., 2011;Bluestone et al. 2010;Poicot et al., 2010;Todd et al., 2010;Todd et al., 2007).But their contribution is minimal in comparison to the HLA locus (Gillespie, 2014).Also, research has shown that less than 10% of individuals with HLA-conferred diabetes susceptibility actually progress to clinical disease (Knip andSiljandera, 2008, Wenzlau et al., 2008).This implies that additional factors are needed to trigger and drive -cell destruction in genetically predisposed persons (Knip and Siljandera, 2008).Environmental factors are believed to influence the expression of T1D.The reason being that in the case of identical twins, if one twin has T1D, the other twin only has it 30%-50% of the time, despite having the same genome.This means that other factors contribute to the prevalence or onset of this disease (Knip et al., 2005)." + ], + [ + "Zerbino, D. R., Achuthan, P., Akanni, W., Amode, M. R., Barrell,D., Bhai, J., Billis, K., Cummins, C., Gall, A., Girn, C. G., Gil,L., Gordon, L., Haggerty, L., Haskell, E., Hourlier, T., Izuogu, O.G., Janacek, S. H., Juettemann, T., To, J. K., Laird, M. R., Lavidas, I., Liu, Z., Loveland, J. E., Maurel, T., McLaren, W., Moore,B., Mudge, J., Murphy, D. N., Newman, V., Nuhn, M., Ogeh, D.,Ong, C. K., Parker, A., Patricio, M., Riat, H. S., Schuilenburg,H., Sheppard, D., Sparrow, H., Taylor, K., Thormann, A., Vullo,A., Walts, B., Zadissa, A., Frankish, A., Hunt, S. E., Kostadima,M., Langridge, N., Martin, F. J., Muffato, M., Perry, E., Ruffier,M., Staines, D. M., Trevanion, S. J., Aken, B. L., Cunningham,F., Yates, A., and Flicek, P.: Ensembl 2018, Nucl.", + "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carriedout and the way the results are displayed. 4.4.1 EnsemblEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the WellcomeTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbardet al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide awindow on the draft genome, curating the results of a series of computational analyses.Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequenceassemblies as its starting point, but it is now based upon NCBI assemblies. TheEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify confirmed genes that arecomputationally predicted (by the GENSCAN gene prediction program) and alsosupported by a significant BLAST match to one or more expressed sequences orproteins. Ensembl also identifies the positions of known human genes from publicsequence database entries, usually using GENEWISE to predict their exon structures.Data retrieval is extremely well catered for in Ensembl, with text searches of alldatabase entries, BLAST searches of all sequences archived, and the availability of bulkdownloads of all Ensembl data and even software source code. Ensembl annotationcan also be viewed interactively on ones local machine with the Apollo viewer (Lewiset al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome BrowserThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;it, too, provides annotation of the NCBI assemblies, and it displays a similar array offeatures, including confirmed genes from Ensembl.Ensembl provides a DAS referenceserver giving access to a wide range of specialist annotations of the humangenome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy ahypothesis (top-down data mining), or to interrogate a database in order togenerate new hypotheses based on rigorous statistical correlations (bottom-updata mining). Domain (protein) A region of special biological interest within a single proteinsequence.", + "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carriedout and the way the results are displayed. 4.4.1 EnsemblEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the WellcomeTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbardet al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide awindow on the draft genome, curating the results of a series of computational analyses.Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequenceassemblies as its starting point, but it is now based upon NCBI assemblies. TheEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify confirmed genes that arecomputationally predicted (by the GENSCAN gene prediction program) and alsosupported by a significant BLAST match to one or more expressed sequences orproteins. Ensembl also identifies the positions of known human genes from publicsequence database entries, usually using GENEWISE to predict their exon structures.Data retrieval is extremely well catered for in Ensembl, with text searches of alldatabase entries, BLAST searches of all sequences archived, and the availability of bulkdownloads of all Ensembl data and even software source code. Ensembl annotationcan also be viewed interactively on ones local machine with the Apollo viewer (Lewiset al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome BrowserThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;it, too, provides annotation of the NCBI assemblies, and it displays a similar array offeatures, including confirmed genes from Ensembl.Ensembl provides a DAS referenceserver giving access to a wide range of specialist annotations of the humangenome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy ahypothesis (top-down data mining), or to interrogate a database in order togenerate new hypotheses based on rigorous statistical correlations (bottom-updata mining). Domain (protein) A region of special biological interest within a single proteinsequence.", + "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carriedout and the way the results are displayed. 4.4.1 EnsemblEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the WellcomeTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbardet al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide awindow on the draft genome, curating the results of a series of computational analyses.Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequenceassemblies as its starting point, but it is now based upon NCBI assemblies. TheEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify confirmed genes that arecomputationally predicted (by the GENSCAN gene prediction program) and alsosupported by a significant BLAST match to one or more expressed sequences orproteins. Ensembl also identifies the positions of known human genes from publicsequence database entries, usually using GENEWISE to predict their exon structures.Data retrieval is extremely well catered for in Ensembl, with text searches of alldatabase entries, BLAST searches of all sequences archived, and the availability of bulkdownloads of all Ensembl data and even software source code. Ensembl annotationcan also be viewed interactively on ones local machine with the Apollo viewer (Lewiset al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome BrowserThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;it, too, provides annotation of the NCBI assemblies, and it displays a similar array offeatures, including confirmed genes from Ensembl.Ensembl provides a DAS referenceserver giving access to a wide range of specialist annotations of the humangenome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy ahypothesis (top-down data mining), or to interrogate a database in order togenerate new hypotheses based on rigorous statistical correlations (bottom-updata mining). Domain (protein) A region of special biological interest within a single proteinsequence.", + "EnsemblEnsembl is a publicly available web resource that contains automatically annotated genomes.It is integrated with other available biological databases like Jasper for binding motifs.It is a much larger web resource than T1Dbase, and contains general information about the human genome including variants.These include SNPs, insertions, deletions and somatic mutations (Alterations in DNA that occur after conception, meaning that they are not inherited) for several species.Data from Ensembl can be accessed in a number of ways.The names of all the SNPs that occur in the T1D susceptibility regions can be collected from Ensembl using the Biomart tool (Kinsella et al., 2011).To achieve this, the coordinates of the T1D regions obtained from T1Dbase are uploaded to the biomart query page which allows one to search the genome browser and retrieve data like the names, chromosomal positions, and genic positions (referred to as \"consequence to transcript\", in Ensembl) of the SNPs.The SNP genic positions tell if a SNP is located within a gene, adjacent to a gene or whether they occur in inter-genic positions between gene coding regions, as well as the particular genes in which they are located.Advantages of Ensembl:There is a number of advantages to using Ensembl. (i) It is a larger web resource than T1Dbase and integrates data from a wide range of biological research sources into its database.Therefore, available information is quite comprehensive. (ii) Genic positions for 99% of the variants obtained from T1Dbase could be retrieved. (iii) Ensembl contains quality checks for genetic variants in its variation pipeline.A variant is flagged as failed if certain quality criteria are not met, for instance if none of the variant alleles match the reference allele of the variant.Generally, Ensembl was found to give more detailed information regarding the genic positions of variants compared to T1Dbase.Information about genes, including gene names, chromosomal coordinates, biotype (coding or non-coding), and number of splice variants, can also be retrieved from Ensembl.", + "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carriedout and the way the results are displayed. 4.4.1 EnsemblEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the WellcomeTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbardet al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide awindow on the draft genome, curating the results of a series of computational analyses.Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequenceassemblies as its starting point, but it is now based upon NCBI assemblies. TheEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify confirmed genes that arecomputationally predicted (by the GENSCAN gene prediction program) and alsosupported by a significant BLAST match to one or more expressed sequences orproteins. Ensembl also identifies the positions of known human genes from publicsequence database entries, usually using GENEWISE to predict their exon structures.Data retrieval is extremely well catered for in Ensembl, with text searches of alldatabase entries, BLAST searches of all sequences archived, and the availability of bulkdownloads of all Ensembl data and even software source code. Ensembl annotationcan also be viewed interactively on ones local machine with the Apollo viewer (Lewiset al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome BrowserThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;it, too, provides annotation of the NCBI assemblies, and it displays a similar array offeatures, including confirmed genes from Ensembl.Ensembl provides a DAS referenceserver giving access to a wide range of specialist annotations of the humangenome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy ahypothesis (top-down data mining), or to interrogate a database in order togenerate new hypotheses based on rigorous statistical correlations (bottom-updata mining). Domain (protein) A region of special biological interest within a single proteinsequence." + ], + [ + "The database contains trait data for severalhundred phenotypes including common inbreds, consomics, 80 BXD recombinant inbreds,hybrids, and over 60,0000 mutagenised mice including ENU mutants and several knockoutlines. SOPs are employed for phenotypic data acquisition. This publicly accessible databaseis an excellent example of one that can be made significantly more valuable to thecommunity with a standard in place for the reporting of these protocols. PhenoSITE (http://www.gsc.riken.go.jp/Mouse/phenotype/top.htm) provides baselinephenotype data for three inbred strains and their F1 hybrids.", + "The MouseGenome Database (MGD) has structured their mouse genomic data in terms of the Mammalian Phenotype Ontology[10]. Similarly, the Rat Genome Database (RGD) [11] alsodeveloped a phenome database, integrated with its genomicdata. In humans, the GeneNetwork (WebQTL) provides adatabase of complex traits with mappings to quantitative traitloci [12]. And several studies have focused on integratinghuman phenome and genome resources. For example, Butteet al. created a large-scale phenomegenome network byintegrating the Unied Medical Language System with humanmicroarray gene expression data [13]; and Aerts et al.de la Cruz N, Bromberg S, Pasko D, Shimoyama M, Twigger S, et al. (2005)The Rat Genome Database (RGD): Developments towards a phenomedatabase. Nucleic Acids Res 33: D485D491. Wang J, Williams RW, Manly KF (2003) WebQTL: Web-based complex traitanalysis. Neuroinformatics 1: 299308. Butte AJ, Kohane IS (2006) Creation and implications of a phenomegenome network. Nat Biotechnol 24: 5562. Aerts S, Lambrechts D, Maity S, Van Loo P, Coessens B, et al. (2006) Geneprioritization through genomic data fusion. Nat Biotechnol 24: 537544.", + "Shur-Jen Wang provided an overview of the Rat Genome Database, which provides a platform to improve model selection.The database includes a quantitative phenotype tool that provides expected ranges for a phenotype of interest across strain groups, drawing from published literature and other deposited data and resources.This tool can also be used to link phenotypic variation to damaging genomic variants, which are shown in parallel.", + "This is apublicly available database that contains phenotypes from hundreds of studies and alsolists basal gene expression data for many tissues, including brain regions. 3.4. Why Mice? The European house mouse (Mus musculus) has served as human analogue in basicresearch for many decades. Ethical and logistic limitations preclude almost all toxicogeneticresearch in humans. Genome-wide association studies in humans have revealed the geneticbasis for individual differences in several diseases; however, the exact mechanisms for geneaction are difficult to ascertain. Thus, the use of animal models to uncover mechanismsbecomes the approach [61,62].", + "A number of public data resources are also being established to provide freelyaccessible microarray data on drug- and toxicity-related phenotypes. For example,the Chemical Effects in Biological Systems (CEBS) database (Mattes et al. , 2004) isa highly recommended resource that accommodates gene-expression profiles, andproteomics and metabolomics data and allows very complex queries across morethan 100 experiments, mostly performed in rat liver. These experiments include datagenerated after exposure to members of key drug classes, including the antidiabetic,troglitazone (Rezulin); the antiepileptic, valproic acid; and the antidepressive, fluoxetine (Prozac) among other drugs (Mattes et al. , 2004).", + "Although these as yet include only alimited number of laboratories and genotypes, they all try to enlist larger groupsof researchers and to expand the animalmodels covered, and they are publicly available. It will be beneficial for the redesign ofnew behavioral measures that raw behavioral data will be available as well in thesedatabases. Access to this information will allowexperimenters to extract from the databasethe size of the genotype-by-laboratory interaction relevant to their experiment.", + ", 2014; see Section 9). GeneNetwork is a database that enables searching for 4000 phenotypes from multiple studies in the BXD, HXB, and in other recombinant inbred rodent families, as well as in other model organismsand even humans (Mulligan et al. , 2017). GeneNetwork employed asomewhat dierent strategy than MPD in that it did not rely solely onresearchers submitting their data. Instead the database operators extracted the data from the scientic literature and integrated them into auniform format (Chesler et al. , 2003).In the future, these two dataresources, the per strain phenotype data storage with thorough protocoldocumentation in MPD, the Rat Genome Database, and genetic analysissuite in GeneNetwork.org will be more closely integrated (Mulliganet al. , 2017). The public database of the International Mouse Phenotyping221Neuroscience and Biobehavioral Reviews 87 (2018) 218232N. Kafka et al. Consortium (IMPC) is intended to be the rst truly comprehensivefunctional catalogue of a mammalian genome (Morgan et al. , 2009;Koscielny et al. , 2014).", + "Useful Databases for the Exploration of Relationships Among Genetic Variations and Specific Phenotypes.", + "Shimoyama M, De Pons J, Hayman GT, Laulederkind SJ, Liu W, Nigam R, Petri V, Smith JR,Tutaj M, Wang S-J, The Rat Genome Database 2015: genomic, phenotypic and environmentalvariations and disease, Nucleic acids research 43(D1) (2014) D743D750. [PubMed: 25355511][24]. Dickinson ME, Flenniken AM, Ji X, Teboul L, Wong MD, White JK, Meehan TF, Weninger WJ,Westerberg H, Adissu H, High-throughput discovery of novel developmental phenotypes, Nature537(7621) (2016) 508. [PubMed: 27626380][25].", + "All data presented in this paper were deposited in the online databaseGeneNetwork (www.genenetwork.org), an open web resource that containsgenotypic, gene expression, and phenotypic data from several genetic referencepopulations of multiple species (e.g. mouse, rat and human) and various celltypes and tissues.35;36 It provides a valuable tool to integrate gene networks andphenotypic traits, and also allows cross-cell type and cross-species comparativegene expression and eQTL analyses.", + "This is apublicly available database that contains phenotypes from hundreds of studies and alsolists basal gene expression data for many tissues, including brain regions. 3.4. Why Mice? The European house mouse (Mus musculus) has served as human analogue in basicresearch for many decades. Ethical and logistic limitations preclude almost all toxicogeneticresearch in humans. Genome-wide association studies in humans have revealed the geneticbasis for individual differences in several diseases; however, the exact mechanisms for geneaction are difficult to ascertain. Thus, the use of animal models to uncover mechanismsbecomes the approach [61,62].", + "The Mouse Phenome Database would be a natural choice: it already provides acontrolled vocabulary for representing phenotype measurements and enforces correct strain nomenclature tofacilitate accurate comparisons across studies. Effectiveintegration of phenotypic and genetic data, facilitated bythe databases and analytical tools presented in this review,is critical to realizing the promise of the CC as it existstoday.", + "A number of public data resources are also being established to provide freelyaccessible microarray data on drug- and toxicity-related phenotypes. For example,the Chemical Effects in Biological Systems (CEBS) database (Mattes et al. , 2004) isa highly recommended resource that accommodates gene-expression profiles, andproteomics and metabolomics data and allows very complex queries across morethan 100 experiments, mostly performed in rat liver. These experiments include datagenerated after exposure to members of key drug classes, including the antidiabetic,troglitazone (Rezulin); the antiepileptic, valproic acid; and the antidepressive, fluoxetine (Prozac) among other drugs (Mattes et al. , 2004).", + "The GeneNetwork database provides open accessto BXD and other RI strain derived microarray data, single nucleotide polymorphism (SNP) data,and phenotypic data for quantitative trait loci analysis and gene expression correlation analyses. Gene expression data were exported for manually selected probes in the PDNN hippocampusdatabase (Hippocampus Consortium M430v2), and the PDNN whole brain database (INIA BrainmRNA M430). The Hippocampus database was chosen as one of the most elaborate brain databases,as well as most highly recommended dataset on GeneNetwork itself (http://www.genenetwork.org/webqtl/main.py?FormID=sharinginfo&GN_AccessionId=112).", + "The Mouse Phenome Database would be anatural choice: it already provides a controlled vocabulary for representing phenotypemeasurements and enforces correct strain nomenclature to facilitate accurate comparisonsacross studies. Effective integration of phenotypic and genetic data, facilitated by thedatabases and analytical tools presented in this review, is critical to realizing the promise ofthe CC as it exists today.", + "RGD database (www.rgd.mcw.edu) provides updated genetic,genomic, phenotype, and disease data generated from mouse, rat,and human. A total of 450 genes were downloaded using cardiomyocyte, myocyte, and cardiomyopathy as the keywords. GWAS Catalog (www.ebi.ac.uk/gwas) database provides published genome-wide association studies in human populations. Atotal of 126 genes associated with cardiomyopathy disease with pvalue 5 10 6 were downloaded using cardiomyopathy asthe key word. IMPC database (http://www.mousephenotype.org/) provides detailed phenotype data for the knockout mouse. A total of 636genes were downloaded using cardiomyocyte, myocyte, andcardiomyopathy as key words. collaborative eort [19].", + "A number of public data resources are also being established to provide freelyaccessible microarray data on drug- and toxicity-related phenotypes. For example,the Chemical Effects in Biological Systems (CEBS) database (Mattes et al. , 2004) isa highly recommended resource that accommodates gene-expression profiles, andproteomics and metabolomics data and allows very complex queries across morethan 100 experiments, mostly performed in rat liver. These experiments include datagenerated after exposure to members of key drug classes, including the antidiabetic,troglitazone (Rezulin); the antiepileptic, valproic acid; and the antidepressive, fluoxetine (Prozac) among other drugs (Mattes et al. , 2004).", + "A number of public data resources are also being established to provide freelyaccessible microarray data on drug- and toxicity-related phenotypes. For example,the Chemical Effects in Biological Systems (CEBS) database (Mattes et al. , 2004) isa highly recommended resource that accommodates gene-expression profiles, andproteomics and metabolomics data and allows very complex queries across morethan 100 experiments, mostly performed in rat liver. These experiments include datagenerated after exposure to members of key drug classes, including the antidiabetic,troglitazone (Rezulin); the antiepileptic, valproic acid; and the antidepressive, fluoxetine (Prozac) among other drugs (Mattes et al. , 2004)." + ], + [ + "d", + "Summary", + "b gg n n e e r c S", + "G", + "d", + "npg", + "Hence only G2D and Gentrepid will be discussed here.", + "F, forward; R, reverse.", + "~~~.", + "n.d.n.d.", + "3KR", + "What Is Relevant?", + "R5. Ubuntu philosophya)R5. Ubuntu philosophy (See page 66)", + "RSet in 10/12 pt Dutch801BT by Aptara\u0002Inc., New Delhi, IndiaDisclaimerThe publisher and the author make no representations or warranties with respect to the accuracy orcompleteness of the contents of this work and specically disclaim all warranties, including withoutlimitation warranties of tness for a particular purpose. No warranty may be created or extended bysales or promotional materials. The advice and strategies contained herein may not be suitable forevery situation. This work is sold with the understanding that the publisher is not engaged inrendering legal, accounting, or other professional services.", + "vid", + "npg", + "HG LG HG LG HG LG HG LG HG LG HG LG HG LG", + "rMZ" + ], + [ + "Pathway analysisSignificant over-representation of biochemical pathways from KEGG and Reactome as well as gene ontology terms were taken from the output of g:Profiler, http://biit.cs.ut.ee/gprofiler/ [15].Lists of genes (n > 10) pertaining to a given type of GxE interaction, i.e., either a particular phenotype or environmental factor, served as input to the pathway/ontology tool.g:Profiler was run with default settings.", + "Pathway EnrichmentPathway analyses were performed to explore possible biological mechanisms that may underlie the associations between the identified genes and aging pathways.We used The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, GO ontology, Pathway commons, and disease-associated genes from WebGestalt for our analyses (Wang et al. 2013).For each pathway, the hypergeometric test was used to detect the overrepresentation of our set of genes among all genes in the pathway.Lastly, FDR was controlled using the Benjamini-Hochberg procedure.In all cases, the complete set of proteincoding genes was used as the background.", + "Multiple exploratory dataanalysis will be used since different analysis can reveal different aspects of the data (Leung Y.F. ,Cavalieri D.). The program EASE (Expression Analysis Systematic Explorer) will furtheranalyze the data by looking at over-represented functional categories of genes in the network. Ingenuity Pathway Analysis will help to identify biological pathways that are relevant to thegenes of interest. The data will be analyzed using WebQTL which will link gene expressionwith behavioral data. Important specific genes found in the study will be further confirmed byreal time PCR.", + "Pathway analysisThe identified CpGs were annotated to nearest genes and evaluated for enrichment of gene-sets in the Reactome and the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways using Gene-Set Enrichment Analysis (GSEA) (http://www.broadinstitute.org/gsea/index.jsp).", + "Ingenuity Pathway Analysis (IPA)The IPA software (Ingenuity Systems, Inc.) was used to carry out the network composition analyses.The Ingenuity Canonical Pathways analysis was used to identify the most significant pathways that were set from the Ingenuity Pathway Analysis library.The significance of the association between a data set and the canonical pathway was measured in two ways: (1) a ratio of the number of molecules from the data set that map to the pathway divided by the total number of molecules that map to the canonical pathway was displayed, and (2) Fisher's exact test was used to calculate a p-value to determine the probability that the association between the genes in the dataset and the canonical pathway can be explained by chance alone [28].", + "Pathway analysisPathway analyses were carried out using the core analysis function of the Ingenuity Pathway Analysis software (IPA, Ingenuity Systems).We performed gene-based tests for association based on results from the PAR-dr and WL-dr discovery GWAS, using the Versatile Gene-based Association Study (VEGAS) software. (16) The full list of genes and gene-based p-values generated by VEGAS was uploaded into IPA for use as a reference set (16,965 genes were available for the PAR-dr analysis and 16,953 for the WL-dr analysis).From this list p-value cut-offs of 0.01 or 0.05 were used to identify IPA focus molecules (Supplemental Section 7).Networks generated by IPA provide insight into the molecular interactions of the focus molecules, independent of any predictions of biological function.", + "Inmetabolic pathways analysis , using bioinformatics toolssuch as RegulonDB, WIT.UM-BBD, EcoCyc,MetaCyc,Enzyme and metabolic pathways database, KEGG bythe researchers willprovide them with theencyclopaedic information about biochemical products ,substrates, catalysing enzymes,amino acids,carbohydrates, lipids and toxic compounds etc. and theirmetabolic pathways specific diseases related to thefailure in their functions. Bioinformatics tools likeKEGG, KEGG BRITE, Gene network database,Genepath help the researchers in analysis of genetic pathwaysand regulatory networks in such a ways that giveinformation about the genes, transcriptional factors,miRNA, genes encode enzymes involved in geneticrelated diseases.The techniques integrate the molecular information from thedatabases with simulation of metabolic networks. These methods also help in representation of genes, proteins andmetabolic pathways in combination with dynamic simulated environment. In this paper we reviewed someapplicable bioinformatics tools for analytical study of three types of pathways such as metabolic, genetics andsignalling pathways along with the information about their principle, work system and their direct access link to thedatabases and programs. This study helps scientists in fast, economic, high accuracy and large scale based outputs ofpathways analysis of their appropriate research involving the biochemical pathways.", + "Well-established methodologies such as Gene Set EnrichmentAnalysis (GSEA) [41] help in differentiating pathways as functionalunits from experimental populations. Manually curated pathwaysbased on expert knowledge and existing literature obtained fromthe Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/pathway.html) are another alternative measure used for validation [21]. Biological Network Inference from Microarray Data, Current Solutions, and AssessmentsTo evaluate the biological significance of a inference method,researchers explored an alternative measure based on Gene Ontology (GO) against functional, biological enrichment of a group ofgenes derived from inferred network modules [34].", + "Pathway analyses.We used two different programs for pathway analysis: Ingenuity (see URLs), version August 2012, application build 172788, content version 14197757) and the Disease Association Protein-Protein Link Evaluator (DAPPLE) 39 .", + "PATHWAYASSIST includes an automatedtext-mining tool, which enables the software to generate pathways from the entire PubMed database and other publicsources. Thus, we surveyed all published work in PubMedand extracted data on each candidate gene relating to itstranscriptional regulation, its binding partners and any othergene/protein that modifies or interacts with it. This analysiswas presented graphically and colour-coding genes identifiedin our study enabled easy identification of the genes lying inoverlapping pathways.", + "For example, Gene Ontology [1], Biocarta [2], GenMAPP[3] and KEGG [4] all allow a list of genes to be crossedwith biological functions and genetic networks, includingmetabolic, signalling or other regulation pathways. Basicstatistical analysis (e.g. , [5,6]) can then determinewhether a pathway is over-represented in the list, andwhether it is over-activated or under-activated. However,one can argue that introducing information on the pathway at this point in the analysis process sacrifices somestatistical power to the simplicity of the approach.", + "Gene Ontology and Pathway analysisData sets were interrogated using the Ingenuity Pathways Analysis (IPA) application (Ingenuity Systems, Redwood City, CA; http://www.ingenuity.com).IPA was used to identify enriched canonical pathways, gene networks, functional classes, and toxicity lists (molecules involved in known toxicity processes).", + "Analysing participating pathways is an important aspectof any genes functional analysis strategy. In this view,REACTOME (http://www.reactome.org) [13] is a crossreferenced, manually curated and peer reviewed pathwaydatabase. LitInspector (http://www.litinspector.org) [14]and NetPath (http://www.netpath.org/index.html) [15]allow one to access curated signal transduction related literature and interaction pathways respectively. PredictiveNetworks (http://predictivenetworks.org/) [16] integratesgene interactions and networks information from PubMedliterature and other online biological databases and presents it in an accessible and efficient user interface. Twoother noteworthy commercial tools are GeneGo andIngenuity IPA.", + ", 2011; Kim et al. , 2011b; Zhang et al. ,2011). A number of pathway analysis software packages are available such as PathwayStudio(http://www.ariadnegenomics.com/),and MetaCoreTM (http://www.genego.com/metacore.php). In such software packages, thealgorithms calculate the statistical signicanceof the expression changes across every group orpathway in the database, thus, allowing identication of groups or pathways most stronglyaffected by the observed expression changes(http://www.ariadnegenomics.com/technologyresearch/pathway-analysis/).", + "Network analyses.Network analyses were carried out using the Ingenuity Pathway Analysis tool 66 .P values for canonical pathways and functions were calculated from the observed number of candidate genes in the gene set, compared with the number expected under the null hypothesis and corrected (Bonferroni) for the number of pathways tested.", + "Pathway enrichment analysis.Pathway enrichment analysis for the predicted genomic key driver variants was performed using the ClueGo(v2.1.7) 74and CluePedia(v1.1.7) 75plugins in Cytoscape(v.3.1.0) 76with the GO database (29.02.2016 download).Pathways with a Bonferroni-corrected p-value are shown with full data in Supplementary Data 4. Pathway enrichment analysis for the coexpression modules from transcriptomic analysis was performed by R package goseq with default parameters 77 .", + "Pathway analysisFor the 85 learning-associated genes, we used a combination of bioinformatics software that included E! Ensemble, Protonet, Pandora, and Pubmed and Pubmatrix searches (Becker et al., 2003).We also used http://bind.cafor protein-protein interaction information.Using this approach (Burger et al., 2007;Velardo et al., 2004) we found information on 50 genes (Table 3 and Supplementary Table 3); the other 35 transcripts were expressed sequence tags (EST).", + "Finally, using the top 24 results, we conducted a pathway analysis with the Database for Annotation, Visualization and Integrated Discovery (http://david.abcc.ncifcrf.gov/).", + "Pathway analysis helps to add structure to the very large amount of data generated by microarrays.This type of analysis allows determining whether differentially methylated genes belong to predefined networks more than by chance alone.Gene ontology enrichment was performed using the Ingenuity Pathway Analysis (IPA) software (Ingenuity System).IPA compares a provided list of genes (differentially methylated genes in this case) to a reference list of genes included in various biological pathways.It provides a P value based on a hypergeometric test identifying over-represented gene ontology categories." + ] + ], + "task_id": [ + "029A427CEEBABE644F12EE390469B134", + "7C028B1D0013EA11574B094986ABE4C2", + "55562016699AFE4B8AD9A7F29A806CB5", + "C9B1B98F9207B79EBBC98790A769CB51", + "242918F32291CC085DEB319A7EE3284B" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_2.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_2.json new file mode 100644 index 0000000..ca4b36c --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_2.json @@ -0,0 +1,108 @@ +{ + "question": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?" + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs." + ], + "contexts": [ + [ + "Selection could occur at multiple levels, from germ cell generation and propagation to fertilization and early embryonic growth.Chromosomal abnormalities, including aneuploidy, were found in 10-20% of spermatozoa and oocytes (20) and in the cleaved embryo, with a 21% rate of abnormalities in preimplantation embryos (21).These findings led to a model for natural selection against chromosome abnormalities (21).Selection extends to the end of gestation: Only approximately 30% of all conceptions result in a live birth, with more than half of aborted fetuses containing chromosomal abnormalities (22), a number likely to be an underestimate because of technological limitations in measuring all possible mutations.But even in the very small fraction of germ cell duos that survive this withering genome attack and result in a live birth, a number of severe de novo mutations will still be found (23).The data on gross chromosomal alterations suggest that overall, mutation frequency early in life is very high.The functional consequence, however, is limited because of selection.Somewhat surprisingly, this picture points toward an initial decline in genomic alterations, allowing the adult individual to acquire a somatic genome optimally equipped to provide function.", + "The phenotype ofthe F1 hybrids is compared to those of the parental inbred strains to revealdominance or semi-dominance relationships between the alleles that aect thephenotype. Phenotypic dierences between reciprocal F1 hybrids indicate thatone or more of the following factors may aect the trait: (1) sex linkage (X- or Ylinked traits), (2) genomic imprinting of QTLs that aect the phenotype, (3)prenatal maternal eects (eects of intrauterine environment), and/or (4)postnatal maternal or paternal eects (eects of maternal and/or paternalparenting behaviour on ospring).", + "Sex brings harmful alleles together into thesame genetic background, allowing selection to more efficiently purge them fromthe population and potentially producing some offspring that are fitter than eitherparent. However, the benefit of recombining deleterious mutations may depend on thenature of the epistatic interactions between them. The mutational deterministic hypothesis(Kondrashov 1988) depends partly on this epistasis.In most plants and animals, sexis a necessary component of reproduction, and the question for evolutionary biologistsis why reproductive mechanisms have evolved that way. In one of the experimentsdescribed next, evolutionary geneticists have nevertheless devised a way to compareevolution with and without recombination in the obligately sexual fruit fly.This disparity in investment is the basis for the twofold cost: asexualfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. Butsex usually also involves the basic process of physical recombination: the breakage andreunion of two different DNA or RNA molecules. Of these two processes, recombinationis clearly the more widespread feature of sexual reproduction. A variety of reproductivesystems, such as selfing and automixis, involve recombination but not outcrossing. Incontrast, relatively few reproductive systems have outcrossing without recombination.", + "Crossing over-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes cross over one another.Crossing over results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.This process is also known as meiotic recombination.The reason for the rarity of these mutations is natural selection: If the mutations result in disorders that decrease health and reproductive fitness, they will eventually be eliminated from a population.In exceptional cases, mutations may cause both beneficial and detrimental consequences, resulting in opposing forces of positive selection and negative selection that may cause the mutations to be preserved at nonrare frequencies in a population.For example, the HbS mutation in the HBB gene (which produces the subunit of hemoglobin) causes sickle cell disease when present in both alleles, a detrimental consequence, but protects against malaria when present in 1 allele, a beneficial consequence, ensuring that the mutation persists in populations in areas of the world where malaria is endemic.Genes are passed from parents to offspring via the process of meiosis by which gametes, the egg cells in the mother and the sperm cells in the father, are generated.Ordinarily, each cell has 23 pairs of chromosomes; the gametes have 23 unpaired chromosomes.In meiosis, the 23 pairs are split so that each gamete receives 1 chromosome from each pair (Figures 8 and 9).Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full complement of 23 chromosome pairs restored.If all goes well, the zygote gives rise to a live offspring.Recombination (meiotic recombination)-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes recombine with one another.Recombination results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.Also known as crossing over.", + "In the generation of gametes, crossing over regularly occurs, and genetic information is swapped between members of a chromosome pair.That doesn't matter within inbred animals, because the swapped parts are identical.In an F 1 animal, however, the chromosomes of a particular pair are genetically different, one each having come from each parent.Each gamete produced will be unique, as will be each F 2 zygote formed by uniting of the gametes from two F 1 parents.An F 2 group thus provides for expression of some genetic variability.This variability is limited to the allelic differences existing between the parent strains of the F 1 s, so that another F 2 , derived from different inbred strains, will express different genetic differences.", + "Sex brings harmful alleles together into thesame genetic background, allowing selection to more efficiently purge them fromthe population and potentially producing some offspring that are fitter than eitherparent. However, the benefit of recombining deleterious mutations may depend on thenature of the epistatic interactions between them. The mutational deterministic hypothesis(Kondrashov 1988) depends partly on this epistasis.In most plants and animals, sexis a necessary component of reproduction, and the question for evolutionary biologistsis why reproductive mechanisms have evolved that way. In one of the experimentsdescribed next, evolutionary geneticists have nevertheless devised a way to compareevolution with and without recombination in the obligately sexual fruit fly.This disparity in investment is the basis for the twofold cost: asexualfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. Butsex usually also involves the basic process of physical recombination: the breakage andreunion of two different DNA or RNA molecules. Of these two processes, recombinationis clearly the more widespread feature of sexual reproduction. A variety of reproductivesystems, such as selfing and automixis, involve recombination but not outcrossing. Incontrast, relatively few reproductive systems have outcrossing without recombination.", + "Aberrant recombination patterns on chromosomes that have missegregated have also been identified as an important factor, in both male and female gametes (Table I).This is because recombination together with cohesion of sister chromatids establish the unique 'bivalent' chromosome structure where homologous partner chromosomes are tethered together, a configuration that is critical for their accurate segregation in meiosis I (Fig. 2A).The remarkable feature is that recombination occurs in foetal oocytes whereas chromosome segregation takes place decades later (Fig. 2A).Since mammalian oocytes are arrested at the G2/M transition (or dictyate stage), this raises the intriguing question of how the bivalent is maintained until the meiotic divisions.", + "Traditionally, it has been agreed that thenal sex of an individual (phenotypic sex)depends on two sequential processes: the sexdetermination system of the species and thegonad differentiation process (Valenzuela,2008). However, recently, these two seeminglydistinct processes are viewed as part of a general process leading to gonad formation andsex ratios (Sarre et al. , 2004; Quinn et al. , 2011;Uller and Helantera, 2011).However, we expect thatonly at this level, the most signicant contributions brought by integrating epigenetics will bemade. Concluding Remarks and FutureProspectsFish sex ratios are the result of a complex interaction between genetic, biochemical, and environmental interactions. The ultimate resultof these interactions at the individual level isgender: male or female. However, at the population level, the combination of sex determination and differentiation sets the sex ratio. Inturn, sex ratios dene the reproductive capacityof populations and, if sex growth dimorphismexists, also the growth characteristics, something very important in an aquaculture context.", + "Obehav is, in turn, influenced by offspring genesand environment (Ogene and Oenvir respectively). Hence, indirect genetic effects (blue arrows)and direct genetic effects (red arrow) are important influencers of behaviour. B) Parentoffspring conflict theory predicts that parental resource investment and offspring solicitationbehaviours are influenced by the fitness benefit to a focal individual (O), cost to a socialpartner such as a sibling (S1 and S2) or parent (P), and by their coefficient of relatedness(black arrows). 42Figure 2: Genomic imprinting can result in divergent phenotypes from the samegenotype. A) A paternally imprinted gene, i.e. maternally expressed.", + "Because of the small contribution, through the sperm, ofthe paternal transcriptome to the fertilized zygote, and because of the stronger maternal contributionto child rearing in most model organisms, parental effects are typically thought of as synonymous withmaternal effects, although true paternal effects are known to exist (Rando, 2012). Maternal effects have been shown to be important during embryonic development, leading todifferences in the birth weight of mice depending on the genotype of the mother (Cowley et al. ,1989; Wolf et al. , 2011).Therefore, the resulting phenotypic patterns lag a generationbehind the genetic transmission of the causal variants. The most well-studied parental genetic effectsare caused by deposition of maternal transcripts into the egg prior to fertilization, resulting indifferences in early embryonic development depending on the genotype of the mother. Certain geneshave also been shown to respond to maternal influence after birth through genetically definedmaternal behaviors (Weaver et al. , 2004).", + "The phenotype ofthe F1 hybrids is compared to those of the parental inbred strains to revealdominance or semi-dominance relationships between the alleles that aect thephenotype. Phenotypic dierences between reciprocal F1 hybrids indicate thatone or more of the following factors may aect the trait: (1) sex linkage (X- or Ylinked traits), (2) genomic imprinting of QTLs that aect the phenotype, (3)prenatal maternal eects (eects of intrauterine environment), and/or (4)postnatal maternal or paternal eects (eects of maternal and/or paternalparenting behaviour on ospring).", + "It was believed by many that for each trait variant we should expect to find acorresponding genetic change, or gene for that trait. Through historical happenstance therelationship between genes and traits was set up and treated as if it were one-to-one. But theproduction of a trait involves not only genes, but also their interactions with each other and theenvironment, and chance." + ], + [ + "distinguishing prenatalfrom postnatal maternal effects, see below). Maternal effects canaccount for a large proportion of phenotypic variance, especiallyduring early life, and for some traits explain more variation thandirect genetic effects [33, 97, 99, 100, 102115]. However, maternal and offspring genotype are correlated (i.e. half their genes areshared), and in inbred lines they are fully confounded, thus separating the effects of their respective genotypes is difficult. To removethis confounding effect cross-fostering has been used, both in thelaboratory and in the field [119, 131].", + "Using genetic markers, the pattern of inheritance can be tracked throughfamilies. For example, by analyzing a marker linked to the eye color genein several generations, it is possible to determine from which grandparents achild has inherited its eye color alleles. More importantly, nding a markerlinked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.", + "Although autosomal SNPs are commonly used as genetic markers to infer ancestry or race/ethnicity membership, haploid such as mitochondria, Y-DNA, and X-lined markers are also important to provide separate stories of ancestry of individuals from paternal and maternal sides [42,43].Therefore, genetic structure created due to autosomal markers could be different from those of lineage markers (often influenced by political, social, and migration history of individuals/populations).mitochondrial DNA or mtDNA haploid is the maternally inherited mitochondrial genome (mtDNA) [44].All children inherit mtDNA from their mother, with no admixture from the father.Like Y-line DNA, mtDNA is passed intact from one generation to the next but through maternal line.a) Autosomal DNA (testing both sexes) markers: autosomal DNA tests utilize DNA from the 22 pairs of autosomal chromosomes.Autosomal DNA is inherited from both parents.Autosomal testing provides percentages of ethnicity using autosomal DNA SNP test (i.e., ancestry informative markers), and it is the most commonly used test to infer ancestry across diploid genome.b) Y-DNA or Y-SNPs (paternal line testing) markers: a haploid Y-DNA is the paternally inherited non-recombining portion of the Y chromosome, and it tests only for males.The Y-DNA testing tests the Y chromosome which is passed intact from father to son with no DNA from the mother.Y-DNA testing can then be used to trace direct paternal line.Y-DNA remains the same in each generation, allowing us to compare surname from different regions to see if we are from the same family.Y-line testing does not indicate anything about the contributions of the other ancestors in a family tree.In other words, you could be 3/4th Native American, with only the direct paternal line being European, and this test would tell you nothing at all about those other three Native lines.When testing the Y-chromosome, there are two types of tests, short tandem repeat (STR) and SNP markers.STR tests are best for recent ancestry while SNP tests tell about more ancient ancestry.c) Mitochondrial DNA (maternal line testing) markers:", + "Additional information about past breeding practices can be gleaned by quantifying the number of reproductive males and females in a population.This can be achieved by comparing levels of genetic diversity between sex chromosomes, autosomes and mtDNA 99 .In cattle, for example, gene flow from aurochs is evident in the autosomes but is absent in mtDNA 41 .This has been interpreted as a management strategy that may have involved allowing insemination of domesticated females by wild bulls 41,100 .In horses, a comparison of the levels of diversity of the Y chromosome and the autosomal chromosomes demonstrated that some cultures allowed fewer males to breed and instead selected specific stallion bloodlines 55 .This male-oriented breeding strategy was not practised by the Romans and only became increasingly prominent in the past 1,000 years as a result of the growing influence of Oriental stallions (Arabian, Persian and Turkmen) 101 .", + "Dr Ring: What makes the maternal gene so peculiar compared to the paternal?Dr Cookson: If you look in the epidemiologic sense, many studies show that there is increased risk of allergic disease if the mother is affected.However, very few studies have actually set out to test that formally and most of them might suffer from some sort of selection bias because the mother is more likely to be aware of her symptoms and feel guilty, and so on.It is very difficult to explain.Is it genomic imprinting, where the gene is only active when transmitted through the mother?I do not think all of these genes would be imprinted, though it is possible.It also seems that there are effects of the maternal phenotype.The maternal phenotype, if the mother is affected or unaffected, determines the strength of the maternal effect.Again, if a gene was imprinted, you would not expect maternal phenotype to be important.So, I think that this has something to do with maternal/fetal interaction, either through the placenta or shortly after birth.There is the issue of immune conflict between mother and child.At the same time, the mother is trying to prime the infant's immune system.", + "Genetic and Genomic Discovery Using Family StudiesIngrid B. Borecki, PhD; Michael A. Province, PhD G enetic studies traditionally have been performed on sets of related individuals, that is, families.Mendel's early studies in sweet peas (Pisum sativum) on the inheritance patterns of discrete traits from parents with specific mating types to offspring has shed light on the basic mechanisms of inheritance, including the fundamental laws of segregation of discrete factors (genes) from parents to offspring and the cosegregation of genes that are closely located on a chromosome (linkage).The distribution of traits within families exhibited mathematical segregation ratios in offspring from known mating types.These expected segregation ratios have been used as an important discovery tool in the study of human diseases in pedigrees, providing evidence for a multitude of single-gene disorders.Furthermore, in some cases, trait cosegregation with genetic markers with known positions provides mapping information that enables localization and, ultimately, identification of the relevant causative gene.", + "In fact, this idea has been pursued before in thecontext of signatures of reproductive isolation and shown to revealpatterns consistent with epistatic gene interactions that arise in theshape of Dobzhansky-Muller incompatibilities [10,11]. In contrast to the mouse data, the available human genotypeswere derived from outbred, ethnically distinct populations. In thiscase pairs of functionally interacting genes can be detectedfollowing a slightly different approach.", + "Family StructureThe first re-identification method (FAMILY) employs genealogical data accompanying genomic data.Genealogies, rich in depth and structure, permit the construction of complex familial relationships.Consider a simple family structure of two parents and one child.Since the parental genders are guaranteed, there exist 2 variants of this structure, since the child's gender is either male or female.When disease status is taken into account, it is represented as a Boolean variable; either an individual afflicted or not afflicted.In this aspect, all three family members can be represented as three attributes {Father, Mother, Child}, and there exist (father's disease status)*(mother's disease status)*(child's disease status)*(child's gender) = 2*2*2*2 = 16 possible family-disease combinations.In reality, pedigrees are much more robust than a simple nuclear family.For example, a three-generation family of two children per family permits on the order of 10 5 distinct variants of the family-disease structure and 10 6 individuals that could be uniquely characterized.The number of combinationsk is larger when supplementary information, such as living status or medical/genetic features, is considered. 16e ability to determine unique family structures is only one part of the re-identification process.These structures must be linked to identifiable information, which, in many instances, is publicly available in the form of various genealogical databases.These databases are accessible both offline and via the World Wide Web.For example, genealogical records are available in many public databases, including ,Ancestry.com>,,Infospace.com>,,RootsWeb.com>,,GeneaNet.com>,,FamilySearch.org>, and ,Genealogy.com>. {From such data, it is not difficult to construct family structures and, with such information in hand, an adversary can link disease-labeled family structures to named individuals.", + "Fig. 3. Illustrations of the three CEU pedigrees (black) showing how genetic information from distant patrilineal relatives (arrow; red, patrilineal lines) can identify individuals.Filled squares represent sequenced individuals.To respect the privacy of these families, only abbreviated versions are presented.The sex of the CEU grandchildren was randomized.The numbers of grandchildren are not given.", + "When I was in high school, I remember often trying to match my friends to their parents at various school functions and being surprised at how easy this was.As human geneticists, in spite of the enormous advances being made in our field, we still cannot answer many of the everyday questions that we are asked, such as: \"Why does he look just like his mother? \"Max Perutz [1], in a recent editorial comment in the New Scientist entitled \"The Molecular Biology of the Future,\" suggested some questions, for, as he put it, \"an examination in some future century. \"Here are two of them: (1) \"The time has come\" the Walrus said, \"To talk of many things ...And why the sea is boiling hot And whether pigs have wings. \"Calculate the amount of genetic information this would require in megacricks.", + "Using genetic markers, the pattern of inheritance can be tracked throughfamilies. For example, by analyzing a marker linked to the eye color genein several generations, it is possible to determine from which grandparents achild has inherited its eye color alleles. More importantly, nding a markerlinked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.", + "Incontrast, genomic imprinting is due to epigenetic changes withinthe individual causing differential gene expression characterizedby either complete or partial silencing of one parental allele(Barlow, 2011; Abramowitz and Bartolomei, 2012; Ashbrook andHager, 2013). As both mothers and fathers had contact with thepups in our study, our observed PGEs could come from eitherparent. Among quantitative USV traits only peak amplitude of calldisplayed a possible parent-of-origin effect. For call number, callduration, mean peak frequency, and all morphological traits,there were no significant parent-of-origin effect in reciprocalF1 females. In contrast, Thornton et al.", + "Another way of avoiding stratification is to use family-based samples.This approach has several theoretical advantages: as well as being immune to stratification 114 , these samples can be used to determine whether an allele has different effects on disease when it is inherited maternally or paternally 115 , and DISCORDANT SIB designs [116][117][118] can control for the effects of shared environment.Furthermore, more complex family-based designs are possible 119 that might allow combined association and linkage analysis 120 , and family-based association tests have also been developed for quantitative traits [94][95][96][97][98] .However, pure sibship-based association studies are underpowered relative to case-control studies 107,116,117 , and the requirement for living parents might introduce an age-of-onset bias towards younger patients for diseases that usually arise late in life.Furthermore, family-based samples are often much more difficult to collect, particularly if larger pedigrees are sought.Finally, the most commonly used family-based design, the TRANSMISSION DISEQUILIBIRIUM TEST (TDT; see REF. 114) is susceptible to technical artefacts (see below).", + "Because mtDNA is not subjected (as far as we know) to sexual recombination and crossover at the time of nuclear meiosis, nature must call on other means to ensure that inevitable germ plasm mtDNA mutations (Medvedev, 1981) are not transmitted.These mutations among primary oocytes, on the face of it, can be expected to increase with time, that is with maternal age.Empirical data on this question are incomplete and conflicting, being mostly confined so far to searches for deletions rather than point mutations (Chen et al., 1995;Keefe et al., 1995).It is inevitable, however, that there will be such mutations and that there must therefore be a reliable physiological mechanism (a) for giving an opportunity for back-mutations to occur, (b) for selecting in favor of those back-mutations (thus preserving the genome) and in favor of rare advantageous mutations, and (c) for preventing the spread of persistent harmful mutations through the population -mutations that are too slight (or too late in origin) to have escaped intraovarian culling.The sheer conservation of the mitochondrial genome over 0.5 billion years or more, despite a mutation rate estimated at 10 -20 times that of nuclear DNA, is ample reason to conclude that such a physiological purification process must exist.", + "To scrutinize the polygenic networks underlying complex diseases, however, mouse resourcesthat are optimized to study the actions of isolated genetic loci ona fixed background will be insufficient on their own. For example, predisposition to the metabolic syndrome is inherited ina non-Mendelian fashion stressing genetic heterogeneity andmultigenetic pathogenesis (Nandi et al. , 2004). With the reawakening as to the extraordinary genetic resources and phenotypicdiversity archived in extant inbred strains, however, a foundationis in place for tracking down these complex traits and quantitative trait loci (QTL).", + "Otherwise, tens of thousands or markers will appear significant inthe genome-wise association studies using up to one million geneticmarkers. Approaches to control for stratification include using ofself report of ancestry or genetically derived principle componentsin the analysis. For studies using inbred mouse lines, a cladogramwhich is a hierarchical grouping based on phylogenetic analysis ofstrain relatedness can be created to subdivide inbred strains intomore genetically homogenous subgroups.", + "Although bilateral descent is the norm in Western societies, it is not universal and there is variation with cultural practices around lineage.In certain societies, individuals place greater importance on (and have greater knowledge about) one side of the family than another (unilineal descent).Thus, individuals in patrilineal groups trace relationships through males only so that your father's brother's children are members of your family, but not your father's sisters (Kottak, 2007).They are members of their husband's group or family.Efforts to create a family pedigree may be hampered if the participant is not familiar with her mother's relatives, but her mother's brother's children (her cousins) may be able to supplement her overall family history.Knowledge about the cultural system of unilineal descent avoids assuming the universality of bilateral descent.Cultural beliefs such as these also have implications in the conduct of genetic research in terms of confidentiality and autonomy (Benkendorf et al., 1997;Wertz, 1997).One cannot assume that the named proband is in a position to speak for the extended family in agreeing to participate in any genetic research (DudokdeWit et al., 1997).", + "In particular in polygynous species, a femalesoffspring may have different fathers and are thus more closely related through the maternalthan the paternal line. Therefore, any fitness cost to mothers, such as increased provisioningand care, affect maternally derived genes more strongly than paternally derived genes,leading to the silencing of the maternal copy (i.e. paternal expression) of genes that increaseresource transfer. 5. Coadaptation between offspring and maternal traitsThe genetics of the co-evolution of parental and offspring traits has been investigated usingquantitative genetics models and in several empirical studies (Agrawal et al.In thisscenario, genes expressed in parents will be selected for their effects on parental behaviourwhile genes expressed in offspring will be selected for their effects on influencing parentalbehaviour. At the genetic level the predicted conflict between paternal and maternal genomes isthought to have led to the evolution of genomic imprinting (monoallelic gene expression). Genomic imprinting effects are good examples of offspring genetic effects on maternal carebecause of the impact on the quality of maternal care and level of resource provisioning (e.g. Li et al. , 1999)." + ], + [ + "When a cell divides in two, both daughter cells must receive a copy of allthe DNA, i.e. the whole genome. During replication the two strands of themother cell DNA are separated, and new nucleotides are put together to maketwo double helices identical to the original one, see Figure 2.1. ATAAGACCG. . . . . . . . ATTCTGGCGACCG. . . . TGGCTA. . ATTCCG. . . CTGGCFigure 2.1: A DNA chain consists of two strands of complementary nucleotides.WhenDNA is replicated, two double chains identical to the original one are created. The human genome consists of approximately 3 billion nucleotide pairs. The chain is divided into pieces called chromosomes. A gene is a short segment of a chromosome where the nucleotide sequence gives the blueprint fora particular substance in the body, for example insulin. Only a small fractionof the DNA consists of genes. In between the genes there are long non-codingregions of which the function is largely unknown.Germ cells originate from 46-chromosomecells, and a sophisticated process called meiosis ensures that exactly 23 chromosomes, and exactly one from each homologous pair, ends up in each daughter cell. Before the homologous chromosomes are distributed to the daughtercells they are paired up side by side. While they are positioned close togethera process called crossover often occurs, see Figure 2.2. The homologous chromosomes randomly exchange large chunks of DNA. As a result, each chromosome that a child has inherited from a parent will most often contain segmentsfrom both grandparents.", + "Replication handlingReplication is a significant part of any comparative experimentation to raise accuracy and more significantly to deliver a basis for recognized statistical interpretation which is nowadays becoming broadly accepted for genomic data.In genetic and genomic context, replication can have various forms [97]: technical replicates, duplicate gene spots, and biological replicates.It is vital to understand that any sort of replication provides information only concerning the specific source of changeability related to that kind of replication and no other.Based on the experimental setting, it may consequently be imperative to consider one, two or all these categories of replicates.", + "Central dogma-An explanation of the flow of genetic information within a cell.Information is stored in the DNA of the genome, transcribed into RNA, and translated into protein.With a few exceptions, genetic information follows this path only in the forward direction.Basics of Molecular BiologyDeoxyribonucleic acid (DNA) is a molecule with 2 strands that are wrapped around each other in a helical formation, hence its description as a double helix (Figure 1).The outer portion of the helix contains the sugar and phosphate backbone; the inner portion contains the coding bases: adenine (A), cytosine (C), guanine (G), and thymine (T).The genetic information of an organism is determined by the order of the sequence of the bases; with 4 bases available, the number of potential sequences is almost infinite.The versatility of DNA results from the obligatory pairing of bases in the 2 strands, forming base pairs.An adenine in 1 strand is always matched up with a thymine in the other strand, and cytosine is always paired with guanine.Thus, the 2 strands contain redundant information, and each can serve as a template on which a new complementary strand can be synthesized.This allows easy duplication of the DNA so that, when a cell divides into 2 cells, each descendant cell receives the same genetic information as the original cell.Figure 1.The structure of DNA.Each DNA strand has a sugarphosphate backbone (not shown in detail) with a sequence of bases that come in 4 versions: adenine (A), cytosine (C), guanine (G), and thymine (T).Two DNA strands can combine to form a double helix, the stable form of DNA found in chromosomes.Holding the strands together are base pairs: Guanine on 1 strand binds to cytosine on the other strand, and adenine on 1 strand binds to thymine on the other strand.Thus, the 2 strands are complementary and contain redundant information.Figure 8. Meiosis, part 1.Before the first cell division, meiotic recombination (crossing over) between a chromosome pair occurs.Figure 9. Meiosis, part 2. The second cell division yields gametes, which have only half of the complete genome (unpaired chromosomes).Two gametes subsequently fuse (fertilization) to create a zygote that has a complete genome and can give rise to an organism.Figure 8. Meiosis, part 1.Before the first cell division, meiotic recombination (crossing over) between a chromosome pair occurs.Figure 9. Meiosis, part 2. The second cell division yields gametes, which have only half of the complete genome (unpaired chromosomes).Two gametes subsequently fuse (fertilization) to create a zygote that has a complete genome and can give rise to an organism.", + "When a cell divides in two, both daughter cells must receive a copy of allthe DNA, i.e. the whole genome. During replication the two strands of themother cell DNA are separated, and new nucleotides are put together to maketwo double helices identical to the original one, see Figure 2.1. ATAAGACCG. . . . . . . . ATTCTGGCGACCG. . . . TGGCTA. . ATTCCG. . . CTGGCFigure 2.1: A DNA chain consists of two strands of complementary nucleotides.WhenDNA is replicated, two double chains identical to the original one are created. The human genome consists of approximately 3 billion nucleotide pairs. The chain is divided into pieces called chromosomes. A gene is a short segment of a chromosome where the nucleotide sequence gives the blueprint fora particular substance in the body, for example insulin. Only a small fractionof the DNA consists of genes. In between the genes there are long non-codingregions of which the function is largely unknown.", + ". . . . . . . Appendices301Appendix ASummaryAll organisms have a genome made of DNA (deoxyribonucleic acid). The genome can be found in nearly every cell and is the blueprint for thegrowth, development, maintenance and repair of the body. It performsthese functions by transcribing small pieces of DNA, the genes, fromthe genome and translating them to proteins. These proteins are thetiny workhorses of the body that break down food, give bones theirstrength, make muscles move, let brains think, and so on.", + "Every nucleated cell in our body, with the exception of egg and sperm, has a complete genome in its nucleus.Each time the cell divides by the process of mitosis, all the DNA in that cell is replicated, so that each of the two new daughter cells has its own copy of the entire genome.The mitochondria, which produce the energy required for all the cell's functions, contain a small circular DNA molecule that is also part of the genome.Every living organism has a complete genome in each of its cells.And the structure of all DNA is the same.The DNA in human cells has the same structure as the DNA in the cells of a butterfly, a whale, a flower, or a worm.What differs is simply the amount of DNA carried by each organism and the order of the nucleotides in each strand.", + "IntroduclJonEver since the structure of DNA was elucidated by Watson and Crick in 1953, it has been generally assumed that genomic DNA, in view of its vital role in transferring hereditary information from generation to generation, is a stable molecule unaltered in its structure by the surrounding events.This taken for granted, its remarkable attribute of stability has turned out to be a myth.As noted by Haynes (1988) DNA is made up of rather ordinary molecules that are not endowed with any peculiar kind of quantum mechanical stability.As such, DNA must be able to undergo all kinds of structural modifications at the body temperature and with many other chemicals in proximity.Much evidence has accumulated in recent years to prove that this is indeed the case, and normal cellular metabolism itself is enough to cause various types of damage to the genomic apparatus.If the genomic DNA can be assaulted in so many ways the natural question that would emerge is: How is genetic informational integrity maintained and transmitted through generations?", + "The second form of genome partitioning was by DNA replication direction.Since the entire genome is replicated every time a cell divides (but only a portion is transcribed), replication direction has the potential to exert larger asymmetries in mutational data.However, determining direction is much more challenging for replication than transcription, since the precise locations of replication origins in the human genome are not known.This has precluded a comprehensive analysis of replicative strand asymmetry thus far.", + "Each gene is a segment of deoxyribonucleicacid (DNA) and the genes are joined together to make up a set of very long DNA moleculescalled chromosomes. In diploid organisms like humans and mouse, there are two copies of eachchromosome. One copy is inherited from each parent. DNA is comprised of a sequence of nucleotides and the four primary DNA bases found innucleotides are Adenine(A), Cytosine(C), Guanine(G), and Thymine(T). Each base binds withanother specific base (T with A and C with G).", + "If this DNA were to be uncoiled and laid out end toend, it would extend about 3 m. Obviously, this cannot possibly fit into a cell,and extended DNA would be susceptible to breakage during replication andcell division. In eukaryotes, genetic material is thus organized into complexesof DNA with core histones and other chromosomal proteins that together formchromatin. The chromatin repeating unit includes two copies each of four corehistones H2A, H2B, H3 and H4 (collective molecular mass 206,000) wrappedby 146 bp of DNA.", + "The core of the human genome is a DNAdouble helix containing ~3 billion base pairsof genetic information. It is continuously challenged by a variety of genotoxic stresses thatcause ruptures of the DNA sugar-phosphatebackbone. DSBs are the most lethal type ofDNA damage. They can be caused by collapseof the DNA replication fork or, less commonly,induced directly by environmental insults suchas ionizing radiation or radiomimetic drugs. To manage these lesions, cells have evolved twomain pathways of DSB repair. Homologousrecombination occurs in mitotic cells, usuallyduring the S and G2 phases.", + "Cellular and Genetic ChangesThis section will explain how cells normally divide.It will also describe how an unexpected change in the structure of DNA can sometimes cause harm to the body.New tools to study genetic variations of common diseases and to identify genetic variations common to specific diseases will also be presented.Table 1. -Glossary of Genetic and Genomic TermsDeoxyribonucleic acid (DNA) -The chemical inside the nucleus of a cell that carries genetic instructions for making living organisms.Double Helix -The structural arrangement of DNA, which looks something like an immensely long ladder twisted into a helix or coil.The sides of the \"ladder\" are formed by a backbone of sugar and phosphate molecules, and the \"rungs\" consist of nucleotide bases joined weakly in the middle by hydrogen bonds." + ], + [ + "Gene editing has gained considerable interest with the identification of the CRISPR-Cas9 system, 27 which allows for a targeted modification in the DNA sequence of an organism.Researchers can utilize their knowledge of the basic biology of the gene and its protein function to precisely change the DNA sequence, thus altering the protein function of the gene and allowing for edits to stay within the species.Researchers at the University of Missouri used the CRISPR-Cas9 system to modify the CD163 gene such that the PRRS virus is not able to replicate inside the pig. 28This slight modification of the swine genome through gene editing keeps the pigs from succumbing to PRRS which has an annual estimated loss to the United States swine industry of over $660 million per year.Despite this benefit, given the public's concerns over food safety, it is likely that approval for such technology is years away in the US, Canada and Europe.However, in some cultures, there is a wide range of non-livestock species that are consumed.Therefore, it is conceivable that these countries and cultures may be open to transgenic/gene edited livestock.They may see the importance of useful gene editing which may lead to approval and consumption of reasonable genetically edited animal products such as those with modifications that are already found in nature or those that offer a substantial welfare benefit to society.", + "As a researcher who has devoted an entire career since 1994 to the development of genome editing tools and methods, I have been amazed by the rapid progress in the field over the last few years.Considering the widespread use of the tools, I am sure that the pace will continue to accelerate.Indeed, programmable nucleases, may eventually enable humans-products of evolution-to become masters of evolution.delivered preassembled recombinant Cas9-guide RNA ribonucleoproteins (RNPs) into animal embryos 6,9 and plant 11 and mammalian cells [73][74][75] .Indeed, Cas9 RNPs were rapidly turned over in cells 73 , reducing off-target effects and mosaicism in gene-edited organisms 11 .Cas9 RNPs can be delivered into cells by various methods, including microinjection 6,9 , electroporation 73 , lipofection 74 and protein transduction 75 .Importantly-and unlike in conventional gene therapy, where therapeutic genes are delivered via plasmids or viral vectors-Cas9 RNP delivery does not involve the use of exogenous DNA; host innate immune responses against foreign DNA are not elicited, and undesired integration of foreign DNA into the host genome is avoided.", + "In comparison to a transgenic approach, a gene editing technique such as CRISPR-Cas9 offers the advantage that gene-edited crops are not considered genetically modified organism (GMO) in some countries, such as the US, where the demand for natural food colorants such as anthocyanins is high.Indeed, the use of GMO crops as a source of natural pigments may be inconsistent with consumer interests.However, carrot cultivars engineered with either the transgenic or gene editing approach have not been reported so far, but their development is possible.", + "The notable accuracy and versatility of CRISPR-Cas for genome editing also opened the door to its use in preclinical and translational settings.In the latter case, CRISPR in vivo gene editing has led to several proof-of-concept studies that would have been unachievable without it, as in the first ever correction of inherited pathogenic mutations linked to degenerative disease in a living organism [22] and even shown to be possible in human embryos [23,24].It also has great potential in the field of precision medicine as large-scale population DNA sequencing studies have provided vast amounts of information linking particular diseases with specific genetic mutations which could, in theory, be targeted through CRISPR [25,26].This could be used during the identification and validation of potential DNA targets during the development of personalised drug or cell therapies, which will require the generation of engineered cell lines and/or animal models.Techniques such as HDR-mediated gene targeting are too labour intensive, with low targeting efficiencies and long times necessary for their establishment, and consequently are not ideally suited for drug discovery purposes.Conversely, CRISPR-Cas has been proven to be efficient for editing virtually any kind of cell line, from primary immune cells to induced pluripotent stem cells (iPSCs) [27,28].Additionally, CRISPR can also be used for functional screening in the development of combined inhibitory therapy aimed at strengthening the efficiency of targeted therapeutics.An example of the latter is shown in a study where a variation of the technology known as CRISPR interference (CRISPRi) was used in genome-wide scale to identify different survival pathways used by cancer cells after oncogene inactivation and allowing the identification of successful combination therapies [29].In terms of translational applications, the overall safety of CRISPR genome editing in humans will require long-term scrutiny before its adoption in the clinic.Nonetheless, a number of CRISPR-based clinical trials are currently in progress, including studies focused on targeting patients' own T cells in order to improve the immune response towards some forms of malignant cancer [30,31], and others aimed at correcting pathogenic mutations in the hematopoietic cells of patients with beta-thalassemia and sickle cell disease [32].Caveats and Ethical Concerns of CRISPR-Cas ApplicationsDespite the presence of both a PAM sequence and a specific gRNA, the CRISPR-Cas9 system is not infallible.In fact, DSBs can occur at different sites in the genome, potentially causing so-called \"off-target\" effects.This eventuality remains to date the biggest concern in the field, as possible undesirable modifications must be properly identified and followed in order to guarantee safety for medical purposes.Nevertheless, there is still little evidence of the biological consequence of Cas9 off-target effects.Two recent studies describe new methods to investigate potential off-target effects in both mammals and plants [33,34].In both cases, whole-genome sequencing revealed that selective nucleotide changes, such as conversion of an adenine to a guanine, caused off-target occurrence very rarely, with a frequency comparable to the one of spontaneous mutations.However, substitution of a cytosine with a thymidine was linked to a sizable number of off-target mutations.This newly acquired information adds to the plethora of studies conducted on the safety of CRISPR, which altogether highlight the need for the establishment of clinical standards for the future use of genome-editing techniques in the clinic.Despite this and other technical challenges still ahead for CRISPR genome editing, the pace at which this technology has developed in recent years suggests many of these concerns could be addressed soon, as long as proper ethical guidelines and regulatory mechanisms are established.ConclusionsThere is no reason to doubt that the development of CRISPR-Cas genome editing represents an unprecedented breakthrough in modern science, as it has potential applications in a wide array of disciplines ranging from agriculture, zoology and renewable energy to biomedicine and synthetic biology.This powerful tool holds promise for further elucidating the molecular causes of ageing by allowing scientists to probe genetic and epigenetic pathways with a level of sophistication that was unattainable just a few years ago.It will allow so in traditional animal and cell models of ageing, but it will also drastically accelerate the generation of refined versions of those models or even allow the development of new research approaches in non-model organisms.Moreover, CRISPR-based genome editing is already having a significant impact in research aiming to understand the cellular and molecular origins of age-related diseases, as well as developing potential treatments against them.The application of CRISPR-Cas gene editing for the treatment of age-related diseases is not over the horizon yet, as it will require the identification of causative genes and their role under a variety of contexts that could be as diverse as the ageing process is across individuals.However, CRISPR-Cas might also hold the key for solving such conundrum, as it has opened the way for achieving true personalised medicine by providing both the precision and scalability required for conducting genome-wide functional screens during the refinement of drug-and cell-based therapies for age-related diseases.Since its discovery, CRISPR-Cas technology has ignited a biological revolution by providing a highly versatile platform that allows fast and efficient genome editing in an ever-growing list of organisms.In this chapter we will first describe the most recent advances in the development and application of the CRISPR-Cas platform in biomedical research.Then we will discuss the most recent and notable basic research applications of this technology in the study of the molecular causes of ageing.Finally, we will review how CRISPR-Cas has been used for creating new models for the study of age-related diseases, as well as for manipulating diseaseassociated gene pathways.", + "Caveats of advanced genome editing toolsOff-target effects.The DNA-binding domains of ZFNs and TALENs need to be very specific for the target site to avoid off-target cleavage, which results in unwanted mutations and potentially cytotoxic effects [27].CRISPR/Cas9 is also known to generate off-target alterations, albeit apparently at low incidence [28,29], since mispairing is allowed between the guide RNA and the genomic DNA.Nonetheless, caution is required in their design and use.Some strategies involving the optimization of the guide RNA/Cas9 include using of software tools to predict potential off-target sites (http://omictools.com/crispr-cas9-Figure1: Genome editing methodologies which can be applied to human pluripotent stem cells.Homologous recombination (HR), or the more advanced tools such as zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) or clustered regularly interspaced short palindromic repeat (CRISPR)/Cas system can be applied to human pluripotent stem cells (hPSCs) either to 1) create naturally occurring mutations or 2) repair a mutation to generate isogenic controls in hPSCs, to understand the function of a gene of interest.c1268-p1.html),truncating the guide RNA (<20 nucleotides) to decrease off-target mutagenesis [30], lowering the dosage of guide RNA and Cas9 plasmids, and decreasing the number of mismatches between the guide RNA and the genomic DNA.A \"double nick\" system with Cas9 nickase, which contains a single inactive catalytic domain, may also be used [31e33].", + "CRISPR screening technologiesThe discovery of CRISPR-Cas9 as a sequence-specific programmable nuclease democratized gene editing and fueled progress in forward genetic screening [20 , 66] .Genetic screens using Cas9 with a pooled singleguide RNA (sgRNA) library allow the interrogation of seemingly all genes in a genome in a single experiment [96 , 97] [null] .Engineered Cas9 variants further extend the versatility of forward genetic screening.Catalytically inactive Cas9 (dCas9) fused with chromatin effector domains permit specific activation (CRISPRa) or inhibition (CRISPRi) of gene expression [37 , 54] .Recently developed and emerging technologies -base editors, prime editors, and Cas transposases -are beginning to enable new types of genetic screens with directed, controlled, and on demand mutations by allowing the creation of user specified modifications, such as single base conversion, deletions, and insertions [4 , 42 , 58] .", + "Coming on the heels of engineered nucleases, CRISPR-Cas9 tools have accelerated the pace of genomic research by permitting highly efficient knockouts or edits of virtually any gene in cells or model organisms.Multiple CRISPR-Cas9-based clinical trials are in progress or are expected to begin soon.Although Cas9engineered cells haven't yet demonstrated efficacy at scale, early trial results suggest that such cells are stable and don't cause acute adverse reactions in humans.Long-term safety is yet to be determined.Current applications largely focus on single-gene disorders for which gene editing can be carried out ex vivo on appropriate cells, such as bone marrow hematopoietic stem cells in the case of sickle cell anemia.Exploration is under way to develop delivery systems that can target the gene-editing apparatus to the appropriate tissue in vivo.Over the past 8 years, CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 (CRISPR-associated protein 9) technologies have emerged as accessible and adaptable tools for studying and altering genomes. 5RISPR-Cas9 can be used to induce genome edits by creating targeted DNA breaks that trigger site-specific DNA repair.In nextgeneration formats, it can also control the transcriptional output of genes or alter genome sequences using a process of nucleotide base editing that does not require repair of DNA breaks.As these technologies continue to mature, it will become increasingly possible to alter cellular genomes efficiently and accurately.", + "The type II CRISPR-Cas9 systems, repurposed from prokaryotic adaptive immune responses, are now widely used for targeted genome modifications in plants, animals, and human cells (Kim et al. 2014;Woo et al. 2015;Zuris et al. 2015).In particular, Cas9 nucleases have shown promise for gene and cell therapy (Maeder and Gersbach 2016).Typically, these nucleases are expressed or delivered in vivo using plasmid DNA or viruses (Yin et al. 2014;Ran et al. 2015).However, plasmid DNA delivery is often inefficient, especially in vivo, and can cause integration of small plasmid fragments degraded by endogenous nucleases at on-target and offtarget sites in the genome (Kim et al. 2014).Viral delivery of Cas9 can be highly efficient in vivo (Ran et al. 2015;Long et al. 2016;Nelson et al. 2016;Tabebordbar et al. 2016), but may be hampered by antibodies or T cells induced against the protein (Shankar et al. 2007;Calcedo et al. 2015;Chew et al. 2016).We and others have shown that preassembled Cas9 ribonucleoproteins (RNPs) can be delivered to human primary and stem cells and mice to modify target genes (Kim et al. 2014;Schumann et al. 2015;Zuris et al. 2015).Cas9 RNPs are rapidly turned over in cells, reducing off-target effects.Furthermore, Cas9 RNPs are unlikely to be limited by host immune systems because they function and disappear before the generation of antibodies and T cells directed against them.Currently, despite these advantages of RNPs, the difficult delivery of Cas9 RNPs in vivo limits its utility for therapeutic applications (Zuris et al. 2015).Here, we show that in vivo genome editing of an wild-type gene, whose up-regulation is responsible for pathogenesis, could be a new therapeutic modality for the treatment of nongenetic degenerative diseases.Our ultimate goal is to harness Cas9 RNPs for a clinical application of therapeutic genome surgery in patients with AMD.", + "Clustered regularly interspaced short palindromic repeat (CRISPR)-Cas nucleases have revolutionized the field of gene editing and have tremendous application in the field of molecular medicine [98][99][100][101][102].Despite a significant surge in CRISPR/Cas9mediated genome editing in various disease models, the progress in the field of AD has lagged behind substantially.We believe that genome editing can significantly improve the development of AD models and also create novel opportunities for the development of the next generation precision targeted AD gene and stem cell therapies.Since there are several excellent review articles on CRISPR/Cas9-mediated genome editing, here we will limit our focus on select recent articles that are noteworthy.CRISPR/Cas9 system can be engineered to either activate transcription (gain-of-function) or achieve gene silencing (Loss-of-function).Dahlman et al. have developed a CRISPR-based system that uses catalytically active Cas9 and distinct single guide (sgRNA) constructs to activate and knockout different genes in the same cell [103].Konermann et al. have used structure-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci [104].Using crystallographic studies, they have engineered a combination of sgRNA2.0,NLS-dCas9-VP64 and MS2-p65-HSF1 to develop one of the most effective transcription activation system.", + "Limitations of CRISPR-Cas9CRISPR provides a simple and easy tool not only for in vitro use but potentially also for in vivo genome editing.However, there are limitations and downsides to this approach.First, and despite considerable improvements in the technology, the risk of the offtarget effect remains and must be considered carefully.Second, DSB may lead to wide-ranging deletions or recombination events involving the on-target site (204).Third, in cycling cells, DNA double strand breaks caused by Cas9 cleavage may trigger a P53 response leading to apoptosis and enrichment for potentially oncogenic P53-deficient cells (205,206).Fourth, subjects may generate antibodies to Cas9, potentially limiting gene therapies (207,208).Genome editing tools that target the desired genomic region and allow for variants to be altered (e.g. from risk to protective), or for more substantial changes to be made (e.g. the deletion of a longer stretch of DNA harbouring a number of variants) and can help to answer each of these questions.These technologies are evolving rapidly (Figure 1 and Table 2).The most recently developed of these, Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) technology, originally developed by Doudna, Charpentier and their colleagues (72,73) and Zhang and his colleagues (50) has become a widely used tool for this purpose.Engineered CRISPR/Cas9 technology uses a guide RNA (gRNA) to direct CRISPR-associated endonuclease (Cas) to the target DNA and generate a double strand DNA break.Correction of a mutation or variant in the target DNA sequence can then be carried out by homology-directed DNA repair (HDR) with a donor template.Since its discovery eight years ago, CRISPR technology has evolved quickly to be a critical part of the molecular biologist's toolbox.", + "INTRODUCTIONGenome editing technologies based on the clustered regularly interspaced short palindromic repeats (CRISPR)-associated endonuclease Cas9 enable rapid and efficient modification of endogenous genes in a variety of cell types, allowing for analysis of gene function in many organs in vivo.CRISPR-Cas9 induces DNA double strand breaks (DSBs) at single-guide RNA (sgRNA)-specific loci in the genome, which are repaired through either non-homologous end-joining (NHEJ) or homology-directed repair (HDR) pathways.While NHEJ introduces unpredictable pattern of insertion or deletion (indel) mutations, HDR directs a precise recombination event between a homologous DNA donor template and the damaged DNA site (Cong et al., 2013;Cox et al., 2015;Doudna and Charpentier, 2014;Heidenreich and Zhang, 2016;Jinek et al., 2012;Mali et al., 2013;Sander and Joung, 2014;Wang et al., 2013;Yang et al., 2013).Thus, HDR can be used to precisely introduce sequence insertions, deletions or mutations by encoding the desired changes in the donor template DNA.", + "CRISPR technology has rapidly changed the face of biological research, such that precise genome editing has now become routine for many labs within several years of its initial development.What makes CRISPR/Cas9 so revolutionary is the ability to target a protein (Cas9) to an exact genomic locus, through designing a specific short complementary nucleotide sequence, that together with a common scaffold sequence, constitute the guide RNA bridging the protein and the DNA.Wild-type Cas9 cleaves both DNA strands at its target sequence, but this protein can also be modified to exert many other functions.For instance, by attaching an activation domain to catalytically inactive Cas9 and targeting a promoter region, it is possible to stimulate the expression of a specific endogenous gene.In principle, any genomic region can be targeted, and recent efforts have successfully generated pooled guide RNA libraries for coding and regulatory regions of human, mouse and Drosophila genomes with high coverage, thus facilitating functional phenotypic screening.In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening.CRISPR technology has rapidly changed the face of biological research, such that precise genome editing has now become routine for many labs within several years of its initial development.What makes CRISPR/Cas9 so revolutionary is the ability to target a protein (Cas9) to an exact genomic locus, through designing a specific short complementary nucleotide sequence, that together with a common scaffold sequence, constitute the guide RNA bridging the protein and the DNA.Wild-type Cas9 cleaves both DNA strands at its target sequence, but this protein can also be modified to exert many other functions.For instance, by attaching an activation domain to catalytically inactive Cas9 and targeting a promoter region, it is possible to stimulate the expression of a specific endogenous gene.In principle, any genomic region can be targeted, and recent efforts have successfully generated pooled guide RNA libraries for coding and regulatory regions of human, mouse and Drosophila genomes with high coverage, thus facilitating functional phenotypic screening.In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening.The recent development of clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 for experimental purposes has dismantled the perception that genome editing technology is off-limits for screening in mammalian systems (Heintze et al., 2013).Since this system employs the basic principle of Watson-Crick base pairing for gene targeting, generation of libraries with whole-genome target coverage is relatively easy and cost-effective.For instance, simple protocols are available to synthesize pooled lentiviral libraries by in silico design of oligonucleotides, which can then be cloned, packaged and delivered to cells by viral transduction (Paddison et al., 2004;LeProust et al., 2010).Similarly, the generation of arrayed libraries can be achieved by following protocols originally developed for arrayed shRNA library production that have been in use for a number of years (Moffat et al., 2006).All in all, the stage is set for CRISPR to make an enormous impact on genomic screening and thus scientific discovery in the coming years, and recent demonstrations of this system have shown great promise (Shalem et al., 2015).However, a number of technical challenges must be addressed in order to maximize the benefit of this technology.In this review, we will discuss current applications of CRISPR in functional genomics and provide a perspective on future developments in this area.", + "Genome editing for crop improvementReports of CRISPR-Cas9-based genome editing first appeared in 2013 (Cong et al., 2013;Feng et al., 2013;Mao et al., 2013).Since then, genome editing technologies have proven to be powerful and efficient tools for the improvement of many crop species.At present, genome editing has been widely used to introduce/modify agronomically important traits, such as increased yield, improved nutritional quality, and resistance to biotic and abiotic stresses, in multiple crops, including rice, wheat, maize, tomato, and potato (Lu et al., 2017;Soyk et al., 2017;Tang et al., 2017;D'Ambrosio et al., 2018;Ye et al., 2018;Miao et al., 2019;Zhang et al., 2019;Zhong et al., 2019;Butt et al., 2020;Zhang et al., 2020c;Li et al., 2021b;Zhan et al., 2021).CRISPR-Cas-based genome editing has been extended to targeted mutagenesis, base editing, and precisely targeted gene/allele replacement or tagging in plants.mportantly, using CRISPR-Cas9 technology, transgenes present in the genomes of genome-edited plants can be removed by chromosomal segregation via a simple self-pollination or hybridization step.Gene editing technologies continue to be developed and utilized (Mao et al., 2013;Lu and Zhu, 2017;Lu et al., 2020)." + ], + [ + "Second, and perhaps moreimportant, is the difference in the size and types of thegenetic reference populations. In our previous study, wemapped the QTL with 36 F2 mice that were genotyped at82 markers. In the current study, by comparison, we wereable to map QTLs after examining 342 mice from 55 strainsthat were genotyped at approximately 4000 markers.", + "This contrast can be exploited to identify subregions that underlie the trans-QTLs [67]. SNPs were counted for all four pairs of parental haplotypesBvs D, B vs H, B vs C, and L vs Sand SNP profiles for the fourcrosses were compared (figure 6). Qrr1 is a highly polymorphicPLoS Genetics | www.plosgenetics.org8November 2008 | Volume 4 | Issue 11 | e1000260QTL Hotspot on Mouse Distal Chromosome 1Figure 5. QTL for aminoacyl-tRNA synthetases in distal Qrr1.", + "The traditional approach to QTL mapping is to usetwo strains that differ maximally in the phenotype asparental strains for genetic crosses, with the followingcaveats. QTL analysis based on a single cross will mostlikely reflect only a small portion of the net geneticvariation, and QTL detection will be limited to regionswhere the two progenitor strains have functional polymorphisms. Data from multiple crosses, or from an HS,will overcome this limitation and can also be used toreduce QTL intervals [5,30].", + "These candidate genes are then sequenced in the two parental inbredstrains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausiblecandidate polymorphisms, the major challenge remains \u0001 proving denitivelywhich nucleotide polymorphism underlies the QTL. The most direct proofwould be replacing one strains allele with another strains allele (creating aFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mousegenerations are depicted.", + "Furthermore, splicing QTLs(sQTLs) rather than eQTLs could comprise the molecular mechanism linking DNA variants with YFP53; thus, sQTL analysis could uncover genes that would not normally bedetected at the level of differential gene expression (DGE),53 and thus, a differentially181182Molecular-Genetic and Statistical Techniques for Behavioral and Neural ResearchFigure 8.5 Schematic for immediate, rapid ne mapping in select F2 recombinants of the RCC-F2cross. Top panel: Genome-wide signicant QTL (green trace; red dashed line signicance threshold;blue vertical lines Bayes credible interval).", + "The fuzzy functional boundaries of genes andthe high density of sequence variants in linkage disequilibrium shifts the burden of prooffrom pure mapping to functional genomics, comparative analysis of human cohorts,complementary animal models, and direct pharmacological and genetic engineering (Smemoet al. , 2014). Author ManuscriptMapping with the BXDs has high powerHow many replicates and strains are needed to detect and resolve QTLs? To start with theconclusionit is almost always better to study small numbers of as many strains as possible(Andreux et al. , 2012; Belknap, 1998).", + "Interval-specific haplotype analysisApproximately 97% of the genetic variation betweeninbred mouse strains is ancestral [22], so regions ofidentity by descent (IBD) between two strains used todetect a QTL are highly unlikely to contain the causalgenetic polymorphism underlying the QTL [28]. Forexample, a cross between C57BL/6J and A/J mice detectedwww.sciencedirect.coma blood pressure QTL on Chr 1 [7].", + "Interval-specific haplotype analysisApproximately 97% of the genetic variation betweeninbred mouse strains is ancestral [22], so regions ofidentity by descent (IBD) between two strains used todetect a QTL are highly unlikely to contain the causalgenetic polymorphism underlying the QTL [28]. Forexample, a cross between C57BL/6J and A/J mice detectedwww.sciencedirect.coma blood pressure QTL on Chr 1 [7].", + "At present, the BXD panel is composed of 80 different strains that all have beenfully genotyped.26 Variation in any quantifiable trait can be associated with thesegregation of parental alleles, and linkage genetics can map this variation toquantitative trait loci (QTLs), thereby identifying the genomic region(s) affectingthat trait. An overview of the QTL mapping approach is depicted in Figure 2. Classical QTL analysis has permitted the identification of loci that areassociated with variation in HSC traits.", + "The progenitor mouse strainsshould have sufficient variation for the traits of interest and they should be genetically diverseenough to enable genetic mapping (BENNETT et al. 2006; FLINT 2003; GRISEL 2000). Thesample size required for the identification of QTL depends largely on the effect size that aQTL contributes to phenotypes on interest. Inference about QTL can be made if one or moregenetic markers are over- or underrepresented in the analysed individuals. Genotyping isoften done by means of microsatellite markers, which contains mono, di-, tri-, ortetranucleotide tandem repeats flanked by specific sequences (Figure 4a).This comparison gives information about the reliability of the observed genotypeinformation: The more the marker locations differ between the two maps (which signifiesvariation in marker positions), the higher the possibility of genotyping errors. QTL mapping was done in several stages to identify loci acting individually and QTL thatinteracted, either additively or epistatically. To determine individually-acting QTL, a singleQTL genome scan was conducted with the function scanone.In general,linking genetic variation with trait variation identifies QTL and a significant linkage ofphenotype and genotype suggest that the DNA status helps to determine trait expression. As stated above, mouse QTL studies provide distinct advantages over human studiesin the examination of genetic causes of a quantitative trait (e.g. alcoholism), even in theabsence of specific hypotheses regarding its aetiology or candidate genes.", + "Importantly, whereasthese studies required substantial labor, time, and resources, X-QTL is a quick and easyapproach to achieve a comparable level of genetic dissection. The levels of complexityobserved here (e.g. 14 loci explaining 70% of the genetic variance for 4-NQO resistance) arestill dramatically lower than those seen in for some human traits in GWAS (e.g. 40 lociexplaining 5% of the variance for height 2,5). One obvious explanation is the difference inexperimental designs (line crosses vs. population association studies), but differences ingenetic architectures among species and traits may also contribute.", + "The method uses two pieces of information: mapping data from crosses thatinvolve more than two inbred strains and sequence variants in the progenitor strains within the intervalcontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that anysequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals exceptat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.", + "The method uses two pieces of information: mapping data from crosses thatinvolve more than two inbred strains and sequence variants in the progenitor strains within the intervalcontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that anysequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals exceptat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.", + "Genotyping all the individual progeny formarkers that show allelic variation between the parental strains (either single nucleotide polymorphisms or simple sequence repeats) will allow the detection of associations between trait values and marker genotype, and in this way demonstrate to whichset of markers a QTL is linked. To reduce the genotyping effort, selective genotypingof the individuals at the extremes of the phenotypic spectrum can be performed (20,23). Although these three approaches are in general considered to be the best to detect andmap QTL, they have several disadvantages for quantitative traits involving HSC.", + "So, how do you go about planning and performing a QTL study, and howdo you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbredstrains that have a markedly different trait. One can now look up many differenttraits of inbred mice online at the Mouse Phenome Database (http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). However, the trait you maywant to study may not be present in wild type mice, so you may want to crossa mutant (or genetically engineered) strain onto several inbred strains.", + "In any case, precision much finerthan this, while welcome, will often not be critical. The fuzzy functional boundaries of genes and the high density of sequence variants in linkage disequilibrium shifts the burden of proof frompure mapping to functional genomics, comparative analysis ofhuman cohorts, complementary animal models, and direct pharmacological and genetic engineering (Smemo et al. , 2014). Mapping with BXDs has high powerHow many replicates and strains are needed to detect andresolve QTLs?", + "These candidate genes are then sequenced in the two parental inbredstrains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausiblecandidate polymorphisms, the major challenge remains \u0001 proving denitivelywhich nucleotide polymorphism underlies the QTL. The most direct proofwould be replacing one strains allele with another strains allele (creating aFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mousegenerations are depicted.", + "QTL mapping studies thenseek to detect the polymorphisms underlying the complex traits of interest byscanning for alleles that co-vary withthe traits. Similar experiments also can be conducted with special derivatives of inbredstrains known as recombinant inbred(RI) mice. These animals are derivedby cross-breeding two or more distinctparental strains (which often divergewidely for the trait of interest), followedby inbreeding of the offspring for severalgenerations (Bailey 1971). Given thecorrect breeding strategy, this method1This is an issue faced by GWASs researchers when classifyingsamples as cases or controls." + ] + ], + "task_id": [ + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "471A145E9CA1E517E462499ABCA8EA2D" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_3.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_3.json new file mode 100644 index 0000000..7cf9999 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_3.json @@ -0,0 +1,103 @@ +{ + "question": [ + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?", + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Why do males have two Y chromosomes and females only one?" + ], + "answer": [ + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.", + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "contexts": [ + [ + "For certain types of important digital objects, there are well-curated, deeply-integrated,special-purpose repositories such as Genbank3, Worldwide Protein Data Bank (wwPDB4), andUniProt5 in the life sciences; Space Physics Data Facility (SPDF; http://spdf.gsfc.nasa.gov/) and Set ofIdentications, Measurements and Bibliography for Astronomical Data (SIMBAD6) in the spacesciences. These foundational and critical core resources are continuously curating and capturing highvalue reference datasets and ne-tuning them to enhance scholarly output, provide support for bothhuman and mechanical users, and provide extensive tooling to access their content in rich, dynamicways.", + "These centralized data resources can often be veryhelpful for gaining a quick overview of an unfamiliar pathway or gene, but inevitablyone needs to re-enter the literature to build up a fuller picture and to answer the questions that are most relevant to the target phenotype or gene. The Internet is also anexcellent resource to help in this process; this probably makes the ubiquitous searchengine Google (http://www.google.com) one of the most powerful bioinformaticstools.The expansionof Web-linked interoperativity and interrogation tools means that new options willalready be available by the time this book is in print. One consequence of these advances could be the perception of a diminished necessity to perform bioinformaticanalysis. Although this is true in the sense that secondary database include an increasing amount of precooked bioinformatic data, there is a paradox in that the moresophisticated the public annotation becomes, the more important it is to understandthe underlying principles.Data retrieval at UCSC is facilitated by text and BLAT (Kent, 2002; a BLAST-likealgorithm) searches and bulk downloads of annotation or sequence data. Other complementary tools at UCSC have extended the functionality of UCSC. For instance,the Proteome Browser graphically displays protein properties such as hydrophobicity, charge and structural features across any publicly available protein sequence(Hinrichs et al. , 2006). As with Ensembl, the UCSC website has been well designedand is sympathetic to the naive user, but the UCSC graphical interface is more Spartan. If Ensembl is Disney, then UCSC is South Park.Data retrieval is extremely well catered for in Ensembl, with text searches of alldatabase entries, BLAST searches of all sequences archived, and the availability of bulkdownloads of all Ensembl data and even software source code. Ensembl annotationcan also be viewed interactively on ones local machine with the Apollo viewer (Lewiset al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome BrowserThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;it, too, provides annotation of the NCBI assemblies, and it displays a similar array offeatures, including confirmed genes from Ensembl.", + "Toachieve this goal, we integrated and make available big reference datain chapters 2 and 3, bridged model organism to human data in chapter4, translated generic methods into clinical applications in chapters 5and 6, and developed a platform to bring innovations into practice inchapter 7. The resources currently available are already plentiful, and both theamount and types of molecular life science data is growing at a tremendous pace.", + "We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. Students integrate information gathered from websites dealing with anatomy (Mouse BrainLibrary), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and geneexpression analyses (University of California, Santa Cruz Genome Browser, National Center forBiotechnology Informations Entrez Gene, and the Allen Brain Atlas), and information resources(PubMed).", + "Useful Online Genomics Resources.", + "These centralized data resources can often be veryhelpful for gaining a quick overview of an unfamiliar pathway or gene, but inevitablyone needs to re-enter the literature to build up a fuller picture and to answer the questions that are most relevant to the target phenotype or gene. The Internet is also anexcellent resource to help in this process; this probably makes the ubiquitous searchengine Google (http://www.google.com) one of the most powerful bioinformaticstools.The expansionof Web-linked interoperativity and interrogation tools means that new options willalready be available by the time this book is in print. One consequence of these advances could be the perception of a diminished necessity to perform bioinformaticanalysis. Although this is true in the sense that secondary database include an increasing amount of precooked bioinformatic data, there is a paradox in that the moresophisticated the public annotation becomes, the more important it is to understandthe underlying principles.Data retrieval at UCSC is facilitated by text and BLAT (Kent, 2002; a BLAST-likealgorithm) searches and bulk downloads of annotation or sequence data. Other complementary tools at UCSC have extended the functionality of UCSC. For instance,the Proteome Browser graphically displays protein properties such as hydrophobicity, charge and structural features across any publicly available protein sequence(Hinrichs et al. , 2006). As with Ensembl, the UCSC website has been well designedand is sympathetic to the naive user, but the UCSC graphical interface is more Spartan. If Ensembl is Disney, then UCSC is South Park.", + "These centralized data resources can often be veryhelpful for gaining a quick overview of an unfamiliar pathway or gene, but inevitablyone needs to re-enter the literature to build up a fuller picture and to answer the questions that are most relevant to the target phenotype or gene. The Internet is also anexcellent resource to help in this process; this probably makes the ubiquitous searchengine Google (http://www.google.com) one of the most powerful bioinformaticstools.The expansionof Web-linked interoperativity and interrogation tools means that new options willalready be available by the time this book is in print. One consequence of these advances could be the perception of a diminished necessity to perform bioinformaticanalysis. Although this is true in the sense that secondary database include an increasing amount of precooked bioinformatic data, there is a paradox in that the moresophisticated the public annotation becomes, the more important it is to understandthe underlying principles.Data retrieval at UCSC is facilitated by text and BLAT (Kent, 2002; a BLAST-likealgorithm) searches and bulk downloads of annotation or sequence data. Other complementary tools at UCSC have extended the functionality of UCSC. For instance,the Proteome Browser graphically displays protein properties such as hydrophobicity, charge and structural features across any publicly available protein sequence(Hinrichs et al. , 2006). As with Ensembl, the UCSC website has been well designedand is sympathetic to the naive user, but the UCSC graphical interface is more Spartan. If Ensembl is Disney, then UCSC is South Park.Data retrieval is extremely well catered for in Ensembl, with text searches of alldatabase entries, BLAST searches of all sequences archived, and the availability of bulkdownloads of all Ensembl data and even software source code. Ensembl annotationcan also be viewed interactively on ones local machine with the Apollo viewer (Lewiset al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome BrowserThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;it, too, provides annotation of the NCBI assemblies, and it displays a similar array offeatures, including confirmed genes from Ensembl.", + "There are online bioinformatics resources from which this type of information may be sourced.", + "There aremany resources for annotating the results of a genome-wide study,all located in diverse databases and other web content. Having theability to harmonize and analyze historic data, together with highlycurated public resource data such as that found in model organismdatabases, adds tremendous depth and orthogonal informationsources to prioritize and refine the results of genetic analysis.", + "These centralized data resources can often be veryhelpful for gaining a quick overview of an unfamiliar pathway or gene, but inevitablyone needs to re-enter the literature to build up a fuller picture and to answer the questions that are most relevant to the target phenotype or gene. The Internet is also anexcellent resource to help in this process; this probably makes the ubiquitous searchengine Google (http://www.google.com) one of the most powerful bioinformaticstools.The expansionof Web-linked interoperativity and interrogation tools means that new options willalready be available by the time this book is in print. One consequence of these advances could be the perception of a diminished necessity to perform bioinformaticanalysis. Although this is true in the sense that secondary database include an increasing amount of precooked bioinformatic data, there is a paradox in that the moresophisticated the public annotation becomes, the more important it is to understandthe underlying principles.Data retrieval at UCSC is facilitated by text and BLAT (Kent, 2002; a BLAST-likealgorithm) searches and bulk downloads of annotation or sequence data. Other complementary tools at UCSC have extended the functionality of UCSC. For instance,the Proteome Browser graphically displays protein properties such as hydrophobicity, charge and structural features across any publicly available protein sequence(Hinrichs et al. , 2006). As with Ensembl, the UCSC website has been well designedand is sympathetic to the naive user, but the UCSC graphical interface is more Spartan. If Ensembl is Disney, then UCSC is South Park." + ], + [ + "Annotation, preprocessing and categorization of dataWe used Ensembl (version 39) as the annotation reference database.Homology between human and mouse genes was derived via BioMart.The total number of genes under study comprises 15,277 Ensembl mouse genes representing the union of the homologue genes from all data sources.An overview about the T2DM specific datasets is given in Table 1.", + "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carriedout and the way the results are displayed. 4.4.1 EnsemblEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the WellcomeTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbardet al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide awindow on the draft genome, curating the results of a series of computational analyses.Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequenceassemblies as its starting point, but it is now based upon NCBI assemblies. TheEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify confirmed genes that arecomputationally predicted (by the GENSCAN gene prediction program) and alsosupported by a significant BLAST match to one or more expressed sequences orproteins. Ensembl also identifies the positions of known human genes from publicsequence database entries, usually using GENEWISE to predict their exon structures.Data retrieval is extremely well catered for in Ensembl, with text searches of alldatabase entries, BLAST searches of all sequences archived, and the availability of bulkdownloads of all Ensembl data and even software source code. Ensembl annotationcan also be viewed interactively on ones local machine with the Apollo viewer (Lewiset al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome BrowserThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;it, too, provides annotation of the NCBI assemblies, and it displays a similar array offeatures, including confirmed genes from Ensembl.Ensembl provides a DAS referenceserver giving access to a wide range of specialist annotations of the humangenome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy ahypothesis (top-down data mining), or to interrogate a database in order togenerate new hypotheses based on rigorous statistical correlations (bottom-updata mining). Domain (protein) A region of special biological interest within a single proteinsequence.", + "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carriedout and the way the results are displayed. 4.4.1 EnsemblEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the WellcomeTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbardet al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide awindow on the draft genome, curating the results of a series of computational analyses.Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequenceassemblies as its starting point, but it is now based upon NCBI assemblies. TheEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify confirmed genes that arecomputationally predicted (by the GENSCAN gene prediction program) and alsosupported by a significant BLAST match to one or more expressed sequences orproteins. Ensembl also identifies the positions of known human genes from publicsequence database entries, usually using GENEWISE to predict their exon structures.Data retrieval is extremely well catered for in Ensembl, with text searches of alldatabase entries, BLAST searches of all sequences archived, and the availability of bulkdownloads of all Ensembl data and even software source code. Ensembl annotationcan also be viewed interactively on ones local machine with the Apollo viewer (Lewiset al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome BrowserThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;it, too, provides annotation of the NCBI assemblies, and it displays a similar array offeatures, including confirmed genes from Ensembl.Ensembl provides a DAS referenceserver giving access to a wide range of specialist annotations of the humangenome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy ahypothesis (top-down data mining), or to interrogate a database in order togenerate new hypotheses based on rigorous statistical correlations (bottom-updata mining). Domain (protein) A region of special biological interest within a single proteinsequence.Anothergrowing area of activity is in cataloguing the genetic variation present in humanpopulations as Ensembl reflects the progress of the International Haplotype MapProject (Thorisson et al. , 2005). More speculative data, such as GENSCAN-predicted exons that have not beenincorporated into Ensembl-confirmed genes, may also be viewed. This means thatthe display can be used as a workbench for the user to develop personalized annotation.", + "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carriedout and the way the results are displayed. 4.4.1 EnsemblEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the WellcomeTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbardet al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide awindow on the draft genome, curating the results of a series of computational analyses.Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequenceassemblies as its starting point, but it is now based upon NCBI assemblies. TheEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify confirmed genes that arecomputationally predicted (by the GENSCAN gene prediction program) and alsosupported by a significant BLAST match to one or more expressed sequences orproteins. Ensembl also identifies the positions of known human genes from publicsequence database entries, usually using GENEWISE to predict their exon structures.", + "EnsemblEnsembl is a publicly available web resource that contains automatically annotated genomes.It is integrated with other available biological databases like Jasper for binding motifs.It is a much larger web resource than T1Dbase, and contains general information about the human genome including variants.These include SNPs, insertions, deletions and somatic mutations (Alterations in DNA that occur after conception, meaning that they are not inherited) for several species.Data from Ensembl can be accessed in a number of ways.The names of all the SNPs that occur in the T1D susceptibility regions can be collected from Ensembl using the Biomart tool (Kinsella et al., 2011).To achieve this, the coordinates of the T1D regions obtained from T1Dbase are uploaded to the biomart query page which allows one to search the genome browser and retrieve data like the names, chromosomal positions, and genic positions (referred to as \"consequence to transcript\", in Ensembl) of the SNPs.The SNP genic positions tell if a SNP is located within a gene, adjacent to a gene or whether they occur in inter-genic positions between gene coding regions, as well as the particular genes in which they are located.Advantages of Ensembl:There is a number of advantages to using Ensembl. (i) It is a larger web resource than T1Dbase and integrates data from a wide range of biological research sources into its database.Therefore, available information is quite comprehensive. (ii) Genic positions for 99% of the variants obtained from T1Dbase could be retrieved. (iii) Ensembl contains quality checks for genetic variants in its variation pipeline.A variant is flagged as failed if certain quality criteria are not met, for instance if none of the variant alleles match the reference allele of the variant.Generally, Ensembl was found to give more detailed information regarding the genic positions of variants compared to T1Dbase.Information about genes, including gene names, chromosomal coordinates, biotype (coding or non-coding), and number of splice variants, can also be retrieved from Ensembl.", + "doi:10.1093/nar/gkp858Cunningham F, Amode MR, Barrell D, Beal K,Billis K, Brent S, Carvalho-Silva D, ClaphamP, Coates G, Fitzgerald S, Gil L, Giron CG,Gordon L, Hourlier T, Hunt SE, Janacek SH,Johnson N, Juettemann T, Kahari AK, KeenanS, Martin FJ, Maurel T, McLaren W, MurphyDN, Nag R, Overduin B, Parker A, PatricioM, Perry E, Pignatelli M, Riat HS, SheppardD, Taylor K, Thormann A, Vullo A, WilderSP, Zadissa A, Aken BL, Birney E, Harrow J,Kinsella R, Muffato M, Ruffier M, Searle SM,Spudich G, Trevanion SJ, Yates A, ZerbinoDR, Flicek P (2015) Ensembl 2015.", + "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carriedout and the way the results are displayed. 4.4.1 EnsemblEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the WellcomeTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbardet al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide awindow on the draft genome, curating the results of a series of computational analyses.Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequenceassemblies as its starting point, but it is now based upon NCBI assemblies. TheEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify confirmed genes that arecomputationally predicted (by the GENSCAN gene prediction program) and alsosupported by a significant BLAST match to one or more expressed sequences orproteins. Ensembl also identifies the positions of known human genes from publicsequence database entries, usually using GENEWISE to predict their exon structures.Data retrieval is extremely well catered for in Ensembl, with text searches of alldatabase entries, BLAST searches of all sequences archived, and the availability of bulkdownloads of all Ensembl data and even software source code. Ensembl annotationcan also be viewed interactively on ones local machine with the Apollo viewer (Lewiset al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome BrowserThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;it, too, provides annotation of the NCBI assemblies, and it displays a similar array offeatures, including confirmed genes from Ensembl.Ensembl provides a DAS referenceserver giving access to a wide range of specialist annotations of the humangenome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy ahypothesis (top-down data mining), or to interrogate a database in order togenerate new hypotheses based on rigorous statistical correlations (bottom-updata mining). Domain (protein) A region of special biological interest within a single proteinsequence." + ], + [ + "However, the twomethods do not necessarily give the same result because theyare measuring complementary aspects of an association. Asquantitative trait loci (QTL) are added to Q, we expect highercompleteness because the QTL in Q cover more segments ofeach chromosome. However, if these quantitative trait loci(QTL) are unrelated to G, we expect many of them to beempty. Similarly, as genes are added to G, we expect higheraccuracy because selected genes are found in more locations.", + "QTL can be mapped through GWAS or GWLS. eQTLAn expression Quantitative Trait Locus is a region in the genome at which allelic variation correlates with the mRNA expression level variation of a certain gene. Distant eQTLA distant (or trans) eQTL is an eQTL which is located far from the gene it controls (forexample on a different chromosome). Local eQTLA local (or cis) eQTL is an eQTL which is located nearby the gene it controls in the genome.", + "QTL mapping, GWAS and genomic selectionInformation from SNP markers are being increasingly used to generate a deeper knowledge of the genetic basis of important traits and speed up the genetic progress in aquaculture species by means of GWAS and genomic selection, respectively (Y aez et al., 2014).GWAS allows the identification of genetic variants associated with complex traits (i.e QTL).When one or few QTL explain a high percentage of genetic variance for a particular trait, it is possible to improve the trait more rapidly by means of MAS.However, the complexity of some traits and the absence of QTL with major effects constrain the successful implementation of MAS.In contrast, genomic selection is the most appropriate way to select for traits that are controlled by several loci of small effects (i.e.polygenic traits) (Meuwissen et al., 2013).", + "Mouse QTL mapping has high power but low precision (i.e., we can detect a QTL, but do not know which of tens or hundreds of genes is causal), whereas human GWAS has low power but high precision (tens or hundreds of thousands of individuals are needed, but candidate regions are often smaller).By combining the power of mouse QTL mapping and the precision of human PheWAS, we can do more than both individually.Candidate genes might show up in our analysis here that did not show up in our above analysis for several reasons, the most common being that gene expression was not measured in the relevant cell type or timepoint.", + "In order to differentiate eQTL from QTLfor phenotypes other than gene expression (phenotypic, physiologic, or clinical QTL),here we will refer to the latter as pQTL. Although the GG approach is relatively recent and is still evolving, there are somegeneral features o f this approach that can be summarized as follows: (1) mapping o ftranscripts profiles as quantitative traits, (2) classification o f eQTL in cis and trans actingmode o f action, (3) identification o f loci where large number o f transcripts map, whichReproduced with permission of the copyright owner.", + "Quantitative trait locus-mapping is a statistical methodused to map chromosomal intervals (loci) that contribute toheritable variance in phenotypes. The method simply compares the inheritance of allelic variants (B or D genotypesin our case) with differences in phenotypes. A QTL willgenerally cover a region that includes 10100 genes, andthese positional candidates can then be ranked roughly onthe basis of criteria such as the types of DNA variants, patterns of mRNA expression, data from complementary humangenetic cohorts (GWAS and linkage) and relevant literatureabout gene effects on central nervous system structure andfunction.", + "These approaches were used to identify dozens of quantitative trait loci (QTL) for BMD and other bone traits [15,16]. However, identifying causative genes underlying QTL proved challenging [17]. Over the last decade, genemapping approaches have transitioned from low-resolution linkage mapping to high-resolution GWASs [11]. The first GWASs in mice used panels of inbred mouse strains [1821] andby leveraging accumulated recombinations, this approach significantly increased mapping resolution [19].", + "The process of QTL mapping includes phenotyping andgenotyping at least several hundred animals from an informative mapping population (e.g. , B6D2 F2). Once a QTL isdetected and confirmed, additional work is needed to identify the specific gene(s) in the QTL interval responsible forthe phenotypic variation. An elegant way to improve QTLmapping resolution is through the development and testingof interval-specific congenic strains (Darvasi, 1997). Thismethod has been successfully used to fine map an ethanolwithdrawal QTL on mouse chromosome 4 (Fehr et al. ,2002; Shirley et al. , 2004).", + "However, the twomethods do not necessarily give the same result because theyare measuring complementary aspects of an association. Asquantitative trait loci (QTL) are added to Q, we expect highercompleteness because the QTL in Q cover more segments ofeach chromosome. However, if these quantitative trait loci(QTL) are unrelated to G, we expect many of them to beempty. Similarly, as genes are added to G, we expect higheraccuracy because selected genes are found in more locations.", + "This comparison gives information about the reliability of the observed genotypeinformation: The more the marker locations differ between the two maps (which signifiesvariation in marker positions), the higher the possibility of genotyping errors. QTL mapping was done in several stages to identify loci acting individually and QTL thatinteracted, either additively or epistatically. To determine individually-acting QTL, a singleQTL genome scan was conducted with the function scanone.", + "Importantly, whereasthese studies required substantial labor, time, and resources, X-QTL is a quick and easyapproach to achieve a comparable level of genetic dissection. The levels of complexityobserved here (e.g. 14 loci explaining 70% of the genetic variance for 4-NQO resistance) arestill dramatically lower than those seen in for some human traits in GWAS (e.g. 40 lociexplaining 5% of the variance for height 2,5). One obvious explanation is the difference inexperimental designs (line crosses vs. population association studies), but differences ingenetic architectures among species and traits may also contribute.", + "Forward genetics approaches that combine traditional QTLmapping with expression quantitative trait mapping (eQTL; in which case transcript abundance is the quantitative trait) [32] are increasingly being used to successfully transition fromQTL to QTG [3335]. Traditional QTL analysis will identify the genomic regions affectingtrait variation, while eQTL analysis can help in understanding which genes, pathways, and biological processes are also under the influence of a given QTL.", + "On the onehand, the genomic location that are in suspicion to be involved in the trait can still involvelarge genomic segments, e.g. , millions of basepairs that include many genes within the segment. On the other hand, GWAS may point toseveral or even many genomic locations for thetrait of interest, complicating further functionalanalysis. Analysis of Quantitative Trait Loci (QTL)QTL analysis reveals statistically signicantlinkage between phenotypes and genotypes,thereby providing explanation for the geneticbasis of variation in complex traits (Falconerand Mackay, 1996; Lynch and Walsh, 1998).", + "This feature of eQTL mapping alone can often make theeffort worthwhile; even with inexpensive genotyping, it is oftencost effective in the long term to generate a TDM map of severalthousand markers, which usually ensures that any two recombination events have a marker between them and that locations ofgenetic cross-overs are well-defined. TDMs can be integrated withGenotyping by Sequencing (GBS) approaches to anchor geneticmaps to physical maps (Poland et al. , 2012; Sonah et al. , 2013). Surprisingly, eQTL mapping is actually the simple portionof eQTL analysis work.", + ", we can detect a QTL, butdo not know which of tens or hundreds of genes is causal), whereas human GWAS has lowpower but high precision (tens or hundreds of thousands of individuals are needed, butGenes 2022, 13, 61411 of 17candidate regions are often smaller). By combining the power of mouse QTL mapping andthe precision of human PheWAS, we can do more than both individually.", + ", we can detect a QTL, butdo not know which of tens or hundreds of genes is causal), whereas human GWAS has lowpower but high precision (tens or hundreds of thousands of individuals are needed, butGenes 2022, 13, 61411 of 17candidate regions are often smaller). By combining the power of mouse QTL mapping andthe precision of human PheWAS, we can do more than both individually.", + "The remarkable success in mappinggenes linked to a number of disease traits using genomewide association studies (GWAS) in human cohorts hasrenewed interest in applying this same technique in modelorganisms such as inbred laboratory mice (Su et al. 2010). Unlike classical phenotypic traits, gene expression traitsgiving rise to cis-acting eQTL provide us with a prioriknowledge of the true QTL location (Doss et al. 2005),which can be used to empirically estimate the power of aGWAS performed at a similar scale (Hao et al. 2008;Schadt et al. 2008).", + "QTLmapping has been highly successful in determining causative loci underlying severaldisease phenotypes (Wang et al. 2004; Cervino et al. 2005; Abboud and Kaplowitz 2007)and can broadly be subdivided into two classes: linkage mapping and association mapping. For standard linkage mapping in experimental crosses, likelihood or regression approachesare used to map QTL, with flanking markers used to infer genotypes in the intervalsbetween widely spaced markers (i.e. > 1cM) (Lander and Botstein 1989; Haley and Knott1992).38Quantitative Trait Locus (QTL) mapping has been used to associate a specificgenotype with the variation in a single measured phenotype like high density lipoproteins(Wang and Paigen 2005) and ethanol tolerance (Grisel et al. 2002). At each locus in asegregating population, a model is fit which estimates the likelihood that this locus explainsthe variation in phenotype versus the likelihood that there is no genotypic effect on thephenotype. Interval mapping (Lander and Botstein 1989) is a variation on QTL mapping whichuses maximum likelihood estimation.", + "QTL mapping studies thenseek to detect the polymorphisms underlying the complex traits of interest byscanning for alleles that co-vary withthe traits. Similar experiments also can be conducted with special derivatives of inbredstrains known as recombinant inbred(RI) mice. These animals are derivedby cross-breeding two or more distinctparental strains (which often divergewidely for the trait of interest), followedby inbreeding of the offspring for severalgenerations (Bailey 1971). Given thecorrect breeding strategy, this method1This is an issue faced by GWASs researchers when classifyingsamples as cases or controls." + ], + [ + "Prior belief or knowledge about the number of true causal and trueindependent links that might be expected in a typical QTL, depending on the studydesign, should be considered to safeguard against high false-positive rates (lowpositive predictive values). In studies that involve mapping gene expression (eQTL),protein (pQTL) or metabolite (mQTL) traits, information about co-localization ofQTL and genes that are functionally linked to the trait provides information aboutthe likelihood of causal links.", + "The next step is to investigate whether the expression of these genes correlates with the phenotype(s) of interest.This would suggest a chain of causality: a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest.To do this, we created a correlation matrix between all genes within a QTL with a cis-eQTL in any brain tissue as well as the phenotypes that contributed to the QTL (Supplementary Table S6).Any gene with a cis-eQTL and a significantly correlated expression was considered a good candidate.If the gene only had a cis-eQTL and correlation in a single brain region, then it suggested that this brain region might also be of interest for the phenotype (adding another link to this chain).", + "One possible approach to facilitate this endeavor is to identify quantitative trait loci(QTL) that contribute to the phenotype and consequently unravel the candidategenes within these loci. Each proposed candidate locus contains multiple genes and,therefore, further analysis is required to choose plausible candidate genes. One ofsuch methods is to use comparative genomics in order to narrow down the QTL to aregion containing only a few genes. We illustrate this strategy by applying it togenetic findings regarding physical activity (PA) in mice and human.", + "Network analysesWe now have two QTL, and we have picked potentially interesting genes within each, but nowwe want to build up more evidence for which gene in our QTL interval is causal. The first, andmost obvious way, is to see what genes our trait of interest correlates with, in tissues that weexpect to be related to the trait. We calculated the Spearmans correlation between the traitBXD_17850 and all probes with expression data in T helper cells (GN319).", + "Anotherapproach to help to determine if a gene located near the mapped QTL wouldhave effects to influence the quantitative trait will be to use genetically engineered mice to determine if altering the expression of a candidate gene will alterthe phenotype of interest (38). However, it is possible that a quantitative trait isa combined effect of multiple genes located near the QTL (39).", + "With a known QTL and abody of evidence suggesting possible roles for the affected gene,phenotypes can be predicted that may be modulated as a resultof this sequence variation. If this phenotype is of interest, itcan be directly measured and a traditional forward QTL analysis carried out to confirm the prediction. Such an approach isextremely attractive when the enormous cost and time requiredfor phenotyping a large panel is considered.", + "The firststep is to narrow down the list ofcandidate causal genes within aFig1. IntervalmappingofoviductgrosspathologyacrosstheBXDstrainsQuantitative Trait Locus (QTL)arevealsaQTLondistalChr3. TheL RSvaluesareplottedinblueacrossthechromosomal region containinggenomeandmeasurethestrengthoftheassociationbetweensequence variants stronglychromosomeandMbposition(topandbottomX-axis,respectively)andassociated with phenotypicphenotypeexpression. Allelecontributionisshownbythered(C57BL/6J)andgreen(DBA/2J)lines. Redandgreyhorizontallinesindicategenome-variation.", + "QTL mapping of traits in mouse cohorts often ends up with a genetic locus, composed of a list of candidategenes. Several studies proposed the use of mediation analysis to identify the causal gene (mediator) betweenthe genetic variant (independent variable) and the trait-of-interest (dependent variable) (Figure 1.4B) [7, 47,61, 77]. Mediation analysis can be used either on gene expression levels to identify the regulatory mechanisms[7, 47, 61], or on phenotypic traits to discover the potential causal drivers contributing to the phenotypicvariances [77] (Figure 1.4C upper).", + "1a). Second-generation offspring are thenphenotyped and genotyped, and linkage analysis is carried out to identify a region that isassociated with the trait1. This approach has led to the identification of thousands of quantitative trait loci (QTLs) forvarious phenotypes and diseases. However, each QTL region is large, often tens ofmegabases, and contains hundreds of genes. The process of identifying the causal variantand the gene involved is therefore difficult and costly. Of the thousands of QTLs identified,only a small fraction of genes has been identified. NIH-PA Author Manuscript 2012 Macmillan Publishers Limited.", + "Network analysesWe now have two QTL, and we have picked potentially interesting genes within each, but nowwe want to build up more evidence for which gene in our QTL interval is causal. The first, andmost obvious way, is to see what genes our trait of interest correlates with, in tissues that weexpect to be related to the trait. We calculated the Spearmans correlation between the traitBXD_17850 and all probes with expression data in T helper cells (GN319).", + "10 JUNE 2016 VOL 352 ISSUE 6291aad0189-5R ES E A RC H | R E S EA R C H A R T I C LESolving QTLs: Finding the quantitativetrait geneFor cis-QTLs, the causal factors can be quicklyidentified: With few exceptions, they will be driven by variants within the gene itself or immediately adjacent. For trans-QTLs, mQTLs, andcQTLs, the identification of the causal quantitative trait gene (QTG) is challenging due to thewidth of the QTLs.", + "Once the QTL interval is reduced to a reasonable size,the next step in the process involves sorting through thegenes within the interval and attempting to determinewhich is the QTG. This step is daunting because more thanone gene may be involved and the function of some geneswithin the interval may be unknown. Until recently, thisstep emphasized the detection of polymorphisms withincoding sequence (reviewed in Korstanje and Paigen, 2002and Glazier et al. 2002); for a polymorphism that producesan amino acid substitution, one can often infer and thentest for a functional consequence.", + "To understand the genetic networks that underliequantitative variation in the trait, it is also very important todiscover genes whose expression is correlated with the traitafter accounting for the known effects of the QTL on thetrait. Many of these genes may have expression that isassociated with QTL genotype, and would therefore beidentified as important via the tests described above. Othergenes, however, may have expression values that are correlated with the trait but unassociated with genotype at theQTL.Theapproach is motivated by the fact that a research project isoften focused on a specific classical quantitative trait. If amajor QTL for this classical trait has been identified, it isoften desirable to test whether this QTL is also associatedwith the transcription level of any genes, which will provide clues as to which genes belong to the pathway that theQTL uses to modulate the classical trait.", + "Quantitative trait loci (QTLs) can be identified in several ways, but isthere a definitive test of whether a candidate locus actually corresponds to a specific QTL? NIH-PA Author ManuscriptMuch of the genetic variation that underlies disease susceptibility and morphology is complexand is governed by loci that have quantitative effects on the phenotype. Gene-gene and geneenvironment interactions are common and make these loci difficult to analyse. Here, we presenta communitys view on the steps that are necessary to identify genetic loci that governquantitative traits, along with a set of interpretive guidelines.", + "Confirmation of Candidate GenesThe next step is to prove that a particular gene is involved in the quantitative traitunder study. This is done by complementation of a QTL, which can be achieved inseveral ways (911,40). In principle, transgenic complementation is the most straightforward. This approach has been used successfully to demonstrate that Pla2g2a wasthe correct candidate gene for Mom1, a modifier of the apcmin allele that causesadenomatous polyposis coli (41).", + "So, how do you go about planning and performing a QTL study, and howdo you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbredstrains that have a markedly different trait. One can now look up many differenttraits of inbred mice online at the Mouse Phenome Database (http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). However, the trait you maywant to study may not be present in wild type mice, so you may want to crossa mutant (or genetically engineered) strain onto several inbred strains.", + "Along with correlations, this tool also derives new traits representing theprincipal components (Figure 2d). The user can add these principal components to their TraitCollection and proceed to perform QTL mapping, as in the case of a single trait QTLmapping. The R/QTL (Broman et al. 2003) and R/CAPE (Tyler et al. 2013) packages can beused for deeper analysis of epistasis and pleiotropy for multiple traits and multipleregulatory loci. Prioritizing Candidate Genes7Author ManuscriptFollowing the identification of a significant QTL, focus shifts to identifying theparticular gene(s) that cause the QTL.", + "The investigatorsfirst identified all QTLs associated witha classical phenotype and then winnowed the list of potentially associatedgene-expression traits on the basis oftheir correlation or eQTL overlap withthe phenotype of interest. Candidategenes then were ranked by applyingthe LCMS technique, which uses theeQTL data to establish causal relationships between DNA loci and transcripts as well as between transcriptsand phenotypes and finally identifiesa model that best fits the data.", + "The goal of QTL mapping is clearly theidentification and eventual confirmation of candidate genes(QTGs) underlying the phenotype. The evidence required forsuch confirmation has engendered much discussion (ComplexTrait Consortium 2003; Glazier et al. 2002) and is likely to varydepending on the nature of the trait and specific resourcesavailable to pin down underlying genes (e.g. availability ofknock-in or knock-outs, specific antibodies, siRNA, etc.). Thepaucity of QTGs meeting such multifaceted standards is testament to the difficulty of narrowing the confidence intervalsufficiently to identify and test suitable candidate genes (Flintet al." + ], + [ + "Y chromosome in peripheral blood cells increases with age in men (6) and is correlated with increased risk of cancer mortality and Alzheimer's disease (6,7).X chromosome mosaicism in women also increases with age (8), as does autosomal mosaicism in both sexes (9,10).Recent studies have shown that the prevalence of age-related mosaic abnormalities is greater in men than women (9,10); however, mechanisms underlying the sex differences observed in chromosomal mosaicism in humans are unknown.", + "Recent reports suggested a role of Y chromosome loss in risk for all-cause mortality and common age-related disease such as cancer, Alzheimer disease as well as severe atherosclerosis [12][13][14][15][16][17][18][19][20].Building on such reports, we aimed to evaluate the contribution of male Y chromosome mosaicism to the risk for late-stage AMD.", + "Box 1. Sex-specific cytonuclear interactionsSeveral predictions about the nature of cytonuclear conflicts follow from the patterns of chromosomal inheritance (Table I).In a mated pair of animals, mtDNA is co-transmitted with half of the autosomal genes, two-thirds of the X-linked genes and none of the Y-linked genes [76].This predicts that, relative to the autosomal case, positive nuclear-mitochondrial interactions are more likely to evolve for X-linked loci whereas deleterious interactions between Y-linked genes and mtDNA should accumulate (or cannot be purged efficiently).", + "In addition to genetic data, the 9p Network Cohort dataset also lists the gender for all 719 individuals.Of these individuals, 406 individuals are female and 313 are male, indicating a female bias (Binomial test p 0.0006).This result was surprising considering that no female bias has been previously reported in 9p deletion and duplication syndromes.A possible explanation for the significant bias in the 9p Network Cohort dataset is the XY sex reversal phenotype, which is commonly observed in individuals with 9p deletion syndrome.This phenotype could lead to individuals with XY sex chromosomes being listed in the dataset as having a female gender.To further examine this hypothesis, we subset our dataset to include only the 236 individuals whose sex chromosomes are listed in their genetic information.For this much smaller subset, 125 individuals had female sex chromosomes and 111 had male sex chromosomes, indicating no significant sex bias (Binomial test p 0.4).We also found no significant gender bias in this group (Binomial test p 0.2), although we did confirm that four of the individuals with XY sex chromosomes had a gender of female.This comparison suggests that the XY sex reversal phenotype may be responsible for a female gender bias, but not a sex bias, in 9p deletion and duplication syndrome cohorts.", + "Duplicated variants with multiple alternative alleles and variants in sex chromosomes X and Y", + "Autosome-One of the numbered, or nonsex, chromosomes (1 through 22).X and Y are the sex chromosomes.", + "Given such a high abundance of young male-biased genes, we asked whether their parental genes are also male-biased.We found that fewer parental genes of X-linked male-biased duplicates were also male-biased (20%, 2/10) compared to the parental genes of autosomal young male-biased duplicates (32%, 12/37).These data, despite the small sample sizes and being statistically not significant, may suggest that compared to autosomal young genes, X-linked young genes more often evolved novel male-biased expression.However, as the majority of young genes are the result of intrachromosomal duplication events, the pattern might also reflect the fact that X-linked old genes are less likely to be male-biased.A slight excess of X-linked female-biased genes was also detected (Fig. 2).Although most of them are old, a few recently arose on the X chromosome over 4 to 6 Myr in the common ancestor of the D. melanogaster and D. simulans clade (branch 5).This can be interpreted in the context of the dominance model of the sexual antagonism hypothesis.In this case, a dominant, X-linked gene that is favorable to females but disadvantageous for males can become fixed.The slow accumulation of female-biased genes in the X reflects an overall low rate of female gene origination, either due to a small dominance effect (the degree of dominance h!1/2), or a minor disadvantageous effect on males (the ratio of fitness effects of male relative to female k!0) along with a favorable effect on females (Vicoso and Charlesworth 2006, Equation 10).Regarding the second step in the evolution of male-biased genes, namely X!A transposition, sexual antagonism favorable for autosomal fixation (Vicoso and Charlesworth 2006) and/or MSCI (Lifschytz and Lindsley 1972;Betran et al. 2002) may play a role in this process.On the other hand, the within-chromosomal duplication rate is higher than the between-chromosomal duplication rate (Emerson et al. 2008), which may contribute to the slow pace of X!A transposition.It has been observed that male-biased genes in Drosophila are overrepresented on autosomes (Parisi et al. 2003;Ranz et al. 2003).Consistent with this result, a dynamic process that can explain the nonrandom autosomal distribution has also been observed, in which autosomal new genes with X-linked parental genes are often male-biased.Specifically, a significant excess of autosomal testisexpressed retrogenes were identified as RNA-duplicates of X-linked parental genes (Betran et al. 2002).Recently, similar X!A gene traffic was observed in the DNA-level duplication and relocation data set of the Drosophila genus (Vibranovski et al. 2009b), and was further confirmed for DNA-level duplications in the D. pseudoobscura neo-X chromosome (Meisel et al. 2009).In addition, selective extinction of neo-X linked male-biased genes also occurred in D. pseudoobscura (Sturgill et al. 2007).These three lines of genome-wide investigation support a common pattern of outof-X traffic for male-biased genes, resulting in an enrichment of these genes on autosomes in the long term.It has been reported that the initial manifestations of new gene emergence, namely polymorphic duplicates, occur at a lower frequency on the X chromosome, thus indicating that these duplicates are subject to stronger purifying selection (Emerson et al. 2008).Therefore, the excessive fixation of X-linked duplicates might not occur via neutral processes.Positive selection could have facilitated the fixation of X-linked young genes in addition to driving their subsequent sequence evolution.", + "Occasionally, Y chromosome DNA is detected in the maternal plasma, and the fetus appears to have female genitalia on sonographic examination.The underlying mechanisms for this include a twin demise, a maternal disorder of sexual differentiation, such as Swyer syndrome, or that the mother has undergone a bone marrow or solid organ transplant from a male donor (Bianchi, 2018;Hartwig, Ambye, Sorensen, & Jorgensen, 2017).", + "Becauseof the differences in sex chromosome number, the sexunmatched comparison contains internal controls, i.e. ,in this comparison, genes on the X-chromosome andY-chromosome (but not those on the autosomes) shouldshow copy number imbalances reective of a single copychange. We showed that the sample that is not sexmatched had readily detectable differences in aCGHsignals for genes on the X and Y chromosomes. No suchpatterns were evident for the autosomes of the sexunmatched individuals or for the sex chromosomes of thesex matched samples.", + "Sex chromosome:The X or Y chromosome in human beings that determines the sex of an individual.Females have two X chromosomes in diploid cells; males have an X and a Y chromosome.The sex chromosomes comprise the 23rd chromosome pair in a karyotype.See also: autosome Sex-linked: Traits or diseases associated with the X or Y chromosome; generally seen in males.X chromosome: One of the two sex chromosomes, X and Y. See also: Y chromosome, sex chromosome Y chromosome: One of the two sex chromosomes, X and Y. See also; X chromosome, sex chromosome", + "The male heterogamety (XY) is the mostcommon reported system, but many specieshave female heterogamety (ZW), and moreoccasionally, multiple chromosome systems(Almeida-Toledo and Foresti, 2001; Devlinand Nagahama, 2002; Penman and Piferrer,2008). Given the low resolution of optical microscopy to differentiate sex chromosomes insh, researchers have looked for an alternativein the tenfold longer meiotic chromosomes todetect mispairing tracts at the synaptonemalcomplex as an indication of the sex differentiated region with variable success.The exclusive femaleconstitution of gynogenetic genomes providesinformation on the SD system, especially in aXX/XY system, where all female progenies areexpected. If ZZ/ZW is the underlying system,male offspring always will be present, but theinterpretation is more complex and will dependon the distance of the SD region to centromereand on the viability of WW offspring (Devlinand Nagahama, 2002; Penman and Piferrer,2008). Induced triploids, on the other hand, areconstituted by the combination of two femaleand one male genomes (Piferrer et al.", + "The existence of a maternally silenced X-linked imprinted locus playing a role in social cognition could explain why males (X m Y) are more vulnerable to disorders of social cognition such as autism spectrum disorders than are females (X m X p ).The absence of the expression of this gene would not lead to autism itself, but would eliminate a putative protective factor, making an individual more susceptible to the effects of other ASD-predisposing genetic mutations or environmental factors.", + "When meiosis takes place, a pair of chromosomes may fail to separate properly, creating a sperm or egg that has either two copies or no copy of a specific chromosome.This is a sporadic event and it is called nondisjunction.Nondisjunction can lead to an extra chromosome, called trisomy, or a missing chromosome, called monosomy (GHR, 2008l).Down syndrome is an example of trisomy.Individuals who have Down syndrome have an extra chromosome number 21. Turner syndrome is an example of monosomy.Girls who have Turner syndrome have only one X chromosome.This causes them to have short stature and be infertile (NHGRI, 2008l).X-Linked InheritanceX-linked genetic disorders (also called sex-linked) are caused by gene mutations on the X chromosome.Most often X-linked genetic disorders are seen in males.Males inherit the X chromosome from their mother and the Y chromosome from their father.Because males have only one X chromosome, if they inherit a gene mutation on the X chromosome from their mother, they will have the disorder.Examples of X-linked genetic disorders occurring in males include hemophilia and Duchenne muscular dystrophy (GHR, 2008o)." + ] + ], + "task_id": [ + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB", + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "C6B9A982C9283DE065A3371F1264095C" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_4.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_4.json new file mode 100644 index 0000000..567ccf4 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_4.json @@ -0,0 +1,111 @@ +{ + "question": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "How can I add a new species to the GeneNetwork database?", + "which genes are typically associated with diabetes in QTL analyses?", + "In which diseases is the gene TCF7L2 involved?" + ], + "answer": [ + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases." + ], + "contexts": [ + [ + "Genetic mapping inmouse strains enhances the power of detecting modifier genes and identifying complexgenetic interactions. Genomewide quantitative trait locus (QTL) analysis, as described inmore detail below, represents a promising approach to detect genetic variants that areassociated with specific phenotypes and interact with each other. 16ACCEPTED MANUSCRIPTIn experimental crosses of two (inbred) strains the first generation (F1) ofoffsprings is genetically heterozygous but equal. Then in the next generation (F2) thePTstrain-specific genetic information is distributed across the genomes of their progeny andRIeach offspring is genetically unique.", + "This contrast can be exploited to identify subregions that underlie the trans-QTLs [67]. SNPs were counted for all four pairs of parental haplotypesBvs D, B vs H, B vs C, and L vs Sand SNP profiles for the fourcrosses were compared (figure 6). Qrr1 is a highly polymorphicPLoS Genetics | www.plosgenetics.org8November 2008 | Volume 4 | Issue 11 | e1000260QTL Hotspot on Mouse Distal Chromosome 1Figure 5. QTL for aminoacyl-tRNA synthetases in distal Qrr1.", + "The traditional approach to QTL mapping is to usetwo strains that differ maximally in the phenotype asparental strains for genetic crosses, with the followingcaveats. QTL analysis based on a single cross will mostlikely reflect only a small portion of the net geneticvariation, and QTL detection will be limited to regionswhere the two progenitor strains have functional polymorphisms. Data from multiple crosses, or from an HS,will overcome this limitation and can also be used toreduce QTL intervals [5,30].", + "These candidate genes are then sequenced in the two parental inbredstrains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausiblecandidate polymorphisms, the major challenge remains \u0001 proving denitivelywhich nucleotide polymorphism underlies the QTL. The most direct proofwould be replacing one strains allele with another strains allele (creating aFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mousegenerations are depicted.", + "Furthermore, splicing QTLs(sQTLs) rather than eQTLs could comprise the molecular mechanism linking DNA variants with YFP53; thus, sQTL analysis could uncover genes that would not normally bedetected at the level of differential gene expression (DGE),53 and thus, a differentially181182Molecular-Genetic and Statistical Techniques for Behavioral and Neural ResearchFigure 8.5 Schematic for immediate, rapid ne mapping in select F2 recombinants of the RCC-F2cross. Top panel: Genome-wide signicant QTL (green trace; red dashed line signicance threshold;blue vertical lines Bayes credible interval).", + "Interval-specific haplotype analysisApproximately 97% of the genetic variation betweeninbred mouse strains is ancestral [22], so regions ofidentity by descent (IBD) between two strains used todetect a QTL are highly unlikely to contain the causalgenetic polymorphism underlying the QTL [28]. Forexample, a cross between C57BL/6J and A/J mice detectedwww.sciencedirect.coma blood pressure QTL on Chr 1 [7].", + "Interval-specific haplotype analysisApproximately 97% of the genetic variation betweeninbred mouse strains is ancestral [22], so regions ofidentity by descent (IBD) between two strains used todetect a QTL are highly unlikely to contain the causalgenetic polymorphism underlying the QTL [28]. Forexample, a cross between C57BL/6J and A/J mice detectedwww.sciencedirect.coma blood pressure QTL on Chr 1 [7].", + "At present, the BXD panel is composed of 80 different strains that all have beenfully genotyped.26 Variation in any quantifiable trait can be associated with thesegregation of parental alleles, and linkage genetics can map this variation toquantitative trait loci (QTLs), thereby identifying the genomic region(s) affectingthat trait. An overview of the QTL mapping approach is depicted in Figure 2. Classical QTL analysis has permitted the identification of loci that areassociated with variation in HSC traits.", + "This comparison gives information about the reliability of the observed genotypeinformation: The more the marker locations differ between the two maps (which signifiesvariation in marker positions), the higher the possibility of genotyping errors. QTL mapping was done in several stages to identify loci acting individually and QTL thatinteracted, either additively or epistatically. To determine individually-acting QTL, a singleQTL genome scan was conducted with the function scanone.In general,linking genetic variation with trait variation identifies QTL and a significant linkage ofphenotype and genotype suggest that the DNA status helps to determine trait expression. As stated above, mouse QTL studies provide distinct advantages over human studiesin the examination of genetic causes of a quantitative trait (e.g. alcoholism), even in theabsence of specific hypotheses regarding its aetiology or candidate genes.The progenitor mouse strainsshould have sufficient variation for the traits of interest and they should be genetically diverseenough to enable genetic mapping (BENNETT et al. 2006; FLINT 2003; GRISEL 2000). Thesample size required for the identification of QTL depends largely on the effect size that aQTL contributes to phenotypes on interest. Inference about QTL can be made if one or moregenetic markers are over- or underrepresented in the analysed individuals. Genotyping isoften done by means of microsatellite markers, which contains mono, di-, tri-, ortetranucleotide tandem repeats flanked by specific sequences (Figure 4a).", + "Importantly, whereasthese studies required substantial labor, time, and resources, X-QTL is a quick and easyapproach to achieve a comparable level of genetic dissection. The levels of complexityobserved here (e.g. 14 loci explaining 70% of the genetic variance for 4-NQO resistance) arestill dramatically lower than those seen in for some human traits in GWAS (e.g. 40 lociexplaining 5% of the variance for height 2,5). One obvious explanation is the difference inexperimental designs (line crosses vs. population association studies), but differences ingenetic architectures among species and traits may also contribute.", + "The method uses two pieces of information: mapping data from crosses thatinvolve more than two inbred strains and sequence variants in the progenitor strains within the intervalcontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that anysequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals exceptat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.", + "The method uses two pieces of information: mapping data from crosses thatinvolve more than two inbred strains and sequence variants in the progenitor strains within the intervalcontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that anysequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals exceptat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.", + "which allows the the estimation of the degree of dominance of detected QTLs.but point out that various designs or linkage analysis methods have advantages and disadvantages and need to be chosen for the question at hand. In general, there are two common statistical approaches for detecting and/or locating QTL. Both approaches involve moving along the chromosome and considering data for one or several markers at a time and relating these to the traits of interest.", + "Genotyping all the individual progeny formarkers that show allelic variation between the parental strains (either single nucleotide polymorphisms or simple sequence repeats) will allow the detection of associations between trait values and marker genotype, and in this way demonstrate to whichset of markers a QTL is linked. To reduce the genotyping effort, selective genotypingof the individuals at the extremes of the phenotypic spectrum can be performed (20,23). Although these three approaches are in general considered to be the best to detect andmap QTL, they have several disadvantages for quantitative traits involving HSC.", + "So, how do you go about planning and performing a QTL study, and howdo you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbredstrains that have a markedly different trait. One can now look up many differenttraits of inbred mice online at the Mouse Phenome Database (http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). However, the trait you maywant to study may not be present in wild type mice, so you may want to crossa mutant (or genetically engineered) strain onto several inbred strains.By using a segregating populationit is possible to determine whether the observed difference in mRNA abundance betweentwo parental lines is caused by a sequence difference within the gene itself or close to it(cis-eQTL) or to a factor from a different location (trans or distal eQTL). If it is possibleto map both the gene itself (a binary trait) and the variation in the abundance of mRNAexpressed from that gene (a quantitative trait) onto the chromosomes (as above) then cisand trans-regulation can be easily differentiated.", + "These candidate genes are then sequenced in the two parental inbredstrains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausiblecandidate polymorphisms, the major challenge remains \u0001 proving denitivelywhich nucleotide polymorphism underlies the QTL. The most direct proofwould be replacing one strains allele with another strains allele (creating aFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mousegenerations are depicted.", + "QTL mapping studies thenseek to detect the polymorphisms underlying the complex traits of interest byscanning for alleles that co-vary withthe traits. Similar experiments also can be conducted with special derivatives of inbredstrains known as recombinant inbred(RI) mice. These animals are derivedby cross-breeding two or more distinctparental strains (which often divergewidely for the trait of interest), followedby inbreeding of the offspring for severalgenerations (Bailey 1971). Given thecorrect breeding strategy, this method1This is an issue faced by GWASs researchers when classifyingsamples as cases or controls." + ], + [ + "(1234567890)Complex trait variation in natural and experimental populations is due to specific DNA sequence polymorphisms, environmental effects, and the interactions between these factors(Johannes et al. 2009). Testis weight is a complex trait thatholds direct implications for reproductive success, as developmental abnormalities can lead to irregular sperm production and infertility in adulthood (Sharpe 2001). Variation intestis size has been linked to environmental factors such associal dominance, social organization, and seasonal changesacross numerous species.", + "Selection could occur at multiple levels, from germ cell generation and propagation to fertilization and early embryonic growth.Chromosomal abnormalities, including aneuploidy, were found in 10-20% of spermatozoa and oocytes (20) and in the cleaved embryo, with a 21% rate of abnormalities in preimplantation embryos (21).These findings led to a model for natural selection against chromosome abnormalities (21).Selection extends to the end of gestation: Only approximately 30% of all conceptions result in a live birth, with more than half of aborted fetuses containing chromosomal abnormalities (22), a number likely to be an underestimate because of technological limitations in measuring all possible mutations.But even in the very small fraction of germ cell duos that survive this withering genome attack and result in a live birth, a number of severe de novo mutations will still be found (23).The data on gross chromosomal alterations suggest that overall, mutation frequency early in life is very high.The functional consequence, however, is limited because of selection.Somewhat surprisingly, this picture points toward an initial decline in genomic alterations, allowing the adult individual to acquire a somatic genome optimally equipped to provide function.", + "We now need to investigate the candidates identified here and how their effects on parental and offspring traits are integrated into thegene networks determining individual development. By controlling for genetic variation in eithermothers or offspring we have been able to show that levels of maternal provisioning and offspringsolicitation are unique to specific genotypes (here each BXD line) and that solicitation is costly.", + "Sex brings harmful alleles together into thesame genetic background, allowing selection to more efficiently purge them fromthe population and potentially producing some offspring that are fitter than eitherparent. However, the benefit of recombining deleterious mutations may depend on thenature of the epistatic interactions between them. The mutational deterministic hypothesis(Kondrashov 1988) depends partly on this epistasis.In most plants and animals, sexis a necessary component of reproduction, and the question for evolutionary biologistsis why reproductive mechanisms have evolved that way. In one of the experimentsdescribed next, evolutionary geneticists have nevertheless devised a way to compareevolution with and without recombination in the obligately sexual fruit fly.This disparity in investment is the basis for the twofold cost: asexualfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. Butsex usually also involves the basic process of physical recombination: the breakage andreunion of two different DNA or RNA molecules. Of these two processes, recombinationis clearly the more widespread feature of sexual reproduction. A variety of reproductivesystems, such as selfing and automixis, involve recombination but not outcrossing. Incontrast, relatively few reproductive systems have outcrossing without recombination.Longago, Wright (1931) noted that sex may destroy adaptation because a successful combination of characteristics is attained in individuals only to be broken up in the next generation by the mechanisms of meiosis itself. Similarly, if alleles at different loci werejointly responsible for the production of phenotypes, sex has the potential to break apartcoadapted gene complexes, as it moves alleles away from genetic backgrounds wherebeneficial epistatic interactions have evolved through natural selection. Why should sex therefore be so common, given the obvious costs?", + "The reason for the rarity of these mutations is natural selection: If the mutations result in disorders that decrease health and reproductive fitness, they will eventually be eliminated from a population.In exceptional cases, mutations may cause both beneficial and detrimental consequences, resulting in opposing forces of positive selection and negative selection that may cause the mutations to be preserved at nonrare frequencies in a population.For example, the HbS mutation in the HBB gene (which produces the subunit of hemoglobin) causes sickle cell disease when present in both alleles, a detrimental consequence, but protects against malaria when present in 1 allele, a beneficial consequence, ensuring that the mutation persists in populations in areas of the world where malaria is endemic.Genes are passed from parents to offspring via the process of meiosis by which gametes, the egg cells in the mother and the sperm cells in the father, are generated.Ordinarily, each cell has 23 pairs of chromosomes; the gametes have 23 unpaired chromosomes.In meiosis, the 23 pairs are split so that each gamete receives 1 chromosome from each pair (Figures 8 and 9).Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full complement of 23 chromosome pairs restored.If all goes well, the zygote gives rise to a live offspring.Crossing over-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes cross over one another.Crossing over results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.This process is also known as meiotic recombination.Recombination (meiotic recombination)-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes recombine with one another.Recombination results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.Also known as crossing over.", + "Sex brings harmful alleles together into thesame genetic background, allowing selection to more efficiently purge them fromthe population and potentially producing some offspring that are fitter than eitherparent. However, the benefit of recombining deleterious mutations may depend on thenature of the epistatic interactions between them. The mutational deterministic hypothesis(Kondrashov 1988) depends partly on this epistasis.In most plants and animals, sexis a necessary component of reproduction, and the question for evolutionary biologistsis why reproductive mechanisms have evolved that way. In one of the experimentsdescribed next, evolutionary geneticists have nevertheless devised a way to compareevolution with and without recombination in the obligately sexual fruit fly.This disparity in investment is the basis for the twofold cost: asexualfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. Butsex usually also involves the basic process of physical recombination: the breakage andreunion of two different DNA or RNA molecules. Of these two processes, recombinationis clearly the more widespread feature of sexual reproduction. A variety of reproductivesystems, such as selfing and automixis, involve recombination but not outcrossing. Incontrast, relatively few reproductive systems have outcrossing without recombination.Longago, Wright (1931) noted that sex may destroy adaptation because a successful combination of characteristics is attained in individuals only to be broken up in the next generation by the mechanisms of meiosis itself. Similarly, if alleles at different loci werejointly responsible for the production of phenotypes, sex has the potential to break apartcoadapted gene complexes, as it moves alleles away from genetic backgrounds wherebeneficial epistatic interactions have evolved through natural selection. Why should sex therefore be so common, given the obvious costs?", + "However, we expect thatonly at this level, the most signicant contributions brought by integrating epigenetics will bemade. Concluding Remarks and FutureProspectsFish sex ratios are the result of a complex interaction between genetic, biochemical, and environmental interactions. The ultimate resultof these interactions at the individual level isgender: male or female. However, at the population level, the combination of sex determination and differentiation sets the sex ratio. Inturn, sex ratios dene the reproductive capacityof populations and, if sex growth dimorphismexists, also the growth characteristics, something very important in an aquaculture context.Traditionally, it has been agreed that thenal sex of an individual (phenotypic sex)depends on two sequential processes: the sexdetermination system of the species and thegonad differentiation process (Valenzuela,2008). However, recently, these two seeminglydistinct processes are viewed as part of a general process leading to gonad formation andsex ratios (Sarre et al. , 2004; Quinn et al. , 2011;Uller and Helantera, 2011).", + "Obehav is, in turn, influenced by offspring genesand environment (Ogene and Oenvir respectively). Hence, indirect genetic effects (blue arrows)and direct genetic effects (red arrow) are important influencers of behaviour. B) Parentoffspring conflict theory predicts that parental resource investment and offspring solicitationbehaviours are influenced by the fitness benefit to a focal individual (O), cost to a socialpartner such as a sibling (S1 and S2) or parent (P), and by their coefficient of relatedness(black arrows). 42Figure 2: Genomic imprinting can result in divergent phenotypes from the samegenotype. A) A paternally imprinted gene, i.e. maternally expressed.", + "Therefore, the resulting phenotypic patterns lag a generationbehind the genetic transmission of the causal variants. The most well-studied parental genetic effectsare caused by deposition of maternal transcripts into the egg prior to fertilization, resulting indifferences in early embryonic development depending on the genotype of the mother. Certain geneshave also been shown to respond to maternal influence after birth through genetically definedmaternal behaviors (Weaver et al. , 2004).Because of the small contribution, through the sperm, ofthe paternal transcriptome to the fertilized zygote, and because of the stronger maternal contributionto child rearing in most model organisms, parental effects are typically thought of as synonymous withmaternal effects, although true paternal effects are known to exist (Rando, 2012). Maternal effects have been shown to be important during embryonic development, leading todifferences in the birth weight of mice depending on the genotype of the mother (Cowley et al. ,1989; Wolf et al. , 2011).", + "It was believed by many that for each trait variant we should expect to find acorresponding genetic change, or gene for that trait. Through historical happenstance therelationship between genes and traits was set up and treated as if it were one-to-one. But theproduction of a trait involves not only genes, but also their interactions with each other and theenvironment, and chance." + ], + [ + "The hierarchical organization of GNs main Select and Search menu issimple and makes it relatively easy to find relevant data sets (Fig. 1). To get data, afteropening the browser, select the most appropriate Species from the dropdown menu. For anopen-ended search of phenotypes you can also select All Species at the bottom of the menu. The next steps are to select the Group, Type, and Data Set from the drop-down menus. Formany groups, a combination of phenotypes, genotypes, and molecular data are available.", + "GeneNetwork contains data from awide range of species, from humans to soybeans, but most of the available phenotypic data isfrom mice. Within the mouse dataset there are groups of families, crosses, non-geneticgroupings, and individual data. The type of dataset must be selected after defining the speciesand sample population. While genotypes, mRNA, methylated DNA, protein, metagenomic, and2bioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. metabolome datasets are available (i.e.", + "The hierarchical organization of GNs main Select and Search menu issimple and makes it relatively easy to find relevant data sets (Fig. 1). To get data, afteropening the browser, select the most appropriate Species from the dropdown menu. For anopen-ended search of phenotypes you can also select All Species at the bottom of the menu. The next steps are to select the Group, Type, and Data Set from the drop-down menus. Formany groups, a combination of phenotypes, genotypes, and molecular data are available.", + "Search and Data RetrievalPoint your browser to www.genenetwork.org. This brings you by default tothe Search page, from which you can retrieve data from many GN data sets. We will focus on the default data set, defined by Species: Mouse, Group: BXD,Type: Whole Brain, Database: INIA Brain mRNA M430 (Apr05) PDNNEnter Kcnj* into the ALL or ANY field and click the Search button. Notethe location and annotation of available potassium channel genes in the SearchResults page that opens. Use the browser Back button to return to previous page.", + "Addinformation on data provenance by giving details in Investigation, Protocols and ProtocolApplicationsCustomize Customize my XGAP database with extended variants of Trait and Subject. In the online XGAP demonstrator, Probe traits have asequence and genome location and Strain subjects have parent strains and (in)breeding method. Describe extensions using MOLGENISlanguage and the generator automatically changes XGAP database software to your researchUploadUpload data from measurement devices, public databases, collaborating XGAP databases, or a public XGAP repository with communitydata.However, a suitable and customizable integration ofthese elements to support high throughput genotype-tophenotype experiments is still needed [34]: dbGaP, GeneNetwork and the model organism databases aredesigned as international repositories and not to serveas general data infrastructure for individual projects;many of the existing bespoke data models are too complicated and specialized, hard to integrate between profiling technologies, or lack software support to easilyconnect to new analysis tools; and customization of theexisting infrastructures dbGaP, GeneNetwork or otherinternational repositories [35,36] or assembly of Bioconductor and generic model organism database components to suit particular experimental designs, organismsand biotechnologies still requires many minor andsometimes major manual changes in the software codethat go beyond what individual lab bioinformaticianscan or should do, and result in duplicated effortsbetween labs if attempted.", + ", 2014; see Section 9). GeneNetwork is a database that enables searching for 4000 phenotypes from multiple studies in the BXD, HXB, and in other recombinant inbred rodent families, as well as in other model organismsand even humans (Mulligan et al. , 2017). GeneNetwork employed asomewhat dierent strategy than MPD in that it did not rely solely onresearchers submitting their data. Instead the database operators extracted the data from the scientic literature and integrated them into auniform format (Chesler et al. , 2003).", + "GeneNetwork contains data from awide range of species, from humans to soybeans, but most of the available phenotypic data isfrom mice. Within the mouse dataset there are groups of families, crosses, non-geneticgroupings, and individual data. The type of dataset must be selected after defining the speciesand sample population. While genotypes, mRNA, methylated DNA, protein, metagenomic, and2bioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. metabolome datasets are available (i.e.", + "However, a suitable and customizable integration of these elementsto support high throughput genotype-to-phenotype experiments is stillneeded[340]: dbGaP, GeneNetwork and the model organism databasesare designed as international repositories and not to serve as generaldata infrastructure for individual projects; many of the existing bespokedata models are too complicated and specialized, hard to integrate between proling technologies, or lack software support to easily connectto new analysis tools; and customization of the existing infrastructuresdbGaP, GeneNetwork or other international repositories[384, 154] orassembly of Bioconductor and generic model organism database components to suit particular experimental designs, organisms and biotechnologies still requires many minor and sometimes major manual changes382.1.", + "All data presented in this paper were deposited in the online databaseGeneNetwork (www.genenetwork.org), an open web resource that containsgenotypic, gene expression, and phenotypic data from several genetic referencepopulations of multiple species (e.g. mouse, rat and human) and various celltypes and tissues.35;36 It provides a valuable tool to integrate gene networks andphenotypic traits, and also allows cross-cell type and cross-species comparativegene expression and eQTL analyses.", + "There is a good chance that you will be able to apply these newtechniques to specific problems, even while you read. If you have a computer with anInternet connectionso much the better, and you can read and work along at the same time. This short review and primer will take you on a tour of a web site called GeneNetwork thatembeds many large data sets that are relevant to studies of behavioral variation. GeneNetwork is an unusual site because it contains a coherent \"universe\" of data, as well asmany powerful analytic tools.", + "The GeneNetwork database provides open accessto BXD and other RI strain derived microarray data, single nucleotide polymorphism (SNP) data,and phenotypic data for quantitative trait loci analysis and gene expression correlation analyses. Gene expression data were exported for manually selected probes in the PDNN hippocampusdatabase (Hippocampus Consortium M430v2), and the PDNN whole brain database (INIA BrainmRNA M430). The Hippocampus database was chosen as one of the most elaborate brain databases,as well as most highly recommended dataset on GeneNetwork itself (http://www.genenetwork.org/webqtl/main.py?FormID=sharinginfo&GN_AccessionId=112).", + "2016) and canalso be accessed in GeneNetwork by entering Record ID 18494 in the Get Anyspace on the Search page and clicking on the Search button. Alternatively, enterdata by hand into the designated boxes provided by GeneNetwork. These latteroptions also allow for the inclusion of trait variance. It is a good idea to namethe trait in the box provided. Then click Next, and manually enter the data foreach RI strain, F1, and founder strain. 3Author ManuscriptAfter entering the data, click on the blue plus sign button called Add.To submit multiple phenotypes at the sametime, select the option for Batch Submission under the Home tab. This allowsusers to submit up to 100 traits for analysis by GeneNetwork. Here, select BXDas the cross or RI set to analyze from the first pull-down menu. The phenotypefile should follow the format described in the Sample text (http://genenetwork.org/sample.txt). After uploading the appropriate file using theBrowse button, enter a name for the file in the Dataset space. The data will bestored in the GeneNetwork server for 24 hours. Click Next.Author ManuscriptMaterialsHere we will provide detailed instructions for using GeneNetwork along with someworked examples taken from the recent study of intravenous cocaine self-administrationby Dickson et al. (2016) in BXD RI mice. A complete overview of GeneNetwork is beyondthe scope of this protocol, but is extensively covered in elsewhere (see Mulligan et al. 2016;Williams & Mulligan 2012 for excellent reviews on GeneNetwork). A computer with an internet connection and current web browser. See the GeneNetwork.orgsite for information on supported browser versions. Author ManuscriptMethodEntering DataAuthor Manuscript1Link to http://www.genenetwork.org.", + "Species in GenAge model organisms", + "Data are reviewed before entry inGeneNetwork by the senior author. Phenotypes are currently split into 15 broadphenotypic categories (Supplementary Data 1). Phenome curation and descriptionwas initiated by R.W.W. and Dr Elissa Chesler in 2002 by literature review and dataextraction. The early work is described briey in Chesler et al.51,52. Most work overthe past 5 years has been performed by two of the coauthors (R.W.W. andM.K.M.). We have used a controlled vocabulary and set of rules described here(http://www.genenetwork.org/faq.html#Q-22).", + "9) To bring your data to GeneWeaver,click on the GeneWeaver icon, making sure to be previouslylogin to your GeneWeaver account. You will be brought to theGeneSet upload page with the Genes Uploaded and theGeneweaver Analysis Platform139Fig. 5 Default settings at GeneNetwork.org are set to search Mouse, Phenotypes, from among the BXDPublished Phenotypes data set. Here the term nociception was searched forFig. 6 The search results page in GeneNetwork showing the 33 records retrieved from the phenotype searchfor nociception.Users may also share their data with other users selectively,make it public, or keep it restricted to a private account. Data can beimported by users, uploading their gene set data directly or exporting to GeneWeaver from within another online resource such asNeuro Informatics Framework (NIF) [8], Grappa [9], MousePhenome Database (MPD) [10] or GeneNetwork [11]. These datasets can then be added to your collection to be analyzed togetherwith other gene sets retrieved from the GeneWeaver database. To begin a GeneWeaver analysis a user must collect GeneSetstogether in a Project.Alternatively the spreadsheet can be saved as a .txt fileand uploaded by clicking on Switch to file upload. Oncecomplete click on upload GeneSet. 7. Once completed you are taken to the GeneSet detail page. Ifthere are errors in your uploaded data you can correct them byclicking on Edit. 8. Use the Add Selected to Project, and create a new project, e.g. Chronic Cocaine. 9. Now using the Search function populate this project with additional gene sets related to this study trying Queries such asCocaine Addiction, Chronic Cocaine." + ], + [ + "Figure 3 | Association statistics from one of the five type 2 diabetes genome-wide association studies 20 .The y axis represents the -log10 p value and the x axis represents each of the ~400,000 SNPs used in this scan.The point of each arrow indicates the location of the most strongly associated SNP in each of nine known type 2 diabetes gene regions.Two signals, in SLC30A8 and TCF2, were not captured on the Affymetrix chip.The plot was generated using Haploview.CDKAL1, CDK5 regulatorysubunit-associated protein 1-like 1; CDKN2, cyclin-dependent kinase inhibitor 2A; FTO, fat mass and obesity-associated; HHEX, haematopoietically expressed homeobox; IDE, insulin-degrading enzyme; IGF2BP2, insulin-like growth factor 2 mRNA-binding protein 2; KCNJ11, potassium inwardly-rectifying channel, subfamily J, member 11; PPARG, peroxisome proliferator-activated receptor- gene; SLC30A8, solute carrier family 30 (zinc transporter), member 8; TCF2, transcription factor 2, hepatic; TCF7L2, transcription factor 7-like 2 (T-cell specific, HMg-box).", + ", for the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 9Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D) [1][2][3][4][5][6][7][8][9][10][11] .Established associations to common and rare variants explain only a small proportion of the heritability of T2D.As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and B2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975.We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P 5.0 10 -14 ), CDC123-CAMK1D (P 1.2 10 -10 ), TSPAN8-LGR5 (P 1.1 10 -9 ), THADA (P 1.1 10 -9 ), ADAMTS9 (P 1.2 10 -8 ) and NOTCH2 (P 4.1 10 -8 ) gene regions.Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D. [3][4][5]7,10 , for the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 9Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D) [1][2][3][4][5][6][7][8][9][10][11] .Established associations to common and rare variants explain only a small proportion of the heritability of T2D.As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and B2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975.We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P 5.0 10 -14 ), CDC123-CAMK1D (P 1.2 10 -10 ), TSPAN8-LGR5 (P 1.1 10 -9 ), THADA (P 1.1 10 -9 ), ADAMTS9 (P 1.2 10 -8 ) and NOTCH2 (P 4.1 10 -8 ) gene regions.Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.", + "Although these are considered to be loci convincingly associated with susceptibility to type 2 diabetes in populations of European descent, other genes related to susceptibility to the disease are probably still unidentified, particularly those for populations of other ancestries.In order to uncover genetic variants that increase the risk of type 2 diabetes, we conducted a genome-wide association study in Japanese individuals with type 2 diabetes and unrelated controls.We first genotyped 268,068 SNPs, which covered approximately 56% of common SNPs in the Japanese, in 194 individuals with type 2 diabetes and diabetic retinopathy (case 1) and in 1,558 controls (control 1) collected in the BioBank Japan.We compared the allele frequencies of 207,097 successfully genotyped SNPs and selected the 8,323 SNPs showing the lowest P values.We then attempted to genotype these 8,323 SNPs in 1,367 individuals with type 2 diabetes and diabetic retinopathy (case 2) and for 1,266 controls (control 2) (stage 2), and successfully obtained data for 6,731 SNPs (the P value distribution in the second test is shown in Supplementary Fig. 1a online).The results of principal component analysis 8 in the stage 1 and 2 samples and HapMap samples revealed that there was no evidence for population stratification between the case and control groups throughout the present tests (Supplementary Fig. 1b,c).We selected the 9 SNP loci showing P values o0.0001 (additive model in stage 2, Table 1) and genotyped a third set of cases and controls comprising 3,557 Japanese individuals with type 2 diabetes (cases 3,4,5) and 1,352 controls (controls 3,4).We evaluated the differences in the population structure among these three sets of case and two sets of control groups by Wright's F test.As the results indicated that there was no difference in the population structure among these groups (Supplementary Table 1b online), we combined these populations for the third test of case-control study.The third set of analysis identified the significant associations for six SNPs (Table 1), including the CDKAL1 locus at 6p22.3 (rs4712524, rs9295475 and rs9460546), the IGF2BP2 locus at 3q27.2 (rs6769511 and rs4376068) and the KCNQ1 locus at 11p15.5 (rs2283228).The remaining three SNPs (rs13259803, rs612774 and rs10836097) had P values of 40.05 in the third test and were not further examined.CDKAL1 and IGF2BP2 were previously reported as susceptibility genes for type 2 diabetes in the Japanese population 9 .Therefore, we focused on the KCNQ1 locus, which was highly associated with type 2 diabetes.", + "Genome-wide association studies (GWAS) have recently revealed many novel SNPs associated with type 2 diabetes.These include SNPs located in the regions near TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, and CDKN2A-CDKN2B [8][9][10][11][12][13].A second phase of studies identified many additional variants, including those near JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, and KCNQ1 [14,15].The two genes in which common variants were previously convincingly associated with type 2 diabetes, PPARG and KCNJ11, were also identified in these GWAS [12,16,17].More recently, numerous other SNPs have been identified in additional GWAS and meta-analyses [18].", + "As noted by Below et al. and others [34,44], there is a significant enrichment of eQTLs among top type 2 diabetesassociated loci.Genetic heritability estimates for type 2 diabetes are markedly higher than can be explained by the variation identified to date; to characterize this Bmissing^heritability, Torres et al. composed multiple SNP subsets by partitioning interrogated maker sets into groups by status as eQTL in several insulin-responsive peripheral tissues [45].They discovered that these subsets explain a greater portion of type 2 diabetes risk than expected by chance, suggesting a significant role of regulatory variation in diabetes susceptibility.Several reasons have been suggested as to why so much of the genetic heritability of type 2 diabetes remains unmapped to risk loci [46].Conclusive identification of less common (0.5-5 % MAF) variation of modest effect will require investments in extremely large sample sizes.The heterogeneous nature of Hispanic populations increases the challenge because to detect variation or effects specific to groups or environments may require sample sizes beyond what exist to be collected.There is evidence that parent of origin may influence effects of variants on type 2 diabetes risk [47].Studies in mouse models also demonstrate that some genetic effects on type 2 diabetes and related traits are modified by sex, diet, and epigenetic effects, indicating that careful environmental modelling and stratification will be necessary to identify some loci subject to interaction effects [48].Genetic characterizations of larger Hispanic samples are underway, but especially in the case of extremely rare or private variation, a return to family-based study designs will improve power through enrichment of allelic observations and increased environmental and genetic homogeneity [49].] performed a GWA study for lipid traits in a cohort of 3642 Hispanic participants from the Women's Health Initiative SNP Health Association Resource (WHI-SHARe) and reported genome-wide significant signals within or near the genes GCKR, LPL, and APOA/APOC for TG and CETP and APOA/APOC for HDL-C.These authors also showed that there is a substantial overlap in the genes associated with lipid traits in different population groups.When testing the markers showing genome-wide significance or suggestive evidence of association (p 10 5 ) in European GWA studies in the Hispanic and African American WHI cohorts, a strong enrichment of small p values was observed in both cohorts.Additionally, there was a significant correlation of the allelic effects of markers with p 10 5 identified in Europeans in the Hispanic and African American cohorts.The genomic regions showing association in Europeans accounted for a disproportionate amount of variance in both cohorts.", + "Attempts to apply similar approaches to families in which either common forms of diabetes or obesity is segregating have proved to be largely unrewarding, 5 and the second wave of discovery involved a switch to tests of association.Although intrinsically more powerful than linkage analysis, association analysis suffers from the disadvantage that the signal can be detected only if one examines the causal variant itself or a nearby marker with which it is tightly correlated.Until the advent of methods that enabled genomewide surveys of association, researchers were therefore obliged to direct their attention to specific candidate variants or genes of interest. 6In retrospect, it is obvious that most such studies were seriously underpowered or focused on inappropriate candidates. 6Nevertheless, by accruing data over the course of multiple studies, some genuine susceptibility variants were identified.Common coding variants in PPARG and KCNJ11 (each of which encodes a protein that acts as a target for classes of therapeutic agents widely used in diabetes management) were shown to have modest effects on the risk of type 2 diabetes. 7,8Resequencing of the gene encoding the melanocortin-4 receptor (MC4R) resulted in the identification of low-frequency coding variants that explain approximately 2 to 3% of cases of severe obesity. 9he third, and most successful, wave of discovery has been driven by systematic, large-scale surveys of association between common DNA sequence variants and disease.The first demonstration that unbiased discovery efforts could reveal new insights into the pathogenesis of type 2 diabetes resulted from identification of the association between type 2 diabetes and variants within TCF7L2 (encoding transcription factor 7-like 2, a protein not previously identified as a biologic candidate). 10TCF7L2 has now been shown to modulate pancreatic islet function. 113][14][15][16][17][18] Together, these studies revealed six new associations, including variants near CDKAL1, CDKN2A, and CDKN2B (which encode putative or known regulators of cyclin-dependent kinases) and HHEX (which is transcribed into a homeobox protein implicated in beta-cell development).Typically each copy of a susceptibility allele at one of these loci is associated with a 15 to 20% increase in the risk of diabetes.Since then, the dominant approach to discovery has involved ever-larger aggregations of genomewide association data from multiple samples so as to improve the power to identify variants of modest effect: these studies have revealed more than 20 additional confirmed signals of susceptibility to type 2 diabetes [19][20][21][22] (Table 1 and Fig. 1).Though early studies were restricted to samples obtained from persons of European descent, genomewide association analyses conducted in other ethnic groups are now emerging. 23,24,29he current total of approximately 40 confirmed type 2 diabetes loci includes variants in or near WFS1 (wolframin) and the hepatocyte nuclear factors HNF1A and HNF1B (genes that also harbor rare mutations responsible for monogenic forms of diabetes) [30][31][32][33] ; the melatonin-receptor gene MTNR1B (which highlights the link between circadian and metabolic regulation) [26][27][28] ; and IRS1 (encoding insulin-receptor substrate 1), one of a limited number of type 2 diabetes loci with a primary effect on insulin action rather than on secretion. 25enomewide association studies of genetic variants influencing body-mass index (BMI) and obesity have been similarly productive, with three main strategies being adopted (Table 2 and Fig. 2).Genomewide association studies of population-based samples to examine the full range of BMI values have identified approximately 30 loci influencing BMI and the risk of obesity.The strongest signal remains the association with variants within FTO (the fat-mass and obesity-related gene). 13,34,45Other signals near BDNF, SH2B1, and NEGR1 (all implicated in aspects of neuronal function) reinforce the view of obesity as a disorder of hypothalamic function. 35,37,38,43A second approach, focusing on case-control analysis of persons selected from the extremes of the BMI distribution, has delivered a complementary, only partly overlapping, set of loci. 39,42,46,47Finally, genomewide analyses of patterns of fat distribution, prompted by the particularly deleterious health effects of visceral fat accumulation, have characterized approximately 15 loci that are largely distinct from those influencing overall adiposity 36,40,41,44 : many of the 15 display markedly stronger associations in women than in men.", + "Recent large genome-wide association studies (GWAS) have identified multiple loci which harbor genetic variants associated with type 2 diabetes mellitus (T2D), many of which encode proteins not previously suspected to be involved in the pathogenesis of T2D.Most GWAS for T2D have focused on populations of European descent, and GWAS conducted in other populations with different ancestry offer a unique opportunity to study the genetic architecture of T2D.We performed genome-wide association scans for T2D in 3,955 Chinese (2,010 cases, 1,945 controls), 2,034 Malays (794 cases, 1,240 controls), and 2,146 Asian Indians (977 cases, 1,169 controls).In addition to the search for novel variants implicated in T2D, these multi-ethnic cohorts serve to assess the transferability and relevance of the previous findings from European descent populations in the three major ethnic populations of Asia, comprising half of the world's population.Of the SNPs associated with T2D in previous GWAS, only variants at CDKAL1 and HHEX/IDE/KIF11 showed the strongest association with T2D in the meta-analysis including all three ethnic groups.However, consistent direction of effect was observed for many of the other SNPs in our study and in those carried out in European populations.Close examination of the associations at both the CDKAL1 and HHEX/IDE/KIF11 loci provided some evidence of locus and allelic heterogeneity in relation to the associations with T2D.We also detected variation in linkage disequilibrium between populations for most of these loci that have been previously identified.These factors, combined with limited statistical power, may contribute to the failure to detect associations across populations of diverse ethnicity.These findings highlight the value of surveying across diverse racial/ethnic groups towards the fine-mapping efforts for the casual variants and also of the search for variants, which may be population-specific.", + "Discovery of Novel Genes Associated With Type 1 DiabetesMellitus A total of 24,984 genes were analyzed in the initial gene-based GWAS.Three quantile-quantile plots for gene-based P-values, SNP-based P-values inside genes and SNP-based P-values outside genes are shown in Figure 1.We observed dramatic deviations at the tails of the distributions for the three plots.The deviation was much stronger for the plot of gene-based P-values than the other two plots, suggesting relatively higher power for gene-based association analysis.In short, through a gene-based association study, we identified 183 type 1 diabetes mellitus-associated genes that were insignificant in the original SNP-based association tests.Among the 183 genes, 171 genes are 'novel' genes identified for type 1 diabetes mellitus.Replication studies and/or differential expression studies further supported the significance of 53 genes to type 1 diabetes mellitus.In particular, four non-HLA genes (RASIP1, STRN4, BCAR1 and MYL2) and three HLA genes (FYN, HLA-J and PPP1R11) were validated by both replication and differential expression studies.", + "Genome-wide association studies (GWAS) have identified >100 independent SNPs that modulate the risk of type 2 diabetes (T2D) and related traits.However, the pathogenic mechanisms of most of these SNPs remain elusive.Here, we examined genomic, epigenomic, and transcriptomic profiles in human pancreatic islets to understand the links between genetic variation, chromatin landscape, and gene expression in the context of T2D.We first integrated genome and transcriptome variation across 112 islet samples to produce dense cis-expression quantitative trait loci (cis-eQTL) maps.Additional integration with chromatin-state maps for islets and other diverse tissue types revealed that cis-eQTLs for islet-specific genes are specifically and significantly enriched in islet stretch enhancers.High-resolution chromatin accessibility profiling using assay for transposase-accessible chromatin sequencing (ATACseq) in two islet samples enabled us to identify specific transcription factor (TF) footprints embedded in active regulatory elements, which are highly enriched for islet cis-eQTL.Aggregate allelic bias signatures in TF footprints enabled us de novo to reconstruct TF binding affinities genetically, which support the high-quality nature of the TF footprint predictions.Interestingly, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprints.Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions.Together, these results suggest that common regulatory variations have shaped islet TF footprints and the transcriptome and that a confluent RFX regulatory grammar plays a significant role in the genetic component of T2D predisposition.", + "Attention turned instead to association approaches in larger, unrelated samples sets (Merikangas and Risch 2003).Association analyses, however, rely upon typing the causal variant or a closely correlated proxy, and hence, initial efforts were constrained by practical limitations of genotyping cost and capacity to the evaluation of variants within pre-defined candidate genes.Nonetheless, this approach heralded the first wave of robustly associated variants.For T2D, non-synonymous variants in genes encoding the targets of two drugs widely used in T2D management [P12A in PPARG (Altshuler et al. 2000) for thiazolidinediones and E23K in KCNJ11 (Gloyn et al. 2003) for sulfonylureas] showed consistent, though modest (per-allele odds ratios of *1.2), evidence of association with disease risk.For obesity, variants within two genes already known to harbour mutations implicated in monogenic obesity-MC4R (V103I, I251L) and PCSK1 (N221D, Q665E-S690T)-were shown to be associated with common obesity risk (Heid et al. 2005;Geller et al. 2004;Benzinou et al. 2008).However, the candidate gene approach is restricted by its intrinsic reliance upon prior knowledge and expectation.When, as with T2D and obesity, our understanding of disease pathogenesis is imperfect, there is a manifest need to extend the search for susceptibility variants across the entire genome in an unbiased, hypothesis-free manner.The first gene to be implicated in T2D susceptibility without prior biological candidacy was TCF7L2, discovered following systematic association analysis across a region of previously identified linkage (Grant et al. 2006).The most strongly associated variants at this locus have the greatest effect on T2D susceptibility of any common variant so far identified.", + "Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge.Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose-tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs.We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls.We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the region of CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk.This brings the number of T2D loci now confidently identified to at least 10.", + "Large-scale association studies conducted by DIAGRAM, in individuals overwhelmingly of European descent, have reported 65 lead SNPs associated with susceptibility to type 2 diabetes (1). Figure 1 illustrates how these SNPs and closely correlated proxy SNPs were systematically selected for allelic expression analysis.In brief, 1,525 proxy SNPs (r 2 .0.8, CEU, 1,000 Genomes Phase 1) were found.Of these SNPs (lead + proxies), 45/1,590 (2.8%) map to exons of 23 human RefSeq genes.For 18 of these genes, TaqMan SNP genotyping assays could be designed to map entirely to exonic sequence, thus allowing for amplification and measurement of mature (i.e., spliced) mRNA species and normalization of allelic expression using genomic DNA from the same individual.After exclusion of SNPs with ,4 heterozygotes (rs1801282, PPARG; rs3734621, KIF6) and assays where .50%cDNA samples yielded Ct values .36(rs2793823, ADAM30; rs7377, SRGN), indicating very low levels of gene expression, allelic expression could be determined for 14 genes in samples from 36 white nondiabetic donors.", + "Associations of seven genes with type 2 diabetes in Chinese and Korean populationsRESULTSWe genotyped 13 representative SNPs from 7 genes implicated in type 2 diabetes in recent GWA studies in 3,041 type 2 diabetic case subjects and 3,678 nondiabetic control subjects from a Chinese population in Hong Kong and two Korean populations.The clinical characteristics of the subjects are summarized in Table 1.Table 2 showed the meta-analyses of type 2 diabetes association under a log additive model.There was no heterogeneity of ORs among the three study populations except for CDKN2A/B (rs10811661) (Q statistic P 0.03), with a random effect OR of 1.32 (1.15-1.52).Apart from two SNPs at CDKN2A/B (rs564398 and rs1333040), all other 11 SNPs were significantly associated with type 2 diabetes, with ORs ranging from 1.09 to 1.35 (1.3 10 12 P 0.016) in the combined samples (Table 2).Eight of the 11 SNPs remained significant after adjustment for multiple comparison by permutation (1.0 10 4 P empirical 0.012) (Table 2) despite nonsignificance of CDKN2A/B (rs10757278), TCF7L2 (rs7903146), and FTO (rs8050136).Because multiple SNPs with little or moderate linkage disequilibrium at CDKAL1 (r 2 0.56), CDKN2A/B (r 2 0.002-0.31),and HHEX (r 2 0.25-0.55)were studied (Supplementary Table 2), we examined haplotype associations but did not reveal more significant association than single marker analyses (Supplementary Table 3).Further haplotype analyses by conditioning rs7756992 on CDKAL1 haplotypes and rs7923837 on HHEX haplotypes revealed no significant residual associations (P 0.05; data not shown), suggesting that these two SNPs are sufficient to explain the respective multiple associations at CDKAL1 and HHEX.Although residual association was observed after conditioning rs10811661 on CDKN2A/B haplotypes (P 0.023), the much stronger single marker association of rs10811661 compared with rs10757278 (P 1.3 10 12 vs. 0.015; Table 2) suggests the former is the key associated SNP.Taken together, seven key SNPs from these genes were significant without correction for multiple comparisons.In this regard, TCF7L2 (rs7903146) showed the strongest effect on type 2 diabetes risk (OR 1.35), followed by CDKN2A/B (rs10811661), CDKAL1 (rs7756992), HHEX (rs7923837), IGF2BP2 (rs4402960), SLC30A8 (rs13266634), and FTO (rs8050136).These seven SNPs were further examined in the subsequent analyses.DISCUSSIONOur study provides important insights for the impact of the new type 2 diabetes genes identified through GWA studies.To our knowledge, this is the largest replication study in Asians up to now.We confirm the type 2 diabetes association of seven representative risk alleles for these seven genes found in Europeans (3)(4)(5)(6)(7)(8), suggesting many of the variants associated with type 2 diabetes in Europeans are also associated in Asians.These genetic effects seem to be additive.Despite differences in effect size of each gene, a crude estimate suggests up to 3.3-fold increased type 2 diabetes risk in subjects carrying eight or more risk alleles compared with those carrying two or fewer risk alleles (Supplementary Fig. 1).Two adjacent regions near CDKN2A/B have been reported to be associated with type 2 diabetes and cardiovascular diseases.Our data confirm the association of type 2 diabetes for rs10811661, found in the European type 2 diabetes studies (3,4,8), but not rs564398, found only in the Wellcome Trust Case Control Consortium Study (8).In addition, we found that the cardiovascular disease risk loci (rs1333040 and rs10757278) (14 -16) were not associated with type 2 diabetes.", + "Finally, we examined whether genes identified using our association studies were enriched within diabetes-related pathways.We collated a list of 42 genes to which 53 CpG sites associated with T2D traits (CS score 1.77, combined P < 0.017) mapped.Even in this small dataset, pathway analysis (Supplementary Material, Table S12) indicated significant enrichment in 31 pathways (Fisher's exact P < 0.05), including those related to circadian clock (P = 0.005), adipocytokine signaling (P = 0.009), leptin pathway (P = 0.023), HDL-mediated lipid transport (P = 0.031) and insulin signaling (P = 0.033).", + "These associations were subsequently confirmed in three reports simultaneously published in Science in April 2007 [13, 14. 23].Although several GWAS had been performed on T2DM in recent years, these latter studies are particularly important because of: the unprecedented joint collaborative effort to combine findings and to perform replication and meta-analysis; the large number of cases examined (each had 1,900 or more cases and controls with a combined number of 14,586 patients and 17,968 controls); and the common European ancestry of all subjects (reviewed in [23]).Despite some differences in the selection of phenotypes, there was remarkable consistency in the genes identified in these studies as linked to T2DM.In addition to replicating positive associations for TCF/L2, KCNJ11, PPARG, HHEX-IDE and SLC30A8, new variants were found in an intron (non-coding, spacer, section of a gene) of cyclin-dependent kinase 5 (CDK5)-regulatory subunit associated protein 1-like 1 (CDKAL1), in an intron of insulin-like growth factor binding protein 2 (IGF2BP2), in non-coding regions near the genes for cyclin-dependent kinase-inhibitor A and B (CDKN2A/B) on chromosome 9, and in the fat mass and obesity associated (FTO) region.", + "A r t i c l e sBy combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P < 5 10 8 .These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A).The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation.We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits." + ], + [ + "Indicative diseases associated with the candidate aging genes", + "In our study, genes regulated in NF1 blood samples appear to be potentially interesting for understanding the pathogenesis of NF1 and the function of NF1 gene.Adhesion molecules and genes involved in matrix remodeling may provide insights into the mechanism of bone malformations seen in some NF1 patients.Interestingly, six genes down-regulated in NF1 are implicated in craniofacial dysplasia (defects in neural crest development) and long bone dysplasia (defects in mesoderm development).The Armadillo repeat gene, deleted in velocardiofacial syndrome (ARVCF), is involved in some phenotypes associated with velocardiofacial syndrome, an autosomal dominant disorder characterized by cleft palate and facial dysmorphology and conotruncal heart defects [24].Chondroitin 6 sulfotransferase catalyzes the formation of choidroitin sulfate and a deficiency in this gene has been associated with a heritable form of spondyloepiphyseal dysplasia [28].Domain-specific mutations in transforming growth factor beta 1 have been implicated in Camurati -Engelmann disease, an autosomal dominant, progressive diaphyseal dysplasia characterized by hyperostosis and sclerosis of the diaphyses of long bones [12].Core-binding factor alpha1 subunit (CBFA1) is a runtdomain containing gene, mutations of which are responsible for cleidocranial dysplasia, a disease characterized by hypoplasia/aplasia of clavicles, patent fontanelles, supernumerary teeth, short stature, and other changes in skeletal patterning and growth [16].Mutations of TCOF1, are responsible for Treacher Collins Syndrome, one of the most common mandibulofacial dysostosis disorders [34].Finally, mutation of Pax 9 is associated with hypodontia [5,26].", + "A Syndromic Intellectual Disability Disorder Caused by Variants in TELO2, a Gene Encoding a Component of the TTT Complex. Am J Hum Genet. 2016; 98: 909918. https://doi.org/10.1016/j.ajhg.2016.03.014 PMID: 2713259397. Moosa S, Altmuller J, Lyngbye T, Christensen R, Li Y, Nurnberg P, et al. Novel compound heterozygous mutations in TELO2 in a patient with severe expression of You-Hoover-Fong syndrome. MolGenet Genomic Med. 2017; 5: 580584. https://doi.org/10.1002/mgg3.287 PMID: 2894424098. Choy KR, Watters DJ. Neurodegeneration in ataxia-telangiectasia: Multiple roles of ATM kinase in cellular homeostasis. Developmental Dynamics. John Wiley and Sons Inc.; 2018. pp. 3346. https://doi. org/10.1002/dvdy.24522 PMID: 2854393599.", + "Human Immunodeficiency-Centromeric Instability-Facial Anomalies (ICF) SyndromeICF syndrome is a rare genetic disorder caused by mutations in one of four identified genes: DNMT3b [57], HELLS [58], CDCA7 [58], and ZBTB24 [59].Most cases of ICF syndrome (55%) are caused by mutation in DNMT3b, the gene coding for the human de novo DNA methyltransferase [60].This disease causes severe immunodeficiency, increased susceptibility to infection, abnormal facial features, and cognitive disabilities [61,62].ICF syndrome is often diagnosed by the presence of stretched and fragile juxtacentromeric heterochromatin on chromosomes 1 and 16 in activated lymphocytes.Perhaps as a result of this phenotype, these chromosomes are more susceptible to breakage, missegregation resulting in aneuploidy, and micronuclei formation [62].Mutation in CDCA7, HELLS, and ZBTB24 also results in DNA methylation defects at alpha-satellites and satellite II DNA that is enriched on juxtacentromeric heterochromatin on chromosomes 1 and 16 [58,63].HELLS and CDCA7 work together in a complex known as CHIRRC (CDCA7-HELLS ICF-Related nucleosome Remodeling Complex) to catalyze nucleosome remodeling, which could modulate the accessibility of DNA for methylation [64].DNA methylation profiles in ICF patients with mutations in any of these three genes are different from that in ICF patients with DNMT3b mutation, indicating that these proteins may not all work in the same pathway [65].All observed ICF patients, however, have hypomethylation of the juxtacentromeric satellite II repeats, leading to the hypothesis that the chromosome fragility and disease symptoms are directly linked to DNA hypomethylation.", + "A genetic factorsuch as M5ahl8/Tmc1m4 may be involved in the samepathway and could modify the deleterious effects associated with defects in other pathway components (such asFSCN2 and TMC1). D2 mice also have a deleteriousvariant of CDH23 (Noben-Trauth et al. 2003) that is epistatic to the effects of the FSCN2 variant (Johnson et al. 2008). CDH23 is a component of the stereocilia tip link,supporting the view that the polygenic nature of the progressive hearing loss of D2 mice is at least partially due todeficiencies in multiple stereocilia components conferringfunctional integrity.", + "TCM Gene Disease", + "To further understand these genes and potential phenotypic consequences, we looked for enrichment of deletions or duplications in a dataset of 29,085 individuals with NDDs and 19,584 controls. 50Six of the pLI >0.9 genes were enriched for deletions in individuals with NDDs (CDC37L1, NFIB, PTPRD, RFX3, SMARCA2, UHRF2), and all 27 were enriched for duplications in individuals with NDDs (BNC2, CDC37L1, CLTA, CNTFR, ELAVL2, MLLT3, NFIB, NOL6, PAX5, PSIP1, PTPRD, RFX3, RNF38, RPS6, RUSC2, SHB, SMARCA2, SMU1, TAF1L, TEK, TESK1, TLN1, TOPORS, UBAP1, UBE2R2, UHRF2, VCP).This observation suggests that the dosage of these genes may play a role in NDDs.The mappability of most of 9p is quite high for short-read WGS data, indicating that the detection of CNV should be robust (Figure S2).Copy-number assessments generated from short-read WGS data in individuals from the 1000 Genomes Project 54 reveal that the copy number of the majority of 9p is not variable in the population (Figure S2).expand beyond NDDs, a search for other gene/disease associations was carried out (Table S3; Figure 2).This analysis revealed two genes in blood phenotypes 115501], TPM2).Importantly, 29 of these genes are known to be involved in autosomal recessive conditions, including DNAI1 in primary ciliary dyskinesia and GALT in galactosemia.Fourteen of these autosomal recessive genes are asso-ciated with neurological phenotypes (e.g., KANK1 in cerebral palsy and MPDZ in congenital hydrocephalus), which may contribute to atypical or severe NDD phenotypes in some patients with 9p CNVs.Disruption of these genes can thus potentially unmask recessive traits and contribute to phenotypic variability and should be explored in patients with complex presentations.", + "CTCF has also been suggested to play a role in two human syndromes, Silver-Russell (SRS) and Beckwith-Wiedeman Syndrome (BWS) (Sparago et al. 2004;Eggermann et al. 2008).BWS is a developmental disorder with variable clinical symptoms including increased frequency of tumors, macroglossia, and overgrowth.It is caused by aberrant activation of the normally maternally imprinted H19/Igf2 locus on chromosome 11.Several sporadic and familial mutations have been characterized in the H19/Igf2 region, which lead to the loss of CTCF binding sites, directly implicating CTCF in the disease (Sparago et al. 2004;Eggermann et al. 2008).Similarly, in SRS, a developmental disorder characterized by severe growth retardation and body asymmetry caused by activation of the paternal allele of Igf2, mutations in CTCF binding sites have been identified (Scho nherr et al. 2008).CTCF has been implicated in various diseases.Trinucleotide repeat expansion diseases including Huntington's disease, fragile X mental retardation, and myotonic dystrophy are caused by excessive lengthening of microsatellite repeat sequences (reviewed in Orr and Zoghbi 2007).For example, although a healthy individual has a stretch of fewer than 27 CAG repeats in their HTT gene, a Huntington's disease patient has typically more than 35.The trigger for repeat expansion is unknown.Recent evidence suggests that CTCF might contribute to generating fragile sites within repeats, thus facilitating their expansion (Libby et al. 2008).Mutation of a CTCF-binding site near a repeat leads to increased genomic instability and increased repeat length, similar to that seen in disease situations.Although not tested, it is possible that interference with CTCF binding, either by mutation of its target site or mutations in an interacting partner, may contribute to trinucleotide repeat diseases (Libby et al. 2008).Defects in CTCF, and other genome organizers, may also play a key role in destabilizing expanded microsatellite repeats in other trinucleotide repeat diseases (reviewed in Dion and Wilson 2009).", + "This is a particular problem for very rare diseases where only one or twofamilies are diagnosed with the condition. An example of the latter is tylosis withoesophageal cancer (TOC). This is an autosomal dominant single gene disorder thatoccurs in three families, with two of the families related by disease haplotype analysis. The entire minimal region (34 kb) has been sequenced (except for highly repetitiveregions) and no obvious disease-causing mutation has been identified, but a diseasemechanism has been postulated (MacDonald et al. , 2006).", + "9(HLA-DR4) [108], cystic fibrosis (CFTR) [22], and thrombophilia (FV, FII, MTHFR)[109-111] have been widely used to guide preventive care. For example, prophylacticmastectomy or oophorectomy is recommended to predisposed individuals and has shownto reduce the risk of cancer by 90-95% in women [112]. Similarly, genetic screenings areavailable for prenatal and newborns to detect birth defects and genetic diseases includingcystic fibrosis, severe combined immunodeficiencies, phenylketonuria, tyrosinemia,sickle cell anemia, hearing loss, and congenital heart defects.The firstsuccessful application of this approach identified genomic loci responsible for an Xlinked phagocytic disorderchronic granulomatous disease (CYBB) [20]. This was soonfollowed by identification of the loci and ultimately genes responsible for other geneticdisorders including Duchene muscular dystrophy (DMD) [21], cystic fibrosis (CFTR)[22], Huntington disease (HTT) [23,24], polycystic kidney disease (PKD1, PKD2 andPKHD1) [25-27] , phenylketonuria (PAH)[28], albinism (TYR) [29] and many more. Currently, Online Mendelian Inheritance in Man (OMIM) catalogues 4,500 humandisorders for which the underlying genetic mutations are known(http://omim.org/statistics/entry).", + "In addition, Mendelian disorders showing certain symptoms are extremely helpful in identifying genes influencing the same symptoms: the gene polymorphism for Netherton's syndrome (ichthyotic dermatosis, severe atopy) was associated with the development of asthma and eczema in children without Netherton's syndrome (Cookson and Moffatt 2000).As the deficiency of the FOXP3/Scurfin gene causes intractable diarrhea, eczema, hemolytic anemia, diabetes mellitus, or thyroid autoimmunity in humans (Patel 2001), dysregulation of FOXP3/ Scurfin gene expression may promote the development of autoimmune diseases, especially type 1 diabetes, thyroid autoimmunity and hemolytic anemia under various effects from circumstances.From an immunogenetical point of view, the FOXP3/Scurfin gene is located on chromosome (Chr) Xp11.23, which includes one of the type 1 diabetes susceptiblity loci (Cucca et al. 1998;Nerup and Pociot 2001).", + "Studies of syndromes that include CTDs, such as the 22q11 deletion syndrome, have provided some clues regarding the specific genes that may be involved in determining the risk of CTDs (e.g.TBX1 [13]).In addition, studies of rare, presumably pathogenic, copy number variants [14][15][16], and inherited [17] and de novo [17,18] single nucleotide variants have identified genes that may contribute to the risk of CTDs [18,19].Yet, most affected patients do not carry a confirmed or suspected rare, causative variant.Moreover, rare variants, in particular rare de novo variants, do not account for the observed increase in risk of CTDs among the relatives of affected patients.", + "This is a particular problem for very rare diseases where only one or twofamilies are diagnosed with the condition. An example of the latter is tylosis withoesophageal cancer (TOC). This is an autosomal dominant single gene disorder thatoccurs in three families, with two of the families related by disease haplotype analysis. The entire minimal region (34 kb) has been sequenced (except for highly repetitiveregions) and no obvious disease-causing mutation has been identified, but a diseasemechanism has been postulated (MacDonald et al. , 2006).", + "Examplesof this application include the detection of over 98% of deletions occurring onthe dystrophin gene for the diagnosis of Duchenne muscular dystrophy (9,10),;an 8-bp deletion in exon 3 of the P450c21B gene in individuals affected by21-hydroxylase deficiency, a recessively inherited disease (11), and the F508mutation, a 3-bp deletion in the gene CFTR that is the most frequently mutation found in individuals affected with cystic fibrosis (12). Another exampleis detection of the internal tandem duplication (ITD) in the juxtamembranedomain-coding sequence of the FLT3 gene in acute leukemias.", + "This is a particular problem for very rare diseases where only one or twofamilies are diagnosed with the condition. An example of the latter is tylosis withoesophageal cancer (TOC). This is an autosomal dominant single gene disorder thatoccurs in three families, with two of the families related by disease haplotype analysis. The entire minimal region (34 kb) has been sequenced (except for highly repetitiveregions) and no obvious disease-causing mutation has been identified, but a diseasemechanism has been postulated (MacDonald et al. , 2006).", + "The location of this gene in a genomic regionresponsible for the disorder, as well as its strong coexpression with other known mitochondrial genesacross tissues, hinted that it may be involved in thedisease. Additional experiments proved that the genedid indeed cause the disorder and was most likely amitochondrial gene. Tissue gene expression databases, along with other databases, have also been used tocategorize, at a whole-genome level, genes potentiallyinvolved in a particular type of disease category (Calvoet al. 2006).", + "A Syndromic Intellectual Disability Disorder Caused by Variants in TELO2, a Gene Encoding a Component of the TTT Complex. Am J Hum Genet. 2016; 98: 909918. https://doi.org/10.1016/j.ajhg.2016.03.014 PMID: 2713259397. Moosa S, Altmuller J, Lyngbye T, Christensen R, Li Y, Nurnberg P, et al. Novel compound heterozygous mutations in TELO2 in a patient with severe expression of You-Hoover-Fong syndrome. MolGenet Genomic Med. 2017; 5: 580584. https://doi.org/10.1002/mgg3.287 PMID: 2894424098. Choy KR, Watters DJ. Neurodegeneration in ataxia-telangiectasia: Multiple roles of ATM kinase in cellular homeostasis. Developmental Dynamics. John Wiley and Sons Inc.; 2018. pp. 3346. https://doi. org/10.1002/dvdy.24522 PMID: 2854393599.", + "This is a particular problem for very rare diseases where only one or twofamilies are diagnosed with the condition. An example of the latter is tylosis withoesophageal cancer (TOC). This is an autosomal dominant single gene disorder thatoccurs in three families, with two of the families related by disease haplotype analysis. The entire minimal region (34 kb) has been sequenced (except for highly repetitiveregions) and no obvious disease-causing mutation has been identified, but a diseasemechanism has been postulated (MacDonald et al. , 2006)." + ] + ], + "task_id": [ + "C0015BEE5FE41769A65126B79BB1E40D", + "3A16235DA1E02B9148B9288A06EE567E", + "E94FFD042BB146E8A429200590A6792D", + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "FFA6EADA5502933C0C30C9D16DCAA073" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_5.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_5.json new file mode 100644 index 0000000..1fa087a --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_5.json @@ -0,0 +1,89 @@ +{ + "question": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert." + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity." + ], + "contexts": [ + [ + "Selection could occur at multiple levels, from germ cell generation and propagation to fertilization and early embryonic growth.Chromosomal abnormalities, including aneuploidy, were found in 10-20% of spermatozoa and oocytes (20) and in the cleaved embryo, with a 21% rate of abnormalities in preimplantation embryos (21).These findings led to a model for natural selection against chromosome abnormalities (21).Selection extends to the end of gestation: Only approximately 30% of all conceptions result in a live birth, with more than half of aborted fetuses containing chromosomal abnormalities (22), a number likely to be an underestimate because of technological limitations in measuring all possible mutations.But even in the very small fraction of germ cell duos that survive this withering genome attack and result in a live birth, a number of severe de novo mutations will still be found (23).The data on gross chromosomal alterations suggest that overall, mutation frequency early in life is very high.The functional consequence, however, is limited because of selection.Somewhat surprisingly, this picture points toward an initial decline in genomic alterations, allowing the adult individual to acquire a somatic genome optimally equipped to provide function.", + "We now need to investigate the candidates identified here and how their effects on parental and offspring traits are integrated into thegene networks determining individual development. By controlling for genetic variation in eithermothers or offspring we have been able to show that levels of maternal provisioning and offspringsolicitation are unique to specific genotypes (here each BXD line) and that solicitation is costly.", + "In most plants and animals, sexis a necessary component of reproduction, and the question for evolutionary biologistsis why reproductive mechanisms have evolved that way. In one of the experimentsdescribed next, evolutionary geneticists have nevertheless devised a way to compareevolution with and without recombination in the obligately sexual fruit fly.Sex brings harmful alleles together into thesame genetic background, allowing selection to more efficiently purge them fromthe population and potentially producing some offspring that are fitter than eitherparent. However, the benefit of recombining deleterious mutations may depend on thenature of the epistatic interactions between them. The mutational deterministic hypothesis(Kondrashov 1988) depends partly on this epistasis.Longago, Wright (1931) noted that sex may destroy adaptation because a successful combination of characteristics is attained in individuals only to be broken up in the next generation by the mechanisms of meiosis itself. Similarly, if alleles at different loci werejointly responsible for the production of phenotypes, sex has the potential to break apartcoadapted gene complexes, as it moves alleles away from genetic backgrounds wherebeneficial epistatic interactions have evolved through natural selection. Why should sex therefore be so common, given the obvious costs?This disparity in investment is the basis for the twofold cost: asexualfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. Butsex usually also involves the basic process of physical recombination: the breakage andreunion of two different DNA or RNA molecules. Of these two processes, recombinationis clearly the more widespread feature of sexual reproduction. A variety of reproductivesystems, such as selfing and automixis, involve recombination but not outcrossing. Incontrast, relatively few reproductive systems have outcrossing without recombination.", + "The reason for the rarity of these mutations is natural selection: If the mutations result in disorders that decrease health and reproductive fitness, they will eventually be eliminated from a population.In exceptional cases, mutations may cause both beneficial and detrimental consequences, resulting in opposing forces of positive selection and negative selection that may cause the mutations to be preserved at nonrare frequencies in a population.For example, the HbS mutation in the HBB gene (which produces the subunit of hemoglobin) causes sickle cell disease when present in both alleles, a detrimental consequence, but protects against malaria when present in 1 allele, a beneficial consequence, ensuring that the mutation persists in populations in areas of the world where malaria is endemic.Genes are passed from parents to offspring via the process of meiosis by which gametes, the egg cells in the mother and the sperm cells in the father, are generated.Ordinarily, each cell has 23 pairs of chromosomes; the gametes have 23 unpaired chromosomes.In meiosis, the 23 pairs are split so that each gamete receives 1 chromosome from each pair (Figures 8 and 9).Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full complement of 23 chromosome pairs restored.If all goes well, the zygote gives rise to a live offspring.Crossing over-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes cross over one another.Crossing over results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.This process is also known as meiotic recombination.Recombination (meiotic recombination)-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes recombine with one another.Recombination results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.Also known as crossing over.", + "In most plants and animals, sexis a necessary component of reproduction, and the question for evolutionary biologistsis why reproductive mechanisms have evolved that way. In one of the experimentsdescribed next, evolutionary geneticists have nevertheless devised a way to compareevolution with and without recombination in the obligately sexual fruit fly.Sex brings harmful alleles together into thesame genetic background, allowing selection to more efficiently purge them fromthe population and potentially producing some offspring that are fitter than eitherparent. However, the benefit of recombining deleterious mutations may depend on thenature of the epistatic interactions between them. The mutational deterministic hypothesis(Kondrashov 1988) depends partly on this epistasis.Longago, Wright (1931) noted that sex may destroy adaptation because a successful combination of characteristics is attained in individuals only to be broken up in the next generation by the mechanisms of meiosis itself. Similarly, if alleles at different loci werejointly responsible for the production of phenotypes, sex has the potential to break apartcoadapted gene complexes, as it moves alleles away from genetic backgrounds wherebeneficial epistatic interactions have evolved through natural selection. Why should sex therefore be so common, given the obvious costs?This disparity in investment is the basis for the twofold cost: asexualfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. Butsex usually also involves the basic process of physical recombination: the breakage andreunion of two different DNA or RNA molecules. Of these two processes, recombinationis clearly the more widespread feature of sexual reproduction. A variety of reproductivesystems, such as selfing and automixis, involve recombination but not outcrossing. Incontrast, relatively few reproductive systems have outcrossing without recombination.", + "Aberrant recombination patterns on chromosomes that have missegregated have also been identified as an important factor, in both male and female gametes (Table I).This is because recombination together with cohesion of sister chromatids establish the unique 'bivalent' chromosome structure where homologous partner chromosomes are tethered together, a configuration that is critical for their accurate segregation in meiosis I (Fig. 2A).The remarkable feature is that recombination occurs in foetal oocytes whereas chromosome segregation takes place decades later (Fig. 2A).Since mammalian oocytes are arrested at the G2/M transition (or dictyate stage), this raises the intriguing question of how the bivalent is maintained until the meiotic divisions.", + "However, we expect thatonly at this level, the most signicant contributions brought by integrating epigenetics will bemade. Concluding Remarks and FutureProspectsFish sex ratios are the result of a complex interaction between genetic, biochemical, and environmental interactions. The ultimate resultof these interactions at the individual level isgender: male or female. However, at the population level, the combination of sex determination and differentiation sets the sex ratio. Inturn, sex ratios dene the reproductive capacityof populations and, if sex growth dimorphismexists, also the growth characteristics, something very important in an aquaculture context.Traditionally, it has been agreed that thenal sex of an individual (phenotypic sex)depends on two sequential processes: the sexdetermination system of the species and thegonad differentiation process (Valenzuela,2008). However, recently, these two seeminglydistinct processes are viewed as part of a general process leading to gonad formation andsex ratios (Sarre et al. , 2004; Quinn et al. , 2011;Uller and Helantera, 2011).", + "Obehav is, in turn, influenced by offspring genesand environment (Ogene and Oenvir respectively). Hence, indirect genetic effects (blue arrows)and direct genetic effects (red arrow) are important influencers of behaviour. B) Parentoffspring conflict theory predicts that parental resource investment and offspring solicitationbehaviours are influenced by the fitness benefit to a focal individual (O), cost to a socialpartner such as a sibling (S1 and S2) or parent (P), and by their coefficient of relatedness(black arrows). 42Figure 2: Genomic imprinting can result in divergent phenotypes from the samegenotype. A) A paternally imprinted gene, i.e. maternally expressed.", + "Therefore, the resulting phenotypic patterns lag a generationbehind the genetic transmission of the causal variants. The most well-studied parental genetic effectsare caused by deposition of maternal transcripts into the egg prior to fertilization, resulting indifferences in early embryonic development depending on the genotype of the mother. Certain geneshave also been shown to respond to maternal influence after birth through genetically definedmaternal behaviors (Weaver et al. , 2004).Because of the small contribution, through the sperm, ofthe paternal transcriptome to the fertilized zygote, and because of the stronger maternal contributionto child rearing in most model organisms, parental effects are typically thought of as synonymous withmaternal effects, although true paternal effects are known to exist (Rando, 2012). Maternal effects have been shown to be important during embryonic development, leading todifferences in the birth weight of mice depending on the genotype of the mother (Cowley et al. ,1989; Wolf et al. , 2011).", + "It was believed by many that for each trait variant we should expect to find acorresponding genetic change, or gene for that trait. Through historical happenstance therelationship between genes and traits was set up and treated as if it were one-to-one. But theproduction of a trait involves not only genes, but also their interactions with each other and theenvironment, and chance." + ], + [ + "SpliceAid2, a tool that allows us to know which proteins can bind to the desired RNA sequence, was used to investigate the effects of studied SNPs in protein binding affinity.This tool is entirely based on the human true splicing site and experimentally assessed target motif.The results of mutation effects on splicing binding in spliceAid2 database demonstrate information on creation of splice sites or strengthening of cryptic splice sites and provide information on intron retention, appearance, and disappearance of new alternative splice site forms (Piva et al., 2012).", + "The variations of binding sites can be collected from knowntarget genes, mutagenesis studies (Hallikas et al. , 2006), phylogenetic shadowing (orthologous binding sites in different species) (Ostrin et al. , 2006), and in vitro SELEXexperiments (Liu and Stormo, 2005). Several recent technologies, such as SELEXSAGE (Roulet et al. , 2002) and protein-binding microarray (PBM) (Mukherjee et al. ,2004), allow the determination of binding specificity in a high throughput manner. The profile or motif of binding sites can then be described with a consensussequence.An alternative mechanism for translation initiation has been identified thatdoes not obey the first AUG rule; this involves cap-independent internal264CH 11 PREDICTIVE FUNCTIONAL ANALYSIS OF POLYMORPHISMSribosome binding mediated by a Y-shaped secondary structure, denoted the internal ribosome entry site (IRES), located in the 5 UTR of 510 per cent ofhuman mRNA molecules (see Le and Maizel, 1997, for a review of these elements). IRES elements are complex stem loop structures, and there is no reliablesequence consensus to allow prediction of the possible functional effects of polymorphisms in these elements.", + "Based on structural modeling of the observedprotein sequences, the authors suggest that the glycine-rich conserved regions offerflexibility to the protein in the form of a wobble (Riede et al. 1987; Drexler et al. 1989). A number of recent studies have explicitly investigated the issue of specialist-generalisttrade-offs. For example, Turner and Elena (2000) evolved replicate populations of anRNA virus on novel hosts using a single novel host or alternating novel hosts. Theauthors observed improvements in fitness on the novel hosts.", + "5.5.3 Candidates in Qrr1d and Possible Link with Local Protein SynthesisTransfer RNAs are direct biological partners of the ARS, and the cluster oftRNAs in the highly polymorphic intergenic region of Qrr1d is an enticing candidate. Inaddition to their role in shuttling amino acids, tRNAs also act as sensors of cellular aminoacid levels and regulate transcription of genes involved in amino acid metabolism and theARS (Ryckelynck et al. , 2005). There is tissue specificity in the expression of differenttRNA isoforms (Dittmar et al.", + "The elucidation of the structure of the ribonuclease inhibitor, a protein containing 15 LRRs,revealed that the LRR sequence is a structural motif,each consisting of a b-strand connected by variableloops to a helical or extended part of about 24 aminoacid residues in length with a conserved 11-residuesegment corresponding to the b-strand and adjacentCorrespondence: Dr Antoni Matilla Duenas, Institute of Child Health, University College London, 30 Guilford Street, London WC1N 1EH, UK. Fax: +44(0)207 905 2301.", + "The elucidation of the structure of the ribonuclease inhibitor, a protein containing 15 LRRs,revealed that the LRR sequence is a structural motif,each consisting of a b-strand connected by variableloops to a helical or extended part of about 24 aminoacid residues in length with a conserved 11-residuesegment corresponding to the b-strand and adjacentCorrespondence: Dr Antoni Matilla Duenas, Institute of Child Health, University College London, 30 Guilford Street, London WC1N 1EH, UK. Fax: +44(0)207 905 2301.", + "Based on structural modeling of the observedprotein sequences, the authors suggest that the glycine-rich conserved regions offerflexibility to the protein in the form of a wobble (Riede et al. 1987; Drexler et al. 1989). A number of recent studies have explicitly investigated the issue of specialist-generalisttrade-offs. For example, Turner and Elena (2000) evolved replicate populations of anRNA virus on novel hosts using a single novel host or alternating novel hosts. Theauthors observed improvements in fitness on the novel hosts.", + "Figure 4. Structure of the ribosomal DNA gene cluster in S. cerevisiae.The location of the rDNA cluster on chromosome XII is shown at the top, with the telomere (TEL) and centromere (CEN) indicated.A detailed view of an rDNA repeat unit is shown below.The 35S and 5S rRNA gene-coding regions are indicated, as is the rDNA origin of replication (rARS).The RFB (red box) is bound by Fob1p (pink).The locations of the 35S promoter and the bidirectional noncoding promoter E-pro (blue box), silenced by Sir2p, are indicated.", + "An alternative mechanism for translation initiation has been identified thatdoes not obey the first AUG rule; this involves cap-independent internal264CH 11 PREDICTIVE FUNCTIONAL ANALYSIS OF POLYMORPHISMSribosome binding mediated by a Y-shaped secondary structure, denoted the internal ribosome entry site (IRES), located in the 5 UTR of 510 per cent ofhuman mRNA molecules (see Le and Maizel, 1997, for a review of these elements). IRES elements are complex stem loop structures, and there is no reliablesequence consensus to allow prediction of the possible functional effects of polymorphisms in these elements.The variations of binding sites can be collected from knowntarget genes, mutagenesis studies (Hallikas et al. , 2006), phylogenetic shadowing (orthologous binding sites in different species) (Ostrin et al. , 2006), and in vitro SELEXexperiments (Liu and Stormo, 2005). Several recent technologies, such as SELEXSAGE (Roulet et al. , 2002) and protein-binding microarray (PBM) (Mukherjee et al. ,2004), allow the determination of binding specificity in a high throughput manner. The profile or motif of binding sites can then be described with a consensussequence.", + "Schluenzen F, Tocilj A, Zarivach R, Harms J, Glyehmann M, Janell D, Bashan A,Bartels H, Agmon I, Franceschi F, Yonath A (2000) Structure of functionally activated small ribosomal subunit at 3.3 Angstroms resolution. Cell 102:61562319. Hendrickson WA (1991) Determination of macromolecular structures from anomalous diffraction of synchrotron radiation. Science 254:515820. Yang C, Pflugrath JW, Courville DA, Stence CN, Ferrara JD (2003) Away fromthe edge: SAD phasing from the sulfur anomalous signal measured in-house withchromium radiation. Acta Crystallogr D59(11):1943195721. Kissinger CR, Gehlhaar DK, Smith BA, Bouzida D (2001) Molecular replacementby evolutionary search.Using this technique, the 6xHis-tagged ExoS ADP-ribosyltransferaseprotein was shown to bind 14-3-3 in a direct fashion (56). The two purifiedproteins were mixed together, and were subjected to nickel-charged affinitychromatography to isolate 6xHis-protein complexes. To quantify the interaction, the amount of 14-3-3 eluted from the 6xHis-ExoS resin was determined. It was found that essentially molar equivalents of ExoS and 14-3-3 werereversibly bound to the affinity matrix while a ligand binding mutant of 14-3-3or egg albumin was detected primarily in the unbound phase (56).Proc Natl Acad Sci USA91:9022902630. Wilson DS, Keefe AD, Szostak JW (2001) The use of mRNA display to selecthigh-affinity protein-binding peptides. Proc Natl Acad Sci USA 98:3750375531. Hanes J, Jermutus L, Weber-Bornhauser S, Bosshard HR, Plckthun A (1998)Ribosome display efficiently selects and evolves high-affinity antibodies in vitrofrom immune libraries. Proc Natl Acad Sci USA 95:141301413532. Fields S, Song O (1989) A novel genetic system to detect protein-protein interactions. Nature 340:24524633. Ma J, Ptashne M (1988) Converting a eukaryotic transcriptional inhibitor into anactivator. Cell 55:44344634.Khler F, Mller KM (2003) Adaptation of the Ras-recruitment system to the analysisof interactions between membrane-associated proteins. Nucleic Acids Res 31:e2841. Osborne MA, Dalton S, Kochan JP (1995) The yeast tribrid system-geneticdetection of trans-phosphorylated ITAM-SH2-interactions. Biotechnology (NY)13:1474147842. Zhang J, Lautar S (1996) A yeast three-hybrid method to clone ternary proteincomplex components. Anal Biochem 242:687243. Licitra EJ, Liu JO (1996) A three-hybrid system for detecting small lig-and-proteinreceptor interactions. Proc Natl Acad Sci USA 93:128171282144. Tafelmeyer P, Johnsson N, Johnsson K (2004) Transforming a (beta/alpha)8-barrelenzyme into a split-protein sensor through directed evolution.", + "The variations of binding sites can be collected from knowntarget genes, mutagenesis studies (Hallikas et al. , 2006), phylogenetic shadowing (orthologous binding sites in different species) (Ostrin et al. , 2006), and in vitro SELEXexperiments (Liu and Stormo, 2005). Several recent technologies, such as SELEXSAGE (Roulet et al. , 2002) and protein-binding microarray (PBM) (Mukherjee et al. ,2004), allow the determination of binding specificity in a high throughput manner. The profile or motif of binding sites can then be described with a consensussequence.An alternative mechanism for translation initiation has been identified thatdoes not obey the first AUG rule; this involves cap-independent internal264CH 11 PREDICTIVE FUNCTIONAL ANALYSIS OF POLYMORPHISMSribosome binding mediated by a Y-shaped secondary structure, denoted the internal ribosome entry site (IRES), located in the 5 UTR of 510 per cent ofhuman mRNA molecules (see Le and Maizel, 1997, for a review of these elements). IRES elements are complex stem loop structures, and there is no reliablesequence consensus to allow prediction of the possible functional effects of polymorphisms in these elements.", + "Gribskov M, Robinson NL: Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching. Computerand Chemistry 1996, 20:25-33. Ren B, Robert F, Wyrick JJ, Aparicio O, Jennings EG, Simon I, Zeitlinger J, Schreiber J, Hannett N, Kanin E, Volkert TL, Wilson CJ, Bell SP,Young RA: Genome-wide location and function of DNA binding proteins. Science 2000, 290(5500):2306-2309. Iyer VR, Horak CE, Scafe CS, Botstein D, Snyder M, Brown PO:Genomic binding sites of the yeast cell-cycle transcriptionfactors SBF and MBF. Nature 2001, 409(6819):533-538.", + "The variations of binding sites can be collected from knowntarget genes, mutagenesis studies (Hallikas et al. , 2006), phylogenetic shadowing (orthologous binding sites in different species) (Ostrin et al. , 2006), and in vitro SELEXexperiments (Liu and Stormo, 2005). Several recent technologies, such as SELEXSAGE (Roulet et al. , 2002) and protein-binding microarray (PBM) (Mukherjee et al. ,2004), allow the determination of binding specificity in a high throughput manner. The profile or motif of binding sites can then be described with a consensussequence.An alternative mechanism for translation initiation has been identified thatdoes not obey the first AUG rule; this involves cap-independent internal264CH 11 PREDICTIVE FUNCTIONAL ANALYSIS OF POLYMORPHISMSribosome binding mediated by a Y-shaped secondary structure, denoted the internal ribosome entry site (IRES), located in the 5 UTR of 510 per cent ofhuman mRNA molecules (see Le and Maizel, 1997, for a review of these elements). IRES elements are complex stem loop structures, and there is no reliablesequence consensus to allow prediction of the possible functional effects of polymorphisms in these elements." + ], + [ + "(1234567890)Complex trait variation in natural and experimental populations is due to specific DNA sequence polymorphisms, environmental effects, and the interactions between these factors(Johannes et al. 2009). Testis weight is a complex trait thatholds direct implications for reproductive success, as developmental abnormalities can lead to irregular sperm production and infertility in adulthood (Sharpe 2001). Variation intestis size has been linked to environmental factors such associal dominance, social organization, and seasonal changesacross numerous species.", + "Selection could occur at multiple levels, from germ cell generation and propagation to fertilization and early embryonic growth.Chromosomal abnormalities, including aneuploidy, were found in 10-20% of spermatozoa and oocytes (20) and in the cleaved embryo, with a 21% rate of abnormalities in preimplantation embryos (21).These findings led to a model for natural selection against chromosome abnormalities (21).Selection extends to the end of gestation: Only approximately 30% of all conceptions result in a live birth, with more than half of aborted fetuses containing chromosomal abnormalities (22), a number likely to be an underestimate because of technological limitations in measuring all possible mutations.But even in the very small fraction of germ cell duos that survive this withering genome attack and result in a live birth, a number of severe de novo mutations will still be found (23).The data on gross chromosomal alterations suggest that overall, mutation frequency early in life is very high.The functional consequence, however, is limited because of selection.Somewhat surprisingly, this picture points toward an initial decline in genomic alterations, allowing the adult individual to acquire a somatic genome optimally equipped to provide function.", + "It is a cornerstone of modern biology that a purposeful genetic program drives all biological processes that occur from conception to reproductive maturation.But, once reproductive maturation is reached, thought is divided in respect to whether the aging process is a continuation of the genetic program or whether it is the result of random losses in molecular fidelity.Despite the claim by many researchers to the contrary, there is no direct evidence that genes drive age changes.I will discuss how genes are involved in the finitude of life subsequently.", + "In most plants and animals, sexis a necessary component of reproduction, and the question for evolutionary biologistsis why reproductive mechanisms have evolved that way. In one of the experimentsdescribed next, evolutionary geneticists have nevertheless devised a way to compareevolution with and without recombination in the obligately sexual fruit fly.Sex brings harmful alleles together into thesame genetic background, allowing selection to more efficiently purge them fromthe population and potentially producing some offspring that are fitter than eitherparent. However, the benefit of recombining deleterious mutations may depend on thenature of the epistatic interactions between them. The mutational deterministic hypothesis(Kondrashov 1988) depends partly on this epistasis.Longago, Wright (1931) noted that sex may destroy adaptation because a successful combination of characteristics is attained in individuals only to be broken up in the next generation by the mechanisms of meiosis itself. Similarly, if alleles at different loci werejointly responsible for the production of phenotypes, sex has the potential to break apartcoadapted gene complexes, as it moves alleles away from genetic backgrounds wherebeneficial epistatic interactions have evolved through natural selection. Why should sex therefore be so common, given the obvious costs?This disparity in investment is the basis for the twofold cost: asexualfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. Butsex usually also involves the basic process of physical recombination: the breakage andreunion of two different DNA or RNA molecules. Of these two processes, recombinationis clearly the more widespread feature of sexual reproduction. A variety of reproductivesystems, such as selfing and automixis, involve recombination but not outcrossing. Incontrast, relatively few reproductive systems have outcrossing without recombination.", + "Crossing over-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes cross over one another.Crossing over results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.This process is also known as meiotic recombination.The reason for the rarity of these mutations is natural selection: If the mutations result in disorders that decrease health and reproductive fitness, they will eventually be eliminated from a population.In exceptional cases, mutations may cause both beneficial and detrimental consequences, resulting in opposing forces of positive selection and negative selection that may cause the mutations to be preserved at nonrare frequencies in a population.For example, the HbS mutation in the HBB gene (which produces the subunit of hemoglobin) causes sickle cell disease when present in both alleles, a detrimental consequence, but protects against malaria when present in 1 allele, a beneficial consequence, ensuring that the mutation persists in populations in areas of the world where malaria is endemic.Genes are passed from parents to offspring via the process of meiosis by which gametes, the egg cells in the mother and the sperm cells in the father, are generated.Ordinarily, each cell has 23 pairs of chromosomes; the gametes have 23 unpaired chromosomes.In meiosis, the 23 pairs are split so that each gamete receives 1 chromosome from each pair (Figures 8 and 9).Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full complement of 23 chromosome pairs restored.If all goes well, the zygote gives rise to a live offspring.Recombination (meiotic recombination)-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes recombine with one another.Recombination results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.Also known as crossing over.", + "In most plants and animals, sexis a necessary component of reproduction, and the question for evolutionary biologistsis why reproductive mechanisms have evolved that way. In one of the experimentsdescribed next, evolutionary geneticists have nevertheless devised a way to compareevolution with and without recombination in the obligately sexual fruit fly.Sex brings harmful alleles together into thesame genetic background, allowing selection to more efficiently purge them fromthe population and potentially producing some offspring that are fitter than eitherparent. However, the benefit of recombining deleterious mutations may depend on thenature of the epistatic interactions between them. The mutational deterministic hypothesis(Kondrashov 1988) depends partly on this epistasis.Longago, Wright (1931) noted that sex may destroy adaptation because a successful combination of characteristics is attained in individuals only to be broken up in the next generation by the mechanisms of meiosis itself. Similarly, if alleles at different loci werejointly responsible for the production of phenotypes, sex has the potential to break apartcoadapted gene complexes, as it moves alleles away from genetic backgrounds wherebeneficial epistatic interactions have evolved through natural selection. Why should sex therefore be so common, given the obvious costs?This disparity in investment is the basis for the twofold cost: asexualfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. Butsex usually also involves the basic process of physical recombination: the breakage andreunion of two different DNA or RNA molecules. Of these two processes, recombinationis clearly the more widespread feature of sexual reproduction. A variety of reproductivesystems, such as selfing and automixis, involve recombination but not outcrossing. Incontrast, relatively few reproductive systems have outcrossing without recombination.", + "However, we expect thatonly at this level, the most signicant contributions brought by integrating epigenetics will bemade. Concluding Remarks and FutureProspectsFish sex ratios are the result of a complex interaction between genetic, biochemical, and environmental interactions. The ultimate resultof these interactions at the individual level isgender: male or female. However, at the population level, the combination of sex determination and differentiation sets the sex ratio. Inturn, sex ratios dene the reproductive capacityof populations and, if sex growth dimorphismexists, also the growth characteristics, something very important in an aquaculture context.Traditionally, it has been agreed that thenal sex of an individual (phenotypic sex)depends on two sequential processes: the sexdetermination system of the species and thegonad differentiation process (Valenzuela,2008). However, recently, these two seeminglydistinct processes are viewed as part of a general process leading to gonad formation andsex ratios (Sarre et al. , 2004; Quinn et al. , 2011;Uller and Helantera, 2011).", + "Obehav is, in turn, influenced by offspring genesand environment (Ogene and Oenvir respectively). Hence, indirect genetic effects (blue arrows)and direct genetic effects (red arrow) are important influencers of behaviour. B) Parentoffspring conflict theory predicts that parental resource investment and offspring solicitationbehaviours are influenced by the fitness benefit to a focal individual (O), cost to a socialpartner such as a sibling (S1 and S2) or parent (P), and by their coefficient of relatedness(black arrows). 42Figure 2: Genomic imprinting can result in divergent phenotypes from the samegenotype. A) A paternally imprinted gene, i.e. maternally expressed.", + "Because of the small contribution, through the sperm, ofthe paternal transcriptome to the fertilized zygote, and because of the stronger maternal contributionto child rearing in most model organisms, parental effects are typically thought of as synonymous withmaternal effects, although true paternal effects are known to exist (Rando, 2012). Maternal effects have been shown to be important during embryonic development, leading todifferences in the birth weight of mice depending on the genotype of the mother (Cowley et al. ,1989; Wolf et al. , 2011).Therefore, the resulting phenotypic patterns lag a generationbehind the genetic transmission of the causal variants. The most well-studied parental genetic effectsare caused by deposition of maternal transcripts into the egg prior to fertilization, resulting indifferences in early embryonic development depending on the genotype of the mother. Certain geneshave also been shown to respond to maternal influence after birth through genetically definedmaternal behaviors (Weaver et al. , 2004).", + "It was believed by many that for each trait variant we should expect to find acorresponding genetic change, or gene for that trait. Through historical happenstance therelationship between genes and traits was set up and treated as if it were one-to-one. But theproduction of a trait involves not only genes, but also their interactions with each other and theenvironment, and chance." + ], + [ + "One of the major drawbacks in NGS technologies is the relatively small reads obtained, which are up to 500pb length in pyrosequencing (Roche 454 genome sequencer) and roughly 50pb for Illumina Gene Analyzer.In order to generate new sequencing technologies that boost the size of sequencing reads, great efforts have been made to develop single-molecule sequencing (SMS) platforms.In SMS sequencing, synthesis is accomplished by single DNA molecule arrays without the amplification step performed in NGS sequencing platforms.This no-amplification procedure allows an increase in the number of DNA molecules tested to improve the throughput, the absence of PCR reduces the price of sequencing, and this technology also increases the length of sequencing reads [12][13][14].", + "However, the quality and completeness of sequencesshould be considered when undertaking an analysis. For a finished sequence, theaccuracy is expected to be high; with less than one nucleotide error per 100 000 nucleotides and fewer than one insertion/deletion error per 200 000 nucleotides, the vastmajority of which are located in tandemly repetitive sequence (IHGSC, 2004), andthere should be no gaps in sequence coverage. The quality of draft sequences dependsto a large degree on the depth of coverage.", + "Thesignificantly longer error-corrected SMRT reads as comparedto other available technologies are expected to enable sequencing through complex repeats, close genomic gaps, allowhaplotype phasing, and catalog alternative splicing isoformsthat are generally out of reach for second-generation sequencing technologies. There are still many challenges ahead forthis third-generation sequencing technology particularly inincreasing sequencing accuracy and throughput. Lee Hood focused the challenge of high throughputdata centers on signal to noise issues as he addressedthe advances that have occurred in dealing with analyzing the complexity of biological systems (Tian et al. 2012).Michael Schatz and Tim Smith reported on the use of SMRTDNA along with other second-generation technologies tosequence and assemble various small genomes includingEscherichia coli strains and budding yeast. SMRT long readshave a mean length of 12 kb for most genomes tested withthe longest reads reaching up to 15 kb. The read accuracy isabout 85% with the vast majority of the errors correspondingto base insertions, ranging from one to several bases, followedby deletions and mismatches.", + "The improvements associated with 3rdGS technologies have led to advances in read quality but significant advances are required to overcome some limitations, such as fresh starting material or intact cells and, moreover, issues associated with high sequencing error rate Long-read sequencing technologies are particularly efficient in de novo genome assembly [62] or complex structural genomic sequencing.SMRT, for example, allowed the sequencing of long stretches of CGG as a Mycobacterium tuberculosis genome, characterized by high redundancy (65.6% of GC bases) or short tandem repeats (STPs) implicated in X fragile syndrome (FXS) [63,64].", + "On the other hand, short-insertpaired-end capability enables high-resolutionre-sequencing, transcriptome analysis (RANSeq) and miRNA discovery. Comparison of Illumina Sequencingand 454 PyrosequencingBoth Illumina and 454 Roche/Pyrosequencinghave been used in different rainbow troutgenome projects, which showed the pros andcons of each technology. The main differences are read length and throughput: Illumina sequencing generates relatively short sequence reads of approximately 100 bp, but withenormously high throughput of approximately20 Gb; in contrast, 454 sequencing generatesrelatively long reads of approximately 400 bp,but its throughput is only approximately 0.4 Gb.Based upon different choices ofsequencing technology, the sequencing yieldsand read lengths vary. Currently, three main next-generationsequencing platforms are widely used in theRNA-Seq, the 454, Illumina and ABI SOLiD. Among these platforms, the throughput variesfrom hundreds of thousands of reads for the454 system to hundreds of millions of readsfor the Illumina and ABI SOLiD systems(Marguerat and Bahler, 2010). The readlengths typically range from 30100 bp forIllumina and SOLiD to 200500 bp for 454.In general, Illumina and SOLiD platforms arerelatively inexpensive, while the 454 technologyoffers longer reads, but is more expensive perrun. Illumina, SOLiD and 454 technologiescan be combined in a hybrid assemblystrategy: short reads that are sequenced at agreater depth are assembled into contigs, andlong reads are subsequently used to scaffoldthe contigs and resolve variants (Martin andWang, 2011). Two main approaches can be used for RNASeq data analysis. One way is to map the resulting reads to a reference genome or referencetranscriptome. This is usually taken in wellstudied species with sequenced genome.The most problematic compromise of NGS isthe relatively shorter read length of 35400 bpas compared with 7501000 bp of thecapillary-based sequencing. Discussions on thischapter will mainly focus on the 454 Roche/pyrosequencing and the Illumina sequencingtechnologies that have been used in rainbowtrout studies. Detailed description of NGStechnologies can be found in the literature(Mardis, 2008b, 2008a; Shendure and Ji, 2008;Zhou et al. , 2010; Kircher and Kelso, 2010). 454 Roche/PyrosequencingSequencing workow of the 454 Roche GSFLX and GS Junior platforms consists of thefollowing basic steps (Figure 11.1A).These technologiesallow sequencing of millions of reads in a relatively short time (Voelkerding et al. , 2009). The assembly and sequencing analysis, as wellas the whole process, require signicant computational and economic investment, but thehigh volume of data allows a full genome tobe sequenced in a relatively short time. Severaldifferent platforms are available, including 454Life Sciences (Roche), Solexa (Illumina), Solid(Applied Biosystems), and single molecule sequencing (Helicos Biosciences). All platformsshare a common approach, involving massiveparallel sequencing of DNA molecules thatare spatially separated. (Brenner et al.Technologically, 454 Roche/Pyrosequencing ismore suitable for de novo characterization ofnonmodel genomes/transcriptomes, and Illumina is more suitable for re-sequencing knowngenomes, digital gene expression, and miRNAdiscovery. However, such technological capability difference is complicated by the cost associated with the sequencing platforms. Forinstance, Illumina HiSeq 2000 instrument cangenerate 20 Gb sequence per sequencing lanethat cost only $30004000, while one run of 454sequencing can only generate 400 million bpthat cost over $10,000.With theseNGS technologies, libraries are constructedand sequenced from amplied fragments ofsingle-stranded DNA. The most signicantadvantage of NGS technology is saving thetime-consuming, laborious, and equipmentassociated steps of cloning and colony pickingof the Sanger-based capillary sequencing. Thesecond most important advantage of NGS isthe tremendous increase in sequencing outputin terms of the number of reads (14800million reads) and total bases per run (0.4300 Gbp) compared with 96 reads of 7501000 bp each in the capillary-based sequencing.Although shorter reads produced by Illumina or SOLiD compared with the 454 technology may be more challenging for de novosequence assembly, the preexisting ESTs produced by Sanger sequencing can be used tofacilitate the assembly (Liu et al. , 2011), andthe algorithms for short reads de novo assembly are being developed (e.g. , Grabherret al. , 2011). Xiang et al. assembled the shortreads from Illumina RNA-Seq deep sequencing to generate the nonredundant consensuswhich is subsequently used as references forDGE prole analysis (Xiang et al. , 2010).", + "There are currently three general WGS strategies (Figure 2): (1) short-read WGS using the Illumina technology, which currently yields paired-end 150 bp reads with low error rates in the range of 0.1%-0.5%;(2) long-read WGS using singlemolecule technologies from Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), which yield 10-100 kb reads-and occasionally much longer-with high error rates in the range of 10%-15%; and (3) linked-read WGS using the technology from 10X Genomics, which generates barcoded Illumina short-reads from longer molecules (e.g., 50 kb).Due to considerations of cost, ease of use, and accuracy, the overwhelming majority of human genetics studies employ short-read WGS using the Illumina HiSeq or NovaSeq platform, and we therefore focus primarily on analysis of this data type.", + "Currently, there are two main types of long-read technologies: single-molecule real-time sequencing approaches and synthetic approaches that rely on existing shortread technologies to construct long reads in silico.The single-molecule approaches differ from short-read approaches in that they do not rely on a clonal population of amplified DNA fragments to generate detectable Following cluster generation or bead deposition onto a slide, fragments are sequenced by ligation, in which a fluorophore-labelled two-base-encoded probe, which is composed of known nucleotides in the first and second positions (dark blue), followed by degenerate or universal bases (pink), is added to the DNA library.The two-base probe is ligated onto an anchor (light purple) that is complementary to an adapter (red), and the slide is imaged to identify the first two bases in each fragment.Unextended strands are capped by unlabelled probes or phosphatase to maintain cycle synchronization.Finally, the terminal degenerate bases and the fluorophore are cleaved off the probe, leaving a 5 bp extended fragment.The process is repeated ten times until two out of every five bases are identified.At this point, the entire strand is reset by removing all of the ligated probes and the process of probe binding, ligation, imaging and cleavage is repeated four times, each with an n + 1, n + 2, n + 3 or n + 4 offset anchor.b | Complete Genomics.DNA is sequenced using the combinatorial probe -anchor ligation (cPAL) approach.After DNA nanoball deposition, an anchor complementary to one of four adapter sequences and a fluorophore-labelled probe are bound to each nanoball.The probe is degenerate at all but the first position.The anchor and probe are then ligated into position and imaged to identify the first base on either the 3 or the 5 side of the anchor.Next, the probe-anchor complex is removed and the process begins again with the same anchor but a different probe with the known base at the n + 1 position.This is repeated until five bases from the 3 end of the anchor and five bases from the 5 end of the anchor are identified.Another round of hybridization occurs, this time using anchors with a five-base offset identifying an additional five bases on either side of the anchor.Finally, this whole process is repeated for each of the remaining three adapter sequences in the nanoball, generating 100 bp paired-end reads.Figure 5 | Real-time and synthetic long-read sequencing approaches.A | Real-time long-read sequencing platforms.Aa | Single-molecule real-time (SMRT) sequencing from Pacific Biosciences (PacBio).Template fragments are processed and ligated to hairpin adapters at each end, resulting in a circular DNA molecule with constant single-stranded DNA (ssDNA) regions at each end with the double-stranded DNA (dsDNA) template in the middle.The resulting 'SMRTbell' template undergoes a size-selection protocol in which fragments that are too large or too small are removed to ensure efficient sequencing.Primers and an efficient 29 DNA polymerase are attached to the ssDNA regions of the SMRTbell.The prepared library is then added to the zero-mode waveguide (ZMW) SMRT cell, where sequencing can take place.To visualize sequencing, a mixture of labelled nucleotides is added; as the polymerase-bound DNA library sits in one of the wells in the SMRT cell, the polymerase incorporates a fluorophore-labelled nucleotide into an elongating DNA strand.During incorporation, the nucleotide momentarily pauses through the activity of the polymerase at the bottom of the ZMW, which is being monitored by a camera.Ab | Oxford Nanopore Technologies (ONT).DNA is initially fragmented to 8-10 kb.Two different adapters, a leader and a hairpin, are ligated to either end of the fragmented dsDNA.Currently, there is no method to direct the adapters to a particular end of the DNA molecule, so there are three possible library conformations: leader -leader, leaderhairpin and hairpin-hairpin.The leader adapter is a double-stranded adapter containing a sequence required to direct the DNA into the pore and a tether sequence to help direct the DNA to the membrane surface.Without this leader adapter, there is minimal interaction of the DNA with the pore, which prevents any hairpin-hairpin fragments from being sequenced.The ideal library conformation is the leader-hairpin.In this conformation the leader sequence directs the DNA fragment to the pore with current passing through.As the DNA translocates through the pore, a characteristic shift in voltage through the pore is observed.Various parameters, including the magnitude and duration of the shift, are recorded and can be interpreted as a particular k-mer sequence.As the next base passes into the pore, a new k-mer modulates the voltage and is identified.At the hairpin, the DNA continues to be translocated through the pore adapter and onto the complement strand.This allows the forward and reverse strands to be used to create a consensus sequence called a '2D' read.B | Synthetic long-read sequencing platforms.Ba | Illumina.Genomic DNA templates are fragmented to 8-10 kb pieces.They are then partitioned into a microtitre plate such that there are around 3,000 templates in a single well.Within the plate, each fragment is sheared to around 350 bp and barcoded with a single barcode per well.The DNA can then be pooled and sent through standard short-read pipelines.Bb | 10X Genomics' emulsion-based sequencing.With as little as 1 ng of starting material, the GemCode can partition arbitrarily large DNA fragments, up to ~100 kb, into micelles (also called 'GEMs') along with gel beads containing adapter and barcode sequences.The GEMs typically contain ~0.3 copies of the genome and 1 unique barcode out of 750,000.Within each GEM, the gel bead dissolves and smaller fragments of DNA are amplified from the original large fragments, each with a barcode identifying the source GEM.After sequencing, the reads are aligned and linked together to form a series of anchored fragments across a span of ~50 kb.Unlike the Illumina system, this approach does not attempt to get full end-to-end coverage of a single DNA fragment.Instead, the reads from a single GEM are dispersed across the original DNA fragment and the cumulative coverage is derived from multiple GEMs with dispersed -but linked -reads.Part Aa is adapted from REF.18, Nature Publishing Group.Part Ba is adapted from REF.62.Synthetic long-reads.Unlike true sequencing platforms, synthetic long-read technology relies on a system of barcoding to associate fragments that are sequenced on existing short-read sequencers 61 .These approaches partition large DNA fragments into either microtitre wells or an emulsion such that very few molecules exist in each partition.Within each partition the template fragments are sheared and barcoded.This approach allows for sequencing on existing short-read instrumentation, after which data are split by barcode and reassembled with the knowledge that fragments sharing barcodes Barcodes A series of known bases added to a template molecule either through ligation or amplification.After sequencing, these barcodes can be used to identify which sample a particular read is derived from.Template fragments are processed and ligated to hairpin adapters at each end, resulting in a circular DNA molecule with constant single-stranded DNA (ssDNA) regions at each end with the double-stranded DNA (dsDNA) template in the middle.The resulting 'SMRTbell' template undergoes a size-selection protocol in which fragments that are too large or too small are removed to ensure efficient sequencing.Primers and an efficient 29 DNA polymerase are attached to the ssDNA regions of the SMRTbell.The prepared library is then added to the zero-mode waveguide (ZMW) SMRT cell, where sequencing can take place.To visualize sequencing, a mixture of labelled nucleotides is added; as the polymerase-bound DNA library sits in one of the wells in the SMRT cell, the polymerase incorporates a fluorophore-labelled nucleotide into an elongating DNA strand.During incorporation, the nucleotide momentarily pauses through the activity of the polymerase at the bottom of the ZMW, which is being monitored by a camera.Ab | Oxford Nanopore Technologies (ONT).DNA is initially fragmented to 8-10 kb.Two different adapters, a leader and a hairpin, are ligated to either end of the fragmented dsDNA.Currently, there is no method to direct the adapters to a particular end of the DNA molecule, so there are three possible library conformations: leader -leader, leaderhairpin and hairpin-hairpin.The leader adapter is a double-stranded adapter containing a sequence required to direct the DNA into the pore and a tether sequence to help direct the DNA to the membrane surface.Without this leader adapter, there is minimal interaction of the DNA with the pore, which prevents any hairpin-hairpin fragments from being sequenced.The ideal library conformation is the leader-hairpin.In this conformation the leader sequence directs the DNA fragment to the pore with current passing through.As the DNA translocates through the pore, a characteristic shift in voltage through the pore is observed.Various parameters, including the magnitude and duration of the shift, are recorded and can be interpreted as a particular k-mer sequence.As the next base passes into the pore, a new k-mer modulates the voltage and is identified.At the hairpin, the DNA continues to be translocated through the pore adapter and onto the complement strand.This allows the forward and reverse strands to be used to create a consensus sequence called a '2D' read.B | Synthetic long-read sequencing platforms.Ba | Illumina.Genomic DNA templates are fragmented to 8-10 kb pieces.They are then partitioned into a microtitre plate such that there are around 3,000 templates in a single well.Within the plate, each fragment is sheared to around 350 bp and barcoded with a single barcode per well.The DNA can then be pooled and sent through standard short-read pipelines.Bb | 10X Genomics' emulsion-based sequencing.With as little as 1 ng of starting material, the GemCode can partition arbitrarily large DNA fragments, up to ~100 kb, into micelles (also called 'GEMs') along with gel beads containing adapter and barcode sequences.The GEMs typically contain ~0.3 copies of the genome and 1 unique barcode out of 750,000.Within each GEM, the gel bead dissolves and smaller fragments of DNA are amplified from the original large fragments, each with a barcode identifying the source GEM.After sequencing, the reads are aligned and linked together to form a series of anchored fragments across a span of ~50 kb.Unlike the Illumina system, this approach does not attempt to get full end-to-end coverage of a single DNA fragment.Instead, the reads from a single GEM are dispersed across the original DNA fragment and the cumulative coverage is derived from multiple GEMs with dispersed -but linked -reads.Part Aa is adapted from REF. 18, Nature Publishing Group.Part Ba is adapted from REF. 62.There are currently two systems available for generating synthetic long-reads: the Illumina synthetic long-read sequencing platform (FIG.5c) and the 10X Genomics emulsion-based system (FIG.5d).The Illumina system (formerly Moleculo) partitions DNA into a microtitre plate and does not require specialized instrumentation.However, the 10X Genomics instruments (GemCode and Chromium) use emulsion to partition DNA and require the use of a microfluidic instrument to perform pre-sequencing reactions.With as little as 1 ng of starting material, the 10X Genomics instruments can partition arbitrarily large DNA fragments, up to ~100 kb, into micelles called 'GEMs' , which typically contain 0.3 copies of the genome and one unique barcode.Within each GEM, a gel bead dissolves and smaller fragments of DNA are amplified from the original large fragments, each with a barcode identifying the source GEM.After sequencing, the reads are aligned and linked together to form a series of anchored fragments across the span of the original fragment.Unlike the Illumina system, this approach does not attempt gapless, end-to-end coverage of a single DNA fragment.Instead it relies on linked reads, in which dispersed, small fragments that are derived from a single long molecule share a communal barcode.Although these fragments leave segments of the original large molecule without any coverage, the gaps are overcome by ensuring that there are many long fragments from the same genomic region in the initial preparation, thus generating a read cloud wherein linked reads from each long fragment can be stacked, combining their individual coverage into an overall map (FIG.5d).Comparison of single-molecule and synthetic longread sequencing. There is growing interest in the field of long-read sequencing, and each system has its own advantages and drawbacks (TABLE 1).Currently, the most widely used instrument in long-read sequencing is the PacBio RS II instrument.This device is capable of generating single polymerase reads in excess of 50 kb with average read lengths of 10-15 kb for a long-insert library.Such properties are ideal for de novo genome assembly applications 63 , for revealing complex longrange genomic structures 64 and for full-length transcript sequencing.There are, however, several notable limitations.The single-pass error rate for long reads is as high as 15% with indel errors dominating 65 , raising concerns about the utility of the instrument 66 .Fortunately, these errors are randomly distributed within each read and hence sufficiently high coverage can overcome the high error rate 67 .The use of a circular template by PacBio also provides a level of error correction.The more frequently a single molecule is sequenced, the higher the resulting accuracy -up to ~99.999% for insert sequences derived from at least 10 subreads 59,68 .This high accuracy rivals that of Sanger sequencing, leading researchers to speculate that this technology can be used in a manner analogous to Sanger-based SNP validation 65 .The runtimes and throughput of this instrument can be tuned by controlling the length of time for which the sensor monitors the ZMW; longer templates require longer times.For example, a 1 kb library that is run for 1 hour will generate around 7,500 bases of sequence per molecule, with an average of 8 passes, whereas a 4-hour run will generate around 30,000 bases per molecule and ~30 passes.Conversely, a 10 kb library requires a 4-hour run to generate ~30,000 bases with ~3 passes.The limited throughput and high costs of PacBio RS II (around $1,000 per Gb), in addition to the need for high coverage, place this instrument out of reach of many small laboratories.However, in an attempt to ameliorate these concerns, PacBio has launched the Sequel System, which reportedly has a throughput 7 that of the RS II, thus halving the cost of sequencing a human genome at 30 coverage 69 .Single-end and paired-end sequencingIn single-end sequencing, a DNA template is sequenced only in one direction.In paired-end sequencing, a DNA template is sequenced from both sides; the forward and reverse reads may or may not overlap.A deviation in the expected genome alignment between two ends of a paired-end read can indicate astructural variation." + ], + [ + "A good starting point is with the databases called Integrated Microbial Genomes (IMG; http://img.jgi.doe. gov/) or National Center for Biotechnology Information (NCBI; http://www.ncbi.nlm.nih.gov/). Many genomes identified inIMG as belonging to the Genome Encyclopedia of Bacteria and Archaea (GEBA; http://jgi.doe.gov/our-science/science-programs/microbial-genomics/phylogenetic-diversity/) project are relatively unstudied. In Step 2, either the instructor or the student chooses an annotation approach. A few of the annotation questions that one might ask using GENI-ACT are these: An initial automated gene call is made following sequencing. Is the automated gene call accurate?", + "The GO Consortium coordinates an effort to maximize the utility of a large and representative set of key genomes, which we refer to as reference genomes.The Reference Genome project has two aspects: (i) to encourage complete and precise annotations of the proteins for the species widely used as model organisms; and (ii) to provide inferred annotations for proteins for which no experimental data are available [4].We describe here the homology-based method and software we have developed to achieve those goals.", + "Although comparative immunogenomic analyses clearly benefit from highly quality WG assemblies, costs may still prevent large-scale analyses involving many species.Nevertheless, the availability of high-quality reference genomes for select species within target clades can enable much more accurate assembly and annotation of other species using SRseq, providing a way forward whilst minimising costs (e.g., [65], Figure 2, Key figure).", + "Each of these has advantages anddisadvantages, and, although the main genome portals are generally consistent, theymay not give the same answers in every case. Bearing in mind that only the first two ofthese gene location methods are based on stable (almost) unambiguous information,it is better to use at least two ways to define and store the results: for example, a sectionof raw sequence and a gene name, or a primary accession number and a set of genomecoordinates. The BACE1 gene will be used as an example of a known gene to locate.This problem can be overcomeby generating several multiple sequence alignments, one with each of the sequencesunder study as the reference. This solution is time-consuming, raises the additionalproblem of integrating results between alignments, and exposes the second majordrawback to the reference sequence approach; that is, the potential for inconsistencieswhen using alternate sequences as the reference. A solution to the problems presented by reference sequence-based alignment andanalysis has been proposed in the form of a threaded blockset (Blanchette et al. ,2004).", + "Toachieve this goal, we integrated and make available big reference datain chapters 2 and 3, bridged model organism to human data in chapter4, translated generic methods into clinical applications in chapters 5and 6, and developed a platform to bring innovations into practice inchapter 7. The resources currently available are already plentiful, and both theamount and types of molecular life science data is growing at a tremendous pace.Interpretation can be sped up using the huge amount of useful information collected by laboratories, public databases and biobanks. Unfortunately, for now, all these sources of useful data cannot be easily integrated and explored in unison. Further, while many innovative analysismethods emerge from research on a regular basis, a lack of standardization makes it dicult to adopt, share, compare and validate them inpractice. Here we report a lightweight framework for genome interpretationpipelines that aims to enable rapid implementation and adaptation ofanalysis protocols that integrate reference annotation data (e.g. ClinVar, ExAC, GoNL), run best-practice analysis tools (e.g.", + "Links to external resources, including genome browsers, sequence databases, protein structure databases, and many other webresources, make it possible to place results in a rich bioinformatic context. It is practical to integrate data across allof these domains and scales for the simple reason that datahave been collected from a common genetic referencepanel of strains. The integration of data across domains in the RI strainsalso allows us to easily study pleiotropic effects of variability in gene expression.", + ", 2012), or the 1001 Genomes Project for A. thaliana (1001 Genomes Consortium,2016), may even be better suited for similar analyses. In thecase of humans, however, it is almost impossible to simultaneously phenotype individuals and sample multi-tissue andmulti-omic data, while controlling the environmental sources ofvariation. Assessing the use of these tools may require cohortsthat have extensive multi-omics datasets available or have relevant samples biobanked, e.g. , the Framingham Heart Study(Mahmood et al. , 2014). Imputation of gene expression in deeptissues from either reference transcriptome datasets (Gamazonet al.", + "The use oftrue strain-specific genomes for read alignment, rather thanthe reference genome or imputed pseudogenomes, willpose new analytical challenges. It will also offer theopportunity to capture biological signals which are notapparent in the present framework. One remaining gap in the CC infrastructure is the lackof a centralized, public platform for sharing and integrating phenotype data on CC lines. The Mouse PhenomeDatabase (http://phenome.jax.org/) (Grubb et al.The genome assembly and annotations are made available to the community via many onlinegenome browsers, the most popular of which are hosted bythe University of California at Santa Cruz [UCSC, (Karolchik et al. 2014)] and Ensembl (Flicek et al. 2013). Use ofa single haploid reference sequence as an anchor for allstudies of genetic variation in mouse offers many practicaladvantages. But the dependency on a reference genomerequires several assumptions about the nature of geneticvariation which may be violated in practicethe strongestof which is that of genomic collinearity (i.e.", + "Each of these has advantages anddisadvantages, and, although the main genome portals are generally consistent, theymay not give the same answers in every case. Bearing in mind that only the first two ofthese gene location methods are based on stable (almost) unambiguous information,it is better to use at least two ways to define and store the results: for example, a sectionof raw sequence and a gene name, or a primary accession number and a set of genomecoordinates. The BACE1 gene will be used as an example of a known gene to locate.This problem can be overcomeby generating several multiple sequence alignments, one with each of the sequencesunder study as the reference. This solution is time-consuming, raises the additionalproblem of integrating results between alignments, and exposes the second majordrawback to the reference sequence approach; that is, the potential for inconsistencieswhen using alternate sequences as the reference. A solution to the problems presented by reference sequence-based alignment andanalysis has been proposed in the form of a threaded blockset (Blanchette et al. ,2004).", + "The large number of bioinformatic tools that have beenmade available to scientists during the last few years has presented theproblem of which to use and how best to obtain scientifically valid answers(3). In this chapter, we will provide a guide for the most efficient way toanalyze a given sequence or to collect information regarding a gene, protein,structure, or interaction of interest by applying current publicly available software and databases that mainly use the World Wide Web.", + "The genome assemblyand annotations are made available to the community via many online genome browsers, themost popular of which are hosted by the University of California at Santa Cruz [UCSC,(Karolchik et al. 2014)] and Ensembl (Flicek et al. 2013). Use of a single haploid referencesequence as an anchor for all studies of genetic variation in mouse offers many practicaladvantages. But the dependency on a reference genome requires several assumptions aboutthe nature of genetic variation which may be violated in practicethe strongest of which isthat of genomic collinearity (i.e.Detailed knowledge of the subspecies contributions to CC genomes, obtained by integratingCC lines haplotype mosaics with data from the Mouse Phylogeny Viewer, will be critical tothis effort. Author ManuscriptAuthor ManuscriptMost of the resources discussed in this review ultimately depend on the mouse referencegenome. A high-quality, well-annotated reference assembly for any model organism isextremely valuable for the research community. In addition to the genomic sequence itself, areference genome provides a backbone for annotation and a common coordinate system toanchor genetic maps.", + "Each of these has advantages anddisadvantages, and, although the main genome portals are generally consistent, theymay not give the same answers in every case. Bearing in mind that only the first two ofthese gene location methods are based on stable (almost) unambiguous information,it is better to use at least two ways to define and store the results: for example, a sectionof raw sequence and a gene name, or a primary accession number and a set of genomecoordinates. The BACE1 gene will be used as an example of a known gene to locate.This problem can be overcomeby generating several multiple sequence alignments, one with each of the sequencesunder study as the reference. This solution is time-consuming, raises the additionalproblem of integrating results between alignments, and exposes the second majordrawback to the reference sequence approach; that is, the potential for inconsistencieswhen using alternate sequences as the reference. A solution to the problems presented by reference sequence-based alignment andanalysis has been proposed in the form of a threaded blockset (Blanchette et al. ,2004).", + "The general conclusion of this comparison is thus that co-assembly and co-binning approaches would be useful for retrieving substantially more genomes in relatively long (>5) subject-specific time series, whereas the potential advantage of retrieving more low-abundance species in a cross-sectional co-assembly is overcome by the disadvantage of having to use more complex approaches such as DESMAN to resolve the strain variation.That is perhaps more appropriate where the aim is to extract as much information as possible from a single study rather than to produce a single comprehensive high fidelity strain catalog.Because time series comprising more than 5 samples from the same subject and body site are very rare in the available cohorts (only 70 individuals -i.e., 1.0% -in our database), co-assembly is not considered in the present work as it would not provide advantages.", + "Each of these has advantages anddisadvantages, and, although the main genome portals are generally consistent, theymay not give the same answers in every case. Bearing in mind that only the first two ofthese gene location methods are based on stable (almost) unambiguous information,it is better to use at least two ways to define and store the results: for example, a sectionof raw sequence and a gene name, or a primary accession number and a set of genomecoordinates. The BACE1 gene will be used as an example of a known gene to locate." + ] + ], + "task_id": [ + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_6.json b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_6.json new file mode 100644 index 0000000..c2a25f9 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/human/dataset_domainexpert_general_6.json @@ -0,0 +1,65 @@ +{ + "question": [ + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?" + ], + "answer": [ + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits." + ], + "contexts": [ + [ + "At the intermediate level, there are regulatory unitsmade up of multiple components, such as gene-promoter pairs. At the highest level, regulatory units interact to create a particulargene circuit, e.g. , two gene-promoter pairs can be arranged in amutually inhibitory network to create a genetic toggle switch. Ateach of these levels, one can identify sequence representationsthat define certain aspects of regulation and control, as well ascompositional relationships (e.g. , spatial arrangement and orientation) and interactions between biomolecules, molecular components, and/or sub-components that impact functional outputsand behaviors.", + "These regulatory programs are apparent across a variety of jointcontributions, from the independent contribution of each of the regulatory mechanisms to acooperative contribution of several mechanisms. A regulatory program may include a varietyof mechanisms such as transcription factors, chromatin remodeling complexes, and promoterregulatory elements. Natural genetic variations may provide important insights into regulatory programs. Inparticular, transcription profiles can be integrated with genotypic data across a population toidentify genomic loci that have an effect on gene expression (Mackay et al. , 2009), and hence itis possible to use these loci as potential regulatory mechanisms.", + "During the development,genes are turned on and off in a pre-programmed fashion, a process orchestratedby TFs, whose binding sites aggregate in the promoters near their controlled genes. A combinatorial control is achieved via different combinations of ubiquitous andcell-specific regulatory factors. Moreover, genes can initiate transcription at multiple loci (alternative promoters), creating RNA isoforms with different 5 regions. Alternative promoters are potentially important for gene-expression regulation orgenerating different protein products. Complex regulation in vivo can also involvemany more features, such as enhancers, locus control regions (LCRs), and/or scaffold/matrix attachment regions (S/MARs).Tightly regulated gene expression for specific cell types and developmental stages inresponse to different physiological conditions is driven by the orchestration of complex and multilayered gene regulatory networks (GRNs) (Maniatis and Reed, 2002). Inferring GRNs is of fundamental importance and a great challenge for molecularbiologists and geneticists. Mutations, including point mutations, insertions and deletions, translocations,and duplications, play critical roles in determining biological phenotypes and disease susceptibilities by perturbing the GRNs. Among them, single nucleotide polymorphisms (SNPs) generated by point mutations occur approximately one per 1000bases and are the predominant variations in man.", + "Gene expression directs the process of cellular differentiation, in which19specialized cells are generated for the different tissue types. The regulation of gene expression (i.e. gene regulation) controls the amount and timing of changes to the geneproduct. This is the basic mechanism for modifying cell function and thereby the versatility and adaptability of an organism. Therefore, gene expression and regulation functionas a bridge between genetic makeup and expression of observable traits. Despite its vital importance, determining the precise roles of given transcripts remainsa fundamental challenge.", + "INTRODUCTIONThe field of gene regulation is currently undergoing a renaissance.With the successful annotation of most of the protein-coding portion of the human genome [1], the focus of much research has shifted toward deciphering the regulatory logic governing the temporal, spatial and quantitative aspects of gene expression that is embedded in the remaining 98% of DNA that does not encode for protein [2].A flurry of papers stemming, in large part, from two broad areas of investigation has recently made a significant impact on the field of gene regulation.The first revolves around the genetic basis of human disease.Fueled by the power of linkage and genome-wide association studies, an ever-expanding list of human diseases has been associated with single nucleotide polymorphisms (SNPs) residing in noncoding regions of the genome [3].These disease-associated SNPs are thought to directly control some aspect of target gene expression, or are linked to other DNA variants that possess regulatory activity.In a small but growing number of cases, the regulatory SNPs identified in human genetic studies have led to the identification of disease susceptibility loci and have served as useful entry points for unraveling the complexities of the gene regulatory landscape (Table 1) [3].The second line of investigation that has revitalized gene expression research relates to the development of functional genomic approaches to screen noncoding DNA for regulatory potential.Genome-wide surveys of sequence conservation [4][5][6], histone modifications [7][8], DNAse I hypersensitivity [9] and DNA structure [10], have all significantly improved the detection of functional cis-acting regulatory sequences.This review will highlight recent examples from the literature that have successfully integrated genetic and genomic approaches to uncover the molecular basis by which cis-regulatory mutations alter gene expression and contribute to human disease.", + "Complexity of gene regulationGene regulation is a complex multi-layered process involving numerous proteins and non-coding RNAs which may act at a great distance from their target gene.Elaborate multi-protein/RNA complexes must be assembled at the site of regulation.The regulatory mechanism may be intricate and variable, potentially involving transcript rearrangement and mRNA degradation.It is now clear that RNA has a diverse set of functions and is more than just a messenger between gene and protein.The mammalian genome is extensively transcribed, giving rise to thousands of RNA transcripts that are never translated into proteins.Whether all of these transcripts are functional is currently debatable, but it is evident that these include families of RNA molecules with a regulatory function [34].The presence of a gene expression change, which is strongly correlated with relevant physiological changes, in the absence of proximate significant GWAS signals, suggests that relatively distant regulatory variants (and potentially many such variants) may act in combination to regulate the expression of the target gene of interest.Such putative gene expression-modulating variants could potentially act upon target gene expression through the mediation of non-protein-coding regulatory RNAs.For example, recent studies have shown that the expression of many genes is modulated by small interfering RNAs (siRNAs) and micro-RNAs, e.g.reviews by [10,30], which do not encode proteins.In addition to microRNAs, many nonprotein-coding RNA species (or \"RNA genes\"), such as long noncoding RNAs [42], are transcribed from the genome.Thus, there is compelling evidence that most of the genome may be transcribed [5,6,9,19,38,53,58,59,62] and the potential role of non-protein coding RNA genes in the modulation of protein-coding gene expression remains to be fully evaluated.", + "Transcription factors that bind to DNA recognize this sequence and use it to correctly position RNA polymerase, the enzyme that actually generates the transcript.Other sequences, called enhancers and repressors, speed up and slow down, respectively, the rate of transcription.Enhancer and repressor sequences can be quite distant from the gene's coding region.Other transcription factors recognize these sequences and further control how much and how fast mRNA is generated.All of these sequences are part of a gene and are required to generate the many proteins that control the overall maintenance and general metabolism of all of our cells.Genes that are expressed in all cell types, such as RNA polymerase and transcription factors, are called housekeeping genes.Concepts in the 21st Century: Genetic and Epigenetic Regulation of Gene ExpressionWe now know that only about 1% of our genome encodes proteins.Alternative splicing is the primary mechanism by which our approximately 20,000 genes can code for hundreds of thousands of proteins.Alternative splicing refers to modification of the primary mRNA produced during transcription (Figure 8).Only a portion of the transcript contains sequences that are translated into a protein.Introns, or intervening sequences, are removed after transcription, and the remaining sequences, known as exons, are spliced together.One transcript can be processed in multiple ways, such that different combinations of exons can be spliced together, producing many different proteins from the same primary transcript.The discovery of alternative splicing has changed our thinking about the central dogma because we now know that the concept of one gene encoding one protein is not true.", + "Of the total 20,000-25,000 protein-coding genes, occupying only 1.2% of the human genome, about six percent are functionally classified as TFs [8].However, some 93% of our genome is transcribed, by far the greatest part expressed as non-protein-coding RNAs (ncRNA), including the miR-NAs [9].An order of magnitude more numerous than all the proteins which make up living organisms are the transcrip-*Address correspondence to this author at the School of Medicine, University of Louisville, 580 S. Preston St., Louisville, KY 40202, USA; Tel: 502-852-2554; Fax: 502-852-2555; E-mail: Eugenia.Wang@Louisville.edution start sites (TSSs), located in promoter-proximal element regions, as well as an increasing number of putative promoter-distal elements, identified by the pilot ENCODE project [9].These recent findings, together with the fact that nonprotein-coding genomic sequence elements-such as miR-NAs-predominate and are evolutionarily conserved in our genome, challenge our traditional understanding of the definition of a gene, which has been generally considered a unit of genome sequence that is transcribed to produce a protein product for a given cellular function.Nevertheless, as the ENCODE consortium suggests, a gene may be defined as \"a union of genomic sequences encoding a coherent set of potentially overlapping functional products\" that eventually orchestrate the complex regulation and function of the host organism's cellular activities [10].An even bolder scenario is proposed by John S. Mattick, who suggests that the genome may consist largely of massively embedded RNA coding sequences directing regulatory networks, which may have co-evolved with proteins.These two complementary genomic sets may ultimately form the interacting RNAprotein regulatory networks which control the complex layers of signaling communication within all cells [11,12].Thus, the intriguing notion of epigenomic regulation of essential processes such as cell proliferation, differentiation, apoptosis, etc., characterized by feed-forward RNA regulatory networks, is becoming increasingly important in our appreciation of the epigenetic information required for the development of multi-cellular organisms [11].In this report, we focus our discussion on the suggestion that derailment of the RNA-protein interaction, and its subsequent impact on the regulatory networks which they direct, may constitute a significant fraction of the molecular mechanisms controlling the aging process.", + "During the development,genes are turned on and off in a pre-programmed fashion, a process orchestratedby TFs, whose binding sites aggregate in the promoters near their controlled genes. A combinatorial control is achieved via different combinations of ubiquitous andcell-specific regulatory factors. Moreover, genes can initiate transcription at multiple loci (alternative promoters), creating RNA isoforms with different 5 regions. Alternative promoters are potentially important for gene-expression regulation orgenerating different protein products. Complex regulation in vivo can also involvemany more features, such as enhancers, locus control regions (LCRs), and/or scaffold/matrix attachment regions (S/MARs).Tightly regulated gene expression for specific cell types and developmental stages inresponse to different physiological conditions is driven by the orchestration of complex and multilayered gene regulatory networks (GRNs) (Maniatis and Reed, 2002). Inferring GRNs is of fundamental importance and a great challenge for molecularbiologists and geneticists. Mutations, including point mutations, insertions and deletions, translocations,and duplications, play critical roles in determining biological phenotypes and disease susceptibilities by perturbing the GRNs. Among them, single nucleotide polymorphisms (SNPs) generated by point mutations occur approximately one per 1000bases and are the predominant variations in man.Gene expression regulation can take place at any step during the path of expression, including transcription, mRNA splicing and processing, export and subcellularlocalization, translation and post-translational modifications. These steps are oftencoupled with each other (Maniatis and Reed, 2002). Currently, it is still too early tobuild comprehensive and accurate dynamic models for truly realistic GRNs. The majority of computational methods attempt to detect cis-trans relationships, the basicbuilding blocks of GRNs, by modern statistical or machine learning approaches.", + "Other possible regulatory regions includeenhancers and silencer etc. In the coding regions of a gene, Triplets of nucleotides,known as codons, each encode for one of 20 amino acids or a signal. 3The process that a ribonucleic acid (RNA) synthesized from DNA is calledtranscription. One strand of DNA is served as template during transcription. The RNAtranscribed from the template DNA is identical in sequence with the other strand of theDNA which is called coding strand.", + "Third, instructions encoded within the embryos DNA can directly control if, andwhen, a nearby gene becomes activated; this is known as cis-regulation. Finally, similar instructionscan also control genes that are situated elsewhere in the embryos DNA through indirectmechanisms; this is known as trans-regulation. Now, Spies, Smith et al. have investigated these four processes in the offspring of two differentstrains of mice, one originally from Europe and the other from Southeast Asia. The two strains werecrossbred and the resulting embryos were analyzed to see which of the four processes affected geneactivity.", + "During the development,genes are turned on and off in a pre-programmed fashion, a process orchestratedby TFs, whose binding sites aggregate in the promoters near their controlled genes. A combinatorial control is achieved via different combinations of ubiquitous andcell-specific regulatory factors. Moreover, genes can initiate transcription at multiple loci (alternative promoters), creating RNA isoforms with different 5 regions. Alternative promoters are potentially important for gene-expression regulation orgenerating different protein products. Complex regulation in vivo can also involvemany more features, such as enhancers, locus control regions (LCRs), and/or scaffold/matrix attachment regions (S/MARs).Tightly regulated gene expression for specific cell types and developmental stages inresponse to different physiological conditions is driven by the orchestration of complex and multilayered gene regulatory networks (GRNs) (Maniatis and Reed, 2002). Inferring GRNs is of fundamental importance and a great challenge for molecularbiologists and geneticists. Mutations, including point mutations, insertions and deletions, translocations,and duplications, play critical roles in determining biological phenotypes and disease susceptibilities by perturbing the GRNs. Among them, single nucleotide polymorphisms (SNPs) generated by point mutations occur approximately one per 1000bases and are the predominant variations in man.", + "REGULATION OF GENE EXPRESSIONApart from the protein coding sequences, there are other biologically relevant nucleic acid sequences that play other important roles in the genome such as regulation of gene expression and maintenance of the chromatin structure (Pique-Regis et al., 2011).Regulation of gene expression involves a process that leads to increase or decrease in the production of specific proteins (Jacob and Monod, 1961).It is an important aspect of the cell because it increases the versatility and adaptability of an organism by allowing the cell to produce proteins only when they are needed (Payankaulam, 2010;Jacob and Monod, 1961).Gene expression is regulated at the level of transcription (described in 2.8), which can only occur if transcription factors bind to the DNA.Binding occurs within special nucleotide sequences called regulatory regions that are usually several hundred base pairs long (Lodish et al., 2000).Regulatory regions surround transcription start sites (TSSs) of genes apart from some sequences called enhancers that are located far upstream or downstream of their target gene (Birney et al., 2007;Dineen et al., 2007).", + "During the development,genes are turned on and off in a pre-programmed fashion, a process orchestratedby TFs, whose binding sites aggregate in the promoters near their controlled genes. A combinatorial control is achieved via different combinations of ubiquitous andcell-specific regulatory factors. Moreover, genes can initiate transcription at multiple loci (alternative promoters), creating RNA isoforms with different 5 regions. Alternative promoters are potentially important for gene-expression regulation orgenerating different protein products. Complex regulation in vivo can also involvemany more features, such as enhancers, locus control regions (LCRs), and/or scaffold/matrix attachment regions (S/MARs).Tightly regulated gene expression for specific cell types and developmental stages inresponse to different physiological conditions is driven by the orchestration of complex and multilayered gene regulatory networks (GRNs) (Maniatis and Reed, 2002). Inferring GRNs is of fundamental importance and a great challenge for molecularbiologists and geneticists. Mutations, including point mutations, insertions and deletions, translocations,and duplications, play critical roles in determining biological phenotypes and disease susceptibilities by perturbing the GRNs. Among them, single nucleotide polymorphisms (SNPs) generated by point mutations occur approximately one per 1000bases and are the predominant variations in man." + ], + [ + "Examples of economically important traits, their heritabilities, and relative economic values.", + "Genetic correlation is different from pleiotropy.Two traits have a pleiotropic relationship if many variants affect both.Genetic correlation is a stronger condition than pleiotropy: to exhibit genetic correlation, the directions of effect must also be consistently aligned.", + "This means that it is the developmentalbasis of trait integration, not simply the strength of the genetic correlations and observable patterns of covariation among traits, that will affect how components of a scalingrelationship can evolve. Although these powerful phenotype landscape models have generated important insights into the evolution of complex traits such as scaling relationships, they are difficult totest empirically (see Rice 2008).A, Shape variation within a group of organisms isshown by a line fit to a data cloud representing the size of two traits for a group of organisms. Shapevariation within a group of organisms is shown by a line (dark line) fit to a data cloud (gray elipse)representing the size of two traits for a group of organisms, in this case the brain-body size relationshipin humans (data from Koh 2005). B, Scaling relationships are divided into three classes based on thepattern of variation they describe.At the phenotypic level, detailed studies of physiology, morphology, and biochemistry canelucidate whether a higher-level trait has evolved via changes in different subordinate traits. At the genetic level, a first-pass black box approach to determine whether different genesunderlie the response to selection in replicate lines is to cross those lines and examine thetraits of interest in the F1, F2, and/or backcross populations (see also Rhodes and Kaweckithis volume).Particularly relevant for the evolution of scaling relationships,these models have revealed that the developmental basis of genetic correlations (e.g. , thedegree to which a given genetic correlation results from additive or nonadditive epistaticinteractions among traits) can profoundly affect the evolutionary malleability of the correlation, trait covariation, and the evolutionary trajectory of the complex phenotype (Wolfet al. 2001, 2004; Rice 2002, 2004a, 2008).The phenotype landscapeapproach has been extended to connect with existing quantitative genetic treatments ofmultivariate evolution, yielding an emergent theory exploring how developmental integration, or entanglement, among traits affects the symmetry and rates of trait evolution;the evolution of heritabilities; the impact of genetic correlations on evolutionary trajectoriesacross different time scales; the evolutionary relationships among trait means, variances,THE EVOLUTION OF ANIMAL FORM437and covariances; and the distribution of traits in phenotypic space (Wolf et al. 2001,2004; Rice 2004b, 2008).", + "In contrast, and consistently with our goal of identifying novel relationships among traits, module nos. 3, 4 and 5 suggest previously unknown connections between traits. We next characterized pairs of traits within each group of traits (trait pairs) to show that thequality of these pairs is not lower than in existing methods. We focused on three main properties oftrait pairs: the correlation among traits in a pair; the correlation between a trait pair and thetranscripts; and the knowledge-based relationships among traits.However, in most cases the genes and molecular mechanisms involved are not yet known so it ismore difficult to work out how the traits are connected. Computing techniques make it possible to assess the relationships between hundreds orthousands of traits at the same time. These high volume analyses can also allow scientists to identifyless obvious relationships that might be missed in more traditional types of study. Here, Oren et al. created a new computer algorithm to identify related traits, their shared geneticbasis, and the molecular mechanisms behind them.", + "This means that it is the developmentalbasis of trait integration, not simply the strength of the genetic correlations and observable patterns of covariation among traits, that will affect how components of a scalingrelationship can evolve. Although these powerful phenotype landscape models have generated important insights into the evolution of complex traits such as scaling relationships, they are difficult totest empirically (see Rice 2008).A, Shape variation within a group of organisms isshown by a line fit to a data cloud representing the size of two traits for a group of organisms. Shapevariation within a group of organisms is shown by a line (dark line) fit to a data cloud (gray elipse)representing the size of two traits for a group of organisms, in this case the brain-body size relationshipin humans (data from Koh 2005). B, Scaling relationships are divided into three classes based on thepattern of variation they describe.At the phenotypic level, detailed studies of physiology, morphology, and biochemistry canelucidate whether a higher-level trait has evolved via changes in different subordinate traits. At the genetic level, a first-pass black box approach to determine whether different genesunderlie the response to selection in replicate lines is to cross those lines and examine thetraits of interest in the F1, F2, and/or backcross populations (see also Rhodes and Kaweckithis volume).Particularly relevant for the evolution of scaling relationships,these models have revealed that the developmental basis of genetic correlations (e.g. , thedegree to which a given genetic correlation results from additive or nonadditive epistaticinteractions among traits) can profoundly affect the evolutionary malleability of the correlation, trait covariation, and the evolutionary trajectory of the complex phenotype (Wolfet al. 2001, 2004; Rice 2002, 2004a, 2008).The phenotype landscapeapproach has been extended to connect with existing quantitative genetic treatments ofmultivariate evolution, yielding an emergent theory exploring how developmental integration, or entanglement, among traits affects the symmetry and rates of trait evolution;the evolution of heritabilities; the impact of genetic correlations on evolutionary trajectoriesacross different time scales; the evolutionary relationships among trait means, variances,THE EVOLUTION OF ANIMAL FORM437and covariances; and the distribution of traits in phenotypic space (Wolf et al. 2001,2004; Rice 2004b, 2008).", + "As outlined by Lewontin (2011), the relationship between genotype and phenotype can be described by four basic models that have been, and still are, used in genetics: one-to-one, one-to-many, many-to-one, and many-to-many (see Fig. 1).The first goes back to the unit factor theory at the beginning of the twentieth century, i.e., one gene gives rise to one trait (Mayr 1982).The second model describes one gene affecting many traits (pleiotropy), while the third model accounts for many genes affecting one trait (polygeny).It is undoubtedly correct that every part of the genome is connected causally with the phenome (a set of phenotypes) by at least some molecular mechanistic pathways, but there is variation in this relation, which can make all of these four models valid at least for some cases.But generally for most eukaryotic organisms, model 4 (many-to-many) is the most acceptable description for most cases of the relationship between phenotype and genotype (Lewontin 2011).And often, the many-to-many model is insufficient, since genes and environment are usually both involved in the development of phenotypes, as captured by the norm-of-reaction concept (see, e.g., Falk 2001).", + "At the otherend are traits, such as growth, which are likely to be affected by many genes that each contributea small portion to the overall phenotype. Between these two extremes are traits that areregulated by more than one genetic locus (and are possibly also influenced by environmentalfactors), which show several intermediate phenotypes. Generally, the more loci that areinvolved in determining a quantitative trait, the more difficult it is to map and identify all ofthe causative QTLs.", + "Genetic Correlations Among Multiple TraitsWhen a sufficient number of traits have been tested in the same inbred strains, the geneticrelationships among the traits can be determined and a genetic framework developed usingmultivariate statistical methods. A growing literature of SI and RI strain surveys exists, withonline resources to search these data and to directly compare previous and new strain surveysMethods Mol Biol. Author manuscript; available in PMC 2011 January 1. Lariviere and MogilPage 4NIH-PA Author Manuscript(e.g. , http://www.jax.org/phenome, http://www.genenetwork.org).", + "However, common practice in genetics treats this relationshipas a straightforward one-to-one mapping from genotype to phenotype. The roots of this practicecan be traced to Mendel who chose traits with a direct relationship between genetic variation andphenotypic variation in formulating his particulate theory of inheritance. It has been furthersolidified by the successes of modern genetics in identifying genes involved in many simpleWtraits, such as rare human diseases. However, most traits are not simple and to understandcomplex traits it is necessary to decipher the developmental processes that occur between genesIEand traits.It was believed by many that for each trait variant we should expect to find acorresponding genetic change, or gene for that trait. Through historical happenstance therelationship between genes and traits was set up and treated as if it were one-to-one. But theproduction of a trait involves not only genes, but also their interactions with each other and theenvironment, and chance.Two approaches to understanding the genotype-phenotype relationship are describedand examples given of how both lead to a many-to-many relationship. First, cellular and geneticmechanisms, such as alternative splicing, DNA and chromatin modification, cellular gene choice,and gene regulation, which lead from DNA sequence to protein structure, are discussed. And,second, examples of variation in the genotype-phenotype relationship which can producevariable phenotypes from the same genetic information and stable phenotypes despite geneticvariation are presented. iiiTo examine how normal variation in complex repeated traits such as the mammaliandentition is produced two experimental approaches are taken." + ], + [ + "Another striking finding has been the revelation of the existence ofgenome regions to which variation in large number of traits can be mapped [29];such regions have been designated as QTL hotspots. This genetic information wasthen used to try to infer biological relationships between those traits and to connectthem into networks [30] (for example transcriptional networks). In more recentstudies, efforts have been devoted to the integration of phenotypes from differentlevels, jointly studying gene expression, proteome, metabolome and sometimesclassical traits such as diseases [31, 32].", + "First, it is possible to map Mendelian traitsand even quantitative traits with modest LOD scores with good precision, even whenusing a small numbers of strains7577. Second, a good way to transition from QTLs tospecific genes, variants, and mechanisms is often to use complementary resources suchas panels of common inbred strains, Collaborative Cross (CC), or Diversity Outbred (DO)cases, efficient screens of candidate genes using in vitro and in vivo assays 48,76, and evenhuman genome-wide association study (GWAS) data 7882.", + "For example, in comparative genomics, QTLs coming from different species andassociated with a given complex phenotype are aligned based on the syntenybetween these species. The overlapping genetic region is considered very likely tocontain the causal gene for this complex trait. In Chapter 9, we wondered whether it197Chapter 10is possible to apply this approach to the currently available data regarding thegenetic basis of physical activity in mice and humans in order to discover novelcandidate genes for this phenotype.", + "It is now widely appreciated that even when an association can be localized to a singlegene, that gene may not be the cause of the association [Smemo-2014], meaning that proximity tothe peak SNP is not sufficient to identify the causal gene. Therefore, a major goal of our study was tointegrate behavioral QTL and eQTL data. eQTLs can provide the crucial link between a regionimplicated by GWAS and the biological processes that underlie that association. We exploited theeasy access to tissue, which is a critical advantage of model organisms, to map eQTLs.Theseexamples illustrate the utility of combining GWAS with eQTL data to identify the molecularmechanism by which a chromosomal region influences a complex trait. DiscussionWe performed a GWAS in a commercially available outbred mouse population, which identifiednumerous physiological, behavioral, and expression QTLs. In several cases the implicated loci weresmaller than 1 Mb and contained just a handful of genes that included an obvious candidate. Inaddition, we used the eQTL results to further parse among the genes in the intervals that wereimplicated in the behavioral traits.", + "The authors analyzed GWAS data to confirm that annotating SNPs with a scorereflecting the strength of the evidence that the SNP is an eQTL can improve the ability todiscover true associations and may further clarify the nature of the mechanism driving theassociations. This raises the possibility that eQTL data may increase the proportion ofheritability explained by identifiable genetic factors, and be used to gain a betterunderstanding of the biology underlying complex traits.", + "Network analysesWe now have two QTL, and we have picked potentially interesting genes within each, but nowwe want to build up more evidence for which gene in our QTL interval is causal. The first, andmost obvious way, is to see what genes our trait of interest correlates with, in tissues that weexpect to be related to the trait. We calculated the Spearmans correlation between the traitBXD_17850 and all probes with expression data in T helper cells (GN319).", + "The advent of largerpanels and denser marker maps, in conjunction with high quality gene expression data, now means that expression QTLs arestatistically robust enough to be considered starting points forfurther study in their own right. This can be used to great effectin reverse complex trait analysis, a powerful new approach inwhich segregating genetic variation, as evidenced by a strongQTL, is mapped to other potentially interacting genes, and ultimately back to candidate phenotypes.", + "Since our driving application is toidentify the genes that cause variation in complex traits, it is necessary to show the relationship or distance between genes and QTLs. For that, we need an additional relationaltable describing the exact location of QTLs in the unit of megabases. Graph theoretic algorithms provide valuable information that is otherwise hard to discern about the data. However, many such algorithms incur long compute times and arefar from being interactive.", + "Using this tool, a QTL analysis may also shed light onwhether differences in phenotype are due to one or two largeeffect genes or many loci of small effect (Stapley et al. , 2010). A model constructed by Malcom (2011) highlights the importance of considering the genetic architecture when attempting topredict evolutionary trajectories by suggesting that a trait controlled by a small gene network will adapt more rapidly but reacha less than optimal endpoint, whereas a trait controlled by a largegene network will evolve more slowly but more accurately.", + "Network analysesWe now have two QTL, and we have picked potentially interesting genes within each, but nowwe want to build up more evidence for which gene in our QTL interval is causal. The first, andmost obvious way, is to see what genes our trait of interest correlates with, in tissues that weexpect to be related to the trait. We calculated the Spearmans correlation between the traitBXD_17850 and all probes with expression data in T helper cells (GN319).", + "We [16,18], and others [19,20] have indicated that the combined use of gene expression datatogether with QTL (quantitative trait locus) analysis canprovide for a better understanding of the genetics of complex traits.", + "These relationships provide important information forbiologists to understand and search for the genetic basis ofeQTL. An eQTL can span physically a large genomicregion, depending on the mapping experimental design. Due to the limitations of linkage studies it is difficult topin down which gene within an eQTL is the source ofeTrait variation [20]. By relating eTraits and genetic markers to their corresponding genes, our eQTL Viewer organizes each eQTL as a list of pairwise relationships betweenan eTrait gene and the multiple candidate genes in theeQTL region.", + "On the onehand, the genomic location that are in suspicion to be involved in the trait can still involvelarge genomic segments, e.g. , millions of basepairs that include many genes within the segment. On the other hand, GWAS may point toseveral or even many genomic locations for thetrait of interest, complicating further functionalanalysis. Analysis of Quantitative Trait Loci (QTL)QTL analysis reveals statistically signicantlinkage between phenotypes and genotypes,thereby providing explanation for the geneticbasis of variation in complex traits (Falconerand Mackay, 1996; Lynch and Walsh, 1998).", + "It is now widely appreciated that even when an association can be localized to a singlegene, that gene may not be the cause of the association [Smemo-2014], meaning that proximity tothe peak SNP is not sufficient to identify the causal gene. Therefore, a major goal of our study was tointegrate behavioral QTL and eQTL data. eQTLs can provide the crucial link between a regionimplicated by GWAS and the biological processes that underlie that association. We exploited theeasy access to tissue, which is a critical advantage of model organisms, to map eQTLs.Theseexamples illustrate the utility of combining GWAS with eQTL data to identify the molecularmechanism by which a chromosomal region influences a complex trait. DiscussionWe performed a GWAS in a commercially available outbred mouse population, which identifiednumerous physiological, behavioral, and expression QTLs. In several cases the implicated loci weresmaller than 1 Mb and contained just a handful of genes that included an obvious candidate. Inaddition, we used the eQTL results to further parse among the genes in the intervals that wereimplicated in the behavioral traits.", + "The remarkable success in mappinggenes linked to a number of disease traits using genomewide association studies (GWAS) in human cohorts hasrenewed interest in applying this same technique in modelorganisms such as inbred laboratory mice (Su et al. 2010). Unlike classical phenotypic traits, gene expression traitsgiving rise to cis-acting eQTL provide us with a prioriknowledge of the true QTL location (Doss et al. 2005),which can be used to empirically estimate the power of aGWAS performed at a similar scale (Hao et al. 2008;Schadt et al. 2008).", + "Genomic regions linked to complex traits can be identified by genetic mappingand quantitative trait locus (QTL) analysis (Shehzad and Okuno 2014). 7QTL mappingQTL mapping with molecular markers is the first strategy in genetic studies. In plantbreeding, QTL mapping is an essential step required for marker-assisted selection(Mohan et al. 1997; Shehzad and Okuno 2014). The fundamental idea underlying QTLanalysis is to associate genotype and phenotype in a population exhibiting a geneticvariation (Broman and Sen 2009).", + "QTL mapping studies thenseek to detect the polymorphisms underlying the complex traits of interest byscanning for alleles that co-vary withthe traits. Similar experiments also can be conducted with special derivatives of inbredstrains known as recombinant inbred(RI) mice. These animals are derivedby cross-breeding two or more distinctparental strains (which often divergewidely for the trait of interest), followedby inbreeding of the offspring for severalgenerations (Bailey 1971). Given thecorrect breeding strategy, this method1This is an issue faced by GWASs researchers when classifyingsamples as cases or controls.The investigatorsfirst identified all QTLs associated witha classical phenotype and then winnowed the list of potentially associatedgene-expression traits on the basis oftheir correlation or eQTL overlap withthe phenotype of interest. Candidategenes then were ranked by applyingthe LCMS technique, which uses theeQTL data to establish causal relationships between DNA loci and transcripts as well as between transcriptsand phenotypes and finally identifiesa model that best fits the data." + ] + ], + "task_id": [ + "44B088326CD80B4980D810738D88A284", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B" + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/old/aging1_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/aging1_dataset.json new file mode 100644 index 0000000..f5b979c --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/aging1_dataset.json @@ -0,0 +1,128 @@ +{ + "question": [ + "List as many studies are you can that include rapamycin", + "Why is it so difficult to map gene loci that control aging in humans?", + "How would one evaluate a candidate gene for mouse lifespan?", + "Do we find significant quantitative trait loci in aging studies, if so give the top three examples with descriptions?", + "What are the most fundamental human life expectancy extending geroscience studies?" + ], + "answer": [ + "1. A study assessing the impact of a rapamycin derivative on healthy aging in people (Mannick et al. 2014). 2. A veterinary clinical trial testing rapamycin as a treatment for osteosarcoma (Paoloni et al. 2010). 3. A veterinary clinical trial assessing the side effect profile and effects on age-associated cardiac function following 10 weeks of rapamycin treatment in healthy elderly dogs (Kaeberlein 2015). 4. A study on the pharmacokinetic analysis of rapamycin treatment in healthy dogs (Larson et al. 2016). 5. A study on the effects of rapamycin in aging mice (Johnson et al. 2015). 6. A study on the effects of rapamycin in rodents (Johnson et al. 2013). 7. A study on the effects of rapamycin on the replicative life of yeast (Reference [57]). 8. A study on the effects of rapamycin in 20-month-old mice (Reference [58]). 9. A study on the combination of rapamycin with other compounds for anti-aging activity (Reference [59]).", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and their interactions with each other and the environment. Secondly, the effects of common genetic variations on longevity are small, requiring large studies for identification. Thirdly, human studies often face issues such as environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of social, economic, and cultural factors, which are unique to humans, can't be replicated in traditional model organisms, adding to the complexity.", + "One would evaluate a candidate gene for mouse lifespan by conducting gene mapping methods and using mouse life span as a surrogate for aging. This involves studying the genetic contributions to mortality rates, conducting sex-specific analyses, and determining any changes in pathology associated with the loci. The study would involve a large population of mice, and the results would be compared to Mendelian expectations. Additionally, epigenetic predictors and differential methylation analysis could be used to assess the relationship between the gene and lifespan. The gene's influence on specific late-life diseases and its effect on the risk of these diseases would also be evaluated.", + "Yes, significant quantitative trait loci (QTLs) have been found in aging studies. Three examples include: 1. Lfdr1: This QTL was found on chromosome 7 and affects lifespan and fertility in response to dietary restriction. It also has suggestive effects on fuel efficiency (FE). 2. Fedr1 and Fedr2: These QTLs were identified on chromosomes 9 and 15 respectively. They have significant effects on fuel efficiency in response to dietary restriction. 3. QTLs associated with age-related thymic involution: These were identified in a study using C57BL/6 J X DBA/2 J (BXD) recombinant inbred (RI) strains of mice.", + "The most fundamental human life expectancy extending geroscience studies include genetic and genomic research into longevity. These studies use both hypothesis-driven candidate gene approaches and explorative genome-wide analyses. They also involve the study of biomarkers related to aging, deep phenotyping, and the use of novel technologies like next-generation sequencing. Additionally, they examine the role of environmental factors, lifestyle, and heritability in human lifespan variation." + ], + "contexts": [ + [ + "Intervention trials and cell-based monotherapy", + "Rapamycin has been shown to be well tolerated in dogs, improves outcome in a glycogen storage disease model (Yi et al. 2014), and is currently being tested in veterinary clinical trials as a treatment for osteosarcoma (Paoloni et al. 2010).A veterinary clinical trial is underway to assess the side effect profile and effects on age-associated cardiac function following 10 weeks of rapamycin treatment in healthy elderly dogs (Kaeberlein 2015).", + "Rapamycin is used clinically to prevent organ transplant rejection, for some forms of cancer, and to prevent restenosis in cardiac stents (Kaeberlein 2013b).Shortterm treatment with the rapamycin derivative RAD001 improves ageassociated decline in immune function, as measured by antibody response to an influenza vaccine, in healthy elderly people (Mannick et al. 2014).", + "To date, only one study has been performed assessing the impact of a rapamycin derivative on healthy aging in people.In this trial, it was observed that 6 weeks of treatment with the rapamycin derivative RAD001 (everolimus) was sufficient to enhance function of the aged immune system, as assessed by response to an influenza vaccine (Mannick et al. 2014).This recapitulates what was observed in elderly mice (Chen et al. 2009), and suggests that at least some of the mechanisms by which rapamycin delays aging in mice work similarly in humans.Although both compounds have essentially identical biological activities, RAD001 was used in this study instead of rapamycin because the study was funded by Novartis, who holds the patent rights for RAD001 (rapamycin is now off patent and sold as a generic drug).The doses of RAD001 used in the human immune aging study were lower than those typically used to prevent organ transplant rejection and showed improved side effect profiles, although some adverse effects, including the presence of mouth sores in a subset of the patients, were noted.", + "This trial is designed to determine whether treatment with the drug rapamycin (see Table 1) can significantly reduce age-related disease and disability as well as mortality in middle-aged large dogs.The initial phase of this trial, which is in progress at the time of this writing, is intended to enroll at least 32 dogs 6 years of age or older and 40 lb in weight or greater.Each animal receives an initial veterinary exam and comprehensive blood work along with a cardiac exam including echocardiography (Fig. 3).Those dogs that do not present with any abnormalities or significant pre-existing health conditions are randomized into either placebo or rapamycin treatment groups for a 10-week treatment period.Initial rapamycin dosing regimens were determined, in part, based on pharmacokinetic analysis of rapamycin treatment in healthy dogs (Larson et al. 2016).After 10 weeks in the study, each dog receives another full exam and blood chemistry panel as well as repeat cardiac exam.The primary goals of this first phase are to establish appropriate dosing of rapamycin in the absence of significant adverse events and to determine whether similar improvements in cardiac function are achieved in aged dogs after 10 weeks of rapamycin treatment, as has been observed in laboratory mice (Dai et al. 2014;Flynn et al. 2013).", + "Fig. 3 Design of the current short-term rapamycin intervention trial.Dogs must weigh at least 40 pounds and be at least 6 years old at time of entry into the study.If no significant pre-existing health conditions are detected at the first exam, dogs are randomized into either placebo or one of the rapamycin treatment groups.Red indicates the 10-week period during which the dogs receive either rapamycin or placebo.Dogs receive the same generic rapamycin (sirolimus) pill that is provided to human patients.Asterisk Serum and feces are collected at each appointment for future metabolomic and microbiome analyses and for quantitation of circulating rapamycin levels", + "Pending the outcome of phase 1, we anticipate enrolling several hundred additional dogs with similar entry criteria into a longer-term, 3-5 year study, to carefully assess the extent to which rapamycin improves health and reduces mortality in middle-age companion dogs.In addition to cardiac function, assessments of multiple age-related phenotypes will be performed including measures of cognitive function, muscle function, kidney function, glucose homeostasis, and cancer incidence.Many of these parameters are beneficially impacted by rapamycin in aging mice (Johnson et al. 2015), and we predict that rapamycin will induce similar improvements in aging dogs.", + "Rapamycin is currently the most effective pharmacological intervention for extending lifespan and delaying a broad range of age-related functional declines in rodents (Johnson et al. 2013).However, the doses used clinically to prevent organ transplant rejection are associated with side effects, such as impaired wound healing, edema, elevated circulating triglycerides, impaired glucose homeostasis, gastrointestinal discomfort, and mouth ulcers (Augustine et al. 2007;de Oliveira et al. 2011).These adverse side effects would likely preclude long-term use of rapamycin at these levels in otherwise healthy people.With the possible exception of impaired glucose homeostasis (Lamming et al. 2012), these side effects have not been observed at doses that are associated with increased lifespan and healthspan in mice, however, raising the possibility that lower doses of this drug could promote healthy aging with minimal adverse effects.", + "Rapamycin Rapamycin is a macrolide isolated from Streptomyces hygroscopicus, a bacteria from Pascua Island (Rapa Nui).It has functions as an antibiotic, an immune suppressant drug, and it is also proposed as a CRM.After the first studies, it was found that rapamycin could induce the extension of the replicative life of yeast through the inhibition of TOR signaling [57].This compound could extend the lifetime useful in 20-month-old mice in correlation with TOR activity [58].These studies were the basis of the research to determine the function of rapamycin as a CRM, due to its modulating properties over proteostasis.In addition, studies suggest that rapamycin can be combined with other compounds (metformin, losartan, statins, propranolol, and aspirin among others) to potentiate their anti-aging activity [59].", + "Rapamycin Rapamycin is a macrolide isolated from Streptomyces hygroscopicus, a bacteria from Pascua Island (Rapa Nui).It has functions as an antibiotic, an immune suppressant drug, and it is also proposed as a CRM.After the first studies, it was found that rapamycin could induce the extension of the replicative life of yeast through the inhibition of TOR signaling [57].This compound could extend the lifetime useful in 20-month-old mice in correlation with TOR activity [58].These studies were the basis of the research to determine the function of rapamycin as a CRM, due to its modulating properties over proteostasis.In addition, studies suggest that rapamycin can be combined with other compounds (metformin, losartan, statins, propranolol, and aspirin among others) to potentiate their anti-aging activity [59].", + "One out of the 25 FDA approved Breast cancer drugs (Gemcitabine), was found in the top 20 drug list from LINCS from breast cancer stage I (dark magenta). As shown in Fig. 12, one drug out of 25 FDA approved Breast cancer drugs, Gemcitabine, was found as repurposed drug from LINCS for breast cancer stage III. Letrozole (Breast cancer drug) has similar structure (greater than 60%) with Ruxolitinib (repurposed drug from LINCS) a drug for the treatment of intermediate or high-risk myelofibrosis (Fig. 13).", + "One out of the 25 FDA approved Breast cancer drugs (Palbociclib), was found in the top 20 drug list from LINCS from breast cancer stage II (deep pink). Scientific Reports | 6:20518 | DOI: 10.1038/srep20518 13 www.nature.com/scientificreports/ Figure 11. Highlighted target genes that physically interact with genes from the breast cancer stage II common network pattern and their corresponding repurposed drugs from LINCS, along with their structurally similar Breast cancer drugs. As shown in Figs 16\u201317 two target genes (TOP2A and TYMS) are also involved in the Triple Negative pattern.", + "Two of them (Gemcitabine and Palbociclib) are included in the list of the 25 known FDA-approved Breast cancer therapeutic drugs. We performed a Hypergeometric distribution test in order to find the statistical significance of this drug overlapping. More precisely, LINCS_L1000 database is comprised from 20,413 chemical reagents. Twenty two out of twenty five breast cancer drugs are also included in LINCS database. Finally, from the 105 drugs that were found from our analysis, the probability of finding two drugs to overlap with the Breast Cancer drugs in LINCS is 0.005471157, pointing out that there is statistical significance in their selection.", + "Two from the 25 FDA approved Breast cancer drugs (Gemcitabine and Palbociclib), was found in the top 20 drug list from LINCS from Luminal A breast cancer (dark magenta and deep pink respectively).", + "18 two drugs out of 25 FDA approved Breast cancer drugs \u2013 Gemcitabine and Palbociclib \u2013 were also found as repurposed drugs from LINCS for breast cancer Luminal A (Fig. 18). Two genes from the Luminal A network pattern physically interact with four genes that involved in Histone deacetylases class (HDAC1, HDAC2, HDAC3 and HDAC8), which are target genes of Vorinostat (repurposed drug from LINCS). Vorinostat is a member of a larger class of compounds that inhibit histone deacetylases (HDAC) and it is used to treat cutaneous T cell lymphoma (CTCL).", + "One out of the 25 FDA approved Breast cancer drugs (Gemcitabine), was found in the top 20 drug list from LINCS from breast cancer stage III (dark magenta). that was found from the drug repurposing analysis of HER2 pattern. It has similar structure - 75% with WZ-4002 repurposed drug, which is a novel mutant-selective inhibitor of EGFR. Finally, both Palbociclib and WZ-4002 are structurally similar to Dasatinib (more than 60%), which is a cancer drug used to treat acute lymphoblastic leukemia.", + "Network pattern for each breast cancer subtype and the common interactions across Luminal A and Luminal B. As shown in Fig. 8, one drug out of 25 FDA approved Breast cancer drugs, Gemcitabine, was proposed as repurposed drug by the LINCS for breast cancer stage I. Furthermore, Gemcitabine is quite similar (tanimoto31 similarity greater than 80%) with Clofarabine and Kinetin-riboside (repurposed drugs from LINCS). Clofarabine is also an anti-cancer, antineoplastic chemotherapy drug and is classified as an antimetabolite.", + "Hierarchical clustering using tanimoto similarity (Soergel distance) was applied to each of the top 20 drug list from LINCS and the 25 known FDA-approved Breast cancer therapeutic drugs (Supplementary Figs 54\u201361). LINCS Drug Names were transformed into ChemSpider IDs (see Supplementary Table 1) In synopsis, the unique drugs for the breast cancer stages were 63 and for the breast cancer subtypes 58, as we have located common drugs across them. Taking their union and removing the duplicates we conclude to a total of 105 repurposed drugs.", + "13, is also structurally similar (greater than 60%) with 6-(1,3-Benzodioxol-5-yl)-N-(cyclopentylmethyl)-4-quinazolinamine (repurposed small molecule from LINCS). As in breast cancer stages I and III one drug out of 25 FDA approved Breast cancer drugs \u2013 Gemcitabine \u2013 was found as repurposed drug from LINCS for breast cancer stage IV (Fig. 14). A repurposed drug from LINCS \u2013 Homoharringtonine was found to be structurally similar with Everolimus and Vinblastine Breast cancer drugs (greater than 70%). On the other hand, as shown in Fig.", + "Rapamycin has serious side effects, particularly as an immunosuppressor, and thus it is not suitable as an antiaging drug.As in sirtuins, however, these studies highlight the road from basic discovery on the biology of aging to antiaging interventions.Further studies of the TOR pathway and of repressors more specific of its downstream signaling pathway are ongoing.Whether rapamycin produces a change in another parameter related to energy uptake or utilization is unknown, and determining which of its effects modulate lifespan is an important unsolved question.Like resveratrol, TOR has attracted considerable attention from the pharmaceutical industry, particularly in the context of cancer (Meric-Bernstam and Gonzalez-Angulo, 2009)." + ], + [ + "FUTURE DIRECTIONS: HIGHER RESOLUTION DATA VIA HIGHER THROUGHPUT ASSAYS One inescapable conclusion of the aggregate results of genome-wide studies of aging to date (see summary Table 1) is that we have not come close to saturating the number of potentially lifespan-altering genes in any organism.This is in no small part because directly generating survival curves is a relatively time-consuming process in most model organisms using current methods.There are several possible ways to address this.One way that has been tried is by attempting to find surrogate phenotypes [72,73,126] that can be screened more rapidly, or even scored under selection.Another is mining candidates from the many whole-genome expression profiles.Results to date with these have been very fruitful, but have not suggested that these methods alone will rapidly saturate our search for lifespan-and healthspan-altering genes in tractable model organisms.", + "Genetic linkage studies of long-lived human families identified a longevity locus while candidate gene approaches have been used to identify and confirm the association between specific variants in the FOXO3A gene and human longevity [3\u20137]. Genome-wide association studies have also been used to identify the association of APOE with life 123 Aging Clin Exp Res span and have yielded insights into potential biological pathways and processes related to aging. Despite these successes, several problems are inherent in human longevity studies including potentially high degrees of environmental heterogeneity, genetic diversity, and lack of birth matched controls, among others [8].", + "Additional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process.", + "The aging process most certainly is under highly polygenic controls\u2026 This should not discourage us from pursuing a search for those loci which may be of profound importance to human aging as it ordinarily occurs in most human beings.", + "In most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes.", + "1993), and gene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not provide very useful evidence with respect to the question of the number of loci that affect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of evolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now amenable to the application of genomic methods.", + "Accepted Article \u00a9 2013 The Authors Aging Cell \u00a9 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland over 90 years and 1,955 controls between 55 and 80 years did not reveal genome-wide significant loci (Newman et al., 2010) and neither did the analyses of all-cause mortality and survival free of major disease in this cohort (Walter et al., 2011).A smaller Dutch study of 403 nonagenarians and 1,670 controls younger than 65 years identified the APOE gene as a mortality locus (Deelen et al., 2011), which was confirmed in a German study of 763 long-lived individuals and 1,085 younger controls (Nebel et al., 2011) and a longitudinal study of 1,606 Danes showed that the effect size of this association increases at the highest ages (Jacobsen et al., 2010).Apparently, the influence of the common genetic variation on longevity is small which requires large meta-GWA studies for identification.Alternatively, rare genetic variants may play a more important role in longevity.Since the previous linkage studies showed contradictory results potentially due to heterogeneity in the longevity phenotype, it is expected that longevity is influenced by many private rare variants.", + "The lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010).", + "Several explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect.", + "Second, the largely negative findings of this and other studies contrast with the intriguing animal studies of longevity.Very large effects of single genes on lifespan have indeed been observed in laboratory animals, but humans often have several homologues of these genes which might significantly differ in function or compensate for mutated genes through redundant mechanisms (Kuningas et al., 2008).This could explain why our top findings did not include genes in these pathways found in animal models.Animal models also represent genetically homogenous populations and are exposed to controlled environmental influences.The lack of replication of animal model findings in humans suggests that the use of knockout animals may not provide the optimal approach to understanding the variation in survival in humans as interactions with environmental factors may obscure the associations and prevent the identification of loci in humans.", + "1993), and gene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not provide very useful evidence with respect to the question of the number of loci that affect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of evolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now amenable to the application of genomic methods.", + "The remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha \u02dces et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design.", + "Results from mutational analysis across eukaryote model organisms have shown unexpected conservation of genes and processes regulating aging.While unique properties exist within particular organisms that modulate these foundational networks, the conservation provides a tool to refine human genetic studies.As noted, GWAS for human longevity metrics suffer from large sample size requirements to obtain statistical resolution due to multiple hypothesis testing across the genome.Assuming that evolutionary genesets for longevity could be generated with confidence, an intersection of them with human variation data would increase the sensitivity of association studies.This would serve as a selective filter to refine the number of loci investigated for association in human populations.Similarly, such evolutionary filters could refine analysis of rare, unique variation within genome sequence data from extremely long-lived cohorts.A similar approach to refine human longevity GWAS used an intersection with age-related disease datasets.This 'disease-informed' GWAS helped refine candidates (iGWAS, Fortney et al., 2015), though, it should be noted that this particular strategy would further blur the distinction between aging and longevity as discussed above.The definition of gene sets from evolutionary experiments in longevity, across clades, would similarly empower detection of networks previously hidden under GWAS in human population analyses (Figure 3).", + "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY Heritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE \u03b52), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans.", + "TRANSLATION OF LONGEVITY MODEL ORGANISMS AND CORE AGING PATHWAYS Genetic studies on lifespan have proven to be challenging.While longevity is a defining trait for a given species, the lifespan of individuals is of limited heritability, making analyses more difficult.Exceptional human life span, although a rare phenotype, is likely multifactorial; refined analyses are required to obtain statistically robust genomic signatures of longevity (Zhang et al., 2020) and these have proven elusive.Unlike laboratory models, the effect of environmental variance cannot be controlled in human studies, potentially masking purely biological aging mechanisms.Even laboratory models cannot replicate the complex \"environment\" of humans; it includes psychosocial, economic, and cultural factors, rather than strictly biological.These human-specific confounders are difficult or impossible to target in traditional model organisms.Despite these limitations, experimentally tractable model organisms have proven invaluable in deciphering the purely genetic contribution to lifespan, including genes and pathways conserved across the tree of life.", + "Our analyses show that it is extremely unlikely that there is a single gene harboring rare protein-altering variants shared by all supercentenarians but no controls.It is not surprising that a highly complex trait such as longevity is not explained by a single Mendelian gene.", + "With modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms.", + "Although the models data set comprises all genes (to our knowledge) shown by the time of the latest update to statistically increase longevity or alter the aging process in a noticeable way, in the human data set we try to evaluate whether a given intervention is affecting the aging process itself or not.For example, many mutations may increase longevity by decreasing the incidence of specific diseases, rather than by altering the basic process of aging (de Magalh\u00e3es et al ., 2005a(de Magalh\u00e3es et al ., , 2005b)).Therefore, the human data set is not merely an extension of the work conducted in model organisms and of its bibliography, but a manually selected list of the most pertinent human aging candidate genes, each presented with a higher annotation level.We cite studies on whether the functions of aging-associated genes in model organisms are conserved in their human orthologues.Likewise, we cite flaws in previous studies based on new published observations, although we have a neutral stance on conflicting findings from different research groups.Our policy is to cite all conflicting reports and let visitors make their own decisions on how to interpret them.By contrast, each entry in GenAge model organisms has only one reference: the first publication reporting an association of the gene with longevity or aging.Moreover, one of the latest enhancements in the human data set was the inclusion of Gene Ontology annotation.Gene Ontology terms and annotation files were obtained from the Gene Ontology Consortium website (http://www.geneontology.org/ ) and provide an additional layer of description for the gene products in a cellular context (Ashburner et al ., 2000).", + "Conclusions and prospects Over the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges.", + "Most of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + ], + [ + "Funding: See page 22 Preprinted: 24 June 2021 Received: 03 November 2021 Accepted: 01 April 2022 Published: 07 April 2022 Reviewing Editor: Joris Deelen, Max Planck Institute for Biology of Ageing, Germany \u200d \u200dCopyright Mozhui et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. Editor's evaluation This article used three newly generated epigenetic predictors to test how they differ between genetically diverse mice from the BXD family (by looking at metabolic traits and lifespan).", + "Longevity data was obtained from a parallel cohort of BXD mice housed in the same UTHSC colony, and members of this \u2018longevity cohort\u2019 were allowed to age until natural death (more detail on the longevity cohort can be found in Roy et al. , 2021). Males were excluded and strain-\u00adby-\u00addiet lifespan summary statistics were derived. Only strain-\u00adby-\u00addiet groups with five or more observations for lifespan were included in the correlational analyses with the epigenetic predictors. Multivariable EWAS Site-\u00adby-\u00adsite differential methylation analysis (EWAS) was performed on the 27,966 CpGs using a multivariable regression model.", + "Funding: See page 22 Preprinted: 24 June 2021 Received: 03 November 2021 Accepted: 01 April 2022 Published: 07 April 2022 Reviewing Editor: Joris Deelen, Max Planck Institute for Biology of Ageing, Germany \u200d \u200dCopyright Mozhui et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. Editor's evaluation This article used three newly generated epigenetic predictors to test how they differ between genetically diverse mice from the BXD family (by looking at metabolic traits and lifespan).", + "Longevity data was obtained from a parallel cohort of BXD mice housed in the same UTHSC colony, and members of this \u2018longevity cohort\u2019 were allowed to age until natural death (more detail on the longevity cohort can be found in Roy et al. , 2021). Males were excluded and strain-\u00adby-\u00addiet lifespan summary statistics were derived. Only strain-\u00adby-\u00addiet groups with five or more observations for lifespan were included in the correlational analyses with the epigenetic predictors. Multivariable EWAS Site-\u00adby-\u00adsite differential methylation analysis (EWAS) was performed on the 27,966 CpGs using a multivariable regression model.", + "Conclusions These results suggest a novel locus influencing survival in the B6/D2 genetic background, perhaps via a metabolic disorder that emerges by 200 days of age in male animals. Keywords Pathology Longevity \u2401 Lifespan \u2401 Mouse \u2401 Linkage \u2401 Introduction Longevity, the quintessential complex trait, likely reflects all aspects of an organism\u2019s life history. In humans, the estimated heritability of age at death is estimated at 25\u201333 % [1]. Genetic contributions to mortality rates are thus of great interest and may aid in the understanding of disease etiology and the process of aging itself [2].", + "Leduc MS, Hageman RS, Meng Q et al (2010) Identification of genetic determinants of IGF-1 levels and longevity among mouse inbred strains. Aging Cell 9(5):823\u2013836. doi:10.1111/j.14749726.2010.00612.x 10. Lang DH, Gerhard GS, Griffith JW et al (2010) Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clin Exp Res 22(1):8\u201319 11. Gelman R, Watson A, Bronson R et al (1988) Murine chromosomal regions correlated with longevity. Genetics 118(4):693\u2013704 12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci associated with life span exhibit sex-specific and epistatic effects.", + "Here, we have extended this analysis to search for genotypes related to survival to the age of 800 days in a population of a reciprocal F2 cross between (B6) and (D2) mice. Since QTL for longevity in mice have shown strong sex specificity [10, 12], we conducted sex-specific analyses. In addition, we also determined whether there were any change in pathology changes associated with the loci that showed frequency distortions with aging. To confirm the associations of the loci of interest with longevity and pathology, we performed replication analyses on a panel of BXD recombinant inbred strains.", + "Methods We examined a population of 1200 mice that were F2 generation offspring of a 4-way reciprocal cross between C57BL6/J and DBA2/J strains. Animals were sacrificed at age 200, 500, or 800 days and genotyped at 96 markers. The 800 days old cohort, which were the survivors of a much larger breeding group, were examined for enriched frequency of alleles that benefit survival and depletion of alleles that reduce survival. Results Loci on Chr 13 in males and on Chr X in females were significantly distorted from Mendelian expectations, even after conservative correction for multiple testing.", + "Assessing epigenetic age in long-lived mice The epigenetic-aging model was applied to the methylation profiles of long-lived mice and the age-matched controls not used for training (Additional file 2: Datasets used summary).Reductions in age were calculated by subtracting the epigenetic ages of the untreated, wild-type mice from those of the treated mice of the same genetic background.To assess the significance, we used an ANOVA for all 22-month-old mice or only 22-month-old UM-HET3 mice.We also compared the epigenetic ages between treatments with their agematched controls from the same genetic background using a t-test (Additional file 4: Treatment vs wild type stats).", + "Editor's evaluation This article used three newly generated epigenetic predictors to test how they differ between genetically diverse mice from the BXD family (by looking at metabolic traits and lifespan).The authors subsequently identified several quantitative trait loci for the different predictors, using linkage analysis, and performed transcriptome and proteome analyses of liver and adipose tissue.The described results provide some important new insights on the underlying biology of epigenetic mouse aging and may be used to inform future studies in other model organisms and humans focused on studying the relationship between epigenetic aging and metabolism.", + "352(6291): p. aad0189. Liao, C.Y. , et al. , Genetic variation in the murine lifespan response to dietary restriction: from life extension to life shortening. Aging Cell, 2010. 9(1): p. 92-5. Johnson, M., Laboratory Mice and Rats. Mater. Methods, 2012. 2: p. 113. Fontaine, D.A. and D.B. Davis, Attention to Background Strain Is Essential for Metabolic Research: C57BL/6 and the International Knockout Mouse Consortium. Diabetes, 2016. 65(1): p. 25-33. Simon, M.M. , et al. , A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol, 2013. 14(7): p. R82. Lilue, J., et al.", + "Materials and Methods Study Design.Female mice of the long-lived F 1 hybrid strain C3B10RF1 were fed and maintained as described (7).Briefly, mice were weaned at 28 days, individually housed, given free access to water, and randomly assigned to study groups.Comparisons between five groups of mice were used to determine the effects of aging and CR on gene expression.Control young (7-month-old; n \u03ed 3) and old (27-month-old; n \u03ed 3) mice were fed 95 kcal of a semipurified control diet (Harlan Teklad, Madison, WI; no.TD94145) per week after weaning.Long-term CR (LT-CR) young (7-month-old; n \u03ed 3) and old (27-month-old; n \u03ed 3) mice were fed 53 kcal of a semipurified CR diet (Harlan Teklad; no.TD94146) per week after weaning.Short-term CR (ST-CR) mice were 34-monthold control mice that were switched to 80 kcal of CR diet for 2 weeks, followed by 53 kcal for 2 weeks (n \u03ed 3).The effects of age on gene expression in control mice were determined by comparison between results from the young control and the old control groups.The effects of LT-CR on gene expression were determined by comparison between results from the young control and the young LT-CR groups, and from the old control and the old LT-CR groups.The effects of ST-CR were determined by comparison between results from the old control and the ST-CR groups.Mice were fasted for 48 h before killing.Mice were killed by cervical dislocation, and the livers were rapidly excised and flash frozen in liquid nitrogen.No signs of pathology were detected in any of the animals used.All animal use protocols were approved by the institutional animal use committee of the University of California, Riverside.", + "Accessing data resources in the mouse phenome database for genetic analysis of murine life span and health span. J. Gerontol. A Biol. Sci. Med. Sci. 71 (2), 170\u2013177. Brown, R.E. , Stanford, L., Schellinck, H.M., 2000. Developing standardized behavioral tests for knockout and mutant mice. ILAR J. 41 (3), 163\u2013174. Bubier, J.A. , Jay, J.J., Baker, C.L. , Bergeson, S.E. , Ohno, H., Metten, P., Crabbe, J.C., Chesler, E.J. , 2014. Identi\ufb01cation of a QTL in Mus musculus for alcohol preference, withdrawal, and Ap3m2 expression using integrative functional genomics and precision genetics. Genetics 197 (4), 1377\u20131393. Burn, C.C. , 2008.", + "Our own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice.", + "The available dataset also provides examples in which genetic variants seem to influence the risk of specific late-life diseases.Figure 8-6, for example, shows longevity results for mice stratified by their inheritance at the 12th chromosome locus D12Mit167.This is a locus associated with differential longevity in both male and female mice, with the strongest effect (adjusted p < 0.01) seen in those mice living more than 657 days (Jackson et al., unpublished results).The longest-lived mice are those that inherit both the C57BL/6 allele from their mother and the C3H allele from their father; on average, they survive 93 days longer than siblings with the BALB plus C3H combination.Figure 8-6 shows that the D12Mit167, like the pair of loci illustrated in Figure 8-5, has significant and similar effects in mice dying of cancer (85 days) and in mice dying of non-neoplastic diseases (126 days).A more detailed analysis of the cancers, however, suggests that while lymphoma and hepatoma victims are equally protected by the favorable alleles (effect sizes of 93 and 167 days, respec- mice of two subgroups: those dying of the urinary syndrome MUS, and those dying of all other causes.The genetic analysis contrasts mice with both the C57BL/6 allele at D4Mit84 and the C3H allele at D9Mit110 to mice with any of the three other allele combinations.In the males dying of causes other than MUS, this allele pair is associated with a 170-day increment in longevity (post-hoc p < 0.00003).But for males that do die of MUS, the same allele combination is associated with a 187-day decline in mean life span (post-hoc p < 0.03).This effect is thus pleiotropic, in that these alleles accelerate death in mice susceptible to MUS, while postponing death for all other males in the population.Although these loci are associated with differential longevity in mice that do develop MUS, they do not have a significant effect on the chances that MUS will indeed occur (not shown).The risk of developing MUS seems to be under control of a separate locus on chromosome 6.As shown in the bottom panel of Figure 8-7, males that inherit the C3H allele at D6Mit268 are far more likely to develop MUS (28 percent risk) than are their brothers who receive the DBA/2 allele at this locus (7 percent risk; p = 0.012 by two-tailed Fisher's exact test).", + "Previously, the methylation status of CpG sites within the genes Prima1, Hsf4, Kcns1 was shown to qualify as a reliable predictor of chronological age of B6 mice.10 This same study also revealed enhanced epigenetic aging of the D2 strain in accordance with its general reduced mean life span, supporting the possibility that the panel might also serve as a marker for the biological age in mice. Applying this B6trained marker panel to our (congenic) experimental strains, we observed that epigenetic age predictions correlated with chronological age in B6 (R2=0.93) and line A mice (R2=0.89).", + "34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with longevity. Genetics 118, 693\u2013704 (1988). [PubMed: 3163317] 35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011). 36. Houtkooper RHet al.Mitonuclear protein imbalance as a conserved longevity mechanism. Nature497, 451\u2013457 (2013). [PubMed: 23698443] 37. Williams EGet al.An Evolutionarily conserved role for the aryl hydrocarbon receptor in the regulation of movement. PLOS Genet. 10, e1004673 (2014). [PubMed: 25255223] 38. Lang DHet al.Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clin. Exp. Res. 22, 8\u201319 (2010).", + "For females, hairs of the congenic mice grew 31% faster, also highly significant (P = 0.0006, 1-tailed). These results validated the presence of a gene in the differential region affecting FE. Discussion We report the outcomes of a quantitative genetic study on aging and longevity in the mouse. We studied an extant series of recombinant inbred strains (ILSXISS) that have been used both in DR aging studies as well as to study alcohol sensitivity (Williams et al. , 2004).", + "FOURTH STEP: MEDICAL TESTING OF CANDIDATE DRUGS Many genes are common between fruit flies and mammals, but by no means all.Therefore, it is important to test biochemical pathways that work in fruit flies with mammals.Mice are the system of choice, as they have relatively short lifespans (2 -3 years) and a great deal is known of their genetics.Mortality rate measurements, like those studied in fruit flies, [10] might speed up mouse trials to just 6-12 months.Mouse trials would also help address issues of safety, such as liver and kidney toxicity, before going on to human trials.", + "Experimental Procedures Mouse Breeding, Maintenance, and Longevity.Cdc42GAP \u03ea/\u03ea and p53 \u03ea/\u03ea mice were generated as previously described (6,35), and the mice used in the studies were mixed C57BL/6 \u03e9/\u03ea 129/Sv inbred.Littermates of different genotypes were housed and fed freely with standard mouse chow over their life span in a pathogen-free environment and were monitored for vitality and longevity.Mice exhibiting extreme morbidity were euthanized and subjected to necropsy.All animal procedures were approved by the Institutional Animal Care and Use Committee at the Children's Hospital Research Foundation." + ], + [ + "Genetic associations for two biological age measures point to distinct aging phenotypes. Aging Cell 20:e13376. DOI: https://doi.org/10.1111/acel.13376, PMID: 34038024 Lang DH, Gerhard GS, Griffith JW, Vogler GP, Vandenbergh DJ, Blizard DA, Stout JT, Lakoski JM, McClearn GE. 2010. Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clinical and Experimental Research 22:8\u201319. DOI: https://doi.org/10.1007/BF03324809, PMID: 20305363 Lappalainen T. 2015. Functional genomics bridges the gap between quantitative genetics and molecular biology. Genome Research 25:1427\u20131431.", + "Pharmacol Biochem Behav 81, 764\u2013768. Hsu, H.C., Lu, L., Yi, N., Van Zant, G., Williams, R.W. & Mountz, J.D. (2007) Quantitative trait locus (QTL) mapping in aging systems. Methods Mol Biol 371, 321\u2013348. Hurlin, P.J. & Huang, J. (2006) The MAX-interacting transcription factor network. Semin Cancer Biol 16, 265\u2013274. Jones, B.C. , Tarantino, L.M. , Rodriguez, L.A., Reed, C.L. , McClearn, G.E. , Plomin, R. & Erwin, V.G. (1999) Quantitative-trait loci analysis of cocaine-related behaviours and neurochemistry. Pharmacogenetics 9, 607\u2013617. Jones, B.C. , Beard, J.L. , Gibson, J.N. , Unger, E.L., Allen, R.P. , McCarthy, K.A. & Earley, C.J.", + "Genetic associations for two biological age measures point to distinct aging phenotypes. Aging Cell 20:e13376. DOI: https://doi.org/10.1111/acel.13376, PMID: 34038024 Lang DH, Gerhard GS, Griffith JW, Vogler GP, Vandenbergh DJ, Blizard DA, Stout JT, Lakoski JM, McClearn GE. 2010. Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clinical and Experimental Research 22:8\u201319. DOI: https://doi.org/10.1007/BF03324809, PMID: 20305363 Lappalainen T. 2015. Functional genomics bridges the gap between quantitative genetics and molecular biology. Genome Research 25:1427\u20131431.", + "Interestingly, the correlation analysis indicates QTL Mapping in Aging Systems 333 Fig. 5. Basic statistics provided by the WebQTL GeneNetwork website. The strain distribution pattern (SDP) of the quantitative trait is presented in the basic statistics page of WebQTL in the following ways: (A) the raw data of the quantitative trait obtained from each BXD recombinant inbred (RI) strain, (B) data mean and distribution, (C) bar graph showing the mean and variable of each strain, and (D) the normal probability plot of the SDP.", + "23 Quantitative Trait Locus (QTL) Mapping in Aging Systems Hui-Chen Hsu, Lu Lu, Nengjun Yi, Gary Van Zant, Robert W. Williams, and John D. Mountz Summary Understanding the genetic basis of the effects of aging on the decline in the immune response is an enormous undertaking. The most prominent age-related change in the immune system is thymic involution. This chapter will focus on the use of C57BL/6 J X DBA/2 J (BXD) recombinant inbred (RI) strains of mice to map genetic loci associated with age-related thymic involution in mice.", + "For further prioritization, we converted the mouse QTL regions to the corresponding syntenic regions in the human genome and retrieved GWAS annotations for these intervals (Buniello et al., 2019).We specifically searched for the traits: epigenetic aging, longevity, age of menarche/menopause/puberty, Alzheimer's disease, and age-related cognitive decline and dementia.This highlighted five genes in Eaa11 and three genes in Eaa19 (Supplementary file 4c).We also identified a GWAS that found associations between variants near Myof-Cyp26a1 and human longevity (Yashin et al., 2018), and a meta-GWAS that found gene-level associations between Nkx2-3 and Cutc, and epigenetic aging (Supplementary file 4c; McCartney et al., 2021).", + "Jiang, C. and Zeng, Z. B. (1995). Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140, 1111\u20131127. Jin, W., Riley, R. M., Wolfinger, R. D.et al. (2001). The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat Genet 29, 389\u2013395. Kempermann, G., Chesler, E. J., Lu, L. et al. (2006). Natural variation and genetic covariance in adult hippocampal neurogenesis. Proc Natl Acad Sci U S A 103, 780\u2013785. Kendziorski, C. M., Chen, M., Yuan, M. et al. (2006). Statistical methods for expression quantitative trait loci (eQTL) mapping. Biometrics 62, 19\u201327.", + "Hypothesis-free genome-wide approaches have also been undertaken.Genome-wide linkage scans reported evidence for linkage with longevity on chromosome 4q25 (Puca et al., 2001), 3p24-22, 9q31-34, and12q24 (Boyden &Kunkel, 2010).However, the evidence for these loci is still very weak as the results, obtained in centenarians and their families, could not be replicated in nonagenarian sibling pairs (Beekman et al., 2006) or have yet to be tested in other studies.A meta GWAS of survival to 90 years or older in 1836 cases and 1955 controls did not find any significant genome-wide associations (Newman et al., 2010).Thus far, hypothesis-free approaches have not identified any loci involved in longevity.", + "Abiola O, Angel JM, Avner P, Bachmanov AA, Belknap JK, Bennett B, et al. The nature and identification of quantitative trait loci: a community\u2019s view. Nat Rev Genet. Nature Publishing Group; 2003; 4: 911\u2013916. https://doi.org/10.1038/nrg1206 PMID: 14634638 18. Grupe A, Germer S, Usuka J, Aud D, Belknap JK, Klein RF, et al. In silico mapping of complex diseaserelated traits in mice. Science. American Association for the Advancement of Science; 2001; 292: 1915\u20131918. https://doi.org/10.1126/science.1058889 PMID: 11397946 19. Pletcher MT, McClurg P, Batalov S, Su AI, Barnes SW, Lagler E, et al.", + "coid levels, etc.The mapping project should thus help to guide the search for human genes that regulate these interesting phenotypes and at the same time spark new investigations, in animal models, for the biochemical differences that mediate the genetic effects we detect.At the same time, the dataset that emerges should also allow us to test more general questions about the nature of aging and its genetic control.We may, for example, be able to identify QTLs that not only retard the development of one or more age-sensitive T-cell subsets, but also retard age-dependent changes in protein conformation, bone matrix turnover, and brain GFAP levels.Such a finding would imply that these changes are influenced, together, by a common biochemical pathway, and the corresponding QTLs would be excellent candidates for genes that regulate aging per se, rather than merely one among the many more agesensitive traits.In the same way, it will be of particular interest to determine if QTLs that regulate age-sensitive traits also are associated with differences in life span, and conversely if QTLs identified on the basis of longevity effects modify one (or nearly all?) of the age-sensitive traits in our test battery.", + "The strategy for mapping such quantitative trait loci (QTL) involves looking for preferential segregation of specific alleles or allele combina-tions in mice that differ in life span (or, more generally, any age-sensitive trait of interest).Our test population, called UM-HET3, consisted of a group of mice bred as the progeny of females of the (BALB/c \u00d7 C57BL/6)F1 genotype and males of the (C3H/HeJ \u00d7 DBA/2)F1 genotype.Mice bred in this way are, from a genetic perspective, all siblings; each shares a random half of its alleles with every other animal in the UM-HET3 population.The current set of analyses was conducted when genotype and longevity data were available from a group of 110 virgin males and 143 virgin females.The analytical method adjusted, by permutation testing, for Type I errors attributable to the simultaneous evaluation of multiple linkage hypotheses, and also included gender as a covariate to look for instances of sex-specific genetic effects.Because we had particular interest in regulation of late-life diseases rather than in causes of premature death, and because of evidence that genetic influences on mouse longevity were particularly strong when early deaths were not considered (Covelli et al., 1989), we repeated each analysis after exclusion of those animals dying before 657 days of age, i.e., the age at which 20 percent of the animals had already died.", + "The proportion of the phenotypic variance accounted for by the QTL yield for Hbact and Hbrear was substantial and of the same order of magnitude as that contributed by age. A small number of age-dependent QTL were found in the midst of a majority of age-stable QTL (see discussion above). These age-sensitive loci point toward genes whose functions are correlated with important behavioral changes during aging.", + "Ageing genes and pathways.Assessing the loci of interest for colocalisation with gene expression quantitative trait loci (eQTL), we find strong evidence (FDR SMR < 5%; P HEIDI > 1%; see \"Methods\") of cis-acting eQTL colocalisation for eight out of 10 loci.In total, we highlight 27 unique genes acting across 32 tissues, especially whole blood (12 genes) and the tibial nerve (7 genes) (Supplementary Data 5).In blood, higher expression levels of BCL3 and CKM (near APOE); CTC-510F12.2, ILF3, KANK2 and PDE4A (near LDLR); USP28 and ANKK1 (near ZW10); and CDKN2B are linked to an increase in multivariate ageing traits (i.e.improved survival), while the opposite is true for EXOC3L2 (near APOE), TTC12 (near ZW10), and FOXO3.For the multivariate signal near SLC4A7 we find colocalisation with expression of NEK10 (liver); for the signal near LPA we find colocalisation with expression of SLC22A1/A3 (multiple tissues) and MAP3K4 (pituitary); and for the signal near FGD6 we find colocalisation with expression of FGD6 itself (adipose/arterial).Including trans-acting eQTL from blood, while keeping the same thresholds for colocalisation, we additionally discover higher expression levels of FOXO3B colocalises with the life-extending signal near FOXO3.When we include genes which could not be tested for heterogeneity (N eQTL < 3), we identify one additional cis-acting and 49 additional trans-acting genes (of which 10 colocalise with the signal near LINC02513) (Table 2; Supplementary Data 5).", + "Jiang, C. and Zeng, Z. B. (1995). Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140, 1111\u20131127. Jin, W., Riley, R. M., Wolfinger, R. D.et al. (2001). The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat Genet 29, 389\u2013395. Kempermann, G., Chesler, E. J., Lu, L. et al. (2006). Natural variation and genetic covariance in adult hippocampal neurogenesis. Proc Natl Acad Sci U S A 103, 780\u2013785. Kendziorski, C. M., Chen, M., Yuan, M. et al. (2006). Statistical methods for expression quantitative trait loci (eQTL) mapping. Biometrics 62, 19\u201327.", + "Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL? NIH-PA Author Manuscript Much of the genetic variation that underlies disease susceptibility and morphology is complex and is governed by loci that have quantitative effects on the phenotype. Gene-gene and geneenvironment interactions are common and make these loci difficult to analyse. Here, we present a community\u2019s view on the steps that are necessary to identify genetic loci that govern quantitative traits, along with a set of interpretive guidelines.", + "QTL Analysis in Hematopoiesis 47 3 Quantitative Trait Analysis in the Investigation of Function and Aging of Hematopoietic Stem Cells Hans-Willem Snoeck Summary Extensive genetically determined quantitative variation exists in the number and function of hematopoietic stem cells in inbred mouse strains. Furthermore, aging of hematopoietic stem cells is genetically determined. Gene identification of quantitative trait loci involved in the regulation and aging of hematopoietic stem cells would provide novel insights into regulatory mechanisms that are relevant in vivo and may be clinically important.", + "In order to find the causal loci for heritable differences in transcript levels and possible interactions between age and genotype, we applied a two-time-point model.In this model, we used three factors-(1) relative age, (2) genotype (marker), and (3) the interaction between factors 1 and 2-to explain the differences in gene expression between RILs and age groups.With this mapping procedure, we found almost 900 genes that had an eQTL or gxa eQTL in developing and/or aging worms (P < 0.0001; Fig. 2).Almost half of these genes with heritable transcript differences were found to have a genotype-by-age effect (396 at P < 0.0001; Table 1) allocated to a specific marker, which we coined genotype-by-age expression-QTL ( gxa eQTL).One specific hotspot (trans-band) for gxa eQTL was found on chromosome IV for aging worms and a trans-band for eQTL on chromosome I was detected in developing worms (Fig. 2).", + "NIH-PA Author Manuscript We found three significant QTLs (genetic regions harboring genes controlling these various aging traits, Supplementary Table 5). On chromosome 7, we found a QTL affecting lifespan and fertility after DR that we have named Lfdr1 for \u201clongevity and fertility response to dietary restriction, QTL 1; this QTL also has suggestive effects on FE (Fig. 5D). Two QTLs having significant effects on FE were identified on chromosomes 9 and 15. These we have named Fedr1 and Fedr2, respectively, for \u201cfuel efficiency response to dietary restriction\u201d QTLs 1 and 2.", + "Quantitative trait locus (QTL) mapping in aging systems. Methods in Molecular Biology (Clifton, NJ ). 2007; 371:321\u2013348. Hunter KW, Crawford NPS. The future of mouse QTL mapping to diagnose disease in mice in the age of whole-genome association studies. Annual Review of Genetics. 2008; 42:131\u2013141. Ito R, Robbins TW, Everitt BJ. Differential control over cocaine-seeking behavior by nucleus accumbens core and shell. Nature Neuroscience. 2004; 7:389\u2013397. [PubMed: 15034590] Kapp MB. Ethical and legal issues in research involving human subjects: do you want a piece of me? Journal of Clinical Pathology. 2006; 59:335\u2013339.", + "Jiang, C. and Zeng, Z. B. (1995). Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140, 1111\u20131127. Jin, W., Riley, R. M., Wolfinger, R. D.et al. (2001). The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat Genet 29, 389\u2013395. Kempermann, G., Chesler, E. J., Lu, L. et al. (2006). Natural variation and genetic covariance in adult hippocampal neurogenesis. Proc Natl Acad Sci U S A 103, 780\u2013785. Kendziorski, C. M., Chen, M., Yuan, M. et al. (2006). Statistical methods for expression quantitative trait loci (eQTL) mapping. Biometrics 62, 19\u201327." + ], + [ + "Introduction With the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging.", + "INTRODUCTION Human aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining \u223c20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging.", + "Introduction Geroscience refers to research aimed at understanding the mechanisms of biological aging (Kennedy et al. 2014).A major goal of geroscience is to define the genetic, epigenetic, and environmental features that determine individual rates of aging.From a translational perspective, a further goal is to use this knowledge to develop interventions that can slow or delay aging in order to promote healthy longevity and increase healthspan, the period of life spent in good health free from chronic disease and disability (Burch et al. 2014;Pitt and Kaeberlein 2015).", + "the maximum human life span.Several avenues to studying aging have placed us on Department of Biology Massachusetts Institute of Technology the threshold of understanding basic underlying mechanisms.These approaches include the identification of Cambridge, Massachusetts 02139 key genes and pathways important in aging; genetic studies of heritable diseases that cause the appearance of premature aging in affected people; physiological ex-Introduction periments that relate the pace of aging to caloric intake; Is aging the final act in the script of developmental bioland advances in human genetics, as well as cell and ogy?The characteristic changes that are part and parcel molecular biology leading to an understanding of the of aging appear similar to developmentally regulated basis of many diseases of aging.Strikingly, single gene programs.But why would aging mechanisms have been mutations have been found to significantly extend the evolutionarily selected as advantageous?Indeed, evolife span in C. elegans, yeast, and, most recently, Drolutionary biologists might argue that aging occurs by sophila, suggesting that aging may be relatively simple, default due to the absence of selection in the postreproat least in these organisms.Further, the limited replicaductive phase of life.By this view, the aging process is tion potential of human cells in culture has been attribnot programmed, but, rather, the detritus of the absence uted to a specific mechanism (i.e., the shortening of of selection for maintenance (Medawar, 1952; Kirkwood, telomeric ends of chromosomes).An important chal- 1977).However, it is quite reasonable that any mechalenge is now to relate these recent findings to the more nisms that sprang up to slow or regulate the pace of complex case of human aging.aging would be selected, because lucky individualsIn this review, we will discuss several important mocould potentially give rise to more progeny.Therefore, lecular models of aging that come from current research.it is reasonable to suppose that life span extending pro-These are damage by reactive oxygen species (ROS) cesses have been selected and that these can be viewed generated by metabolism, genome instability, genetias an elaboration of development itself.In principle, cally programmed extension mechanisms, cell death, such extension mechanisms may act to slow or forestall and systemic aging.Questions to be posed include the deleterious changes in an organism that progressively following.What evidence exists for and against these lead to death.The life span of an organism, therefore, models?Can more than one of these models apply to is the sum of deleterious changes and counteracting aging of different tissues in humans-specifically do repair and maintenance mechanisms that respond to organs with continually dividing cells age by the same the damage (Figure 1).mechanism as organs that are postmitotic?Finally, is A priori, one imagines such longevity mechanisms to aging amenable to therapeutic intervention, and would be much less complex than those regulating embryonic such intervention be advisable?development.The spatial and temporal constraints on embryonic development are many, while requirements Oxidative Damage for longevity mechanisms might be much more specific One theory of aging proposes that ROS which are generif there were a single process (or a few processes) whose ated by metabolism cause cumulative damage over a breakdown is the limiting event in longevity (i.e., the lifetime (Harman, 1981).Roughly two to three percent Achilles heel).of oxygen taken up is chemically reduced by the addition Aging is defined when two criteria are met.First, the of single electrons, which are sequentially converted probability of death at any point in time increases with into ROS, including the superoxide anion, hydrogen perthe age of the organism.This statistical definition applies oxide, and the hydroxyl radical.ROS have been shown from yeast to mammals and reflects the progressive to cause molecular damage relatively indiscriminately nature of aging.Second, characteristic changes in pheto proteins, lipids, and nucleic acids.In addition, specific notype occur in all individuals over time due to the limdamage has been observed in the mitochondrial DNA, iting processes.which we consider below in Genome Instability.The phenotypic definition is equally general and is What is the evidence that oxidative damage causes useful in distinguishing the aging process itself from aging?One category of study that is supportive of this diseases of aging, such as cancer and heart disease.view involves animals transgenic for genes encoding Phenotypes of aging affect all of the individuals in a antioxidants.Transgenic Drosophila overexpressing both population, while diseases of aging affect only a subset.Cu/Zn SOD and catalase live 34% longer than controls Both impact on life span, but in different ways.For exam-(Orr and Sohal, 1994).A more recent study shows that ple, the many advances in medicine and public health expression of human SOD1 exclusively in Drosophila in this century have caused a large increase in the averadult motor neurons leads to a 40% extension in life age life span of humans in developed countries.Howspan (Parkes et al., 1998).Further experiments are necever, because these advances have not altered the aging essary to clarify the nature of this primary role of motor neurons in life span.Conversely, mice knocked out for either GPX1 (encoding glutathione peroxidase), SOD1,", + "the maximum human life span.Several avenues to studying aging have placed us on Department of Biology Massachusetts Institute of Technology the threshold of understanding basic underlying mechanisms.These approaches include the identification of Cambridge, Massachusetts 02139 key genes and pathways important in aging; genetic studies of heritable diseases that cause the appearance of premature aging in affected people; physiological ex-Introduction periments that relate the pace of aging to caloric intake; Is aging the final act in the script of developmental bioland advances in human genetics, as well as cell and ogy?The characteristic changes that are part and parcel molecular biology leading to an understanding of the of aging appear similar to developmentally regulated basis of many diseases of aging.Strikingly, single gene programs.But why would aging mechanisms have been mutations have been found to significantly extend the evolutionarily selected as advantageous?Indeed, evolife span in C. elegans, yeast, and, most recently, Drolutionary biologists might argue that aging occurs by sophila, suggesting that aging may be relatively simple, default due to the absence of selection in the postreproat least in these organisms.Further, the limited replicaductive phase of life.By this view, the aging process is tion potential of human cells in culture has been attribnot programmed, but, rather, the detritus of the absence uted to a specific mechanism (i.e., the shortening of of selection for maintenance (Medawar, 1952; Kirkwood, telomeric ends of chromosomes).An important chal- 1977).However, it is quite reasonable that any mechalenge is now to relate these recent findings to the more nisms that sprang up to slow or regulate the pace of complex case of human aging.aging would be selected, because lucky individuals", + "Currently prevailing studies of genetic and biological origin of human health and longevity follow largely two approaches which focus on the aging-related diseases and on individuals with exceptionally long lives (Martin et al. 2007).This study provides de facto the rationale for a new approach.Specifically, Fig. 2 suggests that a promising strategy could be to focus on individuals who died prematurely.Studies of genetic profiles of short-lived subjects compared to those who aged more successfully (i.e., those who lived longer and perhaps healthier lives) can be a core of this strategy.Importantly, this strategy can be naturally implemented in longitudinal studies of aging and longevity by focusing on individuals who died first.", + "T he average human life expectancy has been increasing for centuries 1 .Based on twin studies, the heritability of human lifespan has been estimated to be ~25%, although this estimate differs among studies 2 .On the other hand, the heritability of lifespan based on the correlation of the mid-parent (i.e., the average of the father and mother) and offspring difference between age at death and expected lifespan was estimated to be 12% 3 .A recent study has indicated that the different heritability estimates may be inflated due to assortative mating, leaving a true heritability that is below 10% 4 .The heritability of lifespan, estimated using the sibling relative risk, increases with age 5 and is assumed to be enriched in long-lived families, particularly when belonging to the 10% longest-lived of their generation 6 .To identify genetic associations with human lifespan, several genome-wide association (GWA) studies have been performed [7][8][9][10][11][12][13][14][15][16][17][18][19][20] .These studies have used a discrete (i.e., older cases versus younger controls) or a continuous phenotype (such as age at death of individuals or their parents).The selection of cases for the studies using a discrete longevity phenotype has been based on the survival to ages above 90 or 100 years or belonging to the top 10% or 1% of survivors in a population.Studies defining cases using a discrete longevity phenotype often need to rely on controls from more contemporary birth cohorts, because all others from the case birth cohorts have died before sample collection.Previous GWA studies have identified several genetic variants, but the only locus that has shown genome-wide significance (P \u2264 5 \u00d7 10 \u22128 ) in multiple independent meta-analyses of GWA studies is apolipoprotein E (APOE) 21 , where the ApoE \u03b54 variant is associated with lower odds of being a long-lived case.", + "Introduction Worldwide human populations have shown an increase in mean life expectancy in the past two centuries (Oeppen & Vaupel, 2002).This is mainly because of environmental factors such as improved hygiene, nutrition, and health care.The large variation in healthy lifespan among the elderly has prompted research into the determinants of aging and lifespan regulation.The genetic contribution to human lifespan variation was estimated at 25-30% in twin studies (Gudmundsson et al., 2000;Skytthe et al., 2003;Hjelmborg et al., 2006).The most prominent genetic influence is observed in families in which the capacity to attain a long lifespan clusters (Perls et al., 2000;Schoenmaker et al., 2006).Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al., 2008;Terry et al., 2008), raising the question whether protective mechanisms against disease exist in long-lived subjects.", + "Introduction Human life expectancies are increasing almost everywhere in the world where socio-economic circumstances are permissive (Tuljapurkar et al., 2000) and there is no evidence that a limit to life is anywhere near (Oeppen and Vaupel, 2002).While this increase in life span would prevent a proposed compression of morbidity (Fries, 1980), there is no evidence that higher average life spans are associated with an extension of the period of increased morbidity (Manton and Gu, 2001).On the contrary, older individuals have never been so healthy and further improvements in life style, environmental conditions and medical care are likely to help this trend to continue.Especially the medical sciences now seem poised to push the biological limits of longevity further by a number of innovations that seem to affect basic mechanisms of ageing and disease rather than merely alleviating its symptoms.While in the past medicine contributed mainly to public health advances by redu-cing infectious diseases, thereby helping infant mortality to decline, more recent developments hold promise for a more basic intervention in the processes that underlie age-related decline.An example is atherosclerosis, a common problem in ageing and, along with hypertension, the cause of most cardiovascular disease.Basic medical research has likely contributed significantly to the current dramatic decline in cardiovascular disease by actively intervening in some of its main risk factors, i.e., lipid levels and hypertension (Levi et al., 2002).However, one could question whether age-related diseases should be seen as separate from ageing.In this respect, ageing has been considered as a process of cellular degeneration and death universal to all or most species, increasing the risk of fatal disease in humans and other mammals.Would it be possible to define such a process and ultimately understand it in terms of the timedependent, coordinated action of the products of multiple genes interacting with the environment?If so, then ageing per se rather than the diseases associated with it, may offer a more logical starting point for further increasing healthy life expectancies through prevention and therapy.This is especially true now that we have a working draft of the human genome and are in a position to determine the functional significance of each gene as part of the dynamic network of all genes that ultimately determine the physiology of an organism.Termed 'Functional Genomics', this new discipline is now often called upon to solve the complex problems in biology, such as to understand functional control mechanisms and investigate the role that genotype and environment play in determining disease phenotypes.The question is then if this same approach would apply to ageing as a complex phenotype.What is ageing, how does it differ from its diametrical opposite, i.e., organismal development, and what role can functional genomics play in unraveling the basic causes of ageing and exploit such knowledge for developing new, rational strategies for extending healthy life span?", + "Introduction As a result of improvements in health care and living conditions over the past two centuries, the average human life expectancy has dramatically increased in many regions of the world [1].This major success reflects the great malleability of the ageing process.Unfortunately, for most people, ageing is accompanied with an increased risk of developing age-related illnesses/disabilities and frailty.Therefore new approaches are required to understand the genetic, cellular, and molecular factors controlling ageing to identify strategies to extend healthy life span.", + "The search for the genetic determinants of extreme human longevity has been challenged by the phenotype's rarity and its nonspecific definition by investigators.To address these issues, we established a consortium of four studies of extreme longevity that contributed 2,070 individuals who survived to the oldest one percentile of survival for the 1900 U.S. birth year cohort.We conducted various analyses to discover longevity-associated variants (LAV) and characterized those LAVs that differentiate survival to extreme age at death (eSAVs) from those LAVs that become more frequent in centenarians because of mortality selection (eg, survival to younger years).The analyses identified new rare variants in chromosomes 4 and 7 associated with extreme survival and with reduced risk for cardiovascular disease and Alzheimer's disease.The results confirm the importance of studying truly rare survival to discover those combinations of common and rare variants associated with extreme longevity and longer health span.", + "The search for the genetic determinants of extreme human longevity has been challenged by the phenotype's rarity and its nonspecific definition by investigators.To address these issues, we established a consortium of four studies of extreme longevity that contributed 2,070 individuals who survived to the oldest one percentile of survival for the 1900 U.S. birth year cohort.We conducted various analyses to discover longevity-associated variants (LAV) and characterized those LAVs that differentiate survival to extreme age at death (eSAVs) from those LAVs that become more frequent in centenarians because of mortality selection (eg, survival to younger years).The analyses identified new rare variants in chromosomes 4 and 7 associated with extreme survival and with reduced risk for cardiovascular disease and Alzheimer's disease.The results confirm the importance of studying truly rare survival to discover those combinations of common and rare variants associated with extreme longevity and longer health span.", + "Introduction The recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005).", + "DESIGNS TO STUDY PARAMETERS OF HEALTHY AGEING, MORBIDITY, MORTALITY AND LONGEVITY Human cohorts may vary considerably in their morbidity, mortality and longevity characteristics and yet they have shown a common increase in mean life expectancy in the past two centuries [5].This is mainly due to improved hygiene, nutrition and healthcare.There is a large variation in healthy lifespan among the elderly and remarkably exceptional longevity (EL) can be reached with a low degree of agerelated disability [6,7].Heritability studies comparing the concordance of lifespan in monozygous and dizygous twins estimated a 25 -30% genetic contribution to human lifespan variation [8 -11], which becomes increasingly important at higher ages.The most prominent genetic influence is present in families in which survival to high ages clusters [12,13].Unlike model systems where single-gene mutations have major life extension effects, human longevity is presumed to be a complex trait [14].", + "INTRODUCTION Genomic studies into human longevity are inspired by the fact that, in animal models, healthy lifespan has proved to be remarkably plastic, and major pathways of lifespan regulation have been identified.Considerable lifespan extension has been induced in models as diverse as yeast, worms, fish, flies and rodents by applying genetic manipulation and dietary restriction (DR) (see [1] for review).Reduced activity of nutrient-sensing pathways such as insulin/insulin-like growth factor (IGF-1) signalling (IIS) and target of rapamycin (TOR) signalling mediated lifespan extension, and also the extension of lifespan by DR [2].An interesting observation from the perspective of human ageing is that, in rodents and monkeys, diets restricted in glucose, fat or protein uptake reduced or delayed the risk of cancer and metabolic disease, thus extending the healthspan of the animals [2].Following the discovery of genes and pathways involved in animal lifespan extension, human research has focused on the corresponding candidate human genes with genetic, genomic and epigenetic studies into ageing and longevity.The designs of these studies differ with respect to the selection of naturally occurring phenotypes and the study populations, which include population-based, patient-based, family-based and exposure-based cohorts.", + "GENETIC STUDIES OF HUMAN LONGEVITY Genetic and genomic studies into longevity have been performed based on a hypothesis, referred to as a candidate gene approach.Alternatively, explorative genome-wide analyses have been applied in which genetic variation and gene transcription across the complete genome are being studied for associations with longevity and related traits.Genetic studies into human disease and longevity include candidate gene approaches, genome-wide association studies (GWASs) and genome-wide linkage studies.", + "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY Heritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE \u03b52), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans.", + "Introduction Human longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006).", + "Conclusions and prospects Over the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges.", + "Human lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + ] + ] +} diff --git a/gnqa/paper1_eval/src/data/datasets/old/aging2_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/aging2_dataset.json new file mode 100644 index 0000000..b560590 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/aging2_dataset.json @@ -0,0 +1,128 @@ +{ + "question": [ + "List and describe the grand challenges related to aging research in genetics and genomics.", + "How do gene-by-drug effects (GxD) modulate life expectancies and health?", + "What loci and gene variants modulate normal variation in lifespan, and in which cases is this sex-specific (GxS)?", + "what is the influence of rapamycin and aging on the in vivo physiological and metabolic states and cellular bioenergetic, and how do these relate to the rapamycin-induced health and lifespan modification for that genometype?", + "Describe the genotypes related to alzheimers and dementia which have commonalities with those for aging?" + ], + "answer": [ + "The grand challenges related to aging research in genetics and genomics include: 1. Lack of awareness and interest: Health professionals, governments, and researchers often lack awareness and interest in genetic diseases, which hinders progress. 2. Limited funding and access to technology: Research funding is often limited, and access to high-end technology is not always available, which restricts the scope of research. 3. Economic and health disparities: These disparities can affect the application and benefits of genomic medicine, making it less accessible to those in lower income brackets. 4. Ethical, legal, and social issues: The interpretation, storage, and sharing of genetic data raise ethical and legal concerns, including issues of privacy and consent. 5. Rapid advancements: The fast pace of developments in genetics and genomics can outpace the ability to address emerging ethical, legal, and social questions. 6. Public knowledge and trust: There is a need to increase public knowledge about genomic research and address issues of trust in medicine and scientific research. 7. Integration of genomics into public health: This is a major future challenge for healthcare systems, requiring dialogue with all stakeholders in society.", + "Gene-by-drug effects (GxD) can modulate life expectancies and health by influencing how individuals respond to medications. Genetic variations can affect how drugs are absorbed, disseminated, metabolized, and excreted in the body, which can lead to differences in drug efficacy and the occurrence of adverse drug reactions. For example, a genetic variant in the HMG-CoA reductase gene can modify the LDL-C response to pravastatin. Additionally, certain genes are involved in DNA damage repair and oxidative stress, which can influence susceptibility to adverse drug effects. Therefore, understanding these genetic variations can help in personalizing treatment and potentially improving health outcomes.", + "The study identified 10 genomic loci which influence healthspan, parental lifespan, and longevity. These include loci near FOXO3, SLC4A7, LINC02513, ZW10, and FGD6. The life-extending variant near FOXO3 is associated with a delay in the age at menarche, suggesting a possible sex-specific effect. However, the study did not perform sex-stratified analyses, so it's unclear if there are other sex-specific longevity-related genetic variants.", + "Rapamycin, an mTOR inhibitor, has been shown to extend the lifespan of various organisms, including mice, by delaying or reversing aging in multiple cell types. It does this by regulating rates of protein synthesis and energy utilization, which are processes associated with aging. In the hematopoietic system, rapamycin limits age-related increases in stem cells and biomarkers of aging, enhancing the performance of these cells. However, rapamycin also has potential unwanted metabolic effects, such as insulin resistance and glucose intolerance, though these effects are controversial and potentially reversible. The mTOR pathway, which rapamycin targets, integrates signals from insulin, cytokines, nutrients, oxygen, and mitogenic stimuli, and its regulation has implications for longevity and against the negative effects of aging. Rapamycin also induces autophagy, a process important for cellular homeostasis and damage prevention. Despite these benefits, the exact mechanisms by which rapamycin extends lifespan and whether it delays aging or affects specific diseases remain unclear.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset Alzheimer's disease. The APOE gene is the strongest genetic risk factor for later onset Alzheimer's. The heritability of late-onset Alzheimer's disease (LOAD) is estimated to be ~60-80%, suggesting a large proportion of individual differences in LOAD risk is driven by genetics." + ], + "contexts": [ + [ + "There is a great need for continuing efforts to increase public knowledge about genomic research.As individuals and communities from diverse social backgrounds become more aware of genomic research and the potential role of genetics in contributing to health outcomes, the public will hopefully be more informed about the implications of genomic research for personal medical care, public health and more broadly the public representation of diverse population groups based on genetic findings.This knowledge should reinforce the ability of potential participants to make informed choices about joining a genetic study.There are complicated issues underlying public trust in medicine as well as scientific and genetic research that must be addressed.Innovative strategies for public education and community engagement should take into account cultural settings and historical experiences that have contributed to distrust in the past.", + "The issues discussed in this section refl ect key current concerns, but, given the rapid advances in genetic and genomic research, new issues will continue to confront families in the next few years.For example, major advances in the developing area of neuropsychiatric genetics, studies of the heritable nature of psychiatric and other nervous system disorders, characterized at the molecular, cellular, or behavioral levels, will challenge family members to address the potential role genes play in the development of schizophrenia, bipolar, or affective disorders (Genomics Network, n.d.).", + "Future Implications and Communication Research Directions Given ever-expanding research on genetics and genomics, scholars interested in family interaction will be challenged to stay abreast of the implications for family disclosure and discussion of genetic health.We believe that the following issues will emerge as key concerns:", + "Conclusion After more than four decades of working, genetics and genomic medicine still faces a considerable challenge to be addressed.Lack of awareness of health professionals and government, lack of interest of researcher on genetic diseases, limited research funding, limited access to high technology, low national health budget and low income family are seem to be the main obstacles to be overcome in implementation of genetics and genomic medicine.Despite these conditions, several research centers still managed to do some studies and few numbers of genetic testing.Several collaborations with countries abroad have been done to overcome some obstacles.Yet, Indonesia still has to accelerate this effort to be able to catch up its lag.Mentoring and collaborations are needed to enable Indonesia in doing so.", + "Opportunities for Population-Based Research on Aging Human Subjects: Pathology and Genetics", + "Concluding remarks The next decade will provide a window of opportunity to prepare health professionals, public health practitioners, the public and policy makers for the advent of genomics on health and health care.This will be a doable project but will require regional, national, European and global coordination on both the vertical and horizontal levels.We argue that there is an ethical obligation to prepare society to meet this challenge and to take up the opportunities provided by the science in a medically useful, effective, efficient, socially desirable and ethically justifiable manner.Here, health literacy, health communication and empowerment in managing risks are key for opening the doors to a truly beneficial Public Health Genomics practice.This can be facilitated by implementing ethical benchmarks and legal safeguards 70 such as respect for autonomy and social justice in the context of policy development.", + "Clarifying the general conditions under which genomic knowledge can be put to best practice in the field of public health, paying particular consideration to the ethical, legal and social implications 12,17,35 is currently the most pressing task in Public Health Genomics.Aiming the application of genetic and molecular science to the promotion of health and disease prevention through the organised efforts of society, integral to its activities is a dialogue with all stakeholders in society, including industry, governments, health professionals and the general public. 18Thus, the integration of genomics into public health research, policy and practice is one of the major future challenges for our health-care systems. 36,37Expertise is already feasible and can be clustered and evaluated for a socially accountable use.", + "Public health needs to prepare itself for the upcoming challenges, which derive from genomics.In this sense, it needs to strengthen the communication efforts among all sciences involved.Public health can serve as the umbrella, that spans the disciplines such as genetics, ethics, law and all other stakeholders.", + "Economic and health disparities related to genetics and genomics.", + "Capabilities and limitations of current genetic/genomic technologies.", + "Identify ethical, legal, and social issues associated with genetic/genomic information.", + "Ongoing research contributing to improved understanding of the genetic/genomic influences on health.", + "Economic and health disparities related to genetics and genomics. Integrate knowledge from psychology, history, politics, sociology and culture when delivering genetic and genomic care.", + "Ethical and legal issues surrounding genetic and genomic information and services.", + "Developments in genetics and genomics occur very rapidly and bring with them new ethical, legal and social questions that need swift, sensible and responsible responses (Pepper, 2011).Examples include next-generation sequencing, genetic cohort studies and biobanks, which have raised questions about data management, including quality of interpretation of data, data storage, data sharing, consent for re-use of data, as well as concerns about identifiability and privacy interests of those who provide samples (Kaye, 2012;Wolf, 2013;Pinxten and Howard, 2014).However, the rapidity of advancement poses difficulties for those who must determine the responses to these questions.They are often slow or even overtaken by further advancements.Ethical, legal and social-related challenges should be prioritised for policymakers, researchers, clinicians and public health practitioners to maximise the benefits of genomic and genetic applications while minimising the risk of harm to people (Geller et al., 2014).Any education strategy developed should therefore be dynamic.", + "Query 2. Perceptions of Genetics and Genomics Awareness of Genetic and Genomic Advancements.", + "In addition, 4 scholarly commentaries in this issue provide insights into several current practical issues and developments in genetics and genomics.Feero and colleagues 11 describe advances in genomics science and explore many of the issues surrounding translation of these advances to routine \"personalized\" patient care.Offit 12 discusses the increasing availability of direct-to-consumer marketing of genomic and genetic testing and sounds an appropriately cautionary note about the need for standards, quality control, and appropriate regulation.Uhlmann and Guttmacher 13 present a useful collection of practical Internet genetics resources for clinicians and patients, including genetics information on specific diseases; guidelines for genetic testing; and educational resources to help clinicians integrate genetics into patient care.Ginsberg and colleagues 14 discuss the importance of centralized biorepositories for genetics and genomics research and empha-size the need to develop and implement standards for informed consent, informatics, and governance.", + "Key Themes Relevant To Genomic Research . . . . . . . . . . . . . . . . . . . . . . . . . . 3", + "A first step is to define the challenges that stand in the way of realizing the promise of genomic medicine.These include addressing gaps in the oversight of genetic testing (including regulation of companies providing test interpretation services), ensuring that realistic claims are made in promotional materials for genetic testing, determining the appropriate role of new genomic technologies in patient care, ensuring the privacy of patients' genomic data, and improving insurance coverage and reimbursement for genetic services.The Secretary's Advisory Committee on Genetics, Health, and Society (SACGHS), on which two of us serve, advises the secretary of health and human services and reports on these issues.", + "How can we maximize the benefits of these new developments and minimize the harms?How can we encourage patients' involvement and autonomy yet establish appropriate safeguards while avoiding inappropriate paternalism?How do we promote Preparing for a Consumer-Driven Genomic Age the understanding that interpretations of genomic information may evolve as research unravels the meaning of gene-gene and gene-environment interactions and the roles of noncoding DNA sequences, copy-number variants, epigenetic mechanisms, and behavioral factors in health and disease?" + ], + [ + "A supervised (pathway driven) approach was used to specifically query three general gene ontology (GO) areas of interest, namely xenobiotic metabolism, DNA damage repair, and oxidative stress-related genes (Table 1).These gene categories are hypothesized to play important roles in sex-and age-related susceptibility to adverse drug effects [18,30].Of the 122 genes included in the xenobiotic metabolism gene list in the Ingenuity Knowledge Base, 61 were differentially expressed.These included Cyp2d4, the rat ortholog of human gene CYP2D6, which is speculated to metabolize up to 25% of commonly prescribed drugs [31].Genes involved in DNA Damage Repair, derived from Ingenuity, were combined with the list by Wood et al. [32] to give 222 genes involved in DNA damage repair.Sixty-five of these genes (approximately 25%) were found to be differentially expressed in the liver.Oxidative Stress genes were defined by 68 genes included in \"response to oxidative stress\" (IPA) of which 23 genes were differentially expressed (Table 1).", + "Pharmacogenomics has advanced the field of drug-response assessment.For example, the first experiences with guiding vitamin K antagonist therapy with the aid of CYP2C9 (cytochrome P450, family 2, subfamily C, polypeptide 9) or VKORC1 (vitamin K epox- ide reductase complex, subunit 1) polymorphisms (93 ), and the use of cytochrome P450 polymorphisms for assessing clopidogrel response have entered US Food and Drug Administration recommendations (94 ).Disease prevention lags behind.Gene chips and modern sequencing approaches that allow largescale interrogation of the genome at the population level will generate novel hypotheses of disease causation.Furthermore, with the continuing drop in the costs of whole-genome sequencing, the practicing physician may soon be faced with having to comment on the disease risks of a patient's \u03fe4 \u03eb 10 6 sequence variants before any clinical signs occur, a task that no certified genetic counselor could fulfill at present.With advent of GWASs, ethical and practical concerns of reporting genetic research results have become apparent.Initial efforts at defining rules of reporting large-scale association results and assessing the level of evidence also apply to nextgeneration large-scale genomics (95,96 ).Reports have suggested that on the consumer side, genomewide genetic profiling of employees of health and technology companies does not change anxiety symptoms, dietary fat intake, or exercise behavior (i.e., lifestyle factors) over a 6-month period (97 ); however, the association of genetic variation with risk and the dissection of objective markers of risk and risk factors that reside in the causal pathways of disease will need careful assessment before these approaches can enter clinical decision making (98 ).A data set containing 80 genes associated with coronary heart disease in GWASs was uploaded and overlaid onto the molecular networks developed from information contained in the Ingenuity Knowledge Base.Networks of Network Eligible Molecules were then algorithmically generated on the basis of their connectivity.The most substantially enriched network, as shown, comprises 36 genes, of which 20 are coronary heart disease genes.", + "19.3.1 An environmental or pharmacogenetic basis for drug efficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many non-genetic factors also influence the efficacy of medications, including the patient\u2019s age, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit juice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the first-pass metabolism of many medications.", + "Finally, it is possible that other molecules (or drugs) might modulate the biological context within which the drug\u2013 target interaction takes place. Variation in any of the elements that control these types of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related candidates. 19.3 PHARMACOGENETICS (PGx) 519 19.3.5 Using bioinformatics to gain understanding of adverse drug reaction (ADR) One of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient.", + "19.3 Pharmacogenetics (PGx) It is well known that after exposure to a drug, almost any given cohort of patients show a wide variety of responses. In an ideal situation, patients show a beneficial response to the therapy, although they may also show no response or a weak response, and perhaps most worryingly, they may experience an adverse drug reaction (ADR), which in extreme situations could lead to serious illness or even death. ADR is an increasingly serious problem with a huge toll in lives and health-care costs every year.", + "A good understanding of disease biology and effective chemistry is not the only requirement for an efficacious drug; we also must understand how variation at the target affects drug action, and how variation in other genes affects the way drugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the drug development paradigm also faces some unique challenges; for example, the exquisite rarity of some adverse reactions makes collection of sufficient samples for well-powered genetic analysis almost impossible.", + "19.3.1 An environmental or pharmacogenetic basis for drug efficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many non-genetic factors also influence the efficacy of medications, including the patient\u2019s age, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit juice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the first-pass metabolism of many medications.", + "Finally, it is possible that other molecules (or drugs) might modulate the biological context within which the drug\u2013 target interaction takes place. Variation in any of the elements that control these types of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related candidates. 19.3 PHARMACOGENETICS (PGx) 519 19.3.5 Using bioinformatics to gain understanding of adverse drug reaction (ADR) One of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient.", + "19.3 Pharmacogenetics (PGx) It is well known that after exposure to a drug, almost any given cohort of patients show a wide variety of responses. In an ideal situation, patients show a beneficial response to the therapy, although they may also show no response or a weak response, and perhaps most worryingly, they may experience an adverse drug reaction (ADR), which in extreme situations could lead to serious illness or even death. ADR is an increasingly serious problem with a huge toll in lives and health-care costs every year.", + "A good understanding of disease biology and effective chemistry is not the only requirement for an efficacious drug; we also must understand how variation at the target affects drug action, and how variation in other genes affects the way drugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the drug development paradigm also faces some unique challenges; for example, the exquisite rarity of some adverse reactions makes collection of sufficient samples for well-powered genetic analysis almost impossible.", + "Drug-Gene Interactions Predicting Efficacy In 1 candidate gene study, a genetic variant in the HMG-CoA reductase gene, present in 6.7% of patients, modified the LDL-C response to pravastatin by 6.4 mg/dL. 244][247] However, these effect sizes are small and difficult to distinguish from random variation in individual patients.Indeed, the metformin finding is less important for its potential clinical applications than for the biological insight provided by this link between glucose control and a gene involved in the response to DNA damage. 245,246", + "Nutrition and metabolism The power of these new experimental protocols, comparing gene expression profiles to understand spontaneous differences in phenotype due to disease, was extended by inducing phenotypic differences using creative molecular intervention.The first experiments to manipulate phenotype in this way used drugs.A comparison of the gene expression of a drug-induced phenotype with that of the normal phenotype was brilliantly executed in a single study that simultaneously identified a mechanism for the regulation of sterol uptake in the intestine and a genetic disease, sitosterolemia [17 \u2022 ], mice were treated with a lipid-metabolism altering compound and the expression profiles of various tissues compared with normal mice using gene arrays.Differentially expressed genes were evaluated 'in silico,' and an unknown gene was found using bioinformatic tools to be homologous to the ATP-binding cassette (ABC) family of genes.Members of the ABC family include cellular cholesterol transport proteins.Defects in a member of this family (ABCA1) form the basis for the poor cholesterol delivery to high-density lipoprotein (HDL) that underlies Tangiers disease [18], another cholesterol-related disease [19].Through the use of a variety of in silico techniques, Berge et al. [17 \u2022\u2022 ] concluded that the proteins produced from the newly discovered genes, ABCG5 and ABCG8, were responsible for the regulated reverse transport of newly absorbed cholesterol and phytosterols out of the apical surface of intestinal cells.Using public gene databases, a human homolog of the putative mouse transporter was identified, cloned and used to screen sitosterolemic humans.Dysfunctional mutations were found in these genes in all individuals suffering from sitosterolemia.Thus, individuals suffering from sitosterolemia lack the machinery responsible for the selective and controlled transport of cholesterol, and therefore hyperabsorb various sterols (including plant sterols).This study illustrated many of the strengths of genomic experimentation: the identification of phenotypically important genes using global differential gene expression analysis; querying internet databases to deduce structure/function relationships from sequence comparison; and the characterization of individual variation (polymorphism) linked to health.These findings have transformed our understanding of lipid absorption and metabolism, begging the question: how long would this knowledge have waited to be discovered without genomics?", + "19.3.1 An environmental or pharmacogenetic basis for drug efficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many non-genetic factors also influence the efficacy of medications, including the patient\u2019s age, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit juice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the first-pass metabolism of many medications.", + "Finally, it is possible that other molecules (or drugs) might modulate the biological context within which the drug\u2013 target interaction takes place. Variation in any of the elements that control these types of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related candidates. 19.3 PHARMACOGENETICS (PGx) 519 19.3.5 Using bioinformatics to gain understanding of adverse drug reaction (ADR) One of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient.", + "19.3 Pharmacogenetics (PGx) It is well known that after exposure to a drug, almost any given cohort of patients show a wide variety of responses. In an ideal situation, patients show a beneficial response to the therapy, although they may also show no response or a weak response, and perhaps most worryingly, they may experience an adverse drug reaction (ADR), which in extreme situations could lead to serious illness or even death. ADR is an increasingly serious problem with a huge toll in lives and health-care costs every year.", + "A good understanding of disease biology and effective chemistry is not the only requirement for an efficacious drug; we also must understand how variation at the target affects drug action, and how variation in other genes affects the way drugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the drug development paradigm also faces some unique challenges; for example, the exquisite rarity of some adverse reactions makes collection of sufficient samples for well-powered genetic analysis almost impossible.", + "19.3.1 An environmental or pharmacogenetic basis for drug efficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many non-genetic factors also influence the efficacy of medications, including the patient\u2019s age, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit juice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the first-pass metabolism of many medications.", + "Finally, it is possible that other molecules (or drugs) might modulate the biological context within which the drug\u2013 target interaction takes place. Variation in any of the elements that control these types of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related candidates. 19.3 PHARMACOGENETICS (PGx) 519 19.3.5 Using bioinformatics to gain understanding of adverse drug reaction (ADR) One of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient.", + "19.3 Pharmacogenetics (PGx) It is well known that after exposure to a drug, almost any given cohort of patients show a wide variety of responses. In an ideal situation, patients show a beneficial response to the therapy, although they may also show no response or a weak response, and perhaps most worryingly, they may experience an adverse drug reaction (ADR), which in extreme situations could lead to serious illness or even death. ADR is an increasingly serious problem with a huge toll in lives and health-care costs every year.", + "A good understanding of disease biology and effective chemistry is not the only requirement for an efficacious drug; we also must understand how variation at the target affects drug action, and how variation in other genes affects the way drugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the drug development paradigm also faces some unique challenges; for example, the exquisite rarity of some adverse reactions makes collection of sufficient samples for well-powered genetic analysis almost impossible." + ], + [ + "In one case, a gene identified by mutation recovered from a genetic screen in the laboratory, methuselah, may have variants in natural populations.In particular, the common ATATC haplotype has a sharp geographic (north-south) cline in U.S. populations, which, intriguingly, is associated with an 18% difference in life span (97).It would be interesting to examine these natural populations for differences in their reproductive schedule.Extensive studies show that life span can be rapidly selected as an indirect outcome of artificial selection for age at reproduction.Samples from natural populations of Drosophila contain genetic variants that can be rapidly selected, within 15 generations, for 50% or greater differences in life span on the basis of choosing individuals that are reproductive at early versus later ages (93).Selection was reversible, indicating that these life history variants depended on existing gene combinations not new mutations.Among the genes that differed in quantitative expression between young-and old-selected lines were heat shock proteins, e.g., hsp 22 (60).An overarching conclusion from fly aging genetics is that stress resistance is coupled to longevity (94), as in C. elegans.Other gene candidates are being sought by QTL analysis and show complex interactions with gender and population density (17,115).", + "Murabito JM, Yuan R, Lunetta KL (2012) The search for longevity and healthy aging genes: insights from epidemiological studies and samples of long-lived individuals. J Gerontol A Biol Sci Med Sci 67(5):470\u2013479. doi:10.1093/gerona/gls089 20. Nuzhdin SV, Pasyukova EG, Dilda CL et al (1997) Sex-specific quantitative trait loci affecting longevity in Drosophila melanogaster. Proc Natl Acad Sci USA 94(18):9734\u20139739 21. Gems D, Riddle DL (2000) Genetic, behavioral and environmental determinants of male longevity in Caenorhabditis elegans. Genetics 154(4):1597\u20131610 123 22.", + "Somatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18].", + "Our study has several limitations.First, we did not analyse the sex and mitochondrial chromosomes, since we were unable to gather enough cohorts that could contribute to the analysis of these chromosomes.However, these chromosomes may harbour loci associated with longevity that we thus have missed.Second, although we included as many cohorts as possible, the sample size of our study is still relatively small (especially for the 99th percentile analysis) in comparison to GWA studies of age-related diseases, such as T2D and cardiovascular disease, and parental age at death 11,51,52 .Hence, this limited our power to detect loci with a low MAF (<1%) that contribute to longevity.Third, we did not perform sex-stratified analyses and may thus have missed sexspecific longevity-related genetic variants.The reason for this is that (1) we only identified a limited number of suggestive significant associations in our unstratified 90th and 99th percentile analyses, (2) our sample size is modest (especially when stratified by sex), and (3) thus far, there has been no report of any genomewide significant sex-specific longevity locus.", + "In most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes.", + "Previously, it has been suggested that genetic variation in the FOXO1 gene is specifically contributing to human female longevity (reviewed in Chung et al., 2010).However, at chromosome 13q14.11harboring the FOXO1 gene we found no evidence for linkage with female longevity (LOD<0.05)and at the gene position of FOXO1 we found no evidence for association in the females-only metaanalysis (p-values>0.042) in the GEHA Study.Potentially, the effect of this locus is not only influenced by gender but also by genetic background.", + ", 2003), to study GXE and consequences of treatments as a function of age, diet, and sex (Fleet et al. , 2016; Philip et al. , 2010; Roy et al. , 2020; Sandoval-Sierra et al. , 2020; Williams et al. , 2016, 2020), gene pleiotropy (Wang et al. , 2016a), and to test behavioral predictions based on differences in brain architecture (Yang et al. , 2008). Author Manuscript Author Manuscript Here we summarize the current status of this resource with a focus on genetic structure, and on the power and precision of mapping trait variance to loci and genes.", + "Somatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18].", + "The Height-Life Span Nexus Several observations and lines of experimentation have raised the issue of whether interindividual differences in aging rate are influenced by genes that modulate body size and early-life growth patterns.These include (a) the association between small stature and exceptional longevity in calorically restricted rodents (Yu et al., 1985), methionine-restricted rats (Orentreich et al., 1993), and mutant dwarf mice (Brown-Borg et al., 1996;Miller, 1999); and (b) the association between small body size and longer life span in natural populations of mice (Falconer et al., 1978), flies (Hillesheim and Stearns, 1992), dogs (Li et al., 1996), and, possibly, people (Samaras andStorms, 1992).The correlation in dogs is particularly striking: selective breeding for dogs of different body size has produced breeds varying in size from Chihuahua to Irish wolfhound.These breeds also vary greatly in mean longevity, from approximately 7 to 10.5 years, and the correlation between breed longevity and breed body weight (Miller, 1999) is a remarkable R 2 = 0.56.These differences are genetic and affect stature rather than obesity: no amount of overeating will convert a West Highland white terrier to a St. Bernard.The selective pressures applied were designed to create dogs of specific sizes and temperaments and were not intended to influence aging rate or life span.The clear implication is that the effects on longevity are pleiotropic, i.e., that genes selected for their effect on body size and conformation influenced life span as a side effect.It is of interest to note that the few analyses (Eigenmann et al., 1984(Eigenmann et al., , 1988) ) of the hormonal basis for interbreed differences in body size have shown that the genes in question influence levels of IGF-1, the most likely mediator of the life-span effects in the long-lived df/df and dw/dw mouse mutants.Could it be mere coincidence that long-lived mutant nematode worms (Kimura et al., 1997) also show mutations in genes related to insulin and IGF-1 receptors?", + "The antagonistic pleiotropy and hyperfunction theories of ageing predict the presence of genetic variants important for growth and development in early life with deleterious effects towards the end of the reproductive window 19,20 .While we are unable to directly capture the genetic effects on individuals before age 40 due to the study design of our datasets, we found that the life-extending variant near FOXO3 is associated with a delay in the age at menarche and a decrease in intracranial volume and cognitive abilities.It thus appears that there are loci exhibiting antagonistic effects, although we are unable to discern whether this is due to true pleiotropy or due to linkage of causal variants within a region Genes which showed a significant effect (FDR < 5%) of gene expression on ageing traits are displayed here.Gene names are annotated with the direction of effect, where + andindicate whether the life-extending association of the locus is linked with higher or lower gene expression, respectively.Locus: nearest gene to lead variant in the multivariate analysis, Chr: chromosome, Position: base-pair position of lead variant (GRCh37), Cis-genes: genes in physical proximity (<500 kb) to the lead variant of the locus which colocalise with the multivariate signal, Trans-genes: genes located more than 500 kb from the lead variant of the locus.", + "Ageing phenotypes, such as years lived in good health (healthspan), total years lived (lifespan), and survival until an exceptional old age (longevity), are of interest to us all but require exceptionally large sample sizes to study genetically.Here we combine existing genome-wide association summary statistics for healthspan, parental lifespan, and longevity in a multivariate framework, increasing statistical power, and identify 10 genomic loci which influence all three phenotypes, of which five (near FOXO3, SLC4A7, LINC02513, ZW10, and FGD6) have not been reported previously at genome-wide significance.The majority of these 10 loci are associated with cardiovascular disease and some affect the expression of genes known to change their activity with age.In total, we implicate 78 genes, and find these to be enriched for ageing pathways previously highlighted in model organisms, such as the response to DNA damage, apoptosis, and homeostasis.Finally, we identify a pathway worthy of further study: haem metabolism.", + "Here, we assess the degree of genetic overlap between published GWAS of three different kinds of ageing phenotypeshealthspan, parental lifespan, and longevity (defined as survival to an age above the 90th percentile)-and perform a multivariate meta-analysis to identify genetic variants related to healthy ageing.We subsequently characterise the sex-and age-specific effects of loci which affect all three ageing traits and look up reported associations with age-related phenotypes and diseases.Finally, we link the observed signal in these loci to the expression of specific genes, including some that are currently studied in model organisms, and identify pathways involved in healthy ageing.", + "Ageing phenotypes, such as years lived in good health (healthspan), total years lived (lifespan), and survival until an exceptional old age (longevity), are of interest to us all but require exceptionally large sample sizes to study genetically.Here we combine existing genome-wide association summary statistics for healthspan, parental lifespan, and longevity in a multivariate framework, increasing statistical power, and identify 10 genomic loci which influence all three phenotypes, of which five (near FOXO3, SLC4A7, LINC02513, ZW10, and FGD6) have not been reported previously at genome-wide significance.The majority of these 10 loci are associated with cardiovascular disease and some affect the expression of genes known to change their activity with age.In total, we implicate 78 genes, and find these to be enriched for ageing pathways previously highlighted in model organisms, such as the response to DNA damage, apoptosis, and homeostasis.Finally, we identify a pathway worthy of further study: haem metabolism.", + "LongevityMap--human genetic variants associated with longevity Variation in human lifespan has been found to be 20-30% heritable, with increasing heritability at advanced ages (27).As next-generation sequencing and genome-wide approaches advance, so does the capacity for performing longevity association studies.To catalog the increasing volume of data in genetic studies of human longevity, we created LongevityMap (http://genomics.senescence.info/longevity/), a database of genes, gene variants and chromosomal locations associated with longevity (28).This differs from the GenAge database, which focuses mostly on data from model organisms and the few genes associated with human ageing (e.g.genes causing progeroid syndromes).", + "Genes/loci identified by genome-wide association studies of longevity and lifespan traits.", + "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY Heritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE \u03b52), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans.", + "Put more simply: What is the strength of evidence in favor of GXE effects on lifespan? We ask if youthful adult body weight (~120 days) predicts lifespan. Is the change in body weight in adults in response to a HFD a causal predictor of lifespan? Finally, we ask whether levels of classic serum metabolites or metabolic hormones measured in middle-age or old-age predict variation in lifespan? Our focus is both on overall effects and on strain-specific difference in effect of diet on lifespan and weight gain, rather than on specific genetic modifiers or loci of lifespan.", + "Studies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways.", + "Introduction Approximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go \u00a8gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha \u00a8chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches.", + "Studies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches." + ], + [ + "One surprising result of our experiment was the relatively weak support for involvement of the insulin/insulin-like signaling (IIS) or target-of-rapamycin (TOR) pathways in the evolution of late-life performance.Mutations in genes within these pathways can alter life span and fertility in flies and other organisms (Partridge and Gems 2002); natural genetic variation in expression of IIS/TOR-pathway genes has been reported to predict agingrelated phenotypes (Nuzhdin et al. 2009), and natural clinal variation in the insulin receptor gene InR has been associated with variation in stress resistance and fecundity (Paaby et al. 2010).We therefore expected that some of these genes would contribute to the evolution of life span and late-life fecundity in our experiment.Only one gene previously annotated with the Gene Ontology biological function \"determination of adult life span\" (Cct1) was among the genes bearing the strongest signature of selection, no more than would be expected by chance (1/96 of the candidate genes that had some biological process annotation, compared to 116/10,792 of all genes with some biological-process annotation, \u03c7 [1] 2 = 0.002, P > 0.96).Genes annotated with the functions \"aging\" or \"determination of adult life span\" were also significantly underrepresented among differentially expressed genes (43/215 transcripts with these annotations had P < 0.05 for line or line-by-age effects, compared to 4488/13,258 of all annotated transcripts, \u03c7 [1] 2 = 18.1, P < 0.0001).Most of the genes we identified are therefore novel candidates for the regulation of life span and late-age performance.", + "Rapamycin Rapamycin has been shown to robustly increase lifespan in at least three different mouse strains and to improve healthspan measures including cognitive function, cardiac function, immune function, obesity, and cancer incidence (Johnson et al. 2015;Kaeberlein 2014).", + "mTOR activates the kinase S6K, which phosphorylates S6, inhibiting autophagy [92].Rapamycin can extend the life span of organisms from yeast to mammals in a dose-dependent manner [95].However, some data suggest that rapamycin has unwanted metabolic effects, including insulin resistance, hyperlipidemia, glucose intolerance, and hypophosphatemia; however, whether rapamycin is responsible for these effects remains controversial, and some of the effects are reversible [96,97].The mTOR pathway integrates different signals from insulin, cytokines, nutrients, oxygen, and mitogenic stimuli, and its regulation has important implications for longevity and against the negative effects of aging [92].", + "The molecular mechanisms that drive cellular senescence in proliferative and nonproliferative cells are being discovered.One of the metabolic pathways associated with aging is the growth-promoting mitogen/nutrient-sensing pathway, in which the target of rapamycin (mTOR) is considered a central signaling molecule that affects multiple cellular pathways associated with aging [137].In particular, mTOR participates in the transition of cells from quiescence to senescence [138].", + "Inductors of Autophagy and its Impact on Aging Autophagy has a role in homeostasis, which plays an essential role in the maintenance of cellular physiology and the prevention of cellular damage.Among the inducers of autophagy have been described the already-mentioned rapamycin, resveratrol, and polyamines; however, only polyamines have demonstrated results in clinical research in humans [65].It is known that these compounds can induce the canonical autophagy pathway, which includes inactivation of the mammalian objective of the rapamycin complex 1 (mTORC1), allowing phosphorylation and activation of the Unc-51 complex (Ulk1/2), where the cascade of the other members of the complex is subsequently activated, ULK as FIP200 and ATG13 [65].", + "A third example illustrates that pharmacological targeting of pathways that have been implicated in promoting aging may also restore youthfulness at cellular and biochemical levels.Among the key regulators associated with interventions that extend life span is the enzyme mTOR, which senses cellular nutrient levels and in turn regulates rates of protein synthesis and energy utilization.Notably, administration of rapamycin, an mTOR inhibitor, starting at midlife can extend the life span of mice, suggesting that aging can be delayed or reversed in multiple cell types (Harrison et al., 2009).In the hematopoietic system, aging is associated with an increase in mTOR activation in stem cells and progenitors (Chen et al., 2009).Administration of rapamycin to old mice to inhibit mTOR not only limited the normal age-related increases in hematopoietic stem cells and biomarkers of aging in those cells, but also enhanced the performance of the stem cells to become as effective as young stem cells in heterochronic transplantation experiments (Chen et al., 2009) (Figure 1).", + "Rapamycin inhibits TOR signalling to alter nDNA translation, inducing mitonuclear protein imbalance35, and increases lifespan in various species, including mice33. Rapamycin also increased mean worm lifespan (by 16%)34 in a ubl-5-dependent manner, induced UPRmt, but not UPRER or heat shock response, and increased respiration (Fig. 6a, c and Supplementary Fig. 9a). This was associated with increased ATP levels, equal citrate synthase activity and altered nDNA/mtDNA oxidative phosphorylation protein ratio (Fig. 6d, e). Additionally, rapamycin changed the balance between nDNA- and mtDNA-encoded oxidative phosphorylation subunits in mouse hepatocytes in a dose dependent manner (Fig. 6f, g).", + "Zylbee, E., Vesco, C. & Penman, S. Selective inhibition of the synthesis of mitochondria-associated RNA by ethidium bromide. J. Mol. Biol. 44, 195\u2013204 (1969). 33. Harrison, D. E. et al. Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature 460, 392\u2013395 (2009). 34. Robida-Stubbs, S. et al. TOR signaling and rapamycin influence longevity by regulating SKN-1/Nrf and DAF-16/FoxO. Cell Metab. 15, 713\u2013724 (2012). 35. Zid, B. M. et al. 4E-BP extends lifespan upon dietary restriction by enhancing mitochondrial activity in Drosophila. Cell 139, 149\u2013160 (2009). 36. Schulz, T. J. et al.", + "a, Rapamycin (Rapa, 1 nM) extends worm lifespan in a ubl-5-dependent manner; b, ubl-5-dependently induced UPRmt (hsp-6::GFP) but not UPRER (hsp-4::GFP) (n 5 4). c\u2013e, Rapamycin increased respiration (c, n 5 10) and ATP content but not citrate synthase activity (d, n 5 3) and induced mitonuclear protein imbalance (e). f\u2013h, In mouse hepatocytes, rapamycin induces mitonuclear protein imbalance (f, g) and induces UPRmt as shown at the protein (f, g, n 5 3), and transcriptional (h, n 5 8) level. i, Resveratrol (Resv, 25 mM) induced mitonuclear protein imbalance in mouse hepatocytes (n 5 4).", + "pivotal in this aspect providing molecular insights and having huge conceptual contributions in the field.Characterising the contribution of individual mutants in ageing is a continuously active and informative activity in the field.On top of these studies, genome-wide screens have provided insights on the role of evolutionarily conserved processes and signalling pathways in ageing such as nutrient response [17,18], protein translation, oxidative damage [19,20], mitochondrial function [21,22] and autophagy [22,23] opening new avenues for biogerontology research.Yeasts have proved informative and helped in understanding mechanisms of highly conserved pathways (from yeast to human) in physiology, health and disease such as the Target of Rapamycin (TOR) [24], glucose sensing (PKA) and stress response pathways (Sty1/p38) [25].", + "mTOR activates the kinase S6K, which phosphorylates S6, inhibiting autophagy [92].Rapamycin can extend the life span of organisms from yeast to mammals in a dose-dependent manner [95].However, some data suggest that rapamycin has unwanted metabolic effects, including insulin resistance, hyperlipidemia, glucose intolerance, and hypophosphatemia; however, whether rapamycin is responsible for these effects remains controversial, and some of the effects are reversible [96,97].The mTOR pathway integrates different signals from insulin, cytokines, nutrients, oxygen, and mitogenic stimuli, and its regulation has important implications for longevity and against the negative effects of aging [92].", + "The molecular mechanisms that drive cellular senescence in proliferative and nonproliferative cells are being discovered.One of the metabolic pathways associated with aging is the growth-promoting mitogen/nutrient-sensing pathway, in which the target of rapamycin (mTOR) is considered a central signaling molecule that affects multiple cellular pathways associated with aging [137].In particular, mTOR participates in the transition of cells from quiescence to senescence [138].", + "Inductors of Autophagy and its Impact on Aging Autophagy has a role in homeostasis, which plays an essential role in the maintenance of cellular physiology and the prevention of cellular damage.Among the inducers of autophagy have been described the already-mentioned rapamycin, resveratrol, and polyamines; however, only polyamines have demonstrated results in clinical research in humans [65].It is known that these compounds can induce the canonical autophagy pathway, which includes inactivation of the mammalian objective of the rapamycin complex 1 (mTORC1), allowing phosphorylation and activation of the Unc-51 complex (Ulk1/2), where the cascade of the other members of the complex is subsequently activated, ULK as FIP200 and ATG13 [65].", + "Background Genetic, dietary and drug interventions can enhance longevity and suppress age-associated disease, such as cancer.Prominent genetic interventions that robustly extend longevity and healthspan in mammals include those that decrease growth hormone (GH) and insulin-like growth factor (IGF) signalling; for example, Ames dwarf mice live more than 50% longer than their wild-type siblings [1].These diminutive mice result from a point mutation in a gene (Prop1 df/df ) that drives development of the pituitary gland, so that mutant mice are deficient in specific hormones.The GH deficiency, in particular, has been shown to underlie their enhanced health span and extended lifespan.Ames mice are highly insulinsensitive, resistant to some stresses and the incidence of cancer is delayed [2][3][4].Dietary and drug interventions that extend lifespan include calorie restriction (CR) and the mTOR inhibitor rapamycin [5].Like the Ames dwarf mutation, CR and rapamycin also suppress and/ or delay the incidence of cancer [5][6][7].A detailed understanding of how these interventions exert their beneficial effects is essential to develop strategies to promote healthy aging in humans [8].Currently, these interventions are thought to exert their effects by related and interconnected effects on some or all of the following: genome stability, the epigenome, telomere attrition and/or function, protein quality control, mitochondrial function, nutrient sensing, cellular senescence, stem cell exhaustion, cellular stress responses and altered intercellular communication [9].Of note, the effects of longevity promoting interventions on the epigenome, a key determinant of cell phenotype, are poorly understood.", + "The target of rapamycin (TOR) signaling pathway has also emerged as a major regulator of lifespan.TOR is a highly conserved kinase that transduces signals from nutrients to regulate cell size, cell growth, and metabolism (Martin & Hall, 2005).Genetic studies in yeast Saccharomyces cerevisiae have shown that reduced levels of nutrients, namely amino acids and sugars, can extend yeast lifespan through regulation of the TOR signaling pathway (Kaeberlein et al ., 2005;Powers et al ., 2006).In Drosophila , recent studies have shown that amino acid restriction, rather than 'calorie restriction', extends lifespan (Min & Tatar, 2006).In C. elegans , either inactivation of CeTOR/let-363 by RNAi, or mutations in Raptor/daf-15 , encoding a regulatory subunit of CeTOR, leads to lifespan extension (Vellai et al ., 2003;Jia et al ., 2004).", + "As mentioned above, a number of genes regulating longevity also control growth and development.Some of these, such as the insulin/IGF1/GH pathway, have been suggested to play a role in the mechanisms of CR (Fig. 1).An emerging critical player is the target of rapamycin (TOR) signaling pathway, which involves both nutrient sensing and regulation of growth.Several genes in the TOR pathway, and the TOR gene itself, regulate longevity in flies (Kapahi et al., 2004) and both longevity and dauer diapause in worms (Jia et al., 2004).Strikingly, not only have genetic manipulations of the TOR gene extended lifespan in yeast and worms (Stanfel et al., 2009) but also feeding rapamycin (which inhibits TOR and is also known as sirolimus) to middle-aged mice significantly (9 -14%) increased lifespan (Harrison et al., 2009).Whether rapamycin is extending lifespan by delaying of aging or by affecting a specific disease, such as cancer, remains unclear.More recent studies show that starting rapamycin administration earlier in life does AGING GENES AS TARGETS FOR DRUG DISCOVERY not result in a significantly greater increase in lifespan (10 -18%) than that obtained in middle-aged mice (Miller et al., 2011).", + "Replacement of the C/ebp\u03b1 gene with C/ebp\u03b2 increases lifespan by 20% [35,36], and may alter the rate of aging [37], indicating that altering the isoform expression of these genes can affect lifespan.Moreover, the life-extending drug rapamycin may affect isoform ratios of C/ebp\u03b2.Rapamycin has been shown to increase lifespan via the suppression of Mtor [38] which in turn controls the isoform ratios of C/ebp\u03b2 [39].Therefore, we speculate that rapamycin may in part exert its life extending effect through C/ebp\u03b2.", + "The genome-wide RNAi study conducted by the Ruvkun lab, authored by Hamilton et al. [88], identified a total of 89 additional aging genes with disparate functions including cell structure, cell surface proteins, cell signaling, cellular metabolism, and protein turnover.Of the 66 genes with previously known functions, 17 corresponded to various aspects of carbon metabolism, including citric acid cycle enzymes and subunits of complexes I, IV, and V of the ETC.Researchers also speculated that protein translation might play a role in lifespan regulation, based on the identification of iff-1 (T05G5.10),a gene that has homology to the translation initiation factor eIF5A.Other hits from this screen included two genes containing PH domains known to interact with phosphatidylinositol lipids, multiple G protein-coupled receptors, protein processing and degradation genes such as proteases and ubiquitin ligases/hydrolases, and chromatin modifying factors.", + "How cellular processes that regulate aging impact genome stability also remain unclear.Compelling evidence now exists that in all eukaryotes, aging is regulated by conserved insulin/insulin-like growth factor (I-(IFG-1)) pathways and growth-signaling pathways regulated by the target of rapamycin (TOR) family of kinases (4).In general, experimental manipulations that upregulate these pathways promote aging, and manipulations that downregulate these pathways-including mutational inactivation or caloric restriction-extend life span and mitigate age-related pathologies.Downregulation of these pathways often leads to a reduction in oxidative stress and oxidative damage to DNA and other cellular constituents.For the most part, however, the relationship between aging and changes in oxidative damage downstream of alterations in growth-signaling pathways remains correlative rather than causal.", + "The potential of interventional approaches targeted at aging has yet to be realized in part because aging is a complicated multisystem process that has remained enigmatic.However, research over the last two decades has led to significant excitement.One of the most striking findings is that it is possible to administer a clinically approved drug, rapamycin, to mice at 20 months of age and extend both their life span and health span (Harrison et al., 2009).Surprisingly, much of the recent success of aging research can be traced back to one of its simplest model organisms: yeast.Two of the major pathways studied in the context of aging and age-related disease are the sirtuin pathway and the TOR signaling pathway, and yeast was pivotal in their discovery." + ], + [ + "We briefly comment on rare mutations that shorten life span through the early onset of diseases that are increasingly common during aging in the general population, e.g., familial forms of Alzheimer, breast cancer, coronary artery disease, type II diabetes, etc.The later onset forms of these diseases are associated with causes of death at later ages.A major question is what role the more common allelic variants of these same genes have in \"normal aging\".Although examination of this huge emerging topic goes beyond the present discussion, we may consider the example of Werner's syndrome, a rare autosomal recessive that causes adult onset progeria with a high incidence of cancer and atherosclerosis (70).The absence of Alzheimer-type dementia in Werner's syndrome illustrates the \"segmental\" nature of this and other progerias (70).Thus, heritable shortening of life span should not be considered as a simple acceleration of general aging processes.The Werner's lesion maps to a defective gene encoding a helicase and exonuclease, which also has several polymorphisms.In Japan, 1367Arg was associated with a lower risk of myocardial infarction (70), although it was not associated with longevity in Finland (14).In general, we know little of the genetic factors involved in frailty and morbidity at later ages, which are important to the geneenvironment interactions implied in the major longevity increase seen during the twentieth century.", + "Indicative diseases associated with the candidate aging genes", + "D ementia has an age-and sex-standardized prevalence of ~7.1% in Europeans 1 , with Alzheimer's disease (AD) being the most common form of dementia (50-70% of cases) 2 .AD is pathologically characterized by the presence of amyloid-beta plaques and tau neurofibrillary tangles in the brain 3 .Most patients are diagnosed with AD after the age of 65, termed late-onset AD (LOAD), while only 1% of AD cases have an early onset (before the age of 65) 3 .On the basis of twin studies, the heritability of LOAD is estimated to be ~60-80% (refs. 4,5 ), suggesting that a large proportion of individual differences in LOAD risk is driven by genetics.The heritability of LOAD is spread across many genetic variants; however, Zhang et al. 6 suggested that LOAD is more of an oligogenic than a polygenic disorder due to the large effects of APOE variants.Zhang et al. 6 and Holland et al. 7 predicted there to be ~100-10,000 causal variants contributing to LOAD; however, only a fraction have been identified.Increasing the sample size of genome-wide association studies (GWAS) will improve the statistical power to identify the missing causal variants and may highlight additional disease mechanisms.In combination with increasing the number of samples, it is beneficial to use different approaches to identify rare and private variation to help identify additional causal variants and increase understanding of disease mechanisms; however, we deem this to be out of the scope of the current analysis.", + "Dementia has an age-and sex-standardized prevalence of ~7.1% in Europeans 1 , with Alzheimer's disease (AD) being the most common form of dementia (50-70% of cases) 2 .AD is pathologically characterized by the presence of amyloid-beta plaques and tau neurofibrillary tangles in the brain 3 .Most patients are diagnosed with AD after the age of 65, termed late-onset AD (LOAD), while only 1% of AD cases have an early onset (before the age of 65) 3 .On the basis of twin studies, the heritability of LOAD is estimated to be ~60-80% (refs. 4,5 ), suggesting that a large proportion of individual differences in LOAD risk is driven by genetics.The heritability of LOAD is spread across many genetic variants; however, Zhang et al. 6 suggested that LOAD is more of an oligogenic than a polygenic disorder due to the large effects of APOE variants.Zhang et al. 6 and Holland et al. 7 predicted there to be ~100-10,000 causal variants contributing to LOAD; however, only a fraction have been identified.Increasing the sample size of genome-wide association studies (GWAS) will improve the statistical power to identify the missing causal variants and may highlight additional disease mechanisms.In combination with increasing the number of samples, it is beneficial to use different approaches to identify rare and private variation to help identify additional causal variants and increase understanding of disease mechanisms; however, we deem this to be out of the scope of the current analysis.The largest previous GWAS of LOAD, identified 29 risk loci from 71,880 (46,613 proxy) cases and 383,378 (318,246 proxy) controls 8 .Our current study expands this to include 90,338 (46,613 proxy) cases and 1,036,225 (318,246 proxy) controls.The recruitment of LOAD cases can be difficult due to the late age of onset, so proxy cases can allow for the inclusion of younger individuals by estimating their risk of LOAD using parental status.Proxy cases and controls were defined on the basis of known parental LOAD status weighted by parental age (Supplementary Information).In the current study, we identified 38 loci, including seven loci that have not been reported previously.Functional follow-up analyses implicated tissues, cell types and genes of interest through tissue and cell type enrichment, colocalization and statistical fine-mapping.This study highlights microglia, immune cells and protein catabolism as relevant to LOAD, while identifying previously unidentified genes of potential interest. ResultsGenome-wide inferences.We performed meta-analysis on data from 13 cohorts, totaling 1,126,563 individuals (Supplementary", + "Introduction Alzheimer's disease (AD) is a complex disorder and is the most common form of dementia [1].After age, family history is the single greatest risk factor for AD.AD can be classified into early and late onset forms.Mutations in three genes: PSEN1/2 and APP are known to cause early onset AD in an autosomal dominant manner [2,3].The majority of AD cases, however, are late onset (LOAD) and the APOE e4 allele is the strongest known genetic risk factor.Many additional genetic polymorphisms have been identified, though with substantially lower risk estimates [1,4,5,6,7,8,9,10].LOAD appears to be inherited and/or sporadic and there is evidence of a maternal inheritance pattern [11].Current estimates suggest that more than 20% of inherited LOAD cases are maternally inherited [12].", + "INTRODUCTION Many common noninfectious diseases exhibit a more severe clinical presentation in older individuals.These diseases often exhibit complex etiology and can affect different tissues and cell types, with a wide spectrum of clinical outcomes.Prominent aging-associated neurodegenerative diseases are Alzheimer's disease (AD), Parkinson's disease (PD), and age-related macular degeneration (AMD), all of which can severely compromise the quality of life and have serious repercussions on both the individual and society at large.These late-onset diseases generally result from the interplay between multiple genetic susceptibility factors and environmental components.Sequencing of the human genome, cataloging of millions of single nucleotide polymorphisms (SNPs) together with the development of a map of common haplotypes, and technological innovations in genotyping are among the major milestones that are facilitating exploration of the genetic basis of common diseases (1,7,50).In the field of AMD genetics, these advances have led to the identification of several genetic susceptibility factors and enabled us to start dissecting the relationship between environmental risk factors and the genetic constitution of each individual (66,118,148).As a result, new opportunities are emerging for improved understanding of disease pathogenesis that may lead to better management and treatment of AMD.Clinical aspects of AMD are discussed only briefly (for a more in-depth discussion, see Reference 79).", + "Aging-associated neurodegenerative diseases significantly influence the quality of life of affected individuals.Genetic approaches, combined with genomic technology, have provided powerful insights into common late-onset diseases, such as age-related macular degeneration (AMD).Here, we discuss current findings on the genetics of AMD to highlight areas of rapid progress and new challenges.We also attempt to integrate available genetic and biochemical data with cellular pathways involved in aging to formulate an integrated model of AMD pathogenesis.", + "Aging-associated neurodegenerative diseases significantly influence the quality of life of affected individuals.Genetic approaches, combined with genomic technology, have provided powerful insights into common late-onset diseases, such as age-related macular degeneration (AMD).Here, we discuss current findings on the genetics of AMD to highlight areas of rapid progress and new challenges.We also attempt to integrate available genetic and biochemical data with cellular pathways involved in aging to formulate an integrated model of AMD pathogenesis.", + "Genetics of Alzheimer Disease: Early-Onset AD In the early to mid-1990s, genetic studies of AD focused on extended families with high burden of disease (two or more cases among first-degree relatives), and used linkage analysis of highly polymorphic genetic markers called short tandem repeats (STRs, or microsattelites) in order to identify genomic regions co-transmitting with disease in affected family members.This strategy, followed by \"fine mapping\"-the positional cloning of candidate genes-was used to identify genes and genetic variants contributing to AD risk.The first three genes known to cause AD were identified among families with multiple early-onset cases (age-at-onset <60 years): APP, encoding amyloid precursor protein [Goate et al., 1991], and PS1 and PS2, encoding presenilins I and II respectively [Levy-Lahad et al., 1995;Rogaev et al., 1995;Sherrington et al., 1995], each transmitting disease-causing variants in the predicted autosomal-dominant fashion.", + "Alzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia.With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets.Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately $20 genes with common variants contributing to LOAD risk.In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses.Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple lowfrequency and rare variant contributors to AD, and discuss ongoing efforts with next-generation sequencing studies to develop statistically well-powered and comprehensive genomic studies of AD.Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD.", + "Alzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia.With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets.Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately $20 genes with common variants contributing to LOAD risk.In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses.Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple lowfrequency and rare variant contributors to AD, and discuss ongoing efforts with next-generation sequencing studies to develop statistically well-powered and comprehensive genomic studies of AD.Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD.", + "Indeed, as age increases, there is an exponential increase in the incidence of AD, with a corresponding effect on healthcare costs and quality of life. AD is a complex disease involving several genetic and environmental components (Hardy, 1997; Munoz & Feldman, 2000), and 15% of patients have a genetic predisposition. Almost 100 candidate genes are currently known to be involved in the development of AD, and only 4 (APP, PSEN1, PSEN2, APOE) in humans have been proven to play a direct role in AD pathogenesis (Thomas & Fenech, 2007).", + "T he genetics of Alzheimer disease (AD) to date support an age-dependent dichotomous model whereby earlier age of disease onset (\u03fd60 years) is explained by 3 fully penetrant genes (APP [NCBI Entrez gene 351], PSEN1 [NCBI Entrez gene 5663], and PSEN2 [NCBI Entrez gene 5664]), whereas later age of disease onset (\u054665 years) representing most cases of AD has yet to be explained by a purely genetic model.The APOE gene (NCBI Entrez gene 348) is the strongest genetic risk factor for later onset, although it is neither sufficient nor necessary to explain all occurrences of disease.Numerous putative genetic risk alleles and genetic variants have been reported.Although all have relevance to biological mechanisms that may be associated with AD pathogenesis, they await replication in large representative populations.Genome-wide association studies have emerged as an increasingly effective tool for identifying genetic contributions to complex diseases and represent the next frontier for furthering our understanding of the underlying etiologic, biological, and pathologic mechanisms associated with chronic complex disorders.There have already been success stories for diseases such as macular degeneration and diabetes mellitus.Whether this will hold true for a genetically complex and heterogeneous disease such as AD is not known, although early reports are encouraging.This review considers recent publications from studies that have successfully applied genome-wide association methods to investigations of AD by taking advantage of the currently available high-throughput arrays, bioinformatics, and software advances.The inherent strengths, limitations, and challenges associated with study design issues in the context of AD are presented herein.", + "Arch Neurol.2008;65(3): 329-334 Alzheimer disease (AD) is the most common cause of dementia and the most prevalent neurodegenerative disorder associated with aging. 1 Alzheimer disease is a heterogeneous disorder with a complex etiology owing to genetic and environmental influences as causal or risk modifiers.The neuropathologic hallmarks of disease are extracellular amyloid plaques and intracellular neurofibrillary tangles of hyperphosphorylated tau protein. 2 Only 10% of AD cases occurring before 60 years of age (early-onset AD) are due to rare, fully penetrant (autosomal dominant) mutations in 3 genes: A\u2424 precursor protein (APP) on chromosome 21, 3 presenilin 1 (PSEN1) on chromosome 14, 4 and presenilin 2 (PSEN2) on chromosome 1. 5,6In contrast, most cases of AD are later in onset (\u0546 65 years of age) (late-onset AD), are nonfamilial, and are likely the result of highly prevalent genetic variants with low penetrance. 7To date, the only genetic risk factor for lateonset AD remains the apolipoprotein E gene (APOE), specifically the \u03b54 allele, which is moderately penetrant, accounting for up to 50% of cases. 8owever, a robust literature reports numerous putative genetic risk alleles and promising genetic variants.Recent reports from individual studies reveal significant associations with the sortilin-related receptor (SORL1 [NCBI Entrez gene 6653]) 9,10 and glycine-rich protein 2-associated binding protein 2 (GAB2 [NCBI Entrez gene 9846]) 11 on chromosome 11; death-associated protein kinase 1 (DAPK1 [NCBI Entrez gene 1612]), 12 ubiquilin 1 (UBQLN1 [NCBI Entrez gene 299798]), 13 and adenosine triphosphate-binding cassette transporter 1, subfamily A (ABCA1 [NCBI Entrez gene 19]), on chromosome 9 14 ; and low-density lipoprotein receptor-related protein 6 (LRP6 [NCBI Entrez gene 4040]) on chromosome 12. 15 All of these putative variants still lack replication in large representative populations but have relevance to neuropathologic mechanisms and pathways that may be associated with AD pathogenesis ( A large meta-analysis from the AlzGene database 16 17 All are associated with relevant biological mechanisms and pathways but await replication to further elucidate their utility as significant markers for AD.", + "Background Alzheimer's disease (AD) is the most common neurodegenerative disorder and the leading cause of dementia in the elderly [1].Diagnosis of AD is based on the presence of neurofibrillary tangles and amyloid plaques [2], and symptoms typically include memory loss and impaired cognitive ability.Although the pathological hallmarks associated with dementia-related symptoms in AD appear largely similar between both the early-onset and late-onset forms of the disease, their underlying etiologies contrast [3].Whereas early-onset AD is a familial autosomal dominant disorder caused by rare, highly penetrant mutations in one of a small set of genes (APP, PSEN1, and PSEN2), the more common late-onset form of the disease (accounting for 90-95 % of cases) occurs sporadically, and risk is determined by complex underlying mechanisms [3][4][5][6].Estimates based on twin concordance rates suggest heritability of late-onset AD is as high as 70 %, implicating major roles for genetic as well as non-genetic factors [6].Indeed, through candidate gene studies, as well as more recent genome-wide association studies (GWASs) and whole-exome sequencing, both common and rare variants associated with the late-onset form of AD have been identified [7][8][9][10][11].Collectively, however, common GWAS variants account for only a modest proportion (~30 %) of the underlying variance in disease susceptibility [12].Several environmental factors are also thought to play a role [5,6], yet exactly how these contribute to risk, onset, and progression remains poorly defined.", + "Alzheimer's disease is the most common type of dementia, and it is characterized by a decline in memory or other thinking skills.The greatest risk factor for Alzheimer's disease is advanced age.A recent genome-wide study identified a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11 is probably responsible for the association.The association of a protective haplotype with a 10-year delay in the onset of Alzheimer's disease and the identification of a CCL11 variant with possible functional roles in this association might allow the future development of immunomodulators with the potential to halve disease incidence.", + "Alzheimer's disease is the most common type of dementia, and it is characterized by a decline in memory or other thinking skills.The greatest risk factor for Alzheimer's disease is advanced age.A recent genome-wide study identified a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11 is probably responsible for the association.The association of a protective haplotype with a 10-year delay in the onset of Alzheimer's disease and the identification of a CCL11 variant with possible functional roles in this association might allow the future development of immunomodulators with the potential to halve disease incidence.", + "INTRODUCTION Alzheimer's disease (AD) is a common debilitating disorder with a prevalence that rises steeply with age from below 1% at 65 years to as high as 40% after the age of 90 [Bachman et al., 1992].Genes are known to play a role in the development of AD.Twin studies show heritabilities of around 60% [Bergem et al., 1997;Gatz et al., 1997].Indeed, variation in four genes has already been shown to cause rare forms of early-onset AD [the Amyloid Precursor Protein Gene (APP); Goate et al., 1991; Presenilin 1 (PS1); Sherrington et al., 1995; Presenilin 2 (PS2); Levy Lahad et al., 1995, Rogaev et al., 1995] or increase the general risk of disease development [Apolipoprotein E (APOE), Corder et al., 1993].As well as increasing disease susceptibility, APOE e4 alleles are associated with reduced age at onset (AAO) and appear to show their strongest effect below 70 years [Farrer et al., 1997].There is also evidence from both twin [Pedersen et al., 2001] and family studies [Tunstall et al., 2000;Li et al., 2002] that AAO in AD is heritable.Daw et al. [2000] have estimated that in addition to APOE, there are at least four loci with similar effect sizes, which contribute to AAO in AD.", + "Introduction Alzheimer's disease (AD), a devastating neurodegenerative disease, is the most common form of dementia among the elderly.Genetically, AD is a complex and multifactorial disease with the possible involvement of multiple genes.The rare early-onset form of the disease usually follows an autosomal-dominant inheritance pattern and to date three genes have been identified: amyloid precursor protein (APP) and presenilin 1 and 2 (PSEN1 and PSEN2).The common late-onset form of the disease is much more complex than the early-onset form and until recently the apolipoprotein E (APOE) gene was the only major genetic factor accounting for 20-29% of the risk for late-onset AD. 1,2 Recent large genome-wide association studies (GWAS) have identi-fied nine additional genes for late-onset AD, including CR1, BIN1, CLU (a.k.a.4][5][6][7] There is high heritability for AD risk (up to 80%), 8 but the total risk attributable to all confirmed loci is about 50%, indicating the presence of additional risk genes for late-onset AD.", + "NE OF EVERY 5 PERSONS AGED 65 years is predicted to develop Alzheimer disease (AD) in their lifetime, and genetic variants may play an important part in the development of the disease. 1 The apparent substantial heritability of late-onset AD 2 is inadequately explained by genetic variation within the well-replicated genes (apolipoprotein E [APOE; RefSeq NG_007084], presenilin-1 [PSEN1; RefSeq NG_007386], presenilin-2 [PSEN2; RefSeq NG_007381], and amyloid beta precursor protein [APP;" + ] + ] +} diff --git a/gnqa/paper1_eval/src/data/datasets/old/diabetes_1_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/diabetes_1_dataset.json new file mode 100644 index 0000000..9354ac7 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/diabetes_1_dataset.json @@ -0,0 +1,128 @@ +{ + "question": [ + "What is the age range in mouse for the onset of diabetes?", + "Do we find significant quantitative trait loci in the studies for diabetes?", + "What environmental factors effect mice that also influence diabetes?", + "List mouse studies that involve insulin.", + "Are there phenotypes or genotypes associated with early onset diabetes in mouse?" + ], + "answer": [ + "The onset of diabetes in mice can occur as early as 3 to 4 weeks of age, with blood sugar concentrations often elevating between 5 and 8 weeks. However, in some cases, hyperglycemia can develop at approximately 20 weeks of age.", + "Yes, the studies identified significant quantitative trait loci associated with both type 1 and type 2 diabetes. These include loci such as TCF7L2, DNER, SRR, HNF1A, KCNQ1, CDKN2A/CDKN2B, JAZF1, CDC123, CAMK1D, TSPAN8, LGR5, THADA, ADAMTS9, NOTCH2, and others. However, the significance of some loci varied across different populations and studies.", + "Several environmental factors can affect mice and influence diabetes. These include the stress response, which can be greater in male mice and can confound feeding behavior, the unique gut microbiota of rodents from different production facilities, cage position, single versus group housing, the skill level of the researcher, ambient room temperature, and the type of cage bedding. Diet, particularly high-fat feeding, can also induce obesity and diabetes. Additionally, the intrauterine environment can impact the subsequent development of diabetes amongst offspring.", + "1. Diabetes incidence study where mice were tested for diabetes monthly by blood glucose and weekly by urine assessment. 2. Studies involving transgenic/knockout animal models of type 2 diabetes to study the role of genes and their effects on peripheral insulin action. 3. Study involving male type-2 diabetic db/db mice and genetic control non-diabetic db/ mice. 4. Study involving young adult mice fed a high-fat or Western diet to elicit DIO and insulin resistance. 5. Study involving Akita mouse genotyping. 6. Study involving 12-week-old male type 2 diabetic db/db mice and contemporary control wild-type mice. 7. Study involving transgenic mice to create specific models of type 1 and type 2 diabetes. 8. Study involving AKITA mice derived from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene. 9. Study monitoring mice for the development of diabetes.", + "Yes, there are phenotypes and genotypes associated with early onset diabetes in mice. The db/db mouse strain, which carries the diabetes (db) mutation, is one such genotype. These mice develop an abnormal and characteristic deposition of fat beginning at 3 to 4 weeks of age, which is an associated phenotype. The C57BL/Ks strain also exhibits early onset diabetes. The severity of the diabetes condition in these mice depends on the interaction of the mutant gene with the inbred background." + ], + "contexts": [ + [ + "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end.", + "Subsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female \u03eb diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering \u03f396% of the genome).", + "The Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased.", + "The animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway.", + "Diabetes-obesity syndromes in rodents", + "Diabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age.", + "However, in other contexts, B6 mice are more likely than D2 to spontaneously develop diabetic syndromes, Aging Clin Exp Res indicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these murine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29\u201331], much like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might underlie diabetes-related traits, including RASA1, Nnt, and PSK1. RASA1 show strong sequence differences between B6 and D2 strains [34]. Rasche et al.", + "In total, about 360 male mice (10 for each strain) were fed with either a regular chow diet (CD) or a high-fat diet (HFD) to induce obesity and associated metabolic stress. At 20 weeks of age, a test meal bolus was administered orally, and postprandial BAs and blood glucose levels were analyzed at three different time points (before and 30 or 60 min after gavage). Nine weeks later, the mice were sacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake are captured.", + "BB rats usually develop diabetes just after puberty and have similar incidence in males and females.Around 90% of rats develop diabetes between 8 and 16 weeks of age.The diabetic phenotype is quite severe, and the rats require insulin therapy for survival.Although the animals have insulitis with the presence of T cells, B cells, macrophages and NK cells, the animals are lymphopenic with a severe reduction in CD4 + T cells and a near absence of CD8 + T cells (Mordes et al., 2004).Lymphopenia is not a characteristic of type 1 diabetes in humans or NOD mice (Mordes et al., 2004) and is seen to be a disadvantage in using the BB as a model of type 1 diabetes in humans.Also, in contrast to NOD mice, the insulitis is not preceded by peri-insulitis.However, the model has been valuable in elucidating more about the genetics of type 1 diabetes (Wallis et al., 2009), and it has been suggested that it may be the preferable small animal model for islet transplantation tolerance induction (Mordes et al., 2004).In addition, BB rats have been used in intervention studies (Hartoft-Nielsen et al., 2009;Holmberg et al., 2011) and studies of diabetic neuropathy (Zhang et al., 2007).", + "Ageing likewise affects metabolic parameters in rodents.Analogous to what occurs in humans, the body weight of the C57BL/6J mouse, the most commonly used mouse strain for metabolic studies, increases with age, peaking at ~9 months 133 , and older C57BL/6J mice (22 months) have reduced lean mass and increased fat mass compared with young 3-month-old mice 134 .In both rats and mice, fasting glucose levels are mostly stable throughout life, but whereas glucose tolerance generally worsens with age in rats, mice are less affected [135][136][137][138][139][140] .In fact, 2-year-old male C57BL/6J mice were significantly more glucose tolerant than their 5-month-old counterparts 138 .Consistent with these findings, glucosestimulated insulin release from the pancreas decreases with age in rats, but not in mice 137,138 .", + "All mice h o m o z y g o u s for t h e d i a b e t e s gene (db/db) b e c o m e diabetic, t h e first d i s t i n g u i s h i n g f e a t u r e being a m a r k e d t e n d e n c y to o b e s i t y w i t h large f a t d e p o s i t i o n s o b s e r v e d in t h e a x i l l a r y a n d i n g u i n a l regions a t a b o u t 3 t o 4 weeks of age.", + "In many of these diabetic mice blood sugar concentration tends to increase gradually between 5 and 12 weeks of age, after which it may rise sharply to over 500 rag/100 ml of blood almost overnight. The diabetic condition, thus, appears to develop in two phases, an early one when there is some regulation of blood sugar concentration, and a later stage characterized by a marked increase in hyperglycemia and a complete loss of metabolic control. A few exceptional diabetics, usually females, exhibit a pattern similar to that shown in Fig. 3. Although 16 240 D.L. COLEMANand K.P.", + "Results All mice homozygous for the trait, diabetes (db), develop an abnormal and characteristic deposition of fat beginning at 3 to 4 weeks of age, making their early identification possible. The difference in size and appearance of litter-mate 6-week old mice, one normal and one diabetic, is shown in Fig. 1. Weight increases Fig. 1. C57BL/Ks-db litter-mates a t 6 weeks.", + "of age; m o r e o f t e n this e l e v a t i o n occurs b e t w e e n 5 a n d 8 weeks. I n older d i a b e t i c mice b l o o d sugar c o n c e n t r a t i o n s g r e a t e r t h a n 600 m g / 1 0 0 m l are n o t u n c o m m o n .", + "I n older mice with blood sugar concentrations over 250 rag/100 ml, injections of up t o 100 units / 100 g were completely ineffective in reducing blood sugar to normal levels. Continued treatment of young diabetic mice with daily injections of insulin, although controlling Mood sugar concentrations initially, did not prevent or delay either the obesity or the uncontrollable high blood sugar concentrations, which usually develop at about 6 to 8 weeks of age.", + "Although the early onset of diabetes in db mice coincides with t h a t in juvenile diabetes in man, the symptoms of obesity and elevated serum insulin are more suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet, none of the lesions associated with advanced diabetes in humans such as retinopathies, cardiovascular and kidney lesions have been observed, possibly because of the early onset of the diabetes and the relatively rapid deterioration and death of these mice.", + "To screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo \u00a8ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49).", + "Renal lesions in diabetic mouse models Db/db mice, which have a recessive mutation in the hypothalamic leptin receptor, develop obesity at 4 wk of age and type 2 diabetes at approximately 8 wk of age.In C57BL/6J background, the diabetes and the obesity are usually less severe than in the C57BL/KsJ background (44).Kidneys are generally enlarged in this mouse strain, and structural glomerular changes (e.g., diffuse glomerulosclerosis, GBM thickening) occur without evidence of tubulointerstitial disease (40).Glomerular lesions of the KK mice are characterized by diffuse and nodular mesangial sclerosis without evidence of tubular disease (45).The lack of reliable mouse models prompted the National Institute of Diabetes and Digestive and Kidney Diseases to fund a consortium for the development and phenotyping of new diabetic mouse models that would resemble closely human DNP.", + "In total, about 360 male mice (10 for each strain) were fed with either a regular chow diet (CD) or a high-fat diet (HFD) to induce obesity and associated metabolic stress. At 20 weeks of age, a test meal bolus was administered orally, and postprandial BAs and blood glucose levels were analyzed at three different time points (before and 30 or 60 min after gavage). Nine weeks later, the mice were sacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake are captured.", + "Assessment of Diabetes Mice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + ], + [ + "Additional large-scale meta-analysis predicated upon increased sample size, were carried out on existing datasets from the WTCCC [18] and the Genetics of Kidneys in Diabetes (GoKinD) study [69][70][71] plus control data derived from the National Institute of Mental Health.These investigators observed significant association of previously observed loci.Importantly, they did not observe evidence of new T1D loci reaching the threshold for genome-wide significance.Instead they re-analyzed the most nominally significant associated SNP in an independent British cohort of approximately 6000 cases, 7000 controls and in 2800 families, where they uncovered four additional loci, BACH2 (previously reported [67]), 10p15 harboring protein kinase C theta (PRKCQ), 15q24 harboring nine genes including the cathepsin H (CTSH), complement 1q (C1q), tumor necrosis factor related protein 6 (C1QTNF6) and somatostatin receptor 3 (SSTR3) genes.Table 1 summarizes the 16 T1D loci reported to date.An example of a tag-SNP that captures the association with T1D in each instance is highlighted together with its relative minor allele frequency in controls and what magnitude of risk or protection it confers.Key references regarding the role of each locus in the context of the disease are included and along with the chromosomal band where each locus resides, the main candidate gene (symbol and full name) is highlighted.", + "Detection of established loci We explored the extent to which previously reported type 2 diabetes association signals could be detected in African-descent individuals.Based on the previously reported effect sizes and the effect allele frequency and sample size from our African meta-analysis, we had sufficient power (80%) to detect three signals (TCF7L2, DNER and SRR) at genome-wide significance (p < 2.5 \u00d7 10 \u22128 ) (ESM Table 2).Only the TCF7L2 variant reached genome-wide significance in our study, whereas both variants in DNER (rs1861612) and SRR (rs391300), originally discovered in Pima Indians and East Asians, respectively, had p > 0.1 (ESM Table 2).", + "On the basis of the combined stage 1-3 analyses, we found that six signals reached compelling levels of evidence (P \u00bc 5.0 \u00c2 10 -8 or better) for association with T2D (Table 2).As in all linkage disequilibrium (LD)-mapping approaches, characterization of the causal variants responsible, their effect sizes and the genes through which they act will require extensive resequencing and fine-mapping.However, on the basis of current evidence, we found that the most associated variants in each of these signals map to intron 1 of JAZF1, between CDC123 and CAMK1D, between TSPAN8 and LGR5, in exon 24 of THADA, near ADAMTS9 and in intron 5 of NOTCH2.", + "Replication study of newly identified type 1 diabetes risk loci", + "Although these are considered to be loci convincingly associated with susceptibility to type 2 diabetes in populations of European descent, other genes related to susceptibility to the disease are probably still unidentified, particularly those for populations of other ancestries.In order to uncover genetic variants that increase the risk of type 2 diabetes, we conducted a genome-wide association study in Japanese individuals with type 2 diabetes and unrelated controls.We first genotyped 268,068 SNPs, which covered approximately 56% of common SNPs in the Japanese, in 194 individuals with type 2 diabetes and diabetic retinopathy (case 1) and in 1,558 controls (control 1) collected in the BioBank Japan.We compared the allele frequencies of 207,097 successfully genotyped SNPs and selected the 8,323 SNPs showing the lowest P values.We then attempted to genotype these 8,323 SNPs in 1,367 individuals with type 2 diabetes and diabetic retinopathy (case 2) and for 1,266 controls (control 2) (stage 2), and successfully obtained data for 6,731 SNPs (the P value distribution in the second test is shown in Supplementary Fig. 1a online).The results of principal component analysis 8 in the stage 1 and 2 samples and HapMap samples revealed that there was no evidence for population stratification between the case and control groups throughout the present tests (Supplementary Fig. 1b,c).We selected the 9 SNP loci showing P values o0.0001 (additive model in stage 2, Table 1) and genotyped a third set of cases and controls comprising 3,557 Japanese individuals with type 2 diabetes (cases 3,4,5) and 1,352 controls (controls 3,4).We evaluated the differences in the population structure among these three sets of case and two sets of control groups by Wright's F test.As the results indicated that there was no difference in the population structure among these groups (Supplementary Table 1b online), we combined these populations for the third test of case-control study.The third set of analysis identified the significant associations for six SNPs (Table 1), including the CDKAL1 locus at 6p22.3 (rs4712524, rs9295475 and rs9460546), the IGF2BP2 locus at 3q27.2 (rs6769511 and rs4376068) and the KCNQ1 locus at 11p15.5 (rs2283228).The remaining three SNPs (rs13259803, rs612774 and rs10836097) had P values of 40.05 in the third test and were not further examined.CDKAL1 and IGF2BP2 were previously reported as susceptibility genes for type 2 diabetes in the Japanese population 9 .Therefore, we focused on the KCNQ1 locus, which was highly associated with type 2 diabetes.", + "We consider these data as an interesting preliminary result that surely requires additional independent studies including a higher number of patients in order to confirm and clarify the possible contribution of this locus to the development of T2DM complications.", + "DISCUSSION Taken together, our full second-stage approach and combined meta-analysis have revealed additional loci associated with type 1 diabetes.Clearly the risks are relatively modest compared with previously described associations, and it was only with this sample size at our disposal that we could we detect and establish these signals as true positives through an independent validation effort.", + "Identification of susceptibility loci The degree of evidence for all reported T2D loci was quantified as follows: a locus with a logarithm of odds ratio (LOD) score of 3 or more was considered significant, a LOD score between 2.2 and 3 was considered suggestive and a LOD score between 1 and 2.2 was considered nominal.For T2D, only those loci were included that were significant at least once, or were suggestive in at least one study and at least nominal in two or more studies.The inclusion of the second category of loci was based on a study by Wiltshire et al. [72], in which it was postulated that locus counting is a useful additional tool for the evaluation of genome scan data for complex trait loci.We used the same two criteria to determine the loci from the five papers published on obesity since 2004 and combined these loci with those from Bell et al. [7].As obesity phenotypes, BMI, serum leptin levels, abdominal subcutaneous and visceral fat, and percentage body fat were included.All of these phenotypes were used as continuous quantitative traits, as well as with various cut-off levels.", + "Today, more than 100 loci for type 2 diabetes and glycemic traits have been identified through numerous GWA studies of common and rare variation in populations of diverse ancestral origins [31]; however, to date, very few GWA studies have been published in cohorts of Mexican ancestry.The first GWA study performed in a non-European cohort was published in 2007 and comprised 561 Mexican American type 2 diabetes cases and controls drawn from the Starr County Health Studies [32].Although no loci reached genome-wide significance, several loci identified in prior GWA studies in Europeans were replicated [32].This analysis was subsequently expanded (N = 1273) and meta-analyzed with a cohort from Mexico City (N = 1310) in 2011 [33,34].The most significant variants observed in this meta-analysis included known regions near HNF1A and KCNQ1.Top association signals were then meta-analyzed with the DIAGRAM and DIAGRAM+ datasets of European ancestry individuals, resulting in two regions reaching genome-wide significance: HNF1A and CDKN2A/CDKN2B (Table 1).Top association signals in both studies were annotated to explore their roles as expression quantitative trait loci (eQTL) in both adipose and muscle tissues, revealing a marked excess of transacting eQTL in top signals in both tissue types.", + "75\u00b179 The main conclusion is that there is no major locus for T2D (analogous to HLA in type 1 diabetes).This is not surprising given the modest l s for T2D (approximately 3.5 in Europeans), imposing a limit on the magnitude of any single gene eect. 4Many scans have consequently been signi\u00aecantly underpowered to detect the modest gene eects anticipated.Certainly, few T2D scans have reported linkages meeting the established criteria for genomewide signi\u00aecance. 80This modest power, combined with the diversity of the pedigrees sampled and the analytical techniques used, means that the replication of positive \u00aendings between data sets has been the exception rather than the rule.", + "Quantitative Trait Analysis Exploration of putative T2DM variants with quantitative glycemic traits in a subset of African-American samples (n = 671 from the IRAS and IRASFS control samples, Table S5) revealed limited insight into the biological mechanism associated with T2DM risk.In addition, the five putative African-American T2DM susceptibility loci were tested for association with quantitative measures of glucose homeostasis in the European Caucasian population, in silico, by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC; [16]).These results did not provide further insight into the probable role these variants may have in disease susceptibility (Table S6).The most significantly associated SNP in African Americans, rs7560163, failed quality controls filters and was not included in analysis likely due to being monomorphic as seen in a representative Caucasian population from the HapMap project (Table S4).", + "Discovery of novel loci for T2D susceptibility.We tested for T2D association with ~27 million variants passing quality-control filters, ~21 million of which had a minor allele frequency (MAF) < 5%.Our meta-analysis identified variants at 231 loci reaching genomewide significance (P < 5 \u00d7 10 \u22128 ) in the BMI-unadjusted analysis (N eff 231,436) and 152 in the smaller (N eff 157,401) BMI-adjusted analysis.Of the 243 loci identified across these two analyses, 135 mapped outside regions previously implicated in T2D risk (Methods, Fig. 1 and Supplementary Table 2).", + "Genetic studies performed since 2012 have identified many additional T2D loci based on risk alleles common in one population but less common in others.Studies in African Americans identified RND3-RBM43 (28), HLA-B and INS-IGF2 (29).Studies in South Asians identified TMEM163 (30) and SGCG (31).One locus, SLC16A11-SLC16A13, was simultaneously identified in Japanese and Mexican Americans (32,33), and studies in East Asians identified ANK1 (34), GRK5 and RASGRP1 (35), LEP and GPSM1 (32), and CCDC63 and C12orf51 (36).A study of individuals from Greenland identified TBC1D4 (37), and a sequencing-based study of Danes with follow-up in other Europeans identified MACF1 (38).Finally, the largest GWAS to date in American Indians identified DNER at near genome-wide significance (P = 6.6 \u00d7 10 \u22128 ) (39).Three of these studies imputed GWAS data using the 1000 Genomes Project sequence-based reference panels, providing better genome coverage (29,32,33,40).Taken together, these studies highlight the value of diverse populations, including founder and historically isolated populations, to detect risk loci.", + "Finally, a recent study identified additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs. 21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds.Thus, by combining data involving 10,128 samples, the authors found in the initial stages of the analysis highly associated variants (they followed only 69 signals out of over 2 million metaanalyzed SNPs) with P values \u03fd10 \u03ea4 in unknown loci, and 11 of these type 2 diabetes' associated SNPs were taken forward to further stages of analysis.Large stage replication testing allowed the detection of at least six previously unknown loci with robust evidence for association with type 2 diabetes.", + "Surprisingly, data about previous published loci associated with type 2 diabetes were not sufficiently powerful to reach a significant P value in individual scans.For example, variants at SLC30A8 and PPARG were significantly associated with type 2 diabetes only when pooling all the GWAs data, whereas in a single genome scan (DGI), no gene showed a positive signal (P value: 0.92 and 0.83, respectively).Thus, this may suggest that GWAs are still underpowered to find SNPs with small effect size.", + "Background: The two genome-wide association studies published by us and by the Wellcome Trust Case-Control Consortium (WTCCC) revealed a number of novel loci, but neither had the statistical power to elucidate all of the genetic components of type 1 diabetes risk, a task for which larger effective sample sizes are needed.Methods: We analysed data from two sources: (1) The previously published second stage of our study, with a total sample size of the two stages consisting of 1046 Canadian case-parent trios and 538 multiplex families with 929 affected offspring from the Type 1 Diabetes Genetics Consortium (T1DGC); (2) the Rapid Response 2 (RR2) project of the T1DGC, which genotyped 4417 individuals from 1062 non-overlapping families, including 2059 affected individuals (mostly sibling pairs) for the 1536 markers with the highest statistical significance for type 1 diabetes in the WTCCC results.Results: One locus, mapping to a linkage disequilibrium (LD) block at chr15q14, reached statistical significance by combining results from two markers (rs17574546 and rs7171171) in perfect LD with each other (r 2 = 1).We obtained a joint p value of 1.3610 26 , which exceeds by an order of magnitude the conservative threshold of 3.26610 25 obtained by correcting for the 1536 single nucleotide polymorphisms (SNPs) tested in our study.Meta-analysis with the original WTCCC genome-wide data produced a p value of 5.83610 29 .Conclusions: A novel type 1 diabetes locus was discovered.It involves RASGRP1, a gene known to play a crucial role in thymocyte differentiation and T cell receptor (TCR) signalling by activating the Ras signalling pathway.", + "Finally, we examined whether genes identified using our association studies were enriched within diabetes-related pathways.We collated a list of 42 genes to which 53 CpG sites associated with T2D traits (CS score \u22651.77, combined P < 0.017) mapped.Even in this small dataset, pathway analysis (Supplementary Material, Table S12) indicated significant enrichment in 31 pathways (Fisher's exact P < 0.05), including those related to circadian clock (P = 0.005), adipocytokine signaling (P = 0.009), leptin pathway (P = 0.023), HDL-mediated lipid transport (P = 0.031) and insulin signaling (P = 0.033).", + "In recent years, progress has been made in following up mechanistic studies of GWAS type 2 diabetes-association signals [6,7,9,[25][26][27][28][29][30], but challenges remain in sifting through the many associated variants at a locus to identify those influencing disease.We hypothesized that a common variant with modest effect underlies the association at the CDC123/CAMK1D locus and evaluated the location of high LD variants (r 2 $.7; n = 11) at the locus relative to known transcripts and to putative DNA regulatory elements.We identified two variants that overlapped putative islet and/or liver regulatory regions and none located in exons.We did not assess variants in lower LD (r 2 ,.7), and additional functional SNPs may exist at this locus acting through alternate functional mechanisms untested in the current study.", + "Meta-analysis results for T2D SNPs for insulin and glucose-related traits.", + "A r t i c l e s By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P < 5 \u00d7 10 \u22128 .These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A).The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation.We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits." + ], + [ + "Methods Mouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m \u00fe / \u00fe lepr db/J) and genetic control non-diabetic db/ \u00fe mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg \u00c0 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl \u00c0 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals.", + "In these models, adult offspring of diabetic animals were noted to have normal development of the endocrine pancreas (Aerts et al., 1997;Ma et al., 2012).However, they develop glucose intolerance and impaired insulin response to glucose challenge, and display insulin resistance, mainly in the liver and muscle, highlighting the presence of both insulin resistance and b-cell dysfunction (Aerts et al., 1988;Holemans et al., 1991a,b).The key role of the intrauterine environment was demonstrated by a series of embryo transfer experiments, which showed that the diabetes risk in a low genetic risk strain can be substantially increased by the hyperglycaemic environment of a dam with a high genetic risk of diabetes (Gill-Randall et al., 2004).", + "Diabetes-obesity syndromes in rodents", + "However, in other contexts, B6 mice are more likely than D2 to spontaneously develop diabetic syndromes, Aging Clin Exp Res indicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these murine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29\u201331], much like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might underlie diabetes-related traits, including RASA1, Nnt, and PSK1. RASA1 show strong sequence differences between B6 and D2 strains [34]. Rasche et al.", + "Other diet-induced rodent models of type 2 diabetes.Although rats and mice are the most commonly used models for studies of type 2 diabetes, other rodents have also been identified as useful models.These include the desert gerbil and the newly described Nile grass rat, both of which tend to develop obesity in captivity.", + "Summary of rodent models of type 2 diabetes", + "Since the obesity is induced by environmental manipulation rather than genes, it is thought to model the human situation more accurately than genetic models of obesityinduced diabetes.High fat feeding is often used in transgenic or knock-out models, which may not show an overt diabetic phenotype under normal conditions, but when the beta cells are 'pushed', the gene may be shown to be of importance.It should be noted that the background strain of the mice can determine the susceptibility to diet-induced metabolic changes, and thus, effects could be missed if a more resistant strain is used (Surwit et al., 1995;Bachmanov et al., 2001;Almind and Kahn, 2004).It has also been reported that there is heterogeneity of the response to high fat feeding within the inbred C57BL/6 strain, indicating that differential responses to a high-fat diet are not purely genetic (Burcelin et al., 2002).", + "Other considerations and limitations A myriad of factors affect animal experiments.Men elicit a greater stress response in mice than women 292 , likely confounding feeding behaviour.Rodents from different production facilities (for example, Jackson Laboratory and Taconic) have unique gut microbiotas 293 , perhaps contributing to differences in their susceptibility to DIO and related diabetic complications 293 .Similarly, cage position within a rack of cages, single versus group housing, the skill level of the researcher, ambient room temperature or the type of cage bedding can all affect experimental outcomes.", + "We believe there are several factors that researchers should consider when conducting obesity and diabetes mellitus research in rodents (FIG.2).Although our list is by no means an exhaustive, it demonstrates the complexity and interconnectedness of the myriad of factors that can confound experimental outcomes.Although it is impossible to control for everything, researchers should accurately detail all experimental conditions and methods to allow for better interpretation of the results and, importantly, for better reproducibility.", + "Figure2| Important experimental parameters and potential confounders of experimental outcomes in obesity and diabetes research and their interrelatedness.Countless factors influence experimental outcomes when using animal models, and what is enumerated here is by no means a complete list.This figure is one depiction of the multifactorial and interconnected genetic and environmental matrix that makes it virtually impossible to design the perfect experiment.For example, single-housing mice to obtain more accurate food intake data introduces a stress that in turn affects food intake.The severity of this stress response is both strain-specific and sex-dependent.What is important is to be aware of these challenges and to control for them in the most optimal manner.It is equally, if not more, important to accurately and comprehensively detail all experimental conditions in research papers, as these have bearing on the interpretation and reproducibility of the published results.DIO, diet-induced obesity.", + "Another concern pertains to control mice.Compared with free-living mice in the wild, laboratory control mice with ad libitum access to food are sedentary, overweight, glucose intolerant and tend to die at a younger age 297 .Comparisons between mice with DIO and control mice might be analogous to investigating the genetic cause of obesity-resistance by comparing humans who are overweight or obese.This potential problem with control mice could explain why the use of DIO diets that have 40% to 60% of total energy from fat is so prevalent, as this might be necessary to achieve divergent weight gains.With free access to running wheels, C57BL/6J mice voluntarily run 5-10 km per day 298,299 .As is the case with humans 300 , mice get health benefits from regular physical activity including weight loss, decreased adiposity and improved insulin sensitivity 301,302 .Physical activity might also affect the epigenome over several generations 303 .An enriched physical and social cage environment alone improves leptin sensitivity and energy expenditure in mice, independent of physical activity 304,305 .Overall, these data suggest that with standard mouse husbandry, chow-fed laboratory mice are not the ideal healthy and lean control group for meaningful obesity research.", + "To better address these points, various animal models have been developed.For example, using HFD-T2DM male rats, the F1 female offspring showed reduced \u03b2 cell area and insulin secretion, together with glucose intolerance, without changes in body weight [145].The islets of the F1 female offspring showed differential expression of many genes involved in Ca 2+ , mitogen-activated protein kinase and Wnt signaling, apoptosis and cell cycle regulation [145].Similarly, in pregnant C57BL6J mice, food deprivation resulted in \u03b2 cell mass reduction and an increased risk of \u03b2 cell failure in offspring [146].", + "They are probably typical of those few mice that develop diabetes more slowly and do not tax the pancreatic insulin supply as severely early in the course of the disease. Attempts at therapy. Attempts to keep the weight of diabetic mice within normal limits by total or partial food restriction resulted in premature deaths. After it was discovered that gluconeogenesis is greatly increased in diabetic mice, attempts were made to regulate blood sugar levels and also weight gain by feeding rations devoid of carbohydrate.", + "The degree of dependence of adiposity, hyperglycemia, and islet hypertrophy on food consumption varies among these mice, but in all, the increase in islet volume and consequent fi-eell hyperplasia appears to be an effective 247 means of maintaining blood sugar concentrations at near normal levels. I n contrast, neither the diabetic sand rat [5] nor the diabetic mouse has hypertrophied islets and neither effectively controls blood sugar levels.", + "HV~MEI,: Studies with the Mutation, Diabetes almost undetectable. Similarly, the activities of citrate lyase and glucose-6-phosphate dehydrogenase were greatly decreased in these older diabetic as compared Diabetologia the diabetic mice have attained m a x i m u m weight, after which no further accumulation of adipose tissue is noted. Fig. 8.", + "Rodent models of monogenic obesity and diabetes Obesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory \u03b2 -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their \u03b2 -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity.", + "As with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes.", + "Animal models of diabetes in pregnancy and the role of intrauterine environment Another important field of diabetes research that has relied heavily on animal experimentation is the study of diabetes in pregnancy and the role of the intrauterine environment on the subsequent development of diabetes amongst offspring.", + "Animal models of Type 2 diabetes mellitus", + "Assessment of Diabetes Mice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + ], + [ + "Methods Mouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m \u00fe / \u00fe lepr db/J) and genetic control non-diabetic db/ \u00fe mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg \u00c0 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl \u00c0 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals.", + "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end.", + "Animal group and study design First, one set of animals comprising 12-week-old male type 2 diabetic db/db (C57BL/KsJ-db\u2212/db\u2212, n = 8) and contemporary control wild-type (C57BL/KsJ-db+/db\u2212, n = 8) mice (Jackson Laboratories) were included in this study.Their weights and blood glucose levels were analysed to eliminate variation.Erectile functions of the animals were evaluated by the apomorphine-induced penile erection test, according to a previously described protocol (Pan et al. 2014).Afterwards, intracavernous pressure (ICP) investigations and histological measurements were applied to further confirm the results of the function tests.Then, all mice were sacrificed and the corpus cavernosum (CC) was collected from each mouse.Because the tissue of the CC is difficult to crush, we randomly collected the CCs from two mice and mixed them into one subgroup.As a result, four diabetic subgroups (DB groups) and four normal control subgroups (NC groups) were used for molecular measurements.Second, another set of animals, including three T2DMED and three normal control mice that were independent from the original set of animals, were included in the validation experiments using qRT-PCR.Third, another separate set of animals, including five T2DMED and five control mice, were used to verify one of the predicted targets, IGF-1, using ELISA.A luciferase reporter assay was performed to verify the binding of the differentially expressed miRNAs to the target gene IGF-1.All procedures were approved by the Institutional Animal Care and Use committee at Nanjing Medical University.", + "Summary of rodent models of type 2 diabetes", + "Summary of rodent models of type 1 diabetes", + "Knock-out and transgenic mice in diabetes research Transgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003).", + "Genetically induced insulin-dependent diabetes AKITA mice.The AKITA mouse was derived in Akita, Japan from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene preventing correct processing of proinsulin.This causes an overload of misfolded proteins and subsequent ER stress.This results in a severe insulindependent diabetes starting from 3 to 4 weeks of age, which is characterized by hyperglycaemia, hypoinsulinaemia, polyuria and polydipsia.Untreated homozygotes rarely survive longer than 12 weeks.The lack of beta cell mass in this model makes it an alternative to streptozotocin-treated mice in transplantation studies (Mathews et al., 2002).It has also been used as a model of type 1 diabetic macrovascular disease (Zhou et al., 2011) and neuropathy (Drel et al., 2011).In addition, this model is commonly used to study potential alleviators of ER stress in the islets and in this respect models some of the pathology of type 2 diabetes (Chen et al., 2011).", + "To achieve a slow pathogenesis of T2DM, young adult mice 284 or rats 285 are fed a high-fat or Western diet to elicit DIO and insulin resistance.Single or multiple injections with low-dose streptozotocin (~30-40 mg/kg intraperitoneally) then elicit partial loss of \u03b2-cells, which results in hypoinsulinaemia and hyperglycaemia.Protocols are being continuously refined and likely differ between species and even strains 283 .The HFD streptozotocin rat is sensitive to metformin, further demonstrating the utility of this model 285 .Downsides of streptozotocin treatment include liver and kidney toxicity and mild carcinogenic adverse effects (TABLE 1).", + "Materials and methods 2.1 Mouse models 2.1.1 Mouse strains 2.1.2 Induction of type 1 diabetes 8 2.1.3 Insulin treatment on diabetic mice 2.1.4 Akita mouse genotyping 2.2 Characterization of diabetic nephropathy in mice 2.2.1 Proteinuria measurement 2.2.2 Glomerular cells quantification 2.2.3 Methenamine silver staining quantification 3. 4. 5. 6.", + "ii) Rodent models of diabetic retinopathy", + "There are some good reviews available in the literatures describing the transgenic/knockout animal models of type 2 diabetes [114][115][116][117][118] .The transgenic and knockout models are developed for studying the role of genes and their effects on peripheral insulin action such as insulin receptor, IRS-1, IRS-2, glucose transporter (GLUT 4), peroxisome proliferator activated receptor-g (PPAR-g) and tumour necrosis factor-a (TNF-a) as well as in insulin secretion such as GLUT-2, glucokinase (GK), islet amyloid polypeptide (IAPP) and GLP-1 and in hepatic glucose production (expression of PEPCK) associated with development of type 2 diabetes.Further, combination or double knockout mouse models including defect in insulin action and insulin secretion (e.g., IRS-1 +/-/GK +/-double knockout) have been produced which clearly illustrate the mechanisms associated with development of insulin resistance and beta cell dysfunction leading to overt hyperglycaemic state in human type 2 diabetes.These above genetically modified animals exhibit various phenotypic features of type 2 diabetes varying from mild to severe hyperglycaemia, insulin resistance, hyperinsulinaemia, impaired glucose tolerance and others as explained in detail elsewhere 6,9,[114][115][116][117][118] .Very recently, tissue specific knockout mouse models have been achieved, allowing further insight into the insulin action with respect to particular target tissues (muscle, adipose tissue and liver) associated with insulin resistance and type 2 diabetes 115,117,118 .The transgenic/knockout animals are currently used mostly for the mechanistic study in diabetes research and not usually recommended for screening programme as they are more complicated and costly.", + "Functional deficits refs Non-Alzheimer-disease mouse [71][72][73][74]76,78,81,85,87 and rat 59,75,77 ,79,95,97 Mouse [81][82][83][84][85] and rat 79,111 Cerebral effects of inducing diabetes or insulin resistance in normal rodents (that is, non-Alzheimer-disease rodent models) and in rodents genetically modified to accumulate amyloid\u03b2 in the brain (that is, rodent models of Alzheimer disease). Common intervetions to induce diabetic conditions in rodents included recessive mutations in the leptin gene (Lep; also known as Ob), defects in the leptin receptor (LEPR; also known as OB-R), diet and administration of streptozotocin. Rodents with pancratic overexpression of human amylin spontaneously develop both type 2 diabetes mellitus and dementia-like pathology.", + "Animal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice.", + "Animal models of Type 2 diabetes mellitus", + "As with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes.", + "Animal models of Type 1 diabetes", + "Animal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice.", + "Rodent models of monogenic obesity and diabetes Obesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory \u03b2 -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their \u03b2 -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity.", + "Introduction Animal experimentation has a long history in the field of diabetes research.The aim of this article is to review the commonly used animal models and discuss the recent technological advances that are being employed in the discipline.The review is based on an extensive literature search using the terms rodent, mouse, rat, animal model, transgenics, knockout, diabetes and pathogenesis, in scientific journal databases such as MEDLINE \u00ae.In addition, abstracts presented at meetings of Diabetes UK, the European Association for the Study of Diabetes and the American Diabetes Association over the last 5 years were examined in order to gain an appreciation of recent and ongoing research projects.", + "Assessment of Diabetes Mice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + ], + [ + "Subsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female \u03eb diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering \u03f396% of the genome).", + "Effects of Inbred Background (Table 2).The syndrome produced in BL/Ks diabetes (db) mice, while similar in early development to that of BL/6 obese (ob) mice, has a more severe diabetes-like condition and a less pronounced obesity.However, both mutations when maintained on the same inbred background exhibit identical syndromes from 3 weeks of age on [9,21].Both diabetes and obese mice of the BL/Ks strain have the severe diabetes characterized by insulinopaenia and islet atrophy, whereas both mutations maintained on the BL/6 strain have mild diabetes characterized by islet hypertrophy and hyperplasia of the beta cells.Islet hypertrophy is either sustained or followed by atrophy depending on modifiers in the genetic background rather than the specific action of the mutant gene.The markedly different obesity-diabetes states exhibited when obese and diabetes mice are on different backgrounds points out the importance of strict genetic control in studies with all types of obese-hyperglycaemic mutants.Genetic studies [11] have shown that the modifiers leading to islet hypertrophy and well-compensated diabetes compatible with a near normal lifespan are dominant to those factors causing severe diabetes.Two other mutations, yellow and fat, cause similar diabetes-syndromes and yet have identical symptoms on both inbred backgrounds (Table 2).This may suggest that the primary insult caused by these mutations is not as severe as that for obese and diabetes and that this more gradual initiation of obesity permits the host genome to make a response (islet hypertrophy) compatible with life rather than islet atrophy, insulinopaenia, and life-shortening diabetes.", + "The animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway.", + "Diabetes-obesity syndromes in rodents", + "The Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased.", + "Diabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age.", + "Results We generated an F2 inter-cross between diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains, made genetically obese in response to the Lep ob mutation [24].The cross consisted of .500mice, evenly split between males and females.A comprehensive set of ,5000 genotype markers were used to genotype each F2 mouse (,2000 informative SNPs were used for analysis), and the expression levels of ,40 K transcripts (corresponding to 25,901 unique genes) were monitored in five tissues (adipose, liver, pancreatic islets, hypothalamus, and gastroc (gastrocnemius muscle)) that were harvested from each mouse at 10 weeks of age.In addition to gene expression, several key T2D-related traits were determined for each mouse.The medians, and 1st and 3rd quartiles for the following traits: body weight, the number of islets harvested per pancreas, HOMA, plasma insulin, glucose, triglyceride, and C-peptide are listed in Table 1.", + "However, in other contexts, B6 mice are more likely than D2 to spontaneously develop diabetic syndromes, Aging Clin Exp Res indicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these murine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29\u201331], much like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might underlie diabetes-related traits, including RASA1, Nnt, and PSK1. RASA1 show strong sequence differences between B6 and D2 strains [34]. Rasche et al.", + "Thus, there is a rich literature indicating strong genetic effects on glucose metabolism in the B6 and D2 genetic background, and a male-specific form of diabetes is known to spontaneously occur in hybrids of this strain. Dental traits The reported link between a Chr 13 locus and dental malocclusions [46] might provide an alternative or additional explanation of the associations we observe. Dental malocclusions were the only major male-specific cause of death we observed in this mouse population (20 % of males that died before the 750-day phenotyping tests, 0 % of females).", + "Obesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease.", + "Polygenic basis of ''diabesity'' in mice: the interaction of obesity and diabetes genes Obesity-associated diabetes (''diabesity'') is due to interaction of genes causing obesity with diabetes genes.This conclusion is based on findings indicating that obesity is a necessary but not sufficient condition for the type 2 diabetes-like hyperglycaemia: Obese mice are insulin resistant and therefore more or less glucose intolerant, but in some strains such as C57BL/6J-ob/ob, insulin resistance is compensated by hyperinsulinemia and beta cell hyperplasia, and plasma glucose is only moderately elevated.Other models such as C57BLKS/J-db/db and NZO present overt diabetes mellitus as defined by a threshold of 16.6 mM (300 mg/dl) plasma glucose (Leiter et al. 1998); mice crossing this threshold usually exhibit progressive failure and subsequent apoptosis of beta cells.This type 2 diabetes-like condition is not due to the obesity-causing gene variants but to other genes in the genetic background of the strain, which cause obesity-associated diabetes.The severe and early onsetting diabetes of the C57BLKS/J-db/ db strain is due to the C57BLKS/J background, since mice carrying the db mutation on the C57BL/6J background are not diabetic (Stoehr et al. 2000).Conversely, C57BL/6Job/ob mice are normoglycemic, whereas introgression of the ob mutation into the C57BLKS/J background produced a severely diabetic strain (Coleman 1978).Furthermore, it has been shown that in crosses of lean, normoglycaemic strains with diabetic strains the lean strain can introduce variants that markedly aggravate the diabetic phenotype (Leiter et al. 1998;Plum et al. 2000).", + "Obesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease.", + "Spontaneous type 2 diabetic models Spontaneously diabetic animals of type 2 diabetes may be obtained from the animals with one or several genetic mutations transmitted from generation to generation (e.g., ob/ob, db/db mice) or by selected from non-diabetic outbred animals by repeated breeding over several generation [e.g., (GK) rat, Tsumara Suzuki Obese Diabetes (TSOD) mouse].These animals generally inherited diabetes either as single or multigene defects.The metabolic peculiarities result from single gene defect (monogenic) which may be due to dominant gene (e.g., Yellow obese or KK/A y mouse) or recessive gene (diabetic or db/db mouse, Zucker fatty rat) or it can be of polygenic origin [e.g., Kuo Kondo (KK) mouse, New Zealand obese (NZO) mouse] 13 .Type 2 diabetes occurring in majority of human being is a result of interaction between environmental and multiple gene defects though certain subtype of diabetes do also exist with well defined cause [i.e., maturity onset diabetes of youth (MODY) due to defect in glucokinase gene] and this single gene defects may cause type 2 diabetes only in few cases.", + "Mice of the KK strain exhibit a multigenic syndrome of hyperphagia, moderate obesity, hyperinsulinemia, and hyperglycemia (Ikeda 1994;Nakamura andYamada 1963, 1967;Reddi and Camerini-Davalos 1988).Most KK males develop non-insulindependent diabetes after 4 months of age (Leiter and Herberg 1997).While KK females are much less diabetes prone, they do become obese.Previous analyses indicate that the inheritance of obesity and diabetes phenotypes in KK mice is multigenic (Nakamura and Yamada 1963;Reddi and Camerini-Davalos 1988).In the present study, we have searched for QTLs affecting male and female adiposity and related traits in an intercross between strains KK and B6.", + "We have previously shown that diabetes traits show strong heritability in an F2 intercross between the diabetes-resistant C57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob mouse strains. We assume that the disease phenotype is brought about by a complex pattern of gene expression changes in key tissues [21,22]. However, we also recognize the complexity inherent in discriminating the gene expression changes that cause diabetes from those that occur as a consequence of the disease. For example, many genes are known to be responsive to elevated blood glucose levels [43].", + "Although the early onset of diabetes in db mice coincides with t h a t in juvenile diabetes in man, the symptoms of obesity and elevated serum insulin are more suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet, none of the lesions associated with advanced diabetes in humans such as retinopathies, cardiovascular and kidney lesions have been observed, possibly because of the early onset of the diabetes and the relatively rapid deterioration and death of these mice.", + "Key-words: Spontaneous Diabetes, Genotype : C57BL/ K5-db, Diabetes in mice, Mutation: diabetes, Obesity, Prediabetes, Insulin in plasma, Insulin in pancreas.", + "Results All mice homozygous for the trait, diabetes (db), develop an abnormal and characteristic deposition of fat beginning at 3 to 4 weeks of age, making their early identification possible. The difference in size and appearance of litter-mate 6-week old mice, one normal and one diabetic, is shown in Fig. 1. Weight increases Fig. 1. C57BL/Ks-db litter-mates a t 6 weeks.", + "Diabetologia 3, 238-248 (1967) Studies with the Mutation, Diabetes, in the Mouse* D . L . COT.EMA~ a n d I ~ T H A a I ~ P. t I u M ~ L The Jackson Laboratory, Bar Harbor, Maine Summary. The mutation, diabetes:,(db), t h a t occurred in the C57BL/Ks strain of mice is a unit autosomal recessive gene with full penetrance, and causes metabolic disturbances in homozygous mice resembling diabetes mellitus in man.", + "To screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo \u00a8ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/old/diabetes_2_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/diabetes_2_dataset.json new file mode 100644 index 0000000..97480b7 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/diabetes_2_dataset.json @@ -0,0 +1,128 @@ +{ + "question": [ + "How do gene-environment interactions influence diabetes risk and progression?", + "What non-coding RNAs are involved in diabetes, and what roles do they play?", + "How do gene-environment interactions influence diabetes risk and progression?", + "Can we identify genetic predictors of diabetes complications?", + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?" + ], + "answer": [ + "Gene-environment interactions influence diabetes risk and progression by the interplay of genetic predisposition and environmental factors such as diet, physical activity, and lifestyle. Certain genetic variants may increase the risk of type 2 diabetes (T2D), but this risk can be modified by environmental factors. For instance, the adverse effect of some T2D-associated genetic variants may be attenuated by higher physical activity levels or a healthy lifestyle. Conversely, low physical activity and dietary factors characterizing a Western dietary pattern may augment the risk. Understanding these interactions can help in the development of personalized prevention strategies and treatments for T2D.", + "MicroRNAs and long noncoding RNAs (lncRNAs) are involved in diabetes. MicroRNAs modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs. They can regulate pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism. They also hold potential as diagnostic biomarkers and possible drug-targets for regulation of dysfunctional cell responses. LncRNAs are implicated in complications associated with diabetes, such as diabetic retinopathy and diabetic nephropathy. They can regulate cell proliferation, viability, migration, and the expression of pathological genes via post-transcriptional mechanisms.", + "Gene-environment interactions influence diabetes risk and progression by the interplay of genetic predisposition and environmental factors such as diet, physical activity, and lifestyle. Certain genetic variants may increase the risk of type 2 diabetes (T2D), but this risk can be modified by environmental factors. For instance, the adverse effect of some T2D-associated genetic variants may be attenuated by higher physical activity levels or a healthy lifestyle. Conversely, low physical activity and dietary factors characterizing a Western dietary pattern may augment the risk. Understanding these interactions can help in the development of personalized prevention strategies and treatments for T2D.", + "Yes, genetic predictors of diabetes complications can be identified. Studies have revealed several susceptibility loci for diabetic complications such as retinopathy and nephropathy. However, these genetic factors only explain a small proportion of the phenotypic variation observed in type 2 diabetes patients, indicating a need for the identification of more novel genetic risk factors.", + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy." + ], + "contexts": [ + [ + "Additional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele.", + "Gene\u2013exercise interaction in type 2 diabetes When studying gene\u2013environment interaction on the quantitative traits that underlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002). Achievement of optimal glycaemic control is the focus of traditional treatment paradigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients (Sigal et al 2007).", + "Gene-Environment Interaction Evidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and \u03b2-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131].", + "A person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3).", + "Genes, environment, and development of type 2 diabetes Genes and the environment together are important determinants of insulin resistance and \u03b2-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic.", + "Type 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors.", + "Type 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors.", + "Gene and Environment Selection Environmental factors selected for recent G \u00d7 E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13).", + "We have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G \u00d7 E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G \u00d7 E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G \u00d7 E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G \u00d7 E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D.", + "The purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G \u00d7 E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field.", + "FUTURE PERSPECTIVES Continued investment in studies of G \u00d7 E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G \u00d7 E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G \u00d7 E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications.", + "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM Recently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ).", + "Introduction Genome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene \u00c2 environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4].", + "The literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative.", + "The genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases.", + "The genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases.", + "Predisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation.", + "T2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes.", + "Gene-Environment Interactions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS.", + "Other aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + ], + [ + "It is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop \u00c0 / \u00c0 mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression.", + "It is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop \u00c0 / \u00c0 mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression.", + "Platelets are key partaker in CVD and their involvement in the development of cardiovascular complications is strengthened in diabetes (148).Platelets play an important role in the pathophysiology of thrombosis and represent an important source of different RNA species, including pseudogenes, intronic transcripts, non-coding RNAs, and antisense transcripts (149,150).These molecules can be released by platelets through microvescicles, contributing to the horizontal transfer of molecular signals delivered through the bloodstream to specific sites of action (151).The downregulation of miR-223, miR-126, or 146a observed in diabetic and hyperglycemic patients (137,152) has been associated with increased platelet reactivity and aggregation (153,154).In line with these findings, silencing of miR-223 in mice caused a hyperreactive and hyperadhesive platelet phenotype, and was associated with calpain activation through the increased expression of beta1 integrin, kindlin-3, and factor XIII (153,155).Moreover, the modulation of the expression levels of platelet miRNAs can also be measured in plasma.In fact, plasma levels of miR-223 and miR-126 are decreased in diabetics (137,156).This leads to the upregulation of the P2Y12 receptor, as well as P-selectin, further contributing to platelet dysfunction (156).As a result of this interaction, activation level of platelets in type 2 DM is increased (149,156,157).Consistently with this, circulating miR-223 levels are independent predictors of high on-treatment platelet reactivity (158).Another interesting mechanism linking platelets and diabetes involves miR-103b, a platelet-derived biomarker proposed for the early diagnosis of type 2 DM, and the secreted frizzledrelated protein-4 (SFRP4), a potential biomarker of early \u03b2 cell dysfunction and diabetes.In fact, platelet-derived miR-103b is able to downregulate SFRP4, whose expression levels are significantly increased in pancreatic islets and in the blood of patients with prediabetes or overt diabetes (159).These interesting results identify miR-103b as a novel potential marker of prediabetes and diabetes, and disclose a novel potential therapeutic target in type 2 DM.", + "In vitro and in vivo studies concerning the mechanisms that are responsible for the endothelial dysfunction in diabetes demonstrated that, in the presence of high glucose concentrations, upregulation of miR-185 reduced the expression of the glutathione peroxidase-1 (GPx-1) gene, which encodes an enzyme that is important in the prevention of oxidative stress (129); instead upregulation of miR-34a and miR-204 contributed to endothelial cell senescence by impairing SIRT-1 expression and function (130,131).In the endothelium, miR-126 exerts proangiogenic, and anti-inflammatory activities.At a functional level, it enhances VEGF and fibroblast growth factor activities, contributing to vascular integrity and angiogenesis (132,133), recruits progenitor cells through the chemokine CXCL12 (134), while it suppresses inflammation by inhibiting TNF-\u03b1, ROS, and NADPH oxidase via HMGB1 (135).Consistently, miR-126 levels are down-regulated in both myocardial tissue and plasma from type 2 diabetic patients without any known anamnestic data for CVD (136,137), and in patients with CAD (138), suggesting that it could represent a new diagnostic marker for diabetes and CVD.Other studies in endothelial colony-forming cells, as well as in progenitor endothelial cells (EPCs) exposed to high glucose, demonstrated that miR-134 and miR-130a affected cell motility and apoptosis, respectively (139,140).", + "Numerous recent reports have demonstrated abnormal expression of various miRNAs in renal, vascular and retinal cells under diabetic conditions, and in vivo models of related diabetic complications [8,[87][88][89][90][91]. Notably, the functional relevance of these miRNAs has been highlighted by the fact they target key genes associated with the progression of, or protection against, these complications.In particular, the role of miRNAs in diabetic nephropathy has been extensively studied, including in the actions of TGF-\u03b2 related to fibrosis and other key renal outcomes in vitro and in vivo [8,[87][88][89][90].In diabetic retinopathy, several miRNAs have been reported to modulate the disease by targeting factors associated with angiogenesis, inflammation, and oxidant stress in RECs and in diabetic retinas [88,89].Reports have also implicated various miRNAs in the aberrant expression of genes associated with diabetic cardiomyopathy [88,91].In addition, effective in vivo targeting of miRNAs has now been demonstrated thanks to advances in nucleotide chemistry and the design of nuclease-resistant anti-miRNAs, which suggest future translational potential of miRNA-based therapies for human diabetic complications [8].Importantly, since miRNAs are stable in biological fluids such as urine and serum [8], they are being assessed in samples from various clinical cohorts as valuable biomarkers for the early detection of diabetic complications, for which there is a major unmet clinical need.It is clear that research in the field of miRNAs and diabetic complications will continue at a rapid pace.", + "Introduction Diabetes-related complications represent one of the most important health problems worldwide with dire social and economic projections (Cooper, 2012).One of the most important medical concerns of the diabetes epidemic is diabetic nephropathy (DN).Diabetic nephropathy is regarded as a prototypical disease of gene and environmental interactions because not all diabetic subjects with traditional risk factors develop clinically evident nephropathy, indicating a role for individual susceptibility.The majority (>85%) of GWAS-identified single nucleotide polymorphisms (SNPs) are located in the non-coding regions of the genome and thus their functional implication lies in identifying the target genes, cell types, and the mode of dysregulation caused by these non-coding SNPs (Maurano et al., 2012).Recent studies indicate that complex trait-causing variants localize to cell-type-specific, functionally important gene regulatory regions where they can disrupt or create transcription factor binding sites to alter transcript levels only in disease-target cell types (Ko and Susztak, 2013;Susztak, 2014).Several elements of the immune system including cytokines and resident chemokines, macrophage recruitment, T lymphocytes, and immune complex deposition have recently been associated with DN (Navarro-Gonz\u00e1lez and Mora-Fern\u00e1ndez, 2008;Gaballa and Farag, 2013).Since renal cells are also capable of synthesizing pro-inflammatory cytokines such as tumor necrotic factor-alpha (TNF-\u03b1), interleukin-1\u03b2 (IL-1\u03b2) and interleukin-6 (IL-6), therefore, these cytokines acting in a paracrine or autocrine manner may induce significant effects leading to the development and progression of several renal disorders (Matoba et al., 2010;Pruijm et al., 2012;Shankar et al., 2011).The rationale of this study involved a concerted effort of genotyping, correlation and gene expression techniques involving three pro-inflammatory cytokine genes in the development and progression of DN as well as identification of high risk patients involving susceptibility or poor clinical outcome.", + "They also identified enrichment in coagulation and complement pathways, signaling pathways, tissue remodeling, and antigen presentation, including PI3K-Akt, Rap1, Toll-like, and NOD-like. Sun et al. [25] studied diabetic retinopathy and identified four stress-inducible genes Rmb3, Cirbp, Mt1, and Mt2 which commonly exist in most retinal cell types. Diabetes increases the inflammatory factor gene expressions in retinal microglia and stimulates the immediate early gene expressions (IEGs) in retinal astrocytes. Van Zyl et al. [30] studied glaucoma cases and identified the cell types that represent gene expressions implicated in glaucoma.", + "One of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3\u03b2 isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies.", + "M A N U S C R I P T A C C E P T E D In relation to the regulation of gene expression, the role of microRNAs (miRNAs) in diabetic retinopathy has been gaining more emphasis.miRNAs are non-coding small RNAs which modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs.miRNAs can be detected in serum (free, associated with proteins or within membrane-bound particles) (Weiland et al., 2012), vitreous (Ragusa et al., 2013) and aqueous (Dunmire et al., 2013).As reviewed by Mastropasqua et al., miRNAs hold considerable interest for diabetic retinopathy since they can regulate important pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism (Mastropasqua et al., 2014).Although based on a small patient sample, it has been reported that three separate miRNAs (miR-21, miR-181c, and miR-1179) in serum of patients with diabetic retinopathy have potential to be used as biomarkers for early detection of disease (Li et al., 2014;Qing et al., 2014).While this is still a growing research area, miRNAs hold considerable clinical potential in the diabetic retinopathy field, both as possible drug-targets for regulation of dysfunctional cell responses and as diagnostic biomarkers.", + "Roles of lncRNAs in diabetic complications Apart from being involved in major metabolic tissues during diabetes as discussed above, lncRNAs are implicated in complications associated with diabetes.Diabetic retinopathy is one of the common complications in diabetic patients, which leads to impaired or loss of vision.Altered expression of lncRNAs, namely MALAT1 [82,83] and MEG3 [84], are reported to be associated with diabetic retinopathy.In STZ-induced diabetic rats, the expression of MALAT1 is elevated in the endothelial cells of the retina and knockdown of MALAT1 ameliorates retinopathy in STZ-induced rats [82].The lncRNA, MEG3, was also found to be downregulated in the retina of STZ-induced diabetic mice and its in vitro knockdown in retinal endothelial cells was found to regulate cell proliferation, viability, and migration [84].Hyperglycemia as in diabetes causes upregulation of ANRIL levels in endothelial cells [85,86], and this elevates the levels of the PRC2 subunit, EZH2 that consequently promotes the expression of VEGF, a key promoter of angiogenesis [85].Another major complication associated with diabetes is diabetic nephropathy, and this is considered a major cause of end-stage renal disease and disability in diabetic patients [87].Recent studies show that lncRNAs play important roles in the development of diabetic nephropathy and accumulation of extracellular matrix (ECM) proteins.There is higher expression of the lncRNA, PVT1, during diabetic nephropathy, and this increase leads to increased fibrosis due to accumulation of ECM proteins in renal cells [88]; downregulation of PVT1 reduces ECM accumulation [88].LncRNA PVT1 is also a host to miR-1207-5p and this miRNA is shown to regulate the expression of fibronectin1 (FN1), plasminogen activator inhibitor-1 (PAI1), and transforming growth factor beta 1 (TGF\u03b21) [89].In renal tube injury during diabetes, the lncRNA, MIAT, is under-expressed, and this negatively correlates with creatinine and BUN levels in the serum of these subjects.It has been shown to regulate cell viability of proximal convoluted renal tubules [90].In diabetic nephropathic mice, the lncRNA, MGC, is increased in renal mesangial cells.Interestingly, this lncRNA harbours a cluster of approximately 40 miRNAs, and is regulated by the ER stress marker C/EBP homologous protein (CHOP) [91].In CHOP -deficient mice, there is decreased expression of the lncRNA, MGC, and the clustered miRNAs, and these mice have shown an improvement in diabetic nephropathy [91].Diabetic nephropathy is also associated with increased levels of lincRNA, Gm4419, and this exerts its action by interacting with NF-\u03ba\u03b2.Knockdown of this lincRNA in renal mesangial cells lowers cellular proliferation and inhibits expression of NF-\u03ba\u03b2 in hyperglycemic states [92].The lncRNA, TUG1, that is upregulated in diabetic nephropathy acts as sponge for miR-377 and regulates PPAR-\u03b3 expression which further modulates the expression of FN1, collagen type IV alpha 1 chain (COL4A1), PAI1, and TGF\u03b21 in renal mesangial cells [93].Diabetic cardiomyopathy is a critical end-stage complication associated with diabetes.Several such cardiovascular complications and myocardial dysfunction in diabetic patients lead to heart failure [94].Differential expression analysis in cardiac tissue from normal and diabetic rats shows that the lncRNA, MALAT1, is upregulated during cardiomyopathy and knockdown of this lncRNA improves left ventricular systolic function by reducing myocardial inflammation in diabetic rats [95,96].Decreased expression of the lncRNA, H19, is also reported during diabetes [68,70], and this often results in decreased expression of the exonic miRNA, miR-675 [97,98].mir-675 directly targets the voltage-dependent anion channel 1 (VDAC1) which is involved in mitochondria-mediated apoptosis in the cardiac tissue during diabetes.H19 overexpression in diabetic rats reduces oxidative stress, apoptosis, and inflammation, and improves ventricle function [98].LncRNAs NONRATT021972 and uc.48+ are reported to be associated with diabetic neuropathic pain [99,100], and inhibition of both have been shown to alleviate such neuropathic pain by activating the P2X3 receptor.Impaired wound closure is a notable complication associated with diabetes and a recent report shows decreased levels of the lncRNA, Lethe in such impaired dorsal wounds of diabetic mice.This was demonstrated to be associated with increased ROS production, possibly through regulation of NOX2 expression [101].", + "All these suggest towards important roles of various lncRNAs in complications associated with diabetes and, therefore, assume importance to be studied in detail.", + "An overall important consideration in study design is that similar to RNA, noncoding RNAs are tissue and cell specific [24,[77][78][79][80][81][82].Given that it is still unknown if pathogenic changes in AMD are localized to specific ocular tissues or systemic, one must take into consideration that potential biomarkers identified in the peripheral blood as \"disease associated\" may not reflect the disease mechanism occurring in the neural retina and/or RPE.", + "Skol et al. developed methods to study genomics and transcriptomics together to help discover genes that cause diabetic retinopathy.Genes involved in how cells respond to high blood sugar were first identified using cells grown in the lab.By comparing the activity of these genes in people with and without retinopathy the study identified genes associated with an increased risk of retinopathy in diabetes.In people with retinopathy, the activity of the folliculin gene (FLCN) increased more in response to high blood sugar.This was further verified with independent groups of people and using computer models to estimate the effect of different versions of the folliculin gene.", + "miRNAs in Kidney Disease and Diabetic Nephropathy Diabetic nephropathy is a progressive kidney disease and a major debilitating complication of both type 1 and type 2 diabetes that can lead to end-stage renal disease (ESRD) and related cardiovascular disorders.Absence or lower levels of particular miRNAs in the kidney compared with other organs may permit renal specific expression of target proteins that are important for kidney functions [45].Figure 4 depicts the connection between the role of miRNAs and kidney fibrosis.Altered expression of miRNAs causes renal fibrosis by inducing EMT, EndMT, and other fibrogenic stimuli.The accumulative effects of hyperglycaemia, inflammatory cytokines, proteinuria, ageing, high blood pressure, and hypoxia result into alteration of miRNAs expression profiles.The altered miRNAs level causes the initiation of such transition program in normal kidney, finally fibrosis.Some of the miRNAs that are more abundant in the kidney compared with other organs include miR-192, miR-194, miR-204, miR-215, and miR-216.A critical role of miRNA regulation in the progression of glomerular and tubular damage and the development of proteinuria been suggested by studies in mice with podocytespecific deletion of Dicer [46].There was a rapid progression of renal disease with initial development of albuminuria followed by pathological features of glomerulosclerosis and tubulointerstitial fibrosis.It is likely that these phenotypes are due to the global loss of miRNAs because of Dicer deletion, but, given multiple miRNAs and their myriad targets, the precise pathways responsible require identification.These investigators also identified specific miRNA changes, for example, the downregulation of the miR-30 family when Dicer was deleted.Of relevance, the miR-30 family was found to target connective tissue growth factor, a profibrotic molecule that is also downstream of transforming growth factor (TGF)- [47].Thus, the targets of these miRNAs may regulate critical glomerular and podocyte functions.These findings have also been complemented by an elegant study revealing a developmental role for the miR-30 family during pronephric kidney development in Xenopus [48].Sun et al. [49] identified five miRNAs (-192, -194, -204, -215, and -216) that were highly expressed in human and mouse kidney using miRNA microarray.A recent report using new proteomic approaches to profile and identify miRNA targets demonstrated that miR-NAs repress their targets at both the mRNA and translational levels and that the effects are mostly relatively mild [50].The role of miR-192 remains controversial and highlights the complex nature of miRNA research.Kato et al. [51] observed increased renal expression of miR-192 in streptozotocin-(STZ-) induced diabetes and in the db/db mouse and demonstrated that transforming growth factor (TGF-1) upregulated miR-192 in mesangial cells (MCs).miR-192 repressed the translation of Zeb2, a transcriptional repressor that binds to the E-box in the collagen 12 (col12) gene.They proposed that miR-192 repressed Zeb2 and resulted in increased col12 expression in vitro and contributed to increased collagen deposition in vivo.These data suggest a role for miR-192 in the development of the matrix accumulation observed in DN.It is interesting that the expression of miR-192 was increased by TGF- in mouse MCs (mesangial cells), whereas, conversely, the expression of its target, Zeb2, was decreased [51].This also paralleled the increased Col1 2 and TGF- expression [51].These results suggested that the increase in TGF- in vivo in diabetic glomeruli and in vitro in MCs can induce miR-192 expression, which can target and downregulate Zeb2 thereby to increase Col1 2.This is supported by the report showing that miR-192 is upregulated in human MCs treated with high glucose [51].TGF- induced downregulation of Zeb2 (via miR-192) and Zeb1 (via potentially another miRNA) can cooperate to enhance Col1 2 expression via de-repression at E-box elements [51].In contrast to the above, other reports suggest the relationship between miR-192 and renal fibrosis may be more complicated.Krupa et al. [52] identified two miRNAs in human renal biopsies, the expression of which differed by more than twofold between progressors and nonprogressors with respect to DN, the greatest change occurring in miR-192 which was significantly lower in patients with advanced DN, correlating with tubulointerstitial fibrosis and low glomerular filtration rate.They also reported, in contrast to the Kato et al. [51] study in MCs, that TGF-1 decreased expression of miR-192 in cultured proximal tubular cells (PTCs).These investigators concluded that a decrease in miR-192 is associated with increased renal fibrosis in vivo.Interestingly, connective tissue growth factor (CTGF) treatment also resulted in fibrogenesis but caused the induction of miR-192/215 and, consequently, decreased Zeb2 and increased E-cadherin.The contrasting findings above highlight the complex nature of miRNA research.Some of the differences may relate to models and/or experimental conditions; however, one often overlooked explanation is that some effects of miRNAs and inhibitors are likely to be indirect in nature.A recent report also showed that BMP6-induced miR-192 decreases the expression of Zeb1 in breast cancer cells [53].Thus, TGF- induced increase in the expression of key miRNAs (miR-192 and miR-200 family members) might coordinately downregulate E-box repressors Zeb1 and Zeb2 to increase Col12 expression in MCs related to the pathogenesis of DN.The proximal promoter of the Col1a2 gene responds to TGF- via smads and SP1.Conversely, the downregulation of Zeb1 and Zeb2 by TGF- via miR-200 family and miR-192 can affect upstream E-box regions.Because E-boxes are present in the upstream genomic regions of the miR-200 family, miR-200 family members may themselves be regulated by Zeb1 and Zeb2 [54].It is possible that the miR-200 family upregulated by TGF- or in diabetic glomeruli under early stages of the disease can also regulate collagen expression related to diabetic kidney disease by targeting and downregulating E-box repressors.miR-192 might initiate signaling from TGF- to upregulate miR-200 family members, which subsequently could amplify the signaling by further regulating themselves through down regulation of Ebox repressors.Such events could lead to progressive renal dysfunction under pathologic conditions such as diabetes, in which TGF- levels are enhanced.Conversely, there are several reports that miR-200 family members and miR-192 can be suppressed by TGF-, and this promotes epithelial-tomesenchymal transition (EMT) in cancer and other kidneyderived epithelial cell lines via subsequent upregulation of targets Zeb1 and Zeb2 to repress E-cadherin [54,55].", + "DR. HARRINGTON: You mentioned Liu's data from China [abstract; Liu Z-H et al J Am Soc Nephrol 14:400A, 2003], which overwhelmed me.Apparently there are 182 genes whose expression is up-or down-regulated significantly in patients with diabetes.If I asked you to pick the \"top three\" genes other than the ACE polymorphisms, which three would you choose and why?DR.ADLER: Well, actually I didn't see all of their results nor did they report all 182.But I guess my favorite ones would be some that relate to the ROS pathway because this is an all-purpose pathway of cell injury fueled by a hyperglycemic environment; some that relate to podocyte structure to explain the development of proteinuria; and TGF-b, which is a master regulator of sclerosis and fibrosis.", + "IncRNAs and microRNAs Figure 1 | Emerging molecular mechanisms of diabetic nephropathy.Diabetic conditions induce the expression of growth factors such as transforming growth factor \u03b21 and angiotensin II, cytokines and AGEs to promote inflammation, fibrosis and hypertrophy, which contribute to the progression of diabetic nephropathy.These factors stimulate various signal transduction mechanisms that activate downstream transcription factors.They can also affect DNA methylation and histone modifications, which result in increased chromatin accessibility to transcription factors near pathological genes in renal cells.Coordinated interactions between transcription factors and epigenetic mechanisms can increase the expression of not only coding RNAs, but also noncoding RNAs such as microRNAs and lncRNAs.Furthermore, microRNAs and lncRNAs can also increase the expression of pathological genes via post-transcriptional mechanisms.Notably, the induction of key coding genes and proteins, lncRNAs and microRNAs can also 'lock' open chromatin states to create persistent expression of genes, which could be one mechanism of metabolic memory.Abbreviations: AGE, advanced glycation end-product; lncRNA, long noncoding RNA.", + "Key points \u25a0 Diabetic conditions induce inflammation, fibrosis and hypertrophy in renal cells through various cytokines and growth factors such as transforming growth factor \u03b21, angiotensin II and platelet-derived growth factor \u25a0 The engagement of cytokines and growth factors with their receptors triggers signal transduction cascades that result in the activation of transcription factors to increase expression of inflammatory and fibrotic genes \u25a0 These signalling mechanisms affect epigenetic states-such as DNA methylation and chromatin histone modifications-to augment the expression of profibrotic and inflammatory genes, as well as noncoding RNAs \u25a0 Noncoding RNAs that are induced by diabetic conditions can also promote the expression of pathological genes via various post-transcriptional and post-translational mechanisms \u25a0 These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for 'metabolic memory' \u25a0 Key epigenetic regulators, microRNAs and long noncoding RNAs could serve as new therapeutic targets for diabetic nephropathy", + "| Diabetic nephropathy (DN), a severe microvascular complication frequently associated with both type 1 and type 2 diabetes mellitus, is a leading cause of renal failure.The condition can also lead to accelerated cardiovascular disease and macrovascular complications.Currently available therapies have not been fully efficacious in the treatment of DN, suggesting that further understanding of the molecular mechanisms underlying the pathogenesis of DN is necessary for the improved management of this disease.Although key signal transduction and gene regulation mechanisms have been identified, especially those related to the effects of hyperglycaemia, transforming growth factor \u03b21 and angiotensin II, progress in functional genomics, high-throughput sequencing technology, epigenetics and systems biology approaches have greatly expanded our knowledge and uncovered new molecular mechanisms and factors involved in DN.These mechanisms include DNA methylation, chromatin histone modifications, novel transcripts and functional noncoding RNAs, such as microRNAs and long noncoding RNAs.In this Review, we discuss the significance of these emerging mechanisms, how they mediate the actions of growth factors to augment the expression of extracellular matrix and inflammatory genes associated with DN and their potential usefulness as diagnostic biomarkers or novel therapeutic targets for DN.", + "| microRNAs relevant to the pathogenesis of diabetic nephropathy", + "Review criteria A search for original published articles focusing on \"diabetic nephropathy\", \"signal transduction\", \"noncoding RNAs\", \"microRNAs\", \"long noncoding RNAs\", \"genetics\" and \"epigenetics\" was performed in MEDLINE and PubMed.All articles identified were English-language, full-text papers.We also searched the reference lists of identified articles for further relevant papers." + ], + [ + "Additional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele.", + "Gene\u2013exercise interaction in type 2 diabetes When studying gene\u2013environment interaction on the quantitative traits that underlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002). Achievement of optimal glycaemic control is the focus of traditional treatment paradigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients (Sigal et al 2007).", + "Gene-Environment Interaction Evidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and \u03b2-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131].", + "A person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3).", + "Genes, environment, and development of type 2 diabetes Genes and the environment together are important determinants of insulin resistance and \u03b2-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic.", + "Type 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors.", + "Type 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors.", + "Gene and Environment Selection Environmental factors selected for recent G \u00d7 E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13).", + "We have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G \u00d7 E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G \u00d7 E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G \u00d7 E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G \u00d7 E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D.", + "The purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G \u00d7 E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field.", + "FUTURE PERSPECTIVES Continued investment in studies of G \u00d7 E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G \u00d7 E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G \u00d7 E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications.", + "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM Recently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ).", + "Introduction Genome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene \u00c2 environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4].", + "The literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative.", + "The genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases.", + "The genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases.", + "Predisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation.", + "T2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes.", + "Gene-Environment Interactions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS.", + "Other aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + ], + [ + "Researchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today.", + "A considerable amount of work has focused on dissecting the genetics of diabetes itself; however, fewer studies have been conducted on the molecular mechanisms leading to its specific complications such as DR.To identify susceptibility loci that are associated with T2D retinopathy in Taiwanese population, we conducted a genome-wide association study involving 749 T2D cases (174 with retinopathy and 575 without retinopathy) and 100 nondiabetic controls and identified 12 previously unknown susceptibility loci related to DR.", + "Progress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions.", + "Future directions Delays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4.", + "Recent advances in GWAS have substantially improved our understanding of the pathophysiology of diabetes, but the currently identified genetic susceptibility loci are insufficient to explain differences in diabetes risk across different ethnic groups or the rapid rise in diabetes prevalence over the past several decades.Clinical utility of these loci in predicting future risk of diabetes is also limited.", + "Conclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations.", + "Studies show evidence of considerable genetic component predisposing to diabetic complications, explaining even around 50% of the risk of proliferative retinopathy [11].In the last few decades, genetic research including genome-wide association studies (GWAS), linkage analysis, and candidate gene approach has revealed several susceptibility loci for diabetic retinopathy and nephropathy (VEGF, CAT , FTO, UCP1, and INSR), and also macrovascular complications (ADIPOQ).Nevertheless, they explain only a small proportion of the phenotypic variation observed in T2DM patients [12][13][14][15][16][17], justifying a need for identification of novel genetic risk factors for T2DM complications and improvement of knowledge about molecular mechanisms underlying these comorbid conditions.", + "Methods: We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications.", + "Background: Type 2 diabetes complications cause a serious emotional and economical burden to patients and healthcare systems globally.Management of both acute and chronic complications of diabetes, which dramatically impair the quality of patients' life, is still an unsolved issue in diabetes care, suggesting a need for early identification of individuals with high risk for developing diabetes complications. Methods:We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications. Results:The analysis revealed ten novel associations showing genome-wide significance, including rs1132787 (GYPA, OR = 2.71; 95% CI = 2.02-3.64)and diabetic neuropathy, rs2477088 (PDE4DIP, OR = 2.50; 95% CI = 1.87-3.34),rs4852954 (NAT8, OR = 2.27; 95% CI = 2.71-3.01),rs6032 (F5, OR = 2.12; 95% CI = 1.63-2.77),rs6935464 (RPS6KA2, OR = 2.25; 95% CI = 6.69-3.01)and macrovascular complications, rs3095447 (CCDC146, OR = 2.18; 95% CI = 1.66-2.87)and ophthalmic complications.By applying the targeted approach of previously reported susceptibility loci we managed to replicate three associations: MAPK14 (rs3761980, rs80028505) and diabetic neuropathy, APOL1 (rs136161) and diabetic nephropathy.Conclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations.", + "Discussion Here we present the results of the genome-wide association study for T2DM complications performed in a population of Latvia for the first time, revealing 10 susceptibility loci for T2DM complications, including diabetic neuropathy, macrovascular and ophthalmic complications.As in other reports aimed to identify the risk factors of T2DM complications [15,32], the control group of our study consisted of T2DM patients with no evidence of the complication type of interest instead of conventional healthy subjects, since the implementation of healthy controls would rather reveal genetic associations with the diagnosis of T2DM itself, not the T2DM complications.", + "Genetic determinants of diabetes and metabolic syndromes.", + "Conclusions As compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up.", + "Research Gaps After consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations.", + "COMPLICATIONS In addition to the genetic determinants of diabetes, several gene mutations and polymorphisms have been associated with the clinical complications of diabetes.The cumulative data on diabetes patients with a variety of micro-and macrovascular complications support the presence of strong genetic factors involved in the development of various complications [200] .A list of genes have been reported that are associated with diabetes complications including ACE and AKR1B1 in nephropathy, VEGF and AKRB1 in retinopathy and ADIPOQ and GLUL in cardiovascular diseases [200] .", + "How do we identify the major 'culprits' at the implicated genome-wide association study loci? If population-based genetics, including genome-wide association studies, have allowed progress in the identification of Type 2 diabetes loci to be rapid over the past few years, progress towards determining which of the gene variants close to the implicated loci confer altered disease risk and how (at the molecular, cellular and whole body level) has lagged some way behind.Indeed, given the number of possible single nucleotide polymorphisms and genes, unravelling these questions represents a monumental challenge, requiring multiple, complementary approaches.Nonetheless, the rewards of success, in terms of new understanding of disease mechanisms and even the identification of new targets for therapeutic intervention, are likely to be great, potentially allowing the treatment of underlying disease aetiology in a personalized (stratified) manner.", + "During the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized.", + "During the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized.", + "Conclusions and Future Directions GWAS and GWAS meta-analyses have by far been the most efficient way to identify new T2D genes (Figure 2), but their predictive value for future occurrence of T2D has been very limited compared to classic risk factors such as obesity and fasting glucose levels (Walford et al., 2014).Although it might be good news that our genome does not fully dictate our future, the knowledge of its specificities may help us to improve our health.Early genetic studies showed that the higher risk for T2D conferred by TCF7L2 variant can be reversed by lifestyle intervention (Florez et al., 2006), opening avenues for strategies targeted on genetically selected individuals with pre-diabetes.TCF7L2 has also been shown to be associated with a lower efficiency of oral sulfonylureas in newly diagnosed T2D patients (Pearson et al., 2007), but a more recent Danish study suggested that in contrast to clinical markers, all known T2D-associated variants do not significantly affect the time to prescription of the first drug after disease onset (Hornbak et al., 2014).In other words, frequent SNPs are not helpful to predict patients' futures, though the good use of genetic data may contribute to provide better care to newly diagnosed T2D patients who are currently all treated the same (with metformin).", + "Background Multiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus.We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone.", + "Genetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + ], + [ + "Genetics and pharmacogenomics We are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor \u03b3 (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a \u03b2 cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18).", + "With further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients.", + "Pharmacogenomics of Type 2 Diabetes With the advent of GWAS, studies on the roles of inherited and acquired genetic variations in drug response have undergone an evolution from pharmacogenetics into pharmacogenomics, with a shift from the focus on individual candidate genes to GWAS [147].Clinically, it is often observed that even patients who receive similar antidiabetic regimens demonstrate large variability in drug disposition, glycemic response, tolerability, and incidence of adverse effects [148].This interindividual variability can be attributed to specific gene polymorphisms involved in the metabolism, transportation, and therapeutic mechanisms of oral antidiabetic drugs.Pharmacogenomics is on the agenda to explore feasible genetic testing to predict treatment outcome, so that appropriate steps could be taken to treat type 2 diabetes more efficiently.", + "Future directions Delays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4.", + "Genomics of T2D Diet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes.", + "Genetics & genomics of T2D \u2022 Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D.However, only close to 10% heritability is explained by these variants.Other genetic variants, particularly those which are rare but with significant effects need to be identified.\u2022 Genetic variability is responsible for the difference in response to antidiabetic drugs seen across individuals.", + "The aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484", + "Diabetes progression is a multifactorial process; however, pharmacogenetics seems to play an important role in understanding the different phenotypes and progression rates among diabetic patients.Genetic variants associated with decreased effect of a certain drug might explain why some individuals are more likely to experience glycemic deterioration on a given treatment.In the following sections, different genetic variants and their impact on treatment efficacy and outcome will be addressed.", + "The aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484", + "To date, a number of genetic variants have been identified to be associated with response to antidiabetic drugs.Of these, some variants are present in either drug receptors or drug metabolizers as for OCT genes, KCNJ11, ABCC8, and CYP2C9.Other variants are known T2D susceptibility variants such as TCF7L2.To identify variants of importance for antiglycemic drug response, GWAS in large cohorts of patients with diabetes with detailed measures of pharmacotherapy are lacking.The pharmacologic management of patients with diabetes often involves drug classes other than antidiabetics.Pharmacogenetic studies on statin and antihypertensive treatment have reported several genetic variants associated with treatment response and adverse drug reactions [101,102].It therefore seems natural to conclude that the future perspectives in pharmacogenetics is to conduct genetic studies in large cohorts with wellphenotyped individuals, thorough data collection on baseline treatment, concomitant treatment, adherence to therapy as well as data collection on comorbidity and additional disease diagnoses.These types of pharmacogenetic studies may provide unique opportunities for future genotype-based treatment standards and may help in delaying or changing the slope of disease progression among patients with T2D.", + "Genetic determinants of diabetes and metabolic syndromes.", + "Thus, specific answers are lacking as to the genetic basis for type 2 diabetes.Still, speculations can be made about what eventually will be found.It is almost certain the genetic basis for type 2 diabetes and other common metabolic diseases will be extremely complex-that a predisposition for the disease will require several genetic hits as opposed to just one.Also, it is generally assumed there will be many susceptibility genes for type 2 diabetes, with enormous variability in different families and ethnic groups.Not known is whether there will be a common form of type 2 diabetes, with any one or even a few susceptibility genes accounting for a sizeable percentage of affected persons.As such, identifying diabetes genes will be slow and difficult.", + "Ta rge ted T r e atmen t a nd Pr e v en t ion 4][75] In monogenic forms of diabetes, at least, genetic testing already drives the choice of therapy.For example, in patients who have maturity-onset diabetes of the young due to mutations in the gene encoding glucokinase (GCK), the hyperglycemia is mild and stable, the risk of complications is low, and dietary management is often sufficient.In contrast, in patients who have maturity-onset diabetes of the young due to mutations in HNF1A, the disease follows a more aggressive course, with a greater risk of severe complications, but is particularly responsive to the hypoglycemic effects of sulfonylureas. 62,73Most children with neonatal diabetes have mutations in KCNJ11 or ABCC8, adjacent genes that jointly encode the beta-cell ATP-sensitive potassium channel that mediates glucose-stimulated insulin secretion and is the target of sulfonylureas.In such children, treatment with sulfonylureas has proved more effective and convenient than the lifelong insulin therapy previously considered the default option. 74,75n children with severe obesity due to profound leptin deficiency, exogenous leptin therapy is lifesaving. 76s yet, there are insufficient genetic data to support management decisions for common forms of type 2 diabetes and obesity. 77Although the TCF7L2 genotype is associated with variation in the response to sulfonylurea treatment, 78 the effect is too modest to guide the care of individual patients.For the time being, the contribution of genetic information to therapy is most likely to come through the drug-discovery pipeline.Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physiological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32 and to characterize high-risk groups to enable more efficient clinical trials of agents designed to reduce the progression of type 2 diabetes or obesity or the risk of complications.", + "Type 2 Diabetes While a subset of genetic variants are linked to both type 1 and type 2 diabetes (42,43), the two diseases have a largely distinct genetic basis, which could be leveraged toward classification of diabetes (44).Genome-wide association studies have identified more than 130 genetic variants associated with type 2 diabetes, glucose levels, or insulin levels; however, these variants explain less than 15% of disease heritability (45)(46)(47).There are many possibilities for explaining the majority of type 2 diabetes heritability, including disease heterogeneity, gene-gene interactions, and epigenetics.Most type 2 variants are in noncoding genomic regions.Some variants, such as those in KCNQ1, show strong parent-of-origin effects (48).It is possible that children of mothers carrying KCNQ1 are born with a reduced functional b-cell mass and thereby are less able to increase their insulin secretion when exposed to insulin resistance (49).Another area of particular interest has been the search for rare variants protecting from type 2 diabetes, such as loss-of-function mutations in SLC30A8 (50), which could offer potential new drug targets for type 2 diabetes.", + "Research Gaps After consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations.", + "Genome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 \u00d7 10 \u22128 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF \u2264 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total \u223c88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes.", + "Together, the findings from these studies were among the first to demonstrate that the genetic etiology of hyperglycemia may modulate response to hypoglycemia agents.Such results yielded strong implications for patient management and paved the way toward elucidating additional genetic factors that might influence drug response in the treatment of T2D.", + "A number of studies have implicated a genetic basis for type 2 diabetes (1).The discovery of monogenic forms of the disease underscored the phenotypic and genotypic heterogeneity, although monogenic forms account for only a few percent of the disease (1).Defining the genetic basis of the far more common polygenic form of the disease presents more difficulties (2,3).Nevertheless, some interesting results have recently emerged.A genome scan of Hispanic-American families (330 affected sib-pairs [ASPs]) found linkage to chromosome 2q37 (logarithm of odds [LOD] 4.15) (4), and the causative gene has been recently reported (5).A number of other genome scans in various racial groups have identified other putative susceptibility loci (6 -8).The largest genome-wide scan for type 2 diabetes loci reported to date studied 477 Finnish families (716 ASPs) and found evidence for linkage to chromosome 20q12-13.1(LOD 2.06 at D20S107) (9).Interestingly, similar results have been reported by at least three other groups (10 -12).", + "Because more than one genetic mutation contributes to T1D, the differences that occur between individuals of different backgrounds (for instance, race and locality) may need to be considered in the design of treatments.Personalized medicine is about the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or in their response to a specific treatment (Blau and Liakopoulou, 2013;Timmeman, 2013).This will allow for a more accurate diagnosis per individual, and design of specific treatment plans including gene therapy.", + "Genetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/old/experts_aging1_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/experts_aging1_dataset.json new file mode 100644 index 0000000..6429e09 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/experts_aging1_dataset.json @@ -0,0 +1,128 @@ +{ + "question": [ + "Which mouse genes have been associated with longevity?", + "Is lifespan determined by genetics?", + "Is there a direct association between aging and susceptibility to having diabetes?", + "Which genes are associated with aging in humans?", + "What genetic factors influence aging in humans?" + ], + "answer": [ + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "Lifespan is partially determined by genetics, with studies estimating that genetics contribute to about 25-30% of the variation in human lifespan. However, it is also significantly influenced by non-heritable factors such as environment and lifestyle.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "The genes associated with aging in humans are APOE and FOXO3A.", + "Several genetic factors influence aging in humans. These include the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling pathway, and the exonuclease 1 (EXO1) gene. Other genes associated with aging are those involved in pathways such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response. Additionally, genes related to immune response, energy metabolism, signal transduction pathways, and cellular senescence also play a role in aging." + ], + "contexts": [ + [ + "DOI: https://doi.org/10.7554/eLife.75244 \b 24 of 30 Chromosomes and Gene Expression | Genetics and Genomics Research article\ufeff\ufeff\ufeff\ufeff\ufeff\ufeff Continued Author(s) Year Dataset title Dataset URL Database and Identifier Longevityteam 2021 Genetics of longevity in BXD mice http://www.\u200b BDL_10006, 10006 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10006&\u200bdataset=\u200bBXD-\u200b LongevityPublish Longevityteam 2021 Genetics of longevity in BXD mice http://www.\u200b BDL_10010, 10010 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10010&\u200bdataset=\u200bBXD-\u200b LongevityPublish Longevityteam 2021 Genetics of longevity in BXD mice http://www.\u200b BDL_10011, 10011 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10011&\u200bdataset=\u200bBXD-\u200b LongevityPublish Longevityteam 2020 Genetics of longevity in BXD mice http://www.\u200b BDL_10021, 10021 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10021&\u200bdataset=\u200bBXD-\u200b LongevityPublish Longevityteam 2020 Genetics of longevity in BXD mice http://www.\u200b BDL_10022, 10022 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10022&\u200bdataset=\u200bBXD-\u200b LongevityPublish Longevityteam 2020 Genetics of longevity in BXD mice http://www.\u200b BDL_10025, 10025 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10025&\u200bdataset=\u200bBXD-\u200b LongevityPublish Longevityteam 2021 Genetics and epigenetics of aging and longevity in BXD mice http://www.\u200b BDL_10066, 10066 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10066&\u200bdataset=\u200bBXD-\u200b LongevityPublish References Albertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P, Carlson M. 1994.", + "DOI: https://doi.org/10.7554/eLife.75244 \b 24 of 30 Chromosomes and Gene Expression | Genetics and Genomics Research article\ufeff\ufeff\ufeff\ufeff\ufeff\ufeff Continued Author(s) Year Dataset title Dataset URL Database and Identifier Longevityteam 2021 Genetics of longevity in BXD mice http://www.\u200b BDL_10006, 10006 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10006&\u200bdataset=\u200bBXD-\u200b LongevityPublish Longevityteam 2021 Genetics of longevity in BXD mice http://www.\u200b BDL_10010, 10010 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10010&\u200bdataset=\u200bBXD-\u200b LongevityPublish Longevityteam 2021 Genetics of longevity in BXD mice http://www.\u200b BDL_10011, 10011 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10011&\u200bdataset=\u200bBXD-\u200b LongevityPublish Longevityteam 2020 Genetics of longevity in BXD mice http://www.\u200b BDL_10021, 10021 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10021&\u200bdataset=\u200bBXD-\u200b LongevityPublish Longevityteam 2020 Genetics of longevity in BXD mice http://www.\u200b BDL_10022, 10022 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10022&\u200bdataset=\u200bBXD-\u200b LongevityPublish Longevityteam 2020 Genetics of longevity in BXD mice http://www.\u200b BDL_10025, 10025 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10025&\u200bdataset=\u200bBXD-\u200b LongevityPublish Longevityteam 2021 Genetics and epigenetics of aging and longevity in BXD mice http://www.\u200b BDL_10066, 10066 genenetwork.\u200borg/\u200b show_\u200btrait?\u200btrait_\u200bid=\u200b 10066&\u200bdataset=\u200bBXD-\u200b LongevityPublish References Albertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P, Carlson M. 1994.", + "Leduc MS, Hageman RS, Meng Q et al (2010) Identification of genetic determinants of IGF-1 levels and longevity among mouse inbred strains. Aging Cell 9(5):823\u2013836. doi:10.1111/j.14749726.2010.00612.x 10. Lang DH, Gerhard GS, Griffith JW et al (2010) Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clin Exp Res 22(1):8\u201319 11. Gelman R, Watson A, Bronson R et al (1988) Murine chromosomal regions correlated with longevity. Genetics 118(4):693\u2013704 12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci associated with life span exhibit sex-specific and epistatic effects.", + "Conclusions These results suggest a novel locus influencing survival in the B6/D2 genetic background, perhaps via a metabolic disorder that emerges by 200 days of age in male animals. Keywords Pathology Longevity \\ Lifespan \\ Mouse \\ Linkage \\ Introduction Longevity, the quintessential complex trait, likely reflects all aspects of an organism\u2019s life history. In humans, the estimated heritability of age at death is estimated at 25\u201333 % [1]. Genetic contributions to mortality rates are thus of great interest and may aid in the understanding of disease etiology and the process of aging itself [2].", + "Here, we have extended this analysis to search for genotypes related to survival to the age of 800 days in a population of a reciprocal F2 cross between (B6) and (D2) mice. Since QTL for longevity in mice have shown strong sex specificity [10, 12], we conducted sex-specific analyses. In addition, we also determined whether there were any change in pathology changes associated with the loci that showed frequency distortions with aging. To confirm the associations of the loci of interest with longevity and pathology, we performed replication analyses on a panel of BXD recombinant inbred strains.", + "352(6291): p. aad0189. Liao, C.Y. , et al. , Genetic variation in the murine lifespan response to dietary restriction: from life extension to life shortening. Aging Cell, 2010. 9(1): p. 92-5. Johnson, M., Laboratory Mice and Rats. Mater. Methods, 2012. 2: p. 113. Fontaine, D.A. and D.B. Davis, Attention to Background Strain Is Essential for Metabolic Research: C57BL/6 and the International Knockout Mouse Consortium. Diabetes, 2016. 65(1): p. 25-33. Simon, M.M. , et al. , A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol, 2013. 14(7): p. R82. Lilue, J., et al.", + "Mamm Genome 2001;12: 930\u20132. 21 Gelman R, Watson A, Bronson R, Yunis E. Murine chromosomal regions correlated with longevity. Genetics 1988;118:693\u2013704. 22 Peirce JL, Lu L, Gu J, Silver LM, Williams RW. A new set of BXD recombinant inbred lines from advanced intercross populations in mice. BMC Genet 2004;5:7. 23 Rahman ZS, Tin SK, Buenaventura PN et al. A novel susceptibility locus on chromosome 2 in the (New Zealand Black \\ New Zealand White) F1 hybrid mouse model of systemic lupus erythematosus. J Immunol 2002;168:3042\u20139. 24 Kono DH, Burlingame RW, Owens DG et al.", + "Conversely, the BXD strain with the shortest life span (BXD14) has the lowest responsiveness to the stimulatory effect of TGF-\u24242 when old (48). The region on chromosome 2 where a suggestive QTL regulating the responsiveness to TGF-\u24242 in old mice is located also contains two QTL for longevity (32). Finally, the strongest support for this hypothesis is the correlation between longevity and the age-related increase in the serum-dependent effect of TGF-\u24242 on LSK cells, the extent of which may determine stem cell function in aged mice.", + "FIGURE 8-5 Genetic regulation of longevity in mice stratified by cause of death.Female mice that inherit the C3H allele at D2Mit58 plus the BALB allele at D16Mit182 (light gray bars) have significantly higher longevity than their sisters (dark gray bars) with the C57BL/6 plus DBA/2 allele combination (\"all causes\" of death combined).Subsets of mice that died either of cancer or of a nonneoplastic (\"benign\") illness both show the association between genotype and longevity.Among the mice dying of neoplasia, subsets dying of lymphoma or of fibrosarcoma show equivalent, and significant, genotypic effects.Bars indicate means plus standard error of the mean.SOURCE:Miller et al. (unpublished results).", + "The available dataset also provides examples in which genetic variants seem to influence the risk of specific late-life diseases.Figure 8-6, for example, shows longevity results for mice stratified by their inheritance at the 12th chromosome locus D12Mit167.This is a locus associated with differential longevity in both male and female mice, with the strongest effect (adjusted p < 0.01) seen in those mice living more than 657 days (Jackson et al., unpublished results).The longest-lived mice are those that inherit both the C57BL/6 allele from their mother and the C3H allele from their father; on average, they survive 93 days longer than siblings with the BALB plus C3H combination.Figure 8-6 shows that the D12Mit167, like the pair of loci illustrated in Figure 8-5, has significant and similar effects in mice dying of cancer (85 days) and in mice dying of non-neoplastic diseases (126 days).A more detailed analysis of the cancers, however, suggests that while lymphoma and hepatoma victims are equally protected by the favorable alleles (effect sizes of 93 and 167 days, respec- mice of two subgroups: those dying of the urinary syndrome MUS, and those dying of all other causes.The genetic analysis contrasts mice with both the C57BL/6 allele at D4Mit84 and the C3H allele at D9Mit110 to mice with any of the three other allele combinations.In the males dying of causes other than MUS, this allele pair is associated with a 170-day increment in longevity (post-hoc p < 0.00003).But for males that do die of MUS, the same allele combination is associated with a 187-day decline in mean life span (post-hoc p < 0.03).This effect is thus pleiotropic, in that these alleles accelerate death in mice susceptible to MUS, while postponing death for all other males in the population.Although these loci are associated with differential longevity in mice that do develop MUS, they do not have a significant effect on the chances that MUS will indeed occur (not shown).The risk of developing MUS seems to be under control of a separate locus on chromosome 6.As shown in the bottom panel of Figure 8-7, males that inherit the C3H allele at D6Mit268 are far more likely to develop MUS (28 percent risk) than are their brothers who receive the DBA/2 allele at this locus (7 percent risk; p = 0.012 by two-tailed Fisher's exact test).", + "High levels of CD8M cells are associated with diminished longevity in mated females (left panel; p < 0.001), but not in virgin females (center panel).Among virgin males, those dying of diseases other than the urinary syndrome MUS show no association between CD8M and longevity (open circles, upper line), but those dying because of MUS show a nonsignificant trend (filled circles, lower line, R = -0.27,p = 0.13) similar to the relationship observed in mated females.SOURCE : Miller et al. (unpublished results).Male or female mice that inherit the C57BL/6 (maternal) and C3H (paternal) alleles at D12Mit167 (light gray bars) are longer lived than their siblings that inherit the BALB plus C3H combination.The \"effect size\" shown at the right represents that difference in mean longevity between mice in the two genetically different groups, with (**) = p < 0.01 and (*) = p < 0.05 by t-test.Similar effect sizes are seen for mice dying of cancer or of non-neoplastic illnesses (\"benign\"), and among the cancer deaths the genetic effect is similar for deaths due to lymphoma and hepatoma.The genetic effect on longevity seems to be minimal, however, for mice dying of fibrosarcoma.Bars show means plus standard errors.SOURCE : Miller et al. (unpublished results).", + "Our own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice.", + ", Vogler, G.P. , Vandenbergh, D.J. , Blizard, D.A. , Stout, J.T. & McClearn, G.E. Quantitative Trait Locus (QTL) Analysis of Longevity in C57BL/6J byDBA/2J (BXD) Recombinant Inbred Mice. Aging Clin Exp Res (in press). Lionikas, A., Blizard, D.A. , Vandenbergh, D.J. , Glover, M.G. , Stout, J.T. , Vogler, G.P. , McClearn, G.E. & Larsson, L. (2003) Genetic architecture of fast- and slow-twitch skeletal muscle weight in 200-day-old mice of the C57BL/6J and DBA/2J lineage. Physiol Genomics 16, 141\u2013152. Lionikas A., Blizard D.A. , Gerhard G.S. , Vandenbergh D.J. , Stout J.T. , Vogler G.P. , McClearn G.E.", + "Deficiency mapping of quantitative trait loci affecting longevity in Drosophila melanogaster. Genetics 2000;156:1129\u20131146. [PubMed: 11063689] 33. Ma RZ, et al. Identification of Bphs, an autoimmune disease locus, as histamine receptor H1. Science 2002;297:620\u2013623. [PubMed: 12142541] Nat Rev Genet. Author manuscript; available in PMC 2007 November 5. Page 12 NIH-PA Author Manuscript 34. Vivian JL, Chen Y, Yee D, Schneider E, Magnuson T. An allelic series of mutations in Smad2 and Smad4 identified in a genotype-based screen of N-ethyl-N-nitrosourea-mutagenized mouse embryonic stem cells. Proc. Natl Acad. Sci. USA 2002;99:15542\u201315547. [PubMed: 12432092] 35. Vogel G. Scientists dream of 1001 complex mice.", + "34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with longevity. Genetics 118, 693\u2013704 (1988). [PubMed: 3163317] 35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011). 36. Houtkooper RHet al.Mitonuclear protein imbalance as a conserved longevity mechanism. Nature497, 451\u2013457 (2013). [PubMed: 23698443] 37. Williams EGet al.An Evolutionarily conserved role for the aryl hydrocarbon receptor in the regulation of movement. PLOS Genet. 10, e1004673 (2014). [PubMed: 25255223] 38. Lang DHet al.Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clin. Exp. Res. 22, 8\u201319 (2010).", + "In addition, the B6 mouse strain is one of the longest-lived mouse strains with a mean lifespan of 3 years versus other mouse strains with mean lifespan from 1.5-2 years. Therefore, it is evident that the genetic background of a particular mouse strain can have a profound effect on the biology of the HSC population as well as organismal longevity. Indeed, it is for this reason that it is difficult to compare findings from various laboratories where different mouse strains are used.", + "NIH-PA Author Manuscript This study indicated a large amount of genetic variation for mouse longevity; heritability was 34% for AL and 36% for DR (60% of AL food intake). There was no significant correlation between mean longevity under these two conditions, although maximum lifespans of the AL and DR mice were significantly correlated. Similar observations were made at the UTHSCSA on the ILSXISS RI mice (Liao et al. , 2010a, b; Mattson 2010), where they also observed similar heritability (28% AL males, 36% AL females, 55% DR males, 53% DR females).", + "For females, hairs of the congenic mice grew 31% faster, also highly significant (P = 0.0006, 1-tailed). These results validated the presence of a gene in the differential region affecting FE. Discussion We report the outcomes of a quantitative genetic study on aging and longevity in the mouse. We studied an extant series of recombinant inbred strains (ILSXISS) that have been used both in DR aging studies as well as to study alcohol sensitivity (Williams et al. , 2004).", + "(2007) is a separate issue from the analyses conducted in this study (the AL efficiency model will be tested in future studies). Exp Gerontol. Author manuscript; available in PMC 2011 September 1. Rikke et al. Page 8 NIH-PA Author Manuscript Other studies have also reported that individual mice that maintained the highest BW were likely to be the longest-lived individuals among cohorts of genetically identical mice (Weindruch et al. , 1986; Harper et al. , 2006).", + "Age-associated changes are conserved between mouse strains Life span and aging vary between mouse strains.For example, C57BL/6 mice are long-lived compared to the short-lived DBA/2 mice (Turturro et al. 1999).To test the generality of our observations, we also examined LT-HSCs, ST-HSC and MPPs in young and old mice from the DBA/2 strain, which originates from a distinct breeding lineage (Fox 1997)." + ], + [ + "Our results show that longevity is partly determined by the predisposition to common diseases and, to an even greater extent, by modifiable risk factors.The genetic architecture of lifespan appears complex and diverse and there appears to be no single genetic elixir of long life.", + "L ongevity is of interest to us all, and philosophers have long speculated on the extent to which it is pre-determined by fate.Here we focus on a narrower question-the extent and nature of its genetic basis and how this inter-relates with that of health and disease traits.In what follows, we shall use longevity as an umbrella term.We shall also more specifically refer to lifespan (the duration of life) and long-livedness (living to extreme old age, usually defined by a threshold, such as 90 years).Up to 25% of the variability in human lifespan has been estimated to be genetic 1 , but genetic variation at only three loci (near APOE, FOXO3A and CHRNA3/5) [2][3][4][5] have so far been demonstrated to be robustly associated with lifespan.", + "GENETICS OF LIFE SPAN IN HUMANS Most studies of human twins agree that the heritability of life span is less than 50% (45,68).Of particular interest is an ongoing study of aging in Swedish twins that includes a large group of adopted twins who were reared separately.Ljungquist et al. (68) concluded that \"a maximum of one-third the variance in integrated mortality risk is attributable to genetic factors and that almost all of the remaining variance is due to nonshared, individually unique environmental factors. \"Moreover, this heritability declined with age and was negligible after the age of 85 in men and 90 in women.", + "How can lifespan be controlled by a single gene?Two possibilities are, first, that the mutations that extend lifespan are in genes whose products regulate the activity of many other genes and, second, that these genes do not in fact control the rate of ageing.", + "Since that time, observations across species have shown that life span can be extended by genetic factors.One of the first demonstrations of this entailed the study of recombinant inbred populations of the nematode worm Caenorhabditis elegans by Thomas E. Johnson.Then a postdoc in William (Bill) Wood's lab at the University of Colorado Boulder, Tom and Bill demonstrated that crosses of C. elegans strains did not display the heterosis effect that interfered with many other studies, \"As predicted, we found significant genetic effects on life span as well as other life history traits. \"This finding established a method for evaluating genetic factors that influenced life-span variation.In fact, their measurements of life span of the recombinant inbred strains demonstrated the heritability of life span to be 19%-51% (1).Consistent with theories of the 1970s and 1980s, it was concluded that these genetic factors were a collection of small influences across many genes.This finding was one of the first steps in demonstrating that genetic factors influence aging.As genetic analysis was making great progress in understanding other biological processes, such as developmental programming, the realization that aging could be investigated using the same tools was highly significant.", + "Although it is known that health and lifespan are heavily influenced by genetics [14], variations in the lifespan of different individuals within the same species seem to be more the result of the accumulation over time of molecular damage that compromises the function of the cells [15].These molecular alterations can occur both at the genetic and epigenetic levels and depend on genetic, environmental, and stochastic factors [16].This complex multifactorial mix determined characteristics, such as longevity and a healthy lifespan, which are central concerns of human existence (Fig. 13.1).This chapter describes different types of tools in genomics used in ageing research and their different applications in clinical scenarios.", + "Age at death in adulthood has a moderate genetic component overall, with a heritability of approximately 25% (Murabito et al., 2012).Heritability of longevity increases with age, with a negligible genetic contribution to survival up to approximately 60 years of age, after which an increasing genetic component to survival is observed (Brooks-Wilson, 2013;Christensen et al., 2006).Most genetic studies of aging have focused on long-lived individuals, typically defined as centenarians 100 years or older, who may have had exceptional survival due to medical interventions (Murabito et al., 2012).A number of genetic associations with exceptional longevity have been made (Atzmon et al., 2006;Bojesen and Nordestgaard, 2008;Hurme et al., 2005;Kuningas et al., 2007;Melzer et al., 2007;Pawlikowska et al., 2009;Sanders et al., 2010;Suh et al., 2008;Willcox et al., 2008), with only markers at APOE and FOXO3A being well replicated (Murabito et al., 2012).Overall, the results of genetic and epidemiological longevity studies suggest aging is a complex trait and that achievement of exceptional longevity may not best capture the genetics of resistance to or delay of age-associated disease (Christensen et al., 2006).", + "Introduction Worldwide human populations have shown an increase in mean life expectancy in the past two centuries (Oeppen & Vaupel, 2002).This is mainly because of environmental factors such as improved hygiene, nutrition, and health care.The large variation in healthy lifespan among the elderly has prompted research into the determinants of aging and lifespan regulation.The genetic contribution to human lifespan variation was estimated at 25-30% in twin studies (Gudmundsson et al., 2000;Skytthe et al., 2003;Hjelmborg et al., 2006).The most prominent genetic influence is observed in families in which the capacity to attain a long lifespan clusters (Perls et al., 2000;Schoenmaker et al., 2006).Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al., 2008;Terry et al., 2008), raising the question whether protective mechanisms against disease exist in long-lived subjects.", + "Introduction Human lifespan is a highly complex trait, the product of myriad factors involving health, lifestyle, genetics, environment, and chance.The extent of the role of genetic variation in human lifespan has been widely debated (van den Berg et al., 2017), with estimates of broad sense heritability ranging from around 25% based on twin studies (Ljungquist et al., 1998;Herskind et al., 1996;McGue et al., 1993) (perhaps over-estimated [Young et al., 2018]) to around 16.1%, (narrow sense 12.2%) based on large-scale population data (Kaplanis et al., 2018).One very recent study suggests it is much lower still (<7%) (Ruby et al., 2018), pointing to assortative mating as the source of resemblance amongst kin.", + "Many factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone.Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases.If such genes exist, their effects were too small to be detected in this study.The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease.", + "Life Span During the last decade a variety of twin studies have shown that approximately 25 percent of the variation in life span is caused by genetic differences.This seems to be a rather consistent finding in various Nordic countries in different time periods and even so among other species not living in the wild (Herskind et al., 1996;Iachine et al., 1999;Finch and Tanzi, 1997).their relative magnitude and pattern depend on sex and on the socioeconomic environment experienced by successive birth cohorts.Genetic effects were most pronounced in periods with consciously controlled fertility, suggesting that the genetic disposition primarily affects fertility behavior and motivation for having children.Analyses of fertility motivation in some of the more recent twin cohorts, measured by age at first attempt to have children, supported this interpretation.", + "The Height-Life Span Nexus Several observations and lines of experimentation have raised the issue of whether interindividual differences in aging rate are influenced by genes that modulate body size and early-life growth patterns.These include (a) the association between small stature and exceptional longevity in calorically restricted rodents (Yu et al., 1985), methionine-restricted rats (Orentreich et al., 1993), and mutant dwarf mice (Brown-Borg et al., 1996;Miller, 1999); and (b) the association between small body size and longer life span in natural populations of mice (Falconer et al., 1978), flies (Hillesheim and Stearns, 1992), dogs (Li et al., 1996), and, possibly, people (Samaras andStorms, 1992).The correlation in dogs is particularly striking: selective breeding for dogs of different body size has produced breeds varying in size from Chihuahua to Irish wolfhound.These breeds also vary greatly in mean longevity, from approximately 7 to 10.5 years, and the correlation between breed longevity and breed body weight (Miller, 1999) is a remarkable R 2 = 0.56.These differences are genetic and affect stature rather than obesity: no amount of overeating will convert a West Highland white terrier to a St. Bernard.The selective pressures applied were designed to create dogs of specific sizes and temperaments and were not intended to influence aging rate or life span.The clear implication is that the effects on longevity are pleiotropic, i.e., that genes selected for their effect on body size and conformation influenced life span as a side effect.It is of interest to note that the few analyses (Eigenmann et al., 1984(Eigenmann et al., , 1988) ) of the hormonal basis for interbreed differences in body size have shown that the genes in question influence levels of IGF-1, the most likely mediator of the life-span effects in the long-lived df/df and dw/dw mouse mutants.Could it be mere coincidence that long-lived mutant nematode worms (Kimura et al., 1997) also show mutations in genes related to insulin and IGF-1 receptors?", + "Altogether, the twin and genealogical studies have shown that human lifespan is heritable, but is significantly influenced by non-heritable factors, which may explain why genetic studies of lifespan have proven to be challenging.", + "Twin studies have shown that the heritability of lifespan ranges between 0.01 and 0.27 in various European populations (Ljungquist et al., 1998;van den Berg et al., 2017).Large genealogical studies are more powered to address questions FIGURE 1 | Relationship between aging and lifespan variation versus species defining lifespan. (A) Lifespan comparisons within species, measured as mean (50%) or portion of a population living till extended limits of lifespan (90-95%).Differences between populations (orange and green) can identify specific genetic or environmental changes associating with long life.These factors promote viability and often associate with increasing healthspan.Mutant analysis within a particular model organism often encompasses these types of changes as it relates to lifespan. (B) Maximum lifespans recorded for different species (A-E).While lifespan variation within a species is capped to a certain extent, variation between species can range dramatically.Changes to maximum lifespan often are associated with protective mechanisms for genomic and genetic fidelity as well as life history changes as they relate to maturation and reproduction.", + "The genetic component of human lifespan based on twin studies has been estimated to be around 20-30 percent in the normal population [7], but higher in long-lived families [8][9][10].Furthermore, siblings, parents, and offspring of centenarians also live well beyond average [11,12].Lifestyle choices in terms of smoking, alcohol consumption, exercise, or diet does not appear to differ between centenarians and controls [13].Taken together, these findings provide ample evidence that extreme longevity has a genetic component .", + "Introduction Human longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006).", + "Unraveling the heritability of human longevity was one of the first problems faced by geneticists.Just over a century ago, Mary Beeton and Karl Pearson [1] described a resemblance among relatives for the duration of life.A short time later, Yule [2] and Fisher [3] proved that the correlation is to be expected if lifespan is influenced by what had recently been termed 'genes' [4].Indeed, a century of correlation studies have established that something on the order of 30-50% of the total variation in human life span is attributable to genetic variation [5].Despite the wealth of diversity, specific genes contributing to this variation have proven notoriously difficult to identify.Sample size and issues of shared environment limit family-based methods such as linkage analysis, where rough genomic positions of important genetic variants are identified by comparing a small number of exceptionally long-lived people in defined pedigrees.", + "Human lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation.", + "Human lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation.", + "Studies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches." + ], + [ + "Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.", + "There are two major factors that underlie these alarming projections.The first is T2D is associated with age, and Western populations are aging rapidly.The second major explanation is our lifestyles have changed dramatically in recent years.Epidemiological studies have identified strong T2D risk relationships for obesity, sedentary behavior [2][3][4], and diets rich in energy [5], processed carbohydrates [6], and animal fats [7].Collectively, these lifestyle factors impede the actions of insulin and raise hepatic glucose production, which can result in the diminution of endogenous insulin production and T2D.The strongest evidence for a causal relationship between adverse lifestyle behaviors and T2D comes from randomized controlled trials that show intensive lifestyle interventions involving structured exercise regimes which promote habitual physical activity (PA) and have a major beneficial impact on diabetes incidence in high-risk individuals [8,9].", + "Epidemiological studies examining the associations between lifestyle behaviors and diabetes risk have reached similar conclusions as the clinical trials described above.For example, the 14-year follow-up University of Pennsylvania Alumni Health Study [52] (n = 5,990 men aged 39-68 years) showed PA (leisure time physical activity [LTPA] expressed in kcal expended per week through walking, stair climbing, and sports) was inversely associated with the incidence of T2D.Incidence rates declined as energy expenditure rose from 500 through 3,500 kcal/week.The age-adjusted relative risk ratio (RR) of T2D was reduced by about 6% for each 500 kcal increment increase in PA energy expenditure.", + "Overall, results were similar in analyses restricted to diabetes mellitus identified at baseline only, although the confidence interval included 1.These results suggest that diabetes mellitus is related to risk of AD in old age.These findings are consistent with the results of 2 large longitudinal cohort studies. 5,6In one study, 5 diabetes mellitus doubled the risk of AD during 2 years of follow-up in a sample of more than 6000 older persons from a defined cohort.The other study, 6 using data from about 2500 Japanese American men, found a similar result: diabetes mellitus approximately doubled the risk of AD.In contrast, 2 other longitudinal studies 7,8 did not demonstrate a significant association between diabetes mellitus and incident AD, but in both, the results were in the direction of increased risk.Some, [9][10][11] but not all, 12 previous studies found that diabetes mellitus was related to change in cognitive function.One factor that may contribute to variability from study to study is that diabetes mellitus may be related to decline in some cognitive systems but not others.4][15] Although diabetes mellitus was related to level of global cognition and multiple cognitive domains at baseline, we found that diabetes mellitus was only related to decline in perceptual speed.The one study 12 that did not find a relation between diabetes mellitus and cognitive decline did not include a measure of perceptual speed.", + "COMMENT In a cohort of more than 800 older persons, we found that diabetes mellitus sometime in the study was associated with an increased risk of developing AD during a mean of 5.5 years of observation.The risk of incident AD was 65% higher in those with diabetes mellitus than in those without it.", + "In summary, these findings suggest that diabetes mellitus is associated with AD and decline in cognitive function in older persons.December 12, 2003.", + "DIABETES MELLITUS AND RISK OF AD During the follow-up evaluations, 151 persons developed AD, of whom 31 had diabetes mellitus.In a proportional hazards model adjusted for age, sex, and educational level, there was a 65% increase in the risk of developing AD in those with diabetes mellitus compared with those without diabetes mellitus (hazard ratio, 1.65; 95% confidence interval, 1.10-2.47).The cumulative hazard of AD over time, adjusted for age, sex, and educational level, is shown graphically in Figure 1 for typical participants with and without diabetes mellitus.Similar results were found in analyses with diabetes mellitus identified at baseline only (hazard ratio, 1.53; 95% confidence interval, 0.96-2.45).", + "Age. Age is another factor that has a considerable effect on outcomes in obesity and T2DM research.In humans, body weight increases with age and peaks at ~55 years in both men and women.Ageing per se is associated with a redistribution of both the fat-free mass and the fat mass, with the latter increase starting at ~30 years of age 129 .Intramuscular and intrahepatic fat are particularly increased in older persons, and this increase has been linked to insulin resistance 130 .Partially on the basis of these changes, ageing has been proposed to be an independent determinant of glucose tolerance, which progressively worsens with age 131,132 .", + "Age also plays a vital role in the onset of diabetes (Cowie & Eberhardt, 1995).In south-east Asia almost 97% diabetic patients are 40 years old or more (IDF Atlas, 2017).In Bangladesh, the reported age of diabetes is \u226540 years in 71% urban and 85% rural female, while in the case of male the proportion is 85.5% urban and 86.5% in rural population (IDF Atlas, 2017).The current study also pinpointed an exponential increase in the risk of onset of T2DM with the increase of age when 40 years was chosen as the reference (Table S4).", + "Whether age and stress variables are risk factors for type 2 diabetes incidence was assessed by multivariate logistic regression (Table S4).Subjects in the age groups of (40-60) and >60 years had 1.78\u00d7 (p = .005)and 3.19\u00d7 (p = .006)greater risk for type 2 diabetes respectively than group of <40 years.Overall, patients under stressful condition are more likely to develop T2DM than that of nonstressed respondent (p = .000).Moreover, when stress is divided into two groups-low stress and high stress, we found that both males (p = .000)and females (p = .000)with high stress were at high risk of diabetes mellitus, whereas the association between low stress and T2DM incidence was significant only among males (Male: p = .002;Female: p = .115).The distribution and association of the genotypes, age, and stress with T2DM have been summarized in Table 3 and Figure 3.There was no difference in T2DM incidence between CT (p = .030)and TT/CC (p = .034)genotype containing people who were in age group of 40-60 years (Table 3).In contrast, people who were more than 60 years old with CT genotype (OR = 4.636, p = .029)were more prone to T2DM than that of TT/CC genotype (OR = 3.714, p = .007)subjects (Table 3).", + "Research Gaps There is a clear correlation of environmental influences to diabetes risk.Yet, the assembled experts agreed that hypothesis-driven research is needed to define direct causal relationships between specific environmental factors and pathophysiologies leading to diabetes.Research efforts need to address environmental etiologies of type 1 diabetes and determine their relative contribution to onset of autoimmunity and progression to symptomatic disease.Whether there is a direct causal role of the intestinal microbiota in pathogenesis of type 1 and type 2 diabetes and response to therapies needs to be determined.Public health interventions that successfully reduce the levels of consumption of energy-dense foods and/or reduce sedentary time and increase time spent in physical activity need to be evaluated to determine whether they can reduce type 2 diabetes incidence at a population level.", + "In sum, it is clear that multiple risk factors are involved in diabetes-associated cognitive decrements as well as in dementia in relation to diabetes 38 .On the basis of our assessment of the literature, it is also clear that there are still substantial knowledge gaps on how the risk factors interconnect, how the risk factors translate to potentially modifiable mechanisms and which genetic factors are involved.", + "The aim of this study was to investigate the association between age at natural menopause and risk of developing type 2 diabetes, and to assess whether this association is independent of potential intermediate risk factors for type 2 diabetes.Furthermore, we examined the role of endogenous sex hormone levels in the association between age at natural menopause and type 2 diabetes.", + "Aims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens.", + "Aims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens.Results During a median follow-up of 9.2 years, we identified 348 individuals with incident type 2 diabetes.After adjustment for confounders, HRs for type 2 diabetes were 3.7 (95% CI 1.8, 7.5), 2.4 (95% CI 1.3, 4.3) and 1.60 (95% CI 1.0, 2.8) for women with premature, early and normal menopause, respectively, relative to those with late menopause (ptrend <0.001).The HR for type 2 diabetes per 1 year older at menopause was 0.96 (95% CI 0.94, 0.98).Further adjustment for BMI, glycaemic traits, metabolic risk factors, C-reactive protein, endogenous sex hormone levels or shared genetic factors did not affect this association.Conclusions/interpretation Early onset of natural menopause is an independent marker for type 2 diabetes in postmenopausal women.", + "association and explore whether the timing of natural menopause can add value to diabetes prediction and prevention.", + "Although drawing of definitive conclusions is difficult from these observational studies, their results suggest that young-onset type 2 diabetes is associated with a much more frequent occurrence of adverse macrovascular and microvascular outcomes and a more rapidly progressing severity of complications than is seen in type 1 diabetes or later-onset type 2 diabetes.", + "In a study of the age-specific incidence of type 2 diabetes in the UK (a retrospective cohort study of patients with newly diagnosed type 2 diabetes between 1990 and 2010), the investigators reported a substantial increase in the proportion of people aged 40 years or younger at diagnosis", + "The prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults.", + "The biological processes linking aging and disease risk are poorly understood.Still, aging is considered to date as one of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes." + ], + [ + "Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "Background: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study.", + "Genetic linkage studies of long-lived human families identified a longevity locus while candidate gene approaches have been used to identify and confirm the association between specific variants in the FOXO3A gene and human longevity [3\u20137]. Genome-wide association studies have also been used to identify the association of APOE with life 123 Aging Clin Exp Res span and have yielded insights into potential biological pathways and processes related to aging. Despite these successes, several problems are inherent in human longevity studies including potentially high degrees of environmental heterogeneity, genetic diversity, and lack of birth matched controls, among others [8].", + "Additional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process.", + "In conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies.", + "The only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained.", + "Ageing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.", + "Ageing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.", + "In most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes.", + "The lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010).", + "Human longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p \u03fd 5 \u03eb 10 \u03ea8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p \u03fd 10 \u03ea5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.", + "Human longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p \u03fd 5 \u03eb 10 \u03ea8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p \u03fd 10 \u03ea5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.", + "In addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha \u02dces et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations.", + "The genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha \u00a8chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity.", + "GenAge: the aging gene database Philosophy and overview of resources It is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalh\u00e3es, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging.", + "The only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained.", + "Ageing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.", + "Ageing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future.", + "Most of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + ], + [ + "Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "Recent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals.", + "Background: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study.", + "Introduction With the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging.", + "INTRODUCTION Human aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining \u223c20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging.", + "Somatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18].", + "Influence of Genetic Factors in Ageing and Lifespan Ageing is defined as the decline of physiological functions in several tissues and organs inducing an increasing probability of death [17].The understanding of genetic factors involved in ageing has been limited due to the complexity of this process and the heterogeneity among individuals and even among tissues [18][19][20].Tissue cells adopt a senescent phenotype as a consequence of multiple intrinsic, extrinsic, and stochastic factors [21].The combination of these genetic factors is related to longevity and healthy ageing [22].Although this decline is somewhat predictable, some individuals show a much slower decline and get to live past the age of 100.Studies in these individuals showed polymorphisms in some genes which are associated with long life, such as APOE and FOXO3.However, these associations have not been consistent across different populations, suggesting that ageing is rather polygenic [23].", + "Before the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26].", + "M OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10).", + "Introduction The recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005).", + "Human longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p \u03fd 5 \u03eb 10 \u03ea8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p \u03fd 10 \u03ea5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.", + "Translational A LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors.", + "Many factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics.", + "The genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha \u00a8chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity.", + "Introduction Human longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006).", + "Introduction Approximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go \u00a8gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha \u00a8chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches.", + "GenAge: the aging gene database Philosophy and overview of resources It is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalh\u00e3es, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging.", + "I NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity.", + "Human lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/old/experts_aging2_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/experts_aging2_dataset.json new file mode 100644 index 0000000..4153c30 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/experts_aging2_dataset.json @@ -0,0 +1,128 @@ +{ + "question": [ + "Why is it so diffuclut to map gene loci that control aging in humans?", + "what causes the aging process", + "What genes are associated with aging?", + "what genetic factor are associated with aging", + "which genes are involved in the aging process" + ], + "answer": [ + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "The genes associated with aging include the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, HLA-DQA1/DRB1, LPA, CHRNA3/5, CDKN2A/B, SH2B3, and AKT1. Other genes involved in processes like growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition are also associated with aging.", + "Several genetic factors are associated with aging. These include the APOE, FOXO3A, and CHRNA3/5 genes, as well as the CDKN2A/B, SH2B3, and MTP genes. Other factors include the HLA-DQA1/DRB1 and LPA regions, and the AKAP2 gene. Additionally, genes highly expressed in the brain, genes involved in neural development and function, and genes associated with autophagy have been linked to aging.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others." + ], + "contexts": [ + [ + "Recent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals.", + "FUTURE DIRECTIONS: HIGHER RESOLUTION DATA VIA HIGHER THROUGHPUT ASSAYS One inescapable conclusion of the aggregate results of genome-wide studies of aging to date (see summary Table 1) is that we have not come close to saturating the number of potentially lifespan-altering genes in any organism.This is in no small part because directly generating survival curves is a relatively time-consuming process in most model organisms using current methods.There are several possible ways to address this.One way that has been tried is by attempting to find surrogate phenotypes [72,73,126] that can be screened more rapidly, or even scored under selection.Another is mining candidates from the many whole-genome expression profiles.Results to date with these have been very fruitful, but have not suggested that these methods alone will rapidly saturate our search for lifespan-and healthspan-altering genes in tractable model organisms.", + "Chromosome mapping of genes that were differentially expressed in mice of different ages and/or in response to CR revealed a wide distribution of genes with some physical clustering of responsive genes within the genome.The latter findings are consistent with the concept that aging is a complex process and that evolutionary adaptations to aging, if they exist, may or may not involve geographic clustering of functionally related genes.", + "Genetic linkage studies of long-lived human families identified a longevity locus while candidate gene approaches have been used to identify and confirm the association between specific variants in the FOXO3A gene and human longevity [3\u20137]. Genome-wide association studies have also been used to identify the association of APOE with life 123 Aging Clin Exp Res span and have yielded insights into potential biological pathways and processes related to aging. Despite these successes, several problems are inherent in human longevity studies including potentially high degrees of environmental heterogeneity, genetic diversity, and lack of birth matched controls, among others [8].", + "The aging process most certainly is under highly polygenic controls\u2026 This should not discourage us from pursuing a search for those loci which may be of profound importance to human aging as it ordinarily occurs in most human beings.", + "In most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes.", + "1993), and gene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not provide very useful evidence with respect to the question of the number of loci that affect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of evolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now amenable to the application of genomic methods.", + "Accepted Article \u00a9 2013 The Authors Aging Cell \u00a9 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland over 90 years and 1,955 controls between 55 and 80 years did not reveal genome-wide significant loci (Newman et al., 2010) and neither did the analyses of all-cause mortality and survival free of major disease in this cohort (Walter et al., 2011).A smaller Dutch study of 403 nonagenarians and 1,670 controls younger than 65 years identified the APOE gene as a mortality locus (Deelen et al., 2011), which was confirmed in a German study of 763 long-lived individuals and 1,085 younger controls (Nebel et al., 2011) and a longitudinal study of 1,606 Danes showed that the effect size of this association increases at the highest ages (Jacobsen et al., 2010).Apparently, the influence of the common genetic variation on longevity is small which requires large meta-GWA studies for identification.Alternatively, rare genetic variants may play a more important role in longevity.Since the previous linkage studies showed contradictory results potentially due to heterogeneity in the longevity phenotype, it is expected that longevity is influenced by many private rare variants.", + "Ageing is complex and takes a long time to study -a lifetime in fact.This makes it difficult to discern its causes, among the countless possibilities based on an individual's genes, behaviour or environment.While thousands of regions in an individual's genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle.Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average.", + "Several explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect.", + "Second, the largely negative findings of this and other studies contrast with the intriguing animal studies of longevity.Very large effects of single genes on lifespan have indeed been observed in laboratory animals, but humans often have several homologues of these genes which might significantly differ in function or compensate for mutated genes through redundant mechanisms (Kuningas et al., 2008).This could explain why our top findings did not include genes in these pathways found in animal models.Animal models also represent genetically homogenous populations and are exposed to controlled environmental influences.The lack of replication of animal model findings in humans suggests that the use of knockout animals may not provide the optimal approach to understanding the variation in survival in humans as interactions with environmental factors may obscure the associations and prevent the identification of loci in humans.", + "The lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010).", + "In addition to timing differences, a small proportion of genes (10%-15%) exhibit opposite trends of expression changes with age in humans and macaques (Supplemental Fig. S13).Interestingly, such differences are ;1.5 times more common in aging than in development, an observation consistent with the lower strength of purifying selection on the gene regulation at old age (discussed below).These differences could also reflect extreme shifts in developmental timing between species, as well as technical artifacts.Future studies, using additional species and alternative methodology, are needed to address this issue.", + "1993), and gene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not provide very useful evidence with respect to the question of the number of loci that affect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of evolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now amenable to the application of genomic methods.", + "The remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha \u02dces et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design.", + "TRANSLATION OF LONGEVITY MODEL ORGANISMS AND CORE AGING PATHWAYS Genetic studies on lifespan have proven to be challenging.While longevity is a defining trait for a given species, the lifespan of individuals is of limited heritability, making analyses more difficult.Exceptional human life span, although a rare phenotype, is likely multifactorial; refined analyses are required to obtain statistically robust genomic signatures of longevity (Zhang et al., 2020) and these have proven elusive.Unlike laboratory models, the effect of environmental variance cannot be controlled in human studies, potentially masking purely biological aging mechanisms.Even laboratory models cannot replicate the complex \"environment\" of humans; it includes psychosocial, economic, and cultural factors, rather than strictly biological.These human-specific confounders are difficult or impossible to target in traditional model organisms.Despite these limitations, experimentally tractable model organisms have proven invaluable in deciphering the purely genetic contribution to lifespan, including genes and pathways conserved across the tree of life.", + "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY Heritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE \u03b52), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans.", + "With modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms.", + "Most of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31].", + "Conclusions and prospects Over the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + ], + [ + "There are multiple definitions of the aging process.Aging may be perceived as the random, systemic loss of molecular fidelity that, after reproductive maturity, accumulates to levels that eventually exceed tissue repair, turnover, or maintenance capacity (Hayflick 2004).The underlying molecular mechanisms of aging remain a subject of debates (de Magalhaes et al. 2009): tissue deterioration might not be programmed, being just a function of increase in entropy (Hayflick 2004).No genes are necessary to drive a stochastic process; however, there are genes that act to prevent an organism from destruction and disorganization.It may be due to the absence of specific disease-causing alleles or due to the presence of favorable alleles (Halaschek-Wiener et al. 2009).These genes may inhibit entropy, regulate inflammation, maintain DNA repair (such as telomere maintenance factors), or provide antioxidant functions (e.g., antagonists of reactive oxygen species).As healthy cells adapt to degeneration, differential expression of genes with age may indicate a transcriptional response to aging rather than a deleterious mechanism of aging per se (de Magalhaes et al. 2009).It might be postulated that there exist alleles that confer a pleiotropic effect on structure and function during aging (Lunetta et al. 2007).These alleles should regulate the ability of an organism to withstand challenging endogenous and exogenous influences.", + "Why does ageing evolve? The intrinsic decline in function that occurs during ageing appears to be caused by the accumulation of damage, particularly at the molecular level.As far as we know, no genes have evolved specifically because they cause damage to accumulate, and the evolution of ageing can therefore be understood only as a side-effect of other causes of evolutionary change.The mechanisms by which ageing can evolve were first elucidated by J.B.S. Haldane [14], P.B. Medawar [15] and G.C. Williams [16].Extrinsic hazards from disease, predation and accidents mean that even potentially immortal organisms will die.Genetic effects that become apparent only later in life encounter a reduced force of natural selection, because not all their bearers will survive to express them.Haldane pointed out that late-onset genetic diseases in humans, such as Huntington's disease, encounter only weak selection, because most reproduction is complete by the age of onset [14].Ageing could therefore result from the accumulation under mutation pressure of age-specific, deleterious mutations.In addition, if some mutations have pleiotropic effects, with beneficial effects in youth, such as high fecundity, but also with a higher subsequent rate of ageing, then they could be incorporated into the population by natural selection, which will act more strongly on the early, beneficial effect.Thus, variation in the rate of ageing would result from the readjustment of a tradeoff between youthful benefits and the subsequent rate of ageing.Both processes imply that faster ageing will evolve where the extrinsic hazard to adults is greatest, a hypothesis in general supported by the data [1,2,17].", + "A. Theories In looking back at the development of aging studies, we can see that it did not follow a straight or logical course.On the contrary, it can be compared with the flow of several convergent streams winding in their course.To date, numerous proposals have been made for the paradigm of aging.These include Hayflick's contributions (153) on programmed cellular incapacitation derived from flbroblast studies, a decrease in immunologic response, deleterious endocrinological changes, nuclear somatic gene mutation, mitochondrial somatic gene mutation, oxygen free radical damage to proteins and nucleic acids, molecular instabilities, molecular cross-linking, glycation reactions, and so on.There is little doubt that many of these factors contribute to the overall aging, but what are primary causes, and what are secondary outcomes?", + "Ageing Is Adjusted by Genetic, Environmental, and Stochastic Processes Enough evidence suggests that ageing is the result of different events such as molecular damage, mutations, incomplete repair, genetic programs, and continued development, among others [16].These events, in turn, are caused by genetic factors, environmental conditions, and even stochastic factors, which are mentioned below in this chapter.", + "Different stochastic theories of ageing focus on specific mechanisms that may lead to ageing.The catastrophic error theory poses that the accumulation of errors in protein synthesis causes damage in cell function.The theory of cross-linking holds this process between proteins and other macromolecules responsible for ageing, while the theory of free radicals suggests that ageing is the result of inadequate protection against cell and tissue damage by free radicals and oxidative stress throughout life.Finally, the wear-and-tear theory poses that the cumulative damage that eventually leads to ageing and death is, in fact, the result of the continuous functioning of vital processes, during which stochastic errors gradually arise.", + "Introduction Aging is a natural and irreversible process characterized by a progressive decay in physiological, biochemical, and structural functions of individuals.Aging is a multifactorial process that can be affected by two main factors: environmental and genetic.Environmental factors are nutrition, pathologies, pollution exposure, physical activity, and microbiota, while genetic factors are issues that have been associated with antioxidant and DNA damage responses, the fidelity of genetic information transfer, the efficiency of protein degradation, the extent of cellular responsiveness to stress, the mechanisms of epigenetic regulation, and the ability to elongate telomeres.All of them can determine how fast we age.Traditionally, aging studies had used several model organisms, from yeast to mammals, especially rodents (rats and mice).Most of the studies are made under controlled conditions, where only a few variables are observed, and the subjects are members of the same strain with the same genetic backgrounds or the same mutations.The information that so far has been obtained about aging has helped us to describe different factors that influence this process and that are the fundamental concepts of the various theories of aging.However, these theories do not fully explain the aging process in the different models of aging study.This is the case of the study of aging in humans, where it is very difficult to control the environmental and genetic variables.That is why issues haven't been solved such as the following: How does time influence aging?When do we start to age?How do we know we are old?Is it possible to delay aging?Those and more questions are the cornerstones for aging studies.Biological aging has been associated with the decrease in the repair and regeneration capacity of tissues and organs; it is a time-dependent process.This reduction can be observed by an increase in the acquisition of diseases and functional and reproductive disability, which eventually lead to death.On the other hand, it has been observed that in humans, people with the same chronological age exhibit different trajectories in the decrease of physiological functions associated with biological aging and what complicates the understanding of the molecular and physiological phenomena that drive the complex and multifactorial processes that underlie biological aging in humans.", + "The underlying cause of aging remains one of the central mysteries of biology.Recent studies in several different systems suggest that not only may the rate of aging be modified by environmental and genetic factors, but also that the aging clock can be reversed, restoring characteristics of youthfulness to aged cells and tissues.This Review focuses on the emerging biology of rejuvenation through the lens of epigenetic reprogramming.By defining youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges.", + "Aging does not happen in a vacuum.Aging must be the result of changes that occur in molecules that have existed at one time with no age changes.It is the state of these pre-existing molecules that governs longevity determination.The pre-existing state is, as I have already described, maintained by repair and turnover systems that themselves eventually succumb to irreparable age changes.Longevity determination is the state of all molecules prior to succumbing to irreparable loss of molecular structure.", + "Biological aging is more than simply the occurrence of random changes in molecules.It also includes the role of the many repair systems found within cells.Thus, a more complete, but less concise, explanation of the first causes of aging in biological systems is the following:", + "U nderstanding the deleterious processes that cause aging has been a human endeavor ever since we figured out that we grew old and that we didn't like it.Many hypotheses have been proposed to explain the root cause of aging (1).One broad-based hypothesis is that generalized homeostatic failure leads to age-related decline.Although notions of time-and use-related deterioration may be applicable to mechanical objects, they fall short as analogies to biological systems because energy input should theoretically maintain living systems indefinitely.Yet, despite the regenerative potential of biological organisms, progressive deterioration accompanies postmaturational aging.That the organism's repair capabilities cannot keep up with wear and tear is, according to evolutionary theory, explained by the inevitable declining force of natural selection with age.According to this reasoning, there is no selective advantage to maintaining somatic cells in perfect order much beyond reproductive maturation (1).Hence, a long life depends on the timing of maturation and the quality of somatic cell maintenance.", + "Wear and tear on the DNA often has been touted as a possible basis for our progressive age-related decline.Supporting this notion is the work of de Boer et al. (2) reported on page 1276 of this week's issue.They reveal important evidence for imperfect genome maintenance of DNA damage as a possible causal factor in aging.Harman, with his \"free radical theory of aging\" (3), was the first to propose that metabolic by-products called reactive oxygen species (ROS) continually damage cellular macromolecules, including DNA.Incomplete repair of such damage would lead to its accumulation over time and eventually result in age-related deterioration.A number of observations support the free radical theory, including the discovery that dietary restriction delays aging and extends life-span in a wide range of rodents and other species, possibly by reducing free radical damage.The notion that genomic DNA could be a major target of continual free radical attack over time is supported by the recent observation that genetic lesions accumulate with age and that dietary restriction reduces this accumulation in rodents (4).In addition, deletion of p66 shc , a signaling protein that maintains oxidant levels, increases resistance to oxidative damage and extends the life-span of mice (5).", + "Instead, aging is expected to be a pervasive failure of adaptation across most, if not all, of the physiological mechanisms that sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute \u201cthe cause of aging\u201d to any one physiological mechanism or gene for aging or programmed death. Although common genetic pathways might be identified that contribute to aging among a variety of organisms (cf.", + "Background Aging is a complex process characterized by the progressive degeneration of a healthy phenotype and correlated with a decline in the ability to withstand cellular stress and damage.The subject of investigation for decades, the underlying molecular genetic causes of and responses to aging remain an area of active study.Research from model systems has characterized a range of physiological and molecular phenotypes associated with aging.These include genomic instability caused by accumulation of DNA damage, dysregulation of repair mechanisms, and telomere attrition; epigenetic alterations; dysregulation of transcription; loss of proteostasis; cellular senescence; and deregulated nutrient sensing, metabolic pathways, and energy use (reviewed in [1]).Separating causation from correlation between these phenotypes and aging remains a challenge, however.", + "Introduction Understanding what actually causes ageing remains admittedly a fundamental and fascinating problem in biology [1].Experimental data accumulated in the last three decades have led to the identification of various environmental and genetic factors, as well as chemical substances that influence lifespan in divergent eukaryotic species [1,2].Organisms normally age faster and hence live shorter under stress conditions that can lead to the generation of DNA mutations and, often as a consequence of mutations, damaged cytoplasmic constituents (including injured proteins, lipids, carbohydrates and organelles).Such types of damage can interfere with cellular functioning; thereby, they should be eliminated by effective repair and self-cleaning mechanisms to maintain cellular homeostasis.These mechanisms include DNA repair pathways, molecular chaperons, as well as the proteasome-ubiquitin system and lysosome-mediated autophagy, the main forms of cellular self-degradation [3].This has led to the attractive model that the gradual, lifelong accumulation of unrepaired cellular damage drives the ageing process and determines the incidence of age-related fatal diseases [4,5].", + "In conclusion, aging may not be primarily due to damage accumulating from the basic biochemical reactions that make up life but rather the result of the developmental program or of changes brought about by it.Our hypothesis is that the timing of development regulates the rate of aging among mammals, with a subset of developmental mechanisms determining the pace and causing most agerelated changes.Maybe people change as they grow old due to the same mechanisms that drive changes throughout the earlier stages in life.", + "Instead, aging is expected to be a pervasive failure of adaptation across most, if not all, of the physiological mechanisms that sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute \u201cthe cause of aging\u201d to any one physiological mechanism or gene for aging or programmed death. Although common genetic pathways might be identified that contribute to aging among a variety of organisms (cf.", + "In 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13].", + "Introduction The fundamental manifestation of the aging process is a progressive decline in the functional maintenance of tissue homeostasis and an increasing propensity to degenerative diseases and death [1].It has attracted significant interest to study the underlying mechanisms of aging, and many theories have been put forward to explain the phenomenon of aging.There is an emerging consensus that aging is a multifactorial process, which is genetically determined and influenced epigenetically by environment [2].Most aging theories postulate a single physiological cause of aging, and likely these theories are correct to a certain degree and in certain aspects of aging.", + "Many factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics.", + "Trying to explain aging in terms of a singular process would be in conflict with evolutionary theory.Even if loss of genome sequence integrity was the most conserved cause of aging, already active in the first replicators (Vijg, 2007), natural selection would allow a multitude of mutations with late adverse effects to accumulate in the germline, many of which would be positively selected for because of their beneficial effects early in life (Williams, 1957), In this respect, somatic mutation accumulation could be a conserved, inevitable cause of aging but superposed on multiple other processes that usually cause the earlier demise of an individual." + ], + [ + "Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "Recent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals.", + "Studies revealed from 300 to 750 genes related to longevity that are critically involved in a variety of life activities, such as growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [5,6].Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability, and the endocrine-related pathway [7][8][9].In addition, the candidates for longevity encompass genes related to drug metabolism, the ones involved in protein folding, stabilization, and degradation, as well those related to coagulation and regulation of circulation [10], etc.In most cases, these genes or their polymorphic sites were examined in multiple population replication studies, which discovered certain longevity-associated genes or pathways [4][5][6][7][8][9][10].", + "Additional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process.", + "Somatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18].", + "Before the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26].", + "Unbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases.", + "Involvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes.", + "Gene associations with age-related traits found using longitudinal study data.", + "In most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes.", + "The lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010).", + "Thus, substantially more work is needed in this area to establish whether longevity is driven by nuclear genomic stability.Diverse and unexpected bits of evidence support a relationship.For example, a disproportionate number of genes identified in unbiased and targeted genome-wide association studies (GWASs) as associated with longevity are involved in genome maintenance (75).One study involved age of natural menopause in \u223c70,000 women and led to the identification of 44 genetic variants associated with early or late menopause, a strong biomarker of healthy TIFs (telomere dysfunction-induced foci): co-localization of multiple DNA damage response factors and repair proteins on uncapped telomeric DNA aging (76).Approximately two-thirds of these are associated with genome maintenance genes.Seven of ten significantly associated pathways are involved in DNA repair.The highly significant overrepresentation of DNA repair pathways indicates an intimate connection between genome maintenance and aging phenotypes.From unrelated studies, we know that reduced expression of the repair endonuclease ERCC1-XPF causes accelerated aging (3), whereas ERCC1 is one of the top genes under positive selective pressure in the longest-lived mammalian species, the bowhead whale (77).Intriguingly, hepatocytes from old rats have impaired NER, whereas caloric restriction, which extends longevity, restored the NER capacity of old rats to that of youthful levels (42).In a human interventional study, brief caloric restriction increased NER capacity in PBMCs of individuals who had low NER prior to dietary intervention (78).Therefore, increased DNA repair capacity could promote longevity and may even prove amenable to improvement.", + "In addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha \u02dces et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations.", + "Genes/loci identified by genome-wide association studies of longevity and lifespan traits.", + "The genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha \u00a8chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity.", + "Since many alleles will fit the two patterns just described, it follows that we expect many genetic and biochemical mechanisms of aging.There are some experiments that have attempted to estimate the number of genes involved in aging, particularly in Drosophila.Quantitative genetic estimates of gene number have probably been subject to artifacts, [6,8] and are highly imprecise.Molecular genetic estimates using 2-D gels [3] and high-density geneexpression arrays [12] indicate the involvement of at least 300 genetic loci in Drosophila aging, and that estimate is highly conservative.For now, the best conclusion is probably that many genes are involved in aging in fruit flies.Vertebrates are unlikely to have fewer genes involved in aging, in view of their larger genomes.", + "GenAge consists of several searchable data sets.Considering the extraordinary discoveries in the genetics of aging in model organisms, GenAge includes a data set of genes associated with longevity and/or aging in model organisms.We consider a given gene for inclusion in GenAge if genetic manipulations of the gene result in noticeable changes in the aging phenotype and/or longevity.Most genes in GenAge are from the four typical model organisms: mice, worms, fruit flies and yeast (Table 1).Strikingly, homologues of many genes -such as insulin receptors and sirtuins -have been shown to regulate aging in model organisms separated by large evolutionary distances (Kenyon, 2005;Liu et al ., 2005;Smith et al ., 2008).Moreover, we have shown that genes associated with aging and/or longevity in model organisms are evolutionary conserved in terms of having more homologues than predicted by chance (Budovsky et al ., 2007(Budovsky et al ., , 2008) ) and exhibiting slower molecular evolution rates (de Magalh\u00e3es & Church, 2007).Therefore, it is now clear that at least some genes identified in model organisms may be relevant to human aging.", + "Gene associations with age-related traits found using longitudinal study data.", + "Most of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + ], + [ + "Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "Genomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.", + "Recent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals.", + "Background: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study.", + "Background: Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality.Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.Results: Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively.We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function.Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs.A polygenic score for GrimAge acceleration showed strong associations with adiposityrelated traits, educational attainment, parental longevity, and C-reactive protein levels.Conclusion: This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.", + "INTRODUCTION Human aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining \u223c20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging.", + "Before the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26].", + "Even more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010).", + "On the other hand, the same evolutionary-motivated strategy suggesting to focus on more heterogeneous phenotypes (as opposite to more homogenous) can be highly beneficial for unraveling genetic predisposition to fundamental mechanisms of intrinsic biological aging and, consequently, to geriatric diseases.Indeed, aging is associated with systemic remodeling of an organism's functioning which increases chances of virtually all geriatric disorders (Franco et al. 2009;Franceschi et al. 2000;Martin et al. 2007;Cutler and Mattson 2006).Experiments with laboratory animals (Johnson 2006) and heritability estimates in humans (Christensen et al. 2006;Iachine et al. 1998) show that aging can be genetically regulated (Finch and Tanzi 1997;Martin et al. 2007;Vaupel 2010).Accordingly, yielding insights in genetic predisposition to aging-related processes in an organism could be a major breakthrough in preventing and/or ameliorating not one geriatric trait, but perhaps a major subset of such traits (Martin et al. 2007) that can greatly advance progress in solving the problem of extending healthy lifespan in humans.", + "In conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies.", + "M OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10).", + "The lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010).", + "Human longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p \u03fd 5 \u03eb 10 \u03ea8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p \u03fd 10 \u03ea5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.", + "Introduction The recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005).", + "Human longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p \u03fd 5 \u03eb 10 \u03ea8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p \u03fd 10 \u03ea5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.", + "Many factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics.", + "The genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha \u00a8chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity.", + "Introduction Approximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go \u00a8gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha \u00a8chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches.", + "GenAge: the aging gene database Philosophy and overview of resources It is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalh\u00e3es, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging.", + "I NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + ], + [ + "Indicative biological pathways associated with the candidate aging genes", + "Fig. 2 Significant biological processes associated with the candidate aging genes", + "Following are examples of the identified genes and experimental or GWAS link between these genes and aging.On the list of the 25 top genes, NAP1L4 encodes a member of the nucleosome assembly protein (NAP) family, which interacts with both core and linker histones, and shuttles between the cytoplasm and nucleus, suggesting a role as histone chaperone.Histone protein levels decline during aging, and dramatically affect chromatin structure.Remarkably, the lifespan can be extended by manipulations that reverse the age-dependent changes to chromatin structure, indicating the pivotal role of chromatin structure in aging [32].In another example, gene expression of NAP1L4 increases with age in the skin tissue [33].Findings of GWAS link a number of the identified genes to age-related disorders, such as GAB2 and late onset Alzheimer's disease [86], and QKI and coronary heart disease/myocardial infarction [79].Interestingly, GWAS reports also link QKI to successful aging [87].", + "Examples of biological candidate genes with pleiotropic functions, which are involved in aging in general and in musculoskeletal aging in particular, are numerous: (a) in addition to the IGF-1 and vitamin D genes, estrogen metabolism pathway genes, including estrogen receptors and aromatase (CYP19), are associated with fat-free mass (Walsh et al. 2005) and BMD (Shearman et al. 2004), prostate and breast cancer (Gallicchio et al. 2006), and cardiovascular disease risk (Shearman et al. 2003).", + "In-depth analysis of the age-regulated genes revealed that multiple genes in the DNA damage response pathway were upregulated with age including those that function in non-homologous end-joining repair (mre11, rad50, Ku80 and mus308) and in translesion DNA synthesis (mus205 and DNApol-eta) [44][45][46].Genes that encoded enzymes with antioxidant properties, such as the thioredoxin reductase Trxr-1, and antioxidant genes involved in glutamate metabolism, such as GlnRS, isoQC and QC, were also upregulated with age [47][48][49][50].We also observed increased age-associated expression of chaperone genes (Cct1, Cct4, Cct5, Cct6, Hsc70-4) and the unfolded protein response transcription factor Xbp1, consistent with an induction of the unfolded protein response [51][52][53].Under stress conditions, there is a translational switch that favors production of stressrelated proteins while decreasing translation of other proteins [54].Paralogs of canonical translation factors such as NAT1 and Rack1, which were both upregulated, promote this switch to cap-independent translation [55,56].Notably, Rheb, which is downregulated with age, positively regulates ribosome production and capdependent translation by activating the mechanistic target of rapamycin (mTOR) kinase pathway [57].Thus, decreased Rheb levels during aging could decrease mTOR pathway activity, which extends lifespan and is protective against age-related pathology [58].Together, these data suggest that multiple genes are induced in aging photoreceptors to mitigate the effects of oxidative stress, protein misfolding and DNA damage.", + "CellAge vs human orthologues of longevity-associated model organism genes To understand how senescence is linked to the genetics of aging processes, we looked at the intersection of CellAge genes and the 869 genes in the human orthologues of model organisms' longevity-associated genes (LAGs) dataset, collected based on quantitative changes in lifespan [34].Like CellAge, where genes are classified based on whether their upregulation induces, inhibits, or has an unknown impact on CS, the longevity orthologues dataset also provides information on the effect of upregulation of its genes, namely whether it promotes (pro, 421) or inhibits (anti, 448) longevity (Additional file 1: Table S7; Additional file 2: Fig. S2).", + "Using network biology, we implicated the CellAge genes in various processes, particularly cell division and immune system processes.We used network topology to identify potential regulators of CS and bottlenecks that could impact various downstream processes if deregulated.Indeed, we identified 11 genes that have already been shown to contribute towards CS, which will be added to future versions of CellAge.Finally, we experimentally verified 26 genes that induce CS morphology or biomarkers when knocked down in human mammary fibroblasts.Of these, 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) were strong hits in inducing a senescent phenotype.", + "Results: We develop CellAge (http://genomics.senescence.info/cells),a manually curated database of 279 human genes driving cellular senescence, and perform various integrative analyses.Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes.Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates.We also build cellular senescence protein-protein interaction and co-expression networks.Clusters in the networks are enriched for cell cycle and immunological processes.Network topological parameters also reveal novel potential cellular senescence regulators.Using siRNAs, we observe that all 26 candidates tested induce at least one marker of senescence with 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) decreasing cell number, activating p16/p21, and undergoing morphological changes that resemble cellular senescence.Conclusions: Overall, our work provides a benchmark resource for researchers to study cellular senescence, and our systems biology analyses reveal new insights and gene regulators of cellular senescence.", + "Genomics-a fundamental basis for understanding skin aging In the last decade, genomic tools such as gene chips have been widely developed.This accomplishment has provided us with deeper insights into the molecular events underlying skin aging. 137Gene expression profiling has led to identification of pathways affected by aging, and this information has led to the development of new strategies to enable better skin repair and antiaging benefits. 138ene expression patterns were examined in sun-protected (buttocks) and sun-exposed skin (extensor forearm) from 10 young (age 19 to 20 years) and 10 older women (age 63 to 67 years) to examine gene expression profiles associated with chronologic skin aging and photoaging.Chronologic and photoaging were both associated with downregulation of the biologic process of lipid synthesis.In particular, genes involved in cholesterol and fatty acid synthesis were downregulated, as were genes associated with epidermal differentiation, including keratin filaments and cornified envelope components.An upregulation of the biologic processes of inflammatory response and wound healing, the molecular functions of cytokine activity and protease activity and the cellular component theme of extracellular matrix was also observed in both skin aging types.Elastin gene expression was upregulated with aging only in the photodamaged arm and remained unchanged in the sunprotected buttock.This finding corresponds to the histopathologic findings that show typical elastotic changes, the \"solar elastosis,\" in photoaged skin. 139urther studies conducted to investigate changes in gene expression during skin aging have been performed on naturally aged human foreskin obtained from children and elderly men.Some of the mechanisms proposed to be involved in the induction of aging comprise disturbed lipid metabolism, altered insulin and STAT3 signalling, upregulation of apoptotic genes partly due to the deregulation of FOXO1, downregulation of members of the jun and fos family, differential expression of cytoskeletal proteins (eg, keratin 2A, 6A, and 16A), extracellular matrix components (eg, PI3, S100A2, A7, A9, SPRR2B), and proteins involved in cell-cycle control (eg, CDKs, GOS2). 140Similar results have been presented by a study related to aging of skeletal muscle. 141n a previous study, we proposed that one of the factors significantly involved in the initiation of aging might be the physiologic decline of hormones occurring with age.Human SZ95 sebocytes in vitro treated with hormone levels that can be found in 60 year-old women produce less lipids than sebocytes treated with a hormone mixture representing that found in the serum of 20 year-old women. 6A differential gene expression between SZ95 sebocytes under the 20 and 60 year-old hormone mixture detected differentially expressed genes that are involved in biologic processes such as DNA repair and stability, mitochondrial function, oxidative stress, cell cycle and apoptosis, ubiquitin-induced proteolysis, and transcriptional regulation. 139,140A comparison of these results with data obtained from the aged kidney 142 identified key genes that may be of great importance for global aging.The most significantly altered signalling pathway was that of TGF-\u03b2.A disturbed function of this cascade has been also c-Fos, which heterodimerize to form the activator protein 1 (AP-1) complex.AP-1 is a key regulator of skin aging, because it induces the expression of the MMP family and inhibits type I procollagen gene expression through interference with TGF-\u03b2 signalling pathway.It has been postulated that MAP kinases may be activated by excess production of reactive oxygen species (ROS) that occurs with advanced age and may be superimposed by extrinsic factors such as ultraviolet irradiation.Excess ROS production also leads to accumulation of cellular damage, which includes oxidation of DNA resulting in mutations, oxidation of proteins leading to reduced function, and oxidation of membrane lipids resulting in reduced transport efficiency and altered transmembrane signalling.IL, interleukin; NF-\u03baB, nuclear factor-\u03baB; TGF-\u03b2, transforming growth factor-\u03b2; TSP-1, thrombospondin-1; TSP-2, thrombospondin-2; VEGF, vascular endothelial growth factor.associated with tumorigenesis, such as in pancreatic, prostate, intestine, breast, and uterine cancer.", + "Analysis of prior research (Online Resource 5) shows that the revealed genes can be explicitly involved in other key biological processes in an organism whose role is known to be changing with aging.Specifically, ten genes (BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, and ZKSCAN1) regulate transcription which is believed to be disrupted when an organism is getting older (Roy et al. 2002).The DBH, TPO, and LSS genes are involved in synthesis of catecholamine, thyroid, and vitamin D hormones, respectively.The GPER binds estrogen and HCRTR2 binds orexin-A and orexin-B neuropeptid hormones.Hormonal deregulation with aging is considered to be one of the major components of senescent processes in an organism (Barzilai and Gabriely 2010).Five genes (ATG2A, NEDD4L, PSMB1, UBXN4, and USP6) are involved in degradation of proteins through ubiquitin-proteasome and the lysosomal/autophagic system.Dysfunction of this system leads to accumulation of damaged proteins in an organism that is associated with aging (Koga et al. 2011).Protein degradation through ubiquitin-mediated proteolysis plays an important role in cell-cycle regulation (Reed 2003).The PSMB1, SIK1, TRIP13, and TTN genes in the revealed set coordinate cell cycle.Cell cycle is linked with the aging-related processes in humans through a gradual increase in cell division errors in all tissues in an organism (Ly et al. 2000).Five genes (EEF1A2, DBH, ITGB2, TUBB2C, and WRN) take part in regulation of apoptosis which plays an important role in the aging process and tumorigenesis (Salvioli et al. 2008).Seven genes (ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, and SOAT2) are involved in lipid metabolism which plays one of the key roles in human longevity and healthy aging (Barzilai et al. 2003).", + "Genes that are age-regulated in all tissues would reveal genes involved in core mechanisms that underlie cellular ageing.Zahn et al. [63] discovered genetic pathways that show common age regulation in human kidney, brain and muscle.They used microarrays to analyse expression in 81 skeletal muscle samples from patients aged 16 -86 years and found 250 age-regulated muscle genes [63].Similar to the ageing expression profile for the kidney, the overall expression behaviour of this set of age-regulated muscle genes correlated with the physiological as well as chronological age of the muscle sample.Next, they compared their muscle-ageing results to previously published data on kidney and brain ageing of similarly large sample size [56,60].Although most of the age-related changes were tissue specific, they found evidence for common age regulation of six genetic pathways in all three tissues.Specifically, there is an overall increase in expression of the extracellular matrix genes, the ribosomal genes, the cell growth genes and the complement activation genes in all three tissues.Increased overall expression of the extracellular matrix and complement activation gene sets with advancing age may contribute to widespread fibrosis and inflammation in the elderly.There is an overall decrease in expression of the chloride transport genes and the electron transport genes in all three tissues.Decreased overall expression of electron transport chain genes with age might support the mitochondrial free-radical theory of ageing [67], as free-radical generation by mitochondria would preferentially damage the electron transport chain protein complexes.Decreased expression of the electron transport genes (encoded in the nucleus) might be caused by feedback regulation from damage to the electron transport chain protein complexes [63].However, it is also possible that increased oxidative damage occurs as a consequence of the decreased expression of the electron transport chain genes.In addition, an increasing number of studies in model organisms have critically challenged the mitochondrial free-radical theory of ageing [68].", + "Discussion Aging studies from model organisms such as yeast, worms, and flies have repeatedly shown that changes in the expression of certain genes have an effect upon longevity.Although similar aging processes are likely to operate across multiple species [30], it has been much more difficult to identify longevity candidate genes in human studies [30].A key question in human aging is to what extent a signature of aging may be detectable across tissues.Until now there has been a lack of large transcriptional profiles from the same human individuals in multiple tissues.The MuTHER study provides insight into the human aging process by interrogating the largest multiple human tissue gene expression resource to identify genes in which expression was affected by chronological age.The analysis of the skin and adipose tissues samples identified several hundred genes responsive to changes in chronological age.However, the 43 shared genes in skin and adipose tissue showed a single common identifiable pathway related to the stress response.From over 1,800 transcripts that have altered expression with age in skin and adipose tissues, 14 also had age-related differential expression in brain.The limited overlap in these two experiments may partly reflect the smaller sample size of the brain expression dataset, the differences in age range between the studies (16 to 83 years for brain samples; 39 to 85 years for MUTHER samples), or the inclusion of males in the brain samples.But it may also imply, as other studies have suggested, that the effects of age on gene transcription are tissue specific [6,31,32].This hypothesis was supported by the comparison with known related aging genes from the GenAge database, which identified an overlap for a small number of aging-related genes with our data.The GenAge database was the result of a meta-analysis using age-related expression profiles from human brain, kidney, and skeletal muscle, and several expression profiles from mouse and rat; no adipose tissue or skin samples were included (Additional file, Table 1 in [7]).The limited overlap between these datasets supports the idea that molecular signatures of aging reflect predominantly a tissue-specific transcriptional response.The lack of age-related genes in transformed LCLs, suggest that the transformation to immortalize a cell line may mask or even remove the age-related signatures in gene expression.The transformation of primary B lymphocytes into LCLs requires infection by the Epstein-Barr virus which has the effect of disrupting the p53 signaling pathway in order to induce growth and survival [33].Joehanes et al. [15] identified only five genes with age-associated expression in LCLs, including p53 itself (TP53).Although the authors attribute the lack of age-affected genes to their small sample size (n=50) and narrow age range, our analysis with a much larger sample size found even fewer age-related changes, suggesting a lack of detectable aging signature in LCLs.The analysis in the subset of fresh lymphocytes suggested an age influence in fresh lymphocytes may potentially be detectable with a larger sample size.", + "Genes Whose Expression Decreased with Age.Of the 26 genes that decreased expression with age in control mice, 23% are involved in DNA replication and the cell cycle (Table 2).Most of these have a negative effect on cell growth and division.Among these, the product of phosphatase and tensin homolog (Pten) gene is a tumor suppressor that induces cell-cycle arrest through inhibition of the phosphoinositide 3-kinase pathway (28).B cell translocation gene 2 (Btg2) is a tumor suppressor that increases expression in response to DNA damage (29).The murine gene product of the amino-terminal enhancer of split (Aes) is a potent corepressor of gene expression and cellular proliferation (30).Calcium-binding protein A11 (S100a10) binds to and regulates the activity of annexin II, which is involved in the transduction of calcium-related mitogenic signals (31).Insulin-like growth factor (IGF) binding protein 1 (Igfbp1) plays an important role in the negative regulation of the IGF-1 system, a stimulator of mitogenesis (32).", + "daf-16 dependent genes Among the 52 genes that we have tested, 29 genes act almost completely in a daf-16 dependent manner, to regulate lifespan (Table 2).One of the genes identified was daf-2 (Y55D5A_391.b).This serves as a proof of principle that our screen is effective in identification of aging genes.", + "Several of the genes we identify have previously been shown to influence lifespan in experiments on model organisms.For example, knockouts of the orthologs of APOE, LDLR, CDKN2B, and RBM38 in mice shortens their lifespan [24][25][26][27] , while knockout of IGF1R has the opposite effect 28 .Similarly, overexpression of the FOXO3 orthologue in Drosophila melanogaster 29 and the SNCA orthologue in Caenorhabditis elegans 30 have shown to extend their respective lifespans.Many of our genes are also enriched for pathways previously related to ageing in eukaryotic model organisms, including genomic stability, cellular senescence, and nutrient sensing 31 .For example, FOXO3 and IGF1R are well-known players modulating survival in response to dietary restriction 32 , but we also highlight genes involved in the response to DNA damage and apoptosis, such as CDKN2B, USP28, E2F2, and BCL3.In addition to hallmarks discovered in model organisms, our results suggest that haem metabolism may play a role in human ageing.This pathway includes genes involved in processing haem and differentiation of erythroblasts 33 .Although the enrichment is largely driven by genes linked to the LDLR locus, genes linked to other loci of interest (such as FOXO3, CDKN2B, LINC02513) are involved in similar biological pathways: myeloid differentiation, erythrocyte homeostasis, and chemical homeostasis.", + "Hundreds of genes in several pathways act as regulators of ageing (1,32).However, analysis of DrugAge and other HAGR databases has revealed that the overlap between the targets of lifespan-extending drugs and known ageing related genes is modest (31).This indicates that most ageing-related pathways have yet to be targeted pharmacologically; DrugAge may aid in guiding further assays.This was recently demonstrated in one study where machine learning was used to predict whether a compound would increase lifespan in worms using data from Dru-gAge.The best model had 80% prediction accuracy and the top hit compounds could broadly be divided into compounds affecting mitochondria, inflammation, cancer, and gonadotropin-releasing hormone (33).", + "Top 25genes co-expressed with aging related genes", + "Aging-related gene prediction and putative transcriptional mechanisms GeneFriends was used to identify genes related to aging.A seed list of genes known to be consistently overexpressed with age in mammals was used [18].In total, 1119 genes were co-expressed with the aging seed list at p <10 -6 ; Table 1 shows the top 25 genes.Many of these genes have been associated with age-related diseases.Several other genes that have been shown to play a role in aging such as lysosomal-associated membrane protein-2 Lamp2 [19] (p = 5.68 -30 ), Fas [20] (p = 2.70 -31 ) and growth hormone receptor Ghr [21] (p = 1.34 -19 ) also showed a significant co-expression.Anxa2, Anxa3 and Anxa4 also show a low p-value (p < 10 -25 ) as well as several S100 calcium binding proteins which have been shown to interact with annexins [22].", + "Genetic studies have shown that aging can be slowed in mutants that are defective in a wide range of cellular processes (such as mitochondrial function, chromatin regulation, insulin signaling, transcriptional regulation, and genome stability).This indicates that aging is a complex process driven by diverse molecular pathways and biochemical events.As such, a powerful approach to study aging is to use systems biology, which allows a multitude of factors affecting aging to be analyzed in parallel.For example, DNA microarrays and gene expression chips have been used to perform a genome-wide analysis of changes in gene expres-sion in old age.Extensive studies in Caenorhabditis elegans and Drosophila melanogaster have identified hundreds of ageregulated genes (Hill et al. 2000;Zou et al. 2000;Lund et al. 2002;Pletcher et al. 2002;Murphy et al. 2003).Several studies have described age-regulated genes in the muscle and brain of mice (Lee et al. 1999(Lee et al. , 2000) ) and the retina and muscle of humans (Yoshida et al. 2002;Welle et al. 2003Welle et al. , 2004).These age-regulated genes may serve as markers of aging, enabling one to assess physiological age independently of chronological age.Analysis of the functions of these age-regulated genes has identified specific biochemical mechanisms that change toward the end of life.", + "Age-Regulated Genes Involved in Reproductive Capacity.Decline in reproductive capacity is an age-related phenotype, and the reproductive system seems to play an important role in longevity (22).For example, signals from germ cells can affect lifespan in C. elegans (23).In our study, we observed decreased RNA levels for several genes involved in reproduction (Fig. 3).These include two genes that encode members of the Acp family.The Acp from male flies stimulates female egg-laying and facilitates storage of sperm in the female genital tract (24).In addition, two ESTs showing age-regulated decrease of transcript levels represent different genes with homology to Arabidopsis MALE STERIL- In Northern analysis, the ratios were calculated by dividing mRNA levels at 25-, 40-, and 50-day time points by those at 3-day time points after normalization with mRNA levels of the control gene rp49.Ratios in microarray analysis are provided from each of the duplicate experiments for comparison.ITY 2 (MS2; ref. 25), a gene involved in gametogenesis.Furthermore, an EST with homology to peanut, a member of the septin family (26), is down-regulated in older flies.This downregulation may reflect a decrease in spermatogenesis." + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/old/experts_general1_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/experts_general1_dataset.json new file mode 100644 index 0000000..5643ab9 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/experts_general1_dataset.json @@ -0,0 +1,128 @@ +{ + "question": [ + "How do I determine which gene in my QTL is causal for the trait?", + "Why do males have two Y chromosomes and females only one?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2?", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?" + ], + "answer": [ + "To determine which gene in your QTL is causal for the trait, you can follow several steps. First, narrow down the QTL interval to a reasonable size. Then, sort through the genes within the interval to identify potential Quantitative Trait Genes (QTGs). This can be challenging as more than one gene may be involved and the function of some genes may be unknown. You can use techniques like LCMS to establish causal relationships between DNA loci and transcripts. Once potential QTGs are identified, you need to prove that a particular gene is involved in the quantitative trait under study. This can be done by complementation of a QTL, which can be achieved in several ways. You can also use mediation analysis to identify the causal gene between the genetic variant and the trait-of-interest. Finally, investigate whether the expression of these genes correlates with the phenotype(s) of interest. If the gene expression strongly correlates with the QTL phenotype, this further strengthens the assumption that this gene might be causal.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.", + "GeneNetwork utilizes datasets containing phenotypes and genotypes for QTL mapping analysis. These datasets may include genetic, genomic, and phenotypic data. The data are often formatted and normalized, and come with metadata. The database also includes data on complex phenotypes ranging from gene expression to behavior in standard inbred strains, and panels of mouse recombinant inbred strains. Rat and Arabidopsis populations are also represented in the database.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined." + ], + "contexts": [ + [ + "Prior belief or knowledge about the number of true causal and true independent links that might be expected in a typical QTL, depending on the study design, should be considered to safeguard against high false-positive rates (low positive predictive values). In studies that involve mapping gene expression (eQTL), protein (pQTL) or metabolite (mQTL) traits, information about co-localization of QTL and genes that are functionally linked to the trait provides information about the likelihood of causal links.", + "The next step is to investigate whether the expression of these genes correlates with the phenotype(s) of interest.This would suggest a chain of causality: a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest.To do this, we created a correlation matrix between all genes within a QTL with a cis-eQTL in any brain tissue as well as the phenotypes that contributed to the QTL (Supplementary Table S6).Any gene with a cis-eQTL and a significantly correlated expression was considered a good candidate.If the gene only had a cis-eQTL and correlation in a single brain region, then it suggested that this brain region might also be of interest for the phenotype (adding another link to this chain).", + "One possible approach to facilitate this endeavor is to identify quantitative trait loci (QTL) that contribute to the phenotype and consequently unravel the candidate genes within these loci. Each proposed candidate locus contains multiple genes and, therefore, further analysis is required to choose plausible candidate genes. One of such methods is to use comparative genomics in order to narrow down the QTL to a region containing only a few genes. We illustrate this strategy by applying it to genetic findings regarding physical activity (PA) in mice and human.", + "Network analyses We now have two QTL, and we have picked potentially interesting genes within each, but now we want to build up more evidence for which gene in our QTL interval is causal. The first, and most obvious way, is to see what genes our trait of interest correlates with, in tissues that we expect to be related to the trait. We calculated the Spearman\u2019s correlation between the trait BXD_17850 and all probes with expression data in T helper cells (GN319).", + "Another approach to help to determine if a gene located near the mapped QTL would have effects to influence the quantitative trait will be to use genetically engineered mice to determine if altering the expression of a candidate gene will alter the phenotype of interest (38). However, it is possible that a quantitative trait is a combined effect of multiple genes located near the QTL (39).", + "With a known QTL and a body of evidence suggesting possible roles for the affected gene, phenotypes can be predicted that may be modulated as a result of this sequence variation. If this phenotype is of interest, it can be directly measured and a traditional \u2018forward\u2019 QTL analysis carried out to confirm the prediction. Such an approach is extremely attractive when the enormous cost and time required for phenotyping a large panel is considered.", + "The first step is to narrow down the list of candidate causal genes within a Fig 1. Interval mapping of oviduct gross pathology across the BXD strains Quantitative Trait Locus (QTL)\u2014a reveals a QTL on distal Chr 3. The L RS values are plotted in blue across the chromosomal region containing genome and measure the strength of the association between sequence variants strongly chromosome and Mb position (top and bottom X-\u00ad\u2010axis, respectively) and associated with phenotypic phenotype expression. Allele contribution is shown by the red (C57BL/6J) and green (DBA/2J) lines. Red and grey horizontal lines indicate genome-\u00ad\u2010 variation.", + "A special case is the correlation of the target phenotype with the expression of the priorized gene(s) (RNA or protein amounts). This refers to colocalization of the QTL of the target phenotype with the eQTL position. Correlation can also be examined between the target QTL phenotype and expression of all genes in the QTL interval. If the gene expression strongly correlates with the QTL phenotype, this further strengthens the assumption that this gene might be causal (see Note 12). For performing a correlation analysis: \u2013 Go to the Trait Overview Page, as described in step 3, point 1.", + "QTL mapping of traits in mouse cohorts often ends up with a genetic locus, composed of a list of candidate genes. Several studies proposed the use of mediation analysis to identify the causal gene (mediator) between the genetic variant (independent variable) and the trait-of-interest (dependent variable) (Figure 1.4B) [7, 47, 61, 77]. Mediation analysis can be used either on gene expression levels to identify the regulatory mechanisms [7, 47, 61], or on phenotypic traits to discover the potential causal drivers contributing to the phenotypic variances [77] (Figure 1.4C upper).", + "1a). Second-generation offspring are then phenotyped and genotyped, and linkage analysis is carried out to identify a region that is associated with the trait1. This approach has led to the identification of thousands of quantitative trait loci (QTLs) for various phenotypes and diseases. However, each QTL region is large, often tens of megabases, and contains hundreds of genes. The process of identifying the causal variant and the gene involved is therefore difficult and costly. Of the thousands of QTLs identified, only a small fraction of genes has been identified. NIH-PA Author Manuscript \u00a9 2012 Macmillan Publishers Limited.", + "Network analyses We now have two QTL, and we have picked potentially interesting genes within each, but now we want to build up more evidence for which gene in our QTL interval is causal. The first, and most obvious way, is to see what genes our trait of interest correlates with, in tissues that we expect to be related to the trait. We calculated the Spearman\u2019s correlation between the trait BXD_17850 and all probes with expression data in T helper cells (GN319).", + "10 JUNE 2016 \u2022 VOL 352 ISSUE 6291 aad0189-5 R ES E A RC H | R E S EA R C H A R T I C LE Solving QTLs: Finding the quantitative trait gene For cis-QTLs, the causal factors can be quickly identified: With few exceptions, they will be driven by variants within the gene itself or immediately adjacent. For trans-QTLs, mQTLs, and cQTLs, the identification of the causal quantitative trait gene (QTG) is challenging due to the width of the QTLs.", + "Once the QTL interval is reduced to a reasonable size, the next step in the process involves sorting through the genes within the interval and attempting to determine which is the QTG. This step is daunting because more than one gene may be involved and the function of some genes within the interval may be unknown. Until recently, this step emphasized the detection of polymorphisms within coding sequence (reviewed in Korstanje and Paigen, 2002 and Glazier et al. 2002); for a polymorphism that produces an amino acid substitution, one can often infer and then test for a functional consequence.", + "To understand the genetic networks that underlie quantitative variation in the trait, it is also very important to discover genes whose expression is correlated with the trait after accounting for the known effects of the QTL on the trait. Many of these genes may have expression that is associated with QTL genotype, and would therefore be identified as important via the tests described above. Other genes, however, may have expression values that are correlated with the trait but unassociated with genotype at the QTL.", + "The approach is motivated by the fact that a research project is often focused on a specific classical quantitative trait. If a major QTL for this classical trait has been identified, it is often desirable to test whether this QTL is also associated with the transcription level of any genes, which will provide clues as to which genes belong to the pathway that the QTL uses to modulate the classical trait.", + "Confirmation of Candidate Genes The next step is to prove that a particular gene is involved in the quantitative trait under study. This is done by complementation of a QTL, which can be achieved in several ways (9\u201311,40). In principle, transgenic complementation is the most straightforward. This approach has been used successfully to demonstrate that Pla2g2a was the correct candidate gene for Mom1, a modifier of the apcmin allele that causes adenomatous polyposis coli (41).", + "So, how do you go about planning and performing a QTL study, and how do you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbred strains that have a markedly different trait. One can now look up many different traits of inbred mice online at the Mouse Phenome Database (http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). However, the trait you may want to study may not be present in wild type mice, so you may want to cross a mutant (or genetically engineered) strain onto several inbred strains.", + "Along with correlations, this tool also derives new traits representing the principal components (Figure 2d). The user can add these principal components to their Trait Collection and proceed to perform QTL mapping, as in the case of a single trait QTL mapping. The R/QTL (Broman et al. 2003) and R/CAPE (Tyler et al. 2013) packages can be used for deeper analysis of epistasis and pleiotropy for multiple traits and multiple regulatory loci. Prioritizing Candidate Genes 7 Author Manuscript Following the identification of a significant QTL, focus shifts to identifying the particular gene(s) that cause the QTL.", + "The investigators first identified all QTLs associated with a classical phenotype and then winnowed the list of potentially associated gene-expression traits on the basis of their correlation or eQTL overlap with the phenotype of interest. Candidate genes then were ranked by applying the LCMS technique, which uses the eQTL data to establish causal relationships between DNA loci and transcripts as well as between transcripts and phenotypes and finally identifies a model that best fits the data.", + "The goal of QTL mapping is clearly the identification and eventual confirmation of candidate genes (QTGs) underlying the phenotype. The evidence required for such confirmation has engendered much discussion (Complex Trait Consortium 2003; Glazier et al. 2002) and is likely to vary depending on the nature of the trait and specific resources available to pin down underlying genes (e.g. availability of knock-in or knock-outs, specific antibodies, siRNA, etc.). The paucity of QTGs meeting such multifaceted standards is testament to the difficulty of narrowing the confidence interval sufficiently to identify and test suitable candidate genes (Flint et al." + ], + [ + "Y chromosome in peripheral blood cells increases with age in men (6) and is correlated with increased risk of cancer mortality and Alzheimer's disease (6,7).X chromosome mosaicism in women also increases with age (8), as does autosomal mosaicism in both sexes (9,10).Recent studies have shown that the prevalence of age-related mosaic abnormalities is greater in men than women (9,10); however, mechanisms underlying the sex differences observed in chromosomal mosaicism in humans are unknown.", + "Recent reports suggested a role of Y chromosome loss in risk for all-cause mortality and common age-related disease such as cancer, Alzheimer disease as well as severe atherosclerosis [12][13][14][15][16][17][18][19][20].Building on such reports, we aimed to evaluate the contribution of male Y chromosome mosaicism to the risk for late-stage AMD.", + "Box 1. Sex-specific cytonuclear interactions Several predictions about the nature of cytonuclear conflicts follow from the patterns of chromosomal inheritance (Table I).In a mated pair of animals, mtDNA is co-transmitted with half of the autosomal genes, two-thirds of the X-linked genes and none of the Y-linked genes [76].This predicts that, relative to the autosomal case, positive nuclear-mitochondrial interactions are more likely to evolve for X-linked loci whereas deleterious interactions between Y-linked genes and mtDNA should accumulate (or cannot be purged efficiently).", + "In addition to genetic data, the 9p Network Cohort dataset also lists the gender for all 719 individuals.Of these individuals, 406 individuals are female and 313 are male, indicating a female bias (Binomial test p \u00bc 0.0006).This result was surprising considering that no female bias has been previously reported in 9p deletion and duplication syndromes.A possible explanation for the significant bias in the 9p Network Cohort dataset is the XY sex reversal phenotype, which is commonly observed in individuals with 9p deletion syndrome.This phenotype could lead to individuals with XY sex chromosomes being listed in the dataset as having a female gender.To further examine this hypothesis, we subset our dataset to include only the 236 individuals whose sex chromosomes are listed in their genetic information.For this much smaller subset, 125 individuals had female sex chromosomes and 111 had male sex chromosomes, indicating no significant sex bias (Binomial test p \u00bc 0.4).We also found no significant gender bias in this group (Binomial test p \u00bc 0.2), although we did confirm that four of the individuals with XY sex chromosomes had a gender of female.This comparison suggests that the XY sex reversal phenotype may be responsible for a female gender bias, but not a sex bias, in 9p deletion and duplication syndrome cohorts.", + "Duplicated variants with multiple alternative alleles and variants in sex chromosomes X and Y", + "Autosome-One of the numbered, or nonsex, chromosomes (1 through 22).X and Y are the sex chromosomes.", + "Given such a high abundance of young male-biased genes, we asked whether their parental genes are also male-biased.We found that fewer parental genes of X-linked male-biased duplicates were also male-biased (20%, 2/10) compared to the parental genes of autosomal young male-biased duplicates (32%, 12/37).These data, despite the small sample sizes and being statistically not significant, may suggest that compared to autosomal young genes, X-linked young genes more often evolved novel male-biased expression.However, as the majority of young genes are the result of intrachromosomal duplication events, the pattern might also reflect the fact that X-linked old genes are less likely to be male-biased.", + "A slight excess of X-linked female-biased genes was also detected (Fig. 2).Although most of them are old, a few recently arose on the X chromosome over 4 to 6 Myr in the common ancestor of the D. melanogaster and D. simulans clade (branch 5).This can be interpreted in the context of the dominance model of the sexual antagonism hypothesis.In this case, a dominant, X-linked gene that is favorable to females but disadvantageous for males can become fixed.The slow accumulation of female-biased genes in the X reflects an overall low rate of female gene origination, either due to a small dominance effect (the degree of dominance h!1/2), or a minor disadvantageous effect on males (the ratio of fitness effects of male relative to female k!0) along with a favorable effect on females (Vicoso and Charlesworth 2006, Equation 10).", + "Regarding the second step in the evolution of male-biased genes, namely X!A transposition, sexual antagonism favorable for autosomal fixation (Vicoso and Charlesworth 2006) and/or MSCI (Lifschytz and Lindsley 1972;Betran et al. 2002) may play a role in this process.On the other hand, the within-chromosomal duplication rate is higher than the between-chromosomal duplication rate (Emerson et al. 2008), which may contribute to the slow pace of X!A transposition.", + "It has been observed that male-biased genes in Drosophila are overrepresented on autosomes (Parisi et al. 2003;Ranz et al. 2003).Consistent with this result, a dynamic process that can explain the nonrandom autosomal distribution has also been observed, in which autosomal new genes with X-linked parental genes are often male-biased.Specifically, a significant excess of autosomal testisexpressed retrogenes were identified as RNA-duplicates of X-linked parental genes (Betran et al. 2002).Recently, similar X!A gene traffic was observed in the DNA-level duplication and relocation data set of the Drosophila genus (Vibranovski et al. 2009b), and was further confirmed for DNA-level duplications in the D. pseudoobscura neo-X chromosome (Meisel et al. 2009).In addition, selective extinction of neo-X linked male-biased genes also occurred in D. pseudoobscura (Sturgill et al. 2007).These three lines of genome-wide investigation support a common pattern of outof-X traffic for male-biased genes, resulting in an enrichment of these genes on autosomes in the long term.", + "It has been reported that the initial manifestations of new gene emergence, namely polymorphic duplicates, occur at a lower frequency on the X chromosome, thus indicating that these duplicates are subject to stronger purifying selection (Emerson et al. 2008).Therefore, the excessive fixation of X-linked duplicates might not occur via neutral processes.Positive selection could have facilitated the fixation of X-linked young genes in addition to driving their subsequent sequence evolution.", + "Occasionally, Y chromosome DNA is detected in the maternal plasma, and the fetus appears to have female genitalia on sonographic examination.The underlying mechanisms for this include a twin demise, a maternal disorder of sexual differentiation, such as Swyer syndrome, or that the mother has undergone a bone marrow or solid organ transplant from a male donor (Bianchi, 2018;Hartwig, Ambye, Sorensen, & Jorgensen, 2017).", + "Because of the differences in sex chromosome number, the sexunmatched comparison contains internal controls, i.e. , in this comparison, genes on the X-chromosome and Y-chromosome (but not those on the autosomes) should show copy number imbalances re\ufb02ective of a single copy change. We showed that the sample that is not sexmatched had readily detectable differences in aCGH signals for genes on the X and Y chromosomes. No such patterns were evident for the autosomes of the sex unmatched individuals or for the sex chromosomes of the sex matched samples.", + "Sex chromosome: The X or Y chromosome in human beings that determines the sex of an individual.Females have two X chromosomes in diploid cells; males have an X and a Y chromosome.The sex chromosomes comprise the 23rd chromosome pair in a karyotype.See also: autosome Sex-linked: Traits or diseases associated with the X or Y chromosome; generally seen in males.", + "X chromosome: One of the two sex chromosomes, X and Y. See also: Y chromosome, sex chromosome Y chromosome: One of the two sex chromosomes, X and Y. See also; X chromosome, sex chromosome", + "The male heterogamety (XY) is the most common reported system, but many species have female heterogamety (ZW), and more occasionally, multiple chromosome systems (Almeida-Toledo and Foresti, 2001; Devlin and Nagahama, 2002; Penman and Piferrer, 2008). Given the low resolution of optical microscopy to differentiate sex chromosomes in \ufb01sh, researchers have looked for an alternative in the tenfold longer meiotic chromosomes to detect mispairing tracts at the synaptonemal complex as an indication of the sex differentiated region with variable success.", + "The exclusive female constitution of gynogenetic genomes provides information on the SD system, especially in a XX/XY system, where all female progenies are expected. If ZZ/ZW is the underlying system, male offspring always will be present, but the interpretation is more complex and will depend on the distance of the SD region to centromere and on the viability of WW offspring (Devlin and Nagahama, 2002; Penman and Piferrer, 2008). Induced triploids, on the other hand, are constituted by the combination of two female and one male genomes (Piferrer et al.", + "The existence of a maternally silenced X-linked imprinted locus playing a role in social cognition could explain why males (X m Y) are more vulnerable to disorders of social cognition such as autism spectrum disorders than are females (X m X p ).The absence of the expression of this gene would not lead to autism itself, but would eliminate a putative protective factor, making an individual more susceptible to the effects of other ASD-predisposing genetic mutations or environmental factors.", + "When meiosis takes place, a pair of chromosomes may fail to separate properly, creating a sperm or egg that has either two copies or no copy of a specific chromosome.This is a sporadic event and it is called nondisjunction.Nondisjunction can lead to an extra chromosome, called trisomy, or a missing chromosome, called monosomy (GHR, 2008l).Down syndrome is an example of trisomy.Individuals who have Down syndrome have an extra chromosome number 21. Turner syndrome is an example of monosomy.Girls who have Turner syndrome have only one X chromosome.This causes them to have short stature and be infertile (NHGRI, 2008l).", + "X-Linked Inheritance X-linked genetic disorders (also called sex-linked) are caused by gene mutations on the X chromosome.Most often X-linked genetic disorders are seen in males.Males inherit the X chromosome from their mother and the Y chromosome from their father.Because males have only one X chromosome, if they inherit a gene mutation on the X chromosome from their mother, they will have the disorder.Examples of X-linked genetic disorders occurring in males include hemophilia and Duchenne muscular dystrophy (GHR, 2008o)." + ], + [ + "The project also provides online analysis tools to allow identification of correlations within its data set. GeneNetwork (http://www.genenetwork.org), encompassing WebQTL, is a database of genotypes and complex phenotypes ranging from gene expression to behaviour in standard inbred strains, and six panels of mouse recombinant inbred strains including the two largest sets (BXD and LXS) of approximately 80 strains each. Rat and Arabidopsis populations are also represented. Approximately 1500 phenotypes spanning the 25 year history of these strains are incorporated in this public resource, many of which were retrieved from the literature.", + "GN spares the user most of these problem. Data are formatted and normalized, and usually come with good metadata (often in the form of links to more information). This greatly simplifies QTL and eQTL analysis, candidate gene discovery, coexpression analysis, and hypothesis testing [3, 10].", + "Suitable for quantitative genetics (QTL mapping) and systems genetics, including correlation and network analysis to compare associations between tissues and between other rodent or human data sets Description and usage [32] [31] [30] [11] References Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential usage.", + "Bioinformatics All of the genetic analyses were carried out in GeneNetwork, which is an open source bioinformatics resource for systems genetics that exists as both a repository for genetic, genomic and phenotypic data together with a suite of statistical programs for data analysis that includes mapping and evaluating QTLs, examining phenotype/genotype correlations and building interaction networks. QTL mapping The QTL mapping module of GeneNetwork was used to identify QTLs for hippocampal morphometry and radial maze trait data. This module enables interval mapping, composite interval mapping and a pairwise scan option to identify epistatic effects.", + "There are four options for QTL mapping on the GeneNetwork website: interval mapping, marker regression analysis, composite interval mapping, and pairscan analysis. In this case, interval mapping was used to compute linkage maps for the entire genome. The log of odds (LOD) score was used to assert that a causal relation exists between a chromosomal location and a phenotypic variant, such as Gsto1 expression variation.", + "Webqtl is an online database [110] of linked datasets, including genotype and expression data, covering multiple species including mouse, macaque monkey, rat, drosophila, arabidopsis, plants and humans [60]. While this tool cannot be used to calculate eQTLs, it can be used to find and visualize eQTLs in different species, strains and tissues. It can perform single- and multiple-interval QTL mapping of up to 100 selected traits. Users can also upload their own trait data for populations included in the database. It can also calculate and display trait-correlation matrices and network graphs (also for up to 100 traits).", + "GN spares the user most of these problem. Data are formatted and normalized, and usually come with good metadata (often in the form of links to more information). This greatly simplifies QTL and eQTL analysis, candidate gene discovery, coexpression analysis, and hypothesis testing [3, 10].", + "Suitable for quantitative genetics (QTL mapping) and systems genetics, including correlation and network analysis to compare associations between tissues and between other rodent or human data sets Description and usage [32] [31] [30] [11] References Many of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential usage.", + "QTL MAPPING AND QTG DISCOVERY IN THE RCC A variety of statistical methods and tools have been developed for QTL mapping and implemented in free software for public use. These methods are well suited for simple backcross and F2 RCC populations. R/qtl9,39 was developed for identi\ufb01cation of QTLs and higher order modeling. Another Web-based tool, GeneNetwork or WebQTL (GeneNetwork.org),40 was developed for QTL mapping and to explore associations between variants, molecular traits (e.g. , gene expression), and higher order phenotypes (e.g. , behavior) and facilitate QTG identi\ufb01cation.", + "This enables gene expression correlation and interval mapping, candidate gene searches and multitrait analyses. Each exported dataset was subject to an interval mapping analysis, which uses GeneNetwork\u2019s embedded MapManager software (Manly et al . 2001) to perform Haley\u2013Knott regression. Empirical P values were derived using 1000 permutations using the incorporated permutation feature of WebQTL. The peak of each statistically significant (P -value <0.05) or suggestive (P -value <0.63) (Lander & Kruglyak 1995) QTL was determined based on empirical P values (Doerge & Churchill 1996). A one-LOD drop-off was used to determine the QTL confidence interval about each peak.", + "The peak linkage value and position was databased in GeneNetwork and users can rapidly retrieve and view these mapping results for any probe set. Any of the QTL maps can also be rapidly regenerated using the same Haley-Knott methods, again using functions imbedded in GeneNetwork. GeneNetwork also enable a search for epistatic interactions (pair scanning function) and composite interval mapping with control for a single marker. Data quality control We used two simple but effective methods to confirm correct sample identification of all data entered into GeneNetwork.", + "QTL analysis All QTL mapping for phenotypes was performed using the WebQTL software module of the 170 GeneNetwork (www.genenetwork.org) [34]. Interval mapping to evaluate potential QTLs was calculated from the likelihood ratio statistics (LRS) as the software\u2019s default measurement of the association between differences in traits and differences in particular genotype markers. Another common measure score, the log of the odds (LOD) ratio, can be converted from the LRS (LRS/4.61). Suggestive and significant LRS values were determined by applying 1000 175 permutations.", + "Unlike interval-specific haplotype analysis, which is most useful for narrowing a QTL shared by multiple crosses, genome-wide haplotype analysis requires only phenotype information from many inbred strains and can effectively narrow a QTL identified in only one experimental cross [36]. After narrowing the QTL to an interval that is !5 Mb using these bioinformatics techniques or classical experimental methods, strain-specific sequence and gene expression comparisons are effective for focusing on a few strong candidate genes (Figure 7).", + "We considered QTL intervals that achieved genome-wide significance for one phenotype, and genome-wide suggestive for others, as highest priority for candidate gene analysis. The January 2017 BXD genotype file was used4 . Updated linear mixed model mapping algorithms are now available on GeneNetwork 25 (Sloan et al. , 2016), that account for kinship among strains. These new algorithms include GEMMA (Zhou and Stephens, 2012), pyLMM6 (Sul et al. , 2016), and R/qtl27 .", + "The peak linkage value and position was databased in GeneNetwork and users can rapidly retrieve and view these mapping results for any probe set. Any of the QTL maps can also be rapidly regenerated using the same Haley-Knott methods, again using functions imbedded in GeneNetwork. GeneNetwork also enable a search for epistatic interactions (pair scanning function) and composite interval mapping with control for a single marker. Data quality control We used two simple but effective methods to confirm correct sample identification of all data entered into GeneNetwork.", + "There are four options for QTL mapping on the GeneNetwork website: interval mapping, marker regression analysis, composite interval mapping, and pairscan analysis. In this case, interval mapping was used to compute linkage maps for the entire genome. The log of odds (LOD) score was used to assert that a causal relation exists between a chromosomal location and a phenotypic variant, such as Gsto1 expression variation.", + "eQTL mapping QTL mapping was performed with GeneNetwork, an online bioinformatics resource featuring tools for systems genetic and complex trait analysis [9, 35]. QTL mapping involves entering VMB and CP iron data (strain means and SEM) as quantitative traits; the software generates whole-genome interval maps for each trait. The interval maps graphically illustrate phenotype\u2013genotype associations as peaks (QTL) indicating the strength of association between genomic polymorphisms and the quantitative trait throughout the genome.", + "Genetic Mapping In this study we utilize GeneNetwork, a database containing phenotypes and genotypes, and also serves as an analysis engine for quantitative trait locus (QTL) mapping, genetic correlations, and phenome-wide association studies (PheWAS) (Sloan et al. , 2016; Mulligan et al. , 2017; Watson and Ashbrook, 2020). QTL analysis involves connecting phenotype data with genotype data to examine genetic variation in traits controlled by multiple genes and their interaction with the environment (also called complex traits)(Lynch et al. , 1998; Myles and Wayne, 2008; Goddard et al. , 2016).", + "Once the resulting record set of the query is returned, it can be further restricted by selecting relevant records based on attached annotations before forwarding it for further analysis. To map genetic loci associated with mRNA abundance or trait phenotypes, any one of the three QTL mapping functions currently employed by GeneNetwork's WebQTL module can be used. These are 1. interval mapping, 2. single-marker regression, or 3. composite mapping [29,30].", + "genenetwork.org/) a set of 3795 markers. Linkage is reported with genome-wide significance levels based on 2000 permutation tests. Two types of QTL mapping analyses\u2013simple mapping using the Haley\u2013 Knott regression equation, and composite interval mapping\u2013were utilized in this study. Simple interval mapping was performed to illustrate the significance of any QTLs that regulate the TID. As a secondary analysis, composite interval mapping which controlled for the influence of Tyrp1 was also performed with the goal of identifying any secondary QTLs that may have been masked by the major QTL on Chr 4." + ], + [ + "A number of additional ethical implications must be considered.Associating financial investments with the prevention of disease, especially where reproductive decisions are involved, requires sensitivity, caution, and ethical rigor.Funding decisions based on imputed cost-savings must not result in implicit pressure on individuals to violate personal ethics to reduce financial burden on society.As discussions regarding prenatal testing have demonstrated, 35 is risk that \"routinization\" of testing may lead to social or medical expectations of testing in all eligible individuals.These expectations, if linked with financial incentives for the health system, could risk applying implicit pressure on serious, and potentially irreversible, personal decisions.Such expectations, if applied at the population level, could risk becoming normalized, compromising the values of informed consent and individual autonomy.", + "With regard to pregnancies affected by a genetic condition identified through population carrier screening, we modeled the decision to terminate affected pregnancies conservatively (0.50).This is despite the literature suggesting rates above 0.90 for elective TOP for conditions such as Down syndrome 33 and SMA. 34We recognize this issue is controversial, and that laws and ethical positions vary considerably between countries/ jurisdictions.Variations in population attitudes based on age, religion, and other factors, as well as the criticality of preserving individual choice, were acknowledged in adopting this highly conservative estimate.", + "The use of genetic testing from pre-conception through adulthood is expanding rapidly.As a result of this expansion, new ethical issues are emerging related to genetic testing and informed consent.These new issues create ethical challenges for nurses and all healthcare providers.Currently expanding areas include newborn screening and genetic testing of children.These new ethical challenges will be described below.", + "The use of genetic testing from pre-conception through adulthood is expanding rapidly. Psychological risks for parents who are carriers may include parental guilt.", + "Ethnic and cultural backgrounds may also play a role in the decisions that families make regarding prenatal testing.Moyer et al. (1999) concluded that Caucasian women more often undergo prenatal diagnoses than African American or Asian women, or Latinas.Furthermore, Awwad et al. (2008) found American couples less inclined to involve extended relatives in the prenatal decision-making process than Native Palestinian couples.Both of these examples clearly indicate that cultural differences can impact the ways in which families negotiate prenatal decisions.Further research needs to investigate how different families engage in such discussions and decision-making processes, especially as prenatal testing becomes more common and better able to predict or prevent a wider range of genetic conditions.Tightly closed ethnic groups remain at high risk of serving as carriers for genetic mutations, but the management of this possibility varies greatly.For example, some Ashkenazi Jewish groups use screening for mutations for Tay-Sachs disease (TSD) as the basis for rabbinical marriage advice; whereas, children born to Amish families in Pennsylvania more often present with glutaric aciduria type 1 (GA1) but, given their beliefs, parents tend not to accept prenatal testing because of the implication of abortion (McKusick, 2000).", + "Researchers studying factors that contribute toward a couple's choice to undergo prenatal testing have determined that partners base their decision upon several factors, including, but not limited to: parental beliefs about abortion, attitudes regarding disability and their \"perceptions of the usefulness of having the information revealed by genetic tests\" (Moyer et al., 1999, p. 522).Abortion beliefs constitute a key issue in the decision-making process.Even though a majority of parents receiving abnormal prenatal test results terminate their pregnancies (Redlinger-Grosse, Bernhardt, Berg, Muenke, & Biesecker, 2002), Moyer et al. noted that, when asked, more families reported that they would make use of prenatal testing than would be willing to terminate a pregnancy.The decision to continue or terminate a pregnancy after prenatal testing Downloaded by [University of the Sunshine Coast] at 10:32 05 August 2017 comprises a joint decision between both parents (e.g., Awwad et al., 2008;Beeson & Golbus, 1985); however, the nature of the conversations leading to the decision and the involvement of extended family members in the decisionmaking process remains highly understudied.", + "The Genetic Divide(s) and Communication The ability of scientists to \"map\" disease through several generations (Collins, 1999) raises practical and ethical issues of access to resulting opportunities and creates family communication challenges.Currently, prenatal testing for chromosomal diseases has become increasingly common (Moyer et al., 1999).Options such as pre-implantation genetic diagnosis (PGD) can identify over 1,250 disease-related mutations creating an opportunity for parents to select unaffected embryos for implantation in the womb (R. M. Green, 2008).Test results provide potential parents with information that may lead to decisions involving intervention in the genetic makeup of future children.Although some families welcome such options, others may be unable or unwilling to consider such procedures, due to fi nancial concerns or moral/ethical/religious beliefs.", + "Privacy Issues Finally, privacy issues should be seriously considered when the use of genetic testing is contemplated, especially with respect to whole-genome sequencing of healthy people.It is an unanswered question under what circumstances, to what extent, and by what means genetic data should be incorporated into the medical record.Although easy access to such data could be helpful to providers in improving patient care, it remains to be seen how other parties (eg, insurance companies) might act on the data in ways that do not benefit patients.The US Congress acted to prohibit discrimination by employers and health insurers on the basis of genetic testing with the Genetic Information Nondiscrimination Act in 2008, but further safeguards will undoubtedly be needed as the health implications of genetic data become clearer.", + "The ethical evaluation of genetic testing in children is traditionally based on the balance of clinical benefits and risks (American Society of Human Genetics Board of Directors and the American College of Medical Genetics All correspondence concerning this article should be addressed to Benjamin Wilfond, MD, Treuman Katz Center for Pediatric Bioethics, Seattle Children's Hospital, Metropolitan Park West M/S: MPW 8-2, 1100 Olive Way, Room 876, Seattle WA 98101, USA.E-mail: benjamin.wilfond@seattlechildrens.org Board of Directors, 1995;Andrews, Fullerton, Holtzman, & Motolsky, 1994;Clarke, 1994;Wertz, Fanos, & Reilly, 1994).In the early 1990s, when there were only scant data about children who had received genetic tests results, the presumption was to give greater weight to the potential risks and to restrict testing.However, this criterion is not necessarily consistent with the general practice of respecting broad parental discretion in health care decisionmaking for and on behalf of their children.In general, parents are the presumed decision makers for their children and their decisions are respected unless they are abusive or neglectful (Buchanan & Brock, 1989;Goldstein, Freud, & Solnit, 1979;Ross, 1998).The tension between assessments of benefits and risks made by health care providers and policy makers, and the procedural respect owed to parental authority will be clearly tested as the ability to conduct and interpret whole-genome sequencing and related technologies gain in momentum.", + "Ethical Considerations in Developing Policy for ''Comprehensive'' Genomic Testing In the near future, genomic testing is likely to become more accessible and will provide both information about the risks of common conditions such as heart disease, diabetes, and hypertension as well as predictions about individual responses to specific pharmaceuticals and other medical therapies (Aspinall & Hamermesh, 2007).Over time, the number and range of conditions for which such testing is available is likely to expand to include more behavioral traits, ranging from information about anxiety and depression, to attention and addiction (Rothstein, 2005).", + "Objective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical benefits and risks.However, this focus can be inconsistent with the general practice of respecting parental decision-making about their children's health care.We argue that respect for parental decision-making should play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions to use emerging genomic technologies.Methods Genomic testing involves the examination of thousands of DNA markers spanning genes throughout the genome and their interrelationships, yielding virtually limitless interpretations.We presume that parents and providers should proceed cautiously in applying genomic testing in children, as we explore how genomic testing will stress the fault lines of the traditional ethical analysis.Results Empirical data about the psychosocial risks and benefits of genetic testing of children do not reveal serious harms, yet virtually no such data exist yet about genomic testing.Unless empirical social and behavioral data indicate that genomic testing is highly likely to cause serious harms to the children, parental decisions to obtain comprehensive genomic testing in their children should be respected.Once comprehensive genomic testing of children becomes routine, resultant information may be more easily integrated by families than anticipated.Conclusions Research on the social and behavioral impact of comprehensive genomic testing on children and their families is needed to further inform parents, clinicians, and policy makers.", + "Objective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical benefits and risks.However, this focus can be inconsistent with the general practice of respecting parental decision-making about their children's health care.We argue that respect for parental decision-making should play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions to use emerging genomic technologies.Methods Genomic testing involves the examination of thousands of DNA markers spanning genes throughout the genome and their interrelationships, yielding virtually limitless interpretations.We presume that parents and providers should proceed cautiously in applying genomic testing in children, as we explore how genomic testing will stress the fault lines of the traditional ethical analysis.Results Empirical data about the psychosocial risks and benefits of genetic testing of children do not reveal serious harms, yet virtually no such data exist yet about genomic testing.Unless empirical social and behavioral data indicate that genomic testing is highly likely to cause serious harms to the children, parental decisions to obtain comprehensive genomic testing in their children should be respected.Once comprehensive genomic testing of children becomes routine, resultant information may be more easily integrated by families than anticipated.Conclusions Research on the social and behavioral impact of comprehensive genomic testing on children and their families is needed to further inform parents, clinicians, and policy makers.", + "To the extent that ''personal meaning'' gains wider acceptance as a legitimate criterion for expanding the availability of new tests and applications of genomic technology, the current policies and practices of restricting some genetic testing of children and mandating other tests will need to be reevaluated.There will be some parents who will find the information that becomes available through new technologies and data useful in shaping their parenting practices, while others will be more skeptical of their value.These disparate parental judgments may be independent of professional assessments of clinical validity and utility.Extrapolating from the empirical data about predictive genetic testing of children in at-risk families discussed earlier, we speculate that once comprehensive genomic testing of children becomes routine, the information may be more easily integrated by families than might be predicted.This is not meant to imply that whatever information parents want about their children should be provided carte blanche.Clearly, education and counseling will be crucial to ensure that families understand the limitations of the information.However, restrictions and mandates should be based on a criterion of risk of serious harm (Diekema, 2004).Given the lack of data confirming harm and the related data that indicate children may fare better than anticipated, such restrictions and mandates cannot be justified.Policies and practices will also need to clarify the role of the older adolescent in the decision-making process, although the issues related to balancing and assessing parental and adolescent interests and preferences goes beyond the focus of this article.This is also not meant to ignore the professional and moral obligation to educate parents and to help parents make good decisions on behalf of their children.It is morally appropriate for providers to strongly recommend particular tests in infancy and young childhood (i.e., PKU testing), and to strongly discourage other tests (e.g., ApoE testing of children for adult onset Alzheimer disease and heart disease because ApoE is not predictive but only provides an increased relative risk and has limited sensitivity and specificity) (Roberts, Cupples, Relkin, Whitehouse, & Green, 2005).Selective and directive recommendations are a routine aspect of pediatric practice.However, it will become increasingly important for professional organizations to begin to reconcile their support for mandatory genetic testing for some conditions and their support for restrictions for other conditions with the broad discretion that parents have and need in the health care arena in order to promote their children's well-being.", + "What limits should be imposed, if any, need to be determined prior to commercial feasibility.In this article, we consider how genetic testing decisions for children have been made traditionally and how the anticipation of comprehensive genomic testing in the near future will stress the fault lines of traditional approaches.The potential for comprehensive genomic testing in children could shift the equilibrium towards expanding or reducing parental discretion, and forces us to reexamine the evidence for our genetic testing policies and practices.We will highlight specific domains where further empirical social and behavioral research is necessary to inform policy and practice.", + "Prenatal genetics is largely practiced by maternal-fetal medicine specialists due to severe deficiency in the number of qualified clinical geneticists.Recent years have witnessed a tremendous growth in the demand for chorionic villous sampling and amniocentesis for the diagnosis of single gene disorders.At KFSHRC alone, the number of prenatal samples that are tested for single gene disorders has increased from 5 in 2004 to 250 in 2013.Therapeutic abortion is permitted by law if performed within 120 days from the time of fertilization in order to comply with the Islamic view of the timing of ensoulment (Alkuraya and Kilani 2001).However, the approved indication for the procedure, which is \"severe malformation\", must be authorized by three attending-level physicians.The definition of \"severe\" is left to the discretion of the medical team after consulting with the family.For example, intellectual disability is a common indication for many therapeutic abortion procedures.Contrary to commonly held views, we have shown that early prenatal diagnosis is the method of choice for couples who had one or more children with single gene disorders, as long as they are provided with a culturally sensitive genetic counseling that addresses their religious and cultural concerns (Alkuraya and Kilani 2001).Nearly 45% of these couples opt for early prenatal diagnosis compared to 35% who choose preimplantation genetic diagnosis (PGD) (Alkuraya 2013a).PGD is available freely at KFSHRC but is also provided by the private sector.Noninvasive prenatal screening using cell-free fetal DNA in maternal blood is quickly becoming integrated in prenatal care.KFSHRC offers this test routinely to all pregnant women regardless of their perceived risk and the MOH is considering making this test available throughout its vast network of hospitals and medical centers.", + "Social and psychological implications of accessing genetic services and information.", + "A corollary of the predictive power of genetic information is the limited ability to prevent or treat many conditions with significant genetic factors involved.Indeed, virtually all of the complex ethical and legal issues relevant to genetic testing would disappear if there were effective preventions or treatments available for genetic conditions.The ability to predict future disease in conjunction with a limited ability to do much about it has important social and psychological implications that must be addressed in conducting genetic research.", + "Interpretations of the literature will likely mirror the priorities and evaluative tendencies of the reader.Are you willing to accept the overall trends in genetic and genomic testing evaluation and to trust that the existing clinical approaches will apply informed consent appropriately while identifying and supporting the rare individual who has a serious adverse response to the testing?If so, you might advocate that attention be turned more toward other issues relevant to the effective implementation of genetic and genomic testing.Or do you feel a strong need to understand in more detail the possible psychosocial harms of the testing, particularly the subtler impacts or responses of individuals who do not fit the norm?In that case, you would likely encourage renewed and innovative efforts to study the psychosocial consequences of the receipt of risk information from genetic and genomic testing.", + "Other social issues require our attention if genomic medicine is to benefit our patients.How should genetic tests be regulated?What, if any, are the appropriate uses of direct-to-consumer marketing of genetic tests?The Internet has recently had a proliferation of genetic-testing sites that feature claims grounded in greed and pseudoscience, rather than in data or reality.How will health care providers and the public distinguish between these and responsible testing services, whether they are available through the Internet or in the hospital?", + "Environmental Factors As widespread use of genetic testing increases, it is the responsibility of the medical community to ensure its equitable use across socioeconomic and cultural spectrums." + ], + [ + "Gene editing has gained considerable interest with the identification of the CRISPR-Cas9 system, 27 which allows for a targeted modification in the DNA sequence of an organism.Researchers can utilize their knowledge of the basic biology of the gene and its protein function to precisely change the DNA sequence, thus altering the protein function of the gene and allowing for edits to stay within the species.Researchers at the University of Missouri used the CRISPR-Cas9 system to modify the CD163 gene such that the PRRS virus is not able to replicate inside the pig. 28This slight modification of the swine genome through gene editing keeps the pigs from succumbing to PRRS which has an annual estimated loss to the United States swine industry of over $660 million per year.Despite this benefit, given the public's concerns over food safety, it is likely that approval for such technology is years away in the US, Canada and Europe.However, in some cultures, there is a wide range of non-livestock species that are consumed.Therefore, it is conceivable that these countries and cultures may be open to transgenic/gene edited livestock.They may see the importance of useful gene editing which may lead to approval and consumption of reasonable genetically edited animal products such as those with modifications that are already found in nature or those that offer a substantial welfare benefit to society.", + "As a researcher who has devoted an entire career since 1994 to the development of genome editing tools and methods, I have been amazed by the rapid progress in the field over the last few years.Considering the widespread use of the tools, I am sure that the pace will continue to accelerate.Indeed, programmable nucleases, may eventually enable humans-products of evolution-to become masters of evolution.delivered preassembled recombinant Cas9-guide RNA ribonucleoproteins (RNPs) into animal embryos 6,9 and plant 11 and mammalian cells [73][74][75] .Indeed, Cas9 RNPs were rapidly turned over in cells 73 , reducing off-target effects and mosaicism in gene-edited organisms 11 .Cas9 RNPs can be delivered into cells by various methods, including microinjection 6,9 , electroporation 73 , lipofection 74 and protein transduction 75 .Importantly-and unlike in conventional gene therapy, where therapeutic genes are delivered via plasmids or viral vectors-Cas9 RNP delivery does not involve the use of exogenous DNA; host innate immune responses against foreign DNA are not elicited, and undesired integration of foreign DNA into the host genome is avoided.", + "In comparison to a transgenic approach, a gene editing technique such as CRISPR-Cas9 offers the advantage that gene-edited crops are not considered genetically modified organism (GMO) in some countries, such as the US, where the demand for natural food colorants such as anthocyanins is high.Indeed, the use of GMO crops as a source of natural pigments may be inconsistent with consumer interests.However, carrot cultivars engineered with either the transgenic or gene editing approach have not been reported so far, but their development is possible.", + "The notable accuracy and versatility of CRISPR-Cas for genome editing also opened the door to its use in preclinical and translational settings.In the latter case, CRISPR in vivo gene editing has led to several proof-of-concept studies that would have been unachievable without it, as in the first ever correction of inherited pathogenic mutations linked to degenerative disease in a living organism [22] and even shown to be possible in human embryos [23,24].It also has great potential in the field of precision medicine as large-scale population DNA sequencing studies have provided vast amounts of information linking particular diseases with specific genetic mutations which could, in theory, be targeted through CRISPR [25,26].This could be used during the identification and validation of potential DNA targets during the development of personalised drug or cell therapies, which will require the generation of engineered cell lines and/or animal models.Techniques such as HDR-mediated gene targeting are too labour intensive, with low targeting efficiencies and long times necessary for their establishment, and consequently are not ideally suited for drug discovery purposes.Conversely, CRISPR-Cas has been proven to be efficient for editing virtually any kind of cell line, from primary immune cells to induced pluripotent stem cells (iPSCs) [27,28].Additionally, CRISPR can also be used for functional screening in the development of combined inhibitory therapy aimed at strengthening the efficiency of targeted therapeutics.An example of the latter is shown in a study where a variation of the technology known as CRISPR interference (CRISPRi) was used in genome-wide scale to identify different survival pathways used by cancer cells after oncogene inactivation and allowing the identification of successful combination therapies [29].In terms of translational applications, the overall safety of CRISPR genome editing in humans will require long-term scrutiny before its adoption in the clinic.Nonetheless, a number of CRISPR-based clinical trials are currently in progress, including studies focused on targeting patients' own T cells in order to improve the immune response towards some forms of malignant cancer [30,31], and others aimed at correcting pathogenic mutations in the hematopoietic cells of patients with beta-thalassemia and sickle cell disease [32].", + "Caveats and Ethical Concerns of CRISPR-Cas Applications Despite the presence of both a PAM sequence and a specific gRNA, the CRISPR-Cas9 system is not infallible.In fact, DSBs can occur at different sites in the genome, potentially causing so-called \"off-target\" effects.This eventuality remains to date the biggest concern in the field, as possible undesirable modifications must be properly identified and followed in order to guarantee safety for medical purposes.Nevertheless, there is still little evidence of the biological consequence of Cas9 off-target effects.Two recent studies describe new methods to investigate potential off-target effects in both mammals and plants [33,34].In both cases, whole-genome sequencing revealed that selective nucleotide changes, such as conversion of an adenine to a guanine, caused off-target occurrence very rarely, with a frequency comparable to the one of spontaneous mutations.However, substitution of a cytosine with a thymidine was linked to a sizable number of off-target mutations.This newly acquired information adds to the plethora of studies conducted on the safety of CRISPR, which altogether highlight the need for the establishment of clinical standards for the future use of genome-editing techniques in the clinic.Despite this and other technical challenges still ahead for CRISPR genome editing, the pace at which this technology has developed in recent years suggests many of these concerns could be addressed soon, as long as proper ethical guidelines and regulatory mechanisms are established.", + "Conclusions There is no reason to doubt that the development of CRISPR-Cas genome editing represents an unprecedented breakthrough in modern science, as it has potential applications in a wide array of disciplines ranging from agriculture, zoology and renewable energy to biomedicine and synthetic biology.This powerful tool holds promise for further elucidating the molecular causes of ageing by allowing scientists to probe genetic and epigenetic pathways with a level of sophistication that was unattainable just a few years ago.It will allow so in traditional animal and cell models of ageing, but it will also drastically accelerate the generation of refined versions of those models or even allow the development of new research approaches in non-model organisms.Moreover, CRISPR-based genome editing is already having a significant impact in research aiming to understand the cellular and molecular origins of age-related diseases, as well as developing potential treatments against them.The application of CRISPR-Cas gene editing for the treatment of age-related diseases is not over the horizon yet, as it will require the identification of causative genes and their role under a variety of contexts that could be as diverse as the ageing process is across individuals.However, CRISPR-Cas might also hold the key for solving such conundrum, as it has opened the way for achieving true personalised medicine by providing both the precision and scalability required for conducting genome-wide functional screens during the refinement of drug-and cell-based therapies for age-related diseases.", + "Since its discovery, CRISPR-Cas technology has ignited a biological revolution by providing a highly versatile platform that allows fast and efficient genome editing in an ever-growing list of organisms.In this chapter we will first describe the most recent advances in the development and application of the CRISPR-Cas platform in biomedical research.Then we will discuss the most recent and notable basic research applications of this technology in the study of the molecular causes of ageing.Finally, we will review how CRISPR-Cas has been used for creating new models for the study of age-related diseases, as well as for manipulating diseaseassociated gene pathways.", + "Caveats of advanced genome editing tools Off-target effects.The DNA-binding domains of ZFNs and TALENs need to be very specific for the target site to avoid off-target cleavage, which results in unwanted mutations and potentially cytotoxic effects [27].CRISPR/Cas9 is also known to generate off-target alterations, albeit apparently at low incidence [28,29], since mispairing is allowed between the guide RNA and the genomic DNA.Nonetheless, caution is required in their design and use.Some strategies involving the optimization of the guide RNA/Cas9 include using of software tools to predict potential off-target sites (http://omictools.com/crispr-cas9-Figure1: Genome editing methodologies which can be applied to human pluripotent stem cells.Homologous recombination (HR), or the more advanced tools such as zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) or clustered regularly interspaced short palindromic repeat (CRISPR)/Cas system can be applied to human pluripotent stem cells (hPSCs) either to 1) create naturally occurring mutations or 2) repair a mutation to generate isogenic controls in hPSCs, to understand the function of a gene of interest.c1268-p1.html),truncating the guide RNA (<20 nucleotides) to decrease off-target mutagenesis [30], lowering the dosage of guide RNA and Cas9 plasmids, and decreasing the number of mismatches between the guide RNA and the genomic DNA.A \"double nick\" system with Cas9 nickase, which contains a single inactive catalytic domain, may also be used [31e33].", + "CRISPR screening technologies The discovery of CRISPR-Cas9 as a sequence-specific programmable nuclease democratized gene editing and fueled progress in forward genetic screening [20 , 66] .Genetic screens using Cas9 with a pooled singleguide RNA (sgRNA) library allow the interrogation of seemingly all genes in a genome in a single experiment [96 , 97] [null] .Engineered Cas9 variants further extend the versatility of forward genetic screening.Catalytically inactive Cas9 (dCas9) fused with chromatin effector domains permit specific activation (CRISPRa) or inhibition (CRISPRi) of gene expression [37 , 54] .Recently developed and emerging technologies -base editors, prime editors, and Cas transposases -are beginning to enable new types of genetic screens with directed, controlled, and on demand mutations by allowing the creation of user specified modifications, such as single base conversion, deletions, and insertions [4 , 42 , 58] .", + "Coming on the heels of engineered nucleases, CRISPR-Cas9 tools have accelerated the pace of genomic research by permitting highly efficient knockouts or edits of virtually any gene in cells or model organisms.Multiple CRISPR-Cas9-based clinical trials are in progress or are expected to begin soon.Although Cas9engineered cells haven't yet demonstrated efficacy at scale, early trial results suggest that such cells are stable and don't cause acute adverse reactions in humans.Long-term safety is yet to be determined.Current applications largely focus on single-gene disorders for which gene editing can be carried out ex vivo on appropriate cells, such as bone marrow hematopoietic stem cells in the case of sickle cell anemia.Exploration is under way to develop delivery systems that can target the gene-editing apparatus to the appropriate tissue in vivo.", + "Over the past 8 years, CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 (CRISPR-associated protein 9) technologies have emerged as accessible and adaptable tools for studying and altering genomes. 5RISPR-Cas9 can be used to induce genome edits by creating targeted DNA breaks that trigger site-specific DNA repair.In nextgeneration formats, it can also control the transcriptional output of genes or alter genome sequences using a process of nucleotide base editing that does not require repair of DNA breaks.As these technologies continue to mature, it will become increasingly possible to alter cellular genomes efficiently and accurately.", + "The type II CRISPR-Cas9 systems, repurposed from prokaryotic adaptive immune responses, are now widely used for targeted genome modifications in plants, animals, and human cells (Kim et al. 2014;Woo et al. 2015;Zuris et al. 2015).In particular, Cas9 nucleases have shown promise for gene and cell therapy (Maeder and Gersbach 2016).Typically, these nucleases are expressed or delivered in vivo using plasmid DNA or viruses (Yin et al. 2014;Ran et al. 2015).However, plasmid DNA delivery is often inefficient, especially in vivo, and can cause integration of small plasmid fragments degraded by endogenous nucleases at on-target and offtarget sites in the genome (Kim et al. 2014).Viral delivery of Cas9 can be highly efficient in vivo (Ran et al. 2015;Long et al. 2016;Nelson et al. 2016;Tabebordbar et al. 2016), but may be hampered by antibodies or T cells induced against the protein (Shankar et al. 2007;Calcedo et al. 2015;Chew et al. 2016).We and others have shown that preassembled Cas9 ribonucleoproteins (RNPs) can be delivered to human primary and stem cells and mice to modify target genes (Kim et al. 2014;Schumann et al. 2015;Zuris et al. 2015).Cas9 RNPs are rapidly turned over in cells, reducing off-target effects.Furthermore, Cas9 RNPs are unlikely to be limited by host immune systems because they function and disappear before the generation of antibodies and T cells directed against them.Currently, despite these advantages of RNPs, the difficult delivery of Cas9 RNPs in vivo limits its utility for therapeutic applications (Zuris et al. 2015).Here, we show that in vivo genome editing of an wild-type gene, whose up-regulation is responsible for pathogenesis, could be a new therapeutic modality for the treatment of nongenetic degenerative diseases.Our ultimate goal is to harness Cas9 RNPs for a clinical application of therapeutic genome surgery in patients with AMD.", + "Clustered regularly interspaced short palindromic repeat (CRISPR)-Cas nucleases have revolutionized the field of gene editing and have tremendous application in the field of molecular medicine [98][99][100][101][102].Despite a significant surge in CRISPR/Cas9mediated genome editing in various disease models, the progress in the field of AD has lagged behind substantially.We believe that genome editing can significantly improve the development of AD models and also create novel opportunities for the development of the next generation precision targeted AD gene and stem cell therapies.Since there are several excellent review articles on CRISPR/Cas9-mediated genome editing, here we will limit our focus on select recent articles that are noteworthy.CRISPR/Cas9 system can be engineered to either activate transcription (gain-of-function) or achieve gene silencing (Loss-of-function).Dahlman et al. have developed a CRISPR-based system that uses catalytically active Cas9 and distinct single guide (sgRNA) constructs to activate and knockout different genes in the same cell [103].Konermann et al. have used structure-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci [104].Using crystallographic studies, they have engineered a combination of sgRNA2.0,NLS-dCas9-VP64 and MS2-p65-HSF1 to develop one of the most effective transcription activation system.", + "Limitations of CRISPR-Cas9 CRISPR provides a simple and easy tool not only for in vitro use but potentially also for in vivo genome editing.However, there are limitations and downsides to this approach.First, and despite considerable improvements in the technology, the risk of the offtarget effect remains and must be considered carefully.Second, DSB may lead to wide-ranging deletions or recombination events involving the on-target site (204).Third, in cycling cells, DNA double strand breaks caused by Cas9 cleavage may trigger a P53 response leading to apoptosis and enrichment for potentially oncogenic P53-deficient cells (205,206).Fourth, subjects may generate antibodies to Cas9, potentially limiting gene therapies (207,208).", + "Genome editing tools that target the desired genomic region and allow for variants to be altered (e.g. from risk to protective), or for more substantial changes to be made (e.g. the deletion of a longer stretch of DNA harbouring a number of variants) and can help to answer each of these questions.These technologies are evolving rapidly (Figure 1 and Table 2).The most recently developed of these, Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) technology, originally developed by Doudna, Charpentier and their colleagues (72,73) and Zhang and his colleagues (50) has become a widely used tool for this purpose.Engineered CRISPR/Cas9 technology uses a guide RNA (gRNA) to direct CRISPR-associated endonuclease (Cas) to the target DNA and generate a double strand DNA break.Correction of a mutation or variant in the target DNA sequence can then be carried out by homology-directed DNA repair (HDR) with a donor template.Since its discovery eight years ago, CRISPR technology has evolved quickly to be a critical part of the molecular biologist's toolbox.", + "INTRODUCTION Genome editing technologies based on the clustered regularly interspaced short palindromic repeats (CRISPR)-associated endonuclease Cas9 enable rapid and efficient modification of endogenous genes in a variety of cell types, allowing for analysis of gene function in many organs in vivo.CRISPR-Cas9 induces DNA double strand breaks (DSBs) at single-guide RNA (sgRNA)-specific loci in the genome, which are repaired through either non-homologous end-joining (NHEJ) or homology-directed repair (HDR) pathways.While NHEJ introduces unpredictable pattern of insertion or deletion (indel) mutations, HDR directs a precise recombination event between a homologous DNA donor template and the damaged DNA site (Cong et al., 2013;Cox et al., 2015;Doudna and Charpentier, 2014;Heidenreich and Zhang, 2016;Jinek et al., 2012;Mali et al., 2013;Sander and Joung, 2014;Wang et al., 2013;Yang et al., 2013).Thus, HDR can be used to precisely introduce sequence insertions, deletions or mutations by encoding the desired changes in the donor template DNA.", + "CRISPR technology has rapidly changed the face of biological research, such that precise genome editing has now become routine for many labs within several years of its initial development.What makes CRISPR/Cas9 so revolutionary is the ability to target a protein (Cas9) to an exact genomic locus, through designing a specific short complementary nucleotide sequence, that together with a common scaffold sequence, constitute the guide RNA bridging the protein and the DNA.Wild-type Cas9 cleaves both DNA strands at its target sequence, but this protein can also be modified to exert many other functions.For instance, by attaching an activation domain to catalytically inactive Cas9 and targeting a promoter region, it is possible to stimulate the expression of a specific endogenous gene.In principle, any genomic region can be targeted, and recent efforts have successfully generated pooled guide RNA libraries for coding and regulatory regions of human, mouse and Drosophila genomes with high coverage, thus facilitating functional phenotypic screening.In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening.", + "CRISPR technology has rapidly changed the face of biological research, such that precise genome editing has now become routine for many labs within several years of its initial development.What makes CRISPR/Cas9 so revolutionary is the ability to target a protein (Cas9) to an exact genomic locus, through designing a specific short complementary nucleotide sequence, that together with a common scaffold sequence, constitute the guide RNA bridging the protein and the DNA.Wild-type Cas9 cleaves both DNA strands at its target sequence, but this protein can also be modified to exert many other functions.For instance, by attaching an activation domain to catalytically inactive Cas9 and targeting a promoter region, it is possible to stimulate the expression of a specific endogenous gene.In principle, any genomic region can be targeted, and recent efforts have successfully generated pooled guide RNA libraries for coding and regulatory regions of human, mouse and Drosophila genomes with high coverage, thus facilitating functional phenotypic screening.In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening.", + "The recent development of clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 for experimental purposes has dismantled the perception that genome editing technology is off-limits for screening in mammalian systems (Heintze et al., 2013).Since this system employs the basic principle of Watson-Crick base pairing for gene targeting, generation of libraries with whole-genome target coverage is relatively easy and cost-effective.For instance, simple protocols are available to synthesize pooled lentiviral libraries by in silico design of oligonucleotides, which can then be cloned, packaged and delivered to cells by viral transduction (Paddison et al., 2004;LeProust et al., 2010).Similarly, the generation of arrayed libraries can be achieved by following protocols originally developed for arrayed shRNA library production that have been in use for a number of years (Moffat et al., 2006).All in all, the stage is set for CRISPR to make an enormous impact on genomic screening and thus scientific discovery in the coming years, and recent demonstrations of this system have shown great promise (Shalem et al., 2015).However, a number of technical challenges must be addressed in order to maximize the benefit of this technology.In this review, we will discuss current applications of CRISPR in functional genomics and provide a perspective on future developments in this area.", + "Genome editing for crop improvement Reports of CRISPR-Cas9-based genome editing first appeared in 2013 (Cong et al., 2013;Feng et al., 2013;Mao et al., 2013).Since then, genome editing technologies have proven to be powerful and efficient tools for the improvement of many crop species.At present, genome editing has been widely used to introduce/modify agronomically important traits, such as increased yield, improved nutritional quality, and resistance to biotic and abiotic stresses, in multiple crops, including rice, wheat, maize, tomato, and potato (Lu et al., 2017;Soyk et al., 2017;Tang et al., 2017;D'Ambrosio et al., 2018;Ye et al., 2018;Miao et al., 2019;Zhang et al., 2019;Zhong et al., 2019;Butt et al., 2020;Zhang et al., 2020c;Li et al., 2021b;Zhan et al., 2021).CRISPR-Cas-based genome editing has been extended to targeted mutagenesis, base editing, and precisely targeted gene/allele replacement or tagging in plants.mportantly, using CRISPR-Cas9 technology, transgenes present in the genomes of genome-edited plants can be removed by chromosomal segregation via a simple self-pollination or hybridization step.Gene editing technologies continue to be developed and utilized (Mao et al., 2013;Lu and Zhu, 2017;Lu et al., 2020)." + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/old/experts_general2_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/experts_general2_dataset.json new file mode 100644 index 0000000..20e7d00 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/experts_general2_dataset.json @@ -0,0 +1,128 @@ +{ + "question": [ + "What about recombination in human centromeres?", + "What about recombination in the human genome?", + "How can I add a new species to the GeneNetwork database?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "what is ensembl?" + ], + "answer": [ + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "The text mentions the application of site-specific recombinase technology, which allows investigators to engineer genes in the mouse that will allow for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. However, it does not provide specific details about recombination in the human genome.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute. It is a publicly available web resource that contains automatically annotated genomes and integrates data from a wide range of biological research sources. Launched in 1999, the Ensembl database was the first to provide a window on the draft genome, curating the results of a series of computational analyses. It provides comprehensive information about the human genome including variants like SNPs, insertions, deletions, and somatic mutations for several species. Ensembl also offers tools for data retrieval and analysis." + ], + "contexts": [ + [ + "Primate and mouse centromeric DNA contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B.In humans, the CENP-B box is present on every chromosome except for the Y chromosome [20].It is paradoxically necessary for formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres, and mice lacking CENP-B are viable and fertile [21,22].", + "Box 3 Mechanism of homologous recombination and end joining The severe phenotype of the mouse mutants and the highly cancer-prone human syndromes highlight the importance of homologous recombination.Mouse KU mutants display sensitivity to agents that lead to breaks in DNA, and have immunological problems because the KU proteins are involved in V(D)J recombination of antibody gene sequences.In addition, these mutants display poor development, several features of premature ageing and increased apoptosis of postmitotic neurons in the developing brain.Mice with defects in DNA-PK cs (SCID mice) display a similar but generally milder phenotype.In contrast, XRCC4-and ligase IV-knockout mice seem more severe, with late embryonic lethality resulting from massive ATM-and p53-dependent neuronal apoptosis 33,38 .", + "Cells in G1 have only the homologous chromosome for recombination repair.However, this may be difficult to find in the complex genome.Moreover, it is potentially dangerous as a template for repair as it may lead to homozygosity for recessive mutations.As an alternative, the end-joining reaction simply links ends of a DSB together, without any template, using the end-binding KU70/80 complex and DNA-PK cs , followed by ligation by XRCC4-ligase4 (reviewed by 27,33; see the right panel of the figure, stages V-VII).The function of KU70/80 might involve end protection and approximating the ends, in addition to a signalling function by DNA-PK cs .End joining may be further facilitated when the ends are still held together through nucleosomes or other structures.End joining is sometimes associated with gain or loss of a few nucleotides if internal microhomologies are used for annealing before sealing.This implies the involvement of DNA polymerases and/or nucleases.Note that the KU complex is also involved in telomere metabolism 27,62 .found to be lethal 34 .Inactivation of ATR by itself is inviable already at the blastocyst stage.Inactivation of BRCA1 and BRCA2 in mice is also embryonically lethal; cell lines display defects in homologous recombination [35][36][37] .", + "371 A tentative scenario for the homologousrecombination reaction is depicted in the left panel of the figure.To promote strand invasion into homologous sequences, the 5\u1371-3\u1371 exonuclease activity of the RAD50/MRE11/NBS1 complex (also a substrate for ATM phosphorylation) exposes both 3\u1371 ends 30 (I).RPA facilitates assembly of a RAD51 nucleoprotein filament that probably includes RAD51-related proteins XRCC2, XRCC3, RAD51B, C and D. RAD52 stimulates filament assembly (II).RAD51 has, like its Escherichia coli RecA counterpart, the ability to exchange the single strand with the same sequence from a double-stranded DNA molecule.Correct positioning of the sister chromatids by cohesins probably facilitates the identification of a homologous sequence.A candidate for the complex chromatin transactions associated with these DNA gymnastics is RAD54, a member of the SWI/SNF family of DNA-dependent ATPases.After identification of the identical sister chromatid sequence, the intact double-stranded copy is used as a template to properly heal the broken ends by DNA synthesis (III).Finally, the so-called Hollidayjunctions are resolved by resolvases 27,33,60 (IV).Homologous recombination involves the simultaneous action of large numbers of the same molecules, which are found to be concentrated in radiation-induced nuclear foci.These depend on, and also include, the BRCA1 and BRCA2 proteins 36 .Recent evidence implicates BRCA2 directly or indirectly in nuclear translocation of RAD51 (ref.61).", + "This picture poses more questions than it seeks to answer.Is the grouping of the regions by product rather than by type of region correct?Given that the recombina- tion fraction between HLA-A and HLA-B is of the order of .08%,and that this is likely to represent a distance of at least hundreds of thousands of nucleotides, how are the pieces put together over such relatively long distances?Is it possible that regions of the DNA loop out, so that transcripts can be made directly from noncon- tiguous DNA sequences, the loops being held in place by small RNAs as suggested for the control of splicing by Steitz, and her colleagues [24] and by others [25]?If these small RNAs are coded for well outside the HLA region, does this provide a mechanism for control of expression of products by unlinked genes, as may be the case for one of the constituent polypeptides of the HLA-DR product?What might be the nature of the signals that control which of a multiple set of alternative regions is expressed by any given chromosome?", + "Mamm Genome. 2006; 17:220\u2013229. [PubMed: 16518689] 72. Romanoski CE, et al. Systems genetics analysis of gene-by-environment interactions in human cells. Am J Hum Genet. 2010; 86:399\u2013410. [PubMed: 20170901] 73. Myers S, Freeman C, Auton A, Donnelly P, McVean G. A common sequence motif associated with recombination hot spots and genome instability in humans. Nature Genet. 2008; 40:1124\u2013 1129. [PubMed: 19165926] 74. Myers S, et al. Drive against hotspot motifs in primates implicates the PRDM9 gene in meiotic recombination. Science. 2010; 327:876\u2013879. [PubMed: 20044541] 75. Cordell HJ. Detecting gene-gene interactions that underlie human diseases. Nature Rev Genet. 2009; 10:392\u2013404.", + "Classification of common conserved sequences in mammalian intergenic regions. Hum. Mol. Genet. 2002, 11, 669\u2013674. 25. Zhu, L.; Swergold, G.D.; Seldin, M.F. Examination of sequence homology between human chromosome 20 and the mouse genome: Intense conservation of many genomic elements. Hum. Genet. 2003, 113, 60\u201370. 26. Pevzner, P.; Tesler, G. Human and mouse genomic sequences reveal extensive breakpoint reuse in mammalian evolution. Proc. Natl. Acad. Sci. USA 2003, 100, 7672\u20137677. 27. Christmann, R.B. ; Sampaio-Barros, P.; Stifano, G.; Borges, C.L. ; de Carvalho, C.R. ; Kairalla, R.; Parra, E.R. ; Spira, A.; Simms, R.; Capellozzi, V.L. ; et al.", + "a The table lists proteins in which mutations have been shown to increase homologous recombination (HR), gross chromosomal rearrangements (GCRs), chromosomal instability (CIN), sister chromatid exchanges (SCEs), tri-nucleotide repeat expansions and contractions (TNR), telomere fusions (Tel fusion), or fragile telomeres (Tel fragility).A phenotype inside brackets ([ ]) indicates that it is caused by overexpression of the protein.For further details and references see Supplementary Table1.Abbreviations: DSB, double-strand break; PCNA, proliferating cell nuclear antigen; RFC, replication factor C complex; SCF, Skp1-Cdc53/Cullin-F-box.", + "Figure 3 Intermediates and chromosome structural alterations, as observed by different techniques. (a) Replication fork stalling, as monitored by 2D-gel electrophoresis and Southern analysis in yeast (for details about the technique, see Reference 161). (b) Slower human replication forks covering shorter DNA synthesis tracks, as determined by incorporation of IdU and CldU via DNA combing (52), which permits visualization of the process of replication on DNA fibers. (c) Accumulation of double-strand breaks (DSBs) or replicative stress, as inferred by \u03b3H2AX foci or by \u03b3H2AX pan staining, respectively, in human cells. (d ) DSBs or ssDNA (single-stranded DNA) gaps as seen directly by nuclear \"comet tails\" via single-cell electrophoresis assays in human cells (52). (e) Sister-chromatid exchanges (SCEs), as determined by Giemsa staining in human cells (207). ( f ) Hyper-recombination, as determined by colony sectoring in yeast (5). ( g) Gross chromosomal rearrangements (GCRs), as determined by spectral karyotyping in mouse cells (118). (h) Translocations, as visualized by pulse-field gel electrophoresis in yeast (168). (i ) Fragile sites, as detected by mitotic spreads in human cells (109). ( j) Telomere fusions, as determined by CO-FISH (chromosome-orientation fluorescent in situ hybridization) in mouse cells (124). (k) Anaphase bridges, presumably resulting from unfinished replication, dicentric chromosomes, and sister-chromatid nondisjunction, as detected by fluorescence microscopy in mouse cells.Arrows indicate the specific structural alterations referred to in each panel; in panel h, closed and open arrows indicate the position where the translocated or missing parental chromosome migrate or should migrate, respectively.When necessary, a normal control is shown on top of the panel, with the exception of panel a, which is shown on the left.Detailed description of each technique can be found in the references provided.Photos are from the laboratories of A. Nussenzweig ( g), A. Losada (k), M. Blasco ( j), L. Tora (i ), and ours (all others).Abbreviations: HR, homologous recombination; NHEJ, nonhomologous end-joining.", + "In humans, the pericentromeric region of chromosome 9 is densely packed with segmental genomic duplications (segdups) and is prone to microdeletions and microduplications. 5In order to evaluate this region for microdeletions and microduplications in family T, we screened genomic DNA from affected individual II-7 by arrayCGH with the Nimblegen HD2 platform with the previously described CHP-SKN sample 6 as the reference.Data were normalized and CNVs were called by identifying regions where Z-scores consistently deviated from the diploid mean.At 9q21.11, a genomic duplication of ~270 kb was apparent in the genomic DNA of II-7 (Figure 1D).The Genomic duplications may or may not be in tandem with their parent segment and may be either in the same or inverted orientation. 7We developed primers that would uniquely amplify genomic DNA with the duplication under each of these conditions.Forward (5 0 -CCCAGCAGA AGCAATGGTGGTAGCC-3 0 ) and reverse (5 0 -GGTGGTGAA TCCAAAAACACAAGAACAAAGTC-3 0 ) primers diagnostic for a tandem inverted duplication (Figure 2A) yielded products of expected size in family T relatives with hearing loss, but yielded no product in unaffected family T relatives (Figure 2B).Genotypes of all 58 participating relatives in family T indicated that the tandem inverted duplication was coinherited with hearing loss.The duplication spans approximately positions 71,705,804 to 71,974,823 (hg19) on chromosome 9 for a size of ~269,023 bp.The duplication includes the entire locus for the tight junction protein TJP2, which spans positions 71,788,971 to 71,870,124 (hg19).", + "Chromosomal context of human NORs Human NORs are positioned on the short arms of the acrocentric chromosomes that still remain unsequenced and thus missing from the current human genome draft, GRCh38.p7.Seeking an understanding of the chromosomal context of human NORs and to identify potential NOR regulatory elements, my laboratory has begun to characterize the sequences on both proximal (centromeric) and distal (telomeric) sides of the rDNA arrays (Fig. 3A; Floutsakou et al. 2013).Building on earlier reports of sequences distal and proximal to the rDNA array on HSA21 and HSA22, respectively (Worton et al. 1988;Sakai et al. 1995;Gonzalez and Sylvester 1997), 207 kb of sequence immediately proximal and 379 kb distal to rDNA arrays have been reported recently (Floutsakou et al. 2013).Consensus proximal junction (PJ) and distal junction (DJ) sequences were constructed mostly from chromosome 21 BACs (bacterial artificial chromosomes).Comparison of these sequences with BACs and cosmids derived from the other acrocentrics revealed that the PJ and DJ sequences are, respectively, \u223c95% and 99% identical between all five acrocentric chromosomes.Conservation of DJ sequences among the acrocentrics is consistent with frequent recombination between the rDNA arrays on each of the acrocentric chromosomes (Worton et al. 1988).However, conservation of PJ sequences suggests that there must also be frequent recombination events in the interval between the centromere and rDNA arrays.Proximal sequences are almost entirely segmentally duplicated, similar to the regions bordering centromeres.Consequently, they are unlikely to contain any specific elements that would regulate the activity of the linked NOR.In contrast, the distal sequence is predominantly unique to the acrocentric short arms and is dominated by a very large inverted repeat.Each arm of the inverted repeat is >100 kb, and they share an average sequence identity of 80%.There is a large (\u223c40-kb) block of a 48base-pair (bp) satellite repeat, CER, at the distal end of the DJ (Fig. 3A).CER blocks are found distal to the rDNA on all acrocentric chromosomes, with additional pericentromeric blocks on chromosomes 14 and 22. Finally, there are two blocks of a novel 138-bp tandem repeat, ACRO138, present within the DJ.", + "The conservation of DJ sequence between the five human acrocentric chromosomes provides a unique opportunity to visualize NORs by FISH.Whereas the rDNA content of NORs can vary greatly, probing of human metaphase chromosome spreads with a DJ BAC results in signal that is consistent between NORs (Floutsakou et al. 2013).Using this probing scheme, it was observed that in most human cell lines analyzed, including multiple primary lines, at least one and sometimes as many as four of the NORs present have very little or no detectable rDNA (C van Vuuren and B McStay, unpubl. ).Many studies have used silver staining of metaphase spreads prepared from stimulated human peripheral blood lymphocytes to determine how many NORs are active in normal human cells.The number of active NORs ranges from seven to 10, with an average of eight (Heliot et al. 2000).Possibly, NORs with low rDNA content are active but fall below a detection threshold in silver staining.At this point, it is worth considering the distribution of active versus silent rDNA repeats in humans and other mammals.If 50% of rDNA repeats are truly repressed, there are insufficient \"silent\" NORs to house them.We must conclude that active NORs are a mosaic of active and silent repeats.", + "However, excluding some cases, recombination suppression occurs in a small genomic tract where these genes are located, and it does not extend over most of the sex chromosome pair, as occurs in mammals and birds (Bergero and Charlesworth, 2009). It is not clear if this suppression occurs by the presence of inversions or as a modulation of the recombination mechanism itself, but both could be involved (Bergero and Charlesworth, 2009). Evidence of recombination in the SD region in sex reversal individuals supports the second hypothesis.", + "Orthologous chromosomes between baboon and human", + "Lichter P, Cremer T, Borden J, Manuelidis L, Ward DC (1988) Delineation of individual human chromosomes in metaphase and interphase cells by in situ suppression hybridization using recombinant DNA libraries. Hum Genet 80:224\u2013234 3. Jang W, Yonescu R, Knutsen T, Brown T, Reppert T, Sirotkin K, Schuler GD, Ried T, Kirsch IR (2006) Linking the human cytogenetic map with nucleotide sequence: the CCAP clone set. Cancer Genet Cytogenet 168:89\u201397 4.", + "Nature Genet 1:222\u2013225 55. Foote S, Vollrath D, Hilton A, Page DC (1992) The human Y chromosome: overlapping DNA clones spanning the euchromatic region. Science 258:60\u201366 56. Chumakov IM, Rigault P, Le Gall I et al (1995) A YAC contig map of the human genome. Nature 377:175\u2013297 57. Hudson TJ, Stein LD, Gerety SS et al (1995) An STS-based map of the human genome. Science 270:1945\u20131954 58. Coffey AJ, Roberts RG, Green ED et al (1992) Construction of a 2.6-Mb contig in yeast artificial chromosomes spanning the human dystrophin gene using an STSbased approach. Genomics 12:474\u2013484 59.", + "Figure 4 Schematic depiction of proposed mechanisms for observed intrachromosomal rearrangements.The blue and red arrows indicate the orientation of the integrated plasmid loci and the recovered mouse sequences, respectively, on the original non-rearranged chromosome (left column).All four combinations are given for an arbitrarily orientated chromosome (green line).The middle column shows how two breakpoints (lightning signs) could lead to the inversion or deletion of the encompassed chromosomal sequence (yellow-orange dual tone line) and result in a recoverable mutation in the right column.The last row indicates the two options for a transposition, in which either the transgene locus or the recovered mouse sequence is copied or excised (as indicated by the pink and light blue arrows) and integrates in the breakpoint at the other location.", + "As mentioned above, by taking into account that for a genome rearrangement to be detected, the 5\u0408 plasmid sequence of the breakpoint in lacZ must remain intact and end immediately in front of the recovered mouse sequence, the simplest intrachromosomal mutation that could have taken place was inferred (Fig. 4).Rearrangements with breakpoints in the mouse genome on either site of the integrated plasmid concatamer, but with reversely orientated sequences, could be inversions (Fig. 4).Rearrangements in the direction of the integrated plasmids, proximal for chromosome 3 and distal for chromosome 4 (Fig. 3), with similarly orientated breakpoints in the mouse genome, could be deletions (Fig. 4).Rearrangements in the reverse direction of the integrated plasmids, with reversely orientated mouse sequences, are more complicated and might be owing to transpositions (Fig. 4).According to these schemes, half of the intrachromosomal rearrangements would have been inversions, whereas deletions and transpositions each made up one fourth (Fig. 3).Alternatively, these rearrangements could be explained by translocations involving the transgene clusters integrated on either the homolog or the other chromosome.", + "FIGURE 3. Telomere arrays of chicken and human chromosomes: the chicken genome contains more telomere sequence than the human genome.Chicken (a) and human (b) metaphase chromosomes and interphase cells hybridized with a telomeric sequence-peptide nucleic acid (PNA)-fluorescein probe.Human and chicken slide preparations were processed, and images were captured using the same parameters.Qualitatively, the telomere-positive fluorescent signals (white spots) from chicken cells and chromosomes have greater intensity than those of human (4\u2032,6 diamidino-2-phenylindole, DAPI counterstain).", + "In a previous study on the accumulation of spontaneous genome rearrangements in normal mice with aging, we discovered that 50% of the events were intrachromosomal, i.e., large deletions or inversions [22].In contrast, in this present study most of the rearrangements resulted from inter-chromosomal recombination, in both the Ercc1-mutant and control animals (Table 3).Previously, we used lacZ-plasmid line 60 mice with integration sites on Chromosomes 3 and 4, while in the present study line 30 mice were used with a single integration site on Chromosome 11.This indicates that the relative frequency of translocations is founder line specific and could be due to the position of the lacZ-plasmid cluster on the chromosome.Indeed, the chromosomal integration sites in line 60 mice are in the E1 region of Chromosome 3 (half way along the chromosome) and the C5 region of Chromosome 4 (two-thirds of the way along the chromosome) [22], while the integration site of founder line 30 (used in this study) is on the centromeric tip of Chromosome 11 (region A1-A2; not shown).The proximal location on Chromosome 11 prevents the detection of all but relatively small intra-chromosomal recombinations; larger events would lead to loss of the centromere and, therefore, the entire chromosome.If the orientation of the integration site in line 30, which is currently unknown, is towards the centromere, transpositions and inversions towards the distal end are the only detectable large intra-chromosomal rearrangements (for a detailed explanation of the different chromosomal events that can occur at the lacZ locus, see [22])." + ], + [ + "Genome Res, 2011, 21: 1769\u20131776 Mattick JS, Dinger ME. The extent of functionality in the human genome. HUGO J, 2013, 7, doi:10.1186/1877-6566-1187-1182 ENCODE Project Consortium, Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M. An integrated encyclopedia of DNA elements in the human genome. Nature, 2012, 489: 57\u201374 Pheasant M, Mattick JS. Raising the estimate of functional human sequences. Genome Res, 2007, 17: 1245\u20131253 Hu T, Long M, Yuan D, Zhu Z, Huang Y, Huang S. The genetic equidistance result, misreading by the molecular clock and neutral theory and reinterpretation nearly half of a century later.", + "This approach enables, on the one hand, studying the process of mammalian evolution and, on the other hand, translational studies using model organisms of complex human phenotypes. Detection of regions conserved between distant species points to high functional importance of these fragments of the DNA sequence. Human and mouse developmental lines diverged about 75 million years ago, and ever since evolutionary forces shaped the two genotypes in a different manner (Waterston et al. , 2002). Nevertheless, the extent of the changes is, however, small enough for conservation of local gene order (Waterston et al. , 2002).", + "First, the human and mouse genome projects elucidated the sequences of over 20,000 genes [Lander et al. , 2001; Venter et al. , 2001], and most are expressed in the CNS. The availability of gene sequences has allowed rapid analysis of candidate human disease and disorder genes and the isolation of the mouse homologues. Second, the application of site-speci\ufb01c recombinase technology provides investigators with the opportunity to engineer genes in the mouse that will allow for the deletion, insertion, inversion, or exchange of chromosomal DNA with high \ufb01delity (for review see Branda and Dymechi, 2004].", + "In some cases, structural variations, such as copy number polymorphisms, exist (Feuk et al. , 2006); however, because of the nature of the genome assembly process, these will invariably be collapsed into a single contig that does not reflect the natural sequence. To address the technical challenges of whole-genome assembly, the human genome is released as defined \u2018builds\u2019 on a quarterly basis (Lander et al. , 2001; reviewed in Chapter 4). The increasing complexity of processes that map data to the genome implicitly involves some lag in availability of the most current sequence assembly.", + "In practical terms, this has meant that we acquire many fragments, from a few hundred bases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human genome, two unfinished \u2018draft\u2019 sequences were produced by different methods, one by the International Human Genome Sequencing Consortium (IHGSC) and one by Celera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001).", + "4 Assembling a View of the Human Genome Colin A. M. Semple Bioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK 4.1 Introduction The miraculous birth of the draft human genome sequence took place against the odds. It was only made possible by parallel revolutions in the technologies used to produce, store and analyse the sequence data, and by the development of new, large-scale consortia to organize and obtain funding for the work (Watson, 1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.", + "THE HUMAN GENOME PROJECT IS generating vast amounts of new information at breakneck speed and causing a fundamental shift in disease research.Now with the availability of a nearly complete, high-accuracy sequence of the mouse genome (7), a new and powerful paradigm for biomedical research is established.The remarkable similarity of mouse and human genomes, in both synteny and sequence, unconditionally validates the mouse as an exceptional model organism for understanding human biology.The discovery among inbred mouse strains of defined regions of high and low genomic variation inherited primarily from two ancestral Mus subspecies (6) holds great promise to make mapping and positional cloning more rapid and feasible.Haplotype maps of inbred mouse strains combined with sophisticated delineation of their phenotypic variation and gene expression patterns will enable complex trait analysis on an unprecedented scale.This issue of Journal of Applied Physiology highlights inbred strain surveys exploring phenotypic variation in drug responses [see Crabbe et al. (1) and Watters et al. (8) in this issue].These mouse initiatives demonstrate a viable, cost-effective alternative to human research requiring family studies, population linkage analysis, or genome-wide genotyping on a multitude of individuals for association mapping.", + "How Many Genes are There in the Human Genome?", + "The Landscape of Human Genome Variation", + "In some cases, structural variations, such as copy number polymorphisms, exist (Feuk et al. , 2006); however, because of the nature of the genome assembly process, these will invariably be collapsed into a single contig that does not reflect the natural sequence. To address the technical challenges of whole-genome assembly, the human genome is released as defined \u2018builds\u2019 on a quarterly basis (Lander et al. , 2001; reviewed in Chapter 4). The increasing complexity of processes that map data to the genome implicitly involves some lag in availability of the most current sequence assembly.", + "In practical terms, this has meant that we acquire many fragments, from a few hundred bases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human genome, two unfinished \u2018draft\u2019 sequences were produced by different methods, one by the International Human Genome Sequencing Consortium (IHGSC) and one by Celera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001).", + "4 Assembling a View of the Human Genome Colin A. M. Semple Bioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK 4.1 Introduction The miraculous birth of the draft human genome sequence took place against the odds. It was only made possible by parallel revolutions in the technologies used to produce, store and analyse the sequence data, and by the development of new, large-scale consortia to organize and obtain funding for the work (Watson, 1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.", + "Science 291:1304\u2013 1351 3. Lander ES et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860\u2013921 4. Engle LJ, Simpson CL, Landers JE (2006) Using high-throughput SNP technologies to study cancer. Oncogene 25:1594\u20131601 5. Elston RC, Anne Spence M (2006) Advances in statistical human genetics over the last 25 years. Stat Med 25:3049\u20133080 6. Larson GP et al (2005) Genetic linkage of prostate cancer risk to the chromosome 3 region bearing FHIT. Cancer Res 65:805\u2013814 7. Botstein D, Risch N (2003) Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease.", + "McPherson JD, Marra M, Hillier L et al (2001) A physical map of the human genome. Nature 409:934\u2013941 13. Burke DT, Carle GF, Olson MV. (1987) Cloning of large segments of exogenous DNA into yeast by means of artificial chromosome vectors. Science 236:806\u2013812 14. Fleischmann RD, Adams MD, White O et al (1995) Whole-genome random sequencing and assembly of Haemophilus influenzae Rd Science 269:496\u2013512 15. Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408:796\u2013815 16.", + "T he human genome has been cracked wide open in recent years and is spilling many of its secrets.More than 100 genome wide association studies have been conducted for scores of hu man diseases, identifying hun dreds of polymorphisms that are widely seen to influence disease risk.After many years in which the study of complex human traits was mired in false claims and methodologic inconsistencies, ge nomics has brought not only com prehensive representation of com mon variation but also welcome rigor in the interpretation of sta tistical evidence.Researchers now know how to properly account for most of the multiple hypothesis testing involved in mining the ge nome for associations, and most reported associations reflect real biologic causation.But do they matter?", + "In some cases, structural variations, such as copy number polymorphisms, exist (Feuk et al. , 2006); however, because of the nature of the genome assembly process, these will invariably be collapsed into a single contig that does not reflect the natural sequence. To address the technical challenges of whole-genome assembly, the human genome is released as defined \u2018builds\u2019 on a quarterly basis (Lander et al. , 2001; reviewed in Chapter 4). The increasing complexity of processes that map data to the genome implicitly involves some lag in availability of the most current sequence assembly.", + "In practical terms, this has meant that we acquire many fragments, from a few hundred bases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human genome, two unfinished \u2018draft\u2019 sequences were produced by different methods, one by the International Human Genome Sequencing Consortium (IHGSC) and one by Celera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001).", + "In some cases, structural variations, such as copy number polymorphisms, exist (Feuk et al. , 2006); however, because of the nature of the genome assembly process, these will invariably be collapsed into a single contig that does not reflect the natural sequence. To address the technical challenges of whole-genome assembly, the human genome is released as defined \u2018builds\u2019 on a quarterly basis (Lander et al. , 2001; reviewed in Chapter 4). The increasing complexity of processes that map data to the genome implicitly involves some lag in availability of the most current sequence assembly.", + "In practical terms, this has meant that we acquire many fragments, from a few hundred bases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human genome, two unfinished \u2018draft\u2019 sequences were produced by different methods, one by the International Human Genome Sequencing Consortium (IHGSC) and one by Celera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001).", + "4 Assembling a View of the Human Genome Colin A. M. Semple Bioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK 4.1 Introduction The miraculous birth of the draft human genome sequence took place against the odds. It was only made possible by parallel revolutions in the technologies used to produce, store and analyse the sequence data, and by the development of new, large-scale consortia to organize and obtain funding for the work (Watson, 1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + ], + [ + "The hierarchical organization of GN\u2019s main Select and Search menu is simple and makes it relatively easy to find relevant data sets (Fig. 1). To get data, after opening the browser, select the most appropriate Species from the dropdown menu. For an open-ended search of phenotypes you can also select All Species at the bottom of the menu. The next steps are to select the Group, Type, and Data Set from the drop-down menus. For many groups, a combination of phenotypes, genotypes, and molecular data are available.", + "GeneNetwork contains data from a wide range of species, from humans to soybeans, but most of the available phenotypic data is from mice. Within the mouse dataset there are groups of families, crosses, non-genetic groupings, and individual data. The type of dataset must be selected after defining the species and sample population. While genotypes, mRNA, methylated DNA, protein, metagenomic, and 2 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. metabolome datasets are available (i.e.", + "The hierarchical organization of GN\u2019s main Select and Search menu is simple and makes it relatively easy to find relevant data sets (Fig. 1). To get data, after opening the browser, select the most appropriate Species from the dropdown menu. For an open-ended search of phenotypes you can also select All Species at the bottom of the menu. The next steps are to select the Group, Type, and Data Set from the drop-down menus. For many groups, a combination of phenotypes, genotypes, and molecular data are available.", + "Search and Data Retrieval Point your browser to www.genenetwork.org. This brings you by default to the Search page, from which you can retrieve data from many GN data sets. We will focus on the default data set, defined by Species: Mouse, Group: BXD, Type: Whole Brain, Database: INIA Brain mRNA M430 (Apr05) PDNN Enter \u201cKcnj*\u201d into the ALL or ANY field and click the Search button. Note the location and annotation of available potassium channel genes in the Search Results page that opens. Use the browser Back button to return to previous page.", + "Add information on data provenance by giving details in Investigation, Protocols and ProtocolApplications Customize Customize \u2018my\u2019 XGAP database with extended variants of Trait and Subject. In the online XGAP demonstrator, Probe traits have a sequence and genome location and Strain subjects have parent strains and (in)breeding method. Describe extensions using MOLGENIS language and the generator automatically changes XGAP database software to your research Upload Upload data from measurement devices, public databases, collaborating XGAP databases, or a public XGAP repository with community data.", + "However, a suitable and customizable integration of these elements to support high throughput genotype-tophenotype experiments is still needed [34]: dbGaP, GeneNetwork and the model organism databases are designed as international repositories and not to serve as general data infrastructure for individual projects; many of the existing bespoke data models are too complicated and specialized, hard to integrate between profiling technologies, or lack software support to easily connect to new analysis tools; and customization of the existing infrastructures dbGaP, GeneNetwork or other international repositories [35,36] or assembly of Bioconductor and generic model organism database components to suit particular experimental designs, organisms and biotechnologies still requires many minor and sometimes major manual changes in the software code that go beyond what individual lab bioinformaticians can or should do, and result in duplicated efforts between labs if attempted.", + ", 2014; see Section 9). GeneNetwork is a database that enables searching for \u223c4000 phenotypes from multiple studies in the BXD, HXB, and in other recombinant inbred rodent families, as well as in other model organisms and even humans (Mulligan et al. , 2017). GeneNetwork employed a somewhat di\ufb00erent strategy than MPD in that it did not rely solely on researchers submitting their data. Instead the database operators extracted the data from the scienti\ufb01c literature and integrated them into a uniform format (Chesler et al. , 2003).", + "GeneNetwork contains data from a wide range of species, from humans to soybeans, but most of the available phenotypic data is from mice. Within the mouse dataset there are groups of families, crosses, non-genetic groupings, and individual data. The type of dataset must be selected after defining the species and sample population. While genotypes, mRNA, methylated DNA, protein, metagenomic, and 2 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. metabolome datasets are available (i.e.", + "However, a suitable and customizable integration of these elements to support high throughput genotype-to-phenotype experiments is still needed[340]: dbGaP, GeneNetwork and the model organism databases are designed as international repositories and not to serve as general data infrastructure for individual projects; many of the existing bespoke data models are too complicated and specialized, hard to integrate between pro\ufb01ling technologies, or lack software support to easily connect to new analysis tools; and customization of the existing infrastructures dbGaP, GeneNetwork or other international repositories[384, 154] or assembly of Bioconductor and generic model organism database components to suit particular experimental designs, organisms and biotechnologies still requires many minor and sometimes major manual changes 38 2.1.", + "All data presented in this paper were deposited in the online database GeneNetwork (www.genenetwork.org), an open web resource that contains genotypic, gene expression, and phenotypic data from several genetic reference populations of multiple species (e.g. mouse, rat and human) and various cell types and tissues.35;36 It provides a valuable tool to integrate gene networks and phenotypic traits, and also allows cross-cell type and cross-species comparative gene expression and eQTL analyses.", + "There is a good chance that you will be able to apply these new techniques to specific problems, even while you read. If you have a computer with an Internet connection\u2014so much the better, and you can read and work along at the same time. This short review and primer will take you on a tour of a web site called GeneNetwork that embeds many large data sets that are relevant to studies of behavioral variation. GeneNetwork is an unusual site because it contains a coherent \"universe\" of data, as well as many powerful analytic tools.", + "The GeneNetwork database provides open access to BXD and other RI strain derived microarray data, single nucleotide polymorphism (SNP) data, and phenotypic data for quantitative trait loci analysis and gene expression correlation analyses. Gene expression data were exported for manually selected probes in the PDNN hippocampus database (Hippocampus Consortium M430v2), and the PDNN whole brain database (INIA Brain mRNA M430). The Hippocampus database was chosen as one of the most elaborate brain databases, as well as most highly recommended dataset on GeneNetwork itself (http://www.genenetwork.org/ webqtl/main.py?FormID=sharinginfo&GN_AccessionId=112).", + "2016) and can also be accessed in GeneNetwork by entering Record ID 18494 in the Get Any space on the Search page and clicking on the Search button. Alternatively, enter data by hand into the designated boxes provided by GeneNetwork. These latter options also allow for the inclusion of trait variance. It is a good idea to name the trait in the box provided. Then click Next, and manually enter the data for each RI strain, F1, and founder strain. 3 Author Manuscript After entering the data, click on the blue plus sign button called Add.", + "To submit multiple phenotypes at the same time, select the option for Batch Submission under the Home tab. This allows users to submit up to 100 traits for analysis by GeneNetwork. Here, select BXD as the cross or RI set to analyze from the first pull-down menu. The phenotype file should follow the format described in the Sample text (http:// genenetwork.org/sample.txt). After uploading the appropriate file using the Browse button, enter a name for the file in the Dataset space. The data will be stored in the GeneNetwork server for 24 hours. Click Next.", + "Author Manuscript Materials Here we will provide detailed instructions for using GeneNetwork along with some \u201cworked\u201d examples taken from the recent study of intravenous cocaine self-administration by Dickson et al. (2016) in BXD RI mice. A complete overview of GeneNetwork is beyond the scope of this protocol, but is extensively covered in elsewhere (see Mulligan et al. 2016; Williams & Mulligan 2012 for excellent reviews on GeneNetwork). A computer with an internet connection and current web browser. See the GeneNetwork.org site for information on supported browser versions. Author Manuscript Method Entering Data Author Manuscript 1 Link to http://www.genenetwork.org.", + "Species in GenAge model organisms", + "Data are reviewed before entry in GeneNetwork by the senior author. Phenotypes are currently split into 15 broad phenotypic categories (Supplementary Data 1). Phenome curation and description was initiated by R.W.W. and Dr Elissa Chesler in 2002 by literature review and data extraction. The early work is described brie\ufb02y in Chesler et al.51,52. Most work over the past 5 years has been performed by two of the coauthors (R.W.W. and M.K.M.). We have used a controlled vocabulary and set of rules described here (http://www.genenetwork.org/faq.html#Q-22).", + "9) To bring your data to GeneWeaver, click on the GeneWeaver icon, making sure to be previously login to your GeneWeaver account. You will be brought to the GeneSet upload page with the Genes Uploaded and the Geneweaver Analysis Platform 139 Fig. 5 Default settings at GeneNetwork.org are set to search \u201cMouse\u201d, \u201cPhenotypes\u201d, from among the \u201cBXD Published Phenotypes\u201d data set. Here the term nociception was searched for Fig. 6 The search results page in GeneNetwork showing the 33 records retrieved from the phenotype search for nociception.", + "Users may also share their data with other users selectively, make it public, or keep it restricted to a private account. Data can be imported by users, uploading their gene set data directly or exporting to GeneWeaver from within another online resource such as Neuro Informatics Framework (NIF) [8], Grappa [9], Mouse Phenome Database (MPD) [10] or GeneNetwork [11]. These datasets can then be added to your collection to be analyzed together with other gene sets retrieved from the GeneWeaver database. To begin a GeneWeaver analysis a user must collect \u201cGeneSets\u201d together in a \u201cProject\u201d.", + "Alternatively the spreadsheet can be saved as a .txt file and uploaded by clicking on \u201cSwitch to file upload.\u201d Once complete click on upload GeneSet. 7. Once completed you are taken to the GeneSet detail page. If there are errors in your uploaded data you can correct them by clicking on \u201cEdit\u201d. 8. Use the Add Selected to Project, and create a new project, e.g. \u201cChronic Cocaine\u201d. 9. Now using the Search function populate this project with additional gene sets related to this study trying Queries such as \u201cCocaine Addiction\u201d, \u201cChronic Cocaine\u201d." + ], + [ + "Oxidative stress and mitochondrial DNA Not long after it was discovered that mitochondria have their own genetic apparatus, Harman proposed that mitochondria play a central role in the free radical theory of aging [16].This idea was developed further by Miquel et al. [330], and the notion that mtDNA mutagenesis played a role in aging took hold.The phenotypical importance of mutations in mtDNA was demonstrated by Wallace et al. [331] and Holt et al. [332], who first showed that Leber's hereditary optic neuropathy and mitochondrial myopathies were caused by mtDNA mutations (reviewed in [333]).Because mtDNA is so close to the site of mitochondrial ROS production, it is exposed to considerably higher oxidative stress, resulting in 3-fold higher levels of DNA oxidative damage (the previously quoted 20-fold figure is apparently due to an isolation artifact [334,335]).In the 1990s a series of papers reported that the frequency of mitochondrial DNA deletions increases dramatically with age, being essentially undetectable in young individuals and reaching levels as high as 2% of mtDNA in old individuals.This age-related increase in mtDNA deletions was found in organisms as diverse as worms, mice, and humans (reviewed in [24,336]).The same is also true with mtDNA point mutations [337,338].Certain mtDNA polymorphisms have been found in increased frequency in centenarians, implying a protective effect during aging [339][340][341].Similar protective effects of mtDNA polymorphisms have been reported for the age-related neurodegenerative condition, Parkinson's disease [342].", + "Variation in the structure and function of mitochondria underlies variation in organismal energetics broadly (Seebacher et al., 2010) and evidence for the importance of mitochondrial function in the evolution of natural populations continues to accumulate (Ballard and Melvin, 2010;Glanville et al., 2012;Hicks et al., 2012;Kurbalija Novi\u010di\u0107 et al., 2015).For example, variation in mitochondrial DNA sequences (mtDNA) can determine whole-organism metabolism, i.e., the rate at which organisms process energy from their environment, a phenomenon widespread across animal taxa (Arnqvist et al., 2010;Ballard et al., 2007;Ballard and Pichaud, 2014;Havird et al., 2019;Hood et al., 2018;James et al., 2016;Wolff et al., 2014).Specifically, mtDNA sequence variants are linked to functional metabolic differences in fish (Chapdelaine et al., 2020;Flight et al., 2011;Healy et al., 2019), birds (Scott et al., 2011), and mammals (Fontanillas et al., 2005), including humans (Amo and Brand, 2007;Dato et al., 2004;Niemi et al., 2003;Tranah et al., 2011).These mtDNA variants are often correlated with environmental factors such as temperature and altitude (Storz et al., 2010).However, other studies attempting to link mitochondrial function to mitochondrial DNA (mtDNA) sequence variation or environmental factors have offered mixed reports (Amo and Brand, 2007;Flight et al., 2011;Fontanillas et al., 2005;Hicks et al., 2012).", + "The results here point to several potentially fruitful research directions.We have identified how nonsynonymous mutations in the mitochondrial genome associate with variation in whole-organism metabolism (including CytB, ND1, ND5 and ND6).A next step will be to characterize the molecular details of how these changes affect molecular function.It would also be beneficial to describe how variation in cellular oxygen consumption rate scales up to determine whole-organism metabolic rate across a range of temperatures, thus identifying potential mismatches across levels of organization that may impact organismal performance (Gangloff and Telemeco, 2018).While the interconnected processes that shape organismal and population-level responses to environmental variation do not lend themselves to simple narratives, and many molecular processes interact to produce the emergent ecotypic divergences at the phenotypic level, it is clear that the mitochondria play a central role even as that role may change across populations and ecological contexts (Fig. 1).Research within well-characterized natural systems, such as these garter snake populations, can offer illustrative case studies of how mitochondria respond to their environments, and thus impact physiological pathways and evolutionary patterns, creating variation in life histories and aging.", + "Despite the complexities underlying observed variation in mitochondrial function, recent work has demonstrated examples of how evolution and plasticity in mitochondrial function across populations within a species can shape life histories.For example, evidence from Drosophila has demonstrated the effect of temperature on components of the ETC and has linked mtDNA variants to metabolic thermosensitivity (Pichaud et al., 2012), to differences in whole-organism metabolic rates (Kurbalija Novi\u010di\u0107 et al., 2015), and to fitness-related traits (Ballard et al., 2007;Pichaud et al., 2011;Pichaud et al., 2010).In general, studies in birds and mammals demonstrate that mitochondria of longer-lived species are more efficient in ATP production, produce less reactive oxygen species, and demonstrate increased antioxidant capacities (Barja and Herrero, 2000;Ku et al., 1993;Lambert et al., 2007).While some studies in lizards and snakes demonstrate a similar pattern (Olsson et al., 2008;Robert et al., 2007), the extent to which these results are generalizable across vertebrate taxa is not yet known.The diversity of life-history traits and immense variation in longevity demonstrated by reptiles, both within and among species, make these taxa ideal candidates for understanding how variation in mitochondrial physiology drives this variation in whole-organism traits (reviewed in Hoekstra et al., 2019).Such work has moved to the forefront with a recent focus on the ecological and evolutionary significance of aging processes in wild populations (reviewed in Nussey et al., 2013;Fletcher and Selman, 2015;Gaillard and Lema\u00eetre, 2020).", + "Over evolutionary time, differential mortality rates are a selective force in shaping genetic structure.This results in divergence of a variety of physiological networks that shape, ultimately, patterns of aging and longevity in different habitats (Monaghan et al., 2008;Stojkovi\u0107 et al., 2017).Such selective pressures can have differential effects on the nuclear and mitochondrial genomes (McKenzie et al., 2019;Wolff et al., 2014).Genetic variation in the mitochondrial genome is known to drive mitochondrial function in many species (Ballard and Melvin, 2010;McKenzie et al., 2019;Novelletto et al., 2016) and we find this in our system as well.Whole organism metabolic rate varies with the mitochondrial genome haplogroups we identified in this study.T. elegans individuals with the introgressed T. sirtalis mitochondrial genome had the lowest metabolic rate and had 68 amino acid changes in the ETC genes relative to the T. elegans mitochondrial genomes.As species divergence are a continuation of population divergence, this introgression provides additional insight into how genetic variation can alter mitochondrial function.Whether the lower metabolic rate in our snakes with the introgressed mitochondrial genome is due to the fixed amino acid changes between the species or a mismatch between the coadapted nuclear and mitochondrially-encoded ETC proteins that could alter function of the mitochondria (Burton et al., 2013;Haenel, 2017;Rawson and Burton, 2002;Toews et al., 2014;Wolff et al., 2014) will require further comparisons to T. sirtalis individuals.", + "Building on previous work in this system, the current study tests three primary hypotheses about how variation in mtDNA and mitochondrial function relate to variation in life-history traits and aging within this system (Fig. 1): (1) First, we test whether rates of cellular oxygen consumption in isolated immune cells exhibit patterns that are consistent with the hypothesis that cellular processes drive whole-organism senescence and aging, and if these patterns differ between the SA and FA ecotypes and between sexes.By measuring basal, ATP-production associated, and maximal rates of cellular oxygen consumption, we further test for evidence that phenotypic divergence is dependent on a specific aspect of oxidative phosphorylation within immune cells.The energetics of these cells are particularly important given their essential role in modulating disease and infection, important factors contributing to senescence (Metcalf et al., 2019).We predict that SA snakes will maintain levels of cellular oxygen consumption across age, whereas the FA snakes will show a decline with age, especially in ATP-associated rates, possibly due to continual degradation of electron transport chain functionality from accumulating oxidative damage and reduced DNA repair mechanisms (Robert and Bronikowski, 2010;Schwartz and Bronikowski, 2013). ( 2) Second, we expand our mitochondrial genomics dataset to quantify mtDNA genetic structure across the landscape and test whether mtDNA haplotypes, and alleles at a nonsynonymous SNP in the Cytochrome B (CytB) gene correlate with aging ecotypes. (3) Third, we test the hypothesis that variation in mtDNA correlates with whole-organism variation in metabolic rates, suggesting a pathway linking mitochondrial genetic variation in mtDNA to whole-organism energetics.We first test whether different haplotypes differ in resting metabolic rate.Then, we test the effects of the nonsynonymous SNP in CytB on resting metabolic rate.The CytB gene encodes a component of complex III of the ETC, and was previously found to segregate between these life-history ecotypes (Schwartz et al., 2015).This SNP results in an amino acid substitution from isoleucine (aliphatic, hydrophobic) to threonine (hydrophilic) on a region that comes into close contact with a nuclear-encoded subunit (Schwartz et al., 2015).We combine previously published and new data on whole-organism resting metabolic rates (oxygen consumption) to test for the effects of this nonsynonymous mutation in three populations where we find heterogeneity at this nucleotide, thus allowing us to disentangle the effects of shared environment (population) from sequence variation (SNP).We predict that this SNP will correlate with variation in whole-organism metabolic rate, demonstrating a putatively adaptive difference between the derived and ancestral sequence.By utilizing this integrative data setfrom genes to organelles to whole organisms to populationsin a known life-history context, we are able to test hypotheses across levels of organization to provide a more complete picture of the complicated story of mitochondria and life history (Havird et al., 2019).", + "mtDNA Diversity Unlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage.It is unclear what advantage a uniparental mtDNA transmission confers, but one possibility is to minimize the number of distinct genomes to maximize the efficiency of a multi-genomic system (Hill et al. 2019).In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca and O'Farrell 2012; Rojansky et al. 2016).Paternal mitochondria from sperms that enter into the egg during fertilization are actively and selectively eliminated via mitophagy through two E3 ligases, PARKIN, and MUL1 (Rojansky et al. 2016).PARKIN and MUL1 serve redundant purposes, and mitophagy becomes insufficient to eliminate paternal mtDNA only in the absence of both (Rojansky et al. 2016).Even though oocytes have at least a thousand-fold more mitochondria than a sperm cell (Rojansky et al. 2016) and heteroplasmy levels would be very low if paternal mtDNA were to contaminate the embryo, the results can still be non-trivial.However, challenging this notion, a recent study provides evidence of potential paternal transmission (Luo et al. 2018), but awaits further corroborating studies (Lutz-Bonengel and Parson 2019).", + "MtDNA exhibit a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms (van Oven and Kayser 2009; Wallace 1999; Wallace and Chalkia 2013).In fact, the co-evolution of the mitonuclear genomes has been proposed to be driven by mtDNA mutations that select for compensatory changes in the nuclear genome (Havird and Sloan 2016).Populations that share similar mtDNA polymorphisms can be clustered into distinct haplogroups that are designated using all letters of the alphabet (i.e., A through Z).The mtDNA haplogroups represent major branch points on the mitochondrial phylogenetic tree that have strong regional ties around the globe, thus supporting the concept of a 'mitochondrial eve' (Wallace 1999).Haplogroups present inherently different mitonuclear interactions (Zaidi and Makova 2019), which eventually affect the aging process (Wolff et al. 2016).For example, one haplogroup commonly found in Ashkenazi Jews can interact with a specific enrichment of an amino acid sequence in complex I, and result in altered susceptibility to type 2 diabetes mellitus (Gershoni et al. 2014).The effect of mitonuclear compatibility on lifespan is influenced by environmental cues in flies (Drummond et al. 2019).It is unclear if mitonuclear compatibility is invariable throughout an organism's life, or antagonistically pleiotropic during aging, making it a difficult moving target to understand.", + "Background: The accumulation of mitochondrial DNA (mtDNA) mutations, and the reduction of mtDNA copy number, both disrupt mitochondrial energetics, and may contribute to aging and age-associated phenotypes.However, there are few genetic and epidemiological studies on the spectra of blood mtDNA heteroplasmies, and the distribution of mtDNA copy numbers in different age groups and their impact on age-related phenotypes.In this work, we used whole-genome sequencing data of isolated peripheral blood mononuclear cells (PBMCs) from the UK10K project to investigate in parallel mtDNA heteroplasmy and copy number in 1511 women, between 17 and 85 years old, recruited in the TwinsUK cohorts.", + "Discussion Two significant questions are raised by the findings that mitochondrial DNA can integrate into the nucleus.Firstly, is this an extraordinarily rare event or is it occurring continually and at high frequency?Secondly, can such an event have pathological consequences to the organism?", + "Phylogeny The mtDNA is maternally inherited (120) by offspring through the oocyte cytoplasm; namely, the mother transmits her mtDNAs to all of her offspring, and her daughters transmit their mtDNAs to the next generation.This is the consequence of the fact that the mature oocyte such as mouse (304) or bovine (144) contains lOO-1,000 times more mtDNA than is found in somatic cells.Hence, the few sperm mtDNAs that enter the egg (130) have little effect on the genotype.The maternal inheritance results in sequentially diverged mtDNA polymorphism of modern human, as shown in Figure 2. The polymorphism derives from the combinations of small deletions and additions of <14 bp in noncoding region and base substitutions including some point mutations in coding region.", + "There have been few reports on distinct correlation between mitochondrial morphology and human aging, except changes in number and size of mitochondria associated with age.Concerning the gross structure of mitochondria, the overwhelming importance of the cell nucleus in mitochondrial biogenesis should be noted, because the major parts of mitochondrial proteins are encoded by nuclear genes that are stable during life with the efficient repair mechanism for nDNA.", + "Early data on DNA polymorphism detected by restriction endonuclease (263) have suggested that the evolutionary change of mtDNA in higher animals occurs mainly by nucleotide substitution rather than by deletion and insertion.The mtDNA nucleotide sequence evolves 6-17 times faster than comparable nuclear DNA gene sequences (51,52,405).Rapid evolution of mtDNA of higher primates including human, 0.02 base substitutions per site per million years, was calculated from the restriction map of mtDNA (51).Because orthodox recombination mechanism appears to be absent in mtDNA (128), germline mutation seems to go down to posterity as maternal inheritance from our common ancestor (57).", + "A number of conclusions may be drawn from these results.Firstly, the data begin to answer the question of how closely mtDNA replication is kept in synchrony with nuclear DNA replication: it would appear to be regulated not by direct coupling to the nuclear DNA replication, but rather by the cell mass to be serviced by mitochondria.", + "It may be that high mtDNA levels are indeed indicative of compromised mitochondria, but that the underlying defects are unrelated to alterations in the DNA sequence.Alternatively, elevated quantities of mtDNA might be associated with increased metabolic requirements of the embryo, rather than organelles of suboptimal function.It is possible that embryos produced by older oocytes are under some form of stress and therefore have larger energy requirements.Functional experiments will be required to address these questions.Whatever the underlying basis, the current study has unequivocally demonstrated that female reproductive aging is associated with changes in the mtDNA content at the blastocyst stage.", + "Age-associated alterations of the mitochondrial genome occur in several different species; however, their physiological relevance remains unclear.The age-associated changes of mitochondrial DNA (mtDNA) include nucleotide point mutations and modifications, as well as deletions.In this review, we summarize the current literature on age-associated mtDNA mutations and deletions and comment on their abundance.A clear need exists for a more thorough evaluation of the total damage to the mitochondrial genome that accumulates in aged tissues.\u1b67 1997 Elsevier Science Inc.", + "Mitochondrial genetics One underexplored avenue for determining maternal risk for preterm birth involves the influence of the mitochondrial genome.The high mutation rate of mito chondrial DNA (mtDNA), together with the fact that most of its encoded proteins are evolutionarily con served, allowing for the selection of neutral or beneficial variants, has generated interest in defining human mtDNA variations and their roles in human biology [58].", + "Clearly, as mitochondrial metabolic and genetic therapies advance for treating mitochondrial disease, they will also be available to enhance the personal lives of others.However, mitochondrial genetic variation appears to have been one of the primary factors that permitted our ancestors to adapt to new environments, survive adverse conditions, and multiple throughout the globe.Is it possible that by taking over control of individual mtDNA variation, we might also be setting our species on the road to functional decline and ultimately extinction?", + "Mitochondrial therapeutics and performance enhancement It is now clear that not all mtDNA variation is deleterious.Indeed, about 25% of all ancient mtDNA variation appears to have caused functional mitochondrial changes and thus been adaptive.Those mtDNA variants that are adapted to warm climates have mtDNA variants that result in tightly coupled OXPHOS, thus maximizing ATP output and minimizing heat production.The presence of these mtDNAs permits maximum muscle performance but also predispose sedentary individuals that consume excess calories to multiple problems.They would be prone to be overweight and their mitochondria would generate excessive ROS, thus making them susceptible to a variety of degenerative diseases, cancer and premature aging.Partially uncoupled mitochondria generate more heat, but at the expense of ATP production.Individual's with these variants are better able to tolerate the cold, and are less prone to obesity.They also generate less ROS making then resistant to degenerative diseases and aging.Finally, the mitochondria are why we breathe.Hence, mitochondrial variation might be an important factor in individual predisposition to altitude sickness.", + "Human mtDNA codes for 13 essential polypeptide components of the mitochondrial oxidative phosphorylation (OXPHOS) system.mtDNA undergoes strict maternal inheritance, resulting in the absence of bi-parental recombination (Elson et al., 2001) and has a high mutation rate (Tuppen et al., 2010).As such, the evolution of mtDNA is characterised by the emergence of distinct lineages (or haplogroups) (Hernstadt et al., 2002).This results in high levels of mtDNA variation at the population level despite its rather small size, which is also illustrated by the large number of sub-haplogroups (van Oven and Kayser, 2009).Africa" + ], + [ + "Annotation, preprocessing and categorization of data We used Ensembl (version 39) as the annotation reference database.Homology between human and mouse genes was derived via BioMart.The total number of genes under study comprises 15,277 Ensembl mouse genes representing the union of the homologue genes from all data sources.An overview about the T2DM specific datasets is given in Table 1.", + "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried out and the way the results are displayed. 4.4.1 Ensembl Ensembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome Trust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard et al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a window on the draft genome, curating the results of a series of computational analyses.", + "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence assemblies as its starting point, but it is now based upon NCBI assemblies. The Ensembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify \u2018confirmed\u2019 genes that are computationally predicted (by the GENSCAN gene prediction program) and also supported by a significant BLAST match to one or more expressed sequences or proteins. Ensembl also identifies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures.", + "Data retrieval is extremely well catered for in Ensembl, with text searches of all database entries, BLAST searches of all sequences archived, and the availability of bulk downloads of all Ensembl data and even software source code. Ensembl annotation can also be viewed interactively on one\u2019s local machine with the Apollo viewer (Lewis et al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome Browser The UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl; it, too, provides annotation of the NCBI assemblies, and it displays a similar array of features, including confirmed genes from Ensembl.", + "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried out and the way the results are displayed. 4.4.1 Ensembl Ensembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome Trust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard et al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a window on the draft genome, curating the results of a series of computational analyses.", + "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence assemblies as its starting point, but it is now based upon NCBI assemblies. The Ensembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify \u2018confirmed\u2019 genes that are computationally predicted (by the GENSCAN gene prediction program) and also supported by a significant BLAST match to one or more expressed sequences or proteins. Ensembl also identifies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures.", + "Data retrieval is extremely well catered for in Ensembl, with text searches of all database entries, BLAST searches of all sequences archived, and the availability of bulk downloads of all Ensembl data and even software source code. Ensembl annotation can also be viewed interactively on one\u2019s local machine with the Apollo viewer (Lewis et al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome Browser The UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl; it, too, provides annotation of the NCBI assemblies, and it displays a similar array of features, including confirmed genes from Ensembl.", + "Ensembl provides a DAS reference server giving access to a wide range of specialist annotations of the human genome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy a hypothesis (\u2018top-down\u2019 data mining), or to interrogate a database in order to generate new hypotheses based on rigorous statistical correlations (\u2018bottom-up\u2019 data mining). Domain (protein) A region of special biological interest within a single protein sequence.", + "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried out and the way the results are displayed. 4.4.1 Ensembl Ensembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome Trust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard et al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a window on the draft genome, curating the results of a series of computational analyses.", + "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence assemblies as its starting point, but it is now based upon NCBI assemblies. The Ensembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify \u2018confirmed\u2019 genes that are computationally predicted (by the GENSCAN gene prediction program) and also supported by a significant BLAST match to one or more expressed sequences or proteins. Ensembl also identifies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures.", + "Data retrieval is extremely well catered for in Ensembl, with text searches of all database entries, BLAST searches of all sequences archived, and the availability of bulk downloads of all Ensembl data and even software source code. Ensembl annotation can also be viewed interactively on one\u2019s local machine with the Apollo viewer (Lewis et al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome Browser The UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl; it, too, provides annotation of the NCBI assemblies, and it displays a similar array of features, including confirmed genes from Ensembl.", + "Ensembl provides a DAS reference server giving access to a wide range of specialist annotations of the human genome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy a hypothesis (\u2018top-down\u2019 data mining), or to interrogate a database in order to generate new hypotheses based on rigorous statistical correlations (\u2018bottom-up\u2019 data mining). Domain (protein) A region of special biological interest within a single protein sequence.", + "Ensembl Ensembl is a publicly available web resource that contains automatically annotated genomes.It is integrated with other available biological databases like Jasper for binding motifs.It is a much larger web resource than T1Dbase, and contains general information about the human genome including variants.These include SNPs, insertions, deletions and somatic mutations (Alterations in DNA that occur after conception, meaning that they are not inherited) for several species.Data from Ensembl can be accessed in a number of ways.The names of all the SNPs that occur in the T1D susceptibility regions can be collected from Ensembl using the Biomart tool (Kinsella et al., 2011).To achieve this, the coordinates of the T1D regions obtained from T1Dbase are uploaded to the biomart query page which allows one to search the genome browser and retrieve data like the names, chromosomal positions, and genic positions (referred to as \"consequence to transcript\", in Ensembl) of the SNPs.The SNP genic positions tell if a SNP is located within a gene, adjacent to a gene or whether they occur in inter-genic positions between gene coding regions, as well as the particular genes in which they are located.", + "Advantages of Ensembl: There is a number of advantages to using Ensembl. (i) It is a larger web resource than T1Dbase and integrates data from a wide range of biological research sources into its database.Therefore, available information is quite comprehensive. (ii) Genic positions for 99% of the variants obtained from T1Dbase could be retrieved. (iii) Ensembl contains quality checks for genetic variants in its variation pipeline.A variant is flagged as failed if certain quality criteria are not met, for instance if none of the variant alleles match the reference allele of the variant.Generally, Ensembl was found to give more detailed information regarding the genic positions of variants compared to T1Dbase.", + "Information about genes, including gene names, chromosomal coordinates, biotype (coding or non-coding), and number of splice variants, can also be retrieved from Ensembl.", + "doi:10.1093/nar/gkp858 Cunningham F, Amode MR, Barrell D, Beal K, Billis K, Brent S, Carvalho-Silva D, Clapham P, Coates G, Fitzgerald S, Gil L, Giron CG, Gordon L, Hourlier T, Hunt SE, Janacek SH, Johnson N, Juettemann T, Kahari AK, Keenan S, Martin FJ, Maurel T, McLaren W, Murphy DN, Nag R, Overduin B, Parker A, Patricio M, Perry E, Pignatelli M, Riat HS, Sheppard D, Taylor K, Thormann A, Vullo A, Wilder SP, Zadissa A, Aken BL, Birney E, Harrow J, Kinsella R, Muffato M, Ruffier M, Searle SM, Spudich G, Trevanion SJ, Yates A, Zerbino DR, Flicek P (2015) Ensembl 2015.", + "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried out and the way the results are displayed. 4.4.1 Ensembl Ensembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome Trust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard et al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a window on the draft genome, curating the results of a series of computational analyses.", + "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence assemblies as its starting point, but it is now based upon NCBI assemblies. The Ensembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify \u2018confirmed\u2019 genes that are computationally predicted (by the GENSCAN gene prediction program) and also supported by a significant BLAST match to one or more expressed sequences or proteins. Ensembl also identifies the positions of known human genes from public sequence database entries, usually using GENEWISE to predict their exon structures.", + "Data retrieval is extremely well catered for in Ensembl, with text searches of all database entries, BLAST searches of all sequences archived, and the availability of bulk downloads of all Ensembl data and even software source code. Ensembl annotation can also be viewed interactively on one\u2019s local machine with the Apollo viewer (Lewis et al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome Browser The UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl; it, too, provides annotation of the NCBI assemblies, and it displays a similar array of features, including confirmed genes from Ensembl.", + "Ensembl provides a DAS reference server giving access to a wide range of specialist annotations of the human genome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy a hypothesis (\u2018top-down\u2019 data mining), or to interrogate a database in order to generate new hypotheses based on rigorous statistical correlations (\u2018bottom-up\u2019 data mining). Domain (protein) A region of special biological interest within a single protein sequence." + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/old/experts_suga1_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/experts_suga1_dataset.json new file mode 100644 index 0000000..b9cfeb6 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/experts_suga1_dataset.json @@ -0,0 +1,152 @@ +{ + "question": [ + "what genes are associated with diabetes?", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabetes", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabetes", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?", + "How does genetics influence the emergency of diabetes?", + "How many types of diabetes exist?" + ], + "answer": [ + "The genes associated with diabetes mentioned in the text include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, HNFA4, LOC387761, KCNQ1, MTNR1B, GCKR, BACH2, CTNNB1, CREB5, PARD3B, PARD6G, PRKCZ, EBF1, GIGYF1, ADCY5, PROX1, DGKB-TMEM195, CRY2, FADS1, GLIS3, and C2CD4B.", + "Genomics provides a comprehensive understanding of the genetic factors influencing the development and progression of diabetes. Nutritional genomics focuses on the interaction between genes and dietary patterns, which plays a crucial role in diabetes. By studying gene-nutrient interactions and genetic variations, researchers can develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. Furthermore, genomics can help identify how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake, potentially leading to diabetes. Thus, genomics offers a promising approach to understanding the nutritional factors of diabetes and developing personalized dietary interventions.", + "Genomics can be used to understand the nutritional factors of diabetes by studying nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This approach can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. Additionally, genomics can identify gene variants that interact with specific nutrients, providing insights into how diet and genetics interact to influence diabetes risk.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any association between the genes SH2B3 or ERBB3 and diabetes.", + "Genetics plays a significant role in the emergence of diabetes. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus contribute to 50-60% of the genetic risk by affecting protein binding and antigen presentation to T cells. Other gene variants that modulate immune regulation, viral responses, environmental signals, and endocrine function also contribute to the disease. The risk for type 1 diabetes is strongly influenced by multiple genetic loci. In type 2 diabetes, many common variants, largely with small effect size, contribute to overall disease risk. Monogenic forms of diabetes, such as MODY, result from mutations in a single gene. Family history also plays a role, suggesting that genetics conveys more than heritable information; it probably includes non-genetic familial behaviors and norms.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY)." + ], + "contexts": [ + [ + "To see which other significant genes were likely to have a role in diabetes we looked at all variant sets with a significant glucose, HbA1c, or T2D association and examined whether they had associations with additional diabetes traits (p \u2264 0.0016, correcting for 32 sets tested).Damaging missense variants in PDX1 and PFAS, which significantly associated with HbA1c levels in our primary analysis, associated with T2D diagnosis using this threshold (Table 3 and Supplementary Table 14).", + "Identification of genes with a biological role in diabetes. Variants in two genes, GCK and GIGYF1, significantly associated with glucose, HbA1c and T2D diagnosis, strongly suggesting a biological role in diabetes; GCK is involved in Mendelian forms of diabetes while GIGYF1 has not previously been implicated by genetics in the disease.Both GCK and GIGYF1 are located on chromosome 7 but are 56 Mb apart, strongly suggesting that these signals are independent; this independence was confirmed by conditional analysis (Supplementary Table 13).Two additional variant sets, HNF1A pLOF and TNRC6B pLOF, had genome-wide associations with both T2D diagnosis and HbA1c levels while G6PC2 damaging missense variants associated with decreased levels of both glucose and HbA1c but not T2D diagnosis (Table 3).", + "One obvious locus to consider is TCF7L2 in the context of type 2 diabetes.Common genetic variation located within the gene encoding transcription factor 7 like 2 (TCF7L2) has been consistently reported to be strongly associated with the disease.Such reports range from 2006, when we first published the association [3], to the recent transethnic meta-analysis GWAS of type 2 diabetes [4].", + "Testing of these loci for association with T2D as a dichotomous trait in up to 40,655 cases and 87,022 nondiabetic controls demonstrated that the fasting glucose-raising alleles at seven loci (in or near ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 and the known T2D genes TCF7L2 and SLC30A8) are robustly associated (P < 5 \u00d7 10 \u22128 ) with increased risk of T2D (Table 2).The association of a highly correlated SNP in ADCY5 with T2D in partially overlapping samples is reported by our companion manuscript 29 .We found less significant T2D associations (P < 5 \u00d7 10 \u22123 ) for variants in or near CRY2, FADS1, GLIS3 and C2CD4B (Table 2).These data clearly show that loci with similar fasting glucose effect sizes may have very different T2D risk effects (see, for example, ADCY5 and MADD in Table 2).", + "Among the confirmed and potential type 2 diabetes risk genes described in Tables 1 and 2, eight genes influence whole-body or peripheral insulin sensitivity: ADIPOQ (47, 52, 250 -257), AHSG (75, 258), CAPN10 (259 -264), ENPP1 (265)(266)(267)(268)(269)(270)(271), PPARG (272)(273)(274)(275)(276)(277)(278)(279)(280)(281)(282)(283), PPARGC1A (284,285), SREBF1 (65), and TCF7L2 (133,151,286,287).", + "Despite identification of many putative causative genetic variants, few have generated credible susceptibility variants for type 2 diabetes.Indeed, the most important finding using linkage studies is the discovery that the alteration of TCF7L2 (TCF-4) gene expression or function (33) disrupts pancreatic islet function and results in enhanced risk of type 2 diabetes.Candidate gene studies have also reported many type 2 diabetes-associated loci and the coding variants in the nuclear receptor peroxisome proliferator-activated receptor-g (34), the potassium channel KCNJ11 (34), WFS1 (35), and HNF1B (TCF2) (36) are among the few that have been replicated (Table 2).Recently, there have been great advances in the analysis of associated variants in GWA and replication studies due to highthroughput genotyping technologies, the International HapMap Project, and the Human Genome Project.Type 2 susceptibility loci such as JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2, and ADCY5 (37,38) are among some of the established loci (Table 2).CDKN2A/B, CDKAL1, SLC30A8, IGF2BP2, HHEX/IDE, and FTO are other established susceptibility loci for diabetes (Table 2) (34,39,40).GWA studies have also identified the potassium voltage-gated channel KCNQ1 (32) as an associated gene variant for diabetes.A recent GWA study reporting a genetic variant with a strong association with insulin resistance, hyperinsulinemia, and type 2 diabetes, located adjacent to the insulin receptor substrate 1 (IRS1) gene, is the C allele of rs2943641 (41).Interestingly, the parental origin of the single nucleotide polymorphism is of importance because the allele that confers risk when paternally inherited is protected when maternally transmitted.GWA studies for glycemic traits have identified loci such as MTNR1B (42), GCK (glucokinase) (42), and GCKR (glucokinase receptor) (42); however, further investigation of genetic loci on glucose homeostasis and their impact on type 2 diabetes is needed.Indeed, a recent study by Soranzo et al. (42) using GWA studies identified ten genetic loci associated with HbA 1c .Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin may be associated with changes in levels of HbA 1c .", + "G enome-wide association studies (GWAS) have iden- tified several type 2 diabetes mellitus (T2DM) susceptibility loci including CDKAL1, CDKN2B, IGF2BP2, HHEX, SLC30A8, PKN2, LOC387761 (1)(2)(3)(4)(5), and KCNQ1, which was recently identified by similar GWAS approach in two independent Japanese samples (6,7).Although these associations have been well replicated in Japanese populations (8), the role of these loci in other East Asian populations remains less clear.For example, a study in China by Wu et al. (9) did not find significant associations between single-nucleotide polymorphisms (SNPs) in IGF2BP2 and SLC30A8 with T2DM, whereas an association between SNPs at the HHEX locus and T2DM was reported among Chinese living in Shanghai, but not among Chinese in Beijing.Another study in Hong Kong Chinese (10) also did not find an association with SNPs at the IGF2BP2 locus; however, they reported an association between T2DM with SNPs at the HHEX and SLC30A8 loci.", + "In studies where overt T2D has been the phenotype the majority of associated polymorphisms have encoded proteins known to be involved in \u03b2-cell metabolism; for example TCF7L2, KCNJ11 and HHEX have shown robust association [170,171].This suggests that these genes could prove useful in predicting \u03b2-cell preservation during the course of T2D.The glucokinase gene (GCK) coding for the initial glucose-sensing step in the \u03b2-cell can have activating mutations causing hypoglycemia that might provide structural and functional models leading to drug targets for treating T2D [172].In the GoDARTs study, investigators examined the medication response of metformin and sulphonylurea based on the TCF7L2 variants mainly affecting the \u03b2-cell.The carriers of the at risk 'T' allele responded less well to sulphonylurea therapy than metformin [173].Also it is of significant public health interest that in the Diabetes Prevention Program, lifestyle modifications were shown to reduce the risk of diabetes conferred by risk variants of TCF7L2 at rs7093146, and in placebo participants who carried the homozygous risk genotype (TT), there was 80% higher risk for developing diabetes compared to the lifestyle intervention group carrying the same risk genotypes [35].These findings could herald significant future progress in the field of T2D pharmacogenomics, possibly leading to the development and use of agents tailored on the basis of genotype.", + "Despite sharing only 9 loci (among 26 and 17 total in the two analyses, respectively), the separate analyses both identified genes involved in diabetes-related biological functions, including \"glucose homeostasis,\" \"pancreas development\" and \"insulin secretion\" (Supplementary Tables 3 and 5).Three of the top eleven scoring genes in our independent replication analysis have verified causal links to T2D, as annotated in the OMIM 41 .These include genes encoding transcription factors TCF7L2 (TCF4), which has extensive evidence of being causal in T2D 61,62 , and HNF1B, which is a known cause of maturity onset diabetes of the young 63 .Other high-ranking candidate genes have been identified as therapeutic targets in T2D (for example, CTBP1 (ref.64) and LEP 65 ), and the high-scoring gene HHEX has recently been shown to play a key role in islet function 66 .", + "Similar findings to AMD are now unfolding with type 2 DM.Grant et al. (24) first reported on a variant of the gene TCF7L2, which has been linked to reduced beta cell function and poor insulin response to oral glucose loads (51).Since its first discovery, this gene has been widely confirmed in independent studies as a pivotal susceptibility marker for type 2 DM (23,(25)(26)(27)(28)40).Recently, 6 genome-wide SNP association studies have identified and replicated in separate stages several additional novel genes conferring susceptibility to type 2 DM (23,(25)(26)(27)(28)40) (Table 2).Interestingly, these loci primarily include genes involved in pancreatic beta cell development and function as opposed to insulin resistance-the current accepted mechanism for type 2 DM.This development casts doubt on our traditional pathophysiological modeling of the type 2 diabetic patient and underscores the need for genomic studies to further define pathobiological processes of complex traits.", + "Of the 16 loci that have been associated with type 2 diabetes previously, [8][9][10][11][12][13][14][15] we showed that 11 -TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEXwere associated with an enhanced risk of future diabetes.Many of the variants that we genotyped appear to influence beta-cell function, possibly through effects on proliferation, regeneration, and apoptosis.There was a time-dependent increase in the BMI and a decrease in insulin sensitivity in the subjects from the Botnia study, an increase in insulin resistance that was reflected by an increase in insulin secretion.However, this increase was inadequate to compensate for the increase in insulin resistance in carriers with a high genetic risk, which resulted in a markedly impaired disposition index.Only variants in FTO were associated with an increased BMI.Both FTO and PPARG together with TCF7L2 and KCNJ11 predicted transition from impaired fasting glucose levels or impaired glucose tolerance to manifest diabetes, which suggests that a combination of increased obesity and insulin resistance with a deterioration in beta-cell function contribute to the manifestation of diabetes in these subjects.Collectively, our findings emphasize the critical role of inherited defects in beta-cell function for the development of type 2 diabetes.", + "Type 2 Diabetes Common variants in 11 genes were significantly associated with the risk of future type 2 diabetes in the MPP cohort, including TCF7L2 (odds ratio, 1.30; P = 9.5\u00d710 \u221213 ), PPARG (odds ratio, 1.20; P = 4.0\u00d710 \u22124 ), FTO (odds ratio, 1.14; P = 9.2\u00d710 \u22125 ), KCNJ11 (odds ratio, 1.13; P = 3.6\u00d710 \u22124 ), NOTCH2 (odds ratio, 1.13; P = 0.02), WFS1 (odds ratio, 1.12; P = 0.001), CDKAL1 (odds ratio, 1.11; P = 0.004), IGF2BP2 (odds ratio, 1.10; P = 0.008), SLC30A8 (odds ratio, 1.10; P = 0.008), JAZF1 (odds ratio, 1.08; P = 0.03), and HHEX (odds ratio, 1.07; P = 0.03) (Table 2).Although these findings could not be fully replicated in the smaller Botnia study, there was little heterogeneity between the studies with respect to the risk conferred by different genotypes.", + "To date, more than 70 genes have been identified as involved in T2DM, primarily by association analysis [34].In addition, via GWAS arrays, more than 100 SNPs have been identified for T2DM [35].From the 50 novel loci associated with T2DM previously identified, more than 40 loci have been associated with T2DM-related traits, including fasting proinsulin, insulin and glucose (Table 1) [36][37][38][39].However, for T2DM-related traits, such as the HOMA index or pancreatic \u03b2 cell function, there are virtually no published data examining the relationship between these traits or the genotype and environment interactions.Clinical investigations of some loci have suggested that the genetic components of T2DM risk act preferentially through \u03b2 cell function [40].Among all 40 loci associated with T2DM-related traits, only transcription factor-7-like 2 (TCF7L2) was shown to clearly contribute to T2DM risk [41].Several studies in white European [42], Indian [43], Japanese [44], Mexican American [45] and West African [46] individuals have shown a strong association between TCF7L2 and T2DM.It is also noteworthy that these populations represent the major racial groups with a high prevalence of T2DM.In all populations, TCF7L2 showed a strong association, with the odds of developing T2DM increased by 30%-50% for each allele inherited.This finding indicates an approximately double odds ratio compared to most other diabetes susceptibility polymorphisms.TCF7L2 is a transcription factor involved in the Wnt signaling pathway that is ubiquitously expressed, and it has been observed that TCF7L2 risk alleles result in the overexpression of TCF7L2 in pancreatic \u03b2 cells.This overexpression causes reduced nutrient-induced insulin secretion, which results in a direct predisposition to T2DM as well as an indirect predisposition via an increase in hepatic glucose production [47].", + "Most Relevant T2DM Susceptibility Genes Gene and environment interaction studies have shown a nice association between variants in peroxisome proliferator-activated receptor gamma (PPARG), TCF7L2 and fat mass and obesity-associated protein (FTO) genes, a Western dietary pattern and T2DM.", + "One of these genes associated with type 2 diabetes is the insulin receptor substrate 1 (IRS1, OMIM association number, 147545) (Alharbi, Khan, Abotalib, & Al-Hakeem, 2014;Alharbi, Khan, Munshi et al., 2014;Brender et al., 2013;Brunetti, Chiefari, & Foti, 2014) and another is the C-C motif chemokine receptor5(CCR5, OMIM association number, 601373) (Balistreri et al., 2007;Mokubo et al., 2006;Muntinghe et al., 2009).", + "Genes boosted in type 2 diabetes Before the Wellcome Trust study, PPARG, KCNJ11, and TCF7L2 had all been identified as genes involved in type 2 diabetes through genome-wide association studies and replicated in follow-up studies (for review, see Bonnefond et al. 2010).The strongest candidate gene for type 2 diabetes, TCF7L2, was also the strongest signal seen in the Wellcome trust study, although the others were not so strong.However, the exact mechanism by which TCF7L2 acts was not entirely clear.In our analysis (Fig. 5), we find it directly connected to the b-catenin/WNT signaling pathway by its functional connection to CTNNB1, as well as to BACH2, a gene that has been repeatedly implicated in type 1 diabetes (e.g., Cooper et al. 2008;Madu et al. 2009), but which has not yet been linked to type 2 diabetes.BACH2 is among the genes most strongly boosted by network linkages, deriving additional signal from CREB5 and PARD3B, which both score highly in the GWAS data.PARD6G, PARD3B, and CDC42 are also emphasized by the method.Notably, these genes form a complex with PRKCZ (Koh et al. 2008), a variant of which correlates with type 2 diabetes in Han Chinese (Qin et al. 2008).EBF1, a known regulator of adipocyte differentiation (Akerblad et al. 2005) is also strongly boosted by the network, supporting a possible role in type 2 diabetes.", + "RESULTS- We confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 \u03eb 10 \u03ea12 \u03fd P unadjusted \u03fd 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted \u03ed 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations.", + "OBJECTIVE-Recent genome-wide association studies have identified six novel genes for type 2 diabetes and obesity and confirmed TCF7L2 as the major type 2 diabetes gene to date in Europeans.However, the implications of these genes in Asians are unclear.RESEARCH DESIGN AND METHODS-We studied 13 associated single nucleotide polymorphisms from these genes in 3,041 patients with type 2 diabetes and 3,678 control subjects of Asian ancestry from Hong Kong and Korea. RESULTS-We confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 \u03eb 10 \u03ea12 \u03fd P unadjusted \u03fd 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted \u03ed 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations. CONCLUSIONS-Ourfindings support the important but differential contribution of these genetic variants to type 2 diabetes and obesity in Asians compared with Europeans.Diabetes 57: 2226-2233, 2008T ype 2 diabetes is a major health problem affecting more than 170 million people worldwide.In the next 20 years, Asia will be hit hardest, with the diabetic populations in India and China more than doubling (1).Type 2 diabetes is characterized by the presence of insulin resistance and pancreatic \u2424-cell dysfunction, resulting from the interaction of genetic and environmental factors.Until recently, few genes identified through linkage scans or the candidate gene approach have been confirmed to be associated with type 2 diabetes (e.g., PPARG, KCNJ11, CAPN10, and TCF7L2).Under the common variant-common disease hypothesis, several genome-wide association (GWA) studies on type 2 diabetes have been conducted in large-scale case-control samples.Six novel genes (SLC30A8, HHEX, CDKAL1, CDKN2A and CDKN2B, IGF2BP2, and FTO) with modest effect for type 2 diabetes (odds ratio [OR] 1.14 -1.20) had been reproducibly demonstrated in multiple populations of European ancestry.Moreover, TCF7L2 was shown to have the largest effect for type 2 diabetes (1.37) in the European populations to date (2-8).Although many of these genes may be implicated in the insulin production/secretion pathway (TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, and IGF2BP2) (6,9 -11), FTO is associated with type 2 diabetes through its regulation of adiposity (8,12,13).Moreover, two adjacent regions near CDKN2A/B are associated with type 2 diabetes and cardiovascular diseases risks, respectively (7,14 -16).Despite the consistent associations among Europeans, the contributions of these genetic variants in other ethnic groups are less clear.Given the differences in environmental factors (e.g., lifestyle), risk factor profiles (body composition and insulin secretion/resistance patterns), and genetic background (linkage disequilibrium pattern and risk allele frequencies) between Europeans and Asians, it is important to understand the role of these genes in Asians.A recent case-control study in 1,728 Japanese subjects revealed nominal association to type 2 diabetes for variants at the SLC30A8, HHEX, CDKAL1, CDKN2B, and FTO genes but not IGF2BP2 (17).In the present large-scale case-control replication study of 6,719 Asians, we aimed to test for the association of six novel genes from GWA studies and TCF7L2, which had the largest effect in Europeans, and their joint effects on type 2 diabetes risk and metabolic traits. RESEARCH DESIGN AND METHODSAll subjects were recruited from Hong Kong and Korea and of Asian ancestry.The subjects in the Hong Kong case-control study were of southern Han Chinese ancestry residing in Hong Kong.Participants for the case cohort consisting of 1,481 subjects with type 2 diabetes were selected from two", + "OBJECTIVE-Common variants in PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 genes have been shown to be associated with type 2 diabetes in European populations by genome-wide association studies.We have studied the association of common variants in these eight genes with type 2 diabetes and related traits in Indians by combining the data from two independent case-control studies.", + "Introduction Many genes have been evaluated as candidates for T2D susceptibility.However, only variants in the TCF7L2, PPARG, KCNJ11 and HNFA4 genes have been extensively replicated in populations around the world, showing their indisputable association with T2D risk (Zeggini 2007).In the particular case of the HNF4A gene, it has been implicated in maturity-onset diabetes of the young type 1 (MODY 1) (Mitchell and Frayling 2002;Zhu et al. 2003).HNF4A is a member of the nuclear receptor super-family that plays a critical role in embryogenesis and metabolism, by regulating gene expression in pancreatic beta cells, liver and other tissues.The HNF4A gene is localized to chromosome 20q13, a region that has demonstrated evidence for linkage with T2D (Sladek et al. 1990;Ghosh et al. 1999).Several genetic studies, mainly in Caucasian and Asian populations, have provided evidence for the association of the variants in HNF4A with T2D (Ghosh et al. 1999;Silander et al. 2004;Winckler et al. 2005)." + ], + [ + "Researchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today.", + "enetic factors for many decades have been known to play a critical role in the etiology of diabetes, but it has been only recently that the specific genes have been identified.The identification of the underlying molecular genetics opens the possibility for understanding the genetic architecture of clinically defined categories of diabetes, new biological insights, new clinical insights, and new clinical applications.This article examines the new insights that have arisen from defining the etiological genes in monogenic diabetes and the predisposing polymorphisms in type 2 diabetes.", + "Genomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.", + "Diabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes.", + "Genetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner.", + "In conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes.", + "To date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow.", + "In recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management.", + "Nonetheless, \"evidence\" for the genetics of diabetes risk is mounting, often at the expense of understanding the social context and determinants of the disease.Biogenetic views tend to trump sociological views in the diabetes research imaginary of consortium members.However, the genetic epidemiologists who make up part of the diabetes consortium are not ignorant of the effects of proper diet and adequate exercise. \"Take away the television and the automobile and diabetes would all but disappear,\" quipped the head of one lab.Neither are researchers unsympathetic to those who suffer from social inequality in the United States.Their career and intellectual interests lie in genetic explanations of diabetes, which, as I aim to show in this discussion, involves folding political and economic social relationships into biomedical discourse.In fact, the case of diabetes genetic epidemiology illustrates how, in spite of the sympathies of diabetes scientists, arrangements of racial inequality in the United States find their way into diabetes research publications and drug company promotional campaigns.To illustrate this phenomenon further, I present two tales from the field, one dealing with the naming of a publication article, the other with the marketing of a diabetes drug.", + "Discussion Our study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured.", + "Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression,", + "Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression,", + "The aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM.", + "It is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8].", + "A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.", + "In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.", + "In a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases.", + "Genetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + ], + [ + "Researchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today.", + "Genomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.", + "Genomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective.", + "Genetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner.", + "In conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes.", + "Genomics of T2D Diet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes.", + "It is possible that there are genes that because of their known metabolic involvement are likely to interact with specific nutrients.For example, SLC30A8 which encodes a zinc transporter localized in secretory granules, interacted with dietary zinc to effect fasting insulin levels [132].However, the majority of GWAS variants have not shown interaction with environmental factors for effect on diabetes or related traits.Therefore, it is likely that prospective future studies will utilize improved assessment methods to increase power and avoid false interpretation [133,134].This could be enhanced by prioritizing variants that are most likely to have effects [135] or selective sampling according to extremes of the environmental factor could reduce the requirement for sample size [136].These and other strategies such as meta-analysis, nested case control and genotype-based studies have been recently reviewed [123,133] and the difficulties in measuring environmental exposures have been emphasized, including the application of analyses based on logistic regression [124] and problems with instruments such as physical activity questionnaires [137].Validated food frequency questionnaires are popular instruments for evaluation diabetes risk and are often used in conjunction with food analysis software [138,139].Similar methodology has been adapted to assess two predominant food consumption patterns by Prudent and Western [140], and demonstrated synergistic interaction with genotype and a less healthy Western dietary pattern in determining male risk for T2D by showing that the gene-diet interaction was higher in men with a high genetic risk score determined by a gene counting method [141].Also the effects of diet may predominate at specific developmental periods [142] suggesting that age and associated physiological changes are important as well as differences between genders.It has also been observed that homogeneity of an environmental factor such as physical activity in an Asian Indian study, may reduce ability to detect interaction, but could be solved by subgrouping by the level of activity [143], but increased recruitment would be needed to maintain power.", + "To date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow.", + "In recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management.", + "Discussion Our study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured.", + "Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression,", + "Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression,", + "The aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM.", + "Thus, studies performed during the last decade have provided strong evidence to support a diet-genome interaction as an important factor leading to the development of T2DM.", + "It is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8].", + "A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "In a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases.", + "The public health genomics approach to type 2 diabetes.So, while exciting gene discoveries are being made, what can we do?The answer may lie in the relatively new field of public health genomics, \"a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health\" (12).Researchers, policymakers, and practitioners in public health genomics use populationbased data on genetic variation and gene-environment interactions to develop, implement, and evaluate evidencebased tools for improving health and preventing disease.They also apply systematic evidence-based knowledge synthesis and appraisal of the clinical validity and utility of genomic applications in health practice.Validated genomic information is then integrated into disease control and prevention programs (13).", + "Genomics for Type 2 Diabetes Many research studies have been carried out on genetic-based illness prediction.Incorporating machine learning approaches with genetic-based illness prediction could result in an accurate outcome.This has intensified the role of Artificial Intelligence (AI) in healthcare.It has been estimated that approximately $36 billion will be invested in AI by 2025 [48].Deep genomics through machine learning approaches has outperformed accuracy in predicting and diagnosing illnesses such as cancer with minimal inclusion of radiologists.It is desired to have sufficient biological knowledge to understand how genetics can help us predict various conditions and analyze each chromosome to identify the disease-causing gene.Pre-existing research studies have focused on genomics and gene interaction patterns of various persistent illnesses such as Alzheimer's, multiple cancers, and Parkinson's." + ], + [ + "Figure 8 Molecular changes in the islets of patients with T2D mirror the processes altered in NOD mice.mRNA expression in human pancreatic islets from healthy individuals (n = 105) and those diagnosed with T2D (n = 14) was assessed through RNA-seq analysis. (a) Relationship between GLIS3 and MANF expression in healthy individuals (Spearman correlation P value = 0.043), individuals with T2D (Spearman correlation P value = 0.075) and all individuals (Spearman correlation P value = 0.028). (b-e) Expression of XRCC4 (b), LIG4 (c), H2AFX (d) and CDKN1A (e) in healthy islets as compared to i slets from patients withT2D (P values shown after multiple-testing correction).The median and interquartile range (IQR; box) are shown, with error bars indicating 1.5 times the IQR.Individual values are shown if beyond 1.5 times the IQR. (f) Relationship between H2AFX and LIG4 expression in human islets (Spearman correlation P value = 5 \u00d7 10 \u22129 ).", + "All the genes involved in these pathways, as well as the genes involved in b-cells development and turnover, may be considered candidate genes for T2DM with predominant insulin deficiency.", + "One method of searching for the cause of NIDDM is via the candidate gene approach.Possible candidates for NIDDM include genes involved in specifying pancreatic islet (3-cell phenotype and in directing fj-cell development and (3-cell responses of glucose-mediated insulin synthesis and secretion.The transcription factor islet-1 (Isl-1) has been shown to be a unique protein that binds to the mini-enhancer or Far-FLAT region (nucleotide -247 to -198) of the rat insulin I gene (7).Isl-1, a protein comprised of 349 residues (38 kD), is a member of the LIM/homeodomain family of proteins, named for the first three members described: lin-11, isl-1, and mec-3 (8,9).These proteins are comprised of three putative regulatory regions, two LIM domains (cysteine-rich motifs) in the amino terminus of the protein, a homeobox domain near the middle, and a glutamine-rich transcriptional activation domain at the carboxyl end (7,9).With the use of an antibody to Isl-1, expression was shown to be restricted to a subset of endocrine cells, including islets, neurons involved in autonomic and endocrine control, and selected other tissues in the adult rat (10)(11)(12).", + "Results Impairment or alteration of the insulin-signaling pathway is a commonly recognized feature of type 2 diabetes.It is therefore notable that the IS-HD gene set (Dataset S4) was not detected to be significantly transcriptionally altered by application of either hypergeometric enrichmentt test, DEA or GSEA.In particular, applying GSEA to the transcriptional profile dataset of diabetic and normal glucose-tolerant skeletal muscle described in Mootha et al. [10] did not identify a significant level of alteration in the IS-HD gene set (p \u00bc 0.536), while DEA produced a comparably weak enrichment score (p \u00bc 0.607).The failure to detect a significant transcriptional alteration in IS-HD may be explained by a number of factors.The enrichment results depended on the specific choice of the IS-HD gene set, and it is possible that an alternatively defined insulin-signaling gene set would be determined as significantly enriched.Additionally, expression changes in a few critical genes in IS-HD may be sufficient to substantially alter insulin signaling, and running DEA on the large IS-HD set may miss the contributions from these few genes.", + "35 ABSTRACT 11 A GENE EXPRESSION NETWORK MODEL OF TYPE 2 DIABETES ESTABLISHES A RELATIONSHIP BETWEEN CELL CYCLE REGULATION IN ISLETS AND DIABETES SUSCEPTIBILITY MP Keller, YJ Choi, P Wang, DB Davis, ME Rabaglia, AT Oler, DS Stapleton, C Argmann, KL Schueler, S Edwards, HA Steinberg, EC Neto, R Klienhanz, S Turner, MK Hellerstein, EE Schadt, BS Yandell, C Kendziorski, and AD Attie Depts.", + "Second, we performed an extensive manual curation according to a previously described b-cell-targeted annotation (Kutlu et al, 2003;Ortis et al, 2010).In partial agreement with the IPA, we found these genes to fall into three broad categories: (1) genes related to b-cell dysfunction and death, (2) genes potentially facilitating the adaptation of the pancreatic islets to the altered metabolic situation in T2D and (3) genes whose role in disease pathogenesis remains to be unearthed (Figure 6B).The adaptation-related gene category contains few metabolism-associated genes (e.g., HK1, FBP2; Figure 6B, right part, Figure 7) and many more genes involved in signal transduction or encoding hormones, growth factors (e.g., EGF, FGF1, IGF2/IGF2AS; Figure 7), or transcription factors involved in important regulatory networks (for instance, FOXA2/HNF3B, PAX4 and SOX6) (Figure 6B, right part, Figure 7).In the b-cell dysfunction and death category, there were hypomethylated genes related to DNA damage and oxidative stress (e.g., GSTP1, ALDH3B1; Figure 7), the endoplasmic reticulum (ER) stress response (NIBAN, PPP2R4, CHAC1), and apoptosis (CASP10, NR4A1, MADD; Figure 6B, left part, Figure 7).Some genes of interest from the highlighted categories are depicted in Figure 7. Their annotated functions provide possible explanations of how the epigenetic dysregulation of these genes in diabetic islets is connected to T2D pathogenesis.Numerous genes that were identified by our methylation profiling approach have been functionally implicated in insulin secretion.Examination of the available literature on the function of these genes revealed three aspects of insulin secretion with which they interfere: some of these genes influence the expression of the insulin gene, like MAPK1 and SOX6, or its post-translational maturation, like PPP2R4 (cf. Figure 7 and references therein).Others can deregulate the process of insulin secretion itself (SLC25A5, Ahuja et al, 2007;RALGDS, Ljubicic et al, 2009) or influence synthesis as well as secretion (vitronectin, Kaido et al, 2006).A third group of differentially methylated genes affects (i) signalling processes in the b-cell leading to insulin secretion or (ii) glucose homeostasis in b-cells, thereby modulating insulin response upon stimulation.GRB10 (Yamamoto et al, 2008), FBP2 and HK1 (Figure 7) are examples for these genes.Additional genes found in our study have been implicated in the b-cells' capability to secrete insulin, though the mechanisms have not yet been fully established.The putative functions of these genes indicate a potential epigenetic impact on insulin secretion at multiple levels, namely signalling, expression/synthesis and secretion.", + "In summary, we have associated mutations in the SLC29A3 gene with diabetes mellitus in humans and the insulin signaling pathway in Drosophila.The mechanistic basis of these findings remains to be determined.This is strong evidence supporting the investment of resources to further investigate the role of SLC29A3 and its orthologs in diabetes and glucose metabolism in model systems.", + "DISCUSSION We have identified mutations in the equilibrative nucleoside transporter 3 protein that are associated with an inherited syndrome of insulin-dependent DM, and provide prima facie evidence that the Drosophila ortholog of this protein interacts with the insulin signaling pathway.This is the first evidence that mutations in the human SLC29A3 gene can be associated with a diabetic phenotype.", + "These observations taken together suggest that molecules involved in innate immunity could serve as candidate genes that determine the susceptibility of sensitive strains of mice to virusinduced diabetes.Interestingly, deficiency of the Tyk2 gene results in a reduced antiviral response 24 .In addition, the human TYK2 gene was mapped to the possible type 1 diabetes susceptibility locus 25 .", + "A recent sequencing study provides an example of detection of rare variants in type 1 diabetes.Targeted sequencing in a series of candidate coding regions resulted in IFIH1 being identified as the causal gene in a region associated with type 1 diabetes by GWA studies (58).IFIH1 encodes a cytoplasmic helicase that mediates induction of the interferon response to viral RNA.The discovery of IFIH1 as a contributor to susceptibility to type 1 diabetes has strengthened the hypothesis (70) about a mechanism of disease pathogenesis involving virusgenetic interplay and raised type 1 interferon levels as a cofactor in \u2424-cell destruction.Nonetheless, it should be recognized that a component of the missing heritability (familial aggregation) in type 1 diabetes could well be due to unrecognized intra-familial environmental factors.Disease pathogenesis.Contemporary models of pathogenesis of type 1 diabetes support the involvement of two primary dramatis personae: the immune system and the \u2424-cell.The known and newly identified genetic risk factors for type 1 diabetes present exciting opportunities to build on to the current cast of disease mechanisms and networks.Most of the listed genes of interest (Table 2) and those in extended regions are assumed to regulate immune function.Some of these genes, however, may also have roles in the \u2424-cell (insulin being the most obvious example).Another gene, PTPN2, encoding a protein tyrosine phosphatase, was identified as affecting the risk for type 1 diabetes as well as for Crohn disease (47,71).PTPN2 is expressed in immune cells, and its expression is highly regulated by cytokines.However, PTPN2 is expressed also in \u2424-cells, where it modulates interferon (IFN)-\u2425 signal transduction and has been shown to regulate cytokineinduced apoptosis (72).Other candidate genes, such as NOS2A, IL1B, reactive oxygen species scavengers, and candidate genes, identified in large GWA studies of type 2 diabetes, have not been found to be significant contributors to the susceptibility of type 1 diabetes (73).", + "Differential Expression Analyses of Type 1 Diabetes Mellitus Associated Genes For the aforementioned 171 'novel' genes, we used t-test to compare ribonucleic acid expression signals in PBMCs or monocytes between type 1 diabetes mellitus patients and healthy controls.We found that 37 genes, including 21 non-HLA genes (e.g.FAM46B, OLFML3 and HIPK1), were differentially expressed between type 1 diabetes mellitus patients and controls (Table 2).For the differential expression study, the significance level of P < 5.0E-02 was used.", + "In this study, we have correlated the function and genotype of human islets obtained from diabetic and nondiabetic (ND) donors.We have analyzed a panel of 14 gene variants robustly associated with T2D susceptibility identified by recent genetic association studies.We have identified four genetic variants that confer reduced b-cell exocytosis and six variants that interfere with insulin granule distribution.Based on these observations, we calculate a genetic risk score for islet dysfunction leading to T2D that involves decreased docking of insulin-containing secretory granules, impaired insulin exocytosis, and reduced insulin secretion.", + "At present, insulin [15], glucokinase [16], amylin [17], mitochondrial DNA [18], and several transcriptional factors [19][20][21][22] are recognized as diabetogenic genes in pancreatic b-cells.In the present study we used the candidate gene approach in the examination of genomic variation in the a 1D and Kir6.2 channel genes in type 2 diabetic patients.", + "In summary, we report AEIs that are consistent with type 2 diabetes-associated variation regulating the expression of cis-linked genes in human islets.For some of the genes where significant AEI was identified (e.g., SLC30A8, WFS1), there is strong evidence from human genetics that small changes in gene dosage may have significant consequences for the pancreatic b-cell.For other genes with significant AEI (e.g., ANPEP, HMG20A), their role is less well defined, and hence this study should provide a platform for further work examining the effects of carefully manipulating the expression of these genes in human islets.", + "Results.Pathway analysis of genes with differentially methylated promoters identified the top 3 enriched pathways as maturity onset diabetes of the young (MODY), type 2 diabetes, and Notch signaling.Several genes in these pathways are known to affect pancreatic development and insulin secretion.", + "The authors then used mouse liver and adipose expression data from several mouse crosses to construct causal expression networks for the ERBB3 and RPS26 orthologs in the mouse. They then showed that ERBB3 is not associated with any known Type I diabetes genes whereas RPS26 is associated a network of several genes that are part of the KEGG Type I diabetes pathway (Schadt et al. 2008). This type of analysis demonstrates the power of combining human and mouse data with a network based approach that has been proposed for use in drug discovery (Schadt et al.", + "In conclusion, GWAS studies focusing on the causes of T2D have implicated islet dysfunction as a major contributing factor (18,71).By examining isolated islets for stress responses and cross-referencing gene hits with genes associated with glucose-stimulated insulin release in human populations with T2D, we identified 7 genes that may play a role in promoting or preventing islet decline in T2D.By further examining stress-induced expression changes in each of these genes, we identified 5 genes that stood out: F13a1 as a novel stress-inhibited gene in islets, Klhl6 and Pamr1 as induced genes specific to ER stress, Ripk2 as a broadly stress-induced gene, and Steap4 as an exceptionally cytokine-sensitive gene.These genes provide promising leads in elucidating islet stress responses and islet dysfunction during the development of T2D.", + "Genome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of \u2424-cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of \u03fe2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D.", + "Genome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of \u2424-cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of \u03fe2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D.", + "Finally, several of the linking nodes introduced into this islet network through their PPI connections represent interesting candidates for a role in T2D pathogenesis, and there are several examples where external data provides validation of those assignments.An interesting example involves the gene GINS4 which maps at the ANK1 locus.Though this gene generated a low PCS [0.03] and was not included in the set of seed genes for this locus, GINS4 knock-down has an impact in a human beta-cell line [14].In addition, cyclin-dependent kinase 2 (CDK2) has been shown to influence beta-cell mass in a compensatory mechanism related to age-and diet-induced stress, connecting beta-cell dysfunction and progressive beta-cell mass deterioration [54].YHWAG is a member of the 14-3-3 family, known to be signalling hubs for beta-cell survival [55], and disruption of SMAD4 drives islet hypertrophy [56]." + ], + [ + "A. Genetic Screening We have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is \u03f310 -15 times greater.Although \u03f370% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3).", + "II. THE GENETICS OF TYPE 1 DIABETES A comprehensive overview of genetic data in mouse and human is beyond the scope of this article.Instead, we will focus on how the various susceptibility genes and environmental triggers can fit in a mechanistic model for T1D etiology.", + "The relative prevalence of mutations causal for monogenic forms of diabetes suggests that mutations in \u2424-cellrelated processes are a more frequent cause of severe early-onset diabetes than those influencing insulin action (see above).Studies of the relative heritabilities of indexes of \u2424-cell function and insulin action in the general population also hint at a preponderance of \u2424-cell effects (52).", + "In 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention.Type 1 diabetes becomes clinically apparent after a preclinical period of varying length, during which autoimmune destruction reduces the mass of beta cells in the pancreatic islets to a level at which blood glucose levels can no longer be maintained in a physiologic range.The disease has two subtypes: 1A, which includes the common, immune-mediated forms of the disease; and 1B, which includes nonimmune forms.In this review, we focus on subtype 1A, which for simplicity will be referred to as type 1 diabetes.Although there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied. Gene tic S t udiesBecause of the uncertainty regarding the number and action of genes involved in type 1 diabetes, genetic studies have tended to focus on approaches that require few assumptions about the underlying model of disease risk.The two primary approaches have been linkage studies (using pairs of affected relatives, typically siblings) and association studies (using either case-control or family-based designs).Linkage studies using affected sibling pairs seek to identify regions of the genome that are shared", + "Environment The second factor in Figure 1 is environmental aspects.An important concept is the diabetes genotype typically causes only a predisposition for glucose intolerance (note the terminology susceptibility gene was used in the preceding paragraphs).Whether one develops the diabetes phenotype depends on environmental factors, some obvious in how they act, others less so.For instance, the Nurses Health Survey showed positive associations between obesity and lack of physical activity in the development of type 2 diabetes (as expected), but also protection by not smoking and moderate alcohol intake (14).Already discussed, many studies have shown an association between TV watching, high calorie diets, and lack of physical activity with risk of diabetes, i.e., our modern lifestyle, so it is not surprising that there is an explosion in the incidence of diabetes worldwide.", + "The genetics of type 1 diabetes There is a strong genetic risk to T1D.This is exemplified by (Redondo et al., 2001) who demonstrated a strong concordance of genetic inheritance (65%) and T1D susceptibility in monozygotic twin pairs.That is, when one sibling is afflicted, there is a high probability that the other twin will develop T1D by the age of 60 years.Additionally, autoantibody positivity and islet destruction was observed after a prospective long-term follow-up of monozygotic twins of patients with T1D, despite initial disease-discordance among the twins (Redondo et al., 2008).", + "Type 1 diabetes has unusual epidemiological features related to gender Type 1 diabetes also displays unusual patterns of inheritance that may yield insights into etiology and provide clues to the best methods for analyzing genetic studies.The risk to the offspring is generally greater from a mother or father who was diagnosed at an early age (again suggesting that early-onset cases are more heavily genetically 'loaded').However, the risk of diabetes is approximately two to four times higher for a child whose father has type 1 diabetes than one whose mother is affected [see (52,53) and references therein].This parental difference is largely due to a low risk for offspring of mothers who were diagnosed at a later age (53).The difference could be explained by at least three different factors.First, the risk alleles could only be active when transmitted by the father (such as is seen in imprinting, where only one of the parental alleles is expressed).Alternatively, a maternal environmental factor during pregnancy could be protective.However, it is difficult to see how this protective effect would be restricted to mothers diagnosed at a later age, especially since the protective effect was unrelated to the mother's duration of diabetes or even diabetic status at delivery (53).Finally, mothers who are diagnosed at a later age could represent more 'environmental' cases of diabetes, and thus be less likely to pass on risk genes to their offspring.", + "Type 1 diabetes is a genetic disease Family studies have indicated that genetic factors are important determinants of type 1 diabetes risk.First, the risk to a sibling of an affected individual is approximately 6%, as compared with an average risk of 0.4% (depending on the population), or a relative increased risk of 15-fold (17).The increased risk to siblings is referred to as l s (18) and is one measure of the degree of familial clustering of the disease.", + "Family and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.", + "Family and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.", + "Genes affecting type 1 diabetes diagnosis age / A. Syreeni et al.", + "Thus, the most likely scenario is that these genes are more poised for activation in the case group compared with the control group, contributing to various diabetes complications in the long term.This could be a consequence of the early exposure to hyperglycemia (measured by HbA 1c level), which is known to be associated with increased rates of long-term diabetes complications.", + "The risk for T1D is strongly influenced by multiple genetic loci and environmental factors.The disease is heritable, with first-degree relatives of patients with T1D being at 15-fold greater risk for developing the condition than the general population.", + "Genetic Background and Environment Both type 1 and 2 diabetes as well as other rare forms of diabetes that are directly inherited, including MODY and diabetes due to mutations in mitochondrial DNA, are caused by a combination of genetic and environmental risk factors.Unlike some traits, diabetes does not seem to be inherited in a simple pattern.Undoubtedly, however, some people are born prone to developing diabetes more so than others.Several epidemiological patterns suggest that environmental factors contribute to the etiology of T1D.Interestingly, the recent elevated number of T1D incidents projects a changing global environment, which acts either as initiator and/or accelerator of beta cell autoimmunity rather than variation in the gene pool.Several genetic factors are involved in the development of the disease [127].There is evidence that more than twenty regions of the genome are involved in the genetic susceptibility to T1D.", + "Type 1 Diabetes The higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk.", + "Genetics Both type 1 and type 2 diabetes are polygenic diseases where many common variants, largely with small effect size, contribute to overall disease risk.Disease heritability (h 2 ), defined as sibling-relative risk, is 3 for type 2 diabetes and 15 for type 1 diabetes (17).The lifetime risk of developing type 2 diabetes is ;40% if one parent has type 2 diabetes and higher if the mother has the disease (18).The risk for type 1 diabetes is ;5% if a parent has type 1 diabetes and higher if the father has the disease (19).Maturity-onset diabetes of the young (MODY) is a monogenic disease and has a high h 2 of ;50 (20).Mutations in any 1 of 13 different individual genes have been identified to cause MODY (21), and a genetic diagnosis can be critical for selecting the most appropriate therapy.For example, children with mutations in KCJN11 causing MODY should be treated with sulfonylureas rather than insulin.", + "Type 1 diabetes as well as type 2 diabetes shows a genetic predisposition, although only type 1 diabetes is HLA dependent [32,33,36,40].", + "Genetic factors have an important role in the development of diabetes, with some forms of the disease resulting from mutations in a single gene.Others are multifactorial in origin.The monogenic forms of diabetes account for approximately 5% of cases and are caused by mutations in genes encoding insulin 3 , the insulin receptor 4 , the glycolytic enzyme glucokinase 5 , and the transcription factors hepatocyte nuclear factor-1\u03b1 (HNF-1\u03b1), HNF-1\u03b2, HNF-4\u03b1, insulin promoter factor-1 and NeuroD1/BETA2 (refs 6-10).Mutations in maternally inherited mitochondrial genes can also cause diabetes, often in association with hearing loss 11 .", + "Studies [71][72][73][74] in Mexican and Asian populations have identified several mutations associated with type 2 diabetes in young people.The high prevalence of type 2 diabetes in the parents of young people diagnosed with type 2 diabetes could reflect a stronger genetic predisposition, even when monogenic diabetes is excluded.This hypothesis suggests that efforts to define genes that cause type 2 diabetes by linkage might be more powerful if focused on young adults with diabetes, raising the question of whether type 2 diabetes in older populations has a relatively smaller genetic contribution and a stronger environmental contribution. 66", + "We found that the presence or absence of parental diabetes and the genotype score were independently associated with the risk of diabetes.This suggests that family history as a risk factor for diabetes conveys more than heritable genetic information; it probably includes nongenetic familial behaviors and norms.The lower relative risks for diabetes associated with observed parental diabetes as compared with those associated with self-reported family history (approximately 1.8 vs. approximately 2.2) support the contention that family history contains more risk information than is implied by inheritance of the diabetes phenotype alone.One of the limitations of our study is that the 18 SNPs we included are probably insufficient to account for the familial risk of diabetes.They account for a minority of diabetes heritability, and the SNP array platforms from which they were chosen capture only approximately 80% of common variants in Europeans.In addition, we have not considered structural variants that might confer a risk of diabetes.It is possible that the addition of rare risk alleles with large effects, or a much larger number of common risk alleles with small individual effects, could improve discrimination. 36Indeed, as many as 500 loci may underlie the genetic risk of type 2 diabetes. 16Also, we did not study interactions among genes or between genes and the environment that might alter the genetic risk in exposed persons.As more diabetes risk variants become known, their incorporation into the genotype score may explain more of the genetic risk implied by parental diabetes." + ], + [ + "The prevalence of diabetes mellitus worldwide is sobering; the International Diabetes Federation estimates that 415 million people have diabetes mellitus, with 90% of these individuals having type 2 diabetes mellitus (T2DM) 1 .T2DM occurs when pancreatic \u03b2-cells fail to release enough insulin to meet the demands of insulin-responsive tissues, which safely store and metabolize glucose.Driven by both genetic and environmental risk factors, T2DM is a complex, multifactorial disorder.Although the increasing prevalence of T2DM is driven by changes in our environment and mirrors the increase in obesity, the greater concordance for T2DM found in monozygotic compared with dizygotic twins has long provided evidence for a genetic component in T2DM risk 2 .", + "In the UK alone, nearly 1.8 million people are already recognized to have this disorder (consuming w5% of the total National Health Service budget), and the search is on to find the 'missing million' who are living with the condition but in whom the diagnosis has yet to be made. 3In the USA, the situation appears to be even more serious with some commentators predicting that one in every three Americans born in the year 2000 will go on to develop diabetes during their lifetime, bringing unprecedented costs in terms of healthcare dollars as well as human morbidity and mortality. 4The majority (w90%) of these cases will be type 2 in origin, reflecting a trend towards obesity and more sedentary lifestyles as the 'norm' rather than the exception in 'developed' societies.Indeed, the face of T2DM is changing, as a condition that was once considered the preserve of middle/old age is increasingly diagnosed in young adults and even children, reflecting the high rates of obesity (and, in particular, visceral adiposity) in these populations.", + "Table 1 lists the various subtypes of diabetes based on the classification suggested by the ADA [4].", + "The ADA lists four subtypes of diabetes based on the clinical symptoms at time of presentation, [4] namely, Type 1 diabetes, Type 2 diabetes (T2D), gestational diabetes, and diabetes due to specific causes (genetic defects causing deficient insulin secretion or action, diseases of pancreas, use of certain drugs such as steroids, thiazides among others).Of these, T2D is the most prevalent (close to 90% of all cases) and is the major cause of morbidity and mortality in both developed and developing nations [1].At times it is difficult to assign a patient to a particular subtype due to the difference in conditions associated with hyperglycemia at the time of diagnosis [4,7].For example, a lady diagnosed with gestational diabetes mellitus during pregnancy is highly susceptible to develop T2D later.Therefore, other than proper treatment during and post pregnancy, a regular follow-up is required for stratifying disease risk, and for timely management before progression to another subtype.It is clear that the classification of diabetes may not be as simple as just categorizing it into any one of the four given subtypes due to its miscellaneous nature.Every case needs to be considered at the time of presentation, on the basis of the risk factors or underlying cause of hyperglycemia, the clinical symptoms, and disease prognosis.", + "Introduction Globally, diabetes affects more than 400 million people (World Health Organization, 2016), with Type 1 (insulin-dependent) diabetes (T1D) accounting for up to 10 percent of cases (American Diabetes Association, 2009).In the United States, T1D occurs at a rate of 15-30 cases per 100,000 children aged 0-14 years annually (International Diabetes Foundation, 2017;Maahs et al., 2010), with similar prevalence in Canada, Europe, Australia, and New Zealand (Fig. 1) (Derraik et al., 2012;International Diabetes Foundation, 2017;Maahs et al., 2010).By contrast, the estimated incidence rate of T1D among Asians, South Americans, and Africans is below 15 cases per 100,000 children (Fig. 1) (International Diabetes Foundation, 2017;Maahs et al., 2010).The global incidence of T1D has been rising by 3-5% per annum over the past two decades, with a notable increase in children below 10 years of age (Diamond Project, 2006;Patterson et al., 2009).", + "Animal Models 9.2% in women and 9.8% in men, with approximately 347 million people suffering from the disease worldwide in 2008 (Danaei et al., 2011).There are several different classifications of diabetes, the most common being type 1 and type 2 diabetes.", + "Type 2 diabetes is the most common type of diabetes with prevalence in the United Kingdom of around 4%.It is most commonly diagnosed in middle-aged adults, although more recently the age of onset is decreasing with increasing levels of obesity (Pinhas-Hamiel and Zeitler, 2005).Indeed, although development of the disease shows high hereditability, the risk increases proportionally with body mass index (Lehtovirta et al., 2010).Type 2 diabetes is associated with insulin resistance, and a lack of appropriate compensation by the beta cells leads to a relative insulin deficiency.Insulin resistance can be improved by weight reduction and exercise (Solomon et al., 2008).If lifestyle intervention fails, there are a variety of drugs available to treat type 2 diabetes (Krentz et al., 2008), which can be divided into five main classes: drugs that stimulate insulin production from the beta cells (e.g.sulphonylureas), drugs that reduce hepatic glucose production (e.g.biguanides), drugs that delay carbohydrate uptake in the gut (e.g.a-glucosidase inhibitors), drugs that improve insulin action (e.g.thiazolidinediones) or drugs targeting the GLP-1 axis (e.g.GLP-1 receptor agonists or DPP-4 inhibitors).", + "Introduction Diabetes impacts the lives of approximately 200 million people worldwide [1], with chronic complications including accelerated development of cardiovascular disease.Over 90% of cases are of type 2 diabetes (T2D), with the bulk of the remainder presenting with type 1 diabetes (T1D).", + "Classification of Diabetes On the basis of insulin deficiency, diabetes can be classified into the following types as follows.", + "| INTRODUCTION Today, more than 265 million people are affected across the world.It is estimated that by the year 2030 this number will reach 366 million people (about 4/4 percent of the world's population), and now the cause of death is more than 1.1 million per year (including 50% of the population under-70 years of age and 55% of women).On the other hand, given its negative effect on the economic growth of developing countries, it calls for universal mobilization to combat this disease (Bhattacharya, Dey, & Roy, 2007).Diabetes or diabetes mellitus is referred to as a heterogeneous group of metabolic disorders characterized by chronic hyperglycemia and carbohydrate, fat and protein metabolism disorders that result from a defect in the secretion of insulin, or impairment in its function, or both.Types of diabetes mellitus include type 1, type 2 diabetes and other kind of diabetes, but the two most common types of diabetes mellitus are type 1 and type 2, which are different in several aspects (Meshkani, Taghikhani, Mosapour et al., 2007).Type 1 diabetes has been identified with autoimmune destruction of pancreatic beta cells (insulin secreting cells) and accounts for about 5% of all diabetic people, while type 2 diabetes is a predominant disorder characterized by insulin resistance or a relative decline in insulin production, and accounts for about 90% of all types of diabetes mellitus (Meshkani, Taghikhani, Al-Kateb et al., 2007).Important factors that predispose a person to type 2 diabetes are multifactorial, including genetic factors and environments.However, its inheritance has certainly not been proven, but it is believed that first-degree relatives of diabetic patients have a higher chance to develop the disease.In this regard, recognizing gene polymorphisms of this disease seems to be necessary (H\u00e4ring et al., 2014).Multiple genes have been studied in the pathogenesis of type 2 diabetes.", + "CONCLUSIONS Diabetes is currently broadly classified as type 1, type 2, gestational, and a group of \"other specific syndromes. \"However, increasing evidence suggests that there are populations of individuals within these broad categories that have subtypes of disease with a well-defined etiology that may be clinically characterized (e.g., LADA, MODY).These developments suggest that perhaps, with more focused research in critical areas, we are approaching a point where it would be possible to categorize diabetes in a more precise manner that can inform individual treatment decisions.", + "Type 2 Diabetes In the U.S., an estimated 95% of the nearly 30 million people living with diabetes have type 2 diabetes.An additional 86 million have prediabetes, putting them at high risk for developing type 2 diabetes (9).Among the demographic associations for type 2 diabetes are older age, race/ ethnicity, male sex, and socioeconomic status (9).", + "Type 1 Diabetes Between 2001 and 2009, there was a 21% increase in the number of youth with type 1 diabetes in the U.S. (7).Its prevalence is increasing at a rate of ;3% per year globally (8).Though diagnosis of type 1 diabetes frequently occurs in childhood, 84% of people living with type 1 diabetes are adults (9).Type 1 diabetes affects males and females equally (10) and decreases life expectancy by an estimated 13 years (11).An estimated 5-15% of adults diagnosed with type 2 diabetes actually have type 1 diabetes or latent autoimmune diabetes of adults (LADA) (12).", + "Background Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous.A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis.", + "Diabetes mellitus now affects ~8% of the world's adult population [1], including ~3 000 000 individuals in the UK (with a further 600 000 people affected but presently undiagnosed) [2].Of these cases, > 90% have Type 2 diabetes.Treatments of the complications of the disease, which range from stroke, blindness and kidney failure to lower limb amputations and cancer, presently consume ~10% of the National Health Service budget, some \u00a314 bn per year [3].These figures are anticipated to increase further in the next 10 years, driven by increasingly sedentary lifestyles and increases in obesity; the collision between these 'environmental' factors and genetic susceptibility (see below) being the key underlying driver.Whilst existing treatments ameliorate the symptoms of the disease, notably hyperglyca-emia, none target the underlying molecular aetiology.In particular, no available treatments tackle the progressive and largely irreversible loss of insulin production [4] which, in the face of insulin resistance, underlies the progressive deterioration in glucose control.Reductions in b-cell mass [5,6] and dysfunction [7] both contribute to this gradual impairment in insulin release.Recent years have seen an increase in the view that the former may play a less important role than the latter, with a 2008 study by Rahier et al. [6] reporting that b-cell mass (and insulin content) in people with Type 2 diabetes was on average ~35% lower than that of healthy control subjects.However, this difference was only ~24% within 5 years of diagnosis, far below levels likely to lead to the symptoms of diabetes.Indeed, given our present inability to monitor b-cell mass prospectively over the course of the disease, it is conceivable that the differences observed post mortem between healthy individuals and those with Type 2 diabetes [5,6] may reflect an increased predisposition to diabetes in those born with a lower than average b-cell mass.", + "INTRODUCTION Type 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 (1).The disease usually involves insulin resistance but is ultimately the result of pancreatic b cell failure, a sine qua non for disease development (2).In contrast, Type 1 diabetes (T1D) affects a smaller proportion of people with diabetes and is chiefly the result of pancreatic b cell destruction mediated by immune cells (3).", + "Introduction Diabetes is a complex and heterogeneous disease with a staggering global impact and the most recent estimates indicate 346 million people worldwide suffer from this disease (WHO Diabetes Fact sheet No. 312, 2011).Type 2 diabetes mellitus (T2DM) is the most common form of diabetes, accounting for >90% of cases, and occurs when peripheral tissue insulin resistance accompanies insufficient b-cell insulin production.While >80% of diabetes deaths occur in low-and middle-income countries [1].India and China have the highest reported prevalence of diabetes with 65 and 98 million in 2013, respectively [2].", + "The disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition.", + "The disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition.", + "Introduction: Is Type 2 Diabetes a Genetic Disorder?According to the World Health Organization (WHO), approximately 350 million people worldwide have diabetes, and this disorder is likely to be the seventh leading cause of death in 2030.Diabetes is an economic burden on healthcare systems, especially in developing countries (World Health Organization, 2013)." + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/old/full_aging_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/full_aging_dataset.json new file mode 100644 index 0000000..9e2008f --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/full_aging_dataset.json @@ -0,0 +1,248 @@ +{ + "question": [ + "List as many studies are you can that include rapamycin", + "Why is it so difficult to map gene loci that control aging in humans?", + "How would one evaluate a candidate gene for mouse lifespan?", + "Do we find significant quantitative trait loci in aging studies, if so give the top three examples with descriptions?", + "What are the most fundamental human life expectancy extending geroscience studies?", + "List and describe the grand challenges related to aging research in genetics and genomics.", + "How do gene-by-drug effects (GxD) modulate life expectancies and health?", + "What loci and gene variants modulate normal variation in lifespan, and in which cases is this sex-specific (GxS)?", + "what is the influence of rapamycin and aging on the in vivo physiological and metabolic states and cellular bioenergetic, and how do these relate to the rapamycin-induced health and lifespan modification for that genometype?", + "Describe the genotypes related to alzheimers and dementia which have commonalities with those for aging?" + ], + "answer": [ + "1. A study assessing the impact of a rapamycin derivative on healthy aging in people (Mannick et al. 2014). 2. A veterinary clinical trial testing rapamycin as a treatment for osteosarcoma (Paoloni et al. 2010). 3. A veterinary clinical trial assessing the side effect profile and effects on age-associated cardiac function following 10 weeks of rapamycin treatment in healthy elderly dogs (Kaeberlein 2015). 4. A study on the pharmacokinetic analysis of rapamycin treatment in healthy dogs (Larson et al. 2016). 5. A study on the effects of rapamycin in aging mice (Johnson et al. 2015). 6. A study on the effects of rapamycin in rodents (Johnson et al. 2013). 7. A study on the effects of rapamycin on the replicative life of yeast (Reference [57]). 8. A study on the effects of rapamycin in 20-month-old mice (Reference [58]). 9. A study on the combination of rapamycin with other compounds for anti-aging activity (Reference [59]).", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and their interactions with each other and the environment. Secondly, the effects of common genetic variations on longevity are small, requiring large studies for identification. Thirdly, human studies often face issues such as environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of social, economic, and cultural factors, which are unique to humans, can't be replicated in traditional model organisms, adding to the complexity.", + "One would evaluate a candidate gene for mouse lifespan by conducting gene mapping methods and using mouse life span as a surrogate for aging. This involves studying the genetic contributions to mortality rates, conducting sex-specific analyses, and determining any changes in pathology associated with the loci. The study would involve a large population of mice, and the results would be compared to Mendelian expectations. Additionally, epigenetic predictors and differential methylation analysis could be used to assess the relationship between the gene and lifespan. The gene's influence on specific late-life diseases and its effect on the risk of these diseases would also be evaluated.", + "Yes, significant quantitative trait loci (QTLs) have been found in aging studies. Three examples include: 1. Lfdr1: This QTL was found on chromosome 7 and affects lifespan and fertility in response to dietary restriction. It also has suggestive effects on fuel efficiency (FE). 2. Fedr1 and Fedr2: These QTLs were identified on chromosomes 9 and 15 respectively. They have significant effects on fuel efficiency in response to dietary restriction. 3. QTLs associated with age-related thymic involution: These were identified in a study using C57BL/6 J X DBA/2 J (BXD) recombinant inbred (RI) strains of mice.", + "The most fundamental human life expectancy extending geroscience studies include genetic and genomic research into longevity. These studies use both hypothesis-driven candidate gene approaches and explorative genome-wide analyses. They also involve the study of biomarkers related to aging, deep phenotyping, and the use of novel technologies like next-generation sequencing. Additionally, they examine the role of environmental factors, lifestyle, and heritability in human lifespan variation.", + "The grand challenges related to aging research in genetics and genomics include: 1. Lack of awareness and interest: Health professionals, governments, and researchers often lack awareness and interest in genetic diseases, which hinders progress. 2. Limited funding and access to technology: Research funding is often limited, and access to high-end technology is not always available, which restricts the scope of research. 3. Economic and health disparities: These disparities can affect the application and benefits of genomic medicine, making it less accessible to those in lower income brackets. 4. Ethical, legal, and social issues: The interpretation, storage, and sharing of genetic data raise ethical and legal concerns, including issues of privacy and consent. 5. Rapid advancements: The fast pace of developments in genetics and genomics can outpace the ability to address emerging ethical, legal, and social questions. 6. Public knowledge and trust: There is a need to increase public knowledge about genomic research and address issues of trust in medicine and scientific research. 7. Integration of genomics into public health: This is a major future challenge for healthcare systems, requiring dialogue with all stakeholders in society.", + "Gene-by-drug effects (GxD) can modulate life expectancies and health by influencing how individuals respond to medications. Genetic variations can affect how drugs are absorbed, disseminated, metabolized, and excreted in the body, which can lead to differences in drug efficacy and the occurrence of adverse drug reactions. For example, a genetic variant in the HMG-CoA reductase gene can modify the LDL-C response to pravastatin. Additionally, certain genes are involved in DNA damage repair and oxidative stress, which can influence susceptibility to adverse drug effects. Therefore, understanding these genetic variations can help in personalizing treatment and potentially improving health outcomes.", + "The study identified 10 genomic loci which influence healthspan, parental lifespan, and longevity. These include loci near FOXO3, SLC4A7, LINC02513, ZW10, and FGD6. The life-extending variant near FOXO3 is associated with a delay in the age at menarche, suggesting a possible sex-specific effect. However, the study did not perform sex-stratified analyses, so it's unclear if there are other sex-specific longevity-related genetic variants.", + "Rapamycin, an mTOR inhibitor, has been shown to extend the lifespan of various organisms, including mice, by delaying or reversing aging in multiple cell types. It does this by regulating rates of protein synthesis and energy utilization, which are processes associated with aging. In the hematopoietic system, rapamycin limits age-related increases in stem cells and biomarkers of aging, enhancing the performance of these cells. However, rapamycin also has potential unwanted metabolic effects, such as insulin resistance and glucose intolerance, though these effects are controversial and potentially reversible. The mTOR pathway, which rapamycin targets, integrates signals from insulin, cytokines, nutrients, oxygen, and mitogenic stimuli, and its regulation has implications for longevity and against the negative effects of aging. Rapamycin also induces autophagy, a process important for cellular homeostasis and damage prevention. Despite these benefits, the exact mechanisms by which rapamycin extends lifespan and whether it delays aging or affects specific diseases remain unclear.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset Alzheimer's disease. The APOE gene is the strongest genetic risk factor for later onset Alzheimer's. The heritability of late-onset Alzheimer's disease (LOAD) is estimated to be ~60-80%, suggesting a large proportion of individual differences in LOAD risk is driven by genetics." + ], + "contexts": [ + [ + "Intervention trials and cell-based monotherapy", + "Rapamycin has been shown to be well tolerated in dogs, improves outcome in a glycogen storage disease model (Yi et al. 2014), and is currently being tested in veterinary clinical trials as a treatment for osteosarcoma (Paoloni et al. 2010).A veterinary clinical trial is underway to assess the side effect profile and effects on age-associated cardiac function following 10 weeks of rapamycin treatment in healthy elderly dogs (Kaeberlein 2015).", + "Rapamycin is used clinically to prevent organ transplant rejection, for some forms of cancer, and to prevent restenosis in cardiac stents (Kaeberlein 2013b).Shortterm treatment with the rapamycin derivative RAD001 improves ageassociated decline in immune function, as measured by antibody response to an influenza vaccine, in healthy elderly people (Mannick et al. 2014).", + "To date, only one study has been performed assessing the impact of a rapamycin derivative on healthy aging in people.In this trial, it was observed that 6 weeks of treatment with the rapamycin derivative RAD001 (everolimus) was sufficient to enhance function of the aged immune system, as assessed by response to an influenza vaccine (Mannick et al. 2014).This recapitulates what was observed in elderly mice (Chen et al. 2009), and suggests that at least some of the mechanisms by which rapamycin delays aging in mice work similarly in humans.Although both compounds have essentially identical biological activities, RAD001 was used in this study instead of rapamycin because the study was funded by Novartis, who holds the patent rights for RAD001 (rapamycin is now off patent and sold as a generic drug).The doses of RAD001 used in the human immune aging study were lower than those typically used to prevent organ transplant rejection and showed improved side effect profiles, although some adverse effects, including the presence of mouth sores in a subset of the patients, were noted.", + "This trial is designed to determine whether treatment with the drug rapamycin (see Table 1) can significantly reduce age-related disease and disability as well as mortality in middle-aged large dogs.The initial phase of this trial, which is in progress at the time of this writing, is intended to enroll at least 32 dogs 6 years of age or older and 40 lb in weight or greater.Each animal receives an initial veterinary exam and comprehensive blood work along with a cardiac exam including echocardiography (Fig. 3).Those dogs that do not present with any abnormalities or significant pre-existing health conditions are randomized into either placebo or rapamycin treatment groups for a 10-week treatment period.Initial rapamycin dosing regimens were determined, in part, based on pharmacokinetic analysis of rapamycin treatment in healthy dogs (Larson et al. 2016).After 10 weeks in the study, each dog receives another full exam and blood chemistry panel as well as repeat cardiac exam.The primary goals of this first phase are to establish appropriate dosing of rapamycin in the absence of significant adverse events and to determine whether similar improvements in cardiac function are achieved in aged dogs after 10 weeks of rapamycin treatment, as has been observed in laboratory mice (Dai et al. 2014;Flynn et al. 2013).", + "Fig. 3 Design of the current short-term rapamycin intervention trial.Dogs must weigh at least 40 pounds and be at least 6 years old at time of entry into the study.If no significant pre-existing health conditions are detected at the first exam, dogs are randomized into either placebo or one of the rapamycin treatment groups.Red indicates the 10-week period during which the dogs receive either rapamycin or placebo.Dogs receive the same generic rapamycin (sirolimus) pill that is provided to human patients.Asterisk Serum and feces are collected at each appointment for future metabolomic and microbiome analyses and for quantitation of circulating rapamycin levels", + "Pending the outcome of phase 1, we anticipate enrolling several hundred additional dogs with similar entry criteria into a longer-term, 3-5 year study, to carefully assess the extent to which rapamycin improves health and reduces mortality in middle-age companion dogs.In addition to cardiac function, assessments of multiple age-related phenotypes will be performed including measures of cognitive function, muscle function, kidney function, glucose homeostasis, and cancer incidence.Many of these parameters are beneficially impacted by rapamycin in aging mice (Johnson et al. 2015), and we predict that rapamycin will induce similar improvements in aging dogs.", + "Rapamycin is currently the most effective pharmacological intervention for extending lifespan and delaying a broad range of age-related functional declines in rodents (Johnson et al. 2013).However, the doses used clinically to prevent organ transplant rejection are associated with side effects, such as impaired wound healing, edema, elevated circulating triglycerides, impaired glucose homeostasis, gastrointestinal discomfort, and mouth ulcers (Augustine et al. 2007;de Oliveira et al. 2011).These adverse side effects would likely preclude long-term use of rapamycin at these levels in otherwise healthy people.With the possible exception of impaired glucose homeostasis (Lamming et al. 2012), these side effects have not been observed at doses that are associated with increased lifespan and healthspan in mice, however, raising the possibility that lower doses of this drug could promote healthy aging with minimal adverse effects.", + "Rapamycin Rapamycin is a macrolide isolated from Streptomyces hygroscopicus, a bacteria from Pascua Island (Rapa Nui).It has functions as an antibiotic, an immune suppressant drug, and it is also proposed as a CRM.After the first studies, it was found that rapamycin could induce the extension of the replicative life of yeast through the inhibition of TOR signaling [57].This compound could extend the lifetime useful in 20-month-old mice in correlation with TOR activity [58].These studies were the basis of the research to determine the function of rapamycin as a CRM, due to its modulating properties over proteostasis.In addition, studies suggest that rapamycin can be combined with other compounds (metformin, losartan, statins, propranolol, and aspirin among others) to potentiate their anti-aging activity [59].", + "Rapamycin Rapamycin is a macrolide isolated from Streptomyces hygroscopicus, a bacteria from Pascua Island (Rapa Nui).It has functions as an antibiotic, an immune suppressant drug, and it is also proposed as a CRM.After the first studies, it was found that rapamycin could induce the extension of the replicative life of yeast through the inhibition of TOR signaling [57].This compound could extend the lifetime useful in 20-month-old mice in correlation with TOR activity [58].These studies were the basis of the research to determine the function of rapamycin as a CRM, due to its modulating properties over proteostasis.In addition, studies suggest that rapamycin can be combined with other compounds (metformin, losartan, statins, propranolol, and aspirin among others) to potentiate their anti-aging activity [59].", + "One out of the 25 FDA approved Breast cancer drugs (Gemcitabine), was found in the top 20 drug list from LINCS from breast cancer stage I (dark magenta). As shown in Fig. 12, one drug out of 25 FDA approved Breast cancer drugs, Gemcitabine, was found as repurposed drug from LINCS for breast cancer stage III. Letrozole (Breast cancer drug) has similar structure (greater than 60%) with Ruxolitinib (repurposed drug from LINCS) a drug for the treatment of intermediate or high-risk myelofibrosis (Fig. 13).", + "One out of the 25 FDA approved Breast cancer drugs (Palbociclib), was found in the top 20 drug list from LINCS from breast cancer stage II (deep pink). Scientific Reports | 6:20518 | DOI: 10.1038/srep20518 13 www.nature.com/scientificreports/ Figure 11. Highlighted target genes that physically interact with genes from the breast cancer stage II common network pattern and their corresponding repurposed drugs from LINCS, along with their structurally similar Breast cancer drugs. As shown in Figs 16\u201317 two target genes (TOP2A and TYMS) are also involved in the Triple Negative pattern.", + "Two of them (Gemcitabine and Palbociclib) are included in the list of the 25 known FDA-approved Breast cancer therapeutic drugs. We performed a Hypergeometric distribution test in order to find the statistical significance of this drug overlapping. More precisely, LINCS_L1000 database is comprised from 20,413 chemical reagents. Twenty two out of twenty five breast cancer drugs are also included in LINCS database. Finally, from the 105 drugs that were found from our analysis, the probability of finding two drugs to overlap with the Breast Cancer drugs in LINCS is 0.005471157, pointing out that there is statistical significance in their selection.", + "Two from the 25 FDA approved Breast cancer drugs (Gemcitabine and Palbociclib), was found in the top 20 drug list from LINCS from Luminal A breast cancer (dark magenta and deep pink respectively).", + "18 two drugs out of 25 FDA approved Breast cancer drugs \u2013 Gemcitabine and Palbociclib \u2013 were also found as repurposed drugs from LINCS for breast cancer Luminal A (Fig. 18). Two genes from the Luminal A network pattern physically interact with four genes that involved in Histone deacetylases class (HDAC1, HDAC2, HDAC3 and HDAC8), which are target genes of Vorinostat (repurposed drug from LINCS). Vorinostat is a member of a larger class of compounds that inhibit histone deacetylases (HDAC) and it is used to treat cutaneous T cell lymphoma (CTCL).", + "One out of the 25 FDA approved Breast cancer drugs (Gemcitabine), was found in the top 20 drug list from LINCS from breast cancer stage III (dark magenta). that was found from the drug repurposing analysis of HER2 pattern. It has similar structure - 75% with WZ-4002 repurposed drug, which is a novel mutant-selective inhibitor of EGFR. Finally, both Palbociclib and WZ-4002 are structurally similar to Dasatinib (more than 60%), which is a cancer drug used to treat acute lymphoblastic leukemia.", + "Network pattern for each breast cancer subtype and the common interactions across Luminal A and Luminal B. As shown in Fig. 8, one drug out of 25 FDA approved Breast cancer drugs, Gemcitabine, was proposed as repurposed drug by the LINCS for breast cancer stage I. Furthermore, Gemcitabine is quite similar (tanimoto31 similarity greater than 80%) with Clofarabine and Kinetin-riboside (repurposed drugs from LINCS). Clofarabine is also an anti-cancer, antineoplastic chemotherapy drug and is classified as an antimetabolite.", + "Hierarchical clustering using tanimoto similarity (Soergel distance) was applied to each of the top 20 drug list from LINCS and the 25 known FDA-approved Breast cancer therapeutic drugs (Supplementary Figs 54\u201361). LINCS Drug Names were transformed into ChemSpider IDs (see Supplementary Table 1) In synopsis, the unique drugs for the breast cancer stages were 63 and for the breast cancer subtypes 58, as we have located common drugs across them. Taking their union and removing the duplicates we conclude to a total of 105 repurposed drugs.", + "13, is also structurally similar (greater than 60%) with 6-(1,3-Benzodioxol-5-yl)-N-(cyclopentylmethyl)-4-quinazolinamine (repurposed small molecule from LINCS). As in breast cancer stages I and III one drug out of 25 FDA approved Breast cancer drugs \u2013 Gemcitabine \u2013 was found as repurposed drug from LINCS for breast cancer stage IV (Fig. 14). A repurposed drug from LINCS \u2013 Homoharringtonine was found to be structurally similar with Everolimus and Vinblastine Breast cancer drugs (greater than 70%). On the other hand, as shown in Fig.", + "Rapamycin has serious side effects, particularly as an immunosuppressor, and thus it is not suitable as an antiaging drug.As in sirtuins, however, these studies highlight the road from basic discovery on the biology of aging to antiaging interventions.Further studies of the TOR pathway and of repressors more specific of its downstream signaling pathway are ongoing.Whether rapamycin produces a change in another parameter related to energy uptake or utilization is unknown, and determining which of its effects modulate lifespan is an important unsolved question.Like resveratrol, TOR has attracted considerable attention from the pharmaceutical industry, particularly in the context of cancer (Meric-Bernstam and Gonzalez-Angulo, 2009)." + ], + [ + "FUTURE DIRECTIONS: HIGHER RESOLUTION DATA VIA HIGHER THROUGHPUT ASSAYS One inescapable conclusion of the aggregate results of genome-wide studies of aging to date (see summary Table 1) is that we have not come close to saturating the number of potentially lifespan-altering genes in any organism.This is in no small part because directly generating survival curves is a relatively time-consuming process in most model organisms using current methods.There are several possible ways to address this.One way that has been tried is by attempting to find surrogate phenotypes [72,73,126] that can be screened more rapidly, or even scored under selection.Another is mining candidates from the many whole-genome expression profiles.Results to date with these have been very fruitful, but have not suggested that these methods alone will rapidly saturate our search for lifespan-and healthspan-altering genes in tractable model organisms.", + "Genetic linkage studies of long-lived human families identified a longevity locus while candidate gene approaches have been used to identify and confirm the association between specific variants in the FOXO3A gene and human longevity [3\u20137]. Genome-wide association studies have also been used to identify the association of APOE with life 123 Aging Clin Exp Res span and have yielded insights into potential biological pathways and processes related to aging. Despite these successes, several problems are inherent in human longevity studies including potentially high degrees of environmental heterogeneity, genetic diversity, and lack of birth matched controls, among others [8].", + "Additional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process.", + "The aging process most certainly is under highly polygenic controls\u2026 This should not discourage us from pursuing a search for those loci which may be of profound importance to human aging as it ordinarily occurs in most human beings.", + "In most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes.", + "1993), and gene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not provide very useful evidence with respect to the question of the number of loci that affect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of evolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now amenable to the application of genomic methods.", + "Accepted Article \u00a9 2013 The Authors Aging Cell \u00a9 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland over 90 years and 1,955 controls between 55 and 80 years did not reveal genome-wide significant loci (Newman et al., 2010) and neither did the analyses of all-cause mortality and survival free of major disease in this cohort (Walter et al., 2011).A smaller Dutch study of 403 nonagenarians and 1,670 controls younger than 65 years identified the APOE gene as a mortality locus (Deelen et al., 2011), which was confirmed in a German study of 763 long-lived individuals and 1,085 younger controls (Nebel et al., 2011) and a longitudinal study of 1,606 Danes showed that the effect size of this association increases at the highest ages (Jacobsen et al., 2010).Apparently, the influence of the common genetic variation on longevity is small which requires large meta-GWA studies for identification.Alternatively, rare genetic variants may play a more important role in longevity.Since the previous linkage studies showed contradictory results potentially due to heterogeneity in the longevity phenotype, it is expected that longevity is influenced by many private rare variants.", + "The lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010).", + "Several explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect.", + "Second, the largely negative findings of this and other studies contrast with the intriguing animal studies of longevity.Very large effects of single genes on lifespan have indeed been observed in laboratory animals, but humans often have several homologues of these genes which might significantly differ in function or compensate for mutated genes through redundant mechanisms (Kuningas et al., 2008).This could explain why our top findings did not include genes in these pathways found in animal models.Animal models also represent genetically homogenous populations and are exposed to controlled environmental influences.The lack of replication of animal model findings in humans suggests that the use of knockout animals may not provide the optimal approach to understanding the variation in survival in humans as interactions with environmental factors may obscure the associations and prevent the identification of loci in humans.", + "1993), and gene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not provide very useful evidence with respect to the question of the number of loci that affect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of evolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now amenable to the application of genomic methods.", + "The remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha \u02dces et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design.", + "Results from mutational analysis across eukaryote model organisms have shown unexpected conservation of genes and processes regulating aging.While unique properties exist within particular organisms that modulate these foundational networks, the conservation provides a tool to refine human genetic studies.As noted, GWAS for human longevity metrics suffer from large sample size requirements to obtain statistical resolution due to multiple hypothesis testing across the genome.Assuming that evolutionary genesets for longevity could be generated with confidence, an intersection of them with human variation data would increase the sensitivity of association studies.This would serve as a selective filter to refine the number of loci investigated for association in human populations.Similarly, such evolutionary filters could refine analysis of rare, unique variation within genome sequence data from extremely long-lived cohorts.A similar approach to refine human longevity GWAS used an intersection with age-related disease datasets.This 'disease-informed' GWAS helped refine candidates (iGWAS, Fortney et al., 2015), though, it should be noted that this particular strategy would further blur the distinction between aging and longevity as discussed above.The definition of gene sets from evolutionary experiments in longevity, across clades, would similarly empower detection of networks previously hidden under GWAS in human population analyses (Figure 3).", + "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY Heritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE \u03b52), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans.", + "TRANSLATION OF LONGEVITY MODEL ORGANISMS AND CORE AGING PATHWAYS Genetic studies on lifespan have proven to be challenging.While longevity is a defining trait for a given species, the lifespan of individuals is of limited heritability, making analyses more difficult.Exceptional human life span, although a rare phenotype, is likely multifactorial; refined analyses are required to obtain statistically robust genomic signatures of longevity (Zhang et al., 2020) and these have proven elusive.Unlike laboratory models, the effect of environmental variance cannot be controlled in human studies, potentially masking purely biological aging mechanisms.Even laboratory models cannot replicate the complex \"environment\" of humans; it includes psychosocial, economic, and cultural factors, rather than strictly biological.These human-specific confounders are difficult or impossible to target in traditional model organisms.Despite these limitations, experimentally tractable model organisms have proven invaluable in deciphering the purely genetic contribution to lifespan, including genes and pathways conserved across the tree of life.", + "Our analyses show that it is extremely unlikely that there is a single gene harboring rare protein-altering variants shared by all supercentenarians but no controls.It is not surprising that a highly complex trait such as longevity is not explained by a single Mendelian gene.", + "With modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms.", + "Although the models data set comprises all genes (to our knowledge) shown by the time of the latest update to statistically increase longevity or alter the aging process in a noticeable way, in the human data set we try to evaluate whether a given intervention is affecting the aging process itself or not.For example, many mutations may increase longevity by decreasing the incidence of specific diseases, rather than by altering the basic process of aging (de Magalh\u00e3es et al ., 2005a(de Magalh\u00e3es et al ., , 2005b)).Therefore, the human data set is not merely an extension of the work conducted in model organisms and of its bibliography, but a manually selected list of the most pertinent human aging candidate genes, each presented with a higher annotation level.We cite studies on whether the functions of aging-associated genes in model organisms are conserved in their human orthologues.Likewise, we cite flaws in previous studies based on new published observations, although we have a neutral stance on conflicting findings from different research groups.Our policy is to cite all conflicting reports and let visitors make their own decisions on how to interpret them.By contrast, each entry in GenAge model organisms has only one reference: the first publication reporting an association of the gene with longevity or aging.Moreover, one of the latest enhancements in the human data set was the inclusion of Gene Ontology annotation.Gene Ontology terms and annotation files were obtained from the Gene Ontology Consortium website (http://www.geneontology.org/ ) and provide an additional layer of description for the gene products in a cellular context (Ashburner et al ., 2000).", + "Conclusions and prospects Over the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges.", + "Most of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + ], + [ + "Funding: See page 22 Preprinted: 24 June 2021 Received: 03 November 2021 Accepted: 01 April 2022 Published: 07 April 2022 Reviewing Editor: Joris Deelen, Max Planck Institute for Biology of Ageing, Germany \u200d \u200dCopyright Mozhui et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. Editor's evaluation This article used three newly generated epigenetic predictors to test how they differ between genetically diverse mice from the BXD family (by looking at metabolic traits and lifespan).", + "Longevity data was obtained from a parallel cohort of BXD mice housed in the same UTHSC colony, and members of this \u2018longevity cohort\u2019 were allowed to age until natural death (more detail on the longevity cohort can be found in Roy et al. , 2021). Males were excluded and strain-\u00adby-\u00addiet lifespan summary statistics were derived. Only strain-\u00adby-\u00addiet groups with five or more observations for lifespan were included in the correlational analyses with the epigenetic predictors. Multivariable EWAS Site-\u00adby-\u00adsite differential methylation analysis (EWAS) was performed on the 27,966 CpGs using a multivariable regression model.", + "Funding: See page 22 Preprinted: 24 June 2021 Received: 03 November 2021 Accepted: 01 April 2022 Published: 07 April 2022 Reviewing Editor: Joris Deelen, Max Planck Institute for Biology of Ageing, Germany \u200d \u200dCopyright Mozhui et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. Editor's evaluation This article used three newly generated epigenetic predictors to test how they differ between genetically diverse mice from the BXD family (by looking at metabolic traits and lifespan).", + "Longevity data was obtained from a parallel cohort of BXD mice housed in the same UTHSC colony, and members of this \u2018longevity cohort\u2019 were allowed to age until natural death (more detail on the longevity cohort can be found in Roy et al. , 2021). Males were excluded and strain-\u00adby-\u00addiet lifespan summary statistics were derived. Only strain-\u00adby-\u00addiet groups with five or more observations for lifespan were included in the correlational analyses with the epigenetic predictors. Multivariable EWAS Site-\u00adby-\u00adsite differential methylation analysis (EWAS) was performed on the 27,966 CpGs using a multivariable regression model.", + "Conclusions These results suggest a novel locus influencing survival in the B6/D2 genetic background, perhaps via a metabolic disorder that emerges by 200 days of age in male animals. Keywords Pathology Longevity \u2401 Lifespan \u2401 Mouse \u2401 Linkage \u2401 Introduction Longevity, the quintessential complex trait, likely reflects all aspects of an organism\u2019s life history. In humans, the estimated heritability of age at death is estimated at 25\u201333 % [1]. Genetic contributions to mortality rates are thus of great interest and may aid in the understanding of disease etiology and the process of aging itself [2].", + "Leduc MS, Hageman RS, Meng Q et al (2010) Identification of genetic determinants of IGF-1 levels and longevity among mouse inbred strains. Aging Cell 9(5):823\u2013836. doi:10.1111/j.14749726.2010.00612.x 10. Lang DH, Gerhard GS, Griffith JW et al (2010) Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clin Exp Res 22(1):8\u201319 11. Gelman R, Watson A, Bronson R et al (1988) Murine chromosomal regions correlated with longevity. Genetics 118(4):693\u2013704 12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci associated with life span exhibit sex-specific and epistatic effects.", + "Here, we have extended this analysis to search for genotypes related to survival to the age of 800 days in a population of a reciprocal F2 cross between (B6) and (D2) mice. Since QTL for longevity in mice have shown strong sex specificity [10, 12], we conducted sex-specific analyses. In addition, we also determined whether there were any change in pathology changes associated with the loci that showed frequency distortions with aging. To confirm the associations of the loci of interest with longevity and pathology, we performed replication analyses on a panel of BXD recombinant inbred strains.", + "Methods We examined a population of 1200 mice that were F2 generation offspring of a 4-way reciprocal cross between C57BL6/J and DBA2/J strains. Animals were sacrificed at age 200, 500, or 800 days and genotyped at 96 markers. The 800 days old cohort, which were the survivors of a much larger breeding group, were examined for enriched frequency of alleles that benefit survival and depletion of alleles that reduce survival. Results Loci on Chr 13 in males and on Chr X in females were significantly distorted from Mendelian expectations, even after conservative correction for multiple testing.", + "Assessing epigenetic age in long-lived mice The epigenetic-aging model was applied to the methylation profiles of long-lived mice and the age-matched controls not used for training (Additional file 2: Datasets used summary).Reductions in age were calculated by subtracting the epigenetic ages of the untreated, wild-type mice from those of the treated mice of the same genetic background.To assess the significance, we used an ANOVA for all 22-month-old mice or only 22-month-old UM-HET3 mice.We also compared the epigenetic ages between treatments with their agematched controls from the same genetic background using a t-test (Additional file 4: Treatment vs wild type stats).", + "Editor's evaluation This article used three newly generated epigenetic predictors to test how they differ between genetically diverse mice from the BXD family (by looking at metabolic traits and lifespan).The authors subsequently identified several quantitative trait loci for the different predictors, using linkage analysis, and performed transcriptome and proteome analyses of liver and adipose tissue.The described results provide some important new insights on the underlying biology of epigenetic mouse aging and may be used to inform future studies in other model organisms and humans focused on studying the relationship between epigenetic aging and metabolism.", + "352(6291): p. aad0189. Liao, C.Y. , et al. , Genetic variation in the murine lifespan response to dietary restriction: from life extension to life shortening. Aging Cell, 2010. 9(1): p. 92-5. Johnson, M., Laboratory Mice and Rats. Mater. Methods, 2012. 2: p. 113. Fontaine, D.A. and D.B. Davis, Attention to Background Strain Is Essential for Metabolic Research: C57BL/6 and the International Knockout Mouse Consortium. Diabetes, 2016. 65(1): p. 25-33. Simon, M.M. , et al. , A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol, 2013. 14(7): p. R82. Lilue, J., et al.", + "Materials and Methods Study Design.Female mice of the long-lived F 1 hybrid strain C3B10RF1 were fed and maintained as described (7).Briefly, mice were weaned at 28 days, individually housed, given free access to water, and randomly assigned to study groups.Comparisons between five groups of mice were used to determine the effects of aging and CR on gene expression.Control young (7-month-old; n \u03ed 3) and old (27-month-old; n \u03ed 3) mice were fed 95 kcal of a semipurified control diet (Harlan Teklad, Madison, WI; no.TD94145) per week after weaning.Long-term CR (LT-CR) young (7-month-old; n \u03ed 3) and old (27-month-old; n \u03ed 3) mice were fed 53 kcal of a semipurified CR diet (Harlan Teklad; no.TD94146) per week after weaning.Short-term CR (ST-CR) mice were 34-monthold control mice that were switched to 80 kcal of CR diet for 2 weeks, followed by 53 kcal for 2 weeks (n \u03ed 3).The effects of age on gene expression in control mice were determined by comparison between results from the young control and the old control groups.The effects of LT-CR on gene expression were determined by comparison between results from the young control and the young LT-CR groups, and from the old control and the old LT-CR groups.The effects of ST-CR were determined by comparison between results from the old control and the ST-CR groups.Mice were fasted for 48 h before killing.Mice were killed by cervical dislocation, and the livers were rapidly excised and flash frozen in liquid nitrogen.No signs of pathology were detected in any of the animals used.All animal use protocols were approved by the institutional animal use committee of the University of California, Riverside.", + "Accessing data resources in the mouse phenome database for genetic analysis of murine life span and health span. J. Gerontol. A Biol. Sci. Med. Sci. 71 (2), 170\u2013177. Brown, R.E. , Stanford, L., Schellinck, H.M., 2000. Developing standardized behavioral tests for knockout and mutant mice. ILAR J. 41 (3), 163\u2013174. Bubier, J.A. , Jay, J.J., Baker, C.L. , Bergeson, S.E. , Ohno, H., Metten, P., Crabbe, J.C., Chesler, E.J. , 2014. Identi\ufb01cation of a QTL in Mus musculus for alcohol preference, withdrawal, and Ap3m2 expression using integrative functional genomics and precision genetics. Genetics 197 (4), 1377\u20131393. Burn, C.C. , 2008.", + "Our own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice.", + "The available dataset also provides examples in which genetic variants seem to influence the risk of specific late-life diseases.Figure 8-6, for example, shows longevity results for mice stratified by their inheritance at the 12th chromosome locus D12Mit167.This is a locus associated with differential longevity in both male and female mice, with the strongest effect (adjusted p < 0.01) seen in those mice living more than 657 days (Jackson et al., unpublished results).The longest-lived mice are those that inherit both the C57BL/6 allele from their mother and the C3H allele from their father; on average, they survive 93 days longer than siblings with the BALB plus C3H combination.Figure 8-6 shows that the D12Mit167, like the pair of loci illustrated in Figure 8-5, has significant and similar effects in mice dying of cancer (85 days) and in mice dying of non-neoplastic diseases (126 days).A more detailed analysis of the cancers, however, suggests that while lymphoma and hepatoma victims are equally protected by the favorable alleles (effect sizes of 93 and 167 days, respec- mice of two subgroups: those dying of the urinary syndrome MUS, and those dying of all other causes.The genetic analysis contrasts mice with both the C57BL/6 allele at D4Mit84 and the C3H allele at D9Mit110 to mice with any of the three other allele combinations.In the males dying of causes other than MUS, this allele pair is associated with a 170-day increment in longevity (post-hoc p < 0.00003).But for males that do die of MUS, the same allele combination is associated with a 187-day decline in mean life span (post-hoc p < 0.03).This effect is thus pleiotropic, in that these alleles accelerate death in mice susceptible to MUS, while postponing death for all other males in the population.Although these loci are associated with differential longevity in mice that do develop MUS, they do not have a significant effect on the chances that MUS will indeed occur (not shown).The risk of developing MUS seems to be under control of a separate locus on chromosome 6.As shown in the bottom panel of Figure 8-7, males that inherit the C3H allele at D6Mit268 are far more likely to develop MUS (28 percent risk) than are their brothers who receive the DBA/2 allele at this locus (7 percent risk; p = 0.012 by two-tailed Fisher's exact test).", + "Previously, the methylation status of CpG sites within the genes Prima1, Hsf4, Kcns1 was shown to qualify as a reliable predictor of chronological age of B6 mice.10 This same study also revealed enhanced epigenetic aging of the D2 strain in accordance with its general reduced mean life span, supporting the possibility that the panel might also serve as a marker for the biological age in mice. Applying this B6trained marker panel to our (congenic) experimental strains, we observed that epigenetic age predictions correlated with chronological age in B6 (R2=0.93) and line A mice (R2=0.89).", + "34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with longevity. Genetics 118, 693\u2013704 (1988). [PubMed: 3163317] 35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011). 36. Houtkooper RHet al.Mitonuclear protein imbalance as a conserved longevity mechanism. Nature497, 451\u2013457 (2013). [PubMed: 23698443] 37. Williams EGet al.An Evolutionarily conserved role for the aryl hydrocarbon receptor in the regulation of movement. PLOS Genet. 10, e1004673 (2014). [PubMed: 25255223] 38. Lang DHet al.Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clin. Exp. Res. 22, 8\u201319 (2010).", + "For females, hairs of the congenic mice grew 31% faster, also highly significant (P = 0.0006, 1-tailed). These results validated the presence of a gene in the differential region affecting FE. Discussion We report the outcomes of a quantitative genetic study on aging and longevity in the mouse. We studied an extant series of recombinant inbred strains (ILSXISS) that have been used both in DR aging studies as well as to study alcohol sensitivity (Williams et al. , 2004).", + "FOURTH STEP: MEDICAL TESTING OF CANDIDATE DRUGS Many genes are common between fruit flies and mammals, but by no means all.Therefore, it is important to test biochemical pathways that work in fruit flies with mammals.Mice are the system of choice, as they have relatively short lifespans (2 -3 years) and a great deal is known of their genetics.Mortality rate measurements, like those studied in fruit flies, [10] might speed up mouse trials to just 6-12 months.Mouse trials would also help address issues of safety, such as liver and kidney toxicity, before going on to human trials.", + "Experimental Procedures Mouse Breeding, Maintenance, and Longevity.Cdc42GAP \u03ea/\u03ea and p53 \u03ea/\u03ea mice were generated as previously described (6,35), and the mice used in the studies were mixed C57BL/6 \u03e9/\u03ea 129/Sv inbred.Littermates of different genotypes were housed and fed freely with standard mouse chow over their life span in a pathogen-free environment and were monitored for vitality and longevity.Mice exhibiting extreme morbidity were euthanized and subjected to necropsy.All animal procedures were approved by the Institutional Animal Care and Use Committee at the Children's Hospital Research Foundation." + ], + [ + "Genetic associations for two biological age measures point to distinct aging phenotypes. Aging Cell 20:e13376. DOI: https://doi.org/10.1111/acel.13376, PMID: 34038024 Lang DH, Gerhard GS, Griffith JW, Vogler GP, Vandenbergh DJ, Blizard DA, Stout JT, Lakoski JM, McClearn GE. 2010. Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clinical and Experimental Research 22:8\u201319. DOI: https://doi.org/10.1007/BF03324809, PMID: 20305363 Lappalainen T. 2015. Functional genomics bridges the gap between quantitative genetics and molecular biology. Genome Research 25:1427\u20131431.", + "Pharmacol Biochem Behav 81, 764\u2013768. Hsu, H.C., Lu, L., Yi, N., Van Zant, G., Williams, R.W. & Mountz, J.D. (2007) Quantitative trait locus (QTL) mapping in aging systems. Methods Mol Biol 371, 321\u2013348. Hurlin, P.J. & Huang, J. (2006) The MAX-interacting transcription factor network. Semin Cancer Biol 16, 265\u2013274. Jones, B.C. , Tarantino, L.M. , Rodriguez, L.A., Reed, C.L. , McClearn, G.E. , Plomin, R. & Erwin, V.G. (1999) Quantitative-trait loci analysis of cocaine-related behaviours and neurochemistry. Pharmacogenetics 9, 607\u2013617. Jones, B.C. , Beard, J.L. , Gibson, J.N. , Unger, E.L., Allen, R.P. , McCarthy, K.A. & Earley, C.J.", + "Genetic associations for two biological age measures point to distinct aging phenotypes. Aging Cell 20:e13376. DOI: https://doi.org/10.1111/acel.13376, PMID: 34038024 Lang DH, Gerhard GS, Griffith JW, Vogler GP, Vandenbergh DJ, Blizard DA, Stout JT, Lakoski JM, McClearn GE. 2010. Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clinical and Experimental Research 22:8\u201319. DOI: https://doi.org/10.1007/BF03324809, PMID: 20305363 Lappalainen T. 2015. Functional genomics bridges the gap between quantitative genetics and molecular biology. Genome Research 25:1427\u20131431.", + "Interestingly, the correlation analysis indicates QTL Mapping in Aging Systems 333 Fig. 5. Basic statistics provided by the WebQTL GeneNetwork website. The strain distribution pattern (SDP) of the quantitative trait is presented in the basic statistics page of WebQTL in the following ways: (A) the raw data of the quantitative trait obtained from each BXD recombinant inbred (RI) strain, (B) data mean and distribution, (C) bar graph showing the mean and variable of each strain, and (D) the normal probability plot of the SDP.", + "23 Quantitative Trait Locus (QTL) Mapping in Aging Systems Hui-Chen Hsu, Lu Lu, Nengjun Yi, Gary Van Zant, Robert W. Williams, and John D. Mountz Summary Understanding the genetic basis of the effects of aging on the decline in the immune response is an enormous undertaking. The most prominent age-related change in the immune system is thymic involution. This chapter will focus on the use of C57BL/6 J X DBA/2 J (BXD) recombinant inbred (RI) strains of mice to map genetic loci associated with age-related thymic involution in mice.", + "For further prioritization, we converted the mouse QTL regions to the corresponding syntenic regions in the human genome and retrieved GWAS annotations for these intervals (Buniello et al., 2019).We specifically searched for the traits: epigenetic aging, longevity, age of menarche/menopause/puberty, Alzheimer's disease, and age-related cognitive decline and dementia.This highlighted five genes in Eaa11 and three genes in Eaa19 (Supplementary file 4c).We also identified a GWAS that found associations between variants near Myof-Cyp26a1 and human longevity (Yashin et al., 2018), and a meta-GWAS that found gene-level associations between Nkx2-3 and Cutc, and epigenetic aging (Supplementary file 4c; McCartney et al., 2021).", + "Jiang, C. and Zeng, Z. B. (1995). Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140, 1111\u20131127. Jin, W., Riley, R. M., Wolfinger, R. D.et al. (2001). The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat Genet 29, 389\u2013395. Kempermann, G., Chesler, E. J., Lu, L. et al. (2006). Natural variation and genetic covariance in adult hippocampal neurogenesis. Proc Natl Acad Sci U S A 103, 780\u2013785. Kendziorski, C. M., Chen, M., Yuan, M. et al. (2006). Statistical methods for expression quantitative trait loci (eQTL) mapping. Biometrics 62, 19\u201327.", + "Hypothesis-free genome-wide approaches have also been undertaken.Genome-wide linkage scans reported evidence for linkage with longevity on chromosome 4q25 (Puca et al., 2001), 3p24-22, 9q31-34, and12q24 (Boyden &Kunkel, 2010).However, the evidence for these loci is still very weak as the results, obtained in centenarians and their families, could not be replicated in nonagenarian sibling pairs (Beekman et al., 2006) or have yet to be tested in other studies.A meta GWAS of survival to 90 years or older in 1836 cases and 1955 controls did not find any significant genome-wide associations (Newman et al., 2010).Thus far, hypothesis-free approaches have not identified any loci involved in longevity.", + "Abiola O, Angel JM, Avner P, Bachmanov AA, Belknap JK, Bennett B, et al. The nature and identification of quantitative trait loci: a community\u2019s view. Nat Rev Genet. Nature Publishing Group; 2003; 4: 911\u2013916. https://doi.org/10.1038/nrg1206 PMID: 14634638 18. Grupe A, Germer S, Usuka J, Aud D, Belknap JK, Klein RF, et al. In silico mapping of complex diseaserelated traits in mice. Science. American Association for the Advancement of Science; 2001; 292: 1915\u20131918. https://doi.org/10.1126/science.1058889 PMID: 11397946 19. Pletcher MT, McClurg P, Batalov S, Su AI, Barnes SW, Lagler E, et al.", + "coid levels, etc.The mapping project should thus help to guide the search for human genes that regulate these interesting phenotypes and at the same time spark new investigations, in animal models, for the biochemical differences that mediate the genetic effects we detect.At the same time, the dataset that emerges should also allow us to test more general questions about the nature of aging and its genetic control.We may, for example, be able to identify QTLs that not only retard the development of one or more age-sensitive T-cell subsets, but also retard age-dependent changes in protein conformation, bone matrix turnover, and brain GFAP levels.Such a finding would imply that these changes are influenced, together, by a common biochemical pathway, and the corresponding QTLs would be excellent candidates for genes that regulate aging per se, rather than merely one among the many more agesensitive traits.In the same way, it will be of particular interest to determine if QTLs that regulate age-sensitive traits also are associated with differences in life span, and conversely if QTLs identified on the basis of longevity effects modify one (or nearly all?) of the age-sensitive traits in our test battery.", + "The strategy for mapping such quantitative trait loci (QTL) involves looking for preferential segregation of specific alleles or allele combina-tions in mice that differ in life span (or, more generally, any age-sensitive trait of interest).Our test population, called UM-HET3, consisted of a group of mice bred as the progeny of females of the (BALB/c \u00d7 C57BL/6)F1 genotype and males of the (C3H/HeJ \u00d7 DBA/2)F1 genotype.Mice bred in this way are, from a genetic perspective, all siblings; each shares a random half of its alleles with every other animal in the UM-HET3 population.The current set of analyses was conducted when genotype and longevity data were available from a group of 110 virgin males and 143 virgin females.The analytical method adjusted, by permutation testing, for Type I errors attributable to the simultaneous evaluation of multiple linkage hypotheses, and also included gender as a covariate to look for instances of sex-specific genetic effects.Because we had particular interest in regulation of late-life diseases rather than in causes of premature death, and because of evidence that genetic influences on mouse longevity were particularly strong when early deaths were not considered (Covelli et al., 1989), we repeated each analysis after exclusion of those animals dying before 657 days of age, i.e., the age at which 20 percent of the animals had already died.", + "The proportion of the phenotypic variance accounted for by the QTL yield for Hbact and Hbrear was substantial and of the same order of magnitude as that contributed by age. A small number of age-dependent QTL were found in the midst of a majority of age-stable QTL (see discussion above). These age-sensitive loci point toward genes whose functions are correlated with important behavioral changes during aging.", + "Ageing genes and pathways.Assessing the loci of interest for colocalisation with gene expression quantitative trait loci (eQTL), we find strong evidence (FDR SMR < 5%; P HEIDI > 1%; see \"Methods\") of cis-acting eQTL colocalisation for eight out of 10 loci.In total, we highlight 27 unique genes acting across 32 tissues, especially whole blood (12 genes) and the tibial nerve (7 genes) (Supplementary Data 5).In blood, higher expression levels of BCL3 and CKM (near APOE); CTC-510F12.2, ILF3, KANK2 and PDE4A (near LDLR); USP28 and ANKK1 (near ZW10); and CDKN2B are linked to an increase in multivariate ageing traits (i.e.improved survival), while the opposite is true for EXOC3L2 (near APOE), TTC12 (near ZW10), and FOXO3.For the multivariate signal near SLC4A7 we find colocalisation with expression of NEK10 (liver); for the signal near LPA we find colocalisation with expression of SLC22A1/A3 (multiple tissues) and MAP3K4 (pituitary); and for the signal near FGD6 we find colocalisation with expression of FGD6 itself (adipose/arterial).Including trans-acting eQTL from blood, while keeping the same thresholds for colocalisation, we additionally discover higher expression levels of FOXO3B colocalises with the life-extending signal near FOXO3.When we include genes which could not be tested for heterogeneity (N eQTL < 3), we identify one additional cis-acting and 49 additional trans-acting genes (of which 10 colocalise with the signal near LINC02513) (Table 2; Supplementary Data 5).", + "Jiang, C. and Zeng, Z. B. (1995). Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140, 1111\u20131127. Jin, W., Riley, R. M., Wolfinger, R. D.et al. (2001). The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat Genet 29, 389\u2013395. Kempermann, G., Chesler, E. J., Lu, L. et al. (2006). Natural variation and genetic covariance in adult hippocampal neurogenesis. Proc Natl Acad Sci U S A 103, 780\u2013785. Kendziorski, C. M., Chen, M., Yuan, M. et al. (2006). Statistical methods for expression quantitative trait loci (eQTL) mapping. Biometrics 62, 19\u201327.", + "Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL? NIH-PA Author Manuscript Much of the genetic variation that underlies disease susceptibility and morphology is complex and is governed by loci that have quantitative effects on the phenotype. Gene-gene and geneenvironment interactions are common and make these loci difficult to analyse. Here, we present a community\u2019s view on the steps that are necessary to identify genetic loci that govern quantitative traits, along with a set of interpretive guidelines.", + "QTL Analysis in Hematopoiesis 47 3 Quantitative Trait Analysis in the Investigation of Function and Aging of Hematopoietic Stem Cells Hans-Willem Snoeck Summary Extensive genetically determined quantitative variation exists in the number and function of hematopoietic stem cells in inbred mouse strains. Furthermore, aging of hematopoietic stem cells is genetically determined. Gene identification of quantitative trait loci involved in the regulation and aging of hematopoietic stem cells would provide novel insights into regulatory mechanisms that are relevant in vivo and may be clinically important.", + "In order to find the causal loci for heritable differences in transcript levels and possible interactions between age and genotype, we applied a two-time-point model.In this model, we used three factors-(1) relative age, (2) genotype (marker), and (3) the interaction between factors 1 and 2-to explain the differences in gene expression between RILs and age groups.With this mapping procedure, we found almost 900 genes that had an eQTL or gxa eQTL in developing and/or aging worms (P < 0.0001; Fig. 2).Almost half of these genes with heritable transcript differences were found to have a genotype-by-age effect (396 at P < 0.0001; Table 1) allocated to a specific marker, which we coined genotype-by-age expression-QTL ( gxa eQTL).One specific hotspot (trans-band) for gxa eQTL was found on chromosome IV for aging worms and a trans-band for eQTL on chromosome I was detected in developing worms (Fig. 2).", + "NIH-PA Author Manuscript We found three significant QTLs (genetic regions harboring genes controlling these various aging traits, Supplementary Table 5). On chromosome 7, we found a QTL affecting lifespan and fertility after DR that we have named Lfdr1 for \u201clongevity and fertility response to dietary restriction, QTL 1; this QTL also has suggestive effects on FE (Fig. 5D). Two QTLs having significant effects on FE were identified on chromosomes 9 and 15. These we have named Fedr1 and Fedr2, respectively, for \u201cfuel efficiency response to dietary restriction\u201d QTLs 1 and 2.", + "Quantitative trait locus (QTL) mapping in aging systems. Methods in Molecular Biology (Clifton, NJ ). 2007; 371:321\u2013348. Hunter KW, Crawford NPS. The future of mouse QTL mapping to diagnose disease in mice in the age of whole-genome association studies. Annual Review of Genetics. 2008; 42:131\u2013141. Ito R, Robbins TW, Everitt BJ. Differential control over cocaine-seeking behavior by nucleus accumbens core and shell. Nature Neuroscience. 2004; 7:389\u2013397. [PubMed: 15034590] Kapp MB. Ethical and legal issues in research involving human subjects: do you want a piece of me? Journal of Clinical Pathology. 2006; 59:335\u2013339.", + "Jiang, C. and Zeng, Z. B. (1995). Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140, 1111\u20131127. Jin, W., Riley, R. M., Wolfinger, R. D.et al. (2001). The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat Genet 29, 389\u2013395. Kempermann, G., Chesler, E. J., Lu, L. et al. (2006). Natural variation and genetic covariance in adult hippocampal neurogenesis. Proc Natl Acad Sci U S A 103, 780\u2013785. Kendziorski, C. M., Chen, M., Yuan, M. et al. (2006). Statistical methods for expression quantitative trait loci (eQTL) mapping. Biometrics 62, 19\u201327." + ], + [ + "Introduction With the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging.", + "INTRODUCTION Human aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining \u223c20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging.", + "Introduction Geroscience refers to research aimed at understanding the mechanisms of biological aging (Kennedy et al. 2014).A major goal of geroscience is to define the genetic, epigenetic, and environmental features that determine individual rates of aging.From a translational perspective, a further goal is to use this knowledge to develop interventions that can slow or delay aging in order to promote healthy longevity and increase healthspan, the period of life spent in good health free from chronic disease and disability (Burch et al. 2014;Pitt and Kaeberlein 2015).", + "the maximum human life span.Several avenues to studying aging have placed us on Department of Biology Massachusetts Institute of Technology the threshold of understanding basic underlying mechanisms.These approaches include the identification of Cambridge, Massachusetts 02139 key genes and pathways important in aging; genetic studies of heritable diseases that cause the appearance of premature aging in affected people; physiological ex-Introduction periments that relate the pace of aging to caloric intake; Is aging the final act in the script of developmental bioland advances in human genetics, as well as cell and ogy?The characteristic changes that are part and parcel molecular biology leading to an understanding of the of aging appear similar to developmentally regulated basis of many diseases of aging.Strikingly, single gene programs.But why would aging mechanisms have been mutations have been found to significantly extend the evolutionarily selected as advantageous?Indeed, evolife span in C. elegans, yeast, and, most recently, Drolutionary biologists might argue that aging occurs by sophila, suggesting that aging may be relatively simple, default due to the absence of selection in the postreproat least in these organisms.Further, the limited replicaductive phase of life.By this view, the aging process is tion potential of human cells in culture has been attribnot programmed, but, rather, the detritus of the absence uted to a specific mechanism (i.e., the shortening of of selection for maintenance (Medawar, 1952; Kirkwood, telomeric ends of chromosomes).An important chal- 1977).However, it is quite reasonable that any mechalenge is now to relate these recent findings to the more nisms that sprang up to slow or regulate the pace of complex case of human aging.aging would be selected, because lucky individualsIn this review, we will discuss several important mocould potentially give rise to more progeny.Therefore, lecular models of aging that come from current research.it is reasonable to suppose that life span extending pro-These are damage by reactive oxygen species (ROS) cesses have been selected and that these can be viewed generated by metabolism, genome instability, genetias an elaboration of development itself.In principle, cally programmed extension mechanisms, cell death, such extension mechanisms may act to slow or forestall and systemic aging.Questions to be posed include the deleterious changes in an organism that progressively following.What evidence exists for and against these lead to death.The life span of an organism, therefore, models?Can more than one of these models apply to is the sum of deleterious changes and counteracting aging of different tissues in humans-specifically do repair and maintenance mechanisms that respond to organs with continually dividing cells age by the same the damage (Figure 1).mechanism as organs that are postmitotic?Finally, is A priori, one imagines such longevity mechanisms to aging amenable to therapeutic intervention, and would be much less complex than those regulating embryonic such intervention be advisable?development.The spatial and temporal constraints on embryonic development are many, while requirements Oxidative Damage for longevity mechanisms might be much more specific One theory of aging proposes that ROS which are generif there were a single process (or a few processes) whose ated by metabolism cause cumulative damage over a breakdown is the limiting event in longevity (i.e., the lifetime (Harman, 1981).Roughly two to three percent Achilles heel).of oxygen taken up is chemically reduced by the addition Aging is defined when two criteria are met.First, the of single electrons, which are sequentially converted probability of death at any point in time increases with into ROS, including the superoxide anion, hydrogen perthe age of the organism.This statistical definition applies oxide, and the hydroxyl radical.ROS have been shown from yeast to mammals and reflects the progressive to cause molecular damage relatively indiscriminately nature of aging.Second, characteristic changes in pheto proteins, lipids, and nucleic acids.In addition, specific notype occur in all individuals over time due to the limdamage has been observed in the mitochondrial DNA, iting processes.which we consider below in Genome Instability.The phenotypic definition is equally general and is What is the evidence that oxidative damage causes useful in distinguishing the aging process itself from aging?One category of study that is supportive of this diseases of aging, such as cancer and heart disease.view involves animals transgenic for genes encoding Phenotypes of aging affect all of the individuals in a antioxidants.Transgenic Drosophila overexpressing both population, while diseases of aging affect only a subset.Cu/Zn SOD and catalase live 34% longer than controls Both impact on life span, but in different ways.For exam-(Orr and Sohal, 1994).A more recent study shows that ple, the many advances in medicine and public health expression of human SOD1 exclusively in Drosophila in this century have caused a large increase in the averadult motor neurons leads to a 40% extension in life age life span of humans in developed countries.Howspan (Parkes et al., 1998).Further experiments are necever, because these advances have not altered the aging essary to clarify the nature of this primary role of motor neurons in life span.Conversely, mice knocked out for either GPX1 (encoding glutathione peroxidase), SOD1,", + "the maximum human life span.Several avenues to studying aging have placed us on Department of Biology Massachusetts Institute of Technology the threshold of understanding basic underlying mechanisms.These approaches include the identification of Cambridge, Massachusetts 02139 key genes and pathways important in aging; genetic studies of heritable diseases that cause the appearance of premature aging in affected people; physiological ex-Introduction periments that relate the pace of aging to caloric intake; Is aging the final act in the script of developmental bioland advances in human genetics, as well as cell and ogy?The characteristic changes that are part and parcel molecular biology leading to an understanding of the of aging appear similar to developmentally regulated basis of many diseases of aging.Strikingly, single gene programs.But why would aging mechanisms have been mutations have been found to significantly extend the evolutionarily selected as advantageous?Indeed, evolife span in C. elegans, yeast, and, most recently, Drolutionary biologists might argue that aging occurs by sophila, suggesting that aging may be relatively simple, default due to the absence of selection in the postreproat least in these organisms.Further, the limited replicaductive phase of life.By this view, the aging process is tion potential of human cells in culture has been attribnot programmed, but, rather, the detritus of the absence uted to a specific mechanism (i.e., the shortening of of selection for maintenance (Medawar, 1952; Kirkwood, telomeric ends of chromosomes).An important chal- 1977).However, it is quite reasonable that any mechalenge is now to relate these recent findings to the more nisms that sprang up to slow or regulate the pace of complex case of human aging.aging would be selected, because lucky individuals", + "Currently prevailing studies of genetic and biological origin of human health and longevity follow largely two approaches which focus on the aging-related diseases and on individuals with exceptionally long lives (Martin et al. 2007).This study provides de facto the rationale for a new approach.Specifically, Fig. 2 suggests that a promising strategy could be to focus on individuals who died prematurely.Studies of genetic profiles of short-lived subjects compared to those who aged more successfully (i.e., those who lived longer and perhaps healthier lives) can be a core of this strategy.Importantly, this strategy can be naturally implemented in longitudinal studies of aging and longevity by focusing on individuals who died first.", + "T he average human life expectancy has been increasing for centuries 1 .Based on twin studies, the heritability of human lifespan has been estimated to be ~25%, although this estimate differs among studies 2 .On the other hand, the heritability of lifespan based on the correlation of the mid-parent (i.e., the average of the father and mother) and offspring difference between age at death and expected lifespan was estimated to be 12% 3 .A recent study has indicated that the different heritability estimates may be inflated due to assortative mating, leaving a true heritability that is below 10% 4 .The heritability of lifespan, estimated using the sibling relative risk, increases with age 5 and is assumed to be enriched in long-lived families, particularly when belonging to the 10% longest-lived of their generation 6 .To identify genetic associations with human lifespan, several genome-wide association (GWA) studies have been performed [7][8][9][10][11][12][13][14][15][16][17][18][19][20] .These studies have used a discrete (i.e., older cases versus younger controls) or a continuous phenotype (such as age at death of individuals or their parents).The selection of cases for the studies using a discrete longevity phenotype has been based on the survival to ages above 90 or 100 years or belonging to the top 10% or 1% of survivors in a population.Studies defining cases using a discrete longevity phenotype often need to rely on controls from more contemporary birth cohorts, because all others from the case birth cohorts have died before sample collection.Previous GWA studies have identified several genetic variants, but the only locus that has shown genome-wide significance (P \u2264 5 \u00d7 10 \u22128 ) in multiple independent meta-analyses of GWA studies is apolipoprotein E (APOE) 21 , where the ApoE \u03b54 variant is associated with lower odds of being a long-lived case.", + "Introduction Worldwide human populations have shown an increase in mean life expectancy in the past two centuries (Oeppen & Vaupel, 2002).This is mainly because of environmental factors such as improved hygiene, nutrition, and health care.The large variation in healthy lifespan among the elderly has prompted research into the determinants of aging and lifespan regulation.The genetic contribution to human lifespan variation was estimated at 25-30% in twin studies (Gudmundsson et al., 2000;Skytthe et al., 2003;Hjelmborg et al., 2006).The most prominent genetic influence is observed in families in which the capacity to attain a long lifespan clusters (Perls et al., 2000;Schoenmaker et al., 2006).Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al., 2008;Terry et al., 2008), raising the question whether protective mechanisms against disease exist in long-lived subjects.", + "Introduction Human life expectancies are increasing almost everywhere in the world where socio-economic circumstances are permissive (Tuljapurkar et al., 2000) and there is no evidence that a limit to life is anywhere near (Oeppen and Vaupel, 2002).While this increase in life span would prevent a proposed compression of morbidity (Fries, 1980), there is no evidence that higher average life spans are associated with an extension of the period of increased morbidity (Manton and Gu, 2001).On the contrary, older individuals have never been so healthy and further improvements in life style, environmental conditions and medical care are likely to help this trend to continue.Especially the medical sciences now seem poised to push the biological limits of longevity further by a number of innovations that seem to affect basic mechanisms of ageing and disease rather than merely alleviating its symptoms.While in the past medicine contributed mainly to public health advances by redu-cing infectious diseases, thereby helping infant mortality to decline, more recent developments hold promise for a more basic intervention in the processes that underlie age-related decline.An example is atherosclerosis, a common problem in ageing and, along with hypertension, the cause of most cardiovascular disease.Basic medical research has likely contributed significantly to the current dramatic decline in cardiovascular disease by actively intervening in some of its main risk factors, i.e., lipid levels and hypertension (Levi et al., 2002).However, one could question whether age-related diseases should be seen as separate from ageing.In this respect, ageing has been considered as a process of cellular degeneration and death universal to all or most species, increasing the risk of fatal disease in humans and other mammals.Would it be possible to define such a process and ultimately understand it in terms of the timedependent, coordinated action of the products of multiple genes interacting with the environment?If so, then ageing per se rather than the diseases associated with it, may offer a more logical starting point for further increasing healthy life expectancies through prevention and therapy.This is especially true now that we have a working draft of the human genome and are in a position to determine the functional significance of each gene as part of the dynamic network of all genes that ultimately determine the physiology of an organism.Termed 'Functional Genomics', this new discipline is now often called upon to solve the complex problems in biology, such as to understand functional control mechanisms and investigate the role that genotype and environment play in determining disease phenotypes.The question is then if this same approach would apply to ageing as a complex phenotype.What is ageing, how does it differ from its diametrical opposite, i.e., organismal development, and what role can functional genomics play in unraveling the basic causes of ageing and exploit such knowledge for developing new, rational strategies for extending healthy life span?", + "Introduction As a result of improvements in health care and living conditions over the past two centuries, the average human life expectancy has dramatically increased in many regions of the world [1].This major success reflects the great malleability of the ageing process.Unfortunately, for most people, ageing is accompanied with an increased risk of developing age-related illnesses/disabilities and frailty.Therefore new approaches are required to understand the genetic, cellular, and molecular factors controlling ageing to identify strategies to extend healthy life span.", + "The search for the genetic determinants of extreme human longevity has been challenged by the phenotype's rarity and its nonspecific definition by investigators.To address these issues, we established a consortium of four studies of extreme longevity that contributed 2,070 individuals who survived to the oldest one percentile of survival for the 1900 U.S. birth year cohort.We conducted various analyses to discover longevity-associated variants (LAV) and characterized those LAVs that differentiate survival to extreme age at death (eSAVs) from those LAVs that become more frequent in centenarians because of mortality selection (eg, survival to younger years).The analyses identified new rare variants in chromosomes 4 and 7 associated with extreme survival and with reduced risk for cardiovascular disease and Alzheimer's disease.The results confirm the importance of studying truly rare survival to discover those combinations of common and rare variants associated with extreme longevity and longer health span.", + "The search for the genetic determinants of extreme human longevity has been challenged by the phenotype's rarity and its nonspecific definition by investigators.To address these issues, we established a consortium of four studies of extreme longevity that contributed 2,070 individuals who survived to the oldest one percentile of survival for the 1900 U.S. birth year cohort.We conducted various analyses to discover longevity-associated variants (LAV) and characterized those LAVs that differentiate survival to extreme age at death (eSAVs) from those LAVs that become more frequent in centenarians because of mortality selection (eg, survival to younger years).The analyses identified new rare variants in chromosomes 4 and 7 associated with extreme survival and with reduced risk for cardiovascular disease and Alzheimer's disease.The results confirm the importance of studying truly rare survival to discover those combinations of common and rare variants associated with extreme longevity and longer health span.", + "Introduction The recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005).", + "DESIGNS TO STUDY PARAMETERS OF HEALTHY AGEING, MORBIDITY, MORTALITY AND LONGEVITY Human cohorts may vary considerably in their morbidity, mortality and longevity characteristics and yet they have shown a common increase in mean life expectancy in the past two centuries [5].This is mainly due to improved hygiene, nutrition and healthcare.There is a large variation in healthy lifespan among the elderly and remarkably exceptional longevity (EL) can be reached with a low degree of agerelated disability [6,7].Heritability studies comparing the concordance of lifespan in monozygous and dizygous twins estimated a 25 -30% genetic contribution to human lifespan variation [8 -11], which becomes increasingly important at higher ages.The most prominent genetic influence is present in families in which survival to high ages clusters [12,13].Unlike model systems where single-gene mutations have major life extension effects, human longevity is presumed to be a complex trait [14].", + "INTRODUCTION Genomic studies into human longevity are inspired by the fact that, in animal models, healthy lifespan has proved to be remarkably plastic, and major pathways of lifespan regulation have been identified.Considerable lifespan extension has been induced in models as diverse as yeast, worms, fish, flies and rodents by applying genetic manipulation and dietary restriction (DR) (see [1] for review).Reduced activity of nutrient-sensing pathways such as insulin/insulin-like growth factor (IGF-1) signalling (IIS) and target of rapamycin (TOR) signalling mediated lifespan extension, and also the extension of lifespan by DR [2].An interesting observation from the perspective of human ageing is that, in rodents and monkeys, diets restricted in glucose, fat or protein uptake reduced or delayed the risk of cancer and metabolic disease, thus extending the healthspan of the animals [2].Following the discovery of genes and pathways involved in animal lifespan extension, human research has focused on the corresponding candidate human genes with genetic, genomic and epigenetic studies into ageing and longevity.The designs of these studies differ with respect to the selection of naturally occurring phenotypes and the study populations, which include population-based, patient-based, family-based and exposure-based cohorts.", + "GENETIC STUDIES OF HUMAN LONGEVITY Genetic and genomic studies into longevity have been performed based on a hypothesis, referred to as a candidate gene approach.Alternatively, explorative genome-wide analyses have been applied in which genetic variation and gene transcription across the complete genome are being studied for associations with longevity and related traits.Genetic studies into human disease and longevity include candidate gene approaches, genome-wide association studies (GWASs) and genome-wide linkage studies.", + "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY Heritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE \u03b52), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans.", + "Introduction Human longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006).", + "Conclusions and prospects Over the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges.", + "Human lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + ], + [ + "There is a great need for continuing efforts to increase public knowledge about genomic research.As individuals and communities from diverse social backgrounds become more aware of genomic research and the potential role of genetics in contributing to health outcomes, the public will hopefully be more informed about the implications of genomic research for personal medical care, public health and more broadly the public representation of diverse population groups based on genetic findings.This knowledge should reinforce the ability of potential participants to make informed choices about joining a genetic study.There are complicated issues underlying public trust in medicine as well as scientific and genetic research that must be addressed.Innovative strategies for public education and community engagement should take into account cultural settings and historical experiences that have contributed to distrust in the past.", + "The issues discussed in this section refl ect key current concerns, but, given the rapid advances in genetic and genomic research, new issues will continue to confront families in the next few years.For example, major advances in the developing area of neuropsychiatric genetics, studies of the heritable nature of psychiatric and other nervous system disorders, characterized at the molecular, cellular, or behavioral levels, will challenge family members to address the potential role genes play in the development of schizophrenia, bipolar, or affective disorders (Genomics Network, n.d.).", + "Future Implications and Communication Research Directions Given ever-expanding research on genetics and genomics, scholars interested in family interaction will be challenged to stay abreast of the implications for family disclosure and discussion of genetic health.We believe that the following issues will emerge as key concerns:", + "Conclusion After more than four decades of working, genetics and genomic medicine still faces a considerable challenge to be addressed.Lack of awareness of health professionals and government, lack of interest of researcher on genetic diseases, limited research funding, limited access to high technology, low national health budget and low income family are seem to be the main obstacles to be overcome in implementation of genetics and genomic medicine.Despite these conditions, several research centers still managed to do some studies and few numbers of genetic testing.Several collaborations with countries abroad have been done to overcome some obstacles.Yet, Indonesia still has to accelerate this effort to be able to catch up its lag.Mentoring and collaborations are needed to enable Indonesia in doing so.", + "Opportunities for Population-Based Research on Aging Human Subjects: Pathology and Genetics", + "Concluding remarks The next decade will provide a window of opportunity to prepare health professionals, public health practitioners, the public and policy makers for the advent of genomics on health and health care.This will be a doable project but will require regional, national, European and global coordination on both the vertical and horizontal levels.We argue that there is an ethical obligation to prepare society to meet this challenge and to take up the opportunities provided by the science in a medically useful, effective, efficient, socially desirable and ethically justifiable manner.Here, health literacy, health communication and empowerment in managing risks are key for opening the doors to a truly beneficial Public Health Genomics practice.This can be facilitated by implementing ethical benchmarks and legal safeguards 70 such as respect for autonomy and social justice in the context of policy development.", + "Clarifying the general conditions under which genomic knowledge can be put to best practice in the field of public health, paying particular consideration to the ethical, legal and social implications 12,17,35 is currently the most pressing task in Public Health Genomics.Aiming the application of genetic and molecular science to the promotion of health and disease prevention through the organised efforts of society, integral to its activities is a dialogue with all stakeholders in society, including industry, governments, health professionals and the general public. 18Thus, the integration of genomics into public health research, policy and practice is one of the major future challenges for our health-care systems. 36,37Expertise is already feasible and can be clustered and evaluated for a socially accountable use.", + "Public health needs to prepare itself for the upcoming challenges, which derive from genomics.In this sense, it needs to strengthen the communication efforts among all sciences involved.Public health can serve as the umbrella, that spans the disciplines such as genetics, ethics, law and all other stakeholders.", + "Economic and health disparities related to genetics and genomics.", + "Capabilities and limitations of current genetic/genomic technologies.", + "Identify ethical, legal, and social issues associated with genetic/genomic information.", + "Ongoing research contributing to improved understanding of the genetic/genomic influences on health.", + "Economic and health disparities related to genetics and genomics. Integrate knowledge from psychology, history, politics, sociology and culture when delivering genetic and genomic care.", + "Ethical and legal issues surrounding genetic and genomic information and services.", + "Developments in genetics and genomics occur very rapidly and bring with them new ethical, legal and social questions that need swift, sensible and responsible responses (Pepper, 2011).Examples include next-generation sequencing, genetic cohort studies and biobanks, which have raised questions about data management, including quality of interpretation of data, data storage, data sharing, consent for re-use of data, as well as concerns about identifiability and privacy interests of those who provide samples (Kaye, 2012;Wolf, 2013;Pinxten and Howard, 2014).However, the rapidity of advancement poses difficulties for those who must determine the responses to these questions.They are often slow or even overtaken by further advancements.Ethical, legal and social-related challenges should be prioritised for policymakers, researchers, clinicians and public health practitioners to maximise the benefits of genomic and genetic applications while minimising the risk of harm to people (Geller et al., 2014).Any education strategy developed should therefore be dynamic.", + "Query 2. Perceptions of Genetics and Genomics Awareness of Genetic and Genomic Advancements.", + "In addition, 4 scholarly commentaries in this issue provide insights into several current practical issues and developments in genetics and genomics.Feero and colleagues 11 describe advances in genomics science and explore many of the issues surrounding translation of these advances to routine \"personalized\" patient care.Offit 12 discusses the increasing availability of direct-to-consumer marketing of genomic and genetic testing and sounds an appropriately cautionary note about the need for standards, quality control, and appropriate regulation.Uhlmann and Guttmacher 13 present a useful collection of practical Internet genetics resources for clinicians and patients, including genetics information on specific diseases; guidelines for genetic testing; and educational resources to help clinicians integrate genetics into patient care.Ginsberg and colleagues 14 discuss the importance of centralized biorepositories for genetics and genomics research and empha-size the need to develop and implement standards for informed consent, informatics, and governance.", + "Key Themes Relevant To Genomic Research . . . . . . . . . . . . . . . . . . . . . . . . . . 3", + "A first step is to define the challenges that stand in the way of realizing the promise of genomic medicine.These include addressing gaps in the oversight of genetic testing (including regulation of companies providing test interpretation services), ensuring that realistic claims are made in promotional materials for genetic testing, determining the appropriate role of new genomic technologies in patient care, ensuring the privacy of patients' genomic data, and improving insurance coverage and reimbursement for genetic services.The Secretary's Advisory Committee on Genetics, Health, and Society (SACGHS), on which two of us serve, advises the secretary of health and human services and reports on these issues.", + "How can we maximize the benefits of these new developments and minimize the harms?How can we encourage patients' involvement and autonomy yet establish appropriate safeguards while avoiding inappropriate paternalism?How do we promote Preparing for a Consumer-Driven Genomic Age the understanding that interpretations of genomic information may evolve as research unravels the meaning of gene-gene and gene-environment interactions and the roles of noncoding DNA sequences, copy-number variants, epigenetic mechanisms, and behavioral factors in health and disease?" + ], + [ + "A supervised (pathway driven) approach was used to specifically query three general gene ontology (GO) areas of interest, namely xenobiotic metabolism, DNA damage repair, and oxidative stress-related genes (Table 1).These gene categories are hypothesized to play important roles in sex-and age-related susceptibility to adverse drug effects [18,30].Of the 122 genes included in the xenobiotic metabolism gene list in the Ingenuity Knowledge Base, 61 were differentially expressed.These included Cyp2d4, the rat ortholog of human gene CYP2D6, which is speculated to metabolize up to 25% of commonly prescribed drugs [31].Genes involved in DNA Damage Repair, derived from Ingenuity, were combined with the list by Wood et al. [32] to give 222 genes involved in DNA damage repair.Sixty-five of these genes (approximately 25%) were found to be differentially expressed in the liver.Oxidative Stress genes were defined by 68 genes included in \"response to oxidative stress\" (IPA) of which 23 genes were differentially expressed (Table 1).", + "Pharmacogenomics has advanced the field of drug-response assessment.For example, the first experiences with guiding vitamin K antagonist therapy with the aid of CYP2C9 (cytochrome P450, family 2, subfamily C, polypeptide 9) or VKORC1 (vitamin K epox- ide reductase complex, subunit 1) polymorphisms (93 ), and the use of cytochrome P450 polymorphisms for assessing clopidogrel response have entered US Food and Drug Administration recommendations (94 ).Disease prevention lags behind.Gene chips and modern sequencing approaches that allow largescale interrogation of the genome at the population level will generate novel hypotheses of disease causation.Furthermore, with the continuing drop in the costs of whole-genome sequencing, the practicing physician may soon be faced with having to comment on the disease risks of a patient's \u03fe4 \u03eb 10 6 sequence variants before any clinical signs occur, a task that no certified genetic counselor could fulfill at present.With advent of GWASs, ethical and practical concerns of reporting genetic research results have become apparent.Initial efforts at defining rules of reporting large-scale association results and assessing the level of evidence also apply to nextgeneration large-scale genomics (95,96 ).Reports have suggested that on the consumer side, genomewide genetic profiling of employees of health and technology companies does not change anxiety symptoms, dietary fat intake, or exercise behavior (i.e., lifestyle factors) over a 6-month period (97 ); however, the association of genetic variation with risk and the dissection of objective markers of risk and risk factors that reside in the causal pathways of disease will need careful assessment before these approaches can enter clinical decision making (98 ).A data set containing 80 genes associated with coronary heart disease in GWASs was uploaded and overlaid onto the molecular networks developed from information contained in the Ingenuity Knowledge Base.Networks of Network Eligible Molecules were then algorithmically generated on the basis of their connectivity.The most substantially enriched network, as shown, comprises 36 genes, of which 20 are coronary heart disease genes.", + "19.3.1 An environmental or pharmacogenetic basis for drug efficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many non-genetic factors also influence the efficacy of medications, including the patient\u2019s age, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit juice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the first-pass metabolism of many medications.", + "Finally, it is possible that other molecules (or drugs) might modulate the biological context within which the drug\u2013 target interaction takes place. Variation in any of the elements that control these types of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related candidates. 19.3 PHARMACOGENETICS (PGx) 519 19.3.5 Using bioinformatics to gain understanding of adverse drug reaction (ADR) One of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient.", + "19.3 Pharmacogenetics (PGx) It is well known that after exposure to a drug, almost any given cohort of patients show a wide variety of responses. In an ideal situation, patients show a beneficial response to the therapy, although they may also show no response or a weak response, and perhaps most worryingly, they may experience an adverse drug reaction (ADR), which in extreme situations could lead to serious illness or even death. ADR is an increasingly serious problem with a huge toll in lives and health-care costs every year.", + "A good understanding of disease biology and effective chemistry is not the only requirement for an efficacious drug; we also must understand how variation at the target affects drug action, and how variation in other genes affects the way drugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the drug development paradigm also faces some unique challenges; for example, the exquisite rarity of some adverse reactions makes collection of sufficient samples for well-powered genetic analysis almost impossible.", + "19.3.1 An environmental or pharmacogenetic basis for drug efficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many non-genetic factors also influence the efficacy of medications, including the patient\u2019s age, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit juice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the first-pass metabolism of many medications.", + "Finally, it is possible that other molecules (or drugs) might modulate the biological context within which the drug\u2013 target interaction takes place. Variation in any of the elements that control these types of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related candidates. 19.3 PHARMACOGENETICS (PGx) 519 19.3.5 Using bioinformatics to gain understanding of adverse drug reaction (ADR) One of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient.", + "19.3 Pharmacogenetics (PGx) It is well known that after exposure to a drug, almost any given cohort of patients show a wide variety of responses. In an ideal situation, patients show a beneficial response to the therapy, although they may also show no response or a weak response, and perhaps most worryingly, they may experience an adverse drug reaction (ADR), which in extreme situations could lead to serious illness or even death. ADR is an increasingly serious problem with a huge toll in lives and health-care costs every year.", + "A good understanding of disease biology and effective chemistry is not the only requirement for an efficacious drug; we also must understand how variation at the target affects drug action, and how variation in other genes affects the way drugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the drug development paradigm also faces some unique challenges; for example, the exquisite rarity of some adverse reactions makes collection of sufficient samples for well-powered genetic analysis almost impossible.", + "Drug-Gene Interactions Predicting Efficacy In 1 candidate gene study, a genetic variant in the HMG-CoA reductase gene, present in 6.7% of patients, modified the LDL-C response to pravastatin by 6.4 mg/dL. 244][247] However, these effect sizes are small and difficult to distinguish from random variation in individual patients.Indeed, the metformin finding is less important for its potential clinical applications than for the biological insight provided by this link between glucose control and a gene involved in the response to DNA damage. 245,246", + "Nutrition and metabolism The power of these new experimental protocols, comparing gene expression profiles to understand spontaneous differences in phenotype due to disease, was extended by inducing phenotypic differences using creative molecular intervention.The first experiments to manipulate phenotype in this way used drugs.A comparison of the gene expression of a drug-induced phenotype with that of the normal phenotype was brilliantly executed in a single study that simultaneously identified a mechanism for the regulation of sterol uptake in the intestine and a genetic disease, sitosterolemia [17 \u2022 ], mice were treated with a lipid-metabolism altering compound and the expression profiles of various tissues compared with normal mice using gene arrays.Differentially expressed genes were evaluated 'in silico,' and an unknown gene was found using bioinformatic tools to be homologous to the ATP-binding cassette (ABC) family of genes.Members of the ABC family include cellular cholesterol transport proteins.Defects in a member of this family (ABCA1) form the basis for the poor cholesterol delivery to high-density lipoprotein (HDL) that underlies Tangiers disease [18], another cholesterol-related disease [19].Through the use of a variety of in silico techniques, Berge et al. [17 \u2022\u2022 ] concluded that the proteins produced from the newly discovered genes, ABCG5 and ABCG8, were responsible for the regulated reverse transport of newly absorbed cholesterol and phytosterols out of the apical surface of intestinal cells.Using public gene databases, a human homolog of the putative mouse transporter was identified, cloned and used to screen sitosterolemic humans.Dysfunctional mutations were found in these genes in all individuals suffering from sitosterolemia.Thus, individuals suffering from sitosterolemia lack the machinery responsible for the selective and controlled transport of cholesterol, and therefore hyperabsorb various sterols (including plant sterols).This study illustrated many of the strengths of genomic experimentation: the identification of phenotypically important genes using global differential gene expression analysis; querying internet databases to deduce structure/function relationships from sequence comparison; and the characterization of individual variation (polymorphism) linked to health.These findings have transformed our understanding of lipid absorption and metabolism, begging the question: how long would this knowledge have waited to be discovered without genomics?", + "19.3.1 An environmental or pharmacogenetic basis for drug efficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many non-genetic factors also influence the efficacy of medications, including the patient\u2019s age, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit juice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the first-pass metabolism of many medications.", + "Finally, it is possible that other molecules (or drugs) might modulate the biological context within which the drug\u2013 target interaction takes place. Variation in any of the elements that control these types of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related candidates. 19.3 PHARMACOGENETICS (PGx) 519 19.3.5 Using bioinformatics to gain understanding of adverse drug reaction (ADR) One of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient.", + "19.3 Pharmacogenetics (PGx) It is well known that after exposure to a drug, almost any given cohort of patients show a wide variety of responses. In an ideal situation, patients show a beneficial response to the therapy, although they may also show no response or a weak response, and perhaps most worryingly, they may experience an adverse drug reaction (ADR), which in extreme situations could lead to serious illness or even death. ADR is an increasingly serious problem with a huge toll in lives and health-care costs every year.", + "A good understanding of disease biology and effective chemistry is not the only requirement for an efficacious drug; we also must understand how variation at the target affects drug action, and how variation in other genes affects the way drugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the drug development paradigm also faces some unique challenges; for example, the exquisite rarity of some adverse reactions makes collection of sufficient samples for well-powered genetic analysis almost impossible.", + "19.3.1 An environmental or pharmacogenetic basis for drug efficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many non-genetic factors also influence the efficacy of medications, including the patient\u2019s age, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit juice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the first-pass metabolism of many medications.", + "Finally, it is possible that other molecules (or drugs) might modulate the biological context within which the drug\u2013 target interaction takes place. Variation in any of the elements that control these types of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related candidates. 19.3 PHARMACOGENETICS (PGx) 519 19.3.5 Using bioinformatics to gain understanding of adverse drug reaction (ADR) One of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient.", + "19.3 Pharmacogenetics (PGx) It is well known that after exposure to a drug, almost any given cohort of patients show a wide variety of responses. In an ideal situation, patients show a beneficial response to the therapy, although they may also show no response or a weak response, and perhaps most worryingly, they may experience an adverse drug reaction (ADR), which in extreme situations could lead to serious illness or even death. ADR is an increasingly serious problem with a huge toll in lives and health-care costs every year.", + "A good understanding of disease biology and effective chemistry is not the only requirement for an efficacious drug; we also must understand how variation at the target affects drug action, and how variation in other genes affects the way drugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the drug development paradigm also faces some unique challenges; for example, the exquisite rarity of some adverse reactions makes collection of sufficient samples for well-powered genetic analysis almost impossible." + ], + [ + "In one case, a gene identified by mutation recovered from a genetic screen in the laboratory, methuselah, may have variants in natural populations.In particular, the common ATATC haplotype has a sharp geographic (north-south) cline in U.S. populations, which, intriguingly, is associated with an 18% difference in life span (97).It would be interesting to examine these natural populations for differences in their reproductive schedule.Extensive studies show that life span can be rapidly selected as an indirect outcome of artificial selection for age at reproduction.Samples from natural populations of Drosophila contain genetic variants that can be rapidly selected, within 15 generations, for 50% or greater differences in life span on the basis of choosing individuals that are reproductive at early versus later ages (93).Selection was reversible, indicating that these life history variants depended on existing gene combinations not new mutations.Among the genes that differed in quantitative expression between young-and old-selected lines were heat shock proteins, e.g., hsp 22 (60).An overarching conclusion from fly aging genetics is that stress resistance is coupled to longevity (94), as in C. elegans.Other gene candidates are being sought by QTL analysis and show complex interactions with gender and population density (17,115).", + "Murabito JM, Yuan R, Lunetta KL (2012) The search for longevity and healthy aging genes: insights from epidemiological studies and samples of long-lived individuals. J Gerontol A Biol Sci Med Sci 67(5):470\u2013479. doi:10.1093/gerona/gls089 20. Nuzhdin SV, Pasyukova EG, Dilda CL et al (1997) Sex-specific quantitative trait loci affecting longevity in Drosophila melanogaster. Proc Natl Acad Sci USA 94(18):9734\u20139739 21. Gems D, Riddle DL (2000) Genetic, behavioral and environmental determinants of male longevity in Caenorhabditis elegans. Genetics 154(4):1597\u20131610 123 22.", + "Somatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18].", + "Our study has several limitations.First, we did not analyse the sex and mitochondrial chromosomes, since we were unable to gather enough cohorts that could contribute to the analysis of these chromosomes.However, these chromosomes may harbour loci associated with longevity that we thus have missed.Second, although we included as many cohorts as possible, the sample size of our study is still relatively small (especially for the 99th percentile analysis) in comparison to GWA studies of age-related diseases, such as T2D and cardiovascular disease, and parental age at death 11,51,52 .Hence, this limited our power to detect loci with a low MAF (<1%) that contribute to longevity.Third, we did not perform sex-stratified analyses and may thus have missed sexspecific longevity-related genetic variants.The reason for this is that (1) we only identified a limited number of suggestive significant associations in our unstratified 90th and 99th percentile analyses, (2) our sample size is modest (especially when stratified by sex), and (3) thus far, there has been no report of any genomewide significant sex-specific longevity locus.", + "In most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes.", + "Previously, it has been suggested that genetic variation in the FOXO1 gene is specifically contributing to human female longevity (reviewed in Chung et al., 2010).However, at chromosome 13q14.11harboring the FOXO1 gene we found no evidence for linkage with female longevity (LOD<0.05)and at the gene position of FOXO1 we found no evidence for association in the females-only metaanalysis (p-values>0.042) in the GEHA Study.Potentially, the effect of this locus is not only influenced by gender but also by genetic background.", + ", 2003), to study GXE and consequences of treatments as a function of age, diet, and sex (Fleet et al. , 2016; Philip et al. , 2010; Roy et al. , 2020; Sandoval-Sierra et al. , 2020; Williams et al. , 2016, 2020), gene pleiotropy (Wang et al. , 2016a), and to test behavioral predictions based on differences in brain architecture (Yang et al. , 2008). Author Manuscript Author Manuscript Here we summarize the current status of this resource with a focus on genetic structure, and on the power and precision of mapping trait variance to loci and genes.", + "Somatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18].", + "The Height-Life Span Nexus Several observations and lines of experimentation have raised the issue of whether interindividual differences in aging rate are influenced by genes that modulate body size and early-life growth patterns.These include (a) the association between small stature and exceptional longevity in calorically restricted rodents (Yu et al., 1985), methionine-restricted rats (Orentreich et al., 1993), and mutant dwarf mice (Brown-Borg et al., 1996;Miller, 1999); and (b) the association between small body size and longer life span in natural populations of mice (Falconer et al., 1978), flies (Hillesheim and Stearns, 1992), dogs (Li et al., 1996), and, possibly, people (Samaras andStorms, 1992).The correlation in dogs is particularly striking: selective breeding for dogs of different body size has produced breeds varying in size from Chihuahua to Irish wolfhound.These breeds also vary greatly in mean longevity, from approximately 7 to 10.5 years, and the correlation between breed longevity and breed body weight (Miller, 1999) is a remarkable R 2 = 0.56.These differences are genetic and affect stature rather than obesity: no amount of overeating will convert a West Highland white terrier to a St. Bernard.The selective pressures applied were designed to create dogs of specific sizes and temperaments and were not intended to influence aging rate or life span.The clear implication is that the effects on longevity are pleiotropic, i.e., that genes selected for their effect on body size and conformation influenced life span as a side effect.It is of interest to note that the few analyses (Eigenmann et al., 1984(Eigenmann et al., , 1988) ) of the hormonal basis for interbreed differences in body size have shown that the genes in question influence levels of IGF-1, the most likely mediator of the life-span effects in the long-lived df/df and dw/dw mouse mutants.Could it be mere coincidence that long-lived mutant nematode worms (Kimura et al., 1997) also show mutations in genes related to insulin and IGF-1 receptors?", + "The antagonistic pleiotropy and hyperfunction theories of ageing predict the presence of genetic variants important for growth and development in early life with deleterious effects towards the end of the reproductive window 19,20 .While we are unable to directly capture the genetic effects on individuals before age 40 due to the study design of our datasets, we found that the life-extending variant near FOXO3 is associated with a delay in the age at menarche and a decrease in intracranial volume and cognitive abilities.It thus appears that there are loci exhibiting antagonistic effects, although we are unable to discern whether this is due to true pleiotropy or due to linkage of causal variants within a region Genes which showed a significant effect (FDR < 5%) of gene expression on ageing traits are displayed here.Gene names are annotated with the direction of effect, where + andindicate whether the life-extending association of the locus is linked with higher or lower gene expression, respectively.Locus: nearest gene to lead variant in the multivariate analysis, Chr: chromosome, Position: base-pair position of lead variant (GRCh37), Cis-genes: genes in physical proximity (<500 kb) to the lead variant of the locus which colocalise with the multivariate signal, Trans-genes: genes located more than 500 kb from the lead variant of the locus.", + "Ageing phenotypes, such as years lived in good health (healthspan), total years lived (lifespan), and survival until an exceptional old age (longevity), are of interest to us all but require exceptionally large sample sizes to study genetically.Here we combine existing genome-wide association summary statistics for healthspan, parental lifespan, and longevity in a multivariate framework, increasing statistical power, and identify 10 genomic loci which influence all three phenotypes, of which five (near FOXO3, SLC4A7, LINC02513, ZW10, and FGD6) have not been reported previously at genome-wide significance.The majority of these 10 loci are associated with cardiovascular disease and some affect the expression of genes known to change their activity with age.In total, we implicate 78 genes, and find these to be enriched for ageing pathways previously highlighted in model organisms, such as the response to DNA damage, apoptosis, and homeostasis.Finally, we identify a pathway worthy of further study: haem metabolism.", + "Here, we assess the degree of genetic overlap between published GWAS of three different kinds of ageing phenotypeshealthspan, parental lifespan, and longevity (defined as survival to an age above the 90th percentile)-and perform a multivariate meta-analysis to identify genetic variants related to healthy ageing.We subsequently characterise the sex-and age-specific effects of loci which affect all three ageing traits and look up reported associations with age-related phenotypes and diseases.Finally, we link the observed signal in these loci to the expression of specific genes, including some that are currently studied in model organisms, and identify pathways involved in healthy ageing.", + "Ageing phenotypes, such as years lived in good health (healthspan), total years lived (lifespan), and survival until an exceptional old age (longevity), are of interest to us all but require exceptionally large sample sizes to study genetically.Here we combine existing genome-wide association summary statistics for healthspan, parental lifespan, and longevity in a multivariate framework, increasing statistical power, and identify 10 genomic loci which influence all three phenotypes, of which five (near FOXO3, SLC4A7, LINC02513, ZW10, and FGD6) have not been reported previously at genome-wide significance.The majority of these 10 loci are associated with cardiovascular disease and some affect the expression of genes known to change their activity with age.In total, we implicate 78 genes, and find these to be enriched for ageing pathways previously highlighted in model organisms, such as the response to DNA damage, apoptosis, and homeostasis.Finally, we identify a pathway worthy of further study: haem metabolism.", + "LongevityMap--human genetic variants associated with longevity Variation in human lifespan has been found to be 20-30% heritable, with increasing heritability at advanced ages (27).As next-generation sequencing and genome-wide approaches advance, so does the capacity for performing longevity association studies.To catalog the increasing volume of data in genetic studies of human longevity, we created LongevityMap (http://genomics.senescence.info/longevity/), a database of genes, gene variants and chromosomal locations associated with longevity (28).This differs from the GenAge database, which focuses mostly on data from model organisms and the few genes associated with human ageing (e.g.genes causing progeroid syndromes).", + "Genes/loci identified by genome-wide association studies of longevity and lifespan traits.", + "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY Heritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE \u03b52), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans.", + "Put more simply: What is the strength of evidence in favor of GXE effects on lifespan? We ask if youthful adult body weight (~120 days) predicts lifespan. Is the change in body weight in adults in response to a HFD a causal predictor of lifespan? Finally, we ask whether levels of classic serum metabolites or metabolic hormones measured in middle-age or old-age predict variation in lifespan? Our focus is both on overall effects and on strain-specific difference in effect of diet on lifespan and weight gain, rather than on specific genetic modifiers or loci of lifespan.", + "Studies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways.", + "Introduction Approximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go \u00a8gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha \u00a8chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches.", + "Studies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches." + ], + [ + "One surprising result of our experiment was the relatively weak support for involvement of the insulin/insulin-like signaling (IIS) or target-of-rapamycin (TOR) pathways in the evolution of late-life performance.Mutations in genes within these pathways can alter life span and fertility in flies and other organisms (Partridge and Gems 2002); natural genetic variation in expression of IIS/TOR-pathway genes has been reported to predict agingrelated phenotypes (Nuzhdin et al. 2009), and natural clinal variation in the insulin receptor gene InR has been associated with variation in stress resistance and fecundity (Paaby et al. 2010).We therefore expected that some of these genes would contribute to the evolution of life span and late-life fecundity in our experiment.Only one gene previously annotated with the Gene Ontology biological function \"determination of adult life span\" (Cct1) was among the genes bearing the strongest signature of selection, no more than would be expected by chance (1/96 of the candidate genes that had some biological process annotation, compared to 116/10,792 of all genes with some biological-process annotation, \u03c7 [1] 2 = 0.002, P > 0.96).Genes annotated with the functions \"aging\" or \"determination of adult life span\" were also significantly underrepresented among differentially expressed genes (43/215 transcripts with these annotations had P < 0.05 for line or line-by-age effects, compared to 4488/13,258 of all annotated transcripts, \u03c7 [1] 2 = 18.1, P < 0.0001).Most of the genes we identified are therefore novel candidates for the regulation of life span and late-age performance.", + "Rapamycin Rapamycin has been shown to robustly increase lifespan in at least three different mouse strains and to improve healthspan measures including cognitive function, cardiac function, immune function, obesity, and cancer incidence (Johnson et al. 2015;Kaeberlein 2014).", + "mTOR activates the kinase S6K, which phosphorylates S6, inhibiting autophagy [92].Rapamycin can extend the life span of organisms from yeast to mammals in a dose-dependent manner [95].However, some data suggest that rapamycin has unwanted metabolic effects, including insulin resistance, hyperlipidemia, glucose intolerance, and hypophosphatemia; however, whether rapamycin is responsible for these effects remains controversial, and some of the effects are reversible [96,97].The mTOR pathway integrates different signals from insulin, cytokines, nutrients, oxygen, and mitogenic stimuli, and its regulation has important implications for longevity and against the negative effects of aging [92].", + "The molecular mechanisms that drive cellular senescence in proliferative and nonproliferative cells are being discovered.One of the metabolic pathways associated with aging is the growth-promoting mitogen/nutrient-sensing pathway, in which the target of rapamycin (mTOR) is considered a central signaling molecule that affects multiple cellular pathways associated with aging [137].In particular, mTOR participates in the transition of cells from quiescence to senescence [138].", + "Inductors of Autophagy and its Impact on Aging Autophagy has a role in homeostasis, which plays an essential role in the maintenance of cellular physiology and the prevention of cellular damage.Among the inducers of autophagy have been described the already-mentioned rapamycin, resveratrol, and polyamines; however, only polyamines have demonstrated results in clinical research in humans [65].It is known that these compounds can induce the canonical autophagy pathway, which includes inactivation of the mammalian objective of the rapamycin complex 1 (mTORC1), allowing phosphorylation and activation of the Unc-51 complex (Ulk1/2), where the cascade of the other members of the complex is subsequently activated, ULK as FIP200 and ATG13 [65].", + "A third example illustrates that pharmacological targeting of pathways that have been implicated in promoting aging may also restore youthfulness at cellular and biochemical levels.Among the key regulators associated with interventions that extend life span is the enzyme mTOR, which senses cellular nutrient levels and in turn regulates rates of protein synthesis and energy utilization.Notably, administration of rapamycin, an mTOR inhibitor, starting at midlife can extend the life span of mice, suggesting that aging can be delayed or reversed in multiple cell types (Harrison et al., 2009).In the hematopoietic system, aging is associated with an increase in mTOR activation in stem cells and progenitors (Chen et al., 2009).Administration of rapamycin to old mice to inhibit mTOR not only limited the normal age-related increases in hematopoietic stem cells and biomarkers of aging in those cells, but also enhanced the performance of the stem cells to become as effective as young stem cells in heterochronic transplantation experiments (Chen et al., 2009) (Figure 1).", + "Rapamycin inhibits TOR signalling to alter nDNA translation, inducing mitonuclear protein imbalance35, and increases lifespan in various species, including mice33. Rapamycin also increased mean worm lifespan (by 16%)34 in a ubl-5-dependent manner, induced UPRmt, but not UPRER or heat shock response, and increased respiration (Fig. 6a, c and Supplementary Fig. 9a). This was associated with increased ATP levels, equal citrate synthase activity and altered nDNA/mtDNA oxidative phosphorylation protein ratio (Fig. 6d, e). Additionally, rapamycin changed the balance between nDNA- and mtDNA-encoded oxidative phosphorylation subunits in mouse hepatocytes in a dose dependent manner (Fig. 6f, g).", + "Zylbee, E., Vesco, C. & Penman, S. Selective inhibition of the synthesis of mitochondria-associated RNA by ethidium bromide. J. Mol. Biol. 44, 195\u2013204 (1969). 33. Harrison, D. E. et al. Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature 460, 392\u2013395 (2009). 34. Robida-Stubbs, S. et al. TOR signaling and rapamycin influence longevity by regulating SKN-1/Nrf and DAF-16/FoxO. Cell Metab. 15, 713\u2013724 (2012). 35. Zid, B. M. et al. 4E-BP extends lifespan upon dietary restriction by enhancing mitochondrial activity in Drosophila. Cell 139, 149\u2013160 (2009). 36. Schulz, T. J. et al.", + "a, Rapamycin (Rapa, 1 nM) extends worm lifespan in a ubl-5-dependent manner; b, ubl-5-dependently induced UPRmt (hsp-6::GFP) but not UPRER (hsp-4::GFP) (n 5 4). c\u2013e, Rapamycin increased respiration (c, n 5 10) and ATP content but not citrate synthase activity (d, n 5 3) and induced mitonuclear protein imbalance (e). f\u2013h, In mouse hepatocytes, rapamycin induces mitonuclear protein imbalance (f, g) and induces UPRmt as shown at the protein (f, g, n 5 3), and transcriptional (h, n 5 8) level. i, Resveratrol (Resv, 25 mM) induced mitonuclear protein imbalance in mouse hepatocytes (n 5 4).", + "pivotal in this aspect providing molecular insights and having huge conceptual contributions in the field.Characterising the contribution of individual mutants in ageing is a continuously active and informative activity in the field.On top of these studies, genome-wide screens have provided insights on the role of evolutionarily conserved processes and signalling pathways in ageing such as nutrient response [17,18], protein translation, oxidative damage [19,20], mitochondrial function [21,22] and autophagy [22,23] opening new avenues for biogerontology research.Yeasts have proved informative and helped in understanding mechanisms of highly conserved pathways (from yeast to human) in physiology, health and disease such as the Target of Rapamycin (TOR) [24], glucose sensing (PKA) and stress response pathways (Sty1/p38) [25].", + "mTOR activates the kinase S6K, which phosphorylates S6, inhibiting autophagy [92].Rapamycin can extend the life span of organisms from yeast to mammals in a dose-dependent manner [95].However, some data suggest that rapamycin has unwanted metabolic effects, including insulin resistance, hyperlipidemia, glucose intolerance, and hypophosphatemia; however, whether rapamycin is responsible for these effects remains controversial, and some of the effects are reversible [96,97].The mTOR pathway integrates different signals from insulin, cytokines, nutrients, oxygen, and mitogenic stimuli, and its regulation has important implications for longevity and against the negative effects of aging [92].", + "The molecular mechanisms that drive cellular senescence in proliferative and nonproliferative cells are being discovered.One of the metabolic pathways associated with aging is the growth-promoting mitogen/nutrient-sensing pathway, in which the target of rapamycin (mTOR) is considered a central signaling molecule that affects multiple cellular pathways associated with aging [137].In particular, mTOR participates in the transition of cells from quiescence to senescence [138].", + "Inductors of Autophagy and its Impact on Aging Autophagy has a role in homeostasis, which plays an essential role in the maintenance of cellular physiology and the prevention of cellular damage.Among the inducers of autophagy have been described the already-mentioned rapamycin, resveratrol, and polyamines; however, only polyamines have demonstrated results in clinical research in humans [65].It is known that these compounds can induce the canonical autophagy pathway, which includes inactivation of the mammalian objective of the rapamycin complex 1 (mTORC1), allowing phosphorylation and activation of the Unc-51 complex (Ulk1/2), where the cascade of the other members of the complex is subsequently activated, ULK as FIP200 and ATG13 [65].", + "Background Genetic, dietary and drug interventions can enhance longevity and suppress age-associated disease, such as cancer.Prominent genetic interventions that robustly extend longevity and healthspan in mammals include those that decrease growth hormone (GH) and insulin-like growth factor (IGF) signalling; for example, Ames dwarf mice live more than 50% longer than their wild-type siblings [1].These diminutive mice result from a point mutation in a gene (Prop1 df/df ) that drives development of the pituitary gland, so that mutant mice are deficient in specific hormones.The GH deficiency, in particular, has been shown to underlie their enhanced health span and extended lifespan.Ames mice are highly insulinsensitive, resistant to some stresses and the incidence of cancer is delayed [2][3][4].Dietary and drug interventions that extend lifespan include calorie restriction (CR) and the mTOR inhibitor rapamycin [5].Like the Ames dwarf mutation, CR and rapamycin also suppress and/ or delay the incidence of cancer [5][6][7].A detailed understanding of how these interventions exert their beneficial effects is essential to develop strategies to promote healthy aging in humans [8].Currently, these interventions are thought to exert their effects by related and interconnected effects on some or all of the following: genome stability, the epigenome, telomere attrition and/or function, protein quality control, mitochondrial function, nutrient sensing, cellular senescence, stem cell exhaustion, cellular stress responses and altered intercellular communication [9].Of note, the effects of longevity promoting interventions on the epigenome, a key determinant of cell phenotype, are poorly understood.", + "The target of rapamycin (TOR) signaling pathway has also emerged as a major regulator of lifespan.TOR is a highly conserved kinase that transduces signals from nutrients to regulate cell size, cell growth, and metabolism (Martin & Hall, 2005).Genetic studies in yeast Saccharomyces cerevisiae have shown that reduced levels of nutrients, namely amino acids and sugars, can extend yeast lifespan through regulation of the TOR signaling pathway (Kaeberlein et al ., 2005;Powers et al ., 2006).In Drosophila , recent studies have shown that amino acid restriction, rather than 'calorie restriction', extends lifespan (Min & Tatar, 2006).In C. elegans , either inactivation of CeTOR/let-363 by RNAi, or mutations in Raptor/daf-15 , encoding a regulatory subunit of CeTOR, leads to lifespan extension (Vellai et al ., 2003;Jia et al ., 2004).", + "As mentioned above, a number of genes regulating longevity also control growth and development.Some of these, such as the insulin/IGF1/GH pathway, have been suggested to play a role in the mechanisms of CR (Fig. 1).An emerging critical player is the target of rapamycin (TOR) signaling pathway, which involves both nutrient sensing and regulation of growth.Several genes in the TOR pathway, and the TOR gene itself, regulate longevity in flies (Kapahi et al., 2004) and both longevity and dauer diapause in worms (Jia et al., 2004).Strikingly, not only have genetic manipulations of the TOR gene extended lifespan in yeast and worms (Stanfel et al., 2009) but also feeding rapamycin (which inhibits TOR and is also known as sirolimus) to middle-aged mice significantly (9 -14%) increased lifespan (Harrison et al., 2009).Whether rapamycin is extending lifespan by delaying of aging or by affecting a specific disease, such as cancer, remains unclear.More recent studies show that starting rapamycin administration earlier in life does AGING GENES AS TARGETS FOR DRUG DISCOVERY not result in a significantly greater increase in lifespan (10 -18%) than that obtained in middle-aged mice (Miller et al., 2011).", + "Replacement of the C/ebp\u03b1 gene with C/ebp\u03b2 increases lifespan by 20% [35,36], and may alter the rate of aging [37], indicating that altering the isoform expression of these genes can affect lifespan.Moreover, the life-extending drug rapamycin may affect isoform ratios of C/ebp\u03b2.Rapamycin has been shown to increase lifespan via the suppression of Mtor [38] which in turn controls the isoform ratios of C/ebp\u03b2 [39].Therefore, we speculate that rapamycin may in part exert its life extending effect through C/ebp\u03b2.", + "The genome-wide RNAi study conducted by the Ruvkun lab, authored by Hamilton et al. [88], identified a total of 89 additional aging genes with disparate functions including cell structure, cell surface proteins, cell signaling, cellular metabolism, and protein turnover.Of the 66 genes with previously known functions, 17 corresponded to various aspects of carbon metabolism, including citric acid cycle enzymes and subunits of complexes I, IV, and V of the ETC.Researchers also speculated that protein translation might play a role in lifespan regulation, based on the identification of iff-1 (T05G5.10),a gene that has homology to the translation initiation factor eIF5A.Other hits from this screen included two genes containing PH domains known to interact with phosphatidylinositol lipids, multiple G protein-coupled receptors, protein processing and degradation genes such as proteases and ubiquitin ligases/hydrolases, and chromatin modifying factors.", + "How cellular processes that regulate aging impact genome stability also remain unclear.Compelling evidence now exists that in all eukaryotes, aging is regulated by conserved insulin/insulin-like growth factor (I-(IFG-1)) pathways and growth-signaling pathways regulated by the target of rapamycin (TOR) family of kinases (4).In general, experimental manipulations that upregulate these pathways promote aging, and manipulations that downregulate these pathways-including mutational inactivation or caloric restriction-extend life span and mitigate age-related pathologies.Downregulation of these pathways often leads to a reduction in oxidative stress and oxidative damage to DNA and other cellular constituents.For the most part, however, the relationship between aging and changes in oxidative damage downstream of alterations in growth-signaling pathways remains correlative rather than causal.", + "The potential of interventional approaches targeted at aging has yet to be realized in part because aging is a complicated multisystem process that has remained enigmatic.However, research over the last two decades has led to significant excitement.One of the most striking findings is that it is possible to administer a clinically approved drug, rapamycin, to mice at 20 months of age and extend both their life span and health span (Harrison et al., 2009).Surprisingly, much of the recent success of aging research can be traced back to one of its simplest model organisms: yeast.Two of the major pathways studied in the context of aging and age-related disease are the sirtuin pathway and the TOR signaling pathway, and yeast was pivotal in their discovery." + ], + [ + "We briefly comment on rare mutations that shorten life span through the early onset of diseases that are increasingly common during aging in the general population, e.g., familial forms of Alzheimer, breast cancer, coronary artery disease, type II diabetes, etc.The later onset forms of these diseases are associated with causes of death at later ages.A major question is what role the more common allelic variants of these same genes have in \"normal aging\".Although examination of this huge emerging topic goes beyond the present discussion, we may consider the example of Werner's syndrome, a rare autosomal recessive that causes adult onset progeria with a high incidence of cancer and atherosclerosis (70).The absence of Alzheimer-type dementia in Werner's syndrome illustrates the \"segmental\" nature of this and other progerias (70).Thus, heritable shortening of life span should not be considered as a simple acceleration of general aging processes.The Werner's lesion maps to a defective gene encoding a helicase and exonuclease, which also has several polymorphisms.In Japan, 1367Arg was associated with a lower risk of myocardial infarction (70), although it was not associated with longevity in Finland (14).In general, we know little of the genetic factors involved in frailty and morbidity at later ages, which are important to the geneenvironment interactions implied in the major longevity increase seen during the twentieth century.", + "Indicative diseases associated with the candidate aging genes", + "D ementia has an age-and sex-standardized prevalence of ~7.1% in Europeans 1 , with Alzheimer's disease (AD) being the most common form of dementia (50-70% of cases) 2 .AD is pathologically characterized by the presence of amyloid-beta plaques and tau neurofibrillary tangles in the brain 3 .Most patients are diagnosed with AD after the age of 65, termed late-onset AD (LOAD), while only 1% of AD cases have an early onset (before the age of 65) 3 .On the basis of twin studies, the heritability of LOAD is estimated to be ~60-80% (refs. 4,5 ), suggesting that a large proportion of individual differences in LOAD risk is driven by genetics.The heritability of LOAD is spread across many genetic variants; however, Zhang et al. 6 suggested that LOAD is more of an oligogenic than a polygenic disorder due to the large effects of APOE variants.Zhang et al. 6 and Holland et al. 7 predicted there to be ~100-10,000 causal variants contributing to LOAD; however, only a fraction have been identified.Increasing the sample size of genome-wide association studies (GWAS) will improve the statistical power to identify the missing causal variants and may highlight additional disease mechanisms.In combination with increasing the number of samples, it is beneficial to use different approaches to identify rare and private variation to help identify additional causal variants and increase understanding of disease mechanisms; however, we deem this to be out of the scope of the current analysis.", + "Dementia has an age-and sex-standardized prevalence of ~7.1% in Europeans 1 , with Alzheimer's disease (AD) being the most common form of dementia (50-70% of cases) 2 .AD is pathologically characterized by the presence of amyloid-beta plaques and tau neurofibrillary tangles in the brain 3 .Most patients are diagnosed with AD after the age of 65, termed late-onset AD (LOAD), while only 1% of AD cases have an early onset (before the age of 65) 3 .On the basis of twin studies, the heritability of LOAD is estimated to be ~60-80% (refs. 4,5 ), suggesting that a large proportion of individual differences in LOAD risk is driven by genetics.The heritability of LOAD is spread across many genetic variants; however, Zhang et al. 6 suggested that LOAD is more of an oligogenic than a polygenic disorder due to the large effects of APOE variants.Zhang et al. 6 and Holland et al. 7 predicted there to be ~100-10,000 causal variants contributing to LOAD; however, only a fraction have been identified.Increasing the sample size of genome-wide association studies (GWAS) will improve the statistical power to identify the missing causal variants and may highlight additional disease mechanisms.In combination with increasing the number of samples, it is beneficial to use different approaches to identify rare and private variation to help identify additional causal variants and increase understanding of disease mechanisms; however, we deem this to be out of the scope of the current analysis.The largest previous GWAS of LOAD, identified 29 risk loci from 71,880 (46,613 proxy) cases and 383,378 (318,246 proxy) controls 8 .Our current study expands this to include 90,338 (46,613 proxy) cases and 1,036,225 (318,246 proxy) controls.The recruitment of LOAD cases can be difficult due to the late age of onset, so proxy cases can allow for the inclusion of younger individuals by estimating their risk of LOAD using parental status.Proxy cases and controls were defined on the basis of known parental LOAD status weighted by parental age (Supplementary Information).In the current study, we identified 38 loci, including seven loci that have not been reported previously.Functional follow-up analyses implicated tissues, cell types and genes of interest through tissue and cell type enrichment, colocalization and statistical fine-mapping.This study highlights microglia, immune cells and protein catabolism as relevant to LOAD, while identifying previously unidentified genes of potential interest. ResultsGenome-wide inferences.We performed meta-analysis on data from 13 cohorts, totaling 1,126,563 individuals (Supplementary", + "Introduction Alzheimer's disease (AD) is a complex disorder and is the most common form of dementia [1].After age, family history is the single greatest risk factor for AD.AD can be classified into early and late onset forms.Mutations in three genes: PSEN1/2 and APP are known to cause early onset AD in an autosomal dominant manner [2,3].The majority of AD cases, however, are late onset (LOAD) and the APOE e4 allele is the strongest known genetic risk factor.Many additional genetic polymorphisms have been identified, though with substantially lower risk estimates [1,4,5,6,7,8,9,10].LOAD appears to be inherited and/or sporadic and there is evidence of a maternal inheritance pattern [11].Current estimates suggest that more than 20% of inherited LOAD cases are maternally inherited [12].", + "INTRODUCTION Many common noninfectious diseases exhibit a more severe clinical presentation in older individuals.These diseases often exhibit complex etiology and can affect different tissues and cell types, with a wide spectrum of clinical outcomes.Prominent aging-associated neurodegenerative diseases are Alzheimer's disease (AD), Parkinson's disease (PD), and age-related macular degeneration (AMD), all of which can severely compromise the quality of life and have serious repercussions on both the individual and society at large.These late-onset diseases generally result from the interplay between multiple genetic susceptibility factors and environmental components.Sequencing of the human genome, cataloging of millions of single nucleotide polymorphisms (SNPs) together with the development of a map of common haplotypes, and technological innovations in genotyping are among the major milestones that are facilitating exploration of the genetic basis of common diseases (1,7,50).In the field of AMD genetics, these advances have led to the identification of several genetic susceptibility factors and enabled us to start dissecting the relationship between environmental risk factors and the genetic constitution of each individual (66,118,148).As a result, new opportunities are emerging for improved understanding of disease pathogenesis that may lead to better management and treatment of AMD.Clinical aspects of AMD are discussed only briefly (for a more in-depth discussion, see Reference 79).", + "Aging-associated neurodegenerative diseases significantly influence the quality of life of affected individuals.Genetic approaches, combined with genomic technology, have provided powerful insights into common late-onset diseases, such as age-related macular degeneration (AMD).Here, we discuss current findings on the genetics of AMD to highlight areas of rapid progress and new challenges.We also attempt to integrate available genetic and biochemical data with cellular pathways involved in aging to formulate an integrated model of AMD pathogenesis.", + "Aging-associated neurodegenerative diseases significantly influence the quality of life of affected individuals.Genetic approaches, combined with genomic technology, have provided powerful insights into common late-onset diseases, such as age-related macular degeneration (AMD).Here, we discuss current findings on the genetics of AMD to highlight areas of rapid progress and new challenges.We also attempt to integrate available genetic and biochemical data with cellular pathways involved in aging to formulate an integrated model of AMD pathogenesis.", + "Genetics of Alzheimer Disease: Early-Onset AD In the early to mid-1990s, genetic studies of AD focused on extended families with high burden of disease (two or more cases among first-degree relatives), and used linkage analysis of highly polymorphic genetic markers called short tandem repeats (STRs, or microsattelites) in order to identify genomic regions co-transmitting with disease in affected family members.This strategy, followed by \"fine mapping\"-the positional cloning of candidate genes-was used to identify genes and genetic variants contributing to AD risk.The first three genes known to cause AD were identified among families with multiple early-onset cases (age-at-onset <60 years): APP, encoding amyloid precursor protein [Goate et al., 1991], and PS1 and PS2, encoding presenilins I and II respectively [Levy-Lahad et al., 1995;Rogaev et al., 1995;Sherrington et al., 1995], each transmitting disease-causing variants in the predicted autosomal-dominant fashion.", + "Alzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia.With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets.Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately $20 genes with common variants contributing to LOAD risk.In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses.Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple lowfrequency and rare variant contributors to AD, and discuss ongoing efforts with next-generation sequencing studies to develop statistically well-powered and comprehensive genomic studies of AD.Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD.", + "Alzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia.With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets.Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately $20 genes with common variants contributing to LOAD risk.In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses.Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple lowfrequency and rare variant contributors to AD, and discuss ongoing efforts with next-generation sequencing studies to develop statistically well-powered and comprehensive genomic studies of AD.Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD.", + "Indeed, as age increases, there is an exponential increase in the incidence of AD, with a corresponding effect on healthcare costs and quality of life. AD is a complex disease involving several genetic and environmental components (Hardy, 1997; Munoz & Feldman, 2000), and 15% of patients have a genetic predisposition. Almost 100 candidate genes are currently known to be involved in the development of AD, and only 4 (APP, PSEN1, PSEN2, APOE) in humans have been proven to play a direct role in AD pathogenesis (Thomas & Fenech, 2007).", + "T he genetics of Alzheimer disease (AD) to date support an age-dependent dichotomous model whereby earlier age of disease onset (\u03fd60 years) is explained by 3 fully penetrant genes (APP [NCBI Entrez gene 351], PSEN1 [NCBI Entrez gene 5663], and PSEN2 [NCBI Entrez gene 5664]), whereas later age of disease onset (\u054665 years) representing most cases of AD has yet to be explained by a purely genetic model.The APOE gene (NCBI Entrez gene 348) is the strongest genetic risk factor for later onset, although it is neither sufficient nor necessary to explain all occurrences of disease.Numerous putative genetic risk alleles and genetic variants have been reported.Although all have relevance to biological mechanisms that may be associated with AD pathogenesis, they await replication in large representative populations.Genome-wide association studies have emerged as an increasingly effective tool for identifying genetic contributions to complex diseases and represent the next frontier for furthering our understanding of the underlying etiologic, biological, and pathologic mechanisms associated with chronic complex disorders.There have already been success stories for diseases such as macular degeneration and diabetes mellitus.Whether this will hold true for a genetically complex and heterogeneous disease such as AD is not known, although early reports are encouraging.This review considers recent publications from studies that have successfully applied genome-wide association methods to investigations of AD by taking advantage of the currently available high-throughput arrays, bioinformatics, and software advances.The inherent strengths, limitations, and challenges associated with study design issues in the context of AD are presented herein.", + "Arch Neurol.2008;65(3): 329-334 Alzheimer disease (AD) is the most common cause of dementia and the most prevalent neurodegenerative disorder associated with aging. 1 Alzheimer disease is a heterogeneous disorder with a complex etiology owing to genetic and environmental influences as causal or risk modifiers.The neuropathologic hallmarks of disease are extracellular amyloid plaques and intracellular neurofibrillary tangles of hyperphosphorylated tau protein. 2 Only 10% of AD cases occurring before 60 years of age (early-onset AD) are due to rare, fully penetrant (autosomal dominant) mutations in 3 genes: A\u2424 precursor protein (APP) on chromosome 21, 3 presenilin 1 (PSEN1) on chromosome 14, 4 and presenilin 2 (PSEN2) on chromosome 1. 5,6In contrast, most cases of AD are later in onset (\u0546 65 years of age) (late-onset AD), are nonfamilial, and are likely the result of highly prevalent genetic variants with low penetrance. 7To date, the only genetic risk factor for lateonset AD remains the apolipoprotein E gene (APOE), specifically the \u03b54 allele, which is moderately penetrant, accounting for up to 50% of cases. 8owever, a robust literature reports numerous putative genetic risk alleles and promising genetic variants.Recent reports from individual studies reveal significant associations with the sortilin-related receptor (SORL1 [NCBI Entrez gene 6653]) 9,10 and glycine-rich protein 2-associated binding protein 2 (GAB2 [NCBI Entrez gene 9846]) 11 on chromosome 11; death-associated protein kinase 1 (DAPK1 [NCBI Entrez gene 1612]), 12 ubiquilin 1 (UBQLN1 [NCBI Entrez gene 299798]), 13 and adenosine triphosphate-binding cassette transporter 1, subfamily A (ABCA1 [NCBI Entrez gene 19]), on chromosome 9 14 ; and low-density lipoprotein receptor-related protein 6 (LRP6 [NCBI Entrez gene 4040]) on chromosome 12. 15 All of these putative variants still lack replication in large representative populations but have relevance to neuropathologic mechanisms and pathways that may be associated with AD pathogenesis ( A large meta-analysis from the AlzGene database 16 17 All are associated with relevant biological mechanisms and pathways but await replication to further elucidate their utility as significant markers for AD.", + "Background Alzheimer's disease (AD) is the most common neurodegenerative disorder and the leading cause of dementia in the elderly [1].Diagnosis of AD is based on the presence of neurofibrillary tangles and amyloid plaques [2], and symptoms typically include memory loss and impaired cognitive ability.Although the pathological hallmarks associated with dementia-related symptoms in AD appear largely similar between both the early-onset and late-onset forms of the disease, their underlying etiologies contrast [3].Whereas early-onset AD is a familial autosomal dominant disorder caused by rare, highly penetrant mutations in one of a small set of genes (APP, PSEN1, and PSEN2), the more common late-onset form of the disease (accounting for 90-95 % of cases) occurs sporadically, and risk is determined by complex underlying mechanisms [3][4][5][6].Estimates based on twin concordance rates suggest heritability of late-onset AD is as high as 70 %, implicating major roles for genetic as well as non-genetic factors [6].Indeed, through candidate gene studies, as well as more recent genome-wide association studies (GWASs) and whole-exome sequencing, both common and rare variants associated with the late-onset form of AD have been identified [7][8][9][10][11].Collectively, however, common GWAS variants account for only a modest proportion (~30 %) of the underlying variance in disease susceptibility [12].Several environmental factors are also thought to play a role [5,6], yet exactly how these contribute to risk, onset, and progression remains poorly defined.", + "Alzheimer's disease is the most common type of dementia, and it is characterized by a decline in memory or other thinking skills.The greatest risk factor for Alzheimer's disease is advanced age.A recent genome-wide study identified a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11 is probably responsible for the association.The association of a protective haplotype with a 10-year delay in the onset of Alzheimer's disease and the identification of a CCL11 variant with possible functional roles in this association might allow the future development of immunomodulators with the potential to halve disease incidence.", + "Alzheimer's disease is the most common type of dementia, and it is characterized by a decline in memory or other thinking skills.The greatest risk factor for Alzheimer's disease is advanced age.A recent genome-wide study identified a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11 is probably responsible for the association.The association of a protective haplotype with a 10-year delay in the onset of Alzheimer's disease and the identification of a CCL11 variant with possible functional roles in this association might allow the future development of immunomodulators with the potential to halve disease incidence.", + "INTRODUCTION Alzheimer's disease (AD) is a common debilitating disorder with a prevalence that rises steeply with age from below 1% at 65 years to as high as 40% after the age of 90 [Bachman et al., 1992].Genes are known to play a role in the development of AD.Twin studies show heritabilities of around 60% [Bergem et al., 1997;Gatz et al., 1997].Indeed, variation in four genes has already been shown to cause rare forms of early-onset AD [the Amyloid Precursor Protein Gene (APP); Goate et al., 1991; Presenilin 1 (PS1); Sherrington et al., 1995; Presenilin 2 (PS2); Levy Lahad et al., 1995, Rogaev et al., 1995] or increase the general risk of disease development [Apolipoprotein E (APOE), Corder et al., 1993].As well as increasing disease susceptibility, APOE e4 alleles are associated with reduced age at onset (AAO) and appear to show their strongest effect below 70 years [Farrer et al., 1997].There is also evidence from both twin [Pedersen et al., 2001] and family studies [Tunstall et al., 2000;Li et al., 2002] that AAO in AD is heritable.Daw et al. [2000] have estimated that in addition to APOE, there are at least four loci with similar effect sizes, which contribute to AAO in AD.", + "Introduction Alzheimer's disease (AD), a devastating neurodegenerative disease, is the most common form of dementia among the elderly.Genetically, AD is a complex and multifactorial disease with the possible involvement of multiple genes.The rare early-onset form of the disease usually follows an autosomal-dominant inheritance pattern and to date three genes have been identified: amyloid precursor protein (APP) and presenilin 1 and 2 (PSEN1 and PSEN2).The common late-onset form of the disease is much more complex than the early-onset form and until recently the apolipoprotein E (APOE) gene was the only major genetic factor accounting for 20-29% of the risk for late-onset AD. 1,2 Recent large genome-wide association studies (GWAS) have identi-fied nine additional genes for late-onset AD, including CR1, BIN1, CLU (a.k.a.4][5][6][7] There is high heritability for AD risk (up to 80%), 8 but the total risk attributable to all confirmed loci is about 50%, indicating the presence of additional risk genes for late-onset AD.", + "NE OF EVERY 5 PERSONS AGED 65 years is predicted to develop Alzheimer disease (AD) in their lifetime, and genetic variants may play an important part in the development of the disease. 1 The apparent substantial heritability of late-onset AD 2 is inadequately explained by genetic variation within the well-replicated genes (apolipoprotein E [APOE; RefSeq NG_007084], presenilin-1 [PSEN1; RefSeq NG_007386], presenilin-2 [PSEN2; RefSeq NG_007381], and amyloid beta precursor protein [APP;" + ] + ] +} diff --git a/gnqa/paper1_eval/src/data/datasets/old/full_general_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/full_general_dataset.json new file mode 100644 index 0000000..5e8a3b4 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/full_general_dataset.json @@ -0,0 +1,248 @@ +{ + "question": [ + "What is the most cited environmental factor for the onset of asthma.", + "What is apoptosis?", + "How would one extract the DNA, from say, mammals or plants?", + "What is a genetic marker?", + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?" + ], + "answer": [ + "Air pollution is the most cited environmental factor for the onset of asthma.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "For mammals, DNA can be extracted using a DNA Isolation Kit for Mammalian Blood, following the manufacturer's instructions. This process typically involves mixing blood with lysis buffer and Proteinase K, followed by the addition of isopropanol and Inhibitor Removal Buffer. The DNA is then washed, centrifuged, and the supernatant discarded. The remaining pellet, which contains the purified genomic DNA, is diluted in TE buffer.\n\nFor plants, the mixed alkyl trimethyl ammonium bromide (MATAB) procedure can be used. This involves grinding plant material in liquid nitrogen, incubating it in extraction buffer, and then purifying it with chloroform:isoamylalcohol. The DNA extracts are then precipitated with isopropanol and resuspended in a buffer. The extracts are further purified on anion exchange columns.", + "A genetic marker is a measurable polymorphic sequence of DNA whose chromosomal location is known. They often have no known functional significance but are used as pointers to a particular chromosomal location. They are used to track the inheritance of genes and can be a gene or some section of DNA with no known function.", + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text." + ], + "contexts": [ + [ + "INTRODUCTION Asthma is a chronic disease of the airways defined by its symptoms, which include reversible airflow obstruction, inflammation, and bronchial hyperresponsiveness.The ancient Egyptians, Greeks, and Romans made reference to the symptoms of asthma, and today the disease is estimated to affect 235-334 million people worldwide (44,53).", + " The atopic triad.Perhaps the most widely recognized pattern of co-occurrence is the one of asthma, atopic dermatitis (eczema), and allergic rhinitis (hay fever), which together are referred to as the atopic triad and characteristically present clinically in a temporal sequence known as the atopic march.Within this sequence, atopic dermatitis is typically the first component to manifest, with approximately 20-30% of individuals with mild disease and 70% of those with severe disease going on to develop asthma.Individuals who undergo this distinctive sequence of disease progression frequently exhibit a more severe and persistent phenotype, with increased risk of allergen sensitization.", + " Clinically, asthma is characterized by episodes of coughing, chest tightness, wheezing, dyspnea, or sputum production.Often, asthma sufferers experience a combination of these symptoms, or some symptoms more than others.Pulmonary breathing tests typically demonstrate variable airway obstruction and hyperreactivity, but may be normal, even in patients with severe and uncontrolled disease [8].Thus, the diagnosis of asthma, which is based on general clinical symptoms and variable lung function testing, is non-specific and heavily dependent on clinical history.Within the \"umbrella\" diagnosis of asthma there exists a diverse array of differing clinical phenotypes [9].For example, childhood asthma is often associated with personal and parental atopic diseases (i.e., atopic dermatitis, food allergy, eosinophilic esophagitis, allergic rhinitis), viral infections, and tobacco smoke exposure [10].Alternatively, adult-onset asthma is less associated with atopic disease [11,12], but more associated with female sex [13], sinus disease [14], and preceding respiratory infections such as pneumonia [15].In addition, adult-onset disease is often of higher severity [12,16] with a faster and more persistent decline in lung function [17].Moreover, although severe patients are found in every demographic and age group, the most common phenotype is an adult female that is older and obese [18].", + "Introduction An estimated 9% of children and 6% of adults in the United States have asthma [1].The total number of asthma sufferers worldwide is estimated to be over 300 million, with an additional 100 million expected to develop asthma by 2025 [2][3][4][5].Developed countries are the most affected, with some of the highest rates found in the United Kingdom, Australia, New Zealand and the Republic of Ireland [3].Asthma prevalence is rising significantly in developing countries in transition to a more Western lifestyle [3].In 2007, the cost of disease in the United States was estimated to be $56 billion in relation to medical expenses, missed days of work, and early deaths [1].The rate of asthma deaths has likely plateaued, but is still as high as 250,000 per year worldwide [6].Morbidity and mortality are particularly high in ethnic minorities living below or near the poverty line, and African American children had a death rate 10 times that of non-Hispanic white children in 2015 [7].Thus, asthma is a costly, growing health problem associated with high morbidity and mortality.", + "Getting accurate estimates of exposures is difficult, whether this is air pollution or toxins in our food and drink, but these are important questions. Rutter: That is an important point. From the twin study data it is clear that environmental effects account for quite a lot of the variance on all the multifactorial disorders. Yet the kinds of measures that are used aren\u2019t terribly solid. They include broad thing such as socio-economic status (SES). Even where there are good measures the care taken in testing for environmental mediation is usually poor.", + "Bronchiolitis, a disease that happens in the first year of life in many infants, is strongly associated with subsequent asthma. We ascertained it in the first years of life and have been following these people to age 25 now. For the people who had bronchiolitis and now have asthma, their parents recall much better that they had bronchiolitis than those who don\u2019t have asthma now. It is at least twice more. Extraordinarily, some of these latter parents don\u2019t recall that they took their child to the doctor in the fi rst year of life.", + "If you arrive in the USA when you are young you have almost the same prevalence of asthma as an adult as those who are born in the USA and who are not Mexican. But if you arrive at older ages you have less asthma. If you arrive at the age of 20 you have the same asthma risk as those born in Mexico (Eldeirawi et al 2005). Kotb: This is extremely interesting. There is a relationship between depression and the immune system. This especially applies to natural killer (NK) cells, which are the main cells that fight cancers.", + "A colleague of mine in Georgia found this may have a protective effect against later development of asthma (Ownby et al 2002). Martinez: We find significantly decreased likelihood of asthma if you have a dog in a home, but not if you have a cat. The reason for this is not that I hate cats, which I do, but most likely because cats are stealth hunters, and they have to be very clean. Dogs are collective hunters and they don\u2019t care if they smell.", + "; Guffey, S.E. Investigation into pedestrian exposure to near-vehicle exhaust emissions. Environ. Health 2009, 8, 13. [CrossRef] [PubMed] Our World in Data.org. 2017. Available online: https://ourworldindata.org/data-review-air-pollution-deaths (accessed on 10 January 2022). Pope, C.A. , III. Respiratory disease associated with community air pollution and a steel mill, Utah Valley. Am. J. Public Health 1989, 79, 623\u2013628. [CrossRef] [PubMed] Pope, C.A. , III. What do epidemiologic findings tell us about the health effects of environmental aerosols? J. Aerosol. Med. 2000, 13, 335\u2013354. [CrossRef] [PubMed] Pope, C.A. , III.", + "Case for Support BBSRC Grant Application September 2005 \u201cIntegrative Analysis of the Genetic Factors behind Asthma and Atopic Dermatitis\u201d Part I: Research Proposal Background A Introduction of topic of research and its academic and wider context Asthma is the most common disease of childhood, and affects one child in seven in the United Kingdom. Atopic Dermatitis (AD, eczema) affects similar numbers of children. About 60% of children with severe AD will have concomitant asthma. Treatments for both diseases are unsatisfactory. Abandonment of orthodox medical therapy for AD is common in many families who have children with the disease.", + "This is most common during the rainy season when aerosols are created, which results in repeated inhalation of Bp [43, 44]. Environmental sampling studies reveal there is a positive association between the prevalence of disease and the degree of environmental contamination [7]. In addition to environmental factors, data suggests that host factors play an important role in mounting an immune response against infectious diseases [45] such as melioidosis. While healthy persons can contract melioidosis, most patients in endemic regions have an underlying predisposition [28], which suggests that the immunological status of the patient can influence disease initiation and progression [15].", + "Sensitivity analysis We did two sets of post-hoc sensitivity analyses to assess the effects of potential poor recall of age of onset among individuals with adult-onset asthma, and the effects of misclassification of COPD as asthma among the adultonset cases, even with exclusion of cases with a reported diagnosis of COPD, emphysema, or chronic bronchitis.First, to assure that the adult-onset cases did not include a significant proportion of childhood-onset asthma in which symptoms remitted in early life but then relapsed in adulthood, we replaced adult-onset cases with increasing proportions of randomly selected childhood-onset cases, and then tested for association at the two most significant childhood onset-specific loci.This procedure was repeated 20 times for each proportion to quantify the sampling variability (appendix pp 7-8).Second, we did two analyses in which we removed either individuals with ages of asthma onset between 46 and 65 years or adult-onset cases and controls with FEV\u2081/FVC <0\u202270.For each, we compared p values and ORs with the GWAS including all adult-onset cases (appendix pp 8-9).", + " We used data for British white individuals from UK Biobank data release July 19, 2017. 8We extracted disease status (asthma, allergic rhinitis, atopic dermatitis, food allergy, chronic obstructive pulmonary disease (COPD), emphysema, and chronic bronchitis), age of on set of asthma, and sex from self-reported question naires and hospital records (International Classification of Diseases 10th revision [ICD-10] codes) by querying our in-house protected UK Biobank database server. 9For our main case analysis, we included individuals who self-reported that they had doctor-diagnosed asthma.Further details of our research approach are provided in the appendix (pp 4-7).", + "; Guffey, S.E. Investigation into pedestrian exposure to near-vehicle exhaust emissions. Environ. Health 2009, 8, 13. [CrossRef] [PubMed] Our World in Data.org. 2017. Available online: https://ourworldindata.org/data-review-air-pollution-deaths (accessed on 10 January 2022). Pope, C.A. , III. Respiratory disease associated with community air pollution and a steel mill, Utah Valley. Am. J. Public Health 1989, 79, 623\u2013628. [CrossRef] [PubMed] Pope, C.A. , III. What do epidemiologic findings tell us about the health effects of environmental aerosols? J. Aerosol. Med. 2000, 13, 335\u2013354. [CrossRef] [PubMed] Pope, C.A. , III.", + "8 The socio-ecologic framework posits that various aspects of a child\u2019s environment directly and indirectly impact the child\u2019s health and development.9 Drawing on this framework, Beck and colleagues10 examined several biologic, social and ecologic variables to provide a greater understanding of factors influencing asthma-related hospital readmissions for black children compared to their white counterparts. The study revealed that black children were over two times as likely to be readmitted for an asthma-related illness compared to white children; this resulted from significant differences in almost every socio-ecologic variable measured, including disease management practices and access to primary care.", + "Specific Aims Asthma is the most common chronic pediatric medical condition in the United States, with a prevalence over 9.6% in children under 18 years of age.1, 2 Low-income, urban children incur a disproportionate share of asthma prevalence and morbidity;2-4 13% of children living below the poverty threshold are diagnosed with asthma compared to 8% of non-poor (>200% poverty),3 and poverty is associated with higher rates of asthma attacks.1 Living in an urban area confers additional risk for asthma and increased ED utilization.4, 5 Implementation of the National Asthma Education and Prevention Program\u2019s (NAEPP) Guidelines has contributed to reductions in asthma morbidity and mortality rates, and these guidelines emphasize establishing a partnership between healthcare providers and patients/families to promote effective asthma management.6 The NAEPP expert panel states, \u201cbuilding a partnership requires that clinicians promote open communication and ensure that patients have a basic and accurate foundation of knowledge about asthma\u2026\u201d (p.124),6 yet care partnerships also require that the patient/parent effectively communicate issues such as emerging symptoms or response to medications.", + "Vital & health statistics Series 3, Analytical and epidemiological studies. 2012(35):1-58. CDC. Current Asthma Prevalence. https://www.cdc.gov/asthma/most_recent_data.htm. 2015. Updated June 2017. Accessed March 9, 2018. Northridge J, Ramirez OF, Stingone JA, Claudio L. The role of housing type and housing quality in urban children with asthma. Journal of urban health : bulletin of the New York Academy of Medicine. 2010;87(2):211-224. Flores G, Snowden-Bridon C, Torres S, et al. Urban minority children with asthma: substantial morbidity, compromised quality and access to specialists, and the importance of poverty and specialty care.", + "Asthma Prevalence and Disparities Asthma is the most common chronic pediatric medical condition in the United States,1 affecting an estimated 6.2 million children annually.2 Poorly controlled pediatric asthma contributes to over 700,000 visits a year to emergency departments (ED).1 Children living in impoverished, urban settings are disproportionately affected by asthma,3 and the disparate impact of asthma is even worse among black and Latino children, and children whose parents have limited English proficiency (LEP) in these urban low-income areas.4-6 A 2017 longitudinal study revealed that black race and Latino ethnicity are significantly associated with worse asthma outcomes including 1) asthma knowledge, 2) asthma-related quality of life, 3) asthma severity, and 4) asthma control.", + "The Journal of asthma : official journal of the Association for the Care of Asthma. 2017:16. Inkelas M, Garro N, McQuaid EL, Ortega AN. Race/ethnicity, language, and asthma care: findings from a 4-state survey. Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology. 2008;100(2):120-127. National Asthma Education and Prevention Program. Expert Panel Report 3: Guidelines for the Diagnosis and Management of Asthma Bethesda, MD: National Institutes of Health, National Heart, Lung, and Blood Institute; 2007. Publication no. 08-045.1. NIH Consensus Group. Video report: What is mHealth?", + "Contact PD/PI: Coker, Tumaini Rucker INTRODUCTION TO APPLICATION Research Plan Overview Childhood asthma is the most common pediatric medical condition in the United States, and disproportionately affects children living in low-income, urban settings. Many low-income, urban families rely on emergency department (ED) services as their source for sick care for their child. This is often due to not having a primary care provider or sufficient access to their primary care provider for asthma management." + ], + [ + " Apoptosis, or controlled cell death [62], is another major stressed-cell response, and was also represented in our results (Fig. 9e).A large body of direct evidence points to apoptosis as one of the main routes of RPE degeneration in AMD [63].Induction of apoptosis upon stress is dictated by the action of master regulator p53, and it was recently shown that aging increases the activity of p53 in RPE cells and the likelihood for apoptotic cell death [64].Consistent with this evidence, we found association with pathways in Transcriptional regulation by TP53 group (Fig. 9d).In particular, Regulation of TP53 activity through methylation was among the top pathway in our association analysis (Table 1), suggesting that p53 modification by methylation and the closely related histone modifications [Protein lysine methyltransferases (PKMTs) methylate histone lysine in Fig. 9e] play important roles in RPE apoptosis regulation.In the intrinsic apoptotic pathway induced by oxidative stress, cytochrome c is released from mitochondria into the cytosol, binding and activating caspases, the main proteases central to apoptotic action.We found association in pathways involving 'inhibitor of apoptosis' (IAP) and its negative regulator 'second mitochondrial activator of caspases' (SMAC) [65], which suggests that disruption to regulatory mechanisms preventing apoptosis in RPE cells may play roles in AMD.", + "Apoptosis Persistent DNA damage", + "42 ABSTRACT 18 A MODULARIZED MODEL OF APOPTOSIS HA Harrington, KHo, Sk Ghosh, KC Tung , CY Kao, and B Aguda Imperial College London, Courant Institute of Mathematical Sciences New York University, University of Texas at Arlington, University of Texas Southwestern Medical Center, Mathematical Biosciences Institute, and Department of Mathematics, The Ohio State University Columbus, OH, USA Background: One of the key physiological mechanisms employed by the cell (during development and for maintenance of homeostasis) in multi-cellular organism is apoptosis, which is characterized by a sequence of well-defined events resulting in cell destruction.", + "14 Apoptosis is caused by the activation of the caspase cascade, which is initiated by two signaling routes (stress-induced death and death-domain receptor-induced death) (Domen 2001). This process can be prevented by antiapoptotic molecules, such as Bcl-2 (Domen and Weissman 2000). Direct evidence for the involvement of apoptosis in HSC number regulation came from the findings that overexpression of the anti-apoptotic gene bcl-2 led to increased numbers of Thy-1.1low, Sca-1+, c-kit+, Lin- cells, a population with long-term multi-lineage repopulation potential (Domen et al. 2000).", + "Several lines of evidence have indicated that apoptosis acts as an important regulator of stem cells. First of all, expression of some apoptosisrelated genes were detected in human and/or murine HSCs (Domen 2001). Secondly, targeted disruption of some of these genes in null and dominant negative mutant mice interfered with normal apoptotic processes in HSCs. For example, overexpression of Bcl-2, a negative regulator of apoptosis, increased not only the numbers and competitive repopulation capabilities of HSCs, but also the resistance of HSCs to apoptosis induced by ionizing radiation (Domen and Weissman 2003).", + "Apoptosis Cell suicide, or apoptosis, is a well-studied biological phenomenon in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism.The lack of an apparent evolutionary benefit for such a process in a single-celled organism initially caused controversy about the presence of an apoptotic pathway in yeast.Today, however, a number of yeast orthologues to mammalian apoptosis genes have been discovered and apoptotic-like cell death has been linked to mating, colony formation, and aging (Buttner et al. 2006;Eisenberg et al. 2007;Frohlich et al. 2007).With respect to aging, both replicatively and chronologically aged cells that die have increased ROS and display apoptotic phenotypes (Fabrizio et al. 2004a;Herker et al. 2004;Laun et al. 2001).", + " The importance of apoptosis in yeast aging has yet to be fully characterized.At the very least, yeast apoptosis provides a useful pathway for studying genetic interactions for age-related diseases that affect humans, such as cancer.Readers interested in further information related to yeast apoptosis are referred to several in-depth reviews (Buttner et al. 2006;Eisenberg et al. 2007;Frohlich et al. 2007).", + "Early redistribution of plasma membrane phosphatidylserine is a general feature of apoptosis regardless of the initiating stimulus: inhibition by overexpression of Bcl-2 and Abl. J Exp Med 182: 1545-56. Mathew CG (2006). Fanconi anaemia genes and susceptibility to cancer. Oncogene 25: 5875-84. McBride MW, Carr FJ, Graham D, Anderson NH, Clark JS, Lee WK et al (2003). Microarray analysis of rat chromosome 2 congenic strains. Hypertension 41: 847-53. Merino-Trigo A, Kerr MC, Houghton F, Lindberg A, Mitchell C, Teasdale RD et al (2004).", + " When a cell harbors such severe DNA damage that it is beyond repair, it is disposed of through apoptosis.Alternatively, DNA damage can induce cellular senescence, the irreversible cessation of mitosis.Both processes are critically dependent on p53, which is known as the guardian of the genome [3] .DNA damage may also trigger autophagy, a cellular catabolic process that maintains homeostasis [4] .It should be noted that under normal conditions cells are rarely exposed to very high doses of DNAdamaging agents, which may be the explanation why we do not age and die because we run out of cells.However, aging is associated with some atrophy [1] and it is conceivable that at older ages bursts of DNA damage, for example from free radical reactions associated with inflammation, do occur and give rise to an increasingly high rate of apoptosis or cellular senescence.While there is some evidence for increased apoptosis and cellular senescence at old age, it is doubtful that under normal conditions this would lead to a significant loss of functional cells.", + " Apoptosis, or programmed cell death, literally eliminates cells at risk for neoplastic transformation.Senescence, by contrast, permanently arrests their growth.Both processes are controlled by the p53 tumor suppressor protein (Amundson, Myers, & Fornace, 1998;Bringold & Serrano, 2000;Hickman, Moroni, & Helin, 2002;Itahana, Dimri, & Campisi, 2001).p53 is a transcriptional regulator that both transactivates and transrepresses target genes in response to stress (Prives & Hall, 1999;Ryan, Phillips, & Voudsen, 2001).These target genes, in turn, stimulate DNA repair, transient cell cycle arrest, permanent cell cycle arrest (senescence) or cell death (apoptosis), depending on cell type, degree and type of damage, and other variables.In contrast, cells that lack normal p53 regulation or function -for example, tumor cells -tend to die in response to telomere dysfunction.Some normal human cells, on the other hand, undergo a senescence growth arrest.In either case, when present, p53 is crucial for mediating the cellular response to telomere dysfunction (Yaswen & Stampfer, 2002) (Fig. 4).", + "Cell death, and in particular apoptosis, can be caused by a number of mechanisms including loss of growth factors and excitotoxicity (e.g. , Bhutta and Anand, 2002; Nikolic\u0301 et al. , 2013). It is of interest therefore, that proximal to the region of the QTL there are several genes that are related to growth factors including the latent transforming growth factor protein 2 (ltbp2), placental growth factor (pgf), and transforming growth factor beta (Tgf beta).", + " Apoptosis-related gene expression profiles", + " Apoptosis.Programmed death of cells during embryogenesis and metamorphosis or during cell turnover in adult tissues.", + "14 Apoptosis is caused by the activation of the caspase cascade, which is initiated by two signaling routes (stress-induced death and death-domain receptor-induced death) (Domen 2001). This process can be prevented by antiapoptotic molecules, such as Bcl-2 (Domen and Weissman 2000). Direct evidence for the involvement of apoptosis in HSC number regulation came from the findings that overexpression of the anti-apoptotic gene bcl-2 led to increased numbers of Thy-1.1low, Sca-1+, c-kit+, Lin- cells, a population with long-term multi-lineage repopulation potential (Domen et al. 2000).", + "Several lines of evidence have indicated that apoptosis acts as an important regulator of stem cells. First of all, expression of some apoptosisrelated genes were detected in human and/or murine HSCs (Domen 2001). Secondly, targeted disruption of some of these genes in null and dominant negative mutant mice interfered with normal apoptotic processes in HSCs. For example, overexpression of Bcl-2, a negative regulator of apoptosis, increased not only the numbers and competitive repopulation capabilities of HSCs, but also the resistance of HSCs to apoptosis induced by ionizing radiation (Domen and Weissman 2003).", + " Fraction of cells displaying apoptosis", + " It has been known that mitochondria play a central role in the life and death of cells (Kroemer & Reed, 2000).Apoptosis was observed in developmentally arrested embryos by 72 h, but not at 24 h after FCCP treatment, despite considerable telomere attrition at this early stage, suggesting that telomere attrition occurs prior to apoptosis and may serve as an intermediate step between mitochondrial dysfunction and apoptosis.These results also suggest that telomere shortening may signal apoptosis (Lee et al ., 1998;Karlseder et al ., 1999).", + "Cell Death A form of programmed cell death, apoptosis is necessary for normal cell turnover and is essential to a plethora of other biological processes.Apoptosis can be executed via Bcl-2 activation of caspases, via signals from the death receptor on the plasma membrane, or via induction by granzyme B secreted from cytotoxic T cells (Tc cells) [35].Endonucleases and proteases are activated by active caspases, eventually leading to the death of the cell.With age, however, apoptotic activity changes.In heart [36], kidney [37], skeletal muscle [38], and Tc cells [39], increased apoptosis has been reported, perhaps contributing to loss of cellularity in these tissues.This escalation across various tissues may be attributed to the increased production of free radicals [40] and furthermore exacerbated by the accumulation of DNA damage in the aged cells [41].As the risk increases for cells to turn cancerous and dysfunctional with advancing age, increased apoptosis in aged cells is argued to be a defense strategy.In other tissues, such as the colon, apoptosis appears to decrease with age perhaps contributing to the accumulation of senescent cells and age-associated carcinogenesis [42].", + " The regulation and execution of apoptosis in endothelial cells is a complex process involving paracrine factors, membrane receptors, interaction of pro-and anti-apoptotic factors and cysteinyl aspartate-specific proteases (caspases).Recent studies suggest that in aging there is an imbalance in the expression of pro-and anti-apoptotic genes resulting in an enhanced apoptosis in the myocardium (19), central nervous system (24), skeletal muscle (10), lung (33), and liver (2,33).Yet, age-related alterations in the expression of pro-and anti-apoptotic genes in coronary arteries have not been elucidated.", + "Apoptosis modulating genes Apopotosis or programmed cell death is associated with alterations in cell morphology, particularly the nucleus, with endonucleatytic cleavage of DNA into nucleosomal length fragments.Apoptosis may result from withdrawal of growth signals." + ], + [ + "DNA and RNA extraction of tissues Genomic DNA was extracted from frozen placentae (n \u03ed 3/group) and liver (n \u03ed 9/group) using a modified version of an established protocol (28,29).Total RNA was extracted from the remaining tissue using TRIzol, as per the manufacturer's instructions (Invitrogen Canada Inc).Genomic DNA and RNA purity and concentration were assessed using spectrophotometric anal-ysis, and integrity was verified using agarose gel [1% (wt/vol)] electrophoresis.", + "Taxon Sampling and DNA Extractions We extracted DNA from 72 pinned specimens from the National Museum of Natural History (NMNH) Entomology collection for this study.We plucked middle legs from the pinned bees using a pair of sterilized forceps and washed the tissue in 95% ethanol to remove dust, pollen, and other forms of accumulated debris on the bee legs.After evaporation of the ethanol (by drying the tissue on a clean Kimwipe \u2122 ), the samples were placed in a freezer for several hours.DNA was then extracted destructively by grinding the frozen tissue with a sterile pestle, using a DNeasy Blood and TissueKit (Qiagen, Valencia, CA, USA) and following the manufacturer's protocol, except the DNA was eluted in 130\u03bcL ddH 2 O instead of the supplied buffer.We ran 10\u03bcL of each extract for 60 min at 100 volt on 1.5% agarose SB (sodium borate) gels, to estimate size of the genomic DNA.", + "DNA extraction DNA was extracted from PBMCs using the QIAamp DNA Mini kit (Qiagen, CA, USA), following the manufacturer's instructions for the spin protocol.The DNA was eluted in 60 \u03bcl of AE elution buffer and stored at -20\u00b0C.The concentration and quality of the DNA was assessed with the Qubit dsDNA HS Assay (Invitrogen, Eugene, OR, USA).", + "Methods Laboratory procedures.We initially screened 107 ancient samples (Supplementary Data 1) in dedicated clean facilities at the ancient DNA lab of Jilin University, China, following published protocols for DNA extraction and library preparation 36,37 .Prior to sampling, we wiped all skeletal elements with 5% bleach and irradiated with UV-light for 30 min from each side.We drilled teeth to obtain fine powder using a dental drill (Dremel, USA).We sampled the dense part of petrous bones around the cochlea by first removing the outer part using the sandblaster (Renfert, Germany), and then grinding the clean inner part into fine powder with the mixer mill (Retsch, Germany).We digested the powder (50-100 mg) in 900 \u03bcl 0.5 M EDTA (Sigma-Aldrich), 16.7 \u03bcl of Proteinase K (Sigma-Aldrich), and 83.3 \u03bcl ddH 2 O (Thermo Fisher, USA) at 37 \u00b0C for 18 h.Then we transferred the supernatant to a MinElute silica spin column (QIAGEN, Germany) after fully mixed with the 13 ml custom binding buffer [5 M guanidine hydrochloride (MW 95.53), 40% Isopropanol, 90 mM Sodium Acetate (3 M), and 0.05% Tween-20] followed by two washes with PE buffer (80% ethanol).Then we eluted the DNA with 100 \u03bcl TET buffer (QIAGEN, Germany).", + "DNA Extraction After blood was drawn into EDTA tubes, genomic DNA was extracted using a DNA Isolation Kit for Mammalian Blood Kit (Roche Applied Science, Indianapolis, IN, USA) according to the manufacturer's recommendations.Briefly, 300 \u03bcl of whole blood from each sample was mixed with 200 \u03bcl of lysis buffer (50 mM Tris pH 8.0, 100 mM EDTA, 100 mM NaCl, 1% SDS) and 40 \u03bcl of Proteinase K, followed by addition of 100 \u03bcl of isoproponal and 500 \u03bcl of Inhibitor Removal Buffer (5M guanidine-HCl, 20 mM Tris-HCl pH 6.6).The DNA was washed with a buffer (20 mM NaCl; 2 mM Tris-HCl; pH 7.5), centrifuged twice at 2000 rpm, washed using cold 70% ethanol and centrifuged at 3000 rpm.The supernatant was discarded and the pellet containing purified genomic DNA was diluted in TE buffer (1 mM EDTA; 10 mM Tris-HCl, pH 7.5) to a concentration of approximately 50 ng/\u03bcl.", + "Genomic DNA extraction Leukocytes were isolated from 5-ml peripheral blood samples.DNA was prepared by phenol extraction and chloroform extraction followed by isopropanol precipitation, washed with ethanol, and air-dried.Tris-EDTA buffer pH 8.0 was used to dissolve the final genomic DNA product.", + " The pulled down DNA fragments were extracted and purified using phenolchloroform extraction/ethanol precipitation.The samples were stored at -20 \u00b0C until use.", + "DNA extraction for genotyping For the majority of samples, DNA was extracted from either spleen or the exocrine fraction of the islet isolation using the Tissue DNA Purification Kit according to manufacturer's instructions on an automated Maxwell 16 system (both Promega, USA).When no other tissue was available, DNA was extracted from human islets using the Trizol fraction remaining after extraction of RNA (see above).To precipitate the DNA, 300\u03bcl 100% ethanol was added to the thawed solution.This mixture was incubated at room temperature for a minimum of 30 minutes.DNA was then pelleted by centrifugation at 4,000 x g for 5 minutes at 4\u00b0C.After removing the supernatant, the pellet was twice washed with 0.1M trisodium citrate (Sigma Aldrich, UK) in 10% ethanol and left at room temperature for 30 minutes, followed by another wash step with 75% ethanol.After the final wash step, pellets were air-dried for 10 minutes to remove residual ethanol and re-suspended in a minimum of 100 \u03bcL 8mM NaOH (Sigma Aldrich).Extracted DNA was stored at -20\u00b0C before further use.", + "DNA extraction Tissue samples were incubated at 50\u00b0C overnight with shaking in DNA extraction buffer (100 mM NaCl, 10 mM Tris.HCl pH8, 25 mM EDTA, 0.5% (w/v) SDS), containing 200 \u03bcg/ml proteinase K. DNA was isolated by two rounds of phenol:chloroform extraction, followed by RNAse A treatment, precipitation in absolute ethanol containing 10% (v/v) sodium acetate (3 M, pH 5.2), and resuspended in 100 \u03bcl nuclease-free water (Ambion, Austin, TX, USA) or using salting out method followed by purification with Qiagen blood and tissue kit (Qiagen, Mississauga, ON, USA).DNA was stored at -20\u00b0C.", + "Methods Human DNA samples DNA was extracted from human patient tissue samples acquired from the University of Minnesota Tissue Procurement Facility from BioNet (IRB#0805E32181).See Supplemental Table S4 for patient data.Briefly, 2 mg of tissue was digested overnight at 55\u00b0C on a rotating platform in 710 mL of digest buffer (1 M Tris at pH 8.0, 1 mM EDTA, 13 SSC, 1% SDS, 1 Mm NaCl, 10 mg/mL Proteinase K).Following digest, DNA was purified using phenolchloroform-isoamyl alcohol (Life Sciences) isolation protocol.", + "3.2.2 Isolation of genomic DNA Genomic DNA was isolated from frozen liver tissue. The isolation was conducted using the Qiagen DNeasy\uf8e8 Blood & Tissue Kit (Qiagen) according to the manufacturer\u2019s protocol. DNA concentration was evaluated photometrically at a wavelength of 260 nm using the FusionTM Universal Microplate Analyzer. For nucleic acid quantification, the Beer-Lambert (A = \u03b5 * b * c) equation is modified to use an extinction coefficient with units of M-1 cm-1.", + " Most typically, DNA is extracted from blood samples, dried blood spots, buccal swabs, saliva, tissue and even urine and stool samples.In forensic science, other sources have been validated e.g.bone, tooth pulp, dandruff and others.", + "DNA isolation High-molecular weight DNAs was isolated from the samples by organic solvent extraction method, followed by precipitation in cold ethanol [14].", + "Genomic DNA extraction DNA from MEF cultures or mouse liver was isolated by phenol/chloroform extraction, as described [11].", + "DNA is usually recovered from cells by methods that include cell rupture but that prevent the DNA from fragmenting by mechanical shearing. This is generally undertaken in the presence of EDTA, which chelates the magnesium ions needed as cofactors for enzymes that degrade DNA, termed DNase. Ideally, cell walls, if present, should be digested enzymatically (e.g. , lysozyme in the bacteria or bacterial cell). In addition the cell membrane should be solubilized using detergent.", + "DNA solutions can be stored frozen, although repeated freezing and thawing tends to damage long DNA molecules by shearing. A flow diagram summarizing the extraction of DNA is given in Fig. 1.2. The above-described procedure is suitable for total cellular DNA. If the DNA from a specific organelle or viral particle is needed, it is best to isolate the organelle or virus before extracting its DNA, because the recovery of a particular type of DNA from a mixture is usually rather difficult.", + "Genomic DNA extraction Genomic DNA was extracted by the mixed alkyl trimethyl ammonium bromide (MATAB) procedure.Briefly, 250 mg of plant material was ground in liquid nitrogen and immediately incubated in 2 ml of pre-warmed extraction buffer (100 mM Tris-HCl, pH 8, containing 20 mM EDTA, 1.4 M NaCl, 2% (w/v) MATAB, 1% (w/v) PEG6000 (polyethylene glycol), 0.5% (w/v) sodium sulfite, 20% (w/v) Igepal CA630, 20% (w/v) lithium dodecyl sulfate, and 20% (w/v) sodium deoxycholate) at 74 \u00b0C for 20 min.After purification with 2 ml of chloroform:isoamylalcohol (24:1, v/v), DNA extracts were precipitated with 1.6 ml of isopropanol then resuspended in 1 ml of buffer (50 mM Tris-HCl, pH 8, containing 10 mM EDTA and 0.7 M NaCl).The extracts were purified on anion exchange columns (QIAGEN-tip 20) following the manufacturer's instructions (QIAGEN, Valencia, CA).", + "DNA extraction and enzymatic digestion Total DNA was isolated from whole blood and separated blood subtypes using a Qiagen DNeasy Blood & Tissue Kit following the manufacturer instructions.After extraction, DNA was quantified by NanoDrop (Thermo Scientific NanoDrop products, Wilmington, DE).The isolated genomic DNA was enzymatically digested according to previously described method.Briefly, DNA (3 \u03bcg) was first denatured by heating at 95 \u00b0C for 5 min and then chilling on ice for 2 min.Then, 1/10 volume of S1 nuclease buffer (30 mM CH 3 COONa, pH 4.6, 280 mM NaCl, 1 mM ZnSO 4 ) and 100 units of S1 nuclease were added before the mixture (20 \u03bcL) was incubated at 37 \u00b0C for 16 h.Subsequently, after 1/10 volume of alkaline phosphatase buffer (50 mM Tris-HCl, 10 mM MgCl 2 , pH 9.0), 0.002 units of venom phosphodiesterase I, and 10 units of alkaline phosphatase were added, the solution was incubated at 37 \u00b0C for an additional 4 h followed by extraction with an equal volume of chloroform for twice.The aqueous layer was collected and lyophilized to dryness and then reconstituted in 100 \u03bcL water.About 30 \u03bcL of the obtained samples were then subjected to liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) analysis.", + " The conventional DNA extraction procedure involved the homogenization of single D. magna in 400 l of sperm lysis buffer (100 mM Tris-HCl, pH 8; 500 mM NaCl; 10 mM ethylenediaminetetraacetic acid [EDTA], pH 8; 1% SDS; 2% mercaptoethanol) followed by RNase treatment (40 g, 37\u040aC for 1.5 h).The DNA was then extracted in phenol (pH 8) and chloroform:isoamyl alcohol (1:1).The DNA was finally precipitated by two volumes of ice-cold ethanol in the presence of 3 M sodium acetate (1/10 of the DNA volume) and was incubated at \u03ea80\u040aC overnight.Precipitated DNA was harvested by centrifugation, dried in air, and the final pellet dissolved in sterile analytic grade water.", + "DNA extraction and quantification DNA was extracted from whole organs by standard techniques (34) with emphasis on minimizing shearing or nicking of DNA as nicked DNA has been shown to be refractory to LX-PCR (35).DNA from the brain was extracted from the right hemisphere.Extracted DNA was resuspended in 10 mM Tris 1 mM EDTA (pH 8) (TE) and stored at 4_C.A number of samples were normalized for mtDNA content by dot blotting and hybridization with digoxigenin-labeled full-length mtDNA and densitometry.In cases where mtDNA quantification was not carried out, the DNAs were normalized by A 260 of total DNA." + ], + [ + "Using genetic markers, the pattern of inheritance can be tracked through families. For example, by analyzing a marker linked to the eye color gene in several generations, it is possible to determine from which grandparents a child has inherited its eye color alleles. More importantly, \ufb01nding a marker linked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.", + " It is well known, however, that not all genomic markers are independent (Frazer et al., 2007).Genetic variation is often inherited in contiguous segments of DNA, such that there tends to be correlation between the inheritance of alleles at markers close to each other on the same chromosome.This genetic correlation is called linkage disequilibrium (LD), and, as a result, the effective number of independent tests (M eff ) conducted is less than the total number of markers (M).By effective number of tests, we mean the number of independent tests that would have to be conducted to lead to a null distribution for the minimum P-values that was approximately the same as that obtained when conducting tests that are necessarily correlated due to LD.", + "Genetic mapping is a powerful strategy that exploits genomic information to dissect complex traits into Mendelian loci (quantitative trait loci or QTL) and identifies genetic * Correspondence: marioenrico.pe@sssup.it 1 Institute of Life Sciences, Scuola Superiore Sant\u2019Anna, Pisa, Italy Full list of author information is available at the end of the article determinants that may lead to crop improvement. As marker density ceases to be a limiting factor [3], our ability to discover specific genetic determinants in a single mapping study depends upon the availability of populations with high genetic diversity and recombination density [4].", + "This capacity allows samples to be placed into meaningful genetic groups that reflect evolutionary relationships (more stable, lower diversity markers), while simultaneously permitting high levels of strain resolution (high diversity markers). From a clinical perspective, markers that accurately reflect broad evolutionary relationships are valuable for comparing the genetic similarity of an isolate to isolates on a regional or global scale, whereas high-resolution markers are valuable for detailed epidemiological tracking in an outbreak. Variable-number tandem repeats (VNTRs) are genetic markers that can span a range of variability and, therefore, can capture genetic relationships on multiple scales (18\u201319).", + "Identifying the genetic loci that modulate a trait based on correlation between variation in phenotype and variation in genotype is the essence of genetic mapping. This first involves systematically genotyping a genetically diverse population using microsatellite or SNP markers. The phenotype of interest is then measured and its variability in the population assessed. A statistical test is then carried out to identify chromosomal regions that segregate with the trait and show linkage with the trait, i.e. , 3 identify genetic regions that have the same genotype among individuals with similar trait values but differ between individuals with dissimilar trait values.", + "Using genetic markers, the pattern of inheritance can be tracked through families. For example, by analyzing a marker linked to the eye color gene in several generations, it is possible to determine from which grandparents a child has inherited its eye color alleles. More importantly, \ufb01nding a marker linked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.", + "Genetic variation For decades researchers used single markers to elucidate clinal differentiation and spatial variation in allele frequencies.This approach revealed multiple markers with variation that tracked the clines, including some with the same allele at higher frequency at the same latitude in the Northern and Southern hemispheres.Examples include alcohol dehydrogenase (Adh), a-glycerol-3-phosphate dehydrogenase (Gpdh), glucose-6-phosphate dehydrogenase (G6pd), esterase-6 (Est-6), octanol dehydrogenase (Odh), and 6-phosphogluconate dehydrogenase (Pgd) [30][31][32][33] (Table 1).Perhaps the most heavily explored locus in D. melanogaster has been Adh, the first step in the ethanol detoxification pathway.The Adh-F allele encodes high catalytic activity of ADH, but this increase in activity trades off with enzyme stability at higher temperatures [34,35].Unsurprisingly, the Adh-F allele is found at a higher frequency in cooler high-latitude populations, and differentiation has occurred in parallel along clines in", + "In the case of genetic markers, this easily runs in the several hundreds to thousands. Moreover, the optimal subset of markers is heavily dependent on how these markers are combined, i.e. dependent on the optimal Boolean function . Altogether, one frequently has to rely on greedy search strategies that easily get stuck in local optima or near exhaustive searches that are computationally too expensive, especially when employed in permutation procedures required to assess statistical significance. Our solution to this problem hinges upon two observations.", + "GENE MAPPING The opportunity to merge advances in molecular genetic technology with advances in statistical techniques expanded in earnest with the development of DNA markers such as restriction fragment length polymorphisms (Lander and Botstein, 1989).Research exploded in the past decade with the continued refinement of molecular technology yielding a variety of DNA markers-e.g., short tandem repeats (STRs) or microsatellites; variable number of tandem repeats (VNTRs); single nucleotide polymorpohisms (SNPs), and gene expression microarrays or gene chips.A genetic marker is a measurable polymorphic sequence of DNA whose chromosomal location is known.Markers often have no known functional significance but are used as pointers to a particular chromosomal location.The logic of gene mapping technology is simple: Determine if there is a relationship between variability in a phenotype and variability in an anonymous DNA marker of known chromosomal location.If there is a relationship, it is taken as evidence that there is a gene that influences the trait at or near the marker.", + "Genetic drift. Genetic changes in populations caused by random phenomena rather than by selection.Genetic marker.A segment of DNA with an identifiable physical location on a chromosome whose inheritance can be followed.A marker can be a gene, or it can be some section of DNA with no known function.", + " Biological characteristics indicating initial resiliency or susceptibility of an organism include genetic profiles.As noted above, genetic markers need to have a high prevalence in the population and have a reasonably strong effect on common population health outcomes, or have an interaction effect with other health-affecting mechanisms, to be candidates for inclusion in population studies.At the moment, the only known genetic marker of clear value in a population survey is the apolipoprotein E gene (APOE), although this is likely to change in the very near future.APOE allele status is clearly related to a number of major health outcomes in older populations which are reasonably well measured in population surveys: mortality, heart disease, and cognitive functioning (Albert et al., 1995b;Corder et al., 1993;Evans et al., 1997;Ewbank, 1997;Hofman et al., 1997;Hyman et al., 1996;Luc et al., 1994;Saunders et al., 1993).Both the prevalence of alleles indicating higher risk and the size of the effect are large enough to be of importance in explaining variability in currently studied health outcomes.APOE allele status has been shown to have independent effects on health outcomes and to interact with other life circumstances such as sex and race in its effect on health outcomes (Jarvik et al., 1995;Maestre et al., 1995;Payami et al., 1992).Incorporation of information on this genetic indicator could lead to increased knowledge of the interactive mechanisms of this genetic marker and other social and behavioral variables and thus clarify some of the mechanisms leading to population differentials in cognition, heart disease, and mortality.", + " As described by Hermalin (1999), if genetic markers are modeled as part of an individual's physiological structure, they can provide controls for predisposing factors that affect more proximate mid-level markers of function as well as downstream health outcomes.This potential benefit of genetic information-i.e., its power in explicating the black box of Figure 11-1-may outweigh, or at least precede, its near-term potential for discovering genetic links to chronic disease.As discussed by Weiss (1998b), the situation with chronic disease differs from single locus disorders that are inherited following well-identified Mendelian rules.In general, we cannot expect to find relationships that are even as straightforward as the APOE links to cardiovascular and Alzheimer's disease.Variation across populations, difficulty in identifying a small enough area on the chromosome to search for disease-associated genes, and the problems inherent in identifying continuous outcomes with particular genes may limit finding the connections.", + "This capacity allows samples to be placed into meaningful genetic groups that reflect evolutionary relationships (more stable, lower diversity markers), while simultaneously permitting high levels of strain resolution (high diversity markers). From a clinical perspective, markers that accurately reflect broad evolutionary relationships are valuable for comparing the genetic similarity of an isolate to isolates on a regional or global scale, whereas high-resolution markers are valuable for detailed epidemiological tracking in an outbreak. Variable-number tandem repeats (VNTRs) are genetic markers that can span a range of variability and, therefore, can capture genetic relationships on multiple scales (18\u201319).", + "These variations provide a species the ability of adapting to the environment change (Liu and Cordes, 2004). DNA markers are among the most powerful tools for revealing genetic variations in organisms. Historically, many different types of markers have been used for aquaculture studies Functional Genomics in Aquaculture, First Edition. Edited by Marco Saroglia and Zhanjiang (John) Liu. \u2402 C 2012 John Wiley & Sons, Inc. Published 2012 by John Wiley & Sons, Inc. 41 42 Functional Genomics in Aquaculture Table 2.1 A summary of characteristics of various molecular markers used in aquaculture species.", + "For instance, mapping of a trait or a phenotype would require polymorphic DNA markers such as microsatellites (SSRs) or single nucleotide polymorphisms (SNPs); expression pro\ufb01ling would require genome annotation information; microarray design would require sequence information of genes, etc. The objective of this chapter is to provide a general review of genomic resources needed, and currently present for aquaculture species, for functional genomics studies. Polymorphic DNA Markers The key factor behind the signi\ufb01cant differences at the level of individuals, species, and higher order of taxonomic groups is genetic variation (polymorphism).", + "Functional genomics: The study of genes, their resulting proteins, and the role played by the proteins in the biochemical processes of the body.Gene: A unit of inheritance; a working subunit of DNA.Each of the 20 000 to 25 000 genes in the body contains the code for a specific product, typically a protein such as an enzyme.Gene expression: The process by which the coded information of a gene is translated into the structures present and operating in the cell (either proteins or ribonucleic acids).Gene markers: Landmarks for a target gene, either detectable traits that are inherited along with the gene or distinctive segments of DNA.Gene map: A description of the relative positions of genes on a chromosome and the distance between them.Genetic counseling: A short-term educational counseling process for individuals and families who have a genetic disease or who are at risk for such a disease.Genetic counseling provides patients with information about their condition and helps them make informed decisions.Genetic linkage maps: DNA maps that assign relative chromosomal locations to genetic landmarks-either genes for known traits or distinctive sequences of DNA (ie, genetic markers)-on the basis of how frequently they are inherited together.Genetic testing: Examining a sample of blood or other body fluid or tissue for biochemical, chromosomal, or genetic markers that indicate the presence or absence of genetic disease.Genetics: The scientific study of heredity, how particular qualities or traits are transmitted from parents to offspring.Genome: All the genetic material in the chromosomes of a particular organism.Genome-wide: Descriptor that indicates that the entire breadth of the genome has been examined in a study (eg, a linkage or association study).Genome-wide studies do not resequence the entire genome but type (an increasingly large set of) markers distributed throughout the genome.Genomics: A \"scaled-up\" version of the science of genetics that investigates the structure and function of large sections of the genome simultaneously.Genotype: The actual genes carried by an individual (as distinct from phenotype-ie, the physical, bodily characteristics into which genes are translated).Haplotype: A way of denoting the collective genotype of a number of closely linked loci on a chromosome.Heritability (h 2 ): For any trait, the proportion of the phenotypic variability resulting from genetic variance.Note that heritability does not indicate the degree to which a trait is \"genetic. \"Nor does a high h 2 mean that the trait cannot be influenced by environment.A heritability significantly \u03fe0, however, can provide a rationale for further genetic and genomic study of a trait of interest.Heterozygous: Possessing 2 different sequences (ie, genotypes) of a particular gene, 1 inherited from each parent.High-throughput genotyping: In contrast to the older labor-and time-intensive genotyping methods, high-throughput genotyping makes use of robots, computers, and other evolving technologies, thus enabling laboratories to type up to hundreds of thousands of polymorphisms in many samples in a relatively short period of time.Homozygous: Possessing 2 identical sequences of a particular gene, 1 inherited from each parent.Interaction: The differing effect of 1 independent variable on the dependent variable, depending on the particular level of another independent variable.For example, there would be an interaction between the factors sex and treatment if the effect of treatment was not the same for male and female subjects in a drug trial.Linkage analysis: A gene-hunting technique that traces patterns of heredity in large, high-risk families in an attempt to locate a disease-causing gene mutation by identifying traits that are coinherited with it.Linkage disequilibrium: Two alleles at different loci that occur together on the same chromosome more often than would be predicted by chance alone.It is a measure of cosegregation of alleles in a population.", + "Source: Kearsey and Pooni (1996). Genetic maps consist of a series of markers or identifiable features at known, or perhaps best described as estimated, locations on the genome (see Figure 9). For some discrete traits, simple Mendelian inheritance is followed and the phenotype has a one to one correspondence with the genes controlling it. These are so called morphological markers, which were then related to continuous or quantitative traits of interest. Examples are shape, colour, size or height in particular varieties of peas, as studied by Mendel. For another example, see Appendix A.2.", + "Genomic markers used in linkage mapping have evolved from restriction fragment length polymorphisms (RFLPs) to microsatellites (simple sequence repeat polymorphisms; SSRPs), to single-nucleotide polymorphisms (SNPs), with the more modern markers exhibiting higher frequencies in the genome (thus ensuring fuller coverage). Linkage mapping of a trait is in fact the demonstration of linkage between the phenotype and a genomic marker, followed by an inference of linkage between the genomic marker and the responsible DNA variant. Transitive logic ties the phenotype with the DNA variant, which is of course the point of the exercise. See Fig.", + "However, because of time constraints it is often more practicable to choose an appropriate mapping population that is already available through the current stock centers. Plant species chosen for study will depend largely on the availability of suitable plant resources. Obtain appropriate mapping population information to include information on markers/genotypes (see Note 4). A marker is an identifying factor; a gene or other DNA of known location that is used to track the inheritance and so on of other genes whose exact location is not yet known.", + "The closer two genes are together on a chromosome, the less likely it is for a recombination event to occur between the two, causing a non-random association. This is the basis for genetic linkage. The development of genetic markers allowed the theory of linkage disequilibrium (LD) to be used in mapping genes. Genetic markers are speci c genetic di\u241berences between species or cultivars, and genetic linkage of these markers to particular morphological traits can allow genetic markers to be used to represent the gene of interest (Collard et al. , 2005)." + ], + [ + "This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction The association between a complex phenotypic trait and genetic markers on the chromosomes can be detected through statistical analysis, leading to the identification of quantitative trait loci (QTL)\u2014regions of the chromosomes that appear to be associated with the phenotype. Quantitative trait loci (QTL) are expected to be associated with the genes controlling some aspects of the phenotype.", + "Nowadays many different cost-efficient genotyping solutions (including sequencing and Single Nucleotide Polymorphisms arrays) have opened the way to systematic genome-wide fine mapping of quantitative traits (Quantitative Trait Locus or QTL mapping). The process of QTL mapping (Figure 1) consists in searching for genome regions that influence the value of a given trait. For example, identifying a QTL for plant height means finding a DNA region at which the plants that carry a certain allele tend to be significantly higher or lower than those carrying another allele.", + "QTLs are regions within the genome whose genetic variation modulates quantitatively a phenotype characteristic of the particular trait under study (Lynch and Walsh, 1998). Determining the association between variations in specific disease phenotypes or a trait, with variations in genotypes of a reference population can be used to locate a QTL. One of the methods used for mapping QTLs associated with complex traits is genetic markers-trait association. Genetic markers associated with certain loci can be inherited in linkage disequilibrium. Generating populations with linked loci in disequilibrium is achieved though either crosses between inbred lines, or use of the out-bred populations.", + "Often, the first step in analysis of new trait data is single-marker regression across all chromosomes. A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).", + "One possible approach to facilitate this endeavor is to identify quantitative trait loci (QTL) that contribute to the phenotype and consequently unravel the candidate genes within these loci. Each proposed candidate locus contains multiple genes and, therefore, further analysis is required to choose plausible candidate genes. One of such methods is to use comparative genomics in order to narrow down the QTL to a region containing only a few genes. We illustrate this strategy by applying it to genetic findings regarding physical activity (PA) in mice and human.", + "Elucidation of the molecular basis of these traits has proven difficult as they are under the control of multiple genes and genetic loci. The standard approach to gene identification involves mapping by linkage analysis in experimental crosses, and this has led to the localization in the rat genome of hundreds of quantitative trait loci (QTLs) underlying trait variation (68). We refer to these loci as physiological quantitative trait loci (pQTLs).", + " Often, the first step in analysis of new trait data is single-marker regression across all chromosomes.A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992).For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).By default, it returns a list of marker loci that show greater than sugges-tive association with the trait according to standard criteria (Lander and Kruglyak, 1995), but it will also accept user-defined criteria.Local maxima in the LRS in this list identify loci that are most likely to be near QTLs.WebQTL provides this list within a few seconds.", + "QTLs can be identified through their genetic linkage to visible marker loci with genotypes that can be readily classified [94, 97]. As such, markers that are genetically linked quantitative trait will segregate more often with trait values, whereas unlinked markers will lack an association with the phenotype [94, 98]. The principal goal of a QTL analysis is to identify all QTLs linked to a trait and discern whether phenotypic differences are mainly due to a few loci with large effects, or many loci with small effects [98].", + "This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction The association between a complex phenotypic trait and genetic markers on the chromosomes can be detected through statistical analysis, leading to the identification of quantitative trait loci (QTL)\u2014regions of the chromosomes that appear to be associated with the phenotype. Quantitative trait loci (QTL) are expected to be associated with the genes controlling some aspects of the phenotype.", + "The basic principle of classic QTL is trait segregation along with the markers and necessitated the availability of two or more genetically different lines corresponding with the phenotypic trait. Markers like single nucleotide polymorphisms (SNPs) and microsatellites are used for genotypic distinctions (Vignal et al. , 2002). QTL mapping is achieved in four basic steps; the first one is the measurement of variation for a trait in the individuals. It is a prerequisite to have the traits that show phenotypic variability among the individuals (inbred strains).", + " Often, the first step in analysis of new trait data is single-marker regression across all chromosomes.A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992).For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).By default, it returns a list of marker loci that show greater than sugges-tive association with the trait according to standard criteria (Lander and Kruglyak, 1995), but it will also accept user-defined criteria.Local maxima in the LRS in this list identify loci that are most likely to be near QTLs.WebQTL provides this list within a few seconds.", + "Often, the first step in analysis of new trait data is single-marker regression across all chromosomes. A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).", + "Quantitative Trait Locus (QTL) mapping To map QTL, we used 934 AXB/BXA genetic informative markers obtained from http://www. genenetwork.org. For all the in vitro measurements and gene expression linkage analysis, a genome-wide scan was performed using R/qtl [57]. Significance of QTL logarithm-of-odds (LOD) scores was assessed using 1000 permutations of the phenotype data [114] and the corresponding p-values reported. For the cellular phenotypes, QTL significance was reported at a genome-wide threshold corresponding to p < 0.05.", + "Typically one may obtain a location known to derive from only one of the two parent strains that contains a chromosomal region that correlates with a trait of interest. Since the actual gene and gene product will frequently remain unknown, the region is referred to as quantitative trait locus (QTL), and is simply named for the trait itself (Alberts & Schughart, 2010). Growing sets of strain-dependent marker locations in established RI strains are continually updated in online repositories.", + "By definition, a quantitative trait locus is a chromosomal region that contains a gene, or genes, that regulate a portion of the genetic variation for a particular phenotype (Wehner et al. 2001). The goal of QTL mapping is to identify regions of the genome that harbour genes relevant to a specified trait. QTL map locations are commonly determined by initial screening of mice with specific genetic characteristics, such as recombinant inbred strains, the F2 of two inbred strains, or recombinant congenic strains (Flint 2003).", + "Often, the first step in analysis of new trait data is single-marker regression across all chromosomes. A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).", + "QTL linkage studies are conducted in order to map a region or regions of the genome which affect a continuous or quantitative trait. In agriculture, as soon as markers linked to QTL are found for economically important traits, these markers can be used for selecting individuals in breeding programmes. In human studies, the aim is often to identify markers indicating disease susceptibility. Current techniques for measuring markers are usually relatively slow and laborious. Newer DNA technology, such as SNP or single nucleotide polymorphisms (Kwok, 2001b; Patil et al.", + "Genomic regions linked to complex traits can be identified by genetic mapping and quantitative trait locus (QTL) analysis (Shehzad and Okuno 2014). 7 QTL mapping QTL mapping with molecular markers is the first strategy in genetic studies. In plant breeding, QTL mapping is an essential step required for marker-assisted selection (Mohan et al. 1997; Shehzad and Okuno 2014). The fundamental idea underlying QTL analysis is to associate genotype and phenotype in a population exhibiting a genetic variation (Broman and Sen 2009).", + "Four steps of QTL mapping are (1) development a W population, (2) genotyping the population using molecular markers, (3) phenotyping the population for an interested trait, and (4) QTL analysis using statistical procedures to find IE markers linked to the QTL (Bernardo 2002). PR EV Populations used for genetic mapping can be a segregating population (F2 and backcross) or a permanent population (double haploids or recombinant inbred lines). Recombinant inbred lines (RILs) are developed by selfing of individual progenies of the F2 plants until homozygosity is achieved (F7-F8).", + "This tool allows systems genetic analysis of single genes or small sets of genes using a bottom-up approach. relations define quantitative trait loci (QTLs). Because the marker is not typically the actual site of the polymorphism, interpolative methods have been developed to estimate the distance of the QTL from the marker and the strength of the association. Using multiple-regression and model-fitting methods, the true complexity of the phenotypic variation can be modeled through the consideration of multiple loci and environmental factors as predictors [13]." + ], + [ + " To overcome the lack of phenotypic information in the 1000 Genomes Project, The ClinSeq Project was developed to pilot large-scale genome sequencing for research in genomic medicine at the National Institutes of Health Clinical Research Center in Bethesda, MD. 40 The study seeks to enroll 1000 individuals who will be evaluated for personal health status and family history.The project aims to:", + "We (Hein, Schierup and Wiuf) have published a 300 page book on molecular population genetics titled \u201cGene Genealogies, Sequence Variation and Evolution\u201d Oxford University Press, and are presently developing a tutorial in association mapping that we hope to publish as a booklet in 2006 and are also involved in a very large EU collaboration (Holland, Denmark, Iceland and UK) to find susceptibility genes for breast and prostate cancer. In comparative genomics, the most fundamental investigation is to find genes in a pair of aligned genomes.", + "Key bioinformatic steps to take a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stage regions in man (see Chapter 5). Similar issues also exist in the establishment of true orthology between genes in different species, where one is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation (Fitch, 2000).", + "In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily on Bioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes 2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB) \u2402 C 106 CH 6 COMPARATIVE GENOMICS questions of relevance to human genetics. We begin, in Section 6.2 by presenting an overview of genome structure and content, providing a context for the subsequent discussions.", + "4 Assembling a View of the Human Genome Colin A. M. Semple Bioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK 4.1 Introduction The miraculous birth of the draft human genome sequence took place against the odds. It was only made possible by parallel revolutions in the technologies used to produce, store and analyse the sequence data, and by the development of new, large-scale consortia to organize and obtain funding for the work (Watson, 1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.", + "This fully indexed but semi-intelligible Bioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes 2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB) \u2402 C 4 CH 1 BIOINFORMATICS CHALLENGES FOR THE GENETICIST \u2018book of life\u2019 immediately began to serve as a valuable framework for integration of genetic and biological data. However, knowledge of the genome sequence did not immediately clarify the nature and structure of human genetic variation.", + " Methods for DNA sequencing are constantly being improved, with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000, an end that appears to be in sight (Hayden, 2014).In the very near future, whole-genome sequencing will be routinely available for clinical purposes, perhaps even beginning at birth.The major challenge ahead is the interpretation of this information.How do our genes interact with each other, and how does the environment contribute to the development of health and disease?What are the individual and societal implications of knowing our genome sequence?The answers to these and other important questions will unfold in the years ahead.Thus, we are truly in an era where precision medicine may soon become a reality.", + " Characteristics of genotyping and sequencing technologies", + "Key bioinformatic steps to take a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stage regions in man (see Chapter 5). Similar issues also exist in the establishment of true orthology between genes in different species, where one is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation (Fitch, 2000).", + "In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily on Bioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes 2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB) \u2402 C 106 CH 6 COMPARATIVE GENOMICS questions of relevance to human genetics. We begin, in Section 6.2 by presenting an overview of genome structure and content, providing a context for the subsequent discussions.", + "4 Assembling a View of the Human Genome Colin A. M. Semple Bioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK 4.1 Introduction The miraculous birth of the draft human genome sequence took place against the odds. It was only made possible by parallel revolutions in the technologies used to produce, store and analyse the sequence data, and by the development of new, large-scale consortia to organize and obtain funding for the work (Watson, 1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.", + "Introduction Since the first human genome was sequenced at an estimated cost of $150 million, several advanced high-throughput techniques \u2013 some with lower costs - have come up. At the same time, this resulted in a data deluge and a critical need to connect the heterogeneous sequencing data and associated annotations \u2013 structural and functional with the basic tenets of biology or molecular basis of development and disease.", + "Key bioinformatic steps to take a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stage regions in man (see Chapter 5). Similar issues also exist in the establishment of true orthology between genes in different species, where one is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation (Fitch, 2000).", + "In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily on Bioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes 2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB) \u2402 C 106 CH 6 COMPARATIVE GENOMICS questions of relevance to human genetics. We begin, in Section 6.2 by presenting an overview of genome structure and content, providing a context for the subsequent discussions.", + "4 Assembling a View of the Human Genome Colin A. M. Semple Bioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK 4.1 Introduction The miraculous birth of the draft human genome sequence took place against the odds. It was only made possible by parallel revolutions in the technologies used to produce, store and analyse the sequence data, and by the development of new, large-scale consortia to organize and obtain funding for the work (Watson, 1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.", + " Ample time was allotted to answer questions and a copy of \"A Guide to Your Genome\" (National Human Genome Research Institute 2007) was provided to further assist participants' understanding and ability to communicate results with family members or others.", + " Whether within 10 or 12 (or 8) years, such inexpensive sequencing will change both research and clinical care, and progress does not need to wait even that long.The National Human Genome Research Institute (NHGRI) plans to focus a significant portion of the sequencing capacity that it supports on medical sequencing.For instance, the NHGRI and the National Cancer Institute are actively considering a Human Cancer Genome Project, 22 which would use DNA sequencing and a host of other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of cancer.Medical sequencing should also provide important insight into many other diseases.For example, sequencing all exons in X-linked mental retardation syndromes may reveal much about their etiology.Sequencing candidate genes in the extremes of the distribution of quantitative traits should also reveal much of importance about common diseases, such as coronary atherosclerosis. 23With further technological advances, other previously unimaginable research approaches will become real.", + "Key bioinformatic steps to take a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stage regions in man (see Chapter 5). Similar issues also exist in the establishment of true orthology between genes in different species, where one is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation (Fitch, 2000).", + "In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily on Bioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes 2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB) \u2402 C 106 CH 6 COMPARATIVE GENOMICS questions of relevance to human genetics. We begin, in Section 6.2 by presenting an overview of genome structure and content, providing a context for the subsequent discussions.", + "4 Assembling a View of the Human Genome Colin A. M. Semple Bioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK 4.1 Introduction The miraculous birth of the draft human genome sequence took place against the odds. It was only made possible by parallel revolutions in the technologies used to produce, store and analyse the sequence data, and by the development of new, large-scale consortia to organize and obtain funding for the work (Watson, 1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + ], + [ + " In birds, where erythrocyte telomere length (ETL) is measured, the majority of species sampled have shown no sex difference (36).Nonetheless, bird telomere dynamics are complex and, as with humans, may be affected by environment and stress.For example, a longitudinal study of black-tailed gulls (Larus crassitostris) over 2-5 years found no correlation between ETL and age or sex.Rather, ETL attrition was correlated with reduced food availability and environmental stressors (55).In a captive zebra finch (Taeniopygia guttata) population, male and female mean telomere length decreased with increasing age of the animals, but did differ between sexes (56).As these examples illustrate, the relationship between telomere length, lifespan, and sex is likely to be complex in other vertebrates.", + " Comparative studies of age-related telomere attrition in other species also reveal a variety of patterns.Barrett and Richardson (36) recently summarized the comparative data available on sex differences in telomere length.They found a strong correlation between male-biased mortality and either shorter telomeres or greater telomere attrition in males across bird and mammal taxa.However, telomere length did not differ between males and females in species where females are shorter-lived than males (36), suggesting that telomere shortening is not associated with species-specific longevity in a simple linear fashion.These studies generally suffer from relatively small sample sizes and are largely cross-sectional.Further, the use of diverse assays, different tissues (eg, leukocytes in mammals vs erythrocytes in birds), and lack of standardized benchmarks for accuracy makes comparisons between studies difficult.", + " In some organisms, there is no clear relationship between telomere length and lifespan.Age-related telomere attrition could not be detected in Daphnia pulex (57) or sea urchin species (Strongylocentrotus franciscanus and Lytechinus variegatus) (58).Studies in C. elegans examining natural variation in telomere length and experimentally manipulated telomere length detect no correlation with lifespan (59,60), and in Drosophila, which uses a telomerase-independent mechanism for telomere maintenance, there is a similar lack of correlation between longevity and telomere length (61).Similarly, data on sex differences in age-related telomere shortening are mixed.For example, in the ant species Lasius niger, the rate of telomere shortening is more rapid in short-lived males compared to longer-lived females.But, mean telomere length does not differ between the two types of females, queens and workers, despite the fact that queens live much longer than workers (up to 28 years vs 2-3 months) (62).These findings suggest that the question of how telomere shortening affects aging across species and how sex affects telomere attrition rates are complex.", + " With new methodologies to assess relative telomere length by Q-PCR, studies were designed to address the impact of telomere length on aging, aging associated pathologies, and mortality.One such study has correlated shorter leukocyte telomere lengths at age 60 with a three times higher risk of heart disease and an eightfold increase in risk of infection-related death (36), thereby associating measured relative cellular aging with disease and life expectancy.In a similar way, chronic stress was shown to correlate with short leukocyte telomere length, a phenomenon attributed to higher levels of oxidative stress at the cellular level (70).More recent studies have linked telomere length in smooth muscle cells with senescence and disease severity in patients with atherosclerosis (141,150).Leukocyte telomere length was also short in a cohort of similar patients and associated with a higher risk of developing occult cardiovascular disease (71).More data are needed to understand and validate the use of leukocyte telomere length as a biomarker for cardiovascular and other diseases.", + " Shortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19].", + " New research has indicated how social factors, such as subordination, may translate into biological effects (epel et al. 2004;Chae et al. 2014).In a now classic study, epel et al. ( 2004) examined the telomere lengths of fifty-eight healthy premenopausal women who either had a healthy child (n = 19) or were giving care to a chronically ill child (n = 39. )They measured perceived stress, years of caregiving, telomere length, and oxidative stress.They found highly statistically significant differences in telomere length between women taking care of chronically ill children and those who had healthy children.They found highly statistically significant negative correlations between telomere length and perceived stress and years of caregiving.Telomerase activity had highly statistically significant negative correlations with perceived stress and years of caregiving.Oxidative stress was highly positively correlated with perceived stress and years of caregiving.They concluded that the telomere length shortening was equivalent to 9 to 17 years of aging in the high stress group.Telomere length is considered a biomarker of aging (Finch and Kirkwood 2000).Thus, this study showed that caregiver stress had essentially aged these women 9 to 17 years compared to women who had healthy children.", + " The single, consistent predictor of the rate of telomere attrition shown in multiple adult and the few child longitudinal studies is the baseline measurement of telomere length at the start of each study.This suggests the importance of understanding predictors of telomere length prior to adulthood, as it determines in part the rate of change (Revesz et al. 2014a, b;Nordfjall et al. 2009).Moreover, longitudinal studies in adults have had found that telomere attrition rate is dependent on baseline telomere length independent of any phenotypic predictors of shortening, such as disease or demographic variables (Nordfjall et al. 2009), attesting to the importance of studies to evaluate risk factors for shortening prior to adulthood.", + " Rates of decline in childhood may be particularly relevant for later chronic disease risk as shorter telomere length has been implicated in disease progression through exposure to cellular senescence, inflammatory cytokines and adipocyte hypertrophy (Raschenberger et al. 2015;Willeit et al. 2014;Monickaraj et al. 2012;Fyhrquist et al. 2013).", + " Adult studies have also found a negative correlation with baseline telomere length, suggesting a negative feedback regulation of leukocyte telomere length (Farzaneh-Far et al. 2010;Aviv et al. 2009;Epel et al. 2008;Nordfjall et al. 2009).It is possible that while our follow-up period was shorter than Shalev et al. 2013 and adult studies, which had a minimum of 5 year intervals with the exception of Puterman et al. (2015) who followed for a one-year time period, there may be biological regulation of telomere length at 4 and 5 years of age such that shorter telomeres are more robustly maintained, whereas longer telomeres have greater rates of decline, over a short period of one year.It is unlikely that this relationship is due to assay error or regression to the mean given the consistency of our findings across studies.We have had similar findings of longer telomeres having greater rates of decline and shorter telomeres being maintained in our different studies (Farzaneh-Far et al. 2010;Epel et al. 2008;Puterman et al. 2015).", + " We found primarily maintenance and lengthening from 4 to 5 years of age in children, with minimal telomere attrition, indicating that most of the telomere loss happens in the first 4 years, plateauing by age 4. Lastly, we found close to 10 % of the variance in rate of change in children shared by mothers.While some of this shared variance is genetic, there are likely environmental factors that need to be further identified that impact rate of telomere length change.", + " Abstract Telomeres are the protective complexes at the end of chromosomes, required for genomic stability.Little is known about predictors of attrition in young children or the relationship between parental and child patterns of telomere change.Telomere length was assessed twice over one year, at 4 and at 5 years of age, in Latino preschool children (n = 77) and their mothers (n = 70) in whole blood leukocytes.Maternal and child rates of attrition during the same time period were compared in 70 mother-child pairs.More children showed lengthened telomeres over one year compared to their mothers and very few children showed attrition (2.6 %).Approximately 31 % of children and 16 % of mothers displayed lengthening over one year while 66 % of children showed maintenance in contrast with 74 % of mothers.The strongest predictor for child telomere length change was child's baseline telomere length (r = \u22120.61,p < 0.01).Maternal rate of change was associated with child rate of change (r = 0.33, p < 0.01).After controlling for child baseline telomere length, the relationship between child and maternal rate of change trended towards significance (Coeff = 0.20, 95 % CI \u22120.03 to 0.43; p = 0.08).", + " Blackburn and Epel, a health psychologist who did original research on how specific lifestyle and psychological habits can protect telomeres, published The Telomere Effect (Blackburn & Epel, 2017), in which they suggested that individuals with shorter telomeres developed diseases earlier in life (a shorter \"disease span\").What follows is the evidence from these authors, their colleagues, and other researchers describing how length of telomeres contributes to mind-body connection and healthy longevity.", + " As early as at the time of birth, each of the 92 telomeres of the human genome has its own characteristic length.Additionally, each telomere shortens by its individual attrition rate.In general, longer telomeres at birth are associated with higher age-dependent attrition rates and vice versa.Overall, telomere shortening appears more dynamic in males.", + " In conclusion, a combination of overall and chromosomespecifi c shorter telomeres and more pronounced age-dependent telomere erosion could be observed in males.There is a prospective clinical study strongly suggesting that longer telomeres decrease the risk of dying (Cawthon et al., 2003).With this in mind, the telomere length discrepancies between the sexes may indeed be a factor infl uencing the differences in their life expectancy.", + " In every chromosome a linear decline of telomere length with age was observed, being more pronounced in men independent of the examined chromosome arm.This might suggest that telomere length on single chromosome arms may be infl uenced by the same factors which determine overall telomere length.", + " S. Mayer a S. Br\u00fcderlein a S. Perner a I. Waibel a A. Holdenried a N. Ciloglu a C. Hasel a T. Mattfeldt a K.V. Nielsen b P. M\u00f6ller a a Institute of Pathology, University of Ulm, Ulm (Germany); b DakoCytomation A/S, Glostrup (Denmark) follow uniformity.In previous studies, sex-specifi c differences in telomere length and attrition rate of men and women were found (Benetos et al., 2001;Cawthon et al., 2003;Nawrot et al., 2004), suggesting gender differences in behavior of telomeres.In individual chromosome arms, telomere length was also shown not to be homogeneous (Lansdorp et al., 1996;Benn, 1997;Martens et al., 1998;Surralles et al., 1999;Hao and Tan, 2001;Londono-Vallejo et al., 2001;Graakjaer et al., 2003), some telomeres being signifi cantly shorter, others longer than the average length.", + " To date, these characteristics in telomere lengths could not be set in a biological context, as only a few groups have provided detailed information about chromosome-specifi c patterns of telomere distribution (Lansdorp et al., 1996;Graakjaer et al., 2003).Whether accumulation of short telomeres (Martens et al., 2000;Londono-Vallejo et al., 2001) or rather the shortest telomere of one specifi c chromosome arm (Hemann et al., 2001) elicits senescence, remains an open question so far.", + " In recent literature, there are hints that the average telomere length may be higher in women and that their annual shortening rate may be somewhat lower (Vaziri et al., 1993;Rufer et al., 1998;Jeanclos et al., 2000), but these reported differences failed to reach statistical signifi cance except for one study (Jeanclos et al., 2000).Here, we provide compelling evidence that this is indeed the case.", + " It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo.In individuals, short telomeres are considered to be a sign of advanced age.Cawthon and coworkers (2003) showed that telomere shortening in humans likely contributes to mortality, supporting the hypothesis that they might act as a mitotic clock (Allsopp et al., 1992).Telomere length dynamics, however, does not seem to Abstract.During aging, telomeres are gradually shortened, eventually leading to cellular senescence.By T/C-FISH (telomere/centromere-FISH), we investigated human telomere length differences on single chromosome arms of 205 individuals in different age groups and sexes.For all chromosome arms, we found a linear correlation between telomere length and donor age.Generally, males had shorter telomeres and higher attrition rates.Every chromosome arm had its individual age-specifi c telomere length and erosion pattern, resulting in an unexpected heterogeneity in chromosomespecifi c regression lines.This differential erosion pattern, however, does not seem to be accidental, since we found a correlation between average telomere length of single chromosome arms in newborns and their annual attrition rate.Apart from the above-mentioned sex-specifi c discrepancies, chromosome arm-specifi c telomere lengths were strikingly similar in men and women.This implies a mechanism that arm specifi cally regulates the telomere length independent of gender, thus leading to interchromosomal telomere variations.", + " Shortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19]." + ], + [ + " Selection could occur at multiple levels, from germ cell generation and propagation to fertilization and early embryonic growth.Chromosomal abnormalities, including aneuploidy, were found in 10-20% of spermatozoa and oocytes (20) and in the cleaved embryo, with a 21% rate of abnormalities in preimplantation embryos (21).These findings led to a model for natural selection against chromosome abnormalities (21).Selection extends to the end of gestation: Only approximately 30% of all conceptions result in a live birth, with more than half of aborted fetuses containing chromosomal abnormalities (22), a number likely to be an underestimate because of technological limitations in measuring all possible mutations.But even in the very small fraction of germ cell duos that survive this withering genome attack and result in a live birth, a number of severe de novo mutations will still be found (23).The data on gross chromosomal alterations suggest that overall, mutation frequency early in life is very high.The functional consequence, however, is limited because of selection.Somewhat surprisingly, this picture points toward an initial decline in genomic alterations, allowing the adult individual to acquire a somatic genome optimally equipped to provide function.", + "The phenotype of the F1 hybrids is compared to those of the parental inbred strains to reveal dominance or semi-dominance relationships between the alleles that a\u00a1ect the phenotype. Phenotypic di\u00a1erences between reciprocal F1 hybrids indicate that one or more of the following factors may a\u00a1ect the trait: (1) sex linkage (X- or Ylinked traits), (2) genomic imprinting of QTLs that a\u00a1ect the phenotype, (3) prenatal maternal e\u00a1ects (e\u00a1ects of intrauterine environment), and/or (4) postnatal maternal or paternal e\u00a1ects (e\u00a1ects of maternal and/or paternal parenting behaviour on o\u00a1spring).", + "Sex brings harmful alleles together into the same genetic background, allowing selection to more efficiently purge them from the population and potentially producing some offspring that are fitter than either parent. However, the benefit of recombining deleterious mutations may depend on the nature of the epistatic interactions between them. The mutational deterministic hypothesis (Kondrashov 1988) depends partly on this epistasis.", + "In most plants and animals, sex is a necessary component of reproduction, and the question for evolutionary biologists is why reproductive mechanisms have evolved that way. In one of the experiments described next, evolutionary geneticists have nevertheless devised a way to compare evolution with and without recombination in the obligately sexual fruit fly.", + "This disparity in investment is the basis for the twofold cost: asexual females hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. But sex usually also involves the basic process of physical recombination: the breakage and reunion of two different DNA or RNA molecules. Of these two processes, recombination is clearly the more widespread feature of sexual reproduction. A variety of reproductive systems, such as selfing and automixis, involve recombination but not outcrossing. In contrast, relatively few reproductive systems have outcrossing without recombination.", + " Crossing over-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes cross over one another.Crossing over results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.This process is also known as meiotic recombination.", + " The reason for the rarity of these mutations is natural selection: If the mutations result in disorders that decrease health and reproductive fitness, they will eventually be eliminated from a population.In exceptional cases, mutations may cause both beneficial and detrimental consequences, resulting in opposing forces of positive selection and negative selection that may cause the mutations to be preserved at nonrare frequencies in a population.For example, the HbS mutation in the HBB gene (which produces the \u03b2 subunit of hemoglobin) causes sickle cell disease when present in both alleles, a detrimental consequence, but protects against malaria when present in 1 allele, a beneficial consequence, ensuring that the mutation persists in populations in areas of the world where malaria is endemic.Genes are passed from parents to offspring via the process of meiosis by which gametes, the egg cells in the mother and the sperm cells in the father, are generated.Ordinarily, each cell has 23 pairs of chromosomes; the gametes have 23 unpaired chromosomes.In meiosis, the 23 pairs are split so that each gamete receives 1 chromosome from each pair (Figures 8 and 9).Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full complement of 23 chromosome pairs restored.If all goes well, the zygote gives rise to a live offspring.", + " Recombination (meiotic recombination)-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes recombine with one another.Recombination results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.Also known as crossing over.", + " In the generation of gametes, crossing over regularly occurs, and genetic information is swapped between members of a chromosome pair.That doesn't matter within inbred animals, because the swapped parts are identical.In an F 1 animal, however, the chromosomes of a particular pair are genetically different, one each having come from each parent.Each gamete produced will be unique, as will be each F 2 zygote formed by uniting of the gametes from two F 1 parents.An F 2 group thus provides for expression of some genetic variability.This variability is limited to the allelic differences existing between the parent strains of the F 1 s, so that another F 2 , derived from different inbred strains, will express different genetic differences.", + "Sex brings harmful alleles together into the same genetic background, allowing selection to more efficiently purge them from the population and potentially producing some offspring that are fitter than either parent. However, the benefit of recombining deleterious mutations may depend on the nature of the epistatic interactions between them. The mutational deterministic hypothesis (Kondrashov 1988) depends partly on this epistasis.", + "In most plants and animals, sex is a necessary component of reproduction, and the question for evolutionary biologists is why reproductive mechanisms have evolved that way. In one of the experiments described next, evolutionary geneticists have nevertheless devised a way to compare evolution with and without recombination in the obligately sexual fruit fly.", + "This disparity in investment is the basis for the twofold cost: asexual females hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. But sex usually also involves the basic process of physical recombination: the breakage and reunion of two different DNA or RNA molecules. Of these two processes, recombination is clearly the more widespread feature of sexual reproduction. A variety of reproductive systems, such as selfing and automixis, involve recombination but not outcrossing. In contrast, relatively few reproductive systems have outcrossing without recombination.", + " Aberrant recombination patterns on chromosomes that have missegregated have also been identified as an important factor, in both male and female gametes (Table I).This is because recombination together with cohesion of sister chromatids establish the unique 'bivalent' chromosome structure where homologous partner chromosomes are tethered together, a configuration that is critical for their accurate segregation in meiosis I (Fig. 2A).The remarkable feature is that recombination occurs in foetal oocytes whereas chromosome segregation takes place decades later (Fig. 2A).Since mammalian oocytes are arrested at the G2/M transition (or dictyate stage), this raises the intriguing question of how the bivalent is maintained until the meiotic divisions.", + "Traditionally, it has been agreed that the \ufb01nal sex of an individual (phenotypic sex) depends on two sequential processes: the sex determination system of the species and the gonad differentiation process (Valenzuela, 2008). However, recently, these two seemingly distinct processes are viewed as part of a general process leading to gonad formation and sex ratios (Sarre et al. , 2004; Quinn et al. , 2011; Uller and Helantera\u0308, 2011).", + "However, we expect that only at this level, the most signi\ufb01cant contributions brought by integrating epigenetics will be made. Concluding Remarks and Future Prospects Fish sex ratios are the result of a complex interaction between genetic, biochemical, and environmental interactions. The ultimate result of these interactions at the individual level is gender: male or female. However, at the population level, the combination of sex determination and differentiation sets the sex ratio. In turn, sex ratios de\ufb01ne the reproductive capacity of populations and, if sex growth dimorphism exists, also the growth characteristics, something very important in an aquaculture context.", + "Obehav is, in turn, influenced by offspring genes and environment (Ogene and Oenvir respectively). Hence, indirect genetic effects (blue arrows) and direct genetic effects (red arrow) are important influencers of behaviour. B) Parentoffspring conflict theory predicts that parental resource investment and offspring solicitation behaviours are influenced by the fitness benefit to a focal individual (O), cost to a social partner such as a sibling (S1 and S2) or parent (P), and by their coefficient of relatedness (black arrows). 42 Figure 2: Genomic imprinting can result in divergent phenotypes from the same genotype. A) A paternally imprinted gene, i.e. maternally expressed.", + "Because of the small contribution, through the sperm, of the paternal transcriptome to the fertilized zygote, and because of the stronger maternal contribution to child rearing in most model organisms, parental effects are typically thought of as synonymous with maternal effects, although true paternal effects are known to exist (Rando, 2012). Maternal effects have been shown to be important during embryonic development, leading to differences in the birth weight of mice depending on the genotype of the mother (Cowley et al. , 1989; Wolf et al. , 2011).", + "Therefore, the resulting phenotypic patterns lag a generation behind the genetic transmission of the causal variants. The most well-studied parental genetic effects are caused by deposition of maternal transcripts into the egg prior to fertilization, resulting in differences in early embryonic development depending on the genotype of the mother. Certain genes have also been shown to respond to maternal influence after birth through genetically defined maternal behaviors (Weaver et al. , 2004).", + "The phenotype of the F1 hybrids is compared to those of the parental inbred strains to reveal dominance or semi-dominance relationships between the alleles that a\u00a1ect the phenotype. Phenotypic di\u00a1erences between reciprocal F1 hybrids indicate that one or more of the following factors may a\u00a1ect the trait: (1) sex linkage (X- or Ylinked traits), (2) genomic imprinting of QTLs that a\u00a1ect the phenotype, (3) prenatal maternal e\u00a1ects (e\u00a1ects of intrauterine environment), and/or (4) postnatal maternal or paternal e\u00a1ects (e\u00a1ects of maternal and/or paternal parenting behaviour on o\u00a1spring).", + "It was believed by many that for each trait variant we should expect to find a corresponding genetic change, or \u201egene for\u201f that trait. Through historical happenstance the relationship between genes and traits was set up and treated as if it were one-to-one. But the production of a trait involves not only genes, but also their interactions with each other and the environment, and chance." + ], + [ + "distinguishing prenatal from postnatal maternal effects, see below). Maternal effects can account for a large proportion of phenotypic variance, especially during early life, and for some traits explain more variation than direct genetic effects [33, 97, 99, 100, 102\u2013115]. However, maternal and offspring genotype are correlated (i.e. half their genes are shared), and in inbred lines they are fully confounded, thus separating the effects of their respective genotypes is difficult. To remove this confounding effect cross-fostering has been used, both in the laboratory and in the field [119, 131].", + "Using genetic markers, the pattern of inheritance can be tracked through families. For example, by analyzing a marker linked to the eye color gene in several generations, it is possible to determine from which grandparents a child has inherited its eye color alleles. More importantly, \ufb01nding a marker linked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.", + " Although autosomal SNPs are commonly used as genetic markers to infer ancestry or race/ethnicity membership, haploid such as mitochondria, Y-DNA, and X-lined markers are also important to provide separate stories of ancestry of individuals from paternal and maternal sides [42,43].Therefore, genetic structure created due to autosomal markers could be different from those of lineage markers (often influenced by political, social, and migration history of individuals/populations).mitochondrial DNA or mtDNA haploid is the maternally inherited mitochondrial genome (mtDNA) [44].All children inherit mtDNA from their mother, with no admixture from the father.Like Y-line DNA, mtDNA is passed intact from one generation to the next but through maternal line.", + " a) Autosomal DNA (testing both sexes) markers: autosomal DNA tests utilize DNA from the 22 pairs of autosomal chromosomes.Autosomal DNA is inherited from both parents.Autosomal testing provides percentages of ethnicity using autosomal DNA SNP test (i.e., ancestry informative markers), and it is the most commonly used test to infer ancestry across diploid genome.b) Y-DNA or Y-SNPs (paternal line testing) markers: a haploid Y-DNA is the paternally inherited non-recombining portion of the Y chromosome, and it tests only for males.The Y-DNA testing tests the Y chromosome which is passed intact from father to son with no DNA from the mother.Y-DNA testing can then be used to trace direct paternal line.Y-DNA remains the same in each generation, allowing us to compare surname from different regions to see if we are from the same family.Y-line testing does not indicate anything about the contributions of the other ancestors in a family tree.In other words, you could be 3/4th Native American, with only the direct paternal line being European, and this test would tell you nothing at all about those other three Native lines.When testing the Y-chromosome, there are two types of tests, short tandem repeat (STR) and SNP markers.STR tests are best for recent ancestry while SNP tests tell about more ancient ancestry.c) Mitochondrial DNA (maternal line testing) markers:", + " Additional information about past breeding practices can be gleaned by quantifying the number of reproductive males and females in a population.This can be achieved by comparing levels of genetic diversity between sex chromosomes, autosomes and mtDNA 99 .In cattle, for example, gene flow from aurochs is evident in the autosomes but is absent in mtDNA 41 .This has been interpreted as a management strategy that may have involved allowing insemination of domesticated females by wild bulls 41,100 .In horses, a comparison of the levels of diversity of the Y chromosome and the autosomal chromosomes demonstrated that some cultures allowed fewer males to breed and instead selected specific stallion bloodlines 55 .This male-oriented breeding strategy was not practised by the Romans and only became increasingly prominent in the past 1,000 years as a result of the growing influence of Oriental stallions (Arabian, Persian and Turkmen) 101 .", + " Dr Ring: What makes the maternal gene so peculiar compared to the paternal?Dr Cookson: If you look in the epidemiologic sense, many studies show that there is increased risk of allergic disease if the mother is affected.However, very few studies have actually set out to test that formally and most of them might suffer from some sort of selection bias because the mother is more likely to be aware of her symptoms and feel guilty, and so on.It is very difficult to explain.Is it genomic imprinting, where the gene is only active when transmitted through the mother?I do not think all of these genes would be imprinted, though it is possible.It also seems that there are effects of the maternal phenotype.The maternal phenotype, if the mother is affected or unaffected, determines the strength of the maternal effect.Again, if a gene was imprinted, you would not expect maternal phenotype to be important.So, I think that this has something to do with maternal/fetal interaction, either through the placenta or shortly after birth.There is the issue of immune conflict between mother and child.At the same time, the mother is trying to prime the infant's immune system.", + "Genetic and Genomic Discovery Using Family Studies Ingrid B. Borecki, PhD; Michael A. Province, PhD G enetic studies traditionally have been performed on sets of related individuals, that is, families.Mendel's early studies in sweet peas (Pisum sativum) on the inheritance patterns of discrete traits from parents with specific mating types to offspring has shed light on the basic mechanisms of inheritance, including the fundamental laws of segregation of discrete factors (genes) from parents to offspring and the cosegregation of genes that are closely located on a chromosome (linkage).The distribution of traits within families exhibited mathematical segregation ratios in offspring from known mating types.These expected segregation ratios have been used as an important discovery tool in the study of human diseases in pedigrees, providing evidence for a multitude of single-gene disorders.Furthermore, in some cases, trait cosegregation with genetic markers with known positions provides mapping information that enables localization and, ultimately, identification of the relevant causative gene.", + "In fact, this idea has been pursued before in the context of signatures of reproductive isolation and shown to reveal patterns consistent with epistatic gene interactions that arise in the shape of Dobzhansky-Muller incompatibilities [10,11]. In contrast to the mouse data, the available human genotypes were derived from outbred, ethnically distinct populations. In this case pairs of functionally interacting genes can be detected following a slightly different approach.", + "Family Structure The first re-identification method (FAMILY) employs genealogical data accompanying genomic data.Genealogies, rich in depth and structure, permit the construction of complex familial relationships.Consider a simple family structure of two parents and one child.Since the parental genders are guaranteed, there exist 2 variants of this structure, since the child's gender is either male or female.When disease status is taken into account, it is represented as a Boolean variable; either an individual afflicted or not afflicted.In this aspect, all three family members can be represented as three attributes {Father, Mother, Child}, and there exist (father's disease status)*(mother's disease status)*(child's disease status)*(child's gender) = 2*2*2*2 = 16 possible family-disease combinations.In reality, pedigrees are much more robust than a simple nuclear family.For example, a three-generation family of two children per family permits on the order of 10 5 distinct variants of the family-disease structure and 10 6 individuals that could be uniquely characterized.The number of combinationsk is larger when supplementary information, such as living status or medical/genetic features, is considered. 16e ability to determine unique family structures is only one part of the re-identification process.These structures must be linked to identifiable information, which, in many instances, is publicly available in the form of various genealogical databases.These databases are accessible both offline and via the World Wide Web.For example, genealogical records are available in many public databases, including ,Ancestry.com>,,Infospace.com>,,RootsWeb.com>,,GeneaNet.com>,,FamilySearch.org>, and ,Genealogy.com>. {From such data, it is not difficult to construct family structures and, with such information in hand, an adversary can link disease-labeled family structures to named individuals.", + " Fig. 3. Illustrations of the three CEU pedigrees (black) showing how genetic information from distant patrilineal relatives (arrow; red, patrilineal lines) can identify individuals.Filled squares represent sequenced individuals.To respect the privacy of these families, only abbreviated versions are presented.The sex of the CEU grandchildren was randomized.The numbers of grandchildren are not given.", + " When I was in high school, I remember often trying to match my friends to their parents at various school functions and being surprised at how easy this was.As human geneticists, in spite of the enormous advances being made in our field, we still cannot answer many of the everyday questions that we are asked, such as: \"Why does he look just like his mother? \"Max Perutz [1], in a recent editorial comment in the New Scientist entitled \"The Molecular Biology of the Future,\" suggested some questions, for, as he put it, \"an examination in some future century. \"Here are two of them: (1) \"The time has come\" the Walrus said, \"To talk of many things ...And why the sea is boiling hot And whether pigs have wings. \"Calculate the amount of genetic information this would require in megacricks.", + "Using genetic markers, the pattern of inheritance can be tracked through families. For example, by analyzing a marker linked to the eye color gene in several generations, it is possible to determine from which grandparents a child has inherited its eye color alleles. More importantly, \ufb01nding a marker linked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.", + "In contrast, genomic imprinting is due to epigenetic changes within the individual causing differential gene expression characterized by either complete or partial silencing of one parental allele (Barlow, 2011; Abramowitz and Bartolomei, 2012; Ashbrook and Hager, 2013). As both mothers and fathers had contact with the pups in our study, our observed PGEs could come from either parent. Among quantitative USV traits only peak amplitude of call displayed a possible parent-of-origin effect. For call number, call duration, mean peak frequency, and all morphological traits, there were no significant parent-of-origin effect in reciprocal F1 females. In contrast, Thornton et al.", + " Another way of avoiding stratification is to use family-based samples.This approach has several theoretical advantages: as well as being immune to stratification 114 , these samples can be used to determine whether an allele has different effects on disease when it is inherited maternally or paternally 115 , and DISCORDANT SIB designs [116][117][118] can control for the effects of shared environment.Furthermore, more complex family-based designs are possible 119 that might allow combined association and linkage analysis 120 , and family-based association tests have also been developed for quantitative traits [94][95][96][97][98] .However, pure sibship-based association studies are underpowered relative to case-control studies 107,116,117 , and the requirement for living parents might introduce an age-of-onset bias towards younger patients for diseases that usually arise late in life.Furthermore, family-based samples are often much more difficult to collect, particularly if larger pedigrees are sought.Finally, the most commonly used family-based design, the TRANSMISSION DISEQUILIBIRIUM TEST (TDT; see REF. 114) is susceptible to technical artefacts (see below).", + " Because mtDNA is not subjected (as far as we know) to sexual recombination and crossover at the time of nuclear meiosis, nature must call on other means to ensure that inevitable germ plasm mtDNA mutations (Medvedev, 1981) are not transmitted.These mutations among primary oocytes, on the face of it, can be expected to increase with time, that is with maternal age.Empirical data on this question are incomplete and conflicting, being mostly confined so far to searches for deletions rather than point mutations (Chen et al., 1995;Keefe et al., 1995).It is inevitable, however, that there will be such mutations and that there must therefore be a reliable physiological mechanism (a) for giving an opportunity for back-mutations to occur, (b) for selecting in favor of those back-mutations (thus preserving the genome) and in favor of rare advantageous mutations, and (c) for preventing the spread of persistent harmful mutations through the population -mutations that are too slight (or too late in origin) to have escaped intraovarian culling.The sheer conservation of the mitochondrial genome over 0.5 billion years or more, despite a mutation rate estimated at 10 -20 times that of nuclear DNA, is ample reason to conclude that such a physiological purification process must exist.", + "To scrutinize the polygenic networks underlying complex diseases, however, mouse resources that are optimized to study the actions of isolated genetic loci on a fixed background will be insufficient on their own. For example, predisposition to the metabolic syndrome is inherited in a non-Mendelian fashion stressing genetic heterogeneity and multigenetic pathogenesis (Nandi et al. , 2004). With the reawakening as to the extraordinary genetic resources and phenotypic diversity archived in extant inbred strains, however, a foundation is in place for tracking down these complex traits and quantitative trait loci (QTL).", + "Otherwise, tens of thousands or markers will appear significant in the genome-wise association studies using up to one million genetic markers. Approaches to control for stratification include using of self report of ancestry or genetically derived principle components in the analysis. For studies using inbred mouse lines, a cladogram which is a hierarchical grouping based on phylogenetic analysis of strain relatedness can be created to subdivide inbred strains into more genetically homogenous subgroups.", + " Although bilateral descent is the norm in Western societies, it is not universal and there is variation with cultural practices around lineage.In certain societies, individuals place greater importance on (and have greater knowledge about) one side of the family than another (unilineal descent).Thus, individuals in patrilineal groups trace relationships through males only so that your father's brother's children are members of your family, but not your father's sisters (Kottak, 2007).They are members of their husband's group or family.Efforts to create a family pedigree may be hampered if the participant is not familiar with her mother's relatives, but her mother's brother's children (her cousins) may be able to supplement her overall family history.Knowledge about the cultural system of unilineal descent avoids assuming the universality of bilateral descent.Cultural beliefs such as these also have implications in the conduct of genetic research in terms of confidentiality and autonomy (Benkendorf et al., 1997;Wertz, 1997).One cannot assume that the named proband is in a position to speak for the extended family in agreeing to participate in any genetic research (DudokdeWit et al., 1997).", + "In particular in polygynous species, a female\u2019s offspring may have different fathers and are thus more closely related through the maternal than the paternal line. Therefore, any fitness cost to mothers, such as increased provisioning and care, affect maternally derived genes more strongly than paternally derived genes, leading to the silencing of the maternal copy (i.e. paternal expression) of genes that increase resource transfer. 5. Coadaptation between offspring and maternal traits The genetics of the co-evolution of parental and offspring traits has been investigated using quantitative genetics models and in several empirical studies (Agrawal et al.", + "In this scenario, genes expressed in parents will be selected for their effects on parental behaviour while genes expressed in offspring will be selected for their effects on influencing parental behaviour. At the genetic level the predicted conflict between paternal and maternal genomes is thought to have led to the evolution of genomic imprinting (monoallelic gene expression). Genomic imprinting effects are good examples of offspring genetic effects on maternal care because of the impact on the quality of maternal care and level of resource provisioning (e.g. Li et al. , 1999)." + ], + [ + "Genetic mapping in mouse strains enhances the power of detecting modifier genes and identifying complex genetic interactions. Genomewide quantitative trait locus (QTL) analysis, as described in more detail below, represents a promising approach to detect genetic variants that are associated with specific phenotypes and interact with each other. 16 ACCEPTED MANUSCRIPT In experimental crosses of two (inbred) strains the first generation (F1) of offsprings is genetically heterozygous but equal. Then in the next generation (F2) the PT strain-specific genetic information is distributed across the genomes of their progeny and RI each offspring is genetically unique.", + "Second, and perhaps more important, is the difference in the size and types of the genetic reference populations. In our previous study, we mapped the QTL with 36 F2 mice that were genotyped at 82 markers. In the current study, by comparison, we were able to map QTLs after examining 342 mice from 55 strains that were genotyped at approximately 4000 markers.", + "This contrast can be exploited to identify subregions that underlie the trans-QTLs [67]. SNPs were counted for all four pairs of parental haplotypes\u2014B vs D, B vs H, B vs C, and L vs S\u2014and SNP profiles for the four crosses were compared (figure 6). Qrr1 is a highly polymorphic PLoS Genetics | www.plosgenetics.org 8 November 2008 | Volume 4 | Issue 11 | e1000260 QTL Hotspot on Mouse Distal Chromosome 1 Figure 5. QTL for aminoacyl-tRNA synthetases in distal Qrr1.", + "The traditional approach to QTL mapping is to use two strains that differ maximally in the phenotype as parental strains for genetic crosses, with the following caveats. QTL analysis based on a single cross will most likely reflect only a small portion of the net genetic variation, and QTL detection will be limited to regions where the two progenitor strains have functional polymorphisms. Data from multiple crosses, or from an HS, will overcome this limitation and can also be used to reduce QTL intervals [5,30].", + "These candidate genes are then sequenced in the two parental inbred strains looking for sequence di\u00a1erences in coding or regulatory regions. After \u00a2ne mapping the QTL interval and shortening the list of plausible candidate polymorphisms, the major challenge remains \u2401 proving de\u00a2nitively which nucleotide polymorphism underlies the QTL. The most direct proof would be replacing one strain\u2019s allele with another strain\u2019s allele (creating a FIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mouse generations are depicted.", + "Furthermore, splicing QTLs (sQTLs) rather than eQTLs could comprise the molecular mechanism linking DNA variants with YFP53; thus, sQTL analysis could uncover genes that would not normally be detected at the level of differential gene expression (DGE),53 and thus, a differentially 181 182 Molecular-Genetic and Statistical Techniques for Behavioral and Neural Research Figure 8.5 Schematic for immediate, rapid \ufb01ne mapping in select F2 recombinants of the RCC-F2 cross. Top panel: Genome-wide signi\ufb01cant QTL (green trace; red dashed line \u00bc signi\ufb01cance threshold; blue vertical lines \u00bc Bayes credible interval).", + "Interval-specific haplotype analysis Approximately 97% of the genetic variation between inbred mouse strains is ancestral [22], so regions of identity by descent (IBD) between two strains used to detect a QTL are highly unlikely to contain the causal genetic polymorphism underlying the QTL [28]. For example, a cross between C57BL/6J and A/J mice detected www.sciencedirect.com a blood pressure QTL on Chr 1 [7].", + "Interval-specific haplotype analysis Approximately 97% of the genetic variation between inbred mouse strains is ancestral [22], so regions of identity by descent (IBD) between two strains used to detect a QTL are highly unlikely to contain the causal genetic polymorphism underlying the QTL [28]. For example, a cross between C57BL/6J and A/J mice detected www.sciencedirect.com a blood pressure QTL on Chr 1 [7].", + "At present, the BXD panel is composed of 80 different strains that all have been fully genotyped.26 Variation in any quantifiable trait can be associated with the segregation of parental alleles, and linkage genetics can map this variation to quantitative trait loci (QTLs), thereby identifying the genomic region(s) affecting that trait. An overview of the QTL mapping approach is depicted in Figure 2. Classical QTL analysis has permitted the identification of loci that are associated with variation in HSC traits.", + "In general, linking genetic variation with trait variation identifies QTL and a significant linkage of phenotype and genotype suggest that the DNA status helps to determine trait expression. As stated above, mouse QTL studies provide distinct advantages over human studies in the examination of genetic causes of a quantitative trait (e.g. alcoholism), even in the absence of specific hypotheses regarding its aetiology or candidate genes.", + "The progenitor mouse strains should have sufficient variation for the traits of interest and they should be genetically diverse enough to enable genetic mapping (BENNETT et al. 2006; FLINT 2003; GRISEL 2000). The sample size required for the identification of QTL depends largely on the effect size that a QTL contributes to phenotypes on interest. Inference about QTL can be made if one or more genetic markers are over- or underrepresented in the analysed individuals. Genotyping is often done by means of microsatellite markers, which contains mono, di-, tri-, or tetranucleotide tandem repeats flanked by specific sequences (Figure 4a).", + "This comparison gives information about the reliability of the observed genotype information: The more the marker locations differ between the two maps (which signifies variation in marker positions), the higher the possibility of genotyping errors. QTL mapping was done in several stages to identify loci acting individually and QTL that interacted, either additively or epistatically. To determine individually-acting QTL, a singleQTL genome scan was conducted with the function scanone.", + "Importantly, whereas these studies required substantial labor, time, and resources, X-QTL is a quick and easy approach to achieve a comparable level of genetic dissection. The levels of complexity observed here (e.g. 14 loci explaining 70% of the genetic variance for 4-NQO resistance) are still dramatically lower than those seen in for some human traits in GWAS (e.g. 40 loci explaining 5% of the variance for height 2,5). One obvious explanation is the difference in experimental designs (line crosses vs. population association studies), but differences in genetic architectures among species and traits may also contribute.", + "The method uses two pieces of information: mapping data from crosses that involve more than two inbred strains and sequence variants in the progenitor strains within the interval containing a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that any sequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals except at a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.", + "The method uses two pieces of information: mapping data from crosses that involve more than two inbred strains and sequence variants in the progenitor strains within the interval containing a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that any sequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals except at a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.", + "Genotyping all the individual progeny for markers that show allelic variation between the parental strains (either single nucleotide polymorphisms or simple sequence repeats) will allow the detection of associations between trait values and marker genotype, and in this way demonstrate to which set of markers a QTL is linked. To reduce the genotyping effort, selective genotyping of the individuals at the extremes of the phenotypic spectrum can be performed (20,23). Although these three approaches are in general considered to be the best to detect and map QTL, they have several disadvantages for quantitative traits involving HSC.", + "So, how do you go about planning and performing a QTL study, and how do you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbred strains that have a markedly different trait. One can now look up many different traits of inbred mice online at the Mouse Phenome Database (http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). However, the trait you may want to study may not be present in wild type mice, so you may want to cross a mutant (or genetically engineered) strain onto several inbred strains.", + "QTL Theory and Planning The theory behind the most basic form of QTL mapping is based upon intercrossing two inbred strains. The mouse genome consists of 19 pairs of autosomes (non sex-determining chromosome) and the X and Y chromosomes. In the example shown in Fig. 18.1, we are intercrossing stain A (shown with a black chromosome pair) with strain B (shown with a white chromosome pair). The initial F1 (filial generation 1) mice are true hybrids, with each individual From: Molecular Biomethods Handbook, 2nd Edition.", + "These candidate genes are then sequenced in the two parental inbred strains looking for sequence di\u00a1erences in coding or regulatory regions. After \u00a2ne mapping the QTL interval and shortening the list of plausible candidate polymorphisms, the major challenge remains \u2401 proving de\u00a2nitively which nucleotide polymorphism underlies the QTL. The most direct proof would be replacing one strain\u2019s allele with another strain\u2019s allele (creating a FIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mouse generations are depicted.", + "QTL mapping studies then seek to detect the polymorphisms underlying the complex traits of interest by scanning for alleles that co-vary with the traits. Similar experiments also can be conducted with special derivatives of inbred strains known as recombinant inbred (RI) mice. These animals are derived by cross-breeding two or more distinct parental strains (which often diverge widely for the trait of interest), followed by inbreeding of the offspring for several generations (Bailey 1971). Given the correct breeding strategy, this method 1 This is an issue faced by GWASs researchers when classifying samples as cases or controls." + ] + ] +} diff --git a/gnqa/paper1_eval/src/data/datasets/old/full_test_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/full_test_dataset.json new file mode 100644 index 0000000..8818869 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/full_test_dataset.json @@ -0,0 +1,248 @@ +{ + "question": [ + "List genes related to asthma with their phenotype descriptions.", + "Which types of mouse studies are popular in genenetwork, where a type an ailment, e.g. asthma, allergies, aging, heart failure, diabetes, glaucoma?", + "Do we find significant quantitative trait loci in the studies for diabetes?", + "What environmental factors effect mice that also influence diabetes?", + "List mouse studies that involve insulin.", + "What is the typical weight of a mouse liver?", + "How are C57BL/6 and DBA/2 different?", + "What behavioural QTLs have been found in mice?", + "Where do I look for genes that regulate hippocampal neural stem cell proliferation?", + "What does QTL mean?" + ], + "answer": [ + "The text mentions two severe asthma clusters, SC2 and SC3, but does not specify individual genes. SC2 is characterized by high T2 inflammation, high FeNO, bronchoalveolar lavage and blood eosinophilia, and up-regulated expression of T2 signature and mast cell genes. SC3 is characterized by lower levels of T2 clinical biomarkers and gene expression, a later age of onset, increased incidence of nasal polyposis, and higher levels of neutrophilic inflammation.", + "Mouse studies popular in genenetwork often focus on ailments such as diabetes, glaucoma, obesity-associated diabetes, and metabolic diseases. These studies utilize genetically modified mouse models, inbred mouse models, and polygenic models. They also use specific strains like DBA/2J, Goto-Kakizaki (GK) rat, and KK mice.", + "Yes, the studies identified significant quantitative trait loci associated with both type 1 and type 2 diabetes. These include loci such as TCF7L2, DNER, SRR, HNF1A, KCNQ1, CDKN2A/CDKN2B, JAZF1, CDC123, CAMK1D, TSPAN8, LGR5, THADA, ADAMTS9, NOTCH2, and others. However, the significance of some loci varied across different populations and studies.", + "Several environmental factors can affect mice and influence diabetes. These include the stress response, which can be greater in male mice and can confound feeding behavior, the unique gut microbiota of rodents from different production facilities, cage position, single versus group housing, the skill level of the researcher, ambient room temperature, and the type of cage bedding. Diet, particularly high-fat feeding, can also induce obesity and diabetes. Additionally, the intrauterine environment can impact the subsequent development of diabetes amongst offspring.", + "1. Diabetes incidence study where mice were tested for diabetes monthly by blood glucose and weekly by urine assessment.\n2. Studies involving transgenic/knockout animal models of type 2 diabetes to study the role of genes and their effects on peripheral insulin action.\n3. Study involving male type-2 diabetic db/db mice and genetic control non-diabetic db/ mice.\n4. Study involving young adult mice fed a high-fat or Western diet to elicit DIO and insulin resistance.\n5. Study involving Akita mouse genotyping.\n6. Study involving 12-week-old male type 2 diabetic db/db mice and contemporary control wild-type mice.\n7. Study involving transgenic mice to create specific models of type 1 and type 2 diabetes.\n8. Study involving AKITA mice derived from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene.\n9. Study monitoring mice for the development of diabetes.", + "The typical weight of a mouse liver can vary, but in the context provided, it can weigh up to 4.5 grams in a 40 gram mouse and 1.2 grams in a 20 gram normal mouse.", + "C57BL/6 and DBA/2 mice differ in several ways. C57BL/6 mice are more accurate in tasks with greater demands and make fewer impulsive responses. They also have larger brain weights and are more resistant to radiation-induced genomic instability. DBA/2 mice, on the other hand, make fewer omission errors, respond more vigorously to certain influenza viruses, and perform better in less spatially demanding tasks. They also have larger cortical areas when adjusted for brain weight and are more sensitive to acute nephrotoxicity. Genetic differences between the two strains affect various responses including activity, memory, learning tasks, and responses to alcohol and opioids.", + "Several behavioral QTLs have been found in mice. These include QTLs affecting aggressive behaviors, which have been identified in strains such as the Turku aggressive (TA) and non-aggressive (TNS) strains, the NC900 and NC100 strains, and the short attack latency (SAL) and long attack latency (LAL) strains. Other QTLs have been associated with traits like anxiety-related behavior, seizure, hippocampal volume, and alcohol preference. Additionally, QTLs related to alcohol, morphine or other drugs, painful arthritis, emotionality/anxiety, and seizure susceptibility have been mapped. There are also QTLs that influence exploratory behavior in mice.", + "You can look for genes that regulate hippocampal neural stem cell proliferation in the results of gene expression profiling studies, such as the one mentioned in the text. Specifically, the study identified LRP6 as a novel regulator of adult hippocampal neurogenesis. Other genes like Lef1, Mtdh, and Chchd8 may also be involved.", + "QTL stands for Quantitative Trait Locus. It is a region of the genome that is linked to a trait and contains a gene or genes that regulate a portion of the genetic variation for a particular phenotype. QTLs are associated with quantitative traits, which are traits that have measurable phenotypic variation due to genetic and/or environmental influences." + ], + "contexts": [ + [ + " We present an analytical technique designed to test a priori defined gene sets (for example, pathways) for association with disease phenotypes.We apply this method to gene expression profiles of human diabetic muscle, identifying a set of genes whose expression is correlated with insulin resistance and aerobic capacity.These results suggest hypotheses about pathways contributing to human metabolic disease and, more generally, show the value of incorporating information about functional relationships among genes in the analysis of microarray data.", + " Pathway and gene ontology analysis for select phenotypes and envionmental factors showing GxE interactions.", + " Next, the genes that correlated with FeNO (n = 549) were used to objectively cluster asthma subjects into subgroups.In agreement with Moore et al., most of the severe asthma patients clustered into 2 subject clusters (SCs) (SC2 and SC3).One severe asthma cluster (SC2) had high T2 inflammation, as evidence by a high FeNO, bronchoalveolar lavage and blood eosinophilia, and up-regulated expression of T2 signature and mast cell genes.The other severe asthma cluster (SC3) had lower levels of T2 clinical biomarkers and gene expression, in addition to a later age of onset, increased incidence of nasal polyposis and higher levels of neutrophilic inflammation.Roughly 1/2 of all asthma subjects had evidence of high T2 inflammatory response (by clinical biomarkers and gene expression), confirming the prior findings of Woodruff et al. in a more severe and steroid-treated patient population.In general, both severe asthma clusters (SC2 and SC3) were older and more obese than the other non-severe subclusters.Further, both of the severe SCs demonstrated suppression of genes associated with cilia function, neuronal function, cell adhesion and wound repair.These findings suggested that airway epithelial defense, repair, neuronal function are an integral part of a healthy epithelial layer and perhaps prevention of severe asthma.", + "These genes are high priority candidates, although we acknowledge that causal variants may lie in non-coding regions. For each of these high priority candidates we then examined which GO:biological processes (Consortium, 2015) and KEGG pathways (Kanehisa et al. , 2012) the gene was annotated as being part of, and highlighted those which may relate to our phenotypes. We also reviewed known effects of mutations using the Mouse Genome Informatics (MGI) Phenotypes, Alleles and Disease Models Search (www.informatics.jax.org/allele) (Bello et al. , 2015).", + "Results were displayed as a matrix with all phenotypes/diseases associated with 173 mouse models and human genes found for the candidate gene list. 174 175 2.6. Expression-phenotype correlations 176 For each gene discovered after filtering, an adequate probe within the well-curated INIA Amygdala 177 Cohort Affy MoGene 1.0ST (Mar11) RMA, Hippocampus Consortium M430v2 (Jun06) PDNN, 178 VCU BXD Prefrontal Cortex M430 2.0 (Dec06) RMA, INIA Hypothalamus Affy MoGene 1.0ST 179 (Nov10), and INIA Adrenal Affy MoGene 1.0ST (Jun12) RMA Databases was identified using 180 GeneNetwork (http://www.genenetwork.org; Williams and Mulligan, 2012)).", + " The GeneNetwork website contains extensive phenotypic datasets ranging from behavioral to morphological to pharmacological.To identify phenotypes associated with Gsto1 variation, we queried the BXD phenotype database in GeneNetwork, which contains nearly 3000 phenotypes, to look for the phenotypes that are most closely related to hippocampal expression of Gsto1 (probe set 1416531_at).", + " To examine known causal genes that have been reported in the literature, including related genes and pathways, a gene list was generated consisting of 6264 genes categorized by disorders, pathways, expression, AmiGO terms, and other into 26 sublists (supplemental data).This list was manually collected from different database sources covering all aspects of insulin-and glucose-related genes and disorders.This was done through an extensive literature review using PubMed, Ovid\u00ae, GeneCards\u00ae, and the National Center for Biotechnology Information (NCBI).Gene and protein expression databases such as BioGPS and The Human Protein Atlas were used.Protein interactions and gene network databases, such as AmiGO, BioGRID, GIANT, KEGG, and Reactome, were also used.Knockout mouse databases, such as MGI and IMPC, were also used.However, filtering against the gene list will not replace the manual screening for all variants called; therefore, we did not consider the results of our gene list alone.Once the raw data were obtained, they were filtered and investigated individually.As shown in Fig. 1, mutations went through serial steps ending up with a single nucleotide polymorphism mutation as a potential explanation.Pathogenicity scores were determined by SIFT, PolyPhen-2, PROVEAN, and PhD-SNP.", + "Chesler, E. J., Wang, J., Lu, L., Qu, Y., Manly, K. F., and Williams, R. W. (2003). Genetic correlates of gene expression in recombinant inbred strains: a relational model system to explore neurobehavioral phenotypes. Neuroinformatics 1, 343\u2013357. doi:10.1385/NI:1:4:343. Denny, J. C., Ritchie, M. D., Basford, M. A., Pulley, J. M., Bastarache, L., Brown-Gentry, K., et al. (2010). PheWAS: demonstrating the feasibility of a phenome-wide scan to discover genedisease associations. Bioinformatics 26, 1205\u20131210. doi:10.1093/bioinformatics/btq126. Farrar, C. A., Zhou, W., and Sacks, S. H. (2016). Role of the lectin complement pathway in kidney transplantation. Immunobiology 221, 1068\u20131072. doi:10.1016/j.imbio.2016.05.004. Gene Ontology Consortium (2015).", + "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In this manuscript we will outline some simple use cases, and show how a small number of plausible candidate genes can be identified for an immune phenotype. 1. Data Once you have navigated to genenetwork.org, there are two ways to search for data in GN. The first is to use the global search bar located at the top of the page (Figure 1). This is a new feature in GN that allows researchers to search for genes, mRNAs, or proteins across all of the datasets.", + "Protein interaction data: There is a growing body of protein-interaction data and this data is a useful extension to inferences of functional interaction between disease gene candidates and co-expressed genes. Ontologies for Functional Annotation: This project will lead to a small subset of genes of interest for asthma and AD.. Ontologies are key in making automated and vocabulary controlled statements about function and it will be interesting to interface the analytical framework presented in the proposal with contemporary advances in gene ontology methodology.", + "A network or interaction model will be generated using methods of graphical modelling with both inhouse data and public databases to propose predictive models for epithelial cells and characterise critical molecular interactions within asthma and AD biology. Finally, supporting and extending methodologies from above will contribute to (E) Future Directions of the study and include interfacing and data exchange with contemporary public databases. D(a) Disease Association and eQTL Mapping Mapping the human genome for regions and positions that are responsible for disease susceptibility and differential gene expression is central to this project.", + "For example, time series data sets potentially capture relationships and dependencies of gene expression within and between time points which may suggest causative co-regulation. These dependencies and interactions could be better uncovered using statistical modelling approaches such as Bayesian model based methods that aim to identify co-expressed clusters of genes under a model of temporal dependence between observations, that is utilising gene expression measures in time to better judge cluster membership11,12. Secondly, the asthma and AD expression dataset of sibpairs inherently contains underlying structures of shared genetic disease risk.", + "Genes are arranged based on their genetic positions, and genes annotated to be involved in the module are colored red. Genes with absolute GMAS over 0.268 are considered significantly associated. DDT, BOLA3, and ARID1A are labeled. B, Venn diagram of novel genes associated with respiratory electron transport module in human, mouse and rat. 707 genes were predicted to be mito-proteins by G-MAD in all three species.", + "Chesler, E. J., Wang, J., Lu, L., Qu, Y., Manly, K. F., and Williams, R. W. (2003). Genetic correlates of gene expression in recombinant inbred strains: a relational model system to explore neurobehavioral phenotypes. Neuroinformatics 1, 343\u2013357. doi:10.1385/NI:1:4:343. Denny, J. C., Ritchie, M. D., Basford, M. A., Pulley, J. M., Bastarache, L., Brown-Gentry, K., et al. (2010). PheWAS: demonstrating the feasibility of a phenome-wide scan to discover genedisease associations. Bioinformatics 26, 1205\u20131210. doi:10.1093/bioinformatics/btq126. Farrar, C. A., Zhou, W., and Sacks, S. H. (2016). Role of the lectin complement pathway in kidney transplantation. Immunobiology 221, 1068\u20131072. doi:10.1016/j.imbio.2016.05.004. Gene Ontology Consortium (2015).", + "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In this manuscript we will outline some simple use cases, and show how a small number of plausible candidate genes can be identified for an immune phenotype. 1. Data Once you have navigated to genenetwork.org, there are two ways to search for data in GN. The first is to use the global search bar located at the top of the page (Figure 1). This is a new feature in GN that allows researchers to search for genes, mRNAs, or proteins across all of the datasets.", + "6 Phenotype-matched reports 7 The framework implementation we have presented uses only genomic information to generate a patient or research report. Of course, the clinical features of the sample o\ufb00er vital clues as to which gene is likely responsible for the disease. It would therefore make sense to include phenotype-based gene \ufb01ltering or prioritization to the report. To make this possible, associations of Human Phenotype Ontology (HPO) terms[292] to their known disease genes could be integrated into the system. Users can enter HPO terms that match the phenotypes observed in a patient to shorten their list of candidate genes.", + "Predicted transcriptome association test We used the PrediXcan 16 framework to identify genes that might mediate associations between genetic variants and asthma risk.PrediXcan is a software tool that estimates tissue-specific gene expression profiles from an individual's SNP genotype profile by use of prediction models trained in large reference databases of genotypes and tissue-specific gene expression profiles.With these genotype-imputed expression profiles, PrediXcan can perform gene-based association tests that correlate predicted expression levels with phenotypes (eg, asthma) to identify candidate causal genes from GWAS data.We used a summary version of PrediXcan, which has high concordance with the individual-level version (r\u00b2>0\u202299). 17or predictions, we downloaded elastic net models trained with reference transcriptome data from the Genotype-Tissue Expression consortium 18 for 49 tissues (appendix pp 9, 47).", + " Gene selection was based on searches conducted using the Genetic Association Database (geneticassociationdb.nih.gov).Only genes with multiple, independent indicators of function were included.aPhenotype available for one cohort only.", + "The results from the phenotype-driven searches should then be linked to gene names associated with a given phenotype. These genes are presented as a list from which the user can choose the genes of interest and save them in a shopping cart. It is then possible to feed the genes into the gene-centric use-case and perform a more detailed data mining or meta-analysis. The description and further development of the phenotype-driven use-case may represent a very useful concept for scientists and clinicians outside the mouse community.", + " As a demonstration of the utility of the web interface, we entered the 9 genes that reached suggestive significance in a recent genome-wide association study of opioid cessation (Cox et al. 2020).The graph view of the search results are shown in Fig. 3. Genes and keywords are all shown as circles and lines connecting them show the number of abstracts containing the 2 circles they connect.Keywords under the same main category are shown with the same color in the graphic output.Clicking on the lines brings up a new page that displays all sentences containing the keywords that line connects.An alternative tabular view of the same results is also available, where genes, the keywords, and number of abstracts are shown as separate columns." + ], + [ + "A major advantage of the mouse as an animal model is the availability of well-characterized inbred strains that enable functional genomics on defined genetic backgrounds. Currently, however, exploiting the full utility of mice to study human diseases is hampered by the lack of gene targeting resources for multiple inbred mouse strains. DBA/2J is a common inbred mouse strain critical in studying a diverse range of human diseases. For example, it is widely used as an inherited model of glaucoma. Glaucoma is a neurodegenerative disorder that affects 70 million people worldwide.", + "The network is driven by a common regulator, Ebi2 (also known as Gpr183), which is conserved in rats and humans, is expressed in macrophages and is associated in GWASs with human type 1 diabetes48. Such systemsgenetics studies are possible in rats because of the ready availability of ex vivo tissues and the statistical power gained from studies of inbred strains in controlled environments. Overall, these vignettes provide clear examples of the translational focus of the rat genetics community in an era of unprecedented scientific opportunity enabled by ultra-high-throughput genomics and mathematical biology.", + " Inbred animal models with homogeneous genetic backgrounds have been a powerful adjunct to human studies, providing a sufficiently large number of samples required for an unconstrained genetic analysis.Several polygenic NIDDM rodent models have been developed.These include the Goto-Kakizaki (GK) rat, the Otsuka Long-Evans Tokushima Fatty (OLETF) rat, the Nagoya Shibata Yasuda mouse, the New Zealand Obese mouse (reviewed in Kim et al., 1998), and the Tsumura-Suzuki Obese Diabetes mouse (Suzuki et al., 1999).The underlying genetic factors in these animal models have been studied by quantitative trait locus (QTL) mapping analysis, and several QTLs associated with glucose intolerance, defective insulin secretion, or parameters defining glucose homeostasis have been located (reviewed in Kim et al., 1998;Hirayama et al., 1999;Ueda et al., 1999).", + "In as much as it is quite difficult to conduct certain infectious disease studies in humans, there has been a critical need for small animal models for infectious diseases. Appreciating the limitations of existing models, we developed several novel and complementary mouse models that are ideal for use in systems genetics studies of complex diseases. These models not only allow biological validation of known genetic associations, but importantly they afford an unbiased tool for discovering novel genes and pathways contributing to disease outcomes, under different environments. 2008 Genetic effects on environmental vulnerability to disease.", + "Generalities Mouse models have been developed to give new insights into human diseases.Mouse models can be classified into two main classes: 1) genetically modified mouse models, animals that lack (knockout) or overexpress a specific gene and the protein that is encoded for, 2) mice that acquire a disease/symptom following an experimental procedure, such as diet, chemical injections and specific surgery.", + "However, in other contexts, B6 mice are more likely than D2 to spontaneously develop diabetic syndromes, Aging Clin Exp Res indicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these murine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29\u201331], much like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might underlie diabetes-related traits, including RASA1, Nnt, and PSK1. RASA1 show strong sequence differences between B6 and D2 strains [34]. Rasche et al.", + "In other cases, the rat phenotypes have proved more robust and consistent, such as pristane-induced arthritis as a model for rheumatoid arthritis (Holmdahl et al. 2001) and cresentic glomerulonephritis (Aitman et al. 2006). Decades of careful phenotyping and detailed analyses in rat experimental crosses have led to the localization of hundreds of rat physiological quantitative trait loci (pQTLs) containing genes that confer susceptibility to complex disease phenotypes, including hypertension, type 2 diabetes, autoimmune disorders, and cancer (Flint et al. 2005). The availability of the rat genome sequence in June 2003 (Gibbs et al.", + ", et al. , Harnessing Genetic Complexity to Enhance Translatability of Alzheimer's Disease Mouse Models: A Path toward Precision Medicine. Neuron, 2019. 101(3): p. 399-411 e5. Beura, L.K. , et al. , Normalizing the environment recapitulates adult human immune traits in laboratory mice. Nature, 2016. 532(7600): p. 512-6. Kleinert, M., et al. , Animal models of obesity and diabetes mellitus. Nat Rev Endocrinol, 2018. 14(3): p. 140-162. Kebede, M.A. and A.D. Attie, Insights into obesity and diabetes at the intersection of mouse and human genetics. Trends Endocrinol Metab, 2014. 25(10): p. 493-501. von Scheidt, M., et al.", + "Researchers have access to all the tissue samples in mice, especially those highly relevant in diseases, which is impossible in most human studies because of ethical issues. 8. Mouse models can be used to capture the disease progression stages in longitudinal studies. 9. Mouse genetic populations are able to model the genetic diversity of human populations, and require fewer individuals for genetic association analyses. 10. Unlike human genetic studies where data should always be kept highly confidential, data from mouse studies can be made public available to facilitate its re-analysis to the fullest extent.", + "Knock-out and transgenic mice in diabetes research Transgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003).", + " Polygenic models of obesity.Polygenic models of obesity may provide a more accurate model of the human condition.A variety of different polygenic mouse models of obesity, glucose intolerance and diabetes exist, allowing a variety of genotypes and susceptibilities to be studied.However, unlike the monogenic models, there are no wild-type controls.In addition, the male sex bias is more extreme in these models (Leiter, 2009).These polygenic models have been used in a wide variety of studies that have aimed to reverse the symptoms of type 2 diabetes (Chen et al., 2009;Fukaya et al., 2009;Guo et al., 2010;Mochizuki et al., 2011;Yoshinari and Igarashi, 2011), understand more about the interplay of obesity and glucose homeostasis (Kluth et al., 2011) (Jurgens et al., 2007) or study diabetic complications (Cheng et al., 2007;Fang et al., 2010;Buck et al., 2011;Lee et al., 2011a).KK mice.KK mice are a mildly obese and hyperleptinaemic strain derived from wild-derived ddY mice in Japan by Kondo in 1957 (Clee and Attie, 2007).They develop severe hyperinsulinaemia and demonstrate insulin resistance in both muscle and adipose tissue.The pancreatic islets are hypertrophic and degranulated.This mouse strain also shows signs of diabetic nephropathy (Ikeda, 1994).", + ", 2008) and specific genetic factors for predisposition to DN were recently identified in several diabetic sibling studies (Bleyer et al. , 2008; Schelling et al.,2008; Tanaka et al. , 2005). Similar to humans, inbred strains of mice exhibit differences in their susceptibility to diabetes, renal and cardiovascular diseases (Krolewski et al. , 1996). More recently, differential susceptibilities to DN have also been observed in well-defined strains of 23", + " The third advantage of the mouse model is that after identification of a candidate gene, direct genetic evidence for its involvement in a pathophysiology can be obtained in mice, but very rarely in humans.Thus, inbred mouse models are ideally suited for the investigation of the obesity-associated diabetes.However, the genetic homogeneity of the inbred strains is not only an advantage, it also limits their potential.Individuals of an inbred mouse line are genetically identical, and it cannot be expected that a single strain carries more than a small portion of all relevant gene variants.Currently, more than 2000 mouse QTL for different traits have been identified in crosses between inbred stains, but only about 1 % has been characterized on molecular level (Flint et al. 2005).Thus, more than one model and new resources, e.g., systems biology may be required for a complete genetic analysis of complex traits.Previous and ongoing research supports the view that the combination of individual genomes-by intercross of inbred strains and by the generation of congenic lineswill reveal effects of many more genes and gene interactions than can be observed in a single inbred strain.Because the cross-breeding experiments are time consuming and expensive, selecting the ''right'' models of the obesity-associated diabetes is of crucial importance (Leiter 2009).Another advantage of mouse studies in comparison to human studies is the ability to control the environment and to investigate effects of diets, exercise, and intestinal microbiota.", + "Introduction Rodents, particularly mouse and rat have been widely used for biomedical research in models of human diseases since it is known that almost of all of genes in mouse and rat are similar to that of humans. However, not every genetic pathway or molecular mechanism of diseases or drugs discovered to be efficacious in these models can be extrapolated to human diseases. Thus, while much data from animal studies have been successfully applied to humans, some have not. The present study aims to explore the degrees of differences in the causal pathways for lung fibrosis between humans and mice.", + " These limitations support the increasing need of experimental systems to characterize the fundamental biological mechanisms responsible for diabetes inheritance and the function of risk genes.In the context of diabetes pathogenesis, in vitro systems are useful but often limited, in particular to assess glucose tolerance, insulin sensitivity, islet architecture and function and diabetes complications.The laboratory mouse provides a wide range of experimental models for diabetes gene discovery and for in vivo post-GWAS studies of diabetes that develops either spontaneously or following gene editing [5].The laboratory rat is also a powerful system to implement phenotyping methods required to record biological variables relevant to common chronic diseases.The rat is the preferred model to perform phenotyping procedures that are often technically challenging in mice or require the collection of large volumes of blood or organs.For these reasons, rat models of type 2 diabetes or hypertension have been successfully used to localise in the genome genes controlling endophenotypes relevant to these complex diseases.This review addresses strategies used to map the genetic determinants of physiological and molecular phenotypes relevant to type 2 diabetes pathogenesis and to characterize their biological function in vivo through examples derived from genetic and genomic research in the Goto-Kakizaki (GK) rat strain.", + "However, many of the phenotypes of the homozygous null mutations were extreme and/or did not model the complexity of the metabolic syndrome. For example, IR knockout (IR2/2) mice died because of developmental effects (Accili et al. , 1996), which precluded analysis of adult mice. Likewise, GLUT42/2 mice exhibited only moderate insulin resistance and were not overtly diabetic, suggesting compensatory mechanisms (Katz et al. , 1995). Monogenic GEMMs furthermore ignore the polygenic nature of metabolic diseases, resulting from genetic and environmental factors impacting at multiple levels in signaling cascades. Oligogenic mouse models remedied some of these shortcomings.", + "Since glucokinase2/2 mice are embryonic lethal, this collection of glucokinase mutants is useful for dissecting the pathogenesis of MODY2. Genetic reference populations (GRPs) Perhaps the most \u2018\u2018refreshing\u2019\u2019 mouse resource for investigating complex diseases is the construction of mouse crosses using inbred mice and the subsequent QTL mapping. Inbred mice have an inherent wealth of variation due to past spontaneous mutation events, which have been preserved through systematic and uninterrupted brother-sister matings (Paigen, 2003). Inbred mice are appealing since they are genetically identical within a strain but are diverse between strains.", + "Mouse Models of Oxidative Stress and Mitochondrial Dysfunction in Aging.Genetically engineered mouse models provide great systems to directly dissect the complex relationship between oxidative damage, mitochondrial dysfunction, and aging.Although it is difficult to manipulate mitochondrial genome, genetic engineering of nuclear genes that are involved in oxidative stress response and mitochondrial function has been utilized to study mitochondrial biology and aging.", + "Rodent models of glaucoma have gained favor in the research community due to their ease of handling and the lower costs associated with acquisition and care. In particular, the mouse provides a number of useful genetic approaches to create models and to test specific molecular interactions associated with the disease process. Furthermore, the mouse genome is relatively conserved compared to the human genome.", + "Better Mouse Models. A key point to bear in mind in assessing the usefulness of mouse models is the relative plasticity displayed by rodents faced with gene deletions.Thus, differences between the penetrance of mutations in human genes linked to monogenic forms of diabetes, including maturity onset diabetes of the young (MODY), between humans and mice, are usually observed [114] with the mouse equivalents showing far less marked disturbances in glycemia or changes which are seen only after deletion of both alleles.This clearly reflects the limitations of the use of mice (weight \u223c25 g, life expectancy \u223c3 years) for comparisons with human subjects.Nonetheless, and although the phenotypes of the above murine models are thus often more subtle than the human counterparts, they remain useful models for the study of diabetes, allowing single-targeted gene deletions which are impossible in man.For example, human populations with different genetic backgrounds have different susceptibility to the R235W ZnT8 polymorphism.We should not, therefore, find surprising the results that different genetic backgrounds and different diet reveal different phenotypes in ZnT8 knockout models." + ], + [ + " Additional large-scale meta-analysis predicated upon increased sample size, were carried out on existing datasets from the WTCCC [18] and the Genetics of Kidneys in Diabetes (GoKinD) study [69][70][71] plus control data derived from the National Institute of Mental Health.These investigators observed significant association of previously observed loci.Importantly, they did not observe evidence of new T1D loci reaching the threshold for genome-wide significance.Instead they re-analyzed the most nominally significant associated SNP in an independent British cohort of approximately 6000 cases, 7000 controls and in 2800 families, where they uncovered four additional loci, BACH2 (previously reported [67]), 10p15 harboring protein kinase C theta (PRKCQ), 15q24 harboring nine genes including the cathepsin H (CTSH), complement 1q (C1q), tumor necrosis factor related protein 6 (C1QTNF6) and somatostatin receptor 3 (SSTR3) genes.Table 1 summarizes the 16 T1D loci reported to date.An example of a tag-SNP that captures the association with T1D in each instance is highlighted together with its relative minor allele frequency in controls and what magnitude of risk or protection it confers.Key references regarding the role of each locus in the context of the disease are included and along with the chromosomal band where each locus resides, the main candidate gene (symbol and full name) is highlighted.", + "Detection of established loci We explored the extent to which previously reported type 2 diabetes association signals could be detected in African-descent individuals.Based on the previously reported effect sizes and the effect allele frequency and sample size from our African meta-analysis, we had sufficient power (80%) to detect three signals (TCF7L2, DNER and SRR) at genome-wide significance (p < 2.5 \u00d7 10 \u22128 ) (ESM Table 2).Only the TCF7L2 variant reached genome-wide significance in our study, whereas both variants in DNER (rs1861612) and SRR (rs391300), originally discovered in Pima Indians and East Asians, respectively, had p > 0.1 (ESM Table 2).", + " On the basis of the combined stage 1-3 analyses, we found that six signals reached compelling levels of evidence (P \u00bc 5.0 \u00c2 10 -8 or better) for association with T2D (Table 2).As in all linkage disequilibrium (LD)-mapping approaches, characterization of the causal variants responsible, their effect sizes and the genes through which they act will require extensive resequencing and fine-mapping.However, on the basis of current evidence, we found that the most associated variants in each of these signals map to intron 1 of JAZF1, between CDC123 and CAMK1D, between TSPAN8 and LGR5, in exon 24 of THADA, near ADAMTS9 and in intron 5 of NOTCH2.", + " Replication study of newly identified type 1 diabetes risk loci", + " Although these are considered to be loci convincingly associated with susceptibility to type 2 diabetes in populations of European descent, other genes related to susceptibility to the disease are probably still unidentified, particularly those for populations of other ancestries.In order to uncover genetic variants that increase the risk of type 2 diabetes, we conducted a genome-wide association study in Japanese individuals with type 2 diabetes and unrelated controls.We first genotyped 268,068 SNPs, which covered approximately 56% of common SNPs in the Japanese, in 194 individuals with type 2 diabetes and diabetic retinopathy (case 1) and in 1,558 controls (control 1) collected in the BioBank Japan.We compared the allele frequencies of 207,097 successfully genotyped SNPs and selected the 8,323 SNPs showing the lowest P values.We then attempted to genotype these 8,323 SNPs in 1,367 individuals with type 2 diabetes and diabetic retinopathy (case 2) and for 1,266 controls (control 2) (stage 2), and successfully obtained data for 6,731 SNPs (the P value distribution in the second test is shown in Supplementary Fig. 1a online).The results of principal component analysis 8 in the stage 1 and 2 samples and HapMap samples revealed that there was no evidence for population stratification between the case and control groups throughout the present tests (Supplementary Fig. 1b,c).We selected the 9 SNP loci showing P values o0.0001 (additive model in stage 2, Table 1) and genotyped a third set of cases and controls comprising 3,557 Japanese individuals with type 2 diabetes (cases 3,4,5) and 1,352 controls (controls 3,4).We evaluated the differences in the population structure among these three sets of case and two sets of control groups by Wright's F test.As the results indicated that there was no difference in the population structure among these groups (Supplementary Table 1b online), we combined these populations for the third test of case-control study.The third set of analysis identified the significant associations for six SNPs (Table 1), including the CDKAL1 locus at 6p22.3 (rs4712524, rs9295475 and rs9460546), the IGF2BP2 locus at 3q27.2 (rs6769511 and rs4376068) and the KCNQ1 locus at 11p15.5 (rs2283228).The remaining three SNPs (rs13259803, rs612774 and rs10836097) had P values of 40.05 in the third test and were not further examined.CDKAL1 and IGF2BP2 were previously reported as susceptibility genes for type 2 diabetes in the Japanese population 9 .Therefore, we focused on the KCNQ1 locus, which was highly associated with type 2 diabetes.", + " We consider these data as an interesting preliminary result that surely requires additional independent studies including a higher number of patients in order to confirm and clarify the possible contribution of this locus to the development of T2DM complications.", + "DISCUSSION Taken together, our full second-stage approach and combined meta-analysis have revealed additional loci associated with type 1 diabetes.Clearly the risks are relatively modest compared with previously described associations, and it was only with this sample size at our disposal that we could we detect and establish these signals as true positives through an independent validation effort.", + "Identification of susceptibility loci The degree of evidence for all reported T2D loci was quantified as follows: a locus with a logarithm of odds ratio (LOD) score of 3 or more was considered significant, a LOD score between 2.2 and 3 was considered suggestive and a LOD score between 1 and 2.2 was considered nominal.For T2D, only those loci were included that were significant at least once, or were suggestive in at least one study and at least nominal in two or more studies.The inclusion of the second category of loci was based on a study by Wiltshire et al. [72], in which it was postulated that locus counting is a useful additional tool for the evaluation of genome scan data for complex trait loci.We used the same two criteria to determine the loci from the five papers published on obesity since 2004 and combined these loci with those from Bell et al. [7].As obesity phenotypes, BMI, serum leptin levels, abdominal subcutaneous and visceral fat, and percentage body fat were included.All of these phenotypes were used as continuous quantitative traits, as well as with various cut-off levels.", + " Today, more than 100 loci for type 2 diabetes and glycemic traits have been identified through numerous GWA studies of common and rare variation in populations of diverse ancestral origins [31]; however, to date, very few GWA studies have been published in cohorts of Mexican ancestry.The first GWA study performed in a non-European cohort was published in 2007 and comprised 561 Mexican American type 2 diabetes cases and controls drawn from the Starr County Health Studies [32].Although no loci reached genome-wide significance, several loci identified in prior GWA studies in Europeans were replicated [32].This analysis was subsequently expanded (N = 1273) and meta-analyzed with a cohort from Mexico City (N = 1310) in 2011 [33,34].The most significant variants observed in this meta-analysis included known regions near HNF1A and KCNQ1.Top association signals were then meta-analyzed with the DIAGRAM and DIAGRAM+ datasets of European ancestry individuals, resulting in two regions reaching genome-wide significance: HNF1A and CDKN2A/CDKN2B (Table 1).Top association signals in both studies were annotated to explore their roles as expression quantitative trait loci (eQTL) in both adipose and muscle tissues, revealing a marked excess of transacting eQTL in top signals in both tissue types.", + " 75\u00b179 The main conclusion is that there is no major locus for T2D (analogous to HLA in type 1 diabetes).This is not surprising given the modest l s for T2D (approximately 3.5 in Europeans), imposing a limit on the magnitude of any single gene eect. 4Many scans have consequently been signi\u00aecantly underpowered to detect the modest gene eects anticipated.Certainly, few T2D scans have reported linkages meeting the established criteria for genomewide signi\u00aecance. 80This modest power, combined with the diversity of the pedigrees sampled and the analytical techniques used, means that the replication of positive \u00aendings between data sets has been the exception rather than the rule.", + "Quantitative Trait Analysis Exploration of putative T2DM variants with quantitative glycemic traits in a subset of African-American samples (n = 671 from the IRAS and IRASFS control samples, Table S5) revealed limited insight into the biological mechanism associated with T2DM risk.In addition, the five putative African-American T2DM susceptibility loci were tested for association with quantitative measures of glucose homeostasis in the European Caucasian population, in silico, by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC; [16]).These results did not provide further insight into the probable role these variants may have in disease susceptibility (Table S6).The most significantly associated SNP in African Americans, rs7560163, failed quality controls filters and was not included in analysis likely due to being monomorphic as seen in a representative Caucasian population from the HapMap project (Table S4).", + " Discovery of novel loci for T2D susceptibility.We tested for T2D association with ~27 million variants passing quality-control filters, ~21 million of which had a minor allele frequency (MAF) < 5%.Our meta-analysis identified variants at 231 loci reaching genomewide significance (P < 5 \u00d7 10 \u22128 ) in the BMI-unadjusted analysis (N eff 231,436) and 152 in the smaller (N eff 157,401) BMI-adjusted analysis.Of the 243 loci identified across these two analyses, 135 mapped outside regions previously implicated in T2D risk (Methods, Fig. 1 and Supplementary Table 2).", + " Genetic studies performed since 2012 have identified many additional T2D loci based on risk alleles common in one population but less common in others.Studies in African Americans identified RND3-RBM43 (28), HLA-B and INS-IGF2 (29).Studies in South Asians identified TMEM163 (30) and SGCG (31).One locus, SLC16A11-SLC16A13, was simultaneously identified in Japanese and Mexican Americans (32,33), and studies in East Asians identified ANK1 (34), GRK5 and RASGRP1 (35), LEP and GPSM1 (32), and CCDC63 and C12orf51 (36).A study of individuals from Greenland identified TBC1D4 (37), and a sequencing-based study of Danes with follow-up in other Europeans identified MACF1 (38).Finally, the largest GWAS to date in American Indians identified DNER at near genome-wide significance (P = 6.6 \u00d7 10 \u22128 ) (39).Three of these studies imputed GWAS data using the 1000 Genomes Project sequence-based reference panels, providing better genome coverage (29,32,33,40).Taken together, these studies highlight the value of diverse populations, including founder and historically isolated populations, to detect risk loci.", + " Finally, a recent study identified additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs. 21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds.Thus, by combining data involving 10,128 samples, the authors found in the initial stages of the analysis highly associated variants (they followed only 69 signals out of over 2 million metaanalyzed SNPs) with P values \u03fd10 \u03ea4 in unknown loci, and 11 of these type 2 diabetes' associated SNPs were taken forward to further stages of analysis.Large stage replication testing allowed the detection of at least six previously unknown loci with robust evidence for association with type 2 diabetes.", + " Surprisingly, data about previous published loci associated with type 2 diabetes were not sufficiently powerful to reach a significant P value in individual scans.For example, variants at SLC30A8 and PPARG were significantly associated with type 2 diabetes only when pooling all the GWAs data, whereas in a single genome scan (DGI), no gene showed a positive signal (P value: 0.92 and 0.83, respectively).Thus, this may suggest that GWAs are still underpowered to find SNPs with small effect size.", + " Background: The two genome-wide association studies published by us and by the Wellcome Trust Case-Control Consortium (WTCCC) revealed a number of novel loci, but neither had the statistical power to elucidate all of the genetic components of type 1 diabetes risk, a task for which larger effective sample sizes are needed.Methods: We analysed data from two sources: (1) The previously published second stage of our study, with a total sample size of the two stages consisting of 1046 Canadian case-parent trios and 538 multiplex families with 929 affected offspring from the Type 1 Diabetes Genetics Consortium (T1DGC); (2) the Rapid Response 2 (RR2) project of the T1DGC, which genotyped 4417 individuals from 1062 non-overlapping families, including 2059 affected individuals (mostly sibling pairs) for the 1536 markers with the highest statistical significance for type 1 diabetes in the WTCCC results.Results: One locus, mapping to a linkage disequilibrium (LD) block at chr15q14, reached statistical significance by combining results from two markers (rs17574546 and rs7171171) in perfect LD with each other (r 2 = 1).We obtained a joint p value of 1.3610 26 , which exceeds by an order of magnitude the conservative threshold of 3.26610 25 obtained by correcting for the 1536 single nucleotide polymorphisms (SNPs) tested in our study.Meta-analysis with the original WTCCC genome-wide data produced a p value of 5.83610 29 .Conclusions: A novel type 1 diabetes locus was discovered.It involves RASGRP1, a gene known to play a crucial role in thymocyte differentiation and T cell receptor (TCR) signalling by activating the Ras signalling pathway.", + " Finally, we examined whether genes identified using our association studies were enriched within diabetes-related pathways.We collated a list of 42 genes to which 53 CpG sites associated with T2D traits (CS score \u22651.77, combined P < 0.017) mapped.Even in this small dataset, pathway analysis (Supplementary Material, Table S12) indicated significant enrichment in 31 pathways (Fisher's exact P < 0.05), including those related to circadian clock (P = 0.005), adipocytokine signaling (P = 0.009), leptin pathway (P = 0.023), HDL-mediated lipid transport (P = 0.031) and insulin signaling (P = 0.033).", + " In recent years, progress has been made in following up mechanistic studies of GWAS type 2 diabetes-association signals [6,7,9,[25][26][27][28][29][30], but challenges remain in sifting through the many associated variants at a locus to identify those influencing disease.We hypothesized that a common variant with modest effect underlies the association at the CDC123/CAMK1D locus and evaluated the location of high LD variants (r 2 $.7; n = 11) at the locus relative to known transcripts and to putative DNA regulatory elements.We identified two variants that overlapped putative islet and/or liver regulatory regions and none located in exons.We did not assess variants in lower LD (r 2 ,.7), and additional functional SNPs may exist at this locus acting through alternate functional mechanisms untested in the current study.", + " Meta-analysis results for T2D SNPs for insulin and glucose-related traits.", + "A r t i c l e s By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P < 5 \u00d7 10 \u22128 .These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A).The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation.We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits." + ], + [ + "Methods Mouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m \u00fe / \u00fe lepr db/J) and genetic control non-diabetic db/ \u00fe mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg \u00c0 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl \u00c0 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals.", + " In these models, adult offspring of diabetic animals were noted to have normal development of the endocrine pancreas (Aerts et al., 1997;Ma et al., 2012).However, they develop glucose intolerance and impaired insulin response to glucose challenge, and display insulin resistance, mainly in the liver and muscle, highlighting the presence of both insulin resistance and b-cell dysfunction (Aerts et al., 1988;Holemans et al., 1991a,b).The key role of the intrauterine environment was demonstrated by a series of embryo transfer experiments, which showed that the diabetes risk in a low genetic risk strain can be substantially increased by the hyperglycaemic environment of a dam with a high genetic risk of diabetes (Gill-Randall et al., 2004).", + " Diabetes-obesity syndromes in rodents", + "However, in other contexts, B6 mice are more likely than D2 to spontaneously develop diabetic syndromes, Aging Clin Exp Res indicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these murine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29\u201331], much like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might underlie diabetes-related traits, including RASA1, Nnt, and PSK1. RASA1 show strong sequence differences between B6 and D2 strains [34]. Rasche et al.", + " Other diet-induced rodent models of type 2 diabetes.Although rats and mice are the most commonly used models for studies of type 2 diabetes, other rodents have also been identified as useful models.These include the desert gerbil and the newly described Nile grass rat, both of which tend to develop obesity in captivity.", + " Summary of rodent models of type 2 diabetes", + " Since the obesity is induced by environmental manipulation rather than genes, it is thought to model the human situation more accurately than genetic models of obesityinduced diabetes.High fat feeding is often used in transgenic or knock-out models, which may not show an overt diabetic phenotype under normal conditions, but when the beta cells are 'pushed', the gene may be shown to be of importance.It should be noted that the background strain of the mice can determine the susceptibility to diet-induced metabolic changes, and thus, effects could be missed if a more resistant strain is used (Surwit et al., 1995;Bachmanov et al., 2001;Almind and Kahn, 2004).It has also been reported that there is heterogeneity of the response to high fat feeding within the inbred C57BL/6 strain, indicating that differential responses to a high-fat diet are not purely genetic (Burcelin et al., 2002).", + "Other considerations and limitations A myriad of factors affect animal experiments.Men elicit a greater stress response in mice than women 292 , likely confounding feeding behaviour.Rodents from different production facilities (for example, Jackson Laboratory and Taconic) have unique gut microbiotas 293 , perhaps contributing to differences in their susceptibility to DIO and related diabetic complications 293 .Similarly, cage position within a rack of cages, single versus group housing, the skill level of the researcher, ambient room temperature or the type of cage bedding can all affect experimental outcomes.", + " We believe there are several factors that researchers should consider when conducting obesity and diabetes mellitus research in rodents (FIG.2).Although our list is by no means an exhaustive, it demonstrates the complexity and interconnectedness of the myriad of factors that can confound experimental outcomes.Although it is impossible to control for everything, researchers should accurately detail all experimental conditions and methods to allow for better interpretation of the results and, importantly, for better reproducibility.", + " Figure2| Important experimental parameters and potential confounders of experimental outcomes in obesity and diabetes research and their interrelatedness.Countless factors influence experimental outcomes when using animal models, and what is enumerated here is by no means a complete list.This figure is one depiction of the multifactorial and interconnected genetic and environmental matrix that makes it virtually impossible to design the perfect experiment.For example, single-housing mice to obtain more accurate food intake data introduces a stress that in turn affects food intake.The severity of this stress response is both strain-specific and sex-dependent.What is important is to be aware of these challenges and to control for them in the most optimal manner.It is equally, if not more, important to accurately and comprehensively detail all experimental conditions in research papers, as these have bearing on the interpretation and reproducibility of the published results.DIO, diet-induced obesity.", + " Another concern pertains to control mice.Compared with free-living mice in the wild, laboratory control mice with ad libitum access to food are sedentary, overweight, glucose intolerant and tend to die at a younger age 297 .Comparisons between mice with DIO and control mice might be analogous to investigating the genetic cause of obesity-resistance by comparing humans who are overweight or obese.This potential problem with control mice could explain why the use of DIO diets that have 40% to 60% of total energy from fat is so prevalent, as this might be necessary to achieve divergent weight gains.With free access to running wheels, C57BL/6J mice voluntarily run 5-10 km per day 298,299 .As is the case with humans 300 , mice get health benefits from regular physical activity including weight loss, decreased adiposity and improved insulin sensitivity 301,302 .Physical activity might also affect the epigenome over several generations 303 .An enriched physical and social cage environment alone improves leptin sensitivity and energy expenditure in mice, independent of physical activity 304,305 .Overall, these data suggest that with standard mouse husbandry, chow-fed laboratory mice are not the ideal healthy and lean control group for meaningful obesity research.", + " To better address these points, various animal models have been developed.For example, using HFD-T2DM male rats, the F1 female offspring showed reduced \u03b2 cell area and insulin secretion, together with glucose intolerance, without changes in body weight [145].The islets of the F1 female offspring showed differential expression of many genes involved in Ca 2+ , mitogen-activated protein kinase and Wnt signaling, apoptosis and cell cycle regulation [145].Similarly, in pregnant C57BL6J mice, food deprivation resulted in \u03b2 cell mass reduction and an increased risk of \u03b2 cell failure in offspring [146].", + "They are probably typical of those few mice that develop diabetes more slowly and do not tax the pancreatic insulin supply as severely early in the course of the disease. Attempts at therapy. Attempts to keep the weight of diabetic mice within normal limits by total or partial food restriction resulted in premature deaths. After it was discovered that gluconeogenesis is greatly increased in diabetic mice, attempts were made to regulate blood sugar levels and also weight gain by feeding rations devoid of carbohydrate.", + "The degree of dependence of adiposity, hyperglycemia, and islet hypertrophy on food consumption varies among these mice, but in all, the increase in islet volume and consequent fi-eell hyperplasia appears to be an effective 247 means of maintaining blood sugar concentrations at near normal levels. I n contrast, neither the diabetic sand rat [5] nor the diabetic mouse has hypertrophied islets and neither effectively controls blood sugar levels.", + "HV~MEI,: Studies with the Mutation, Diabetes almost undetectable. Similarly, the activities of citrate lyase and glucose-6-phosphate dehydrogenase were greatly decreased in these older diabetic as compared Diabetologia the diabetic mice have attained m a x i m u m weight, after which no further accumulation of adipose tissue is noted. Fig. 8.", + "Rodent models of monogenic obesity and diabetes Obesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory \u03b2 -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their \u03b2 -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity.", + " As with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes.", + "Animal models of diabetes in pregnancy and the role of intrauterine environment Another important field of diabetes research that has relied heavily on animal experimentation is the study of diabetes in pregnancy and the role of the intrauterine environment on the subsequent development of diabetes amongst offspring.", + " Animal models of Type 2 diabetes mellitus", + "Assessment of Diabetes Mice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + ], + [ + "Methods Mouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m \u00fe / \u00fe lepr db/J) and genetic control non-diabetic db/ \u00fe mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg \u00c0 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl \u00c0 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals.", + "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end.", + "Animal group and study design First, one set of animals comprising 12-week-old male type 2 diabetic db/db (C57BL/KsJ-db\u2212/db\u2212, n = 8) and contemporary control wild-type (C57BL/KsJ-db+/db\u2212, n = 8) mice (Jackson Laboratories) were included in this study.Their weights and blood glucose levels were analysed to eliminate variation.Erectile functions of the animals were evaluated by the apomorphine-induced penile erection test, according to a previously described protocol (Pan et al. 2014).Afterwards, intracavernous pressure (ICP) investigations and histological measurements were applied to further confirm the results of the function tests.Then, all mice were sacrificed and the corpus cavernosum (CC) was collected from each mouse.Because the tissue of the CC is difficult to crush, we randomly collected the CCs from two mice and mixed them into one subgroup.As a result, four diabetic subgroups (DB groups) and four normal control subgroups (NC groups) were used for molecular measurements.Second, another set of animals, including three T2DMED and three normal control mice that were independent from the original set of animals, were included in the validation experiments using qRT-PCR.Third, another separate set of animals, including five T2DMED and five control mice, were used to verify one of the predicted targets, IGF-1, using ELISA.A luciferase reporter assay was performed to verify the binding of the differentially expressed miRNAs to the target gene IGF-1.All procedures were approved by the Institutional Animal Care and Use committee at Nanjing Medical University.", + " Summary of rodent models of type 2 diabetes", + " Summary of rodent models of type 1 diabetes", + "Knock-out and transgenic mice in diabetes research Transgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003).", + "Genetically induced insulin-dependent diabetes AKITA mice.The AKITA mouse was derived in Akita, Japan from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene preventing correct processing of proinsulin.This causes an overload of misfolded proteins and subsequent ER stress.This results in a severe insulindependent diabetes starting from 3 to 4 weeks of age, which is characterized by hyperglycaemia, hypoinsulinaemia, polyuria and polydipsia.Untreated homozygotes rarely survive longer than 12 weeks.The lack of beta cell mass in this model makes it an alternative to streptozotocin-treated mice in transplantation studies (Mathews et al., 2002).It has also been used as a model of type 1 diabetic macrovascular disease (Zhou et al., 2011) and neuropathy (Drel et al., 2011).In addition, this model is commonly used to study potential alleviators of ER stress in the islets and in this respect models some of the pathology of type 2 diabetes (Chen et al., 2011).", + " To achieve a slow pathogenesis of T2DM, young adult mice 284 or rats 285 are fed a high-fat or Western diet to elicit DIO and insulin resistance.Single or multiple injections with low-dose streptozotocin (~30-40 mg/kg intraperitoneally) then elicit partial loss of \u03b2-cells, which results in hypoinsulinaemia and hyperglycaemia.Protocols are being continuously refined and likely differ between species and even strains 283 .The HFD streptozotocin rat is sensitive to metformin, further demonstrating the utility of this model 285 .Downsides of streptozotocin treatment include liver and kidney toxicity and mild carcinogenic adverse effects (TABLE 1).", + "Materials and methods 2.1 Mouse models 2.1.1 Mouse strains 2.1.2 Induction of type 1 diabetes 8 2.1.3 Insulin treatment on diabetic mice 2.1.4 Akita mouse genotyping 2.2 Characterization of diabetic nephropathy in mice 2.2.1 Proteinuria measurement 2.2.2 Glomerular cells quantification 2.2.3 Methenamine silver staining quantification 3. 4. 5. 6.", + " ii) Rodent models of diabetic retinopathy", + " There are some good reviews available in the literatures describing the transgenic/knockout animal models of type 2 diabetes [114][115][116][117][118] .The transgenic and knockout models are developed for studying the role of genes and their effects on peripheral insulin action such as insulin receptor, IRS-1, IRS-2, glucose transporter (GLUT 4), peroxisome proliferator activated receptor-g (PPAR-g) and tumour necrosis factor-a (TNF-a) as well as in insulin secretion such as GLUT-2, glucokinase (GK), islet amyloid polypeptide (IAPP) and GLP-1 and in hepatic glucose production (expression of PEPCK) associated with development of type 2 diabetes.Further, combination or double knockout mouse models including defect in insulin action and insulin secretion (e.g., IRS-1 +/-/GK +/-double knockout) have been produced which clearly illustrate the mechanisms associated with development of insulin resistance and beta cell dysfunction leading to overt hyperglycaemic state in human type 2 diabetes.These above genetically modified animals exhibit various phenotypic features of type 2 diabetes varying from mild to severe hyperglycaemia, insulin resistance, hyperinsulinaemia, impaired glucose tolerance and others as explained in detail elsewhere 6,9,[114][115][116][117][118] .Very recently, tissue specific knockout mouse models have been achieved, allowing further insight into the insulin action with respect to particular target tissues (muscle, adipose tissue and liver) associated with insulin resistance and type 2 diabetes 115,117,118 .The transgenic/knockout animals are currently used mostly for the mechanistic study in diabetes research and not usually recommended for screening programme as they are more complicated and costly.", + "Functional deficits refs Non-Alzheimer-disease mouse [71][72][73][74]76,78,81,85,87 and rat 59,75,77 ,79,95,97 Mouse [81][82][83][84][85] and rat 79,111 Cerebral effects of inducing diabetes or insulin resistance in normal rodents (that is, non-Alzheimer-disease rodent models) and in rodents genetically modified to accumulate amyloid\u03b2 in the brain (that is, rodent models of Alzheimer disease). Common intervetions to induce diabetic conditions in rodents included recessive mutations in the leptin gene (Lep; also known as Ob), defects in the leptin receptor (LEPR; also known as OB-R), diet and administration of streptozotocin. Rodents with pancratic overexpression of human amylin spontaneously develop both type 2 diabetes mellitus and dementia-like pathology.", + " Animal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice.", + " Animal models of Type 2 diabetes mellitus", + " As with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes.", + " Animal models of Type 1 diabetes", + " Animal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice.", + "Rodent models of monogenic obesity and diabetes Obesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory \u03b2 -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their \u03b2 -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity.", + "Introduction Animal experimentation has a long history in the field of diabetes research.The aim of this article is to review the commonly used animal models and discuss the recent technological advances that are being employed in the discipline.The review is based on an extensive literature search using the terms rodent, mouse, rat, animal model, transgenics, knockout, diabetes and pathogenesis, in scientific journal databases such as MEDLINE \u00ae.In addition, abstracts presented at meetings of Diabetes UK, the European Association for the Study of Diabetes and the American Diabetes Association over the last 5 years were examined in order to gain an appreciation of recent and ongoing research projects.", + "Assessment of Diabetes Mice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + ], + [ + " FIG. 6. Hepatic steatosis during DIO is associated with loss of eAT mass.A: Liver weight (adjusted for body weight) of mice fed a HF diet for 1, 4, 8, 12, 16, and 20 weeks.B: Inverse association of eAT mass and liver weight (as in A) between DIO weeks 12 and 20.C: Representative micrographs of hematoxylin and eosin-stained liver sections demonstrating that hepatic macrosteatosis in HF-fed mice is initially evident at DIO week 12 and increases through week 20.", + " RESEARCH DESIGN AND METHODS-Male C57BL/6 mice were fed a high-fat diet for 20 weeks to induce obesity.Every 4 weeks, insulin resistance was assessed by intraperitoneal insulin tolerance tests, and epididymal (eAT) and inguinal subcutaneous AT (iAT) and livers were harvested for histological, immunohistochemical, and gene expression analyses.", + "BXD and HMDP mouse strains, as well as HXB/BXH rat strains, with higher Cd36 expression had increased fat mass and body weight, as well as decreased VO 2 and liver acid beta\u2212glucosidase activity (Figure S2.4B-C), confirming the involvement of Cd36 in metabolism [126] and suggesting a potential role in Gaucher's disease, which results from the deficiency of acid beta\u2212glucosidase [127]. An association between Abca8a liver transcripts and triglyceride levels was also revealed (Figure S2.4D).", + "The mice were sacrificed at 9 am after a 4-hour fast. (A-E) PARPi reduced body weight (A; *, #, and $ indicates significant differences between 27 HFHS and CD, HFHS and PAPRi-Prev, and HFHS and PARPi-Ther, respectively), liver weight (B), epididymal fat pad (C), liver triglyceride content (D), and cholesterol (E) in both preventive and therapeutic cohorts (n=8-10). (F,G) Representative images of livers (F) and liver sections stained with H&E and Oil Red O (lipid content appears in red) (G), (n= 4-5).", + "CD45 positive cells appear brown. (n=4). * P <0.05; ** P < 0.001; *** P< 0.0001. Data are expressed as the mean \u00b1 SEM. One-way ANOVA with a post-hoc Bonferroni test was used for all statistical analyses. Male mice were used in these experiments. Fig. 5. Liver damage in MCD diet-induced NAFLD was reversed by NAD+ repletion. C57BL/6J mice were fed with CD, MCD, or MCD+PARPi (PARPi, 50 mg/kg/day). The mice were sacrificed at 9 am after a 4-hour fast. (A) PARPi reduces global protein PARylation and (B) recovers NAD+ levels in liver tissue (n=6).", + "At 10 weeks of age, male C57BL/6J mice were challenged with an MCD diet for 5 weeks. Similar to the effects seen in mice on a HFHS diet, MCD-fed mice treated with PARPi in a preventive manner exhibited reduced PARylation and increased hepatic NAD+ levels (Fig. 5A and B). Mice fed with a MCD diet for 5 weeks showed classical pathophysiological characteristics of NAFLD, including hepatic steatosis, inflammation and fibrosis. MCD diet increased AST and ALT levels compared to a control diet, while PARPi treatment reduced their levels (Fig. 5C and D).", + " The left inguinal, gonadal, and retroperitoneal fat pads were dissected and weighed individually. (Prior data showed that weights of left and right fat pads are highly correlated. )The mesenteric fat pad was also dissected and weighed.An adiposity index (AI) was computed for each mouse as follows: the left inguinal, gonadal, and retroperitoneal fat pad weights were summed, doubled, added to mesenteric fat pad weight, divided by body weight, and multiplied by 100.The ratios of the individual fat pad weights divided by body weight and expressed as a percentage (for example, 200\u00d7 left gonadal fat pad weight/body weight) were analyzed as separate traits, as were blood glucose level, plasma leptin level (log 10 transformed), body weight, and body length.", + "Metabolic phenotypes were compared between mice in the upper (Lonp1-high) and lower (Lonp1-low) quartiles with respect to WAT Lonp1 expression (n=9\u201310 mice per Copyright \u00a9 2021 Korean Endocrine Society VAT mRNA levels of OXPHOS-complex and UPRmt genes in relation to BMI Among 48 patients, 11 were obese (\u226525 kg/m2), 11 were overweight (23 to 24.9 kg/m2), and 26 were of normal or underweight (<22.9 kg/m2), according to the World Health Organization Asia-Pacific Obesity Classification [16]. Clinical characteristics of the participants stratified by BMI (<23 kg/m2 vs. \u226523 kg/m2) are summarized in Table 1.", + "In an F2 cohort derived from these parental strains, we have shown that the range of blood glucose, insulin levels, and body weight exceeds that of either the C57BL/6 (B6) leptinob/ob or BTBR leptinob/ob parental strains. We went on to identify several diabetesrelated QTL in this F2 sample [21,22]. In the current study, we focused on a subset of 60 F2 mice that have previously been evaluated in detail with regard to liver gene expression profiles [24] to ask if the abundances of hepatic metabolic intermediates would show sufficient heritability to enable us to map metabolic QTL (mQTL).", + "(E\u2013G) Data from CTB6F2 (E) and HMDP (F) mouse cohorts, and the HXB/BXH rat cohort (G) indicate significant negative correlations between liver Rpl26 levels and body weight, and other metabolic traits. adipose tissue (subWAT) mass (Figure 2D), suggesting pleiotropic effects of Pten. The links between Pten and neurobiological and metabolic phenotypes have been confirmed by independent studies (Kwon et al. , 2006; Ortega-Molina et al. , 2012). Overall, PheWAS showed that 4,230 out of 11,548 genes were associated with at least one phenotypic trait and all genes had significant associated molecular traits after phenome-wide correction (Figures 2E; Table S3).", + "Curves of weight ( \u2022 ... \u2022 ) and blood sugar concentration with age in a less typical diabetic mouse Diabetologia (I --I ) Aside from the large accumulations of fat, subcutaneously in axillary and inguinal regions and intraabdominally in mescnteric and gonadal fat pads, the most striking anatomical deviation is the size of the liver. The liver m a y weigh up to 4.5 grams in a 40 gram mouse, compared with 1.2 grams in a 20 gram normal mouse.", + "In mice, within hours after the last meal, the organs respond with changes in gene expression mainly in general metabolism (70). The role of the liver is to provide energy for glucose-dependent tissues, by glycogenolysis, gluconeogenesis, ketogenesis, and fatty-acid \u03b2-oxidation (71). The basic architecture of the lobules and the zonation are not affected, but the cell size declines in prolonged fasting, when murine liver restores partly its glycogen deposits, and much of gene expression returns to control values (72). In Abcb4-/- mice, collagens, fibronectin and vimentin, responsible for the structural integrity of the ECM, were strongly affected by fasting.", + "James SJ, Muskhelishvili L. Rates of apoptosis and proliferation vary with caloric intake and may influence incidence of spontaneous hepatoma in C57BL/6 x C3H F1 mice. Cancer Res 1994 Nov 1;54(21):5508-5510. 50. Hakvoort TB, Moerland PD, Frijters R, Sokolovic A, Labruyere WT, Vermeulen JL, et al. Interorgan coordination of the murine adaptive response to fasting. J Biol Chem 2011 May 6;286(18):16332-16343. 51. Lin S, Saxena NK, Ding X, Stein LL, Anania FA. Leptin increases tissue inhibitor of metalloproteinase I (TIMP-1) gene expression by a specificity protein 1/signal transducer and activator of transcription 3 mechanism. Mol Endocrinol 2006 Dec;20(12):3376-3388. 52.", + " Characterization of lean and obese control and mGHRKO mice", + " Consistent with the broad up-regulation of genes associated with fatty acid synthesis (Table 1), Oil Red O staining of liver sections from 15-d-old pups and naturally aged mice revealed enhanced accumulation of triacylglycerides in both compared to control littermates and 8-wk-old mice (Figure 7C), indicating hepatic steatosis.This and the absence of adipose tissue suggest that Csb m/m /Xpa \u00c0/\u00c0 mice display generalized lipodystrophy (loss and abnormal redistribution of body fat) [31]., and Csb m/m /Xpa \u00c0/\u00c0 mice (n \u00bc 6).The levels of IGF1 (ng/ml) and glucose (mmol/l) in the serum of Csb m/m /Xpa \u00c0/\u00c0 mice are significantly lower than that of control littermates (p , 0.0004 and p , 0.04, respectively). (C) PAS staining for glycogen and Oil Red O staining for triglycerides in livers of 15-d-old wt and Csb m/m /Xpa \u00c0/\u00c0 mice and 96-wk-old wt mice.Pictures were taken at 1003 magnification.Note the large polyploid nuclei in the 96-wk-old wt mouse liver and the reduced glycogen levels in the Csb m/m /Xpa \u00c0/\u00c0 liver after overnight fasting.doi:10.1371/journal.pbio.0050002.g007", + "Association between lifespan and metabolic organ weights We measured weight of certain metabolic organs and tissues of a subsample of cases on both diets at ~500 days of age. HFD mice (n = 63) had 84% greater fat mass, 25% greater heart mass, 19% greater liver mass, and 18% greater kidney mass at ~500 days compared to controls (n = 71). However, HFD did not influence brain mass (Supplemental Table).", + " Young adult dwarf mice have more body fat than normal mice.But, with age, normal mice from this line accumulate fat at a higher rate, and the percent body fat in old DF mice does not differ from that of normal mice, as measured by dual energy X-ray absorptiometry (DEXA) (29).Downregulation of lipid biosynthetic genes and upregulation of \u2424-oxidation-related genes in the liver of DF mice may explain this slower rate of fat deposition.", + "(b) Serum levels of liver injury markers, triglyceride, and cholesterol profiles of 20-month-old WT (n = 6) and Gdf15 KO (n = 6) mice. (c) Serum levels of pro-inflammatory cytokines of 20-month-old WT (n = 6) and Gdf15 KO (n = 6) mice. (d) H&E staining for liver tissues of 20-month-old WT (n = 6) and Gdf15 KO (n = 6) mice. Scale bar, 200 \u03bcm. Arrows indicate fat accumulation. (e) Fixed adipose tissue from 20-month-old WT (n = 6) and Gdf15 KO (n = 6) mice was stained for F4/80 antibodies. Scale bar, 200 \u03bcm.", + "(12) studied liver gene expression changes in Stat5b knockout and wild-type mice, finding 1,603 differentially regulated genes, with 850 being male- and 753 female biased (P \u2b0d 0.05 and FC \u2b0e 1.5). A large study consisting of 344 mice comprising an F2 cross between C57B/6J.apoE\u2afa/\u2afa and C3H/HeJ.apoE\u2afa/\u2afa strains (\u2b0350% from each sex) produced two reports (57, 61) that examined sexually dimorphic gene expression in adipose tissue, brain, liver, and muscle. It was reported that 9,250 genes are dimorphic in the liver (P \u2b0d 0.01 and FC \u2b0e 1).", + "2006) studied liver gene expression changes in Stat5b knockout and wild type mice, finding 1,603 differentially regulated genes, with 850 being male- and 753 female-biased (p<0.05 and FC>1.5). A large study consisting of 344 mice comprising an F2 cross between C57B/6J.apoE-/- and C3H/HeJ.apoE-/- strains (~50% from each sex) produced two reports (Wang et al. 2006; Yang et al. 2006) which examined sexually dimorphic gene expression in adipose tissue, brain, liver and muscle. It was reported that 9,250 genes are dimorphic in the liver (p<0.01 and FC>1)." + ], + [ + "However, when the data were adjusted for brain weight, there was a significant (p = 0.008) difference between DBA/2J and C57BL/6J (2.14 \u00b1 0.06 mm2 and 1.96 \u00b1 0.03 mm2, respectively) making the DBA/2J larger by 8.50%. Total brain weight of DBA/2J animals was significantly (p < 0.0001) smaller than that of C57BL/ 6J animals (0.35 \u00b1 0.01 g, 0.42 \u00b1 0.01 g respectively).", + "Phenotypes are often very different between mouse strains with diverse genetic backgrounds and the strain characteristics of DBA/ 2J are often contrasted with other genetically distinct inbred strains such as C57BL/6J. These defined genetic backgrounds provide an excellent system for mapping modifier genes [20,21,22]. To study these differences a number of DBA/2J-relevant resources have been generated. For instance, a genome-wide panel of congenic strains has been created that contain portions of DBA/2J chromosomes on a C57BL/6J background [23]. These 65 strains contain more than 95% of the DBA/2J genome.", + "Well-documented behavioral differences between C57 and DBA, including enhanced closed-arm preference and deficits in conditional fear, were observed. This suggests at a minimum that the influence of previous testing in the two parental strains was comparable. The use of DBA/2J donor segments for the GTM panel may have implications for loci identified in tests involving auditory stimuli, as this strain is known to undergo progressive hearing loss with age. While no rigorous examination of hearing capacity in the GTM has been conducted, inspection of time course data for individual mice in both the general Mol Psychiatry.", + "Particularly striking is the difference in their locomotor response: the C57BL/6J strain shows a marked locomotor activation following an acute opiate administration, which is virtually absent in DBA/2 mice [6, 25, 29]. After chronic morphine treatment, either tolerance or sensitization of the locomotor response was evidenced in C57BL/6J mice, depending on the treatment paradigm, whereas no altered responses were observed in the DBA/2J strain [1, 22, 29, 31]. Other inter-strain differences in reactions to opioids have also been reported, including a greater sensitivity to opioid reward and stronger withdrawal symptoms in the C57BL/6J strain [2, 6, 17, 30, 35].", + "Although no differences in attentional performance were detected between C57BL/6J and DBA/2J, in line with previous reports in the 5-CSRTT and five-choice CPT (Loos et al . 2010; Young et al . 2009), we observed significant differences among BXD recombinant inbred strains that transgressed beyond the phenotypes of the founders. This suggested the contribution of multiple genetic loci to these phenotypes, of which we detected a significant one on chromosome 16 for response variability.", + "Given the large differences that we found previously (Crusio 2013) between C57BL/6 and DBA/2, this is unexpected. One possible explanation for the lower than expected performance of the C57BL/6 and (at least some) BXD strains lies in the housing conditions. Our animal facility was built to house about 500 cages in one large breeding room. However, the cage-washing installation (and the available personnel) could not handle that many cages at a time. As a result, every day one or two racks of cages were changed. C57BL/6 mice are sensitive to such disruptions and, indeed, breeding results were only mediocre.", + "C57BL/6 and DBA/2 mice is not yet fully understood but involves multiple genetic differences between the two mouse lineages, affecting several pathways and processes (1). Certain influenza viruses grow to higher titers in DBA/2 mice (A/Hong Kong/213/2003 [H5N1] or A/Memphis/33/2008 [H1N1]) (data not shown) while others do not (H7N3 and H10N5) (this study). Irrespective of the difference in viral loads, DBA/2 mice respond more vigorously, producing larger quantities of certain proinflammatory molecules like TNF-\u2423, which was shown to correlate with increased morbidity and mortality in humans (5).", + "Additionally, in this protocol the strains DBA/2J, A/J, NOD/ShiLt/J, C57BL/10J, SM/J, and C57BR/cdJ are AA sensitive; the strains CAST/EiJ and BTBR T\u2af9 tf/J are resistant; and the strains NZW/LacJ, KK,HIJ, and SWR/J have intermediate resistance to AA-induced acute nephrotoxicity (supplementary data; all supplementary material for this article is available online at the journal web site.). For this QTL study, C57BL/6J and DBA/2J mice were used as resistant and sensitive strains, respectively. Each strain has a complete genomic sequence available, and the genetic basis of differences in their ability to respond to xenobiotics is extensively studied (reviewed in Ref. 8).", + "The C57BL/6J X DBA/2J (BXD) recombinant inbred (RI) mouse strains, which are unique mosaic of alleles derived from the parental C57BL/6J (B6) and DBA/2J (D2) strains have been constructed as a high precision genetic reference population for systems genetics in unraveling the genetic architecture of polygenic traits (Ashbrook et al. , 2019). The BXD family consists of more than 150 BXD fully inbred strains that segregate for \u223c6 million genetic variants and thus can be used as an informative murine genetic reference panel.", + "Because we have now shown that the parental strains C57BL/6J and DBA/2J markedly differ in both quantitative measures of cortex area size [6] and shape, this assures variation in the derivative BXD lines, and provides an empirical basis for using the BXD panel to study cortical development. Conclusion C57BL/6J and DBA/2J have markedly different cortical area maps, in both size and shape. These differences suggest polymorphism in genetic factors underlying cortical specification, even between common isogenic strains. Comparing cortical phenotypes between normally varying inbred mice or between genetically modified mice can identify genetic contributions to cortical specification.", + "The C57BL/6 mice were more accurate than DBA/2 mice at the shorter SD where the task demands were greater, and they also made anticipatory (impulsive) responses at a lower rate. In contrast, the DBA/2 mice made fewer omission errors than the C57BL/6 but this effect was not seen until the final stages of the experimental procedures. These findings are in agreement with those of Greco et al. [18]. Although they used different breeders as well as different test chambers, training protocols and reinforcers, the results were similar: DBA/2 males were less accurate and made more anticipatory responses than C57BL/6 males.", + "DBA/2 mice perform poorly in other spatial tasks as well as in the 5-CSRTT (see Section 1) but this is by no means true for paradigms that are less spatially demanding. For instance, in the four-arm baited and cued versions of the radial maze, as well as in auditory fear conditioning, C57BL/6 and DBA/2 do not differ [1,30]; DBA/2 mice even perform better than C57BL/6 with regard to two-way active avoidance learning [37].", + "While the factorial structure of C57BL/6 mice remained the same as under low attentional demands (two factors), there was only one factor for DBA2 mice. This factor was characterised by high positive loadings (>0.78) from the percent of correct responses and omission errors, and a high negative loading (0.87) from anticipatory responses. 4. Discussion The results indicated that both C57BL/6 and DBA/2 mice were able to learn the complex 5-CSRTT task but there were considerable quantitative differences in their performance.", + "It can be seen that at all SD, accuracy was greater for C57BL/6 than for DBA/2 mice. The clearest difference was at 1 s SD where C57BL/6 mice were responding at a mean accuracy of 80% compared with the DBA/2 group for which the mean was 59% (Fig. 1(A)). With a SD of 5 s there was no significant main effect for group (F1,28 = 3.13), whereas at 2 and 1 s SD significant group effects were achieved (F1,28 = 5.44 and 25.1; P < 0.05 and 0.001, respectively).", + "In marked contrast, the C57BL/6J strain was found to have the highest level of oral morphine consumption [6]. However, sensitivity to the reinforcing effects of morphine in conditioned place preference and intravenous self-administration paradigms was higher in DBA mice than in C57BL [10]. The two frequently used laboratory strains of mice C57BL/6J and DBA/2J show remarkable differences in analgesic response to morphine. Moreover, several studies have reported profound differences in morphine induced locomotor activity between the sensitive C57BL/6 and insensitive DBA/2 mice [3,7].", + ", increased exploration of the open areas) in both tests. One explanation is that DBA/2J is \u201csusceptible\u201d to this stressor, whereas C57BL/6J is \u201cresilient.\u201d However, a more circumscribed but potentially more accurate interpretation is that both strains react strongly to this particular stress regime, but differ in the manner in which the response manifests behaviorally. Thus, DBA/2J may develop a classic \u201cpassive\u201d anxiety-like suppression of approach behavior, whereas C57BL/6J may exhibit more of an \u201cactive\u201d response to stress. This could reflect an increased panic-like escape drive or manic-like reaction to stress in C57BL/6J, rather than a decrease in anxiety-like behavior.", + "Differences in radiation sensitivity between the BXD parental strains were first described by Roderick more than 45 years ago, with DBA/2J succumbing more quickly than C57BL/6J to a lethal dose of radiation (26). At more modest doses, C57BL/6J mice were shown to be more resistant to radiation-induced genomic instability than DBA/2J (38, 84, 85).", + "Genetic differences between C57 and DBA mice have been shown to translate into a broad spectrum of CNS related functional and molecular correlates, for example, differences in activity, impulsive action, hippocampal related memory and learning tasks, post- and pre-synaptic protein expression, and synaptic transmission and plasticity [27\u201340]. Through genetic linkage analyses, the genetic and phenotypic differences in the BXD panel of RI strains have resulted in identification of genes and loci involved in complex CNS functions, such as impulsivity [41], reversal learning [42], attention [43], neuronal oscillations [44], hearing loss [45], and fear and spatial learning [39,40].", + "For example, the C57BL/6J (B6) and DBA2/J (D2) inbred mice frequently are used in alcohol research because they clearly differ in various responses to alcohol, including development of functional tolerance (Grieve and Littleton 1979), locomotor activation (Phillips et al. 1998), and sensitivity to withdrawal symptoms (Metten and Crabbe 1994). Because the environmental conditions in these experiments can be controlled, any differences observed between the mouse strains in these phenotypes most likely can be attributed to genetic differences.", + "For example, when subjected to HFD, DBA/2J had 12.5% more body fat compared to C57BL/6J (P < 0.0001, Fig 1A). Additionally, the F1 offspring generated by DBA/2J dams (DBA/2J x C57BL/6J) had 10.6% more body fat (P < 0.001) compared to the F1 from C57BL/ 2J dams (C57BL/6J x DBA/2J). While the source of these latter effects appears to be maternal, further studies are needed to identify the molecular basis of these differences. In general, genetic differences between strains impacted body weight variation throughout the experiment (P < 0.05) (Fig 1B)." + ], + [ + " Quantitative trait locus (QTL) mapping has been carried out in numerous species to associate regions of the genome to phenotypes even before the structure of the genome was well understood (e.g., [3]).Rodents, especially mice, have been the species most prominently used for biomedically relevant traits.Amongst these, the BXD family of recombinant inbred (RI) strains derived from crossing two inbred strains-C57BL/6J and DBA/2J mice-have been extensively used for almost 50 years in fields such as neuropharmacology [4][5][6], immunology [7][8][9][10][11][12][13], behaviour [13][14][15][16][17][18][19][20][21], aging [21][22][23][24][25][26][27][28][29], neurodegeneration [30][31][32][33], and gut microbiome-host interactions [34].", + "Milhaud JM, Halley H, Lassalle JM (2002) Two QTLs located on chromosomes 1 and 5 modulate different aspects of the performance of mice of the B6D Ty RI strain series in the Morris navigation task. Behav Genet 32: 69\u201378. 16. Buck KJ, Rademacher BS, Metten P, Crabbe JC (2002) Mapping murine loci for physical dependence on ethanol. Psychopharmacology (Berl) 160: 398\u2013407. 17. Ferraro TN, Golden GT, Smith GG, Schork NJ, St Jean P, et al. (1997) Mapping murine loci for seizure response to kainic acid. Mamm Genome 8: 200\u2013208. 18.", + "Other aggression QTLs Several lines of mice have been selectively bred for high or low levels of o\u00a1ensive aggression, which con\u00a2rms that a propensity for aggressive behaviours is partially heritable. These lines include the Turku aggressive (TA) and non-aggressive (TNS) strains bred in Finland, the NC900 and NC100 strains bred in North Carolina, and the short attack latency (SAL) and long attack latency (LAL) strains bred in the Netherlands (Miczek et al 2001). In wild mice, there is evidence for a QTL a\u00a1ecting aggressive behaviours in a region of chromosome 17, the t region.", + "QTL ANALYSIS OF AGGRESSIVE BEHAVIOURS IN MICE 65 Progress towards identifying QTLs that a\u00a1ect aggressive behaviours in mice An example of aggression QTLs identi\u00a2ed as part of a whole genome scan One of the few studies to identify intermale aggression QTLs as part of a whole genome scan was published recently (Brodkin et al 2002). This study used NZB/ B1NJ (extremely aggressive) and A/J (extremely unaggressive) inbred mice as parental strains. The methods chosen for housing and aggression testing were designed to control the e\u00a1ect of non-genetic factors on the phenotype.", + "Neuroscientist 4:317^323 Brodkin ES, Goforth SA, Keene AH, Fossella JA, Silver LM 2002 Identi\u00a2cation of quantitative trait loci that a\u00a1ect aggressive behavior in mice. J Neurosci 22:1165^1170 Chesler EJ, Lu L, Wang J, Williams RW, Manly KF 2004 WebQTL: rapid exploratory analysis of gene expression and genetic networks for brain and behavior. Nat Neurosci 7:485^486 Darvasi A 1997 Interval-speci\u00a2c congenic strains (ISCS): an experimental design for mapping a QTL into a 1-centimorgan interval. Mamm Genome 8:163^167 Darvasi A 1998 Experimental strategies for the genetic dissection of complex traits in animal models.", + "Brodkin: Such a course mapping study with only about 400 mice would be unlikely to detect a QTL that accounts for only 2.5% of the phenotypic variance, QTL ANALYSIS OF AGGRESSIVE BEHAVIOURS IN MICE 73 but it should detect a QTL that accounts for approximately 10% of the variance (Lynch & Walsh 1998, Darvasi 1998). QTLs of this magnitude of e\u00a1ect on neurobiological or behavioural traits have been found fairly commonly in crosses between inbred mouse strains (see e.g. Wehner et al 1997).", + "By correlating genotypes with phenotypes in quantitative trait locus (QTL) analysis, a large number of polymorphic regions harboring trait relevant allelic variation have been defined for a wide range of behavioral phenotypes [17]. At present, there are 340 549 QTLs for behavioral phenotypes in the Mouse Genome Informatics database, which are largely derived from crosses of 2 inbred strains of mice [18].", + "A search of the Mouse Genome Informatics database (www.informatics.jax.org, March 16,2006) revealed 34 neurobehavioral- and/or pain-related QTLs mapped to >75 cM; these inc1ude seven traits related to alcohol, six to morphine or other drugs, two to painful arthritis, five to emotionality/anxiety, and one to seizure susceptibility. Several ofthese QTLs have been finely mapped near the peak of linkage of our analgesia QTL.", + "The behavioral QTLs were determined from the MGI database as of October 1, 2004. Alcrsp2 (Erwin et al. , 1997); Ap3q (Bachmanov et al. , 2002); Alcp12 (Gill et al. , 1998). Behavioral QTLs have been mapped using other mouse strains, and their validity in the ILS and ISS strains has not been tested. Mb, megabases. Table 4.", + "In the fourth step, we sought to identify DNA sequence variants that influence both molecular phenotypes as well as phenotypes at the structural and behavioral level. A remarkable region located on the distal end of mouse Chr 1 (172\u2013178 Mb) was the ideal subject for such an integrative study. This region, which we have named as Qrr1 (QTL rich region on distal Chr 1), is known for its unusually high density of QTLs for neural and behavioral traits, e.g. , traits like anxiety-related behavior, seizure, hippocampal volume, and alcohol preference consistently map to this region.", + "Overall, these studies reveal the existence of an extensive polygenic system influencing the exploratory behavior of mice similar to the kind of genetic architecture shown to influence behavior in tests of fear and anxiety (Caldarone et al. 1997; Flint et al. 1995; Gill & Boyle 2005; Henderson et al. 2004; Laarakker et al. 2008; Singer et al. 2005; Turri et al. 2001a,b). The significance of the QTL, and also of the polygenic system, is heightened by the finding that roughly the same set of genes has the potential to influence some behaviors from early adulthood to old age.", + "The behavioral phenotypes with QTLs on distal Chr 17 are (1) prepulse inhibition, assayed by McCaughran et al.41 in a panel of 21 BXD strains (trait ID on Genenetwork is 10396), (2) anxiety trait measure by time spent in open quadrant of zero-maze, assayed in a larger panel of 57 BXD strains42 (trait ID 11696) and (3) handling induced convulsion as an index of ethanol withdrawal severity, measured in 25 BXD strains43 (trait ID 10065). Gene\u2013gene interaction analysis.", + "Quantitative trait locus (QTL) mapping has been carried out in numerous species to associate regions of the genome to phenotypes even before the structure of the genome was well understood (e.g. , [3]). Rodents, especially mice, have been the species most prominently used for biomedically relevant traits. Amongst these, the BXD family of recombinant inbred (RI) strains derived from crossing two inbred strains\u2014C57BL/6J and DBA/2J mice\u2014have been extensively used for almost 50 years in fields such as neuropharmacology [4\u20136], immunology [7\u201313], behaviour [13\u201321], aging [21\u201329], neurodegeneration [30\u201333], and gut microbiome\u2013host interactions [34].", + "Two QTLs located on chromosomes 1 and 5 modulate different aspects of the performance of mice of the BXD Ty RI strain series in the Morris navigation task. Behav Genet. 2002; 32:69\u201378. [PubMed: 11958544] Mozhui RT, Ciobanu DC, Schikorski T, Wang XS, Lu L, Williams RW. Dissection of a QTL hotspot on mouse distal chromosome 1 that modulates neurobehavioral phenotypes and gene expression. PLoS Genetics. 2008; 4:e1000260. [PubMed: 19008955] Mulligan MK, Wang X, Adler AL, Mozhui K, Lu L, Williams RW. Complex control of GABA(A) receptor subunit mRNA expression: variation, covariation, and genetic regulation. PLoS One. 2012; 7(4):e34586.", + "Type I and type II error rates for quantitative trait loci (QTL) mapping studies using recombinant inbred mouse strains. Behav Genet, 26(2): 149-160. Bidwell, L. C., Willcutt, E. G., Defries, J. C., & Pennington, B. F. 2007. Testing for neuropsychological endophenotypes in siblings discordant for attentiondeficit/hyperactivity disorder. Biol Psychiatry, 62(9): 991-998. Bitanihirwe, B. K., Dubroqua, S., Singer, P., Feldon, J., & Yee, B. K. 2011. Sensorimotor gating and vigilance-dependent choice accuracy: a within-subject correlative analysis in wild-type C57BL/6 mice. Behav Brain Res, 217(1): 178-187. 151 References Bitsios, P., & Giakoumaki, S. G. 2005.", + "Quantitative trait locus (QTL) mapping has been carried out in numerous species to associate regions of the genome to phenotypes even before the structure of the genome was well understood (e.g. , [3]). Rodents, especially mice, have been the species most prominently used for biomedically relevant traits. Amongst these, the BXD family of recombinant inbred (RI) strains derived from crossing two inbred strains\u2014C57BL/6J and DBA/2J mice\u2014have been extensively used for almost 50 years in fields such as neuropharmacology [4\u20136], immunology [7\u201313], behaviour [13\u201321], aging [21\u201329], neurodegeneration [30\u201333], and gut microbiome\u2013host interactions [34].", + "Other aggression QTLs Several lines of mice have been selectively bred for high or low levels of o\u00a1ensive aggression, which con\u00a2rms that a propensity for aggressive behaviours is partially heritable. These lines include the Turku aggressive (TA) and non-aggressive (TNS) strains bred in Finland, the NC900 and NC100 strains bred in North Carolina, and the short attack latency (SAL) and long attack latency (LAL) strains bred in the Netherlands (Miczek et al 2001). In wild mice, there is evidence for a QTL a\u00a1ecting aggressive behaviours in a region of chromosome 17, the t region.", + "QTL ANALYSIS OF AGGRESSIVE BEHAVIOURS IN MICE 65 Progress towards identifying QTLs that a\u00a1ect aggressive behaviours in mice An example of aggression QTLs identi\u00a2ed as part of a whole genome scan One of the few studies to identify intermale aggression QTLs as part of a whole genome scan was published recently (Brodkin et al 2002). This study used NZB/ B1NJ (extremely aggressive) and A/J (extremely unaggressive) inbred mice as parental strains. The methods chosen for housing and aggression testing were designed to control the e\u00a1ect of non-genetic factors on the phenotype.", + "Neuroscientist 4:317^323 Brodkin ES, Goforth SA, Keene AH, Fossella JA, Silver LM 2002 Identi\u00a2cation of quantitative trait loci that a\u00a1ect aggressive behavior in mice. J Neurosci 22:1165^1170 Chesler EJ, Lu L, Wang J, Williams RW, Manly KF 2004 WebQTL: rapid exploratory analysis of gene expression and genetic networks for brain and behavior. Nat Neurosci 7:485^486 Darvasi A 1997 Interval-speci\u00a2c congenic strains (ISCS): an experimental design for mapping a QTL into a 1-centimorgan interval. Mamm Genome 8:163^167 Darvasi A 1998 Experimental strategies for the genetic dissection of complex traits in animal models.", + "Brodkin: Such a course mapping study with only about 400 mice would be unlikely to detect a QTL that accounts for only 2.5% of the phenotypic variance, QTL ANALYSIS OF AGGRESSIVE BEHAVIOURS IN MICE 73 but it should detect a QTL that accounts for approximately 10% of the variance (Lynch & Walsh 1998, Darvasi 1998). QTLs of this magnitude of e\u00a1ect on neurobiological or behavioural traits have been found fairly commonly in crosses between inbred mouse strains (see e.g. Wehner et al 1997)." + ], + [ + "Other cell cyclerelated genes, such as p21, p18 and p27, were also reported to be involved in regulating different types of hematopoietic cells (Cheng 2004; Steinman 2002). For example, p21 and p18 specifically control HSC proliferation, whereas p27 only affects hematopoietic progenitor cells. Further study of the chromosome 3 QTL interval in the congenic mouse model may provide a platform leading to the discovery of novel cycle-active gene and/or functions of already known genes. The apoptotic analyses shown in Table 3.2 are novel.", + "Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT, Wiltshire T, Su AI, Vellenga E, Wang J, Manly KF, Lu L, Chesler EJ, Alberts R, Jansen RC, Williams RW, Cooke MP, de Haan G: Uncovering regulatory pathways that affect hematopoietic stem cell function using \u2018genetical genomics\u2019. Nat Genet 2005, 37(3):225-32. 29. Overall RW, Kempermann G, Peirce J, Lu L, Goldowitz D, Gage FH, Goodwin S, Smit AB, Airey DC, Rosen GD, Schalkwyk LC, Sutter TR, Nowakowski RS, Whatley S, Williams RW: Genetics of the hippocampal transcriptome in mouse: a systematic survey and online neurogenomics resource.", + "In summary, I have identified p107 and Snx5 as quantitative trait genes that regulate the number of HSCs in B6 and congenic mice. CAFC assays confirmed that increased expression of both genes increases HSC number in an in vitro setting. Although the increased expression of both Snx5 and p107 resulted in small increases in HSC number, the changes are biologically significant given the extensive proliferative potential of primitive stem cells.", + "The molecular mechanisms that regulate progenitor cell division and differentiation in the RMS remain largely unknown. Here, we surveyed the mouse genome in an unbiased manner to identify candidate gene loci that regulate proliferation in the adult RMS. We quantified neurogenesis in adult C57BL/6J and A/J mice and 27 recombinant inbred lines derived from those parental strains. We showed that the A/J RMS had greater numbers of bromodeoxyuridine-labeled cells than that of C57BL/6J mice with similar cell cycle parameters, indicating that the differences in the number of bromodeoxyuridine-positive cells reflected the number of proliferating cells between the strains.", + "Page 10 NIH-PA Author Manuscript Septin 9 (Sept9) and cyclin-dependent kinase 3 (cdk3) and are two other genes that are worth mentioning because even though they are not directly linked to neurogenesis, they are both cell cycle regulatory genes. Sept9 is involved in the progression through G1 of the cell cycle and it is highly expressed throughout the adult mouse brain (Gonzalez et al. , 2009). Whereas, cdk3 is expressed at low levels throughout the adult mouse brain and it is required for G1-S transition (Braun et al. , 1998).", + "Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT, Wiltshire T et al. (2005). Uncovering regulatory pathways that effect hematopoietic stem cell function using \u2018genetical genomics\u2019. Nat Genet 37:225\u2013232. Cai L, Morrow EM, Cepko CL (2000). Misexpression of basic helix-loop-helix genes in the murine cerebral cortex affects cell fate choices and neuronal survival. Development 127:3021\u20133030. Caldarone B, Saavedra C, Tartaglia K, Wehner JM, Dudek BC, Flaherty L (1997). Quantitative trait loci analysis affecting contextual conditioning in mice. Nat Genet 17:335\u2013337. Calder AJ, Lawrence AD, Young AW (2001). Neuropsychology of fear and loathing. Nature Rev Neurosci 2:352\u2013363.", + "As further step, this finding opens the door to study the molecular networks via which LRP6 acts to regulate proliferation. ! '*! ! +&(/. ((&-*) 5.2. Redox regulation of Adult Hippocampal Precursor Cells 5.2.1. Hypoxia increases AHPCs proliferation and neuronal differentiation Oxygen concentration plays an important role in cellular development and tissue homeostasis. In the brain, depending on the tissue, the oxygen concentration varies from 0.1 to 5% and in the rat hippocampus it is around 3.2% (Studer et al. , 2000).", + "While this study covers only one part in the several conceptual levels of regulation we are confident that this work will lead to finding a central regulatory pathway that regulates adult hippocampal precursor cell proliferation. ! &*! ! +&(/. ((&-*) 5.1.1. Establishment of AHPCs Isolating the precursor cells has become extremely important in order to study them in detail away from the influence of their in vivo niche. Once the cells are in culture they express their autonomous, intrinsic properties without the niche influences such as cell-cell contacts, blood vessels, known and unknown growth factors and network activities.", + "Gene expression profiling using RNA samples from proliferating cultures of the 20 BXD mice strains yielded two cis eQTL candidates that directly regulated proliferation, LRP6 and Chchd8. LRP6 is well known as a co-receptor of Wnt signaling, but the function of Chchd8 is not known. Further experimentation, using over- ! I! ! SUMMARY expression and gene silencing demonstrated that LRP6 negatively regulates AHPCs proliferation. Thus, from this study using a system genetics approach, we were able to identify, LRP6 as a novel regulator of adult hippocampal neurogenesis. ! V! ! INTRODUCTION 2. INTRODUCTION 2.1.", + "Gene expression profiling ...............................................................68 4.1.8. LRP6 is a novel regulator of AHPCs proliferation .........................73 4.2. Redox regulation of Adult Hippocampal Precursor Cells................78 4.2.1. AHPCs yield increased under hypoxic conditions..........................78 ! T! ! TABLE OF CONTENTS 4.2.2. More neuronal differentiation under hypoxic conditions................79 5. DISCUSSION ..............................................................................................81 5.1. Systems genetic approach to identify genes regulating AHPCs proliferation .................................................................................................81 5.1.1. Establishment of AHPCs................................................................82 5.1.2. Variation in proliferative and differentiative properties of AHPCs83 5.1.3. QTL analysis ...................................................................................86 5.1.4. Candidate genes from gene expression profiling ............................87 5.1.5. Lrp6 as negative regulator of AHPCs proliferation ........................89 5.2. Redox regulation of Adult Hippocampal Precursor Cells................92 5.2.1.", + "Mapping determinants of human gene expression by regional and genome-wide association. Nature 437, 1365-1369. Chiasson, B.J. , Tropepe, V., Morshead, C.M. , and van der Kooy, D. (1999). Adult mammalian forebrain ependymal and subependymal cells demonstrate proliferative potential, but only subependymal cells have neural stem cell characteristics. Journal of Neuroscience 19, 4462-4471. Cipolleschi, M.G. , Dello Sbarba, P., and Olivotto, M. (1993). The role of hypoxia in the maintenance of hematopoietic stem cells. Blood 82, 20312037. Clarke, D.L. , Johansson, C.B. , Wilbertz, J., Veress, B., Nilsson, E., Karlstrom, H., Lendahl, U., and Frisen, J. (2000).", + "List of BXD AHPC lines stored Table 3. List of eQTls in 0.6 threshold range Table 4. Cis acting genes regulating proliferation trait ! U#! ! PUBLICATIONS Publications A protocol for isolation and enriched monolayer cultivation of neural precursor cells from mouse dentate gyrus. Harish Babu*, Jan-Hendrik Claasen*, Suresh Kannan, Annette E. R\u00fcnker, Theo Palmer, Gerd Kempermann. Front. Neurosci. 5:89. doi: 10.3389/fnins.2011.00089 System genetics approach yields candidate genes regulating adult hippocampal precursor cells proliferation, Manuscript in preparation (first author paper) ! U##! ! SUMMARY 1. SUMMARY Adult hippocampal neurogenesis is regulated at various levels and by various factors.", + "A recent study suggesting the role of mitochondria and ! &&! ! +&(/. ((&-*) cytochrome oxidase in enhancing hippocampal neurogenesis during inflammation (Voloboueva et al. , 2010) may reveal the link for Chchd8 gene in adult neurogenesis. 5.1.5. Lrp6 as negative regulator of AHPCs proliferation The results from our gene expression profiling suggest that high expression level of Lrp6 is associated with slow proliferating AHPCs and vice versa. We confirmed this result by over expressing LRP6 in AHPCs. This revealed that LRP6 over expression reduced the proliferation of AHPCs by more than 2fold.", + "Two types of collagen and N-Cadherin were also in this pathway. The top upstream regulators of this gene set were Huntingtin (HTT) which regulates 32 of the 193 genes analyzed (p = 1.22 \u00d7 10\u221215), and \u03b2-estradiol which may regulate 39 out of 193 genes in the set (p = 4.06 \u00d7 10\u221210). 3.2.2. Genes regulated by ethanol in the NAC following CIE\u2014Three hundred seventy-eight probesets were exclusively altered by ethanol in the NAC only following CIE (Supplemental Fig. 2 and Table 5).", + "Expression of a subset of these neurogenesis-associated transcripts was controlled in cis across the BXD set. These self-modulating genes are particularly interesting candidates to control neurogenesis. Among these were musashi (Msi1h) and prominin1\u517eCD133 (Prom1), both of which are linked to stem-cell maintenance and division. Twelve neurogenesis-associated transcripts had significant cis-acting quantitative trait loci, and, of these, six had plausible biological association with adult neurogenesis (Prom1, Ssbp2, Kcnq2, Ndufs2, Camk4, and Kcnj9). Only one cis-acting candidate was linked to both neurogenesis and gliogenesis, Rapgef6, a downstream target of ras signaling.", + "Other cell cyclerelated genes, such as p21, p18 and p27, were also reported to be involved in regulating different types of hematopoietic cells (Cheng 2004; Steinman 2002). For example, p21 and p18 specifically control HSC proliferation, whereas p27 only affects hematopoietic progenitor cells. Further study of the chromosome 3 QTL interval in the congenic mouse model may provide a platform leading to the discovery of novel cycle-active gene and/or functions of already known genes. The apoptotic analyses shown in Table 3.2 are novel.", + " and Tgfbr3 (transforming growth factor beta receptor 3).Of the significant genes correlated with the hippocampal cell death phenotype, there were 107 genes that were significant for a strain \u00d7 treatment interaction.Four of these genes also showed an FC > 1.5: Gadd45g (growth arrest and DNA-damage-inducible, gamma), Kcnj13 (potassium inwardly rectifying channel, subfamily J, member 13), Plekhg1 (pleckstrin homology domain containing, family G (with RhoGef domain) member 1), and Sgms2 (sphingomyelin synthase 2).", + "111 Bystrykh, L., E. Weersing, et al. (2005). \"Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'. \"Nat Genet 37(3): 225-32. Cashman, J., A. C. Eaves, et al. (1985). \"Regulated proliferation of primitive hematopoietic progenitor cells in long-term human marrow cultures. \"Blood 66: 1002-1005. Celeste, A., O. Fernandez-Capetillo, et al. (2003). \"Histone H2AX phosphorylation is dispensable for the initial recognition of DNA breaks. \"Nat Cell Biol 5(7): 675-9. Chen, J., B. A. Astle, et al. (1999). \"Development and aging of primitive hematopoietic stem cells in BALB/cBy mice.\"Exp. Hematol. 27: 928-935. Cheng, T., N. Rodrigues, et al.", + " The next category was Cellular Growth and Proliferation, which includes growth, proliferation, expansion and differentiation of cells and is also pertinent to the possible formation of new cells in this area of the hippocampus.37 genes were associated with this function.Not surprisingly, in the Cell Cycle function (Supplementary Table 2) we found thirty genes involved in cell cycle progression indicating the activity of dividing cells in this region.", + "Lef1 is expressed in cultured hippocampal neural stem cells in response to activation of the Wnt signaling pathway (Cui et al. , 2011). Our evidence and the literature both suggest that genes known to be involved in hippocampal adult neurogenesis are targets of Lef1, an important factor in generating granule cells in the dentate gyrus during development (Galceran et al. , 2000). The only two genes not targeted by Lef1 can be closely associated with it: Mtdh regulates the expression of Lef1 (Hu et al. , 2009; Yoo et al." + ], + [ + "QTL Mapping and Identification of Candidate Genes A QTL is a region of the genome shown to be linked to a trait. The purpose of mapping this region is to identify a region of a genome that has a higher probability of harbouring the genetic variations controlling variability in trait values.", + "Often a local eQTL will be caused by allelic variation in the regulatory region of the gene or within the gene itself. mQTL A metabolite Quantitative Trait Locus is a region in the genome at which allelic variation correlates with the abundance variation of a certain metabolite. pQTL A protein Quantitative Trait Locus is a region in the genome at which allelic variation correlates with the abundance variation of a certain protein. Just like eQTL, pQTL can be local or distant according to the genomic position of the gene encoding for the protein relative to the QTL.", + "QTLs are regions within the genome whose genetic variation modulates quantitatively a phenotype characteristic of the particular trait under study (Lynch and Walsh, 1998). Determining the association between variations in specific disease phenotypes or a trait, with variations in genotypes of a reference population can be used to locate a QTL. One of the methods used for mapping QTLs associated with complex traits is genetic markers-trait association. Genetic markers associated with certain loci can be inherited in linkage disequilibrium. Generating populations with linked loci in disequilibrium is achieved though either crosses between inbred lines, or use of the out-bred populations.", + "Quantitative trait locus-mapping is a statistical method used to map chromosomal intervals (loci) that contribute to heritable variance in phenotypes. The method simply compares the inheritance of allelic variants (B or D genotypes in our case) with differences in phenotypes. A QTL will generally cover a region that includes 10\u2013100 genes, and these positional candidates can then be ranked roughly on the basis of criteria such as the types of DNA variants, patterns of mRNA expression, data from complementary human genetic cohorts (GWAS and linkage) and relevant literature about gene effects on central nervous system structure and function.", + "Chromosomal regions containing a gene (or genes) that a\u00a1ect the level of a quantitative trait are called quantitative trait loci (QTLs). The relevant genes in these regions have been called quantitative trait genes (QTGs) (Hitzemann et al 2003). Quantitative trait locus (QTL) analysis is an experimental strategy for identifying QTLs, and ultimately QTGs, that a\u00a1ect quantitative traits. Because of the complexity of these traits, progress in identifying QTGs has been slow compared to that in cloning genes underlying Mendelian traits (Glazier et al 2002).", + "Expression QTL Next, we will examine expression quantitative trait loci (eQTLs). These are QTLs for gene expression traits, a subset of the molecular phenotypes mentioned above. Much like classical phenotypes, expression of transcripts can be influenced by variants within the genome. However, because we know the location of the gene, we can split these eQTL into two categories, trans- (or distal) or cis- (or local) eQTL. A trans-eQTL (or distal-eQTL) describes when the expression of a gene is influenced by a locus far away from that gene, and therefore indicates that the gene of interest is downstream of another gene.", + "These loci which are associated with changes in transcript expression are often termed expression QTL (eQTL): a variant (or variants) within the locus alters the expression of the gene of interest. An eQTL found near to the location (~ \u2264 1Mbp) of the transcript is described as a local eQTL, and are often called ciseQTL. This is in contrast to trans-eQTL which are found more distally. Cis-eQTL are interesting when they are found for a gene within a QTL for another phenotype (e.g.", + "The location of these genotypes are quantitative trait loci (QTLs) [Abiola et al. , 2003]. Detected via statistical methods [Doerge, 2002], QTLs are stretches of DNA highly associated with a specific phenotype, analogous to genetic landmarks which roughly indicate the position of the active gene. QTLs are not defined at very fine granularity; they usually correspond to areas large enough to hold several genes. The genetic polymorphism (genotypes) in neighboring areas of a set of loci, as a group, influence structure and function on both molecular and organismic scales.", + "Quantitative trait loci (QTL) 132 analysis is a means to query the entire genome for DNA variants (markers) that show significant 133 associations with the phenotype (quantitative trait) under investigation. This is the first step to 134 identify candidate genes whose variants (alleles) affect the value of the phenotype. QTL analysis 135 was performed using WebQTL (http://www.genenetwork.org) for each PCA factor. WebQTL 136 performs 2,000 or more permutations of the strain data and significant QTL are defined by the 137 likelihood ratio statistic (LRS) score of correctly ordered data exceeding all other permutations 138 95% of the time, i.e.", + "Expression QTL Next, we will examine expression quantitative trait loci (eQTLs). These are QTLs for gene expression traits, a subset of the molecular phenotypes mentioned above. Much like classical phenotypes, expression of transcripts can be influenced by variants within the genome. However, because we know the location of the gene, we can split these eQTL into two categories, trans- (or distal) or cis- (or local) eQTL. A trans-eQTL (or distal-eQTL) describes when the expression of a gene is influenced by a locus far away from that gene, and therefore indicates that the gene of interest is downstream of another gene.", + "These are referred to as expression QTLs, or eQTLs (Schadt et al. , 2003), which control a portion of expression variation of particular genes in a population. eQTLs result from genetic differences in regulatory elements close to or within the gene (apparent cis-acting eQTLs) as well as those that map elsewhere in the genome from the gene whose expression is modulated (trans-acting eQTLs). By combining microarray and QTL analysis on the same mice, much can be learned about the genetic underpinnings of particular alcohol traits (Hitzemann et al. , 2004; Tabakoff et al. , 2003).", + "Working with complex traits that typically vary in their manifestation across a continuous distribution, in contrast to the binary nature of monogenic traits, QTLs are discovered by simply identifying loci with alleles that consistently covary with a phenotype across a population. Genomic regions that show a sufficiently strong association with a phenotype are considered QTLs. The simplest, or most hopeful, interpretation of a mapped QTL is that the implicated region harbors a single gene affecting manifestation of the associated phenotype.", + "By definition, a quantitative trait locus is a chromosomal region that contains a gene, or genes, that regulate a portion of the genetic variation for a particular phenotype (Wehner et al. 2001). The goal of QTL mapping is to identify regions of the genome that harbour genes relevant to a specified trait. QTL map locations are commonly determined by initial screening of mice with specific genetic characteristics, such as recombinant inbred strains, the F2 of two inbred strains, or recombinant congenic strains (Flint 2003).", + "(2003) and others defined the expression QTLs (eQTLs) as either cis (mapping near the gene locus) or trans (mapping elsewhere in the genome). When behavioral QTLs (bQTLs) and cis-eQTLs overlap, the cis-eQTL genes are inferred as strong quantitative trait gene (QTG) candidates (see e.g. Farris et al. 2010). The situation for trans-eQTLs is more complicated since the QTL confidence interval is generally larger and any gene within the QTL interval could have a regulatory role. The application of genetical genomics to mouse has generally focused on segregating populations involving R. Hitzemann et al.", + "Page 2 Definition of a QTL NIH-PA Author Manuscript A quantitative trait is one that has measurable phenotypic variation owing to genetic and/or environmental influences. This variation can consist of discrete values, such as the number of separate tumours in the intestine of a cancer-prone mouse, or can be continuous, such as measurements of height, weight and blood pressure. Sometimes a threshold must be crossed for the quantitative trait to be expressed; this is common among complex diseases. A QTL is a genetic locus, the alleles of which affect this variation.", + "When the phenotype of interest is a quantitative trait, such as blood pressure or cholesterol levels, the underlying genetic locus is referred to as a \u201cQTL\u201d. A common strategy investigates the association between quantitative traits of transcriptional responses and their underlying DNA loci called \u201cresponse QTLs\u201d (reQTLs) (Albert and Kruglyak 2015). Studies have provided clear evidence for the colocalization of reQTLs and disease-related loci (Caliskan et al. 2015).", + "81 Gene Expression Quantitative Trait Locus Analysis Quantitative trait locus (QTL) mapping is a statistical technique that finds associations between phenotype and genotype in a genetically segregating population (Lander and Botstein 1989). Here, we performed eQTL mapping on the male and female data separately. There were 1,137 significant (q\u22640.5 and p\u22640.025) male and 1,232 female eQTLs. First, we explored differences in patterns of eQTL locations between sexes by plotting the genomic locations of each eQTL versus the transcript location (Figure 4.3a, b).", + "Chromosomal regions containing a gene (or genes) that a\u00a1ect the level of a quantitative trait are called quantitative trait loci (QTLs). The relevant genes in these regions have been called quantitative trait genes (QTGs) (Hitzemann et al 2003). Quantitative trait locus (QTL) analysis is an experimental strategy for identifying QTLs, and ultimately QTGs, that a\u00a1ect quantitative traits. Because of the complexity of these traits, progress in identifying QTGs has been slow compared to that in cloning genes underlying Mendelian traits (Glazier et al 2002).", + "1.4 Q u a n tita tiv e T rait L ocu s M a p p in g Q uantitative tra it loci (QTLs) are genetic regions on a chromosome th a t control certain quantitative traits, such as crop yield or body fat. QTL m apping involves con\u00ad struction of genomic m aps and testing for association between tra its and polymorphic markers. A significant association provides evidence th a t a QTL is near th e m arker.", + " Current data processing capabilities have also made it possible to search genome-wide for QTL (quantitative trait loci) [109].QTL mapping seeks to identify the relationship between various genomic locations and a set of quantitative traits, leading to a chromosomal location and ultimately to identification of gene(s) with the final goal of looking at gene expression.Among other things, this will lead to a better understanding of genetic mechanisms of variation and adaptation [121].Results can then be applied to adjust conservation measures in response to rapid change, for example, by identifying the genetic adaptability potential of individuals to be used in assisted migration or reintroduction [122,123]." + ] + ] +} diff --git a/gnqa/paper1_eval/src/data/datasets/old/general1_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/general1_dataset.json new file mode 100644 index 0000000..b3e9c06 --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/general1_dataset.json @@ -0,0 +1,128 @@ +{ + "question": [ + "What is the most cited environmental factor for the onset of asthma.", + "What is apoptosis?", + "How would one extract the DNA, from say, mammals or plants?", + "What is a genetic marker?", + "Explain the process of finding a genetic marker followed by a quantitative trait loci." + ], + "answer": [ + "Air pollution is the most cited environmental factor for the onset of asthma.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "For mammals, DNA can be extracted using a DNA Isolation Kit for Mammalian Blood, following the manufacturer's instructions. This process typically involves mixing blood with lysis buffer and Proteinase K, followed by the addition of isopropanol and Inhibitor Removal Buffer. The DNA is then washed, centrifuged, and the supernatant discarded. The remaining pellet, which contains the purified genomic DNA, is diluted in TE buffer. For plants, the mixed alkyl trimethyl ammonium bromide (MATAB) procedure can be used. This involves grinding plant material in liquid nitrogen, incubating it in extraction buffer, and then purifying it with chloroform:isoamylalcohol. The DNA extracts are then precipitated with isopropanol and resuspended in a buffer. The extracts are further purified on anion exchange columns.", + "A genetic marker is a measurable polymorphic sequence of DNA whose chromosomal location is known. They often have no known functional significance but are used as pointers to a particular chromosomal location. They are used to track the inheritance of genes and can be a gene or some section of DNA with no known function.", + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait." + ], + "contexts": [ + [ + "INTRODUCTION Asthma is a chronic disease of the airways defined by its symptoms, which include reversible airflow obstruction, inflammation, and bronchial hyperresponsiveness.The ancient Egyptians, Greeks, and Romans made reference to the symptoms of asthma, and today the disease is estimated to affect 235-334 million people worldwide (44,53).", + "The atopic triad.Perhaps the most widely recognized pattern of co-occurrence is the one of asthma, atopic dermatitis (eczema), and allergic rhinitis (hay fever), which together are referred to as the atopic triad and characteristically present clinically in a temporal sequence known as the atopic march.Within this sequence, atopic dermatitis is typically the first component to manifest, with approximately 20-30% of individuals with mild disease and 70% of those with severe disease going on to develop asthma.Individuals who undergo this distinctive sequence of disease progression frequently exhibit a more severe and persistent phenotype, with increased risk of allergen sensitization.", + "Clinically, asthma is characterized by episodes of coughing, chest tightness, wheezing, dyspnea, or sputum production.Often, asthma sufferers experience a combination of these symptoms, or some symptoms more than others.Pulmonary breathing tests typically demonstrate variable airway obstruction and hyperreactivity, but may be normal, even in patients with severe and uncontrolled disease [8].Thus, the diagnosis of asthma, which is based on general clinical symptoms and variable lung function testing, is non-specific and heavily dependent on clinical history.Within the \"umbrella\" diagnosis of asthma there exists a diverse array of differing clinical phenotypes [9].For example, childhood asthma is often associated with personal and parental atopic diseases (i.e., atopic dermatitis, food allergy, eosinophilic esophagitis, allergic rhinitis), viral infections, and tobacco smoke exposure [10].Alternatively, adult-onset asthma is less associated with atopic disease [11,12], but more associated with female sex [13], sinus disease [14], and preceding respiratory infections such as pneumonia [15].In addition, adult-onset disease is often of higher severity [12,16] with a faster and more persistent decline in lung function [17].Moreover, although severe patients are found in every demographic and age group, the most common phenotype is an adult female that is older and obese [18].", + "Introduction An estimated 9% of children and 6% of adults in the United States have asthma [1].The total number of asthma sufferers worldwide is estimated to be over 300 million, with an additional 100 million expected to develop asthma by 2025 [2][3][4][5].Developed countries are the most affected, with some of the highest rates found in the United Kingdom, Australia, New Zealand and the Republic of Ireland [3].Asthma prevalence is rising significantly in developing countries in transition to a more Western lifestyle [3].In 2007, the cost of disease in the United States was estimated to be $56 billion in relation to medical expenses, missed days of work, and early deaths [1].The rate of asthma deaths has likely plateaued, but is still as high as 250,000 per year worldwide [6].Morbidity and mortality are particularly high in ethnic minorities living below or near the poverty line, and African American children had a death rate 10 times that of non-Hispanic white children in 2015 [7].Thus, asthma is a costly, growing health problem associated with high morbidity and mortality.", + "Getting accurate estimates of exposures is difficult, whether this is air pollution or toxins in our food and drink, but these are important questions. Rutter: That is an important point. From the twin study data it is clear that environmental effects account for quite a lot of the variance on all the multifactorial disorders. Yet the kinds of measures that are used aren\u2019t terribly solid. They include broad thing such as socio-economic status (SES). Even where there are good measures the care taken in testing for environmental mediation is usually poor.", + "Bronchiolitis, a disease that happens in the first year of life in many infants, is strongly associated with subsequent asthma. We ascertained it in the first years of life and have been following these people to age 25 now. For the people who had bronchiolitis and now have asthma, their parents recall much better that they had bronchiolitis than those who don\u2019t have asthma now. It is at least twice more. Extraordinarily, some of these latter parents don\u2019t recall that they took their child to the doctor in the fi rst year of life.", + "If you arrive in the USA when you are young you have almost the same prevalence of asthma as an adult as those who are born in the USA and who are not Mexican. But if you arrive at older ages you have less asthma. If you arrive at the age of 20 you have the same asthma risk as those born in Mexico (Eldeirawi et al 2005). Kotb: This is extremely interesting. There is a relationship between depression and the immune system. This especially applies to natural killer (NK) cells, which are the main cells that fight cancers.", + "A colleague of mine in Georgia found this may have a protective effect against later development of asthma (Ownby et al 2002). Martinez: We find significantly decreased likelihood of asthma if you have a dog in a home, but not if you have a cat. The reason for this is not that I hate cats, which I do, but most likely because cats are stealth hunters, and they have to be very clean. Dogs are collective hunters and they don\u2019t care if they smell.", + "; Guffey, S.E. Investigation into pedestrian exposure to near-vehicle exhaust emissions. Environ. Health 2009, 8, 13. [CrossRef] [PubMed] Our World in Data.org. 2017. Available online: https://ourworldindata.org/data-review-air-pollution-deaths (accessed on 10 January 2022). Pope, C.A. , III. Respiratory disease associated with community air pollution and a steel mill, Utah Valley. Am. J. Public Health 1989, 79, 623\u2013628. [CrossRef] [PubMed] Pope, C.A. , III. What do epidemiologic findings tell us about the health effects of environmental aerosols? J. Aerosol. Med. 2000, 13, 335\u2013354. [CrossRef] [PubMed] Pope, C.A. , III.", + "Case for Support BBSRC Grant Application September 2005 \u201cIntegrative Analysis of the Genetic Factors behind Asthma and Atopic Dermatitis\u201d Part I: Research Proposal Background A Introduction of topic of research and its academic and wider context Asthma is the most common disease of childhood, and affects one child in seven in the United Kingdom. Atopic Dermatitis (AD, eczema) affects similar numbers of children. About 60% of children with severe AD will have concomitant asthma. Treatments for both diseases are unsatisfactory. Abandonment of orthodox medical therapy for AD is common in many families who have children with the disease.", + "This is most common during the rainy season when aerosols are created, which results in repeated inhalation of Bp [43, 44]. Environmental sampling studies reveal there is a positive association between the prevalence of disease and the degree of environmental contamination [7]. In addition to environmental factors, data suggests that host factors play an important role in mounting an immune response against infectious diseases [45] such as melioidosis. While healthy persons can contract melioidosis, most patients in endemic regions have an underlying predisposition [28], which suggests that the immunological status of the patient can influence disease initiation and progression [15].", + "Sensitivity analysis We did two sets of post-hoc sensitivity analyses to assess the effects of potential poor recall of age of onset among individuals with adult-onset asthma, and the effects of misclassification of COPD as asthma among the adultonset cases, even with exclusion of cases with a reported diagnosis of COPD, emphysema, or chronic bronchitis.First, to assure that the adult-onset cases did not include a significant proportion of childhood-onset asthma in which symptoms remitted in early life but then relapsed in adulthood, we replaced adult-onset cases with increasing proportions of randomly selected childhood-onset cases, and then tested for association at the two most significant childhood onset-specific loci.This procedure was repeated 20 times for each proportion to quantify the sampling variability (appendix pp 7-8).Second, we did two analyses in which we removed either individuals with ages of asthma onset between 46 and 65 years or adult-onset cases and controls with FEV\u2081/FVC <0\u202270.For each, we compared p values and ORs with the GWAS including all adult-onset cases (appendix pp 8-9).", + "We used data for British white individuals from UK Biobank data release July 19, 2017. 8We extracted disease status (asthma, allergic rhinitis, atopic dermatitis, food allergy, chronic obstructive pulmonary disease (COPD), emphysema, and chronic bronchitis), age of on set of asthma, and sex from self-reported question naires and hospital records (International Classification of Diseases 10th revision [ICD-10] codes) by querying our in-house protected UK Biobank database server. 9For our main case analysis, we included individuals who self-reported that they had doctor-diagnosed asthma.Further details of our research approach are provided in the appendix (pp 4-7).", + "; Guffey, S.E. Investigation into pedestrian exposure to near-vehicle exhaust emissions. Environ. Health 2009, 8, 13. [CrossRef] [PubMed] Our World in Data.org. 2017. Available online: https://ourworldindata.org/data-review-air-pollution-deaths (accessed on 10 January 2022). Pope, C.A. , III. Respiratory disease associated with community air pollution and a steel mill, Utah Valley. Am. J. Public Health 1989, 79, 623\u2013628. [CrossRef] [PubMed] Pope, C.A. , III. What do epidemiologic findings tell us about the health effects of environmental aerosols? J. Aerosol. Med. 2000, 13, 335\u2013354. [CrossRef] [PubMed] Pope, C.A. , III.", + "8 The socio-ecologic framework posits that various aspects of a child\u2019s environment directly and indirectly impact the child\u2019s health and development.9 Drawing on this framework, Beck and colleagues10 examined several biologic, social and ecologic variables to provide a greater understanding of factors influencing asthma-related hospital readmissions for black children compared to their white counterparts. The study revealed that black children were over two times as likely to be readmitted for an asthma-related illness compared to white children; this resulted from significant differences in almost every socio-ecologic variable measured, including disease management practices and access to primary care.", + "Specific Aims Asthma is the most common chronic pediatric medical condition in the United States, with a prevalence over 9.6% in children under 18 years of age.1, 2 Low-income, urban children incur a disproportionate share of asthma prevalence and morbidity;2-4 13% of children living below the poverty threshold are diagnosed with asthma compared to 8% of non-poor (>200% poverty),3 and poverty is associated with higher rates of asthma attacks.1 Living in an urban area confers additional risk for asthma and increased ED utilization.4, 5 Implementation of the National Asthma Education and Prevention Program\u2019s (NAEPP) Guidelines has contributed to reductions in asthma morbidity and mortality rates, and these guidelines emphasize establishing a partnership between healthcare providers and patients/families to promote effective asthma management.6 The NAEPP expert panel states, \u201cbuilding a partnership requires that clinicians promote open communication and ensure that patients have a basic and accurate foundation of knowledge about asthma\u2026\u201d (p.124),6 yet care partnerships also require that the patient/parent effectively communicate issues such as emerging symptoms or response to medications.", + "Vital & health statistics Series 3, Analytical and epidemiological studies. 2012(35):1-58. CDC. Current Asthma Prevalence. https://www.cdc.gov/asthma/most_recent_data.htm. 2015. Updated June 2017. Accessed March 9, 2018. Northridge J, Ramirez OF, Stingone JA, Claudio L. The role of housing type and housing quality in urban children with asthma. Journal of urban health : bulletin of the New York Academy of Medicine. 2010;87(2):211-224. Flores G, Snowden-Bridon C, Torres S, et al. Urban minority children with asthma: substantial morbidity, compromised quality and access to specialists, and the importance of poverty and specialty care.", + "Asthma Prevalence and Disparities Asthma is the most common chronic pediatric medical condition in the United States,1 affecting an estimated 6.2 million children annually.2 Poorly controlled pediatric asthma contributes to over 700,000 visits a year to emergency departments (ED).1 Children living in impoverished, urban settings are disproportionately affected by asthma,3 and the disparate impact of asthma is even worse among black and Latino children, and children whose parents have limited English proficiency (LEP) in these urban low-income areas.4-6 A 2017 longitudinal study revealed that black race and Latino ethnicity are significantly associated with worse asthma outcomes including 1) asthma knowledge, 2) asthma-related quality of life, 3) asthma severity, and 4) asthma control.", + "The Journal of asthma : official journal of the Association for the Care of Asthma. 2017:16. Inkelas M, Garro N, McQuaid EL, Ortega AN. Race/ethnicity, language, and asthma care: findings from a 4-state survey. Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology. 2008;100(2):120-127. National Asthma Education and Prevention Program. Expert Panel Report 3: Guidelines for the Diagnosis and Management of Asthma Bethesda, MD: National Institutes of Health, National Heart, Lung, and Blood Institute; 2007. Publication no. 08-045.1. NIH Consensus Group. Video report: What is mHealth?", + "Contact PD/PI: Coker, Tumaini Rucker INTRODUCTION TO APPLICATION Research Plan Overview Childhood asthma is the most common pediatric medical condition in the United States, and disproportionately affects children living in low-income, urban settings. Many low-income, urban families rely on emergency department (ED) services as their source for sick care for their child. This is often due to not having a primary care provider or sufficient access to their primary care provider for asthma management." + ], + [ + "Apoptosis, or controlled cell death [62], is another major stressed-cell response, and was also represented in our results (Fig. 9e).A large body of direct evidence points to apoptosis as one of the main routes of RPE degeneration in AMD [63].Induction of apoptosis upon stress is dictated by the action of master regulator p53, and it was recently shown that aging increases the activity of p53 in RPE cells and the likelihood for apoptotic cell death [64].Consistent with this evidence, we found association with pathways in Transcriptional regulation by TP53 group (Fig. 9d).In particular, Regulation of TP53 activity through methylation was among the top pathway in our association analysis (Table 1), suggesting that p53 modification by methylation and the closely related histone modifications [Protein lysine methyltransferases (PKMTs) methylate histone lysine in Fig. 9e] play important roles in RPE apoptosis regulation.In the intrinsic apoptotic pathway induced by oxidative stress, cytochrome c is released from mitochondria into the cytosol, binding and activating caspases, the main proteases central to apoptotic action.We found association in pathways involving 'inhibitor of apoptosis' (IAP) and its negative regulator 'second mitochondrial activator of caspases' (SMAC) [65], which suggests that disruption to regulatory mechanisms preventing apoptosis in RPE cells may play roles in AMD.", + "Apoptosis Persistent DNA damage", + "42 ABSTRACT 18 A MODULARIZED MODEL OF APOPTOSIS HA Harrington, KHo, Sk Ghosh, KC Tung , CY Kao, and B Aguda Imperial College London, Courant Institute of Mathematical Sciences New York University, University of Texas at Arlington, University of Texas Southwestern Medical Center, Mathematical Biosciences Institute, and Department of Mathematics, The Ohio State University Columbus, OH, USA Background: One of the key physiological mechanisms employed by the cell (during development and for maintenance of homeostasis) in multi-cellular organism is apoptosis, which is characterized by a sequence of well-defined events resulting in cell destruction.", + "14 Apoptosis is caused by the activation of the caspase cascade, which is initiated by two signaling routes (stress-induced death and death-domain receptor-induced death) (Domen 2001). This process can be prevented by antiapoptotic molecules, such as Bcl-2 (Domen and Weissman 2000). Direct evidence for the involvement of apoptosis in HSC number regulation came from the findings that overexpression of the anti-apoptotic gene bcl-2 led to increased numbers of Thy-1.1low, Sca-1+, c-kit+, Lin- cells, a population with long-term multi-lineage repopulation potential (Domen et al. 2000).", + "Several lines of evidence have indicated that apoptosis acts as an important regulator of stem cells. First of all, expression of some apoptosisrelated genes were detected in human and/or murine HSCs (Domen 2001). Secondly, targeted disruption of some of these genes in null and dominant negative mutant mice interfered with normal apoptotic processes in HSCs. For example, overexpression of Bcl-2, a negative regulator of apoptosis, increased not only the numbers and competitive repopulation capabilities of HSCs, but also the resistance of HSCs to apoptosis induced by ionizing radiation (Domen and Weissman 2003).", + "Apoptosis Cell suicide, or apoptosis, is a well-studied biological phenomenon in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism.The lack of an apparent evolutionary benefit for such a process in a single-celled organism initially caused controversy about the presence of an apoptotic pathway in yeast.Today, however, a number of yeast orthologues to mammalian apoptosis genes have been discovered and apoptotic-like cell death has been linked to mating, colony formation, and aging (Buttner et al. 2006;Eisenberg et al. 2007;Frohlich et al. 2007).With respect to aging, both replicatively and chronologically aged cells that die have increased ROS and display apoptotic phenotypes (Fabrizio et al. 2004a;Herker et al. 2004;Laun et al. 2001).", + "The importance of apoptosis in yeast aging has yet to be fully characterized.At the very least, yeast apoptosis provides a useful pathway for studying genetic interactions for age-related diseases that affect humans, such as cancer.Readers interested in further information related to yeast apoptosis are referred to several in-depth reviews (Buttner et al. 2006;Eisenberg et al. 2007;Frohlich et al. 2007).", + "Early redistribution of plasma membrane phosphatidylserine is a general feature of apoptosis regardless of the initiating stimulus: inhibition by overexpression of Bcl-2 and Abl. J Exp Med 182: 1545-56. Mathew CG (2006). Fanconi anaemia genes and susceptibility to cancer. Oncogene 25: 5875-84. McBride MW, Carr FJ, Graham D, Anderson NH, Clark JS, Lee WK et al (2003). Microarray analysis of rat chromosome 2 congenic strains. Hypertension 41: 847-53. Merino-Trigo A, Kerr MC, Houghton F, Lindberg A, Mitchell C, Teasdale RD et al (2004).", + "When a cell harbors such severe DNA damage that it is beyond repair, it is disposed of through apoptosis.Alternatively, DNA damage can induce cellular senescence, the irreversible cessation of mitosis.Both processes are critically dependent on p53, which is known as the guardian of the genome [3] .DNA damage may also trigger autophagy, a cellular catabolic process that maintains homeostasis [4] .It should be noted that under normal conditions cells are rarely exposed to very high doses of DNAdamaging agents, which may be the explanation why we do not age and die because we run out of cells.However, aging is associated with some atrophy [1] and it is conceivable that at older ages bursts of DNA damage, for example from free radical reactions associated with inflammation, do occur and give rise to an increasingly high rate of apoptosis or cellular senescence.While there is some evidence for increased apoptosis and cellular senescence at old age, it is doubtful that under normal conditions this would lead to a significant loss of functional cells.", + "Apoptosis, or programmed cell death, literally eliminates cells at risk for neoplastic transformation.Senescence, by contrast, permanently arrests their growth.Both processes are controlled by the p53 tumor suppressor protein (Amundson, Myers, & Fornace, 1998;Bringold & Serrano, 2000;Hickman, Moroni, & Helin, 2002;Itahana, Dimri, & Campisi, 2001).p53 is a transcriptional regulator that both transactivates and transrepresses target genes in response to stress (Prives & Hall, 1999;Ryan, Phillips, & Voudsen, 2001).These target genes, in turn, stimulate DNA repair, transient cell cycle arrest, permanent cell cycle arrest (senescence) or cell death (apoptosis), depending on cell type, degree and type of damage, and other variables.In contrast, cells that lack normal p53 regulation or function -for example, tumor cells -tend to die in response to telomere dysfunction.Some normal human cells, on the other hand, undergo a senescence growth arrest.In either case, when present, p53 is crucial for mediating the cellular response to telomere dysfunction (Yaswen & Stampfer, 2002) (Fig. 4).", + "Cell death, and in particular apoptosis, can be caused by a number of mechanisms including loss of growth factors and excitotoxicity (e.g. , Bhutta and Anand, 2002; Nikolic\u0301 et al. , 2013). It is of interest therefore, that proximal to the region of the QTL there are several genes that are related to growth factors including the latent transforming growth factor protein 2 (ltbp2), placental growth factor (pgf), and transforming growth factor beta (Tgf beta).", + "Apoptosis-related gene expression profiles", + "Apoptosis.Programmed death of cells during embryogenesis and metamorphosis or during cell turnover in adult tissues.", + "14 Apoptosis is caused by the activation of the caspase cascade, which is initiated by two signaling routes (stress-induced death and death-domain receptor-induced death) (Domen 2001). This process can be prevented by antiapoptotic molecules, such as Bcl-2 (Domen and Weissman 2000). Direct evidence for the involvement of apoptosis in HSC number regulation came from the findings that overexpression of the anti-apoptotic gene bcl-2 led to increased numbers of Thy-1.1low, Sca-1+, c-kit+, Lin- cells, a population with long-term multi-lineage repopulation potential (Domen et al. 2000).", + "Several lines of evidence have indicated that apoptosis acts as an important regulator of stem cells. First of all, expression of some apoptosisrelated genes were detected in human and/or murine HSCs (Domen 2001). Secondly, targeted disruption of some of these genes in null and dominant negative mutant mice interfered with normal apoptotic processes in HSCs. For example, overexpression of Bcl-2, a negative regulator of apoptosis, increased not only the numbers and competitive repopulation capabilities of HSCs, but also the resistance of HSCs to apoptosis induced by ionizing radiation (Domen and Weissman 2003).", + "Fraction of cells displaying apoptosis", + "It has been known that mitochondria play a central role in the life and death of cells (Kroemer & Reed, 2000).Apoptosis was observed in developmentally arrested embryos by 72 h, but not at 24 h after FCCP treatment, despite considerable telomere attrition at this early stage, suggesting that telomere attrition occurs prior to apoptosis and may serve as an intermediate step between mitochondrial dysfunction and apoptosis.These results also suggest that telomere shortening may signal apoptosis (Lee et al ., 1998;Karlseder et al ., 1999).", + "Cell Death A form of programmed cell death, apoptosis is necessary for normal cell turnover and is essential to a plethora of other biological processes.Apoptosis can be executed via Bcl-2 activation of caspases, via signals from the death receptor on the plasma membrane, or via induction by granzyme B secreted from cytotoxic T cells (Tc cells) [35].Endonucleases and proteases are activated by active caspases, eventually leading to the death of the cell.With age, however, apoptotic activity changes.In heart [36], kidney [37], skeletal muscle [38], and Tc cells [39], increased apoptosis has been reported, perhaps contributing to loss of cellularity in these tissues.This escalation across various tissues may be attributed to the increased production of free radicals [40] and furthermore exacerbated by the accumulation of DNA damage in the aged cells [41].As the risk increases for cells to turn cancerous and dysfunctional with advancing age, increased apoptosis in aged cells is argued to be a defense strategy.In other tissues, such as the colon, apoptosis appears to decrease with age perhaps contributing to the accumulation of senescent cells and age-associated carcinogenesis [42].", + "The regulation and execution of apoptosis in endothelial cells is a complex process involving paracrine factors, membrane receptors, interaction of pro-and anti-apoptotic factors and cysteinyl aspartate-specific proteases (caspases).Recent studies suggest that in aging there is an imbalance in the expression of pro-and anti-apoptotic genes resulting in an enhanced apoptosis in the myocardium (19), central nervous system (24), skeletal muscle (10), lung (33), and liver (2,33).Yet, age-related alterations in the expression of pro-and anti-apoptotic genes in coronary arteries have not been elucidated.", + "Apoptosis modulating genes Apopotosis or programmed cell death is associated with alterations in cell morphology, particularly the nucleus, with endonucleatytic cleavage of DNA into nucleosomal length fragments.Apoptosis may result from withdrawal of growth signals." + ], + [ + "DNA and RNA extraction of tissues Genomic DNA was extracted from frozen placentae (n \u03ed 3/group) and liver (n \u03ed 9/group) using a modified version of an established protocol (28,29).Total RNA was extracted from the remaining tissue using TRIzol, as per the manufacturer's instructions (Invitrogen Canada Inc).Genomic DNA and RNA purity and concentration were assessed using spectrophotometric anal-ysis, and integrity was verified using agarose gel [1% (wt/vol)] electrophoresis.", + "Taxon Sampling and DNA Extractions We extracted DNA from 72 pinned specimens from the National Museum of Natural History (NMNH) Entomology collection for this study.We plucked middle legs from the pinned bees using a pair of sterilized forceps and washed the tissue in 95% ethanol to remove dust, pollen, and other forms of accumulated debris on the bee legs.After evaporation of the ethanol (by drying the tissue on a clean Kimwipe \u2122 ), the samples were placed in a freezer for several hours.DNA was then extracted destructively by grinding the frozen tissue with a sterile pestle, using a DNeasy Blood and TissueKit (Qiagen, Valencia, CA, USA) and following the manufacturer's protocol, except the DNA was eluted in 130\u03bcL ddH 2 O instead of the supplied buffer.We ran 10\u03bcL of each extract for 60 min at 100 volt on 1.5% agarose SB (sodium borate) gels, to estimate size of the genomic DNA.", + "DNA extraction DNA was extracted from PBMCs using the QIAamp DNA Mini kit (Qiagen, CA, USA), following the manufacturer's instructions for the spin protocol.The DNA was eluted in 60 \u03bcl of AE elution buffer and stored at -20\u00b0C.The concentration and quality of the DNA was assessed with the Qubit dsDNA HS Assay (Invitrogen, Eugene, OR, USA).", + "Methods Laboratory procedures.We initially screened 107 ancient samples (Supplementary Data 1) in dedicated clean facilities at the ancient DNA lab of Jilin University, China, following published protocols for DNA extraction and library preparation 36,37 .Prior to sampling, we wiped all skeletal elements with 5% bleach and irradiated with UV-light for 30 min from each side.We drilled teeth to obtain fine powder using a dental drill (Dremel, USA).We sampled the dense part of petrous bones around the cochlea by first removing the outer part using the sandblaster (Renfert, Germany), and then grinding the clean inner part into fine powder with the mixer mill (Retsch, Germany).We digested the powder (50-100 mg) in 900 \u03bcl 0.5 M EDTA (Sigma-Aldrich), 16.7 \u03bcl of Proteinase K (Sigma-Aldrich), and 83.3 \u03bcl ddH 2 O (Thermo Fisher, USA) at 37 \u00b0C for 18 h.Then we transferred the supernatant to a MinElute silica spin column (QIAGEN, Germany) after fully mixed with the 13 ml custom binding buffer [5 M guanidine hydrochloride (MW 95.53), 40% Isopropanol, 90 mM Sodium Acetate (3 M), and 0.05% Tween-20] followed by two washes with PE buffer (80% ethanol).Then we eluted the DNA with 100 \u03bcl TET buffer (QIAGEN, Germany).", + "DNA Extraction After blood was drawn into EDTA tubes, genomic DNA was extracted using a DNA Isolation Kit for Mammalian Blood Kit (Roche Applied Science, Indianapolis, IN, USA) according to the manufacturer's recommendations.Briefly, 300 \u03bcl of whole blood from each sample was mixed with 200 \u03bcl of lysis buffer (50 mM Tris pH 8.0, 100 mM EDTA, 100 mM NaCl, 1% SDS) and 40 \u03bcl of Proteinase K, followed by addition of 100 \u03bcl of isoproponal and 500 \u03bcl of Inhibitor Removal Buffer (5M guanidine-HCl, 20 mM Tris-HCl pH 6.6).The DNA was washed with a buffer (20 mM NaCl; 2 mM Tris-HCl; pH 7.5), centrifuged twice at 2000 rpm, washed using cold 70% ethanol and centrifuged at 3000 rpm.The supernatant was discarded and the pellet containing purified genomic DNA was diluted in TE buffer (1 mM EDTA; 10 mM Tris-HCl, pH 7.5) to a concentration of approximately 50 ng/\u03bcl.", + "Genomic DNA extraction Leukocytes were isolated from 5-ml peripheral blood samples.DNA was prepared by phenol extraction and chloroform extraction followed by isopropanol precipitation, washed with ethanol, and air-dried.Tris-EDTA buffer pH 8.0 was used to dissolve the final genomic DNA product.", + "The pulled down DNA fragments were extracted and purified using phenolchloroform extraction/ethanol precipitation.The samples were stored at -20 \u00b0C until use.", + "DNA extraction for genotyping For the majority of samples, DNA was extracted from either spleen or the exocrine fraction of the islet isolation using the Tissue DNA Purification Kit according to manufacturer's instructions on an automated Maxwell 16 system (both Promega, USA).When no other tissue was available, DNA was extracted from human islets using the Trizol fraction remaining after extraction of RNA (see above).To precipitate the DNA, 300\u03bcl 100% ethanol was added to the thawed solution.This mixture was incubated at room temperature for a minimum of 30 minutes.DNA was then pelleted by centrifugation at 4,000 x g for 5 minutes at 4\u00b0C.After removing the supernatant, the pellet was twice washed with 0.1M trisodium citrate (Sigma Aldrich, UK) in 10% ethanol and left at room temperature for 30 minutes, followed by another wash step with 75% ethanol.After the final wash step, pellets were air-dried for 10 minutes to remove residual ethanol and re-suspended in a minimum of 100 \u03bcL 8mM NaOH (Sigma Aldrich).Extracted DNA was stored at -20\u00b0C before further use.", + "DNA extraction Tissue samples were incubated at 50\u00b0C overnight with shaking in DNA extraction buffer (100 mM NaCl, 10 mM Tris.HCl pH8, 25 mM EDTA, 0.5% (w/v) SDS), containing 200 \u03bcg/ml proteinase K. DNA was isolated by two rounds of phenol:chloroform extraction, followed by RNAse A treatment, precipitation in absolute ethanol containing 10% (v/v) sodium acetate (3 M, pH 5.2), and resuspended in 100 \u03bcl nuclease-free water (Ambion, Austin, TX, USA) or using salting out method followed by purification with Qiagen blood and tissue kit (Qiagen, Mississauga, ON, USA).DNA was stored at -20\u00b0C.", + "Methods Human DNA samples DNA was extracted from human patient tissue samples acquired from the University of Minnesota Tissue Procurement Facility from BioNet (IRB#0805E32181).See Supplemental Table S4 for patient data.Briefly, 2 mg of tissue was digested overnight at 55\u00b0C on a rotating platform in 710 mL of digest buffer (1 M Tris at pH 8.0, 1 mM EDTA, 13 SSC, 1% SDS, 1 Mm NaCl, 10 mg/mL Proteinase K).Following digest, DNA was purified using phenolchloroform-isoamyl alcohol (Life Sciences) isolation protocol.", + "3.2.2 Isolation of genomic DNA Genomic DNA was isolated from frozen liver tissue. The isolation was conducted using the Qiagen DNeasy\uf8e8 Blood & Tissue Kit (Qiagen) according to the manufacturer\u2019s protocol. DNA concentration was evaluated photometrically at a wavelength of 260 nm using the FusionTM Universal Microplate Analyzer. For nucleic acid quantification, the Beer-Lambert (A = \u03b5 * b * c) equation is modified to use an extinction coefficient with units of M-1 cm-1.", + "Most typically, DNA is extracted from blood samples, dried blood spots, buccal swabs, saliva, tissue and even urine and stool samples.In forensic science, other sources have been validated e.g.bone, tooth pulp, dandruff and others.", + "DNA isolation High-molecular weight DNAs was isolated from the samples by organic solvent extraction method, followed by precipitation in cold ethanol [14].", + "Genomic DNA extraction DNA from MEF cultures or mouse liver was isolated by phenol/chloroform extraction, as described [11].", + "DNA is usually recovered from cells by methods that include cell rupture but that prevent the DNA from fragmenting by mechanical shearing. This is generally undertaken in the presence of EDTA, which chelates the magnesium ions needed as cofactors for enzymes that degrade DNA, termed DNase. Ideally, cell walls, if present, should be digested enzymatically (e.g. , lysozyme in the bacteria or bacterial cell). In addition the cell membrane should be solubilized using detergent.", + "DNA solutions can be stored frozen, although repeated freezing and thawing tends to damage long DNA molecules by shearing. A flow diagram summarizing the extraction of DNA is given in Fig. 1.2. The above-described procedure is suitable for total cellular DNA. If the DNA from a specific organelle or viral particle is needed, it is best to isolate the organelle or virus before extracting its DNA, because the recovery of a particular type of DNA from a mixture is usually rather difficult.", + "Genomic DNA extraction Genomic DNA was extracted by the mixed alkyl trimethyl ammonium bromide (MATAB) procedure.Briefly, 250 mg of plant material was ground in liquid nitrogen and immediately incubated in 2 ml of pre-warmed extraction buffer (100 mM Tris-HCl, pH 8, containing 20 mM EDTA, 1.4 M NaCl, 2% (w/v) MATAB, 1% (w/v) PEG6000 (polyethylene glycol), 0.5% (w/v) sodium sulfite, 20% (w/v) Igepal CA630, 20% (w/v) lithium dodecyl sulfate, and 20% (w/v) sodium deoxycholate) at 74 \u00b0C for 20 min.After purification with 2 ml of chloroform:isoamylalcohol (24:1, v/v), DNA extracts were precipitated with 1.6 ml of isopropanol then resuspended in 1 ml of buffer (50 mM Tris-HCl, pH 8, containing 10 mM EDTA and 0.7 M NaCl).The extracts were purified on anion exchange columns (QIAGEN-tip 20) following the manufacturer's instructions (QIAGEN, Valencia, CA).", + "DNA extraction and enzymatic digestion Total DNA was isolated from whole blood and separated blood subtypes using a Qiagen DNeasy Blood & Tissue Kit following the manufacturer instructions.After extraction, DNA was quantified by NanoDrop (Thermo Scientific NanoDrop products, Wilmington, DE).The isolated genomic DNA was enzymatically digested according to previously described method.Briefly, DNA (3 \u03bcg) was first denatured by heating at 95 \u00b0C for 5 min and then chilling on ice for 2 min.Then, 1/10 volume of S1 nuclease buffer (30 mM CH 3 COONa, pH 4.6, 280 mM NaCl, 1 mM ZnSO 4 ) and 100 units of S1 nuclease were added before the mixture (20 \u03bcL) was incubated at 37 \u00b0C for 16 h.Subsequently, after 1/10 volume of alkaline phosphatase buffer (50 mM Tris-HCl, 10 mM MgCl 2 , pH 9.0), 0.002 units of venom phosphodiesterase I, and 10 units of alkaline phosphatase were added, the solution was incubated at 37 \u00b0C for an additional 4 h followed by extraction with an equal volume of chloroform for twice.The aqueous layer was collected and lyophilized to dryness and then reconstituted in 100 \u03bcL water.About 30 \u03bcL of the obtained samples were then subjected to liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) analysis.", + "The conventional DNA extraction procedure involved the homogenization of single D. magna in 400 l of sperm lysis buffer (100 mM Tris-HCl, pH 8; 500 mM NaCl; 10 mM ethylenediaminetetraacetic acid [EDTA], pH 8; 1% SDS; 2% mercaptoethanol) followed by RNase treatment (40 g, 37\u040aC for 1.5 h).The DNA was then extracted in phenol (pH 8) and chloroform:isoamyl alcohol (1:1).The DNA was finally precipitated by two volumes of ice-cold ethanol in the presence of 3 M sodium acetate (1/10 of the DNA volume) and was incubated at \u03ea80\u040aC overnight.Precipitated DNA was harvested by centrifugation, dried in air, and the final pellet dissolved in sterile analytic grade water.", + "DNA extraction and quantification DNA was extracted from whole organs by standard techniques (34) with emphasis on minimizing shearing or nicking of DNA as nicked DNA has been shown to be refractory to LX-PCR (35).DNA from the brain was extracted from the right hemisphere.Extracted DNA was resuspended in 10 mM Tris 1 mM EDTA (pH 8) (TE) and stored at 4_C.A number of samples were normalized for mtDNA content by dot blotting and hybridization with digoxigenin-labeled full-length mtDNA and densitometry.In cases where mtDNA quantification was not carried out, the DNAs were normalized by A 260 of total DNA." + ], + [ + "Using genetic markers, the pattern of inheritance can be tracked through families. For example, by analyzing a marker linked to the eye color gene in several generations, it is possible to determine from which grandparents a child has inherited its eye color alleles. More importantly, \ufb01nding a marker linked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.", + "It is well known, however, that not all genomic markers are independent (Frazer et al., 2007).Genetic variation is often inherited in contiguous segments of DNA, such that there tends to be correlation between the inheritance of alleles at markers close to each other on the same chromosome.This genetic correlation is called linkage disequilibrium (LD), and, as a result, the effective number of independent tests (M eff ) conducted is less than the total number of markers (M).By effective number of tests, we mean the number of independent tests that would have to be conducted to lead to a null distribution for the minimum P-values that was approximately the same as that obtained when conducting tests that are necessarily correlated due to LD.", + "Genetic mapping is a powerful strategy that exploits genomic information to dissect complex traits into Mendelian loci (quantitative trait loci or QTL) and identifies genetic * Correspondence: marioenrico.pe@sssup.it 1 Institute of Life Sciences, Scuola Superiore Sant\u2019Anna, Pisa, Italy Full list of author information is available at the end of the article determinants that may lead to crop improvement. As marker density ceases to be a limiting factor [3], our ability to discover specific genetic determinants in a single mapping study depends upon the availability of populations with high genetic diversity and recombination density [4].", + "This capacity allows samples to be placed into meaningful genetic groups that reflect evolutionary relationships (more stable, lower diversity markers), while simultaneously permitting high levels of strain resolution (high diversity markers). From a clinical perspective, markers that accurately reflect broad evolutionary relationships are valuable for comparing the genetic similarity of an isolate to isolates on a regional or global scale, whereas high-resolution markers are valuable for detailed epidemiological tracking in an outbreak. Variable-number tandem repeats (VNTRs) are genetic markers that can span a range of variability and, therefore, can capture genetic relationships on multiple scales (18\u201319).", + "Identifying the genetic loci that modulate a trait based on correlation between variation in phenotype and variation in genotype is the essence of genetic mapping. This first involves systematically genotyping a genetically diverse population using microsatellite or SNP markers. The phenotype of interest is then measured and its variability in the population assessed. A statistical test is then carried out to identify chromosomal regions that segregate with the trait and show linkage with the trait, i.e. , 3 identify genetic regions that have the same genotype among individuals with similar trait values but differ between individuals with dissimilar trait values.", + "Using genetic markers, the pattern of inheritance can be tracked through families. For example, by analyzing a marker linked to the eye color gene in several generations, it is possible to determine from which grandparents a child has inherited its eye color alleles. More importantly, \ufb01nding a marker linked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.", + "Genetic variation For decades researchers used single markers to elucidate clinal differentiation and spatial variation in allele frequencies.This approach revealed multiple markers with variation that tracked the clines, including some with the same allele at higher frequency at the same latitude in the Northern and Southern hemispheres.Examples include alcohol dehydrogenase (Adh), a-glycerol-3-phosphate dehydrogenase (Gpdh), glucose-6-phosphate dehydrogenase (G6pd), esterase-6 (Est-6), octanol dehydrogenase (Odh), and 6-phosphogluconate dehydrogenase (Pgd) [30][31][32][33] (Table 1).Perhaps the most heavily explored locus in D. melanogaster has been Adh, the first step in the ethanol detoxification pathway.The Adh-F allele encodes high catalytic activity of ADH, but this increase in activity trades off with enzyme stability at higher temperatures [34,35].Unsurprisingly, the Adh-F allele is found at a higher frequency in cooler high-latitude populations, and differentiation has occurred in parallel along clines in", + "In the case of genetic markers, this easily runs in the several hundreds to thousands. Moreover, the optimal subset of markers is heavily dependent on how these markers are combined, i.e. dependent on the optimal Boolean function . Altogether, one frequently has to rely on greedy search strategies that easily get stuck in local optima or near exhaustive searches that are computationally too expensive, especially when employed in permutation procedures required to assess statistical significance. Our solution to this problem hinges upon two observations.", + "GENE MAPPING The opportunity to merge advances in molecular genetic technology with advances in statistical techniques expanded in earnest with the development of DNA markers such as restriction fragment length polymorphisms (Lander and Botstein, 1989).Research exploded in the past decade with the continued refinement of molecular technology yielding a variety of DNA markers-e.g., short tandem repeats (STRs) or microsatellites; variable number of tandem repeats (VNTRs); single nucleotide polymorpohisms (SNPs), and gene expression microarrays or gene chips.A genetic marker is a measurable polymorphic sequence of DNA whose chromosomal location is known.Markers often have no known functional significance but are used as pointers to a particular chromosomal location.The logic of gene mapping technology is simple: Determine if there is a relationship between variability in a phenotype and variability in an anonymous DNA marker of known chromosomal location.If there is a relationship, it is taken as evidence that there is a gene that influences the trait at or near the marker.", + "Genetic drift. Genetic changes in populations caused by random phenomena rather than by selection.Genetic marker.A segment of DNA with an identifiable physical location on a chromosome whose inheritance can be followed.A marker can be a gene, or it can be some section of DNA with no known function.", + "Biological characteristics indicating initial resiliency or susceptibility of an organism include genetic profiles.As noted above, genetic markers need to have a high prevalence in the population and have a reasonably strong effect on common population health outcomes, or have an interaction effect with other health-affecting mechanisms, to be candidates for inclusion in population studies.At the moment, the only known genetic marker of clear value in a population survey is the apolipoprotein E gene (APOE), although this is likely to change in the very near future.APOE allele status is clearly related to a number of major health outcomes in older populations which are reasonably well measured in population surveys: mortality, heart disease, and cognitive functioning (Albert et al., 1995b;Corder et al., 1993;Evans et al., 1997;Ewbank, 1997;Hofman et al., 1997;Hyman et al., 1996;Luc et al., 1994;Saunders et al., 1993).Both the prevalence of alleles indicating higher risk and the size of the effect are large enough to be of importance in explaining variability in currently studied health outcomes.APOE allele status has been shown to have independent effects on health outcomes and to interact with other life circumstances such as sex and race in its effect on health outcomes (Jarvik et al., 1995;Maestre et al., 1995;Payami et al., 1992).Incorporation of information on this genetic indicator could lead to increased knowledge of the interactive mechanisms of this genetic marker and other social and behavioral variables and thus clarify some of the mechanisms leading to population differentials in cognition, heart disease, and mortality.", + "As described by Hermalin (1999), if genetic markers are modeled as part of an individual's physiological structure, they can provide controls for predisposing factors that affect more proximate mid-level markers of function as well as downstream health outcomes.This potential benefit of genetic information-i.e., its power in explicating the black box of Figure 11-1-may outweigh, or at least precede, its near-term potential for discovering genetic links to chronic disease.As discussed by Weiss (1998b), the situation with chronic disease differs from single locus disorders that are inherited following well-identified Mendelian rules.In general, we cannot expect to find relationships that are even as straightforward as the APOE links to cardiovascular and Alzheimer's disease.Variation across populations, difficulty in identifying a small enough area on the chromosome to search for disease-associated genes, and the problems inherent in identifying continuous outcomes with particular genes may limit finding the connections.", + "This capacity allows samples to be placed into meaningful genetic groups that reflect evolutionary relationships (more stable, lower diversity markers), while simultaneously permitting high levels of strain resolution (high diversity markers). From a clinical perspective, markers that accurately reflect broad evolutionary relationships are valuable for comparing the genetic similarity of an isolate to isolates on a regional or global scale, whereas high-resolution markers are valuable for detailed epidemiological tracking in an outbreak. Variable-number tandem repeats (VNTRs) are genetic markers that can span a range of variability and, therefore, can capture genetic relationships on multiple scales (18\u201319).", + "These variations provide a species the ability of adapting to the environment change (Liu and Cordes, 2004). DNA markers are among the most powerful tools for revealing genetic variations in organisms. Historically, many different types of markers have been used for aquaculture studies Functional Genomics in Aquaculture, First Edition. Edited by Marco Saroglia and Zhanjiang (John) Liu. \u2402 C 2012 John Wiley & Sons, Inc. Published 2012 by John Wiley & Sons, Inc. 41 42 Functional Genomics in Aquaculture Table 2.1 A summary of characteristics of various molecular markers used in aquaculture species.", + "For instance, mapping of a trait or a phenotype would require polymorphic DNA markers such as microsatellites (SSRs) or single nucleotide polymorphisms (SNPs); expression pro\ufb01ling would require genome annotation information; microarray design would require sequence information of genes, etc. The objective of this chapter is to provide a general review of genomic resources needed, and currently present for aquaculture species, for functional genomics studies. Polymorphic DNA Markers The key factor behind the signi\ufb01cant differences at the level of individuals, species, and higher order of taxonomic groups is genetic variation (polymorphism).", + "Functional genomics: The study of genes, their resulting proteins, and the role played by the proteins in the biochemical processes of the body.Gene: A unit of inheritance; a working subunit of DNA.Each of the 20 000 to 25 000 genes in the body contains the code for a specific product, typically a protein such as an enzyme.Gene expression: The process by which the coded information of a gene is translated into the structures present and operating in the cell (either proteins or ribonucleic acids).Gene markers: Landmarks for a target gene, either detectable traits that are inherited along with the gene or distinctive segments of DNA.Gene map: A description of the relative positions of genes on a chromosome and the distance between them.Genetic counseling: A short-term educational counseling process for individuals and families who have a genetic disease or who are at risk for such a disease.Genetic counseling provides patients with information about their condition and helps them make informed decisions.Genetic linkage maps: DNA maps that assign relative chromosomal locations to genetic landmarks-either genes for known traits or distinctive sequences of DNA (ie, genetic markers)-on the basis of how frequently they are inherited together.Genetic testing: Examining a sample of blood or other body fluid or tissue for biochemical, chromosomal, or genetic markers that indicate the presence or absence of genetic disease.Genetics: The scientific study of heredity, how particular qualities or traits are transmitted from parents to offspring.Genome: All the genetic material in the chromosomes of a particular organism.Genome-wide: Descriptor that indicates that the entire breadth of the genome has been examined in a study (eg, a linkage or association study).Genome-wide studies do not resequence the entire genome but type (an increasingly large set of) markers distributed throughout the genome.Genomics: A \"scaled-up\" version of the science of genetics that investigates the structure and function of large sections of the genome simultaneously.Genotype: The actual genes carried by an individual (as distinct from phenotype-ie, the physical, bodily characteristics into which genes are translated).Haplotype: A way of denoting the collective genotype of a number of closely linked loci on a chromosome.Heritability (h 2 ): For any trait, the proportion of the phenotypic variability resulting from genetic variance.Note that heritability does not indicate the degree to which a trait is \"genetic. \"Nor does a high h 2 mean that the trait cannot be influenced by environment.A heritability significantly \u03fe0, however, can provide a rationale for further genetic and genomic study of a trait of interest.Heterozygous: Possessing 2 different sequences (ie, genotypes) of a particular gene, 1 inherited from each parent.High-throughput genotyping: In contrast to the older labor-and time-intensive genotyping methods, high-throughput genotyping makes use of robots, computers, and other evolving technologies, thus enabling laboratories to type up to hundreds of thousands of polymorphisms in many samples in a relatively short period of time.Homozygous: Possessing 2 identical sequences of a particular gene, 1 inherited from each parent.Interaction: The differing effect of 1 independent variable on the dependent variable, depending on the particular level of another independent variable.For example, there would be an interaction between the factors sex and treatment if the effect of treatment was not the same for male and female subjects in a drug trial.Linkage analysis: A gene-hunting technique that traces patterns of heredity in large, high-risk families in an attempt to locate a disease-causing gene mutation by identifying traits that are coinherited with it.Linkage disequilibrium: Two alleles at different loci that occur together on the same chromosome more often than would be predicted by chance alone.It is a measure of cosegregation of alleles in a population.", + "Source: Kearsey and Pooni (1996). Genetic maps consist of a series of markers or identifiable features at known, or perhaps best described as estimated, locations on the genome (see Figure 9). For some discrete traits, simple Mendelian inheritance is followed and the phenotype has a one to one correspondence with the genes controlling it. These are so called morphological markers, which were then related to continuous or quantitative traits of interest. Examples are shape, colour, size or height in particular varieties of peas, as studied by Mendel. For another example, see Appendix A.2.", + "Genomic markers used in linkage mapping have evolved from restriction fragment length polymorphisms (RFLPs) to microsatellites (simple sequence repeat polymorphisms; SSRPs), to single-nucleotide polymorphisms (SNPs), with the more modern markers exhibiting higher frequencies in the genome (thus ensuring fuller coverage). Linkage mapping of a trait is in fact the demonstration of linkage between the phenotype and a genomic marker, followed by an inference of linkage between the genomic marker and the responsible DNA variant. Transitive logic ties the phenotype with the DNA variant, which is of course the point of the exercise. See Fig.", + "However, because of time constraints it is often more practicable to choose an appropriate mapping population that is already available through the current stock centers. Plant species chosen for study will depend largely on the availability of suitable plant resources. Obtain appropriate mapping population information to include information on markers/genotypes (see Note 4). A marker is an identifying factor; a gene or other DNA of known location that is used to track the inheritance and so on of other genes whose exact location is not yet known.", + "The closer two genes are together on a chromosome, the less likely it is for a recombination event to occur between the two, causing a non-random association. This is the basis for genetic linkage. The development of genetic markers allowed the theory of linkage disequilibrium (LD) to be used in mapping genes. Genetic markers are speci c genetic di\u241berences between species or cultivars, and genetic linkage of these markers to particular morphological traits can allow genetic markers to be used to represent the gene of interest (Collard et al. , 2005)." + ], + [ + "This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction The association between a complex phenotypic trait and genetic markers on the chromosomes can be detected through statistical analysis, leading to the identification of quantitative trait loci (QTL)\u2014regions of the chromosomes that appear to be associated with the phenotype. Quantitative trait loci (QTL) are expected to be associated with the genes controlling some aspects of the phenotype.", + "Nowadays many different cost-efficient genotyping solutions (including sequencing and Single Nucleotide Polymorphisms arrays) have opened the way to systematic genome-wide fine mapping of quantitative traits (Quantitative Trait Locus or QTL mapping). The process of QTL mapping (Figure 1) consists in searching for genome regions that influence the value of a given trait. For example, identifying a QTL for plant height means finding a DNA region at which the plants that carry a certain allele tend to be significantly higher or lower than those carrying another allele.", + "QTLs are regions within the genome whose genetic variation modulates quantitatively a phenotype characteristic of the particular trait under study (Lynch and Walsh, 1998). Determining the association between variations in specific disease phenotypes or a trait, with variations in genotypes of a reference population can be used to locate a QTL. One of the methods used for mapping QTLs associated with complex traits is genetic markers-trait association. Genetic markers associated with certain loci can be inherited in linkage disequilibrium. Generating populations with linked loci in disequilibrium is achieved though either crosses between inbred lines, or use of the out-bred populations.", + "Often, the first step in analysis of new trait data is single-marker regression across all chromosomes. A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).", + "One possible approach to facilitate this endeavor is to identify quantitative trait loci (QTL) that contribute to the phenotype and consequently unravel the candidate genes within these loci. Each proposed candidate locus contains multiple genes and, therefore, further analysis is required to choose plausible candidate genes. One of such methods is to use comparative genomics in order to narrow down the QTL to a region containing only a few genes. We illustrate this strategy by applying it to genetic findings regarding physical activity (PA) in mice and human.", + "Elucidation of the molecular basis of these traits has proven difficult as they are under the control of multiple genes and genetic loci. The standard approach to gene identification involves mapping by linkage analysis in experimental crosses, and this has led to the localization in the rat genome of hundreds of quantitative trait loci (QTLs) underlying trait variation (68). We refer to these loci as physiological quantitative trait loci (pQTLs).", + "Often, the first step in analysis of new trait data is single-marker regression across all chromosomes.A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992).For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).By default, it returns a list of marker loci that show greater than sugges-tive association with the trait according to standard criteria (Lander and Kruglyak, 1995), but it will also accept user-defined criteria.Local maxima in the LRS in this list identify loci that are most likely to be near QTLs.WebQTL provides this list within a few seconds.", + "QTLs can be identified through their genetic linkage to visible marker loci with genotypes that can be readily classified [94, 97]. As such, markers that are genetically linked quantitative trait will segregate more often with trait values, whereas unlinked markers will lack an association with the phenotype [94, 98]. The principal goal of a QTL analysis is to identify all QTLs linked to a trait and discern whether phenotypic differences are mainly due to a few loci with large effects, or many loci with small effects [98].", + "This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction The association between a complex phenotypic trait and genetic markers on the chromosomes can be detected through statistical analysis, leading to the identification of quantitative trait loci (QTL)\u2014regions of the chromosomes that appear to be associated with the phenotype. Quantitative trait loci (QTL) are expected to be associated with the genes controlling some aspects of the phenotype.", + "The basic principle of classic QTL is trait segregation along with the markers and necessitated the availability of two or more genetically different lines corresponding with the phenotypic trait. Markers like single nucleotide polymorphisms (SNPs) and microsatellites are used for genotypic distinctions (Vignal et al. , 2002). QTL mapping is achieved in four basic steps; the first one is the measurement of variation for a trait in the individuals. It is a prerequisite to have the traits that show phenotypic variability among the individuals (inbred strains).", + "Often, the first step in analysis of new trait data is single-marker regression across all chromosomes.A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992).For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).By default, it returns a list of marker loci that show greater than sugges-tive association with the trait according to standard criteria (Lander and Kruglyak, 1995), but it will also accept user-defined criteria.Local maxima in the LRS in this list identify loci that are most likely to be near QTLs.WebQTL provides this list within a few seconds.", + "Often, the first step in analysis of new trait data is single-marker regression across all chromosomes. A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).", + "Quantitative Trait Locus (QTL) mapping To map QTL, we used 934 AXB/BXA genetic informative markers obtained from http://www. genenetwork.org. For all the in vitro measurements and gene expression linkage analysis, a genome-wide scan was performed using R/qtl [57]. Significance of QTL logarithm-of-odds (LOD) scores was assessed using 1000 permutations of the phenotype data [114] and the corresponding p-values reported. For the cellular phenotypes, QTL significance was reported at a genome-wide threshold corresponding to p < 0.05.", + "Typically one may obtain a location known to derive from only one of the two parent strains that contains a chromosomal region that correlates with a trait of interest. Since the actual gene and gene product will frequently remain unknown, the region is referred to as quantitative trait locus (QTL), and is simply named for the trait itself (Alberts & Schughart, 2010). Growing sets of strain-dependent marker locations in established RI strains are continually updated in online repositories.", + "By definition, a quantitative trait locus is a chromosomal region that contains a gene, or genes, that regulate a portion of the genetic variation for a particular phenotype (Wehner et al. 2001). The goal of QTL mapping is to identify regions of the genome that harbour genes relevant to a specified trait. QTL map locations are commonly determined by initial screening of mice with specific genetic characteristics, such as recombinant inbred strains, the F2 of two inbred strains, or recombinant congenic strains (Flint 2003).", + "Often, the first step in analysis of new trait data is single-marker regression across all chromosomes. A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).", + "QTL linkage studies are conducted in order to map a region or regions of the genome which affect a continuous or quantitative trait. In agriculture, as soon as markers linked to QTL are found for economically important traits, these markers can be used for selecting individuals in breeding programmes. In human studies, the aim is often to identify markers indicating disease susceptibility. Current techniques for measuring markers are usually relatively slow and laborious. Newer DNA technology, such as SNP or single nucleotide polymorphisms (Kwok, 2001b; Patil et al.", + "Genomic regions linked to complex traits can be identified by genetic mapping and quantitative trait locus (QTL) analysis (Shehzad and Okuno 2014). 7 QTL mapping QTL mapping with molecular markers is the first strategy in genetic studies. In plant breeding, QTL mapping is an essential step required for marker-assisted selection (Mohan et al. 1997; Shehzad and Okuno 2014). The fundamental idea underlying QTL analysis is to associate genotype and phenotype in a population exhibiting a genetic variation (Broman and Sen 2009).", + "Four steps of QTL mapping are (1) development a W population, (2) genotyping the population using molecular markers, (3) phenotyping the population for an interested trait, and (4) QTL analysis using statistical procedures to find IE markers linked to the QTL (Bernardo 2002). PR EV Populations used for genetic mapping can be a segregating population (F2 and backcross) or a permanent population (double haploids or recombinant inbred lines). Recombinant inbred lines (RILs) are developed by selfing of individual progenies of the F2 plants until homozygosity is achieved (F7-F8).", + "This tool allows systems genetic analysis of single genes or small sets of genes using a bottom-up approach. relations define quantitative trait loci (QTLs). Because the marker is not typically the actual site of the polymorphism, interpolative methods have been developed to estimate the distance of the QTL from the marker and the strength of the association. Using multiple-regression and model-fitting methods, the true complexity of the phenotypic variation can be modeled through the consideration of multiple loci and environmental factors as predictors [13]." + ] + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/datasets/old/general2_dataset.json b/gnqa/paper1_eval/src/data/datasets/old/general2_dataset.json new file mode 100644 index 0000000..ca8918f --- /dev/null +++ b/gnqa/paper1_eval/src/data/datasets/old/general2_dataset.json @@ -0,0 +1,128 @@ +{ + "question": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?" + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome. 2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence. 3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together. 4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation. 5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms. 6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions. 7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000. 8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease. 9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence. 10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text." + ], + "contexts": [ + [ + "To overcome the lack of phenotypic information in the 1000 Genomes Project, The ClinSeq Project was developed to pilot large-scale genome sequencing for research in genomic medicine at the National Institutes of Health Clinical Research Center in Bethesda, MD. 40 The study seeks to enroll 1000 individuals who will be evaluated for personal health status and family history.The project aims to:", + "We (Hein, Schierup and Wiuf) have published a 300 page book on molecular population genetics titled \u201cGene Genealogies, Sequence Variation and Evolution\u201d Oxford University Press, and are presently developing a tutorial in association mapping that we hope to publish as a booklet in 2006 and are also involved in a very large EU collaboration (Holland, Denmark, Iceland and UK) to find susceptibility genes for breast and prostate cancer. In comparative genomics, the most fundamental investigation is to find genes in a pair of aligned genomes.", + "Key bioinformatic steps to take a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stage regions in man (see Chapter 5). Similar issues also exist in the establishment of true orthology between genes in different species, where one is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation (Fitch, 2000).", + "In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily on Bioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes 2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB) \u2402 C 106 CH 6 COMPARATIVE GENOMICS questions of relevance to human genetics. We begin, in Section 6.2 by presenting an overview of genome structure and content, providing a context for the subsequent discussions.", + "4 Assembling a View of the Human Genome Colin A. M. Semple Bioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK 4.1 Introduction The miraculous birth of the draft human genome sequence took place against the odds. It was only made possible by parallel revolutions in the technologies used to produce, store and analyse the sequence data, and by the development of new, large-scale consortia to organize and obtain funding for the work (Watson, 1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.", + "This fully indexed but semi-intelligible Bioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes 2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB) \u2402 C 4 CH 1 BIOINFORMATICS CHALLENGES FOR THE GENETICIST \u2018book of life\u2019 immediately began to serve as a valuable framework for integration of genetic and biological data. However, knowledge of the genome sequence did not immediately clarify the nature and structure of human genetic variation.", + "Methods for DNA sequencing are constantly being improved, with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000, an end that appears to be in sight (Hayden, 2014).In the very near future, whole-genome sequencing will be routinely available for clinical purposes, perhaps even beginning at birth.The major challenge ahead is the interpretation of this information.How do our genes interact with each other, and how does the environment contribute to the development of health and disease?What are the individual and societal implications of knowing our genome sequence?The answers to these and other important questions will unfold in the years ahead.Thus, we are truly in an era where precision medicine may soon become a reality.", + "Characteristics of genotyping and sequencing technologies", + "Key bioinformatic steps to take a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stage regions in man (see Chapter 5). Similar issues also exist in the establishment of true orthology between genes in different species, where one is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation (Fitch, 2000).", + "In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily on Bioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes 2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB) \u2402 C 106 CH 6 COMPARATIVE GENOMICS questions of relevance to human genetics. We begin, in Section 6.2 by presenting an overview of genome structure and content, providing a context for the subsequent discussions.", + "4 Assembling a View of the Human Genome Colin A. M. Semple Bioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK 4.1 Introduction The miraculous birth of the draft human genome sequence took place against the odds. It was only made possible by parallel revolutions in the technologies used to produce, store and analyse the sequence data, and by the development of new, large-scale consortia to organize and obtain funding for the work (Watson, 1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.", + "Introduction Since the first human genome was sequenced at an estimated cost of $150 million, several advanced high-throughput techniques \u2013 some with lower costs - have come up. At the same time, this resulted in a data deluge and a critical need to connect the heterogeneous sequencing data and associated annotations \u2013 structural and functional with the basic tenets of biology or molecular basis of development and disease.", + "Key bioinformatic steps to take a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stage regions in man (see Chapter 5). Similar issues also exist in the establishment of true orthology between genes in different species, where one is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation (Fitch, 2000).", + "In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily on Bioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes 2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB) \u2402 C 106 CH 6 COMPARATIVE GENOMICS questions of relevance to human genetics. We begin, in Section 6.2 by presenting an overview of genome structure and content, providing a context for the subsequent discussions.", + "4 Assembling a View of the Human Genome Colin A. M. Semple Bioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK 4.1 Introduction The miraculous birth of the draft human genome sequence took place against the odds. It was only made possible by parallel revolutions in the technologies used to produce, store and analyse the sequence data, and by the development of new, large-scale consortia to organize and obtain funding for the work (Watson, 1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond.", + "Ample time was allotted to answer questions and a copy of \"A Guide to Your Genome\" (National Human Genome Research Institute 2007) was provided to further assist participants' understanding and ability to communicate results with family members or others.", + "Whether within 10 or 12 (or 8) years, such inexpensive sequencing will change both research and clinical care, and progress does not need to wait even that long.The National Human Genome Research Institute (NHGRI) plans to focus a significant portion of the sequencing capacity that it supports on medical sequencing.For instance, the NHGRI and the National Cancer Institute are actively considering a Human Cancer Genome Project, 22 which would use DNA sequencing and a host of other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of cancer.Medical sequencing should also provide important insight into many other diseases.For example, sequencing all exons in X-linked mental retardation syndromes may reveal much about their etiology.Sequencing candidate genes in the extremes of the distribution of quantitative traits should also reveal much of importance about common diseases, such as coronary atherosclerosis. 23With further technological advances, other previously unimaginable research approaches will become real.", + "Key bioinformatic steps to take a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stage regions in man (see Chapter 5). Similar issues also exist in the establishment of true orthology between genes in different species, where one is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation (Fitch, 2000).", + "In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily on Bioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes 2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB) \u2402 C 106 CH 6 COMPARATIVE GENOMICS questions of relevance to human genetics. We begin, in Section 6.2 by presenting an overview of genome structure and content, providing a context for the subsequent discussions.", + "4 Assembling a View of the Human Genome Colin A. M. Semple Bioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK 4.1 Introduction The miraculous birth of the draft human genome sequence took place against the odds. It was only made possible by parallel revolutions in the technologies used to produce, store and analyse the sequence data, and by the development of new, large-scale consortia to organize and obtain funding for the work (Watson, 1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + ], + [ + "In birds, where erythrocyte telomere length (ETL) is measured, the majority of species sampled have shown no sex difference (36).Nonetheless, bird telomere dynamics are complex and, as with humans, may be affected by environment and stress.For example, a longitudinal study of black-tailed gulls (Larus crassitostris) over 2-5 years found no correlation between ETL and age or sex.Rather, ETL attrition was correlated with reduced food availability and environmental stressors (55).In a captive zebra finch (Taeniopygia guttata) population, male and female mean telomere length decreased with increasing age of the animals, but did differ between sexes (56).As these examples illustrate, the relationship between telomere length, lifespan, and sex is likely to be complex in other vertebrates.", + "Comparative studies of age-related telomere attrition in other species also reveal a variety of patterns.Barrett and Richardson (36) recently summarized the comparative data available on sex differences in telomere length.They found a strong correlation between male-biased mortality and either shorter telomeres or greater telomere attrition in males across bird and mammal taxa.However, telomere length did not differ between males and females in species where females are shorter-lived than males (36), suggesting that telomere shortening is not associated with species-specific longevity in a simple linear fashion.These studies generally suffer from relatively small sample sizes and are largely cross-sectional.Further, the use of diverse assays, different tissues (eg, leukocytes in mammals vs erythrocytes in birds), and lack of standardized benchmarks for accuracy makes comparisons between studies difficult.", + "In some organisms, there is no clear relationship between telomere length and lifespan.Age-related telomere attrition could not be detected in Daphnia pulex (57) or sea urchin species (Strongylocentrotus franciscanus and Lytechinus variegatus) (58).Studies in C. elegans examining natural variation in telomere length and experimentally manipulated telomere length detect no correlation with lifespan (59,60), and in Drosophila, which uses a telomerase-independent mechanism for telomere maintenance, there is a similar lack of correlation between longevity and telomere length (61).Similarly, data on sex differences in age-related telomere shortening are mixed.For example, in the ant species Lasius niger, the rate of telomere shortening is more rapid in short-lived males compared to longer-lived females.But, mean telomere length does not differ between the two types of females, queens and workers, despite the fact that queens live much longer than workers (up to 28 years vs 2-3 months) (62).These findings suggest that the question of how telomere shortening affects aging across species and how sex affects telomere attrition rates are complex.", + "With new methodologies to assess relative telomere length by Q-PCR, studies were designed to address the impact of telomere length on aging, aging associated pathologies, and mortality.One such study has correlated shorter leukocyte telomere lengths at age 60 with a three times higher risk of heart disease and an eightfold increase in risk of infection-related death (36), thereby associating measured relative cellular aging with disease and life expectancy.In a similar way, chronic stress was shown to correlate with short leukocyte telomere length, a phenomenon attributed to higher levels of oxidative stress at the cellular level (70).More recent studies have linked telomere length in smooth muscle cells with senescence and disease severity in patients with atherosclerosis (141,150).Leukocyte telomere length was also short in a cohort of similar patients and associated with a higher risk of developing occult cardiovascular disease (71).More data are needed to understand and validate the use of leukocyte telomere length as a biomarker for cardiovascular and other diseases.", + "Shortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19].", + "New research has indicated how social factors, such as subordination, may translate into biological effects (epel et al. 2004;Chae et al. 2014).In a now classic study, epel et al. ( 2004) examined the telomere lengths of fifty-eight healthy premenopausal women who either had a healthy child (n = 19) or were giving care to a chronically ill child (n = 39. )They measured perceived stress, years of caregiving, telomere length, and oxidative stress.They found highly statistically significant differences in telomere length between women taking care of chronically ill children and those who had healthy children.They found highly statistically significant negative correlations between telomere length and perceived stress and years of caregiving.Telomerase activity had highly statistically significant negative correlations with perceived stress and years of caregiving.Oxidative stress was highly positively correlated with perceived stress and years of caregiving.They concluded that the telomere length shortening was equivalent to 9 to 17 years of aging in the high stress group.Telomere length is considered a biomarker of aging (Finch and Kirkwood 2000).Thus, this study showed that caregiver stress had essentially aged these women 9 to 17 years compared to women who had healthy children.", + "The single, consistent predictor of the rate of telomere attrition shown in multiple adult and the few child longitudinal studies is the baseline measurement of telomere length at the start of each study.This suggests the importance of understanding predictors of telomere length prior to adulthood, as it determines in part the rate of change (Revesz et al. 2014a, b;Nordfjall et al. 2009).Moreover, longitudinal studies in adults have had found that telomere attrition rate is dependent on baseline telomere length independent of any phenotypic predictors of shortening, such as disease or demographic variables (Nordfjall et al. 2009), attesting to the importance of studies to evaluate risk factors for shortening prior to adulthood.", + "Rates of decline in childhood may be particularly relevant for later chronic disease risk as shorter telomere length has been implicated in disease progression through exposure to cellular senescence, inflammatory cytokines and adipocyte hypertrophy (Raschenberger et al. 2015;Willeit et al. 2014;Monickaraj et al. 2012;Fyhrquist et al. 2013).", + "Adult studies have also found a negative correlation with baseline telomere length, suggesting a negative feedback regulation of leukocyte telomere length (Farzaneh-Far et al. 2010;Aviv et al. 2009;Epel et al. 2008;Nordfjall et al. 2009).It is possible that while our follow-up period was shorter than Shalev et al. 2013 and adult studies, which had a minimum of 5 year intervals with the exception of Puterman et al. (2015) who followed for a one-year time period, there may be biological regulation of telomere length at 4 and 5 years of age such that shorter telomeres are more robustly maintained, whereas longer telomeres have greater rates of decline, over a short period of one year.It is unlikely that this relationship is due to assay error or regression to the mean given the consistency of our findings across studies.We have had similar findings of longer telomeres having greater rates of decline and shorter telomeres being maintained in our different studies (Farzaneh-Far et al. 2010;Epel et al. 2008;Puterman et al. 2015).", + "We found primarily maintenance and lengthening from 4 to 5 years of age in children, with minimal telomere attrition, indicating that most of the telomere loss happens in the first 4 years, plateauing by age 4. Lastly, we found close to 10 % of the variance in rate of change in children shared by mothers.While some of this shared variance is genetic, there are likely environmental factors that need to be further identified that impact rate of telomere length change.", + "Abstract Telomeres are the protective complexes at the end of chromosomes, required for genomic stability.Little is known about predictors of attrition in young children or the relationship between parental and child patterns of telomere change.Telomere length was assessed twice over one year, at 4 and at 5 years of age, in Latino preschool children (n = 77) and their mothers (n = 70) in whole blood leukocytes.Maternal and child rates of attrition during the same time period were compared in 70 mother-child pairs.More children showed lengthened telomeres over one year compared to their mothers and very few children showed attrition (2.6 %).Approximately 31 % of children and 16 % of mothers displayed lengthening over one year while 66 % of children showed maintenance in contrast with 74 % of mothers.The strongest predictor for child telomere length change was child's baseline telomere length (r = \u22120.61,p < 0.01).Maternal rate of change was associated with child rate of change (r = 0.33, p < 0.01).After controlling for child baseline telomere length, the relationship between child and maternal rate of change trended towards significance (Coeff = 0.20, 95 % CI \u22120.03 to 0.43; p = 0.08).", + "Blackburn and Epel, a health psychologist who did original research on how specific lifestyle and psychological habits can protect telomeres, published The Telomere Effect (Blackburn & Epel, 2017), in which they suggested that individuals with shorter telomeres developed diseases earlier in life (a shorter \"disease span\").What follows is the evidence from these authors, their colleagues, and other researchers describing how length of telomeres contributes to mind-body connection and healthy longevity.", + "As early as at the time of birth, each of the 92 telomeres of the human genome has its own characteristic length.Additionally, each telomere shortens by its individual attrition rate.In general, longer telomeres at birth are associated with higher age-dependent attrition rates and vice versa.Overall, telomere shortening appears more dynamic in males.", + "In conclusion, a combination of overall and chromosomespecifi c shorter telomeres and more pronounced age-dependent telomere erosion could be observed in males.There is a prospective clinical study strongly suggesting that longer telomeres decrease the risk of dying (Cawthon et al., 2003).With this in mind, the telomere length discrepancies between the sexes may indeed be a factor infl uencing the differences in their life expectancy.", + "In every chromosome a linear decline of telomere length with age was observed, being more pronounced in men independent of the examined chromosome arm.This might suggest that telomere length on single chromosome arms may be infl uenced by the same factors which determine overall telomere length.", + "S. Mayer a S. Br\u00fcderlein a S. Perner a I. Waibel a A. Holdenried a N. Ciloglu a C. Hasel a T. Mattfeldt a K.V. Nielsen b P. M\u00f6ller a a Institute of Pathology, University of Ulm, Ulm (Germany); b DakoCytomation A/S, Glostrup (Denmark) follow uniformity.In previous studies, sex-specifi c differences in telomere length and attrition rate of men and women were found (Benetos et al., 2001;Cawthon et al., 2003;Nawrot et al., 2004), suggesting gender differences in behavior of telomeres.In individual chromosome arms, telomere length was also shown not to be homogeneous (Lansdorp et al., 1996;Benn, 1997;Martens et al., 1998;Surralles et al., 1999;Hao and Tan, 2001;Londono-Vallejo et al., 2001;Graakjaer et al., 2003), some telomeres being signifi cantly shorter, others longer than the average length.", + "To date, these characteristics in telomere lengths could not be set in a biological context, as only a few groups have provided detailed information about chromosome-specifi c patterns of telomere distribution (Lansdorp et al., 1996;Graakjaer et al., 2003).Whether accumulation of short telomeres (Martens et al., 2000;Londono-Vallejo et al., 2001) or rather the shortest telomere of one specifi c chromosome arm (Hemann et al., 2001) elicits senescence, remains an open question so far.", + "In recent literature, there are hints that the average telomere length may be higher in women and that their annual shortening rate may be somewhat lower (Vaziri et al., 1993;Rufer et al., 1998;Jeanclos et al., 2000), but these reported differences failed to reach statistical signifi cance except for one study (Jeanclos et al., 2000).Here, we provide compelling evidence that this is indeed the case.", + "It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo.In individuals, short telomeres are considered to be a sign of advanced age.Cawthon and coworkers (2003) showed that telomere shortening in humans likely contributes to mortality, supporting the hypothesis that they might act as a mitotic clock (Allsopp et al., 1992).Telomere length dynamics, however, does not seem to Abstract.During aging, telomeres are gradually shortened, eventually leading to cellular senescence.By T/C-FISH (telomere/centromere-FISH), we investigated human telomere length differences on single chromosome arms of 205 individuals in different age groups and sexes.For all chromosome arms, we found a linear correlation between telomere length and donor age.Generally, males had shorter telomeres and higher attrition rates.Every chromosome arm had its individual age-specifi c telomere length and erosion pattern, resulting in an unexpected heterogeneity in chromosomespecifi c regression lines.This differential erosion pattern, however, does not seem to be accidental, since we found a correlation between average telomere length of single chromosome arms in newborns and their annual attrition rate.Apart from the above-mentioned sex-specifi c discrepancies, chromosome arm-specifi c telomere lengths were strikingly similar in men and women.This implies a mechanism that arm specifi cally regulates the telomere length independent of gender, thus leading to interchromosomal telomere variations.", + "Shortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19]." + ], + [ + "Selection could occur at multiple levels, from germ cell generation and propagation to fertilization and early embryonic growth.Chromosomal abnormalities, including aneuploidy, were found in 10-20% of spermatozoa and oocytes (20) and in the cleaved embryo, with a 21% rate of abnormalities in preimplantation embryos (21).These findings led to a model for natural selection against chromosome abnormalities (21).Selection extends to the end of gestation: Only approximately 30% of all conceptions result in a live birth, with more than half of aborted fetuses containing chromosomal abnormalities (22), a number likely to be an underestimate because of technological limitations in measuring all possible mutations.But even in the very small fraction of germ cell duos that survive this withering genome attack and result in a live birth, a number of severe de novo mutations will still be found (23).The data on gross chromosomal alterations suggest that overall, mutation frequency early in life is very high.The functional consequence, however, is limited because of selection.Somewhat surprisingly, this picture points toward an initial decline in genomic alterations, allowing the adult individual to acquire a somatic genome optimally equipped to provide function.", + "The phenotype of the F1 hybrids is compared to those of the parental inbred strains to reveal dominance or semi-dominance relationships between the alleles that a\u00a1ect the phenotype. Phenotypic di\u00a1erences between reciprocal F1 hybrids indicate that one or more of the following factors may a\u00a1ect the trait: (1) sex linkage (X- or Ylinked traits), (2) genomic imprinting of QTLs that a\u00a1ect the phenotype, (3) prenatal maternal e\u00a1ects (e\u00a1ects of intrauterine environment), and/or (4) postnatal maternal or paternal e\u00a1ects (e\u00a1ects of maternal and/or paternal parenting behaviour on o\u00a1spring).", + "Sex brings harmful alleles together into the same genetic background, allowing selection to more efficiently purge them from the population and potentially producing some offspring that are fitter than either parent. However, the benefit of recombining deleterious mutations may depend on the nature of the epistatic interactions between them. The mutational deterministic hypothesis (Kondrashov 1988) depends partly on this epistasis.", + "In most plants and animals, sex is a necessary component of reproduction, and the question for evolutionary biologists is why reproductive mechanisms have evolved that way. In one of the experiments described next, evolutionary geneticists have nevertheless devised a way to compare evolution with and without recombination in the obligately sexual fruit fly.", + "This disparity in investment is the basis for the twofold cost: asexual females hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. But sex usually also involves the basic process of physical recombination: the breakage and reunion of two different DNA or RNA molecules. Of these two processes, recombination is clearly the more widespread feature of sexual reproduction. A variety of reproductive systems, such as selfing and automixis, involve recombination but not outcrossing. In contrast, relatively few reproductive systems have outcrossing without recombination.", + "Crossing over-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes cross over one another.Crossing over results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.This process is also known as meiotic recombination.", + "The reason for the rarity of these mutations is natural selection: If the mutations result in disorders that decrease health and reproductive fitness, they will eventually be eliminated from a population.In exceptional cases, mutations may cause both beneficial and detrimental consequences, resulting in opposing forces of positive selection and negative selection that may cause the mutations to be preserved at nonrare frequencies in a population.For example, the HbS mutation in the HBB gene (which produces the \u03b2 subunit of hemoglobin) causes sickle cell disease when present in both alleles, a detrimental consequence, but protects against malaria when present in 1 allele, a beneficial consequence, ensuring that the mutation persists in populations in areas of the world where malaria is endemic.Genes are passed from parents to offspring via the process of meiosis by which gametes, the egg cells in the mother and the sperm cells in the father, are generated.Ordinarily, each cell has 23 pairs of chromosomes; the gametes have 23 unpaired chromosomes.In meiosis, the 23 pairs are split so that each gamete receives 1 chromosome from each pair (Figures 8 and 9).Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full complement of 23 chromosome pairs restored.If all goes well, the zygote gives rise to a live offspring.", + "Recombination (meiotic recombination)-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes recombine with one another.Recombination results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.Also known as crossing over.", + "In the generation of gametes, crossing over regularly occurs, and genetic information is swapped between members of a chromosome pair.That doesn't matter within inbred animals, because the swapped parts are identical.In an F 1 animal, however, the chromosomes of a particular pair are genetically different, one each having come from each parent.Each gamete produced will be unique, as will be each F 2 zygote formed by uniting of the gametes from two F 1 parents.An F 2 group thus provides for expression of some genetic variability.This variability is limited to the allelic differences existing between the parent strains of the F 1 s, so that another F 2 , derived from different inbred strains, will express different genetic differences.", + "Sex brings harmful alleles together into the same genetic background, allowing selection to more efficiently purge them from the population and potentially producing some offspring that are fitter than either parent. However, the benefit of recombining deleterious mutations may depend on the nature of the epistatic interactions between them. The mutational deterministic hypothesis (Kondrashov 1988) depends partly on this epistasis.", + "In most plants and animals, sex is a necessary component of reproduction, and the question for evolutionary biologists is why reproductive mechanisms have evolved that way. In one of the experiments described next, evolutionary geneticists have nevertheless devised a way to compare evolution with and without recombination in the obligately sexual fruit fly.", + "This disparity in investment is the basis for the twofold cost: asexual females hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. But sex usually also involves the basic process of physical recombination: the breakage and reunion of two different DNA or RNA molecules. Of these two processes, recombination is clearly the more widespread feature of sexual reproduction. A variety of reproductive systems, such as selfing and automixis, involve recombination but not outcrossing. In contrast, relatively few reproductive systems have outcrossing without recombination.", + "Aberrant recombination patterns on chromosomes that have missegregated have also been identified as an important factor, in both male and female gametes (Table I).This is because recombination together with cohesion of sister chromatids establish the unique 'bivalent' chromosome structure where homologous partner chromosomes are tethered together, a configuration that is critical for their accurate segregation in meiosis I (Fig. 2A).The remarkable feature is that recombination occurs in foetal oocytes whereas chromosome segregation takes place decades later (Fig. 2A).Since mammalian oocytes are arrested at the G2/M transition (or dictyate stage), this raises the intriguing question of how the bivalent is maintained until the meiotic divisions.", + "Traditionally, it has been agreed that the \ufb01nal sex of an individual (phenotypic sex) depends on two sequential processes: the sex determination system of the species and the gonad differentiation process (Valenzuela, 2008). However, recently, these two seemingly distinct processes are viewed as part of a general process leading to gonad formation and sex ratios (Sarre et al. , 2004; Quinn et al. , 2011; Uller and Helantera\u0308, 2011).", + "However, we expect that only at this level, the most signi\ufb01cant contributions brought by integrating epigenetics will be made. Concluding Remarks and Future Prospects Fish sex ratios are the result of a complex interaction between genetic, biochemical, and environmental interactions. The ultimate result of these interactions at the individual level is gender: male or female. However, at the population level, the combination of sex determination and differentiation sets the sex ratio. In turn, sex ratios de\ufb01ne the reproductive capacity of populations and, if sex growth dimorphism exists, also the growth characteristics, something very important in an aquaculture context.", + "Obehav is, in turn, influenced by offspring genes and environment (Ogene and Oenvir respectively). Hence, indirect genetic effects (blue arrows) and direct genetic effects (red arrow) are important influencers of behaviour. B) Parentoffspring conflict theory predicts that parental resource investment and offspring solicitation behaviours are influenced by the fitness benefit to a focal individual (O), cost to a social partner such as a sibling (S1 and S2) or parent (P), and by their coefficient of relatedness (black arrows). 42 Figure 2: Genomic imprinting can result in divergent phenotypes from the same genotype. A) A paternally imprinted gene, i.e. maternally expressed.", + "Because of the small contribution, through the sperm, of the paternal transcriptome to the fertilized zygote, and because of the stronger maternal contribution to child rearing in most model organisms, parental effects are typically thought of as synonymous with maternal effects, although true paternal effects are known to exist (Rando, 2012). Maternal effects have been shown to be important during embryonic development, leading to differences in the birth weight of mice depending on the genotype of the mother (Cowley et al. , 1989; Wolf et al. , 2011).", + "Therefore, the resulting phenotypic patterns lag a generation behind the genetic transmission of the causal variants. The most well-studied parental genetic effects are caused by deposition of maternal transcripts into the egg prior to fertilization, resulting in differences in early embryonic development depending on the genotype of the mother. Certain genes have also been shown to respond to maternal influence after birth through genetically defined maternal behaviors (Weaver et al. , 2004).", + "The phenotype of the F1 hybrids is compared to those of the parental inbred strains to reveal dominance or semi-dominance relationships between the alleles that a\u00a1ect the phenotype. Phenotypic di\u00a1erences between reciprocal F1 hybrids indicate that one or more of the following factors may a\u00a1ect the trait: (1) sex linkage (X- or Ylinked traits), (2) genomic imprinting of QTLs that a\u00a1ect the phenotype, (3) prenatal maternal e\u00a1ects (e\u00a1ects of intrauterine environment), and/or (4) postnatal maternal or paternal e\u00a1ects (e\u00a1ects of maternal and/or paternal parenting behaviour on o\u00a1spring).", + "It was believed by many that for each trait variant we should expect to find a corresponding genetic change, or \u201egene for\u201f that trait. Through historical happenstance the relationship between genes and traits was set up and treated as if it were one-to-one. But the production of a trait involves not only genes, but also their interactions with each other and the environment, and chance." + ], + [ + "distinguishing prenatal from postnatal maternal effects, see below). Maternal effects can account for a large proportion of phenotypic variance, especially during early life, and for some traits explain more variation than direct genetic effects [33, 97, 99, 100, 102\u2013115]. However, maternal and offspring genotype are correlated (i.e. half their genes are shared), and in inbred lines they are fully confounded, thus separating the effects of their respective genotypes is difficult. To remove this confounding effect cross-fostering has been used, both in the laboratory and in the field [119, 131].", + "Using genetic markers, the pattern of inheritance can be tracked through families. For example, by analyzing a marker linked to the eye color gene in several generations, it is possible to determine from which grandparents a child has inherited its eye color alleles. More importantly, \ufb01nding a marker linked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.", + "Although autosomal SNPs are commonly used as genetic markers to infer ancestry or race/ethnicity membership, haploid such as mitochondria, Y-DNA, and X-lined markers are also important to provide separate stories of ancestry of individuals from paternal and maternal sides [42,43].Therefore, genetic structure created due to autosomal markers could be different from those of lineage markers (often influenced by political, social, and migration history of individuals/populations).mitochondrial DNA or mtDNA haploid is the maternally inherited mitochondrial genome (mtDNA) [44].All children inherit mtDNA from their mother, with no admixture from the father.Like Y-line DNA, mtDNA is passed intact from one generation to the next but through maternal line.", + "a) Autosomal DNA (testing both sexes) markers: autosomal DNA tests utilize DNA from the 22 pairs of autosomal chromosomes.Autosomal DNA is inherited from both parents.Autosomal testing provides percentages of ethnicity using autosomal DNA SNP test (i.e., ancestry informative markers), and it is the most commonly used test to infer ancestry across diploid genome.b) Y-DNA or Y-SNPs (paternal line testing) markers: a haploid Y-DNA is the paternally inherited non-recombining portion of the Y chromosome, and it tests only for males.The Y-DNA testing tests the Y chromosome which is passed intact from father to son with no DNA from the mother.Y-DNA testing can then be used to trace direct paternal line.Y-DNA remains the same in each generation, allowing us to compare surname from different regions to see if we are from the same family.Y-line testing does not indicate anything about the contributions of the other ancestors in a family tree.In other words, you could be 3/4th Native American, with only the direct paternal line being European, and this test would tell you nothing at all about those other three Native lines.When testing the Y-chromosome, there are two types of tests, short tandem repeat (STR) and SNP markers.STR tests are best for recent ancestry while SNP tests tell about more ancient ancestry.c) Mitochondrial DNA (maternal line testing) markers:", + "Additional information about past breeding practices can be gleaned by quantifying the number of reproductive males and females in a population.This can be achieved by comparing levels of genetic diversity between sex chromosomes, autosomes and mtDNA 99 .In cattle, for example, gene flow from aurochs is evident in the autosomes but is absent in mtDNA 41 .This has been interpreted as a management strategy that may have involved allowing insemination of domesticated females by wild bulls 41,100 .In horses, a comparison of the levels of diversity of the Y chromosome and the autosomal chromosomes demonstrated that some cultures allowed fewer males to breed and instead selected specific stallion bloodlines 55 .This male-oriented breeding strategy was not practised by the Romans and only became increasingly prominent in the past 1,000 years as a result of the growing influence of Oriental stallions (Arabian, Persian and Turkmen) 101 .", + "Dr Ring: What makes the maternal gene so peculiar compared to the paternal?Dr Cookson: If you look in the epidemiologic sense, many studies show that there is increased risk of allergic disease if the mother is affected.However, very few studies have actually set out to test that formally and most of them might suffer from some sort of selection bias because the mother is more likely to be aware of her symptoms and feel guilty, and so on.It is very difficult to explain.Is it genomic imprinting, where the gene is only active when transmitted through the mother?I do not think all of these genes would be imprinted, though it is possible.It also seems that there are effects of the maternal phenotype.The maternal phenotype, if the mother is affected or unaffected, determines the strength of the maternal effect.Again, if a gene was imprinted, you would not expect maternal phenotype to be important.So, I think that this has something to do with maternal/fetal interaction, either through the placenta or shortly after birth.There is the issue of immune conflict between mother and child.At the same time, the mother is trying to prime the infant's immune system.", + "Genetic and Genomic Discovery Using Family Studies Ingrid B. Borecki, PhD; Michael A. Province, PhD G enetic studies traditionally have been performed on sets of related individuals, that is, families.Mendel's early studies in sweet peas (Pisum sativum) on the inheritance patterns of discrete traits from parents with specific mating types to offspring has shed light on the basic mechanisms of inheritance, including the fundamental laws of segregation of discrete factors (genes) from parents to offspring and the cosegregation of genes that are closely located on a chromosome (linkage).The distribution of traits within families exhibited mathematical segregation ratios in offspring from known mating types.These expected segregation ratios have been used as an important discovery tool in the study of human diseases in pedigrees, providing evidence for a multitude of single-gene disorders.Furthermore, in some cases, trait cosegregation with genetic markers with known positions provides mapping information that enables localization and, ultimately, identification of the relevant causative gene.", + "In fact, this idea has been pursued before in the context of signatures of reproductive isolation and shown to reveal patterns consistent with epistatic gene interactions that arise in the shape of Dobzhansky-Muller incompatibilities [10,11]. In contrast to the mouse data, the available human genotypes were derived from outbred, ethnically distinct populations. In this case pairs of functionally interacting genes can be detected following a slightly different approach.", + "Family Structure The first re-identification method (FAMILY) employs genealogical data accompanying genomic data.Genealogies, rich in depth and structure, permit the construction of complex familial relationships.Consider a simple family structure of two parents and one child.Since the parental genders are guaranteed, there exist 2 variants of this structure, since the child's gender is either male or female.When disease status is taken into account, it is represented as a Boolean variable; either an individual afflicted or not afflicted.In this aspect, all three family members can be represented as three attributes {Father, Mother, Child}, and there exist (father's disease status)*(mother's disease status)*(child's disease status)*(child's gender) = 2*2*2*2 = 16 possible family-disease combinations.In reality, pedigrees are much more robust than a simple nuclear family.For example, a three-generation family of two children per family permits on the order of 10 5 distinct variants of the family-disease structure and 10 6 individuals that could be uniquely characterized.The number of combinationsk is larger when supplementary information, such as living status or medical/genetic features, is considered. 16e ability to determine unique family structures is only one part of the re-identification process.These structures must be linked to identifiable information, which, in many instances, is publicly available in the form of various genealogical databases.These databases are accessible both offline and via the World Wide Web.For example, genealogical records are available in many public databases, including ,Ancestry.com>,,Infospace.com>,,RootsWeb.com>,,GeneaNet.com>,,FamilySearch.org>, and ,Genealogy.com>. {From such data, it is not difficult to construct family structures and, with such information in hand, an adversary can link disease-labeled family structures to named individuals.", + "Fig. 3. Illustrations of the three CEU pedigrees (black) showing how genetic information from distant patrilineal relatives (arrow; red, patrilineal lines) can identify individuals.Filled squares represent sequenced individuals.To respect the privacy of these families, only abbreviated versions are presented.The sex of the CEU grandchildren was randomized.The numbers of grandchildren are not given.", + "When I was in high school, I remember often trying to match my friends to their parents at various school functions and being surprised at how easy this was.As human geneticists, in spite of the enormous advances being made in our field, we still cannot answer many of the everyday questions that we are asked, such as: \"Why does he look just like his mother? \"Max Perutz [1], in a recent editorial comment in the New Scientist entitled \"The Molecular Biology of the Future,\" suggested some questions, for, as he put it, \"an examination in some future century. \"Here are two of them: (1) \"The time has come\" the Walrus said, \"To talk of many things ...And why the sea is boiling hot And whether pigs have wings. \"Calculate the amount of genetic information this would require in megacricks.", + "Using genetic markers, the pattern of inheritance can be tracked through families. For example, by analyzing a marker linked to the eye color gene in several generations, it is possible to determine from which grandparents a child has inherited its eye color alleles. More importantly, \ufb01nding a marker linked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.", + "In contrast, genomic imprinting is due to epigenetic changes within the individual causing differential gene expression characterized by either complete or partial silencing of one parental allele (Barlow, 2011; Abramowitz and Bartolomei, 2012; Ashbrook and Hager, 2013). As both mothers and fathers had contact with the pups in our study, our observed PGEs could come from either parent. Among quantitative USV traits only peak amplitude of call displayed a possible parent-of-origin effect. For call number, call duration, mean peak frequency, and all morphological traits, there were no significant parent-of-origin effect in reciprocal F1 females. In contrast, Thornton et al.", + "Another way of avoiding stratification is to use family-based samples.This approach has several theoretical advantages: as well as being immune to stratification 114 , these samples can be used to determine whether an allele has different effects on disease when it is inherited maternally or paternally 115 , and DISCORDANT SIB designs [116][117][118] can control for the effects of shared environment.Furthermore, more complex family-based designs are possible 119 that might allow combined association and linkage analysis 120 , and family-based association tests have also been developed for quantitative traits [94][95][96][97][98] .However, pure sibship-based association studies are underpowered relative to case-control studies 107,116,117 , and the requirement for living parents might introduce an age-of-onset bias towards younger patients for diseases that usually arise late in life.Furthermore, family-based samples are often much more difficult to collect, particularly if larger pedigrees are sought.Finally, the most commonly used family-based design, the TRANSMISSION DISEQUILIBIRIUM TEST (TDT; see REF. 114) is susceptible to technical artefacts (see below).", + "Because mtDNA is not subjected (as far as we know) to sexual recombination and crossover at the time of nuclear meiosis, nature must call on other means to ensure that inevitable germ plasm mtDNA mutations (Medvedev, 1981) are not transmitted.These mutations among primary oocytes, on the face of it, can be expected to increase with time, that is with maternal age.Empirical data on this question are incomplete and conflicting, being mostly confined so far to searches for deletions rather than point mutations (Chen et al., 1995;Keefe et al., 1995).It is inevitable, however, that there will be such mutations and that there must therefore be a reliable physiological mechanism (a) for giving an opportunity for back-mutations to occur, (b) for selecting in favor of those back-mutations (thus preserving the genome) and in favor of rare advantageous mutations, and (c) for preventing the spread of persistent harmful mutations through the population -mutations that are too slight (or too late in origin) to have escaped intraovarian culling.The sheer conservation of the mitochondrial genome over 0.5 billion years or more, despite a mutation rate estimated at 10 -20 times that of nuclear DNA, is ample reason to conclude that such a physiological purification process must exist.", + "To scrutinize the polygenic networks underlying complex diseases, however, mouse resources that are optimized to study the actions of isolated genetic loci on a fixed background will be insufficient on their own. For example, predisposition to the metabolic syndrome is inherited in a non-Mendelian fashion stressing genetic heterogeneity and multigenetic pathogenesis (Nandi et al. , 2004). With the reawakening as to the extraordinary genetic resources and phenotypic diversity archived in extant inbred strains, however, a foundation is in place for tracking down these complex traits and quantitative trait loci (QTL).", + "Otherwise, tens of thousands or markers will appear significant in the genome-wise association studies using up to one million genetic markers. Approaches to control for stratification include using of self report of ancestry or genetically derived principle components in the analysis. For studies using inbred mouse lines, a cladogram which is a hierarchical grouping based on phylogenetic analysis of strain relatedness can be created to subdivide inbred strains into more genetically homogenous subgroups.", + "Although bilateral descent is the norm in Western societies, it is not universal and there is variation with cultural practices around lineage.In certain societies, individuals place greater importance on (and have greater knowledge about) one side of the family than another (unilineal descent).Thus, individuals in patrilineal groups trace relationships through males only so that your father's brother's children are members of your family, but not your father's sisters (Kottak, 2007).They are members of their husband's group or family.Efforts to create a family pedigree may be hampered if the participant is not familiar with her mother's relatives, but her mother's brother's children (her cousins) may be able to supplement her overall family history.Knowledge about the cultural system of unilineal descent avoids assuming the universality of bilateral descent.Cultural beliefs such as these also have implications in the conduct of genetic research in terms of confidentiality and autonomy (Benkendorf et al., 1997;Wertz, 1997).One cannot assume that the named proband is in a position to speak for the extended family in agreeing to participate in any genetic research (DudokdeWit et al., 1997).", + "In particular in polygynous species, a female\u2019s offspring may have different fathers and are thus more closely related through the maternal than the paternal line. Therefore, any fitness cost to mothers, such as increased provisioning and care, affect maternally derived genes more strongly than paternally derived genes, leading to the silencing of the maternal copy (i.e. paternal expression) of genes that increase resource transfer. 5. Coadaptation between offspring and maternal traits The genetics of the co-evolution of parental and offspring traits has been investigated using quantitative genetics models and in several empirical studies (Agrawal et al.", + "In this scenario, genes expressed in parents will be selected for their effects on parental behaviour while genes expressed in offspring will be selected for their effects on influencing parental behaviour. At the genetic level the predicted conflict between paternal and maternal genomes is thought to have led to the evolution of genomic imprinting (monoallelic gene expression). Genomic imprinting effects are good examples of offspring genetic effects on maternal care because of the impact on the quality of maternal care and level of resource provisioning (e.g. Li et al. , 1999)." + ], + [ + "Genetic mapping in mouse strains enhances the power of detecting modifier genes and identifying complex genetic interactions. Genomewide quantitative trait locus (QTL) analysis, as described in more detail below, represents a promising approach to detect genetic variants that are associated with specific phenotypes and interact with each other. 16 ACCEPTED MANUSCRIPT In experimental crosses of two (inbred) strains the first generation (F1) of offsprings is genetically heterozygous but equal. Then in the next generation (F2) the PT strain-specific genetic information is distributed across the genomes of their progeny and RI each offspring is genetically unique.", + "Second, and perhaps more important, is the difference in the size and types of the genetic reference populations. In our previous study, we mapped the QTL with 36 F2 mice that were genotyped at 82 markers. In the current study, by comparison, we were able to map QTLs after examining 342 mice from 55 strains that were genotyped at approximately 4000 markers.", + "This contrast can be exploited to identify subregions that underlie the trans-QTLs [67]. SNPs were counted for all four pairs of parental haplotypes\u2014B vs D, B vs H, B vs C, and L vs S\u2014and SNP profiles for the four crosses were compared (figure 6). Qrr1 is a highly polymorphic PLoS Genetics | www.plosgenetics.org 8 November 2008 | Volume 4 | Issue 11 | e1000260 QTL Hotspot on Mouse Distal Chromosome 1 Figure 5. QTL for aminoacyl-tRNA synthetases in distal Qrr1.", + "The traditional approach to QTL mapping is to use two strains that differ maximally in the phenotype as parental strains for genetic crosses, with the following caveats. QTL analysis based on a single cross will most likely reflect only a small portion of the net genetic variation, and QTL detection will be limited to regions where the two progenitor strains have functional polymorphisms. Data from multiple crosses, or from an HS, will overcome this limitation and can also be used to reduce QTL intervals [5,30].", + "These candidate genes are then sequenced in the two parental inbred strains looking for sequence di\u00a1erences in coding or regulatory regions. After \u00a2ne mapping the QTL interval and shortening the list of plausible candidate polymorphisms, the major challenge remains \u2401 proving de\u00a2nitively which nucleotide polymorphism underlies the QTL. The most direct proof would be replacing one strain\u2019s allele with another strain\u2019s allele (creating a FIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mouse generations are depicted.", + "Furthermore, splicing QTLs (sQTLs) rather than eQTLs could comprise the molecular mechanism linking DNA variants with YFP53; thus, sQTL analysis could uncover genes that would not normally be detected at the level of differential gene expression (DGE),53 and thus, a differentially 181 182 Molecular-Genetic and Statistical Techniques for Behavioral and Neural Research Figure 8.5 Schematic for immediate, rapid \ufb01ne mapping in select F2 recombinants of the RCC-F2 cross. Top panel: Genome-wide signi\ufb01cant QTL (green trace; red dashed line \u00bc signi\ufb01cance threshold; blue vertical lines \u00bc Bayes credible interval).", + "Interval-specific haplotype analysis Approximately 97% of the genetic variation between inbred mouse strains is ancestral [22], so regions of identity by descent (IBD) between two strains used to detect a QTL are highly unlikely to contain the causal genetic polymorphism underlying the QTL [28]. For example, a cross between C57BL/6J and A/J mice detected www.sciencedirect.com a blood pressure QTL on Chr 1 [7].", + "Interval-specific haplotype analysis Approximately 97% of the genetic variation between inbred mouse strains is ancestral [22], so regions of identity by descent (IBD) between two strains used to detect a QTL are highly unlikely to contain the causal genetic polymorphism underlying the QTL [28]. For example, a cross between C57BL/6J and A/J mice detected www.sciencedirect.com a blood pressure QTL on Chr 1 [7].", + "At present, the BXD panel is composed of 80 different strains that all have been fully genotyped.26 Variation in any quantifiable trait can be associated with the segregation of parental alleles, and linkage genetics can map this variation to quantitative trait loci (QTLs), thereby identifying the genomic region(s) affecting that trait. An overview of the QTL mapping approach is depicted in Figure 2. Classical QTL analysis has permitted the identification of loci that are associated with variation in HSC traits.", + "In general, linking genetic variation with trait variation identifies QTL and a significant linkage of phenotype and genotype suggest that the DNA status helps to determine trait expression. As stated above, mouse QTL studies provide distinct advantages over human studies in the examination of genetic causes of a quantitative trait (e.g. alcoholism), even in the absence of specific hypotheses regarding its aetiology or candidate genes.", + "The progenitor mouse strains should have sufficient variation for the traits of interest and they should be genetically diverse enough to enable genetic mapping (BENNETT et al. 2006; FLINT 2003; GRISEL 2000). The sample size required for the identification of QTL depends largely on the effect size that a QTL contributes to phenotypes on interest. Inference about QTL can be made if one or more genetic markers are over- or underrepresented in the analysed individuals. Genotyping is often done by means of microsatellite markers, which contains mono, di-, tri-, or tetranucleotide tandem repeats flanked by specific sequences (Figure 4a).", + "This comparison gives information about the reliability of the observed genotype information: The more the marker locations differ between the two maps (which signifies variation in marker positions), the higher the possibility of genotyping errors. QTL mapping was done in several stages to identify loci acting individually and QTL that interacted, either additively or epistatically. To determine individually-acting QTL, a singleQTL genome scan was conducted with the function scanone.", + "Importantly, whereas these studies required substantial labor, time, and resources, X-QTL is a quick and easy approach to achieve a comparable level of genetic dissection. The levels of complexity observed here (e.g. 14 loci explaining 70% of the genetic variance for 4-NQO resistance) are still dramatically lower than those seen in for some human traits in GWAS (e.g. 40 loci explaining 5% of the variance for height 2,5). One obvious explanation is the difference in experimental designs (line crosses vs. population association studies), but differences in genetic architectures among species and traits may also contribute.", + "The method uses two pieces of information: mapping data from crosses that involve more than two inbred strains and sequence variants in the progenitor strains within the interval containing a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that any sequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals except at a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.", + "The method uses two pieces of information: mapping data from crosses that involve more than two inbred strains and sequence variants in the progenitor strains within the interval containing a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that any sequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals except at a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.", + "Genotyping all the individual progeny for markers that show allelic variation between the parental strains (either single nucleotide polymorphisms or simple sequence repeats) will allow the detection of associations between trait values and marker genotype, and in this way demonstrate to which set of markers a QTL is linked. To reduce the genotyping effort, selective genotyping of the individuals at the extremes of the phenotypic spectrum can be performed (20,23). Although these three approaches are in general considered to be the best to detect and map QTL, they have several disadvantages for quantitative traits involving HSC.", + "So, how do you go about planning and performing a QTL study, and how do you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbred strains that have a markedly different trait. One can now look up many different traits of inbred mice online at the Mouse Phenome Database (http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). However, the trait you may want to study may not be present in wild type mice, so you may want to cross a mutant (or genetically engineered) strain onto several inbred strains.", + "QTL Theory and Planning The theory behind the most basic form of QTL mapping is based upon intercrossing two inbred strains. The mouse genome consists of 19 pairs of autosomes (non sex-determining chromosome) and the X and Y chromosomes. In the example shown in Fig. 18.1, we are intercrossing stain A (shown with a black chromosome pair) with strain B (shown with a white chromosome pair). The initial F1 (filial generation 1) mice are true hybrids, with each individual From: Molecular Biomethods Handbook, 2nd Edition.", + "These candidate genes are then sequenced in the two parental inbred strains looking for sequence di\u00a1erences in coding or regulatory regions. After \u00a2ne mapping the QTL interval and shortening the list of plausible candidate polymorphisms, the major challenge remains \u2401 proving de\u00a2nitively which nucleotide polymorphism underlies the QTL. The most direct proof would be replacing one strain\u2019s allele with another strain\u2019s allele (creating a FIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mouse generations are depicted.", + "QTL mapping studies then seek to detect the polymorphisms underlying the complex traits of interest by scanning for alleles that co-vary with the traits. Similar experiments also can be conducted with special derivatives of inbred strains known as recombinant inbred (RI) mice. These animals are derived by cross-breeding two or more distinct parental strains (which often diverge widely for the trait of interest), followed by inbreeding of the offspring for several generations (Bailey 1971). Given the correct breeding strategy, this method 1 This is an issue faced by GWASs researchers when classifying samples as cases or controls." + ] + ] +}
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b/gnqa/paper1_eval/src/data/queries/gpt4o-queries-partial.json @@ -0,0 +1,45 @@ +[ + { + "level": "domainexpert", + "domain": "gn", + "query": [ + "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?", + "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?", + "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?", + "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?", + "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?", + "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?", + "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?", + "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?", + "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?", + "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + ] + }, + { + "level": "citizenscientist", + "domain": "gn", + "query": [ + "What is GeneNetwork.org, and how does it help scientists understand genetics?", + "How do researchers use GeneNetwork.org to study diseases?", + "What can GeneNetwork.org tell us about how genes interact with each other?", + "How does GeneNetwork.org help in finding the genetic causes of common diseases?", + "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?", + "How does GeneNetwork.org make use of data from different populations around the world?", + "What kinds of genetic data are available on GeneNetwork.org?", + "How do scientists use GeneNetwork.org to study differences in gene expression?", + "Can GeneNetwork.org be used to learn about genetic influences on behavior?", + "What role does GeneNetwork.org play in personalized medicine?", + "How does the information on GeneNetwork.org help in developing new treatments for diseases?", + "What is a gene network, and why is it important for understanding genetics?", + "How do researchers identify which genes are important for certain traits using GeneNetwork.org?", + "How can GeneNetwork.org help in understanding complex traits like height or intelligence?", + "Are there any known genetic mutations that cause premature aging?", + "What are the practical applications of the research done through GeneNetwork.org?", + "How can I access and use the data available on GeneNetwork.org?", + "What are some recent discoveries made using GeneNetwork.org?", + "How do scientists ensure the accuracy of the data on GeneNetwork.org?", + "What’s the difference between looking at one gene and studying a whole gene network?", + "How can GeneNetwork.org contribute to advancements in genetic engineering?" + ] + } +] diff --git a/gnqa/paper1_eval/src/data/queries/gpt4o-queries.json b/gnqa/paper1_eval/src/data/queries/gpt4o-queries.json new file mode 100644 index 0000000..74c18b0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/queries/gpt4o-queries.json @@ -0,0 +1,159 @@ +[ + { + "level": "domainexpert", + "domain": "diabetes", + "query": [ + "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?", + "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?", + "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?", + "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?", + "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?", + "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?", + "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?", + "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alström Syndrome?", + "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?", + "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?", + "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?", + "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?", + "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?", + "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?", + "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?", + "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?", + "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?", + "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?,", + "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?", + "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + ] + }, + { + "level": "citizenscientist", + "domain": "diabetes", + "query": [ + "How do genetic mutations in the insulin gene affect glucose metabolism?", + "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?", + "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?", + "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?", + "How do genetic differences contribute to variations in diabetes prevalence among different populations?", + "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?", + "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?", + "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?", + "How do microRNAs regulate gene expression related to diabetes?", + "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?", + "What genes are most commonly associated with an increased risk of developing diabetes?", + "How can genetic testing help predict a person's risk for diabetes?", + "What role do family genetics play in the likelihood of getting diabetes?", + "Can lifestyle changes affect genetic risk factors for diabetes?", + "What recent breakthroughs have been made in understanding the genetic causes of diabetes?", + "How do genes influence how our bodies respond to sugar and insulin?", + "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?", + "How can new gene therapies potentially cure or treat diabetes?", + "What is the difference between monogenic and polygenic diabetes?", + "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" + ] + }, + { + "level":"citizenscientist", + "domain": "aging", + "query": [ + "What are the main genetic factors that influence aging?", + "How do genes affect the aging process in humans?", + "What lifestyle choices can help slow down genetic aging?", + "How do scientists study the genetics of aging in animals?", + "Are there specific genes that have been linked to longer lifespans?", + "How do telomeres affect the aging process?", + "What role does DNA repair play in aging?", + "Can genetic research lead to treatments that slow down aging?", + "How does mitochondrial DNA influence aging?", + "Are there any known genetic mutations that cause premature aging?", + "What recent discoveries have been made about the genetics of aging?", + "How do epigenetic changes affect aging?", + "What is the role of the gene FOXO3 in longevity?", + "How does the environment interact with genes to influence aging?", + "What are senescent cells and how do they contribute to aging?", + "Are there any known lifestyle interventions that can positively impact genes related to aging?", + "What is the 'epigenetic clock,' and how is it used in aging research?", + "How do researchers use model organisms like yeast or worms to study human aging?", + "Are there any promising anti-aging therapies being developed based on genetic research?", + "How do caloric restriction and diet impact the genetics of aging?" + ] + }, + { + "level":"domainexpert", + "domain":"aging", + "query": [ + "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?", + "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?", + "How do age-related changes in chromatin architecture contribute to the decline in cellular function?", + "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?", + "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?", + "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?", + "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?", + "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?", + "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?", + "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?", + "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?", + "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?", + "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?", + "How do changes in the gut microbiome composition correlate with aging and longevity?", + "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?", + "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?", + "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?", + "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?", + "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?", + "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + ] + }, + { + "level": "domainexpert", + "domain": "gn", + "query": [ + "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?", + "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?", + "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?", + "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?", + "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?", + "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?", + "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?", + "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?", + "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?", + "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?", + "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?", + "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?", + "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?", + "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?", + "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?", + "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?", + "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?", + "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?", + "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?", + "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + ] + }, + { + "level": "citizenscientist", + "domain": "gn", + "query": [ + "What is GeneNetwork.org, and how does it help scientists understand genetics?", + "How do researchers use GeneNetwork.org to study diseases?", + "What can GeneNetwork.org tell us about how genes interact with each other?", + "How does GeneNetwork.org help in finding the genetic causes of common diseases?", + "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?", + "How does GeneNetwork.org make use of data from different populations around the world?", + "What kinds of genetic data are available on GeneNetwork.org?", + "How do scientists use GeneNetwork.org to study differences in gene expression?", + "Can GeneNetwork.org be used to learn about genetic influences on behavior?", + "What role does GeneNetwork.org play in personalized medicine?", + "How does the information on GeneNetwork.org help in developing new treatments for diseases?", + "What is a gene network, and why is it important for understanding genetics?", + "How do researchers identify which genes are important for certain traits using GeneNetwork.org?", + "How can GeneNetwork.org help in understanding complex traits like height or intelligence?", + "Are there any known genetic mutations that cause premature aging?", + "What are the practical applications of the research done through GeneNetwork.org?", + "How can I access and use the data available on GeneNetwork.org?", + "What are some recent discoveries made using GeneNetwork.org?", + "How do scientists ensure the accuracy of the data on GeneNetwork.org?", + "What’s the difference between looking at one gene and studying a whole gene network?", + "How can GeneNetwork.org contribute to advancements in genetic engineering?" + ] + } +] diff --git a/gnqa/paper1_eval/src/data/queries/query_generation_prompt.md b/gnqa/paper1_eval/src/data/queries/query_generation_prompt.md new file mode 100644 index 0000000..5b09832 --- /dev/null +++ b/gnqa/paper1_eval/src/data/queries/query_generation_prompt.md @@ -0,0 +1,14 @@ +# OpenAI gpt4o Query Generation prompt + +## System settings +There is a retrieval augmented generation system, called GNQA, that holds a corpus of 3000 research documents. The documents span the topics of research related to genenetwork.org, research about the genetics and genomics of diabetes and aging. The systems topics will be referred to as GN, aging, and sugah. Two types of individuals query GNQA, citizen scientists and domain experts. A citizen scientist is someone with no more than undergraduate level understanding of biology and is someone who did not major or minor in biology. A domain expert has studied advanced biology and has a graduate degree in a type of biology or majored in biology for undergraduate school. + +## User messages + +Generate 20 questions, for GNQA, about research on GN from the perspective of a citizen scientist. +Generate 20 questions, for GNQA, about research on GN from the perspective of a domain expert. +Generate 20 questions, for GNQA, about research on aging from the perspective of a domain expert. +Generate 20 questions, for GNQA, about research on aging from the perspective of a citizen scientist. +Generate 20 questions, for GNQA, about research on sugah from the perspective of domain expert. +Generate 20 questions, for GNQA, about research on sugah from the perspective of citizen scientist. + diff --git a/gnqa/paper1_eval/src/data/queries/voluteer_queries.json b/gnqa/paper1_eval/src/data/queries/voluteer_queries.json new file mode 100644 index 0000000..d855140 --- /dev/null +++ b/gnqa/paper1_eval/src/data/queries/voluteer_queries.json @@ -0,0 +1,32 @@ +"stuff_a": { + "level": "domainexpert", + "domain": "gn", + "query": [ + "What is ensembl", + "Which database can I use for genetic, genomics, phenotype, and disease-related data generated from rat research?", + "What is RGD?", + "What resources can I use to do pathway analyses?", + "Which genes give a predisposition to developing T1D?" + ], + "task_id": [ + "7C028B1D0013EA11574B094986ABE4C2", + "55562016699AFE4B8AD9A7F29A806CB5", + "C9B1B98F9207B79EBBC98790A769CB51", + "242918F32291CC085DEB319A7EE3284B", + "029A427CEEBABE644F12EE390469B134" + ] +}, +"stuff_b": { + "level": "domainexpert", + "domain": "diabetes", + "query": [ + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?", + "How can I use genenetwork to find genes related with diabetes in humans?", + "How can I use the GeneNetwork tool to find genes related with diabetes in humans?" + ], + "task_id": [ + "173BE1F01E4A6074A0EBB7CC6137AC8D", + "F55160D302C899B2131AA8502A640684", + "B52AF52D46499DE2B98933F1786EC9E0" + ] +} diff --git a/gnqa/paper1_eval/src/data/ratings/.~lock.2024_06_18_gnqa_user_ratings.ods# b/gnqa/paper1_eval/src/data/ratings/.~lock.2024_06_18_gnqa_user_ratings.ods# new file mode 100644 index 0000000..2e571af --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/.~lock.2024_06_18_gnqa_user_ratings.ods# @@ -0,0 +1 @@ +,shebes,nyeusi-shebes,17.07.2024 00:25,file:///home/shebes/.config/libreoffice/4;
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_05_20-gnqa_responses.json b/gnqa/paper1_eval/src/data/ratings/2024_05_20-gnqa_responses.json new file mode 100644 index 0000000..2c3dfd2 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_05_20-gnqa_responses.json @@ -0,0 +1,57 @@ +[{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"F26EEA55FF8FFCB0B9E3E46688C276DA"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"5674818C9037B8A93EDCF35CEF97E33C"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"106C5DDDE1338BE1CC8AE04CEAE3EF62"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"2D8205C79915FF9CEB8DECCE51E6E473"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?","answer":"The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.","weight":1,"task_id":"849E78D8214245F8E8167E78C01BEE60"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"List as many studies as you can that include rapamycin.","answer":"1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.","weight":-1,"task_id":"2C477A3C76794C27A1FBBF437CFF75EE"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"Why is it so diffuclut to map gene loci that control aging in humans?","answer":"Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.","weight":1,"task_id":"CAD6C6C2AB42AA66BFDD65F0F11932B2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.","weight":1,"task_id":"EC2BFCD8E06079A3E595114822D2A44D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does epigenetics inluence gene expression without changing the underlying DNA sequence?","answer":"Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.","weight":-1,"task_id":"59AA8E8D7A97CFF47C6CFD65629F29F9"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.","answer":"Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.","weight":1,"task_id":"C58CFF41F7422B321DF88A110E278FD5"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?","answer":"The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.","weight":1,"task_id":"BCD1175CCB27FBA1E6F9D7670B17E527"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"5341FE6588C6175BC8A688A483928BC0"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"42847DE50D50E6A9B26ED0B03CFD160E"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"12BEAFA9366519672FC8B06959FB2DAF"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"64FEC152131BC6502E15EA6A6348D70B"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is apoptosis?","answer":"Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.","weight":1,"task_id":"78A0CD7E12AFEF6865583142603EE039"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is the most cited environmental factor for the onset of asthma?","answer":"The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.","weight":1,"task_id":"33FC2CC0F61BA22E4D095586B95703BD"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"370380F3A38AC4A788463D14E0EC673A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what is bioinformatics","answer":"Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.","weight":1,"task_id":"1E0DA0931F4E3A8C2893353CCA114B10"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"How would one extract the DNA, from say, flora or fauna?","answer":"DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.","weight":1,"task_id":"59E2406798D265A3CB466B766683E63C"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.","answer":"DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.","weight":-1,"task_id":"3F9EDFE9A0222EA70459EC8985F134C4"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.","weight":1,"task_id":"A010490B55F739DF95BB82DF2B0F5AA3"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Explain the process of finding a genetic marker followed by a quantitative trait loci.","answer":"The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.","weight":1,"task_id":"02A94D1056FDA77BDA9AC6CFDE0D5FC6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.","weight":1,"task_id":"96B873A13E3B386E38940EF6ECA90D4A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"A55310A1C19732792B5894A2DB5E8596"}, +{"user_id":"d2f71149-a0d5-4666-95d8-15c82543243b","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"7CA65F6B192BE7896422284B962CF5C3"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"2EDD65822CDA4669FCAD678288C2E3E9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":1,"task_id":"DA98AC2EA5D1F776D3F04FCBC7F01339"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what causes the aging process","answer":"The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.","weight":1,"task_id":"117299AD06C2B147F49E9C9BC036CEA4"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.","weight":1,"task_id":"F4DA6C97230E89C0226B1433532A16D9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":0,"task_id":"FBBFB0D391778CDE9536F742300C1809"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"32CE1E54032485B73B5968395B3D3538"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"59CCE2D70104154865218876DD53D049"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0AE973110158192E0D1F50E1D79764FB"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"33749EB09AAC7AD0404C8E3E584B98D2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"471A145E9CA1E517E462499ABCA8EA2D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?","answer":"The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.","weight":1,"task_id":"19EBC6CA7E425D0C1279475BD0B411B6"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"what is ensembl?","answer":"Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.","weight":-1,"task_id":"9CFE932D7898C83E473E590BC77B4FCB"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Create a how to guide for genetic sequencing ","answer":"1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.","weight":-1,"task_id":"C52A9690417093A861C669A0753689BD"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What is the significance of the length of telomeres? ","answer":"The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.","weight":1,"task_id":"93DE2EF005059DFEA5A7FBBA3BD17D03"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ","answer":"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.","weight":1,"task_id":"477FC54178046FE98BF97FAAC5FE167F"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Why is genetic tracing matrilineal rather than patrilineal? ","answer":"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.","weight":1,"task_id":"6B80ECC5F657EB7CBDE69D411A30D3EA"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"2DE25ABD7E487B80D0C489319640EACC"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"what type of dataset is useful for qtl mapping analysis in genenetwork2? ","answer":"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.","weight":1,"task_id":"6498ED71891B79908B2E383D9AA5BAC5"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What genetic factors influence aging in humans? ","answer":"Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.","weight":1,"task_id":"B2F5CB7BCD9A827D3A6E0152C030C4B4"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Is there a direct association between aging and susceptibility to having diabetes?","answer":"Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.","weight":1,"task_id":"72FBC4F382B6502EAF41BD6682E63A2D"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does genetics influence the emergency of diabetes? ","answer":"Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.","weight":1,"task_id":"02C953165B9CA94E273DD4A04301C89F"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"19DC9E909DDE6D9CDB3E86D1069F5A69"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F2843EA2D5A239D022186329C8D5D8EF"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"4E1F0C2E792BAF0BA349326375D3EE6E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"FA8ADB009A499F51B0533FDCB72CB29E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.","weight":-1,"task_id":"38BD5864A7928C6DBCA1D844327F3A19"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with diabetes?","answer":"The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.","weight":1,"task_id":"2272C482CC247E746D15C9F55EDD8BCE"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with aging?","answer":"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.","weight":1,"task_id":"C6C7CEF19CE7C27CF4BC6906259CDDF9"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the bioinformatics tools for QTLs analysis?","answer":"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.","weight":1,"task_id":"B4BB83EB5D5C5C042E07173119046A13"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the statistical approaches for qtls analysis?","answer":"The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.","weight":1,"task_id":"D88EF655762CE3D524A7A1EEA3FA16ED"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for GWAS analysis?","answer":"1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.","weight":1,"task_id":"245DD8093F5D16F44C2AD7618245086C"}] diff --git a/gnqa/paper1_eval/src/data/ratings/2024_05_21-gnqa_responses.json b/gnqa/paper1_eval/src/data/ratings/2024_05_21-gnqa_responses.json new file mode 100644 index 0000000..c90818f --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_05_21-gnqa_responses.json @@ -0,0 +1,513 @@ +[ +{ + "user_id": "b4601142-3b57-4d5b-9b55-80bdf0ea4599", + "query": "List as many studies as you can that include rapamycin.", + "answer": "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "weight": 1, + "task_id": "2D8205C79915FF9CEB8DECCE51E6E473" +}, +{ + "user_id": "e031ac19-fec1-4d97-a037-cd39d3817c54", + "query": "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "answer": "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "weight": 1, + "task_id": "849E78D8214245F8E8167E78C01BEE60" +}, +{ + "user_id": "e031ac19-fec1-4d97-a037-cd39d3817c54", + "query": "List as many studies as you can that include rapamycin.", + "answer": "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "weight": -1, + "task_id": "2C477A3C76794C27A1FBBF437CFF75EE" +}, +{ + "user_id": "e031ac19-fec1-4d97-a037-cd39d3817c54", + "query": "Why is it so diffuclut to map gene loci that control aging in humans?", + "answer": "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "weight": 1, + "task_id": "CAD6C6C2AB42AA66BFDD65F0F11932B2" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "answer": "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "weight": 1, + "task_id": "EC2BFCD8E06079A3E595114822D2A44D" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "answer": "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "weight": -1, + "task_id": "59AA8E8D7A97CFF47C6CFD65629F29F9" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "answer": "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "weight": 1, + "task_id": "C58CFF41F7422B321DF88A110E278FD5" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "answer": "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "weight": 1, + "task_id": "BCD1175CCB27FBA1E6F9D7670B17E527" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "Create a how-to guide for genetic sequencing.", + "answer": "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "weight": 1, + "task_id": "5341FE6588C6175BC8A688A483928BC0" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "weight": 1, + "task_id": "42847DE50D50E6A9B26ED0B03CFD160E" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "weight": 1, + "task_id": "12BEAFA9366519672FC8B06959FB2DAF" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "Why is genetic tracing matrilineal rather than patrilineal?", + "answer": "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "weight": 1, + "task_id": "64FEC152131BC6502E15EA6A6348D70B" +}, +{ + "user_id": "e031ac19-fec1-4d97-a037-cd39d3817c54", + "query": "What is apoptosis?", + "answer": "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "weight": 1, + "task_id": "78A0CD7E12AFEF6865583142603EE039" +}, +{ + "user_id": "e031ac19-fec1-4d97-a037-cd39d3817c54", + "query": "What is the most cited environmental factor for the onset of asthma?", + "answer": "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "weight": 1, + "task_id": "33FC2CC0F61BA22E4D095586B95703BD" +}, +{ + "user_id": "b4601142-3b57-4d5b-9b55-80bdf0ea4599", + "query": "genetics", + "answer": "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "weight": 1, + "task_id": "370380F3A38AC4A788463D14E0EC673A" +}, +{ + "user_id": "b4601142-3b57-4d5b-9b55-80bdf0ea4599", + "query": "what is bioinformatics", + "answer": "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "weight": 1, + "task_id": "1E0DA0931F4E3A8C2893353CCA114B10" +}, +{ + "user_id": "e031ac19-fec1-4d97-a037-cd39d3817c54", + "query": "How would one extract the DNA, from say, flora or fauna?", + "answer": "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.", + "weight": 1, + "task_id": "59E2406798D265A3CB466B766683E63C" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "answer": "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "weight": -1, + "task_id": "3F9EDFE9A0222EA70459EC8985F134C4" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "answer": "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "weight": 1, + "task_id": "A010490B55F739DF95BB82DF2B0F5AA3" +}, +{ + "user_id": "d8abfb12-9a11-400a-8cd0-0a436602581c", + "query": "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "answer": "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "weight": 1, + "task_id": "02A94D1056FDA77BDA9AC6CFDE0D5FC6" +}, +{ + "user_id": "d8abfb12-9a11-400a-8cd0-0a436602581c", + "query": "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "answer": "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "weight": 1, + "task_id": "96B873A13E3B386E38940EF6ECA90D4A" +}, +{ + "user_id": "b4601142-3b57-4d5b-9b55-80bdf0ea4599", + "query": "genetics", + "answer": "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "weight": 1, + "task_id": "A55310A1C19732792B5894A2DB5E8596" +}, +{ + "user_id": "d2f71149-a0d5-4666-95d8-15c82543243b", + "query": "genetics", + "answer": "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "weight": 1, + "task_id": "7CA65F6B192BE7896422284B962CF5C3" +}, +{ + "user_id": "b4601142-3b57-4d5b-9b55-80bdf0ea4599", + "query": "genetics", + "answer": "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "weight": -1, + "task_id": "2EDD65822CDA4669FCAD678288C2E3E9" +}, +{ + "user_id": "b4601142-3b57-4d5b-9b55-80bdf0ea4599", + "query": "which genes are involved in the aging process", + "answer": "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "weight": 1, + "task_id": "DA98AC2EA5D1F776D3F04FCBC7F01339" +}, +{ + "user_id": "b4601142-3b57-4d5b-9b55-80bdf0ea4599", + "query": "what causes the aging process", + "answer": "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "weight": 1, + "task_id": "117299AD06C2B147F49E9C9BC036CEA4" +}, +{ + "user_id": "d8abfb12-9a11-400a-8cd0-0a436602581c", + "query": "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "answer": "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "weight": 1, + "task_id": "F4DA6C97230E89C0226B1433532A16D9" +}, +{ + "user_id": "b4601142-3b57-4d5b-9b55-80bdf0ea4599", + "query": "which genes are involved in the aging process", + "answer": "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "weight": 0, + "task_id": "FBBFB0D391778CDE9536F742300C1809" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "Create a how-to guide for genetic sequencing.", + "answer": "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "weight": 1, + "task_id": "32CE1E54032485B73B5968395B3D3538" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "weight": 1, + "task_id": "59CCE2D70104154865218876DD53D049" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "weight": 1, + "task_id": "0AE973110158192E0D1F50E1D79764FB" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "Why is genetic tracing matrilineal rather than patrilineal?", + "answer": "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "weight": 1, + "task_id": "33749EB09AAC7AD0404C8E3E584B98D2" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "answer": "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "weight": 1, + "task_id": "471A145E9CA1E517E462499ABCA8EA2D" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "answer": "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "weight": 1, + "task_id": "19EBC6CA7E425D0C1279475BD0B411B6" +}, +{ + "user_id": "6365589e-a47b-4fa8-a53c-88cb5ee1a991", + "query": "what is ensembl?", + "answer": "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.", + "weight": -1, + "task_id": "9CFE932D7898C83E473E590BC77B4FCB" +}, +{ + "user_id": "6ee46240-38bf-4035-b9a8-0d72e29401b5", + "query": "Create a how to guide for genetic sequencing ", + "answer": "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "weight": -1, + "task_id": "C52A9690417093A861C669A0753689BD" +}, +{ + "user_id": "6ee46240-38bf-4035-b9a8-0d72e29401b5", + "query": "What is the significance of the length of telomeres? ", + "answer": "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "weight": 1, + "task_id": "93DE2EF005059DFEA5A7FBBA3BD17D03" +}, +{ + "user_id": "6ee46240-38bf-4035-b9a8-0d72e29401b5", + "query": "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "answer": "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "weight": 1, + "task_id": "477FC54178046FE98BF97FAAC5FE167F" +}, +{ + "user_id": "6ee46240-38bf-4035-b9a8-0d72e29401b5", + "query": "Why is genetic tracing matrilineal rather than patrilineal? ", + "answer": "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "weight": 1, + "task_id": "6B80ECC5F657EB7CBDE69D411A30D3EA" +}, +{ + "user_id": "6ee46240-38bf-4035-b9a8-0d72e29401b5", + "query": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "answer": "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "weight": 1, + "task_id": "2DE25ABD7E487B80D0C489319640EACC" +}, +{ + "user_id": "6ee46240-38bf-4035-b9a8-0d72e29401b5", + "query": "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "answer": "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "weight": 1, + "task_id": "6498ED71891B79908B2E383D9AA5BAC5" +}, +{ + "user_id": "6ee46240-38bf-4035-b9a8-0d72e29401b5", + "query": "What genetic factors influence aging in humans? ", + "answer": "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "weight": 1, + "task_id": "B2F5CB7BCD9A827D3A6E0152C030C4B4" +}, +{ + "user_id": "6ee46240-38bf-4035-b9a8-0d72e29401b5", + "query": "Is there a direct association between aging and susceptibility to having diabetes?", + "answer": "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "weight": 1, + "task_id": "72FBC4F382B6502EAF41BD6682E63A2D" +}, +{ + "user_id": "6ee46240-38bf-4035-b9a8-0d72e29401b5", + "query": "How does genetics influence the emergency of diabetes? ", + "answer": "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.", + "weight": 1, + "task_id": "02C953165B9CA94E273DD4A04301C89F" +}, +{ + "user_id": "e8855be7-59fd-4224-90ad-575e7158c34c", + "query": "Create a how-to guide for genetic sequencing.", + "answer": "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "weight": -1, + "task_id": "19DC9E909DDE6D9CDB3E86D1069F5A69" +}, +{ + "user_id": "e8855be7-59fd-4224-90ad-575e7158c34c", + "query": "What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "weight": 1, + "task_id": "F2843EA2D5A239D022186329C8D5D8EF" +}, +{ + "user_id": "e8855be7-59fd-4224-90ad-575e7158c34c", + "query": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "weight": 1, + "task_id": "4E1F0C2E792BAF0BA349326375D3EE6E" +}, +{ + "user_id": "e8855be7-59fd-4224-90ad-575e7158c34c", + "query": "Why is genetic tracing matrilineal rather than patrilineal?", + "answer": "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "weight": -1, + "task_id": "FA8ADB009A499F51B0533FDCB72CB29E" +}, +{ + "user_id": "e8855be7-59fd-4224-90ad-575e7158c34c", + "query": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "answer": "The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.", + "weight": -1, + "task_id": "38BD5864A7928C6DBCA1D844327F3A19" +}, +{ + "user_id": "e8855be7-59fd-4224-90ad-575e7158c34c", + "query": "what genes are associated with diabetes?", + "answer": "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "weight": 1, + "task_id": "2272C482CC247E746D15C9F55EDD8BCE" +}, +{ + "user_id": "e8855be7-59fd-4224-90ad-575e7158c34c", + "query": "what genes are associated with aging?", + "answer": "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "weight": 1, + "task_id": "C6C7CEF19CE7C27CF4BC6906259CDDF9" +}, +{ + "user_id": "e8855be7-59fd-4224-90ad-575e7158c34c", + "query": "what are the bioinformatics tools for QTLs analysis?", + "answer": "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "weight": 1, + "task_id": "B4BB83EB5D5C5C042E07173119046A13" +}, +{ + "user_id": "e8855be7-59fd-4224-90ad-575e7158c34c", + "query": "what are the statistical approaches for qtls analysis?", + "answer": "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "weight": 1, + "task_id": "D88EF655762CE3D524A7A1EEA3FA16ED" +}, +{ + "user_id": "e8855be7-59fd-4224-90ad-575e7158c34c", + "query": "Create a how-to guide for GWAS analysis?", + "answer": "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.", + "weight": 1, + "task_id": "245DD8093F5D16F44C2AD7618245086C" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "weight": 1, + "task_id": "B89A904E71CF7F16126FCA9EAAFBC8A6" +}, +{ + "user_id": "d8abfb12-9a11-400a-8cd0-0a436602581c", + "query": "What about recombination in human centromeres?", + "answer": "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "weight": 1, + "task_id": "2F8796A8C3DC633F00DB901C9BA396DA" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Create a how-to guide for genetic sequencing.", + "answer": "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "weight": -1, + "task_id": "6188C7826173CD59FD243F98C537AD50" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "weight": 1, + "task_id": "2C37A2EC74E4B46F13C6FB23D9547DBB" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "answer": "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "weight": 1, + "task_id": "590809C6B2A1504E2AE9A79EFDF3AC9A" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "What is the difference between QTL mapping and GWAS?", + "answer": "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "weight": 1, + "task_id": "6DBC070B2E4DC2FE8036E5BA7480B755" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How do I determine which gene in my QTL is causal for the trait?", + "answer": "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "weight": 1, + "task_id": "5594EA025D9631328071B6A1A7EF1375" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Which mouse genes have been associated with longevity?", + "answer": "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "weight": -1, + "task_id": "AB589D2E046B211A7486A6C4BD4ECFB4" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How is gene expression in the liver affected by diabetes?", + "answer": "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "weight": 1, + "task_id": "8FFF9DCC307B8DBF2C8485637F2ABEF4" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Why do males have two Y chromosomes and females only one?", + "answer": "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.", + "weight": 1, + "task_id": "C6B9A982C9283DE065A3371F1264095C" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " Create a how-to guide for genetic sequencing", + "answer": "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "weight": 1, + "task_id": "F9F7EA3DC28534B161ED70DB401C7D11" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " Create a how-to guide for genetic sequencing.", + "answer": "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "weight": -1, + "task_id": "4A06F8DF54C82D90E02F81D0E1E8B08A" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying.", + "weight": 1, + "task_id": "BA6A505E62A0529DB883D036CBC1FD92" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Which genes are associated with aging in human ", + "answer": "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "weight": 1, + "task_id": "3EC47C56606B02F00CF2449AB311365C" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Create a how-to guide for genetic sequencing", + "answer": "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "weight": 1, + "task_id": "CDFC418BD568E839C09656C57808ADA1" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Create a guide for genetic sequencing", + "answer": "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "weight": -1, + "task_id": "5DEB102510F48D0BF9C278DC895A8BD1" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "weight": 1, + "task_id": "5A562D5F7A266BA057B6833F3A83E7A7" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Why is genetic tracing matrilineal rather than patrilineal?", + "answer": "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "weight": 1, + "task_id": "53905925B4D6F69CE5706896A3C667CE" +}, +{ + "user_id": "0c4a1c26-539f-453a-b7f6-915ab462cf0b", + "query": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs", + "answer": "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.", + "weight": -1, + "task_id": "C0015BEE5FE41769A65126B79BB1E40D" +} +]
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_05_24-gnqa_responses.json b/gnqa/paper1_eval/src/data/ratings/2024_05_24-gnqa_responses.json new file mode 100644 index 0000000..7ae2726 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_05_24-gnqa_responses.json @@ -0,0 +1,92 @@ +{ + "ratings_list": + [ + {"user_id":"b4601143-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"F26EEA55FF8FFCB0B9E3E46688C276DA"}, + {"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"5674818C9037B8A93EDCF35CEF97E33C"}, + {"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"106C5DDDE1338BE1CC8AE04CEAE3EF62"}, + {"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"2D8205C79915FF9CEB8DECCE51E6E473"}, + {"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?","answer":"The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.","weight":1,"task_id":"849E78D8214245F8E8167E78C01BEE60"}, + {"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"List as many studies as you can that include rapamycin.","answer":"1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.","weight":-1,"task_id":"2C477A3C76794C27A1FBBF437CFF75EE"}, + {"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"Why is it so diffuclut to map gene loci that control aging in humans?","answer":"Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.","weight":1,"task_id":"CAD6C6C2AB42AA66BFDD65F0F11932B2"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.","weight":1,"task_id":"EC2BFCD8E06079A3E595114822D2A44D"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does epigenetics inluence gene expression without changing the underlying DNA sequence?","answer":"Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.","weight":-1,"task_id":"59AA8E8D7A97CFF47C6CFD65629F29F9"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.","answer":"Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.","weight":1,"task_id":"C58CFF41F7422B321DF88A110E278FD5"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?","answer":"The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.","weight":1,"task_id":"BCD1175CCB27FBA1E6F9D7670B17E527"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"5341FE6588C6175BC8A688A483928BC0"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"42847DE50D50E6A9B26ED0B03CFD160E"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"12BEAFA9366519672FC8B06959FB2DAF"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"64FEC152131BC6502E15EA6A6348D70B"}, + {"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is apoptosis?","answer":"Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.","weight":1,"task_id":"78A0CD7E12AFEF6865583142603EE039"}, + {"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is the most cited environmental factor for the onset of asthma?","answer":"The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.","weight":1,"task_id":"33FC2CC0F61BA22E4D095586B95703BD"}, + {"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"370380F3A38AC4A788463D14E0EC673A"}, + {"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what is bioinformatics","answer":"Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.","weight":1,"task_id":"1E0DA0931F4E3A8C2893353CCA114B10"}, + {"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"How would one extract the DNA, from say, flora or fauna?","answer":"DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.","weight":1,"task_id":"59E2406798D265A3CB466B766683E63C"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.","answer":"DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.","weight":-1,"task_id":"3F9EDFE9A0222EA70459EC8985F134C4"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.","weight":1,"task_id":"A010490B55F739DF95BB82DF2B0F5AA3"}, + {"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Explain the process of finding a genetic marker followed by a quantitative trait loci.","answer":"The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.","weight":1,"task_id":"02A94D1056FDA77BDA9AC6CFDE0D5FC6"}, + {"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.","weight":1,"task_id":"96B873A13E3B386E38940EF6ECA90D4A"}, + {"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"A55310A1C19732792B5894A2DB5E8596"}, + {"user_id":"d2f71149-a0d5-4666-95d8-15c82543243b","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"7CA65F6B192BE7896422284B962CF5C3"}, + {"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"2EDD65822CDA4669FCAD678288C2E3E9"}, + {"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":1,"task_id":"DA98AC2EA5D1F776D3F04FCBC7F01339"}, + {"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what causes the aging process","answer":"The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.","weight":1,"task_id":"117299AD06C2B147F49E9C9BC036CEA4"}, + {"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.","weight":1,"task_id":"F4DA6C97230E89C0226B1433532A16D9"}, + {"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":0,"task_id":"FBBFB0D391778CDE9536F742300C1809"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"32CE1E54032485B73B5968395B3D3538"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"59CCE2D70104154865218876DD53D049"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0AE973110158192E0D1F50E1D79764FB"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"33749EB09AAC7AD0404C8E3E584B98D2"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"471A145E9CA1E517E462499ABCA8EA2D"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?","answer":"The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.","weight":1,"task_id":"19EBC6CA7E425D0C1279475BD0B411B6"}, + {"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"what is ensembl?","answer":"Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.","weight":-1,"task_id":"9CFE932D7898C83E473E590BC77B4FCB"}, + {"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Create a how to guide for genetic sequencing ","answer":"1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.","weight":-1,"task_id":"C52A9690417093A861C669A0753689BD"}, + {"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What is the significance of the length of telomeres? ","answer":"The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.","weight":1,"task_id":"93DE2EF005059DFEA5A7FBBA3BD17D03"}, + {"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ","answer":"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.","weight":1,"task_id":"477FC54178046FE98BF97FAAC5FE167F"}, + {"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Why is genetic tracing matrilineal rather than patrilineal? ","answer":"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.","weight":1,"task_id":"6B80ECC5F657EB7CBDE69D411A30D3EA"}, + {"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"2DE25ABD7E487B80D0C489319640EACC"}, + {"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"what type of dataset is useful for qtl mapping analysis in genenetwork2? ","answer":"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.","weight":1,"task_id":"6498ED71891B79908B2E383D9AA5BAC5"}, + {"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What genetic factors influence aging in humans? ","answer":"Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.","weight":1,"task_id":"B2F5CB7BCD9A827D3A6E0152C030C4B4"}, + {"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Is there a direct association between aging and susceptibility to having diabetes?","answer":"Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.","weight":1,"task_id":"72FBC4F382B6502EAF41BD6682E63A2D"}, + {"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does genetics influence the emergency of diabetes? ","answer":"Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.","weight":1,"task_id":"02C953165B9CA94E273DD4A04301C89F"}, + {"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"19DC9E909DDE6D9CDB3E86D1069F5A69"}, + {"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F2843EA2D5A239D022186329C8D5D8EF"}, + {"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"4E1F0C2E792BAF0BA349326375D3EE6E"}, + {"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"FA8ADB009A499F51B0533FDCB72CB29E"}, + {"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.","weight":-1,"task_id":"38BD5864A7928C6DBCA1D844327F3A19"}, + {"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with diabetes?","answer":"The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.","weight":1,"task_id":"2272C482CC247E746D15C9F55EDD8BCE"}, + {"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with aging?","answer":"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.","weight":1,"task_id":"C6C7CEF19CE7C27CF4BC6906259CDDF9"}, + {"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the bioinformatics tools for QTLs analysis?","answer":"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.","weight":1,"task_id":"B4BB83EB5D5C5C042E07173119046A13"}, + {"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the statistical approaches for qtls analysis?","answer":"The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.","weight":1,"task_id":"D88EF655762CE3D524A7A1EEA3FA16ED"}, + {"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for GWAS analysis?","answer":"1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.","weight":1,"task_id":"245DD8093F5D16F44C2AD7618245086C"}, + {"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"B89A904E71CF7F16126FCA9EAAFBC8A6"}, + {"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":1,"task_id":"2F8796A8C3DC633F00DB901C9BA396DA"}, + {"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"6188C7826173CD59FD243F98C537AD50"}, + {"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"2C37A2EC74E4B46F13C6FB23D9547DBB"}, + {"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"590809C6B2A1504E2AE9A79EFDF3AC9A"}, + {"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the difference between QTL mapping and GWAS?","answer":"QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.","weight":1,"task_id":"6DBC070B2E4DC2FE8036E5BA7480B755"}, + {"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How do I determine which gene in my QTL is causal for the trait?","answer":"To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.","weight":1,"task_id":"5594EA025D9631328071B6A1A7EF1375"}, + {"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Which mouse genes have been associated with longevity?","answer":"The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.","weight":-1,"task_id":"AB589D2E046B211A7486A6C4BD4ECFB4"}, + {"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How is gene expression in the liver affected by diabetes?","answer":"In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.","weight":1,"task_id":"8FFF9DCC307B8DBF2C8485637F2ABEF4"}, + {"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Why do males have two Y chromosomes and females only one?","answer":"Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.","weight":1,"task_id":"C6B9A982C9283DE065A3371F1264095C"}, + {"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing","answer":"1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.","weight":1,"task_id":"F9F7EA3DC28534B161ED70DB401C7D11"}, + {"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.","weight":-1,"task_id":"4A06F8DF54C82D90E02F81D0E1E8B08A"}, + {"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying.","weight":1,"task_id":"BA6A505E62A0529DB883D036CBC1FD92"}, + {"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Which genes are associated with aging in human ","answer":"The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.","weight":1,"task_id":"3EC47C56606B02F00CF2449AB311365C"}, + {"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"CDFC418BD568E839C09656C57808ADA1"}, + {"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a guide for genetic sequencing","answer":"1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.","weight":-1,"task_id":"5DEB102510F48D0BF9C278DC895A8BD1"}, + {"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"5A562D5F7A266BA057B6833F3A83E7A7"}, + {"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"53905925B4D6F69CE5706896A3C667CE"}, + {"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.","weight":-1,"task_id":"C0015BEE5FE41769A65126B79BB1E40D"}, + {"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":-1,"task_id":"9957BCF2957D9BD083EDCF8B60C0E4DE"}, + {"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How does recombination work in human centromeres?","answer":"In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.","weight":-1,"task_id":"DEE6D385D1B01B4155AA4ABE59515893"}, + {"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How many types of diabetes exist?","answer":"There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).","weight":1,"task_id":"9309F248E5933718BFB625E4EF2D3E42"}, + {"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in the human genome?","answer":"The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome.","weight":-1,"task_id":"10ABD2210053119B18D94F1FE266E73E"}, + {"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"BD671A3AC4F77A74CA8A68EAD627437D"}, + {"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5E243BF70932D1477209FEF6869FDEEA"}, + {"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.","weight":-1,"task_id":"3A16235DA1E02B9148B9288A06EE567E"}, + {"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9BE91FA13BDC0D9EB80B35FC73A0029B"}, + {"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"3D4688621977A9CBD670B50131B8D912"}, + {"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How can I add a new species to the GeneNetwork database?","answer":"The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.","weight":1,"task_id":"E94FFD042BB146E8A429200590A6792D"}, + {"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?","answer":"Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.","weight":1,"task_id":"C319861B08978CF5F7E6F0CD3A517A81"} + ] +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_05_28-gnqa_responses.json b/gnqa/paper1_eval/src/data/ratings/2024_05_28-gnqa_responses.json new file mode 100644 index 0000000..9697b2e --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_05_28-gnqa_responses.json @@ -0,0 +1,93 @@ +[{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"F26EEA55FF8FFCB0B9E3E46688C276DA"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"5674818C9037B8A93EDCF35CEF97E33C"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"106C5DDDE1338BE1CC8AE04CEAE3EF62"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"2D8205C79915FF9CEB8DECCE51E6E473"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?","answer":"The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.","weight":1,"task_id":"849E78D8214245F8E8167E78C01BEE60"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"List as many studies as you can that include rapamycin.","answer":"1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.","weight":-1,"task_id":"2C477A3C76794C27A1FBBF437CFF75EE"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"Why is it so diffuclut to map gene loci that control aging in humans?","answer":"Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.","weight":1,"task_id":"CAD6C6C2AB42AA66BFDD65F0F11932B2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.","weight":1,"task_id":"EC2BFCD8E06079A3E595114822D2A44D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does epigenetics inluence gene expression without changing the underlying DNA sequence?","answer":"Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.","weight":-1,"task_id":"59AA8E8D7A97CFF47C6CFD65629F29F9"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.","answer":"Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.","weight":1,"task_id":"C58CFF41F7422B321DF88A110E278FD5"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?","answer":"The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.","weight":1,"task_id":"BCD1175CCB27FBA1E6F9D7670B17E527"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"5341FE6588C6175BC8A688A483928BC0"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"42847DE50D50E6A9B26ED0B03CFD160E"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"12BEAFA9366519672FC8B06959FB2DAF"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"64FEC152131BC6502E15EA6A6348D70B"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is apoptosis?","answer":"Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.","weight":1,"task_id":"78A0CD7E12AFEF6865583142603EE039"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is the most cited environmental factor for the onset of asthma?","answer":"The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.","weight":1,"task_id":"33FC2CC0F61BA22E4D095586B95703BD"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"370380F3A38AC4A788463D14E0EC673A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what is bioinformatics","answer":"Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.","weight":1,"task_id":"1E0DA0931F4E3A8C2893353CCA114B10"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"How would one extract the DNA, from say, flora or fauna?","answer":"DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.","weight":1,"task_id":"59E2406798D265A3CB466B766683E63C"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.","answer":"DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.","weight":-1,"task_id":"3F9EDFE9A0222EA70459EC8985F134C4"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.","weight":1,"task_id":"A010490B55F739DF95BB82DF2B0F5AA3"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Explain the process of finding a genetic marker followed by a quantitative trait loci.","answer":"The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.","weight":1,"task_id":"02A94D1056FDA77BDA9AC6CFDE0D5FC6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.","weight":1,"task_id":"96B873A13E3B386E38940EF6ECA90D4A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"A55310A1C19732792B5894A2DB5E8596"}, +{"user_id":"d2f71149-a0d5-4666-95d8-15c82543243b","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"7CA65F6B192BE7896422284B962CF5C3"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"2EDD65822CDA4669FCAD678288C2E3E9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":1,"task_id":"DA98AC2EA5D1F776D3F04FCBC7F01339"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what causes the aging process","answer":"The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.","weight":1,"task_id":"117299AD06C2B147F49E9C9BC036CEA4"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.","weight":1,"task_id":"F4DA6C97230E89C0226B1433532A16D9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":0,"task_id":"FBBFB0D391778CDE9536F742300C1809"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"32CE1E54032485B73B5968395B3D3538"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"59CCE2D70104154865218876DD53D049"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0AE973110158192E0D1F50E1D79764FB"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"33749EB09AAC7AD0404C8E3E584B98D2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"471A145E9CA1E517E462499ABCA8EA2D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?","answer":"The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.","weight":1,"task_id":"19EBC6CA7E425D0C1279475BD0B411B6"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"what is ensembl?","answer":"Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.","weight":-1,"task_id":"9CFE932D7898C83E473E590BC77B4FCB"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Create a how to guide for genetic sequencing ","answer":"1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.","weight":-1,"task_id":"C52A9690417093A861C669A0753689BD"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What is the significance of the length of telomeres? ","answer":"The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.","weight":1,"task_id":"93DE2EF005059DFEA5A7FBBA3BD17D03"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ","answer":"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.","weight":1,"task_id":"477FC54178046FE98BF97FAAC5FE167F"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Why is genetic tracing matrilineal rather than patrilineal? ","answer":"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.","weight":1,"task_id":"6B80ECC5F657EB7CBDE69D411A30D3EA"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"2DE25ABD7E487B80D0C489319640EACC"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"what type of dataset is useful for qtl mapping analysis in genenetwork2? ","answer":"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.","weight":1,"task_id":"6498ED71891B79908B2E383D9AA5BAC5"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What genetic factors influence aging in humans? ","answer":"Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.","weight":1,"task_id":"B2F5CB7BCD9A827D3A6E0152C030C4B4"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Is there a direct association between aging and susceptibility to having diabetes?","answer":"Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.","weight":1,"task_id":"72FBC4F382B6502EAF41BD6682E63A2D"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does genetics influence the emergency of diabetes? ","answer":"Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.","weight":1,"task_id":"02C953165B9CA94E273DD4A04301C89F"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"19DC9E909DDE6D9CDB3E86D1069F5A69"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F2843EA2D5A239D022186329C8D5D8EF"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"4E1F0C2E792BAF0BA349326375D3EE6E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"FA8ADB009A499F51B0533FDCB72CB29E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.","weight":-1,"task_id":"38BD5864A7928C6DBCA1D844327F3A19"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with diabetes?","answer":"The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.","weight":1,"task_id":"2272C482CC247E746D15C9F55EDD8BCE"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with aging?","answer":"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.","weight":1,"task_id":"C6C7CEF19CE7C27CF4BC6906259CDDF9"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the bioinformatics tools for QTLs analysis?","answer":"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.","weight":1,"task_id":"B4BB83EB5D5C5C042E07173119046A13"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the statistical approaches for qtls analysis?","answer":"The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.","weight":1,"task_id":"D88EF655762CE3D524A7A1EEA3FA16ED"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for GWAS analysis?","answer":"1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.","weight":1,"task_id":"245DD8093F5D16F44C2AD7618245086C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"B89A904E71CF7F16126FCA9EAAFBC8A6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":1,"task_id":"2F8796A8C3DC633F00DB901C9BA396DA"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"6188C7826173CD59FD243F98C537AD50"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"2C37A2EC74E4B46F13C6FB23D9547DBB"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"590809C6B2A1504E2AE9A79EFDF3AC9A"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the difference between QTL mapping and GWAS?","answer":"QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.","weight":1,"task_id":"6DBC070B2E4DC2FE8036E5BA7480B755"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How do I determine which gene in my QTL is causal for the trait?","answer":"To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.","weight":1,"task_id":"5594EA025D9631328071B6A1A7EF1375"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Which mouse genes have been associated with longevity?","answer":"The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.","weight":-1,"task_id":"AB589D2E046B211A7486A6C4BD4ECFB4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How is gene expression in the liver affected by diabetes?","answer":"In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.","weight":1,"task_id":"8FFF9DCC307B8DBF2C8485637F2ABEF4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Why do males have two Y chromosomes and females only one?","answer":"Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.","weight":1,"task_id":"C6B9A982C9283DE065A3371F1264095C"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing","answer":"1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.","weight":1,"task_id":"F9F7EA3DC28534B161ED70DB401C7D11"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.","weight":-1,"task_id":"4A06F8DF54C82D90E02F81D0E1E8B08A"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying.","weight":1,"task_id":"BA6A505E62A0529DB883D036CBC1FD92"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Which genes are associated with aging in human ","answer":"The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.","weight":1,"task_id":"3EC47C56606B02F00CF2449AB311365C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"CDFC418BD568E839C09656C57808ADA1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a guide for genetic sequencing","answer":"1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.","weight":-1,"task_id":"5DEB102510F48D0BF9C278DC895A8BD1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"5A562D5F7A266BA057B6833F3A83E7A7"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"53905925B4D6F69CE5706896A3C667CE"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.","weight":-1,"task_id":"C0015BEE5FE41769A65126B79BB1E40D"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":-1,"task_id":"9957BCF2957D9BD083EDCF8B60C0E4DE"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How does recombination work in human centromeres?","answer":"In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.","weight":-1,"task_id":"DEE6D385D1B01B4155AA4ABE59515893"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How many types of diabetes exist?","answer":"There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).","weight":1,"task_id":"9309F248E5933718BFB625E4EF2D3E42"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in the human genome?","answer":"The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome.","weight":-1,"task_id":"10ABD2210053119B18D94F1FE266E73E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"BD671A3AC4F77A74CA8A68EAD627437D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5E243BF70932D1477209FEF6869FDEEA"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.","weight":-1,"task_id":"3A16235DA1E02B9148B9288A06EE567E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9BE91FA13BDC0D9EB80B35FC73A0029B"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"3D4688621977A9CBD670B50131B8D912"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How can I add a new species to the GeneNetwork database?","answer":"The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.","weight":1,"task_id":"E94FFD042BB146E8A429200590A6792D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?","answer":"Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.","weight":1,"task_id":"C319861B08978CF5F7E6F0CD3A517A81"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"what genetic factor are associated with aging","answer":"Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.","weight":1,"task_id":"081B2DB92FD09DEBEF28ADBBDE7199D2"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.","weight":0,"task_id":"68EF3BE5EC2106766CA9CC700135E2FA"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes.","weight":1,"task_id":"8590501C57DC5C321AB5E1036F233027"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":-1,"task_id":"C8E21233058E1DEE05CD619ACBF49E42"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"38A8ED57882E2E250B447F93243A8CD5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"5D3CA84D2DCE010AF63188665193B195"}] diff --git a/gnqa/paper1_eval/src/data/ratings/2024_05_28-out.json b/gnqa/paper1_eval/src/data/ratings/2024_05_28-out.json new file mode 100644 index 0000000..0912e5f --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_05_28-out.json @@ -0,0 +1,518 @@ +[ + { + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "F26EEA55FF8FFCB0B9E3E46688C276DA", + "5674818C9037B8A93EDCF35CEF97E33C", + "106C5DDDE1338BE1CC8AE04CEAE3EF62", + "2D8205C79915FF9CEB8DECCE51E6E473", + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "A55310A1C19732792B5894A2DB5E8596", + "2EDD65822CDA4669FCAD678288C2E3E9", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4", + "FBBFB0D391778CDE9536F742300C1809", + "C8E21233058E1DEE05CD619ACBF49E42", + "38A8ED57882E2E250B447F93243A8CD5", + "5D3CA84D2DCE010AF63188665193B195" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 0, + -1, + 1, + 1 + ], + "answer": [ + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents." + ], + "query": [ + "genetics", + "genetics", + "genetics", + "List as many studies as you can that include rapamycin.", + "genetics", + "what is bioinformatics", + "genetics", + "genetics", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in the aging process", + "List as many studies as you can that include rapamycin.", + "List as many studies as you can that include rapamycin.", + "List as many studies as you can that include rapamycin." + ] + } + }, + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "32CE1E54032485B73B5968395B3D3538", + "59CCE2D70104154865218876DD53D049", + "0AE973110158192E0D1F50E1D79764FB", + "33749EB09AAC7AD0404C8E3E584B98D2", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "9957BCF2957D9BD083EDCF8B60C0E4DE", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "d2f71149-a0d5-4666-95d8-15c82543243b": { + "task_id": [ + "7CA65F6B192BE7896422284B962CF5C3" + ], + "weight": [ + 1 + ], + "answer": [ + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits." + ], + "query": [ + "genetics" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "19DC9E909DDE6D9CDB3E86D1069F5A69", + "F2843EA2D5A239D022186329C8D5D8EF", + "4E1F0C2E792BAF0BA349326375D3EE6E", + "FA8ADB009A499F51B0533FDCB72CB29E", + "38BD5864A7928C6DBCA1D844327F3A19", + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + -1, + 1, + 1, + -1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.", + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "B89A904E71CF7F16126FCA9EAAFBC8A6", + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1", + "5A562D5F7A266BA057B6833F3A83E7A7", + "53905925B4D6F69CE5706896A3C667CE", + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6188C7826173CD59FD243F98C537AD50", + "2C37A2EC74E4B46F13C6FB23D9547DBB", + "590809C6B2A1504E2AE9A79EFDF3AC9A", + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "BD671A3AC4F77A74CA8A68EAD627437D", + "5E243BF70932D1477209FEF6869FDEEA", + "3A16235DA1E02B9148B9288A06EE567E", + "9BE91FA13BDC0D9EB80B35FC73A0029B", + "3D4688621977A9CBD670B50131B8D912", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + } +]
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_05_31-gnqa_responses.json b/gnqa/paper1_eval/src/data/ratings/2024_05_31-gnqa_responses.json new file mode 100644 index 0000000..9697b2e --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_05_31-gnqa_responses.json @@ -0,0 +1,93 @@ +[{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"F26EEA55FF8FFCB0B9E3E46688C276DA"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"5674818C9037B8A93EDCF35CEF97E33C"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"106C5DDDE1338BE1CC8AE04CEAE3EF62"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"2D8205C79915FF9CEB8DECCE51E6E473"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?","answer":"The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.","weight":1,"task_id":"849E78D8214245F8E8167E78C01BEE60"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"List as many studies as you can that include rapamycin.","answer":"1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.","weight":-1,"task_id":"2C477A3C76794C27A1FBBF437CFF75EE"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"Why is it so diffuclut to map gene loci that control aging in humans?","answer":"Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.","weight":1,"task_id":"CAD6C6C2AB42AA66BFDD65F0F11932B2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.","weight":1,"task_id":"EC2BFCD8E06079A3E595114822D2A44D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does epigenetics inluence gene expression without changing the underlying DNA sequence?","answer":"Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.","weight":-1,"task_id":"59AA8E8D7A97CFF47C6CFD65629F29F9"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.","answer":"Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.","weight":1,"task_id":"C58CFF41F7422B321DF88A110E278FD5"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?","answer":"The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.","weight":1,"task_id":"BCD1175CCB27FBA1E6F9D7670B17E527"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"5341FE6588C6175BC8A688A483928BC0"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"42847DE50D50E6A9B26ED0B03CFD160E"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"12BEAFA9366519672FC8B06959FB2DAF"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"64FEC152131BC6502E15EA6A6348D70B"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is apoptosis?","answer":"Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.","weight":1,"task_id":"78A0CD7E12AFEF6865583142603EE039"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is the most cited environmental factor for the onset of asthma?","answer":"The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.","weight":1,"task_id":"33FC2CC0F61BA22E4D095586B95703BD"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"370380F3A38AC4A788463D14E0EC673A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what is bioinformatics","answer":"Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.","weight":1,"task_id":"1E0DA0931F4E3A8C2893353CCA114B10"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"How would one extract the DNA, from say, flora or fauna?","answer":"DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.","weight":1,"task_id":"59E2406798D265A3CB466B766683E63C"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.","answer":"DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.","weight":-1,"task_id":"3F9EDFE9A0222EA70459EC8985F134C4"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.","weight":1,"task_id":"A010490B55F739DF95BB82DF2B0F5AA3"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Explain the process of finding a genetic marker followed by a quantitative trait loci.","answer":"The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.","weight":1,"task_id":"02A94D1056FDA77BDA9AC6CFDE0D5FC6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.","weight":1,"task_id":"96B873A13E3B386E38940EF6ECA90D4A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"A55310A1C19732792B5894A2DB5E8596"}, +{"user_id":"d2f71149-a0d5-4666-95d8-15c82543243b","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"7CA65F6B192BE7896422284B962CF5C3"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"2EDD65822CDA4669FCAD678288C2E3E9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":1,"task_id":"DA98AC2EA5D1F776D3F04FCBC7F01339"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what causes the aging process","answer":"The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.","weight":1,"task_id":"117299AD06C2B147F49E9C9BC036CEA4"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.","weight":1,"task_id":"F4DA6C97230E89C0226B1433532A16D9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":0,"task_id":"FBBFB0D391778CDE9536F742300C1809"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"32CE1E54032485B73B5968395B3D3538"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"59CCE2D70104154865218876DD53D049"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0AE973110158192E0D1F50E1D79764FB"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"33749EB09AAC7AD0404C8E3E584B98D2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"471A145E9CA1E517E462499ABCA8EA2D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?","answer":"The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.","weight":1,"task_id":"19EBC6CA7E425D0C1279475BD0B411B6"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"what is ensembl?","answer":"Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.","weight":-1,"task_id":"9CFE932D7898C83E473E590BC77B4FCB"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Create a how to guide for genetic sequencing ","answer":"1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.","weight":-1,"task_id":"C52A9690417093A861C669A0753689BD"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What is the significance of the length of telomeres? ","answer":"The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.","weight":1,"task_id":"93DE2EF005059DFEA5A7FBBA3BD17D03"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ","answer":"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.","weight":1,"task_id":"477FC54178046FE98BF97FAAC5FE167F"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Why is genetic tracing matrilineal rather than patrilineal? ","answer":"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.","weight":1,"task_id":"6B80ECC5F657EB7CBDE69D411A30D3EA"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"2DE25ABD7E487B80D0C489319640EACC"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"what type of dataset is useful for qtl mapping analysis in genenetwork2? ","answer":"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.","weight":1,"task_id":"6498ED71891B79908B2E383D9AA5BAC5"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What genetic factors influence aging in humans? ","answer":"Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.","weight":1,"task_id":"B2F5CB7BCD9A827D3A6E0152C030C4B4"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Is there a direct association between aging and susceptibility to having diabetes?","answer":"Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.","weight":1,"task_id":"72FBC4F382B6502EAF41BD6682E63A2D"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does genetics influence the emergency of diabetes? ","answer":"Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.","weight":1,"task_id":"02C953165B9CA94E273DD4A04301C89F"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"19DC9E909DDE6D9CDB3E86D1069F5A69"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F2843EA2D5A239D022186329C8D5D8EF"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"4E1F0C2E792BAF0BA349326375D3EE6E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"FA8ADB009A499F51B0533FDCB72CB29E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.","weight":-1,"task_id":"38BD5864A7928C6DBCA1D844327F3A19"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with diabetes?","answer":"The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.","weight":1,"task_id":"2272C482CC247E746D15C9F55EDD8BCE"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with aging?","answer":"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.","weight":1,"task_id":"C6C7CEF19CE7C27CF4BC6906259CDDF9"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the bioinformatics tools for QTLs analysis?","answer":"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.","weight":1,"task_id":"B4BB83EB5D5C5C042E07173119046A13"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the statistical approaches for qtls analysis?","answer":"The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.","weight":1,"task_id":"D88EF655762CE3D524A7A1EEA3FA16ED"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for GWAS analysis?","answer":"1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.","weight":1,"task_id":"245DD8093F5D16F44C2AD7618245086C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"B89A904E71CF7F16126FCA9EAAFBC8A6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":1,"task_id":"2F8796A8C3DC633F00DB901C9BA396DA"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"6188C7826173CD59FD243F98C537AD50"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"2C37A2EC74E4B46F13C6FB23D9547DBB"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"590809C6B2A1504E2AE9A79EFDF3AC9A"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the difference between QTL mapping and GWAS?","answer":"QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.","weight":1,"task_id":"6DBC070B2E4DC2FE8036E5BA7480B755"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How do I determine which gene in my QTL is causal for the trait?","answer":"To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.","weight":1,"task_id":"5594EA025D9631328071B6A1A7EF1375"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Which mouse genes have been associated with longevity?","answer":"The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.","weight":-1,"task_id":"AB589D2E046B211A7486A6C4BD4ECFB4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How is gene expression in the liver affected by diabetes?","answer":"In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.","weight":1,"task_id":"8FFF9DCC307B8DBF2C8485637F2ABEF4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Why do males have two Y chromosomes and females only one?","answer":"Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.","weight":1,"task_id":"C6B9A982C9283DE065A3371F1264095C"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing","answer":"1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.","weight":1,"task_id":"F9F7EA3DC28534B161ED70DB401C7D11"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.","weight":-1,"task_id":"4A06F8DF54C82D90E02F81D0E1E8B08A"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying.","weight":1,"task_id":"BA6A505E62A0529DB883D036CBC1FD92"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Which genes are associated with aging in human ","answer":"The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.","weight":1,"task_id":"3EC47C56606B02F00CF2449AB311365C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"CDFC418BD568E839C09656C57808ADA1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a guide for genetic sequencing","answer":"1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.","weight":-1,"task_id":"5DEB102510F48D0BF9C278DC895A8BD1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"5A562D5F7A266BA057B6833F3A83E7A7"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"53905925B4D6F69CE5706896A3C667CE"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.","weight":-1,"task_id":"C0015BEE5FE41769A65126B79BB1E40D"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":-1,"task_id":"9957BCF2957D9BD083EDCF8B60C0E4DE"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How does recombination work in human centromeres?","answer":"In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.","weight":-1,"task_id":"DEE6D385D1B01B4155AA4ABE59515893"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How many types of diabetes exist?","answer":"There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).","weight":1,"task_id":"9309F248E5933718BFB625E4EF2D3E42"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in the human genome?","answer":"The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome.","weight":-1,"task_id":"10ABD2210053119B18D94F1FE266E73E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"BD671A3AC4F77A74CA8A68EAD627437D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5E243BF70932D1477209FEF6869FDEEA"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.","weight":-1,"task_id":"3A16235DA1E02B9148B9288A06EE567E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9BE91FA13BDC0D9EB80B35FC73A0029B"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"3D4688621977A9CBD670B50131B8D912"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How can I add a new species to the GeneNetwork database?","answer":"The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.","weight":1,"task_id":"E94FFD042BB146E8A429200590A6792D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?","answer":"Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.","weight":1,"task_id":"C319861B08978CF5F7E6F0CD3A517A81"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"what genetic factor are associated with aging","answer":"Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.","weight":1,"task_id":"081B2DB92FD09DEBEF28ADBBDE7199D2"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.","weight":0,"task_id":"68EF3BE5EC2106766CA9CC700135E2FA"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes.","weight":1,"task_id":"8590501C57DC5C321AB5E1036F233027"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":-1,"task_id":"C8E21233058E1DEE05CD619ACBF49E42"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"38A8ED57882E2E250B447F93243A8CD5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"5D3CA84D2DCE010AF63188665193B195"}] diff --git a/gnqa/paper1_eval/src/data/ratings/2024_05_31_harm.json b/gnqa/paper1_eval/src/data/ratings/2024_05_31_harm.json new file mode 100644 index 0000000..80d6c8e --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_05_31_harm.json @@ -0,0 +1,35 @@ +{ + "nijveen": { + "query": [ + "Which genes give a predisposition to developing T1D?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?", + "How can I use genenetwork to find genes related with diabetes in humans?", + "How can I use the GeneNetwork tool to find genes related with diabetes in humans?"], + "ratings": [1,1,0,1], + "task_id": [ + "029A427CEEBABE644F12EE390469B134", + "173BE1F01E4A6074A0EBB7CC6137AC8D", + "F55160D302C899B2131AA8502A640684", + "B52AF52D46499DE2B98933F1786EC9E0" + ], + "topic": [0,2,2,2], + "level": "de" + }, +"villani": { + "query": [ + "What is ensembl", + "Which database can I use for genetic, genomics, phenotype, and disease-related data generated from rat research?", + "What is RGD?", + "What resources can I use to do pathway analyses?" + ], + "task_id": [ + "7C028B1D0013EA11574B094986ABE4C2", + "55562016699AFE4B8AD9A7F29A806CB5", + "C9B1B98F9207B79EBBC98790A769CB51", + "242918F32291CC085DEB319A7EE3284B" + ], + "ratings": [1, 1, -1, 1], + "topic": [0, 0, 0, 0], + "level": "de" + } +} diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_05-gnqa_responses.json b/gnqa/paper1_eval/src/data/ratings/2024_06_05-gnqa_responses.json new file mode 100644 index 0000000..ac877ce --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_05-gnqa_responses.json @@ -0,0 +1,95 @@ +[{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"F26EEA55FF8FFCB0B9E3E46688C276DA"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"5674818C9037B8A93EDCF35CEF97E33C"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"106C5DDDE1338BE1CC8AE04CEAE3EF62"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"2D8205C79915FF9CEB8DECCE51E6E473"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?","answer":"The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.","weight":1,"task_id":"849E78D8214245F8E8167E78C01BEE60"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"List as many studies as you can that include rapamycin.","answer":"1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.","weight":-1,"task_id":"2C477A3C76794C27A1FBBF437CFF75EE"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"Why is it so diffuclut to map gene loci that control aging in humans?","answer":"Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.","weight":1,"task_id":"CAD6C6C2AB42AA66BFDD65F0F11932B2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.","weight":1,"task_id":"EC2BFCD8E06079A3E595114822D2A44D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does epigenetics inluence gene expression without changing the underlying DNA sequence?","answer":"Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.","weight":-1,"task_id":"59AA8E8D7A97CFF47C6CFD65629F29F9"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.","answer":"Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.","weight":1,"task_id":"C58CFF41F7422B321DF88A110E278FD5"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?","answer":"The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.","weight":1,"task_id":"BCD1175CCB27FBA1E6F9D7670B17E527"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"5341FE6588C6175BC8A688A483928BC0"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"42847DE50D50E6A9B26ED0B03CFD160E"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"12BEAFA9366519672FC8B06959FB2DAF"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"64FEC152131BC6502E15EA6A6348D70B"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is apoptosis?","answer":"Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.","weight":1,"task_id":"78A0CD7E12AFEF6865583142603EE039"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is the most cited environmental factor for the onset of asthma?","answer":"The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.","weight":1,"task_id":"33FC2CC0F61BA22E4D095586B95703BD"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"370380F3A38AC4A788463D14E0EC673A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what is bioinformatics","answer":"Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.","weight":1,"task_id":"1E0DA0931F4E3A8C2893353CCA114B10"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"How would one extract the DNA, from say, flora or fauna?","answer":"DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.","weight":1,"task_id":"59E2406798D265A3CB466B766683E63C"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.","answer":"DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.","weight":-1,"task_id":"3F9EDFE9A0222EA70459EC8985F134C4"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.","weight":1,"task_id":"A010490B55F739DF95BB82DF2B0F5AA3"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Explain the process of finding a genetic marker followed by a quantitative trait loci.","answer":"The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.","weight":1,"task_id":"02A94D1056FDA77BDA9AC6CFDE0D5FC6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.","weight":1,"task_id":"96B873A13E3B386E38940EF6ECA90D4A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"A55310A1C19732792B5894A2DB5E8596"}, +{"user_id":"d2f71149-a0d5-4666-95d8-15c82543243b","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"7CA65F6B192BE7896422284B962CF5C3"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"2EDD65822CDA4669FCAD678288C2E3E9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":1,"task_id":"DA98AC2EA5D1F776D3F04FCBC7F01339"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what causes the aging process","answer":"The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.","weight":1,"task_id":"117299AD06C2B147F49E9C9BC036CEA4"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.","weight":1,"task_id":"F4DA6C97230E89C0226B1433532A16D9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":0,"task_id":"FBBFB0D391778CDE9536F742300C1809"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"32CE1E54032485B73B5968395B3D3538"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"59CCE2D70104154865218876DD53D049"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0AE973110158192E0D1F50E1D79764FB"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"33749EB09AAC7AD0404C8E3E584B98D2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"471A145E9CA1E517E462499ABCA8EA2D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?","answer":"The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.","weight":1,"task_id":"19EBC6CA7E425D0C1279475BD0B411B6"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"what is ensembl?","answer":"Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.","weight":-1,"task_id":"9CFE932D7898C83E473E590BC77B4FCB"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Create a how to guide for genetic sequencing ","answer":"1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.","weight":-1,"task_id":"C52A9690417093A861C669A0753689BD"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What is the significance of the length of telomeres? ","answer":"The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.","weight":1,"task_id":"93DE2EF005059DFEA5A7FBBA3BD17D03"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ","answer":"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.","weight":1,"task_id":"477FC54178046FE98BF97FAAC5FE167F"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Why is genetic tracing matrilineal rather than patrilineal? ","answer":"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.","weight":1,"task_id":"6B80ECC5F657EB7CBDE69D411A30D3EA"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"2DE25ABD7E487B80D0C489319640EACC"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"what type of dataset is useful for qtl mapping analysis in genenetwork2? ","answer":"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.","weight":1,"task_id":"6498ED71891B79908B2E383D9AA5BAC5"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What genetic factors influence aging in humans? ","answer":"Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.","weight":1,"task_id":"B2F5CB7BCD9A827D3A6E0152C030C4B4"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Is there a direct association between aging and susceptibility to having diabetes?","answer":"Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.","weight":1,"task_id":"72FBC4F382B6502EAF41BD6682E63A2D"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does genetics influence the emergency of diabetes? ","answer":"Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.","weight":1,"task_id":"02C953165B9CA94E273DD4A04301C89F"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"19DC9E909DDE6D9CDB3E86D1069F5A69"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F2843EA2D5A239D022186329C8D5D8EF"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"4E1F0C2E792BAF0BA349326375D3EE6E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"FA8ADB009A499F51B0533FDCB72CB29E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.","weight":-1,"task_id":"38BD5864A7928C6DBCA1D844327F3A19"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with diabetes?","answer":"The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.","weight":1,"task_id":"2272C482CC247E746D15C9F55EDD8BCE"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with aging?","answer":"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.","weight":1,"task_id":"C6C7CEF19CE7C27CF4BC6906259CDDF9"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the bioinformatics tools for QTLs analysis?","answer":"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.","weight":1,"task_id":"B4BB83EB5D5C5C042E07173119046A13"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the statistical approaches for qtls analysis?","answer":"The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.","weight":1,"task_id":"D88EF655762CE3D524A7A1EEA3FA16ED"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for GWAS analysis?","answer":"1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.","weight":1,"task_id":"245DD8093F5D16F44C2AD7618245086C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"B89A904E71CF7F16126FCA9EAAFBC8A6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":1,"task_id":"2F8796A8C3DC633F00DB901C9BA396DA"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"6188C7826173CD59FD243F98C537AD50"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"2C37A2EC74E4B46F13C6FB23D9547DBB"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"590809C6B2A1504E2AE9A79EFDF3AC9A"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the difference between QTL mapping and GWAS?","answer":"QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.","weight":1,"task_id":"6DBC070B2E4DC2FE8036E5BA7480B755"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How do I determine which gene in my QTL is causal for the trait?","answer":"To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.","weight":1,"task_id":"5594EA025D9631328071B6A1A7EF1375"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Which mouse genes have been associated with longevity?","answer":"The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.","weight":-1,"task_id":"AB589D2E046B211A7486A6C4BD4ECFB4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How is gene expression in the liver affected by diabetes?","answer":"In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.","weight":1,"task_id":"8FFF9DCC307B8DBF2C8485637F2ABEF4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Why do males have two Y chromosomes and females only one?","answer":"Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.","weight":1,"task_id":"C6B9A982C9283DE065A3371F1264095C"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing","answer":"1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.","weight":1,"task_id":"F9F7EA3DC28534B161ED70DB401C7D11"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.","weight":-1,"task_id":"4A06F8DF54C82D90E02F81D0E1E8B08A"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying.","weight":1,"task_id":"BA6A505E62A0529DB883D036CBC1FD92"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Which genes are associated with aging in human ","answer":"The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.","weight":1,"task_id":"3EC47C56606B02F00CF2449AB311365C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"CDFC418BD568E839C09656C57808ADA1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a guide for genetic sequencing","answer":"1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.","weight":-1,"task_id":"5DEB102510F48D0BF9C278DC895A8BD1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"5A562D5F7A266BA057B6833F3A83E7A7"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"53905925B4D6F69CE5706896A3C667CE"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.","weight":-1,"task_id":"C0015BEE5FE41769A65126B79BB1E40D"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":-1,"task_id":"9957BCF2957D9BD083EDCF8B60C0E4DE"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How does recombination work in human centromeres?","answer":"In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.","weight":-1,"task_id":"DEE6D385D1B01B4155AA4ABE59515893"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How many types of diabetes exist?","answer":"There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).","weight":1,"task_id":"9309F248E5933718BFB625E4EF2D3E42"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in the human genome?","answer":"The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome.","weight":-1,"task_id":"10ABD2210053119B18D94F1FE266E73E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"BD671A3AC4F77A74CA8A68EAD627437D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5E243BF70932D1477209FEF6869FDEEA"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.","weight":-1,"task_id":"3A16235DA1E02B9148B9288A06EE567E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9BE91FA13BDC0D9EB80B35FC73A0029B"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"3D4688621977A9CBD670B50131B8D912"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How can I add a new species to the GeneNetwork database?","answer":"The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.","weight":1,"task_id":"E94FFD042BB146E8A429200590A6792D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?","answer":"Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.","weight":1,"task_id":"C319861B08978CF5F7E6F0CD3A517A81"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"what genetic factor are associated with aging","answer":"Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.","weight":1,"task_id":"081B2DB92FD09DEBEF28ADBBDE7199D2"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.","weight":0,"task_id":"68EF3BE5EC2106766CA9CC700135E2FA"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes.","weight":1,"task_id":"8590501C57DC5C321AB5E1036F233027"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":-1,"task_id":"C8E21233058E1DEE05CD619ACBF49E42"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"38A8ED57882E2E250B447F93243A8CD5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"5D3CA84D2DCE010AF63188665193B195"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"AB6286D0D8E9196BA0EE1F1CF90536AD"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"00647726F98EE835006D60B12455866D"}] diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_12-gnqa_responses.json b/gnqa/paper1_eval/src/data/ratings/2024_06_12-gnqa_responses.json new file mode 100644 index 0000000..26f98ab --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_12-gnqa_responses.json @@ -0,0 +1,132 @@ +[{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"F26EEA55FF8FFCB0B9E3E46688C276DA"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"5674818C9037B8A93EDCF35CEF97E33C"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"106C5DDDE1338BE1CC8AE04CEAE3EF62"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"2D8205C79915FF9CEB8DECCE51E6E473"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?","answer":"The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.","weight":1,"task_id":"849E78D8214245F8E8167E78C01BEE60"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"List as many studies as you can that include rapamycin.","answer":"1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.","weight":-1,"task_id":"2C477A3C76794C27A1FBBF437CFF75EE"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"Why is it so diffuclut to map gene loci that control aging in humans?","answer":"Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.","weight":1,"task_id":"CAD6C6C2AB42AA66BFDD65F0F11932B2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.","weight":1,"task_id":"EC2BFCD8E06079A3E595114822D2A44D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does epigenetics inluence gene expression without changing the underlying DNA sequence?","answer":"Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.","weight":-1,"task_id":"59AA8E8D7A97CFF47C6CFD65629F29F9"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.","answer":"Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.","weight":1,"task_id":"C58CFF41F7422B321DF88A110E278FD5"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?","answer":"The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.","weight":1,"task_id":"BCD1175CCB27FBA1E6F9D7670B17E527"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"5341FE6588C6175BC8A688A483928BC0"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"42847DE50D50E6A9B26ED0B03CFD160E"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"12BEAFA9366519672FC8B06959FB2DAF"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"64FEC152131BC6502E15EA6A6348D70B"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is apoptosis?","answer":"Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.","weight":1,"task_id":"78A0CD7E12AFEF6865583142603EE039"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is the most cited environmental factor for the onset of asthma?","answer":"The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.","weight":1,"task_id":"33FC2CC0F61BA22E4D095586B95703BD"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"370380F3A38AC4A788463D14E0EC673A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what is bioinformatics","answer":"Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.","weight":1,"task_id":"1E0DA0931F4E3A8C2893353CCA114B10"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"How would one extract the DNA, from say, flora or fauna?","answer":"DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.","weight":1,"task_id":"59E2406798D265A3CB466B766683E63C"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.","answer":"DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.","weight":-1,"task_id":"3F9EDFE9A0222EA70459EC8985F134C4"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.","weight":1,"task_id":"A010490B55F739DF95BB82DF2B0F5AA3"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Explain the process of finding a genetic marker followed by a quantitative trait loci.","answer":"The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.","weight":1,"task_id":"02A94D1056FDA77BDA9AC6CFDE0D5FC6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.","weight":1,"task_id":"96B873A13E3B386E38940EF6ECA90D4A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"A55310A1C19732792B5894A2DB5E8596"}, +{"user_id":"d2f71149-a0d5-4666-95d8-15c82543243b","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"7CA65F6B192BE7896422284B962CF5C3"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"2EDD65822CDA4669FCAD678288C2E3E9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":1,"task_id":"DA98AC2EA5D1F776D3F04FCBC7F01339"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what causes the aging process","answer":"The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.","weight":1,"task_id":"117299AD06C2B147F49E9C9BC036CEA4"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.","weight":1,"task_id":"F4DA6C97230E89C0226B1433532A16D9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":0,"task_id":"FBBFB0D391778CDE9536F742300C1809"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"32CE1E54032485B73B5968395B3D3538"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"59CCE2D70104154865218876DD53D049"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0AE973110158192E0D1F50E1D79764FB"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"33749EB09AAC7AD0404C8E3E584B98D2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"471A145E9CA1E517E462499ABCA8EA2D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?","answer":"The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.","weight":1,"task_id":"19EBC6CA7E425D0C1279475BD0B411B6"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"what is ensembl?","answer":"Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.","weight":-1,"task_id":"9CFE932D7898C83E473E590BC77B4FCB"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Create a how to guide for genetic sequencing ","answer":"1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.","weight":-1,"task_id":"C52A9690417093A861C669A0753689BD"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What is the significance of the length of telomeres? ","answer":"The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.","weight":1,"task_id":"93DE2EF005059DFEA5A7FBBA3BD17D03"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ","answer":"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.","weight":1,"task_id":"477FC54178046FE98BF97FAAC5FE167F"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Why is genetic tracing matrilineal rather than patrilineal? ","answer":"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.","weight":1,"task_id":"6B80ECC5F657EB7CBDE69D411A30D3EA"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"2DE25ABD7E487B80D0C489319640EACC"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"what type of dataset is useful for qtl mapping analysis in genenetwork2? ","answer":"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.","weight":1,"task_id":"6498ED71891B79908B2E383D9AA5BAC5"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What genetic factors influence aging in humans? ","answer":"Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.","weight":1,"task_id":"B2F5CB7BCD9A827D3A6E0152C030C4B4"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Is there a direct association between aging and susceptibility to having diabetes?","answer":"Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.","weight":1,"task_id":"72FBC4F382B6502EAF41BD6682E63A2D"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does genetics influence the emergency of diabetes? ","answer":"Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.","weight":1,"task_id":"02C953165B9CA94E273DD4A04301C89F"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"19DC9E909DDE6D9CDB3E86D1069F5A69"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F2843EA2D5A239D022186329C8D5D8EF"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"4E1F0C2E792BAF0BA349326375D3EE6E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"FA8ADB009A499F51B0533FDCB72CB29E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.","weight":-1,"task_id":"38BD5864A7928C6DBCA1D844327F3A19"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with diabetes?","answer":"The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.","weight":1,"task_id":"2272C482CC247E746D15C9F55EDD8BCE"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with aging?","answer":"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.","weight":1,"task_id":"C6C7CEF19CE7C27CF4BC6906259CDDF9"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the bioinformatics tools for QTLs analysis?","answer":"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.","weight":1,"task_id":"B4BB83EB5D5C5C042E07173119046A13"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the statistical approaches for qtls analysis?","answer":"The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.","weight":1,"task_id":"D88EF655762CE3D524A7A1EEA3FA16ED"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for GWAS analysis?","answer":"1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.","weight":1,"task_id":"245DD8093F5D16F44C2AD7618245086C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"B89A904E71CF7F16126FCA9EAAFBC8A6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":1,"task_id":"2F8796A8C3DC633F00DB901C9BA396DA"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"6188C7826173CD59FD243F98C537AD50"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"2C37A2EC74E4B46F13C6FB23D9547DBB"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"590809C6B2A1504E2AE9A79EFDF3AC9A"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the difference between QTL mapping and GWAS?","answer":"QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.","weight":1,"task_id":"6DBC070B2E4DC2FE8036E5BA7480B755"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How do I determine which gene in my QTL is causal for the trait?","answer":"To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.","weight":1,"task_id":"5594EA025D9631328071B6A1A7EF1375"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Which mouse genes have been associated with longevity?","answer":"The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.","weight":-1,"task_id":"AB589D2E046B211A7486A6C4BD4ECFB4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How is gene expression in the liver affected by diabetes?","answer":"In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.","weight":1,"task_id":"8FFF9DCC307B8DBF2C8485637F2ABEF4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Why do males have two Y chromosomes and females only one?","answer":"Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.","weight":1,"task_id":"C6B9A982C9283DE065A3371F1264095C"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing","answer":"1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.","weight":1,"task_id":"F9F7EA3DC28534B161ED70DB401C7D11"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.","weight":-1,"task_id":"4A06F8DF54C82D90E02F81D0E1E8B08A"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying.","weight":1,"task_id":"BA6A505E62A0529DB883D036CBC1FD92"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Which genes are associated with aging in human ","answer":"The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.","weight":1,"task_id":"3EC47C56606B02F00CF2449AB311365C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"CDFC418BD568E839C09656C57808ADA1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a guide for genetic sequencing","answer":"1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.","weight":-1,"task_id":"5DEB102510F48D0BF9C278DC895A8BD1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"5A562D5F7A266BA057B6833F3A83E7A7"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"53905925B4D6F69CE5706896A3C667CE"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.","weight":-1,"task_id":"C0015BEE5FE41769A65126B79BB1E40D"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":-1,"task_id":"9957BCF2957D9BD083EDCF8B60C0E4DE"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How does recombination work in human centromeres?","answer":"In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.","weight":-1,"task_id":"DEE6D385D1B01B4155AA4ABE59515893"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How many types of diabetes exist?","answer":"There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).","weight":1,"task_id":"9309F248E5933718BFB625E4EF2D3E42"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in the human genome?","answer":"The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome.","weight":-1,"task_id":"10ABD2210053119B18D94F1FE266E73E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"BD671A3AC4F77A74CA8A68EAD627437D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5E243BF70932D1477209FEF6869FDEEA"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.","weight":-1,"task_id":"3A16235DA1E02B9148B9288A06EE567E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9BE91FA13BDC0D9EB80B35FC73A0029B"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"3D4688621977A9CBD670B50131B8D912"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How can I add a new species to the GeneNetwork database?","answer":"The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.","weight":1,"task_id":"E94FFD042BB146E8A429200590A6792D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?","answer":"Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.","weight":1,"task_id":"C319861B08978CF5F7E6F0CD3A517A81"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"what genetic factor are associated with aging","answer":"Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.","weight":1,"task_id":"081B2DB92FD09DEBEF28ADBBDE7199D2"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.","weight":0,"task_id":"68EF3BE5EC2106766CA9CC700135E2FA"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes.","weight":1,"task_id":"8590501C57DC5C321AB5E1036F233027"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":-1,"task_id":"C8E21233058E1DEE05CD619ACBF49E42"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"38A8ED57882E2E250B447F93243A8CD5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"5D3CA84D2DCE010AF63188665193B195"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"AB6286D0D8E9196BA0EE1F1CF90536AD"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"00647726F98EE835006D60B12455866D"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"8F3A81EAB68F709E82006205380AC723"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"ECEB33208BCDDC20908183BF249555AD"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":-1,"task_id":"906F0A0AB4330CB7C3A75DA8764703F4"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"A3B39D0211921BC5581AB03193860970"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"2BF7D398C0BFD1F4D976C9F82343DE51"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with diabetes in QTL analyses?","answer":"The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.","weight":1,"task_id":"FCFCE5BBA2A8B3D8818890B9D2308C5A"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with early aging?","answer":"The genes typically associated with early aging are APOE and FOXO3A.","weight":1,"task_id":"E3FFB15A9901BD8DB87B0F09D335BEA0"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How do I generate a linkage or association mapping study in mice to understand aging?","answer":"To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.","weight":1,"task_id":"38797E46211127E5C7175E707D40325B"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Is the gene TCF7L2 involved in diabetes?","answer":"Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).","weight":1,"task_id":"CD1F7EAE0FDC758A8167118927ADFE71"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"In which diseases is the gene TCF7L2 involved?","answer":"The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.","weight":-1,"task_id":"FFA6EADA5502933C0C30C9D16DCAA073"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"what are confounding factors in diabetes?","answer":"Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors.","weight":1,"task_id":"00BE70B5D71A5926E56942909C8B2A92"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":-1,"task_id":"0BF7A88573F1B5FCC2E8978A6D94CE2B"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"CBC2A69A6A94CFADE9E4077F5B655B6E"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.","weight":1,"task_id":"847F1E1599EECDE92F99B7581728FFE8"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?","answer":"A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.","weight":1,"task_id":"037BAB6CB2DE7A42AAAA73CE5DA8DB73"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":" Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.","weight":1,"task_id":"B2AA6DE557D652A0A660C4E0FAC1124D"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"3A55AE005B07C55283410798C0FBE47F"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.","answer":"Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.","weight":1,"task_id":"7EC697DE62C0C57E601EC3F5B295DF61"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.","answer":"Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.","weight":1,"task_id":"0A6673A0B69F0FF9C9657FB797DD1FE2"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.","answer":"Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.","weight":1,"task_id":"44B088326CD80B4980D810738D88A284"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert","answer":"Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors.","weight":-1,"task_id":"D53462CE61F52F7D31BB627998F4D75A"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"F6FC3B8BBCE2BA90D0EF2C9532DE3F84"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"0F76F85FB406BF74022084C5866C942D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"FE7363764A44969E28C9562A3948143B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F456694025B9C98AA9E4246820D5909A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"E6C75917249BB8C0810B0E709D6FDD0A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"8FA337BF315CFA09716800E096EA8A06"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"D68A2086596023BDD8C01023B03FA89D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How is the immune system related to diabetes?","answer":"The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.","weight":1,"task_id":"CD3820AA1BD96613F78FDF3CF5C8AB3D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the genomic variants associated with immune system components and diabetes?","answer":"The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.","weight":1,"task_id":"A4CE2F2F8E08E5F16C94A1BCF540D881"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the role of the immune system in the metabolomics of diabetes and associated conditions?","answer":"The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.","weight":1,"task_id":"1B8618ADB274F928B3AACAB1C71A927E"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the different relationship between traits?","answer":"The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.","weight":1,"task_id":"BF1705D2C26044038FF1483258548167"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can landscape of QTL and GWAS hits be used to find relationships between traits ?","answer":"Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.","weight":1,"task_id":"68AB7A78543D5B36206274837824091B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?","answer":"Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes.","weight":1,"task_id":"055110B765AA502F9AAECE68CEC0DD24"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"C4FEDD378CD138B141464832D021624B"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"ED89B73DC42AD2ADA03B7C014009A551"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"21CB24A2A589173F1E50ADA5DD6165EC"}] diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_18-gnqa_responses.json b/gnqa/paper1_eval/src/data/ratings/2024_06_18-gnqa_responses.json new file mode 100644 index 0000000..9000fe6 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_18-gnqa_responses.json @@ -0,0 +1,139 @@ +[{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"F26EEA55FF8FFCB0B9E3E46688C276DA"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"5674818C9037B8A93EDCF35CEF97E33C"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"106C5DDDE1338BE1CC8AE04CEAE3EF62"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"2D8205C79915FF9CEB8DECCE51E6E473"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?","answer":"The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.","weight":1,"task_id":"849E78D8214245F8E8167E78C01BEE60"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"List as many studies as you can that include rapamycin.","answer":"1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.","weight":-1,"task_id":"2C477A3C76794C27A1FBBF437CFF75EE"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"Why is it so diffuclut to map gene loci that control aging in humans?","answer":"Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.","weight":1,"task_id":"CAD6C6C2AB42AA66BFDD65F0F11932B2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.","weight":1,"task_id":"EC2BFCD8E06079A3E595114822D2A44D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does epigenetics inluence gene expression without changing the underlying DNA sequence?","answer":"Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.","weight":-1,"task_id":"59AA8E8D7A97CFF47C6CFD65629F29F9"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.","answer":"Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.","weight":1,"task_id":"C58CFF41F7422B321DF88A110E278FD5"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?","answer":"The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.","weight":1,"task_id":"BCD1175CCB27FBA1E6F9D7670B17E527"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"5341FE6588C6175BC8A688A483928BC0"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"42847DE50D50E6A9B26ED0B03CFD160E"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"12BEAFA9366519672FC8B06959FB2DAF"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"64FEC152131BC6502E15EA6A6348D70B"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is apoptosis?","answer":"Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.","weight":1,"task_id":"78A0CD7E12AFEF6865583142603EE039"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is the most cited environmental factor for the onset of asthma?","answer":"The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.","weight":1,"task_id":"33FC2CC0F61BA22E4D095586B95703BD"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"370380F3A38AC4A788463D14E0EC673A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what is bioinformatics","answer":"Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.","weight":1,"task_id":"1E0DA0931F4E3A8C2893353CCA114B10"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"How would one extract the DNA, from say, flora or fauna?","answer":"DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.","weight":1,"task_id":"59E2406798D265A3CB466B766683E63C"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.","answer":"DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.","weight":-1,"task_id":"3F9EDFE9A0222EA70459EC8985F134C4"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.","weight":1,"task_id":"A010490B55F739DF95BB82DF2B0F5AA3"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Explain the process of finding a genetic marker followed by a quantitative trait loci.","answer":"The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.","weight":1,"task_id":"02A94D1056FDA77BDA9AC6CFDE0D5FC6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.","weight":1,"task_id":"96B873A13E3B386E38940EF6ECA90D4A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"A55310A1C19732792B5894A2DB5E8596"}, +{"user_id":"d2f71149-a0d5-4666-95d8-15c82543243b","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"7CA65F6B192BE7896422284B962CF5C3"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"2EDD65822CDA4669FCAD678288C2E3E9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":1,"task_id":"DA98AC2EA5D1F776D3F04FCBC7F01339"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what causes the aging process","answer":"The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.","weight":1,"task_id":"117299AD06C2B147F49E9C9BC036CEA4"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.","weight":1,"task_id":"F4DA6C97230E89C0226B1433532A16D9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":0,"task_id":"FBBFB0D391778CDE9536F742300C1809"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"32CE1E54032485B73B5968395B3D3538"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"59CCE2D70104154865218876DD53D049"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0AE973110158192E0D1F50E1D79764FB"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"33749EB09AAC7AD0404C8E3E584B98D2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"471A145E9CA1E517E462499ABCA8EA2D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?","answer":"The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.","weight":1,"task_id":"19EBC6CA7E425D0C1279475BD0B411B6"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"what is ensembl?","answer":"Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.","weight":-1,"task_id":"9CFE932D7898C83E473E590BC77B4FCB"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Create a how to guide for genetic sequencing ","answer":"1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.","weight":-1,"task_id":"C52A9690417093A861C669A0753689BD"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What is the significance of the length of telomeres? ","answer":"The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.","weight":1,"task_id":"93DE2EF005059DFEA5A7FBBA3BD17D03"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ","answer":"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.","weight":1,"task_id":"477FC54178046FE98BF97FAAC5FE167F"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Why is genetic tracing matrilineal rather than patrilineal? ","answer":"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.","weight":1,"task_id":"6B80ECC5F657EB7CBDE69D411A30D3EA"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"2DE25ABD7E487B80D0C489319640EACC"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"what type of dataset is useful for qtl mapping analysis in genenetwork2? ","answer":"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.","weight":1,"task_id":"6498ED71891B79908B2E383D9AA5BAC5"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What genetic factors influence aging in humans? ","answer":"Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.","weight":1,"task_id":"B2F5CB7BCD9A827D3A6E0152C030C4B4"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Is there a direct association between aging and susceptibility to having diabetes?","answer":"Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.","weight":1,"task_id":"72FBC4F382B6502EAF41BD6682E63A2D"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does genetics influence the emergency of diabetes? ","answer":"Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.","weight":1,"task_id":"02C953165B9CA94E273DD4A04301C89F"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"19DC9E909DDE6D9CDB3E86D1069F5A69"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F2843EA2D5A239D022186329C8D5D8EF"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"4E1F0C2E792BAF0BA349326375D3EE6E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"FA8ADB009A499F51B0533FDCB72CB29E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.","weight":-1,"task_id":"38BD5864A7928C6DBCA1D844327F3A19"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with diabetes?","answer":"The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.","weight":1,"task_id":"2272C482CC247E746D15C9F55EDD8BCE"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with aging?","answer":"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.","weight":1,"task_id":"C6C7CEF19CE7C27CF4BC6906259CDDF9"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the bioinformatics tools for QTLs analysis?","answer":"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.","weight":1,"task_id":"B4BB83EB5D5C5C042E07173119046A13"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the statistical approaches for qtls analysis?","answer":"The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.","weight":1,"task_id":"D88EF655762CE3D524A7A1EEA3FA16ED"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for GWAS analysis?","answer":"1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.","weight":1,"task_id":"245DD8093F5D16F44C2AD7618245086C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"B89A904E71CF7F16126FCA9EAAFBC8A6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":1,"task_id":"2F8796A8C3DC633F00DB901C9BA396DA"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"6188C7826173CD59FD243F98C537AD50"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"2C37A2EC74E4B46F13C6FB23D9547DBB"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"590809C6B2A1504E2AE9A79EFDF3AC9A"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the difference between QTL mapping and GWAS?","answer":"QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.","weight":1,"task_id":"6DBC070B2E4DC2FE8036E5BA7480B755"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How do I determine which gene in my QTL is causal for the trait?","answer":"To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.","weight":1,"task_id":"5594EA025D9631328071B6A1A7EF1375"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Which mouse genes have been associated with longevity?","answer":"The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.","weight":-1,"task_id":"AB589D2E046B211A7486A6C4BD4ECFB4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How is gene expression in the liver affected by diabetes?","answer":"In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.","weight":1,"task_id":"8FFF9DCC307B8DBF2C8485637F2ABEF4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Why do males have two Y chromosomes and females only one?","answer":"Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.","weight":1,"task_id":"C6B9A982C9283DE065A3371F1264095C"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing","answer":"1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.","weight":1,"task_id":"F9F7EA3DC28534B161ED70DB401C7D11"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.","weight":-1,"task_id":"4A06F8DF54C82D90E02F81D0E1E8B08A"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying.","weight":1,"task_id":"BA6A505E62A0529DB883D036CBC1FD92"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Which genes are associated with aging in human ","answer":"The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.","weight":1,"task_id":"3EC47C56606B02F00CF2449AB311365C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"CDFC418BD568E839C09656C57808ADA1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a guide for genetic sequencing","answer":"1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.","weight":-1,"task_id":"5DEB102510F48D0BF9C278DC895A8BD1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"5A562D5F7A266BA057B6833F3A83E7A7"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"53905925B4D6F69CE5706896A3C667CE"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.","weight":-1,"task_id":"C0015BEE5FE41769A65126B79BB1E40D"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":-1,"task_id":"9957BCF2957D9BD083EDCF8B60C0E4DE"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How does recombination work in human centromeres?","answer":"In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.","weight":-1,"task_id":"DEE6D385D1B01B4155AA4ABE59515893"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How many types of diabetes exist?","answer":"There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).","weight":1,"task_id":"9309F248E5933718BFB625E4EF2D3E42"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in the human genome?","answer":"The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome.","weight":-1,"task_id":"10ABD2210053119B18D94F1FE266E73E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"BD671A3AC4F77A74CA8A68EAD627437D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5E243BF70932D1477209FEF6869FDEEA"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.","weight":-1,"task_id":"3A16235DA1E02B9148B9288A06EE567E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9BE91FA13BDC0D9EB80B35FC73A0029B"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"3D4688621977A9CBD670B50131B8D912"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How can I add a new species to the GeneNetwork database?","answer":"The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.","weight":1,"task_id":"E94FFD042BB146E8A429200590A6792D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?","answer":"Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.","weight":1,"task_id":"C319861B08978CF5F7E6F0CD3A517A81"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"what genetic factor are associated with aging","answer":"Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.","weight":1,"task_id":"081B2DB92FD09DEBEF28ADBBDE7199D2"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.","weight":0,"task_id":"68EF3BE5EC2106766CA9CC700135E2FA"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes.","weight":1,"task_id":"8590501C57DC5C321AB5E1036F233027"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":-1,"task_id":"C8E21233058E1DEE05CD619ACBF49E42"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"38A8ED57882E2E250B447F93243A8CD5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"5D3CA84D2DCE010AF63188665193B195"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"AB6286D0D8E9196BA0EE1F1CF90536AD"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"00647726F98EE835006D60B12455866D"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"8F3A81EAB68F709E82006205380AC723"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"ECEB33208BCDDC20908183BF249555AD"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":-1,"task_id":"906F0A0AB4330CB7C3A75DA8764703F4"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"A3B39D0211921BC5581AB03193860970"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"2BF7D398C0BFD1F4D976C9F82343DE51"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with diabetes in QTL analyses?","answer":"The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.","weight":1,"task_id":"FCFCE5BBA2A8B3D8818890B9D2308C5A"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with early aging?","answer":"The genes typically associated with early aging are APOE and FOXO3A.","weight":1,"task_id":"E3FFB15A9901BD8DB87B0F09D335BEA0"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How do I generate a linkage or association mapping study in mice to understand aging?","answer":"To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.","weight":1,"task_id":"38797E46211127E5C7175E707D40325B"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Is the gene TCF7L2 involved in diabetes?","answer":"Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).","weight":1,"task_id":"CD1F7EAE0FDC758A8167118927ADFE71"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"In which diseases is the gene TCF7L2 involved?","answer":"The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.","weight":-1,"task_id":"FFA6EADA5502933C0C30C9D16DCAA073"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"what are confounding factors in diabetes?","answer":"Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors.","weight":1,"task_id":"00BE70B5D71A5926E56942909C8B2A92"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":-1,"task_id":"0BF7A88573F1B5FCC2E8978A6D94CE2B"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"CBC2A69A6A94CFADE9E4077F5B655B6E"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.","weight":1,"task_id":"847F1E1599EECDE92F99B7581728FFE8"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?","answer":"A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.","weight":1,"task_id":"037BAB6CB2DE7A42AAAA73CE5DA8DB73"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":" Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.","weight":1,"task_id":"B2AA6DE557D652A0A660C4E0FAC1124D"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"3A55AE005B07C55283410798C0FBE47F"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.","answer":"Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.","weight":1,"task_id":"7EC697DE62C0C57E601EC3F5B295DF61"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.","answer":"Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.","weight":1,"task_id":"0A6673A0B69F0FF9C9657FB797DD1FE2"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.","answer":"Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.","weight":1,"task_id":"44B088326CD80B4980D810738D88A284"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert","answer":"Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors.","weight":-1,"task_id":"D53462CE61F52F7D31BB627998F4D75A"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"F6FC3B8BBCE2BA90D0EF2C9532DE3F84"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"0F76F85FB406BF74022084C5866C942D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"FE7363764A44969E28C9562A3948143B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F456694025B9C98AA9E4246820D5909A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"E6C75917249BB8C0810B0E709D6FDD0A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"8FA337BF315CFA09716800E096EA8A06"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"D68A2086596023BDD8C01023B03FA89D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How is the immune system related to diabetes?","answer":"The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.","weight":1,"task_id":"CD3820AA1BD96613F78FDF3CF5C8AB3D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the genomic variants associated with immune system components and diabetes?","answer":"The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.","weight":1,"task_id":"A4CE2F2F8E08E5F16C94A1BCF540D881"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the role of the immune system in the metabolomics of diabetes and associated conditions?","answer":"The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.","weight":1,"task_id":"1B8618ADB274F928B3AACAB1C71A927E"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the different relationship between traits?","answer":"The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.","weight":1,"task_id":"BF1705D2C26044038FF1483258548167"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can landscape of QTL and GWAS hits be used to find relationships between traits ?","answer":"Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.","weight":1,"task_id":"68AB7A78543D5B36206274837824091B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?","answer":"Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes.","weight":1,"task_id":"055110B765AA502F9AAECE68CEC0DD24"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"C4FEDD378CD138B141464832D021624B"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"ED89B73DC42AD2ADA03B7C014009A551"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"21CB24A2A589173F1E50ADA5DD6165EC"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"1619A0727D1C6673EE9E05171054F658"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"52B443B815CD46D57219872DFB3D0579"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"EB6B4DCD473BEE9580F47CD12DAFC074"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What causes diabetes?","answer":"Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.","weight":1,"task_id":"2AE18C9AAFB4E3A103F03C86BBEB2DD1"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Define dyslipidemia.","answer":"Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.","weight":1,"task_id":"58D6F365917926445960756A26B3FDC8"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Does cycling reduce risk of diabetes?","answer":"Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.","weight":1,"task_id":"2A2860BB54BC0D36A929838ED41243A7"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is cytochrome?","answer":"Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence.","weight":1,"task_id":"A5DEAEAC441B3BDC65B58EA6923FAE73"}] diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_18-out.json b/gnqa/paper1_eval/src/data/ratings/2024_06_18-out.json new file mode 100644 index 0000000..06d962a --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_18-out.json @@ -0,0 +1,690 @@ +[ + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "32CE1E54032485B73B5968395B3D3538", + "59CCE2D70104154865218876DD53D049", + "0AE973110158192E0D1F50E1D79764FB", + "33749EB09AAC7AD0404C8E3E584B98D2", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "9957BCF2957D9BD083EDCF8B60C0E4DE", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "d2f71149-a0d5-4666-95d8-15c82543243b": { + "task_id": [ + "7CA65F6B192BE7896422284B962CF5C3" + ], + "weight": [ + 1 + ], + "answer": [ + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits." + ], + "query": [ + "genetics" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "19DC9E909DDE6D9CDB3E86D1069F5A69", + "F2843EA2D5A239D022186329C8D5D8EF", + "4E1F0C2E792BAF0BA349326375D3EE6E", + "FA8ADB009A499F51B0533FDCB72CB29E", + "38BD5864A7928C6DBCA1D844327F3A19", + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + -1, + 1, + 1, + -1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.", + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "B89A904E71CF7F16126FCA9EAAFBC8A6", + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1", + "5A562D5F7A266BA057B6833F3A83E7A7", + "53905925B4D6F69CE5706896A3C667CE", + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6188C7826173CD59FD243F98C537AD50", + "2C37A2EC74E4B46F13C6FB23D9547DBB", + "590809C6B2A1504E2AE9A79EFDF3AC9A", + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "BD671A3AC4F77A74CA8A68EAD627437D", + "5E243BF70932D1477209FEF6869FDEEA", + "3A16235DA1E02B9148B9288A06EE567E", + "9BE91FA13BDC0D9EB80B35FC73A0029B", + "3D4688621977A9CBD670B50131B8D912", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "00647726F98EE835006D60B12455866D", + "8F3A81EAB68F709E82006205380AC723", + "ECEB33208BCDDC20908183BF249555AD", + "906F0A0AB4330CB7C3A75DA8764703F4", + "A3B39D0211921BC5581AB03193860970", + "2BF7D398C0BFD1F4D976C9F82343DE51", + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + -1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "0BF7A88573F1B5FCC2E8978A6D94CE2B", + "CBC2A69A6A94CFADE9E4077F5B655B6E", + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "3A55AE005B07C55283410798C0FBE47F", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + -1, + -1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "What is the significance of the length of telomeres?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "61a8e2c6-249c-40b8-a866-134f3a893e4a": { + "task_id": [ + "F6FC3B8BBCE2BA90D0EF2C9532DE3F84", + "0F76F85FB406BF74022084C5866C942D", + "C4FEDD378CD138B141464832D021624B", + "ED89B73DC42AD2ADA03B7C014009A551", + "21CB24A2A589173F1E50ADA5DD6165EC" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "FE7363764A44969E28C9562A3948143B", + "F456694025B9C98AA9E4246820D5909A", + "E6C75917249BB8C0810B0E709D6FDD0A", + "8FA337BF315CFA09716800E096EA8A06", + "D68A2086596023BDD8C01023B03FA89D", + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + }, + { + "8e4fe952-5a61-4d95-86e5-49f974465572": { + "task_id": [ + "1619A0727D1C6673EE9E05171054F658", + "52B443B815CD46D57219872DFB3D0579", + "EB6B4DCD473BEE9580F47CD12DAFC074", + "2AE18C9AAFB4E3A103F03C86BBEB2DD1", + "58D6F365917926445960756A26B3FDC8", + "2A2860BB54BC0D36A929838ED41243A7", + "A5DEAEAC441B3BDC65B58EA6923FAE73" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "What causes diabetes?", + "Define dyslipidemia.", + "Does cycling reduce risk of diabetes?", + "What is cytochrome?" + ] + } + } +] diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_18_gnqa_user_ratings.csv b/gnqa/paper1_eval/src/data/ratings/2024_06_18_gnqa_user_ratings.csv new file mode 100644 index 0000000..016538b --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_18_gnqa_user_ratings.csv @@ -0,0 +1,124 @@ +1,1 +-1,0 +1,1 +1,1 +1,1 +1,1 +1,1 +-1,0 +1,1 +1,1 +1,1 +1,1 +1,1 +1,1 +-1,0 +1,1 +1,1 +1,1 +1,1 +1,1 +1,1 +1,1 +-1,0 +1,1 +1,1 +1,1 +1,1 +-1,0 +-1,0 +1,1 +-1,0 +-1,0 +1,1 +1,1 +1,1 +1,1 +1,1 +1,1 +1,1 +1,1 +-1,0 +1,1 +1,1 +-1,0 +-1,0 +1,1 +1,1 +1,1 +1,1 +1,1 +1,1 +1,1 +1,1 +-1,0 +1,1 +1,1 +-1,0 +-1,0 +1,1 +1,1 +1,1 +1,1 +-1,0 +1,1 +1,1 +1,1 +-1,0 +1,1 +-1,0 +1,1 +-1,0 +1,1 +1,1 +1,1 +1,1 +1,1 +0,0.5 +1,1 +1,1 +1,1 +1,1 +-1,0 +1,1 +-1,0 +1,1 +1,1 +1,1 +1,1 +-1,0 +1,1 +-1,0 +-1,0 +1,1 +1,1 +1,1 +-1,0 +1,1 +1,1 +1,1 +-1,0 +1,1 +1,1 +-1,0 +1,1 +1,1 +1,1 +1,1 +1,1 +1,1 +-1,0 +1,1 +1,1 +1,1 +1,1 +1,1 +1,1 +1,1 +-1,0 +1,1 +1,1 +1,1 +1,1 +1,1 +,0.768292682926829 diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_18_gnqa_user_ratings.ods b/gnqa/paper1_eval/src/data/ratings/2024_06_18_gnqa_user_ratings.ods Binary files differnew file mode 100644 index 0000000..ceb6e74 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_18_gnqa_user_ratings.ods diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_18_queryanswersratings.json b/gnqa/paper1_eval/src/data/ratings/2024_06_18_queryanswersratings.json new file mode 100644 index 0000000..a6af2ac --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_18_queryanswersratings.json @@ -0,0 +1,673 @@ +[ + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "32CE1E54032485B73B5968395B3D3538", + "59CCE2D70104154865218876DD53D049", + "0AE973110158192E0D1F50E1D79764FB", + "33749EB09AAC7AD0404C8E3E584B98D2", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "9957BCF2957D9BD083EDCF8B60C0E4DE", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "19DC9E909DDE6D9CDB3E86D1069F5A69", + "F2843EA2D5A239D022186329C8D5D8EF", + "4E1F0C2E792BAF0BA349326375D3EE6E", + "FA8ADB009A499F51B0533FDCB72CB29E", + "38BD5864A7928C6DBCA1D844327F3A19", + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + -1, + 1, + 1, + -1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.", + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "B89A904E71CF7F16126FCA9EAAFBC8A6", + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1", + "5A562D5F7A266BA057B6833F3A83E7A7", + "53905925B4D6F69CE5706896A3C667CE", + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6188C7826173CD59FD243F98C537AD50", + "2C37A2EC74E4B46F13C6FB23D9547DBB", + "590809C6B2A1504E2AE9A79EFDF3AC9A", + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "BD671A3AC4F77A74CA8A68EAD627437D", + "5E243BF70932D1477209FEF6869FDEEA", + "3A16235DA1E02B9148B9288A06EE567E", + "9BE91FA13BDC0D9EB80B35FC73A0029B", + "3D4688621977A9CBD670B50131B8D912", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "00647726F98EE835006D60B12455866D", + "8F3A81EAB68F709E82006205380AC723", + "ECEB33208BCDDC20908183BF249555AD", + "906F0A0AB4330CB7C3A75DA8764703F4", + "A3B39D0211921BC5581AB03193860970", + "2BF7D398C0BFD1F4D976C9F82343DE51", + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + -1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "0BF7A88573F1B5FCC2E8978A6D94CE2B", + "CBC2A69A6A94CFADE9E4077F5B655B6E", + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "3A55AE005B07C55283410798C0FBE47F", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + -1, + -1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "What is the significance of the length of telomeres?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "61a8e2c6-249c-40b8-a866-134f3a893e4a": { + "task_id": [ + "F6FC3B8BBCE2BA90D0EF2C9532DE3F84", + "0F76F85FB406BF74022084C5866C942D", + "C4FEDD378CD138B141464832D021624B", + "ED89B73DC42AD2ADA03B7C014009A551", + "21CB24A2A589173F1E50ADA5DD6165EC" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "FE7363764A44969E28C9562A3948143B", + "F456694025B9C98AA9E4246820D5909A", + "E6C75917249BB8C0810B0E709D6FDD0A", + "8FA337BF315CFA09716800E096EA8A06", + "D68A2086596023BDD8C01023B03FA89D", + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + }, + { + "8e4fe952-5a61-4d95-86e5-49f974465572": { + "task_id": [ + "1619A0727D1C6673EE9E05171054F658", + "52B443B815CD46D57219872DFB3D0579", + "EB6B4DCD473BEE9580F47CD12DAFC074", + "2AE18C9AAFB4E3A103F03C86BBEB2DD1", + "58D6F365917926445960756A26B3FDC8", + "2A2860BB54BC0D36A929838ED41243A7", + "A5DEAEAC441B3BDC65B58EA6923FAE73" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "What causes diabetes?", + "Define dyslipidemia.", + "Does cycling reduce risk of diabetes?", + "What is cytochrome?" + ] + } + } +] diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_21-gnqa_combined_responses_edit.json b/gnqa/paper1_eval/src/data/ratings/2024_06_21-gnqa_combined_responses_edit.json new file mode 100644 index 0000000..dae920b --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_21-gnqa_combined_responses_edit.json @@ -0,0 +1,245 @@ +[ +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?","answer":"The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.","weight":1,"task_id":"849E78D8214245F8E8167E78C01BEE60"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"List as many studies as you can that include rapamycin.","answer":"1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.","weight":-1,"task_id":"2C477A3C76794C27A1FBBF437CFF75EE"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"Why is it so diffuclut to map gene loci that control aging in humans?","answer":"Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.","weight":1,"task_id":"CAD6C6C2AB42AA66BFDD65F0F11932B2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.","weight":1,"task_id":"EC2BFCD8E06079A3E595114822D2A44D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does epigenetics inluence gene expression without changing the underlying DNA sequence?","answer":"Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.","weight":-1,"task_id":"59AA8E8D7A97CFF47C6CFD65629F29F9"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.","answer":"Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.","weight":1,"task_id":"C58CFF41F7422B321DF88A110E278FD5"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?","answer":"The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.","weight":1,"task_id":"BCD1175CCB27FBA1E6F9D7670B17E527"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"5341FE6588C6175BC8A688A483928BC0"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"42847DE50D50E6A9B26ED0B03CFD160E"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"12BEAFA9366519672FC8B06959FB2DAF"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"64FEC152131BC6502E15EA6A6348D70B"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is apoptosis?","answer":"Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.","weight":1,"task_id":"78A0CD7E12AFEF6865583142603EE039"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is the most cited environmental factor for the onset of asthma?","answer":"The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.","weight":1,"task_id":"33FC2CC0F61BA22E4D095586B95703BD"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"370380F3A38AC4A788463D14E0EC673A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what is bioinformatics","answer":"Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.","weight":1,"task_id":"1E0DA0931F4E3A8C2893353CCA114B10"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"How would one extract the DNA, from say, flora or fauna?","answer":"DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.","weight":1,"task_id":"59E2406798D265A3CB466B766683E63C"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.","answer":"DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.","weight":-1,"task_id":"3F9EDFE9A0222EA70459EC8985F134C4"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.","weight":1,"task_id":"A010490B55F739DF95BB82DF2B0F5AA3"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Explain the process of finding a genetic marker followed by a quantitative trait loci.","answer":"The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.","weight":1,"task_id":"02A94D1056FDA77BDA9AC6CFDE0D5FC6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.","weight":1,"task_id":"96B873A13E3B386E38940EF6ECA90D4A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":1,"task_id":"DA98AC2EA5D1F776D3F04FCBC7F01339"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what causes the aging process","answer":"The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.","weight":1,"task_id":"117299AD06C2B147F49E9C9BC036CEA4"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.","weight":1,"task_id":"F4DA6C97230E89C0226B1433532A16D9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":0,"task_id":"FBBFB0D391778CDE9536F742300C1809"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"32CE1E54032485B73B5968395B3D3538"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"59CCE2D70104154865218876DD53D049"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0AE973110158192E0D1F50E1D79764FB"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"33749EB09AAC7AD0404C8E3E584B98D2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"471A145E9CA1E517E462499ABCA8EA2D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?","answer":"The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.","weight":1,"task_id":"19EBC6CA7E425D0C1279475BD0B411B6"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"what is ensembl?","answer":"Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.","weight":-1,"task_id":"9CFE932D7898C83E473E590BC77B4FCB"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Create a how to guide for genetic sequencing ","answer":"1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.","weight":-1,"task_id":"C52A9690417093A861C669A0753689BD"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What is the significance of the length of telomeres? ","answer":"The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.","weight":1,"task_id":"93DE2EF005059DFEA5A7FBBA3BD17D03"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ","answer":"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.","weight":1,"task_id":"477FC54178046FE98BF97FAAC5FE167F"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Why is genetic tracing matrilineal rather than patrilineal? ","answer":"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.","weight":1,"task_id":"6B80ECC5F657EB7CBDE69D411A30D3EA"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"2DE25ABD7E487B80D0C489319640EACC"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"what type of dataset is useful for qtl mapping analysis in genenetwork2? ","answer":"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.","weight":1,"task_id":"6498ED71891B79908B2E383D9AA5BAC5"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What genetic factors influence aging in humans? ","answer":"Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.","weight":1,"task_id":"B2F5CB7BCD9A827D3A6E0152C030C4B4"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Is there a direct association between aging and susceptibility to having diabetes?","answer":"Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.","weight":1,"task_id":"72FBC4F382B6502EAF41BD6682E63A2D"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does genetics influence the emergency of diabetes? ","answer":"Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.","weight":1,"task_id":"02C953165B9CA94E273DD4A04301C89F"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"19DC9E909DDE6D9CDB3E86D1069F5A69"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F2843EA2D5A239D022186329C8D5D8EF"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"4E1F0C2E792BAF0BA349326375D3EE6E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"FA8ADB009A499F51B0533FDCB72CB29E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.","weight":-1,"task_id":"38BD5864A7928C6DBCA1D844327F3A19"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with diabetes?","answer":"The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.","weight":1,"task_id":"2272C482CC247E746D15C9F55EDD8BCE"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with aging?","answer":"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.","weight":1,"task_id":"C6C7CEF19CE7C27CF4BC6906259CDDF9"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the bioinformatics tools for QTLs analysis?","answer":"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.","weight":1,"task_id":"B4BB83EB5D5C5C042E07173119046A13"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the statistical approaches for qtls analysis?","answer":"The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.","weight":1,"task_id":"D88EF655762CE3D524A7A1EEA3FA16ED"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for GWAS analysis?","answer":"1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.","weight":1,"task_id":"245DD8093F5D16F44C2AD7618245086C"}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "weight": 1, + "task_id": "B89A904E71CF7F16126FCA9EAAFBC8A6" +}, +{ + "user_id": "d8abfb12-9a11-400a-8cd0-0a436602581c", + "query": "What about recombination in human centromeres?", + "answer": "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "weight": 1, + "task_id": "2F8796A8C3DC633F00DB901C9BA396DA" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Create a how-to guide for genetic sequencing.", + "answer": "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "weight": -1, + "task_id": "6188C7826173CD59FD243F98C537AD50" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "weight": 1, + "task_id": "2C37A2EC74E4B46F13C6FB23D9547DBB" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "answer": "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "weight": 1, + "task_id": "590809C6B2A1504E2AE9A79EFDF3AC9A" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "What is the difference between QTL mapping and GWAS?", + "answer": "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "weight": 1, + "task_id": "6DBC070B2E4DC2FE8036E5BA7480B755" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How do I determine which gene in my QTL is causal for the trait?", + "answer": "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "weight": 1, + "task_id": "5594EA025D9631328071B6A1A7EF1375" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Which mouse genes have been associated with longevity?", + "answer": "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "weight": -1, + "task_id": "AB589D2E046B211A7486A6C4BD4ECFB4" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How is gene expression in the liver affected by diabetes?", + "answer": "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "weight": 1, + "task_id": "8FFF9DCC307B8DBF2C8485637F2ABEF4" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Why do males have two Y chromosomes and females only one?", + "answer": "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.", + "weight": 1, + "task_id": "C6B9A982C9283DE065A3371F1264095C" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " Create a how-to guide for genetic sequencing", + "answer": "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "weight": 1, + "task_id": "F9F7EA3DC28534B161ED70DB401C7D11" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " Create a how-to guide for genetic sequencing.", + "answer": "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "weight": -1, + "task_id": "4A06F8DF54C82D90E02F81D0E1E8B08A" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying.", + "weight": 1, + "task_id": "BA6A505E62A0529DB883D036CBC1FD92" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Which genes are associated with aging in human ", + "answer": "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "weight": 1, + "task_id": "3EC47C56606B02F00CF2449AB311365C" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Create a how-to guide for genetic sequencing", + "answer": "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "weight": 1, + "task_id": "CDFC418BD568E839C09656C57808ADA1" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Create a guide for genetic sequencing", + "answer": "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "weight": -1, + "task_id": "5DEB102510F48D0BF9C278DC895A8BD1" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "weight": 1, + "task_id": "5A562D5F7A266BA057B6833F3A83E7A7" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Why is genetic tracing matrilineal rather than patrilineal?", + "answer": "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "weight": 1, + "task_id": "53905925B4D6F69CE5706896A3C667CE" +}, +{ + "user_id": "0c4a1c26-539f-453a-b7f6-915ab462cf0b", + "query": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs", + "answer": "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.", + "weight": -1, + "task_id": "C0015BEE5FE41769A65126B79BB1E40D" +}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":-1,"task_id":"9957BCF2957D9BD083EDCF8B60C0E4DE"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How does recombination work in human centromeres?","answer":"In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.","weight":-1,"task_id":"DEE6D385D1B01B4155AA4ABE59515893"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How many types of diabetes exist?","answer":"There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).","weight":1,"task_id":"9309F248E5933718BFB625E4EF2D3E42"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in the human genome?","answer":"The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome.","weight":-1,"task_id":"10ABD2210053119B18D94F1FE266E73E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"BD671A3AC4F77A74CA8A68EAD627437D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5E243BF70932D1477209FEF6869FDEEA"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.","weight":-1,"task_id":"3A16235DA1E02B9148B9288A06EE567E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9BE91FA13BDC0D9EB80B35FC73A0029B"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"3D4688621977A9CBD670B50131B8D912"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How can I add a new species to the GeneNetwork database?","answer":"The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.","weight":1,"task_id":"E94FFD042BB146E8A429200590A6792D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?","answer":"Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.","weight":1,"task_id":"C319861B08978CF5F7E6F0CD3A517A81"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"what genetic factor are associated with aging","answer":"Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.","weight":1,"task_id":"081B2DB92FD09DEBEF28ADBBDE7199D2"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.","weight":0,"task_id":"68EF3BE5EC2106766CA9CC700135E2FA"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes.","weight":1,"task_id":"8590501C57DC5C321AB5E1036F233027"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"00647726F98EE835006D60B12455866D"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"ECEB33208BCDDC20908183BF249555AD"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":-1,"task_id":"906F0A0AB4330CB7C3A75DA8764703F4"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"A3B39D0211921BC5581AB03193860970"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"2BF7D398C0BFD1F4D976C9F82343DE51"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with diabetes in QTL analyses?","answer":"The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.","weight":1,"task_id":"FCFCE5BBA2A8B3D8818890B9D2308C5A"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with early aging?","answer":"The genes typically associated with early aging are APOE and FOXO3A.","weight":1,"task_id":"E3FFB15A9901BD8DB87B0F09D335BEA0"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How do I generate a linkage or association mapping study in mice to understand aging?","answer":"To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.","weight":1,"task_id":"38797E46211127E5C7175E707D40325B"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Is the gene TCF7L2 involved in diabetes?","answer":"Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).","weight":1,"task_id":"CD1F7EAE0FDC758A8167118927ADFE71"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"In which diseases is the gene TCF7L2 involved?","answer":"The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.","weight":-1,"task_id":"FFA6EADA5502933C0C30C9D16DCAA073"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"what are confounding factors in diabetes?","answer":"Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors.","weight":1,"task_id":"00BE70B5D71A5926E56942909C8B2A92"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":-1,"task_id":"0BF7A88573F1B5FCC2E8978A6D94CE2B"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"CBC2A69A6A94CFADE9E4077F5B655B6E"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.","weight":1,"task_id":"847F1E1599EECDE92F99B7581728FFE8"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?","answer":"A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.","weight":1,"task_id":"037BAB6CB2DE7A42AAAA73CE5DA8DB73"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":" Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.","weight":1,"task_id":"B2AA6DE557D652A0A660C4E0FAC1124D"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"3A55AE005B07C55283410798C0FBE47F"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.","answer":"Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.","weight":1,"task_id":"7EC697DE62C0C57E601EC3F5B295DF61"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.","answer":"Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.","weight":1,"task_id":"0A6673A0B69F0FF9C9657FB797DD1FE2"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.","answer":"Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.","weight":1,"task_id":"44B088326CD80B4980D810738D88A284"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert","answer":"Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors.","weight":-1,"task_id":"D53462CE61F52F7D31BB627998F4D75A"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"F6FC3B8BBCE2BA90D0EF2C9532DE3F84"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"0F76F85FB406BF74022084C5866C942D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"FE7363764A44969E28C9562A3948143B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F456694025B9C98AA9E4246820D5909A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"E6C75917249BB8C0810B0E709D6FDD0A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"8FA337BF315CFA09716800E096EA8A06"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"D68A2086596023BDD8C01023B03FA89D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How is the immune system related to diabetes?","answer":"The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.","weight":1,"task_id":"CD3820AA1BD96613F78FDF3CF5C8AB3D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the genomic variants associated with immune system components and diabetes?","answer":"The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.","weight":1,"task_id":"A4CE2F2F8E08E5F16C94A1BCF540D881"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the role of the immune system in the metabolomics of diabetes and associated conditions?","answer":"The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.","weight":1,"task_id":"1B8618ADB274F928B3AACAB1C71A927E"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the different relationship between traits?","answer":"The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.","weight":1,"task_id":"BF1705D2C26044038FF1483258548167"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can landscape of QTL and GWAS hits be used to find relationships between traits ?","answer":"Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.","weight":1,"task_id":"68AB7A78543D5B36206274837824091B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?","answer":"Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes.","weight":1,"task_id":"055110B765AA502F9AAECE68CEC0DD24"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"C4FEDD378CD138B141464832D021624B"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"ED89B73DC42AD2ADA03B7C014009A551"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"21CB24A2A589173F1E50ADA5DD6165EC"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"1619A0727D1C6673EE9E05171054F658"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"52B443B815CD46D57219872DFB3D0579"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"EB6B4DCD473BEE9580F47CD12DAFC074"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What causes diabetes?","answer":"Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.","weight":1,"task_id":"2AE18C9AAFB4E3A103F03C86BBEB2DD1"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Define dyslipidemia.","answer":"Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.","weight":1,"task_id":"58D6F365917926445960756A26B3FDC8"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Does cycling reduce risk of diabetes?","answer":"Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.","weight":1,"task_id":"2A2860BB54BC0D36A929838ED41243A7"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is cytochrome?","answer":"Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence.","weight":1,"task_id":"A5DEAEAC441B3BDC65B58EA6923FAE73"} + + +]
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_21-gnqa_response_reformat.json b/gnqa/paper1_eval/src/data/ratings/2024_06_21-gnqa_response_reformat.json new file mode 100644 index 0000000..620c6e8 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_21-gnqa_response_reformat.json @@ -0,0 +1,715 @@ +, +[ + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "32CE1E54032485B73B5968395B3D3538", + "59CCE2D70104154865218876DD53D049", + "0AE973110158192E0D1F50E1D79764FB", + "33749EB09AAC7AD0404C8E3E584B98D2", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4", + "FBBFB0D391778CDE9536F742300C1809" + ], + "weight": [ + 1, + 1, + 1, + 1, + 0 + ], + "answer": [ + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others." + ], + "query": [ + "genetics", + "what is bioinformatics", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in the aging process" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "9957BCF2957D9BD083EDCF8B60C0E4DE", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "19DC9E909DDE6D9CDB3E86D1069F5A69", + "F2843EA2D5A239D022186329C8D5D8EF", + "4E1F0C2E792BAF0BA349326375D3EE6E", + "FA8ADB009A499F51B0533FDCB72CB29E", + "38BD5864A7928C6DBCA1D844327F3A19", + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + -1, + 1, + 1, + -1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.", + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "B89A904E71CF7F16126FCA9EAAFBC8A6", + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1", + "5A562D5F7A266BA057B6833F3A83E7A7", + "53905925B4D6F69CE5706896A3C667CE" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6188C7826173CD59FD243F98C537AD50", + "2C37A2EC74E4B46F13C6FB23D9547DBB", + "590809C6B2A1504E2AE9A79EFDF3AC9A", + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "0c4a1c26-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + -1 + ], + "answer": [ + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "BD671A3AC4F77A74CA8A68EAD627437D", + "5E243BF70932D1477209FEF6869FDEEA", + "3A16235DA1E02B9148B9288A06EE567E", + "9BE91FA13BDC0D9EB80B35FC73A0029B", + "3D4688621977A9CBD670B50131B8D912", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "00647726F98EE835006D60B12455866D", + "ECEB33208BCDDC20908183BF249555AD", + "906F0A0AB4330CB7C3A75DA8764703F4", + "A3B39D0211921BC5581AB03193860970", + "2BF7D398C0BFD1F4D976C9F82343DE51", + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + -1, + 1, + -1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "0BF7A88573F1B5FCC2E8978A6D94CE2B", + "CBC2A69A6A94CFADE9E4077F5B655B6E", + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "3A55AE005B07C55283410798C0FBE47F", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + -1, + -1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "What is the significance of the length of telomeres?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "61a8e2c6-249c-40b8-a866-134f3a893e4a": { + "task_id": [ + "F6FC3B8BBCE2BA90D0EF2C9532DE3F84", + "0F76F85FB406BF74022084C5866C942D", + "C4FEDD378CD138B141464832D021624B", + "ED89B73DC42AD2ADA03B7C014009A551", + "21CB24A2A589173F1E50ADA5DD6165EC" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "FE7363764A44969E28C9562A3948143B", + "F456694025B9C98AA9E4246820D5909A", + "E6C75917249BB8C0810B0E709D6FDD0A", + "8FA337BF315CFA09716800E096EA8A06", + "D68A2086596023BDD8C01023B03FA89D", + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + }, + { + "8e4fe952-5a61-4d95-86e5-49f974465572": { + "task_id": [ + "1619A0727D1C6673EE9E05171054F658", + "52B443B815CD46D57219872DFB3D0579", + "EB6B4DCD473BEE9580F47CD12DAFC074", + "2AE18C9AAFB4E3A103F03C86BBEB2DD1", + "58D6F365917926445960756A26B3FDC8", + "2A2860BB54BC0D36A929838ED41243A7", + "A5DEAEAC441B3BDC65B58EA6923FAE73" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "What causes diabetes?", + "Define dyslipidemia.", + "Does cycling reduce risk of diabetes?", + "What is cytochrome?" + ] + } + } +]
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_21_gnqa_combined_responses.json b/gnqa/paper1_eval/src/data/ratings/2024_06_21_gnqa_combined_responses.json new file mode 100644 index 0000000..dae920b --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_21_gnqa_combined_responses.json @@ -0,0 +1,245 @@ +[ +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?","answer":"The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.","weight":1,"task_id":"849E78D8214245F8E8167E78C01BEE60"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"List as many studies as you can that include rapamycin.","answer":"1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.","weight":-1,"task_id":"2C477A3C76794C27A1FBBF437CFF75EE"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"Why is it so diffuclut to map gene loci that control aging in humans?","answer":"Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.","weight":1,"task_id":"CAD6C6C2AB42AA66BFDD65F0F11932B2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.","weight":1,"task_id":"EC2BFCD8E06079A3E595114822D2A44D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does epigenetics inluence gene expression without changing the underlying DNA sequence?","answer":"Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.","weight":-1,"task_id":"59AA8E8D7A97CFF47C6CFD65629F29F9"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.","answer":"Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.","weight":1,"task_id":"C58CFF41F7422B321DF88A110E278FD5"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?","answer":"The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.","weight":1,"task_id":"BCD1175CCB27FBA1E6F9D7670B17E527"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"5341FE6588C6175BC8A688A483928BC0"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"42847DE50D50E6A9B26ED0B03CFD160E"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"12BEAFA9366519672FC8B06959FB2DAF"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"64FEC152131BC6502E15EA6A6348D70B"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is apoptosis?","answer":"Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.","weight":1,"task_id":"78A0CD7E12AFEF6865583142603EE039"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is the most cited environmental factor for the onset of asthma?","answer":"The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.","weight":1,"task_id":"33FC2CC0F61BA22E4D095586B95703BD"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"370380F3A38AC4A788463D14E0EC673A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what is bioinformatics","answer":"Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.","weight":1,"task_id":"1E0DA0931F4E3A8C2893353CCA114B10"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"How would one extract the DNA, from say, flora or fauna?","answer":"DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.","weight":1,"task_id":"59E2406798D265A3CB466B766683E63C"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.","answer":"DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.","weight":-1,"task_id":"3F9EDFE9A0222EA70459EC8985F134C4"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.","weight":1,"task_id":"A010490B55F739DF95BB82DF2B0F5AA3"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Explain the process of finding a genetic marker followed by a quantitative trait loci.","answer":"The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.","weight":1,"task_id":"02A94D1056FDA77BDA9AC6CFDE0D5FC6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.","weight":1,"task_id":"96B873A13E3B386E38940EF6ECA90D4A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":1,"task_id":"DA98AC2EA5D1F776D3F04FCBC7F01339"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what causes the aging process","answer":"The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.","weight":1,"task_id":"117299AD06C2B147F49E9C9BC036CEA4"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.","weight":1,"task_id":"F4DA6C97230E89C0226B1433532A16D9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":0,"task_id":"FBBFB0D391778CDE9536F742300C1809"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"32CE1E54032485B73B5968395B3D3538"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"59CCE2D70104154865218876DD53D049"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0AE973110158192E0D1F50E1D79764FB"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"33749EB09AAC7AD0404C8E3E584B98D2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"471A145E9CA1E517E462499ABCA8EA2D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?","answer":"The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.","weight":1,"task_id":"19EBC6CA7E425D0C1279475BD0B411B6"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"what is ensembl?","answer":"Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.","weight":-1,"task_id":"9CFE932D7898C83E473E590BC77B4FCB"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Create a how to guide for genetic sequencing ","answer":"1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.","weight":-1,"task_id":"C52A9690417093A861C669A0753689BD"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What is the significance of the length of telomeres? ","answer":"The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.","weight":1,"task_id":"93DE2EF005059DFEA5A7FBBA3BD17D03"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ","answer":"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.","weight":1,"task_id":"477FC54178046FE98BF97FAAC5FE167F"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Why is genetic tracing matrilineal rather than patrilineal? ","answer":"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.","weight":1,"task_id":"6B80ECC5F657EB7CBDE69D411A30D3EA"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"2DE25ABD7E487B80D0C489319640EACC"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"what type of dataset is useful for qtl mapping analysis in genenetwork2? ","answer":"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.","weight":1,"task_id":"6498ED71891B79908B2E383D9AA5BAC5"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What genetic factors influence aging in humans? ","answer":"Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.","weight":1,"task_id":"B2F5CB7BCD9A827D3A6E0152C030C4B4"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Is there a direct association between aging and susceptibility to having diabetes?","answer":"Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.","weight":1,"task_id":"72FBC4F382B6502EAF41BD6682E63A2D"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does genetics influence the emergency of diabetes? ","answer":"Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.","weight":1,"task_id":"02C953165B9CA94E273DD4A04301C89F"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"19DC9E909DDE6D9CDB3E86D1069F5A69"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F2843EA2D5A239D022186329C8D5D8EF"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"4E1F0C2E792BAF0BA349326375D3EE6E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"FA8ADB009A499F51B0533FDCB72CB29E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.","weight":-1,"task_id":"38BD5864A7928C6DBCA1D844327F3A19"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with diabetes?","answer":"The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.","weight":1,"task_id":"2272C482CC247E746D15C9F55EDD8BCE"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with aging?","answer":"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.","weight":1,"task_id":"C6C7CEF19CE7C27CF4BC6906259CDDF9"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the bioinformatics tools for QTLs analysis?","answer":"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.","weight":1,"task_id":"B4BB83EB5D5C5C042E07173119046A13"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the statistical approaches for qtls analysis?","answer":"The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.","weight":1,"task_id":"D88EF655762CE3D524A7A1EEA3FA16ED"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for GWAS analysis?","answer":"1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.","weight":1,"task_id":"245DD8093F5D16F44C2AD7618245086C"}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "weight": 1, + "task_id": "B89A904E71CF7F16126FCA9EAAFBC8A6" +}, +{ + "user_id": "d8abfb12-9a11-400a-8cd0-0a436602581c", + "query": "What about recombination in human centromeres?", + "answer": "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "weight": 1, + "task_id": "2F8796A8C3DC633F00DB901C9BA396DA" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Create a how-to guide for genetic sequencing.", + "answer": "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "weight": -1, + "task_id": "6188C7826173CD59FD243F98C537AD50" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "weight": 1, + "task_id": "2C37A2EC74E4B46F13C6FB23D9547DBB" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "answer": "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "weight": 1, + "task_id": "590809C6B2A1504E2AE9A79EFDF3AC9A" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "What is the difference between QTL mapping and GWAS?", + "answer": "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "weight": 1, + "task_id": "6DBC070B2E4DC2FE8036E5BA7480B755" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How do I determine which gene in my QTL is causal for the trait?", + "answer": "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "weight": 1, + "task_id": "5594EA025D9631328071B6A1A7EF1375" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Which mouse genes have been associated with longevity?", + "answer": "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "weight": -1, + "task_id": "AB589D2E046B211A7486A6C4BD4ECFB4" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How is gene expression in the liver affected by diabetes?", + "answer": "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "weight": 1, + "task_id": "8FFF9DCC307B8DBF2C8485637F2ABEF4" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Why do males have two Y chromosomes and females only one?", + "answer": "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.", + "weight": 1, + "task_id": "C6B9A982C9283DE065A3371F1264095C" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " Create a how-to guide for genetic sequencing", + "answer": "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "weight": 1, + "task_id": "F9F7EA3DC28534B161ED70DB401C7D11" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " Create a how-to guide for genetic sequencing.", + "answer": "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "weight": -1, + "task_id": "4A06F8DF54C82D90E02F81D0E1E8B08A" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying.", + "weight": 1, + "task_id": "BA6A505E62A0529DB883D036CBC1FD92" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Which genes are associated with aging in human ", + "answer": "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "weight": 1, + "task_id": "3EC47C56606B02F00CF2449AB311365C" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Create a how-to guide for genetic sequencing", + "answer": "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "weight": 1, + "task_id": "CDFC418BD568E839C09656C57808ADA1" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Create a guide for genetic sequencing", + "answer": "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "weight": -1, + "task_id": "5DEB102510F48D0BF9C278DC895A8BD1" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "weight": 1, + "task_id": "5A562D5F7A266BA057B6833F3A83E7A7" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Why is genetic tracing matrilineal rather than patrilineal?", + "answer": "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "weight": 1, + "task_id": "53905925B4D6F69CE5706896A3C667CE" +}, +{ + "user_id": "0c4a1c26-539f-453a-b7f6-915ab462cf0b", + "query": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs", + "answer": "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.", + "weight": -1, + "task_id": "C0015BEE5FE41769A65126B79BB1E40D" +}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":-1,"task_id":"9957BCF2957D9BD083EDCF8B60C0E4DE"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How does recombination work in human centromeres?","answer":"In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.","weight":-1,"task_id":"DEE6D385D1B01B4155AA4ABE59515893"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How many types of diabetes exist?","answer":"There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).","weight":1,"task_id":"9309F248E5933718BFB625E4EF2D3E42"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in the human genome?","answer":"The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome.","weight":-1,"task_id":"10ABD2210053119B18D94F1FE266E73E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"BD671A3AC4F77A74CA8A68EAD627437D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5E243BF70932D1477209FEF6869FDEEA"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.","weight":-1,"task_id":"3A16235DA1E02B9148B9288A06EE567E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9BE91FA13BDC0D9EB80B35FC73A0029B"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"3D4688621977A9CBD670B50131B8D912"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How can I add a new species to the GeneNetwork database?","answer":"The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.","weight":1,"task_id":"E94FFD042BB146E8A429200590A6792D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?","answer":"Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.","weight":1,"task_id":"C319861B08978CF5F7E6F0CD3A517A81"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"what genetic factor are associated with aging","answer":"Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.","weight":1,"task_id":"081B2DB92FD09DEBEF28ADBBDE7199D2"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.","weight":0,"task_id":"68EF3BE5EC2106766CA9CC700135E2FA"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes.","weight":1,"task_id":"8590501C57DC5C321AB5E1036F233027"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"00647726F98EE835006D60B12455866D"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"ECEB33208BCDDC20908183BF249555AD"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":-1,"task_id":"906F0A0AB4330CB7C3A75DA8764703F4"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"A3B39D0211921BC5581AB03193860970"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"2BF7D398C0BFD1F4D976C9F82343DE51"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with diabetes in QTL analyses?","answer":"The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.","weight":1,"task_id":"FCFCE5BBA2A8B3D8818890B9D2308C5A"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with early aging?","answer":"The genes typically associated with early aging are APOE and FOXO3A.","weight":1,"task_id":"E3FFB15A9901BD8DB87B0F09D335BEA0"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How do I generate a linkage or association mapping study in mice to understand aging?","answer":"To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.","weight":1,"task_id":"38797E46211127E5C7175E707D40325B"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Is the gene TCF7L2 involved in diabetes?","answer":"Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).","weight":1,"task_id":"CD1F7EAE0FDC758A8167118927ADFE71"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"In which diseases is the gene TCF7L2 involved?","answer":"The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.","weight":-1,"task_id":"FFA6EADA5502933C0C30C9D16DCAA073"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"what are confounding factors in diabetes?","answer":"Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors.","weight":1,"task_id":"00BE70B5D71A5926E56942909C8B2A92"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":-1,"task_id":"0BF7A88573F1B5FCC2E8978A6D94CE2B"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"CBC2A69A6A94CFADE9E4077F5B655B6E"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.","weight":1,"task_id":"847F1E1599EECDE92F99B7581728FFE8"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?","answer":"A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.","weight":1,"task_id":"037BAB6CB2DE7A42AAAA73CE5DA8DB73"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":" Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.","weight":1,"task_id":"B2AA6DE557D652A0A660C4E0FAC1124D"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"3A55AE005B07C55283410798C0FBE47F"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.","answer":"Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.","weight":1,"task_id":"7EC697DE62C0C57E601EC3F5B295DF61"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.","answer":"Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.","weight":1,"task_id":"0A6673A0B69F0FF9C9657FB797DD1FE2"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.","answer":"Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.","weight":1,"task_id":"44B088326CD80B4980D810738D88A284"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert","answer":"Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors.","weight":-1,"task_id":"D53462CE61F52F7D31BB627998F4D75A"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"F6FC3B8BBCE2BA90D0EF2C9532DE3F84"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"0F76F85FB406BF74022084C5866C942D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"FE7363764A44969E28C9562A3948143B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F456694025B9C98AA9E4246820D5909A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"E6C75917249BB8C0810B0E709D6FDD0A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"8FA337BF315CFA09716800E096EA8A06"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"D68A2086596023BDD8C01023B03FA89D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How is the immune system related to diabetes?","answer":"The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.","weight":1,"task_id":"CD3820AA1BD96613F78FDF3CF5C8AB3D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the genomic variants associated with immune system components and diabetes?","answer":"The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.","weight":1,"task_id":"A4CE2F2F8E08E5F16C94A1BCF540D881"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the role of the immune system in the metabolomics of diabetes and associated conditions?","answer":"The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.","weight":1,"task_id":"1B8618ADB274F928B3AACAB1C71A927E"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the different relationship between traits?","answer":"The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.","weight":1,"task_id":"BF1705D2C26044038FF1483258548167"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can landscape of QTL and GWAS hits be used to find relationships between traits ?","answer":"Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.","weight":1,"task_id":"68AB7A78543D5B36206274837824091B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?","answer":"Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes.","weight":1,"task_id":"055110B765AA502F9AAECE68CEC0DD24"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"C4FEDD378CD138B141464832D021624B"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"ED89B73DC42AD2ADA03B7C014009A551"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"21CB24A2A589173F1E50ADA5DD6165EC"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"1619A0727D1C6673EE9E05171054F658"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"52B443B815CD46D57219872DFB3D0579"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"EB6B4DCD473BEE9580F47CD12DAFC074"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What causes diabetes?","answer":"Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.","weight":1,"task_id":"2AE18C9AAFB4E3A103F03C86BBEB2DD1"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Define dyslipidemia.","answer":"Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.","weight":1,"task_id":"58D6F365917926445960756A26B3FDC8"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Does cycling reduce risk of diabetes?","answer":"Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.","weight":1,"task_id":"2A2860BB54BC0D36A929838ED41243A7"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is cytochrome?","answer":"Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence.","weight":1,"task_id":"A5DEAEAC441B3BDC65B58EA6923FAE73"} + + +]
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_23-gnqa_response_reformat.json b/gnqa/paper1_eval/src/data/ratings/2024_06_23-gnqa_response_reformat.json new file mode 100644 index 0000000..97291b9 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_23-gnqa_response_reformat.json @@ -0,0 +1,759 @@ +, +[ + { + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "F26EEA55FF8FFCB0B9E3E46688C276DA", + "5674818C9037B8A93EDCF35CEF97E33C", + "106C5DDDE1338BE1CC8AE04CEAE3EF62", + "2D8205C79915FF9CEB8DECCE51E6E473", + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "A55310A1C19732792B5894A2DB5E8596", + "2EDD65822CDA4669FCAD678288C2E3E9", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4", + "FBBFB0D391778CDE9536F742300C1809", + "C8E21233058E1DEE05CD619ACBF49E42", + "38A8ED57882E2E250B447F93243A8CD5", + "5D3CA84D2DCE010AF63188665193B195", + "AB6286D0D8E9196BA0EE1F1CF90536AD" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 0, + -1, + 1, + 1, + -1 + ], + "answer": [ + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits." + ], + "query": [ + "genetics", + "genetics", + "genetics", + "List as many studies as you can that include rapamycin.", + "genetics", + "what is bioinformatics", + "genetics", + "genetics", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in the aging process", + "List as many studies as you can that include rapamycin.", + "List as many studies as you can that include rapamycin.", + "List as many studies as you can that include rapamycin.", + "genetics" + ] + } + }, + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "32CE1E54032485B73B5968395B3D3538", + "59CCE2D70104154865218876DD53D049", + "0AE973110158192E0D1F50E1D79764FB", + "33749EB09AAC7AD0404C8E3E584B98D2", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "9957BCF2957D9BD083EDCF8B60C0E4DE", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "19DC9E909DDE6D9CDB3E86D1069F5A69", + "F2843EA2D5A239D022186329C8D5D8EF", + "4E1F0C2E792BAF0BA349326375D3EE6E", + "FA8ADB009A499F51B0533FDCB72CB29E", + "38BD5864A7928C6DBCA1D844327F3A19", + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + -1, + 1, + 1, + -1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.", + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "B89A904E71CF7F16126FCA9EAAFBC8A6", + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1", + "5A562D5F7A266BA057B6833F3A83E7A7", + "53905925B4D6F69CE5706896A3C667CE" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6188C7826173CD59FD243F98C537AD50", + "2C37A2EC74E4B46F13C6FB23D9547DBB", + "590809C6B2A1504E2AE9A79EFDF3AC9A", + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "0c4a1c26-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + -1 + ], + "answer": [ + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "BD671A3AC4F77A74CA8A68EAD627437D", + "5E243BF70932D1477209FEF6869FDEEA", + "3A16235DA1E02B9148B9288A06EE567E", + "9BE91FA13BDC0D9EB80B35FC73A0029B", + "3D4688621977A9CBD670B50131B8D912", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "00647726F98EE835006D60B12455866D", + "8F3A81EAB68F709E82006205380AC723", + "ECEB33208BCDDC20908183BF249555AD", + "906F0A0AB4330CB7C3A75DA8764703F4", + "A3B39D0211921BC5581AB03193860970", + "2BF7D398C0BFD1F4D976C9F82343DE51", + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + -1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "0BF7A88573F1B5FCC2E8978A6D94CE2B", + "CBC2A69A6A94CFADE9E4077F5B655B6E", + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "3A55AE005B07C55283410798C0FBE47F", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + -1, + -1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "What is the significance of the length of telomeres?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "61a8e2c6-249c-40b8-a866-134f3a893e4a": { + "task_id": [ + "F6FC3B8BBCE2BA90D0EF2C9532DE3F84", + "0F76F85FB406BF74022084C5866C942D", + "C4FEDD378CD138B141464832D021624B", + "ED89B73DC42AD2ADA03B7C014009A551", + "21CB24A2A589173F1E50ADA5DD6165EC" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "FE7363764A44969E28C9562A3948143B", + "F456694025B9C98AA9E4246820D5909A", + "E6C75917249BB8C0810B0E709D6FDD0A", + "8FA337BF315CFA09716800E096EA8A06", + "D68A2086596023BDD8C01023B03FA89D", + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + }, + { + "8e4fe952-5a61-4d95-86e5-49f974465572": { + "task_id": [ + "1619A0727D1C6673EE9E05171054F658", + "52B443B815CD46D57219872DFB3D0579", + "EB6B4DCD473BEE9580F47CD12DAFC074", + "2AE18C9AAFB4E3A103F03C86BBEB2DD1", + "58D6F365917926445960756A26B3FDC8", + "2A2860BB54BC0D36A929838ED41243A7", + "A5DEAEAC441B3BDC65B58EA6923FAE73" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "What causes diabetes?", + "Define dyslipidemia.", + "Does cycling reduce risk of diabetes?", + "What is cytochrome?" + ] + } + } +]
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_24-gnqa_response_reformat_unique_queries.json b/gnqa/paper1_eval/src/data/ratings/2024_06_24-gnqa_response_reformat_unique_queries.json new file mode 100644 index 0000000..691af54 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_24-gnqa_response_reformat_unique_queries.json @@ -0,0 +1,582 @@ +[ + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "topic": [ + 2, 1, 1, 1, 0, 0 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ], + "topic": [ + 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + ] + } + }, + { + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4" + ], + "weight": [ + 1, + 1, + 1, + 1 + ], + "answer": [ + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes." + ], + "query": [ + "genetics", + "what is bioinformatics", + "which genes are involved in the aging process", + "what causes the aging process" + ], + "topic": [ + 0, 0, 1, 1 + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ], + "topic": [ + 0, 1, 1, 0, 0, 2, 0 + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ], + "topic": [ + 0, 0, 0, 0, 0, 0, 1, 2, 2 + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ], + "topic": [ + 2, 1, 0, 0, 0 + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ], + "topic": [ + 0, 0, 1, 2, 0 + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ], + "topic": [ + 0, 0, 0 + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1" + ], + "weight": [ + 1, + 1, + -1 + ], + "answer": [ + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides." + ], + "query": [ + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing" + ], + "topic": [ + 1, 0, 0 + ] + } + }, + { + "0c4a1c26-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + -1 + ], + "answer": [ + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ], + "topic": [ + 0 + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "3A16235DA1E02B9148B9288A06EE567E", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + 1 + ], + "answer": [ + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ], + "topic": [ + 0, 0, 2 + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ], + "topic": [ + 1, 2, 2 + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ], + "topic": [ + 0, 1, 1, 2, 0, 2 + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ], + "topic": [ + 0, 0, 0, 0, 0, 0, 1 + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ], + "topic": [ + 2, 2, 2, 0, 0, 2 + ] + } + }, + { + "8e4fe952-5a61-4d95-86e5-49f974465572": { + "task_id": [ + "2AE18C9AAFB4E3A103F03C86BBEB2DD1", + "58D6F365917926445960756A26B3FDC8", + "2A2860BB54BC0D36A929838ED41243A7", + "A5DEAEAC441B3BDC65B58EA6923FAE73" + ], + "weight": [ + 1, + 1, + 1, + 1 + ], + "answer": [ + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence." + ], + "query": [ + "What causes diabetes?", + "Define dyslipidemia.", + "Does cycling reduce risk of diabetes?", + "What is cytochrome?" + ], + "topic": [ + 2, 0, 2, 0 + ] + } + } +]
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_25-gnqa_combined_responses.json b/gnqa/paper1_eval/src/data/ratings/2024_06_25-gnqa_combined_responses.json new file mode 100644 index 0000000..4cce8d2 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_25-gnqa_combined_responses.json @@ -0,0 +1,277 @@ +[ +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?","answer":"The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.","weight":1,"task_id":"849E78D8214245F8E8167E78C01BEE60"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"List as many studies as you can that include rapamycin.","answer":"1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.","weight":-1,"task_id":"2C477A3C76794C27A1FBBF437CFF75EE"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"Why is it so diffuclut to map gene loci that control aging in humans?","answer":"Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.","weight":1,"task_id":"CAD6C6C2AB42AA66BFDD65F0F11932B2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.","weight":1,"task_id":"EC2BFCD8E06079A3E595114822D2A44D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does epigenetics inluence gene expression without changing the underlying DNA sequence?","answer":"Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.","weight":-1,"task_id":"59AA8E8D7A97CFF47C6CFD65629F29F9"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.","answer":"Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.","weight":1,"task_id":"C58CFF41F7422B321DF88A110E278FD5"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?","answer":"The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.","weight":1,"task_id":"BCD1175CCB27FBA1E6F9D7670B17E527"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"5341FE6588C6175BC8A688A483928BC0"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"42847DE50D50E6A9B26ED0B03CFD160E"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"12BEAFA9366519672FC8B06959FB2DAF"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"64FEC152131BC6502E15EA6A6348D70B"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is apoptosis?","answer":"Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.","weight":1,"task_id":"78A0CD7E12AFEF6865583142603EE039"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is the most cited environmental factor for the onset of asthma?","answer":"The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.","weight":1,"task_id":"33FC2CC0F61BA22E4D095586B95703BD"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"370380F3A38AC4A788463D14E0EC673A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what is bioinformatics","answer":"Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.","weight":1,"task_id":"1E0DA0931F4E3A8C2893353CCA114B10"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"How would one extract the DNA, from say, flora or fauna?","answer":"DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.","weight":1,"task_id":"59E2406798D265A3CB466B766683E63C"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.","answer":"DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.","weight":-1,"task_id":"3F9EDFE9A0222EA70459EC8985F134C4"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.","weight":1,"task_id":"A010490B55F739DF95BB82DF2B0F5AA3"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Explain the process of finding a genetic marker followed by a quantitative trait loci.","answer":"The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.","weight":1,"task_id":"02A94D1056FDA77BDA9AC6CFDE0D5FC6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.","weight":1,"task_id":"96B873A13E3B386E38940EF6ECA90D4A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":1,"task_id":"DA98AC2EA5D1F776D3F04FCBC7F01339"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what causes the aging process","answer":"The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.","weight":1,"task_id":"117299AD06C2B147F49E9C9BC036CEA4"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.","weight":1,"task_id":"F4DA6C97230E89C0226B1433532A16D9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":0,"task_id":"FBBFB0D391778CDE9536F742300C1809"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"32CE1E54032485B73B5968395B3D3538"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"59CCE2D70104154865218876DD53D049"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0AE973110158192E0D1F50E1D79764FB"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"33749EB09AAC7AD0404C8E3E584B98D2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"471A145E9CA1E517E462499ABCA8EA2D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?","answer":"The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.","weight":1,"task_id":"19EBC6CA7E425D0C1279475BD0B411B6"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"what is ensembl?","answer":"Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.","weight":-1,"task_id":"9CFE932D7898C83E473E590BC77B4FCB"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Create a how to guide for genetic sequencing ","answer":"1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.","weight":-1,"task_id":"C52A9690417093A861C669A0753689BD"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What is the significance of the length of telomeres? ","answer":"The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.","weight":1,"task_id":"93DE2EF005059DFEA5A7FBBA3BD17D03"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ","answer":"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.","weight":1,"task_id":"477FC54178046FE98BF97FAAC5FE167F"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Why is genetic tracing matrilineal rather than patrilineal? ","answer":"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.","weight":1,"task_id":"6B80ECC5F657EB7CBDE69D411A30D3EA"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"2DE25ABD7E487B80D0C489319640EACC"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"what type of dataset is useful for qtl mapping analysis in genenetwork2? ","answer":"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.","weight":1,"task_id":"6498ED71891B79908B2E383D9AA5BAC5"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What genetic factors influence aging in humans? ","answer":"Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.","weight":1,"task_id":"B2F5CB7BCD9A827D3A6E0152C030C4B4"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Is there a direct association between aging and susceptibility to having diabetes?","answer":"Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.","weight":1,"task_id":"72FBC4F382B6502EAF41BD6682E63A2D"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does genetics influence the emergency of diabetes? ","answer":"Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.","weight":1,"task_id":"02C953165B9CA94E273DD4A04301C89F"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"19DC9E909DDE6D9CDB3E86D1069F5A69"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F2843EA2D5A239D022186329C8D5D8EF"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"4E1F0C2E792BAF0BA349326375D3EE6E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"FA8ADB009A499F51B0533FDCB72CB29E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.","weight":-1,"task_id":"38BD5864A7928C6DBCA1D844327F3A19"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with diabetes?","answer":"The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.","weight":1,"task_id":"2272C482CC247E746D15C9F55EDD8BCE"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with aging?","answer":"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.","weight":1,"task_id":"C6C7CEF19CE7C27CF4BC6906259CDDF9"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the bioinformatics tools for QTLs analysis?","answer":"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.","weight":1,"task_id":"B4BB83EB5D5C5C042E07173119046A13"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the statistical approaches for qtls analysis?","answer":"The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.","weight":1,"task_id":"D88EF655762CE3D524A7A1EEA3FA16ED"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for GWAS analysis?","answer":"1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.","weight":1,"task_id":"245DD8093F5D16F44C2AD7618245086C"}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "weight": 1, + "task_id": "B89A904E71CF7F16126FCA9EAAFBC8A6" +}, +{ + "user_id": "d8abfb12-9a11-400a-8cd0-0a436602581c", + "query": "What about recombination in human centromeres?", + "answer": "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "weight": 1, + "task_id": "2F8796A8C3DC633F00DB901C9BA396DA" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Create a how-to guide for genetic sequencing.", + "answer": "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "weight": -1, + "task_id": "6188C7826173CD59FD243F98C537AD50" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "weight": 1, + "task_id": "2C37A2EC74E4B46F13C6FB23D9547DBB" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "answer": "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "weight": 1, + "task_id": "590809C6B2A1504E2AE9A79EFDF3AC9A" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "What is the difference between QTL mapping and GWAS?", + "answer": "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "weight": 1, + "task_id": "6DBC070B2E4DC2FE8036E5BA7480B755" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How do I determine which gene in my QTL is causal for the trait?", + "answer": "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "weight": 1, + "task_id": "5594EA025D9631328071B6A1A7EF1375" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Which mouse genes have been associated with longevity?", + "answer": "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "weight": -1, + "task_id": "AB589D2E046B211A7486A6C4BD4ECFB4" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How is gene expression in the liver affected by diabetes?", + "answer": "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "weight": 1, + "task_id": "8FFF9DCC307B8DBF2C8485637F2ABEF4" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Why do males have two Y chromosomes and females only one?", + "answer": "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.", + "weight": 1, + "task_id": "C6B9A982C9283DE065A3371F1264095C" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " Create a how-to guide for genetic sequencing", + "answer": "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "weight": 1, + "task_id": "F9F7EA3DC28534B161ED70DB401C7D11" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " Create a how-to guide for genetic sequencing.", + "answer": "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "weight": -1, + "task_id": "4A06F8DF54C82D90E02F81D0E1E8B08A" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying.", + "weight": 1, + "task_id": "BA6A505E62A0529DB883D036CBC1FD92" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Which genes are associated with aging in human ", + "answer": "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "weight": 1, + "task_id": "3EC47C56606B02F00CF2449AB311365C" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Create a how-to guide for genetic sequencing", + "answer": "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "weight": 1, + "task_id": "CDFC418BD568E839C09656C57808ADA1" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Create a guide for genetic sequencing", + "answer": "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "weight": -1, + "task_id": "5DEB102510F48D0BF9C278DC895A8BD1" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "weight": 1, + "task_id": "5A562D5F7A266BA057B6833F3A83E7A7" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Why is genetic tracing matrilineal rather than patrilineal?", + "answer": "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "weight": 1, + "task_id": "53905925B4D6F69CE5706896A3C667CE" +}, +{ + "user_id": "0c4a1c26-539f-453a-b7f6-915ab462cf0b", + "query": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs", + "answer": "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.", + "weight": -1, + "task_id": "C0015BEE5FE41769A65126B79BB1E40D" +}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":-1,"task_id":"9957BCF2957D9BD083EDCF8B60C0E4DE"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How does recombination work in human centromeres?","answer":"In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.","weight":-1,"task_id":"DEE6D385D1B01B4155AA4ABE59515893"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How many types of diabetes exist?","answer":"There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).","weight":1,"task_id":"9309F248E5933718BFB625E4EF2D3E42"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in the human genome?","answer":"The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome.","weight":-1,"task_id":"10ABD2210053119B18D94F1FE266E73E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"BD671A3AC4F77A74CA8A68EAD627437D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5E243BF70932D1477209FEF6869FDEEA"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.","weight":-1,"task_id":"3A16235DA1E02B9148B9288A06EE567E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9BE91FA13BDC0D9EB80B35FC73A0029B"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"3D4688621977A9CBD670B50131B8D912"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How can I add a new species to the GeneNetwork database?","answer":"The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.","weight":1,"task_id":"E94FFD042BB146E8A429200590A6792D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?","answer":"Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.","weight":1,"task_id":"C319861B08978CF5F7E6F0CD3A517A81"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"what genetic factor are associated with aging","answer":"Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.","weight":1,"task_id":"081B2DB92FD09DEBEF28ADBBDE7199D2"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.","weight":0,"task_id":"68EF3BE5EC2106766CA9CC700135E2FA"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes.","weight":1,"task_id":"8590501C57DC5C321AB5E1036F233027"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"00647726F98EE835006D60B12455866D"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"ECEB33208BCDDC20908183BF249555AD"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":-1,"task_id":"906F0A0AB4330CB7C3A75DA8764703F4"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"A3B39D0211921BC5581AB03193860970"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"2BF7D398C0BFD1F4D976C9F82343DE51"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with diabetes in QTL analyses?","answer":"The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.","weight":1,"task_id":"FCFCE5BBA2A8B3D8818890B9D2308C5A"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with early aging?","answer":"The genes typically associated with early aging are APOE and FOXO3A.","weight":1,"task_id":"E3FFB15A9901BD8DB87B0F09D335BEA0"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How do I generate a linkage or association mapping study in mice to understand aging?","answer":"To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.","weight":1,"task_id":"38797E46211127E5C7175E707D40325B"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Is the gene TCF7L2 involved in diabetes?","answer":"Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).","weight":1,"task_id":"CD1F7EAE0FDC758A8167118927ADFE71"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"In which diseases is the gene TCF7L2 involved?","answer":"The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.","weight":-1,"task_id":"FFA6EADA5502933C0C30C9D16DCAA073"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"what are confounding factors in diabetes?","answer":"Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors.","weight":1,"task_id":"00BE70B5D71A5926E56942909C8B2A92"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":-1,"task_id":"0BF7A88573F1B5FCC2E8978A6D94CE2B"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"CBC2A69A6A94CFADE9E4077F5B655B6E"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.","weight":1,"task_id":"847F1E1599EECDE92F99B7581728FFE8"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?","answer":"A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.","weight":1,"task_id":"037BAB6CB2DE7A42AAAA73CE5DA8DB73"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":" Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.","weight":1,"task_id":"B2AA6DE557D652A0A660C4E0FAC1124D"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"3A55AE005B07C55283410798C0FBE47F"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.","answer":"Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.","weight":1,"task_id":"7EC697DE62C0C57E601EC3F5B295DF61"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.","answer":"Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.","weight":1,"task_id":"0A6673A0B69F0FF9C9657FB797DD1FE2"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.","answer":"Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.","weight":1,"task_id":"44B088326CD80B4980D810738D88A284"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert","answer":"Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors.","weight":-1,"task_id":"D53462CE61F52F7D31BB627998F4D75A"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"F6FC3B8BBCE2BA90D0EF2C9532DE3F84"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"0F76F85FB406BF74022084C5866C942D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"FE7363764A44969E28C9562A3948143B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F456694025B9C98AA9E4246820D5909A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"E6C75917249BB8C0810B0E709D6FDD0A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"8FA337BF315CFA09716800E096EA8A06"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"D68A2086596023BDD8C01023B03FA89D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How is the immune system related to diabetes?","answer":"The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.","weight":1,"task_id":"CD3820AA1BD96613F78FDF3CF5C8AB3D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the genomic variants associated with immune system components and diabetes?","answer":"The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.","weight":1,"task_id":"A4CE2F2F8E08E5F16C94A1BCF540D881"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the role of the immune system in the metabolomics of diabetes and associated conditions?","answer":"The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.","weight":1,"task_id":"1B8618ADB274F928B3AACAB1C71A927E"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the different relationship between traits?","answer":"The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.","weight":1,"task_id":"BF1705D2C26044038FF1483258548167"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can landscape of QTL and GWAS hits be used to find relationships between traits ?","answer":"Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.","weight":1,"task_id":"68AB7A78543D5B36206274837824091B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?","answer":"Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes.","weight":1,"task_id":"055110B765AA502F9AAECE68CEC0DD24"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"C4FEDD378CD138B141464832D021624B"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"ED89B73DC42AD2ADA03B7C014009A551"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"21CB24A2A589173F1E50ADA5DD6165EC"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"1619A0727D1C6673EE9E05171054F658"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"52B443B815CD46D57219872DFB3D0579"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"EB6B4DCD473BEE9580F47CD12DAFC074"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What causes diabetes?","answer":"Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.","weight":1,"task_id":"2AE18C9AAFB4E3A103F03C86BBEB2DD1"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Define dyslipidemia.","answer":"Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.","weight":1,"task_id":"58D6F365917926445960756A26B3FDC8"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Does cycling reduce risk of diabetes?","answer":"Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.","weight":1,"task_id":"2A2860BB54BC0D36A929838ED41243A7"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is cytochrome?","answer":"Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence.","weight":1,"task_id":"A5DEAEAC441B3BDC65B58EA6923FAE73"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in aging","answer":"Several genes are involved in aging, including BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, CDKN2B, USP28, E2F2, BCL3, NAP1L4, GAB2, QKI, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, and several S100 calcium binding proteins.","weight":1,"task_id":"FE094A900BA5B3C48A3A67B18B2F12BD"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"9E388A69975DBDEA3E8DE25294960147"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"29C36228E29604002BB3BAE6654F7762"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"9ED249912768DA58AF97F5600D0CBA8F"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"DEEA82693F72D24109C91089CABC7EBF"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"FC09E6CA3472C1E263743195703162C5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"136D27CEFA12BC3AF0BDD42121FD6DBA"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5DCBD523F5F1663492EB5630EAD981FE"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9C697AF95B263CBD4E243D8AD1062180"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"6F33581CC88C813D20B047A82A78BC7C"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"2C01511CD9C5ABDC085D77F67AA862E1"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.","weight":1,"task_id":"0CDD1C9219114BB2770C28D541F1060A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"BD981EFD76B6C93C620CD92DB9EF0B35"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What is GeneNetwork and how does it relate to aging research?","answer":"GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity.","weight":1,"task_id":"37A26345145679F7539EA8F512623F5E"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"How can GeneNetwork assist in identifying genetic factors involved in diabetes?","answer":"GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network","weight":-1,"task_id":"F35BF9C40081CE0521E562CD95BA4C2F"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?","answer":"GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.","weight":-1,"task_id":"9DD88454267DEF2106A3EA7E6E8B5443"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Explain Protective Genetic Factors Against Diabetes in Elderly Populations","answer":"Studies have shown that foreign genetic admixture can have a protective effect against diabetes. For instance, the prevalence of Type 2 Diabetes (T2D) in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with foreign genetic admixture. This suggests that foreign genotypes can reduce the risk of diabetes. Similar findings have been reported in Pima Indians and other Native American populations.","weight":1,"task_id":"CB93CE86DA18F287DBEF22CB29C560CF"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What role does insulin play in the regulation of blood glucose levels?","answer":"Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present, cells increase insulin output to maintain normal glucose tolerance. However, if cells are incapable of this task, plasma concentrations of glucose increase.","weight":1,"task_id":"732D340E5C8F09381CEFA440AD2A7AB6"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what genes are involved in the aging process","answer":"Several genes are involved in the aging process. These include BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, IGF-1, CYP19, NAP1L4, GAB2, QKI, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, USP28, E2F2, BCL3, mre11, rad50, Ku80, mus308, mus205, DNApol-eta, Trxr-1, GlnRS, isoQC, QC, Cct1, Cct4, Cct5, Cct6, Hsc70-4, Xbp1, NAT1, Rack1, Rheb, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, SIR2, DAF-2/IGF1R, DAF-16/FOXO, AAK-2/AMPK, LET-363/mTOR, SIRT1/SIR2, p53, Werner's syndrome gene, iff-1, SIR2, TERT, ZBTB16, CA9, HEY2, Smarca4, Amarcb1, Hdac1, -5, -6, Dnmt3b, and genes in the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway.","weight":1,"task_id":"8DCEF606839664C8B6C72CF1D181CEEA"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk","answer":"Lifestyle modifications, including increased physical activity, dietary changes, and weight reduction, have been shown to significantly reduce the risk of Type 2 Diabetes (T2D), which is often associated with aging. Regular physical activity improves insulin sensitivity, reducing the stress on insulin-producing cells. Dietary changes, particularly those reducing the intake of processed carbohydrates, energy-rich foods, and animal fats, can also lower T2D risk. Weight loss can directly improve insulin secretion and sensitivity. However, these lifestyle changes require not only individual efforts but also changes in social, built, and food environments.","weight":1,"task_id":"FEE16F5E4D12AF7E7B0DDBF6F047EB76"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":" How does aging affect the risk of developing type 2 diabetes?","answer":"Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes.","weight":1,"task_id":"CE5922BDA6B949A17665AB4E1A8138D5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Explain The Role of Longevity Genes in Protecting Against Diabetes","answer":"The background text does not provide information on the role of longevity genes in protecting against diabetes.","weight":-1,"task_id":"DF05AACA4A1466AC1753DE13631A6ACD"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Can lifestyle changes reverse type 2 diabetes?","answer":"Lifestyle changes, including diet modification and increased physical activity, can help prevent and manage type 2 diabetes. They can improve insulin sensitivity, control blood glucose levels, and promote weight loss, which are all beneficial in managing the disease. However, they may not completely reverse the disease, especially in advanced stages.","weight":1,"task_id":"F0CC742EA104CB2C8B8BCA9CB6EB78F0"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"What are the types of diabetes","answer":"The types of diabetes include Type 1 diabetes, Type 2 diabetes, gestational diabetes, and diabetes due to specific causes such as genetic defects causing deficient insulin secretion or action, diseases of the pancreas, and use of certain drugs. There are also subtypes of adult-onset diabetes identified as severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes.","weight":1,"task_id":"57CB850E74BC7A26A645CAAB823D35CD"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"BFEF55FA3BA8B9460207E8CF981E1A4A"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"A603218836E967137903C8CABAC8C282"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.","weight":-1,"task_id":"A5627A35FFA5C25EE34522D01D7198B1"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"how does environment influence fertilisation","answer":"Environmental factors can influence fertilisation in several ways. Factors such as temperature, pressure, and chemical activity can affect the physiological processes involved in fertilisation. Lifestyle factors like smoking, irradiation, oral contraceptives, and low socioeconomic status can also impact natural conception. Moreover, environmental conditions can affect the quality of maternal care and offspring development. For instance, crowded larval environments can lead to a decline in quality over time, affecting the genetic polymorphism in populations. Additionally, environmental stressors can influence the responsiveness to prenatal stress, potentially affecting fertility.","weight":1,"task_id":"6D733CABEB70E4DBF150EAAFFED6C973"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"how does diet impact someone's height","answer":"The text does not provide information on how diet impacts someone's height.","weight":-1,"task_id":"6267E2FEFF0332F88C2294C8F32C1FC1"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"which animal has the same number of chromosomes as human","answer":"The Bama miniature pig has the same number of chromosomes as humans.","weight":1,"task_id":"3FFA45D7124495B37B6F7F2B7B780AF3"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"what's ensures brains work","answer":"The functioning of the brain is ensured by the complex interplay of neuronal networks, synaptic connections, and the precise regulation of molecular and cellular events. This includes the spatiotemporal regulation of the transcriptome, the signaling dynamics of integrated circuitry, and the balance between activating and inhibiting systems. Additionally, the maintenance of genomic integrity in neuronal cells is crucial for the brain's high metabolic activity.","weight":1,"task_id":"499C63633BB95DE93DC3A89615496443"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"how do our brains maintain emotions","answer":"Our brains maintain emotions through complex neural systems and processes. The reward-motivation system, which includes the striatum, amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex, plays a key role in processing emotional and motivational information. The amygdala, a principal component of the limbic system, controls emotionality. Stress, anxiety, and other emotion-related traits are influenced by multiple genetic, environmental, experiential, and epigenetic factors. Additionally, hormones released by stressful experiences can enhance memory consolidation, further influencing emotional responses.","weight":1,"task_id":"405240F6F75C3927C1088287E19920AD"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"what hormones do our brains release during stressful experiences?","answer":"During stressful experiences, our brains release hormones such as epinephrine, glucocorticoids, and corticosterone. The hypothalamic-pituitary-adrenal (HPA) axis also plays a significant role in this process, stimulating the release of these hormones.","weight":1,"task_id":"DA2C5FBAA7806455F89E896E641DD642"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"what is the use of corticosterone?","answer":"Corticosterone, a type of glucocorticoid, is used in the body's response to stress. It initiates metabolic and physiological responses that help the body cope with stressors. It also provides negative feedback to inhibit activity in the hypothalamic-pituitary-adrenal (HPA) axis, which helps to dampen stress-activated defense reactions, including immune responses. Additionally, corticosterone can influence the expression of certain cytokines, potentially having anti-inflammatory effects.","weight":1,"task_id":"7B0629638DF00DF1183B67EE3BF39B1C"} +]
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_25-gnqa_responses.json b/gnqa/paper1_eval/src/data/ratings/2024_06_25-gnqa_responses.json new file mode 100644 index 0000000..105a5e0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_25-gnqa_responses.json @@ -0,0 +1,173 @@ +[{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"F26EEA55FF8FFCB0B9E3E46688C276DA"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"5674818C9037B8A93EDCF35CEF97E33C"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"106C5DDDE1338BE1CC8AE04CEAE3EF62"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"2D8205C79915FF9CEB8DECCE51E6E473"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?","answer":"The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.","weight":1,"task_id":"849E78D8214245F8E8167E78C01BEE60"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"List as many studies as you can that include rapamycin.","answer":"1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.","weight":-1,"task_id":"2C477A3C76794C27A1FBBF437CFF75EE"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"Why is it so diffuclut to map gene loci that control aging in humans?","answer":"Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.","weight":1,"task_id":"CAD6C6C2AB42AA66BFDD65F0F11932B2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.","weight":1,"task_id":"EC2BFCD8E06079A3E595114822D2A44D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does epigenetics inluence gene expression without changing the underlying DNA sequence?","answer":"Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.","weight":-1,"task_id":"59AA8E8D7A97CFF47C6CFD65629F29F9"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.","answer":"Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.","weight":1,"task_id":"C58CFF41F7422B321DF88A110E278FD5"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?","answer":"The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.","weight":1,"task_id":"BCD1175CCB27FBA1E6F9D7670B17E527"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"5341FE6588C6175BC8A688A483928BC0"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"42847DE50D50E6A9B26ED0B03CFD160E"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"12BEAFA9366519672FC8B06959FB2DAF"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"64FEC152131BC6502E15EA6A6348D70B"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is apoptosis?","answer":"Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.","weight":1,"task_id":"78A0CD7E12AFEF6865583142603EE039"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is the most cited environmental factor for the onset of asthma?","answer":"The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.","weight":1,"task_id":"33FC2CC0F61BA22E4D095586B95703BD"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"370380F3A38AC4A788463D14E0EC673A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what is bioinformatics","answer":"Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.","weight":1,"task_id":"1E0DA0931F4E3A8C2893353CCA114B10"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"How would one extract the DNA, from say, flora or fauna?","answer":"DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.","weight":1,"task_id":"59E2406798D265A3CB466B766683E63C"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.","answer":"DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.","weight":-1,"task_id":"3F9EDFE9A0222EA70459EC8985F134C4"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.","weight":1,"task_id":"A010490B55F739DF95BB82DF2B0F5AA3"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Explain the process of finding a genetic marker followed by a quantitative trait loci.","answer":"The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.","weight":1,"task_id":"02A94D1056FDA77BDA9AC6CFDE0D5FC6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.","weight":1,"task_id":"96B873A13E3B386E38940EF6ECA90D4A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"A55310A1C19732792B5894A2DB5E8596"}, +{"user_id":"d2f71149-a0d5-4666-95d8-15c82543243b","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"7CA65F6B192BE7896422284B962CF5C3"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"2EDD65822CDA4669FCAD678288C2E3E9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":1,"task_id":"DA98AC2EA5D1F776D3F04FCBC7F01339"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what causes the aging process","answer":"The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.","weight":1,"task_id":"117299AD06C2B147F49E9C9BC036CEA4"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.","weight":1,"task_id":"F4DA6C97230E89C0226B1433532A16D9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":0,"task_id":"FBBFB0D391778CDE9536F742300C1809"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"32CE1E54032485B73B5968395B3D3538"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"59CCE2D70104154865218876DD53D049"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0AE973110158192E0D1F50E1D79764FB"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"33749EB09AAC7AD0404C8E3E584B98D2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"471A145E9CA1E517E462499ABCA8EA2D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?","answer":"The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.","weight":1,"task_id":"19EBC6CA7E425D0C1279475BD0B411B6"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"what is ensembl?","answer":"Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.","weight":-1,"task_id":"9CFE932D7898C83E473E590BC77B4FCB"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Create a how to guide for genetic sequencing ","answer":"1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.","weight":-1,"task_id":"C52A9690417093A861C669A0753689BD"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What is the significance of the length of telomeres? ","answer":"The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.","weight":1,"task_id":"93DE2EF005059DFEA5A7FBBA3BD17D03"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ","answer":"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.","weight":1,"task_id":"477FC54178046FE98BF97FAAC5FE167F"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Why is genetic tracing matrilineal rather than patrilineal? ","answer":"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.","weight":1,"task_id":"6B80ECC5F657EB7CBDE69D411A30D3EA"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"2DE25ABD7E487B80D0C489319640EACC"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"what type of dataset is useful for qtl mapping analysis in genenetwork2? ","answer":"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.","weight":1,"task_id":"6498ED71891B79908B2E383D9AA5BAC5"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What genetic factors influence aging in humans? ","answer":"Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.","weight":1,"task_id":"B2F5CB7BCD9A827D3A6E0152C030C4B4"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Is there a direct association between aging and susceptibility to having diabetes?","answer":"Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.","weight":1,"task_id":"72FBC4F382B6502EAF41BD6682E63A2D"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does genetics influence the emergency of diabetes? ","answer":"Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.","weight":1,"task_id":"02C953165B9CA94E273DD4A04301C89F"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"19DC9E909DDE6D9CDB3E86D1069F5A69"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F2843EA2D5A239D022186329C8D5D8EF"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"4E1F0C2E792BAF0BA349326375D3EE6E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"FA8ADB009A499F51B0533FDCB72CB29E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.","weight":-1,"task_id":"38BD5864A7928C6DBCA1D844327F3A19"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with diabetes?","answer":"The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.","weight":1,"task_id":"2272C482CC247E746D15C9F55EDD8BCE"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with aging?","answer":"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.","weight":1,"task_id":"C6C7CEF19CE7C27CF4BC6906259CDDF9"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the bioinformatics tools for QTLs analysis?","answer":"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.","weight":1,"task_id":"B4BB83EB5D5C5C042E07173119046A13"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the statistical approaches for qtls analysis?","answer":"The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.","weight":1,"task_id":"D88EF655762CE3D524A7A1EEA3FA16ED"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for GWAS analysis?","answer":"1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.","weight":1,"task_id":"245DD8093F5D16F44C2AD7618245086C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"B89A904E71CF7F16126FCA9EAAFBC8A6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":1,"task_id":"2F8796A8C3DC633F00DB901C9BA396DA"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"6188C7826173CD59FD243F98C537AD50"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"2C37A2EC74E4B46F13C6FB23D9547DBB"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"590809C6B2A1504E2AE9A79EFDF3AC9A"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the difference between QTL mapping and GWAS?","answer":"QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.","weight":1,"task_id":"6DBC070B2E4DC2FE8036E5BA7480B755"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How do I determine which gene in my QTL is causal for the trait?","answer":"To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.","weight":1,"task_id":"5594EA025D9631328071B6A1A7EF1375"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Which mouse genes have been associated with longevity?","answer":"The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.","weight":-1,"task_id":"AB589D2E046B211A7486A6C4BD4ECFB4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How is gene expression in the liver affected by diabetes?","answer":"In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.","weight":1,"task_id":"8FFF9DCC307B8DBF2C8485637F2ABEF4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Why do males have two Y chromosomes and females only one?","answer":"Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.","weight":1,"task_id":"C6B9A982C9283DE065A3371F1264095C"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing","answer":"1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.","weight":1,"task_id":"F9F7EA3DC28534B161ED70DB401C7D11"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.","weight":-1,"task_id":"4A06F8DF54C82D90E02F81D0E1E8B08A"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying.","weight":1,"task_id":"BA6A505E62A0529DB883D036CBC1FD92"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Which genes are associated with aging in human ","answer":"The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.","weight":1,"task_id":"3EC47C56606B02F00CF2449AB311365C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"CDFC418BD568E839C09656C57808ADA1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a guide for genetic sequencing","answer":"1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.","weight":-1,"task_id":"5DEB102510F48D0BF9C278DC895A8BD1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"5A562D5F7A266BA057B6833F3A83E7A7"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"53905925B4D6F69CE5706896A3C667CE"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.","weight":-1,"task_id":"C0015BEE5FE41769A65126B79BB1E40D"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":-1,"task_id":"9957BCF2957D9BD083EDCF8B60C0E4DE"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How does recombination work in human centromeres?","answer":"In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.","weight":-1,"task_id":"DEE6D385D1B01B4155AA4ABE59515893"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How many types of diabetes exist?","answer":"There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).","weight":1,"task_id":"9309F248E5933718BFB625E4EF2D3E42"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in the human genome?","answer":"The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome.","weight":-1,"task_id":"10ABD2210053119B18D94F1FE266E73E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"BD671A3AC4F77A74CA8A68EAD627437D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5E243BF70932D1477209FEF6869FDEEA"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.","weight":-1,"task_id":"3A16235DA1E02B9148B9288A06EE567E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9BE91FA13BDC0D9EB80B35FC73A0029B"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"3D4688621977A9CBD670B50131B8D912"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How can I add a new species to the GeneNetwork database?","answer":"The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.","weight":1,"task_id":"E94FFD042BB146E8A429200590A6792D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?","answer":"Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.","weight":1,"task_id":"C319861B08978CF5F7E6F0CD3A517A81"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"what genetic factor are associated with aging","answer":"Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.","weight":1,"task_id":"081B2DB92FD09DEBEF28ADBBDE7199D2"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.","weight":0,"task_id":"68EF3BE5EC2106766CA9CC700135E2FA"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes.","weight":1,"task_id":"8590501C57DC5C321AB5E1036F233027"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":-1,"task_id":"C8E21233058E1DEE05CD619ACBF49E42"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"38A8ED57882E2E250B447F93243A8CD5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"5D3CA84D2DCE010AF63188665193B195"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"AB6286D0D8E9196BA0EE1F1CF90536AD"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"00647726F98EE835006D60B12455866D"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"8F3A81EAB68F709E82006205380AC723"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"ECEB33208BCDDC20908183BF249555AD"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":-1,"task_id":"906F0A0AB4330CB7C3A75DA8764703F4"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"A3B39D0211921BC5581AB03193860970"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"2BF7D398C0BFD1F4D976C9F82343DE51"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with diabetes in QTL analyses?","answer":"The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.","weight":1,"task_id":"FCFCE5BBA2A8B3D8818890B9D2308C5A"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with early aging?","answer":"The genes typically associated with early aging are APOE and FOXO3A.","weight":1,"task_id":"E3FFB15A9901BD8DB87B0F09D335BEA0"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How do I generate a linkage or association mapping study in mice to understand aging?","answer":"To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.","weight":1,"task_id":"38797E46211127E5C7175E707D40325B"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Is the gene TCF7L2 involved in diabetes?","answer":"Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).","weight":1,"task_id":"CD1F7EAE0FDC758A8167118927ADFE71"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"In which diseases is the gene TCF7L2 involved?","answer":"The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.","weight":-1,"task_id":"FFA6EADA5502933C0C30C9D16DCAA073"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"what are confounding factors in diabetes?","answer":"Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors.","weight":1,"task_id":"00BE70B5D71A5926E56942909C8B2A92"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":-1,"task_id":"0BF7A88573F1B5FCC2E8978A6D94CE2B"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"CBC2A69A6A94CFADE9E4077F5B655B6E"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.","weight":1,"task_id":"847F1E1599EECDE92F99B7581728FFE8"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?","answer":"A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.","weight":1,"task_id":"037BAB6CB2DE7A42AAAA73CE5DA8DB73"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":" Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.","weight":1,"task_id":"B2AA6DE557D652A0A660C4E0FAC1124D"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"3A55AE005B07C55283410798C0FBE47F"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.","answer":"Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.","weight":1,"task_id":"7EC697DE62C0C57E601EC3F5B295DF61"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.","answer":"Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.","weight":1,"task_id":"0A6673A0B69F0FF9C9657FB797DD1FE2"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.","answer":"Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.","weight":1,"task_id":"44B088326CD80B4980D810738D88A284"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert","answer":"Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors.","weight":-1,"task_id":"D53462CE61F52F7D31BB627998F4D75A"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"F6FC3B8BBCE2BA90D0EF2C9532DE3F84"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"0F76F85FB406BF74022084C5866C942D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"FE7363764A44969E28C9562A3948143B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F456694025B9C98AA9E4246820D5909A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"E6C75917249BB8C0810B0E709D6FDD0A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"8FA337BF315CFA09716800E096EA8A06"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"D68A2086596023BDD8C01023B03FA89D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How is the immune system related to diabetes?","answer":"The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.","weight":1,"task_id":"CD3820AA1BD96613F78FDF3CF5C8AB3D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the genomic variants associated with immune system components and diabetes?","answer":"The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.","weight":1,"task_id":"A4CE2F2F8E08E5F16C94A1BCF540D881"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the role of the immune system in the metabolomics of diabetes and associated conditions?","answer":"The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.","weight":1,"task_id":"1B8618ADB274F928B3AACAB1C71A927E"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the different relationship between traits?","answer":"The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.","weight":1,"task_id":"BF1705D2C26044038FF1483258548167"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can landscape of QTL and GWAS hits be used to find relationships between traits ?","answer":"Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.","weight":1,"task_id":"68AB7A78543D5B36206274837824091B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?","answer":"Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes.","weight":1,"task_id":"055110B765AA502F9AAECE68CEC0DD24"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"C4FEDD378CD138B141464832D021624B"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"ED89B73DC42AD2ADA03B7C014009A551"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"21CB24A2A589173F1E50ADA5DD6165EC"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"1619A0727D1C6673EE9E05171054F658"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"52B443B815CD46D57219872DFB3D0579"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"EB6B4DCD473BEE9580F47CD12DAFC074"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What causes diabetes?","answer":"Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.","weight":1,"task_id":"2AE18C9AAFB4E3A103F03C86BBEB2DD1"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Define dyslipidemia.","answer":"Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.","weight":1,"task_id":"58D6F365917926445960756A26B3FDC8"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Does cycling reduce risk of diabetes?","answer":"Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.","weight":1,"task_id":"2A2860BB54BC0D36A929838ED41243A7"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is cytochrome?","answer":"Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence.","weight":1,"task_id":"A5DEAEAC441B3BDC65B58EA6923FAE73"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in aging","answer":"Several genes are involved in aging, including BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, CDKN2B, USP28, E2F2, BCL3, NAP1L4, GAB2, QKI, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, and several S100 calcium binding proteins.","weight":1,"task_id":"FE094A900BA5B3C48A3A67B18B2F12BD"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"9E388A69975DBDEA3E8DE25294960147"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"29C36228E29604002BB3BAE6654F7762"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"9ED249912768DA58AF97F5600D0CBA8F"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"DEEA82693F72D24109C91089CABC7EBF"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"FC09E6CA3472C1E263743195703162C5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"136D27CEFA12BC3AF0BDD42121FD6DBA"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5DCBD523F5F1663492EB5630EAD981FE"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9C697AF95B263CBD4E243D8AD1062180"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"6F33581CC88C813D20B047A82A78BC7C"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"2C01511CD9C5ABDC085D77F67AA862E1"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.","weight":1,"task_id":"0CDD1C9219114BB2770C28D541F1060A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"BD981EFD76B6C93C620CD92DB9EF0B35"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What is GeneNetwork and how does it relate to aging research?","answer":"GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity.","weight":1,"task_id":"37A26345145679F7539EA8F512623F5E"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"How can GeneNetwork assist in identifying genetic factors involved in diabetes?","answer":"GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network","weight":-1,"task_id":"F35BF9C40081CE0521E562CD95BA4C2F"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?","answer":"GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.","weight":-1,"task_id":"9DD88454267DEF2106A3EA7E6E8B5443"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Explain Protective Genetic Factors Against Diabetes in Elderly Populations","answer":"Studies have shown that foreign genetic admixture can have a protective effect against diabetes. For instance, the prevalence of Type 2 Diabetes (T2D) in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with foreign genetic admixture. This suggests that foreign genotypes can reduce the risk of diabetes. Similar findings have been reported in Pima Indians and other Native American populations.","weight":1,"task_id":"CB93CE86DA18F287DBEF22CB29C560CF"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What role does insulin play in the regulation of blood glucose levels?","answer":"Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present, cells increase insulin output to maintain normal glucose tolerance. However, if cells are incapable of this task, plasma concentrations of glucose increase.","weight":1,"task_id":"732D340E5C8F09381CEFA440AD2A7AB6"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what genes are involved in the aging process","answer":"Several genes are involved in the aging process. These include BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, IGF-1, CYP19, NAP1L4, GAB2, QKI, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, USP28, E2F2, BCL3, mre11, rad50, Ku80, mus308, mus205, DNApol-eta, Trxr-1, GlnRS, isoQC, QC, Cct1, Cct4, Cct5, Cct6, Hsc70-4, Xbp1, NAT1, Rack1, Rheb, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, SIR2, DAF-2/IGF1R, DAF-16/FOXO, AAK-2/AMPK, LET-363/mTOR, SIRT1/SIR2, p53, Werner's syndrome gene, iff-1, SIR2, TERT, ZBTB16, CA9, HEY2, Smarca4, Amarcb1, Hdac1, -5, -6, Dnmt3b, and genes in the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway.","weight":1,"task_id":"8DCEF606839664C8B6C72CF1D181CEEA"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk","answer":"Lifestyle modifications, including increased physical activity, dietary changes, and weight reduction, have been shown to significantly reduce the risk of Type 2 Diabetes (T2D), which is often associated with aging. Regular physical activity improves insulin sensitivity, reducing the stress on insulin-producing cells. Dietary changes, particularly those reducing the intake of processed carbohydrates, energy-rich foods, and animal fats, can also lower T2D risk. Weight loss can directly improve insulin secretion and sensitivity. However, these lifestyle changes require not only individual efforts but also changes in social, built, and food environments.","weight":1,"task_id":"FEE16F5E4D12AF7E7B0DDBF6F047EB76"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":" How does aging affect the risk of developing type 2 diabetes?","answer":"Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes.","weight":1,"task_id":"CE5922BDA6B949A17665AB4E1A8138D5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Explain The Role of Longevity Genes in Protecting Against Diabetes","answer":"The background text does not provide information on the role of longevity genes in protecting against diabetes.","weight":-1,"task_id":"DF05AACA4A1466AC1753DE13631A6ACD"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Can lifestyle changes reverse type 2 diabetes?","answer":"Lifestyle changes, including diet modification and increased physical activity, can help prevent and manage type 2 diabetes. They can improve insulin sensitivity, control blood glucose levels, and promote weight loss, which are all beneficial in managing the disease. However, they may not completely reverse the disease, especially in advanced stages.","weight":1,"task_id":"F0CC742EA104CB2C8B8BCA9CB6EB78F0"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"What are the types of diabetes","answer":"The types of diabetes include Type 1 diabetes, Type 2 diabetes, gestational diabetes, and diabetes due to specific causes such as genetic defects causing deficient insulin secretion or action, diseases of the pancreas, and use of certain drugs. There are also subtypes of adult-onset diabetes identified as severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes.","weight":1,"task_id":"57CB850E74BC7A26A645CAAB823D35CD"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"BFEF55FA3BA8B9460207E8CF981E1A4A"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"A603218836E967137903C8CABAC8C282"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.","weight":-1,"task_id":"A5627A35FFA5C25EE34522D01D7198B1"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"how does environment influence fertilisation","answer":"Environmental factors can influence fertilisation in several ways. Factors such as temperature, pressure, and chemical activity can affect the physiological processes involved in fertilisation. Lifestyle factors like smoking, irradiation, oral contraceptives, and low socioeconomic status can also impact natural conception. Moreover, environmental conditions can affect the quality of maternal care and offspring development. For instance, crowded larval environments can lead to a decline in quality over time, affecting the genetic polymorphism in populations. Additionally, environmental stressors can influence the responsiveness to prenatal stress, potentially affecting fertility.","weight":1,"task_id":"6D733CABEB70E4DBF150EAAFFED6C973"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"how does diet impact someone's height","answer":"The text does not provide information on how diet impacts someone's height.","weight":-1,"task_id":"6267E2FEFF0332F88C2294C8F32C1FC1"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"which animal has the same number of chromosomes as human","answer":"The Bama miniature pig has the same number of chromosomes as humans.","weight":1,"task_id":"3FFA45D7124495B37B6F7F2B7B780AF3"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"what's ensures brains work","answer":"The functioning of the brain is ensured by the complex interplay of neuronal networks, synaptic connections, and the precise regulation of molecular and cellular events. This includes the spatiotemporal regulation of the transcriptome, the signaling dynamics of integrated circuitry, and the balance between activating and inhibiting systems. Additionally, the maintenance of genomic integrity in neuronal cells is crucial for the brain's high metabolic activity.","weight":1,"task_id":"499C63633BB95DE93DC3A89615496443"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"how do our brains maintain emotions","answer":"Our brains maintain emotions through complex neural systems and processes. The reward-motivation system, which includes the striatum, amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex, plays a key role in processing emotional and motivational information. The amygdala, a principal component of the limbic system, controls emotionality. Stress, anxiety, and other emotion-related traits are influenced by multiple genetic, environmental, experiential, and epigenetic factors. Additionally, hormones released by stressful experiences can enhance memory consolidation, further influencing emotional responses.","weight":1,"task_id":"405240F6F75C3927C1088287E19920AD"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"what hormones do our brains release during stressful experiences?","answer":"During stressful experiences, our brains release hormones such as epinephrine, glucocorticoids, and corticosterone. The hypothalamic-pituitary-adrenal (HPA) axis also plays a significant role in this process, stimulating the release of these hormones.","weight":1,"task_id":"DA2C5FBAA7806455F89E896E641DD642"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"what is the use of corticosterone?","answer":"Corticosterone, a type of glucocorticoid, is used in the body's response to stress. It initiates metabolic and physiological responses that help the body cope with stressors. It also provides negative feedback to inhibit activity in the hypothalamic-pituitary-adrenal (HPA) axis, which helps to dampen stress-activated defense reactions, including immune responses. Additionally, corticosterone can influence the expression of certain cytokines, potentially having anti-inflammatory effects.","weight":1,"task_id":"7B0629638DF00DF1183B67EE3BF39B1C"}] diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_25-out-unique.json b/gnqa/paper1_eval/src/data/ratings/2024_06_25-out-unique.json new file mode 100644 index 0000000..5511d97 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_25-out-unique.json @@ -0,0 +1,674 @@ +{ + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ], + "topic": [ + 2, 1, 1, 1, 0, 0 + ], + "level": "cs" + }, + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ], + "topic": [ + 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 + ], + "level": "de" + }, + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4", + "FE094A900BA5B3C48A3A67B18B2F12BD", + "CB93CE86DA18F287DBEF22CB29C560CF", + "8DCEF606839664C8B6C72CF1D181CEEA", + "FEE16F5E4D12AF7E7B0DDBF6F047EB76", + "DF05AACA4A1466AC1753DE13631A6ACD", + "57CB850E74BC7A26A645CAAB823D35CD" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "Several genes are involved in aging, including BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, CDKN2B, USP28, E2F2, BCL3, NAP1L4, GAB2, QKI, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, and several S100 calcium binding proteins.", + "Studies have shown that foreign genetic admixture can have a protective effect against diabetes. For instance, the prevalence of Type 2 Diabetes (T2D) in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with foreign genetic admixture. This suggests that foreign genotypes can reduce the risk of diabetes. Similar findings have been reported in Pima Indians and other Native American populations.", + "Several genes are involved in the aging process. These include BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, IGF-1, CYP19, NAP1L4, GAB2, QKI, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, USP28, E2F2, BCL3, mre11, rad50, Ku80, mus308, mus205, DNApol-eta, Trxr-1, GlnRS, isoQC, QC, Cct1, Cct4, Cct5, Cct6, Hsc70-4, Xbp1, NAT1, Rack1, Rheb, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, SIR2, DAF-2/IGF1R, DAF-16/FOXO, AAK-2/AMPK, LET-363/mTOR, SIRT1/SIR2, p53, Werner's syndrome gene, iff-1, SIR2, TERT, ZBTB16, CA9, HEY2, Smarca4, Amarcb1, Hdac1, -5, -6, Dnmt3b, and genes in the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway.", + "Lifestyle modifications, including increased physical activity, dietary changes, and weight reduction, have been shown to significantly reduce the risk of Type 2 Diabetes (T2D), which is often associated with aging. Regular physical activity improves insulin sensitivity, reducing the stress on insulin-producing cells. Dietary changes, particularly those reducing the intake of processed carbohydrates, energy-rich foods, and animal fats, can also lower T2D risk. Weight loss can directly improve insulin secretion and sensitivity. However, these lifestyle changes require not only individual efforts but also changes in social, built, and food environments.", + "The background text does not provide information on the role of longevity genes in protecting against diabetes.", + "The types of diabetes include Type 1 diabetes, Type 2 diabetes, gestational diabetes, and diabetes due to specific causes such as genetic defects causing deficient insulin secretion or action, diseases of the pancreas, and use of certain drugs. There are also subtypes of adult-onset diabetes identified as severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes." + ], + "query": [ + "genetics", + "what is bioinformatics", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in aging", + "Explain Protective Genetic Factors Against Diabetes in Elderly Populations", + "what genes are involved in the aging process", + "Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk", + "Explain The Role of Longevity Genes in Protecting Against Diabetes", + "What are the types of diabetes" + ], + "topic": [ + 0, 0, 1, 1, 1, 2, 1, 2, 2, 2 + ], + "level": "cs" + }, + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ], + "topic": [ + 0, 1, 1, 0, 0, 2, 0 + ], + "level": "cs" + }, + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ], + "topic": [ + 0, 0, 0, 0, 0, 0, 1, 2, 2 + ], + "level": "cs" + }, + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ], + "topic": [ + 2, 1, 0, 0, 0 + ], + "level": "cs" + }, + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ], + "topic": [ + 0, 0, 1, 2, 0 + ], + "level": "de" + }, + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ], + "topic": [ + 0, 0, 0 + ], + "level": "cs" + }, + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1" + ], + "weight": [ + 1, + 1, + -1 + ], + "answer": [ + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides." + ], + "query": [ + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing" + ], + "topic": [ + 1, 0, 0 + ], + "level": "cs" + }, + "0c4a1c26-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + -1 + ], + "answer": [ + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ], + "topic": [ + 0 + ], + "level": "de" + }, + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "3A16235DA1E02B9148B9288A06EE567E", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + 1 + ], + "answer": [ + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ], + "topic": [ + 0, 0, 2 + ], + "level": "de" + }, + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ], + "topic": [ + 1, 2, 2 + ], + "level": "de" + }, + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ], + "topic": [ + 0, 1, 1, 2, 0, 2 + ], + "level": "de" + }, + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ], + "topic": [ + 0, 0, 0, 0, 0, 0, 1 + ], + "level": "de" + }, + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ], + "topic": [ + 2, 2, 2, 0, 0, 2 + ], + "level": "de" + }, + "8e4fe952-5a61-4d95-86e5-49f974465572": { + "task_id": [ + "2AE18C9AAFB4E3A103F03C86BBEB2DD1", + "58D6F365917926445960756A26B3FDC8", + "2A2860BB54BC0D36A929838ED41243A7", + "A5DEAEAC441B3BDC65B58EA6923FAE73" + ], + "weight": [ + 1, + 1, + 1, + 1 + ], + "answer": [ + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence." + ], + "query": [ + "What causes diabetes?", + "Define dyslipidemia.", + "Does cycling reduce risk of diabetes?", + "What is cytochrome?" + ], + "topic": [ + 2, 0, 2, 0 + ], + "level": "cs" + }, + "5fb03df3-5a04-4dad-ba1c-14eb8e50a241": { + "task_id": [ + "0CDD1C9219114BB2770C28D541F1060A", + "37A26345145679F7539EA8F512623F5E", + "F35BF9C40081CE0521E562CD95BA4C2F", + "9DD88454267DEF2106A3EA7E6E8B5443", + "732D340E5C8F09381CEFA440AD2A7AB6", + "CE5922BDA6B949A17665AB4E1A8138D5", + "F0CC742EA104CB2C8B8BCA9CB6EB78F0" + ], + "weight": [ + 1, + 1, + -1, + -1, + 1, + 1, + 1 + ], + "answer": [ + "The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.", + "GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity.", + "GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network", + "GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.", + "Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present, cells increase insulin output to maintain normal glucose tolerance. However, if cells are incapable of this task, plasma concentrations of glucose increase.", + "Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes.", + "Lifestyle changes, including diet modification and increased physical activity, can help prevent and manage type 2 diabetes. They can improve insulin sensitivity, control blood glucose levels, and promote weight loss, which are all beneficial in managing the disease. However, they may not completely reverse the disease, especially in advanced stages." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is GeneNetwork and how does it relate to aging research?", + "How can GeneNetwork assist in identifying genetic factors involved in diabetes?", + "What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?", + "What role does insulin play in the regulation of blood glucose levels?", + " How does aging affect the risk of developing type 2 diabetes?", + "Can lifestyle changes reverse type 2 diabetes?" + ], + "topic": [ + 0, 1, 2, 2, 2, 2, 2 + ], + "level": "cs" + }, + "24a2e578-5f6e-4576-8942-148d6a9f672d": { + "task_id": [ + "6D733CABEB70E4DBF150EAAFFED6C973", + "6267E2FEFF0332F88C2294C8F32C1FC1", + "3FFA45D7124495B37B6F7F2B7B780AF3", + "499C63633BB95DE93DC3A89615496443", + "405240F6F75C3927C1088287E19920AD", + "DA2C5FBAA7806455F89E896E641DD642", + "7B0629638DF00DF1183B67EE3BF39B1C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "Environmental factors can influence fertilisation in several ways. Factors such as temperature, pressure, and chemical activity can affect the physiological processes involved in fertilisation. Lifestyle factors like smoking, irradiation, oral contraceptives, and low socioeconomic status can also impact natural conception. Moreover, environmental conditions can affect the quality of maternal care and offspring development. For instance, crowded larval environments can lead to a decline in quality over time, affecting the genetic polymorphism in populations. Additionally, environmental stressors can influence the responsiveness to prenatal stress, potentially affecting fertility.", + "The text does not provide information on how diet impacts someone's height.", + "The Bama miniature pig has the same number of chromosomes as humans.", + "The functioning of the brain is ensured by the complex interplay of neuronal networks, synaptic connections, and the precise regulation of molecular and cellular events. This includes the spatiotemporal regulation of the transcriptome, the signaling dynamics of integrated circuitry, and the balance between activating and inhibiting systems. Additionally, the maintenance of genomic integrity in neuronal cells is crucial for the brain's high metabolic activity.", + "Our brains maintain emotions through complex neural systems and processes. The reward-motivation system, which includes the striatum, amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex, plays a key role in processing emotional and motivational information. The amygdala, a principal component of the limbic system, controls emotionality. Stress, anxiety, and other emotion-related traits are influenced by multiple genetic, environmental, experiential, and epigenetic factors. Additionally, hormones released by stressful experiences can enhance memory consolidation, further influencing emotional responses.", + "During stressful experiences, our brains release hormones such as epinephrine, glucocorticoids, and corticosterone. The hypothalamic-pituitary-adrenal (HPA) axis also plays a significant role in this process, stimulating the release of these hormones.", + "Corticosterone, a type of glucocorticoid, is used in the body's response to stress. It initiates metabolic and physiological responses that help the body cope with stressors. It also provides negative feedback to inhibit activity in the hypothalamic-pituitary-adrenal (HPA) axis, which helps to dampen stress-activated defense reactions, including immune responses. Additionally, corticosterone can influence the expression of certain cytokines, potentially having anti-inflammatory effects." + ], + "query": [ + "how does environment influence fertilisation", + "how does diet impact someone's height", + "which animal has the same number of chromosomes as human", + "what's ensures brains work", + "how do our brains maintain emotions", + "what hormones do our brains release during stressful experiences?", + "what is the use of corticosterone?" + ], + "topic": [ + 0, 0, 0, 0, 0, 0, 0 + ], + "level": "cs" + } +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_25-out.json b/gnqa/paper1_eval/src/data/ratings/2024_06_25-out.json new file mode 100644 index 0000000..98fd751 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_25-out.json @@ -0,0 +1,930 @@ +[ + { + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "F26EEA55FF8FFCB0B9E3E46688C276DA", + "5674818C9037B8A93EDCF35CEF97E33C", + "106C5DDDE1338BE1CC8AE04CEAE3EF62", + "2D8205C79915FF9CEB8DECCE51E6E473", + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "A55310A1C19732792B5894A2DB5E8596", + "2EDD65822CDA4669FCAD678288C2E3E9", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4", + "FBBFB0D391778CDE9536F742300C1809", + "C8E21233058E1DEE05CD619ACBF49E42", + "38A8ED57882E2E250B447F93243A8CD5", + "5D3CA84D2DCE010AF63188665193B195", + "AB6286D0D8E9196BA0EE1F1CF90536AD", + "FE094A900BA5B3C48A3A67B18B2F12BD", + "FC09E6CA3472C1E263743195703162C5", + "136D27CEFA12BC3AF0BDD42121FD6DBA", + "6F33581CC88C813D20B047A82A78BC7C", + "2C01511CD9C5ABDC085D77F67AA862E1", + "BD981EFD76B6C93C620CD92DB9EF0B35", + "CB93CE86DA18F287DBEF22CB29C560CF", + "8DCEF606839664C8B6C72CF1D181CEEA", + "FEE16F5E4D12AF7E7B0DDBF6F047EB76", + "DF05AACA4A1466AC1753DE13631A6ACD", + "57CB850E74BC7A26A645CAAB823D35CD" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 0, + -1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Several genes are involved in aging, including BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, CDKN2B, USP28, E2F2, BCL3, NAP1L4, GAB2, QKI, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, and several S100 calcium binding proteins.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Studies have shown that foreign genetic admixture can have a protective effect against diabetes. For instance, the prevalence of Type 2 Diabetes (T2D) in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with foreign genetic admixture. This suggests that foreign genotypes can reduce the risk of diabetes. Similar findings have been reported in Pima Indians and other Native American populations.", + "Several genes are involved in the aging process. These include BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, IGF-1, CYP19, NAP1L4, GAB2, QKI, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, USP28, E2F2, BCL3, mre11, rad50, Ku80, mus308, mus205, DNApol-eta, Trxr-1, GlnRS, isoQC, QC, Cct1, Cct4, Cct5, Cct6, Hsc70-4, Xbp1, NAT1, Rack1, Rheb, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, SIR2, DAF-2/IGF1R, DAF-16/FOXO, AAK-2/AMPK, LET-363/mTOR, SIRT1/SIR2, p53, Werner's syndrome gene, iff-1, SIR2, TERT, ZBTB16, CA9, HEY2, Smarca4, Amarcb1, Hdac1, -5, -6, Dnmt3b, and genes in the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway.", + "Lifestyle modifications, including increased physical activity, dietary changes, and weight reduction, have been shown to significantly reduce the risk of Type 2 Diabetes (T2D), which is often associated with aging. Regular physical activity improves insulin sensitivity, reducing the stress on insulin-producing cells. Dietary changes, particularly those reducing the intake of processed carbohydrates, energy-rich foods, and animal fats, can also lower T2D risk. Weight loss can directly improve insulin secretion and sensitivity. However, these lifestyle changes require not only individual efforts but also changes in social, built, and food environments.", + "The background text does not provide information on the role of longevity genes in protecting against diabetes.", + "The types of diabetes include Type 1 diabetes, Type 2 diabetes, gestational diabetes, and diabetes due to specific causes such as genetic defects causing deficient insulin secretion or action, diseases of the pancreas, and use of certain drugs. There are also subtypes of adult-onset diabetes identified as severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes." + ], + "query": [ + "genetics", + "genetics", + "genetics", + "List as many studies as you can that include rapamycin.", + "genetics", + "what is bioinformatics", + "genetics", + "genetics", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in the aging process", + "List as many studies as you can that include rapamycin.", + "List as many studies as you can that include rapamycin.", + "List as many studies as you can that include rapamycin.", + "genetics", + "which genes are involved in aging", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Explain Protective Genetic Factors Against Diabetes in Elderly Populations", + "what genes are involved in the aging process", + "Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk", + "Explain The Role of Longevity Genes in Protecting Against Diabetes", + "What are the types of diabetes" + ], + "level": "cs" + } + }, + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ], + "level": "cs" + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "32CE1E54032485B73B5968395B3D3538", + "59CCE2D70104154865218876DD53D049", + "0AE973110158192E0D1F50E1D79764FB", + "33749EB09AAC7AD0404C8E3E584B98D2", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ], + "level": "de" + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "9957BCF2957D9BD083EDCF8B60C0E4DE", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ], + "level": "cs" + } + }, + { + "d2f71149-a0d5-4666-95d8-15c82543243b": { + "task_id": [ + "7CA65F6B192BE7896422284B962CF5C3" + ], + "weight": [ + 1 + ], + "answer": [ + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits." + ], + "query": [ + "genetics" + ], + "level": "cs" + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ], + "level": "cs" + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "19DC9E909DDE6D9CDB3E86D1069F5A69", + "F2843EA2D5A239D022186329C8D5D8EF", + "4E1F0C2E792BAF0BA349326375D3EE6E", + "FA8ADB009A499F51B0533FDCB72CB29E", + "38BD5864A7928C6DBCA1D844327F3A19", + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + -1, + 1, + 1, + -1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.", + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ], + "level": "cs" + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "B89A904E71CF7F16126FCA9EAAFBC8A6", + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1", + "5A562D5F7A266BA057B6833F3A83E7A7", + "53905925B4D6F69CE5706896A3C667CE", + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ], + "level": "de" + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6188C7826173CD59FD243F98C537AD50", + "2C37A2EC74E4B46F13C6FB23D9547DBB", + "590809C6B2A1504E2AE9A79EFDF3AC9A", + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "BD671A3AC4F77A74CA8A68EAD627437D", + "5E243BF70932D1477209FEF6869FDEEA", + "3A16235DA1E02B9148B9288A06EE567E", + "9BE91FA13BDC0D9EB80B35FC73A0029B", + "3D4688621977A9CBD670B50131B8D912", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "00647726F98EE835006D60B12455866D", + "8F3A81EAB68F709E82006205380AC723", + "ECEB33208BCDDC20908183BF249555AD", + "906F0A0AB4330CB7C3A75DA8764703F4", + "A3B39D0211921BC5581AB03193860970", + "2BF7D398C0BFD1F4D976C9F82343DE51", + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + -1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "0BF7A88573F1B5FCC2E8978A6D94CE2B", + "CBC2A69A6A94CFADE9E4077F5B655B6E", + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "3A55AE005B07C55283410798C0FBE47F", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + -1, + -1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "What is the significance of the length of telomeres?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "61a8e2c6-249c-40b8-a866-134f3a893e4a": { + "task_id": [ + "F6FC3B8BBCE2BA90D0EF2C9532DE3F84", + "0F76F85FB406BF74022084C5866C942D", + "C4FEDD378CD138B141464832D021624B", + "ED89B73DC42AD2ADA03B7C014009A551", + "21CB24A2A589173F1E50ADA5DD6165EC" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "FE7363764A44969E28C9562A3948143B", + "F456694025B9C98AA9E4246820D5909A", + "E6C75917249BB8C0810B0E709D6FDD0A", + "8FA337BF315CFA09716800E096EA8A06", + "D68A2086596023BDD8C01023B03FA89D", + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + }, + { + "8e4fe952-5a61-4d95-86e5-49f974465572": { + "task_id": [ + "1619A0727D1C6673EE9E05171054F658", + "52B443B815CD46D57219872DFB3D0579", + "EB6B4DCD473BEE9580F47CD12DAFC074", + "2AE18C9AAFB4E3A103F03C86BBEB2DD1", + "58D6F365917926445960756A26B3FDC8", + "2A2860BB54BC0D36A929838ED41243A7", + "A5DEAEAC441B3BDC65B58EA6923FAE73" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "What causes diabetes?", + "Define dyslipidemia.", + "Does cycling reduce risk of diabetes?", + "What is cytochrome?" + ] + } + }, + { + "5fb03df3-5a04-4dad-ba1c-14eb8e50a241": { + "task_id": [ + "9E388A69975DBDEA3E8DE25294960147", + "9ED249912768DA58AF97F5600D0CBA8F", + "DEEA82693F72D24109C91089CABC7EBF", + "9C697AF95B263CBD4E243D8AD1062180", + "0CDD1C9219114BB2770C28D541F1060A", + "37A26345145679F7539EA8F512623F5E", + "F35BF9C40081CE0521E562CD95BA4C2F", + "9DD88454267DEF2106A3EA7E6E8B5443", + "732D340E5C8F09381CEFA440AD2A7AB6", + "CE5922BDA6B949A17665AB4E1A8138D5", + "F0CC742EA104CB2C8B8BCA9CB6EB78F0" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + -1, + -1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.", + "GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity.", + "GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network", + "GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.", + "Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present, cells increase insulin output to maintain normal glucose tolerance. However, if cells are incapable of this task, plasma concentrations of glucose increase.", + "Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes.", + "Lifestyle changes, including diet modification and increased physical activity, can help prevent and manage type 2 diabetes. They can improve insulin sensitivity, control blood glucose levels, and promote weight loss, which are all beneficial in managing the disease. However, they may not completely reverse the disease, especially in advanced stages." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is GeneNetwork and how does it relate to aging research?", + "How can GeneNetwork assist in identifying genetic factors involved in diabetes?", + "What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?", + "What role does insulin play in the regulation of blood glucose levels?", + " How does aging affect the risk of developing type 2 diabetes?", + "Can lifestyle changes reverse type 2 diabetes?" + ] + } + }, + { + "24a2e578-5f6e-4576-8942-148d6a9f672d": { + "task_id": [ + "29C36228E29604002BB3BAE6654F7762", + "5DCBD523F5F1663492EB5630EAD981FE", + "BFEF55FA3BA8B9460207E8CF981E1A4A", + "A603218836E967137903C8CABAC8C282", + "A5627A35FFA5C25EE34522D01D7198B1", + "6D733CABEB70E4DBF150EAAFFED6C973", + "6267E2FEFF0332F88C2294C8F32C1FC1", + "3FFA45D7124495B37B6F7F2B7B780AF3", + "499C63633BB95DE93DC3A89615496443", + "405240F6F75C3927C1088287E19920AD", + "DA2C5FBAA7806455F89E896E641DD642", + "7B0629638DF00DF1183B67EE3BF39B1C" + ], + "weight": [ + 1, + 1, + 1, + -1, + -1, + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.", + "Environmental factors can influence fertilisation in several ways. Factors such as temperature, pressure, and chemical activity can affect the physiological processes involved in fertilisation. Lifestyle factors like smoking, irradiation, oral contraceptives, and low socioeconomic status can also impact natural conception. Moreover, environmental conditions can affect the quality of maternal care and offspring development. For instance, crowded larval environments can lead to a decline in quality over time, affecting the genetic polymorphism in populations. Additionally, environmental stressors can influence the responsiveness to prenatal stress, potentially affecting fertility.", + "The text does not provide information on how diet impacts someone's height.", + "The Bama miniature pig has the same number of chromosomes as humans.", + "The functioning of the brain is ensured by the complex interplay of neuronal networks, synaptic connections, and the precise regulation of molecular and cellular events. This includes the spatiotemporal regulation of the transcriptome, the signaling dynamics of integrated circuitry, and the balance between activating and inhibiting systems. Additionally, the maintenance of genomic integrity in neuronal cells is crucial for the brain's high metabolic activity.", + "Our brains maintain emotions through complex neural systems and processes. The reward-motivation system, which includes the striatum, amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex, plays a key role in processing emotional and motivational information. The amygdala, a principal component of the limbic system, controls emotionality. Stress, anxiety, and other emotion-related traits are influenced by multiple genetic, environmental, experiential, and epigenetic factors. Additionally, hormones released by stressful experiences can enhance memory consolidation, further influencing emotional responses.", + "During stressful experiences, our brains release hormones such as epinephrine, glucocorticoids, and corticosterone. The hypothalamic-pituitary-adrenal (HPA) axis also plays a significant role in this process, stimulating the release of these hormones.", + "Corticosterone, a type of glucocorticoid, is used in the body's response to stress. It initiates metabolic and physiological responses that help the body cope with stressors. It also provides negative feedback to inhibit activity in the hypothalamic-pituitary-adrenal (HPA) axis, which helps to dampen stress-activated defense reactions, including immune responses. Additionally, corticosterone can influence the expression of certain cytokines, potentially having anti-inflammatory effects." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "how does environment influence fertilisation", + "how does diet impact someone's height", + "which animal has the same number of chromosomes as human", + "what's ensures brains work", + "how do our brains maintain emotions", + "what hormones do our brains release during stressful experiences?", + "what is the use of corticosterone?" + ] + } + } +]
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_25-out_combined.json b/gnqa/paper1_eval/src/data/ratings/2024_06_25-out_combined.json new file mode 100644 index 0000000..9cbcabd --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_25-out_combined.json @@ -0,0 +1,874 @@ +[ + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "32CE1E54032485B73B5968395B3D3538", + "59CCE2D70104154865218876DD53D049", + "0AE973110158192E0D1F50E1D79764FB", + "33749EB09AAC7AD0404C8E3E584B98D2", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4", + "FBBFB0D391778CDE9536F742300C1809", + "FE094A900BA5B3C48A3A67B18B2F12BD", + "FC09E6CA3472C1E263743195703162C5", + "136D27CEFA12BC3AF0BDD42121FD6DBA", + "6F33581CC88C813D20B047A82A78BC7C", + "2C01511CD9C5ABDC085D77F67AA862E1", + "BD981EFD76B6C93C620CD92DB9EF0B35", + "CB93CE86DA18F287DBEF22CB29C560CF", + "8DCEF606839664C8B6C72CF1D181CEEA", + "FEE16F5E4D12AF7E7B0DDBF6F047EB76", + "DF05AACA4A1466AC1753DE13631A6ACD", + "57CB850E74BC7A26A645CAAB823D35CD" + ], + "weight": [ + 1, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "Several genes are involved in aging, including BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, CDKN2B, USP28, E2F2, BCL3, NAP1L4, GAB2, QKI, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, and several S100 calcium binding proteins.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Studies have shown that foreign genetic admixture can have a protective effect against diabetes. For instance, the prevalence of Type 2 Diabetes (T2D) in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with foreign genetic admixture. This suggests that foreign genotypes can reduce the risk of diabetes. Similar findings have been reported in Pima Indians and other Native American populations.", + "Several genes are involved in the aging process. These include BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, IGF-1, CYP19, NAP1L4, GAB2, QKI, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, USP28, E2F2, BCL3, mre11, rad50, Ku80, mus308, mus205, DNApol-eta, Trxr-1, GlnRS, isoQC, QC, Cct1, Cct4, Cct5, Cct6, Hsc70-4, Xbp1, NAT1, Rack1, Rheb, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, SIR2, DAF-2/IGF1R, DAF-16/FOXO, AAK-2/AMPK, LET-363/mTOR, SIRT1/SIR2, p53, Werner's syndrome gene, iff-1, SIR2, TERT, ZBTB16, CA9, HEY2, Smarca4, Amarcb1, Hdac1, -5, -6, Dnmt3b, and genes in the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway.", + "Lifestyle modifications, including increased physical activity, dietary changes, and weight reduction, have been shown to significantly reduce the risk of Type 2 Diabetes (T2D), which is often associated with aging. Regular physical activity improves insulin sensitivity, reducing the stress on insulin-producing cells. Dietary changes, particularly those reducing the intake of processed carbohydrates, energy-rich foods, and animal fats, can also lower T2D risk. Weight loss can directly improve insulin secretion and sensitivity. However, these lifestyle changes require not only individual efforts but also changes in social, built, and food environments.", + "The background text does not provide information on the role of longevity genes in protecting against diabetes.", + "The types of diabetes include Type 1 diabetes, Type 2 diabetes, gestational diabetes, and diabetes due to specific causes such as genetic defects causing deficient insulin secretion or action, diseases of the pancreas, and use of certain drugs. There are also subtypes of adult-onset diabetes identified as severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes." + ], + "query": [ + "genetics", + "what is bioinformatics", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in the aging process", + "which genes are involved in aging", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Explain Protective Genetic Factors Against Diabetes in Elderly Populations", + "what genes are involved in the aging process", + "Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk", + "Explain The Role of Longevity Genes in Protecting Against Diabetes", + "What are the types of diabetes" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "9957BCF2957D9BD083EDCF8B60C0E4DE", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "19DC9E909DDE6D9CDB3E86D1069F5A69", + "F2843EA2D5A239D022186329C8D5D8EF", + "4E1F0C2E792BAF0BA349326375D3EE6E", + "FA8ADB009A499F51B0533FDCB72CB29E", + "38BD5864A7928C6DBCA1D844327F3A19", + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + -1, + 1, + 1, + -1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.", + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "B89A904E71CF7F16126FCA9EAAFBC8A6", + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1", + "5A562D5F7A266BA057B6833F3A83E7A7", + "53905925B4D6F69CE5706896A3C667CE" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6188C7826173CD59FD243F98C537AD50", + "2C37A2EC74E4B46F13C6FB23D9547DBB", + "590809C6B2A1504E2AE9A79EFDF3AC9A", + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "0c4a1c26-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + -1 + ], + "answer": [ + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "BD671A3AC4F77A74CA8A68EAD627437D", + "5E243BF70932D1477209FEF6869FDEEA", + "3A16235DA1E02B9148B9288A06EE567E", + "9BE91FA13BDC0D9EB80B35FC73A0029B", + "3D4688621977A9CBD670B50131B8D912", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "00647726F98EE835006D60B12455866D", + "ECEB33208BCDDC20908183BF249555AD", + "906F0A0AB4330CB7C3A75DA8764703F4", + "A3B39D0211921BC5581AB03193860970", + "2BF7D398C0BFD1F4D976C9F82343DE51", + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + -1, + 1, + -1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "0BF7A88573F1B5FCC2E8978A6D94CE2B", + "CBC2A69A6A94CFADE9E4077F5B655B6E", + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "3A55AE005B07C55283410798C0FBE47F", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + -1, + -1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "What is the significance of the length of telomeres?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "61a8e2c6-249c-40b8-a866-134f3a893e4a": { + "task_id": [ + "F6FC3B8BBCE2BA90D0EF2C9532DE3F84", + "0F76F85FB406BF74022084C5866C942D", + "C4FEDD378CD138B141464832D021624B", + "ED89B73DC42AD2ADA03B7C014009A551", + "21CB24A2A589173F1E50ADA5DD6165EC" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "FE7363764A44969E28C9562A3948143B", + "F456694025B9C98AA9E4246820D5909A", + "E6C75917249BB8C0810B0E709D6FDD0A", + "8FA337BF315CFA09716800E096EA8A06", + "D68A2086596023BDD8C01023B03FA89D", + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + }, + { + "8e4fe952-5a61-4d95-86e5-49f974465572": { + "task_id": [ + "1619A0727D1C6673EE9E05171054F658", + "52B443B815CD46D57219872DFB3D0579", + "EB6B4DCD473BEE9580F47CD12DAFC074", + "2AE18C9AAFB4E3A103F03C86BBEB2DD1", + "58D6F365917926445960756A26B3FDC8", + "2A2860BB54BC0D36A929838ED41243A7", + "A5DEAEAC441B3BDC65B58EA6923FAE73" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "What causes diabetes?", + "Define dyslipidemia.", + "Does cycling reduce risk of diabetes?", + "What is cytochrome?" + ] + } + }, + { + "5fb03df3-5a04-4dad-ba1c-14eb8e50a241": { + "task_id": [ + "9E388A69975DBDEA3E8DE25294960147", + "9ED249912768DA58AF97F5600D0CBA8F", + "DEEA82693F72D24109C91089CABC7EBF", + "9C697AF95B263CBD4E243D8AD1062180", + "0CDD1C9219114BB2770C28D541F1060A", + "37A26345145679F7539EA8F512623F5E", + "F35BF9C40081CE0521E562CD95BA4C2F", + "9DD88454267DEF2106A3EA7E6E8B5443", + "732D340E5C8F09381CEFA440AD2A7AB6", + "CE5922BDA6B949A17665AB4E1A8138D5", + "F0CC742EA104CB2C8B8BCA9CB6EB78F0" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + -1, + -1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.", + "GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity.", + "GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network", + "GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.", + "Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present, cells increase insulin output to maintain normal glucose tolerance. However, if cells are incapable of this task, plasma concentrations of glucose increase.", + "Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes.", + "Lifestyle changes, including diet modification and increased physical activity, can help prevent and manage type 2 diabetes. They can improve insulin sensitivity, control blood glucose levels, and promote weight loss, which are all beneficial in managing the disease. However, they may not completely reverse the disease, especially in advanced stages." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is GeneNetwork and how does it relate to aging research?", + "How can GeneNetwork assist in identifying genetic factors involved in diabetes?", + "What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?", + "What role does insulin play in the regulation of blood glucose levels?", + " How does aging affect the risk of developing type 2 diabetes?", + "Can lifestyle changes reverse type 2 diabetes?" + ] + } + }, + { + "24a2e578-5f6e-4576-8942-148d6a9f672d": { + "task_id": [ + "29C36228E29604002BB3BAE6654F7762", + "5DCBD523F5F1663492EB5630EAD981FE", + "BFEF55FA3BA8B9460207E8CF981E1A4A", + "A603218836E967137903C8CABAC8C282", + "A5627A35FFA5C25EE34522D01D7198B1", + "6D733CABEB70E4DBF150EAAFFED6C973", + "6267E2FEFF0332F88C2294C8F32C1FC1", + "3FFA45D7124495B37B6F7F2B7B780AF3", + "499C63633BB95DE93DC3A89615496443", + "405240F6F75C3927C1088287E19920AD", + "DA2C5FBAA7806455F89E896E641DD642", + "7B0629638DF00DF1183B67EE3BF39B1C" + ], + "weight": [ + 1, + 1, + 1, + -1, + -1, + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.", + "Environmental factors can influence fertilisation in several ways. Factors such as temperature, pressure, and chemical activity can affect the physiological processes involved in fertilisation. Lifestyle factors like smoking, irradiation, oral contraceptives, and low socioeconomic status can also impact natural conception. Moreover, environmental conditions can affect the quality of maternal care and offspring development. For instance, crowded larval environments can lead to a decline in quality over time, affecting the genetic polymorphism in populations. Additionally, environmental stressors can influence the responsiveness to prenatal stress, potentially affecting fertility.", + "The text does not provide information on how diet impacts someone's height.", + "The Bama miniature pig has the same number of chromosomes as humans.", + "The functioning of the brain is ensured by the complex interplay of neuronal networks, synaptic connections, and the precise regulation of molecular and cellular events. This includes the spatiotemporal regulation of the transcriptome, the signaling dynamics of integrated circuitry, and the balance between activating and inhibiting systems. Additionally, the maintenance of genomic integrity in neuronal cells is crucial for the brain's high metabolic activity.", + "Our brains maintain emotions through complex neural systems and processes. The reward-motivation system, which includes the striatum, amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex, plays a key role in processing emotional and motivational information. The amygdala, a principal component of the limbic system, controls emotionality. Stress, anxiety, and other emotion-related traits are influenced by multiple genetic, environmental, experiential, and epigenetic factors. Additionally, hormones released by stressful experiences can enhance memory consolidation, further influencing emotional responses.", + "During stressful experiences, our brains release hormones such as epinephrine, glucocorticoids, and corticosterone. The hypothalamic-pituitary-adrenal (HPA) axis also plays a significant role in this process, stimulating the release of these hormones.", + "Corticosterone, a type of glucocorticoid, is used in the body's response to stress. It initiates metabolic and physiological responses that help the body cope with stressors. It also provides negative feedback to inhibit activity in the hypothalamic-pituitary-adrenal (HPA) axis, which helps to dampen stress-activated defense reactions, including immune responses. Additionally, corticosterone can influence the expression of certain cytokines, potentially having anti-inflammatory effects." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "how does environment influence fertilisation", + "how does diet impact someone's height", + "which animal has the same number of chromosomes as human", + "what's ensures brains work", + "how do our brains maintain emotions", + "what hormones do our brains release during stressful experiences?", + "what is the use of corticosterone?" + ] + } + } +]
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_25-out_combined.json.2 b/gnqa/paper1_eval/src/data/ratings/2024_06_25-out_combined.json.2 new file mode 100644 index 0000000..4e06c1c --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_25-out_combined.json.2 @@ -0,0 +1,2553 @@ +, +[ + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4", + "FE094A900BA5B3C48A3A67B18B2F12BD", + "CB93CE86DA18F287DBEF22CB29C560CF", + "8DCEF606839664C8B6C72CF1D181CEEA", + "FEE16F5E4D12AF7E7B0DDBF6F047EB76", + "DF05AACA4A1466AC1753DE13631A6ACD", + "57CB850E74BC7A26A645CAAB823D35CD" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "Several genes are involved in aging, including BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, CDKN2B, USP28, E2F2, BCL3, NAP1L4, GAB2, QKI, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, and several S100 calcium binding proteins.", + "Studies have shown that foreign genetic admixture can have a protective effect against diabetes. For instance, the prevalence of Type 2 Diabetes (T2D) in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with foreign genetic admixture. This suggests that foreign genotypes can reduce the risk of diabetes. Similar findings have been reported in Pima Indians and other Native American populations.", + "Several genes are involved in the aging process. These include BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, IGF-1, CYP19, NAP1L4, GAB2, QKI, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, USP28, E2F2, BCL3, mre11, rad50, Ku80, mus308, mus205, DNApol-eta, Trxr-1, GlnRS, isoQC, QC, Cct1, Cct4, Cct5, Cct6, Hsc70-4, Xbp1, NAT1, Rack1, Rheb, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, SIR2, DAF-2/IGF1R, DAF-16/FOXO, AAK-2/AMPK, LET-363/mTOR, SIRT1/SIR2, p53, Werner's syndrome gene, iff-1, SIR2, TERT, ZBTB16, CA9, HEY2, Smarca4, Amarcb1, Hdac1, -5, -6, Dnmt3b, and genes in the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway.", + "Lifestyle modifications, including increased physical activity, dietary changes, and weight reduction, have been shown to significantly reduce the risk of Type 2 Diabetes (T2D), which is often associated with aging. Regular physical activity improves insulin sensitivity, reducing the stress on insulin-producing cells. Dietary changes, particularly those reducing the intake of processed carbohydrates, energy-rich foods, and animal fats, can also lower T2D risk. Weight loss can directly improve insulin secretion and sensitivity. However, these lifestyle changes require not only individual efforts but also changes in social, built, and food environments.", + "The background text does not provide information on the role of longevity genes in protecting against diabetes.", + "The types of diabetes include Type 1 diabetes, Type 2 diabetes, gestational diabetes, and diabetes due to specific causes such as genetic defects causing deficient insulin secretion or action, diseases of the pancreas, and use of certain drugs. There are also subtypes of adult-onset diabetes identified as severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes." + ], + "query": [ + "genetics", + "what is bioinformatics", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in aging", + "Explain Protective Genetic Factors Against Diabetes in Elderly Populations", + "what genes are involved in the aging process", + "Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk", + "Explain The Role of Longevity Genes in Protecting Against Diabetes", + "What are the types of diabetes" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1" + ], + "weight": [ + 1, + 1, + -1 + ], + "answer": [ + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides." + ], + "query": [ + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing" + ] + } + }, + { + "0c4a1c26-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + -1 + ], + "answer": [ + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "3A16235DA1E02B9148B9288A06EE567E", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + 1 + ], + "answer": [ + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + }, + { + "8e4fe952-5a61-4d95-86e5-49f974465572": { + "task_id": [ + "2AE18C9AAFB4E3A103F03C86BBEB2DD1", + "58D6F365917926445960756A26B3FDC8", + "2A2860BB54BC0D36A929838ED41243A7", + "A5DEAEAC441B3BDC65B58EA6923FAE73" + ], + "weight": [ + 1, + 1, + 1, + 1 + ], + "answer": [ + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence." + ], + "query": [ + "What causes diabetes?", + "Define dyslipidemia.", + "Does cycling reduce risk of diabetes?", + "What is cytochrome?" + ] + } + }, + { + "5fb03df3-5a04-4dad-ba1c-14eb8e50a241": { + "task_id": [ + "0CDD1C9219114BB2770C28D541F1060A", + "37A26345145679F7539EA8F512623F5E", + "F35BF9C40081CE0521E562CD95BA4C2F", + "9DD88454267DEF2106A3EA7E6E8B5443", + "732D340E5C8F09381CEFA440AD2A7AB6", + "CE5922BDA6B949A17665AB4E1A8138D5", + "F0CC742EA104CB2C8B8BCA9CB6EB78F0" + ], + "weight": [ + 1, + 1, + -1, + -1, + 1, + 1, + 1 + ], + "answer": [ + "The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.", + "GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity.", + "GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network", + "GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.", + "Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present, cells increase insulin output to maintain normal glucose tolerance. However, if cells are incapable of this task, plasma concentrations of glucose increase.", + "Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes.", + "Lifestyle changes, including diet modification and increased physical activity, can help prevent and manage type 2 diabetes. They can improve insulin sensitivity, control blood glucose levels, and promote weight loss, which are all beneficial in managing the disease. However, they may not completely reverse the disease, especially in advanced stages." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is GeneNetwork and how does it relate to aging research?", + "How can GeneNetwork assist in identifying genetic factors involved in diabetes?", + "What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?", + "What role does insulin play in the regulation of blood glucose levels?", + " How does aging affect the risk of developing type 2 diabetes?", + "Can lifestyle changes reverse type 2 diabetes?" + ] + } + }, + { + "24a2e578-5f6e-4576-8942-148d6a9f672d": { + "task_id": [ + "6D733CABEB70E4DBF150EAAFFED6C973", + "6267E2FEFF0332F88C2294C8F32C1FC1", + "3FFA45D7124495B37B6F7F2B7B780AF3", + "499C63633BB95DE93DC3A89615496443", + "405240F6F75C3927C1088287E19920AD", + "DA2C5FBAA7806455F89E896E641DD642", + "7B0629638DF00DF1183B67EE3BF39B1C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "Environmental factors can influence fertilisation in several ways. Factors such as temperature, pressure, and chemical activity can affect the physiological processes involved in fertilisation. Lifestyle factors like smoking, irradiation, oral contraceptives, and low socioeconomic status can also impact natural conception. Moreover, environmental conditions can affect the quality of maternal care and offspring development. For instance, crowded larval environments can lead to a decline in quality over time, affecting the genetic polymorphism in populations. Additionally, environmental stressors can influence the responsiveness to prenatal stress, potentially affecting fertility.", + "The text does not provide information on how diet impacts someone's height.", + "The Bama miniature pig has the same number of chromosomes as humans.", + "The functioning of the brain is ensured by the complex interplay of neuronal networks, synaptic connections, and the precise regulation of molecular and cellular events. This includes the spatiotemporal regulation of the transcriptome, the signaling dynamics of integrated circuitry, and the balance between activating and inhibiting systems. Additionally, the maintenance of genomic integrity in neuronal cells is crucial for the brain's high metabolic activity.", + "Our brains maintain emotions through complex neural systems and processes. The reward-motivation system, which includes the striatum, amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex, plays a key role in processing emotional and motivational information. The amygdala, a principal component of the limbic system, controls emotionality. Stress, anxiety, and other emotion-related traits are influenced by multiple genetic, environmental, experiential, and epigenetic factors. Additionally, hormones released by stressful experiences can enhance memory consolidation, further influencing emotional responses.", + "During stressful experiences, our brains release hormones such as epinephrine, glucocorticoids, and corticosterone. The hypothalamic-pituitary-adrenal (HPA) axis also plays a significant role in this process, stimulating the release of these hormones.", + "Corticosterone, a type of glucocorticoid, is used in the body's response to stress. It initiates metabolic and physiological responses that help the body cope with stressors. It also provides negative feedback to inhibit activity in the hypothalamic-pituitary-adrenal (HPA) axis, which helps to dampen stress-activated defense reactions, including immune responses. Additionally, corticosterone can influence the expression of certain cytokines, potentially having anti-inflammatory effects." + ], + "query": [ + "how does environment influence fertilisation", + "how does diet impact someone's height", + "which animal has the same number of chromosomes as human", + "what's ensures brains work", + "how do our brains maintain emotions", + "what hormones do our brains release during stressful experiences?", + "what is the use of corticosterone?" + ] + } + } +], +[ + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4", + "FE094A900BA5B3C48A3A67B18B2F12BD", + "CB93CE86DA18F287DBEF22CB29C560CF", + "8DCEF606839664C8B6C72CF1D181CEEA", + "FEE16F5E4D12AF7E7B0DDBF6F047EB76", + "DF05AACA4A1466AC1753DE13631A6ACD", + "57CB850E74BC7A26A645CAAB823D35CD" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "Several genes are involved in aging, including BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, CDKN2B, USP28, E2F2, BCL3, NAP1L4, GAB2, QKI, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, and several S100 calcium binding proteins.", + "Studies have shown that foreign genetic admixture can have a protective effect against diabetes. For instance, the prevalence of Type 2 Diabetes (T2D) in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with foreign genetic admixture. This suggests that foreign genotypes can reduce the risk of diabetes. Similar findings have been reported in Pima Indians and other Native American populations.", + "Several genes are involved in the aging process. These include BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, IGF-1, CYP19, NAP1L4, GAB2, QKI, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, USP28, E2F2, BCL3, mre11, rad50, Ku80, mus308, mus205, DNApol-eta, Trxr-1, GlnRS, isoQC, QC, Cct1, Cct4, Cct5, Cct6, Hsc70-4, Xbp1, NAT1, Rack1, Rheb, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, SIR2, DAF-2/IGF1R, DAF-16/FOXO, AAK-2/AMPK, LET-363/mTOR, SIRT1/SIR2, p53, Werner's syndrome gene, iff-1, SIR2, TERT, ZBTB16, CA9, HEY2, Smarca4, Amarcb1, Hdac1, -5, -6, Dnmt3b, and genes in the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway.", + "Lifestyle modifications, including increased physical activity, dietary changes, and weight reduction, have been shown to significantly reduce the risk of Type 2 Diabetes (T2D), which is often associated with aging. Regular physical activity improves insulin sensitivity, reducing the stress on insulin-producing cells. Dietary changes, particularly those reducing the intake of processed carbohydrates, energy-rich foods, and animal fats, can also lower T2D risk. Weight loss can directly improve insulin secretion and sensitivity. However, these lifestyle changes require not only individual efforts but also changes in social, built, and food environments.", + "The background text does not provide information on the role of longevity genes in protecting against diabetes.", + "The types of diabetes include Type 1 diabetes, Type 2 diabetes, gestational diabetes, and diabetes due to specific causes such as genetic defects causing deficient insulin secretion or action, diseases of the pancreas, and use of certain drugs. There are also subtypes of adult-onset diabetes identified as severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes." + ], + "query": [ + "genetics", + "what is bioinformatics", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in aging", + "Explain Protective Genetic Factors Against Diabetes in Elderly Populations", + "what genes are involved in the aging process", + "Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk", + "Explain The Role of Longevity Genes in Protecting Against Diabetes", + "What are the types of diabetes" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1" + ], + "weight": [ + 1, + 1, + -1 + ], + "answer": [ + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides." + ], + "query": [ + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing" + ] + } + }, + { + "0c4a1c26-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + -1 + ], + "answer": [ + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "3A16235DA1E02B9148B9288A06EE567E", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + 1 + ], + "answer": [ + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + }, + { + "8e4fe952-5a61-4d95-86e5-49f974465572": { + "task_id": [ + "2AE18C9AAFB4E3A103F03C86BBEB2DD1", + "58D6F365917926445960756A26B3FDC8", + "2A2860BB54BC0D36A929838ED41243A7", + "A5DEAEAC441B3BDC65B58EA6923FAE73" + ], + "weight": [ + 1, + 1, + 1, + 1 + ], + "answer": [ + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence." + ], + "query": [ + "What causes diabetes?", + "Define dyslipidemia.", + "Does cycling reduce risk of diabetes?", + "What is cytochrome?" + ] + } + }, + { + "5fb03df3-5a04-4dad-ba1c-14eb8e50a241": { + "task_id": [ + "0CDD1C9219114BB2770C28D541F1060A", + "37A26345145679F7539EA8F512623F5E", + "F35BF9C40081CE0521E562CD95BA4C2F", + "9DD88454267DEF2106A3EA7E6E8B5443", + "732D340E5C8F09381CEFA440AD2A7AB6", + "CE5922BDA6B949A17665AB4E1A8138D5", + "F0CC742EA104CB2C8B8BCA9CB6EB78F0" + ], + "weight": [ + 1, + 1, + -1, + -1, + 1, + 1, + 1 + ], + "answer": [ + "The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.", + "GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity.", + "GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network", + "GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.", + "Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present, cells increase insulin output to maintain normal glucose tolerance. However, if cells are incapable of this task, plasma concentrations of glucose increase.", + "Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes.", + "Lifestyle changes, including diet modification and increased physical activity, can help prevent and manage type 2 diabetes. They can improve insulin sensitivity, control blood glucose levels, and promote weight loss, which are all beneficial in managing the disease. However, they may not completely reverse the disease, especially in advanced stages." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is GeneNetwork and how does it relate to aging research?", + "How can GeneNetwork assist in identifying genetic factors involved in diabetes?", + "What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?", + "What role does insulin play in the regulation of blood glucose levels?", + " How does aging affect the risk of developing type 2 diabetes?", + "Can lifestyle changes reverse type 2 diabetes?" + ] + } + }, + { + "24a2e578-5f6e-4576-8942-148d6a9f672d": { + "task_id": [ + "6D733CABEB70E4DBF150EAAFFED6C973", + "6267E2FEFF0332F88C2294C8F32C1FC1", + "3FFA45D7124495B37B6F7F2B7B780AF3", + "499C63633BB95DE93DC3A89615496443", + "405240F6F75C3927C1088287E19920AD", + "DA2C5FBAA7806455F89E896E641DD642", + "7B0629638DF00DF1183B67EE3BF39B1C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "Environmental factors can influence fertilisation in several ways. Factors such as temperature, pressure, and chemical activity can affect the physiological processes involved in fertilisation. Lifestyle factors like smoking, irradiation, oral contraceptives, and low socioeconomic status can also impact natural conception. Moreover, environmental conditions can affect the quality of maternal care and offspring development. For instance, crowded larval environments can lead to a decline in quality over time, affecting the genetic polymorphism in populations. Additionally, environmental stressors can influence the responsiveness to prenatal stress, potentially affecting fertility.", + "The text does not provide information on how diet impacts someone's height.", + "The Bama miniature pig has the same number of chromosomes as humans.", + "The functioning of the brain is ensured by the complex interplay of neuronal networks, synaptic connections, and the precise regulation of molecular and cellular events. This includes the spatiotemporal regulation of the transcriptome, the signaling dynamics of integrated circuitry, and the balance between activating and inhibiting systems. Additionally, the maintenance of genomic integrity in neuronal cells is crucial for the brain's high metabolic activity.", + "Our brains maintain emotions through complex neural systems and processes. The reward-motivation system, which includes the striatum, amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex, plays a key role in processing emotional and motivational information. The amygdala, a principal component of the limbic system, controls emotionality. Stress, anxiety, and other emotion-related traits are influenced by multiple genetic, environmental, experiential, and epigenetic factors. Additionally, hormones released by stressful experiences can enhance memory consolidation, further influencing emotional responses.", + "During stressful experiences, our brains release hormones such as epinephrine, glucocorticoids, and corticosterone. The hypothalamic-pituitary-adrenal (HPA) axis also plays a significant role in this process, stimulating the release of these hormones.", + "Corticosterone, a type of glucocorticoid, is used in the body's response to stress. It initiates metabolic and physiological responses that help the body cope with stressors. It also provides negative feedback to inhibit activity in the hypothalamic-pituitary-adrenal (HPA) axis, which helps to dampen stress-activated defense reactions, including immune responses. Additionally, corticosterone can influence the expression of certain cytokines, potentially having anti-inflammatory effects." + ], + "query": [ + "how does environment influence fertilisation", + "how does diet impact someone's height", + "which animal has the same number of chromosomes as human", + "what's ensures brains work", + "how do our brains maintain emotions", + "what hormones do our brains release during stressful experiences?", + "what is the use of corticosterone?" + ] + } + } +], +[ + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4", + "FE094A900BA5B3C48A3A67B18B2F12BD", + "CB93CE86DA18F287DBEF22CB29C560CF", + "8DCEF606839664C8B6C72CF1D181CEEA", + "FEE16F5E4D12AF7E7B0DDBF6F047EB76", + "DF05AACA4A1466AC1753DE13631A6ACD", + "57CB850E74BC7A26A645CAAB823D35CD" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "Several genes are involved in aging, including BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, CDKN2B, USP28, E2F2, BCL3, NAP1L4, GAB2, QKI, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, and several S100 calcium binding proteins.", + "Studies have shown that foreign genetic admixture can have a protective effect against diabetes. For instance, the prevalence of Type 2 Diabetes (T2D) in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with foreign genetic admixture. This suggests that foreign genotypes can reduce the risk of diabetes. Similar findings have been reported in Pima Indians and other Native American populations.", + "Several genes are involved in the aging process. These include BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, IGF-1, CYP19, NAP1L4, GAB2, QKI, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, USP28, E2F2, BCL3, mre11, rad50, Ku80, mus308, mus205, DNApol-eta, Trxr-1, GlnRS, isoQC, QC, Cct1, Cct4, Cct5, Cct6, Hsc70-4, Xbp1, NAT1, Rack1, Rheb, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, SIR2, DAF-2/IGF1R, DAF-16/FOXO, AAK-2/AMPK, LET-363/mTOR, SIRT1/SIR2, p53, Werner's syndrome gene, iff-1, SIR2, TERT, ZBTB16, CA9, HEY2, Smarca4, Amarcb1, Hdac1, -5, -6, Dnmt3b, and genes in the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway.", + "Lifestyle modifications, including increased physical activity, dietary changes, and weight reduction, have been shown to significantly reduce the risk of Type 2 Diabetes (T2D), which is often associated with aging. Regular physical activity improves insulin sensitivity, reducing the stress on insulin-producing cells. Dietary changes, particularly those reducing the intake of processed carbohydrates, energy-rich foods, and animal fats, can also lower T2D risk. Weight loss can directly improve insulin secretion and sensitivity. However, these lifestyle changes require not only individual efforts but also changes in social, built, and food environments.", + "The background text does not provide information on the role of longevity genes in protecting against diabetes.", + "The types of diabetes include Type 1 diabetes, Type 2 diabetes, gestational diabetes, and diabetes due to specific causes such as genetic defects causing deficient insulin secretion or action, diseases of the pancreas, and use of certain drugs. There are also subtypes of adult-onset diabetes identified as severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes." + ], + "query": [ + "genetics", + "what is bioinformatics", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in aging", + "Explain Protective Genetic Factors Against Diabetes in Elderly Populations", + "what genes are involved in the aging process", + "Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk", + "Explain The Role of Longevity Genes in Protecting Against Diabetes", + "What are the types of diabetes" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1" + ], + "weight": [ + 1, + 1, + -1 + ], + "answer": [ + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides." + ], + "query": [ + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing" + ] + } + }, + { + "0c4a1c26-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + -1 + ], + "answer": [ + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "3A16235DA1E02B9148B9288A06EE567E", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + 1 + ], + "answer": [ + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + }, + { + "8e4fe952-5a61-4d95-86e5-49f974465572": { + "task_id": [ + "2AE18C9AAFB4E3A103F03C86BBEB2DD1", + "58D6F365917926445960756A26B3FDC8", + "2A2860BB54BC0D36A929838ED41243A7", + "A5DEAEAC441B3BDC65B58EA6923FAE73" + ], + "weight": [ + 1, + 1, + 1, + 1 + ], + "answer": [ + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence." + ], + "query": [ + "What causes diabetes?", + "Define dyslipidemia.", + "Does cycling reduce risk of diabetes?", + "What is cytochrome?" + ] + } + }, + { + "5fb03df3-5a04-4dad-ba1c-14eb8e50a241": { + "task_id": [ + "0CDD1C9219114BB2770C28D541F1060A", + "37A26345145679F7539EA8F512623F5E", + "F35BF9C40081CE0521E562CD95BA4C2F", + "9DD88454267DEF2106A3EA7E6E8B5443", + "732D340E5C8F09381CEFA440AD2A7AB6", + "CE5922BDA6B949A17665AB4E1A8138D5", + "F0CC742EA104CB2C8B8BCA9CB6EB78F0" + ], + "weight": [ + 1, + 1, + -1, + -1, + 1, + 1, + 1 + ], + "answer": [ + "The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.", + "GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity.", + "GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network", + "GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.", + "Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present, cells increase insulin output to maintain normal glucose tolerance. However, if cells are incapable of this task, plasma concentrations of glucose increase.", + "Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes.", + "Lifestyle changes, including diet modification and increased physical activity, can help prevent and manage type 2 diabetes. They can improve insulin sensitivity, control blood glucose levels, and promote weight loss, which are all beneficial in managing the disease. However, they may not completely reverse the disease, especially in advanced stages." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is GeneNetwork and how does it relate to aging research?", + "How can GeneNetwork assist in identifying genetic factors involved in diabetes?", + "What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?", + "What role does insulin play in the regulation of blood glucose levels?", + " How does aging affect the risk of developing type 2 diabetes?", + "Can lifestyle changes reverse type 2 diabetes?" + ] + } + }, + { + "24a2e578-5f6e-4576-8942-148d6a9f672d": { + "task_id": [ + "6D733CABEB70E4DBF150EAAFFED6C973", + "6267E2FEFF0332F88C2294C8F32C1FC1", + "3FFA45D7124495B37B6F7F2B7B780AF3", + "499C63633BB95DE93DC3A89615496443", + "405240F6F75C3927C1088287E19920AD", + "DA2C5FBAA7806455F89E896E641DD642", + "7B0629638DF00DF1183B67EE3BF39B1C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "Environmental factors can influence fertilisation in several ways. Factors such as temperature, pressure, and chemical activity can affect the physiological processes involved in fertilisation. Lifestyle factors like smoking, irradiation, oral contraceptives, and low socioeconomic status can also impact natural conception. Moreover, environmental conditions can affect the quality of maternal care and offspring development. For instance, crowded larval environments can lead to a decline in quality over time, affecting the genetic polymorphism in populations. Additionally, environmental stressors can influence the responsiveness to prenatal stress, potentially affecting fertility.", + "The text does not provide information on how diet impacts someone's height.", + "The Bama miniature pig has the same number of chromosomes as humans.", + "The functioning of the brain is ensured by the complex interplay of neuronal networks, synaptic connections, and the precise regulation of molecular and cellular events. This includes the spatiotemporal regulation of the transcriptome, the signaling dynamics of integrated circuitry, and the balance between activating and inhibiting systems. Additionally, the maintenance of genomic integrity in neuronal cells is crucial for the brain's high metabolic activity.", + "Our brains maintain emotions through complex neural systems and processes. The reward-motivation system, which includes the striatum, amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex, plays a key role in processing emotional and motivational information. The amygdala, a principal component of the limbic system, controls emotionality. Stress, anxiety, and other emotion-related traits are influenced by multiple genetic, environmental, experiential, and epigenetic factors. Additionally, hormones released by stressful experiences can enhance memory consolidation, further influencing emotional responses.", + "During stressful experiences, our brains release hormones such as epinephrine, glucocorticoids, and corticosterone. The hypothalamic-pituitary-adrenal (HPA) axis also plays a significant role in this process, stimulating the release of these hormones.", + "Corticosterone, a type of glucocorticoid, is used in the body's response to stress. It initiates metabolic and physiological responses that help the body cope with stressors. It also provides negative feedback to inhibit activity in the hypothalamic-pituitary-adrenal (HPA) axis, which helps to dampen stress-activated defense reactions, including immune responses. Additionally, corticosterone can influence the expression of certain cytokines, potentially having anti-inflammatory effects." + ], + "query": [ + "how does environment influence fertilisation", + "how does diet impact someone's height", + "which animal has the same number of chromosomes as human", + "what's ensures brains work", + "how do our brains maintain emotions", + "what hormones do our brains release during stressful experiences?", + "what is the use of corticosterone?" + ] + } + } +], +[ + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4", + "FE094A900BA5B3C48A3A67B18B2F12BD", + "CB93CE86DA18F287DBEF22CB29C560CF", + "8DCEF606839664C8B6C72CF1D181CEEA", + "FEE16F5E4D12AF7E7B0DDBF6F047EB76", + "DF05AACA4A1466AC1753DE13631A6ACD", + "57CB850E74BC7A26A645CAAB823D35CD" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "Several genes are involved in aging, including BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, CDKN2B, USP28, E2F2, BCL3, NAP1L4, GAB2, QKI, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, and several S100 calcium binding proteins.", + "Studies have shown that foreign genetic admixture can have a protective effect against diabetes. For instance, the prevalence of Type 2 Diabetes (T2D) in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with foreign genetic admixture. This suggests that foreign genotypes can reduce the risk of diabetes. Similar findings have been reported in Pima Indians and other Native American populations.", + "Several genes are involved in the aging process. These include BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, IGF-1, CYP19, NAP1L4, GAB2, QKI, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, USP28, E2F2, BCL3, mre11, rad50, Ku80, mus308, mus205, DNApol-eta, Trxr-1, GlnRS, isoQC, QC, Cct1, Cct4, Cct5, Cct6, Hsc70-4, Xbp1, NAT1, Rack1, Rheb, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, SIR2, DAF-2/IGF1R, DAF-16/FOXO, AAK-2/AMPK, LET-363/mTOR, SIRT1/SIR2, p53, Werner's syndrome gene, iff-1, SIR2, TERT, ZBTB16, CA9, HEY2, Smarca4, Amarcb1, Hdac1, -5, -6, Dnmt3b, and genes in the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway.", + "Lifestyle modifications, including increased physical activity, dietary changes, and weight reduction, have been shown to significantly reduce the risk of Type 2 Diabetes (T2D), which is often associated with aging. Regular physical activity improves insulin sensitivity, reducing the stress on insulin-producing cells. Dietary changes, particularly those reducing the intake of processed carbohydrates, energy-rich foods, and animal fats, can also lower T2D risk. Weight loss can directly improve insulin secretion and sensitivity. However, these lifestyle changes require not only individual efforts but also changes in social, built, and food environments.", + "The background text does not provide information on the role of longevity genes in protecting against diabetes.", + "The types of diabetes include Type 1 diabetes, Type 2 diabetes, gestational diabetes, and diabetes due to specific causes such as genetic defects causing deficient insulin secretion or action, diseases of the pancreas, and use of certain drugs. There are also subtypes of adult-onset diabetes identified as severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes." + ], + "query": [ + "genetics", + "what is bioinformatics", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in aging", + "Explain Protective Genetic Factors Against Diabetes in Elderly Populations", + "what genes are involved in the aging process", + "Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk", + "Explain The Role of Longevity Genes in Protecting Against Diabetes", + "What are the types of diabetes" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1" + ], + "weight": [ + 1, + 1, + -1 + ], + "answer": [ + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides." + ], + "query": [ + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing" + ] + } + }, + { + "0c4a1c26-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + -1 + ], + "answer": [ + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "3A16235DA1E02B9148B9288A06EE567E", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + 1 + ], + "answer": [ + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + }, + { + "8e4fe952-5a61-4d95-86e5-49f974465572": { + "task_id": [ + "2AE18C9AAFB4E3A103F03C86BBEB2DD1", + "58D6F365917926445960756A26B3FDC8", + "2A2860BB54BC0D36A929838ED41243A7", + "A5DEAEAC441B3BDC65B58EA6923FAE73" + ], + "weight": [ + 1, + 1, + 1, + 1 + ], + "answer": [ + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence." + ], + "query": [ + "What causes diabetes?", + "Define dyslipidemia.", + "Does cycling reduce risk of diabetes?", + "What is cytochrome?" + ] + } + }, + { + "5fb03df3-5a04-4dad-ba1c-14eb8e50a241": { + "task_id": [ + "0CDD1C9219114BB2770C28D541F1060A", + "37A26345145679F7539EA8F512623F5E", + "F35BF9C40081CE0521E562CD95BA4C2F", + "9DD88454267DEF2106A3EA7E6E8B5443", + "732D340E5C8F09381CEFA440AD2A7AB6", + "CE5922BDA6B949A17665AB4E1A8138D5", + "F0CC742EA104CB2C8B8BCA9CB6EB78F0" + ], + "weight": [ + 1, + 1, + -1, + -1, + 1, + 1, + 1 + ], + "answer": [ + "The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.", + "GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity.", + "GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network", + "GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.", + "Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present, cells increase insulin output to maintain normal glucose tolerance. However, if cells are incapable of this task, plasma concentrations of glucose increase.", + "Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes.", + "Lifestyle changes, including diet modification and increased physical activity, can help prevent and manage type 2 diabetes. They can improve insulin sensitivity, control blood glucose levels, and promote weight loss, which are all beneficial in managing the disease. However, they may not completely reverse the disease, especially in advanced stages." + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is GeneNetwork and how does it relate to aging research?", + "How can GeneNetwork assist in identifying genetic factors involved in diabetes?", + "What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?", + "What role does insulin play in the regulation of blood glucose levels?", + " How does aging affect the risk of developing type 2 diabetes?", + "Can lifestyle changes reverse type 2 diabetes?" + ] + } + }, + { + "24a2e578-5f6e-4576-8942-148d6a9f672d": { + "task_id": [ + "6D733CABEB70E4DBF150EAAFFED6C973", + "6267E2FEFF0332F88C2294C8F32C1FC1", + "3FFA45D7124495B37B6F7F2B7B780AF3", + "499C63633BB95DE93DC3A89615496443", + "405240F6F75C3927C1088287E19920AD", + "DA2C5FBAA7806455F89E896E641DD642", + "7B0629638DF00DF1183B67EE3BF39B1C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "Environmental factors can influence fertilisation in several ways. Factors such as temperature, pressure, and chemical activity can affect the physiological processes involved in fertilisation. Lifestyle factors like smoking, irradiation, oral contraceptives, and low socioeconomic status can also impact natural conception. Moreover, environmental conditions can affect the quality of maternal care and offspring development. For instance, crowded larval environments can lead to a decline in quality over time, affecting the genetic polymorphism in populations. Additionally, environmental stressors can influence the responsiveness to prenatal stress, potentially affecting fertility.", + "The text does not provide information on how diet impacts someone's height.", + "The Bama miniature pig has the same number of chromosomes as humans.", + "The functioning of the brain is ensured by the complex interplay of neuronal networks, synaptic connections, and the precise regulation of molecular and cellular events. This includes the spatiotemporal regulation of the transcriptome, the signaling dynamics of integrated circuitry, and the balance between activating and inhibiting systems. Additionally, the maintenance of genomic integrity in neuronal cells is crucial for the brain's high metabolic activity.", + "Our brains maintain emotions through complex neural systems and processes. The reward-motivation system, which includes the striatum, amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex, plays a key role in processing emotional and motivational information. The amygdala, a principal component of the limbic system, controls emotionality. Stress, anxiety, and other emotion-related traits are influenced by multiple genetic, environmental, experiential, and epigenetic factors. Additionally, hormones released by stressful experiences can enhance memory consolidation, further influencing emotional responses.", + "During stressful experiences, our brains release hormones such as epinephrine, glucocorticoids, and corticosterone. The hypothalamic-pituitary-adrenal (HPA) axis also plays a significant role in this process, stimulating the release of these hormones.", + "Corticosterone, a type of glucocorticoid, is used in the body's response to stress. It initiates metabolic and physiological responses that help the body cope with stressors. It also provides negative feedback to inhibit activity in the hypothalamic-pituitary-adrenal (HPA) axis, which helps to dampen stress-activated defense reactions, including immune responses. Additionally, corticosterone can influence the expression of certain cytokines, potentially having anti-inflammatory effects." + ], + "query": [ + "how does environment influence fertilisation", + "how does diet impact someone's height", + "which animal has the same number of chromosomes as human", + "what's ensures brains work", + "how do our brains maintain emotions", + "what hormones do our brains release during stressful experiences?", + "what is the use of corticosterone?" + ] + } + } +]
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/2024_06_27-gnqa-responses.json b/gnqa/paper1_eval/src/data/ratings/2024_06_27-gnqa-responses.json new file mode 100644 index 0000000..b6fc03b --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_06_27-gnqa-responses.json @@ -0,0 +1,184 @@ +[{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"F26EEA55FF8FFCB0B9E3E46688C276DA"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"5674818C9037B8A93EDCF35CEF97E33C"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"106C5DDDE1338BE1CC8AE04CEAE3EF62"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"2D8205C79915FF9CEB8DECCE51E6E473"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?","answer":"The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.","weight":1,"task_id":"849E78D8214245F8E8167E78C01BEE60"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"List as many studies as you can that include rapamycin.","answer":"1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.","weight":-1,"task_id":"2C477A3C76794C27A1FBBF437CFF75EE"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"Why is it so diffuclut to map gene loci that control aging in humans?","answer":"Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.","weight":1,"task_id":"CAD6C6C2AB42AA66BFDD65F0F11932B2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.","weight":1,"task_id":"EC2BFCD8E06079A3E595114822D2A44D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does epigenetics inluence gene expression without changing the underlying DNA sequence?","answer":"Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.","weight":-1,"task_id":"59AA8E8D7A97CFF47C6CFD65629F29F9"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.","answer":"Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.","weight":1,"task_id":"C58CFF41F7422B321DF88A110E278FD5"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?","answer":"The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.","weight":1,"task_id":"BCD1175CCB27FBA1E6F9D7670B17E527"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"5341FE6588C6175BC8A688A483928BC0"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"42847DE50D50E6A9B26ED0B03CFD160E"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"12BEAFA9366519672FC8B06959FB2DAF"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"64FEC152131BC6502E15EA6A6348D70B"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is apoptosis?","answer":"Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.","weight":1,"task_id":"78A0CD7E12AFEF6865583142603EE039"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is the most cited environmental factor for the onset of asthma?","answer":"The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.","weight":1,"task_id":"33FC2CC0F61BA22E4D095586B95703BD"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"370380F3A38AC4A788463D14E0EC673A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what is bioinformatics","answer":"Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.","weight":1,"task_id":"1E0DA0931F4E3A8C2893353CCA114B10"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"How would one extract the DNA, from say, flora or fauna?","answer":"DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.","weight":1,"task_id":"59E2406798D265A3CB466B766683E63C"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.","answer":"DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.","weight":-1,"task_id":"3F9EDFE9A0222EA70459EC8985F134C4"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.","weight":1,"task_id":"A010490B55F739DF95BB82DF2B0F5AA3"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Explain the process of finding a genetic marker followed by a quantitative trait loci.","answer":"The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.","weight":1,"task_id":"02A94D1056FDA77BDA9AC6CFDE0D5FC6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.","weight":1,"task_id":"96B873A13E3B386E38940EF6ECA90D4A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"A55310A1C19732792B5894A2DB5E8596"}, +{"user_id":"d2f71149-a0d5-4666-95d8-15c82543243b","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":1,"task_id":"7CA65F6B192BE7896422284B962CF5C3"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"2EDD65822CDA4669FCAD678288C2E3E9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":1,"task_id":"DA98AC2EA5D1F776D3F04FCBC7F01339"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what causes the aging process","answer":"The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.","weight":1,"task_id":"117299AD06C2B147F49E9C9BC036CEA4"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.","weight":1,"task_id":"F4DA6C97230E89C0226B1433532A16D9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":0,"task_id":"FBBFB0D391778CDE9536F742300C1809"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"32CE1E54032485B73B5968395B3D3538"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"59CCE2D70104154865218876DD53D049"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0AE973110158192E0D1F50E1D79764FB"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"33749EB09AAC7AD0404C8E3E584B98D2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"471A145E9CA1E517E462499ABCA8EA2D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?","answer":"The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.","weight":1,"task_id":"19EBC6CA7E425D0C1279475BD0B411B6"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"what is ensembl?","answer":"Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.","weight":-1,"task_id":"9CFE932D7898C83E473E590BC77B4FCB"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Create a how to guide for genetic sequencing ","answer":"1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.","weight":-1,"task_id":"C52A9690417093A861C669A0753689BD"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What is the significance of the length of telomeres? ","answer":"The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.","weight":1,"task_id":"93DE2EF005059DFEA5A7FBBA3BD17D03"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ","answer":"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.","weight":1,"task_id":"477FC54178046FE98BF97FAAC5FE167F"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Why is genetic tracing matrilineal rather than patrilineal? ","answer":"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.","weight":1,"task_id":"6B80ECC5F657EB7CBDE69D411A30D3EA"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"2DE25ABD7E487B80D0C489319640EACC"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"what type of dataset is useful for qtl mapping analysis in genenetwork2? ","answer":"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.","weight":1,"task_id":"6498ED71891B79908B2E383D9AA5BAC5"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What genetic factors influence aging in humans? ","answer":"Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.","weight":1,"task_id":"B2F5CB7BCD9A827D3A6E0152C030C4B4"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Is there a direct association between aging and susceptibility to having diabetes?","answer":"Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.","weight":1,"task_id":"72FBC4F382B6502EAF41BD6682E63A2D"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does genetics influence the emergency of diabetes? ","answer":"Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.","weight":1,"task_id":"02C953165B9CA94E273DD4A04301C89F"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"19DC9E909DDE6D9CDB3E86D1069F5A69"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F2843EA2D5A239D022186329C8D5D8EF"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"4E1F0C2E792BAF0BA349326375D3EE6E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"FA8ADB009A499F51B0533FDCB72CB29E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.","weight":-1,"task_id":"38BD5864A7928C6DBCA1D844327F3A19"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with diabetes?","answer":"The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.","weight":1,"task_id":"2272C482CC247E746D15C9F55EDD8BCE"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with aging?","answer":"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.","weight":1,"task_id":"C6C7CEF19CE7C27CF4BC6906259CDDF9"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the bioinformatics tools for QTLs analysis?","answer":"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.","weight":1,"task_id":"B4BB83EB5D5C5C042E07173119046A13"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the statistical approaches for qtls analysis?","answer":"The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.","weight":1,"task_id":"D88EF655762CE3D524A7A1EEA3FA16ED"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for GWAS analysis?","answer":"1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.","weight":1,"task_id":"245DD8093F5D16F44C2AD7618245086C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"B89A904E71CF7F16126FCA9EAAFBC8A6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":1,"task_id":"2F8796A8C3DC633F00DB901C9BA396DA"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"6188C7826173CD59FD243F98C537AD50"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"2C37A2EC74E4B46F13C6FB23D9547DBB"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"590809C6B2A1504E2AE9A79EFDF3AC9A"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"What is the difference between QTL mapping and GWAS?","answer":"QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.","weight":1,"task_id":"6DBC070B2E4DC2FE8036E5BA7480B755"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How do I determine which gene in my QTL is causal for the trait?","answer":"To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.","weight":1,"task_id":"5594EA025D9631328071B6A1A7EF1375"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Which mouse genes have been associated with longevity?","answer":"The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.","weight":-1,"task_id":"AB589D2E046B211A7486A6C4BD4ECFB4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"How is gene expression in the liver affected by diabetes?","answer":"In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.","weight":1,"task_id":"8FFF9DCC307B8DBF2C8485637F2ABEF4"}, +{"user_id":"415d39c0-28b8-4711-8d20-081082660f35","query":"Why do males have two Y chromosomes and females only one?","answer":"Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.","weight":1,"task_id":"C6B9A982C9283DE065A3371F1264095C"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing","answer":"1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.","weight":1,"task_id":"F9F7EA3DC28534B161ED70DB401C7D11"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.","weight":-1,"task_id":"4A06F8DF54C82D90E02F81D0E1E8B08A"}, +{"user_id":"c5e42006-ddba-4b69-b1d1-61b0dceaec86","query":" What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying.","weight":1,"task_id":"BA6A505E62A0529DB883D036CBC1FD92"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Which genes are associated with aging in human ","answer":"The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.","weight":1,"task_id":"3EC47C56606B02F00CF2449AB311365C"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"CDFC418BD568E839C09656C57808ADA1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Create a guide for genetic sequencing","answer":"1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.","weight":-1,"task_id":"5DEB102510F48D0BF9C278DC895A8BD1"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"5A562D5F7A266BA057B6833F3A83E7A7"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"53905925B4D6F69CE5706896A3C667CE"}, +{"user_id":"0c4a1c25-539f-453a-b7f6-915ab462cf0b","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.","weight":-1,"task_id":"C0015BEE5FE41769A65126B79BB1E40D"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":-1,"task_id":"9957BCF2957D9BD083EDCF8B60C0E4DE"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How does recombination work in human centromeres?","answer":"In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.","weight":-1,"task_id":"DEE6D385D1B01B4155AA4ABE59515893"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How many types of diabetes exist?","answer":"There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).","weight":1,"task_id":"9309F248E5933718BFB625E4EF2D3E42"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in the human genome?","answer":"The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome.","weight":-1,"task_id":"10ABD2210053119B18D94F1FE266E73E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"BD671A3AC4F77A74CA8A68EAD627437D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5E243BF70932D1477209FEF6869FDEEA"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.","weight":-1,"task_id":"3A16235DA1E02B9148B9288A06EE567E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9BE91FA13BDC0D9EB80B35FC73A0029B"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"3D4688621977A9CBD670B50131B8D912"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How can I add a new species to the GeneNetwork database?","answer":"The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.","weight":1,"task_id":"E94FFD042BB146E8A429200590A6792D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?","answer":"Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.","weight":1,"task_id":"C319861B08978CF5F7E6F0CD3A517A81"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"what genetic factor are associated with aging","answer":"Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.","weight":1,"task_id":"081B2DB92FD09DEBEF28ADBBDE7199D2"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.","weight":0,"task_id":"68EF3BE5EC2106766CA9CC700135E2FA"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes.","weight":1,"task_id":"8590501C57DC5C321AB5E1036F233027"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":-1,"task_id":"C8E21233058E1DEE05CD619ACBF49E42"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"38A8ED57882E2E250B447F93243A8CD5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"List as many studies as you can that include rapamycin.","answer":"1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.","weight":1,"task_id":"5D3CA84D2DCE010AF63188665193B195"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.","weight":-1,"task_id":"AB6286D0D8E9196BA0EE1F1CF90536AD"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"00647726F98EE835006D60B12455866D"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"8F3A81EAB68F709E82006205380AC723"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"ECEB33208BCDDC20908183BF249555AD"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":-1,"task_id":"906F0A0AB4330CB7C3A75DA8764703F4"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"A3B39D0211921BC5581AB03193860970"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"2BF7D398C0BFD1F4D976C9F82343DE51"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with diabetes in QTL analyses?","answer":"The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.","weight":1,"task_id":"FCFCE5BBA2A8B3D8818890B9D2308C5A"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with early aging?","answer":"The genes typically associated with early aging are APOE and FOXO3A.","weight":1,"task_id":"E3FFB15A9901BD8DB87B0F09D335BEA0"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How do I generate a linkage or association mapping study in mice to understand aging?","answer":"To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.","weight":1,"task_id":"38797E46211127E5C7175E707D40325B"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Is the gene TCF7L2 involved in diabetes?","answer":"Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).","weight":1,"task_id":"CD1F7EAE0FDC758A8167118927ADFE71"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"In which diseases is the gene TCF7L2 involved?","answer":"The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.","weight":-1,"task_id":"FFA6EADA5502933C0C30C9D16DCAA073"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"what are confounding factors in diabetes?","answer":"Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors.","weight":1,"task_id":"00BE70B5D71A5926E56942909C8B2A92"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":-1,"task_id":"0BF7A88573F1B5FCC2E8978A6D94CE2B"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"CBC2A69A6A94CFADE9E4077F5B655B6E"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.","weight":1,"task_id":"847F1E1599EECDE92F99B7581728FFE8"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?","answer":"A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.","weight":1,"task_id":"037BAB6CB2DE7A42AAAA73CE5DA8DB73"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":" Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.","weight":1,"task_id":"B2AA6DE557D652A0A660C4E0FAC1124D"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"3A55AE005B07C55283410798C0FBE47F"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.","answer":"Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.","weight":1,"task_id":"7EC697DE62C0C57E601EC3F5B295DF61"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.","answer":"Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.","weight":1,"task_id":"0A6673A0B69F0FF9C9657FB797DD1FE2"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.","answer":"Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.","weight":1,"task_id":"44B088326CD80B4980D810738D88A284"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert","answer":"Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors.","weight":-1,"task_id":"D53462CE61F52F7D31BB627998F4D75A"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"F6FC3B8BBCE2BA90D0EF2C9532DE3F84"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"0F76F85FB406BF74022084C5866C942D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"FE7363764A44969E28C9562A3948143B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F456694025B9C98AA9E4246820D5909A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"E6C75917249BB8C0810B0E709D6FDD0A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"8FA337BF315CFA09716800E096EA8A06"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"D68A2086596023BDD8C01023B03FA89D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How is the immune system related to diabetes?","answer":"The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.","weight":1,"task_id":"CD3820AA1BD96613F78FDF3CF5C8AB3D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the genomic variants associated with immune system components and diabetes?","answer":"The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.","weight":1,"task_id":"A4CE2F2F8E08E5F16C94A1BCF540D881"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the role of the immune system in the metabolomics of diabetes and associated conditions?","answer":"The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.","weight":1,"task_id":"1B8618ADB274F928B3AACAB1C71A927E"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the different relationship between traits?","answer":"The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.","weight":1,"task_id":"BF1705D2C26044038FF1483258548167"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can landscape of QTL and GWAS hits be used to find relationships between traits ?","answer":"Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.","weight":1,"task_id":"68AB7A78543D5B36206274837824091B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?","answer":"Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes.","weight":1,"task_id":"055110B765AA502F9AAECE68CEC0DD24"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"C4FEDD378CD138B141464832D021624B"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"ED89B73DC42AD2ADA03B7C014009A551"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"21CB24A2A589173F1E50ADA5DD6165EC"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"1619A0727D1C6673EE9E05171054F658"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"52B443B815CD46D57219872DFB3D0579"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"EB6B4DCD473BEE9580F47CD12DAFC074"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What causes diabetes?","answer":"Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.","weight":1,"task_id":"2AE18C9AAFB4E3A103F03C86BBEB2DD1"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Define dyslipidemia.","answer":"Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.","weight":1,"task_id":"58D6F365917926445960756A26B3FDC8"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Does cycling reduce risk of diabetes?","answer":"Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.","weight":1,"task_id":"2A2860BB54BC0D36A929838ED41243A7"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is cytochrome?","answer":"Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence.","weight":1,"task_id":"A5DEAEAC441B3BDC65B58EA6923FAE73"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in aging","answer":"Several genes are involved in aging, including BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, CDKN2B, USP28, E2F2, BCL3, NAP1L4, GAB2, QKI, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, and several S100 calcium binding proteins.","weight":1,"task_id":"FE094A900BA5B3C48A3A67B18B2F12BD"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"9E388A69975DBDEA3E8DE25294960147"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"29C36228E29604002BB3BAE6654F7762"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"9ED249912768DA58AF97F5600D0CBA8F"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"DEEA82693F72D24109C91089CABC7EBF"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"FC09E6CA3472C1E263743195703162C5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"136D27CEFA12BC3AF0BDD42121FD6DBA"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5DCBD523F5F1663492EB5630EAD981FE"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9C697AF95B263CBD4E243D8AD1062180"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"6F33581CC88C813D20B047A82A78BC7C"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"2C01511CD9C5ABDC085D77F67AA862E1"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.","weight":1,"task_id":"0CDD1C9219114BB2770C28D541F1060A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"BD981EFD76B6C93C620CD92DB9EF0B35"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What is GeneNetwork and how does it relate to aging research?","answer":"GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity.","weight":1,"task_id":"37A26345145679F7539EA8F512623F5E"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"How can GeneNetwork assist in identifying genetic factors involved in diabetes?","answer":"GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network","weight":-1,"task_id":"F35BF9C40081CE0521E562CD95BA4C2F"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?","answer":"GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.","weight":-1,"task_id":"9DD88454267DEF2106A3EA7E6E8B5443"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Explain Protective Genetic Factors Against Diabetes in Elderly Populations","answer":"Studies have shown that foreign genetic admixture can have a protective effect against diabetes. For instance, the prevalence of Type 2 Diabetes (T2D) in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with foreign genetic admixture. This suggests that foreign genotypes can reduce the risk of diabetes. Similar findings have been reported in Pima Indians and other Native American populations.","weight":1,"task_id":"CB93CE86DA18F287DBEF22CB29C560CF"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What role does insulin play in the regulation of blood glucose levels?","answer":"Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present, cells increase insulin output to maintain normal glucose tolerance. However, if cells are incapable of this task, plasma concentrations of glucose increase.","weight":1,"task_id":"732D340E5C8F09381CEFA440AD2A7AB6"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what genes are involved in the aging process","answer":"Several genes are involved in the aging process. These include BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, IGF-1, CYP19, NAP1L4, GAB2, QKI, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, USP28, E2F2, BCL3, mre11, rad50, Ku80, mus308, mus205, DNApol-eta, Trxr-1, GlnRS, isoQC, QC, Cct1, Cct4, Cct5, Cct6, Hsc70-4, Xbp1, NAT1, Rack1, Rheb, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, SIR2, DAF-2/IGF1R, DAF-16/FOXO, AAK-2/AMPK, LET-363/mTOR, SIRT1/SIR2, p53, Werner's syndrome gene, iff-1, SIR2, TERT, ZBTB16, CA9, HEY2, Smarca4, Amarcb1, Hdac1, -5, -6, Dnmt3b, and genes in the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway.","weight":1,"task_id":"8DCEF606839664C8B6C72CF1D181CEEA"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk","answer":"Lifestyle modifications, including increased physical activity, dietary changes, and weight reduction, have been shown to significantly reduce the risk of Type 2 Diabetes (T2D), which is often associated with aging. Regular physical activity improves insulin sensitivity, reducing the stress on insulin-producing cells. Dietary changes, particularly those reducing the intake of processed carbohydrates, energy-rich foods, and animal fats, can also lower T2D risk. Weight loss can directly improve insulin secretion and sensitivity. However, these lifestyle changes require not only individual efforts but also changes in social, built, and food environments.","weight":1,"task_id":"FEE16F5E4D12AF7E7B0DDBF6F047EB76"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":" How does aging affect the risk of developing type 2 diabetes?","answer":"Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes.","weight":1,"task_id":"CE5922BDA6B949A17665AB4E1A8138D5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Explain The Role of Longevity Genes in Protecting Against Diabetes","answer":"The background text does not provide information on the role of longevity genes in protecting against diabetes.","weight":-1,"task_id":"DF05AACA4A1466AC1753DE13631A6ACD"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Can lifestyle changes reverse type 2 diabetes?","answer":"Lifestyle changes, including diet modification and increased physical activity, can help prevent and manage type 2 diabetes. They can improve insulin sensitivity, control blood glucose levels, and promote weight loss, which are all beneficial in managing the disease. However, they may not completely reverse the disease, especially in advanced stages.","weight":1,"task_id":"F0CC742EA104CB2C8B8BCA9CB6EB78F0"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"What are the types of diabetes","answer":"The types of diabetes include Type 1 diabetes, Type 2 diabetes, gestational diabetes, and diabetes due to specific causes such as genetic defects causing deficient insulin secretion or action, diseases of the pancreas, and use of certain drugs. There are also subtypes of adult-onset diabetes identified as severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes.","weight":1,"task_id":"57CB850E74BC7A26A645CAAB823D35CD"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"BFEF55FA3BA8B9460207E8CF981E1A4A"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"A603218836E967137903C8CABAC8C282"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.","weight":-1,"task_id":"A5627A35FFA5C25EE34522D01D7198B1"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"how does environment influence fertilisation","answer":"Environmental factors can influence fertilisation in several ways. Factors such as temperature, pressure, and chemical activity can affect the physiological processes involved in fertilisation. Lifestyle factors like smoking, irradiation, oral contraceptives, and low socioeconomic status can also impact natural conception. Moreover, environmental conditions can affect the quality of maternal care and offspring development. For instance, crowded larval environments can lead to a decline in quality over time, affecting the genetic polymorphism in populations. Additionally, environmental stressors can influence the responsiveness to prenatal stress, potentially affecting fertility.","weight":1,"task_id":"6D733CABEB70E4DBF150EAAFFED6C973"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"how does diet impact someone's height","answer":"The text does not provide information on how diet impacts someone's height.","weight":-1,"task_id":"6267E2FEFF0332F88C2294C8F32C1FC1"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"which animal has the same number of chromosomes as human","answer":"The Bama miniature pig has the same number of chromosomes as humans.","weight":1,"task_id":"3FFA45D7124495B37B6F7F2B7B780AF3"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"what's ensures brains work","answer":"The functioning of the brain is ensured by the complex interplay of neuronal networks, synaptic connections, and the precise regulation of molecular and cellular events. This includes the spatiotemporal regulation of the transcriptome, the signaling dynamics of integrated circuitry, and the balance between activating and inhibiting systems. Additionally, the maintenance of genomic integrity in neuronal cells is crucial for the brain's high metabolic activity.","weight":1,"task_id":"499C63633BB95DE93DC3A89615496443"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"how do our brains maintain emotions","answer":"Our brains maintain emotions through complex neural systems and processes. The reward-motivation system, which includes the striatum, amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex, plays a key role in processing emotional and motivational information. The amygdala, a principal component of the limbic system, controls emotionality. Stress, anxiety, and other emotion-related traits are influenced by multiple genetic, environmental, experiential, and epigenetic factors. Additionally, hormones released by stressful experiences can enhance memory consolidation, further influencing emotional responses.","weight":1,"task_id":"405240F6F75C3927C1088287E19920AD"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"what hormones do our brains release during stressful experiences?","answer":"During stressful experiences, our brains release hormones such as epinephrine, glucocorticoids, and corticosterone. The hypothalamic-pituitary-adrenal (HPA) axis also plays a significant role in this process, stimulating the release of these hormones.","weight":1,"task_id":"DA2C5FBAA7806455F89E896E641DD642"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"what is the use of corticosterone?","answer":"Corticosterone, a type of glucocorticoid, is used in the body's response to stress. It initiates metabolic and physiological responses that help the body cope with stressors. It also provides negative feedback to inhibit activity in the hypothalamic-pituitary-adrenal (HPA) axis, which helps to dampen stress-activated defense reactions, including immune responses. Additionally, corticosterone can influence the expression of certain cytokines, potentially having anti-inflammatory effects.","weight":1,"task_id":"7B0629638DF00DF1183B67EE3BF39B1C"}, +{"user_id":"e59f6172-5877-4bc7-b6db-e8621f6c5e0a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"EEF95E20F590A24FD5C5EAE934DE080A"}, +{"user_id":"e59f6172-5877-4bc7-b6db-e8621f6c5e0a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"1DC67DC9808D0B6551A2C2F3B744D787"}, +{"user_id":"e59f6172-5877-4bc7-b6db-e8621f6c5e0a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0360FCEE63D1F10813706D239F4CAE87"}, +{"user_id":"e59f6172-5877-4bc7-b6db-e8621f6c5e0a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"98BC8141880A76BE21A62B8C2868E2B4"}, +{"user_id":"e59f6172-5877-4bc7-b6db-e8621f6c5e0a","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"61599C759677E83684FF252265BDFB9F"}, +{"user_id":"e59f6172-5877-4bc7-b6db-e8621f6c5e0a","query":"Which gene pairs lead to diabetes","answer":"The gene pairs that lead to diabetes include HNF4a and PAX4, PPARG and KCNJ11, SLC30A8 and HHEX, CDKAL1 and CDKN2B, and PGC-1 and TCF7L2.","weight":1,"task_id":"7DBFF16489378D3CCA91BCE753EA4052"}, +{"user_id":"e59f6172-5877-4bc7-b6db-e8621f6c5e0a","query":"What type of mutations cause thyroid disorder?","answer":"The background text does not provide specific information on the type of mutations that cause thyroid disorder.","weight":-1,"task_id":"A616E83051B7F2A98670351C29421F39"}, +{"user_id":"e59f6172-5877-4bc7-b6db-e8621f6c5e0a","query":"What is the relationship between insulin sensitivity and lipid profiles?","answer":"Insulin resistance, a key feature of metabolic syndrome, can lead to dyslipidemia, affecting enzymes involved in lipid metabolism. This typically results in decreased levels of HDL (High-Density Lipoprotein), often referred to as","weight":-1,"task_id":"C2EE6E9125351220DB79AFEFA80575FB"}, +{"user_id":"e59f6172-5877-4bc7-b6db-e8621f6c5e0a","query":"How does one tell the difference between insulin sensitivity and glucose tolerance?","answer":"Insulin sensitivity refers to how responsive the body's cells are to insulin. It is often measured using methods such as the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR), the Cederholm index, or the insulin sensitivity index (ISI) derived from oral glucose-tolerance tests. \n\nOn the other hand, glucose tolerance refers to the body's ability to metabolize glucose and maintain normal blood sugar levels. It is typically assessed using an oral glucose tolerance test (OGTT), where blood glucose levels are measured at various intervals after ingestion of a glucose solution. \n\nIn the context of diabetes, impaired glucose tolerance is generally regarded as a pre-diabetic state, while insulin resistance is a key feature of type 2 diabetes.","weight":1,"task_id":"BA9B766ED3316A509B9499F3E5DF098A"}, +{"user_id":"e59f6172-5877-4bc7-b6db-e8621f6c5e0a","query":"How does body mass index effect glucose tolerance?","answer":"Higher body mass index (BMI) has been associated with impaired glucose tolerance. Studies have shown that weight reduction and enhanced aerobic fitness can improve glucose tolerance. In obese individuals, -cell mass is expanded but -cell function is comparable to individuals with a normal BMI. Weight loss might cause some reduction in both -cell mass and function in non-diabetic individuals. Furthermore, there was a strong correlation between BMI and the number of lipid droplets in b-cells.","weight":1,"task_id":"BD1A1D6FF7F9CB29D94A8F29FA286CCE"}, +{"user_id":"e59f6172-5877-4bc7-b6db-e8621f6c5e0a","query":"What gene sequence usually result in asexual reproduction?","answer":"In the context provided, it is not specified which gene sequence results in asexual reproduction. However, it is mentioned that microbes, including viruses and bacteria, reproduce asexually through various","weight":-1,"task_id":"339D11CF7CB13A68E3546D6A10DCB17A"}] diff --git a/gnqa/paper1_eval/src/data/ratings/2024_07_01-out.json b/gnqa/paper1_eval/src/data/ratings/2024_07_01-out.json new file mode 100644 index 0000000..4618ca4 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_07_01-out.json @@ -0,0 +1,978 @@ +[ + { + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "F26EEA55FF8FFCB0B9E3E46688C276DA", + "5674818C9037B8A93EDCF35CEF97E33C", + "106C5DDDE1338BE1CC8AE04CEAE3EF62", + "2D8205C79915FF9CEB8DECCE51E6E473", + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "A55310A1C19732792B5894A2DB5E8596", + "2EDD65822CDA4669FCAD678288C2E3E9", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4", + "FBBFB0D391778CDE9536F742300C1809", + "C8E21233058E1DEE05CD619ACBF49E42", + "38A8ED57882E2E250B447F93243A8CD5", + "5D3CA84D2DCE010AF63188665193B195", + "AB6286D0D8E9196BA0EE1F1CF90536AD", + "FE094A900BA5B3C48A3A67B18B2F12BD", + "FC09E6CA3472C1E263743195703162C5", + "136D27CEFA12BC3AF0BDD42121FD6DBA", + "6F33581CC88C813D20B047A82A78BC7C", + "2C01511CD9C5ABDC085D77F67AA862E1", + "BD981EFD76B6C93C620CD92DB9EF0B35", + "CB93CE86DA18F287DBEF22CB29C560CF", + "8DCEF606839664C8B6C72CF1D181CEEA", + "FEE16F5E4D12AF7E7B0DDBF6F047EB76", + "DF05AACA4A1466AC1753DE13631A6ACD", + "57CB850E74BC7A26A645CAAB823D35CD" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 0, + -1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Several genes are involved in aging, including BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, CDKN2B, USP28, E2F2, BCL3, NAP1L4, GAB2, QKI, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, and several S100 calcium binding proteins.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Studies have shown that foreign genetic admixture can have a protective effect against diabetes. For instance, the prevalence of Type 2 Diabetes (T2D) in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with foreign genetic admixture. This suggests that foreign genotypes can reduce the risk of diabetes. Similar findings have been reported in Pima Indians and other Native American populations.", + "Several genes are involved in the aging process. These include BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, IGF-1, CYP19, NAP1L4, GAB2, QKI, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, USP28, E2F2, BCL3, mre11, rad50, Ku80, mus308, mus205, DNApol-eta, Trxr-1, GlnRS, isoQC, QC, Cct1, Cct4, Cct5, Cct6, Hsc70-4, Xbp1, NAT1, Rack1, Rheb, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, SIR2, DAF-2/IGF1R, DAF-16/FOXO, AAK-2/AMPK, LET-363/mTOR, SIRT1/SIR2, p53, Werner's syndrome gene, iff-1, SIR2, TERT, ZBTB16, CA9, HEY2, Smarca4, Amarcb1, Hdac1, -5, -6, Dnmt3b, and genes in the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway.", + "Lifestyle modifications, including increased physical activity, dietary changes, and weight reduction, have been shown to significantly reduce the risk of Type 2 Diabetes (T2D), which is often associated with aging. Regular physical activity improves insulin sensitivity, reducing the stress on insulin-producing cells. Dietary changes, particularly those reducing the intake of processed carbohydrates, energy-rich foods, and animal fats, can also lower T2D risk. Weight loss can directly improve insulin secretion and sensitivity. However, these lifestyle changes require not only individual efforts but also changes in social, built, and food environments.", + "The background text does not provide information on the role of longevity genes in protecting against diabetes.", + "The types of diabetes include Type 1 diabetes, Type 2 diabetes, gestational diabetes, and diabetes due to specific causes such as genetic defects causing deficient insulin secretion or action, diseases of the pancreas, and use of certain drugs. There are also subtypes of adult-onset diabetes identified as severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes." + ], + "query": [ + "genetics", + "genetics", + "genetics", + "List as many studies as you can that include rapamycin.", + "genetics", + "what is bioinformatics", + "genetics", + "genetics", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in the aging process", + "List as many studies as you can that include rapamycin.", + "List as many studies as you can that include rapamycin.", + "List as many studies as you can that include rapamycin.", + "genetics", + "which genes are involved in aging", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Explain Protective Genetic Factors Against Diabetes in Elderly Populations", + "what genes are involved in the aging process", + "Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk", + "Explain The Role of Longevity Genes in Protecting Against Diabetes", + "What are the types of diabetes" + ] + } + }, + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "32CE1E54032485B73B5968395B3D3538", + "59CCE2D70104154865218876DD53D049", + "0AE973110158192E0D1F50E1D79764FB", + "33749EB09AAC7AD0404C8E3E584B98D2", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "9957BCF2957D9BD083EDCF8B60C0E4DE", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "d2f71149-a0d5-4666-95d8-15c82543243b": { + "task_id": [ + "7CA65F6B192BE7896422284B962CF5C3" + ], + "weight": [ + 1 + ], + "answer": [ + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits." + ], + "query": [ + "genetics" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "19DC9E909DDE6D9CDB3E86D1069F5A69", + "F2843EA2D5A239D022186329C8D5D8EF", + "4E1F0C2E792BAF0BA349326375D3EE6E", + "FA8ADB009A499F51B0533FDCB72CB29E", + "38BD5864A7928C6DBCA1D844327F3A19", + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + -1, + 1, + 1, + -1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.", + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "B89A904E71CF7F16126FCA9EAAFBC8A6", + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1", + "5A562D5F7A266BA057B6833F3A83E7A7", + "53905925B4D6F69CE5706896A3C667CE", + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6188C7826173CD59FD243F98C537AD50", + "2C37A2EC74E4B46F13C6FB23D9547DBB", + "590809C6B2A1504E2AE9A79EFDF3AC9A", + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "BD671A3AC4F77A74CA8A68EAD627437D", + "5E243BF70932D1477209FEF6869FDEEA", + "3A16235DA1E02B9148B9288A06EE567E", + "9BE91FA13BDC0D9EB80B35FC73A0029B", + "3D4688621977A9CBD670B50131B8D912", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "00647726F98EE835006D60B12455866D", + "8F3A81EAB68F709E82006205380AC723", + "ECEB33208BCDDC20908183BF249555AD", + "906F0A0AB4330CB7C3A75DA8764703F4", + "A3B39D0211921BC5581AB03193860970", + "2BF7D398C0BFD1F4D976C9F82343DE51", + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + -1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "0BF7A88573F1B5FCC2E8978A6D94CE2B", + "CBC2A69A6A94CFADE9E4077F5B655B6E", + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "3A55AE005B07C55283410798C0FBE47F", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + -1, + -1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "What is the significance of the length of telomeres?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "61a8e2c6-249c-40b8-a866-134f3a893e4a": { + "task_id": [ + "F6FC3B8BBCE2BA90D0EF2C9532DE3F84", + "0F76F85FB406BF74022084C5866C942D", + "C4FEDD378CD138B141464832D021624B", + "ED89B73DC42AD2ADA03B7C014009A551", + "21CB24A2A589173F1E50ADA5DD6165EC" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "FE7363764A44969E28C9562A3948143B", + "F456694025B9C98AA9E4246820D5909A", + "E6C75917249BB8C0810B0E709D6FDD0A", + "8FA337BF315CFA09716800E096EA8A06", + "D68A2086596023BDD8C01023B03FA89D", + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + }, + { + "8e4fe952-5a61-4d95-86e5-49f974465572": { + "task_id": [ + "1619A0727D1C6673EE9E05171054F658", + "52B443B815CD46D57219872DFB3D0579", + "EB6B4DCD473BEE9580F47CD12DAFC074", + "2AE18C9AAFB4E3A103F03C86BBEB2DD1", + "58D6F365917926445960756A26B3FDC8", + "2A2860BB54BC0D36A929838ED41243A7", + "A5DEAEAC441B3BDC65B58EA6923FAE73" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.", + "Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.", + "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.", + "Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "What causes diabetes?", + "Define dyslipidemia.", + "Does cycling reduce risk of diabetes?", + "What is cytochrome?" + ] + } + }, + { + "5fb03df3-5a04-4dad-ba1c-14eb8e50a241": { + "task_id": [ + "9E388A69975DBDEA3E8DE25294960147", + "9ED249912768DA58AF97F5600D0CBA8F", + "DEEA82693F72D24109C91089CABC7EBF", + "9C697AF95B263CBD4E243D8AD1062180", + "0CDD1C9219114BB2770C28D541F1060A", + "37A26345145679F7539EA8F512623F5E", + "F35BF9C40081CE0521E562CD95BA4C2F", + "9DD88454267DEF2106A3EA7E6E8B5443", + "732D340E5C8F09381CEFA440AD2A7AB6", + "CE5922BDA6B949A17665AB4E1A8138D5", + "F0CC742EA104CB2C8B8BCA9CB6EB78F0" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + -1, + -1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.", + "GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity.", + "GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network", + "GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.", + "Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present, cells increase insulin output to maintain normal glucose tolerance. However, if cells are incapable of this task, plasma concentrations of glucose increase.", + "Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes.", + "Lifestyle changes, including diet modification and increased physical activity, can help prevent and manage type 2 diabetes. They can improve insulin sensitivity, control blood glucose levels, and promote weight loss, which are all beneficial in managing the disease. However, they may not completely reverse the disease, especially in advanced stages." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is GeneNetwork and how does it relate to aging research?", + "How can GeneNetwork assist in identifying genetic factors involved in diabetes?", + "What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?", + "What role does insulin play in the regulation of blood glucose levels?", + " How does aging affect the risk of developing type 2 diabetes?", + "Can lifestyle changes reverse type 2 diabetes?" + ] + } + }, + { + "24a2e578-5f6e-4576-8942-148d6a9f672d": { + "task_id": [ + "29C36228E29604002BB3BAE6654F7762", + "5DCBD523F5F1663492EB5630EAD981FE", + "BFEF55FA3BA8B9460207E8CF981E1A4A", + "A603218836E967137903C8CABAC8C282", + "A5627A35FFA5C25EE34522D01D7198B1", + "6D733CABEB70E4DBF150EAAFFED6C973", + "6267E2FEFF0332F88C2294C8F32C1FC1", + "3FFA45D7124495B37B6F7F2B7B780AF3", + "499C63633BB95DE93DC3A89615496443", + "405240F6F75C3927C1088287E19920AD", + "DA2C5FBAA7806455F89E896E641DD642", + "7B0629638DF00DF1183B67EE3BF39B1C" + ], + "weight": [ + 1, + 1, + 1, + -1, + -1, + 1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.", + "Environmental factors can influence fertilisation in several ways. Factors such as temperature, pressure, and chemical activity can affect the physiological processes involved in fertilisation. Lifestyle factors like smoking, irradiation, oral contraceptives, and low socioeconomic status can also impact natural conception. Moreover, environmental conditions can affect the quality of maternal care and offspring development. For instance, crowded larval environments can lead to a decline in quality over time, affecting the genetic polymorphism in populations. Additionally, environmental stressors can influence the responsiveness to prenatal stress, potentially affecting fertility.", + "The text does not provide information on how diet impacts someone's height.", + "The Bama miniature pig has the same number of chromosomes as humans.", + "The functioning of the brain is ensured by the complex interplay of neuronal networks, synaptic connections, and the precise regulation of molecular and cellular events. This includes the spatiotemporal regulation of the transcriptome, the signaling dynamics of integrated circuitry, and the balance between activating and inhibiting systems. Additionally, the maintenance of genomic integrity in neuronal cells is crucial for the brain's high metabolic activity.", + "Our brains maintain emotions through complex neural systems and processes. The reward-motivation system, which includes the striatum, amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex, plays a key role in processing emotional and motivational information. The amygdala, a principal component of the limbic system, controls emotionality. Stress, anxiety, and other emotion-related traits are influenced by multiple genetic, environmental, experiential, and epigenetic factors. Additionally, hormones released by stressful experiences can enhance memory consolidation, further influencing emotional responses.", + "During stressful experiences, our brains release hormones such as epinephrine, glucocorticoids, and corticosterone. The hypothalamic-pituitary-adrenal (HPA) axis also plays a significant role in this process, stimulating the release of these hormones.", + "Corticosterone, a type of glucocorticoid, is used in the body's response to stress. It initiates metabolic and physiological responses that help the body cope with stressors. It also provides negative feedback to inhibit activity in the hypothalamic-pituitary-adrenal (HPA) axis, which helps to dampen stress-activated defense reactions, including immune responses. Additionally, corticosterone can influence the expression of certain cytokines, potentially having anti-inflammatory effects." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "how does environment influence fertilisation", + "how does diet impact someone's height", + "which animal has the same number of chromosomes as human", + "what's ensures brains work", + "how do our brains maintain emotions", + "what hormones do our brains release during stressful experiences?", + "what is the use of corticosterone?" + ] + } + }, + { + "e59f6172-5877-4bc7-b6db-e8621f6c5e0a": { + "task_id": [ + "EEF95E20F590A24FD5C5EAE934DE080A", + "1DC67DC9808D0B6551A2C2F3B744D787", + "0360FCEE63D1F10813706D239F4CAE87", + "98BC8141880A76BE21A62B8C2868E2B4", + "61599C759677E83684FF252265BDFB9F", + "7DBFF16489378D3CCA91BCE753EA4052", + "A616E83051B7F2A98670351C29421F39", + "C2EE6E9125351220DB79AFEFA80575FB", + "BA9B766ED3316A509B9499F3E5DF098A", + "BD1A1D6FF7F9CB29D94A8F29FA286CCE", + "339D11CF7CB13A68E3546D6A10DCB17A" + ], + "weight": [ + 1, + 1, + 1, + 1, + 1, + 1, + -1, + -1, + 1, + 1, + -1 + ], + "answer": [ + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The gene pairs that lead to diabetes include HNF4a and PAX4, PPARG and KCNJ11, SLC30A8 and HHEX, CDKAL1 and CDKN2B, and PGC-1 and TCF7L2.", + "The background text does not provide specific information on the type of mutations that cause thyroid disorder.", + "Insulin resistance, a key feature of metabolic syndrome, can lead to dyslipidemia, affecting enzymes involved in lipid metabolism. This typically results in decreased levels of HDL (High-Density Lipoprotein), often referred to as", + "Insulin sensitivity refers to how responsive the body's cells are to insulin. It is often measured using methods such as the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR), the Cederholm index, or the insulin sensitivity index (ISI) derived from oral glucose-tolerance tests. \n\nOn the other hand, glucose tolerance refers to the body's ability to metabolize glucose and maintain normal blood sugar levels. It is typically assessed using an oral glucose tolerance test (OGTT), where blood glucose levels are measured at various intervals after ingestion of a glucose solution. \n\nIn the context of diabetes, impaired glucose tolerance is generally regarded as a pre-diabetic state, while insulin resistance is a key feature of type 2 diabetes.", + "Higher body mass index (BMI) has been associated with impaired glucose tolerance. Studies have shown that weight reduction and enhanced aerobic fitness can improve glucose tolerance. In obese individuals, -cell mass is expanded but -cell function is comparable to individuals with a normal BMI. Weight loss might cause some reduction in both -cell mass and function in non-diabetic individuals. Furthermore, there was a strong correlation between BMI and the number of lipid droplets in b-cells.", + "In the context provided, it is not specified which gene sequence results in asexual reproduction. However, it is mentioned that microbes, including viruses and bacteria, reproduce asexually through various" + ], + "query": [ + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "What is the significance of the length of telomeres?", + "Create a how-to guide for genetic sequencing.", + "Which gene pairs lead to diabetes", + "What type of mutations cause thyroid disorder?", + "What is the relationship between insulin sensitivity and lipid profiles?", + "How does one tell the difference between insulin sensitivity and glucose tolerance?", + "How does body mass index effect glucose tolerance?", + "What gene sequence usually result in asexual reproduction?" + ] + } + } +] diff --git a/gnqa/paper1_eval/src/data/ratings/2024_21_06-gnqa_combined_responses_edit.json b/gnqa/paper1_eval/src/data/ratings/2024_21_06-gnqa_combined_responses_edit.json new file mode 100644 index 0000000..4cce8d2 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/2024_21_06-gnqa_combined_responses_edit.json @@ -0,0 +1,277 @@ +[ +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?","answer":"The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.","weight":1,"task_id":"849E78D8214245F8E8167E78C01BEE60"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"List as many studies as you can that include rapamycin.","answer":"1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.","weight":-1,"task_id":"2C477A3C76794C27A1FBBF437CFF75EE"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"Why is it so diffuclut to map gene loci that control aging in humans?","answer":"Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.","weight":1,"task_id":"CAD6C6C2AB42AA66BFDD65F0F11932B2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.","weight":1,"task_id":"EC2BFCD8E06079A3E595114822D2A44D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does epigenetics inluence gene expression without changing the underlying DNA sequence?","answer":"Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.","weight":-1,"task_id":"59AA8E8D7A97CFF47C6CFD65629F29F9"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.","answer":"Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.","weight":1,"task_id":"C58CFF41F7422B321DF88A110E278FD5"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?","answer":"The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.","weight":1,"task_id":"BCD1175CCB27FBA1E6F9D7670B17E527"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"5341FE6588C6175BC8A688A483928BC0"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"42847DE50D50E6A9B26ED0B03CFD160E"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"12BEAFA9366519672FC8B06959FB2DAF"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"64FEC152131BC6502E15EA6A6348D70B"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is apoptosis?","answer":"Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.","weight":1,"task_id":"78A0CD7E12AFEF6865583142603EE039"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"What is the most cited environmental factor for the onset of asthma?","answer":"The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.","weight":1,"task_id":"33FC2CC0F61BA22E4D095586B95703BD"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"genetics","answer":"Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.","weight":1,"task_id":"370380F3A38AC4A788463D14E0EC673A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what is bioinformatics","answer":"Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.","weight":1,"task_id":"1E0DA0931F4E3A8C2893353CCA114B10"}, +{"user_id":"e031ac19-fec1-4d97-a037-cd39d3817c54","query":"How would one extract the DNA, from say, flora or fauna?","answer":"DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use.","weight":1,"task_id":"59E2406798D265A3CB466B766683E63C"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.","answer":"DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.","weight":-1,"task_id":"3F9EDFE9A0222EA70459EC8985F134C4"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?","answer":"The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.","weight":1,"task_id":"A010490B55F739DF95BB82DF2B0F5AA3"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Explain the process of finding a genetic marker followed by a quantitative trait loci.","answer":"The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.","weight":1,"task_id":"02A94D1056FDA77BDA9AC6CFDE0D5FC6"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.","weight":1,"task_id":"96B873A13E3B386E38940EF6ECA90D4A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":1,"task_id":"DA98AC2EA5D1F776D3F04FCBC7F01339"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what causes the aging process","answer":"The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.","weight":1,"task_id":"117299AD06C2B147F49E9C9BC036CEA4"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.","answer":"The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.","weight":1,"task_id":"F4DA6C97230E89C0226B1433532A16D9"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in the aging process","answer":"The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.","weight":0,"task_id":"FBBFB0D391778CDE9536F742300C1809"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"32CE1E54032485B73B5968395B3D3538"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"59CCE2D70104154865218876DD53D049"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"0AE973110158192E0D1F50E1D79764FB"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"33749EB09AAC7AD0404C8E3E584B98D2"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"471A145E9CA1E517E462499ABCA8EA2D"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?","answer":"The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.","weight":1,"task_id":"19EBC6CA7E425D0C1279475BD0B411B6"}, +{"user_id":"6365589e-a47b-4fa8-a53c-88cb5ee1a991","query":"what is ensembl?","answer":"Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline.","weight":-1,"task_id":"9CFE932D7898C83E473E590BC77B4FCB"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Create a how to guide for genetic sequencing ","answer":"1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.","weight":-1,"task_id":"C52A9690417093A861C669A0753689BD"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What is the significance of the length of telomeres? ","answer":"The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.","weight":1,"task_id":"93DE2EF005059DFEA5A7FBBA3BD17D03"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ","answer":"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.","weight":1,"task_id":"477FC54178046FE98BF97FAAC5FE167F"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Why is genetic tracing matrilineal rather than patrilineal? ","answer":"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.","weight":1,"task_id":"6B80ECC5F657EB7CBDE69D411A30D3EA"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":1,"task_id":"2DE25ABD7E487B80D0C489319640EACC"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"what type of dataset is useful for qtl mapping analysis in genenetwork2? ","answer":"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.","weight":1,"task_id":"6498ED71891B79908B2E383D9AA5BAC5"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"What genetic factors influence aging in humans? ","answer":"Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.","weight":1,"task_id":"B2F5CB7BCD9A827D3A6E0152C030C4B4"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"Is there a direct association between aging and susceptibility to having diabetes?","answer":"Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.","weight":1,"task_id":"72FBC4F382B6502EAF41BD6682E63A2D"}, +{"user_id":"6ee46240-38bf-4035-b9a8-0d72e29401b5","query":"How does genetics influence the emergency of diabetes? ","answer":"Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk.","weight":1,"task_id":"02C953165B9CA94E273DD4A04301C89F"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"19DC9E909DDE6D9CDB3E86D1069F5A69"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F2843EA2D5A239D022186329C8D5D8EF"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"4E1F0C2E792BAF0BA349326375D3EE6E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"FA8ADB009A499F51B0533FDCB72CB29E"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.","weight":-1,"task_id":"38BD5864A7928C6DBCA1D844327F3A19"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with diabetes?","answer":"The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.","weight":1,"task_id":"2272C482CC247E746D15C9F55EDD8BCE"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what genes are associated with aging?","answer":"Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.","weight":1,"task_id":"C6C7CEF19CE7C27CF4BC6906259CDDF9"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the bioinformatics tools for QTLs analysis?","answer":"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.","weight":1,"task_id":"B4BB83EB5D5C5C042E07173119046A13"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"what are the statistical approaches for qtls analysis?","answer":"The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.","weight":1,"task_id":"D88EF655762CE3D524A7A1EEA3FA16ED"}, +{"user_id":"e8855be7-59fd-4224-90ad-575e7158c34c","query":"Create a how-to guide for GWAS analysis?","answer":"1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results.","weight":1,"task_id":"245DD8093F5D16F44C2AD7618245086C"}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "weight": 1, + "task_id": "B89A904E71CF7F16126FCA9EAAFBC8A6" +}, +{ + "user_id": "d8abfb12-9a11-400a-8cd0-0a436602581c", + "query": "What about recombination in human centromeres?", + "answer": "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "weight": 1, + "task_id": "2F8796A8C3DC633F00DB901C9BA396DA" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Create a how-to guide for genetic sequencing.", + "answer": "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "weight": -1, + "task_id": "6188C7826173CD59FD243F98C537AD50" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "weight": 1, + "task_id": "2C37A2EC74E4B46F13C6FB23D9547DBB" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "answer": "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "weight": 1, + "task_id": "590809C6B2A1504E2AE9A79EFDF3AC9A" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "What is the difference between QTL mapping and GWAS?", + "answer": "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "weight": 1, + "task_id": "6DBC070B2E4DC2FE8036E5BA7480B755" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How do I determine which gene in my QTL is causal for the trait?", + "answer": "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "weight": 1, + "task_id": "5594EA025D9631328071B6A1A7EF1375" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Which mouse genes have been associated with longevity?", + "answer": "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "weight": -1, + "task_id": "AB589D2E046B211A7486A6C4BD4ECFB4" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "How is gene expression in the liver affected by diabetes?", + "answer": "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "weight": 1, + "task_id": "8FFF9DCC307B8DBF2C8485637F2ABEF4" +}, +{ + "user_id": "415d39c0-28b8-4711-8d20-081082660f35", + "query": "Why do males have two Y chromosomes and females only one?", + "answer": "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.", + "weight": 1, + "task_id": "C6B9A982C9283DE065A3371F1264095C" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " Create a how-to guide for genetic sequencing", + "answer": "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "weight": 1, + "task_id": "F9F7EA3DC28534B161ED70DB401C7D11" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " Create a how-to guide for genetic sequencing.", + "answer": "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "weight": -1, + "task_id": "4A06F8DF54C82D90E02F81D0E1E8B08A" +}, +{ + "user_id": "c5e42006-ddba-4b69-b1d1-61b0dceaec86", + "query": " What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying.", + "weight": 1, + "task_id": "BA6A505E62A0529DB883D036CBC1FD92" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Which genes are associated with aging in human ", + "answer": "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "weight": 1, + "task_id": "3EC47C56606B02F00CF2449AB311365C" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Create a how-to guide for genetic sequencing", + "answer": "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "weight": 1, + "task_id": "CDFC418BD568E839C09656C57808ADA1" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Create a guide for genetic sequencing", + "answer": "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "weight": -1, + "task_id": "5DEB102510F48D0BF9C278DC895A8BD1" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "weight": 1, + "task_id": "5A562D5F7A266BA057B6833F3A83E7A7" +}, +{ + "user_id": "0c4a1c25-539f-453a-b7f6-915ab462cf0b", + "query": "Why is genetic tracing matrilineal rather than patrilineal?", + "answer": "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "weight": 1, + "task_id": "53905925B4D6F69CE5706896A3C667CE" +}, +{ + "user_id": "0c4a1c26-539f-453a-b7f6-915ab462cf0b", + "query": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs", + "answer": "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process.", + "weight": -1, + "task_id": "C0015BEE5FE41769A65126B79BB1E40D" +}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in human centromeres?","answer":"Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.","weight":-1,"task_id":"9957BCF2957D9BD083EDCF8B60C0E4DE"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How does recombination work in human centromeres?","answer":"In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.","weight":-1,"task_id":"DEE6D385D1B01B4155AA4ABE59515893"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"How many types of diabetes exist?","answer":"There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).","weight":1,"task_id":"9309F248E5933718BFB625E4EF2D3E42"}, +{"user_id":"d8abfb12-9a11-400a-8cd0-0a436602581c","query":"What about recombination in the human genome?","answer":"The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome.","weight":-1,"task_id":"10ABD2210053119B18D94F1FE266E73E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"BD671A3AC4F77A74CA8A68EAD627437D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5E243BF70932D1477209FEF6869FDEEA"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.","weight":-1,"task_id":"3A16235DA1E02B9148B9288A06EE567E"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9BE91FA13BDC0D9EB80B35FC73A0029B"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?","answer":"The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.","weight":1,"task_id":"3D4688621977A9CBD670B50131B8D912"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"How can I add a new species to the GeneNetwork database?","answer":"The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.","weight":1,"task_id":"E94FFD042BB146E8A429200590A6792D"}, +{"user_id":"16aaa58b-ba19-48f9-af5b-db7d2ad98861","query":"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?","answer":"Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.","weight":1,"task_id":"C319861B08978CF5F7E6F0CD3A517A81"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"what genetic factor are associated with aging","answer":"Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.","weight":1,"task_id":"081B2DB92FD09DEBEF28ADBBDE7199D2"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.","weight":0,"task_id":"68EF3BE5EC2106766CA9CC700135E2FA"}, +{"user_id":"316fb5cc-dcca-4d76-bd42-b2010f11799c","query":"nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets","answer":"Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes.","weight":1,"task_id":"8590501C57DC5C321AB5E1036F233027"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"00647726F98EE835006D60B12455866D"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"ECEB33208BCDDC20908183BF249555AD"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":-1,"task_id":"906F0A0AB4330CB7C3A75DA8764703F4"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"A3B39D0211921BC5581AB03193860970"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"2BF7D398C0BFD1F4D976C9F82343DE51"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with diabetes in QTL analyses?","answer":"The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.","weight":1,"task_id":"FCFCE5BBA2A8B3D8818890B9D2308C5A"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"which genes are typically associated with early aging?","answer":"The genes typically associated with early aging are APOE and FOXO3A.","weight":1,"task_id":"E3FFB15A9901BD8DB87B0F09D335BEA0"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"How do I generate a linkage or association mapping study in mice to understand aging?","answer":"To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.","weight":1,"task_id":"38797E46211127E5C7175E707D40325B"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"Is the gene TCF7L2 involved in diabetes?","answer":"Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).","weight":1,"task_id":"CD1F7EAE0FDC758A8167118927ADFE71"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"In which diseases is the gene TCF7L2 involved?","answer":"The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.","weight":-1,"task_id":"FFA6EADA5502933C0C30C9D16DCAA073"}, +{"user_id":"545b58e2-5033-4c96-afe3-96f90e2343af","query":"what are confounding factors in diabetes?","answer":"Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors.","weight":1,"task_id":"00BE70B5D71A5926E56942909C8B2A92"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":-1,"task_id":"0BF7A88573F1B5FCC2E8978A6D94CE2B"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"CBC2A69A6A94CFADE9E4077F5B655B6E"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.","weight":1,"task_id":"847F1E1599EECDE92F99B7581728FFE8"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?","answer":"A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.","weight":1,"task_id":"037BAB6CB2DE7A42AAAA73CE5DA8DB73"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":" Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.","weight":1,"task_id":"B2AA6DE557D652A0A660C4E0FAC1124D"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":-1,"task_id":"3A55AE005B07C55283410798C0FBE47F"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.","answer":"Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.","weight":1,"task_id":"7EC697DE62C0C57E601EC3F5B295DF61"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.","answer":"Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.","weight":1,"task_id":"0A6673A0B69F0FF9C9657FB797DD1FE2"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.","answer":"Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.","weight":1,"task_id":"44B088326CD80B4980D810738D88A284"}, +{"user_id":"3582a98b-2a9c-45fe-bf06-a0fde4e1be21","query":"is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert","answer":"Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors.","weight":-1,"task_id":"D53462CE61F52F7D31BB627998F4D75A"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"F6FC3B8BBCE2BA90D0EF2C9532DE3F84"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"0F76F85FB406BF74022084C5866C942D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"FE7363764A44969E28C9562A3948143B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"F456694025B9C98AA9E4246820D5909A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"E6C75917249BB8C0810B0E709D6FDD0A"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"8FA337BF315CFA09716800E096EA8A06"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"D68A2086596023BDD8C01023B03FA89D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"How is the immune system related to diabetes?","answer":"The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.","weight":1,"task_id":"CD3820AA1BD96613F78FDF3CF5C8AB3D"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the genomic variants associated with immune system components and diabetes?","answer":"The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.","weight":1,"task_id":"A4CE2F2F8E08E5F16C94A1BCF540D881"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What is the role of the immune system in the metabolomics of diabetes and associated conditions?","answer":"The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.","weight":1,"task_id":"1B8618ADB274F928B3AACAB1C71A927E"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"What are the different relationship between traits?","answer":"The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.","weight":1,"task_id":"BF1705D2C26044038FF1483258548167"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can landscape of QTL and GWAS hits be used to find relationships between traits ?","answer":"Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.","weight":1,"task_id":"68AB7A78543D5B36206274837824091B"}, +{"user_id":"200a39ba-aacc-40fe-ad11-d9b7123e3e6a","query":"Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?","answer":"Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes.","weight":1,"task_id":"055110B765AA502F9AAECE68CEC0DD24"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"C4FEDD378CD138B141464832D021624B"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"ED89B73DC42AD2ADA03B7C014009A551"}, +{"user_id":"61a8e2c6-249c-40b8-a866-134f3a893e4a","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"21CB24A2A589173F1E50ADA5DD6165EC"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"1619A0727D1C6673EE9E05171054F658"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"52B443B815CD46D57219872DFB3D0579"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"EB6B4DCD473BEE9580F47CD12DAFC074"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What causes diabetes?","answer":"Diabetes is caused by a combination of genetic and environmental factors. This includes a family history of diabetes, increased age, hypertension, lack of physical exercise, obesity, and certain dietary habits. In type 2 diabetes, the body develops resistance to insulin due to the malfunction of insulin-producing -cells. Some cases of diabetes are also linked to single gene defects. Additionally, exposure to certain environmental pollutants has been associated with the development of diabetes.","weight":1,"task_id":"2AE18C9AAFB4E3A103F03C86BBEB2DD1"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Define dyslipidemia.","answer":"Dyslipidemia is the term for blood fat disorders, which include high triglycerides, low HDL cholesterol, and high LDL cholesterol. These conditions can foster plaque buildups in artery walls.","weight":1,"task_id":"58D6F365917926445960756A26B3FDC8"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"Does cycling reduce risk of diabetes?","answer":"Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.","weight":1,"task_id":"2A2860BB54BC0D36A929838ED41243A7"}, +{"user_id":"8e4fe952-5a61-4d95-86e5-49f974465572","query":"What is cytochrome?","answer":"Cytochrome is a type of protein that contains heme groups and is responsible for the transport of electrons. They are found in aerobic cells and play a crucial role in the respiratory chain, aiding in the process of oxidative phosphorylation. There are different types of cytochromes, including cytochromes a, b, and c, each undergoing oxidation-reduction changes in a determined sequence.","weight":1,"task_id":"A5DEAEAC441B3BDC65B58EA6923FAE73"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"which genes are involved in aging","answer":"Several genes are involved in aging, including BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, CDKN2B, USP28, E2F2, BCL3, NAP1L4, GAB2, QKI, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, and several S100 calcium binding proteins.","weight":1,"task_id":"FE094A900BA5B3C48A3A67B18B2F12BD"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":-1,"task_id":"9E388A69975DBDEA3E8DE25294960147"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"Create a how-to guide for genetic sequencing","answer":"1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.","weight":1,"task_id":"29C36228E29604002BB3BAE6654F7762"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"9ED249912768DA58AF97F5600D0CBA8F"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"DEEA82693F72D24109C91089CABC7EBF"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Create a how-to guide for genetic sequencing.","answer":"1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.","weight":1,"task_id":"FC09E6CA3472C1E263743195703162C5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"136D27CEFA12BC3AF0BDD42121FD6DBA"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"What is the significance of the length of telomeres?","answer":"The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.","weight":1,"task_id":"5DCBD523F5F1663492EB5630EAD981FE"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"9C697AF95B263CBD4E243D8AD1062180"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.","weight":1,"task_id":"6F33581CC88C813D20B047A82A78BC7C"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":1,"task_id":"2C01511CD9C5ABDC085D77F67AA862E1"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.","weight":1,"task_id":"0CDD1C9219114BB2770C28D541F1060A"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.","weight":-1,"task_id":"BD981EFD76B6C93C620CD92DB9EF0B35"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What is GeneNetwork and how does it relate to aging research?","answer":"GeneNetwork is a collaborative web-based resource equipped with tools and features for studying gene/gene interactions and exploring genetic correlates to neurobehavioral phenotypes. It houses gene expression and phenotypic data from various species and brain regions, and offers correlation and mapping strategies for assessing associations among multiple genes and QTLs. In the context of aging research, GeneNetwork can be used to analyze large gene expression data sets, model causal networks linking DNA differences to traits, and identify genes common to cellular senescence and functional cognitive decline. It can also help in identifying potential druggable targets for investigation in longevity.","weight":1,"task_id":"37A26345145679F7539EA8F512623F5E"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"How can GeneNetwork assist in identifying genetic factors involved in diabetes?","answer":"GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network","weight":-1,"task_id":"F35BF9C40081CE0521E562CD95BA4C2F"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?","answer":"GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.","weight":-1,"task_id":"9DD88454267DEF2106A3EA7E6E8B5443"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Explain Protective Genetic Factors Against Diabetes in Elderly Populations","answer":"Studies have shown that foreign genetic admixture can have a protective effect against diabetes. For instance, the prevalence of Type 2 Diabetes (T2D) in elderly Nauruans was reported to be 83% in full-blooded islanders but only 17% in those with foreign genetic admixture. This suggests that foreign genotypes can reduce the risk of diabetes. Similar findings have been reported in Pima Indians and other Native American populations.","weight":1,"task_id":"CB93CE86DA18F287DBEF22CB29C560CF"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"What role does insulin play in the regulation of blood glucose levels?","answer":"Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present, cells increase insulin output to maintain normal glucose tolerance. However, if cells are incapable of this task, plasma concentrations of glucose increase.","weight":1,"task_id":"732D340E5C8F09381CEFA440AD2A7AB6"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"what genes are involved in the aging process","answer":"Several genes are involved in the aging process. These include BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, DBH, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, SOAT2, IGF-1, CYP19, NAP1L4, GAB2, QKI, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, USP28, E2F2, BCL3, mre11, rad50, Ku80, mus308, mus205, DNApol-eta, Trxr-1, GlnRS, isoQC, QC, Cct1, Cct4, Cct5, Cct6, Hsc70-4, Xbp1, NAT1, Rack1, Rheb, Lamp2, Fas, Ghr, Anxa2, Anxa3, Anxa4, SIR2, DAF-2/IGF1R, DAF-16/FOXO, AAK-2/AMPK, LET-363/mTOR, SIRT1/SIR2, p53, Werner's syndrome gene, iff-1, SIR2, TERT, ZBTB16, CA9, HEY2, Smarca4, Amarcb1, Hdac1, -5, -6, Dnmt3b, and genes in the insulin/ insulin-like growth factor (IGF12 )/growth hormone (GH) pathway.","weight":1,"task_id":"8DCEF606839664C8B6C72CF1D181CEEA"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk","answer":"Lifestyle modifications, including increased physical activity, dietary changes, and weight reduction, have been shown to significantly reduce the risk of Type 2 Diabetes (T2D), which is often associated with aging. Regular physical activity improves insulin sensitivity, reducing the stress on insulin-producing cells. Dietary changes, particularly those reducing the intake of processed carbohydrates, energy-rich foods, and animal fats, can also lower T2D risk. Weight loss can directly improve insulin secretion and sensitivity. However, these lifestyle changes require not only individual efforts but also changes in social, built, and food environments.","weight":1,"task_id":"FEE16F5E4D12AF7E7B0DDBF6F047EB76"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":" How does aging affect the risk of developing type 2 diabetes?","answer":"Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes.","weight":1,"task_id":"CE5922BDA6B949A17665AB4E1A8138D5"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"Explain The Role of Longevity Genes in Protecting Against Diabetes","answer":"The background text does not provide information on the role of longevity genes in protecting against diabetes.","weight":-1,"task_id":"DF05AACA4A1466AC1753DE13631A6ACD"}, +{"user_id":"5fb03df3-5a04-4dad-ba1c-14eb8e50a241","query":"Can lifestyle changes reverse type 2 diabetes?","answer":"Lifestyle changes, including diet modification and increased physical activity, can help prevent and manage type 2 diabetes. They can improve insulin sensitivity, control blood glucose levels, and promote weight loss, which are all beneficial in managing the disease. However, they may not completely reverse the disease, especially in advanced stages.","weight":1,"task_id":"F0CC742EA104CB2C8B8BCA9CB6EB78F0"}, +{"user_id":"b4601142-3b57-4d5b-9b55-80bdf0ea4599","query":"What are the types of diabetes","answer":"The types of diabetes include Type 1 diabetes, Type 2 diabetes, gestational diabetes, and diabetes due to specific causes such as genetic defects causing deficient insulin secretion or action, diseases of the pancreas, and use of certain drugs. There are also subtypes of adult-onset diabetes identified as severe autoimmune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, and mild age-related diabetes.","weight":1,"task_id":"57CB850E74BC7A26A645CAAB823D35CD"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?","answer":"Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry genes, which determine the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in traits. Additionally, environmental factors and interactions between genes can influence the expression of traits.","weight":1,"task_id":"BFEF55FA3BA8B9460207E8CF981E1A4A"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"Why is genetic tracing matrilineal rather than patrilineal?","answer":"Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.","weight":-1,"task_id":"A603218836E967137903C8CABAC8C282"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?","answer":"The difference between X and Y DNA in terms of DNA tracing and determining QTLs is not explicitly discussed in the background text. However, it is mentioned that the mouse genome consists of 19 pairs of autosomes and the X and Y chromosomes. In general, the X and Y chromosomes are different in size, gene content, and inheritance patterns, which could potentially influence the process of DNA tracing and determining QTLs. However, specific methods or implications related to these differences are not detailed in the provided text.","weight":-1,"task_id":"A5627A35FFA5C25EE34522D01D7198B1"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"how does environment influence fertilisation","answer":"Environmental factors can influence fertilisation in several ways. Factors such as temperature, pressure, and chemical activity can affect the physiological processes involved in fertilisation. Lifestyle factors like smoking, irradiation, oral contraceptives, and low socioeconomic status can also impact natural conception. Moreover, environmental conditions can affect the quality of maternal care and offspring development. For instance, crowded larval environments can lead to a decline in quality over time, affecting the genetic polymorphism in populations. Additionally, environmental stressors can influence the responsiveness to prenatal stress, potentially affecting fertility.","weight":1,"task_id":"6D733CABEB70E4DBF150EAAFFED6C973"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"how does diet impact someone's height","answer":"The text does not provide information on how diet impacts someone's height.","weight":-1,"task_id":"6267E2FEFF0332F88C2294C8F32C1FC1"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"which animal has the same number of chromosomes as human","answer":"The Bama miniature pig has the same number of chromosomes as humans.","weight":1,"task_id":"3FFA45D7124495B37B6F7F2B7B780AF3"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"what's ensures brains work","answer":"The functioning of the brain is ensured by the complex interplay of neuronal networks, synaptic connections, and the precise regulation of molecular and cellular events. This includes the spatiotemporal regulation of the transcriptome, the signaling dynamics of integrated circuitry, and the balance between activating and inhibiting systems. Additionally, the maintenance of genomic integrity in neuronal cells is crucial for the brain's high metabolic activity.","weight":1,"task_id":"499C63633BB95DE93DC3A89615496443"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"how do our brains maintain emotions","answer":"Our brains maintain emotions through complex neural systems and processes. The reward-motivation system, which includes the striatum, amygdala, ventral tegmental area, orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex, plays a key role in processing emotional and motivational information. The amygdala, a principal component of the limbic system, controls emotionality. Stress, anxiety, and other emotion-related traits are influenced by multiple genetic, environmental, experiential, and epigenetic factors. Additionally, hormones released by stressful experiences can enhance memory consolidation, further influencing emotional responses.","weight":1,"task_id":"405240F6F75C3927C1088287E19920AD"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"what hormones do our brains release during stressful experiences?","answer":"During stressful experiences, our brains release hormones such as epinephrine, glucocorticoids, and corticosterone. The hypothalamic-pituitary-adrenal (HPA) axis also plays a significant role in this process, stimulating the release of these hormones.","weight":1,"task_id":"DA2C5FBAA7806455F89E896E641DD642"}, +{"user_id":"24a2e578-5f6e-4576-8942-148d6a9f672d","query":"what is the use of corticosterone?","answer":"Corticosterone, a type of glucocorticoid, is used in the body's response to stress. It initiates metabolic and physiological responses that help the body cope with stressors. It also provides negative feedback to inhibit activity in the hypothalamic-pituitary-adrenal (HPA) axis, which helps to dampen stress-activated defense reactions, including immune responses. Additionally, corticosterone can influence the expression of certain cytokines, potentially having anti-inflammatory effects.","weight":1,"task_id":"7B0629638DF00DF1183B67EE3BF39B1C"} +]
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/ratings/out.json b/gnqa/paper1_eval/src/data/ratings/out.json new file mode 100644 index 0000000..f8a6c19 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/out.json @@ -0,0 +1,634 @@ +[ + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "32CE1E54032485B73B5968395B3D3538", + "59CCE2D70104154865218876DD53D049", + "0AE973110158192E0D1F50E1D79764FB", + "33749EB09AAC7AD0404C8E3E584B98D2", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "9957BCF2957D9BD083EDCF8B60C0E4DE", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "19DC9E909DDE6D9CDB3E86D1069F5A69", + "F2843EA2D5A239D022186329C8D5D8EF", + "4E1F0C2E792BAF0BA349326375D3EE6E", + "FA8ADB009A499F51B0533FDCB72CB29E", + "38BD5864A7928C6DBCA1D844327F3A19", + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + -1, + 1, + 1, + -1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.", + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "B89A904E71CF7F16126FCA9EAAFBC8A6", + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1", + "5A562D5F7A266BA057B6833F3A83E7A7", + "53905925B4D6F69CE5706896A3C667CE", + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6188C7826173CD59FD243F98C537AD50", + "2C37A2EC74E4B46F13C6FB23D9547DBB", + "590809C6B2A1504E2AE9A79EFDF3AC9A", + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "BD671A3AC4F77A74CA8A68EAD627437D", + "5E243BF70932D1477209FEF6869FDEEA", + "3A16235DA1E02B9148B9288A06EE567E", + "9BE91FA13BDC0D9EB80B35FC73A0029B", + "3D4688621977A9CBD670B50131B8D912", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "00647726F98EE835006D60B12455866D", + "8F3A81EAB68F709E82006205380AC723", + "ECEB33208BCDDC20908183BF249555AD", + "906F0A0AB4330CB7C3A75DA8764703F4", + "A3B39D0211921BC5581AB03193860970", + "2BF7D398C0BFD1F4D976C9F82343DE51", + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + -1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "0BF7A88573F1B5FCC2E8978A6D94CE2B", + "CBC2A69A6A94CFADE9E4077F5B655B6E", + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "3A55AE005B07C55283410798C0FBE47F", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + -1, + -1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "What is the significance of the length of telomeres?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "61a8e2c6-249c-40b8-a866-134f3a893e4a": { + "task_id": [ + "F6FC3B8BBCE2BA90D0EF2C9532DE3F84", + "0F76F85FB406BF74022084C5866C942D", + "C4FEDD378CD138B141464832D021624B", + "ED89B73DC42AD2ADA03B7C014009A551", + "21CB24A2A589173F1E50ADA5DD6165EC" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "FE7363764A44969E28C9562A3948143B", + "F456694025B9C98AA9E4246820D5909A", + "E6C75917249BB8C0810B0E709D6FDD0A", + "8FA337BF315CFA09716800E096EA8A06", + "D68A2086596023BDD8C01023B03FA89D", + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + } +] diff --git a/gnqa/paper1_eval/src/data/ratings/out.json.2 b/gnqa/paper1_eval/src/data/ratings/out.json.2 new file mode 100644 index 0000000..ba96010 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/out.json.2 @@ -0,0 +1,1444 @@ +[ + { + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "F26EEA55FF8FFCB0B9E3E46688C276DA", + "5674818C9037B8A93EDCF35CEF97E33C", + "106C5DDDE1338BE1CC8AE04CEAE3EF62", + "2D8205C79915FF9CEB8DECCE51E6E473", + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "A55310A1C19732792B5894A2DB5E8596", + "2EDD65822CDA4669FCAD678288C2E3E9", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4", + "FBBFB0D391778CDE9536F742300C1809", + "C8E21233058E1DEE05CD619ACBF49E42", + "38A8ED57882E2E250B447F93243A8CD5", + "5D3CA84D2DCE010AF63188665193B195", + "AB6286D0D8E9196BA0EE1F1CF90536AD" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 0, + -1, + 1, + 1, + -1 + ], + "answer": [ + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits." + ], + "query": [ + "genetics", + "genetics", + "genetics", + "List as many studies as you can that include rapamycin.", + "genetics", + "what is bioinformatics", + "genetics", + "genetics", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in the aging process", + "List as many studies as you can that include rapamycin.", + "List as many studies as you can that include rapamycin.", + "List as many studies as you can that include rapamycin.", + "genetics" + ] + } + }, + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "32CE1E54032485B73B5968395B3D3538", + "59CCE2D70104154865218876DD53D049", + "0AE973110158192E0D1F50E1D79764FB", + "33749EB09AAC7AD0404C8E3E584B98D2", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "9957BCF2957D9BD083EDCF8B60C0E4DE", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "d2f71149-a0d5-4666-95d8-15c82543243b": { + "task_id": [ + "7CA65F6B192BE7896422284B962CF5C3" + ], + "weight": [ + 1 + ], + "answer": [ + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits." + ], + "query": [ + "genetics" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "19DC9E909DDE6D9CDB3E86D1069F5A69", + "F2843EA2D5A239D022186329C8D5D8EF", + "4E1F0C2E792BAF0BA349326375D3EE6E", + "FA8ADB009A499F51B0533FDCB72CB29E", + "38BD5864A7928C6DBCA1D844327F3A19", + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + -1, + 1, + 1, + -1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.", + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "B89A904E71CF7F16126FCA9EAAFBC8A6", + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1", + "5A562D5F7A266BA057B6833F3A83E7A7", + "53905925B4D6F69CE5706896A3C667CE", + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6188C7826173CD59FD243F98C537AD50", + "2C37A2EC74E4B46F13C6FB23D9547DBB", + "590809C6B2A1504E2AE9A79EFDF3AC9A", + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "BD671A3AC4F77A74CA8A68EAD627437D", + "5E243BF70932D1477209FEF6869FDEEA", + "3A16235DA1E02B9148B9288A06EE567E", + "9BE91FA13BDC0D9EB80B35FC73A0029B", + "3D4688621977A9CBD670B50131B8D912", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "00647726F98EE835006D60B12455866D", + "8F3A81EAB68F709E82006205380AC723", + "ECEB33208BCDDC20908183BF249555AD", + "906F0A0AB4330CB7C3A75DA8764703F4", + "A3B39D0211921BC5581AB03193860970", + "2BF7D398C0BFD1F4D976C9F82343DE51", + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + -1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "0BF7A88573F1B5FCC2E8978A6D94CE2B", + "CBC2A69A6A94CFADE9E4077F5B655B6E", + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "3A55AE005B07C55283410798C0FBE47F", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + -1, + -1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "What is the significance of the length of telomeres?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "61a8e2c6-249c-40b8-a866-134f3a893e4a": { + "task_id": [ + "F6FC3B8BBCE2BA90D0EF2C9532DE3F84", + "0F76F85FB406BF74022084C5866C942D", + "C4FEDD378CD138B141464832D021624B", + "ED89B73DC42AD2ADA03B7C014009A551", + "21CB24A2A589173F1E50ADA5DD6165EC" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "FE7363764A44969E28C9562A3948143B", + "F456694025B9C98AA9E4246820D5909A", + "E6C75917249BB8C0810B0E709D6FDD0A", + "8FA337BF315CFA09716800E096EA8A06", + "D68A2086596023BDD8C01023B03FA89D", + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + } +], +[ + { + "b4601142-3b57-4d5b-9b55-80bdf0ea4599": { + "task_id": [ + "F26EEA55FF8FFCB0B9E3E46688C276DA", + "5674818C9037B8A93EDCF35CEF97E33C", + "106C5DDDE1338BE1CC8AE04CEAE3EF62", + "2D8205C79915FF9CEB8DECCE51E6E473", + "370380F3A38AC4A788463D14E0EC673A", + "1E0DA0931F4E3A8C2893353CCA114B10", + "A55310A1C19732792B5894A2DB5E8596", + "2EDD65822CDA4669FCAD678288C2E3E9", + "DA98AC2EA5D1F776D3F04FCBC7F01339", + "117299AD06C2B147F49E9C9BC036CEA4", + "FBBFB0D391778CDE9536F742300C1809", + "C8E21233058E1DEE05CD619ACBF49E42", + "38A8ED57882E2E250B447F93243A8CD5", + "5D3CA84D2DCE010AF63188665193B195", + "AB6286D0D8E9196BA0EE1F1CF90536AD" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 0, + -1, + 1, + 1, + -1 + ], + "answer": [ + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Bioinformatics is a field that deals with the application of computer systems to the understanding and organization of biological data. It involves the use of computational tools to gather, store, analyze and integrate biological and genetic information. This can then be used for gene-based drug discovery and development, prediction of protein function from sequence and structural information, and analysis of genomic data.", + "Genetics plays a significant role in various aspects of human life and health, including aging, weight loss, bone traits, physical activity levels, and susceptibility to diseases. Genetic factors can influence lifespan, with certain genes linked to conditions like Alzheimer's disease, heart disease, and cancer. Studies have also shown that genetics can affect the magnitude of weight loss and physical activity levels. In addition, genetics can impact bone traits and the response to different diets. Research on animal models has further demonstrated the influence of genetics on exercise capacity and responses to training regimens.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "1. Study by Kaeberlein (2013b) on the use of Rapamycin in preventing organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. Study by Mannick et al. (2014) on the use of Rapamycin derivative RAD001 in improving age-associated decline in immune function in elderly people.\n3. Study by Yi et al. (2014) on the use of Rapamycin in improving outcomes in a glycogen storage disease model.\n4. Study by Paoloni et al. (2010) on the use of Rapamycin in veterinary clinical trials for osteosarcoma.\n5. Study by Kaeberlein (2015) on the use of Rapamycin in assessing side effects and effects on age-associated cardiac function in elderly dogs.\n6. Study by Meric-Bernstam and Gonzalez-Angulo (2009) on the use of Rapamycin in the context of cancer.\n7. Study by Larson et al. (2016) on the use of Rapamycin in determining initial dosing regimens in healthy dogs.\n8. Study by Dai et al. (2014) and Flynn et al. (2013) on the use of Rapamycin in improving cardiac function in laboratory mice.\n9. Study by Johnson et al. (2015) on the use of Rapamycin in improving health and reducing mortality in middle-aged companion dogs.\n10. Study by Chen et al. (2009) on the use of Rapamycin in delaying aging in mice.\n11. Study by Augustine et al. (2007) and de Oliveira et al. (2011) on the side effects of Rapamycin.\n12. Study by Lamming et al. (2012) on the possible side effects of Rapamycin on glucose homeostasis.\n13. Study on the use of Rapamycin as a CRM due to its modulating properties over proteostasis.\n14. Study by Johnson et al. (2013) on the use of Rapamycin in extending lifespan and delaying age-related functional declines in rodents.", + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits." + ], + "query": [ + "genetics", + "genetics", + "genetics", + "List as many studies as you can that include rapamycin.", + "genetics", + "what is bioinformatics", + "genetics", + "genetics", + "which genes are involved in the aging process", + "what causes the aging process", + "which genes are involved in the aging process", + "List as many studies as you can that include rapamycin.", + "List as many studies as you can that include rapamycin.", + "List as many studies as you can that include rapamycin.", + "genetics" + ] + } + }, + { + "e031ac19-fec1-4d97-a037-cd39d3817c54": { + "task_id": [ + "849E78D8214245F8E8167E78C01BEE60", + "2C477A3C76794C27A1FBBF437CFF75EE", + "CAD6C6C2AB42AA66BFDD65F0F11932B2", + "78A0CD7E12AFEF6865583142603EE039", + "33FC2CC0F61BA22E4D095586B95703BD", + "59E2406798D265A3CB466B766683E63C" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "1. A study by Kaeberlein in 2013b on the use of rapamycin to prevent organ transplant rejection, some forms of cancer, and restenosis in cardiac stents.\n2. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to improve age-associated decline in immune function in healthy elderly people.\n3. A study by Yi et al. in 2014 on the use of rapamycin in dogs to improve outcomes in a glycogen storage disease model.\n4. A study by Paoloni et al. in 2010 on the use of rapamycin in veterinary clinical trials as a treatment for osteosarcoma.\n5. A study by Kaeberlein in 2015 on the use of rapamycin in a veterinary clinical trial to assess side effects and effects on age-associated cardiac function in healthy elderly dogs.\n6. A study by Johnson et al. in 2013 on the use of rapamycin as a pharmacological intervention for extending lifespan and delaying age-related functional declines in rodents.\n7. A study by Augustine et al. in 2007 and de Oliveira et al. in 2011 on the side effects of rapamycin.\n8. A study by Lamming et al. in 2012 on the possible exception of impaired glucose homeostasis as a side effect of rapamycin.\n9. A study by Larson et al. in 2016 on the pharmacokinetic analysis of rapamycin treatment in healthy dogs.\n10. A study by Dai et al. in 2014 and Flynn et al. in 2013 on the improvements in cardiac function in aged dogs and mice after rapamycin treatment.\n11. A study by Johnson et al. in 2015 on the beneficial impacts of rapamycin on multiple age-related phenotypes in aging mice.\n12. A study by Chen et al. in 2009 on the effects of rapamycin on the aged immune system in elderly mice.\n13. A study by Mannick et al. in 2014 on the use of rapamycin derivative RAD001 to enhance the function of the aged immune system in humans.", + "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "The most cited environmental factor for the onset of asthma is living in an urban area, particularly in low-income settings. Other factors include exposure to air pollution, toxins in food and drink, and aerosols, especially during the rainy season.", + "DNA extraction from flora or fauna involves several steps. For flora, a common method is the mixed alkyl trimethyl ammonium bromide (MATAB) procedure. Plant material is ground and incubated in a pre-warmed extraction buffer, then purified with chloroform:isoamylalcohol, and DNA extracts are precipitated with isopropanol. For fauna, DNA can be extracted from various samples such as blood, tissue, or even stool. A common method involves cell rupture in the presence of EDTA to prevent DNA fragmentation, followed by enzymatic digestion of cell walls, solubilization of the cell membrane, and purification using phenol-chloroform extraction and ethanol precipitation. In both cases, the extracted DNA is usually stored at -20 C until use." + ], + "query": [ + "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "List as many studies as you can that include rapamycin.", + "Why is it so diffuclut to map gene loci that control aging in humans?", + "What is apoptosis?", + "What is the most cited environmental factor for the onset of asthma?", + "How would one extract the DNA, from say, flora or fauna?" + ] + } + }, + { + "6365589e-a47b-4fa8-a53c-88cb5ee1a991": { + "task_id": [ + "EC2BFCD8E06079A3E595114822D2A44D", + "59AA8E8D7A97CFF47C6CFD65629F29F9", + "C58CFF41F7422B321DF88A110E278FD5", + "BCD1175CCB27FBA1E6F9D7670B17E527", + "5341FE6588C6175BC8A688A483928BC0", + "42847DE50D50E6A9B26ED0B03CFD160E", + "12BEAFA9366519672FC8B06959FB2DAF", + "64FEC152131BC6502E15EA6A6348D70B", + "3F9EDFE9A0222EA70459EC8985F134C4", + "A010490B55F739DF95BB82DF2B0F5AA3", + "32CE1E54032485B73B5968395B3D3538", + "59CCE2D70104154865218876DD53D049", + "0AE973110158192E0D1F50E1D79764FB", + "33749EB09AAC7AD0404C8E3E584B98D2", + "471A145E9CA1E517E462499ABCA8EA2D", + "19EBC6CA7E425D0C1279475BD0B411B6", + "9CFE932D7898C83E473E590BC77B4FCB" + ], + "weight": [ + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "The potential benefits of gene editing technologies like CRISPR-Cas9 include precise modification of DNA sequences, which can be used to alter gene function, treat genetic diseases, improve crop species, and advance biomedical research. It can also be used for functional screening in drug development and personalized medicine. However, there are risks and limitations associated with this technology. These include off-target effects or unintended modifications, which could potentially lead to harmful consequences. There's also the risk of triggering an immune response, and the potential for wide-ranging deletions or recombination events. Ethical concerns also arise, particularly in the context of editing human genomes.", + "Epigenetics influences gene expression without changing the underlying DNA sequence through mechanisms such as DNA methylation, histone modifications, and chromatin remodeling. These processes can alter the structure of the DNA and its accessibility to transcription factors, thereby regulating gene expression. For instance, DNA methylation typically represses gene expression, while histone modifications can either enhance or repress gene expression depending on the specific modification. These changes can be heritable and are influenced by environmental and lifestyle factors.", + "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "DNA replication is a process where the DNA molecule creates two identical copies of itself. This process begins with the separation of the two strands of the mother cell DNA. New nucleotides are then assembled to form two double helices identical to the original one. This is facilitated by the base pairing rules where adenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine (G). This ensures that each daughter cell receives an exact copy of the DNA. The replication process is crucial during cell division as it allows for the accurate transmission of genetic information from one generation of cells to the next.", + "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text suggests using online bioinformatics resources such as Ensembl, UCSC Human Genome Browser, and others for research and data analysis. It doesn't specifically mention adding books or web resources to a system, but refers to utilizing these online tools and databases for information retrieval and bioinformatic analysis.", + "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute that provides a database for genome data. Launched in 1999, it was the first to provide a window on the draft genome, curating the results of computational analyses. It contains automatically annotated genomes and integrates data from a wide range of biological research sources. Ensembl also provides tools for data retrieval and analysis, and it includes quality checks for genetic variants in its variation pipeline." + ], + "query": [ + "What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", + "How does epigenetics inluence gene expression without changing the underlying DNA sequence?", + "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", + "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", + "what is ensembl?" + ] + } + }, + { + "d8abfb12-9a11-400a-8cd0-0a436602581c": { + "task_id": [ + "02A94D1056FDA77BDA9AC6CFDE0D5FC6", + "96B873A13E3B386E38940EF6ECA90D4A", + "F4DA6C97230E89C0226B1433532A16D9", + "2F8796A8C3DC633F00DB901C9BA396DA", + "9957BCF2957D9BD083EDCF8B60C0E4DE", + "DEE6D385D1B01B4155AA4ABE59515893", + "9309F248E5933718BFB625E4EF2D3E42", + "10ABD2210053119B18D94F1FE266E73E" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + -1, + 1, + -1 + ], + "answer": [ + "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in the CCL11 gene is likely responsible for this association. Additionally, the APOE gene is a strong genetic risk factor for late-onset Alzheimer's, which typically occurs in individuals aged 65 and older. Other genes associated with Alzheimer's include APP, PSEN1, and PSEN2 for early-onset Alzheimer's, and CR1, BIN1, CLU for late-onset Alzheimer's. These genetic factors are believed to interact with environmental components and contribute to the complex etiology of these aging-associated neurodegenerative diseases.", + "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset of Alzheimer's. A specific variant in CCL11 is probably responsible for this association. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset and late-onset forms of Alzheimer's, which are more common in older individuals. The APOE gene, specifically the 4 allele, is a significant genetic risk factor for late-onset Alzheimer's. Genome-wide studies have also identified several genetic susceptibility factors for age-related macular degeneration (AMD), another aging-associated neurodegenerative disease.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "In human centromeres, recombination involves the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Despite this, mice lacking CENP-B are viable and fertile. This suggests that recombination in human centromeres involves complex interactions between specific DNA motifs and proteins.", + "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "The text mentions the application of site-specific recombinase technology, which allows for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. This suggests that recombination, a process that can cause these types of genetic changes, is a significant aspect of the human genome." + ], + "query": [ + "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", + "Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", + "What about recombination in human centromeres?", + "What about recombination in human centromeres?", + "How does recombination work in human centromeres?", + "How many types of diabetes exist?", + "What about recombination in the human genome?" + ] + } + }, + { + "d2f71149-a0d5-4666-95d8-15c82543243b": { + "task_id": [ + "7CA65F6B192BE7896422284B962CF5C3" + ], + "weight": [ + 1 + ], + "answer": [ + "Genetics plays a significant role in aging, lifespan, and various age-related conditions such as type 2 diabetes, cardiovascular disease, and Alzheimer's disease. Genetic factors also influence weight loss, physical activity levels, and bone traits. Research has identified specific genes and DNA regions that affect lifespan and susceptibility to certain diseases. Studies on animal models have further demonstrated the impact of genetics on aging and metabolic traits." + ], + "query": [ + "genetics" + ] + } + }, + { + "6ee46240-38bf-4035-b9a8-0d72e29401b5": { + "task_id": [ + "C52A9690417093A861C669A0753689BD", + "93DE2EF005059DFEA5A7FBBA3BD17D03", + "477FC54178046FE98BF97FAAC5FE167F", + "6B80ECC5F657EB7CBDE69D411A30D3EA", + "2DE25ABD7E487B80D0C489319640EACC", + "6498ED71891B79908B2E383D9AA5BAC5", + "B2F5CB7BCD9A827D3A6E0152C030C4B4", + "72FBC4F382B6502EAF41BD6682E63A2D", + "02C953165B9CA94E273DD4A04301C89F" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association in a genetic study.\n2. Use bioinformatics to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if one is identified to play a role in a disease model.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Understand the genome structure and content to provide context for subsequent discussions.\n6. Utilize genotyping and sequencing technologies to produce, store, and analyze the sequence data.\n7. Use the genome sequence as a framework for integration of genetic and biological data.\n8. Analyze short-read, whole genome, DNA sequences.\n9. Perform comparative analysis of the genome sequences from members of a family to define sequencing errors and genetic heterozygosity.\n10. Track sequence changes/inconsistencies in inheritance from parent to offspring.", + "The length of telomeres is significant as it is associated with aging and disease. Shorter telomeres are considered a sign of advanced age and have been linked to age-related diseases, mortality, and higher risk of heart disease and infection-related death. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and thus, accelerated aging.", + "The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.", + "Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.", + "Several genetic factors influence aging in humans. These include genes such as the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, and the exonuclease 1 (EXO1) gene. Other factors include the insulin-IGF1 signaling pathway, the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis, and the heatshock proteins and heat-shock factors. Additionally, genetic variants within genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old age in humans.", + "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "Genetics plays a significant role in the emergence of diabetes. Certain forms of the disease result from mutations in a single gene, while others are multifactorial in origin. For example, monogenic forms of diabetes, which account for approximately 5% of cases, are caused by mutations in genes encoding insulin, the insulin receptor, and other factors. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus and about 50 other genes contribute to the genetic risk. These genes modulate immune regulation, viral responses, and responses to environmental signals. Genetic susceptibility to type 1 diabetes is also determined by genes related to immune function. Both type 1 and type 2 diabetes are polygenic diseases where many common variants contribute to overall disease risk." + ], + "query": [ + "Create a how to guide for genetic sequencing ", + "What is the significance of the length of telomeres? ", + "Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", + "Why is genetic tracing matrilineal rather than patrilineal? ", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what type of dataset is useful for qtl mapping analysis in genenetwork2? ", + "What genetic factors influence aging in humans? ", + "Is there a direct association between aging and susceptibility to having diabetes?", + "How does genetics influence the emergency of diabetes? " + ] + } + }, + { + "e8855be7-59fd-4224-90ad-575e7158c34c": { + "task_id": [ + "19DC9E909DDE6D9CDB3E86D1069F5A69", + "F2843EA2D5A239D022186329C8D5D8EF", + "4E1F0C2E792BAF0BA349326375D3EE6E", + "FA8ADB009A499F51B0533FDCB72CB29E", + "38BD5864A7928C6DBCA1D844327F3A19", + "2272C482CC247E746D15C9F55EDD8BCE", + "C6C7CEF19CE7C27CF4BC6906259CDDF9", + "B4BB83EB5D5C5C042E07173119046A13", + "D88EF655762CE3D524A7A1EEA3FA16ED", + "245DD8093F5D16F44C2AD7618245086C" + ], + "weight": [ + -1, + 1, + 1, + -1, + -1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of QTLs in the provided text. However, in general, X and Y chromosomes can be differentiated based on their size, gene content, and specific sequences. In the context of QTL mapping, the process would involve identifying the genomic regions affecting a trait, which could be located on any chromosome, including X or Y. The specific methods for tracing and determining QTLs would be the same for all chromosomes.", + "The genes associated with diabetes include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, GIGYF1, HNF1A, TNRC6B, and G6PC2.", + "Several genes are associated with aging. These include NAP1L4, which is involved in chromatin structure and increases with age in skin tissue. Other genes include GAB2, linked to late-onset Alzheimer's disease, and QKI, linked to coronary heart disease and successful aging. Genes such as Lamp2, Fas, and Ghr also show significant co-expression with aging. Other genes involved in aging include those in the IGF-1 and vitamin D pathways, estrogen metabolism pathway genes, and SIR2 genes. Genes like APOE, LDLR, CDKN2B, and RBM38 influence lifespan in model organisms. Genes involved in DNA damage response, antioxidant properties, and protein misfolding also show age-related changes. The gene Cd63 is highly connected in aging-associated gene sets. In muscle aging, genes involved in proteasomal and mitochondrial functions show altered expression. The insulin/insulin-like growth factor 1 (IGF1) signaling pathway also modulates aging.", + "The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching.", + "The statistical approaches for QTLs (Quantitative Trait Loci) analysis mentioned in the text include regression analysis, permutation tests, Pearson's correlation, and analysis of variance (ANOVA). QTL mapping analysis was performed using the WebQTL module of GeneNetwork. The GEMMA method was also used for QTL mapping on all chromosomes. Additionally, quantitative trait association for SNPs was performed using a series of ANOVA tests.", + "1. Carefully select the populations for the study, ensuring a large number of cases.\n2. Employ centralized SNP genotyping, data coordination, and control centers for quality control checks and standardized annotation.\n3. Conduct SNP-level association tests using methods like the likelihood ratio test (LRT) to obtain SNP level summary statistics.\n4. Perform a gene-level GWAS on the summary statistics using a hierarchically structured prior that incorporates the SNP-gene hierarchical structure.\n5. Use methods like meta-analysis to combine the results of multiple surveys and replication studies on promising variants.\n6. Incorporate existing information about the SNPs into the analysis, such as prior information about linkage or association evidence.\n7. Use tools like ePheWAS for applications in human cohorts.\n8. Share GWAS results to enable further understanding and analyses by other researchers.\n9. Deposit data in a public repository for wider scientific community access.\n10. Follow up on SNPs that merit further replication analysis.\n11. Use post-GWAS tools to make biological sense of the statistical genetic associations.\n12. Finally, report summarization and visualization of the GWAS results." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "what genes are associated with diabetes?", + "what genes are associated with aging?", + "what are the bioinformatics tools for QTLs analysis?", + "what are the statistical approaches for qtls analysis?", + "Create a how-to guide for GWAS analysis?" + ] + } + }, + { + "0c4a1c25-539f-453a-b7f6-915ab462cf0b": { + "task_id": [ + "B89A904E71CF7F16126FCA9EAAFBC8A6", + "3EC47C56606B02F00CF2449AB311365C", + "CDFC418BD568E839C09656C57808ADA1", + "5DEB102510F48D0BF9C278DC895A8BD1", + "5A562D5F7A266BA057B6833F3A83E7A7", + "53905925B4D6F69CE5706896A3C667CE", + "C0015BEE5FE41769A65126B79BB1E40D" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + 1, + -1 + ], + "answer": [ + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "The genes associated with aging in humans are APOE, FOXO3A, and to some extent, AKT1.", + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "1. Initiate a project for sequencing, like the '1000 genomes' project or the rainbow trout project, involving collaborators from various institutions.\n2. Choose a sequencing platform, such as Illumina, and prepare the sample for sequencing.\n3. Use bioinformatics tools to analyze the sequence data. Tools like Seqnature for RNA-seq or Acembly for predicting gene structures can be used.\n4. Construct an individualized pseudogenome for the sample using prior knowledge of variant sites in the sample's genome.\n5. Use online resources like NCBI Map Viewer for graphical depictions of genetic and physical maps and to locate genes, markers, and SNPs on the assembled sequences.\n6. Use websites offering annotation of the draft genome for various analyses such as gene predictions and similarity searches.\n7. Monitor the progress of sequencing online and aim for a resolution of selective constraint down to a segment length of eight nucleotides.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining Quantitative Trait Loci (QTLs). However, it discusses the process of identifying QTLs, which involves using mapping data from crosses involving more than two inbred strains and sequence variants in the progenitor strains. The strain distribution pattern in the progenitor strains is tested for consistency with the observed genetic effect of the QTL. This process helps assign a probability that any sequence variant is a Quantitative Trait Nucleotide (QTN). The text does not mention the specific roles of X and Y DNA in this process." + ], + "query": [ + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Which genes are associated with aging in human ", + "Create a how-to guide for genetic sequencing", + "Create a guide for genetic sequencing", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + ] + } + }, + { + "415d39c0-28b8-4711-8d20-081082660f35": { + "task_id": [ + "6188C7826173CD59FD243F98C537AD50", + "2C37A2EC74E4B46F13C6FB23D9547DBB", + "590809C6B2A1504E2AE9A79EFDF3AC9A", + "6DBC070B2E4DC2FE8036E5BA7480B755", + "5594EA025D9631328071B6A1A7EF1375", + "AB589D2E046B211A7486A6C4BD4ECFB4", + "8FFF9DCC307B8DBF2C8485637F2ABEF4", + "C6B9A982C9283DE065A3371F1264095C" + ], + "weight": [ + -1, + 1, + 1, + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "QTL mapping is a statistical method used to identify regions in the genome that correlate with variation in a phenotype. It has high power, meaning it can detect a QTL, but it does not precisely identify which of the many genes within the QTL is causal. On the other hand, GWAS (Genome-Wide Association Studies) is a method used to identify genetic variants associated with complex traits. It has low power, requiring large numbers of individuals, but it has high precision, often identifying smaller candidate regions.", + "To determine which gene in your QTL is causal for the trait, you can start by narrowing down the list of candidate genes within the QTL. This can be done by performing a strain survey, using genetically engineered mice to alter the expression of a candidate gene, or using comparative genomics to narrow down the QTL to a region containing only a few genes. You can then investigate whether the expression of these genes correlates with the phenotype(s) of interest. If a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest, it is considered a good candidate. Network analyses can also be used to build up more evidence for which gene is causal. Finally, you can confirm the candidate genes by complementation of a QTL, which can be achieved in several ways, including transgenic complementation.", + "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "In diabetic conditions, the expression of several key gluconeogenic enzymes in the liver is increased despite higher than normal circulating levels of plasma insulin. Additionally, the activities of insulin-dependent enzymes such as glueokinase, glucose 6-phosphate dehydrogenase, citrate lyase, malic enzyme, and acetyl-CoA synthetase are increased. However, the gene Slc2a2 also shows changes in the liver. Some studies also show that liver cells in diabetic mice are hypertrophied and filled with fat droplets.", + "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "What is the difference between QTL mapping and GWAS?", + "How do I determine which gene in my QTL is causal for the trait?", + "Which mouse genes have been associated with longevity?", + "How is gene expression in the liver affected by diabetes?", + "Why do males have two Y chromosomes and females only one?" + ] + } + }, + { + "c5e42006-ddba-4b69-b1d1-61b0dceaec86": { + "task_id": [ + "F9F7EA3DC28534B161ED70DB401C7D11", + "4A06F8DF54C82D90E02F81D0E1E8B08A", + "BA6A505E62A0529DB883D036CBC1FD92" + ], + "weight": [ + 1, + -1, + 1 + ], + "answer": [ + "1. Choose the appropriate sequencing method: Depending on the specific requirements of your project, you may choose from various sequencing methods such as whole-genome sequencing, exome sequencing, or targeted sequencing.\n\n2. Prepare the sample: Extract the DNA or RNA from your sample. The quality and quantity of the extracted genetic material are crucial for successful sequencing.\n\n3. Library preparation: Prepare a sequencing library. This involves fragmenting the DNA or RNA into smaller pieces, then attaching specific adapters to the ends of these fragments.\n\n4. Sequencing: Use a sequencing platform to read the genetic information. Platforms like Illumina generate massive amounts of short reads.\n\n5. Data analysis: Analyze the sequencing data. This involves aligning the reads to a reference genome, identifying variants, and interpreting the results. Bioinformatics tools and databases such as UCSC Genome Browser, Ensembl, GenBank, MUSCLE, MAFFT, and PRANK can be used for these steps.\n\n6. Validation: Validate the findings using other techniques such as Sanger sequencing or qPCR.\n\n7. Report: Compile the results into a comprehensive report, including methods, findings, and interpretations.\n\nRemember, each step requires careful planning and execution, and the specific details may vary depending on the sequencing method and the research question.", + "1. Start with an initial linkage or association study to identify potential genes of interest.\n2. Use bioinformatics tools to analyze the genomic sequence, which is a common thread through every stage.\n3. Establish true orthology between genes in different species, especially if a gene is identified to play a role in a disease model. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n4. Apply the approaches to sequence data from any collection of organisms, with an emphasis on human genetics.\n5. Present an overview of genome structure and content to provide a context for subsequent discussions.\n6. Use DNA sequencing and other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of diseases.\n7. Analyze the sequence data using advanced technologies and bioinformatics.\n8. Finally, interpret the results of the genetic experiment, which can be a challenge on any level.", + "The length of telomeres is significant as it is associated with aging, age-related diseases, and mortality. Shorter telomeres are generally considered a sign of advanced age and have been linked to higher risks of heart disease, infection-related death, and other diseases. Longer telomeres, on the other hand, are associated with protection from age-related diseases, better cognitive function, and healthier aging. Telomere length can also potentially influence life expectancy, with some studies suggesting that longer telomeres decrease the risk of dying." + ], + "query": [ + " Create a how-to guide for genetic sequencing", + " Create a how-to guide for genetic sequencing.", + " What is the significance of the length of telomeres?" + ] + } + }, + { + "16aaa58b-ba19-48f9-af5b-db7d2ad98861": { + "task_id": [ + "BD671A3AC4F77A74CA8A68EAD627437D", + "5E243BF70932D1477209FEF6869FDEEA", + "3A16235DA1E02B9148B9288A06EE567E", + "9BE91FA13BDC0D9EB80B35FC73A0029B", + "3D4688621977A9CBD670B50131B8D912", + "E94FFD042BB146E8A429200590A6792D", + "C319861B08978CF5F7E6F0CD3A517A81" + ], + "weight": [ + -1, + 1, + -1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Traits are passed onto the resulting lifeform through the process of meiosis. Each parent contributes 23 unpaired chromosomes, which combine to form the full complement of 23 chromosome pairs in the zygote. These chromosomes carry the genetic material that determines the traits of the offspring. The process of recombination or crossing over during meiosis also contributes to genetic variation in the offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The text does not provide specific information on how to differentiate between X and Y DNA in the context of DNA tracing and determining QTLs.", + "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", + "How can I add a new species to the GeneNetwork database?", + "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + ] + } + }, + { + "316fb5cc-dcca-4d76-bd42-b2010f11799c": { + "task_id": [ + "081B2DB92FD09DEBEF28ADBBDE7199D2", + "68EF3BE5EC2106766CA9CC700135E2FA", + "8590501C57DC5C321AB5E1036F233027" + ], + "weight": [ + 1, + 0, + 1 + ], + "answer": [ + "Several genetic factors are associated with aging. These include allele variants, polymorphisms (SNPs), and specific genes such as the microsomal transfer protein (MTP), PKA-anchoring protein (AKAP2) gene, FOXO3A, APOE, and genes in the HLA-DQA1/DRB1 and LPA regions. Other genes associated with aging are those highly expressed in the brain like HECW2, HIP1, BIN2, GRIA1, and genes involved in neural development and function like KCNQ4, LMO4, GRIA1, NETO1. Genes involved in autophagy like ATG4C are also associated with aging.", + "Genomics can be used to better understand the nutritional factors of diabetes through the study of nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This field, known as nutritional genomics, can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. It can also help identify gene variants that interact with specific nutrients, potentially influencing diabetes risk.", + "Genomics provides a comprehensive understanding of the genetic factors contributing to diabetes, a global pandemic. Nutritional genomics studies focus on the interaction between dietary patterns and genetic variations, which play a crucial role in the development and treatment of diabetes. This approach aids in the development of tailored diets, maximizing the use of nutrients and functional ingredients in food to prevent and delay diabetes and its complications. The integration of genomic data with advanced statistical and computational methods can facilitate a better understanding of gene-environment interactions in diabetes manifestation. Furthermore, the identification of novel genetic factors associated with diabetes through advanced genetic techniques can contribute to personalized diabetes management. Therefore, genomics holds significant potential in understanding the nutritional factors of diabetes." + ], + "query": [ + "what genetic factor are associated with aging", + "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", + "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + ] + } + }, + { + "545b58e2-5033-4c96-afe3-96f90e2343af": { + "task_id": [ + "00647726F98EE835006D60B12455866D", + "8F3A81EAB68F709E82006205380AC723", + "ECEB33208BCDDC20908183BF249555AD", + "906F0A0AB4330CB7C3A75DA8764703F4", + "A3B39D0211921BC5581AB03193860970", + "2BF7D398C0BFD1F4D976C9F82343DE51", + "FCFCE5BBA2A8B3D8818890B9D2308C5A", + "E3FFB15A9901BD8DB87B0F09D335BEA0", + "38797E46211127E5C7175E707D40325B", + "CD1F7EAE0FDC758A8167118927ADFE71", + "FFA6EADA5502933C0C30C9D16DCAA073", + "00BE70B5D71A5926E56942909C8B2A92" + ], + "weight": [ + 1, + 1, + 1, + -1, + 1, + -1, + 1, + 1, + 1, + 1, + -1, + 1 + ], + "answer": [ + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The genes typically associated with diabetes in QTL analyses include TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, CDKN2A-CDKN2B, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, KCNQ1, PPARG, and KCNJ11.", + "The genes typically associated with early aging are APOE and FOXO3A.", + "To generate a linkage or association mapping study in mice to understand aging, you would first need to select appropriate mouse strains. You could use inbred strains like C57BL/6J (B6) and DBA/2J (D2), or a recombinant inbred strain like BXD. You would then breed these mice, possibly creating an F2 generation cross or a backcross. After breeding, you would genotype and phenotype the offspring. For aging studies, you would monitor the mice over their lifespan, noting any changes in health, behavior, or physical characteristics. You could also perform genome-wide association mapping and correlation analyses against existing phenotypic and expression data sets to identify candidate genes involved in age-related decline. Additionally, you could use bioinformatics tools to analyze data and find patterns hinting at a common molecular mechanism. Finally, you would validate your findings using statistical analysis.", + "Yes, the gene TCF7L2 is involved in diabetes. Studies have shown that variants of the TCF7L2 gene are associated with an increased risk of type 2 diabetes (T2D).", + "The background text does not provide information on the involvement of the TCF7L2 gene in any diseases.", + "Confounding factors in diabetes include age, sex, BMI, waist circumference, family history, smoking, hypertension, diet quality, physical inactivity, obesity, prediabetes, metabolic syndrome, exposure to environmental pollutants, and certain genetic factors. Socioeconomic status, psychological stress, and certain lifestyle-related risk factors such as physical inactivity and poor diet are also considered confounding factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing.", + "Create a how-to guide for genetic sequencing.", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "which genes are typically associated with diabetes in QTL analyses?", + "which genes are typically associated with early aging?", + "How do I generate a linkage or association mapping study in mice to understand aging?", + "Is the gene TCF7L2 involved in diabetes?", + "In which diseases is the gene TCF7L2 involved?", + "what are confounding factors in diabetes?" + ] + } + }, + { + "3582a98b-2a9c-45fe-bf06-a0fde4e1be21": { + "task_id": [ + "0BF7A88573F1B5FCC2E8978A6D94CE2B", + "CBC2A69A6A94CFADE9E4077F5B655B6E", + "847F1E1599EECDE92F99B7581728FFE8", + "037BAB6CB2DE7A42AAAA73CE5DA8DB73", + "B2AA6DE557D652A0A660C4E0FAC1124D", + "3A55AE005B07C55283410798C0FBE47F", + "7EC697DE62C0C57E601EC3F5B295DF61", + "0A6673A0B69F0FF9C9657FB797DD1FE2", + "44B088326CD80B4980D810738D88A284", + "D53462CE61F52F7D31BB627998F4D75A" + ], + "weight": [ + -1, + -1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + -1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. These genes are located on the chromosomes that each parent contributes. The process of meiosis ensures that each gamete (sperm or egg) contributes one chromosome from each pair, resulting in the offspring having a full set of 23 chromosome pairs. Additionally, certain parental genetic effects, such as maternal transcripts deposited into the egg prior to fertilization, can influence early embryonic development and result in differences in traits depending on the genotype of the mother.", + "A ribosomal binding site is a specific sequence on a molecule of mRNA (messenger RNA) that the ribosome recognizes and binds to when it's time to make a protein. Think of it like a 'start' sign that tells the ribosome where to begin translating the mRNA into a protein. This process is part of how our bodies use genetic information to build the proteins we need to function.", + "Traits are passed onto the resulting lifeform through the process of meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair in the zygote. This process results in a shuffling of genetic material, known as recombination or crossing over, which is a significant cause of genetic variation among offspring. Additionally, certain traits can be influenced by maternal transcripts deposited into the egg prior to fertilization, leading to differences in early embryonic development depending on the genotype of the mother.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "Sequencing with short reads and long reads refers to the length of DNA segments that are sequenced in one go. Short-read sequencing, like that done by Illumina, generates many small pieces of DNA sequence, typically around 100-150 base pairs long. This method is relatively inexpensive and produces a high volume of data, but can struggle with complex parts of the genome.\n\nOn the other hand, long-read sequencing, like that done by Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT), sequences much longer pieces of DNA, sometimes up to 100,000 base pairs. This can provide more complete information about the genome and can handle complex regions better. However, it tends to have a higher error rate and is more expensive.\n\nIn simple terms, imagine trying to solve a jigsaw puzzle: short-read sequencing gives you many small pieces, which can be harder to put together, especially in complex regions. Long-read sequencing gives you fewer, but much larger pieces, which can make the puzzle easier to solve, but might be more costly and have more mistakes.", + "Using a single linear reference, like a specific genome, can limit the scope of genetic variation we can study. It assumes that all genetic variations align neatly with this reference, which isn't always the case in reality. A pangenome-based reference, on the other hand, includes multiple genomes and thus captures a wider range of genetic variations. This can help us better understand and study the complexity of genetic diversity.", + "Genetic regulation is not only done through DNA elements like promoters, repressors, and activators. It also involves other components such as transcription factors, which are proteins that control the rate of transcription of genetic information from DNA to messenger RNA. Additionally, non-coding RNAs, which do not code for proteins, play a significant role in gene regulation. There are also epigenetic factors, which influence gene expression without changing the DNA sequence. These include chemical modifications to the DNA or proteins associated with it. So, genetic regulation is a complex process involving multiple elements and layers of control.", + "Yes, certain genetic variations have been associated with longer lifespans. For example, variations in the APOE, FOXO3A, and EXO1 genes have been linked to longevity. However, it's important to note that these genes don't guarantee a longer life, as longevity is influenced by a combination of genetic, environmental, and lifestyle factors." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Can you explain what a ribosomal binding site at a high level and make it accessable to a non-expert?", + " Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "What is the significance of the length of telomeres?", + "Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", + "Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", + "Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", + "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + ] + } + }, + { + "61a8e2c6-249c-40b8-a866-134f3a893e4a": { + "task_id": [ + "F6FC3B8BBCE2BA90D0EF2C9532DE3F84", + "0F76F85FB406BF74022084C5866C942D", + "C4FEDD378CD138B141464832D021624B", + "ED89B73DC42AD2ADA03B7C014009A551", + "21CB24A2A589173F1E50ADA5DD6165EC" + ], + "weight": [ + 1, + 1, + -1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?" + ] + } + }, + { + "200a39ba-aacc-40fe-ad11-d9b7123e3e6a": { + "task_id": [ + "FE7363764A44969E28C9562A3948143B", + "F456694025B9C98AA9E4246820D5909A", + "E6C75917249BB8C0810B0E709D6FDD0A", + "8FA337BF315CFA09716800E096EA8A06", + "D68A2086596023BDD8C01023B03FA89D", + "CD3820AA1BD96613F78FDF3CF5C8AB3D", + "A4CE2F2F8E08E5F16C94A1BCF540D881", + "1B8618ADB274F928B3AACAB1C71A927E", + "BF1705D2C26044038FF1483258548167", + "68AB7A78543D5B36206274837824091B", + "055110B765AA502F9AAECE68CEC0DD24" + ], + "weight": [ + 1, + 1, + 1, + 1, + -1, + 1, + 1, + 1, + 1, + 1, + 1 + ], + "answer": [ + "1. Identify the gene or genome of interest for sequencing.\n2. Choose the appropriate sequencing technology, such as next-generation sequencing.\n3. Prepare the sample by extracting DNA and creating a library for sequencing.\n4. Run the sequencing process using the chosen platform.\n5. Analyze the sequencing data using bioinformatics tools. This may include aligning the sequence reads to a reference genome, identifying variants, and interpreting the results.\n6. Validate the results using additional experiments or databases.\n7. Document and share the findings in a suitable format, such as a scientific paper or a database entry.\n8. For further analysis or research, use online resources like the '1000 genomes' project, the Mouse Genome Database, or the UCSC genome browser tutorial.\n9. Stay updated with best practice guidelines for the use of next-generation sequencing applications in genome diagnostics.", + "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "The immune system is closely related to diabetes, particularly Type 1 diabetes, which is an autoimmune disorder. In genetically susceptible individuals, the immune system can be triggered by certain environmental factors to produce islet autoantibodies against pancreatic cells, increasing their risk for Type-1 diabetes. In Type 1 diabetes, the immune system destroys insulin-producing cells in the pancreatic islets of Langerhans, leading to insulin deficiency and hyperglycemia. The balance between regulatory and effector T cells determines disease risk, activation, and progression. Genetic factors also play a role in controlling the immune system and influencing susceptibility to Type 1 diabetes.", + "The genomic variants associated with immune system components and diabetes include variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2. These variants have been reported to affect pancreatic -cell functions. Additionally, variants within the HLA locus and non-HLA genetic loci from published GWAS of European background were found to affect immune phenotypes and function. Variants in 63 independent T1D loci were present in the data, and 13 of these were associated with susceptibility to T1D. Other T1D-associated variants were found in the Immunochip, a large scale genotyping platform.", + "The immune system plays a significant role in the metabolomics of diabetes and associated conditions. Chronic low-grade inflammation and activation of the innate immune system are associated with insulin resistance and -cell dysfunction in type 2 diabetes mellitus (T2DM). The infiltration of macrophages into pancreatic islets accelerates -cell dysfunction. These macrophages secrete chemokines and stimulate immune cell migration, as well as the release of pro-inflammatory cytokines. High blood concentrations of pro-inflammatory cytokines, such as C-reactive protein, interleukin-6 (IL-6), and tumour necrosis factor (TNF), are associated with an increased risk of T2DM. Furthermore, cellular oxidative stress, which induces an inflammatory response, is known as one of the leading causes of insulin resistance and islet -cell dysfunction in T2D.", + "The relationships between traits can be described by four basic models: one-to-one, where one gene gives rise to one trait; one-to-many, where one gene affects many traits (pleiotropy); many-to-one, where many genes affect one trait (polygeny); and many-to-many, where multiple genes interact to influence multiple traits. Additionally, traits can also be related through genetic correlation, where the directions of effect are consistently aligned. Furthermore, traits can be interconnected through complex developmental processes and environmental interactions.", + "Yes, the landscape of QTL and GWAS hits can be used to find relationships between traits. This is done by mapping genome regions to variation in a large number of traits, thereby inferring biological relationships between those traits and connecting them into networks. This approach can help identify the genetic basis of variation in complex traits.", + "Yes, the landscape of QTL and GWAS hits can be used to dissect the role of the immune system in diabetes and its complications. The studies mentioned in the text have identified associations between genetic factors and immune-related mechanisms in diabetes. This includes the identification of pathways and genes that may serve as potential intervention targets. Furthermore, the studies have shown a correlation between immune-cell populations and ex vivo cytokine production in response to various stimulations, suggesting a direct link between genetic variants and immune functionality in diabetes." + ], + "query": [ + "Create a how-to guide for genetic sequencing", + "What is the significance of the length of telomeres?", + "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "Why is genetic tracing matrilineal rather than patrilineal?", + "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "How is the immune system related to diabetes?", + "What are the genomic variants associated with immune system components and diabetes?", + "What is the role of the immune system in the metabolomics of diabetes and associated conditions?", + "What are the different relationship between traits?", + "Can landscape of QTL and GWAS hits be used to find relationships between traits ?", + "Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + ] + } + } +] diff --git a/gnqa/paper1_eval/src/data/ratings/out.tmp b/gnqa/paper1_eval/src/data/ratings/out.tmp new file mode 100644 index 0000000..b409757 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/out.tmp @@ -0,0 +1,93 @@ + Create a how-to guide for genetic sequencing + Create a how-to guide for genetic sequencing. + What is the significance of the length of telomeres? +Create a guide for genetic sequencing +Create a how to guide for genetic sequencing +Create a how-to guide for GWAS analysis? +Create a how-to guide for genetic sequencing +Create a how-to guide for genetic sequencing. +Create a how-to guide for genetic sequencing. +Create a how-to guide for genetic sequencing. +Create a how-to guide for genetic sequencing. +Create a how-to guide for genetic sequencing. +Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging. +Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging. +Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA. +Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division. +Explain the process of finding a genetic marker followed by a quantitative trait loci. +For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system? +How can I add a new species to the GeneNetwork database? +How do I determine which gene in my QTL is causal for the trait? +How does epigenetics inluence gene expression without changing the underlying DNA sequence? +How does genetics influence the emergency of diabetes? +How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs? +How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs? +How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs +How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs? +How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs? +How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs? +How does recombination work in human centromeres? +How is gene expression in the liver affected by diabetes? +How many types of diabetes exist? +How would one extract the DNA, from say, flora or fauna? +Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes? +Is there a direct association between aging and susceptibility to having diabetes? +List as many studies as you can that include rapamycin. +List as many studies as you can that include rapamycin. +List as many studies as you can that include rapamycin. +List as many studies as you can that include rapamycin. +List as many studies as you can that include rapamycin. +Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform? +Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? +Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform? +Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform? +Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform? +Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform? +Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform? +What about recombination in human centromeres? +What about recombination in human centromeres? +What about recombination in the human genome? +What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors? +What are the genetic bases for the varying efficacy of diabetes treatments among individuals? +What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9? +What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9? +What genetic factors influence aging in humans? +What is apoptosis? +What is the difference between QTL mapping and GWAS? +What is the most cited environmental factor for the onset of asthma? +What is the significance of the length of telomeres? +What is the significance of the length of telomeres? +What is the significance of the length of telomeres? +What is the significance of the length of telomeres? +What is the significance of the length of telomeres? +What is the significance of the length of telomeres? +Which genes are associated with aging in human +Which mouse genes have been associated with longevity? +Why do males have two Y chromosomes and females only one? +Why is genetic tracing matrilineal rather than patrilineal? +Why is genetic tracing matrilineal rather than patrilineal? +Why is genetic tracing matrilineal rather than patrilineal? +Why is genetic tracing matrilineal rather than patrilineal? +Why is genetic tracing matrilineal rather than patrilineal? +Why is genetic tracing matrilineal rather than patrilineal? +Why is it so diffuclut to map gene loci that control aging in humans? +genetics +genetics +genetics +genetics +genetics +genetics +genetics +nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets +nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets +what are the bioinformatics tools for QTLs analysis? +what are the statistical approaches for qtls analysis? +what causes the aging process +what genes are associated with aging? +what genes are associated with diabetes? +what genetic factor are associated with aging +what is bioinformatics +what is ensembl? +what type of dataset is useful for qtl mapping analysis in genenetwork2? +which genes are involved in the aging process +which genes are involved in the aging process diff --git a/gnqa/paper1_eval/src/data/ratings/user_queries.txt b/gnqa/paper1_eval/src/data/ratings/user_queries.txt new file mode 100644 index 0000000..4b280a1 --- /dev/null +++ b/gnqa/paper1_eval/src/data/ratings/user_queries.txt @@ -0,0 +1,221 @@ +GENERAL +Create a how-to guide for genetic sequencing. +Create a how-to guide for GWAS analysis? +What is the significance of the length of telomeres? +Create a guide for genetic sequencing +Create a how-to guide for genetic sequencing. +Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division. +Explain the process of finding a genetic marker followed by a quantitative trait loci. +For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system? +How can I add a new species to the GeneNetwork database? +How does epigenetics inluence gene expression without changing the underlying DNA sequence? +How does recombination work in human centromeres? +How would one extract the DNA, from say, flora or fauna? +What is the difference between QTL mapping and GWAS? +What is the most cited environmental factor for the onset of asthma? +what are the bioinformatics tools for QTLs analysis? +what are the statistical approaches for qtls analysis? +what is bioinformatics +What is apoptosis? +GENERAL FOR STUDY +How do I determine which gene in my QTL is causal for the trait? +Why do males have two Y chromosomes and females only one? +what type of dataset is useful for qtl mapping analysis in genenetwork2? +What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors? +What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9? +What about recombination in human centromeres? +What about recombination in the human genome? +How can I add a new species to the GeneNetwork database? +Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA. +what is ensembl? +MyQ -- Create a how to guide for genetic sequencing +MyQ -- What is the significance of the length of telomeres? +MyQ -- Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform? +MyQ -- Why is genetic tracing matrilineal rather than patrilineal? +MyQ -- How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs? +MyQ -- Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging. +MyQ -- List as many studies as you can that include rapamycin. +MyQ -- What are the genetic bases for the varying efficacy of diabetes treatments among individuals? +AGING +Which mouse genes have been associated with longevity? +Is lifespan determined by genetics? +Is there a direct association between aging and susceptibility to having diabetes? +Which genes are associated with aging in human +What genetic factors influence aging in humans? +Why is it so diffuclut to map gene loci that control aging in humans? +what causes the aging process +what genes are associated with aging? +what genetic factor are associated with aging +which genes are involved in the aging process +DIABETES +what genes are associated with diabetes? +nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets +nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets +Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes? +How does genetics influence the emergency of diabetes? +How is gene expression in the liver affected by diabetes? +How many types of diabetes exist? + + + +ADDING ALL June 26th 2024 + +Flavia: + - 26681F93BA485656CF56BD71682E7C77: Which database can I use for genetic, genomic, phenotype, and disease-related data generated from rat research? + - What resources can I use to do pathway analyses? + - What is ensembl? + 92B99DB2F9F6265E7582EB8320E742D6: Which genes give a predisposition to developing T1D? +"genetics", +"genetics", +"genetics", +"List as many studies as you can that include rapamycin.", +"genetics", +"what is bioinformatics", +"genetics", +"genetics", +"which genes are involved in the aging process", +"what causes the aging process", +"which genes are involved in the aging process", +"List as many studies as you can that include rapamycin.", +"List as many studies as you can that include rapamycin.", +"List as many studies as you can that include rapamycin.", +"genetics", +"which genes are involved in aging", +"Create a how-to guide for genetic sequencing.", +"What is the significance of the length of telomeres?", +"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", +"Why is genetic tracing matrilineal rather than patrilineal?", +"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", +"Explain Protective Genetic Factors Against Diabetes in Elderly Populations", +"what genes are involved in the aging process", +"Explain Effect of Lifestyle Modifications on Aging-Associated Diabetes Risk", +"Explain The Role of Longevity Genes in Protecting Against Diabetes", +"What are the types of diabetes" + +"What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", +"List as many studies as you can that include rapamycin.", +"Why is it so diffuclut to map gene loci that control aging in humans?", +"What is apoptosis?", +"What is the most cited environmental factor for the onset of asthma?", +"How would one extract the DNA, from say, flora or fauna?" + +"What are the potential benefits and risk associated with gene editing technologies like CRISPRR-Cas9?", +"How does epigenetics inluence gene expression without changing the underlying DNA sequence?", +"Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", +"What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", +"Create a how-to guide for genetic sequencing.", +"What is the significance of the length of telomeres?", +"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", +"Why is genetic tracing matrilineal rather than patrilineal?", +"Explain the process of DNA replication and how it ensures accurate copying of genetic information during cell division.", +"What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", +"Create a how-to guide for genetic sequencing.", +"What is the significance of the length of telomeres?", +"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", +"Why is genetic tracing matrilineal rather than patrilineal?", +"How does one tell the difference between X and Y DNA, with repsect to DNA tracing and determining QTLs?", +"For text and biological resources, do you mean add some books (on biology stuff) or/and web resources (as ensembl) on your system?", +"what is ensembl?" + +"Explain the process of finding a genetic marker followed by a quantitative trait loci.", +"Describe the genotypes related to Alzheimers and dementia which have commonalities with those for aging.", +"Describe the genotypes related to Alzheimer's and dementia which have commonalities with those for aging.", +"What about recombination in human centromeres?", +"What about recombination in human centromeres?", +"How does recombination work in human centromeres?", +"How many types of diabetes exist?", +"What about recombination in the human genome?" + + +"Create a how to guide for genetic sequencing ", +"What is the significance of the length of telomeres? ", +"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ", +"Why is genetic tracing matrilineal rather than patrilineal? ", +"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", +"what type of dataset is useful for qtl mapping analysis in genenetwork2? ", +"What genetic factors influence aging in humans? ", +"Is there a direct association between aging and susceptibility to having diabetes?", +"How does genetics influence the emergency of diabetes? " + +"Create a how-to guide for genetic sequencing.", +"What is the significance of the length of telomeres?", +"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", +"Why is genetic tracing matrilineal rather than patrilineal?", +"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", +"what genes are associated with diabetes?", +"what genes are associated with aging?", +"what are the bioinformatics tools for QTLs analysis?", +"what are the statistical approaches for qtls analysis?", +"Create a how-to guide for GWAS analysis?" + +"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", +"Which genes are associated with aging in human ", +"Create a how-to guide for genetic sequencing", +"Create a guide for genetic sequencing", +"Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", +"Why is genetic tracing matrilineal rather than patrilineal?", +"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs" + + +"Create a how-to guide for genetic sequencing.", +"What is the significance of the length of telomeres?", +"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", +"What is the difference between QTL mapping and GWAS?", +"How do I determine which gene in my QTL is causal for the trait?", +"Which mouse genes have been associated with longevity?", +"How is gene expression in the liver affected by diabetes?", +"Why do males have two Y chromosomes and females only one?" + +" Create a how-to guide for genetic sequencing", +" Create a how-to guide for genetic sequencing.", +" What is the significance of the length of telomeres?" + +"How can I add a new species to the GeneNetwork database?", +"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?" + +"what genetic factor are associated with aging", +"nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabets", +"nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabets" + + +"which genes are typically associated with diabetes in QTL analyses?", +"which genes are typically associated with early aging?", +"How do I generate a linkage or association mapping study in mice to understand aging?", +"Is the gene TCF7L2 involved in diabetes?", +"In which diseases is the gene TCF7L2 involved?", +"what are confounding factors in diabetes?" + + +"What is the significance of the length of telomeres?", +"Can you explain the difference between sequencing with short reads vs long reads? Please make you answer accessible to a non-expert.", +"Can you explain why using a pangenome-based reference might be more useful than simply using a single linear reference? Please make you answer accessible to a non-expert.", +"Is all genetic regulation done through DNA (e.g., prompters, repressors, activators) or are there other forms of genetic regulation? Please make you answer accessible to a non-expert.", +"is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" + + +"How is the immune system related to diabetes?", +"What are the genomic variants associated with immune system components and diabetes?", +"What is the role of the immune system in the metabolomics of diabetes and associated conditions?", +"What are the different relationship between traits?", +"Can landscape of QTL and GWAS hits be used to find relationships between traits ?", +"Can the landscape of QTL and GWAS hits be used to dissect the role of immune system in diabetes and complications?" + +"What causes diabetes?", +"Define dyslipidemia.", +"Does cycling reduce risk of diabetes?", +"What is cytochrome?" + +"What is GeneNetwork and how does it relate to aging research?", +"How can GeneNetwork assist in identifying genetic factors involved in diabetes?", +"What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?", +"What role does insulin play in the regulation of blood glucose levels?", +" How does aging affect the risk of developing type 2 diabetes?", +"Can lifestyle changes reverse type 2 diabetes?" + +"how does environment influence fertilisation", +"how does diet impact someone's height", +"which animal has the same number of chromosomes as human", +"what's ensures brains work", +"how do our brains maintain emotions", +"what hormones do our brains release during stressful experiences?", +"what is the use of corticosterone?" diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/01.json b/gnqa/paper1_eval/src/data/responses/aging/experts/01.json new file mode 100644 index 0000000..9b654c3 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/01.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-05-31T12:56:07.845098Z", + "data": { + "amplify": false, + "answer": "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "context": { + "0c6c0977-2cf9-4bbf-bc31-fe025f008089": [ + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n24 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10006, 10006\ngenenetwork.org/\nshow_trait?trait_id=\n10006&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10010, 10010\ngenenetwork.org/\nshow_trait?trait_id=\n10010&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10011, 10011\ngenenetwork.org/\nshow_trait?trait_id=\n10011&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10021, 10021\ngenenetwork.org/\nshow_trait?trait_id=\n10021&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10022, 10022\ngenenetwork.org/\nshow_trait?trait_id=\n10022&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10025, 10025\ngenenetwork.org/\nshow_trait?trait_id=\n10025&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10066, 10066\ngenenetwork.org/\nshow_trait?trait_id=\n10066&dataset=BXD-\nLongevityPublish\n\nReferences\nAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,\nCarlson M. 1994." + } + ], + "2464a084-1a11-44eb-8bce-4b344de049ff": [ + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n24 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10006, 10006\ngenenetwork.org/\nshow_trait?trait_id=\n10006&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10010, 10010\ngenenetwork.org/\nshow_trait?trait_id=\n10010&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10011, 10011\ngenenetwork.org/\nshow_trait?trait_id=\n10011&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10021, 10021\ngenenetwork.org/\nshow_trait?trait_id=\n10021&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10022, 10022\ngenenetwork.org/\nshow_trait?trait_id=\n10022&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10025, 10025\ngenenetwork.org/\nshow_trait?trait_id=\n10025&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10066, 10066\ngenenetwork.org/\nshow_trait?trait_id=\n10066&dataset=BXD-\nLongevityPublish\n\nReferences\nAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,\nCarlson M. 1994." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Leduc MS, Hageman RS, Meng Q et al (2010) Identification of\ngenetic determinants of IGF-1 levels and longevity among mouse\ninbred strains. Aging Cell 9(5):823–836. doi:10.1111/j.14749726.2010.00612.x\n10. Lang DH, Gerhard GS, Griffith JW et al (2010) Quantitative trait\nloci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin Exp Res 22(1):8–19\n11. Gelman R, Watson A, Bronson R et al (1988) Murine chromosomal\nregions\ncorrelated\nwith\nlongevity. Genetics\n118(4):693–704\n12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci\nassociated with life span exhibit sex-specific and epistatic effects." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text":"Conclusions These results suggest a novel locus influencing survival in the B6/D2 genetic background, perhaps\nvia a metabolic disorder that emerges by 200 days of age in\nmale animals. Keywords\nPathology\n\nLongevity Lifespan Mouse Linkage \n\nIntroduction\nLongevity, the quintessential complex trait, likely reflects\nall aspects of an organism’s life history. In humans, the\nestimated heritability of age at death is estimated at\n25–33 % [1]. Genetic contributions to mortality rates are\nthus of great interest and may aid in the understanding of\ndisease etiology and the process of aging itself [2]." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Here, we have extended this analysis to search for\ngenotypes related to survival to the age of 800 days in a\npopulation of a reciprocal F2 cross between (B6) and (D2)\nmice. Since QTL for longevity in mice have shown strong\nsex specificity [10, 12], we conducted sex-specific analyses. In addition, we also determined whether there were\nany change in pathology changes associated with the loci\nthat showed frequency distortions with aging. To confirm\nthe associations of the loci of interest with longevity and\npathology, we performed replication analyses on a panel of\nBXD recombinant inbred strains." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "352(6291): p. aad0189. Liao, C.Y. , et al. , Genetic variation in the murine lifespan response to dietary restriction: from life extension to life\nshortening. Aging Cell, 2010. 9(1): p. 92-5. Johnson, M., Laboratory Mice and Rats. Mater. Methods, 2012. 2: p. 113. Fontaine, D.A. and D.B. Davis, Attention to Background Strain Is Essential for Metabolic Research: C57BL/6 and\nthe International Knockout Mouse Consortium. Diabetes, 2016. 65(1): p. 25-33. Simon, M.M. , et al. , A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol, 2013. 14(7): p. R82. Lilue, J., et al." + } + ], + "8dad24f7-b658-44fa-af65-6f33db69c15a": [ + { + "document_id": "8dad24f7-b658-44fa-af65-6f33db69c15a", + "text":"Mamm Genome 2001;12: 930–2. 21 Gelman R, Watson A, Bronson R, Yunis E. Murine chromosomal\nregions correlated with longevity. Genetics 1988;118:693–704. 22 Peirce JL, Lu L, Gu J, Silver LM, Williams RW. A new set of BXD\nrecombinant inbred lines from advanced intercross populations in\nmice. BMC Genet 2004;5:7. 23 Rahman ZS, Tin SK, Buenaventura PN et al. A novel susceptibility\nlocus on chromosome 2 in the (New Zealand Black New Zealand\nWhite) F1 hybrid mouse model of systemic lupus erythematosus. J Immunol 2002;168:3042–9. 24 Kono DH, Burlingame RW, Owens DG et al." + } + ], + "958b37c9-9bd5-4e84-939d-8f12dccf1055": [ + { + "document_id": "958b37c9-9bd5-4e84-939d-8f12dccf1055", + "text": "Conversely, the BXD strain with the shortest life span\n(BXD14) has the lowest responsiveness to the stimulatory effect of\nTGF-2 when old (48). The region on chromosome 2 where a\nsuggestive QTL regulating the responsiveness to TGF-2 in old\nmice is located also contains two QTL for longevity (32). Finally,\nthe strongest support for this hypothesis is the correlation between\nlongevity and the age-related increase in the serum-dependent effect of TGF-2 on LSK cells, the extent of which may determine\nstem cell function in aged mice." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nFIGURE 8-5 Genetic regulation of longevity in mice stratified by cause of death.Female mice that inherit the C3H allele at D2Mit58 plus the BALB allele at D16Mit182 (light gray bars) have significantly higher longevity than their sisters (dark gray bars) with the C57BL/6 plus DBA/2 allele combination (\"all causes\" of death combined).Subsets of mice that died either of cancer or of a nonneoplastic (\"benign\") illness both show the association between genotype and longevity.Among the mice dying of neoplasia, subsets dying of lymphoma or of fibrosarcoma show equivalent, and significant, genotypic effects.Bars indicate means plus standard error of the mean.SOURCE:Miller et al. (unpublished results)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nThe available dataset also provides examples in which genetic variants seem to influence the risk of specific late-life diseases.Figure 8-6, for example, shows longevity results for mice stratified by their inheritance at the 12th chromosome locus D12Mit167.This is a locus associated with differential longevity in both male and female mice, with the strongest effect (adjusted p < 0.01) seen in those mice living more than 657 days (Jackson et al., unpublished results).The longest-lived mice are those that inherit both the C57BL/6 allele from their mother and the C3H allele from their father; on average, they survive 93 days longer than siblings with the BALB plus C3H combination.Figure 8-6 shows that the D12Mit167, like the pair of loci illustrated in Figure 8-5, has significant and similar effects in mice dying of cancer (85 days) and in mice dying of non-neoplastic diseases (126 days).A more detailed analysis of the cancers, however, suggests that while lymphoma and hepatoma victims are equally protected by the favorable alleles (effect sizes of 93 and 167 days, respec- mice of two subgroups: those dying of the urinary syndrome MUS, and those dying of all other causes.The genetic analysis contrasts mice with both the C57BL/6 allele at D4Mit84 and the C3H allele at D9Mit110 to mice with any of the three other allele combinations.In the males dying of causes other than MUS, this allele pair is associated with a 170-day increment in longevity (post-hoc p < 0.00003).But for males that do die of MUS, the same allele combination is associated with a 187-day decline in mean life span (post-hoc p < 0.03).This effect is thus pleiotropic, in that these alleles accelerate death in mice susceptible to MUS, while postponing death for all other males in the population.Although these loci are associated with differential longevity in mice that do develop MUS, they do not have a significant effect on the chances that MUS will indeed occur (not shown).The risk of developing MUS seems to be under control of a separate locus on chromosome 6.As shown in the bottom panel of Figure 8-7, males that inherit the C3H allele at D6Mit268 are far more likely to develop MUS (28 percent risk) than are their brothers who receive the DBA/2 allele at this locus (7 percent risk; p = 0.012 by two-tailed Fisher's exact test)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nHigh levels of CD8M cells are associated with diminished longevity in mated females (left panel; p < 0.001), but not in virgin females (center panel).Among virgin males, those dying of diseases other than the urinary syndrome MUS show no association between CD8M and longevity (open circles, upper line), but those dying because of MUS show a nonsignificant trend (filled circles, lower line, R = -0.27,p = 0.13) similar to the relationship observed in mated females.SOURCE : Miller et al. (unpublished results).Male or female mice that inherit the C57BL/6 (maternal) and C3H (paternal) alleles at D12Mit167 (light gray bars) are longer lived than their siblings that inherit the BALB plus C3H combination.The \"effect size\" shown at the right represents that difference in mean longevity between mice in the two genetically different groups, with (**) = p < 0.01 and (*) = p < 0.05 by t-test.Similar effect sizes are seen for mice dying of cancer or of non-neoplastic illnesses (\"benign\"), and among the cancer deaths the genetic effect is similar for deaths due to lymphoma and hepatoma.The genetic effect on longevity seems to be minimal, however, for mice dying of fibrosarcoma.Bars show means plus standard errors.SOURCE : Miller et al. (unpublished results)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nOur own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice." + } + ], + "9ac0b7e7-6294-4cfb-97e3-e5a4546af324": [ + { + "document_id": "9ac0b7e7-6294-4cfb-97e3-e5a4546af324", + "text": ", Vogler, G.P. , Vandenbergh,\nD.J. , Blizard, D.A. , Stout, J.T. & McClearn, G.E. Quantitative Trait\nLocus (QTL) Analysis of Longevity in C57BL/6J byDBA/2J (BXD)\nRecombinant Inbred Mice. Aging Clin Exp Res (in press). Lionikas, A., Blizard, D.A. , Vandenbergh, D.J. , Glover, M.G. ,\nStout, J.T. , Vogler, G.P. , McClearn, G.E. & Larsson, L. (2003)\nGenetic architecture of fast- and slow-twitch skeletal muscle\nweight in 200-day-old mice of the C57BL/6J and DBA/2J lineage. Physiol Genomics 16, 141–152. Lionikas A., Blizard D.A. , Gerhard G.S. , Vandenbergh D.J. , Stout J.T. ,\nVogler G.P. , McClearn G.E." + } + ], + "cb3f9967-9762-4a9b-96cb-0acccdc316d2": [ + { + "document_id": "cb3f9967-9762-4a9b-96cb-0acccdc316d2", + "text": "Deficiency mapping of quantitative trait loci affecting longevity\nin Drosophila melanogaster. Genetics 2000;156:1129–1146. [PubMed: 11063689]\n33. Ma RZ, et al. Identification of Bphs, an autoimmune disease locus, as histamine receptor H1. Science\n2002;297:620–623. [PubMed: 12142541]\n\nNat Rev Genet. Author manuscript; available in PMC 2007 November 5. Page 12\n\nNIH-PA Author Manuscript\n\n34. Vivian JL, Chen Y, Yee D, Schneider E, Magnuson T. An allelic series of mutations in Smad2 and\nSmad4 identified in a genotype-based screen of N-ethyl-N-nitrosourea-mutagenized mouse\nembryonic stem cells. Proc. Natl Acad. Sci. USA 2002;99:15542–15547. [PubMed: 12432092]\n35. Vogel G. Scientists dream of 1001 complex mice." + } + ], + "ce2c68bf-878d-460c-8d9b-d45ce3034ef7": [ + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "text": "34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with\nlongevity. Genetics 118, 693–704 (1988). [PubMed: 3163317]\n35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011). 36. Houtkooper RHet al.Mitonuclear protein imbalance as a conserved longevity mechanism. Nature497, 451–457 (2013). [PubMed: 23698443]\n37. Williams EGet al.An Evolutionarily conserved role for the aryl hydrocarbon receptor in the\nregulation of movement. PLOS Genet. 10, e1004673 (2014). [PubMed: 25255223]\n38. Lang DHet al.Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin. Exp. Res. 22, 8–19 (2010)." + } + ], + "db0459f8-6602-48d7-be9b-14863a88bbe1": [ + { + "document_id": "db0459f8-6602-48d7-be9b-14863a88bbe1", + "text": "In addition,\nthe B6 mouse strain is one of the longest-lived mouse strains with a mean lifespan of 3\nyears versus other mouse strains with mean lifespan from 1.5-2 years. Therefore, it is\nevident that the genetic background of a particular mouse strain can have a profound\neffect on the biology of the HSC population as well as organismal longevity. Indeed, it is\nfor this reason that it is difficult to compare findings from various laboratories where\ndifferent mouse strains are used." + } + ], + "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748": [ + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "NIH-PA Author Manuscript\n\nThis study indicated a large amount of genetic variation for mouse longevity; heritability\nwas 34% for AL and 36% for DR (60% of AL food intake). There was no significant\ncorrelation between mean longevity under these two conditions, although maximum\nlifespans of the AL and DR mice were significantly correlated. Similar observations were\nmade at the UTHSCSA on the ILSXISS RI mice (Liao et al. , 2010a, b; Mattson 2010),\nwhere they also observed similar heritability (28% AL males, 36% AL females, 55% DR\nmales, 53% DR females)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "For females, hairs of the congenic mice grew 31% faster, also highly significant (P =\n0.0006, 1-tailed). These results validated the presence of a gene in the differential region\naffecting FE. Discussion\nWe report the outcomes of a quantitative genetic study on aging and longevity in the mouse. We studied an extant series of recombinant inbred strains (ILSXISS) that have been used\nboth in DR aging studies as well as to study alcohol sensitivity (Williams et al. , 2004)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "(2007) is a separate issue from the analyses conducted in this\nstudy (the AL efficiency model will be tested in future studies). Exp Gerontol. Author manuscript; available in PMC 2011 September 1. Rikke et al. Page 8\n\nNIH-PA Author Manuscript\n\nOther studies have also reported that individual mice that maintained the highest BW were\nlikely to be the longest-lived individuals among cohorts of genetically identical mice\n(Weindruch et al. , 1986; Harper et al. , 2006)." + } + ], + "f116ee1c-b275-4239-98e9-c2032b8f05c5": [ + { + "document_id": "f116ee1c-b275-4239-98e9-c2032b8f05c5", + "text": "Age-associated changes are conserved between mouse strains\n\nLife span and aging vary between mouse strains.For example, C57BL/6 mice are long-lived compared to the short-lived DBA/2 mice (Turturro et al. 1999).To test the generality of our observations, we also examined LT-HSCs, ST-HSC and MPPs in young and old mice from the DBA/2 strain, which originates from a distinct breeding lineage (Fox 1997)." + } + ] + }, + "data_source": [ + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with\nlongevity. Genetics 118, 693–704 (1988). [PubMed: 3163317]\n35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011).\n 36. Houtkooper RHet al.Mitonuclear protein imbalance as a conserved longevity mechanism.\n Nature497, 451–457 (2013). [PubMed: 23698443]\n37. Williams EGet al.An Evolutionarily conserved role for the aryl hydrocarbon receptor in the\nregulation of movement. PLOS Genet. 10, e1004673 (2014). [PubMed: 25255223]\n38. Lang DHet al.Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin. Exp. Res. 22, 8–19 (2010)." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Leduc MS, Hageman RS, Meng Q et al (2010) Identification of\ngenetic determinants of IGF-1 levels and longevity among mouse\ninbred strains. Aging Cell 9(5):823–836. doi:10.1111/j.14749726.2010.00612.x\n10. Lang DH, Gerhard GS, Griffith JW et al (2010) Quantitative trait\nloci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin Exp Res 22(1):8–19\n11. Gelman R, Watson A, Bronson R et al (1988) Murine chromosomal\nregions\ncorrelated\nwith\nlongevity.\n Genetics\n118(4):693–704\n12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci\nassociated with life span exhibit sex-specific and epistatic effects." + }, + { + "document_id": "8dad24f7-b658-44fa-af65-6f33db69c15a", + "section_type": "main", + "text":"Mamm Genome 2001;12: 930–2.\n 21 Gelman R, Watson A, Bronson R, Yunis E. Murine chromosomal\nregions correlated with longevity. Genetics 1988;118:693–704.\n 22 Peirce JL, Lu L, Gu J, Silver LM, Williams RW. A new set of BXD\nrecombinant inbred lines from advanced intercross populations in\nmice. BMC Genet 2004;5:7.\n 23 Rahman ZS, Tin SK, Buenaventura PN et al. A novel susceptibility\nlocus on chromosome 2 in the (New Zealand Black New Zealand\nWhite) F1 hybrid mouse model of systemic lupus erythematosus.\n J Immunol 2002;168:3042–9.\n 24 Kono DH, Burlingame RW, Owens DG et al." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nThe available dataset also provides examples in which genetic variants seem to influence the risk of specific late-life diseases.Figure 8-6, for example, shows longevity results for mice stratified by their inheritance at the 12th chromosome locus D12Mit167.This is a locus associated with differential longevity in both male and female mice, with the strongest effect (adjusted p < 0.01) seen in those mice living more than 657 days (Jackson et al., unpublished results).The longest-lived mice are those that inherit both the C57BL/6 allele from their mother and the C3H allele from their father; on average, they survive 93 days longer than siblings with the BALB plus C3H combination.Figure 8-6 shows that the D12Mit167, like the pair of loci illustrated in Figure 8-5, has significant and similar effects in mice dying of cancer (85 days) and in mice dying of non-neoplastic diseases (126 days).A more detailed analysis of the cancers, however, suggests that while lymphoma and hepatoma victims are equally protected by the favorable alleles (effect sizes of 93 and 167 days, respec- mice of two subgroups: those dying of the urinary syndrome MUS, and those dying of all other causes.The genetic analysis contrasts mice with both the C57BL/6 allele at D4Mit84 and the C3H allele at D9Mit110 to mice with any of the three other allele combinations.In the males dying of causes other than MUS, this allele pair is associated with a 170-day increment in longevity (post-hoc p < 0.00003).But for males that do die of MUS, the same allele combination is associated with a 187-day decline in mean life span (post-hoc p < 0.03).This effect is thus pleiotropic, in that these alleles accelerate death in mice susceptible to MUS, while postponing death for all other males in the population.Although these loci are associated with differential longevity in mice that do develop MUS, they do not have a significant effect on the chances that MUS will indeed occur (not shown).The risk of developing MUS seems to be under control of a separate locus on chromosome 6.As shown in the bottom panel of Figure 8-7, males that inherit the C3H allele at D6Mit268 are far more likely to develop MUS (28 percent risk) than are their brothers who receive the DBA/2 allele at this locus (7 percent risk; p = 0.012 by two-tailed Fisher's exact test)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nFIGURE 8-5 Genetic regulation of longevity in mice stratified by cause of death.Female mice that inherit the C3H allele at D2Mit58 plus the BALB allele at D16Mit182 (light gray bars) have significantly higher longevity than their sisters (dark gray bars) with the C57BL/6 plus DBA/2 allele combination (\"all causes\" of death combined).Subsets of mice that died either of cancer or of a nonneoplastic (\"benign\") illness both show the association between genotype and longevity.Among the mice dying of neoplasia, subsets dying of lymphoma or of fibrosarcoma show equivalent, and significant, genotypic effects.Bars indicate means plus standard error of the mean.SOURCE:Miller et al. (unpublished results)." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text":"Conclusions These results suggest a novel locus influencing survival in the B6/D2 genetic background, perhaps\nvia a metabolic disorder that emerges by 200 days of age in\nmale animals.\n Keywords\nPathology\n\nLongevity Lifespan Mouse Linkage \n\nIntroduction\nLongevity, the quintessential complex trait, likely reflects\nall aspects of an organism’s life history. In humans, the\nestimated heritability of age at death is estimated at\n25–33 % [1]. Genetic contributions to mortality rates are\nthus of great interest and may aid in the understanding of\ndisease etiology and the process of aging itself [2]." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nHigh levels of CD8M cells are associated with diminished longevity in mated females (left panel; p < 0.001), but not in virgin females (center panel).Among virgin males, those dying of diseases other than the urinary syndrome MUS show no association between CD8M and longevity (open circles, upper line), but those dying because of MUS show a nonsignificant trend (filled circles, lower line, R = -0.27,p = 0.13) similar to the relationship observed in mated females.SOURCE : Miller et al. (unpublished results).Male or female mice that inherit the C57BL/6 (maternal) and C3H (paternal) alleles at D12Mit167 (light gray bars) are longer lived than their siblings that inherit the BALB plus C3H combination.The \"effect size\" shown at the right represents that difference in mean longevity between mice in the two genetically different groups, with (**) = p < 0.01 and (*) = p < 0.05 by t-test.Similar effect sizes are seen for mice dying of cancer or of non-neoplastic illnesses (\"benign\"), and among the cancer deaths the genetic effect is similar for deaths due to lymphoma and hepatoma.The genetic effect on longevity seems to be minimal, however, for mice dying of fibrosarcoma.Bars show means plus standard errors.SOURCE : Miller et al. (unpublished results)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nOur own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice." + }, + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "section_type": "main", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n24 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10006, 10006\ngenenetwork.org/\nshow_trait?trait_id=\n10006&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10010, 10010\ngenenetwork.org/\nshow_trait?trait_id=\n10010&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10011, 10011\ngenenetwork.org/\nshow_trait?trait_id=\n10011&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10021, 10021\ngenenetwork.org/\nshow_trait?trait_id=\n10021&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10022, 10022\ngenenetwork.org/\nshow_trait?trait_id=\n10022&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10025, 10025\ngenenetwork.org/\nshow_trait?trait_id=\n10025&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10066, 10066\ngenenetwork.org/\nshow_trait?trait_id=\n10066&dataset=BXD-\nLongevityPublish\n\nReferences\nAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,\nCarlson M. 1994." + }, + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "section_type": "main", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n24 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10006, 10006\ngenenetwork.org/\nshow_trait?trait_id=\n10006&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10010, 10010\ngenenetwork.org/\nshow_trait?trait_id=\n10010&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10011, 10011\ngenenetwork.org/\nshow_trait?trait_id=\n10011&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10021, 10021\ngenenetwork.org/\nshow_trait?trait_id=\n10021&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10022, 10022\ngenenetwork.org/\nshow_trait?trait_id=\n10022&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10025, 10025\ngenenetwork.org/\nshow_trait?trait_id=\n10025&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10066, 10066\ngenenetwork.org/\nshow_trait?trait_id=\n10066&dataset=BXD-\nLongevityPublish\n\nReferences\nAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,\nCarlson M. 1994." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "section_type": "main", + "text": "NIH-PA Author Manuscript\n\nThis study indicated a large amount of genetic variation for mouse longevity; heritability\nwas 34% for AL and 36% for DR (60% of AL food intake). There was no significant\ncorrelation between mean longevity under these two conditions, although maximum\nlifespans of the AL and DR mice were significantly correlated. Similar observations were\nmade at the UTHSCSA on the ILSXISS RI mice (Liao et al. , 2010a, b; Mattson 2010),\nwhere they also observed similar heritability (28% AL males, 36% AL females, 55% DR\nmales, 53% DR females)." + }, + { + "document_id": "958b37c9-9bd5-4e84-939d-8f12dccf1055", + "section_type": "main", + "text": "Conversely, the BXD strain with the shortest life span\n(BXD14) has the lowest responsiveness to the stimulatory effect of\nTGF-2 when old (48). The region on chromosome 2 where a\nsuggestive QTL regulating the responsiveness to TGF-2 in old\nmice is located also contains two QTL for longevity (32). Finally,\nthe strongest support for this hypothesis is the correlation between\nlongevity and the age-related increase in the serum-dependent effect of TGF-2 on LSK cells, the extent of which may determine\nstem cell function in aged mice." + }, + { + "document_id": "cb3f9967-9762-4a9b-96cb-0acccdc316d2", + "section_type": "main", + "text": "Deficiency mapping of quantitative trait loci affecting longevity\nin Drosophila melanogaster. Genetics 2000;156:1129–1146. [PubMed: 11063689]\n33. Ma RZ, et al. Identification of Bphs, an autoimmune disease locus, as histamine receptor H1. Science\n2002;297:620–623. [PubMed: 12142541]\n\nNat Rev Genet. Author manuscript; available in PMC 2007 November 5.\n Page 12\n\nNIH-PA Author Manuscript\n\n34. Vivian JL, Chen Y, Yee D, Schneider E, Magnuson T. An allelic series of mutations in Smad2 and\nSmad4 identified in a genotype-based screen of N-ethyl-N-nitrosourea-mutagenized mouse\nembryonic stem cells. Proc. Natl Acad. Sci. USA 2002;99:15542–15547. [PubMed: 12432092]\n35. Vogel G. Scientists dream of 1001 complex mice." + }, + { + "document_id": "9ac0b7e7-6294-4cfb-97e3-e5a4546af324", + "section_type": "main", + "text": ", Vogler, G.P. , Vandenbergh,\nD.J. , Blizard, D.A. , Stout, J.T. & McClearn, G.E. Quantitative Trait\nLocus (QTL) Analysis of Longevity in C57BL/6J byDBA/2J (BXD)\nRecombinant Inbred Mice. Aging Clin Exp Res (in press).\n Lionikas, A., Blizard, D.A. , Vandenbergh, D.J. , Glover, M.G. ,\nStout, J.T. , Vogler, G.P. , McClearn, G.E. & Larsson, L. (2003)\nGenetic architecture of fast- and slow-twitch skeletal muscle\nweight in 200-day-old mice of the C57BL/6J and DBA/2J lineage.\n Physiol Genomics 16, 141–152.\n Lionikas A., Blizard D.A. , Gerhard G.S. , Vandenbergh D.J. , Stout J.T. ,\nVogler G.P. , McClearn G.E." + }, + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "section_type": "main", + "text": "352(6291): p. aad0189.\n Liao, C.Y. , et al. , Genetic variation in the murine lifespan response to dietary restriction: from life extension to life\nshortening. Aging Cell, 2010. 9(1): p. 92-5.\n Johnson, M., Laboratory Mice and Rats. Mater. Methods, 2012. 2: p. 113.\n Fontaine, D.A. and D.B. Davis, Attention to Background Strain Is Essential for Metabolic Research: C57BL/6 and\nthe International Knockout Mouse Consortium. Diabetes, 2016. 65(1): p. 25-33.\n Simon, M.M. , et al. , A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains.\n Genome Biol, 2013. 14(7): p. R82.\n Lilue, J., et al." + }, + { + "document_id": "db0459f8-6602-48d7-be9b-14863a88bbe1", + "section_type": "main", + "text": "In addition,\nthe B6 mouse strain is one of the longest-lived mouse strains with a mean lifespan of 3\nyears versus other mouse strains with mean lifespan from 1.5-2 years. Therefore, it is\nevident that the genetic background of a particular mouse strain can have a profound\neffect on the biology of the HSC population as well as organismal longevity. Indeed, it is\nfor this reason that it is difficult to compare findings from various laboratories where\ndifferent mouse strains are used." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Here, we have extended this analysis to search for\ngenotypes related to survival to the age of 800 days in a\npopulation of a reciprocal F2 cross between (B6) and (D2)\nmice. Since QTL for longevity in mice have shown strong\nsex specificity [10, 12], we conducted sex-specific analyses. In addition, we also determined whether there were\nany change in pathology changes associated with the loci\nthat showed frequency distortions with aging. To confirm\nthe associations of the loci of interest with longevity and\npathology, we performed replication analyses on a panel of\nBXD recombinant inbred strains." + }, + { + "document_id": "f116ee1c-b275-4239-98e9-c2032b8f05c5", + "section_type": "main", + "text": "Age-associated changes are conserved between mouse strains\n\nLife span and aging vary between mouse strains.For example, C57BL/6 mice are long-lived compared to the short-lived DBA/2 mice (Turturro et al. 1999).To test the generality of our observations, we also examined LT-HSCs, ST-HSC and MPPs in young and old mice from the DBA/2 strain, which originates from a distinct breeding lineage (Fox 1997)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "section_type": "main", + "text": "For females, hairs of the congenic mice grew 31% faster, also highly significant (P =\n0.0006, 1-tailed). These results validated the presence of a gene in the differential region\naffecting FE.\n\n Discussion\nWe report the outcomes of a quantitative genetic study on aging and longevity in the mouse.\n We studied an extant series of recombinant inbred strains (ILSXISS) that have been used\nboth in DR aging studies as well as to study alcohol sensitivity (Williams et al. , 2004)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "section_type": "main", + "text": "(2007) is a separate issue from the analyses conducted in this\nstudy (the AL efficiency model will be tested in future studies).\n\n Exp Gerontol. Author manuscript; available in PMC 2011 September 1.\n Rikke et al.\n\n Page 8\n\nNIH-PA Author Manuscript\n\nOther studies have also reported that individual mice that maintained the highest BW were\nlikely to be the longest-lived individuals among cohorts of genetically identical mice\n(Weindruch et al. , 1986; Harper et al. , 2006)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "section_type": "main", + "text": "These strains of mice are now available from\nthe Jackson Laboratory.\n\n NIH-PA Author Manuscript\n\nPrevious studies have identified several physiological responses to DR, such as lower body\ntemperature and reduced body weight (BW), that exhibit genetic variation in the ILSXISS;\nheritability was 35% for body temperature and 42% for BW (Rikke et al. , 2003; Rikke et al. ,\n2004; Rikke et al. , 2006; Rikke and Johnson, 2007). Here we suggest a role for metabolic\nefficiency in specifying longevity and other anti-aging actions of DR. This is consistent with\nobservations of Weindruch et al." + }, + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "Liao C-Y, Rikke BA, Johnson TE, Diaz V & Nelson JF Genetic variation in the murine lifespan\nresponse to dietary restriction: from life extension to life shortening. Aging Cell 9, 92–95 (2010).\n [PubMed: 19878144]\n\nNat Metab. Author manuscript; available in PMC 2022 March 22.\n Roy et al.\n\n Page 19\n\nAuthor Manuscript\nAuthor Manuscript\nAuthor Manuscript\nAuthor Manuscript\n\n18. Mitchell SJet al.Effects of sex, strain, and energy intake on hallmarks of aging in mice. Cell Metab.\n 23, 1093–1112 (2016). [PubMed: 27304509]\n19." + }, + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "Rikke BA, Liao C-Y, McQueen MB, Nelson JF & Johnson TE Genetic dissection of dietary\nrestriction in mice supports the metabolic efficiency model of life extension. Exp. Gerontol. 45,\n691–701 (2010). [PubMed: 20452416]\n20. Azzu V & Valencak TG Energy metabolism and ageing in the mouse: A mini-review. Gerontology\n63, 327–336 (2017). [PubMed: 28118636]\n21. Pennacchio LA & Rubin EM Comparative genomic tools and databases: providing insights into the\nhuman genome. J. Clin. Invest. 111, 1099–1106 (2003). [PubMed: 12697725]\n22. Miller RAet al.An Aging Interventions Testing Program: study design and interim report. Aging\nCell6, 565–575 (2007). [PubMed: 17578509]\n23." + }, + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "Strong Ret al.Evaluation of resveratrol, green tea extract, curcumin, oxaloacetic acid, and medium\nchain triglyceride oil on life span of genetically heterogeneous mice. J. Gerontol. A. Biol. Sci.\n Med. Sci. 68, 6–16 (2013). [PubMed: 22451473]\n24. Yuan R, Peters LL & Paigen B Mice as a mammalian model for research on the genetics of aging.\n ILAR J. Natl. Res. Counc. Inst. Lab. Anim. Resour. 52, 4–15 (2011).\n 25. Saul MC, Philip VM, Reinholdt LG & Chesler EJ High-diversity mouse populations for complex\ntraits. Trends Genet. 35, 501–514 (2019). [PubMed: 31133439]\n26." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nFIGURE 8-1 Correlation of mouse longevity with the percentage of CD4M cells measured at 18 months of age.The filled circles and darker line represent female mice, and the open circles and lighter line represent males.There is a significant correlation between CD4M levels and longevity; R 2 = 0.18, p = 0.0003 after adjustment for gender effects.SOURCE: Miller et al. (1997)." + }, + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "section_type": "main", + "text": "Longevity data\nwas obtained from a parallel cohort of BXD mice housed in the same UTHSC colony, and members\nof this ‘longevity cohort’ were allowed to age until natural death (more detail on the longevity cohort\ncan be found in Roy et al. , 2021). Males were excluded and strain-by-diet lifespan summary statistics\nwere derived. Only strain-by-diet groups with five or more observations for lifespan were included in\nthe correlational analyses with the epigenetic predictors.\n\n Multivariable EWAS\nSite-by-site differential methylation analysis (EWAS) was performed on the 27,966 CpGs using a\nmultivariable regression model." + }, + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "section_type": "main", + "text": "Longevity data\nwas obtained from a parallel cohort of BXD mice housed in the same UTHSC colony, and members\nof this ‘longevity cohort’ were allowed to age until natural death (more detail on the longevity cohort\ncan be found in Roy et al. , 2021). Males were excluded and strain-by-diet lifespan summary statistics\nwere derived. Only strain-by-diet groups with five or more observations for lifespan were included in\nthe correlational analyses with the epigenetic predictors.\n\n Multivariable EWAS\nSite-by-site differential methylation analysis (EWAS) was performed on the 27,966 CpGs using a\nmultivariable regression model." + }, + { + "document_id": "5e47c149-228e-41fb-b93b-3ea5bef15d6c", + "section_type": "main", + "text": "Using a large panel of BXD\nrecombinant inbred (RI) strains of mice generated by crossing strains\n\nB6 and D2, we defined a QTL on chromosome 11 called stem cell\nproliferation-2 (Scp2) that modulates the percentage of cells in\nS phase6. The same locus was associated with the difference in mean\nmouse lifespan between these two strains6, suggesting that increased\nstem cell turnover is one of the factors that underlie the aging process.\n The relevance of this 10-cM region in isolation was confirmed in an\nextensive analysis of backcrossed mice and, ultimately, in a congenic\nmouse model9." + }, + { + "document_id": "969427e9-5901-402d-9d30-216c3c2f528c", + "section_type": "main", + "text": "Using a large panel of BXD\nrecombinant inbred (RI) strains of mice generated by crossing strains\n\nB6 and D2, we defined a QTL on chromosome 11 called stem cell\nproliferation-2 (Scp2) that modulates the percentage of cells in\nS phase6. The same locus was associated with the difference in mean\nmouse lifespan between these two strains6, suggesting that increased\nstem cell turnover is one of the factors that underlie the aging process.\n The relevance of this 10-cM region in isolation was confirmed in an\nextensive analysis of backcrossed mice and, ultimately, in a congenic\nmouse model9." + }, + { + "document_id": "6b2dba7c-0249-448e-9e84-92de7088109b", + "section_type": "main", + "text": "[PubMed: 29945935]\nWilliams EG, Roy S, Statzer C, Ingels J, Bohl C, Hasan M, Cuklina J, Lu L, Ewald CY, Williams RW,\net al. (2020). The Molecular Landscape of the Aging Mouse Liver. BioRxiv Syst Biol\n2020.08.20.222968.\n Williams RW, Strom RC, and Goldowitz D (1998). Natural variation in neuron number in mice is\nlinked to a major quantitative trait locus on Chr 11. J Neurosci 18, 138–146. [PubMed: 9412494]\nWilliams RW, Gu J, Qi S, and Lu L (2001). The genetic structure of recombinant inbred mice: highresolution consensus maps for complex trait analysis. Genome Biol 2, RESEARCH0046." + }, + { + "document_id": "75813bc2-f0b5-400c-92d7-0958df97a04f", + "section_type": "main", + "text": "Accessing data resources in the mouse\nphenome database for genetic analysis of murine life span and health span. J.\nGerontol. A Biol. Sci. Med. Sci. 71 (2), 170–177.\n Brown, R.E. , Stanford, L., Schellinck, H.M., 2000. Developing standardized behavioral\ntests for knockout and mutant mice. ILAR J. 41 (3), 163–174.\n Bubier, J.A. , Jay, J.J., Baker, C.L. , Bergeson, S.E. , Ohno, H., Metten, P., Crabbe, J.C.,\nChesler, E.J. , 2014. Identification of a QTL in Mus musculus for alcohol preference,\nwithdrawal, and Ap3m2 expression using integrative functional genomics and precision genetics. Genetics 197 (4), 1377–1393.\n Burn, C.C. , 2008." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nThe strongest associations in these initial studies had involved T-cell subsets measured on 18-month-old mice, i.e., mice that had already completed 70 percent of the median life span (approximately 26 months) of the population, but correlations of longevity and T-cells subsets tested in (Tuffery, 1966), which is seen only in nondominant males housed with more aggressive males.This lesion, thought to be secondary to adjustments in dominance hierarchy, typically causes death at relatively early ages, and therefore mice dying of MUS are treated as a separate subgroup.None of the T-cell subsets tested at 8 months of age was able to predict subsequent longevity in the virgin males or virgin females, but there was a significant inverse correlation between CD8M cells and longevity in the mated females.Figure 8-4 shows the scatterplots for all four sets of mice.The correlation for mated females (R = -0.22,p < 0.001) is in the predicted direction, that is, with high levels of memory cells associated with lower life expectancy.There is no correlation in virgin females or in the virgin males dying of causes other than MUS.Males dying of MUS, similar to mated females, show an inverse correlation (R = -0.27,p = 0.13), which, however, is not statistically significant.These data thus support the idea that tests of age-sensitive traits, measured at ages as early as the first third of the life span, may be able to predict subsequent longevity, but raise the concern that the associations may vary with gender and either hormonal exposure or reproductive history.Levels of CD4M and CD8M cells are strongly and positively correlated at all ages (R = 0.70, 0.65, and 0.40 at 8, 14, and 20 months, respectively, all p < 0.005) (Miller, 1997b), and there is no a priori reason to expect that the former subset would be associated with longevity only in virgin animals and the latter only in mated females.We have now initiated a number of collaborations to see if these subsets correlate in expected directions with indices of age-sensitive change in cells and tissues outside the immune system, as well as with life span and protective immune function in these heterogeneous mice." + }, + { + "document_id": "75e0ffe8-7675-4e11-be3e-880bfeb3dabd", + "section_type": "main", + "text": "Bogue MA, Peters LL, Paigen B, Korstanje R, Yuan R, Ackert-Bicknell C, et al. Accessing Data\nResources in the Mouse Phenome Database for Genetic Analysis of Murine Life Span and Health\nSpan. J Gerontol A Biol Sci Med Sci. 2016; 71: 170–177. https://doi.org/10.1093/gerona/glu223 PMID:\n25533306\n\n48.\n\n Ackert-Bicknell CL, Shockley KR, Horton LG, Lecka-Czernik B, Churchill GA, Rosen CJ. Strain-specific\neffects of rosiglitazone on bone mass, body composition, and serum insulin-like growth factor-I. Endocrinology. 2009; 150: 1330–1340. https://doi.org/10.1210/en.2008-0936 PMID: 18948404\n\n49.\n\n Yang H, Ding Y, Hutchins LN, Szatkiewicz J, Bell TA, Paigen BJ, et al." + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "section_type": "main", + "text": "Although genes clustered by treatment,\nconsiderable overlap among treatments was nevertheless observed, suggesting a connection among starvation, dessication, and longevity phenotypes previously noted by\nHoffman and Harshman 1999 and others.\n Expression profiling has also been carried out on mice selected in the laboratory for\nincreased voluntary wheel running (Bronikowski et al. 2004). Gene expression profiles\nwere obtained on hippocampus tissue, as that brain region had previously been shown\nto undergo marked physiological changes in response to wheel running." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "section_type": "main", + "text": "Although genes clustered by treatment,\nconsiderable overlap among treatments was nevertheless observed, suggesting a connection among starvation, dessication, and longevity phenotypes previously noted by\nHoffman and Harshman 1999 and others.\n Expression profiling has also been carried out on mice selected in the laboratory for\nincreased voluntary wheel running (Bronikowski et al. 2004). Gene expression profiles\nwere obtained on hippocampus tissue, as that brain region had previously been shown\nto undergo marked physiological changes in response to wheel running." + }, + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "section_type": "main", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n23 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10072, 10072\ngenenetwork.org/\nshow_trait?trait_id=\n10072&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10073, 10073\ngenenetwork.org/\nshow_trait?trait_id=\n10073&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10074, 10074\ngenenetwork.org/\nshow_trait?trait_id=\n10074&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10075, 10075\ngenenetwork.org/\nshow_trait?trait_id=\n10075&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10076, 10076\ngenenetwork.org/\nshow_trait?trait_id=\n10076&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2022\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10093, 10093\ngenenetwork.org/\nshow_trait?trait_id=\n10093&dataset=BXD-\nLongevityPublish\n\nThe following previously published datasets were used:\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10001, 10001\ngenenetwork.org/\nshow_trait?trait_id=\n10001&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10002, 10002\ngenenetwork.org/\nshow_trait?trait_id=\n10002&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10003, 10003\ngenenetwork.org/\nshow_trait?trait_id=\n10003&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10004, 10004\ngenenetwork.org/\nshow_trait?trait_id=\n10004&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10005, 10005\ngenenetwork.org/\nshow_trait?trait_id=\n10005&dataset=BXD-\nLongevityPublish\n\nContinued on next page\n\nMozhui et al." + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "section_type": "main", + "text": "Burger, J. M. S., K. Munjong, J. Pont, and T. Kawecki. 2008. Learning ability and longevity:\nA symmetrical evolutionary trade-off. Evolution 62:1294–1304.\n Carlson, K. A., and L. G. Harshman. 1999a. Extended longevity lines of Drosophila\nmelanogaster: Abundance of yolk protein gene mRNA in fat body and ovary. Experimental\nGerontology 34:173–184.\n ———. 1999b. Extended longevity lines of Drosophila melanogaster: Characterization of\noocyte stages and ovariole numbers as a function of age and diet. Journal of Gerontology,\nBiological Sciences 54A:B432–B440.\n Carlson, K. A., T. J. Nusbaum, M. R. Rose, and L. G. Harshman. 1998." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "section_type": "main", + "text": "Burger, J. M. S., K. Munjong, J. Pont, and T. Kawecki. 2008. Learning ability and longevity:\nA symmetrical evolutionary trade-off. Evolution 62:1294–1304.\n Carlson, K. A., and L. G. Harshman. 1999a. Extended longevity lines of Drosophila\nmelanogaster: Abundance of yolk protein gene mRNA in fat body and ovary. Experimental\nGerontology 34:173–184.\n ———. 1999b. Extended longevity lines of Drosophila melanogaster: Characterization of\noocyte stages and ovariole numbers as a function of age and diet. Journal of Gerontology,\nBiological Sciences 54A:B432–B440.\n Carlson, K. A., T. J. Nusbaum, M. R. Rose, and L. G. Harshman. 1998." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "section_type": "main", + "text": "Because most of the mice in our lifespan study were\ncannibalized before they were found, we did not conduct pathology studies, nor did we have\nsufficient funds to perform detailed autopsies.\n\n NIH-PA Author Manuscript\n\nIt’s also important to note that our lifespan data correlated significantly with female fertility,\npost DR (R = 0.44, P = 0.006, N = 33 strains). This observation suggests genetic segregation\nof a common anti-aging component, which we called Aging Measure 1. Several previous\nstudies of female reproductive capabilities under DR (Weindruch and Walford, 1988; Merry\nand Holehan, 1991; Johnston et al." + }, + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "section_type": "main", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n23 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10072, 10072\ngenenetwork.org/\nshow_trait?trait_id=\n10072&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10073, 10073\ngenenetwork.org/\nshow_trait?trait_id=\n10073&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10074, 10074\ngenenetwork.org/\nshow_trait?trait_id=\n10074&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10075, 10075\ngenenetwork.org/\nshow_trait?trait_id=\n10075&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10076, 10076\ngenenetwork.org/\nshow_trait?trait_id=\n10076&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2022\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10093, 10093\ngenenetwork.org/\nshow_trait?trait_id=\n10093&dataset=BXD-\nLongevityPublish\n\nThe following previously published datasets were used:\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10001, 10001\ngenenetwork.org/\nshow_trait?trait_id=\n10001&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10002, 10002\ngenenetwork.org/\nshow_trait?trait_id=\n10002&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10003, 10003\ngenenetwork.org/\nshow_trait?trait_id=\n10003&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10004, 10004\ngenenetwork.org/\nshow_trait?trait_id=\n10004&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10005, 10005\ngenenetwork.org/\nshow_trait?trait_id=\n10005&dataset=BXD-\nLongevityPublish\n\nContinued on next page\n\nMozhui et al." + } + ], + "document_id": "2D2D12594F1A6AC91E150695D70A4FFA", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "C57BL/6&allele", + "C3H&allele", + "BALB&allele", + "D2Mit58", + "D16Mit182", + "longevity", + "mouse", + "genetic", + "D12Mit167", + "IGF-1" + ], + "metadata": [ + { + "object": "using in vitro prolactin induced lactogenic differentiation in an HC11 mouse cell model and an in vivo conditional knockout mouse model we showed that mouse Zfhx3 is essential for mouse mammary epithelial cell differentiation and mouse mammary gland development at the lactation stage through regulation of prolactin receptor expression and the downstream Jak2-Stat5 signaling pathway.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab989160" + }, + { + "object": "Genetic variants of mA3 are associated with the restriction factor Rfv3 recovery from Friend leukemia virus and with resistance to mouse mammary tumor virus. We sequenced mA3 from laboratory strains and wild mouse species to examine its evolution. We discovered that the mA3 allele in virus resistant mice such as C57BL/6J but not DBA/2J is disrupted by insertion of the regulatory sequences of a mouse leukemia virus, and this insertion is associated with enhanced mA3 expression. C Kozak", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab2087" + }, + { + "object": "Enhancing IGF-1 expression by astrocytes provided hippocampal neuroprotection and improved memory and motor function after traumatic brain injury. Delivering IGF-1 through reactive astrocytes targeted IGF-1 overexpression to the damaged hippocampus, producing a progressive increase in IGF-1 over 72 h which led to activation of the Akt pro-survival pathway and reduced hippocampal neuron loss in multiple regions.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab259579" + }, + { + "object": "Study found that IL-6, GP130, IGF-1 and IGF-1R were highly expressed in non-small cell lung cancer NSCLC and there was the correlation between GP130, IGF-1, and IGF-1R. Co-stimulation of IL-6 and IGF-1 resulted in significantly enhanced cell proliferation, invasion, and apoptosis of NSCLC cells. This experiment revealed that IL-6 and IGF-1 can synergistically promote the progression of NSCLC.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab741940" + }, + { + "object": "Strong cis eQTL LRS of 60, LRS 22, high B in mouse BXD data sets EPFL/LISP BXD HFD Muscle Affy Mouse Gene 1.0 ST Nov12 RMA Exon Level and in EPFL/LISP BXD CD+HFD and Liver Affy Mouse Gene 1.0 ST Apr13 RMA. Close to Numts and linked to longevity.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab5403" + }, + { + "object": "The rasH2 mouse is a hemizygous transgenic mouse carrying the c-Ha-ras oncogene and that gene's promoter/enhancer within the genetic background of a BALB/cByJ x C57BL/6F1 mouse.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab854885" + }, + { + "object": "review on novel mouse genetic studies that manipulate mHtt to answer questions related to spatio-temporal requirement for mHtt expression in eliciting Huntington's disease-like phenotypes in mouse models and on novel mouse models that aim to address the impact of huntingtin cis-domains or post-translational modifications on disease pathogenesis", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab677590" + }, + { + "object": "IGF-1 has been associated with the pathogenesis of diabetic retinopathy\\r\\nA paracrine effect of IGF-1 in the retina initiated vascular alterations that progressed from nonproliferative to proliferative retinopathy and retinal detachment.\\r\\nIncreased IGF-1 induction of VEGF expression in retinal glial cells\\r\\nThese findings suggest a role of IGF-1 in the development of ocular complications in long-term diabetes.\\r\\nMK, Yates Lab Summer 2015", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab2596" + }, + { + "object": "the effect of genetic inactivation of K-Cl cotransporters KCC1 and KCC3 in a mouse model of beta-thalassemia intermedia; conclude that genetic inactivation of K-Cl cotransport can reverse red cell dehydration and partially attenuate the hematologic phenotype in a mouse model of beta-thalassemia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab804082" + }, + { + "object": "IGF-1 and IGF-1 receptor may be involved in the pathogenesis of Graves' disease; IGF-1 and IGF-1 receptor act by different mechanisms paracrine vs. autocrine as suggested by their differential expression in epithelial and stromal cells.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab376152" + } + ], + "question": "Which mouse genes have been associated with longevity?", + "subquestions": null, + "task_id": "2D2D12594F1A6AC91E150695D70A4FFA", + "usage": { + "chatgpt": 9852, + "gpt-4": 6314, + "gpt-4-turbo-preview": 4895 + }, + "user_id": 2 + }, + "document_id": "2D2D12594F1A6AC91E150695D70A4FFA", + "task_id": "2D2D12594F1A6AC91E150695D70A4FFA" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/02.json b/gnqa/paper1_eval/src/data/responses/aging/experts/02.json new file mode 100644 index 0000000..44da449 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/02.json @@ -0,0 +1,398 @@ +{ + "created_at": "2024-05-31T13:01:42.014610Z", + "data": { + "amplify": false, + "answer": "Lifespan is partially determined by genetics, with studies estimating that genetics contribute to about 25-30% of the variation in human lifespan. However, it is also significantly influenced by non-heritable factors such as environment and lifestyle.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nOur results show that longevity is partly determined by the predisposition to common diseases and, to an even greater extent, by modifiable risk factors.The genetic architecture of lifespan appears complex and diverse and there appears to be no single genetic elixir of long life." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nL ongevity is of interest to us all, and philosophers have long speculated on the extent to which it is pre-determined by fate.Here we focus on a narrower question-the extent and nature of its genetic basis and how this inter-relates with that of health and disease traits.In what follows, we shall use longevity as an umbrella term.We shall also more specifically refer to lifespan (the duration of life) and long-livedness (living to extreme old age, usually defined by a threshold, such as 90 years).Up to 25% of the variability in human lifespan has been estimated to be genetic 1 , but genetic variation at only three loci (near APOE, FOXO3A and CHRNA3/5) [2][3][4][5] have so far been demonstrated to be robustly associated with lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "GENETICS OF LIFE SPAN IN HUMANS\n\nMost studies of human twins agree that the heritability of life span is less than 50% (45,68).Of particular interest is an ongoing study of aging in Swedish twins that includes a large group of adopted twins who were reared separately.Ljungquist et al. (68) concluded that \"a maximum of one-third the variance in integrated mortality risk is attributable to genetic factors and that almost all of the remaining variance is due to nonshared, individually unique environmental factors. \"Moreover, this heritability declined with age and was negligible after the age of 85 in men and 90 in women." + } + ], + "1ccb0d11-1c88-4b08-b40d-4039a954745f": [ + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "text": "\n\nHow can lifespan be controlled by a single gene?Two possibilities are, first, that the mutations that extend lifespan are in genes whose products regulate the activity of many other genes and, second, that these genes do not in fact control the rate of ageing." + } + ], + "4ca8d070-8b58-4bd5-86be-127089b70324": [ + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "text": "\n\nSince that time, observations across species have shown that life span can be extended by genetic factors.One of the first demonstrations of this entailed the study of recombinant inbred populations of the nematode worm Caenorhabditis elegans by Thomas E. Johnson.Then a postdoc in William (Bill) Wood's lab at the University of Colorado Boulder, Tom and Bill demonstrated that crosses of C. elegans strains did not display the heterosis effect that interfered with many other studies, \"As predicted, we found significant genetic effects on life span as well as other life history traits. \"This finding established a method for evaluating genetic factors that influenced life-span variation.In fact, their measurements of life span of the recombinant inbred strains demonstrated the heritability of life span to be 19%-51% (1).Consistent with theories of the 1970s and 1980s, it was concluded that these genetic factors were a collection of small influences across many genes.This finding was one of the first steps in demonstrating that genetic factors influence aging.As genetic analysis was making great progress in understanding other biological processes, such as developmental programming, the realization that aging could be investigated using the same tools was highly significant." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nAlthough it is known that health and lifespan are heavily influenced by genetics [14], variations in the lifespan of different individuals within the same species seem to be more the result of the accumulation over time of molecular damage that compromises the function of the cells [15].These molecular alterations can occur both at the genetic and epigenetic levels and depend on genetic, environmental, and stochastic factors [16].This complex multifactorial mix determined characteristics, such as longevity and a healthy lifespan, which are central concerns of human existence (Fig. 13.1).This chapter describes different types of tools in genomics used in ageing research and their different applications in clinical scenarios." + } + ], + "593b752f-f448-47be-8b83-13bc5e9eb0d4": [ + { + "document_id": "593b752f-f448-47be-8b83-13bc5e9eb0d4", + "text": "\n\nAge at death in adulthood has a moderate genetic component overall, with a heritability of approximately 25% (Murabito et al., 2012).Heritability of longevity increases with age, with a negligible genetic contribution to survival up to approximately 60 years of age, after which an increasing genetic component to survival is observed (Brooks-Wilson, 2013;Christensen et al., 2006).Most genetic studies of aging have focused on long-lived individuals, typically defined as centenarians 100 years or older, who may have had exceptional survival due to medical interventions (Murabito et al., 2012).A number of genetic associations with exceptional longevity have been made (Atzmon et al., 2006;Bojesen and Nordestgaard, 2008;Hurme et al., 2005;Kuningas et al., 2007;Melzer et al., 2007;Pawlikowska et al., 2009;Sanders et al., 2010;Suh et al., 2008;Willcox et al., 2008), with only markers at APOE and FOXO3A being well replicated (Murabito et al., 2012).Overall, the results of genetic and epidemiological longevity studies suggest aging is a complex trait and that achievement of exceptional longevity may not best capture the genetics of resistance to or delay of age-associated disease (Christensen et al., 2006)." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "Introduction\n\nWorldwide human populations have shown an increase in mean life expectancy in the past two centuries (Oeppen & Vaupel, 2002).This is mainly because of environmental factors such as improved hygiene, nutrition, and health care.The large variation in healthy lifespan among the elderly has prompted research into the determinants of aging and lifespan regulation.The genetic contribution to human lifespan variation was estimated at 25-30% in twin studies (Gudmundsson et al., 2000;Skytthe et al., 2003;Hjelmborg et al., 2006).The most prominent genetic influence is observed in families in which the capacity to attain a long lifespan clusters (Perls et al., 2000;Schoenmaker et al., 2006).Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al., 2008;Terry et al., 2008), raising the question whether protective mechanisms against disease exist in long-lived subjects." + } + ], + "78a43a45-84b0-4d73-9396-95b99cfd3983": [ + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "text": "Introduction\n\nHuman lifespan is a highly complex trait, the product of myriad factors involving health, lifestyle, genetics, environment, and chance.The extent of the role of genetic variation in human lifespan has been widely debated (van den Berg et al., 2017), with estimates of broad sense heritability ranging from around 25% based on twin studies (Ljungquist et al., 1998;Herskind et al., 1996;McGue et al., 1993) (perhaps over-estimated [Young et al., 2018]) to around 16.1%, (narrow sense 12.2%) based on large-scale population data (Kaplanis et al., 2018).One very recent study suggests it is much lower still (<7%) (Ruby et al., 2018), pointing to assortative mating as the source of resemblance amongst kin." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "text": "\n\nMany factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone.Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases.If such genes exist, their effects were too small to be detected in this study.The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "Life Span\n\nDuring the last decade a variety of twin studies have shown that approximately 25 percent of the variation in life span is caused by genetic differences.This seems to be a rather consistent finding in various Nordic countries in different time periods and even so among other species not living in the wild (Herskind et al., 1996;Iachine et al., 1999;Finch and Tanzi, 1997).their relative magnitude and pattern depend on sex and on the socioeconomic environment experienced by successive birth cohorts.Genetic effects were most pronounced in periods with consciously controlled fertility, suggesting that the genetic disposition primarily affects fertility behavior and motivation for having children.Analyses of fertility motivation in some of the more recent twin cohorts, measured by age at first attempt to have children, supported this interpretation." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "The Height-Life Span Nexus\n\nSeveral observations and lines of experimentation have raised the issue of whether interindividual differences in aging rate are influenced by genes that modulate body size and early-life growth patterns.These include (a) the association between small stature and exceptional longevity in calorically restricted rodents (Yu et al., 1985), methionine-restricted rats (Orentreich et al., 1993), and mutant dwarf mice (Brown-Borg et al., 1996;Miller, 1999); and (b) the association between small body size and longer life span in natural populations of mice (Falconer et al., 1978), flies (Hillesheim and Stearns, 1992), dogs (Li et al., 1996), and, possibly, people (Samaras andStorms, 1992).The correlation in dogs is particularly striking: selective breeding for dogs of different body size has produced breeds varying in size from Chihuahua to Irish wolfhound.These breeds also vary greatly in mean longevity, from approximately 7 to 10.5 years, and the correlation between breed longevity and breed body weight (Miller, 1999) is a remarkable R 2 = 0.56.These differences are genetic and affect stature rather than obesity: no amount of overeating will convert a West Highland white terrier to a St. Bernard.The selective pressures applied were designed to create dogs of specific sizes and temperaments and were not intended to influence aging rate or life span.The clear implication is that the effects on longevity are pleiotropic, i.e., that genes selected for their effect on body size and conformation influenced life span as a side effect.It is of interest to note that the few analyses (Eigenmann et al., 1984(Eigenmann et al., , 1988) ) of the hormonal basis for interbreed differences in body size have shown that the genes in question influence levels of IGF-1, the most likely mediator of the life-span effects in the long-lived df/df and dw/dw mouse mutants.Could it be mere coincidence that long-lived mutant nematode worms (Kimura et al., 1997) also show mutations in genes related to insulin and IGF-1 receptors?" + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nAltogether, the twin and genealogical studies have shown that human lifespan is heritable, but is significantly influenced by non-heritable factors, which may explain why genetic studies of lifespan have proven to be challenging." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nTwin studies have shown that the heritability of lifespan ranges between 0.01 and 0.27 in various European populations (Ljungquist et al., 1998;van den Berg et al., 2017).Large genealogical studies are more powered to address questions FIGURE 1 | Relationship between aging and lifespan variation versus species defining lifespan. (A) Lifespan comparisons within species, measured as mean (50%) or portion of a population living till extended limits of lifespan (90-95%).Differences between populations (orange and green) can identify specific genetic or environmental changes associating with long life.These factors promote viability and often associate with increasing healthspan.Mutant analysis within a particular model organism often encompasses these types of changes as it relates to lifespan. (B) Maximum lifespans recorded for different species (A-E).While lifespan variation within a species is capped to a certain extent, variation between species can range dramatically.Changes to maximum lifespan often are associated with protective mechanisms for genomic and genetic fidelity as well as life history changes as they relate to maturation and reproduction." + } + ], + "c7361625-831a-44a2-b04d-157a49d00c6a": [ + { + "document_id": "c7361625-831a-44a2-b04d-157a49d00c6a", + "text": "\n\nThe genetic component of human lifespan based on twin studies has been estimated to be around 20-30 percent in the normal population [7], but higher in long-lived families [8][9][10].Furthermore, siblings, parents, and offspring of centenarians also live well beyond average [11,12].Lifestyle choices in terms of smoking, alcohol consumption, exercise, or diet does not appear to differ between centenarians and controls [13].Taken together, these findings provide ample evidence that extreme longevity has a genetic component ." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + } + ], + "dbf4c446-7c25-470a-9532-a564b8683eef": [ + { + "document_id": "dbf4c446-7c25-470a-9532-a564b8683eef", + "text": "\n\nUnraveling the heritability of human longevity was one of the first problems faced by geneticists.Just over a century ago, Mary Beeton and Karl Pearson [1] described a resemblance among relatives for the duration of life.A short time later, Yule [2] and Fisher [3] proved that the correlation is to be expected if lifespan is influenced by what had recently been termed 'genes' [4].Indeed, a century of correlation studies have established that something on the order of 30-50% of the total variation in human life span is attributable to genetic variation [5].Despite the wealth of diversity, specific genes contributing to this variation have proven notoriously difficult to identify.Sample size and issues of shared environment limit family-based methods such as linkage analysis, where rough genomic positions of important genetic variants are identified by comparing a small number of exceptionally long-lived people in defined pedigrees." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nStudies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches." + } + ] + }, + "data_source": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nAltogether, the twin and genealogical studies have shown that human lifespan is heritable, but is significantly influenced by non-heritable factors, which may explain why genetic studies of lifespan have proven to be challenging." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nAlthough it is known that health and lifespan are heavily influenced by genetics [14], variations in the lifespan of different individuals within the same species seem to be more the result of the accumulation over time of molecular damage that compromises the function of the cells [15].These molecular alterations can occur both at the genetic and epigenetic levels and depend on genetic, environmental, and stochastic factors [16].This complex multifactorial mix determined characteristics, such as longevity and a healthy lifespan, which are central concerns of human existence (Fig. 13.1).This chapter describes different types of tools in genomics used in ageing research and their different applications in clinical scenarios." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "main", + "text": "\n\nOur results show that longevity is partly determined by the predisposition to common diseases and, to an even greater extent, by modifiable risk factors.The genetic architecture of lifespan appears complex and diverse and there appears to be no single genetic elixir of long life." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "section_type": "main", + "text": "Introduction\n\nHuman lifespan is a highly complex trait, the product of myriad factors involving health, lifestyle, genetics, environment, and chance.The extent of the role of genetic variation in human lifespan has been widely debated (van den Berg et al., 2017), with estimates of broad sense heritability ranging from around 25% based on twin studies (Ljungquist et al., 1998;Herskind et al., 1996;McGue et al., 1993) (perhaps over-estimated [Young et al., 2018]) to around 16.1%, (narrow sense 12.2%) based on large-scale population data (Kaplanis et al., 2018).One very recent study suggests it is much lower still (<7%) (Ruby et al., 2018), pointing to assortative mating as the source of resemblance amongst kin." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "section_type": "main", + "text": "\n\nMany factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone.Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases.If such genes exist, their effects were too small to be detected in this study.The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "main", + "text": "\n\nL ongevity is of interest to us all, and philosophers have long speculated on the extent to which it is pre-determined by fate.Here we focus on a narrower question-the extent and nature of its genetic basis and how this inter-relates with that of health and disease traits.In what follows, we shall use longevity as an umbrella term.We shall also more specifically refer to lifespan (the duration of life) and long-livedness (living to extreme old age, usually defined by a threshold, such as 90 years).Up to 25% of the variability in human lifespan has been estimated to be genetic 1 , but genetic variation at only three loci (near APOE, FOXO3A and CHRNA3/5) [2][3][4][5] have so far been demonstrated to be robustly associated with lifespan." + }, + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "section_type": "main", + "text": "GENETICS OF LIFE SPAN IN HUMANS\n\nMost studies of human twins agree that the heritability of life span is less than 50% (45,68).Of particular interest is an ongoing study of aging in Swedish twins that includes a large group of adopted twins who were reared separately.Ljungquist et al. (68) concluded that \"a maximum of one-third the variance in integrated mortality risk is attributable to genetic factors and that almost all of the remaining variance is due to nonshared, individually unique environmental factors. \"Moreover, this heritability declined with age and was negligible after the age of 85 in men and 90 in women." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "The Height-Life Span Nexus\n\nSeveral observations and lines of experimentation have raised the issue of whether interindividual differences in aging rate are influenced by genes that modulate body size and early-life growth patterns.These include (a) the association between small stature and exceptional longevity in calorically restricted rodents (Yu et al., 1985), methionine-restricted rats (Orentreich et al., 1993), and mutant dwarf mice (Brown-Borg et al., 1996;Miller, 1999); and (b) the association between small body size and longer life span in natural populations of mice (Falconer et al., 1978), flies (Hillesheim and Stearns, 1992), dogs (Li et al., 1996), and, possibly, people (Samaras andStorms, 1992).The correlation in dogs is particularly striking: selective breeding for dogs of different body size has produced breeds varying in size from Chihuahua to Irish wolfhound.These breeds also vary greatly in mean longevity, from approximately 7 to 10.5 years, and the correlation between breed longevity and breed body weight (Miller, 1999) is a remarkable R 2 = 0.56.These differences are genetic and affect stature rather than obesity: no amount of overeating will convert a West Highland white terrier to a St. Bernard.The selective pressures applied were designed to create dogs of specific sizes and temperaments and were not intended to influence aging rate or life span.The clear implication is that the effects on longevity are pleiotropic, i.e., that genes selected for their effect on body size and conformation influenced life span as a side effect.It is of interest to note that the few analyses (Eigenmann et al., 1984(Eigenmann et al., , 1988) ) of the hormonal basis for interbreed differences in body size have shown that the genes in question influence levels of IGF-1, the most likely mediator of the life-span effects in the long-lived df/df and dw/dw mouse mutants.Could it be mere coincidence that long-lived mutant nematode worms (Kimura et al., 1997) also show mutations in genes related to insulin and IGF-1 receptors?" + }, + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "section_type": "main", + "text": "\n\nHow can lifespan be controlled by a single gene?Two possibilities are, first, that the mutations that extend lifespan are in genes whose products regulate the activity of many other genes and, second, that these genes do not in fact control the rate of ageing." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "dbf4c446-7c25-470a-9532-a564b8683eef", + "section_type": "main", + "text": "\n\nUnraveling the heritability of human longevity was one of the first problems faced by geneticists.Just over a century ago, Mary Beeton and Karl Pearson [1] described a resemblance among relatives for the duration of life.A short time later, Yule [2] and Fisher [3] proved that the correlation is to be expected if lifespan is influenced by what had recently been termed 'genes' [4].Indeed, a century of correlation studies have established that something on the order of 30-50% of the total variation in human life span is attributable to genetic variation [5].Despite the wealth of diversity, specific genes contributing to this variation have proven notoriously difficult to identify.Sample size and issues of shared environment limit family-based methods such as linkage analysis, where rough genomic positions of important genetic variants are identified by comparing a small number of exceptionally long-lived people in defined pedigrees." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "Life Span\n\nDuring the last decade a variety of twin studies have shown that approximately 25 percent of the variation in life span is caused by genetic differences.This seems to be a rather consistent finding in various Nordic countries in different time periods and even so among other species not living in the wild (Herskind et al., 1996;Iachine et al., 1999;Finch and Tanzi, 1997).their relative magnitude and pattern depend on sex and on the socioeconomic environment experienced by successive birth cohorts.Genetic effects were most pronounced in periods with consciously controlled fertility, suggesting that the genetic disposition primarily affects fertility behavior and motivation for having children.Analyses of fertility motivation in some of the more recent twin cohorts, measured by age at first attempt to have children, supported this interpretation." + }, + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "section_type": "main", + "text": "\n\nSince that time, observations across species have shown that life span can be extended by genetic factors.One of the first demonstrations of this entailed the study of recombinant inbred populations of the nematode worm Caenorhabditis elegans by Thomas E. Johnson.Then a postdoc in William (Bill) Wood's lab at the University of Colorado Boulder, Tom and Bill demonstrated that crosses of C. elegans strains did not display the heterosis effect that interfered with many other studies, \"As predicted, we found significant genetic effects on life span as well as other life history traits. \"This finding established a method for evaluating genetic factors that influenced life-span variation.In fact, their measurements of life span of the recombinant inbred strains demonstrated the heritability of life span to be 19%-51% (1).Consistent with theories of the 1970s and 1980s, it was concluded that these genetic factors were a collection of small influences across many genes.This finding was one of the first steps in demonstrating that genetic factors influence aging.As genetic analysis was making great progress in understanding other biological processes, such as developmental programming, the realization that aging could be investigated using the same tools was highly significant." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "abstract", + "text": "\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nTwin studies have shown that the heritability of lifespan ranges between 0.01 and 0.27 in various European populations (Ljungquist et al., 1998;van den Berg et al., 2017).Large genealogical studies are more powered to address questions FIGURE 1 | Relationship between aging and lifespan variation versus species defining lifespan. (A) Lifespan comparisons within species, measured as mean (50%) or portion of a population living till extended limits of lifespan (90-95%).Differences between populations (orange and green) can identify specific genetic or environmental changes associating with long life.These factors promote viability and often associate with increasing healthspan.Mutant analysis within a particular model organism often encompasses these types of changes as it relates to lifespan. (B) Maximum lifespans recorded for different species (A-E).While lifespan variation within a species is capped to a certain extent, variation between species can range dramatically.Changes to maximum lifespan often are associated with protective mechanisms for genomic and genetic fidelity as well as life history changes as they relate to maturation and reproduction." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nStudies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "section_type": "main", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + }, + { + "document_id": "c7361625-831a-44a2-b04d-157a49d00c6a", + "section_type": "main", + "text": "\n\nThe genetic component of human lifespan based on twin studies has been estimated to be around 20-30 percent in the normal population [7], but higher in long-lived families [8][9][10].Furthermore, siblings, parents, and offspring of centenarians also live well beyond average [11,12].Lifestyle choices in terms of smoking, alcohol consumption, exercise, or diet does not appear to differ between centenarians and controls [13].Taken together, these findings provide ample evidence that extreme longevity has a genetic component ." + }, + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "section_type": "main", + "text": "Introduction\n\nWorldwide human populations have shown an increase in mean life expectancy in the past two centuries (Oeppen & Vaupel, 2002).This is mainly because of environmental factors such as improved hygiene, nutrition, and health care.The large variation in healthy lifespan among the elderly has prompted research into the determinants of aging and lifespan regulation.The genetic contribution to human lifespan variation was estimated at 25-30% in twin studies (Gudmundsson et al., 2000;Skytthe et al., 2003;Hjelmborg et al., 2006).The most prominent genetic influence is observed in families in which the capacity to attain a long lifespan clusters (Perls et al., 2000;Schoenmaker et al., 2006).Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al., 2008;Terry et al., 2008), raising the question whether protective mechanisms against disease exist in long-lived subjects." + }, + { + "document_id": "593b752f-f448-47be-8b83-13bc5e9eb0d4", + "section_type": "main", + "text": "\n\nAge at death in adulthood has a moderate genetic component overall, with a heritability of approximately 25% (Murabito et al., 2012).Heritability of longevity increases with age, with a negligible genetic contribution to survival up to approximately 60 years of age, after which an increasing genetic component to survival is observed (Brooks-Wilson, 2013;Christensen et al., 2006).Most genetic studies of aging have focused on long-lived individuals, typically defined as centenarians 100 years or older, who may have had exceptional survival due to medical interventions (Murabito et al., 2012).A number of genetic associations with exceptional longevity have been made (Atzmon et al., 2006;Bojesen and Nordestgaard, 2008;Hurme et al., 2005;Kuningas et al., 2007;Melzer et al., 2007;Pawlikowska et al., 2009;Sanders et al., 2010;Suh et al., 2008;Willcox et al., 2008), with only markers at APOE and FOXO3A being well replicated (Murabito et al., 2012).Overall, the results of genetic and epidemiological longevity studies suggest aging is a complex trait and that achievement of exceptional longevity may not best capture the genetics of resistance to or delay of age-associated disease (Christensen et al., 2006)." + }, + { + "document_id": "c8fbb24d-0a72-4a45-a552-6cd98a4a25a2", + "section_type": "main", + "text": "Translational\n\nA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nThe recent emergence of the UK Biobank has significantly enhanced research on the genetics of lifespan.The most recent effort using parental lifespan data from this databank, as well as several additional studies in the LifeGen initiative, has resulted in the identification of 12 loci that passed threshold for genomewide significance (5 * 10 −8 ).Many of the loci have previously been associated with age-related diseases, including cardiometabolic, autoimmune and neuropsychiatric diseases -all underlying major death causes -which likely explains their association with lifespan in this study (Timmers et al., 2019)." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Influence of Genetic Factors in Ageing and Lifespan\n\nAgeing is defined as the decline of physiological functions in several tissues and organs inducing an increasing probability of death [17].The understanding of genetic factors involved in ageing has been limited due to the complexity of this process and the heterogeneity among individuals and even among tissues [18][19][20].Tissue cells adopt a senescent phenotype as a consequence of multiple intrinsic, extrinsic, and stochastic factors [21].The combination of these genetic factors is related to longevity and healthy ageing [22].Although this decline is somewhat predictable, some individuals show a much slower decline and get to live past the age of 100.Studies in these individuals showed polymorphisms in some genes which are associated with long life, such as APOE and FOXO3.However, these associations have not been consistent across different populations, suggesting that ageing is rather polygenic [23]." + }, + { + "document_id": "da4a9500-831f-48ab-acea-5ec7097276ed", + "section_type": "main", + "text": "\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways." + }, + { + "document_id": "e4773b3b-814d-4306-8250-59dc03f09bc2", + "section_type": "main", + "text": "\n\nLarge differences in species maximum lifespan potential [MLSP] must ultimately be genetically encoded; however, if a specific ''lifespan program'' existed, one might expect that genetic revertants of such a program could be identified to enable immortality.To date, no such observation has been made.So while it is highly unlikely that age of death is programmed, genetic regulation of the many pathways that contribute to survival of the individual (e.g., resistance to stress, damage eradication, and/or somatic repair), as well as genetic regulation of the metabolic pathways that inflict age-related damage, is likely to be directly involved in organismal longevity (Gems and Partridge 2013)." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "section_type": "main", + "text": "\n\nThe DNA of over 500,000 people was read to reveal the specific 'genetic fingerprints' of each participant.Then, after asking each of the participants how long both of their parents had lived, Timmers et al. pinpointed 12 DNA regions that affect lifespan.Five of these regions were new and had not been linked to lifespan before.Across the twelve as a whole several were known to be involved in Alzheimer's disease, smoking-related cancer or heart disease.Looking at the entire genome, Timmers et al. could then predict a lifespan score for each individual, and when they sorted participants into ten groups based on these scores they found that top group lived five years longer than the bottom, on average." + }, + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "section_type": "main", + "text": "\n\nT he average human life expectancy has been increasing for centuries 1 .Based on twin studies, the heritability of human lifespan has been estimated to be ~25%, although this estimate differs among studies 2 .On the other hand, the heritability of lifespan based on the correlation of the mid-parent (i.e., the average of the father and mother) and offspring difference between age at death and expected lifespan was estimated to be 12% 3 .A recent study has indicated that the different heritability estimates may be inflated due to assortative mating, leaving a true heritability that is below 10% 4 .The heritability of lifespan, estimated using the sibling relative risk, increases with age 5 and is assumed to be enriched in long-lived families, particularly when belonging to the 10% longest-lived of their generation 6 .To identify genetic associations with human lifespan, several genome-wide association (GWA) studies have been performed [7][8][9][10][11][12][13][14][15][16][17][18][19][20] .These studies have used a discrete (i.e., older cases versus younger controls) or a continuous phenotype (such as age at death of individuals or their parents).The selection of cases for the studies using a discrete longevity phenotype has been based on the survival to ages above 90 or 100 years or belonging to the top 10% or 1% of survivors in a population.Studies defining cases using a discrete longevity phenotype often need to rely on controls from more contemporary birth cohorts, because all others from the case birth cohorts have died before sample collection.Previous GWA studies have identified several genetic variants, but the only locus that has shown genome-wide significance (P ≤ 5 × 10 −8 ) in multiple independent meta-analyses of GWA studies is apolipoprotein E (APOE) 21 , where the ApoE ε4 variant is associated with lower odds of being a long-lived case." + }, + { + "document_id": "e4773b3b-814d-4306-8250-59dc03f09bc2", + "section_type": "main", + "text": "\n\nAging and longevity research has relied extensively on a battery of commonly used and relatively short-lived eukaryote model organisms, namely yeast, worms, flies, and fish, as well as mice and rats, to explore both genetic and environmental determinants of lifespan.While these short-lived models have each yielded a number of fascinating findings and insights into hypotheses surrounding extended lifespan and healthspan, they may also have constrained this complex, multifactorial field to areas in which they are best suited, most notably short-term intervention studies and genetic manipulations.Studies based upon these organisms revealed that changes in even a single gene (e.g., age-1, phosphatidylinositol 3 kinase) can extend lifespan of Caenorhabditis elegans (Friedman and Johnson 1988).Similar lifespan extension effects are evident in flies and mice when the insulin/IGF, gastric hormone, and the Nrf2/skn-1 detoxification/xenobiotic pathways are genetically manipulated (Kenyon et al. 1993;Brown-Borg et al. 1996;Morris et al. 1996;Clancy et al. 2001;An and Blackwell 2003;Sykiotis and Bohmann 2008;Selman and Withers 2011;Ziv and Hu 2011).Furthermore, various types of dietary restrictions, whether limiting access to calories or amino acids, generally have a conserved effect of enhancing longevity across model systems (McCay et al. 1935;Klass 1977;Weindruch and Walford 1982;Jiang 2000;Selman and Withers 2011;McIsaac et al. 2016), although exceptions do exist (Liao et al. 2010).Collectively, these data support the premise that longevity can be modulated, likely through the regulation of nutrient signaling and stress response, which in turn impacts development, growth, reproduction, and survival.Strikingly, monozygotic human twins, as well as genetically identical individuals of these animal models (e.g., C57BL/6 mice), even when housed in the same environment and fed the same diet do not all have the same lifespans, suggesting that stochastic factors and epigenetic drift influence the hazard rate (i.e., the risk of death as it changes over a lifespan) and subsequent mortality (Finch and Kirkwood 2000;Herndon et al. 2002;Fraga et al. 2005)." + }, + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "section_type": "main", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nGenes do not drive the aging process but by governing the levels of excess physiological capacity, repair, and turnover they indirectly determine potential longevity.There are no genes that specifically drive longevity but there are genes that govern biological processes that increase the likelihood of survival to reproductive maturity.The variations in excess physiological capacity, repair, and turnover accounts for the variations found in longevity both within and between species." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "section_type": "main", + "text": "\n\nAgeing is complex and takes a long time to study -a lifetime in fact.This makes it difficult to discern its causes, among the countless possibilities based on an individual's genes, behaviour or environment.While thousands of regions in an individual's genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle.Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nsuch as to what extent non-additive genetic variance contributes to the heritability of lifespan.Thus, in more than 3 million pairs of relatives, Kaplanis et al. (2018) found that the additive component of lifespan's heritability was 0.16 (comparable to twin studies), while there was only a mild effect of the non-additive component of heritability (∼0.04).Ruby et al. (2018) using an impressive dataset consisting of hundreds of millions of historical individuals showed a similar heritability of lifespan.The study on the heritability of \"longevity\" performed in twins by Ljungquist et al. (1998) found that the heritability of longevity was higher in women and increased with advancing age.Some of the most interesting individuals that may shed reveal secrets of longevity originate from multigenerational, longevity-enriched families, since such families have propensity to be long-lived, but also seem to evade age-related morbidity.Several genealogical studies of long-lived families evidenced that parental longevity could be considered a proxy for lifespan.Long-lived parents have a high probability to beget long-lived offspring, which gives an indication that longevity is indeed heritable (van den Berg et al., 2017).Notably, members of longlived families have an interesting phenotype beyond extended lifespan, as they seem to be escaping or delaying age-related disease and show a compression of late life morbidity (extended healthspan).Unraveling the genetics of these individuals might help identifying novel mechanisms involved in healthy aging that can subsequently be targeted by therapeutic interventions.An important drawback of longevity research is the arbitrary age thresholds that often were used to signify an extreme age (Baghdadi et al., 2020).In the pre-GWAS era, the age-thresholds used to define longevity were relatively low (i.e., reaching an age above 80 or 85 years) and the sample size was limited.van den Berg et al. (2019) used two independent multi-generational genealogical datasets to determine the most optimal definition of longevity.They found that the strongest heritable component of longevity is present in individuals belonging to the top 10% survivors of their birth cohort with equally long-lived family members (reviewed in Baghdadi et al., 2020)." + }, + { + "document_id": "3c78c2be-0bd2-4954-bb47-8b48f6125ed7", + "section_type": "main", + "text": "\n\nNotably, numerous novel determinants of chronological life span were identified in all three competitive-survival screens (Fabrizio et al. 2010;Gresham et al. 2011;Matecic et al. 2010) as well as the candidate gene approach reported by Burtner et al. (2011).This suggests that many genes involved in chronological aging have yet to be identified.The screen of each individual strain from the deletion collection for increased chronological life span that is currently underway is anticipated to identify many of these unknown genes." + }, + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "section_type": "main", + "text": "\n\nIt is also likely that environmental factors and possibly the genetic ancestry may influence the likelihood of an individual to live long ages directly or by interacting with the genetic background.The NECS has shown that the chance of male and female siblings of centenarians to live past 100 can be 8 and 17 times higher than the risk in the general population (Perls et al., 2002).Consistent with this observation, our data suggest that the genetic contribution increases with older and older ages as the limit of lifespan is approached (Sebastiani et al., 2012).The male supercentenarian included in this study had strong longevity in his family.Although we do not have information about the family history of the female supercentenarian, she has living offspring who are approaching their nineties in good health and are currently enrolled in the NECS.The heterogeneity of the results herein suggest that sequencing additional exceptionally old individuals of different genetic ancestry and possibly their family members will provide the critical information to understand roles of common and rare genetic determinants of exceptional longevity and healthspan." + }, + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "section_type": "main", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "LONGEVITY AND AGING -SEPARATE METRICS OF EXTENT AND QUALITY\n\nThe drive to understand why we have a limited license in life has permeated scientific and artistic thought for millennia.Although lifespan has obvious heritable components, the effect of environmental factors and extrinsic mortality factors shape a complex scenario for which clear answers of the regulation of longevity have been difficult to distill.With the discovery of genetic factors underlying aging in experimental laboratory models, forays into the genetic regulation of these properties have rapidly expanded, uncovering conserved mechanisms across diverse metazoa that influence expression of aging phenotypes and lifespan.Yet, the story gets muddled in that these factors are often quite pleiotropic, having broad roles in normal development and physiology of organisms.To date there has not been a singular defining mechanism or factor specifying how and why we age." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "Longevity Genes-A Special Case\n\nDemographers are fascinated by the possibility that one or more genes might determine the rate of decline in multiple organ systems.Several such genes have been identified in other species (Vaupel et al., 1998).These genes are sometimes called gerontogenes or longevity genes.The discovery of one or more genes that act as aging \"clocks\" in humans would be a major breakthrough for genetics.However, the mere existence of such genes would not have a major effect on demographic research.For example, a mutation in a longevity gene that was present in 0.1 percent of the population would still be rare (probably less than 1 percent) among centenarians. 19Such a genotype would not explain much about survival to the oldest ages.Therefore, in order to be important for demographic research, there would have to be common polymorphisms associated with large differences in survival.Vaupel has estimated that there could be hundreds of genotypes with frequencies of 5-10 percent that lower death rates by 5-10 percent (Vaupel, personal communication)." + }, + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "section_type": "main", + "text": "\n\nAnother major challenge is to uncover the genes and processes that determine the differences in lifespan among animal species.Animal lifespans vary to a remarkable degree, and can evolve rapidly.For example, the common ancestors of Homo sapiens and chimpanzees walked the Earth only some 5.4 million years ago, yet our maximum lifespan is twice that of our closest living relative (w110 years versus w59 years).Do the genes and processes that have been the focus of model organism work (e.g.IIS and cellular detoxification) also specify species differences in ageing?Do they also control the remarkable phenotypic plasticity of lifespan seen in, for instance, social insects?Answering these questions will require an approach analogous to that used in understanding the evolution of differences in development that lead to differences in anatomy (i.e.evolutionary developmental biology, or evodevo).One might naturally refer to such an approach as evolutionary gerontology (or evo-gero) (Box 3)." + } + ], + "document_id": "5AE03C65B85643330DE58348F4946E8A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "genetics", + "lifespan", + "heritability", + "environmental&factors", + "twin&studies", + "genealogical&studies", + "longevity", + "genomic&research", + "biomarker&research", + "aging" + ], + "metadata": [ + { + "object": "AGE are an important factor for cardiac aging and fibrosis, whereas the receptor for AGE and TGF-beta/Smad signaling pathway might be involved in the AGE-induced cardiac aging process.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab49862" + }, + { + "object": "Both normal-expression and over-expression of the CG9940 resulted in positive influences on the adaptation of cardiac functions, mobility, and lifespan to exercise in aging Drosophila. Exercise slowed age-related decline of cardiac function, mobility and extent of lifespan in flies, while lower expression of CG9940 led to negative impacts on the adaptation of mobility and lifespan to exercise in Drosophila.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab107731" + }, + { + "object": "Expression of HDAC4 in hippocampus Affy probe set 10356653, UTHSC BXD Aged Hippocampus Affy Mouse Gene 1.0 ST Jun15 Exon Level RMA has a strong negative correlation with age of animal BXD. Like many other age-linked traits, genetic variance of expression maps to Chr 7 at about 87 Mb also see Smc3, top positive age-associated exon probe set in hippocampus. Rupert Overall, Gerd Kempermann, Lu Lu, and Rob Williams Aug 2019 note by RWW", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1771" + }, + { + "object": "Based on a cumulative risk of 0.55% to age 35 for BRCA1 mutation carriers and of 0.56% to age 45 for BRCA2 mutation carriers, we recommend bilateral salpingo-oophorectomy before age 40, but by age 35, for women with a BRCA1 mutation and by age 45 for those with a BRCA2 mutation to maximize prevention and to minimize adverse effects.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab95128" + }, + { + "object": "Study detected age-related differences in the therapeutic effect of calcium-channel blockers, in association with a commonly occurring genetic variant in the COMT gene; proposed a relevant role of estrogen and catecholamines in the age-specific pathogenesis of hypertension and underline the need for individualized therapy approaches taking age into account.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab740177" + }, + { + "object": "Study of genetic risk of prevalent hrHPV infections in Nigerian women found significant associations with SNPs on ribosomal protein gene S19 RPS19 and Thymidylate Synthase gene TYMS, in an allelic model. This risk remained significant, after adjusting for age, body mass index, smoking, age at menarche, age at sexual debut, lifetime total number of sexual partners and the total number of pregnancies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab745428" + }, + { + "object": "4E-BP determines lifespan in the context of temperature changes, revealing a genetic mechanism for cold-induced longevity in this model organism. Our results suggest that the 4E-BP pathway, chiefly thought of as a nutrient sensor, may represent a master metabolic switch responding to diverse environmental factors", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab10515" + }, + { + "object": "Results showed that median age, menarche age, childbearing age, number of children, menopause age, and body-mass indexes were similar in both HER-2 pos and neg groups. # of involved lymph nodes and HER-2 status found to be prog. factors for survival.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab665374" + }, + { + "object": "Women with PAPP-A </=10th percentile in the first trimester are more likely to have an small-for gestational age infant at all gestational ages. PAPP-A >/=90th percentile is protective against small for gestational age, and is associated with an increased risk of large for gestational age for infants born after 32 weeks gestation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1019190" + }, + { + "object": "Top exon level covariate of age in the hippocampus of the BXD family r of 0.59, n = 229 using \tUTHSC BXD Aged Hippocampus Affy Mouse Gene 1.0 ST Jun15 Exon Level RMA and record ID Record ID 10463979. Strongly bimodal expression of this exon probe set genetic effect with nearly +100 day shift in age of those with high D allele expression that maps to Chr 7 at the lncRNA gene Gm32647 lethality associated and ODZ4 no cis effect at all. Show to Rupert Overall and Gerd Kempermann.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab6050" + } + ], + "question": "Is lifespan determined by genetics?", + "subquestions": null, + "task_id": "5AE03C65B85643330DE58348F4946E8A", + "usage": { + "chatgpt": 8198, + "gpt-4": 4730, + "gpt-4-turbo-preview": 3782 + }, + "user_id": 2 + }, + "document_id": "5AE03C65B85643330DE58348F4946E8A", + "task_id": "5AE03C65B85643330DE58348F4946E8A" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/03.json b/gnqa/paper1_eval/src/data/responses/aging/experts/03.json new file mode 100644 index 0000000..09f95d2 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/03.json @@ -0,0 +1,390 @@ +{ + "created_at": "2024-05-31T20:36:11.762580Z", + "data": { + "amplify": false, + "answer": "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "context": { + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nOur result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes." + } + ], + "6e570a0b-a876-4263-b32f-cee85088756d": [ + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "text": "\n\nThere are two major factors that underlie these alarming projections.The first is T2D is associated with age, and Western populations are aging rapidly.The second major explanation is our lifestyles have changed dramatically in recent years.Epidemiological studies have identified strong T2D risk relationships for obesity, sedentary behavior [2][3][4], and diets rich in energy [5], processed carbohydrates [6], and animal fats [7].Collectively, these lifestyle factors impede the actions of insulin and raise hepatic glucose production, which can result in the diminution of endogenous insulin production and T2D.The strongest evidence for a causal relationship between adverse lifestyle behaviors and T2D comes from randomized controlled trials that show intensive lifestyle interventions involving structured exercise regimes which promote habitual physical activity (PA) and have a major beneficial impact on diabetes incidence in high-risk individuals [8,9]." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "text": "\n\nEpidemiological studies examining the associations between lifestyle behaviors and diabetes risk have reached similar conclusions as the clinical trials described above.For example, the 14-year follow-up University of Pennsylvania Alumni Health Study [52] (n = 5,990 men aged 39-68 years) showed PA (leisure time physical activity [LTPA] expressed in kcal expended per week through walking, stair climbing, and sports) was inversely associated with the incidence of T2D.Incidence rates declined as energy expenditure rose from 500 through 3,500 kcal/week.The age-adjusted relative risk ratio (RR) of T2D was reduced by about 6% for each 500 kcal increment increase in PA energy expenditure." + } + ], + "71172700-7bcc-42f5-9354-d8e9290e8743": [ + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "\n\nOverall, results were similar in analyses restricted to diabetes mellitus identified at baseline only, although the confidence interval included 1.These results suggest that diabetes mellitus is related to risk of AD in old age.These findings are consistent with the results of 2 large longitudinal cohort studies. 5,6In one study, 5 diabetes mellitus doubled the risk of AD during 2 years of follow-up in a sample of more than 6000 older persons from a defined cohort.The other study, 6 using data from about 2500 Japanese American men, found a similar result: diabetes mellitus approximately doubled the risk of AD.In contrast, 2 other longitudinal studies 7,8 did not demonstrate a significant association between diabetes mellitus and incident AD, but in both, the results were in the direction of increased risk.Some, [9][10][11] but not all, 12 previous studies found that diabetes mellitus was related to change in cognitive function.One factor that may contribute to variability from study to study is that diabetes mellitus may be related to decline in some cognitive systems but not others.4][15] Although diabetes mellitus was related to level of global cognition and multiple cognitive domains at baseline, we found that diabetes mellitus was only related to decline in perceptual speed.The one study 12 that did not find a relation between diabetes mellitus and cognitive decline did not include a measure of perceptual speed." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "COMMENT\n\nIn a cohort of more than 800 older persons, we found that diabetes mellitus sometime in the study was associated with an increased risk of developing AD during a mean of 5.5 years of observation.The risk of incident AD was 65% higher in those with diabetes mellitus than in those without it." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "\n\nIn summary, these findings suggest that diabetes mellitus is associated with AD and decline in cognitive function in older persons.December 12, 2003." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "DIABETES MELLITUS AND RISK OF AD\n\nDuring the follow-up evaluations, 151 persons developed AD, of whom 31 had diabetes mellitus.In a proportional hazards model adjusted for age, sex, and educational level, there was a 65% increase in the risk of developing AD in those with diabetes mellitus compared with those without diabetes mellitus (hazard ratio, 1.65; 95% confidence interval, 1.10-2.47).The cumulative hazard of AD over time, adjusted for age, sex, and educational level, is shown graphically in Figure 1 for typical participants with and without diabetes mellitus.Similar results were found in analyses with diabetes mellitus identified at baseline only (hazard ratio, 1.53; 95% confidence interval, 0.96-2.45)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAge. Age is another factor that has a considerable effect on outcomes in obesity and T2DM research.In humans, body weight increases with age and peaks at ~55 years in both men and women.Ageing per se is associated with a redistribution of both the fat-free mass and the fat mass, with the latter increase starting at ~30 years of age 129 .Intramuscular and intrahepatic fat are particularly increased in older persons, and this increase has been linked to insulin resistance 130 .Partially on the basis of these changes, ageing has been proposed to be an independent determinant of glucose tolerance, which progressively worsens with age 131,132 ." + } + ], + "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a": [ + { + "document_id": "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a", + "text": "\n\nAge also plays a vital role in the onset of diabetes (Cowie & Eberhardt, 1995).In south-east Asia almost 97% diabetic patients are 40 years old or more (IDF Atlas, 2017).In Bangladesh, the reported age of diabetes is ≥40 years in 71% urban and 85% rural female, while in the case of male the proportion is 85.5% urban and 86.5% in rural population (IDF Atlas, 2017).The current study also pinpointed an exponential increase in the risk of onset of T2DM with the increase of age when 40 years was chosen as the reference (Table S4)." + }, + { + "document_id": "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a", + "text": "\n\nWhether age and stress variables are risk factors for type 2 diabetes incidence was assessed by multivariate logistic regression (Table S4).Subjects in the age groups of (40-60) and >60 years had 1.78× (p = .005)and 3.19× (p = .006)greater risk for type 2 diabetes respectively than group of <40 years.Overall, patients under stressful condition are more likely to develop T2DM than that of nonstressed respondent (p = .000).Moreover, when stress is divided into two groups-low stress and high stress, we found that both males (p = .000)and females (p = .000)with high stress were at high risk of diabetes mellitus, whereas the association between low stress and T2DM incidence was significant only among males (Male: p = .002;Female: p = .115).The distribution and association of the genotypes, age, and stress with T2DM have been summarized in Table 3 and Figure 3.There was no difference in T2DM incidence between CT (p = .030)and TT/CC (p = .034)genotype containing people who were in age group of 40-60 years (Table 3).In contrast, people who were more than 60 years old with CT genotype (OR = 4.636, p = .029)were more prone to T2DM than that of TT/CC genotype (OR = 3.714, p = .007)subjects (Table 3)." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nThere is a clear correlation of environmental influences to diabetes risk.Yet, the assembled experts agreed that hypothesis-driven research is needed to define direct causal relationships between specific environmental factors and pathophysiologies leading to diabetes.Research efforts need to address environmental etiologies of type 1 diabetes and determine their relative contribution to onset of autoimmunity and progression to symptomatic disease.Whether there is a direct causal role of the intestinal microbiota in pathogenesis of type 1 and type 2 diabetes and response to therapies needs to be determined.Public health interventions that successfully reduce the levels of consumption of energy-dense foods and/or reduce sedentary time and increase time spent in physical activity need to be evaluated to determine whether they can reduce type 2 diabetes incidence at a population level." + } + ], + "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6": [ + { + "document_id": "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6", + "text": "\n\nIn sum, it is clear that multiple risk factors are involved in diabetes-associated cognitive decrements as well as in dementia in relation to diabetes 38 .On the basis of our assessment of the literature, it is also clear that there are still substantial knowledge gaps on how the risk factors interconnect, how the risk factors translate to potentially modifiable mechanisms and which genetic factors are involved." + } + ], + "b21bbbce-b53f-416b-8378-b635f4270ace": [ + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\n\nThe aim of this study was to investigate the association between age at natural menopause and risk of developing type 2 diabetes, and to assess whether this association is independent of potential intermediate risk factors for type 2 diabetes.Furthermore, we examined the role of endogenous sex hormone levels in the association between age at natural menopause and type 2 diabetes." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\n\nAims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\nAims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens.Results During a median follow-up of 9.2 years, we identified 348 individuals with incident type 2 diabetes.After adjustment for confounders, HRs for type 2 diabetes were 3.7 (95% CI 1.8, 7.5), 2.4 (95% CI 1.3, 4.3) and 1.60 (95% CI 1.0, 2.8) for women with premature, early and normal menopause, respectively, relative to those with late menopause (ptrend <0.001).The HR for type 2 diabetes per 1 year older at menopause was 0.96 (95% CI 0.94, 0.98).Further adjustment for BMI, glycaemic traits, metabolic risk factors, C-reactive protein, endogenous sex hormone levels or shared genetic factors did not affect this association.Conclusions/interpretation Early onset of natural menopause is an independent marker for type 2 diabetes in postmenopausal women." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\n\nassociation and explore whether the timing of natural menopause can add value to diabetes prediction and prevention." + } + ], + "d1449eee-d4ec-4886-87d1-835fb54a5f56": [ + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\n\nAlthough drawing of definitive conclusions is difficult from these observational studies, their results suggest that young-onset type 2 diabetes is associated with a much more frequent occurrence of adverse macrovascular and microvascular outcomes and a more rapidly progressing severity of complications than is seen in type 1 diabetes or later-onset type 2 diabetes." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\n\nIn a study of the age-specific incidence of type 2 diabetes in the UK (a retrospective cohort study of patients with newly diagnosed type 2 diabetes between 1990 and 2010), the investigators reported a substantial increase in the proportion of people aged 40 years or younger at diagnosis" + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\nThe prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults." + } + ], + "f53cd4d6-2d42-47e1-b58f-5bf8f2d65ef5": [ + { + "document_id": "f53cd4d6-2d42-47e1-b58f-5bf8f2d65ef5", + "text": "\n\nThe biological processes linking aging and disease risk are poorly understood.Still, aging is considered to date as one of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes." + } + ] + }, + "data_source": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "\n\nOur result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes." + }, + { + "document_id": "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a", + "section_type": "main", + "text": "\n\nAge also plays a vital role in the onset of diabetes (Cowie & Eberhardt, 1995).In south-east Asia almost 97% diabetic patients are 40 years old or more (IDF Atlas, 2017).In Bangladesh, the reported age of diabetes is ≥40 years in 71% urban and 85% rural female, while in the case of male the proportion is 85.5% urban and 86.5% in rural population (IDF Atlas, 2017).The current study also pinpointed an exponential increase in the risk of onset of T2DM with the increase of age when 40 years was chosen as the reference (Table S4)." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "section_type": "main", + "text": "\n\nOverall, results were similar in analyses restricted to diabetes mellitus identified at baseline only, although the confidence interval included 1.These results suggest that diabetes mellitus is related to risk of AD in old age.These findings are consistent with the results of 2 large longitudinal cohort studies. 5,6In one study, 5 diabetes mellitus doubled the risk of AD during 2 years of follow-up in a sample of more than 6000 older persons from a defined cohort.The other study, 6 using data from about 2500 Japanese American men, found a similar result: diabetes mellitus approximately doubled the risk of AD.In contrast, 2 other longitudinal studies 7,8 did not demonstrate a significant association between diabetes mellitus and incident AD, but in both, the results were in the direction of increased risk.Some, [9][10][11] but not all, 12 previous studies found that diabetes mellitus was related to change in cognitive function.One factor that may contribute to variability from study to study is that diabetes mellitus may be related to decline in some cognitive systems but not others.4][15] Although diabetes mellitus was related to level of global cognition and multiple cognitive domains at baseline, we found that diabetes mellitus was only related to decline in perceptual speed.The one study 12 that did not find a relation between diabetes mellitus and cognitive decline did not include a measure of perceptual speed." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "section_type": "main", + "text": "COMMENT\n\nIn a cohort of more than 800 older persons, we found that diabetes mellitus sometime in the study was associated with an increased risk of developing AD during a mean of 5.5 years of observation.The risk of incident AD was 65% higher in those with diabetes mellitus than in those without it." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "section_type": "main", + "text": "\n\nIn summary, these findings suggest that diabetes mellitus is associated with AD and decline in cognitive function in older persons.December 12, 2003." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "section_type": "main", + "text": "\n\nThe aim of this study was to investigate the association between age at natural menopause and risk of developing type 2 diabetes, and to assess whether this association is independent of potential intermediate risk factors for type 2 diabetes.Furthermore, we examined the role of endogenous sex hormone levels in the association between age at natural menopause and type 2 diabetes." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "section_type": "main", + "text": "\n\nThere are two major factors that underlie these alarming projections.The first is T2D is associated with age, and Western populations are aging rapidly.The second major explanation is our lifestyles have changed dramatically in recent years.Epidemiological studies have identified strong T2D risk relationships for obesity, sedentary behavior [2][3][4], and diets rich in energy [5], processed carbohydrates [6], and animal fats [7].Collectively, these lifestyle factors impede the actions of insulin and raise hepatic glucose production, which can result in the diminution of endogenous insulin production and T2D.The strongest evidence for a causal relationship between adverse lifestyle behaviors and T2D comes from randomized controlled trials that show intensive lifestyle interventions involving structured exercise regimes which promote habitual physical activity (PA) and have a major beneficial impact on diabetes incidence in high-risk individuals [8,9]." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Research Gaps\n\nThere is a clear correlation of environmental influences to diabetes risk.Yet, the assembled experts agreed that hypothesis-driven research is needed to define direct causal relationships between specific environmental factors and pathophysiologies leading to diabetes.Research efforts need to address environmental etiologies of type 1 diabetes and determine their relative contribution to onset of autoimmunity and progression to symptomatic disease.Whether there is a direct causal role of the intestinal microbiota in pathogenesis of type 1 and type 2 diabetes and response to therapies needs to be determined.Public health interventions that successfully reduce the levels of consumption of energy-dense foods and/or reduce sedentary time and increase time spent in physical activity need to be evaluated to determine whether they can reduce type 2 diabetes incidence at a population level." + }, + { + "document_id": "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a", + "section_type": "main", + "text": "\n\nWhether age and stress variables are risk factors for type 2 diabetes incidence was assessed by multivariate logistic regression (Table S4).Subjects in the age groups of (40-60) and >60 years had 1.78× (p = .005)and 3.19× (p = .006)greater risk for type 2 diabetes respectively than group of <40 years.Overall, patients under stressful condition are more likely to develop T2DM than that of nonstressed respondent (p = .000).Moreover, when stress is divided into two groups-low stress and high stress, we found that both males (p = .000)and females (p = .000)with high stress were at high risk of diabetes mellitus, whereas the association between low stress and T2DM incidence was significant only among males (Male: p = .002;Female: p = .115).The distribution and association of the genotypes, age, and stress with T2DM have been summarized in Table 3 and Figure 3.There was no difference in T2DM incidence between CT (p = .030)and TT/CC (p = .034)genotype containing people who were in age group of 40-60 years (Table 3).In contrast, people who were more than 60 years old with CT genotype (OR = 4.636, p = .029)were more prone to T2DM than that of TT/CC genotype (OR = 3.714, p = .007)subjects (Table 3)." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "section_type": "main", + "text": "\n\nAims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "section_type": "main", + "text": "\n\nAlthough drawing of definitive conclusions is difficult from these observational studies, their results suggest that young-onset type 2 diabetes is associated with a much more frequent occurrence of adverse macrovascular and microvascular outcomes and a more rapidly progressing severity of complications than is seen in type 1 diabetes or later-onset type 2 diabetes." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "section_type": "abstract", + "text": "\nAims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens.Results During a median follow-up of 9.2 years, we identified 348 individuals with incident type 2 diabetes.After adjustment for confounders, HRs for type 2 diabetes were 3.7 (95% CI 1.8, 7.5), 2.4 (95% CI 1.3, 4.3) and 1.60 (95% CI 1.0, 2.8) for women with premature, early and normal menopause, respectively, relative to those with late menopause (ptrend <0.001).The HR for type 2 diabetes per 1 year older at menopause was 0.96 (95% CI 0.94, 0.98).Further adjustment for BMI, glycaemic traits, metabolic risk factors, C-reactive protein, endogenous sex hormone levels or shared genetic factors did not affect this association.Conclusions/interpretation Early onset of natural menopause is an independent marker for type 2 diabetes in postmenopausal women." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "section_type": "main", + "text": "\n\nEpidemiological studies examining the associations between lifestyle behaviors and diabetes risk have reached similar conclusions as the clinical trials described above.For example, the 14-year follow-up University of Pennsylvania Alumni Health Study [52] (n = 5,990 men aged 39-68 years) showed PA (leisure time physical activity [LTPA] expressed in kcal expended per week through walking, stair climbing, and sports) was inversely associated with the incidence of T2D.Incidence rates declined as energy expenditure rose from 500 through 3,500 kcal/week.The age-adjusted relative risk ratio (RR) of T2D was reduced by about 6% for each 500 kcal increment increase in PA energy expenditure." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "section_type": "main", + "text": "\n\nIn a study of the age-specific incidence of type 2 diabetes in the UK (a retrospective cohort study of patients with newly diagnosed type 2 diabetes between 1990 and 2010), the investigators reported a substantial increase in the proportion of people aged 40 years or younger at diagnosis" + }, + { + "document_id": "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6", + "section_type": "main", + "text": "\n\nIn sum, it is clear that multiple risk factors are involved in diabetes-associated cognitive decrements as well as in dementia in relation to diabetes 38 .On the basis of our assessment of the literature, it is also clear that there are still substantial knowledge gaps on how the risk factors interconnect, how the risk factors translate to potentially modifiable mechanisms and which genetic factors are involved." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "section_type": "main", + "text": "DIABETES MELLITUS AND RISK OF AD\n\nDuring the follow-up evaluations, 151 persons developed AD, of whom 31 had diabetes mellitus.In a proportional hazards model adjusted for age, sex, and educational level, there was a 65% increase in the risk of developing AD in those with diabetes mellitus compared with those without diabetes mellitus (hazard ratio, 1.65; 95% confidence interval, 1.10-2.47).The cumulative hazard of AD over time, adjusted for age, sex, and educational level, is shown graphically in Figure 1 for typical participants with and without diabetes mellitus.Similar results were found in analyses with diabetes mellitus identified at baseline only (hazard ratio, 1.53; 95% confidence interval, 0.96-2.45)." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "section_type": "abstract", + "text": "\nThe prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "section_type": "main", + "text": "\n\nassociation and explore whether the timing of natural menopause can add value to diabetes prediction and prevention." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Clinical Factors Predicting Incidence of Diabetes\n\nIn both the MPP and Botnia studies, a family history of diabetes, an increased BMI, and increased levels of blood pressure and serum levels of triglycerides, apolipoprotein A-I, and liver enzymes were independent predictors of future type 2 diabetes (Table 1).In the MPP study, current smoking was also associated with a marked increase in the risk of diabetes.Impaired insulin secretion and action, particularly insulin secretion adjusted for insulin resistance (disposition index), were strong predictors of future diabetes.The presence of a first-degree family history of diabetes doubled the risk of the disease that was seen with an increased BMI (Fig. 2A) and a low disposition index (Fig. 2B)." + }, + { + "document_id": "92004cb7-4f79-4dde-a8e7-d1e93a253dc3", + "section_type": "main", + "text": "\n\nWe identified 164 (78%, >3:4) participants with evidence of age-related chronic disease or risk factors.One hundred eighteen study participants (56%) had evidence of diabetes or risk for diabetes: 15 (7%) had type 2 diabetes, 80 (38%) had prediabetes, and 23 (11%) had insulin resistance suggesting prediabetes risk (based on Quantose IR).Only 19 (9%) reported a history of type 2 diabetes or prediabetes.One hundred twentyfour participants (59%) had evidence of atherosclerotic disease or risk.Thirty-three (16%) had evidence of metabolic syndrome.Twenty-eight participants (13%) met a screening definition for NAFLD, and one had suspected NASH.Many participants had multiple overlapping conditions, including 29 with prediabetes and atherosclerotic disease or risk; 19 with prediabetes, atherosclerotic disease or risk, and metabolic syndrome; and 13 with insulin resistance and atherosclerotic disease or risk.When diabetes, prediabetes, and insulin resistance were considered as a group of diseases and conditions, 28 (11%) had all four of the common diseases and conditions (diabetes and diabetes risk, atherosclerosis or atherosclerosis risk, metabolic syndrome, and NAFLD).As expected, there was a strong effect of age on the prevalence of these conditions, with exception of NAFLD (Fig. 2)." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "section_type": "main", + "text": "\n\nType 2 diabetes is a major risk factor for CVD, and it is unclear whether age at menopause is associated with risk of type 2 diabetes [3,4].Data from cross-sectional studies examining the association between age at menopause and type 2 diabetes are contradictory, with a few studies reporting no association and some other reporting higher odds of having type 2 diabetes with early onset of menopause [5][6][7].Recently, a nested case-cohort study reported that an increased risk of type 2 diabetes is associated with early onset of menopause, but it did not adjust for potential intermediate risk factors such as glucose metabolism, insulin or shared genetic factors [8].Menopause transition is associated with weight gain, an increase in visceral fat and impairment of glucose homeostasis, all of which are important risk factors for type 2 diabetes [9][10][11].However, no study has examined the role of postmenopausal hormone levels in the association between age of menopause and risk of type 2 diabetes.Although the available evidence is not persuasive and the mechanisms remain unclear, age of menopause might be associated with levels of endogenous sex hormones, which might affect the risk of type 2 diabetes in postmenopausal women [12][13][14][15][16][17].Therefore, it is not clear whether the observed association between early onset of menopause and risk of type 2 diabetes can be explained by differences in sex hormones levels in women who experience early vs late menopause." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "section_type": "main", + "text": "Summary and future research directions\n\nAlthough it is tempting to extrapolate the disease course of type 2 diabetes in young people as just an earlier and more rapid form of type 2 diabetes in older adults, distinctive differences are evident.The young-onset phenotype has a stronger family history, a greater association with obesity, early loss of both first and second phases of insulin secretion alongside often severe insulin resistance, early onset and rapid progression of microvascular and macrovascular complications, and poor sustainability of responsiveness to oral glucose-lowering therapies, frequently neces sitating early introduction of insulin." + }, + { + "document_id": "756b902b-cbc7-40e8-84a5-9372221d83a4", + "section_type": "abstract", + "text": "\nBackground: Type 2 diabetes mellitus is an important risk factor for Alzheimer disease and is more prevalent in elderly minority persons compared with non-Hispanic white persons.Objective: To determine whether diabetes is related to a higher risk of mild cognitive impairment (MCI), a transitional stage between normal cognition and Alzheimer disease, in a multiethnic cohort with a high prevalence of diabetes." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "section_type": "main", + "text": "\n\nAge. Age is another factor that has a considerable effect on outcomes in obesity and T2DM research.In humans, body weight increases with age and peaks at ~55 years in both men and women.Ageing per se is associated with a redistribution of both the fat-free mass and the fat mass, with the latter increase starting at ~30 years of age 129 .Intramuscular and intrahepatic fat are particularly increased in older persons, and this increase has been linked to insulin resistance 130 .Partially on the basis of these changes, ageing has been proposed to be an independent determinant of glucose tolerance, which progressively worsens with age 131,132 ." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "section_type": "main", + "text": "\n\nThe prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults." + }, + { + "document_id": "756b902b-cbc7-40e8-84a5-9372221d83a4", + "section_type": "main", + "text": "\n\nObjective: To determine whether diabetes is related to a higher risk of mild cognitive impairment (MCI), a transitional stage between normal cognition and Alzheimer disease, in a multiethnic cohort with a high prevalence of diabetes." + }, + { + "document_id": "756b902b-cbc7-40e8-84a5-9372221d83a4", + "section_type": "main", + "text": "\n\nOur results provide further support to the potentially important independent role of diabetes in the pathogenesis of AD.Diabetes may also be a risk factor for nonamnestic forms of MCI and cognitive impairment, but our analyses need to be repeated in a larger sample." + }, + { + "document_id": "756b902b-cbc7-40e8-84a5-9372221d83a4", + "section_type": "main", + "text": "\n\nBackground: Type 2 diabetes mellitus is an important risk factor for Alzheimer disease and is more prevalent in elderly minority persons compared with non-Hispanic white persons." + }, + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "section_type": "main", + "text": "Aetiological factors\n\nProspective studies suggest that the main pathophysiological defects leading to type 2 diabetes are insulin resistance and a relative insulin secretory defect.The main aetiological risk factors are age, obesity, family history, and physical inactivity.Dietary risk factors have recently emerged: risk is increased by high consumption of red and processed meat 13 and sugar-sweetened beverages, 14 and reduced by intake of fruit and vegetables, 15 some types of dairy products, 16 and some overall dietary patterns. 17Novel strategies to use quantifiable nutritional biomarkers are paving the way for more detailed understanding of the association between diet and diabetes.Although the heritability of type 2 diabetes is high (30e70%) and more than 60 genetic variants related with diabetes risk have now been identified, 18 even when combined into a genetic score, known genes contribute little to the prediction of diabetes.Phenotype-based risk models provide greater discrimination for diabetes, and the addition of genotypic information adds no more than 5e10% improvement in prediction.The current conclusion is that genetic variants provide insights into biological pathways and pathogenesis of diabetes, but not its prediction.It is likely that interactions between the environment/lifestyle and genetic factors provide the explanation for the risk of type 2 diabetes, but demonstrating such interaction is challenging.Encouraging research findings have recently shown higher absolute risk of diabetes associated with obesity at any level of genetic risk. 19evention and screening" + }, + { + "document_id": "195cace4-f298-4910-8b7c-c4e6f208cd35", + "section_type": "main", + "text": "Does a shared pathogenesis underlie both obesity and type 2 diabetes? Although the link between obesity and type 2 diabetes is widely held to involve two discrete lesions-obesityinduced insulin resistance and -cell failure-both disorders may share an underlying defect.This \"unified field theory\" raises questions about whether defects favoring progressive weight gain and metabolic impairment also contribute to -cell decompensation." + }, + { + "document_id": "893e83e6-05f4-4917-9dee-6ec2cb847def", + "section_type": "abstract", + "text": "\nThe worldwide explosion of the rates of diabetes and other metabolic diseases in the last few decades cannot be fully explained only by changes in the prevalence of classical lifestyle-related risk factors, such as physical inactivity and poor diet.For this reason, it has been recently proposed that other \"nontraditional\" risk factors could contribute to the diabetes epidemics.In particular, an increasing number of reports indicate that chronic exposure to and accumulation of a low concentration of environmental pollutants (especially the so-called persistent organic pollutants (POPs)) within the body might be associated with diabetogenesis.In this review, the epidemiological evidence suggesting a relationship between dioxin and other POPs exposure and diabetes incidence will be summarized, and some recent developments on the possible underlying mechanisms, with particular reference to dioxin, will be presented and discussed." + }, + { + "document_id": "92eb0c69-5e98-41aa-9084-506e7f223b1a", + "section_type": "main", + "text": "\n\nAlthough Alzheimer's disease is a chronic neurodegenerative disease, seemingly not related to DM, several studies support the fact DM and AD have a strong causal relationship [86].Alzheimer's disease is often referred to as \"type 3\" diabetes.In [87], authors delved into the relationship between DM and AD via semantic data mining.Following extensive analysis of several paper abstracts, they managed to identify genes related to both diseases.Efforts were also made to construct an interaction network in order to identify existing links (genes and molecules) in the network." + }, + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "section_type": "main", + "text": "\n\nWhat these predisposing factors share is an ability to negatively impact the glucose homeostasis system through worsening of insulin resistance or to impair b-cell function.Superimposing these factors onto a genetically compromised glucose homeostasis system raises the risk of progressing to hyperglycemia.It is the rapid emergence of these disadvantageous environmental factors that is causing the worldwide diabetes epidemic.This concept of environmental changes promoting diabetes was highlighted many years ago by populations that rarely experienced type 2 diabetes, but then moved from a nomadic or farm existence to urban environments followed by an explosion of diabetes, typically with profound obesity: Pima Indians in the Southwest U.S., Saharan nomadic tribes, Australian Aborigines, and many others.Particularly dramatic were studies that showed reversal of the diabetes when they returned to their prior way of life (15).A recent example of this is the rapidly rising incidence of type 2 diabetes in China and India as people move from the country to cities-there is a 0.1-0.2%incidence of diabetes for rural farmers in China as opposed to well more than 5% for city dwellers.Perhaps the scariest example of this is children in the U.S. where the obesity statistics worsen yearly.As many as 20% of U.S. children are now obese, and they are developing all of the elements of the metabolic syndrome-insulin resistance, hypertension, hyperlipidemia, and glucose intolerance (16)." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "section_type": "main", + "text": "Discussion\n\nIn this large population-based study of postmenopausal women free of type 2 diabetes at baseline, we showed that early onset of natural menopause is associated with an increased risk of type 2 diabetes, independent of potential intermediate risk factors for type 2 diabetes (including BMI, glucose and insulin levels) and of levels of endogenous sex hormones and SHBG.We also showed that shared genetic factors could not explain the association between age at natural menopause and risk of type 2 diabetes." + }, + { + "document_id": "29d09d03-fd2f-48b3-a020-ea574d583dc4", + "section_type": "main", + "text": "Diet, Nutrition, and Type 2 Diabetes\n\nObesity is pathophysiologically associated with the development of type II diabetes [199,200].Oxidative stress and inflammation, metabolic impairment and accelerated aging on both the micro-and macrocellular level contribute to the pathogenesis of metabolic diseases [201,202]." + }, + { + "document_id": "f53cd4d6-2d42-47e1-b58f-5bf8f2d65ef5", + "section_type": "main", + "text": "\n\nThe biological processes linking aging and disease risk are poorly understood.Still, aging is considered to date as one of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes." + }, + { + "document_id": "893e83e6-05f4-4917-9dee-6ec2cb847def", + "section_type": "main", + "text": "\n\nThe worldwide explosion of the rates of diabetes and other metabolic diseases in the last few decades cannot be fully explained only by changes in the prevalence of classical lifestyle-related risk factors, such as physical inactivity and poor diet.For this reason, it has been recently proposed that other \"nontraditional\" risk factors could contribute to the diabetes epidemics.In particular, an increasing number of reports indicate that chronic exposure to and accumulation of a low concentration of environmental pollutants (especially the so-called persistent organic pollutants (POPs)) within the body might be associated with diabetogenesis.In this review, the epidemiological evidence suggesting a relationship between dioxin and other POPs exposure and diabetes incidence will be summarized, and some recent developments on the possible underlying mechanisms, with particular reference to dioxin, will be presented and discussed." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "\n\nIndependent of geography, the risk of developing type 2 diabetes is associated with low socioeconomic status.Low educational level increases risk by 41%, low occupation level by 31%, and low income level by 40% (16)." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "section_type": "main", + "text": "\n\nBackground: Few prospective studies have assessed diabetes mellitus as a risk factor for incident Alzheimer disease (AD) and decline in cognitive function." + }, + { + "document_id": "f53cd4d6-2d42-47e1-b58f-5bf8f2d65ef5", + "section_type": "main", + "text": "Discussion\n\nBased on available data, it is still unclear if a faster rate of telomere attrition and the consequent premature cell senescence can be a cause or a consequence of type 2 diabetes 8 .Although telomere length in different cell types may better reflect specific diseases, tissue-specific aging, or cell-specific adaptations, several studies have shown not only a significant association between LTL shortening and T2D 10 , but also a correlation with time of onset, duration of disease and increasing number of diabetes related complications 6,[21][22][23] .Indeed, the attrition of this chromosome region seems to be attenuated in patients with well-controlled diabetes 24 .Therefore, telomere shortening in leukocytes may correspond to a similar shortening of telomeres in organs and tissues such as islet β-cells, which lead to premature senescence and subsequent impaired insulin secretion and glucose tolerance 25,26 .On the other hand, many studies show that physical activity seems to confer a beneficial effect on LTL maintenance in healthy and diseased elderly people 19,[27][28][29][30][31] ." + } + ], + "document_id": "B627313B69829B6ACD923E8E6588A622", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "aging", + "diabetes", + "T2DM", + "age", + "susceptibility", + "onset", + "risk&factors", + "incidence", + "type&2&diabetes", + "age-related&diseases" + ], + "metadata": [ + { + "object": "The NeuroD1-Ala45Thr variation may itself have an important role in susceptibility to or be in disequilibrium with early-onset T2DM in Chinese. The Ala45Thr may affect the onset pattern of T2DM, i.e., early-onset but not late-onset T2DM in Chinese.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab839109" + }, + { + "object": "Data suggest that subjects with point mutation 3243A>G in mtRNA-LeuUUR develop MIDD maternally inherited diabetes and deafness; as compared to patients with T1DM type 1 diabetes mellitus or early-onset T2DM type 2 diabetes mellitus matched for sex, age, duration of diabetes, such MIDD patients have highest rate of osteoporosis.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab211558" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "The mean age of Parkinsonism onset among LRRK2 G2385R carriers was 42.7 years old for early-onset compared to 74.3 for late-onset patients. LRRK2 G2385R mutation appears to be as prevalent among early-onset as late-onset patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab833283" + }, + { + "object": "Study shows nucleotide substitutions in CD101, the human homolog of a diabetes susceptibility gene in non-obese diabetic mouse, in patients with type 1 diabetes. The results raise the possibility that CD101 is a susceptibility gene for type 1 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab750084" + }, + { + "object": "This study investigates the involvement of a 14-bp deletion polymorphism rs371194629 at the 3' untranslated region of HLA-G in the context of T1DM and age of onset.the deletion/deletion DEL/DEL genotype was found to be associated with an early age of onset P = 0.001, while the presence of the insertion allele INS was associated to a later age of onset of type I diabetes mellitus", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab944007" + }, + { + "object": "Data confirm the association between the FTO first intron polymorphism and the presence of type 2 diabetes mellitus in the Slavonic Czech population. The same variant is likely to be associated with development of chronic complications of diabetes mellitus, especially with diabetic neuropathy and diabetic kidney disease in either T2DM or both T1DM and T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab173943" + }, + { + "object": "Blockade of IL-27 significantly delayed the onset of diabetic splenocyte-transferred diabetes, while IL-27-treated diabetic splenocytes promoted the onset of autoimmune diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab103352" + }, + { + "object": "Polymorphism of eNOS G894T is not a risk factor for diabetic foot ulcer formation. T allele is a risk factor for diabetes, but T allele is not a risk factor for diabetic foot ulcer formation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab604531" + } + ], + "question": "Is there a direct association between aging and susceptibility to having diabetes?", + "subquestions": null, + "task_id": "B627313B69829B6ACD923E8E6588A622", + "usage": { + "chatgpt": 5686, + "gpt-4": 3837, + "gpt-4-turbo-preview": 2918 + }, + "user_id": 2 + }, + "document_id": "B627313B69829B6ACD923E8E6588A622", + "task_id": "B627313B69829B6ACD923E8E6588A622" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/04.json b/gnqa/paper1_eval/src/data/responses/aging/experts/04.json new file mode 100644 index 0000000..fd6430e --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/04.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-05-31T20:43:30.117900Z", + "data": { + "amplify": false, + "answer": "The genes associated with aging in humans are APOE and FOXO3A.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + } + ], + "4a27da1c-b184-47e8-bef2-de6435d7c3f5": [ + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + } + ], + "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4": [ + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + } + ], + "5e6ad994-9cad-4b8b-903d-2d5c350e25dc": [ + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nIn addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha ˜es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + } + ], + "f3610ccc-2831-42f6-a3d3-1a0feeba4902": [ + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + } + ] + }, + "data_source": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "section_type": "main", + "text": "\n\nIn addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha ˜es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "section_type": "main", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "section_type": "main", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nGenes/loci identified by genome-wide association studies of longevity and lifespan traits." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "\n\nAlthough the models data set comprises all genes (to our knowledge) shown by the time of the latest update to statistically increase longevity or alter the aging process in a noticeable way, in the human data set we try to evaluate whether a given intervention is affecting the aging process itself or not.For example, many mutations may increase longevity by decreasing the incidence of specific diseases, rather than by altering the basic process of aging (de Magalhães et al ., 2005a(de Magalhães et al ., , 2005b)).Therefore, the human data set is not merely an extension of the work conducted in model organisms and of its bibliography, but a manually selected list of the most pertinent human aging candidate genes, each presented with a higher annotation level.We cite studies on whether the functions of aging-associated genes in model organisms are conserved in their human orthologues.Likewise, we cite flaws in previous studies based on new published observations, although we have a neutral stance on conflicting findings from different research groups.Our policy is to cite all conflicting reports and let visitors make their own decisions on how to interpret them.By contrast, each entry in GenAge model organisms has only one reference: the first publication reporting an association of the gene with longevity or aging.Moreover, one of the latest enhancements in the human data set was the inclusion of Gene Ontology annotation.Gene Ontology terms and annotation files were obtained from the Gene Ontology Consortium website (http://www.geneontology.org/ ) and provide an additional layer of description for the gene products in a cellular context (Ashburner et al ., 2000)." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "main", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "main", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "Candidate gene studies identified APOE and FOXO3A as human longevity genes\n\nThe first genetic longevity studies mainly focused on lifespan regulating loci that emerged from animal models [22].Lifespan Prospects & Overviews .... extension in animal models was obtained by applying caloric restriction or by modifying gene functions (mutagenesis) using RNA interference, knock-out or overexpression of single genes (GenAge; http://genomics.senescence.info/genes/)[23].The most interesting pathways identified using these models are the growth hormone (GH)/insulin/insulin-like growth factor 1 (IGF-1) signaling and mammalian target of rapamycin (mTOR) signaling pathways [24].Thus far, lifespan has been the main phenotype investigated in animal models.In order to make these models more translatable to human studies research should focus on defining the parameters that reflect the physiology and pathology of aging in both animals and humans [25,26]." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "abstract", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "\n\nGenAge consists of several searchable data sets.Considering the extraordinary discoveries in the genetics of aging in model organisms, GenAge includes a data set of genes associated with longevity and/or aging in model organisms.We consider a given gene for inclusion in GenAge if genetic manipulations of the gene result in noticeable changes in the aging phenotype and/or longevity.Most genes in GenAge are from the four typical model organisms: mice, worms, fruit flies and yeast (Table 1).Strikingly, homologues of many genes -such as insulin receptors and sirtuins -have been shown to regulate aging in model organisms separated by large evolutionary distances (Kenyon, 2005;Liu et al ., 2005;Smith et al ., 2008).Moreover, we have shown that genes associated with aging and/or longevity in model organisms are evolutionary conserved in terms of having more homologues than predicted by chance (Budovsky et al ., 2007(Budovsky et al ., , 2008) ) and exhibiting slower molecular evolution rates (de Magalhães & Church, 2007).Therefore, it is now clear that at least some genes identified in model organisms may be relevant to human aging." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "main", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "abstract", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "abstract", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "[PubMed: 18208581]\n3. de Magalhães JP, Wuttke D, Wood SH, Plank M & Vora C Genome-environment interactions that\nmodulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88–101 (2012). [PubMed:\n22090473]\n4. McDaid AFet al.Bayesian association scan reveals loci associated with human lifespan and linked\nbiomarkers. Nat. Commun. 8, 15842 (2017). [PubMed: 28748955]\n5. Fontana L & Partridge L Promoting health and longevity through diet: From model organisms to\nhumans. Cell 161, 106–118 (2015). [PubMed: 25815989]\n6." + }, + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "section_type": "main", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "main", + "text": "\n\nStudies revealed from 300 to 750 genes related to longevity that are critically involved in a variety of life activities, such as growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [5,6].Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability, and the endocrine-related pathway [7][8][9].In addition, the candidates for longevity encompass genes related to drug metabolism, the ones involved in protein folding, stabilization, and degradation, as well those related to coagulation and regulation of circulation [10], etc.In most cases, these genes or their polymorphic sites were examined in multiple population replication studies, which discovered certain longevity-associated genes or pathways [4][5][6][7][8][9][10]." + }, + { + "document_id": "690a2ae6-962a-438c-91ca-60425a0c8d02", + "section_type": "abstract", + "text": "\nClear evidence exists for heritability of human longevity, and much interest is focused on identifying genes associated with longer lives.To identify such longevity alleles, we performed the largest genomewide linkage scan thus far reported.Linkage analyses included 2118 nonagenarian Caucasian sibling pairs that have been enrolled in fifteen study centers of eleven European countries as part of the Genetics of Healthy Ageing (GEHA) project.In the joint linkage analyses we observed four regions that" + }, + { + "document_id": "0fc75a0d-3aa3-481a-8c0f-689bd7ae6104", + "section_type": "abstract", + "text": "\nAging is a complex process affecting different species and individuals in different ways.Comparing genetic variation across species with their aging phenotypes will help understanding the molecular basis of aging and longevity.Although most studies on aging have so far focused on short-lived model organisms, recent comparisons of genomic, transcriptomic, and metabolomic data across lineages with different lifespans are unveiling molecular signatures associated with longevity.Here, we examine the relationship between genomic variation and maximum lifespan across primate species.We used two different approaches.First, we searched for parallel amino-acid mutations that co-occur with increases in longevity across the primate linage.Twenty-five such amino-acid variants were identified, several of which have been previously reported by studies with different experimental setups and in different model organisms.The genes harboring these mutations are mainly enriched in functional categories such as wound healing, blood coagulation, and cardiovascular disorders.We demonstrate that these pathways are highly enriched for pleiotropic effects, as predicted by the antagonistic pleiotropy theory of aging.A second approach was focused on changes in rates of protein evolution across the primate phylogeny.Using the phylogenetic generalized least squares, we show that some genes exhibit strong correlations between their evolutionary rates and longevity-associated traits.These include genes in the Sphingosine 1-phosphate pathway, PI3K signaling, and the Thrombin/protease-activated receptor pathway, among other cardiovascular processes.Together, these results shed light into human senescence patterns and underscore the power of comparative genomics to identify pathways related to aging and longevity." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Murabito JM, Yuan R, Lunetta KL (2012) The search for\nlongevity and healthy aging genes: insights from epidemiological\nstudies and samples of long-lived individuals. J Gerontol A Biol\nSci Med Sci 67(5):470–479. doi:10.1093/gerona/gls089\n20. Nuzhdin SV, Pasyukova EG, Dilda CL et al (1997) Sex-specific\nquantitative trait loci affecting longevity in Drosophila melanogaster. Proc Natl Acad Sci USA 94(18):9734–9739\n21. Gems D, Riddle DL (2000) Genetic, behavioral and environmental determinants of male longevity in Caenorhabditis elegans.\n Genetics 154(4):1597–1610\n\n123\n\n22." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "Discussion\n\nIn our analyses of over 25,000 individuals of 55 years and older followed for an average of 11 years, we did not identify genome-wide significant associations for all-cause mortality and survival free of major diseases.However, both traits highlighted loci with suggestive significance that were in the neighborhood of genes related to neural regulation.In addition, our pathway and network analyses identified an enrichment of genes associated with cellular and neural development and function, and cell communication that may contribute to variation in human aging.Brain development might be responsible for the creation of redundancy in brain circuitry, which is associated with functional reserve and resiliency.Brain function regulates most of the compensatory strategy supporting maintenance of homeostatic equilibrium.Both of these processes are essential to healthy aging and longevity." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "abstract", + "text": "\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "abstract", + "text": "\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "520b36a2-4c9c-4894-a818-9917bd357982", + "section_type": "abstract", + "text": "\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "main", + "text": "\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "main", + "text": "\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "main", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "main", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + }, + { + "document_id": "99a35e24-bbd2-495b-82dc-53d7e2075191", + "section_type": "main", + "text": "\n\nThus, substantially more work is needed in this area to establish whether longevity is driven by nuclear genomic stability.Diverse and unexpected bits of evidence support a relationship.For example, a disproportionate number of genes identified in unbiased and targeted genome-wide association studies (GWASs) as associated with longevity are involved in genome maintenance (75).One study involved age of natural menopause in ∼70,000 women and led to the identification of 44 genetic variants associated with early or late menopause, a strong biomarker of healthy TIFs (telomere dysfunction-induced foci): co-localization of multiple DNA damage response factors and repair proteins on uncapped telomeric DNA aging (76).Approximately two-thirds of these are associated with genome maintenance genes.Seven of ten significantly associated pathways are involved in DNA repair.The highly significant overrepresentation of DNA repair pathways indicates an intimate connection between genome maintenance and aging phenotypes.From unrelated studies, we know that reduced expression of the repair endonuclease ERCC1-XPF causes accelerated aging (3), whereas ERCC1 is one of the top genes under positive selective pressure in the longest-lived mammalian species, the bowhead whale (77).Intriguingly, hepatocytes from old rats have impaired NER, whereas caloric restriction, which extends longevity, restored the NER capacity of old rats to that of youthful levels (42).In a human interventional study, brief caloric restriction increased NER capacity in PBMCs of individuals who had low NER prior to dietary intervention (78).Therefore, increased DNA repair capacity could promote longevity and may even prove amenable to improvement." + }, + { + "document_id": "ae9d5a74-24c1-43f1-b514-5e3f10c91284", + "section_type": "abstract", + "text": "\nIn animal models, single-gene mutations in genes involved in insulin/IGF and target of rapamycin signalling pathways extend lifespan to a considerable extent.The genetic, genomic and epigenetic influences on human longevity are expected to be much more complex.Strikingly however, beneficial metabolic and cellular features of long-lived families resemble those in animals for whom the lifespan is extended by applying genetic manipulation and, especially, dietary restriction.Candidate gene studies in humans support the notion that human orthologues from longevity genes identified in lower species do contribute to longevity but that the influence of the genetic variants involved is small.Here we discuss how an integration of novel study designs, labour-intensive biobanking, deep phenotyping and genomic research may provide insights into the mechanisms that drive human longevity and healthy ageing, beyond the associations usually provided by molecular and genetic epidemiology.Although prospective studies of humans from the cradle to the grave have never been performed, it is feasible to extract life histories from different cohorts jointly covering the molecular changes that occur with age from early development all the way up to the age at death.By the integration of research in different study cohorts, and with research in animal models, biological research into human longevity is thus making considerable progress." + }, + { + "document_id": "520b36a2-4c9c-4894-a818-9917bd357982", + "section_type": "main", + "text": "\n\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases." + }, + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "section_type": "main", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + } + ], + "document_id": "9AA0126F9464E89A7B057D231376A79A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "longevity", + "human", + "aging", + "genes", + "GWAS", + "SNP", + "centenarians", + "genetic&variants" + ], + "metadata": [ + { + "object": "Transient overexpression of WRKY79 in protoplasts results in up-regulation of Gene:542165, Gene:541974, Gene:100274033, Gene:542688, Gene:542150, Gene:542151, Gene:100273457, Gene:100285509, Gene:103626248, Gene:103646045, Gene:100217270, Gene:100279981, Gene:100281950, Gene:542476, Gene:542369, Gene:100281950, and Gene:542260.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab969966" + }, + { + "object": "Uniform Mu insertion results in up-regulation of cytokinin synthesis genes and down-regulation of cytokinin degradation genes. The protein binds to Gene:103632693, Gene:100502174, Gene:100283866, Gene:542044, and Gene:100037786.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab983367" + }, + { + "object": "Data suggest that modulation of ARHGEF3 gene expression in humans with a promoter-localized SNP plays a role in human megakaryocytes and human platelet function-a finding resulting from the biological follow-up of human genetic studies. Arhgef3 KO mice partially recapitulate the human phenotype.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab209151" + }, + { + "object": "NO evident relationship was found between gene polymorphism of Eco RI loci of Apo B gene, Xba I loci of Apo B gene or 3'-VNTR of Apo B gene and ANFH.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab529998" + }, + { + "object": "Data suggest that the redox status of serum apoE might be related to the synthesis of HDL; the cysteine-thiol residue of reduced-apoE is in a naive state, while that of non-reduced-apoE is in a reversibly or irreversibly oxidized state. Data suggest that apoE homodimer and apoE-AII complex are typical reversibly oxidized forms of apoE. apoE-AII complex = a complex of apolipoprotein E and apolipoprotein A-II", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab212832" + }, + { + "object": "Data suggest that modulation of ARHGEF3 gene expression in humans with a promoter-localized SNP plays a role in human megakaryocytes and human platelet function-a finding resulting from the biological follow-up of human genetic studies. Arhgef3 KO mice partially recapitulate the human phenotype.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab209152" + }, + { + "object": "Genome-wide associations P < 5 x 10-8 were found at the PCSK9 gene, the APOB gene, theLPL gene, the APOA1-APOA5 locus, the LIPC gene, the CETP gene, the LDLR gene, and the APOE locus.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab320109" + }, + { + "object": "APOE genotype status moderated the age-related declines in episodic memory: APOE-epsilon4+ middle-aged adults exhibited impairments relative to both APOE-epsilon4- middle-aged participants, and APOE-epsilon4+ younger adults.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab77520" + }, + { + "object": "Very high gene-level linkage in Bayesian GWAS using Kutalik/Timmers data and MAGMA gene-level reanalysis to human longevity LOD 14.5; RW Williams July 2019. Linkage to longevity is second only to the TOMM40-APOE-APOC1-PVRL2-APOC4 region on Chr 19.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab6647" + }, + { + "object": "Studied six SNP loci: rs2279115 of BCL2 gene, rs804270 of NEIL2 gene, rs909253 of LTA gene, rs2294008 of PSCA gene, rs3765524 and rs10509670 of PLCE1 gene to evaluate gastric cancer risk using magnetic nanoparticles and universal tagged arrays.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab732925" + } + ], + "question": "Which genes are associated with aging in humans?", + "subquestions": null, + "task_id": "9AA0126F9464E89A7B057D231376A79A", + "usage": { + "chatgpt": 7766, + "gpt-4": 5012, + "gpt-4-turbo-preview": 4076 + }, + "user_id": 2 + }, + "document_id": "9AA0126F9464E89A7B057D231376A79A", + "task_id": "9AA0126F9464E89A7B057D231376A79A" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/05.json b/gnqa/paper1_eval/src/data/responses/aging/experts/05.json new file mode 100644 index 0000000..3f16805 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/05.json @@ -0,0 +1,398 @@ +{ + "created_at": "2024-05-31T20:40:14.163893Z", + "data": { + "amplify": false, + "answer": "Several genetic factors influence aging in humans. These include the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling pathway, and the exonuclease 1 (EXO1) gene. Other genes associated with aging are those involved in pathways such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response. Additionally, genes related to immune response, energy metabolism, signal transduction pathways, and cellular senescence also play a role in aging.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "Introduction\n\nWith the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging." + } + ], + "1386c8ad-297d-48b1-aa34-41659a9f6544": [ + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Influence of Genetic Factors in Ageing and Lifespan\n\nAgeing is defined as the decline of physiological functions in several tissues and organs inducing an increasing probability of death [17].The understanding of genetic factors involved in ageing has been limited due to the complexity of this process and the heterogeneity among individuals and even among tissues [18][19][20].Tissue cells adopt a senescent phenotype as a consequence of multiple intrinsic, extrinsic, and stochastic factors [21].The combination of these genetic factors is related to longevity and healthy ageing [22].Although this decline is somewhat predictable, some individuals show a much slower decline and get to live past the age of 100.Studies in these individuals showed polymorphisms in some genes which are associated with long life, such as APOE and FOXO3.However, these associations have not been consistent across different populations, suggesting that ageing is rather polygenic [23]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + } + ], + "7291ceb2-482a-4f9b-a116-2b68ff24854f": [ + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + } + ], + "c8fbb24d-0a72-4a45-a552-6cd98a4a25a2": [ + { + "document_id": "c8fbb24d-0a72-4a45-a552-6cd98a4a25a2", + "text": "Translational\n\nA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors." + } + ], + "ca76f85d-9f72-4e15-8ba9-3bf94308c449": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + } + ], + "db90a971-e55a-4ab0-a3b1-05908d6771a4": [ + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + } + ], + "f4e2fa75-559b-4fa9-b722-bdac03f7715a": [ + { + "document_id": "f4e2fa75-559b-4fa9-b722-bdac03f7715a", + "text": "\n\nI NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + } + ] + }, + "data_source": [ + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "section_type": "main", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + }, + { + "document_id": "c8fbb24d-0a72-4a45-a552-6cd98a4a25a2", + "section_type": "main", + "text": "Translational\n\nA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "section_type": "main", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "main", + "text": "Introduction\n\nWith the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging." + }, + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "section_type": "main", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "abstract", + "text": "\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "Conclusions and prospects\n\nOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nAging is an extremely complex process associated with interplay of genetic, biochemical, and metabolic factors in an organism in a given environment.Although genetic studies of various animal models suggest that even a single-gene mutation can remarkably extend lifespan (Kenyon 2005;Johnson 2006) and, thus, modulate aging, no such genes are revealed in humans so far.Given that a human organism is a much more complex system than a model organism (Christensen et al. 2006), it is evident that genetic effects on the aging process should be mediated via coordinate action of a large number of inter-related processes (Kirkwood 2011).Coordinated function is rather relevant to complex biological (Soltow et al. 2010;Slagboom et al. 2011) and genetic (Bloss et al. 2011) networks than to individual genes." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "f4e2fa75-559b-4fa9-b722-bdac03f7715a", + "section_type": "main", + "text": "\n\nI NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "main", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Conclusions and Perspectives\n\nThe advent of new technologies has allowed the identification of conserved pathways involved in the aging process, as well as the association of genomic variants with human longevity.Nevertheless, heritability of human longevity has been estimated from 20% to 30%, reinforcing the fact that external factors such as diet, environment, and physical activity play a critical role in the human life span." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "abstract", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "section_type": "main", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "section_type": "main", + "text": "\n\nMany factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone.Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases.If such genes exist, their effects were too small to be detected in this study.The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "abstract", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "section_type": "main", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nOn the other hand, the same evolutionary-motivated strategy suggesting to focus on more heterogeneous phenotypes (as opposite to more homogenous) can be highly beneficial for unraveling genetic predisposition to fundamental mechanisms of intrinsic biological aging and, consequently, to geriatric diseases.Indeed, aging is associated with systemic remodeling of an organism's functioning which increases chances of virtually all geriatric disorders (Franco et al. 2009;Franceschi et al. 2000;Martin et al. 2007;Cutler and Mattson 2006).Experiments with laboratory animals (Johnson 2006) and heritability estimates in humans (Christensen et al. 2006;Iachine et al. 1998) show that aging can be genetically regulated (Finch and Tanzi 1997;Martin et al. 2007;Vaupel 2010).Accordingly, yielding insights in genetic predisposition to aging-related processes in an organism could be a major breakthrough in preventing and/or ameliorating not one geriatric trait, but perhaps a major subset of such traits (Martin et al. 2007) that can greatly advance progress in solving the problem of extending healthy lifespan in humans." + }, + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "section_type": "main", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + }, + { + "document_id": "a6bc2efd-61a7-4e07-ad5c-49234aa89431", + "section_type": "main", + "text": "\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13]." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "section_type": "main", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Influence of Genetic Factors in Ageing and Lifespan\n\nAgeing is defined as the decline of physiological functions in several tissues and organs inducing an increasing probability of death [17].The understanding of genetic factors involved in ageing has been limited due to the complexity of this process and the heterogeneity among individuals and even among tissues [18][19][20].Tissue cells adopt a senescent phenotype as a consequence of multiple intrinsic, extrinsic, and stochastic factors [21].The combination of these genetic factors is related to longevity and healthy ageing [22].Although this decline is somewhat predictable, some individuals show a much slower decline and get to live past the age of 100.Studies in these individuals showed polymorphisms in some genes which are associated with long life, such as APOE and FOXO3.However, these associations have not been consistent across different populations, suggesting that ageing is rather polygenic [23]." + }, + { + "document_id": "ea036684-619d-4b82-9242-c0b220f2d8df", + "section_type": "main", + "text": "The mechanisms that underlie healthy aging—particularly, the cognitive as-\n\npects—remain poorly understood. Research suggests that genetics play a significant role in determining an individual’s\nsusceptibility or resilience to cognitive decline and dementia\n(Harris and Deary 2011; Ridge et al. , 2013). Identification of precise genetic factors involved would provide insight into\n\nCell Reports 32, 108091, September 1, 2020 ª 2020 The Author(s). 1\nThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).\n ll\nOPEN ACCESS\n\nReport\n\nFigure 1." + }, + { + "document_id": "18e216d9-ea5c-4dfe-a30d-632163fcf39e", + "section_type": "main", + "text": "Current progress and problems of genetic studies of aging and longevity\n\nIn spite of aging being a risk factor for many diseases, a phenotype of aging to date is still tabula rasa.Yet, the choice of a phenotype is critical for the study of a complex genetic process, such as aging (Melzer et al. 2007).Furthermore, proposed treatments to delay or alleviate aging require that validated outcomes exist, which can be measurable earlier rather than later in the life (thus, longevity per se is impractical).To date, however, most of the twin and family studies focused on broad survival measures, primarily on age at death or survival to some arbitrary advanced age (Nicholas et al. 1994).Thus, it has been demonstrated that longevity has moderate heritability ðh 2 ¼ 0:20 À À0:30Þ (McGue et al. 1993;Herskind et al. 1996;Gillespie et al. 1998).There are several challenges in using longevity as a phenotype (reviewed in Karasik et al. 2005 and below).A better strategy would be to investigate a broader outcome such as \"successful\" or \"healthy\" aging (Mulsant et al. 1994;Seeman et al. 2004).However, there is no consensus definition for the latter categories, especially for a genetic study.Similarly, at present, there is no consensus about how to measure aging starting in midlife despite a plethora of publications on the biomarkers and risk factors of aging (Newman et al. 2008).Yet, researchers (Nilsson et al. 2003;Crabtree et al. 2002;Vaillant and Mukamal 2001) have argued that studies of aging genetics should be initiated earlier in life, when there are life expectations permissive of longitudinal studies as well as information on environmental exposures traceable to the outcomes." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nStudies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches." + }, + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "section_type": "main", + "text": "\n\nIn this review, we give an overview of the major environmental factors that modulate aging in animals, in particular those with underlying gene-environment interactions with potential for improving human health and drug discovery.Moreover, we provide a snapshot of the relevance of these to human biology and to antiaging applications in diet, industry, pharmacy, and healthcare." + }, + { + "document_id": "aff67cef-4bf7-42dc-826b-2a259722008d", + "section_type": "abstract", + "text": "\nAs our society is growing older, the consequences of aging have begun to gain particular attention.Improvement of quality of life at old age and prevention of age-associated diseases have become the main focus of the aging research.The process of aging in humans is complex and underlies multiple influences, with the probable involvement of heritable and various environmental factors.In particular, hormones are decisively involved in the generation of aging.Over time, important circulating hormones decline due to a reduced secretion of the pituitary, the adrenal glands and the gonads or due to an intercurrent disease.Among them, serum levels of growth factors and sexual steroids show significant aging-associated changes.Within the scope of the Explorative Project 'Genetic aetiology of human longevity' supported by the German National Genome Research Network 2 (NGFN-2) an in vitro model of human hormonal aging has been developed.Human SZ95 sebocytes were maintained under a hormone-substituted environment consisting of growth factors and sexual steroids in concentrations corresponding to those circulating in 20-and in 60-year-old women.Eight hundred and ninety-nine genes showed a differential expression in SZ95 sebocytes maintained under the 20-and 60-year-old hormone mixture, respectively.Among them genes were regulated which are involved in biological processes which are all hallmarks of aging.The most significantly altered signaling pathway identified was that of the transforming growth factor-b (TGF-b).A disturbed function of this cascade has been associated with tumorigenesis, i.e. in pancreatic, prostate, intestine, breast, and uterine cancer.Interestingly, genes expressed in signaling pathways operative in age-associated diseases such as Huntington's disease (HD), dentatorubral-pallidoluysian atrophy (DRPLA), and amyotrophic lateral sclerosis (ALS) were also identified.These data demonstrate that skin and its appendages may represent an adequate model for aging research.Hormones interact in a complex fashion, and aging may be partly attributed to the changes in their circulating blood levels.Furthermore, a disturbed hormone status may partially act towards the manifestation of neurodegenerative diseases.Thus, these results could be a basis for an integrated and interdisciplinary approach to the analysis of the aging process." + }, + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "[PubMed: 18208581]\n3. de Magalhães JP, Wuttke D, Wood SH, Plank M & Vora C Genome-environment interactions that\nmodulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88–101 (2012). [PubMed:\n22090473]\n4. McDaid AFet al.Bayesian association scan reveals loci associated with human lifespan and linked\nbiomarkers. Nat. Commun. 8, 15842 (2017). [PubMed: 28748955]\n5. Fontana L & Partridge L Promoting health and longevity through diet: From model organisms to\nhumans. Cell 161, 106–118 (2015). [PubMed: 25815989]\n6." + }, + { + "document_id": "0fc75a0d-3aa3-481a-8c0f-689bd7ae6104", + "section_type": "abstract", + "text": "\nAging is a complex process affecting different species and individuals in different ways.Comparing genetic variation across species with their aging phenotypes will help understanding the molecular basis of aging and longevity.Although most studies on aging have so far focused on short-lived model organisms, recent comparisons of genomic, transcriptomic, and metabolomic data across lineages with different lifespans are unveiling molecular signatures associated with longevity.Here, we examine the relationship between genomic variation and maximum lifespan across primate species.We used two different approaches.First, we searched for parallel amino-acid mutations that co-occur with increases in longevity across the primate linage.Twenty-five such amino-acid variants were identified, several of which have been previously reported by studies with different experimental setups and in different model organisms.The genes harboring these mutations are mainly enriched in functional categories such as wound healing, blood coagulation, and cardiovascular disorders.We demonstrate that these pathways are highly enriched for pleiotropic effects, as predicted by the antagonistic pleiotropy theory of aging.A second approach was focused on changes in rates of protein evolution across the primate phylogeny.Using the phylogenetic generalized least squares, we show that some genes exhibit strong correlations between their evolutionary rates and longevity-associated traits.These include genes in the Sphingosine 1-phosphate pathway, PI3K signaling, and the Thrombin/protease-activated receptor pathway, among other cardiovascular processes.Together, these results shed light into human senescence patterns and underscore the power of comparative genomics to identify pathways related to aging and longevity." + }, + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "section_type": "main", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + }, + { + "document_id": "da4a9500-831f-48ab-acea-5ec7097276ed", + "section_type": "main", + "text": "\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "aff67cef-4bf7-42dc-826b-2a259722008d", + "section_type": "main", + "text": "\n\nAs our society is growing older, the consequences of aging have begun to gain particular attention.Improvement of quality of life at old age and prevention of age-associated diseases have become the main focus of the aging research.The process of aging in humans is complex and underlies multiple influences, with the probable involvement of heritable and various environmental factors.In particular, hormones are decisively involved in the generation of aging.Over time, important circulating hormones decline due to a reduced secretion of the pituitary, the adrenal glands and the gonads or due to an intercurrent disease.Among them, serum levels of growth factors and sexual steroids show significant aging-associated changes.Within the scope of the Explorative Project 'Genetic aetiology of human longevity' supported by the German National Genome Research Network 2 (NGFN-2) an in vitro model of human hormonal aging has been developed.Human SZ95 sebocytes were maintained under a hormone-substituted environment consisting of growth factors and sexual steroids in concentrations corresponding to those circulating in 20-and in 60-year-old women.Eight hundred and ninety-nine genes showed a differential expression in SZ95 sebocytes maintained under the 20-and 60-year-old hormone mixture, respectively.Among them genes were regulated which are involved in biological processes which are all hallmarks of aging.The most significantly altered signaling pathway identified was that of the transforming growth factor-b (TGF-b).A disturbed function of this cascade has been associated with tumorigenesis, i.e. in pancreatic, prostate, intestine, breast, and uterine cancer.Interestingly, genes expressed in signaling pathways operative in age-associated diseases such as Huntington's disease (HD), dentatorubral-pallidoluysian atrophy (DRPLA), and amyotrophic lateral sclerosis (ALS) were also identified.These data demonstrate that skin and its appendages may represent an adequate model for aging research.Hormones interact in a complex fashion, and aging may be partly attributed to the changes in their circulating blood levels.Furthermore, a disturbed hormone status may partially act towards the manifestation of neurodegenerative diseases.Thus, these results could be a basis for an integrated and interdisciplinary approach to the analysis of the aging process." + } + ], + "document_id": "7530EBCCAFF1750013433CA62E07A82F", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "IGF", + "insulin", + "GH", + "LPA", + "HLA-DQA1/DRB1", + "CHRNA3/5", + "CDKN2A/B", + "SH2B3" + ], + "metadata": [ + { + "object": "We conclude that 1 GH signaling is normal in obesity, 2 in the obese state, the preservation of IGF-I with fasting and the augmented GH-induced central insulin resistance indicate increased hepatic GH sensitivity, 3 blunted GH levels in obesity may protect against insulin resistance without compromising IGF-I status.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab999203" + }, + { + "object": "insulin and IGF-I activate their cognate receptors and IGF-I also activates naturally occuring IGF-I/insulin hybrid receptors HR IGF-II activates insulin receptor, IGF-I receptor and HR", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab419763" + }, + { + "object": "Data suggest that the redox status of serum apoE might be related to the synthesis of HDL; the cysteine-thiol residue of reduced-apoE is in a naive state, while that of non-reduced-apoE is in a reversibly or irreversibly oxidized state. Data suggest that apoE homodimer and apoE-AII complex are typical reversibly oxidized forms of apoE. apoE-AII complex = a complex of apolipoprotein E and apolipoprotein A-II", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab212832" + }, + { + "object": "By depressing association of IGFs with soluble IGFBPs, Zn2+ is shown to repartition either [125I]-IGF-I or [125I]-IGF-II from soluble IGFBP-5 onto cell surface IGF receptors at physiological doses depressing IGF binding to IGFBP-5 and IGF-2R", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab112518" + }, + { + "object": "Study found that IL-6, GP130, IGF-1 and IGF-1R were highly expressed in non-small cell lung cancer NSCLC and there was the correlation between GP130, IGF-1, and IGF-1R. Co-stimulation of IL-6 and IGF-1 resulted in significantly enhanced cell proliferation, invasion, and apoptosis of NSCLC cells. This experiment revealed that IL-6 and IGF-1 can synergistically promote the progression of NSCLC.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab741940" + }, + { + "object": "Circulating IGF-I appears to be growth hormone GH-independent in GH deficiency GHD patients with a low IGF-I, but remains partially GH-dependent in GHD patients with a normal IGF-I.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab141796" + }, + { + "object": "Prospective associations of insulin, IGF-I, IGF-II and IGFBP-3 with physical performance in Caerphilly Prospective Study and cross-sectional insulin, IGF-I, IGF-II, IGFBP-2 and IGFBP-3 in the Boyd Orr cohort, were examined.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab618236" + }, + { + "object": "Confirmation of the impairment of GH-IGF-1 release in hyperphagic MC4R KO mice suggests a role for insulin in regulating both the release of GH, but also in mediating growth during periods of physiologically suppressed GH-IGF-1 levels", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab154279" + }, + { + "object": "Low apoE and mir-650 plasma concentrations were risk factors for developing Alzheimer's disease AD and were particularly pronounced in severe dementia. APOE E4 allele in both AD patients and controls led to a reduction in apoE, while APOE E3/E3 genotype was associated with an increased apoE concentration and level of miR-107 in AD, which inversely correlated with the number of APOE E4 alleles.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab459467" + }, + { + "object": "Insulin receptor substrates 1 and 2 IRS-1 and IRS-2 were targeted and compared as central distributors of the insulin signal, the insulin receptor, the insulin-like growth factor 1 receptor, and the insulin receptor-related receptor.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab419969" + } + ], + "question": "What genetic factors influence aging in humans?", + "subquestions": null, + "task_id": "7530EBCCAFF1750013433CA62E07A82F", + "usage": { + "chatgpt": 9172, + "gpt-4": 5514, + "gpt-4-turbo-preview": 4604 + }, + "user_id": 2 + }, + "document_id": "7530EBCCAFF1750013433CA62E07A82F", + "task_id": "7530EBCCAFF1750013433CA62E07A82F" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/06.json b/gnqa/paper1_eval/src/data/responses/aging/experts/06.json new file mode 100644 index 0000000..a3204b6 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/06.json @@ -0,0 +1,402 @@ +{ + "created_at": "2024-05-31T20:53:55.182883Z", + "data": { + "amplify": false, + "answer": "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "context": { + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7": [ + { + "document_id": "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7", + "text": "FUTURE DIRECTIONS: HIGHER RESOLUTION DATA VIA HIGHER THROUGHPUT ASSAYS\n\nOne inescapable conclusion of the aggregate results of genome-wide studies of aging to date (see summary Table 1) is that we have not come close to saturating the number of potentially lifespan-altering genes in any organism.This is in no small part because directly generating survival curves is a relatively time-consuming process in most model organisms using current methods.There are several possible ways to address this.One way that has been tried is by attempting to find surrogate phenotypes [72,73,126] that can be screened more rapidly, or even scored under selection.Another is mining candidates from the many whole-genome expression profiles.Results to date with these have been very fruitful, but have not suggested that these methods alone will rapidly saturate our search for lifespan-and healthspan-altering genes in tractable model organisms." + } + ], + "113cb521-b79d-4b44-8250-dc1013ea2cb3": [ + { + "document_id": "113cb521-b79d-4b44-8250-dc1013ea2cb3", + "text": "\n\nChromosome mapping of genes that were differentially expressed in mice of different ages and/or in response to CR revealed a wide distribution of genes with some physical clustering of responsive genes within the genome.The latter findings are consistent with the concept that aging is a complex process and that evolutionary adaptations to aging, if they exist, may or may not involve geographic clustering of functionally related genes." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + } + ], + "4ca8d070-8b58-4bd5-86be-127089b70324": [ + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "text": "\n\nThe aging process most certainly is under highly polygenic controls… This should not discourage us from pursuing a search for those loci which may be of profound importance to human aging as it ordinarily occurs in most human beings." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "606c59c5-5ae4-47e9-b3eb-58afa55669d1": [ + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + } + ], + "690a2ae6-962a-438c-91ca-60425a0c8d02": [ + { + "document_id": "690a2ae6-962a-438c-91ca-60425a0c8d02", + "text": "Accepted Article\n\n© 2013 The Authors Aging Cell © 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland over 90 years and 1,955 controls between 55 and 80 years did not reveal genome-wide significant loci (Newman et al., 2010) and neither did the analyses of all-cause mortality and survival free of major disease in this cohort (Walter et al., 2011).A smaller Dutch study of 403 nonagenarians and 1,670 controls younger than 65 years identified the APOE gene as a mortality locus (Deelen et al., 2011), which was confirmed in a German study of 763 long-lived individuals and 1,085 younger controls (Nebel et al., 2011) and a longitudinal study of 1,606 Danes showed that the effect size of this association increases at the highest ages (Jacobsen et al., 2010).Apparently, the influence of the common genetic variation on longevity is small which requires large meta-GWA studies for identification.Alternatively, rare genetic variants may play a more important role in longevity.Since the previous linkage studies showed contradictory results potentially due to heterogeneity in the longevity phenotype, it is expected that longevity is influenced by many private rare variants." + } + ], + "78a43a45-84b0-4d73-9396-95b99cfd3983": [ + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "text": "\n\nAgeing is complex and takes a long time to study -a lifetime in fact.This makes it difficult to discern its causes, among the countless possibilities based on an individual's genes, behaviour or environment.While thousands of regions in an individual's genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle.Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nSeveral explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nSecond, the largely negative findings of this and other studies contrast with the intriguing animal studies of longevity.Very large effects of single genes on lifespan have indeed been observed in laboratory animals, but humans often have several homologues of these genes which might significantly differ in function or compensate for mutated genes through redundant mechanisms (Kuningas et al., 2008).This could explain why our top findings did not include genes in these pathways found in animal models.Animal models also represent genetically homogenous populations and are exposed to controlled environmental influences.The lack of replication of animal model findings in humans suggests that the use of knockout animals may not provide the optimal approach to understanding the variation in survival in humans as interactions with environmental factors may obscure the associations and prevent the identification of loci in humans." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + } + ], + "97290894-086d-438a-bbd2-907dd4cea2ab": [ + { + "document_id": "97290894-086d-438a-bbd2-907dd4cea2ab", + "text": "\n\nIn addition to timing differences, a small proportion of genes (10%-15%) exhibit opposite trends of expression changes with age in humans and macaques (Supplemental Fig. S13).Interestingly, such differences are ;1.5 times more common in aging than in development, an observation consistent with the lower strength of purifying selection on the gene regulation at old age (discussed below).These differences could also reflect extreme shifts in developmental timing between species, as well as technical artifacts.Future studies, using additional species and alternative methodology, are needed to address this issue." + } + ], + "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed": [ + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nThe remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha ˜es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "TRANSLATION OF LONGEVITY MODEL ORGANISMS AND CORE AGING PATHWAYS\n\nGenetic studies on lifespan have proven to be challenging.While longevity is a defining trait for a given species, the lifespan of individuals is of limited heritability, making analyses more difficult.Exceptional human life span, although a rare phenotype, is likely multifactorial; refined analyses are required to obtain statistically robust genomic signatures of longevity (Zhang et al., 2020) and these have proven elusive.Unlike laboratory models, the effect of environmental variance cannot be controlled in human studies, potentially masking purely biological aging mechanisms.Even laboratory models cannot replicate the complex \"environment\" of humans; it includes psychosocial, economic, and cultural factors, rather than strictly biological.These human-specific confounders are difficult or impossible to target in traditional model organisms.Despite these limitations, experimentally tractable model organisms have proven invaluable in deciphering the purely genetic contribution to lifespan, including genes and pathways conserved across the tree of life." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + } + ], + "efd5747f-9e8b-45e8-9e04-bb31131d44fa": [ + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "text": "\n\nWith modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "Conclusions and prospects\n\nOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + } + ] + }, + "data_source": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nSeveral explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect." + }, + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "section_type": "main", + "text": "\n\nWith modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms." + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "section_type": "main", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists.\n However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "section_type": "main", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists.\n However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + }, + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "section_type": "main", + "text": "\n\nThe aging process most certainly is under highly polygenic controls… This should not discourage us from pursuing a search for those loci which may be of profound importance to human aging as it ordinarily occurs in most human beings." + }, + { + "document_id": "113cb521-b79d-4b44-8250-dc1013ea2cb3", + "section_type": "main", + "text": "\n\nChromosome mapping of genes that were differentially expressed in mice of different ages and/or in response to CR revealed a wide distribution of genes with some physical clustering of responsive genes within the genome.The latter findings are consistent with the concept that aging is a complex process and that evolutionary adaptations to aging, if they exist, may or may not involve geographic clustering of functionally related genes." + }, + { + "document_id": "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7", + "section_type": "main", + "text": "FUTURE DIRECTIONS: HIGHER RESOLUTION DATA VIA HIGHER THROUGHPUT ASSAYS\n\nOne inescapable conclusion of the aggregate results of genome-wide studies of aging to date (see summary Table 1) is that we have not come close to saturating the number of potentially lifespan-altering genes in any organism.This is in no small part because directly generating survival curves is a relatively time-consuming process in most model organisms using current methods.There are several possible ways to address this.One way that has been tried is by attempting to find surrogate phenotypes [72,73,126] that can be screened more rapidly, or even scored under selection.Another is mining candidates from the many whole-genome expression profiles.Results to date with these have been very fruitful, but have not suggested that these methods alone will rapidly saturate our search for lifespan-and healthspan-altering genes in tractable model organisms." + }, + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "section_type": "main", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + }, + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "section_type": "main", + "text": "\n\nThe remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha ˜es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "Conclusions and prospects\n\nOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + }, + { + "document_id": "97290894-086d-438a-bbd2-907dd4cea2ab", + "section_type": "main", + "text": "\n\nIn addition to timing differences, a small proportion of genes (10%-15%) exhibit opposite trends of expression changes with age in humans and macaques (Supplemental Fig. S13).Interestingly, such differences are ;1.5 times more common in aging than in development, an observation consistent with the lower strength of purifying selection on the gene regulation at old age (discussed below).These differences could also reflect extreme shifts in developmental timing between species, as well as technical artifacts.Future studies, using additional species and alternative methodology, are needed to address this issue." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "\n\nAlthough the models data set comprises all genes (to our knowledge) shown by the time of the latest update to statistically increase longevity or alter the aging process in a noticeable way, in the human data set we try to evaluate whether a given intervention is affecting the aging process itself or not.For example, many mutations may increase longevity by decreasing the incidence of specific diseases, rather than by altering the basic process of aging (de Magalhães et al ., 2005a(de Magalhães et al ., , 2005b)).Therefore, the human data set is not merely an extension of the work conducted in model organisms and of its bibliography, but a manually selected list of the most pertinent human aging candidate genes, each presented with a higher annotation level.We cite studies on whether the functions of aging-associated genes in model organisms are conserved in their human orthologues.Likewise, we cite flaws in previous studies based on new published observations, although we have a neutral stance on conflicting findings from different research groups.Our policy is to cite all conflicting reports and let visitors make their own decisions on how to interpret them.By contrast, each entry in GenAge model organisms has only one reference: the first publication reporting an association of the gene with longevity or aging.Moreover, one of the latest enhancements in the human data set was the inclusion of Gene Ontology annotation.Gene Ontology terms and annotation files were obtained from the Gene Ontology Consortium website (http://www.geneontology.org/ ) and provide an additional layer of description for the gene products in a cellular context (Ashburner et al ., 2000)." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nResults from mutational analysis across eukaryote model organisms have shown unexpected conservation of genes and processes regulating aging.While unique properties exist within particular organisms that modulate these foundational networks, the conservation provides a tool to refine human genetic studies.As noted, GWAS for human longevity metrics suffer from large sample size requirements to obtain statistical resolution due to multiple hypothesis testing across the genome.Assuming that evolutionary genesets for longevity could be generated with confidence, an intersection of them with human variation data would increase the sensitivity of association studies.This would serve as a selective filter to refine the number of loci investigated for association in human populations.Similarly, such evolutionary filters could refine analysis of rare, unique variation within genome sequence data from extremely long-lived cohorts.A similar approach to refine human longevity GWAS used an intersection with age-related disease datasets.This 'disease-informed' GWAS helped refine candidates (iGWAS, Fortney et al., 2015), though, it should be noted that this particular strategy would further blur the distinction between aging and longevity as discussed above.The definition of gene sets from evolutionary experiments in longevity, across clades, would similarly empower detection of networks previously hidden under GWAS in human population analyses (Figure 3)." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + }, + { + "document_id": "18e216d9-ea5c-4dfe-a30d-632163fcf39e", + "section_type": "main", + "text": "Conclusions\n\nIn the absence of a consensus phenotype for aging, genetic research is impeded (Melzer et al. 2007).At present, it is difficult to determine whether preventative and therapeutic strategies (such as calorie restriction) have beneficial effects in humans because there are no validated biomarkers that can serve as surrogate markers of aging (Matkovic et al. 1990).To have the \"phenome of aging\" (Xue et al. 2007) much better defined, we propose using the musculoskeletal aging phenotypes as an example and starting point." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + }, + { + "document_id": "04405b2b-901a-423c-9f08-418f5514c535", + "section_type": "main", + "text": "\n\nThese considerations suggest an intriguing question: why did \"Mother Nature\" conserve a common pathway of regulation between two genes involved in a process that is believed to have come out of natural selection?It has been recently proposed that a programmed and altruistic aging may occur in higher eukaryotes [5].Our findings are in line with this idea, although the deep evolutionary force that has driven such an architecture along evolution needs to be explored.The markers used for haplotype analysis are the following (in order): A21631G for PSMD13, G477T and 1-6 VNTR intron5 for SIRT3.Haplotype relative frequencies (RF) and standard errors (SE) are ×100.The p values refer to the null hypothesis of no difference between the transcription activity of the entire 788-bp promoter and the transcription activity of the deletion construct (ANOVA and LSD post hoc tests)." + }, + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "section_type": "main", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nSecond, the largely negative findings of this and other studies contrast with the intriguing animal studies of longevity.Very large effects of single genes on lifespan have indeed been observed in laboratory animals, but humans often have several homologues of these genes which might significantly differ in function or compensate for mutated genes through redundant mechanisms (Kuningas et al., 2008).This could explain why our top findings did not include genes in these pathways found in animal models.Animal models also represent genetically homogenous populations and are exposed to controlled environmental influences.The lack of replication of animal model findings in humans suggests that the use of knockout animals may not provide the optimal approach to understanding the variation in survival in humans as interactions with environmental factors may obscure the associations and prevent the identification of loci in humans." + }, + { + "document_id": "690a2ae6-962a-438c-91ca-60425a0c8d02", + "section_type": "main", + "text": "Accepted Article\n\n© 2013 The Authors Aging Cell © 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland over 90 years and 1,955 controls between 55 and 80 years did not reveal genome-wide significant loci (Newman et al., 2010) and neither did the analyses of all-cause mortality and survival free of major disease in this cohort (Walter et al., 2011).A smaller Dutch study of 403 nonagenarians and 1,670 controls younger than 65 years identified the APOE gene as a mortality locus (Deelen et al., 2011), which was confirmed in a German study of 763 long-lived individuals and 1,085 younger controls (Nebel et al., 2011) and a longitudinal study of 1,606 Danes showed that the effect size of this association increases at the highest ages (Jacobsen et al., 2010).Apparently, the influence of the common genetic variation on longevity is small which requires large meta-GWA studies for identification.Alternatively, rare genetic variants may play a more important role in longevity.Since the previous linkage studies showed contradictory results potentially due to heterogeneity in the longevity phenotype, it is expected that longevity is influenced by many private rare variants." + }, + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "section_type": "main", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "section_type": "main", + "text": "These examples serve to illustrate the general point that the more complex designs of\nexperiments that manipulate the level of imposed mortality rates, unlike the simpler\nprocedure of altering the first age of reproduction in a laboratory population, may in turn\nmake these experiments systematically more difficult to interpret. Futuyma and Bennett\n(this volume) also discuss the merits of simple experimental manipulations.\n THE NUMBER OF GENES AFFECTING AGING\n\nEarly evolutionary discussions of aging, such as those by Williams (1957) and Maynard\nSmith (1966), characteristically concluded that a large number of loci are likely to affect\naging." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "section_type": "main", + "text": "These examples serve to illustrate the general point that the more complex designs of\nexperiments that manipulate the level of imposed mortality rates, unlike the simpler\nprocedure of altering the first age of reproduction in a laboratory population, may in turn\nmake these experiments systematically more difficult to interpret. Futuyma and Bennett\n(this volume) also discuss the merits of simple experimental manipulations.\n THE NUMBER OF GENES AFFECTING AGING\n\nEarly evolutionary discussions of aging, such as those by Williams (1957) and Maynard\nSmith (1966), characteristically concluded that a large number of loci are likely to affect\naging." + }, + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "section_type": "main", + "text": "\n\nThe antagonistic pleiotropy and hyperfunction theories of ageing predict the presence of genetic variants important for growth and development in early life with deleterious effects towards the end of the reproductive window 19,20 .While we are unable to directly capture the genetic effects on individuals before age 40 due to the study design of our datasets, we found that the life-extending variant near FOXO3 is associated with a delay in the age at menarche and a decrease in intracranial volume and cognitive abilities.It thus appears that there are loci exhibiting antagonistic effects, although we are unable to discern whether this is due to true pleiotropy or due to linkage of causal variants within a region Genes which showed a significant effect (FDR < 5%) of gene expression on ageing traits are displayed here.Gene names are annotated with the direction of effect, where + andindicate whether the life-extending association of the locus is linked with higher or lower gene expression, respectively.Locus: nearest gene to lead variant in the multivariate analysis, Chr: chromosome, Position: base-pair position of lead variant (GRCh37), Cis-genes: genes in physical proximity (<500 kb) to the lead variant of the locus which colocalise with the multivariate signal, Trans-genes: genes located more than 500 kb from the lead variant of the locus." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "abstract", + "text": "\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nWhy then are we not devoting significantly greater resources to understanding more about the greatest risk factor for every age-associated pathology by attempting to answer this fundamental question: \"What changes occur in biomolecules that lead to the manifestations of aging at higher orders of complexity and then increase vulnerability to all age-associated pathology?\"" + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "TRANSLATION OF LONGEVITY MODEL ORGANISMS AND CORE AGING PATHWAYS\n\nGenetic studies on lifespan have proven to be challenging.While longevity is a defining trait for a given species, the lifespan of individuals is of limited heritability, making analyses more difficult.Exceptional human life span, although a rare phenotype, is likely multifactorial; refined analyses are required to obtain statistically robust genomic signatures of longevity (Zhang et al., 2020) and these have proven elusive.Unlike laboratory models, the effect of environmental variance cannot be controlled in human studies, potentially masking purely biological aging mechanisms.Even laboratory models cannot replicate the complex \"environment\" of humans; it includes psychosocial, economic, and cultural factors, rather than strictly biological.These human-specific confounders are difficult or impossible to target in traditional model organisms.Despite these limitations, experimentally tractable model organisms have proven invaluable in deciphering the purely genetic contribution to lifespan, including genes and pathways conserved across the tree of life." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "996e02bf-91b2-4e81-89ba-1f661dfc662a", + "section_type": "main", + "text": "Concluding Remarks\n\nRather than expect differences in defensive or protective genes to regulate the pace of aging, which have never been found ( 13), it appears that the genetic factors that drive development may also regulate aging rates.Looking at aging as the unintended outcome of a programmed, well-orchestrated development explains why adult life span is proportional to developmental time among mammals.This perspective is also consistent with the antagonistic pleiotropy theory (53): alleles that favor early reproduction and a faster development may entail deleterious late-life effects and thus cause a faster senescence.Besides, mammals feature a robust set of developmental strategies, particularly compared with amphibians, and therefore it is not surprising that aging in different species of mammals appears to be the same process only timed at radically different rates." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "abstract", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nOn the other hand, the same evolutionary-motivated strategy suggesting to focus on more heterogeneous phenotypes (as opposite to more homogenous) can be highly beneficial for unraveling genetic predisposition to fundamental mechanisms of intrinsic biological aging and, consequently, to geriatric diseases.Indeed, aging is associated with systemic remodeling of an organism's functioning which increases chances of virtually all geriatric disorders (Franco et al. 2009;Franceschi et al. 2000;Martin et al. 2007;Cutler and Mattson 2006).Experiments with laboratory animals (Johnson 2006) and heritability estimates in humans (Christensen et al. 2006;Iachine et al. 1998) show that aging can be genetically regulated (Finch and Tanzi 1997;Martin et al. 2007;Vaupel 2010).Accordingly, yielding insights in genetic predisposition to aging-related processes in an organism could be a major breakthrough in preventing and/or ameliorating not one geriatric trait, but perhaps a major subset of such traits (Martin et al. 2007) that can greatly advance progress in solving the problem of extending healthy lifespan in humans." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nOne way to overcome (part of) this problem is by using a family-based study design (Box 1 and Fig. 1), in which the offspring of long-lived individuals -representing ''healthy agers'' -are compared to similar-aged controls from the general population.The differential gene expression profiles identified using this design may represent markers of healthy aging and familial longevity.This approach has been applied in the LLS to explore the transcriptome in whole blood for association with human familial longevity.Genes belonging to the mTOR pathway, as well as ASF1A and IL7R, were differentially expressed between offspring and controls [59,60].In addition, the expression of mTOR genes in blood associated to prevalent diabetes and serum glucose.However, the association with familial longevity was not dependent on this.Thus, gene expression profiles in blood mark human longevity in middle age and potentially provide information on the pathways that contribute to healthy aging and longevity." + }, + { + "document_id": "fe32b103-5dba-4cf0-b8af-762a71a5f5e6", + "section_type": "main", + "text": "\n\nAlthough many theories have tried to explain aging, only few experimental advances were made prior to the last two decades.Since then rapid progress in the genetics of aging has been made in invertebrate models such as C. elegans and D. melanogaster, demonstrating the existence of regulatory pathways that control the rate of aging in these organisms [1][2][3][4][5][6][7][8][9][10][11][12][13][14].They include the insulin-like pathway, the Jun kinase pathway and the Sir2 deacetylase pathway.Moreover, it was rapidly shown that some of these pathways are conserved from yeast to humans." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "section_type": "main", + "text": "\n\nAgeing is complex and takes a long time to study -a lifetime in fact.This makes it difficult to discern its causes, among the countless possibilities based on an individual's genes, behaviour or environment.While thousands of regions in an individual's genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle.Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "\n\nGenAge consists of several searchable data sets.Considering the extraordinary discoveries in the genetics of aging in model organisms, GenAge includes a data set of genes associated with longevity and/or aging in model organisms.We consider a given gene for inclusion in GenAge if genetic manipulations of the gene result in noticeable changes in the aging phenotype and/or longevity.Most genes in GenAge are from the four typical model organisms: mice, worms, fruit flies and yeast (Table 1).Strikingly, homologues of many genes -such as insulin receptors and sirtuins -have been shown to regulate aging in model organisms separated by large evolutionary distances (Kenyon, 2005;Liu et al ., 2005;Smith et al ., 2008).Moreover, we have shown that genes associated with aging and/or longevity in model organisms are evolutionary conserved in terms of having more homologues than predicted by chance (Budovsky et al ., 2007(Budovsky et al ., , 2008) ) and exhibiting slower molecular evolution rates (de Magalhães & Church, 2007).Therefore, it is now clear that at least some genes identified in model organisms may be relevant to human aging." + }, + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "section_type": "main", + "text": "IV. Genome-Environment Interactions as Targets for Dietary Interventions and Drug Discovery\n\n\"…[It's] possible that we could change a human gene and double our life span. \"-CynthiaKenyon (Duncan, 2004) According to the GenAge database of aging-related genes (http://genomics.senescence.info/genes/),more than 700 genes have been identified that regulate lifespan in model organisms (de Magalha ˜es et al., 2009a).Many of these genes and their associated pathways-such as the insulin/IGF1/GH pathway-have been shown to affect longevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolutionarily conserved and may have potential therapeutic applications (Baur et al., 2006).For example, evidence suggests the use of lowered IGF signaling (e.g., by targeting IGF receptors) to treat certain age-related diseases such as cancer (Pollak et al., 2004), Alzheimer's disease (Cohen et al., 2009), and autoimmune diseases (Smith, 2010).Moreover, a number of genes and pathways associated with longevity and CR are part of nutrient-sensing pathways that also regulate growth and development, including the insulin/IGF1/GH pathway (Narasimhan et al., 2009;Stanfel et al., 2009).Many of these genes modulate the response to environmental signals, such as food availability, and act in signaling pathways that if understood can be targeted (Fig. 1).The genetic regulation of aging is therefore an emerging field with multiple applications in the human nutrition, cosmetic, and pharmaceutical industries." + } + ], + "document_id": "E1F24400EE215327FE987A4DDC0768C8", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "GWAS", + "longevity", + "aging", + "human", + "gene", + "lifespan", + "genetic", + "environment" + ], + "metadata": [ + { + "object": "Transient overexpression of WRKY79 in protoplasts results in up-regulation of Gene:542165, Gene:541974, Gene:100274033, Gene:542688, Gene:542150, Gene:542151, Gene:100273457, Gene:100285509, Gene:103626248, Gene:103646045, Gene:100217270, Gene:100279981, Gene:100281950, Gene:542476, Gene:542369, Gene:100281950, and Gene:542260.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab969966" + }, + { + "object": "Data suggest that the redox status of serum apoE might be related to the synthesis of HDL; the cysteine-thiol residue of reduced-apoE is in a naive state, while that of non-reduced-apoE is in a reversibly or irreversibly oxidized state. Data suggest that apoE homodimer and apoE-AII complex are typical reversibly oxidized forms of apoE. apoE-AII complex = a complex of apolipoprotein E and apolipoprotein A-II", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab212832" + }, + { + "object": "APOE genotype status moderated the age-related declines in episodic memory: APOE-epsilon4+ middle-aged adults exhibited impairments relative to both APOE-epsilon4- middle-aged participants, and APOE-epsilon4+ younger adults.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab77520" + }, + { + "object": "In an Amish population, using expression profiling of genes within regions identified by a meta-analysis GWAS of survival to age 90, we localized PAPSS2 as a candidate gene for extended life span. These results provide novel evidence for genetic loci implicated in longevity and incorporate gene expression results from a unique population to locate positional candidates.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab389107" + }, + { + "object": "Genome-wide associations P < 5 x 10-8 were found at the PCSK9 gene, the APOB gene, theLPL gene, the APOA1-APOA5 locus, the LIPC gene, the CETP gene, the LDLR gene, and the APOE locus.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab320109" + }, + { + "object": "There was no association between the extended psychosis phenotype and BDNF rs6265/COMT rs4680 polymorphisms. The lack of an association between different expression levels of the extended psychosis phenotype and the BDNF rs6265/ COMT rs4680 polymorphism might be related to sample characteristics, underlying gene-gene, gene-environment and gene-environment-gene interactions.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab888369" + }, + { + "object": "Data suggest that modulation of ARHGEF3 gene expression in humans with a promoter-localized SNP plays a role in human megakaryocytes and human platelet function-a finding resulting from the biological follow-up of human genetic studies. Arhgef3 KO mice partially recapitulate the human phenotype.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab209151" + }, + { + "object": "Very high gene-level linkage in Bayesian GWAS using Kutalik/Timmers data and MAGMA gene-level reanalysis to human longevity LOD 14.5; RW Williams July 2019. Linkage to longevity is second only to the TOMM40-APOE-APOC1-PVRL2-APOC4 region on Chr 19.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab6647" + }, + { + "object": "NO evident relationship was found between gene polymorphism of Eco RI loci of Apo B gene, Xba I loci of Apo B gene or 3'-VNTR of Apo B gene and ANFH.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab529998" + }, + { + "object": "Data suggest that modulation of ARHGEF3 gene expression in humans with a promoter-localized SNP plays a role in human megakaryocytes and human platelet function-a finding resulting from the biological follow-up of human genetic studies. Arhgef3 KO mice partially recapitulate the human phenotype.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab209152" + } + ], + "question": "Why is it so diffuclut to map gene loci that control aging in humans?", + "subquestions": null, + "task_id": "E1F24400EE215327FE987A4DDC0768C8", + "usage": { + "chatgpt": 7866, + "gpt-4": 4669, + "gpt-4-turbo-preview": 3717 + }, + "user_id": 2 + }, + "document_id": "E1F24400EE215327FE987A4DDC0768C8", + "task_id": "E1F24400EE215327FE987A4DDC0768C8" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/07.json b/gnqa/paper1_eval/src/data/responses/aging/experts/07.json new file mode 100644 index 0000000..2ff5d59 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/07.json @@ -0,0 +1,402 @@ +{ + "created_at": "2024-05-31T20:55:47.964141Z", + "data": { + "amplify": false, + "answer": "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "context": { + "18e216d9-ea5c-4dfe-a30d-632163fcf39e": [ + { + "document_id": "18e216d9-ea5c-4dfe-a30d-632163fcf39e", + "text": "\n\nThere are multiple definitions of the aging process.Aging may be perceived as the random, systemic loss of molecular fidelity that, after reproductive maturity, accumulates to levels that eventually exceed tissue repair, turnover, or maintenance capacity (Hayflick 2004).The underlying molecular mechanisms of aging remain a subject of debates (de Magalhaes et al. 2009): tissue deterioration might not be programmed, being just a function of increase in entropy (Hayflick 2004).No genes are necessary to drive a stochastic process; however, there are genes that act to prevent an organism from destruction and disorganization.It may be due to the absence of specific disease-causing alleles or due to the presence of favorable alleles (Halaschek-Wiener et al. 2009).These genes may inhibit entropy, regulate inflammation, maintain DNA repair (such as telomere maintenance factors), or provide antioxidant functions (e.g., antagonists of reactive oxygen species).As healthy cells adapt to degeneration, differential expression of genes with age may indicate a transcriptional response to aging rather than a deleterious mechanism of aging per se (de Magalhaes et al. 2009).It might be postulated that there exist alleles that confer a pleiotropic effect on structure and function during aging (Lunetta et al. 2007).These alleles should regulate the ability of an organism to withstand challenging endogenous and exogenous influences." + } + ], + "1ccb0d11-1c88-4b08-b40d-4039a954745f": [ + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "text": "Why does ageing evolve? The intrinsic decline in function that occurs during ageing appears to be caused by the accumulation of damage, particularly at the molecular level.As far as we know, no genes have evolved specifically because they cause damage to accumulate, and the evolution of ageing can therefore be understood only as a side-effect of other causes of evolutionary change.The mechanisms by which ageing can evolve were first elucidated by J.B.S. Haldane [14], P.B. Medawar [15] and G.C. Williams [16].Extrinsic hazards from disease, predation and accidents mean that even potentially immortal organisms will die.Genetic effects that become apparent only later in life encounter a reduced force of natural selection, because not all their bearers will survive to express them.Haldane pointed out that late-onset genetic diseases in humans, such as Huntington's disease, encounter only weak selection, because most reproduction is complete by the age of onset [14].Ageing could therefore result from the accumulation under mutation pressure of age-specific, deleterious mutations.In addition, if some mutations have pleiotropic effects, with beneficial effects in youth, such as high fecundity, but also with a higher subsequent rate of ageing, then they could be incorporated into the population by natural selection, which will act more strongly on the early, beneficial effect.Thus, variation in the rate of ageing would result from the readjustment of a tradeoff between youthful benefits and the subsequent rate of ageing.Both processes imply that faster ageing will evolve where the extrinsic hazard to adults is greatest, a hypothesis in general supported by the data [1,2,17]." + } + ], + "4f010a74-a9b4-4538-94f7-ae8f35c8b96e": [ + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "text": "A. Theories\n\nIn looking back at the development of aging studies, we can see that it did not follow a straight or logical course.On the contrary, it can be compared with the flow of several convergent streams winding in their course.To date, numerous proposals have been made for the paradigm of aging.These include Hayflick's contributions (153) on programmed cellular incapacitation derived from flbroblast studies, a decrease in immunologic response, deleterious endocrinological changes, nuclear somatic gene mutation, mitochondrial somatic gene mutation, oxygen free radical damage to proteins and nucleic acids, molecular instabilities, molecular cross-linking, glycation reactions, and so on.There is little doubt that many of these factors contribute to the overall aging, but what are primary causes, and what are secondary outcomes?" + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Ageing Is Adjusted by Genetic, Environmental, and Stochastic Processes\n\nEnough evidence suggests that ageing is the result of different events such as molecular damage, mutations, incomplete repair, genetic programs, and continued development, among others [16].These events, in turn, are caused by genetic factors, environmental conditions, and even stochastic factors, which are mentioned below in this chapter." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nDifferent stochastic theories of ageing focus on specific mechanisms that may lead to ageing.The catastrophic error theory poses that the accumulation of errors in protein synthesis causes damage in cell function.The theory of cross-linking holds this process between proteins and other macromolecules responsible for ageing, while the theory of free radicals suggests that ageing is the result of inadequate protection against cell and tissue damage by free radicals and oxidative stress throughout life.Finally, the wear-and-tear theory poses that the cumulative damage that eventually leads to ageing and death is, in fact, the result of the continuous functioning of vital processes, during which stochastic errors gradually arise." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Introduction\n\nAging is a natural and irreversible process characterized by a progressive decay in physiological, biochemical, and structural functions of individuals.Aging is a multifactorial process that can be affected by two main factors: environmental and genetic.Environmental factors are nutrition, pathologies, pollution exposure, physical activity, and microbiota, while genetic factors are issues that have been associated with antioxidant and DNA damage responses, the fidelity of genetic information transfer, the efficiency of protein degradation, the extent of cellular responsiveness to stress, the mechanisms of epigenetic regulation, and the ability to elongate telomeres.All of them can determine how fast we age.Traditionally, aging studies had used several model organisms, from yeast to mammals, especially rodents (rats and mice).Most of the studies are made under controlled conditions, where only a few variables are observed, and the subjects are members of the same strain with the same genetic backgrounds or the same mutations.The information that so far has been obtained about aging has helped us to describe different factors that influence this process and that are the fundamental concepts of the various theories of aging.However, these theories do not fully explain the aging process in the different models of aging study.This is the case of the study of aging in humans, where it is very difficult to control the environmental and genetic variables.That is why issues haven't been solved such as the following: How does time influence aging?When do we start to age?How do we know we are old?Is it possible to delay aging?Those and more questions are the cornerstones for aging studies.Biological aging has been associated with the decrease in the repair and regeneration capacity of tissues and organs; it is a time-dependent process.This reduction can be observed by an increase in the acquisition of diseases and functional and reproductive disability, which eventually lead to death.On the other hand, it has been observed that in humans, people with the same chronological age exhibit different trajectories in the decrease of physiological functions associated with biological aging and what complicates the understanding of the molecular and physiological phenomena that drive the complex and multifactorial processes that underlie biological aging in humans." + } + ], + "5030cbc8-e02c-4e3a-8cbc-0156ce123c99": [ + { + "document_id": "5030cbc8-e02c-4e3a-8cbc-0156ce123c99", + "text": "\nThe underlying cause of aging remains one of the central mysteries of biology.Recent studies in several different systems suggest that not only may the rate of aging be modified by environmental and genetic factors, but also that the aging clock can be reversed, restoring characteristics of youthfulness to aged cells and tissues.This Review focuses on the emerging biology of rejuvenation through the lens of epigenetic reprogramming.By defining youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges." + } + ], + "5e157c2e-91b8-466d-a9fd-f91f8f432f0c": [ + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "text": "\n\nAging does not happen in a vacuum.Aging must be the result of changes that occur in molecules that have existed at one time with no age changes.It is the state of these pre-existing molecules that governs longevity determination.The pre-existing state is, as I have already described, maintained by repair and turnover systems that themselves eventually succumb to irreparable age changes.Longevity determination is the state of all molecules prior to succumbing to irreparable loss of molecular structure." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "text": "\n\nBiological aging is more than simply the occurrence of random changes in molecules.It also includes the role of the many repair systems found within cells.Thus, a more complete, but less concise, explanation of the first causes of aging in biological systems is the following:" + } + ], + "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c": [ + { + "document_id": "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c", + "text": "U\n\nnderstanding the deleterious processes that cause aging has been a human endeavor ever since we figured out that we grew old and that we didn't like it.Many hypotheses have been proposed to explain the root cause of aging (1).One broad-based hypothesis is that generalized homeostatic failure leads to age-related decline.Although notions of time-and use-related deterioration may be applicable to mechanical objects, they fall short as analogies to biological systems because energy input should theoretically maintain living systems indefinitely.Yet, despite the regenerative potential of biological organisms, progressive deterioration accompanies postmaturational aging.That the organism's repair capabilities cannot keep up with wear and tear is, according to evolutionary theory, explained by the inevitable declining force of natural selection with age.According to this reasoning, there is no selective advantage to maintaining somatic cells in perfect order much beyond reproductive maturation (1).Hence, a long life depends on the timing of maturation and the quality of somatic cell maintenance." + }, + { + "document_id": "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c", + "text": "\n\nWear and tear on the DNA often has been touted as a possible basis for our progressive age-related decline.Supporting this notion is the work of de Boer et al. (2) reported on page 1276 of this week's issue.They reveal important evidence for imperfect genome maintenance of DNA damage as a possible causal factor in aging.Harman, with his \"free radical theory of aging\" (3), was the first to propose that metabolic by-products called reactive oxygen species (ROS) continually damage cellular macromolecules, including DNA.Incomplete repair of such damage would lead to its accumulation over time and eventually result in age-related deterioration.A number of observations support the free radical theory, including the discovery that dietary restriction delays aging and extends life-span in a wide range of rodents and other species, possibly by reducing free radical damage.The notion that genomic DNA could be a major target of continual free radical attack over time is supported by the recent observation that genetic lesions accumulate with age and that dietary restriction reduces this accumulation in rodents (4).In addition, deletion of p66 shc , a signaling protein that maintains oxidant levels, increases resistance to oxidative damage and extends the life-span of mice (5)." + } + ], + "606c59c5-5ae4-47e9-b3eb-58afa55669d1": [ + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "text": "Instead, aging is expected to\nbe a pervasive failure of adaptation across most, if not all, of the physiological mechanisms\nthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute “the cause of\naging” to any one physiological mechanism or gene for aging or programmed death. Although common genetic pathways might be identified that contribute to aging among a\nvariety of organisms (cf." + } + ], + "846ae0a9-165f-4b25-8bcb-310c7da5eb44": [ + { + "document_id": "846ae0a9-165f-4b25-8bcb-310c7da5eb44", + "text": "Background\n\nAging is a complex process characterized by the progressive degeneration of a healthy phenotype and correlated with a decline in the ability to withstand cellular stress and damage.The subject of investigation for decades, the underlying molecular genetic causes of and responses to aging remain an area of active study.Research from model systems has characterized a range of physiological and molecular phenotypes associated with aging.These include genomic instability caused by accumulation of DNA damage, dysregulation of repair mechanisms, and telomere attrition; epigenetic alterations; dysregulation of transcription; loss of proteostasis; cellular senescence; and deregulated nutrient sensing, metabolic pathways, and energy use (reviewed in [1]).Separating causation from correlation between these phenotypes and aging remains a challenge, however." + } + ], + "870798fd-2c26-4819-9403-fe52836770eb": [ + { + "document_id": "870798fd-2c26-4819-9403-fe52836770eb", + "text": "Introduction\n\nUnderstanding what actually causes ageing remains admittedly a fundamental and fascinating problem in biology [1].Experimental data accumulated in the last three decades have led to the identification of various environmental and genetic factors, as well as chemical substances that influence lifespan in divergent eukaryotic species [1,2].Organisms normally age faster and hence live shorter under stress conditions that can lead to the generation of DNA mutations and, often as a consequence of mutations, damaged cytoplasmic constituents (including injured proteins, lipids, carbohydrates and organelles).Such types of damage can interfere with cellular functioning; thereby, they should be eliminated by effective repair and self-cleaning mechanisms to maintain cellular homeostasis.These mechanisms include DNA repair pathways, molecular chaperons, as well as the proteasome-ubiquitin system and lysosome-mediated autophagy, the main forms of cellular self-degradation [3].This has led to the attractive model that the gradual, lifelong accumulation of unrepaired cellular damage drives the ageing process and determines the incidence of age-related fatal diseases [4,5]." + } + ], + "996e02bf-91b2-4e81-89ba-1f661dfc662a": [ + { + "document_id": "996e02bf-91b2-4e81-89ba-1f661dfc662a", + "text": "\n\nIn conclusion, aging may not be primarily due to damage accumulating from the basic biochemical reactions that make up life but rather the result of the developmental program or of changes brought about by it.Our hypothesis is that the timing of development regulates the rate of aging among mammals, with a subset of developmental mechanisms determining the pace and causing most agerelated changes.Maybe people change as they grow old due to the same mechanisms that drive changes throughout the earlier stages in life." + } + ], + "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed": [ + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "text": "Instead, aging is expected to\nbe a pervasive failure of adaptation across most, if not all, of the physiological mechanisms\nthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute “the cause of\naging” to any one physiological mechanism or gene for aging or programmed death. Although common genetic pathways might be identified that contribute to aging among a\nvariety of organisms (cf." + } + ], + "a6bc2efd-61a7-4e07-ad5c-49234aa89431": [ + { + "document_id": "a6bc2efd-61a7-4e07-ad5c-49234aa89431", + "text": "\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13]." + } + ], + "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65": [ + { + "document_id": "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65", + "text": "Introduction\n\nThe fundamental manifestation of the aging process is a progressive decline in the functional maintenance of tissue homeostasis and an increasing propensity to degenerative diseases and death [1].It has attracted significant interest to study the underlying mechanisms of aging, and many theories have been put forward to explain the phenomenon of aging.There is an emerging consensus that aging is a multifactorial process, which is genetically determined and influenced epigenetically by environment [2].Most aging theories postulate a single physiological cause of aging, and likely these theories are correct to a certain degree and in certain aspects of aging." + } + ], + "ca76f85d-9f72-4e15-8ba9-3bf94308c449": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + } + ], + "f4dd6a1d-062b-42bc-8e22-83fcb3135578": [ + { + "document_id": "f4dd6a1d-062b-42bc-8e22-83fcb3135578", + "text": "\n\nTrying to explain aging in terms of a singular process would be in conflict with evolutionary theory.Even if loss of genome sequence integrity was the most conserved cause of aging, already active in the first replicators (Vijg, 2007), natural selection would allow a multitude of mutations with late adverse effects to accumulate in the germline, many of which would be positively selected for because of their beneficial effects early in life (Williams, 1957), In this respect, somatic mutation accumulation could be a conserved, inevitable cause of aging but superposed on multiple other processes that usually cause the earlier demise of an individual." + } + ] + }, + "data_source": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "section_type": "main", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + }, + { + "document_id": "870798fd-2c26-4819-9403-fe52836770eb", + "section_type": "main", + "text": "Introduction\n\nUnderstanding what actually causes ageing remains admittedly a fundamental and fascinating problem in biology [1].Experimental data accumulated in the last three decades have led to the identification of various environmental and genetic factors, as well as chemical substances that influence lifespan in divergent eukaryotic species [1,2].Organisms normally age faster and hence live shorter under stress conditions that can lead to the generation of DNA mutations and, often as a consequence of mutations, damaged cytoplasmic constituents (including injured proteins, lipids, carbohydrates and organelles).Such types of damage can interfere with cellular functioning; thereby, they should be eliminated by effective repair and self-cleaning mechanisms to maintain cellular homeostasis.These mechanisms include DNA repair pathways, molecular chaperons, as well as the proteasome-ubiquitin system and lysosome-mediated autophagy, the main forms of cellular self-degradation [3].This has led to the attractive model that the gradual, lifelong accumulation of unrepaired cellular damage drives the ageing process and determines the incidence of age-related fatal diseases [4,5]." + }, + { + "document_id": "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c", + "section_type": "main", + "text": "U\n\nnderstanding the deleterious processes that cause aging has been a human endeavor ever since we figured out that we grew old and that we didn't like it.Many hypotheses have been proposed to explain the root cause of aging (1).One broad-based hypothesis is that generalized homeostatic failure leads to age-related decline.Although notions of time-and use-related deterioration may be applicable to mechanical objects, they fall short as analogies to biological systems because energy input should theoretically maintain living systems indefinitely.Yet, despite the regenerative potential of biological organisms, progressive deterioration accompanies postmaturational aging.That the organism's repair capabilities cannot keep up with wear and tear is, according to evolutionary theory, explained by the inevitable declining force of natural selection with age.According to this reasoning, there is no selective advantage to maintaining somatic cells in perfect order much beyond reproductive maturation (1).Hence, a long life depends on the timing of maturation and the quality of somatic cell maintenance." + }, + { + "document_id": "846ae0a9-165f-4b25-8bcb-310c7da5eb44", + "section_type": "main", + "text": "Background\n\nAging is a complex process characterized by the progressive degeneration of a healthy phenotype and correlated with a decline in the ability to withstand cellular stress and damage.The subject of investigation for decades, the underlying molecular genetic causes of and responses to aging remain an area of active study.Research from model systems has characterized a range of physiological and molecular phenotypes associated with aging.These include genomic instability caused by accumulation of DNA damage, dysregulation of repair mechanisms, and telomere attrition; epigenetic alterations; dysregulation of transcription; loss of proteostasis; cellular senescence; and deregulated nutrient sensing, metabolic pathways, and energy use (reviewed in [1]).Separating causation from correlation between these phenotypes and aging remains a challenge, however." + }, + { + "document_id": "996e02bf-91b2-4e81-89ba-1f661dfc662a", + "section_type": "main", + "text": "\n\nIn conclusion, aging may not be primarily due to damage accumulating from the basic biochemical reactions that make up life but rather the result of the developmental program or of changes brought about by it.Our hypothesis is that the timing of development regulates the rate of aging among mammals, with a subset of developmental mechanisms determining the pace and causing most agerelated changes.Maybe people change as they grow old due to the same mechanisms that drive changes throughout the earlier stages in life." + }, + { + "document_id": "5030cbc8-e02c-4e3a-8cbc-0156ce123c99", + "section_type": "abstract", + "text": "\nThe underlying cause of aging remains one of the central mysteries of biology.Recent studies in several different systems suggest that not only may the rate of aging be modified by environmental and genetic factors, but also that the aging clock can be reversed, restoring characteristics of youthfulness to aged cells and tissues.This Review focuses on the emerging biology of rejuvenation through the lens of epigenetic reprogramming.By defining youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges." + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "section_type": "main", + "text": "Instead, aging is expected to\nbe a pervasive failure of adaptation across most, if not all, of the physiological mechanisms\nthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute “the cause of\naging” to any one physiological mechanism or gene for aging or programmed death.\n Although common genetic pathways might be identified that contribute to aging among a\nvariety of organisms (cf." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "section_type": "main", + "text": "Instead, aging is expected to\nbe a pervasive failure of adaptation across most, if not all, of the physiological mechanisms\nthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute “the cause of\naging” to any one physiological mechanism or gene for aging or programmed death.\n Although common genetic pathways might be identified that contribute to aging among a\nvariety of organisms (cf." + }, + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "section_type": "main", + "text": "A. Theories\n\nIn looking back at the development of aging studies, we can see that it did not follow a straight or logical course.On the contrary, it can be compared with the flow of several convergent streams winding in their course.To date, numerous proposals have been made for the paradigm of aging.These include Hayflick's contributions (153) on programmed cellular incapacitation derived from flbroblast studies, a decrease in immunologic response, deleterious endocrinological changes, nuclear somatic gene mutation, mitochondrial somatic gene mutation, oxygen free radical damage to proteins and nucleic acids, molecular instabilities, molecular cross-linking, glycation reactions, and so on.There is little doubt that many of these factors contribute to the overall aging, but what are primary causes, and what are secondary outcomes?" + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Ageing Is Adjusted by Genetic, Environmental, and Stochastic Processes\n\nEnough evidence suggests that ageing is the result of different events such as molecular damage, mutations, incomplete repair, genetic programs, and continued development, among others [16].These events, in turn, are caused by genetic factors, environmental conditions, and even stochastic factors, which are mentioned below in this chapter." + }, + { + "document_id": "a6bc2efd-61a7-4e07-ad5c-49234aa89431", + "section_type": "main", + "text": "\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Introduction\n\nAging is a natural and irreversible process characterized by a progressive decay in physiological, biochemical, and structural functions of individuals.Aging is a multifactorial process that can be affected by two main factors: environmental and genetic.Environmental factors are nutrition, pathologies, pollution exposure, physical activity, and microbiota, while genetic factors are issues that have been associated with antioxidant and DNA damage responses, the fidelity of genetic information transfer, the efficiency of protein degradation, the extent of cellular responsiveness to stress, the mechanisms of epigenetic regulation, and the ability to elongate telomeres.All of them can determine how fast we age.Traditionally, aging studies had used several model organisms, from yeast to mammals, especially rodents (rats and mice).Most of the studies are made under controlled conditions, where only a few variables are observed, and the subjects are members of the same strain with the same genetic backgrounds or the same mutations.The information that so far has been obtained about aging has helped us to describe different factors that influence this process and that are the fundamental concepts of the various theories of aging.However, these theories do not fully explain the aging process in the different models of aging study.This is the case of the study of aging in humans, where it is very difficult to control the environmental and genetic variables.That is why issues haven't been solved such as the following: How does time influence aging?When do we start to age?How do we know we are old?Is it possible to delay aging?Those and more questions are the cornerstones for aging studies.Biological aging has been associated with the decrease in the repair and regeneration capacity of tissues and organs; it is a time-dependent process.This reduction can be observed by an increase in the acquisition of diseases and functional and reproductive disability, which eventually lead to death.On the other hand, it has been observed that in humans, people with the same chronological age exhibit different trajectories in the decrease of physiological functions associated with biological aging and what complicates the understanding of the molecular and physiological phenomena that drive the complex and multifactorial processes that underlie biological aging in humans." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nBiological aging is more than simply the occurrence of random changes in molecules.It also includes the role of the many repair systems found within cells.Thus, a more complete, but less concise, explanation of the first causes of aging in biological systems is the following:" + }, + { + "document_id": "18e216d9-ea5c-4dfe-a30d-632163fcf39e", + "section_type": "main", + "text": "\n\nThere are multiple definitions of the aging process.Aging may be perceived as the random, systemic loss of molecular fidelity that, after reproductive maturity, accumulates to levels that eventually exceed tissue repair, turnover, or maintenance capacity (Hayflick 2004).The underlying molecular mechanisms of aging remain a subject of debates (de Magalhaes et al. 2009): tissue deterioration might not be programmed, being just a function of increase in entropy (Hayflick 2004).No genes are necessary to drive a stochastic process; however, there are genes that act to prevent an organism from destruction and disorganization.It may be due to the absence of specific disease-causing alleles or due to the presence of favorable alleles (Halaschek-Wiener et al. 2009).These genes may inhibit entropy, regulate inflammation, maintain DNA repair (such as telomere maintenance factors), or provide antioxidant functions (e.g., antagonists of reactive oxygen species).As healthy cells adapt to degeneration, differential expression of genes with age may indicate a transcriptional response to aging rather than a deleterious mechanism of aging per se (de Magalhaes et al. 2009).It might be postulated that there exist alleles that confer a pleiotropic effect on structure and function during aging (Lunetta et al. 2007).These alleles should regulate the ability of an organism to withstand challenging endogenous and exogenous influences." + }, + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "section_type": "main", + "text": "\n\nThe dominant theory at the time was that aging was caused by the accumulation of molecular damage generated by oxygen radicals, particularly originating from the mitochondria.Independently, Pamela Larsen and Jacques Vanfleteren exposed wild-type and age-1 mutants to oxidants (hydrogen peroxide and paraquat, respectively) (26,27).The assays were conducted in young animals over days.The long-lived mutants were resistant to oxidative stress.Moreover, age-1 mutant worms had elevated levels of the antioxidant enzymes, superoxide dismutase, and catalase activities which could be sufficient to confer oxidative stress resistance and was consistent with the oxygen radical theory of aging." + }, + { + "document_id": "42cbc297-d57c-4c1f-8d3f-f9e52748b823", + "section_type": "main", + "text": "Conclusions\n\nSkin follows the pathway of aging, whereas in addition to the internal factors, several environmental ones contribute to this process and sometimes accelerate the onset of aging in the skin.Skin functions deteriorate, and this results in the development of a palette of diseases that sometimes jeopardize life quality or even life itself.Awareness of the pathophysiology of age-associated skin diseases as well as of preventive measurements to avoid skin damage is the first step for successful, healthy aging.Genomic technologies, such as gene chips, have identified gene expression signatures associated with skin aging and have become a fundamental basis in helping to develop new skin repair products.Proteomics and metabolomics can complete the increasing knowledge in this field.Research to understand a natural phenomenon such as aging should not only be considered as a privilege of modern Western society but also as the best prevention of age-associated diseases, including cancer." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "abstract", + "text": "\nThe belief that aging is still an unsolved problem in biology is no longer true.Of the two major classes of theories, the one class that is tenable is derivative of a single common denominator that results in only one fundamental theory of aging.In order to address this complex subject, it is necessary to first define the four phenomena that characterize the finitude of life.These phenomena are aging, the determinants of longevity, age-associated diseases, and death.There are only two fundamental ways in which age changes can occur.Aging occurs either as the result of a purposeful program driven by genes or by events that are not guided by a program but are stochastic or random, accidental events.The weight of evidence indicates that genes do not drive the aging process but the general loss of molecular fidelity does.Potential longevity is determined by the energetics of all molecules present at and after the time of reproductive maturation.Thus, every molecule, including those that compose the machinery involved in turnover, replacement, and repair, becomes the substrate that experiences the thermodynamic instability characteristic of the aging process.However, the determinants of the fidelity of all molecules produced before and after reproductive maturity are the determinants of longevity.This process is governed by the genome.Aging does not happen in a vacuum.Aging must be the result of changes that occur in molecules that have existed at one time with no age changes.It is the state of these pre-existing molecules that governs longevity determination.The distinction between the aging process and age-associated disease is not only based on the molecular definition of aging described above but it is also rooted in several practical observations.Unlike any disease, age changes (a) occur in every multicellular animal that reaches a fixed size at reproductive maturity, (b) cross virtually all species barriers, (c) occur in all members of a species only after the age of reproductive maturation, (d) occur in all animals removed from the wild and protected by humans even when that species probably has not experienced aging for thousands or even millions of years, (e) occur in virtually all animate and inanimate matter, and (f ) have the same universal molecular etiology, that is, thermodynamic instability.Unlike aging, there is no disease or pathology that shares these six qualities.Because this critical distinction is poorly understood, there" + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nDifferent stochastic theories of ageing focus on specific mechanisms that may lead to ageing.The catastrophic error theory poses that the accumulation of errors in protein synthesis causes damage in cell function.The theory of cross-linking holds this process between proteins and other macromolecules responsible for ageing, while the theory of free radicals suggests that ageing is the result of inadequate protection against cell and tissue damage by free radicals and oxidative stress throughout life.Finally, the wear-and-tear theory poses that the cumulative damage that eventually leads to ageing and death is, in fact, the result of the continuous functioning of vital processes, during which stochastic errors gradually arise." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nAging then is a catabolic process that is chance driven.Longevity determination is an anabolic process that, indirectly, is genome driven." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nThe evidence for the belief that aging is a stochastic process is, first, that everything in the universe changes or ages in space-time without being driven by a purposeful program.Second, there is no direct evidence that proves that age changes are governed by a genetic program.Finally, there is a huge body of knowledge indicating that age changes are characterized by the loss of molecular fidelity." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nAging does not happen in a vacuum.Aging must be the result of changes that occur in molecules that have existed at one time with no age changes.It is the state of these pre-existing molecules that governs longevity determination.The pre-existing state is, as I have already described, maintained by repair and turnover systems that themselves eventually succumb to irreparable age changes.Longevity determination is the state of all molecules prior to succumbing to irreparable loss of molecular structure." + }, + { + "document_id": "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65", + "section_type": "main", + "text": "Introduction\n\nThe fundamental manifestation of the aging process is a progressive decline in the functional maintenance of tissue homeostasis and an increasing propensity to degenerative diseases and death [1].It has attracted significant interest to study the underlying mechanisms of aging, and many theories have been put forward to explain the phenomenon of aging.There is an emerging consensus that aging is a multifactorial process, which is genetically determined and influenced epigenetically by environment [2].Most aging theories postulate a single physiological cause of aging, and likely these theories are correct to a certain degree and in certain aspects of aging." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nThe belief that aging is still an unsolved problem in biology is no longer true.Of the two major classes of theories, the one class that is tenable is derivative of a single common denominator that results in only one fundamental theory of aging.In order to address this complex subject, it is necessary to first define the four phenomena that characterize the finitude of life.These phenomena are aging, the determinants of longevity, age-associated diseases, and death.There are only two fundamental ways in which age changes can occur.Aging occurs either as the result of a purposeful program driven by genes or by events that are not guided by a program but are stochastic or random, accidental events.The weight of evidence indicates that genes do not drive the aging process but the general loss of molecular fidelity does.Potential longevity is determined by the energetics of all molecules present at and after the time of reproductive maturation.Thus, every molecule, including those that compose the machinery involved in turnover, replacement, and repair, becomes the substrate that experiences the thermodynamic instability characteristic of the aging process.However, the determinants of the fidelity of all molecules produced before and after reproductive maturity are the determinants of longevity.This process is governed by the genome.Aging does not happen in a vacuum.Aging must be the result of changes that occur in molecules that have existed at one time with no age changes.It is the state of these pre-existing molecules that governs longevity determination.The distinction between the aging process and age-associated disease is not only based on the molecular definition of aging described above but it is also rooted in several practical observations.Unlike any disease, age changes (a) occur in every multicellular animal that reaches a fixed size at reproductive maturity, (b) cross virtually all species barriers, (c) occur in all members of a species only after the age of reproductive maturation, (d) occur in all animals removed from the wild and protected by humans even when that species probably has not experienced aging for thousands or even millions of years, (e) occur in virtually all animate and inanimate matter, and (f ) have the same universal molecular etiology, that is, thermodynamic instability.Unlike aging, there is no disease or pathology that shares these six qualities.Because this critical distinction is poorly understood, there" + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nThus, ageing and age-related diseases are probably not mediated by a single factor or primary mechanism, but rather their result of multiple mechanisms, some of which may be genetically determined, and others may be the result of environmental exposures or stochastic.However, not all these processes are currently accounted for, and their precise contribution to ageing remains unclear.It is, therefore, necessary to further aim research efforts at identifying these connections; this may eventually lead to the development of better treatments for age-related diseases and maybe even anti-ageing strategies." + }, + { + "document_id": "489539fd-f7c5-44eb-bb58-5fc19d50a7cf", + "section_type": "main", + "text": "A common theme among many of these\ntheories is to take a reductionist approach and focus attention at the molecular level in\nhopes of understanding the aging of organisms through the aging of their components. In\nour quest to understand the aging process, we must face reality and succumb to the notion\nthat aging is a multifactorial process; therefore it’s likely that all of the aforementioned\nprocesses factor into this phenomenon.\n An important theme emerging in the field of aging research is the role of\nepigenetic alterations in aging mammalian tissues." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "Introduction\n\nDespite recent progress, human aging is a largely controversial process.Many age-related changes have been described, yet there are multiple and conflicting theories regarding what mechanism(s) drive such changes (de Magalhães, 2005).Moreover, we do not know why different species age at different paces, and there is still no proven intervention capable of delaying or postponing the human aging process (Olshansky et al ., 2002).As such, it is clear that aging is a complex, challenging phenomenon that requires extensive research using multiple, interdisciplinary approaches to unravel its puzzles." + }, + { + "document_id": "f4dd6a1d-062b-42bc-8e22-83fcb3135578", + "section_type": "main", + "text": "\n\nTrying to explain aging in terms of a singular process would be in conflict with evolutionary theory.Even if loss of genome sequence integrity was the most conserved cause of aging, already active in the first replicators (Vijg, 2007), natural selection would allow a multitude of mutations with late adverse effects to accumulate in the germline, many of which would be positively selected for because of their beneficial effects early in life (Williams, 1957), In this respect, somatic mutation accumulation could be a conserved, inevitable cause of aging but superposed on multiple other processes that usually cause the earlier demise of an individual." + }, + { + "document_id": "996e02bf-91b2-4e81-89ba-1f661dfc662a", + "section_type": "main", + "text": "\n\nThe developmental theory of aging states that the genetic mechanisms regulating the pace of aging are located in the latter; that is, they are part of the developmental program (FIGURE 1).This concept is supported by observations in a number of animals.In organisms such as the salmon or marsupials of the genus Antechinus, the neuroendocrine system-triggered by reproduction-directly causes the death of organisms (19).Other authors have argued that a morphogenetic program originates aging in response to reproductive impulses (30,38).It is dubious, however, that similar mechanisms occur in animals that rear their offspring, such as most mammals and birds.Besides, not only reproduction but a number of developmental processes have the potential to disrupt homeostasis and cause degeneration (see below).Nonetheless, Antechinus and, particularly, the remarkable physiological degeneration of the salmon after spawning demonstrate how a developmental program optimized for reproduction can trigger senescence (19)." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Stochastic Factors\n\nAgeing is no longer regarded as a programmed process, but rather the result of damage accumulation, which results from stochastic (i.e.random) events or exposures [40].The variables that affect the ageing of an organism are the result of chance and must be studied from a probabilistic approach.According to the stochastic theories of ageing, random factors may induce ageing directly (by nonspecified mechanisms) and increase the probability of developing age-related diseases." + }, + { + "document_id": "a733a920-9896-4ca4-910d-d6f0184a0777", + "section_type": "main", + "text": "Introduction\n\nThe basic similarity of biological processes in living systems pleads for a general mechanism underlying the aging process.Although there is no agreement on the nature of such a unifying mechanism of aging, changes in informational biomolecules are considered to play an important role in the etiology of age-related deteriorative processes.Conceptually, molecular biological theories of aging should first be assigned to the two fundamentally different schools of aging theories, according to which aging is regarded either as a species-specific genetically determined.program or as a series of stochastic events (Schneider 1987)." + }, + { + "document_id": "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c", + "section_type": "main", + "text": "\n\nWear and tear on the DNA often has been touted as a possible basis for our progressive age-related decline.Supporting this notion is the work of de Boer et al. (2) reported on page 1276 of this week's issue.They reveal important evidence for imperfect genome maintenance of DNA damage as a possible causal factor in aging.Harman, with his \"free radical theory of aging\" (3), was the first to propose that metabolic by-products called reactive oxygen species (ROS) continually damage cellular macromolecules, including DNA.Incomplete repair of such damage would lead to its accumulation over time and eventually result in age-related deterioration.A number of observations support the free radical theory, including the discovery that dietary restriction delays aging and extends life-span in a wide range of rodents and other species, possibly by reducing free radical damage.The notion that genomic DNA could be a major target of continual free radical attack over time is supported by the recent observation that genetic lesions accumulate with age and that dietary restriction reduces this accumulation in rodents (4).In addition, deletion of p66 shc , a signaling protein that maintains oxidant levels, increases resistance to oxidative damage and extends the life-span of mice (5)." + }, + { + "document_id": "aff67cef-4bf7-42dc-826b-2a259722008d", + "section_type": "abstract", + "text": "\nAs our society is growing older, the consequences of aging have begun to gain particular attention.Improvement of quality of life at old age and prevention of age-associated diseases have become the main focus of the aging research.The process of aging in humans is complex and underlies multiple influences, with the probable involvement of heritable and various environmental factors.In particular, hormones are decisively involved in the generation of aging.Over time, important circulating hormones decline due to a reduced secretion of the pituitary, the adrenal glands and the gonads or due to an intercurrent disease.Among them, serum levels of growth factors and sexual steroids show significant aging-associated changes.Within the scope of the Explorative Project 'Genetic aetiology of human longevity' supported by the German National Genome Research Network 2 (NGFN-2) an in vitro model of human hormonal aging has been developed.Human SZ95 sebocytes were maintained under a hormone-substituted environment consisting of growth factors and sexual steroids in concentrations corresponding to those circulating in 20-and in 60-year-old women.Eight hundred and ninety-nine genes showed a differential expression in SZ95 sebocytes maintained under the 20-and 60-year-old hormone mixture, respectively.Among them genes were regulated which are involved in biological processes which are all hallmarks of aging.The most significantly altered signaling pathway identified was that of the transforming growth factor-b (TGF-b).A disturbed function of this cascade has been associated with tumorigenesis, i.e. in pancreatic, prostate, intestine, breast, and uterine cancer.Interestingly, genes expressed in signaling pathways operative in age-associated diseases such as Huntington's disease (HD), dentatorubral-pallidoluysian atrophy (DRPLA), and amyotrophic lateral sclerosis (ALS) were also identified.These data demonstrate that skin and its appendages may represent an adequate model for aging research.Hormones interact in a complex fashion, and aging may be partly attributed to the changes in their circulating blood levels.Furthermore, a disturbed hormone status may partially act towards the manifestation of neurodegenerative diseases.Thus, these results could be a basis for an integrated and interdisciplinary approach to the analysis of the aging process." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nAging is an extremely complex process associated with interplay of genetic, biochemical, and metabolic factors in an organism in a given environment.Although genetic studies of various animal models suggest that even a single-gene mutation can remarkably extend lifespan (Kenyon 2005;Johnson 2006) and, thus, modulate aging, no such genes are revealed in humans so far.Given that a human organism is a much more complex system than a model organism (Christensen et al. 2006), it is evident that genetic effects on the aging process should be mediated via coordinate action of a large number of inter-related processes (Kirkwood 2011).Coordinated function is rather relevant to complex biological (Soltow et al. 2010;Slagboom et al. 2011) and genetic (Bloss et al. 2011) networks than to individual genes." + }, + { + "document_id": "a733a920-9896-4ca4-910d-d6f0184a0777", + "section_type": "main", + "text": "\n\nThe fundamental mechanisms involved in the physiological deterioration observed with age in mammalian organisms have not yet been elucidated.It appears that random alterations in informational biomolecules and in their synthesis could be the basis of such physiological changes.There is, however, a lack of knowledge with respect to the frequency and characteristics of changes introduced in the cellular molecular machinery.Moreover, the driving force initiating the generation of such alterations and the order of events in which they occur are unknown at present.In this article, data concerning the hypothesis that the aging process is associated with widespread genetic instability are reviewed in the context of the complex interactions between the three major informational biomolecules, DNA, RNA, and protein.We conclude that the results obtained to date do not rule out the possibility that genetic instability in a wide sense is a major causal factor in a number of age-related phenomena.However, it appears that new strategies based on a new technology are ultimately necessary to elucidate the alterations in the intricately interwoven patterns of molecular control that could underlie the various aspects of the aging process.A first attempt is made to formulate the problems in this field and to provide some solutions." + }, + { + "document_id": "a733a920-9896-4ca4-910d-d6f0184a0777", + "section_type": "abstract", + "text": "\nThe fundamental mechanisms involved in the physiological deterioration observed with age in mammalian organisms have not yet been elucidated.It appears that random alterations in informational biomolecules and in their synthesis could be the basis of such physiological changes.There is, however, a lack of knowledge with respect to the frequency and characteristics of changes introduced in the cellular molecular machinery.Moreover, the driving force initiating the generation of such alterations and the order of events in which they occur are unknown at present.In this article, data concerning the hypothesis that the aging process is associated with widespread genetic instability are reviewed in the context of the complex interactions between the three major informational biomolecules, DNA, RNA, and protein.We conclude that the results obtained to date do not rule out the possibility that genetic instability in a wide sense is a major causal factor in a number of age-related phenomena.However, it appears that new strategies based on a new technology are ultimately necessary to elucidate the alterations in the intricately interwoven patterns of molecular control that could underlie the various aspects of the aging process.A first attempt is made to formulate the problems in this field and to provide some solutions." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nThere are several reasons for the contention that distinguishing between biological aging and disease processes may be problematic.There is little agreement on a precise definition of aging, although many have offered general characteristics; this is usefully discussed by Arking (1998).Most scientific papers on the study of aging, basic or applied, do not offer definitions of aging as an explicit biological process separate from disease and dysfunction.Survivorship and longevity, among the most widely studied attributes of aging across species, are insufficient outcomes for the study of complex animal processes, particularly in humans or other mammals; nearly all humans die of one or more discrete, identifiable medical conditions.Further, most if not all hypothesized biological mechanisms of aging encompass concepts that have also been applied to disease causation and progression.For example, age-related shortening of chromosomal telomeres has been related both to aging processes and to carcinogenesis (Shay, 1997), as have cumulative somatic mutations (Vijg, 2000;Hernandez-Boussard et al., 1999) and age-related, progressively inefficient DNA repair processes (de Boer and Hoeijmakers, 2000).Even an environmental factor that experimentally has been shown to dramatically prolong mammalian survivorship as well as decrease the occurrence of age-related physiological change and disease, caloric restriction, has been shown to alter the rate of change in age-related gene function (Lee et al., 1999)." + }, + { + "document_id": "aff67cef-4bf7-42dc-826b-2a259722008d", + "section_type": "main", + "text": "\n\nAs our society is growing older, the consequences of aging have begun to gain particular attention.Improvement of quality of life at old age and prevention of age-associated diseases have become the main focus of the aging research.The process of aging in humans is complex and underlies multiple influences, with the probable involvement of heritable and various environmental factors.In particular, hormones are decisively involved in the generation of aging.Over time, important circulating hormones decline due to a reduced secretion of the pituitary, the adrenal glands and the gonads or due to an intercurrent disease.Among them, serum levels of growth factors and sexual steroids show significant aging-associated changes.Within the scope of the Explorative Project 'Genetic aetiology of human longevity' supported by the German National Genome Research Network 2 (NGFN-2) an in vitro model of human hormonal aging has been developed.Human SZ95 sebocytes were maintained under a hormone-substituted environment consisting of growth factors and sexual steroids in concentrations corresponding to those circulating in 20-and in 60-year-old women.Eight hundred and ninety-nine genes showed a differential expression in SZ95 sebocytes maintained under the 20-and 60-year-old hormone mixture, respectively.Among them genes were regulated which are involved in biological processes which are all hallmarks of aging.The most significantly altered signaling pathway identified was that of the transforming growth factor-b (TGF-b).A disturbed function of this cascade has been associated with tumorigenesis, i.e. in pancreatic, prostate, intestine, breast, and uterine cancer.Interestingly, genes expressed in signaling pathways operative in age-associated diseases such as Huntington's disease (HD), dentatorubral-pallidoluysian atrophy (DRPLA), and amyotrophic lateral sclerosis (ALS) were also identified.These data demonstrate that skin and its appendages may represent an adequate model for aging research.Hormones interact in a complex fashion, and aging may be partly attributed to the changes in their circulating blood levels.Furthermore, a disturbed hormone status may partially act towards the manifestation of neurodegenerative diseases.Thus, these results could be a basis for an integrated and interdisciplinary approach to the analysis of the aging process." + }, + { + "document_id": "489539fd-f7c5-44eb-bb58-5fc19d50a7cf", + "section_type": "main", + "text": "Poorly repaired\ndamage of chromosomal DNA, stress-related aberrations in structural enzymes or protein\nturnover, and/or deletions in mitochondrial DNA, for example, may compromise organ\nfunction and in turn limit longevity. Given the extremely complex phenotype of aging,\n\n2\nnumerous other theories such as the free radial theory of aging (Harman, 1956) and\nprotein damage accumulation theory (Levine, 2002) have been postulated in an attempt to\nexplain what aging is and why it happens." + }, + { + "document_id": "1e2d93e8-a0a4-4f4a-a470-2dfdd26fa846", + "section_type": "abstract", + "text": "\nLoss of genome maintenance may causally contribute to ageing, as exemplified by the premature appearance of multiple symptoms of ageing in a growing family of human syndromes and in mice with genetic defects in genome maintenance pathways.Recent evidence revealed a similarity between such prematurely ageing mutants and long-lived mice harbouring mutations in growth signalling pathways.At first sight this seems paradoxical as they represent both extremes of ageing yet show a similar 'survival' response that is capable of delaying age-related pathology and extending lifespan.Understanding the mechanistic basis of this response and its connection with genome maintenance would open exciting possibilities for counteracting cancer or agerelated diseases, and for promoting longevity.In Greek mythology, Klotho, Lakhesis and Atropos, the three fates, spun, wove and snipped the thread of life, an unalterable process to which both gods and humans had to submit themselves.Human efforts over recent centuries have succeeded in substantially lengthening the thread, allowing ageing to become a common feature of society.However, despite intense research, the molecular basis of the processes that cause loss of bodily functions, and degeneration of cells and tissues is still unresolved.It is widely accepted that ageing is the consequence of stochastic damage accumulation 1 .Ageing is unique in that it does not seem to be subject to evolutionary selection, as it occurs after the reproductive phase, suggesting that it may occur by default 2 .Nevertheless, it is apparent from studies in many systems that ageing is subject to regulation by evolutionarily highly conserved molecular pathways [3][4][5] .As such, damage drives functional decline with advancing age; however, the existence of universal mechanisms that are able to promote longevity may set the pace on how rapidly damage builds up and function is lost.We discuss the nature of the processes that determine the length and the quality of the thread of life woven by Lakhesis and ultimately snipped by Atropos.Damage and ageing: the DNA perspective Within the complex chemical machinery of each cell, all biomolecules (proteins, lipids and nucleic acids) are subject to indiscriminate damage caused by spontaneous reactions (mostly hydrolysis) and by numerous endogenous and exogenous reactive agents.It is therefore plausible that damage to multiple cellular constituents accounts for ageing 1 .However, damage to certain macromolecules may play a more prominent part than damage to others.The almost exclusive link between an extending class George A." + }, + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "section_type": "main", + "text": "Why does ageing evolve? The intrinsic decline in function that occurs during ageing appears to be caused by the accumulation of damage, particularly at the molecular level.As far as we know, no genes have evolved specifically because they cause damage to accumulate, and the evolution of ageing can therefore be understood only as a side-effect of other causes of evolutionary change.The mechanisms by which ageing can evolve were first elucidated by J.B.S. Haldane [14], P.B. Medawar [15] and G.C. Williams [16].Extrinsic hazards from disease, predation and accidents mean that even potentially immortal organisms will die.Genetic effects that become apparent only later in life encounter a reduced force of natural selection, because not all their bearers will survive to express them.Haldane pointed out that late-onset genetic diseases in humans, such as Huntington's disease, encounter only weak selection, because most reproduction is complete by the age of onset [14].Ageing could therefore result from the accumulation under mutation pressure of age-specific, deleterious mutations.In addition, if some mutations have pleiotropic effects, with beneficial effects in youth, such as high fecundity, but also with a higher subsequent rate of ageing, then they could be incorporated into the population by natural selection, which will act more strongly on the early, beneficial effect.Thus, variation in the rate of ageing would result from the readjustment of a tradeoff between youthful benefits and the subsequent rate of ageing.Both processes imply that faster ageing will evolve where the extrinsic hazard to adults is greatest, a hypothesis in general supported by the data [1,2,17]." + } + ], + "document_id": "0DC57EB6A882DE1734E915DE035D2296", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "genes", + "aging", + "DNA&damage", + "free&radicals", + "telomeres", + "oxidative&stress", + "environmental&factors", + "genetic&factors", + "cellular&repair", + "protein&synthesis" + ], + "metadata": [ + { + "object": "Transient overexpression of WRKY79 in protoplasts results in up-regulation of Gene:542165, Gene:541974, Gene:100274033, Gene:542688, Gene:542150, Gene:542151, Gene:100273457, Gene:100285509, Gene:103626248, Gene:103646045, Gene:100217270, Gene:100279981, Gene:100281950, Gene:542476, Gene:542369, Gene:100281950, and Gene:542260.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab969966" + }, + { + "object": "Uniform Mu insertion results in up-regulation of cytokinin synthesis genes and down-regulation of cytokinin degradation genes. The protein binds to Gene:103632693, Gene:100502174, Gene:100283866, Gene:542044, and Gene:100037786.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab983367" + }, + { + "object": "Part of autosomal recessive retinitis pigmentosa gene network established using RetNet info; Part of autosomal recessive cone_cone-rod gene network established using RetNet info; Part of age-related macular degeneration gene network, cone-dystrophy gene network, and retinitis pigmentosa gene network established using GeneNetwork info -ILMN_2829604\\r\\nused by Irene Whitney", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab4267" + }, + { + "object": "TET1 regulates numerous genes defining differentiation programs in the epiblast and extraembryonic ectoderm. In epiblasts, TET1 demethylates gene promoters via hydroxymethylation and maintains telomere stability. It represses a majority of epiblast target genes independent of methylation, partly by regulation of the JMJD8 gene. Dysregulated gene expression in the absence of TET1 causes embryonic defects.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab769005" + }, + { + "object": "Genome-wide associations P < 5 x 10-8 were found at the PCSK9 gene, the APOB gene, theLPL gene, the APOA1-APOA5 locus, the LIPC gene, the CETP gene, the LDLR gene, and the APOE locus.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab320109" + }, + { + "object": "Genetic risk score GRSNPY analysis found twelve significant P<0.05 serum NPY concentration related SNPs among alpha7 nicotinic acetylcholine receptor gene CHRNA7, insulin receptor gene INSR, leptin receptor gene LEPR, glucocorticoid receptor GR gene NR3C1, and NPY gene. However, after permutation test of gene score the predictive value of GRSNPY remained non-significant P=0.078. CONCLUSIONS: Serum NPY level ...", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab318213" + }, + { + "object": "TYROBP influences a batch of genes that are related to Alzheimer's disease; ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors. By merely leveraging gene expression data, Context Based Dependency Network CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab980273" + }, + { + "object": "Correlation analyses showed that 5hmC enrichment in gene body is positively associated with gene expression level in mouse kidney. Moreover, ischemia reperfusion IR injury-associated genes both up- and down-regulated genes during renal IR injury in mouse kidney exhibit significantly higher 5hmC enrichment in their gene body regions when compared to those un-changed genes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab157853" + }, + { + "object": "PI3/PI4-kinase family, and is closely related to ATM, a protein kinase encoded by the gene mutated in ataxia telangiectasia. Shares similarity with S. pombe rad3, a cell cycle checkpoint gene required for cell cycle arrest and DNA damage repair in response to DNA damage. This kinase has been shown to phosphorylate checkpoint kinase CHK1, checkpoint proteins RAD17, and RAD9, as well as tumor suppressor protein BRCA1. Mutations of this gene are associated with Seckel syndrome.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab4171" + }, + { + "object": "seems that ZnSO4 as a proper antioxidant could improve the aging-related features due to lengthening of the telomeres, increasing the telomerase gene expression, telomerase activity, decreasing aging, and changing the methylation status of hTERT promoter; it could potentially beneficial for enhancing the application of aged-MSCs", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab694596" + } + ], + "question": "what causes the aging process", + "subquestions": null, + "task_id": "0DC57EB6A882DE1734E915DE035D2296", + "usage": { + "chatgpt": 8117, + "gpt-4": 4643, + "gpt-4-turbo-preview": 3712 + }, + "user_id": 2 + }, + "document_id": "0DC57EB6A882DE1734E915DE035D2296", + "task_id": "0DC57EB6A882DE1734E915DE035D2296" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/08.json b/gnqa/paper1_eval/src/data/responses/aging/experts/08.json new file mode 100644 index 0000000..3bf70fb --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/08.json @@ -0,0 +1,406 @@ +{ + "created_at": "2024-05-31T21:16:38.963061Z", + "data": { + "amplify": false, + "answer": "The genes associated with aging include the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, HLA-DQA1/DRB1, LPA, CHRNA3/5, CDKN2A/B, SH2B3, and AKT1. Other genes involved in processes like growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition are also associated with aging.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\n\nStudies revealed from 300 to 750 genes related to longevity that are critically involved in a variety of life activities, such as growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [5,6].Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability, and the endocrine-related pathway [7][8][9].In addition, the candidates for longevity encompass genes related to drug metabolism, the ones involved in protein folding, stabilization, and degradation, as well those related to coagulation and regulation of circulation [10], etc.In most cases, these genes or their polymorphic sites were examined in multiple population replication studies, which discovered certain longevity-associated genes or pathways [4][5][6][7][8][9][10]." + } + ], + "4a27da1c-b184-47e8-bef2-de6435d7c3f5": [ + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + } + ], + "520b36a2-4c9c-4894-a818-9917bd357982": [ + { + "document_id": "520b36a2-4c9c-4894-a818-9917bd357982", + "text": "\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases." + } + ], + "555a1533-2905-4d91-a3b6-2fca3679ab02": [ + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes." + } + ], + "5e6ad994-9cad-4b8b-903d-2d5c350e25dc": [ + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + } + ], + "99a35e24-bbd2-495b-82dc-53d7e2075191": [ + { + "document_id": "99a35e24-bbd2-495b-82dc-53d7e2075191", + "text": "\n\nThus, substantially more work is needed in this area to establish whether longevity is driven by nuclear genomic stability.Diverse and unexpected bits of evidence support a relationship.For example, a disproportionate number of genes identified in unbiased and targeted genome-wide association studies (GWASs) as associated with longevity are involved in genome maintenance (75).One study involved age of natural menopause in ∼70,000 women and led to the identification of 44 genetic variants associated with early or late menopause, a strong biomarker of healthy TIFs (telomere dysfunction-induced foci): co-localization of multiple DNA damage response factors and repair proteins on uncapped telomeric DNA aging (76).Approximately two-thirds of these are associated with genome maintenance genes.Seven of ten significantly associated pathways are involved in DNA repair.The highly significant overrepresentation of DNA repair pathways indicates an intimate connection between genome maintenance and aging phenotypes.From unrelated studies, we know that reduced expression of the repair endonuclease ERCC1-XPF causes accelerated aging (3), whereas ERCC1 is one of the top genes under positive selective pressure in the longest-lived mammalian species, the bowhead whale (77).Intriguingly, hepatocytes from old rats have impaired NER, whereas caloric restriction, which extends longevity, restored the NER capacity of old rats to that of youthful levels (42).In a human interventional study, brief caloric restriction increased NER capacity in PBMCs of individuals who had low NER prior to dietary intervention (78).Therefore, increased DNA repair capacity could promote longevity and may even prove amenable to improvement." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nIn addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha ˜es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nGenes/loci identified by genome-wide association studies of longevity and lifespan traits." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + } + ], + "efd5747f-9e8b-45e8-9e04-bb31131d44fa": [ + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "text": "\n\nSince many alleles will fit the two patterns just described, it follows that we expect many genetic and biochemical mechanisms of aging.There are some experiments that have attempted to estimate the number of genes involved in aging, particularly in Drosophila.Quantitative genetic estimates of gene number have probably been subject to artifacts, [6,8] and are highly imprecise.Molecular genetic estimates using 2-D gels [3] and high-density geneexpression arrays [12] indicate the involvement of at least 300 genetic loci in Drosophila aging, and that estimate is highly conservative.For now, the best conclusion is probably that many genes are involved in aging in fruit flies.Vertebrates are unlikely to have fewer genes involved in aging, in view of their larger genomes." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "\n\nGenAge consists of several searchable data sets.Considering the extraordinary discoveries in the genetics of aging in model organisms, GenAge includes a data set of genes associated with longevity and/or aging in model organisms.We consider a given gene for inclusion in GenAge if genetic manipulations of the gene result in noticeable changes in the aging phenotype and/or longevity.Most genes in GenAge are from the four typical model organisms: mice, worms, fruit flies and yeast (Table 1).Strikingly, homologues of many genes -such as insulin receptors and sirtuins -have been shown to regulate aging in model organisms separated by large evolutionary distances (Kenyon, 2005;Liu et al ., 2005;Smith et al ., 2008).Moreover, we have shown that genes associated with aging and/or longevity in model organisms are evolutionary conserved in terms of having more homologues than predicted by chance (Budovsky et al ., 2007(Budovsky et al ., , 2008) ) and exhibiting slower molecular evolution rates (de Magalhães & Church, 2007).Therefore, it is now clear that at least some genes identified in model organisms may be relevant to human aging." + } + ], + "f3610ccc-2831-42f6-a3d3-1a0feeba4902": [ + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + } + ] + }, + "data_source": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nGenes/loci identified by genome-wide association studies of longevity and lifespan traits." + }, + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "section_type": "main", + "text": "\n\nIn addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha ˜es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations." + }, + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "section_type": "main", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "\n\nGenAge consists of several searchable data sets.Considering the extraordinary discoveries in the genetics of aging in model organisms, GenAge includes a data set of genes associated with longevity and/or aging in model organisms.We consider a given gene for inclusion in GenAge if genetic manipulations of the gene result in noticeable changes in the aging phenotype and/or longevity.Most genes in GenAge are from the four typical model organisms: mice, worms, fruit flies and yeast (Table 1).Strikingly, homologues of many genes -such as insulin receptors and sirtuins -have been shown to regulate aging in model organisms separated by large evolutionary distances (Kenyon, 2005;Liu et al ., 2005;Smith et al ., 2008).Moreover, we have shown that genes associated with aging and/or longevity in model organisms are evolutionary conserved in terms of having more homologues than predicted by chance (Budovsky et al ., 2007(Budovsky et al ., , 2008) ) and exhibiting slower molecular evolution rates (de Magalhães & Church, 2007).Therefore, it is now clear that at least some genes identified in model organisms may be relevant to human aging." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "main", + "text": "\n\nStudies revealed from 300 to 750 genes related to longevity that are critically involved in a variety of life activities, such as growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [5,6].Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability, and the endocrine-related pathway [7][8][9].In addition, the candidates for longevity encompass genes related to drug metabolism, the ones involved in protein folding, stabilization, and degradation, as well those related to coagulation and regulation of circulation [10], etc.In most cases, these genes or their polymorphic sites were examined in multiple population replication studies, which discovered certain longevity-associated genes or pathways [4][5][6][7][8][9][10]." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "main", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "main", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "section_type": "main", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "abstract", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "main", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "main", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "main", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "Candidate gene studies identified APOE and FOXO3A as human longevity genes\n\nThe first genetic longevity studies mainly focused on lifespan regulating loci that emerged from animal models [22].Lifespan Prospects & Overviews .... extension in animal models was obtained by applying caloric restriction or by modifying gene functions (mutagenesis) using RNA interference, knock-out or overexpression of single genes (GenAge; http://genomics.senescence.info/genes/)[23].The most interesting pathways identified using these models are the growth hormone (GH)/insulin/insulin-like growth factor 1 (IGF-1) signaling and mammalian target of rapamycin (mTOR) signaling pathways [24].Thus far, lifespan has been the main phenotype investigated in animal models.In order to make these models more translatable to human studies research should focus on defining the parameters that reflect the physiology and pathology of aging in both animals and humans [25,26]." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "section_type": "main", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "99a35e24-bbd2-495b-82dc-53d7e2075191", + "section_type": "main", + "text": "\n\nThus, substantially more work is needed in this area to establish whether longevity is driven by nuclear genomic stability.Diverse and unexpected bits of evidence support a relationship.For example, a disproportionate number of genes identified in unbiased and targeted genome-wide association studies (GWASs) as associated with longevity are involved in genome maintenance (75).One study involved age of natural menopause in ∼70,000 women and led to the identification of 44 genetic variants associated with early or late menopause, a strong biomarker of healthy TIFs (telomere dysfunction-induced foci): co-localization of multiple DNA damage response factors and repair proteins on uncapped telomeric DNA aging (76).Approximately two-thirds of these are associated with genome maintenance genes.Seven of ten significantly associated pathways are involved in DNA repair.The highly significant overrepresentation of DNA repair pathways indicates an intimate connection between genome maintenance and aging phenotypes.From unrelated studies, we know that reduced expression of the repair endonuclease ERCC1-XPF causes accelerated aging (3), whereas ERCC1 is one of the top genes under positive selective pressure in the longest-lived mammalian species, the bowhead whale (77).Intriguingly, hepatocytes from old rats have impaired NER, whereas caloric restriction, which extends longevity, restored the NER capacity of old rats to that of youthful levels (42).In a human interventional study, brief caloric restriction increased NER capacity in PBMCs of individuals who had low NER prior to dietary intervention (78).Therefore, increased DNA repair capacity could promote longevity and may even prove amenable to improvement." + }, + { + "document_id": "0fc75a0d-3aa3-481a-8c0f-689bd7ae6104", + "section_type": "abstract", + "text": "\nAging is a complex process affecting different species and individuals in different ways.Comparing genetic variation across species with their aging phenotypes will help understanding the molecular basis of aging and longevity.Although most studies on aging have so far focused on short-lived model organisms, recent comparisons of genomic, transcriptomic, and metabolomic data across lineages with different lifespans are unveiling molecular signatures associated with longevity.Here, we examine the relationship between genomic variation and maximum lifespan across primate species.We used two different approaches.First, we searched for parallel amino-acid mutations that co-occur with increases in longevity across the primate linage.Twenty-five such amino-acid variants were identified, several of which have been previously reported by studies with different experimental setups and in different model organisms.The genes harboring these mutations are mainly enriched in functional categories such as wound healing, blood coagulation, and cardiovascular disorders.We demonstrate that these pathways are highly enriched for pleiotropic effects, as predicted by the antagonistic pleiotropy theory of aging.A second approach was focused on changes in rates of protein evolution across the primate phylogeny.Using the phylogenetic generalized least squares, we show that some genes exhibit strong correlations between their evolutionary rates and longevity-associated traits.These include genes in the Sphingosine 1-phosphate pathway, PI3K signaling, and the Thrombin/protease-activated receptor pathway, among other cardiovascular processes.Together, these results shed light into human senescence patterns and underscore the power of comparative genomics to identify pathways related to aging and longevity." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "abstract", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "abstract", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Murabito JM, Yuan R, Lunetta KL (2012) The search for\nlongevity and healthy aging genes: insights from epidemiological\nstudies and samples of long-lived individuals. J Gerontol A Biol\nSci Med Sci 67(5):470–479. doi:10.1093/gerona/gls089\n20. Nuzhdin SV, Pasyukova EG, Dilda CL et al (1997) Sex-specific\nquantitative trait loci affecting longevity in Drosophila melanogaster. Proc Natl Acad Sci USA 94(18):9734–9739\n21. Gems D, Riddle DL (2000) Genetic, behavioral and environmental determinants of male longevity in Caenorhabditis elegans.\n Genetics 154(4):1597–1610\n\n123\n\n22." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "\n\nAlthough the models data set comprises all genes (to our knowledge) shown by the time of the latest update to statistically increase longevity or alter the aging process in a noticeable way, in the human data set we try to evaluate whether a given intervention is affecting the aging process itself or not.For example, many mutations may increase longevity by decreasing the incidence of specific diseases, rather than by altering the basic process of aging (de Magalhães et al ., 2005a(de Magalhães et al ., , 2005b)).Therefore, the human data set is not merely an extension of the work conducted in model organisms and of its bibliography, but a manually selected list of the most pertinent human aging candidate genes, each presented with a higher annotation level.We cite studies on whether the functions of aging-associated genes in model organisms are conserved in their human orthologues.Likewise, we cite flaws in previous studies based on new published observations, although we have a neutral stance on conflicting findings from different research groups.Our policy is to cite all conflicting reports and let visitors make their own decisions on how to interpret them.By contrast, each entry in GenAge model organisms has only one reference: the first publication reporting an association of the gene with longevity or aging.Moreover, one of the latest enhancements in the human data set was the inclusion of Gene Ontology annotation.Gene Ontology terms and annotation files were obtained from the Gene Ontology Consortium website (http://www.geneontology.org/ ) and provide an additional layer of description for the gene products in a cellular context (Ashburner et al ., 2000)." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "0fc75a0d-3aa3-481a-8c0f-689bd7ae6104", + "section_type": "main", + "text": "\n\nAging is a complex process affecting different species and individuals in different ways.Comparing genetic variation across species with their aging phenotypes will help understanding the molecular basis of aging and longevity.Although most studies on aging have so far focused on short-lived model organisms, recent comparisons of genomic, transcriptomic, and metabolomic data across lineages with different lifespans are unveiling molecular signatures associated with longevity.Here, we examine the relationship between genomic variation and maximum lifespan across primate species.We used two different approaches.First, we searched for parallel amino-acid mutations that co-occur with increases in longevity across the primate linage.Twenty-five such amino-acid variants were identified, several of which have been previously reported by studies with different experimental setups and in different model organisms.The genes harboring these mutations are mainly enriched in functional categories such as wound healing, blood coagulation, and cardiovascular disorders.We demonstrate that these pathways are highly enriched for pleiotropic effects, as predicted by the antagonistic pleiotropy theory of aging.A second approach was focused on changes in rates of protein evolution across the primate phylogeny.Using the phylogenetic generalized least squares, we show that some genes exhibit strong correlations between their evolutionary rates and longevity-associated traits.These include genes in the Sphingosine 1-phosphate pathway, PI3K signaling, and the Thrombin/protease-activated receptor pathway, among other cardiovascular processes.Together, these results shed light into human senescence patterns and underscore the power of comparative genomics to identify pathways related to aging and longevity." + }, + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "[PubMed: 18208581]\n3. de Magalhães JP, Wuttke D, Wood SH, Plank M & Vora C Genome-environment interactions that\nmodulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88–101 (2012). [PubMed:\n22090473]\n4. McDaid AFet al.Bayesian association scan reveals loci associated with human lifespan and linked\nbiomarkers. Nat. Commun. 8, 15842 (2017). [PubMed: 28748955]\n5. Fontana L & Partridge L Promoting health and longevity through diet: From model organisms to\nhumans. Cell 161, 106–118 (2015). [PubMed: 25815989]\n6." + }, + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "section_type": "main", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "main", + "text": "\n\nResults: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "section_type": "main", + "text": "\n\nSince many alleles will fit the two patterns just described, it follows that we expect many genetic and biochemical mechanisms of aging.There are some experiments that have attempted to estimate the number of genes involved in aging, particularly in Drosophila.Quantitative genetic estimates of gene number have probably been subject to artifacts, [6,8] and are highly imprecise.Molecular genetic estimates using 2-D gels [3] and high-density geneexpression arrays [12] indicate the involvement of at least 300 genetic loci in Drosophila aging, and that estimate is highly conservative.For now, the best conclusion is probably that many genes are involved in aging in fruit flies.Vertebrates are unlikely to have fewer genes involved in aging, in view of their larger genomes." + }, + { + "document_id": "29c57767-2e2c-4fbe-a8b2-629e1abd5628", + "section_type": "main", + "text": "\n\nLongevity-associated genes I Figure 6 Longevity-associated genes I. Listed genes are those that are differentially expressed with respect to each of four-long lived dwarf models (Snell, Ames, Little, GHR-KO).Each row corresponds to an individual candidate gene, while each column corresponds to one of the contrasts listed in" + }, + { + "document_id": "520b36a2-4c9c-4894-a818-9917bd357982", + "section_type": "abstract", + "text": "\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases." + }, + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "section_type": "main", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nOne way to overcome (part of) this problem is by using a family-based study design (Box 1 and Fig. 1), in which the offspring of long-lived individuals -representing ''healthy agers'' -are compared to similar-aged controls from the general population.The differential gene expression profiles identified using this design may represent markers of healthy aging and familial longevity.This approach has been applied in the LLS to explore the transcriptome in whole blood for association with human familial longevity.Genes belonging to the mTOR pathway, as well as ASF1A and IL7R, were differentially expressed between offspring and controls [59,60].In addition, the expression of mTOR genes in blood associated to prevalent diabetes and serum glucose.However, the association with familial longevity was not dependent on this.Thus, gene expression profiles in blood mark human longevity in middle age and potentially provide information on the pathways that contribute to healthy aging and longevity." + }, + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "section_type": "main", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + }, + { + "document_id": "520b36a2-4c9c-4894-a818-9917bd357982", + "section_type": "main", + "text": "\n\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases." + }, + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "section_type": "main", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "document_id": "B0164472D40098296DA0836E50978AC8", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "HLA-DQA1&DRB1", + "LPA", + "CHRNA3&5", + "CDKN2A&B", + "SH2B3", + "AKT1", + "ERCC1-XPF", + "MTP" + ], + "metadata": [ + { + "object": "Data suggest that the redox status of serum apoE might be related to the synthesis of HDL; the cysteine-thiol residue of reduced-apoE is in a naive state, while that of non-reduced-apoE is in a reversibly or irreversibly oxidized state. Data suggest that apoE homodimer and apoE-AII complex are typical reversibly oxidized forms of apoE. apoE-AII complex = a complex of apolipoprotein E and apolipoprotein A-II", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab212832" + }, + { + "object": "Low apoE and mir-650 plasma concentrations were risk factors for developing Alzheimer's disease AD and were particularly pronounced in severe dementia. APOE E4 allele in both AD patients and controls led to a reduction in apoE, while APOE E3/E3 genotype was associated with an increased apoE concentration and level of miR-107 in AD, which inversely correlated with the number of APOE E4 alleles.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab459467" + }, + { + "object": "Neuronal expression of apoE is controlled by transcription of apoE-intron3 apoE-I3 under normal conditions and by processing of apoE-I3 into mature apoE mRNA in response to injury.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab522285" + }, + { + "object": "FoxO3a was overexpressed in 64.71% cases of hepatocellular carcinoma HCC. FoxO3a overexpression was associated with aggressive phenotypes of HCC, such as histologic grade, stage, and small vessel invasion. FoxO3a overexpression was also correlated with poor disease-free survival. Downregulation of FoxO3a in a HepG2 cell line inhibited cell proliferation and migration.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab303610" + }, + { + "object": "T-type channel signaling is redirected towards the activation of the kinase Akt1, leading to increased expression of the anti-apoptotic protein survivin, and a decrease in the pro-apoptotic mediator FoxO3A. Finally, in iPAH cells, Akt1 is no longer able to regulate caspase 9 activation, whereas T-type channel overexpression reverses PP2A defect in iPAH cells but reinforces the deleterious effects of Akt1 activation", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab762059" + }, + { + "object": "These findings indicate that maternal apo B levels are significantly associated with apo B levels in their pre-school age children, adjusted for confounding variables. Furthermore, the mother-child correlations in apo B levels were independent of mother-child adiposity. Measurement of apo B levels in mothers may identify both high-risk children and mothers who may benefit from intervention.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab902074" + }, + { + "object": "APOE genotype status moderated the age-related declines in episodic memory: APOE-epsilon4+ middle-aged adults exhibited impairments relative to both APOE-epsilon4- middle-aged participants, and APOE-epsilon4+ younger adults.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab77520" + }, + { + "object": "The APOE epsilon2 allele may be protective on cognitive decline among the oldest old. A 22% increased mortality risk for APOE epsilon4 carriers was found. No protective effect of the APOE epsilon2 allele on mortality compared with the APOE epsilon3 allele.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab780736" + }, + { + "object": "Emerging evidences indicate that FOXO3a acts as a tumor suppressor in cancer. FOXO3a is frequently inactivated in cancer cell lines by mutation of the FOXO3a gene or cytoplasmic sequestration of FOXO3a protein. And its inactivation is associated with the initiation and progression of cancer. [review]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab255659" + }, + { + "object": "The preferential interaction of the P. falciparum PFE1590w protein with the human ApoE epsilon3 and ApoE epsilon4 isoforms, but not the ApoE epsilon2 isoform, supports the hypothesis that ApoE genotype affects risk of malaria infection.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab847419" + } + ], + "question": "What genes are associated with aging?", + "subquestions": null, + "task_id": "B0164472D40098296DA0836E50978AC8", + "usage": { + "chatgpt": 7627, + "gpt-4": 4474, + "gpt-4-turbo-preview": 3537 + }, + "user_id": 2 + }, + "document_id": "B0164472D40098296DA0836E50978AC8", + "task_id": "B0164472D40098296DA0836E50978AC8" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/09.json b/gnqa/paper1_eval/src/data/responses/aging/experts/09.json new file mode 100644 index 0000000..fb82f6d --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/09.json @@ -0,0 +1,400 @@ +{ + "created_at": "2024-05-31T21:57:15.838942Z", + "data": { + "amplify": false, + "answer": "Several genetic factors are associated with aging. These include the APOE, FOXO3A, and CHRNA3/5 genes, as well as the CDKN2A/B, SH2B3, and MTP genes. Other factors include the HLA-DQA1/DRB1 and LPA regions, and the AKAP2 gene. Additionally, genes highly expressed in the brain, genes involved in neural development and function, and genes associated with autophagy have been linked to aging.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + } + ], + "0942fb8b-731c-4d6e-9b5a-8a303012eec6": [ + { + "document_id": "0942fb8b-731c-4d6e-9b5a-8a303012eec6", + "text": "\nBackground: Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality.Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.Results: Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively.We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function.Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs.A polygenic score for GrimAge acceleration showed strong associations with adiposityrelated traits, educational attainment, parental longevity, and C-reactive protein levels.Conclusion: This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity." + } + ], + "1386c8ad-297d-48b1-aa34-41659a9f6544": [ + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + } + ], + "555a1533-2905-4d91-a3b6-2fca3679ab02": [ + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nOn the other hand, the same evolutionary-motivated strategy suggesting to focus on more heterogeneous phenotypes (as opposite to more homogenous) can be highly beneficial for unraveling genetic predisposition to fundamental mechanisms of intrinsic biological aging and, consequently, to geriatric diseases.Indeed, aging is associated with systemic remodeling of an organism's functioning which increases chances of virtually all geriatric disorders (Franco et al. 2009;Franceschi et al. 2000;Martin et al. 2007;Cutler and Mattson 2006).Experiments with laboratory animals (Johnson 2006) and heritability estimates in humans (Christensen et al. 2006;Iachine et al. 1998) show that aging can be genetically regulated (Finch and Tanzi 1997;Martin et al. 2007;Vaupel 2010).Accordingly, yielding insights in genetic predisposition to aging-related processes in an organism could be a major breakthrough in preventing and/or ameliorating not one geriatric trait, but perhaps a major subset of such traits (Martin et al. 2007) that can greatly advance progress in solving the problem of extending healthy lifespan in humans." + } + ], + "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4": [ + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + } + ], + "7291ceb2-482a-4f9b-a116-2b68ff24854f": [ + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + } + ], + "ca76f85d-9f72-4e15-8ba9-3bf94308c449": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + } + ], + "db90a971-e55a-4ab0-a3b1-05908d6771a4": [ + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + } + ], + "f4e2fa75-559b-4fa9-b722-bdac03f7715a": [ + { + "document_id": "f4e2fa75-559b-4fa9-b722-bdac03f7715a", + "text": "\n\nI NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + } + ] + }, + "data_source": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + }, + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "section_type": "main", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "main", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "abstract", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "section_type": "main", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nOn the other hand, the same evolutionary-motivated strategy suggesting to focus on more heterogeneous phenotypes (as opposite to more homogenous) can be highly beneficial for unraveling genetic predisposition to fundamental mechanisms of intrinsic biological aging and, consequently, to geriatric diseases.Indeed, aging is associated with systemic remodeling of an organism's functioning which increases chances of virtually all geriatric disorders (Franco et al. 2009;Franceschi et al. 2000;Martin et al. 2007;Cutler and Mattson 2006).Experiments with laboratory animals (Johnson 2006) and heritability estimates in humans (Christensen et al. 2006;Iachine et al. 1998) show that aging can be genetically regulated (Finch and Tanzi 1997;Martin et al. 2007;Vaupel 2010).Accordingly, yielding insights in genetic predisposition to aging-related processes in an organism could be a major breakthrough in preventing and/or ameliorating not one geriatric trait, but perhaps a major subset of such traits (Martin et al. 2007) that can greatly advance progress in solving the problem of extending healthy lifespan in humans." + }, + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "section_type": "main", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + }, + { + "document_id": "ea036684-619d-4b82-9242-c0b220f2d8df", + "section_type": "main", + "text": "The mechanisms that underlie healthy aging—particularly, the cognitive as-\n\npects—remain poorly understood. Research suggests that genetics play a significant role in determining an individual’s\nsusceptibility or resilience to cognitive decline and dementia\n(Harris and Deary 2011; Ridge et al. , 2013). Identification of precise genetic factors involved would provide insight into\n\nCell Reports 32, 108091, September 1, 2020 ª 2020 The Author(s). 1\nThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).\n ll\nOPEN ACCESS\n\nReport\n\nFigure 1." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "Discussion\n\nIn our analyses of over 25,000 individuals of 55 years and older followed for an average of 11 years, we did not identify genome-wide significant associations for all-cause mortality and survival free of major diseases.However, both traits highlighted loci with suggestive significance that were in the neighborhood of genes related to neural regulation.In addition, our pathway and network analyses identified an enrichment of genes associated with cellular and neural development and function, and cell communication that may contribute to variation in human aging.Brain development might be responsible for the creation of redundancy in brain circuitry, which is associated with functional reserve and resiliency.Brain function regulates most of the compensatory strategy supporting maintenance of homeostatic equilibrium.Both of these processes are essential to healthy aging and longevity." + }, + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "section_type": "main", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + }, + { + "document_id": "593b752f-f448-47be-8b83-13bc5e9eb0d4", + "section_type": "main", + "text": "\n\nIn this light, we pursued a genomic study of an alternate but related aging phenotype-healthy aging-in order to expose its potential to uncover genetic factors for protection against age-associated disease.It is important to differentiate longevity from our healthy aging phenotype, which, as we have defined it for our healthy aging cohort (Wellderly), attempts to understand the genetics of disease-free aging in humans without medical interventions.Toward this end, we performed whole-genome sequencing (WGS) of the Wellderly and compared their genetic characteristics to an ethnicity-matched population control.Our findings suggest that healthy aging is associated with a diseaseprotective genetic profile that overlaps with but differs from that observed in exceptional longevity cohorts.These findings include no enrichment of true longevity variants, a lower genetic risk from common susceptibility alleles for Alzheimer and coronary artery disease, and no decrease in the rate of rare pathogenic variants.We identify suggestive common and rare variant genetic associations that implicate genetic protection against cognitive decline in healthy aging.Our data are made available for the discovery of additional disease protective genetic factors by the research community." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "section_type": "main", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + }, + { + "document_id": "99a35e24-bbd2-495b-82dc-53d7e2075191", + "section_type": "main", + "text": "\n\nThus, substantially more work is needed in this area to establish whether longevity is driven by nuclear genomic stability.Diverse and unexpected bits of evidence support a relationship.For example, a disproportionate number of genes identified in unbiased and targeted genome-wide association studies (GWASs) as associated with longevity are involved in genome maintenance (75).One study involved age of natural menopause in ∼70,000 women and led to the identification of 44 genetic variants associated with early or late menopause, a strong biomarker of healthy TIFs (telomere dysfunction-induced foci): co-localization of multiple DNA damage response factors and repair proteins on uncapped telomeric DNA aging (76).Approximately two-thirds of these are associated with genome maintenance genes.Seven of ten significantly associated pathways are involved in DNA repair.The highly significant overrepresentation of DNA repair pathways indicates an intimate connection between genome maintenance and aging phenotypes.From unrelated studies, we know that reduced expression of the repair endonuclease ERCC1-XPF causes accelerated aging (3), whereas ERCC1 is one of the top genes under positive selective pressure in the longest-lived mammalian species, the bowhead whale (77).Intriguingly, hepatocytes from old rats have impaired NER, whereas caloric restriction, which extends longevity, restored the NER capacity of old rats to that of youthful levels (42).In a human interventional study, brief caloric restriction increased NER capacity in PBMCs of individuals who had low NER prior to dietary intervention (78).Therefore, increased DNA repair capacity could promote longevity and may even prove amenable to improvement." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "abstract", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "0942fb8b-731c-4d6e-9b5a-8a303012eec6", + "section_type": "abstract", + "text": "\nBackground: Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality.Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.Results: Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively.We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function.Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs.A polygenic score for GrimAge acceleration showed strong associations with adiposityrelated traits, educational attainment, parental longevity, and C-reactive protein levels.Conclusion: This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "main", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "main", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "abstract", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "593b752f-f448-47be-8b83-13bc5e9eb0d4", + "section_type": "main", + "text": "\n\nAge at death in adulthood has a moderate genetic component overall, with a heritability of approximately 25% (Murabito et al., 2012).Heritability of longevity increases with age, with a negligible genetic contribution to survival up to approximately 60 years of age, after which an increasing genetic component to survival is observed (Brooks-Wilson, 2013;Christensen et al., 2006).Most genetic studies of aging have focused on long-lived individuals, typically defined as centenarians 100 years or older, who may have had exceptional survival due to medical interventions (Murabito et al., 2012).A number of genetic associations with exceptional longevity have been made (Atzmon et al., 2006;Bojesen and Nordestgaard, 2008;Hurme et al., 2005;Kuningas et al., 2007;Melzer et al., 2007;Pawlikowska et al., 2009;Sanders et al., 2010;Suh et al., 2008;Willcox et al., 2008), with only markers at APOE and FOXO3A being well replicated (Murabito et al., 2012).Overall, the results of genetic and epidemiological longevity studies suggest aging is a complex trait and that achievement of exceptional longevity may not best capture the genetics of resistance to or delay of age-associated disease (Christensen et al., 2006)." + }, + { + "document_id": "da4a9500-831f-48ab-acea-5ec7097276ed", + "section_type": "main", + "text": "\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Conclusions and Perspectives\n\nThe advent of new technologies has allowed the identification of conserved pathways involved in the aging process, as well as the association of genomic variants with human longevity.Nevertheless, heritability of human longevity has been estimated from 20% to 30%, reinforcing the fact that external factors such as diet, environment, and physical activity play a critical role in the human life span." + }, + { + "document_id": "0fc75a0d-3aa3-481a-8c0f-689bd7ae6104", + "section_type": "abstract", + "text": "\nAging is a complex process affecting different species and individuals in different ways.Comparing genetic variation across species with their aging phenotypes will help understanding the molecular basis of aging and longevity.Although most studies on aging have so far focused on short-lived model organisms, recent comparisons of genomic, transcriptomic, and metabolomic data across lineages with different lifespans are unveiling molecular signatures associated with longevity.Here, we examine the relationship between genomic variation and maximum lifespan across primate species.We used two different approaches.First, we searched for parallel amino-acid mutations that co-occur with increases in longevity across the primate linage.Twenty-five such amino-acid variants were identified, several of which have been previously reported by studies with different experimental setups and in different model organisms.The genes harboring these mutations are mainly enriched in functional categories such as wound healing, blood coagulation, and cardiovascular disorders.We demonstrate that these pathways are highly enriched for pleiotropic effects, as predicted by the antagonistic pleiotropy theory of aging.A second approach was focused on changes in rates of protein evolution across the primate phylogeny.Using the phylogenetic generalized least squares, we show that some genes exhibit strong correlations between their evolutionary rates and longevity-associated traits.These include genes in the Sphingosine 1-phosphate pathway, PI3K signaling, and the Thrombin/protease-activated receptor pathway, among other cardiovascular processes.Together, these results shed light into human senescence patterns and underscore the power of comparative genomics to identify pathways related to aging and longevity." + }, + { + "document_id": "593b752f-f448-47be-8b83-13bc5e9eb0d4", + "section_type": "abstract", + "text": "\nHighlights d Healthy aging is a complex polygenic trait related but distinct from longevity d Healthy aging is associated with decreased genetic risk for select diseases d Healthy aging is potentially linked to protection against cognitive decline d Genome data are made available for further analysis Authors" + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "section_type": "main", + "text": "This population genetic\nmechanism also can maintain genetic variability for aging, like antagonistic pleiotropy.\n LARGE-EFFECT MUTANTS AND THE GENETICS OF AGING\n\nOne approach that has become increasingly common in the characterization of the genetics of aging is to isolate aging mutants, usually from mutagenesis experiments, and\nthen to determine the mechanistic basis for the unusual life span in the mutants. This\napproach has led to the discovery of genes that can enhance (e.g. , Maynard Smith 1958;\nLin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span\n(e.g. , Pearl and Parker 1922)." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "section_type": "main", + "text": "This population genetic\nmechanism also can maintain genetic variability for aging, like antagonistic pleiotropy.\n LARGE-EFFECT MUTANTS AND THE GENETICS OF AGING\n\nOne approach that has become increasingly common in the characterization of the genetics of aging is to isolate aging mutants, usually from mutagenesis experiments, and\nthen to determine the mechanistic basis for the unusual life span in the mutants. This\napproach has led to the discovery of genes that can enhance (e.g. , Maynard Smith 1958;\nLin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span\n(e.g. , Pearl and Parker 1922)." + }, + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "section_type": "main", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nGenes/loci identified by genome-wide association studies of longevity and lifespan traits." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nSeveral explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nAging is an extremely complex process associated with interplay of genetic, biochemical, and metabolic factors in an organism in a given environment.Although genetic studies of various animal models suggest that even a single-gene mutation can remarkably extend lifespan (Kenyon 2005;Johnson 2006) and, thus, modulate aging, no such genes are revealed in humans so far.Given that a human organism is a much more complex system than a model organism (Christensen et al. 2006), it is evident that genetic effects on the aging process should be mediated via coordinate action of a large number of inter-related processes (Kirkwood 2011).Coordinated function is rather relevant to complex biological (Soltow et al. 2010;Slagboom et al. 2011) and genetic (Bloss et al. 2011) networks than to individual genes." + }, + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "section_type": "main", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "a6bc2efd-61a7-4e07-ad5c-49234aa89431", + "section_type": "main", + "text": "\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13]." + }, + { + "document_id": "f4e2fa75-559b-4fa9-b722-bdac03f7715a", + "section_type": "main", + "text": "\n\nI NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + }, + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "section_type": "main", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + } + ], + "document_id": "22C4FE902B2E9A047267A56F6096B4C6", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "longevity", + "aging", + "genetic", + "SNPs", + "DNA&methylation", + "epigenetic&clock", + "GWAS", + "chromosome&5q33.3" + ], + "metadata": [ + { + "object": "APOE genotype status moderated the age-related declines in episodic memory: APOE-epsilon4+ middle-aged adults exhibited impairments relative to both APOE-epsilon4- middle-aged participants, and APOE-epsilon4+ younger adults.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab77520" + }, + { + "object": "Data suggest that the redox status of serum apoE might be related to the synthesis of HDL; the cysteine-thiol residue of reduced-apoE is in a naive state, while that of non-reduced-apoE is in a reversibly or irreversibly oxidized state. Data suggest that apoE homodimer and apoE-AII complex are typical reversibly oxidized forms of apoE. apoE-AII complex = a complex of apolipoprotein E and apolipoprotein A-II", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab212832" + }, + { + "object": "Low apoE and mir-650 plasma concentrations were risk factors for developing Alzheimer's disease AD and were particularly pronounced in severe dementia. APOE E4 allele in both AD patients and controls led to a reduction in apoE, while APOE E3/E3 genotype was associated with an increased apoE concentration and level of miR-107 in AD, which inversely correlated with the number of APOE E4 alleles.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab459467" + }, + { + "object": "study investigated DNA methylation of the imprinted IGF2/H19 locus; data suggest aging more than population genetics is responsible for the inter-individual variability in DNA methylation patterns; DNA methylation variability appears to be highly region-specific", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab744889" + }, + { + "object": "BDNF mRNA expression and DNA methylation of seven CpG sites were not associated with schizophrenia after accounting for age and PMI effects. BDNF mRNA expression and DNA methylation were not altered by Val66Met after accounting for age and PMI effects. Schizophrenia risk was not associated with differential BDNF mRNA expression and DNA methylation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab97590" + }, + { + "object": "the minimum alleles of rs10895322, rs1784424, rs3781788, and rs1573954 correlated with an increased risk of alcohol-induced ONFH P<0.05. Genetic model analysis revealed significant associations of 9 SNPs with alcohol-induced ONFH occurrence after adjustment for age P<0.05: 2 protective SNPs rs1711423 and rs1784418 and 7 high-risk SNPs rs10895322, rs1784424, rs3781788, rs7126560, rs1573954, rs1711399, rs2292730.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab834824" + }, + { + "object": "1443823_s_at: short probe set - potential SNPs could affect mapping result; 1427465_at: 3 SNPs in target area affect the hybridization of 5 probes; 1434893_at: 6 SNPs in target area could affect the hybridization of 7 probes; 1455136_at generate true cisQTL even 3 SNPs in target area affect mapping accuracy of 4 probes - BUT probes without any SNPs reveal the presence of an eQTL.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab43" + }, + { + "object": "These findings indicate that maternal apo B levels are significantly associated with apo B levels in their pre-school age children, adjusted for confounding variables. Furthermore, the mother-child correlations in apo B levels were independent of mother-child adiposity. Measurement of apo B levels in mothers may identify both high-risk children and mothers who may benefit from intervention.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab902074" + }, + { + "object": "Study of genetic risk of prevalent hrHPV infections in Nigerian women found significant associations with SNPs on ribosomal protein gene S19 RPS19 and Thymidylate Synthase gene TYMS, in an allelic model. This risk remained significant, after adjusting for age, body mass index, smoking, age at menarche, age at sexual debut, lifetime total number of sexual partners and the total number of pregnancies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab745428" + }, + { + "object": "Study surveyed the non-synonymous SNPs of DNASE1L2: 19 SNPs originating from frameshift/nonsense mutations found in DNASE1L2 resulted in loss of function of the enzyme. Thus, the present findings suggest that each of the minor alleles for these SNPs may serve as one of genetic risk factors for parakeratotic skin diseases such as psoriasis, even though they lack a worldwide genetic distribution.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab752673" + } + ], + "question": "what genetic factor are associated with aging", + "subquestions": null, + "task_id": "22C4FE902B2E9A047267A56F6096B4C6", + "usage": { + "chatgpt": 8077, + "gpt-4": 5976, + "gpt-4-turbo-preview": 5057 + }, + "user_id": 2 + }, + "document_id": "22C4FE902B2E9A047267A56F6096B4C6", + "task_id": "22C4FE902B2E9A047267A56F6096B4C6" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_01.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_01.json new file mode 100644 index 0000000..32fe672 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_01.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-05-31T12:56:07.845098Z", + "data": { + "amplify": false, + "answer": "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "context": { + "0c6c0977-2cf9-4bbf-bc31-fe025f008089": [ + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n24 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10006, 10006\ngenenetwork.org/\nshow_trait?trait_id=\n10006&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10010, 10010\ngenenetwork.org/\nshow_trait?trait_id=\n10010&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10011, 10011\ngenenetwork.org/\nshow_trait?trait_id=\n10011&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10021, 10021\ngenenetwork.org/\nshow_trait?trait_id=\n10021&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10022, 10022\ngenenetwork.org/\nshow_trait?trait_id=\n10022&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10025, 10025\ngenenetwork.org/\nshow_trait?trait_id=\n10025&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10066, 10066\ngenenetwork.org/\nshow_trait?trait_id=\n10066&dataset=BXD-\nLongevityPublish\n\nReferences\nAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,\nCarlson M. 1994." + } + ], + "2464a084-1a11-44eb-8bce-4b344de049ff": [ + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n24 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10006, 10006\ngenenetwork.org/\nshow_trait?trait_id=\n10006&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10010, 10010\ngenenetwork.org/\nshow_trait?trait_id=\n10010&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10011, 10011\ngenenetwork.org/\nshow_trait?trait_id=\n10011&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10021, 10021\ngenenetwork.org/\nshow_trait?trait_id=\n10021&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10022, 10022\ngenenetwork.org/\nshow_trait?trait_id=\n10022&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10025, 10025\ngenenetwork.org/\nshow_trait?trait_id=\n10025&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10066, 10066\ngenenetwork.org/\nshow_trait?trait_id=\n10066&dataset=BXD-\nLongevityPublish\n\nReferences\nAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,\nCarlson M. 1994." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Leduc MS, Hageman RS, Meng Q et al (2010) Identification of\ngenetic determinants of IGF-1 levels and longevity among mouse\ninbred strains. Aging Cell 9(5):823–836. doi:10.1111/j.14749726.2010.00612.x\n10. Lang DH, Gerhard GS, Griffith JW et al (2010) Quantitative trait\nloci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin Exp Res 22(1):8–19\n11. Gelman R, Watson A, Bronson R et al (1988) Murine chromosomal\nregions\ncorrelated\nwith\nlongevity. Genetics\n118(4):693–704\n12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci\nassociated with life span exhibit sex-specific and epistatic effects." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Conclusions These results suggest a novel locus influencing survival in the B6/D2 genetic background, perhaps\nvia a metabolic disorder that emerges by 200 days of age in\nmale animals. Keywords\nPathology\n\nLongevity \\ Lifespan \\ Mouse \\ Linkage \\\n\nIntroduction\nLongevity, the quintessential complex trait, likely reflects\nall aspects of an organism’s life history. In humans, the\nestimated heritability of age at death is estimated at\n25–33 % [1]. Genetic contributions to mortality rates are thus of great interest and may aid in the understanding of\ndisease etiology and the process of aging itself [2]." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Here, we have extended this analysis to search for\ngenotypes related to survival to the age of 800 days in a\npopulation of a reciprocal F2 cross between (B6) and (D2)\nmice. Since QTL for longevity in mice have shown strong\nsex specificity [10, 12], we conducted sex-specific analyses. In addition, we also determined whether there were\nany change in pathology changes associated with the loci\nthat showed frequency distortions with aging. To confirm\nthe associations of the loci of interest with longevity and\npathology, we performed replication analyses on a panel of\nBXD recombinant inbred strains." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "352(6291): p. aad0189. Liao, C.Y. , et al. , Genetic variation in the murine lifespan response to dietary restriction: from life extension to life\nshortening. Aging Cell, 2010. 9(1): p. 92-5. Johnson, M., Laboratory Mice and Rats. Mater. Methods, 2012. 2: p. 113. Fontaine, D.A. and D.B. Davis, Attention to Background Strain Is Essential for Metabolic Research: C57BL/6 and\nthe International Knockout Mouse Consortium. Diabetes, 2016. 65(1): p. 25-33. Simon, M.M. , et al. , A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol, 2013. 14(7): p. R82. Lilue, J., et al." + } + ], + "8dad24f7-b658-44fa-af65-6f33db69c15a": [ + { + "document_id": "8dad24f7-b658-44fa-af65-6f33db69c15a", + "text":"Mamm Genome 2001;12: 930–2. 21 Gelman R, Watson A, Bronson R, Yunis E. Murine chromosomal\nregions correlated with longevity. Genetics 1988;118:693–704. 22 Peirce JL, Lu L, Gu J, Silver LM, Williams RW. A new set of BXD\nrecombinant inbred lines from advanced intercross populations in\nmice. BMC Genet 2004;5:7. 23 Rahman ZS, Tin SK, Buenaventura PN et al. A novel susceptibility\nlocus on chromosome 2 in the (New Zealand Black \\ New Zealand\nWhite) F1 hybrid mouse model of systemic lupus erythematosus. J Immunol 2002;168:3042–9. 24 Kono DH, Burlingame RW, Owens DG et al." + } + ], + "958b37c9-9bd5-4e84-939d-8f12dccf1055": [ + { + "document_id": "958b37c9-9bd5-4e84-939d-8f12dccf1055", + "text": "Conversely, the BXD strain with the shortest life span\n(BXD14) has the lowest responsiveness to the stimulatory effect of\nTGF-2 when old (48). The region on chromosome 2 where a\nsuggestive QTL regulating the responsiveness to TGF-2 in old\nmice is located also contains two QTL for longevity (32). Finally,\nthe strongest support for this hypothesis is the correlation between\nlongevity and the age-related increase in the serum-dependent effect of TGF-2 on LSK cells, the extent of which may determine\nstem cell function in aged mice." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nFIGURE 8-5 Genetic regulation of longevity in mice stratified by cause of death.Female mice that inherit the C3H allele at D2Mit58 plus the BALB allele at D16Mit182 (light gray bars) have significantly higher longevity than their sisters (dark gray bars) with the C57BL/6 plus DBA/2 allele combination (\"all causes\" of death combined).Subsets of mice that died either of cancer or of a nonneoplastic (\"benign\") illness both show the association between genotype and longevity.Among the mice dying of neoplasia, subsets dying of lymphoma or of fibrosarcoma show equivalent, and significant, genotypic effects.Bars indicate means plus standard error of the mean.SOURCE:Miller et al. (unpublished results)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nThe available dataset also provides examples in which genetic variants seem to influence the risk of specific late-life diseases.Figure 8-6, for example, shows longevity results for mice stratified by their inheritance at the 12th chromosome locus D12Mit167.This is a locus associated with differential longevity in both male and female mice, with the strongest effect (adjusted p < 0.01) seen in those mice living more than 657 days (Jackson et al., unpublished results).The longest-lived mice are those that inherit both the C57BL/6 allele from their mother and the C3H allele from their father; on average, they survive 93 days longer than siblings with the BALB plus C3H combination.Figure 8-6 shows that the D12Mit167, like the pair of loci illustrated in Figure 8-5, has significant and similar effects in mice dying of cancer (85 days) and in mice dying of non-neoplastic diseases (126 days).A more detailed analysis of the cancers, however, suggests that while lymphoma and hepatoma victims are equally protected by the favorable alleles (effect sizes of 93 and 167 days, respec- mice of two subgroups: those dying of the urinary syndrome MUS, and those dying of all other causes.The genetic analysis contrasts mice with both the C57BL/6 allele at D4Mit84 and the C3H allele at D9Mit110 to mice with any of the three other allele combinations.In the males dying of causes other than MUS, this allele pair is associated with a 170-day increment in longevity (post-hoc p < 0.00003).But for males that do die of MUS, the same allele combination is associated with a 187-day decline in mean life span (post-hoc p < 0.03).This effect is thus pleiotropic, in that these alleles accelerate death in mice susceptible to MUS, while postponing death for all other males in the population.Although these loci are associated with differential longevity in mice that do develop MUS, they do not have a significant effect on the chances that MUS will indeed occur (not shown).The risk of developing MUS seems to be under control of a separate locus on chromosome 6.As shown in the bottom panel of Figure 8-7, males that inherit the C3H allele at D6Mit268 are far more likely to develop MUS (28 percent risk) than are their brothers who receive the DBA/2 allele at this locus (7 percent risk; p = 0.012 by two-tailed Fisher's exact test)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nHigh levels of CD8M cells are associated with diminished longevity in mated females (left panel; p < 0.001), but not in virgin females (center panel).Among virgin males, those dying of diseases other than the urinary syndrome MUS show no association between CD8M and longevity (open circles, upper line), but those dying because of MUS show a nonsignificant trend (filled circles, lower line, R = -0.27,p = 0.13) similar to the relationship observed in mated females.SOURCE : Miller et al. (unpublished results).Male or female mice that inherit the C57BL/6 (maternal) and C3H (paternal) alleles at D12Mit167 (light gray bars) are longer lived than their siblings that inherit the BALB plus C3H combination.The \"effect size\" shown at the right represents that difference in mean longevity between mice in the two genetically different groups, with (**) = p < 0.01 and (*) = p < 0.05 by t-test.Similar effect sizes are seen for mice dying of cancer or of non-neoplastic illnesses (\"benign\"), and among the cancer deaths the genetic effect is similar for deaths due to lymphoma and hepatoma.The genetic effect on longevity seems to be minimal, however, for mice dying of fibrosarcoma.Bars show means plus standard errors.SOURCE : Miller et al. (unpublished results)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nOur own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice." + } + ], + "9ac0b7e7-6294-4cfb-97e3-e5a4546af324": [ + { + "document_id": "9ac0b7e7-6294-4cfb-97e3-e5a4546af324", + "text": ", Vogler, G.P. , Vandenbergh,\nD.J. , Blizard, D.A. , Stout, J.T. & McClearn, G.E. Quantitative Trait\nLocus (QTL) Analysis of Longevity in C57BL/6J byDBA/2J (BXD)\nRecombinant Inbred Mice. Aging Clin Exp Res (in press). Lionikas, A., Blizard, D.A. , Vandenbergh, D.J. , Glover, M.G. ,\nStout, J.T. , Vogler, G.P. , McClearn, G.E. & Larsson, L. (2003)\nGenetic architecture of fast- and slow-twitch skeletal muscle\nweight in 200-day-old mice of the C57BL/6J and DBA/2J lineage. Physiol Genomics 16, 141–152. Lionikas A., Blizard D.A. , Gerhard G.S. , Vandenbergh D.J. , Stout J.T. ,\nVogler G.P. , McClearn G.E." + } + ], + "cb3f9967-9762-4a9b-96cb-0acccdc316d2": [ + { + "document_id": "cb3f9967-9762-4a9b-96cb-0acccdc316d2", + "text": "Deficiency mapping of quantitative trait loci affecting longevity\nin Drosophila melanogaster. Genetics 2000;156:1129–1146. [PubMed: 11063689]\n33. Ma RZ, et al. Identification of Bphs, an autoimmune disease locus, as histamine receptor H1. Science\n2002;297:620–623. [PubMed: 12142541]\n\nNat Rev Genet. Author manuscript; available in PMC 2007 November 5. Page 12\n\nNIH-PA Author Manuscript\n\n34. Vivian JL, Chen Y, Yee D, Schneider E, Magnuson T. An allelic series of mutations in Smad2 and\nSmad4 identified in a genotype-based screen of N-ethyl-N-nitrosourea-mutagenized mouse\nembryonic stem cells. Proc. Natl Acad. Sci. USA 2002;99:15542–15547. [PubMed: 12432092]\n35. Vogel G. Scientists dream of 1001 complex mice." + } + ], + "ce2c68bf-878d-460c-8d9b-d45ce3034ef7": [ + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "text": "34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with\nlongevity. Genetics 118, 693–704 (1988). [PubMed: 3163317]\n35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011). 36. Houtkooper RHet al.Mitonuclear protein imbalance as a conserved longevity mechanism. Nature497, 451–457 (2013). [PubMed: 23698443]\n37. Williams EGet al.An Evolutionarily conserved role for the aryl hydrocarbon receptor in the\nregulation of movement. PLOS Genet. 10, e1004673 (2014). [PubMed: 25255223]\n38. Lang DHet al.Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin. Exp. Res. 22, 8–19 (2010)." + } + ], + "db0459f8-6602-48d7-be9b-14863a88bbe1": [ + { + "document_id": "db0459f8-6602-48d7-be9b-14863a88bbe1", + "text": "In addition,\nthe B6 mouse strain is one of the longest-lived mouse strains with a mean lifespan of 3\nyears versus other mouse strains with mean lifespan from 1.5-2 years. Therefore, it is\nevident that the genetic background of a particular mouse strain can have a profound\neffect on the biology of the HSC population as well as organismal longevity. Indeed, it is\nfor this reason that it is difficult to compare findings from various laboratories where\ndifferent mouse strains are used." + } + ], + "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748": [ + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "NIH-PA Author Manuscript\n\nThis study indicated a large amount of genetic variation for mouse longevity; heritability\nwas 34% for AL and 36% for DR (60% of AL food intake). There was no significant\ncorrelation between mean longevity under these two conditions, although maximum\nlifespans of the AL and DR mice were significantly correlated. Similar observations were\nmade at the UTHSCSA on the ILSXISS RI mice (Liao et al. , 2010a, b; Mattson 2010),\nwhere they also observed similar heritability (28% AL males, 36% AL females, 55% DR\nmales, 53% DR females)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "For females, hairs of the congenic mice grew 31% faster, also highly significant (P =\n0.0006, 1-tailed). These results validated the presence of a gene in the differential region\naffecting FE. Discussion\nWe report the outcomes of a quantitative genetic study on aging and longevity in the mouse. We studied an extant series of recombinant inbred strains (ILSXISS) that have been used\nboth in DR aging studies as well as to study alcohol sensitivity (Williams et al. , 2004)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "(2007) is a separate issue from the analyses conducted in this\nstudy (the AL efficiency model will be tested in future studies). Exp Gerontol. Author manuscript; available in PMC 2011 September 1. Rikke et al. Page 8\n\nNIH-PA Author Manuscript\n\nOther studies have also reported that individual mice that maintained the highest BW were\nlikely to be the longest-lived individuals among cohorts of genetically identical mice\n(Weindruch et al. , 1986; Harper et al. , 2006)." + } + ], + "f116ee1c-b275-4239-98e9-c2032b8f05c5": [ + { + "document_id": "f116ee1c-b275-4239-98e9-c2032b8f05c5", + "text": "Age-associated changes are conserved between mouse strains\n\nLife span and aging vary between mouse strains.For example, C57BL/6 mice are long-lived compared to the short-lived DBA/2 mice (Turturro et al. 1999).To test the generality of our observations, we also examined LT-HSCs, ST-HSC and MPPs in young and old mice from the DBA/2 strain, which originates from a distinct breeding lineage (Fox 1997)." + } + ] + }, + "data_source": [], + "document_id": "2D2D12594F1A6AC91E150695D70A4FFA", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "C57BL/6&allele", + "C3H&allele", + "BALB&allele", + "D2Mit58", + "D16Mit182", + "longevity", + "mouse", + "genetic", + "D12Mit167", + "IGF-1" + ], + "metadata": [], + "question": "Which mouse genes have been associated with longevity?", + "subquestions": null, + "task_id": "2D2D12594F1A6AC91E150695D70A4FFA", + "usage": { + "chatgpt": 9852, + "gpt-4": 6314, + "gpt-4-turbo-preview": 4895 + }, + "user_id": 2 + }, + "document_id": "2D2D12594F1A6AC91E150695D70A4FFA", + "task_id": "2D2D12594F1A6AC91E150695D70A4FFA" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_02.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_02.json new file mode 100644 index 0000000..238aa61 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_02.json @@ -0,0 +1,146 @@ +{ + "created_at": "2024-05-31T13:01:42.014610Z", + "data": { + "amplify": false, + "answer": "Lifespan is partially determined by genetics, with studies estimating that genetics contribute to about 25-30% of the variation in human lifespan. However, it is also significantly influenced by non-heritable factors such as environment and lifestyle.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nOur results show that longevity is partly determined by the predisposition to common diseases and, to an even greater extent, by modifiable risk factors.The genetic architecture of lifespan appears complex and diverse and there appears to be no single genetic elixir of long life." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nL ongevity is of interest to us all, and philosophers have long speculated on the extent to which it is pre-determined by fate.Here we focus on a narrower question-the extent and nature of its genetic basis and how this inter-relates with that of health and disease traits.In what follows, we shall use longevity as an umbrella term.We shall also more specifically refer to lifespan (the duration of life) and long-livedness (living to extreme old age, usually defined by a threshold, such as 90 years).Up to 25% of the variability in human lifespan has been estimated to be genetic 1 , but genetic variation at only three loci (near APOE, FOXO3A and CHRNA3/5) [2][3][4][5] have so far been demonstrated to be robustly associated with lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "GENETICS OF LIFE SPAN IN HUMANS\n\nMost studies of human twins agree that the heritability of life span is less than 50% (45,68).Of particular interest is an ongoing study of aging in Swedish twins that includes a large group of adopted twins who were reared separately.Ljungquist et al. (68) concluded that \"a maximum of one-third the variance in integrated mortality risk is attributable to genetic factors and that almost all of the remaining variance is due to nonshared, individually unique environmental factors. \"Moreover, this heritability declined with age and was negligible after the age of 85 in men and 90 in women." + } + ], + "1ccb0d11-1c88-4b08-b40d-4039a954745f": [ + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "text": "\n\nHow can lifespan be controlled by a single gene?Two possibilities are, first, that the mutations that extend lifespan are in genes whose products regulate the activity of many other genes and, second, that these genes do not in fact control the rate of ageing." + } + ], + "4ca8d070-8b58-4bd5-86be-127089b70324": [ + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "text": "\n\nSince that time, observations across species have shown that life span can be extended by genetic factors.One of the first demonstrations of this entailed the study of recombinant inbred populations of the nematode worm Caenorhabditis elegans by Thomas E. Johnson.Then a postdoc in William (Bill) Wood's lab at the University of Colorado Boulder, Tom and Bill demonstrated that crosses of C. elegans strains did not display the heterosis effect that interfered with many other studies, \"As predicted, we found significant genetic effects on life span as well as other life history traits. \"This finding established a method for evaluating genetic factors that influenced life-span variation.In fact, their measurements of life span of the recombinant inbred strains demonstrated the heritability of life span to be 19%-51% (1).Consistent with theories of the 1970s and 1980s, it was concluded that these genetic factors were a collection of small influences across many genes.This finding was one of the first steps in demonstrating that genetic factors influence aging.As genetic analysis was making great progress in understanding other biological processes, such as developmental programming, the realization that aging could be investigated using the same tools was highly significant." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nAlthough it is known that health and lifespan are heavily influenced by genetics [14], variations in the lifespan of different individuals within the same species seem to be more the result of the accumulation over time of molecular damage that compromises the function of the cells [15].These molecular alterations can occur both at the genetic and epigenetic levels and depend on genetic, environmental, and stochastic factors [16].This complex multifactorial mix determined characteristics, such as longevity and a healthy lifespan, which are central concerns of human existence (Fig. 13.1).This chapter describes different types of tools in genomics used in ageing research and their different applications in clinical scenarios." + } + ], + "593b752f-f448-47be-8b83-13bc5e9eb0d4": [ + { + "document_id": "593b752f-f448-47be-8b83-13bc5e9eb0d4", + "text": "\n\nAge at death in adulthood has a moderate genetic component overall, with a heritability of approximately 25% (Murabito et al., 2012).Heritability of longevity increases with age, with a negligible genetic contribution to survival up to approximately 60 years of age, after which an increasing genetic component to survival is observed (Brooks-Wilson, 2013;Christensen et al., 2006).Most genetic studies of aging have focused on long-lived individuals, typically defined as centenarians 100 years or older, who may have had exceptional survival due to medical interventions (Murabito et al., 2012).A number of genetic associations with exceptional longevity have been made (Atzmon et al., 2006;Bojesen and Nordestgaard, 2008;Hurme et al., 2005;Kuningas et al., 2007;Melzer et al., 2007;Pawlikowska et al., 2009;Sanders et al., 2010;Suh et al., 2008;Willcox et al., 2008), with only markers at APOE and FOXO3A being well replicated (Murabito et al., 2012).Overall, the results of genetic and epidemiological longevity studies suggest aging is a complex trait and that achievement of exceptional longevity may not best capture the genetics of resistance to or delay of age-associated disease (Christensen et al., 2006)." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "Introduction\n\nWorldwide human populations have shown an increase in mean life expectancy in the past two centuries (Oeppen & Vaupel, 2002).This is mainly because of environmental factors such as improved hygiene, nutrition, and health care.The large variation in healthy lifespan among the elderly has prompted research into the determinants of aging and lifespan regulation.The genetic contribution to human lifespan variation was estimated at 25-30% in twin studies (Gudmundsson et al., 2000;Skytthe et al., 2003;Hjelmborg et al., 2006).The most prominent genetic influence is observed in families in which the capacity to attain a long lifespan clusters (Perls et al., 2000;Schoenmaker et al., 2006).Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al., 2008;Terry et al., 2008), raising the question whether protective mechanisms against disease exist in long-lived subjects." + } + ], + "78a43a45-84b0-4d73-9396-95b99cfd3983": [ + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "text": "Introduction\n\nHuman lifespan is a highly complex trait, the product of myriad factors involving health, lifestyle, genetics, environment, and chance.The extent of the role of genetic variation in human lifespan has been widely debated (van den Berg et al., 2017), with estimates of broad sense heritability ranging from around 25% based on twin studies (Ljungquist et al., 1998;Herskind et al., 1996;McGue et al., 1993) (perhaps over-estimated [Young et al., 2018]) to around 16.1%, (narrow sense 12.2%) based on large-scale population data (Kaplanis et al., 2018).One very recent study suggests it is much lower still (<7%) (Ruby et al., 2018), pointing to assortative mating as the source of resemblance amongst kin." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "text": "\n\nMany factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone.Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases.If such genes exist, their effects were too small to be detected in this study.The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "Life Span\n\nDuring the last decade a variety of twin studies have shown that approximately 25 percent of the variation in life span is caused by genetic differences.This seems to be a rather consistent finding in various Nordic countries in different time periods and even so among other species not living in the wild (Herskind et al., 1996;Iachine et al., 1999;Finch and Tanzi, 1997).their relative magnitude and pattern depend on sex and on the socioeconomic environment experienced by successive birth cohorts.Genetic effects were most pronounced in periods with consciously controlled fertility, suggesting that the genetic disposition primarily affects fertility behavior and motivation for having children.Analyses of fertility motivation in some of the more recent twin cohorts, measured by age at first attempt to have children, supported this interpretation." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "The Height-Life Span Nexus\n\nSeveral observations and lines of experimentation have raised the issue of whether interindividual differences in aging rate are influenced by genes that modulate body size and early-life growth patterns.These include (a) the association between small stature and exceptional longevity in calorically restricted rodents (Yu et al., 1985), methionine-restricted rats (Orentreich et al., 1993), and mutant dwarf mice (Brown-Borg et al., 1996;Miller, 1999); and (b) the association between small body size and longer life span in natural populations of mice (Falconer et al., 1978), flies (Hillesheim and Stearns, 1992), dogs (Li et al., 1996), and, possibly, people (Samaras andStorms, 1992).The correlation in dogs is particularly striking: selective breeding for dogs of different body size has produced breeds varying in size from Chihuahua to Irish wolfhound.These breeds also vary greatly in mean longevity, from approximately 7 to 10.5 years, and the correlation between breed longevity and breed body weight (Miller, 1999) is a remarkable R 2 = 0.56.These differences are genetic and affect stature rather than obesity: no amount of overeating will convert a West Highland white terrier to a St. Bernard.The selective pressures applied were designed to create dogs of specific sizes and temperaments and were not intended to influence aging rate or life span.The clear implication is that the effects on longevity are pleiotropic, i.e., that genes selected for their effect on body size and conformation influenced life span as a side effect.It is of interest to note that the few analyses (Eigenmann et al., 1984(Eigenmann et al., , 1988) ) of the hormonal basis for interbreed differences in body size have shown that the genes in question influence levels of IGF-1, the most likely mediator of the life-span effects in the long-lived df/df and dw/dw mouse mutants.Could it be mere coincidence that long-lived mutant nematode worms (Kimura et al., 1997) also show mutations in genes related to insulin and IGF-1 receptors?" + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nAltogether, the twin and genealogical studies have shown that human lifespan is heritable, but is significantly influenced by non-heritable factors, which may explain why genetic studies of lifespan have proven to be challenging." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nTwin studies have shown that the heritability of lifespan ranges between 0.01 and 0.27 in various European populations (Ljungquist et al., 1998;van den Berg et al., 2017).Large genealogical studies are more powered to address questions FIGURE 1 | Relationship between aging and lifespan variation versus species defining lifespan. (A) Lifespan comparisons within species, measured as mean (50%) or portion of a population living till extended limits of lifespan (90-95%).Differences between populations (orange and green) can identify specific genetic or environmental changes associating with long life.These factors promote viability and often associate with increasing healthspan.Mutant analysis within a particular model organism often encompasses these types of changes as it relates to lifespan. (B) Maximum lifespans recorded for different species (A-E).While lifespan variation within a species is capped to a certain extent, variation between species can range dramatically.Changes to maximum lifespan often are associated with protective mechanisms for genomic and genetic fidelity as well as life history changes as they relate to maturation and reproduction." + } + ], + "c7361625-831a-44a2-b04d-157a49d00c6a": [ + { + "document_id": "c7361625-831a-44a2-b04d-157a49d00c6a", + "text": "\n\nThe genetic component of human lifespan based on twin studies has been estimated to be around 20-30 percent in the normal population [7], but higher in long-lived families [8][9][10].Furthermore, siblings, parents, and offspring of centenarians also live well beyond average [11,12].Lifestyle choices in terms of smoking, alcohol consumption, exercise, or diet does not appear to differ between centenarians and controls [13].Taken together, these findings provide ample evidence that extreme longevity has a genetic component ." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + } + ], + "dbf4c446-7c25-470a-9532-a564b8683eef": [ + { + "document_id": "dbf4c446-7c25-470a-9532-a564b8683eef", + "text": "\n\nUnraveling the heritability of human longevity was one of the first problems faced by geneticists.Just over a century ago, Mary Beeton and Karl Pearson [1] described a resemblance among relatives for the duration of life.A short time later, Yule [2] and Fisher [3] proved that the correlation is to be expected if lifespan is influenced by what had recently been termed 'genes' [4].Indeed, a century of correlation studies have established that something on the order of 30-50% of the total variation in human life span is attributable to genetic variation [5].Despite the wealth of diversity, specific genes contributing to this variation have proven notoriously difficult to identify.Sample size and issues of shared environment limit family-based methods such as linkage analysis, where rough genomic positions of important genetic variants are identified by comparing a small number of exceptionally long-lived people in defined pedigrees." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nStudies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches." + } + ] + }, + "data_source": [], + "document_id": "5AE03C65B85643330DE58348F4946E8A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "genetics", + "lifespan", + "heritability", + "environmental&factors", + "twin&studies", + "genealogical&studies", + "longevity", + "genomic&research", + "biomarker&research", + "aging" + ], + "metadata": [], + "question": "Is lifespan determined by genetics?", + "subquestions": null, + "task_id": "5AE03C65B85643330DE58348F4946E8A", + "usage": { + "chatgpt": 8198, + "gpt-4": 4730, + "gpt-4-turbo-preview": 3782 + }, + "user_id": 2 + }, + "document_id": "5AE03C65B85643330DE58348F4946E8A", + "task_id": "5AE03C65B85643330DE58348F4946E8A" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_03.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_03.json new file mode 100644 index 0000000..3f80e3c --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_03.json @@ -0,0 +1,138 @@ +{ + "created_at": "2024-05-31T20:36:11.762580Z", + "data": { + "amplify": false, + "answer": "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "context": { + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nOur result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes." + } + ], + "6e570a0b-a876-4263-b32f-cee85088756d": [ + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "text": "\n\nThere are two major factors that underlie these alarming projections.The first is T2D is associated with age, and Western populations are aging rapidly.The second major explanation is our lifestyles have changed dramatically in recent years.Epidemiological studies have identified strong T2D risk relationships for obesity, sedentary behavior [2][3][4], and diets rich in energy [5], processed carbohydrates [6], and animal fats [7].Collectively, these lifestyle factors impede the actions of insulin and raise hepatic glucose production, which can result in the diminution of endogenous insulin production and T2D.The strongest evidence for a causal relationship between adverse lifestyle behaviors and T2D comes from randomized controlled trials that show intensive lifestyle interventions involving structured exercise regimes which promote habitual physical activity (PA) and have a major beneficial impact on diabetes incidence in high-risk individuals [8,9]." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "text": "\n\nEpidemiological studies examining the associations between lifestyle behaviors and diabetes risk have reached similar conclusions as the clinical trials described above.For example, the 14-year follow-up University of Pennsylvania Alumni Health Study [52] (n = 5,990 men aged 39-68 years) showed PA (leisure time physical activity [LTPA] expressed in kcal expended per week through walking, stair climbing, and sports) was inversely associated with the incidence of T2D.Incidence rates declined as energy expenditure rose from 500 through 3,500 kcal/week.The age-adjusted relative risk ratio (RR) of T2D was reduced by about 6% for each 500 kcal increment increase in PA energy expenditure." + } + ], + "71172700-7bcc-42f5-9354-d8e9290e8743": [ + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "\n\nOverall, results were similar in analyses restricted to diabetes mellitus identified at baseline only, although the confidence interval included 1.These results suggest that diabetes mellitus is related to risk of AD in old age.These findings are consistent with the results of 2 large longitudinal cohort studies. 5,6In one study, 5 diabetes mellitus doubled the risk of AD during 2 years of follow-up in a sample of more than 6000 older persons from a defined cohort.The other study, 6 using data from about 2500 Japanese American men, found a similar result: diabetes mellitus approximately doubled the risk of AD.In contrast, 2 other longitudinal studies 7,8 did not demonstrate a significant association between diabetes mellitus and incident AD, but in both, the results were in the direction of increased risk.Some, [9][10][11] but not all, 12 previous studies found that diabetes mellitus was related to change in cognitive function.One factor that may contribute to variability from study to study is that diabetes mellitus may be related to decline in some cognitive systems but not others.4][15] Although diabetes mellitus was related to level of global cognition and multiple cognitive domains at baseline, we found that diabetes mellitus was only related to decline in perceptual speed.The one study 12 that did not find a relation between diabetes mellitus and cognitive decline did not include a measure of perceptual speed." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "COMMENT\n\nIn a cohort of more than 800 older persons, we found that diabetes mellitus sometime in the study was associated with an increased risk of developing AD during a mean of 5.5 years of observation.The risk of incident AD was 65% higher in those with diabetes mellitus than in those without it." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "\n\nIn summary, these findings suggest that diabetes mellitus is associated with AD and decline in cognitive function in older persons.December 12, 2003." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "DIABETES MELLITUS AND RISK OF AD\n\nDuring the follow-up evaluations, 151 persons developed AD, of whom 31 had diabetes mellitus.In a proportional hazards model adjusted for age, sex, and educational level, there was a 65% increase in the risk of developing AD in those with diabetes mellitus compared with those without diabetes mellitus (hazard ratio, 1.65; 95% confidence interval, 1.10-2.47).The cumulative hazard of AD over time, adjusted for age, sex, and educational level, is shown graphically in Figure 1 for typical participants with and without diabetes mellitus.Similar results were found in analyses with diabetes mellitus identified at baseline only (hazard ratio, 1.53; 95% confidence interval, 0.96-2.45)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAge. Age is another factor that has a considerable effect on outcomes in obesity and T2DM research.In humans, body weight increases with age and peaks at ~55 years in both men and women.Ageing per se is associated with a redistribution of both the fat-free mass and the fat mass, with the latter increase starting at ~30 years of age 129 .Intramuscular and intrahepatic fat are particularly increased in older persons, and this increase has been linked to insulin resistance 130 .Partially on the basis of these changes, ageing has been proposed to be an independent determinant of glucose tolerance, which progressively worsens with age 131,132 ." + } + ], + "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a": [ + { + "document_id": "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a", + "text": "\n\nAge also plays a vital role in the onset of diabetes (Cowie & Eberhardt, 1995).In south-east Asia almost 97% diabetic patients are 40 years old or more (IDF Atlas, 2017).In Bangladesh, the reported age of diabetes is ≥40 years in 71% urban and 85% rural female, while in the case of male the proportion is 85.5% urban and 86.5% in rural population (IDF Atlas, 2017).The current study also pinpointed an exponential increase in the risk of onset of T2DM with the increase of age when 40 years was chosen as the reference (Table S4)." + }, + { + "document_id": "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a", + "text": "\n\nWhether age and stress variables are risk factors for type 2 diabetes incidence was assessed by multivariate logistic regression (Table S4).Subjects in the age groups of (40-60) and >60 years had 1.78× (p = .005)and 3.19× (p = .006)greater risk for type 2 diabetes respectively than group of <40 years.Overall, patients under stressful condition are more likely to develop T2DM than that of nonstressed respondent (p = .000).Moreover, when stress is divided into two groups-low stress and high stress, we found that both males (p = .000)and females (p = .000)with high stress were at high risk of diabetes mellitus, whereas the association between low stress and T2DM incidence was significant only among males (Male: p = .002;Female: p = .115).The distribution and association of the genotypes, age, and stress with T2DM have been summarized in Table 3 and Figure 3.There was no difference in T2DM incidence between CT (p = .030)and TT/CC (p = .034)genotype containing people who were in age group of 40-60 years (Table 3).In contrast, people who were more than 60 years old with CT genotype (OR = 4.636, p = .029)were more prone to T2DM than that of TT/CC genotype (OR = 3.714, p = .007)subjects (Table 3)." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nThere is a clear correlation of environmental influences to diabetes risk.Yet, the assembled experts agreed that hypothesis-driven research is needed to define direct causal relationships between specific environmental factors and pathophysiologies leading to diabetes.Research efforts need to address environmental etiologies of type 1 diabetes and determine their relative contribution to onset of autoimmunity and progression to symptomatic disease.Whether there is a direct causal role of the intestinal microbiota in pathogenesis of type 1 and type 2 diabetes and response to therapies needs to be determined.Public health interventions that successfully reduce the levels of consumption of energy-dense foods and/or reduce sedentary time and increase time spent in physical activity need to be evaluated to determine whether they can reduce type 2 diabetes incidence at a population level." + } + ], + "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6": [ + { + "document_id": "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6", + "text": "\n\nIn sum, it is clear that multiple risk factors are involved in diabetes-associated cognitive decrements as well as in dementia in relation to diabetes 38 .On the basis of our assessment of the literature, it is also clear that there are still substantial knowledge gaps on how the risk factors interconnect, how the risk factors translate to potentially modifiable mechanisms and which genetic factors are involved." + } + ], + "b21bbbce-b53f-416b-8378-b635f4270ace": [ + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\n\nThe aim of this study was to investigate the association between age at natural menopause and risk of developing type 2 diabetes, and to assess whether this association is independent of potential intermediate risk factors for type 2 diabetes.Furthermore, we examined the role of endogenous sex hormone levels in the association between age at natural menopause and type 2 diabetes." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\n\nAims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\nAims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens.Results During a median follow-up of 9.2 years, we identified 348 individuals with incident type 2 diabetes.After adjustment for confounders, HRs for type 2 diabetes were 3.7 (95% CI 1.8, 7.5), 2.4 (95% CI 1.3, 4.3) and 1.60 (95% CI 1.0, 2.8) for women with premature, early and normal menopause, respectively, relative to those with late menopause (ptrend <0.001).The HR for type 2 diabetes per 1 year older at menopause was 0.96 (95% CI 0.94, 0.98).Further adjustment for BMI, glycaemic traits, metabolic risk factors, C-reactive protein, endogenous sex hormone levels or shared genetic factors did not affect this association.Conclusions/interpretation Early onset of natural menopause is an independent marker for type 2 diabetes in postmenopausal women." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\n\nassociation and explore whether the timing of natural menopause can add value to diabetes prediction and prevention." + } + ], + "d1449eee-d4ec-4886-87d1-835fb54a5f56": [ + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\n\nAlthough drawing of definitive conclusions is difficult from these observational studies, their results suggest that young-onset type 2 diabetes is associated with a much more frequent occurrence of adverse macrovascular and microvascular outcomes and a more rapidly progressing severity of complications than is seen in type 1 diabetes or later-onset type 2 diabetes." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\n\nIn a study of the age-specific incidence of type 2 diabetes in the UK (a retrospective cohort study of patients with newly diagnosed type 2 diabetes between 1990 and 2010), the investigators reported a substantial increase in the proportion of people aged 40 years or younger at diagnosis" + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\nThe prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults." + } + ], + "f53cd4d6-2d42-47e1-b58f-5bf8f2d65ef5": [ + { + "document_id": "f53cd4d6-2d42-47e1-b58f-5bf8f2d65ef5", + "text": "\n\nThe biological processes linking aging and disease risk are poorly understood.Still, aging is considered to date as one of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes." + } + ] + }, + "data_source": [], + "document_id": "B627313B69829B6ACD923E8E6588A622", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "aging", + "diabetes", + "T2DM", + "age", + "susceptibility", + "onset", + "risk&factors", + "incidence", + "type&2&diabetes", + "age-related&diseases" + ], + "metadata": [], + "question": "Is there a direct association between aging and susceptibility to having diabetes?", + "subquestions": null, + "task_id": "B627313B69829B6ACD923E8E6588A622", + "usage": { + "chatgpt": 5686, + "gpt-4": 3837, + "gpt-4-turbo-preview": 2918 + }, + "user_id": 2 + }, + "document_id": "B627313B69829B6ACD923E8E6588A622", + "task_id": "B627313B69829B6ACD923E8E6588A622" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_04.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_04.json new file mode 100644 index 0000000..18145a9 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_04.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-05-31T20:43:30.117900Z", + "data": { + "amplify": false, + "answer": "The genes associated with aging in humans are APOE and FOXO3A.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + } + ], + "4a27da1c-b184-47e8-bef2-de6435d7c3f5": [ + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + } + ], + "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4": [ + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + } + ], + "5e6ad994-9cad-4b8b-903d-2d5c350e25dc": [ + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nIn addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha ˜es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + } + ], + "f3610ccc-2831-42f6-a3d3-1a0feeba4902": [ + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + } + ] + }, + "data_source": [], + "document_id": "9AA0126F9464E89A7B057D231376A79A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "longevity", + "human", + "aging", + "genes", + "GWAS", + "SNP", + "centenarians", + "genetic&variants" + ], + "metadata": [], + "question": "Which genes are associated with aging in humans?", + "subquestions": null, + "task_id": "9AA0126F9464E89A7B057D231376A79A", + "usage": { + "chatgpt": 7766, + "gpt-4": 5012, + "gpt-4-turbo-preview": 4076 + }, + "user_id": 2 + }, + "document_id": "9AA0126F9464E89A7B057D231376A79A", + "task_id": "9AA0126F9464E89A7B057D231376A79A" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_05.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_05.json new file mode 100644 index 0000000..d25aaa8 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_05.json @@ -0,0 +1,146 @@ +{ + "created_at": "2024-05-31T20:40:14.163893Z", + "data": { + "amplify": false, + "answer": "Several genetic factors influence aging in humans. These include the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling pathway, and the exonuclease 1 (EXO1) gene. Other genes associated with aging are those involved in pathways such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response. Additionally, genes related to immune response, energy metabolism, signal transduction pathways, and cellular senescence also play a role in aging.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "Introduction\n\nWith the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging." + } + ], + "1386c8ad-297d-48b1-aa34-41659a9f6544": [ + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Influence of Genetic Factors in Ageing and Lifespan\n\nAgeing is defined as the decline of physiological functions in several tissues and organs inducing an increasing probability of death [17].The understanding of genetic factors involved in ageing has been limited due to the complexity of this process and the heterogeneity among individuals and even among tissues [18][19][20].Tissue cells adopt a senescent phenotype as a consequence of multiple intrinsic, extrinsic, and stochastic factors [21].The combination of these genetic factors is related to longevity and healthy ageing [22].Although this decline is somewhat predictable, some individuals show a much slower decline and get to live past the age of 100.Studies in these individuals showed polymorphisms in some genes which are associated with long life, such as APOE and FOXO3.However, these associations have not been consistent across different populations, suggesting that ageing is rather polygenic [23]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + } + ], + "7291ceb2-482a-4f9b-a116-2b68ff24854f": [ + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + } + ], + "c8fbb24d-0a72-4a45-a552-6cd98a4a25a2": [ + { + "document_id": "c8fbb24d-0a72-4a45-a552-6cd98a4a25a2", + "text": "Translational\n\nA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors." + } + ], + "ca76f85d-9f72-4e15-8ba9-3bf94308c449": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + } + ], + "db90a971-e55a-4ab0-a3b1-05908d6771a4": [ + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + } + ], + "f4e2fa75-559b-4fa9-b722-bdac03f7715a": [ + { + "document_id": "f4e2fa75-559b-4fa9-b722-bdac03f7715a", + "text": "\n\nI NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + } + ] + }, + "data_source": [], + "document_id": "7530EBCCAFF1750013433CA62E07A82F", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "IGF", + "insulin", + "GH", + "LPA", + "HLA-DQA1/DRB1", + "CHRNA3/5", + "CDKN2A/B", + "SH2B3" + ], + "metadata": [], + "question": "What genetic factors influence aging in humans?", + "subquestions": null, + "task_id": "7530EBCCAFF1750013433CA62E07A82F", + "usage": { + "chatgpt": 9172, + "gpt-4": 5514, + "gpt-4-turbo-preview": 4604 + }, + "user_id": 2 + }, + "document_id": "7530EBCCAFF1750013433CA62E07A82F", + "task_id": "7530EBCCAFF1750013433CA62E07A82F" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_06.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_06.json new file mode 100644 index 0000000..e47f351 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_06.json @@ -0,0 +1,150 @@ +{ + "created_at": "2024-05-31T20:53:55.182883Z", + "data": { + "amplify": false, + "answer": "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "context": { + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7": [ + { + "document_id": "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7", + "text": "FUTURE DIRECTIONS: HIGHER RESOLUTION DATA VIA HIGHER THROUGHPUT ASSAYS\n\nOne inescapable conclusion of the aggregate results of genome-wide studies of aging to date (see summary Table 1) is that we have not come close to saturating the number of potentially lifespan-altering genes in any organism.This is in no small part because directly generating survival curves is a relatively time-consuming process in most model organisms using current methods.There are several possible ways to address this.One way that has been tried is by attempting to find surrogate phenotypes [72,73,126] that can be screened more rapidly, or even scored under selection.Another is mining candidates from the many whole-genome expression profiles.Results to date with these have been very fruitful, but have not suggested that these methods alone will rapidly saturate our search for lifespan-and healthspan-altering genes in tractable model organisms." + } + ], + "113cb521-b79d-4b44-8250-dc1013ea2cb3": [ + { + "document_id": "113cb521-b79d-4b44-8250-dc1013ea2cb3", + "text": "\n\nChromosome mapping of genes that were differentially expressed in mice of different ages and/or in response to CR revealed a wide distribution of genes with some physical clustering of responsive genes within the genome.The latter findings are consistent with the concept that aging is a complex process and that evolutionary adaptations to aging, if they exist, may or may not involve geographic clustering of functionally related genes." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + } + ], + "4ca8d070-8b58-4bd5-86be-127089b70324": [ + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "text": "\n\nThe aging process most certainly is under highly polygenic controls… This should not discourage us from pursuing a search for those loci which may be of profound importance to human aging as it ordinarily occurs in most human beings." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "606c59c5-5ae4-47e9-b3eb-58afa55669d1": [ + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + } + ], + "690a2ae6-962a-438c-91ca-60425a0c8d02": [ + { + "document_id": "690a2ae6-962a-438c-91ca-60425a0c8d02", + "text": "Accepted Article\n\n© 2013 The Authors Aging Cell © 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland over 90 years and 1,955 controls between 55 and 80 years did not reveal genome-wide significant loci (Newman et al., 2010) and neither did the analyses of all-cause mortality and survival free of major disease in this cohort (Walter et al., 2011).A smaller Dutch study of 403 nonagenarians and 1,670 controls younger than 65 years identified the APOE gene as a mortality locus (Deelen et al., 2011), which was confirmed in a German study of 763 long-lived individuals and 1,085 younger controls (Nebel et al., 2011) and a longitudinal study of 1,606 Danes showed that the effect size of this association increases at the highest ages (Jacobsen et al., 2010).Apparently, the influence of the common genetic variation on longevity is small which requires large meta-GWA studies for identification.Alternatively, rare genetic variants may play a more important role in longevity.Since the previous linkage studies showed contradictory results potentially due to heterogeneity in the longevity phenotype, it is expected that longevity is influenced by many private rare variants." + } + ], + "78a43a45-84b0-4d73-9396-95b99cfd3983": [ + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "text": "\n\nAgeing is complex and takes a long time to study -a lifetime in fact.This makes it difficult to discern its causes, among the countless possibilities based on an individual's genes, behaviour or environment.While thousands of regions in an individual's genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle.Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nSeveral explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nSecond, the largely negative findings of this and other studies contrast with the intriguing animal studies of longevity.Very large effects of single genes on lifespan have indeed been observed in laboratory animals, but humans often have several homologues of these genes which might significantly differ in function or compensate for mutated genes through redundant mechanisms (Kuningas et al., 2008).This could explain why our top findings did not include genes in these pathways found in animal models.Animal models also represent genetically homogenous populations and are exposed to controlled environmental influences.The lack of replication of animal model findings in humans suggests that the use of knockout animals may not provide the optimal approach to understanding the variation in survival in humans as interactions with environmental factors may obscure the associations and prevent the identification of loci in humans." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + } + ], + "97290894-086d-438a-bbd2-907dd4cea2ab": [ + { + "document_id": "97290894-086d-438a-bbd2-907dd4cea2ab", + "text": "\n\nIn addition to timing differences, a small proportion of genes (10%-15%) exhibit opposite trends of expression changes with age in humans and macaques (Supplemental Fig. S13).Interestingly, such differences are ;1.5 times more common in aging than in development, an observation consistent with the lower strength of purifying selection on the gene regulation at old age (discussed below).These differences could also reflect extreme shifts in developmental timing between species, as well as technical artifacts.Future studies, using additional species and alternative methodology, are needed to address this issue." + } + ], + "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed": [ + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nThe remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha ˜es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "TRANSLATION OF LONGEVITY MODEL ORGANISMS AND CORE AGING PATHWAYS\n\nGenetic studies on lifespan have proven to be challenging.While longevity is a defining trait for a given species, the lifespan of individuals is of limited heritability, making analyses more difficult.Exceptional human life span, although a rare phenotype, is likely multifactorial; refined analyses are required to obtain statistically robust genomic signatures of longevity (Zhang et al., 2020) and these have proven elusive.Unlike laboratory models, the effect of environmental variance cannot be controlled in human studies, potentially masking purely biological aging mechanisms.Even laboratory models cannot replicate the complex \"environment\" of humans; it includes psychosocial, economic, and cultural factors, rather than strictly biological.These human-specific confounders are difficult or impossible to target in traditional model organisms.Despite these limitations, experimentally tractable model organisms have proven invaluable in deciphering the purely genetic contribution to lifespan, including genes and pathways conserved across the tree of life." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + } + ], + "efd5747f-9e8b-45e8-9e04-bb31131d44fa": [ + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "text": "\n\nWith modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "Conclusions and prospects\n\nOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + } + ] + }, + "data_source": [], + "document_id": "E1F24400EE215327FE987A4DDC0768C8", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "GWAS", + "longevity", + "aging", + "human", + "gene", + "lifespan", + "genetic", + "environment" + ], + "metadata": [], + "question": "Why is it so diffuclut to map gene loci that control aging in humans?", + "subquestions": null, + "task_id": "E1F24400EE215327FE987A4DDC0768C8", + "usage": { + "chatgpt": 7866, + "gpt-4": 4669, + "gpt-4-turbo-preview": 3717 + }, + "user_id": 2 + }, + "document_id": "E1F24400EE215327FE987A4DDC0768C8", + "task_id": "E1F24400EE215327FE987A4DDC0768C8" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_07.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_07.json new file mode 100644 index 0000000..3848934 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_07.json @@ -0,0 +1,150 @@ +{ + "created_at": "2024-05-31T20:55:47.964141Z", + "data": { + "amplify": false, + "answer": "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "context": { + "18e216d9-ea5c-4dfe-a30d-632163fcf39e": [ + { + "document_id": "18e216d9-ea5c-4dfe-a30d-632163fcf39e", + "text": "\n\nThere are multiple definitions of the aging process.Aging may be perceived as the random, systemic loss of molecular fidelity that, after reproductive maturity, accumulates to levels that eventually exceed tissue repair, turnover, or maintenance capacity (Hayflick 2004).The underlying molecular mechanisms of aging remain a subject of debates (de Magalhaes et al. 2009): tissue deterioration might not be programmed, being just a function of increase in entropy (Hayflick 2004).No genes are necessary to drive a stochastic process; however, there are genes that act to prevent an organism from destruction and disorganization.It may be due to the absence of specific disease-causing alleles or due to the presence of favorable alleles (Halaschek-Wiener et al. 2009).These genes may inhibit entropy, regulate inflammation, maintain DNA repair (such as telomere maintenance factors), or provide antioxidant functions (e.g., antagonists of reactive oxygen species).As healthy cells adapt to degeneration, differential expression of genes with age may indicate a transcriptional response to aging rather than a deleterious mechanism of aging per se (de Magalhaes et al. 2009).It might be postulated that there exist alleles that confer a pleiotropic effect on structure and function during aging (Lunetta et al. 2007).These alleles should regulate the ability of an organism to withstand challenging endogenous and exogenous influences." + } + ], + "1ccb0d11-1c88-4b08-b40d-4039a954745f": [ + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "text": "Why does ageing evolve? The intrinsic decline in function that occurs during ageing appears to be caused by the accumulation of damage, particularly at the molecular level.As far as we know, no genes have evolved specifically because they cause damage to accumulate, and the evolution of ageing can therefore be understood only as a side-effect of other causes of evolutionary change.The mechanisms by which ageing can evolve were first elucidated by J.B.S. Haldane [14], P.B. Medawar [15] and G.C. Williams [16].Extrinsic hazards from disease, predation and accidents mean that even potentially immortal organisms will die.Genetic effects that become apparent only later in life encounter a reduced force of natural selection, because not all their bearers will survive to express them.Haldane pointed out that late-onset genetic diseases in humans, such as Huntington's disease, encounter only weak selection, because most reproduction is complete by the age of onset [14].Ageing could therefore result from the accumulation under mutation pressure of age-specific, deleterious mutations.In addition, if some mutations have pleiotropic effects, with beneficial effects in youth, such as high fecundity, but also with a higher subsequent rate of ageing, then they could be incorporated into the population by natural selection, which will act more strongly on the early, beneficial effect.Thus, variation in the rate of ageing would result from the readjustment of a tradeoff between youthful benefits and the subsequent rate of ageing.Both processes imply that faster ageing will evolve where the extrinsic hazard to adults is greatest, a hypothesis in general supported by the data [1,2,17]." + } + ], + "4f010a74-a9b4-4538-94f7-ae8f35c8b96e": [ + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "text": "A. Theories\n\nIn looking back at the development of aging studies, we can see that it did not follow a straight or logical course.On the contrary, it can be compared with the flow of several convergent streams winding in their course.To date, numerous proposals have been made for the paradigm of aging.These include Hayflick's contributions (153) on programmed cellular incapacitation derived from flbroblast studies, a decrease in immunologic response, deleterious endocrinological changes, nuclear somatic gene mutation, mitochondrial somatic gene mutation, oxygen free radical damage to proteins and nucleic acids, molecular instabilities, molecular cross-linking, glycation reactions, and so on.There is little doubt that many of these factors contribute to the overall aging, but what are primary causes, and what are secondary outcomes?" + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Ageing Is Adjusted by Genetic, Environmental, and Stochastic Processes\n\nEnough evidence suggests that ageing is the result of different events such as molecular damage, mutations, incomplete repair, genetic programs, and continued development, among others [16].These events, in turn, are caused by genetic factors, environmental conditions, and even stochastic factors, which are mentioned below in this chapter." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nDifferent stochastic theories of ageing focus on specific mechanisms that may lead to ageing.The catastrophic error theory poses that the accumulation of errors in protein synthesis causes damage in cell function.The theory of cross-linking holds this process between proteins and other macromolecules responsible for ageing, while the theory of free radicals suggests that ageing is the result of inadequate protection against cell and tissue damage by free radicals and oxidative stress throughout life.Finally, the wear-and-tear theory poses that the cumulative damage that eventually leads to ageing and death is, in fact, the result of the continuous functioning of vital processes, during which stochastic errors gradually arise." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Introduction\n\nAging is a natural and irreversible process characterized by a progressive decay in physiological, biochemical, and structural functions of individuals.Aging is a multifactorial process that can be affected by two main factors: environmental and genetic.Environmental factors are nutrition, pathologies, pollution exposure, physical activity, and microbiota, while genetic factors are issues that have been associated with antioxidant and DNA damage responses, the fidelity of genetic information transfer, the efficiency of protein degradation, the extent of cellular responsiveness to stress, the mechanisms of epigenetic regulation, and the ability to elongate telomeres.All of them can determine how fast we age.Traditionally, aging studies had used several model organisms, from yeast to mammals, especially rodents (rats and mice).Most of the studies are made under controlled conditions, where only a few variables are observed, and the subjects are members of the same strain with the same genetic backgrounds or the same mutations.The information that so far has been obtained about aging has helped us to describe different factors that influence this process and that are the fundamental concepts of the various theories of aging.However, these theories do not fully explain the aging process in the different models of aging study.This is the case of the study of aging in humans, where it is very difficult to control the environmental and genetic variables.That is why issues haven't been solved such as the following: How does time influence aging?When do we start to age?How do we know we are old?Is it possible to delay aging?Those and more questions are the cornerstones for aging studies.Biological aging has been associated with the decrease in the repair and regeneration capacity of tissues and organs; it is a time-dependent process.This reduction can be observed by an increase in the acquisition of diseases and functional and reproductive disability, which eventually lead to death.On the other hand, it has been observed that in humans, people with the same chronological age exhibit different trajectories in the decrease of physiological functions associated with biological aging and what complicates the understanding of the molecular and physiological phenomena that drive the complex and multifactorial processes that underlie biological aging in humans." + } + ], + "5030cbc8-e02c-4e3a-8cbc-0156ce123c99": [ + { + "document_id": "5030cbc8-e02c-4e3a-8cbc-0156ce123c99", + "text": "\nThe underlying cause of aging remains one of the central mysteries of biology.Recent studies in several different systems suggest that not only may the rate of aging be modified by environmental and genetic factors, but also that the aging clock can be reversed, restoring characteristics of youthfulness to aged cells and tissues.This Review focuses on the emerging biology of rejuvenation through the lens of epigenetic reprogramming.By defining youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges." + } + ], + "5e157c2e-91b8-466d-a9fd-f91f8f432f0c": [ + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "text": "\n\nAging does not happen in a vacuum.Aging must be the result of changes that occur in molecules that have existed at one time with no age changes.It is the state of these pre-existing molecules that governs longevity determination.The pre-existing state is, as I have already described, maintained by repair and turnover systems that themselves eventually succumb to irreparable age changes.Longevity determination is the state of all molecules prior to succumbing to irreparable loss of molecular structure." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "text": "\n\nBiological aging is more than simply the occurrence of random changes in molecules.It also includes the role of the many repair systems found within cells.Thus, a more complete, but less concise, explanation of the first causes of aging in biological systems is the following:" + } + ], + "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c": [ + { + "document_id": "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c", + "text": "U\n\nnderstanding the deleterious processes that cause aging has been a human endeavor ever since we figured out that we grew old and that we didn't like it.Many hypotheses have been proposed to explain the root cause of aging (1).One broad-based hypothesis is that generalized homeostatic failure leads to age-related decline.Although notions of time-and use-related deterioration may be applicable to mechanical objects, they fall short as analogies to biological systems because energy input should theoretically maintain living systems indefinitely.Yet, despite the regenerative potential of biological organisms, progressive deterioration accompanies postmaturational aging.That the organism's repair capabilities cannot keep up with wear and tear is, according to evolutionary theory, explained by the inevitable declining force of natural selection with age.According to this reasoning, there is no selective advantage to maintaining somatic cells in perfect order much beyond reproductive maturation (1).Hence, a long life depends on the timing of maturation and the quality of somatic cell maintenance." + }, + { + "document_id": "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c", + "text": "\n\nWear and tear on the DNA often has been touted as a possible basis for our progressive age-related decline.Supporting this notion is the work of de Boer et al. (2) reported on page 1276 of this week's issue.They reveal important evidence for imperfect genome maintenance of DNA damage as a possible causal factor in aging.Harman, with his \"free radical theory of aging\" (3), was the first to propose that metabolic by-products called reactive oxygen species (ROS) continually damage cellular macromolecules, including DNA.Incomplete repair of such damage would lead to its accumulation over time and eventually result in age-related deterioration.A number of observations support the free radical theory, including the discovery that dietary restriction delays aging and extends life-span in a wide range of rodents and other species, possibly by reducing free radical damage.The notion that genomic DNA could be a major target of continual free radical attack over time is supported by the recent observation that genetic lesions accumulate with age and that dietary restriction reduces this accumulation in rodents (4).In addition, deletion of p66 shc , a signaling protein that maintains oxidant levels, increases resistance to oxidative damage and extends the life-span of mice (5)." + } + ], + "606c59c5-5ae4-47e9-b3eb-58afa55669d1": [ + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "text": "Instead, aging is expected to\nbe a pervasive failure of adaptation across most, if not all, of the physiological mechanisms\nthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute “the cause of\naging” to any one physiological mechanism or gene for aging or programmed death. Although common genetic pathways might be identified that contribute to aging among a\nvariety of organisms (cf." + } + ], + "846ae0a9-165f-4b25-8bcb-310c7da5eb44": [ + { + "document_id": "846ae0a9-165f-4b25-8bcb-310c7da5eb44", + "text": "Background\n\nAging is a complex process characterized by the progressive degeneration of a healthy phenotype and correlated with a decline in the ability to withstand cellular stress and damage.The subject of investigation for decades, the underlying molecular genetic causes of and responses to aging remain an area of active study.Research from model systems has characterized a range of physiological and molecular phenotypes associated with aging.These include genomic instability caused by accumulation of DNA damage, dysregulation of repair mechanisms, and telomere attrition; epigenetic alterations; dysregulation of transcription; loss of proteostasis; cellular senescence; and deregulated nutrient sensing, metabolic pathways, and energy use (reviewed in [1]).Separating causation from correlation between these phenotypes and aging remains a challenge, however." + } + ], + "870798fd-2c26-4819-9403-fe52836770eb": [ + { + "document_id": "870798fd-2c26-4819-9403-fe52836770eb", + "text": "Introduction\n\nUnderstanding what actually causes ageing remains admittedly a fundamental and fascinating problem in biology [1].Experimental data accumulated in the last three decades have led to the identification of various environmental and genetic factors, as well as chemical substances that influence lifespan in divergent eukaryotic species [1,2].Organisms normally age faster and hence live shorter under stress conditions that can lead to the generation of DNA mutations and, often as a consequence of mutations, damaged cytoplasmic constituents (including injured proteins, lipids, carbohydrates and organelles).Such types of damage can interfere with cellular functioning; thereby, they should be eliminated by effective repair and self-cleaning mechanisms to maintain cellular homeostasis.These mechanisms include DNA repair pathways, molecular chaperons, as well as the proteasome-ubiquitin system and lysosome-mediated autophagy, the main forms of cellular self-degradation [3].This has led to the attractive model that the gradual, lifelong accumulation of unrepaired cellular damage drives the ageing process and determines the incidence of age-related fatal diseases [4,5]." + } + ], + "996e02bf-91b2-4e81-89ba-1f661dfc662a": [ + { + "document_id": "996e02bf-91b2-4e81-89ba-1f661dfc662a", + "text": "\n\nIn conclusion, aging may not be primarily due to damage accumulating from the basic biochemical reactions that make up life but rather the result of the developmental program or of changes brought about by it.Our hypothesis is that the timing of development regulates the rate of aging among mammals, with a subset of developmental mechanisms determining the pace and causing most agerelated changes.Maybe people change as they grow old due to the same mechanisms that drive changes throughout the earlier stages in life." + } + ], + "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed": [ + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "text": "Instead, aging is expected to\nbe a pervasive failure of adaptation across most, if not all, of the physiological mechanisms\nthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute “the cause of\naging” to any one physiological mechanism or gene for aging or programmed death. Although common genetic pathways might be identified that contribute to aging among a\nvariety of organisms (cf." + } + ], + "a6bc2efd-61a7-4e07-ad5c-49234aa89431": [ + { + "document_id": "a6bc2efd-61a7-4e07-ad5c-49234aa89431", + "text": "\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13]." + } + ], + "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65": [ + { + "document_id": "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65", + "text": "Introduction\n\nThe fundamental manifestation of the aging process is a progressive decline in the functional maintenance of tissue homeostasis and an increasing propensity to degenerative diseases and death [1].It has attracted significant interest to study the underlying mechanisms of aging, and many theories have been put forward to explain the phenomenon of aging.There is an emerging consensus that aging is a multifactorial process, which is genetically determined and influenced epigenetically by environment [2].Most aging theories postulate a single physiological cause of aging, and likely these theories are correct to a certain degree and in certain aspects of aging." + } + ], + "ca76f85d-9f72-4e15-8ba9-3bf94308c449": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + } + ], + "f4dd6a1d-062b-42bc-8e22-83fcb3135578": [ + { + "document_id": "f4dd6a1d-062b-42bc-8e22-83fcb3135578", + "text": "\n\nTrying to explain aging in terms of a singular process would be in conflict with evolutionary theory.Even if loss of genome sequence integrity was the most conserved cause of aging, already active in the first replicators (Vijg, 2007), natural selection would allow a multitude of mutations with late adverse effects to accumulate in the germline, many of which would be positively selected for because of their beneficial effects early in life (Williams, 1957), In this respect, somatic mutation accumulation could be a conserved, inevitable cause of aging but superposed on multiple other processes that usually cause the earlier demise of an individual." + } + ] + }, + "data_source": [], + "document_id": "0DC57EB6A882DE1734E915DE035D2296", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "genes", + "aging", + "DNA&damage", + "free&radicals", + "telomeres", + "oxidative&stress", + "environmental&factors", + "genetic&factors", + "cellular&repair", + "protein&synthesis" + ], + "metadata": [], + "question": "what causes the aging process", + "subquestions": null, + "task_id": "0DC57EB6A882DE1734E915DE035D2296", + "usage": { + "chatgpt": 8117, + "gpt-4": 4643, + "gpt-4-turbo-preview": 3712 + }, + "user_id": 2 + }, + "document_id": "0DC57EB6A882DE1734E915DE035D2296", + "task_id": "0DC57EB6A882DE1734E915DE035D2296" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_08.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_08.json new file mode 100644 index 0000000..abbd2b8 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_08.json @@ -0,0 +1,154 @@ +{ + "created_at": "2024-05-31T21:16:38.963061Z", + "data": { + "amplify": false, + "answer": "The genes associated with aging include the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, HLA-DQA1/DRB1, LPA, CHRNA3/5, CDKN2A/B, SH2B3, and AKT1. Other genes involved in processes like growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition are also associated with aging.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\n\nStudies revealed from 300 to 750 genes related to longevity that are critically involved in a variety of life activities, such as growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [5,6].Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability, and the endocrine-related pathway [7][8][9].In addition, the candidates for longevity encompass genes related to drug metabolism, the ones involved in protein folding, stabilization, and degradation, as well those related to coagulation and regulation of circulation [10], etc.In most cases, these genes or their polymorphic sites were examined in multiple population replication studies, which discovered certain longevity-associated genes or pathways [4][5][6][7][8][9][10]." + } + ], + "4a27da1c-b184-47e8-bef2-de6435d7c3f5": [ + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + } + ], + "520b36a2-4c9c-4894-a818-9917bd357982": [ + { + "document_id": "520b36a2-4c9c-4894-a818-9917bd357982", + "text": "\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases." + } + ], + "555a1533-2905-4d91-a3b6-2fca3679ab02": [ + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes." + } + ], + "5e6ad994-9cad-4b8b-903d-2d5c350e25dc": [ + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + } + ], + "99a35e24-bbd2-495b-82dc-53d7e2075191": [ + { + "document_id": "99a35e24-bbd2-495b-82dc-53d7e2075191", + "text": "\n\nThus, substantially more work is needed in this area to establish whether longevity is driven by nuclear genomic stability.Diverse and unexpected bits of evidence support a relationship.For example, a disproportionate number of genes identified in unbiased and targeted genome-wide association studies (GWASs) as associated with longevity are involved in genome maintenance (75).One study involved age of natural menopause in ∼70,000 women and led to the identification of 44 genetic variants associated with early or late menopause, a strong biomarker of healthy TIFs (telomere dysfunction-induced foci): co-localization of multiple DNA damage response factors and repair proteins on uncapped telomeric DNA aging (76).Approximately two-thirds of these are associated with genome maintenance genes.Seven of ten significantly associated pathways are involved in DNA repair.The highly significant overrepresentation of DNA repair pathways indicates an intimate connection between genome maintenance and aging phenotypes.From unrelated studies, we know that reduced expression of the repair endonuclease ERCC1-XPF causes accelerated aging (3), whereas ERCC1 is one of the top genes under positive selective pressure in the longest-lived mammalian species, the bowhead whale (77).Intriguingly, hepatocytes from old rats have impaired NER, whereas caloric restriction, which extends longevity, restored the NER capacity of old rats to that of youthful levels (42).In a human interventional study, brief caloric restriction increased NER capacity in PBMCs of individuals who had low NER prior to dietary intervention (78).Therefore, increased DNA repair capacity could promote longevity and may even prove amenable to improvement." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nIn addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha ˜es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nGenes/loci identified by genome-wide association studies of longevity and lifespan traits." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + } + ], + "efd5747f-9e8b-45e8-9e04-bb31131d44fa": [ + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "text": "\n\nSince many alleles will fit the two patterns just described, it follows that we expect many genetic and biochemical mechanisms of aging.There are some experiments that have attempted to estimate the number of genes involved in aging, particularly in Drosophila.Quantitative genetic estimates of gene number have probably been subject to artifacts, [6,8] and are highly imprecise.Molecular genetic estimates using 2-D gels [3] and high-density geneexpression arrays [12] indicate the involvement of at least 300 genetic loci in Drosophila aging, and that estimate is highly conservative.For now, the best conclusion is probably that many genes are involved in aging in fruit flies.Vertebrates are unlikely to have fewer genes involved in aging, in view of their larger genomes." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "\n\nGenAge consists of several searchable data sets.Considering the extraordinary discoveries in the genetics of aging in model organisms, GenAge includes a data set of genes associated with longevity and/or aging in model organisms.We consider a given gene for inclusion in GenAge if genetic manipulations of the gene result in noticeable changes in the aging phenotype and/or longevity.Most genes in GenAge are from the four typical model organisms: mice, worms, fruit flies and yeast (Table 1).Strikingly, homologues of many genes -such as insulin receptors and sirtuins -have been shown to regulate aging in model organisms separated by large evolutionary distances (Kenyon, 2005;Liu et al ., 2005;Smith et al ., 2008).Moreover, we have shown that genes associated with aging and/or longevity in model organisms are evolutionary conserved in terms of having more homologues than predicted by chance (Budovsky et al ., 2007(Budovsky et al ., , 2008) ) and exhibiting slower molecular evolution rates (de Magalhães & Church, 2007).Therefore, it is now clear that at least some genes identified in model organisms may be relevant to human aging." + } + ], + "f3610ccc-2831-42f6-a3d3-1a0feeba4902": [ + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + } + ] + }, + "data_source": [], + "document_id": "B0164472D40098296DA0836E50978AC8", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "HLA-DQA1&DRB1", + "LPA", + "CHRNA3&5", + "CDKN2A&B", + "SH2B3", + "AKT1", + "ERCC1-XPF", + "MTP" + ], + "metadata": [], + "question": "What genes are associated with aging?", + "subquestions": null, + "task_id": "B0164472D40098296DA0836E50978AC8", + "usage": { + "chatgpt": 7627, + "gpt-4": 4474, + "gpt-4-turbo-preview": 3537 + }, + "user_id": 2 + }, + "document_id": "B0164472D40098296DA0836E50978AC8", + "task_id": "B0164472D40098296DA0836E50978AC8" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_09.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_09.json new file mode 100644 index 0000000..75ebba9 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_09.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-05-31T21:57:15.838942Z", + "data": { + "amplify": false, + "answer": "Several genetic factors are associated with aging. These include the APOE, FOXO3A, and CHRNA3/5 genes, as well as the CDKN2A/B, SH2B3, and MTP genes. Other factors include the HLA-DQA1/DRB1 and LPA regions, and the AKAP2 gene. Additionally, genes highly expressed in the brain, genes involved in neural development and function, and genes associated with autophagy have been linked to aging.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + } + ], + "0942fb8b-731c-4d6e-9b5a-8a303012eec6": [ + { + "document_id": "0942fb8b-731c-4d6e-9b5a-8a303012eec6", + "text": "\nBackground: Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality.Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.Results: Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively.We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function.Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs.A polygenic score for GrimAge acceleration showed strong associations with adiposityrelated traits, educational attainment, parental longevity, and C-reactive protein levels.Conclusion: This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity." + } + ], + "1386c8ad-297d-48b1-aa34-41659a9f6544": [ + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + } + ], + "555a1533-2905-4d91-a3b6-2fca3679ab02": [ + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nOn the other hand, the same evolutionary-motivated strategy suggesting to focus on more heterogeneous phenotypes (as opposite to more homogenous) can be highly beneficial for unraveling genetic predisposition to fundamental mechanisms of intrinsic biological aging and, consequently, to geriatric diseases.Indeed, aging is associated with systemic remodeling of an organism's functioning which increases chances of virtually all geriatric disorders (Franco et al. 2009;Franceschi et al. 2000;Martin et al. 2007;Cutler and Mattson 2006).Experiments with laboratory animals (Johnson 2006) and heritability estimates in humans (Christensen et al. 2006;Iachine et al. 1998) show that aging can be genetically regulated (Finch and Tanzi 1997;Martin et al. 2007;Vaupel 2010).Accordingly, yielding insights in genetic predisposition to aging-related processes in an organism could be a major breakthrough in preventing and/or ameliorating not one geriatric trait, but perhaps a major subset of such traits (Martin et al. 2007) that can greatly advance progress in solving the problem of extending healthy lifespan in humans." + } + ], + "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4": [ + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + } + ], + "7291ceb2-482a-4f9b-a116-2b68ff24854f": [ + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + } + ], + "ca76f85d-9f72-4e15-8ba9-3bf94308c449": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + } + ], + "db90a971-e55a-4ab0-a3b1-05908d6771a4": [ + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + } + ], + "f4e2fa75-559b-4fa9-b722-bdac03f7715a": [ + { + "document_id": "f4e2fa75-559b-4fa9-b722-bdac03f7715a", + "text": "\n\nI NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + } + ] + }, + "data_source": [], + "document_id": "22C4FE902B2E9A047267A56F6096B4C6", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "longevity", + "aging", + "genetic", + "SNPs", + "DNA&methylation", + "epigenetic&clock", + "GWAS", + "chromosome&5q33.3" + ], + "metadata": [], + "question": "what genetic factor are associated with aging", + "subquestions": null, + "task_id": "22C4FE902B2E9A047267A56F6096B4C6", + "usage": { + "chatgpt": 8077, + "gpt-4": 5976, + "gpt-4-turbo-preview": 5057 + }, + "user_id": 2 + }, + "document_id": "22C4FE902B2E9A047267A56F6096B4C6", + "task_id": "22C4FE902B2E9A047267A56F6096B4C6" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_10.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_10.json new file mode 100644 index 0000000..d400a87 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_10.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-05-31T21:59:24.668100Z", + "data": { + "amplify": false, + "answer": "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "context": { + "0af83a97-18ef-47f4-9f0c-872633ca3414": [ + { + "document_id": "0af83a97-18ef-47f4-9f0c-872633ca3414", + "text": "\n\nIndicative biological pathways associated with the candidate aging genes" + }, + { + "document_id": "0af83a97-18ef-47f4-9f0c-872633ca3414", + "text": "\n\nFig. 2 Significant biological processes associated with the candidate aging genes" + }, + { + "document_id": "0af83a97-18ef-47f4-9f0c-872633ca3414", + "text": "\n\nFollowing are examples of the identified genes and experimental or GWAS link between these genes and aging.On the list of the 25 top genes, NAP1L4 encodes a member of the nucleosome assembly protein (NAP) family, which interacts with both core and linker histones, and shuttles between the cytoplasm and nucleus, suggesting a role as histone chaperone.Histone protein levels decline during aging, and dramatically affect chromatin structure.Remarkably, the lifespan can be extended by manipulations that reverse the age-dependent changes to chromatin structure, indicating the pivotal role of chromatin structure in aging [32].In another example, gene expression of NAP1L4 increases with age in the skin tissue [33].Findings of GWAS link a number of the identified genes to age-related disorders, such as GAB2 and late onset Alzheimer's disease [86], and QKI and coronary heart disease/myocardial infarction [79].Interestingly, GWAS reports also link QKI to successful aging [87]." + } + ], + "18e216d9-ea5c-4dfe-a30d-632163fcf39e": [ + { + "document_id": "18e216d9-ea5c-4dfe-a30d-632163fcf39e", + "text": "\n\nExamples of biological candidate genes with pleiotropic functions, which are involved in aging in general and in musculoskeletal aging in particular, are numerous: (a) in addition to the IGF-1 and vitamin D genes, estrogen metabolism pathway genes, including estrogen receptors and aromatase (CYP19), are associated with fat-free mass (Walsh et al. 2005) and BMD (Shearman et al. 2004), prostate and breast cancer (Gallicchio et al. 2006), and cardiovascular disease risk (Shearman et al. 2003)." + } + ], + "271236e4-60b1-4fe9-a3cc-11748e3cc718": [ + { + "document_id": "271236e4-60b1-4fe9-a3cc-11748e3cc718", + "text": "\n\nIn-depth analysis of the age-regulated genes revealed that multiple genes in the DNA damage response pathway were upregulated with age including those that function in non-homologous end-joining repair (mre11, rad50, Ku80 and mus308) and in translesion DNA synthesis (mus205 and DNApol-eta) [44][45][46].Genes that encoded enzymes with antioxidant properties, such as the thioredoxin reductase Trxr-1, and antioxidant genes involved in glutamate metabolism, such as GlnRS, isoQC and QC, were also upregulated with age [47][48][49][50].We also observed increased age-associated expression of chaperone genes (Cct1, Cct4, Cct5, Cct6, Hsc70-4) and the unfolded protein response transcription factor Xbp1, consistent with an induction of the unfolded protein response [51][52][53].Under stress conditions, there is a translational switch that favors production of stressrelated proteins while decreasing translation of other proteins [54].Paralogs of canonical translation factors such as NAT1 and Rack1, which were both upregulated, promote this switch to cap-independent translation [55,56].Notably, Rheb, which is downregulated with age, positively regulates ribosome production and capdependent translation by activating the mechanistic target of rapamycin (mTOR) kinase pathway [57].Thus, decreased Rheb levels during aging could decrease mTOR pathway activity, which extends lifespan and is protective against age-related pathology [58].Together, these data suggest that multiple genes are induced in aging photoreceptors to mitigate the effects of oxidative stress, protein misfolding and DNA damage." + } + ], + "3a9e80fc-b20d-4828-aaed-1a6ad490020a": [ + { + "document_id": "3a9e80fc-b20d-4828-aaed-1a6ad490020a", + "text": "CellAge vs human orthologues of longevity-associated model organism genes\n\nTo understand how senescence is linked to the genetics of aging processes, we looked at the intersection of CellAge genes and the 869 genes in the human orthologues of model organisms' longevity-associated genes (LAGs) dataset, collected based on quantitative changes in lifespan [34].Like CellAge, where genes are classified based on whether their upregulation induces, inhibits, or has an unknown impact on CS, the longevity orthologues dataset also provides information on the effect of upregulation of its genes, namely whether it promotes (pro, 421) or inhibits (anti, 448) longevity (Additional file 1: Table S7; Additional file 2: Fig. S2)." + }, + { + "document_id": "3a9e80fc-b20d-4828-aaed-1a6ad490020a", + "text": "\n\nUsing network biology, we implicated the CellAge genes in various processes, particularly cell division and immune system processes.We used network topology to identify potential regulators of CS and bottlenecks that could impact various downstream processes if deregulated.Indeed, we identified 11 genes that have already been shown to contribute towards CS, which will be added to future versions of CellAge.Finally, we experimentally verified 26 genes that induce CS morphology or biomarkers when knocked down in human mammary fibroblasts.Of these, 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) were strong hits in inducing a senescent phenotype." + }, + { + "document_id": "3a9e80fc-b20d-4828-aaed-1a6ad490020a", + "text": "\n\nResults: We develop CellAge (http://genomics.senescence.info/cells),a manually curated database of 279 human genes driving cellular senescence, and perform various integrative analyses.Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes.Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates.We also build cellular senescence protein-protein interaction and co-expression networks.Clusters in the networks are enriched for cell cycle and immunological processes.Network topological parameters also reveal novel potential cellular senescence regulators.Using siRNAs, we observe that all 26 candidates tested induce at least one marker of senescence with 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) decreasing cell number, activating p16/p21, and undergoing morphological changes that resemble cellular senescence.Conclusions: Overall, our work provides a benchmark resource for researchers to study cellular senescence, and our systems biology analyses reveal new insights and gene regulators of cellular senescence." + } + ], + "42cbc297-d57c-4c1f-8d3f-f9e52748b823": [ + { + "document_id": "42cbc297-d57c-4c1f-8d3f-f9e52748b823", + "text": "Genomics-a fundamental basis for understanding skin aging\n\nIn the last decade, genomic tools such as gene chips have been widely developed.This accomplishment has provided us with deeper insights into the molecular events underlying skin aging. 137Gene expression profiling has led to identification of pathways affected by aging, and this information has led to the development of new strategies to enable better skin repair and antiaging benefits. 138ene expression patterns were examined in sun-protected (buttocks) and sun-exposed skin (extensor forearm) from 10 young (age 19 to 20 years) and 10 older women (age 63 to 67 years) to examine gene expression profiles associated with chronologic skin aging and photoaging.Chronologic and photoaging were both associated with downregulation of the biologic process of lipid synthesis.In particular, genes involved in cholesterol and fatty acid synthesis were downregulated, as were genes associated with epidermal differentiation, including keratin filaments and cornified envelope components.An upregulation of the biologic processes of inflammatory response and wound healing, the molecular functions of cytokine activity and protease activity and the cellular component theme of extracellular matrix was also observed in both skin aging types.Elastin gene expression was upregulated with aging only in the photodamaged arm and remained unchanged in the sunprotected buttock.This finding corresponds to the histopathologic findings that show typical elastotic changes, the \"solar elastosis,\" in photoaged skin. 139urther studies conducted to investigate changes in gene expression during skin aging have been performed on naturally aged human foreskin obtained from children and elderly men.Some of the mechanisms proposed to be involved in the induction of aging comprise disturbed lipid metabolism, altered insulin and STAT3 signalling, upregulation of apoptotic genes partly due to the deregulation of FOXO1, downregulation of members of the jun and fos family, differential expression of cytoskeletal proteins (eg, keratin 2A, 6A, and 16A), extracellular matrix components (eg, PI3, S100A2, A7, A9, SPRR2B), and proteins involved in cell-cycle control (eg, CDKs, GOS2). 140Similar results have been presented by a study related to aging of skeletal muscle. 141n a previous study, we proposed that one of the factors significantly involved in the initiation of aging might be the physiologic decline of hormones occurring with age.Human SZ95 sebocytes in vitro treated with hormone levels that can be found in 60 year-old women produce less lipids than sebocytes treated with a hormone mixture representing that found in the serum of 20 year-old women. 6A differential gene expression between SZ95 sebocytes under the 20 and 60 year-old hormone mixture detected differentially expressed genes that are involved in biologic processes such as DNA repair and stability, mitochondrial function, oxidative stress, cell cycle and apoptosis, ubiquitin-induced proteolysis, and transcriptional regulation. 139,140A comparison of these results with data obtained from the aged kidney 142 identified key genes that may be of great importance for global aging.The most significantly altered signalling pathway was that of TGF-β.A disturbed function of this cascade has been also c-Fos, which heterodimerize to form the activator protein 1 (AP-1) complex.AP-1 is a key regulator of skin aging, because it induces the expression of the MMP family and inhibits type I procollagen gene expression through interference with TGF-β signalling pathway.It has been postulated that MAP kinases may be activated by excess production of reactive oxygen species (ROS) that occurs with advanced age and may be superimposed by extrinsic factors such as ultraviolet irradiation.Excess ROS production also leads to accumulation of cellular damage, which includes oxidation of DNA resulting in mutations, oxidation of proteins leading to reduced function, and oxidation of membrane lipids resulting in reduced transport efficiency and altered transmembrane signalling.IL, interleukin; NF-κB, nuclear factor-κB; TGF-β, transforming growth factor-β; TSP-1, thrombospondin-1; TSP-2, thrombospondin-2; VEGF, vascular endothelial growth factor.associated with tumorigenesis, such as in pancreatic, prostate, intestine, breast, and uterine cancer." + } + ], + "555a1533-2905-4d91-a3b6-2fca3679ab02": [ + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nAnalysis of prior research (Online Resource 5) shows that the revealed genes can be explicitly involved in other key biological processes in an organism whose role is known to be changing with aging.Specifically, ten genes (BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, and ZKSCAN1) regulate transcription which is believed to be disrupted when an organism is getting older (Roy et al. 2002).The DBH, TPO, and LSS genes are involved in synthesis of catecholamine, thyroid, and vitamin D hormones, respectively.The GPER binds estrogen and HCRTR2 binds orexin-A and orexin-B neuropeptid hormones.Hormonal deregulation with aging is considered to be one of the major components of senescent processes in an organism (Barzilai and Gabriely 2010).Five genes (ATG2A, NEDD4L, PSMB1, UBXN4, and USP6) are involved in degradation of proteins through ubiquitin-proteasome and the lysosomal/autophagic system.Dysfunction of this system leads to accumulation of damaged proteins in an organism that is associated with aging (Koga et al. 2011).Protein degradation through ubiquitin-mediated proteolysis plays an important role in cell-cycle regulation (Reed 2003).The PSMB1, SIK1, TRIP13, and TTN genes in the revealed set coordinate cell cycle.Cell cycle is linked with the aging-related processes in humans through a gradual increase in cell division errors in all tissues in an organism (Ly et al. 2000).Five genes (EEF1A2, DBH, ITGB2, TUBB2C, and WRN) take part in regulation of apoptosis which plays an important role in the aging process and tumorigenesis (Salvioli et al. 2008).Seven genes (ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, and SOAT2) are involved in lipid metabolism which plays one of the key roles in human longevity and healthy aging (Barzilai et al. 2003)." + } + ], + "5e6ad994-9cad-4b8b-903d-2d5c350e25dc": [ + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nGenes that are age-regulated in all tissues would reveal genes involved in core mechanisms that underlie cellular ageing.Zahn et al. [63] discovered genetic pathways that show common age regulation in human kidney, brain and muscle.They used microarrays to analyse expression in 81 skeletal muscle samples from patients aged 16 -86 years and found 250 age-regulated muscle genes [63].Similar to the ageing expression profile for the kidney, the overall expression behaviour of this set of age-regulated muscle genes correlated with the physiological as well as chronological age of the muscle sample.Next, they compared their muscle-ageing results to previously published data on kidney and brain ageing of similarly large sample size [56,60].Although most of the age-related changes were tissue specific, they found evidence for common age regulation of six genetic pathways in all three tissues.Specifically, there is an overall increase in expression of the extracellular matrix genes, the ribosomal genes, the cell growth genes and the complement activation genes in all three tissues.Increased overall expression of the extracellular matrix and complement activation gene sets with advancing age may contribute to widespread fibrosis and inflammation in the elderly.There is an overall decrease in expression of the chloride transport genes and the electron transport genes in all three tissues.Decreased overall expression of electron transport chain genes with age might support the mitochondrial free-radical theory of ageing [67], as free-radical generation by mitochondria would preferentially damage the electron transport chain protein complexes.Decreased expression of the electron transport genes (encoded in the nucleus) might be caused by feedback regulation from damage to the electron transport chain protein complexes [63].However, it is also possible that increased oxidative damage occurs as a consequence of the decreased expression of the electron transport chain genes.In addition, an increasing number of studies in model organisms have critically challenged the mitochondrial free-radical theory of ageing [68]." + } + ], + "6ac32a33-e2af-40bb-aad6-9971c46023d4": [ + { + "document_id": "6ac32a33-e2af-40bb-aad6-9971c46023d4", + "text": "Discussion\n\nAging studies from model organisms such as yeast, worms, and flies have repeatedly shown that changes in the expression of certain genes have an effect upon longevity.Although similar aging processes are likely to operate across multiple species [30], it has been much more difficult to identify longevity candidate genes in human studies [30].A key question in human aging is to what extent a signature of aging may be detectable across tissues.Until now there has been a lack of large transcriptional profiles from the same human individuals in multiple tissues.The MuTHER study provides insight into the human aging process by interrogating the largest multiple human tissue gene expression resource to identify genes in which expression was affected by chronological age.The analysis of the skin and adipose tissues samples identified several hundred genes responsive to changes in chronological age.However, the 43 shared genes in skin and adipose tissue showed a single common identifiable pathway related to the stress response.From over 1,800 transcripts that have altered expression with age in skin and adipose tissues, 14 also had age-related differential expression in brain.The limited overlap in these two experiments may partly reflect the smaller sample size of the brain expression dataset, the differences in age range between the studies (16 to 83 years for brain samples; 39 to 85 years for MUTHER samples), or the inclusion of males in the brain samples.But it may also imply, as other studies have suggested, that the effects of age on gene transcription are tissue specific [6,31,32].This hypothesis was supported by the comparison with known related aging genes from the GenAge database, which identified an overlap for a small number of aging-related genes with our data.The GenAge database was the result of a meta-analysis using age-related expression profiles from human brain, kidney, and skeletal muscle, and several expression profiles from mouse and rat; no adipose tissue or skin samples were included (Additional file, Table 1 in [7]).The limited overlap between these datasets supports the idea that molecular signatures of aging reflect predominantly a tissue-specific transcriptional response.The lack of age-related genes in transformed LCLs, suggest that the transformation to immortalize a cell line may mask or even remove the age-related signatures in gene expression.The transformation of primary B lymphocytes into LCLs requires infection by the Epstein-Barr virus which has the effect of disrupting the p53 signaling pathway in order to induce growth and survival [33].Joehanes et al. [15] identified only five genes with age-associated expression in LCLs, including p53 itself (TP53).Although the authors attribute the lack of age-affected genes to their small sample size (n=50) and narrow age range, our analysis with a much larger sample size found even fewer age-related changes, suggesting a lack of detectable aging signature in LCLs.The analysis in the subset of fresh lymphocytes suggested an age influence in fresh lymphocytes may potentially be detectable with a larger sample size." + } + ], + "71cc1ce5-d23c-42cf-97b8-bb6110ed8d72": [ + { + "document_id": "71cc1ce5-d23c-42cf-97b8-bb6110ed8d72", + "text": "\n\nGenes Whose Expression Decreased with Age.Of the 26 genes that decreased expression with age in control mice, 23% are involved in DNA replication and the cell cycle (Table 2).Most of these have a negative effect on cell growth and division.Among these, the product of phosphatase and tensin homolog (Pten) gene is a tumor suppressor that induces cell-cycle arrest through inhibition of the phosphoinositide 3-kinase pathway (28).B cell translocation gene 2 (Btg2) is a tumor suppressor that increases expression in response to DNA damage (29).The murine gene product of the amino-terminal enhancer of split (Aes) is a potent corepressor of gene expression and cellular proliferation (30).Calcium-binding protein A11 (S100a10) binds to and regulates the activity of annexin II, which is involved in the transduction of calcium-related mitogenic signals (31).Insulin-like growth factor (IGF) binding protein 1 (Igfbp1) plays an important role in the negative regulation of the IGF-1 system, a stimulator of mitogenesis (32)." + } + ], + "8a8bea99-d3b9-4109-88e4-ad459dcd7173": [ + { + "document_id": "8a8bea99-d3b9-4109-88e4-ad459dcd7173", + "text": "daf-16 dependent genes\n\nAmong the 52 genes that we have tested, 29 genes act almost completely in a daf-16 dependent manner, to regulate lifespan (Table 2).One of the genes identified was daf-2 (Y55D5A_391.b).This serves as a proof of principle that our screen is effective in identification of aging genes." + } + ], + "9fed8fd1-fce5-4fc1-9911-05d312f88521": [ + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "text": "\n\nSeveral of the genes we identify have previously been shown to influence lifespan in experiments on model organisms.For example, knockouts of the orthologs of APOE, LDLR, CDKN2B, and RBM38 in mice shortens their lifespan [24][25][26][27] , while knockout of IGF1R has the opposite effect 28 .Similarly, overexpression of the FOXO3 orthologue in Drosophila melanogaster 29 and the SNCA orthologue in Caenorhabditis elegans 30 have shown to extend their respective lifespans.Many of our genes are also enriched for pathways previously related to ageing in eukaryotic model organisms, including genomic stability, cellular senescence, and nutrient sensing 31 .For example, FOXO3 and IGF1R are well-known players modulating survival in response to dietary restriction 32 , but we also highlight genes involved in the response to DNA damage and apoptosis, such as CDKN2B, USP28, E2F2, and BCL3.In addition to hallmarks discovered in model organisms, our results suggest that haem metabolism may play a role in human ageing.This pathway includes genes involved in processing haem and differentiation of erythroblasts 33 .Although the enrichment is largely driven by genes linked to the LDLR locus, genes linked to other loci of interest (such as FOXO3, CDKN2B, LINC02513) are involved in similar biological pathways: myeloid differentiation, erythrocyte homeostasis, and chemical homeostasis." + } + ], + "adf2d31e-e83d-47df-97af-3764e42aa80e": [ + { + "document_id": "adf2d31e-e83d-47df-97af-3764e42aa80e", + "text": "\n\nHundreds of genes in several pathways act as regulators of ageing (1,32).However, analysis of DrugAge and other HAGR databases has revealed that the overlap between the targets of lifespan-extending drugs and known ageing related genes is modest (31).This indicates that most ageing-related pathways have yet to be targeted pharmacologically; DrugAge may aid in guiding further assays.This was recently demonstrated in one study where machine learning was used to predict whether a compound would increase lifespan in worms using data from Dru-gAge.The best model had 80% prediction accuracy and the top hit compounds could broadly be divided into compounds affecting mitochondria, inflammation, cancer, and gonadotropin-releasing hormone (33)." + } + ], + "b1ffece8-f805-4d99-8e3b-402df309f1ed": [ + { + "document_id": "b1ffece8-f805-4d99-8e3b-402df309f1ed", + "text": "\n\nTop 25genes co-expressed with aging related genes" + }, + { + "document_id": "b1ffece8-f805-4d99-8e3b-402df309f1ed", + "text": "Aging-related gene prediction and putative transcriptional mechanisms\n\nGeneFriends was used to identify genes related to aging.A seed list of genes known to be consistently overexpressed with age in mammals was used [18].In total, 1119 genes were co-expressed with the aging seed list at p <10 -6 ; Table 1 shows the top 25 genes.Many of these genes have been associated with age-related diseases.Several other genes that have been shown to play a role in aging such as lysosomal-associated membrane protein-2 Lamp2 [19] (p = 5.68 -30 ), Fas [20] (p = 2.70 -31 ) and growth hormone receptor Ghr [21] (p = 1.34 -19 ) also showed a significant co-expression.Anxa2, Anxa3 and Anxa4 also show a low p-value (p < 10 -25 ) as well as several S100 calcium binding proteins which have been shown to interact with annexins [22]." + } + ], + "dc322053-2672-4c26-b739-5b58c50476ed": [ + { + "document_id": "dc322053-2672-4c26-b739-5b58c50476ed", + "text": "\n\nGenetic studies have shown that aging can be slowed in mutants that are defective in a wide range of cellular processes (such as mitochondrial function, chromatin regulation, insulin signaling, transcriptional regulation, and genome stability).This indicates that aging is a complex process driven by diverse molecular pathways and biochemical events.As such, a powerful approach to study aging is to use systems biology, which allows a multitude of factors affecting aging to be analyzed in parallel.For example, DNA microarrays and gene expression chips have been used to perform a genome-wide analysis of changes in gene expres-sion in old age.Extensive studies in Caenorhabditis elegans and Drosophila melanogaster have identified hundreds of ageregulated genes (Hill et al. 2000;Zou et al. 2000;Lund et al. 2002;Pletcher et al. 2002;Murphy et al. 2003).Several studies have described age-regulated genes in the muscle and brain of mice (Lee et al. 1999(Lee et al. , 2000) ) and the retina and muscle of humans (Yoshida et al. 2002;Welle et al. 2003Welle et al. , 2004).These age-regulated genes may serve as markers of aging, enabling one to assess physiological age independently of chronological age.Analysis of the functions of these age-regulated genes has identified specific biochemical mechanisms that change toward the end of life." + } + ], + "e562a7f1-f43a-4ca4-bf18-4196276b6170": [ + { + "document_id": "e562a7f1-f43a-4ca4-bf18-4196276b6170", + "text": "\n\nAge-Regulated Genes Involved in Reproductive Capacity.Decline in reproductive capacity is an age-related phenotype, and the reproductive system seems to play an important role in longevity (22).For example, signals from germ cells can affect lifespan in C. elegans (23).In our study, we observed decreased RNA levels for several genes involved in reproduction (Fig. 3).These include two genes that encode members of the Acp family.The Acp from male flies stimulates female egg-laying and facilitates storage of sperm in the female genital tract (24).In addition, two ESTs showing age-regulated decrease of transcript levels represent different genes with homology to Arabidopsis MALE STERIL- In Northern analysis, the ratios were calculated by dividing mRNA levels at 25-, 40-, and 50-day time points by those at 3-day time points after normalization with mRNA levels of the control gene rp49.Ratios in microarray analysis are provided from each of the duplicate experiments for comparison.ITY 2 (MS2; ref. 25), a gene involved in gametogenesis.Furthermore, an EST with homology to peanut, a member of the septin family (26), is down-regulated in older flies.This downregulation may reflect a decrease in spermatogenesis." + } + ] + }, + "data_source": [], + "document_id": "A1B97AFC3BB6C31EBB65B7E046A0A55E", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "daf-16", + "aging", + "genes", + "lifespan", + "daf-2", + "transcription", + "protein°radation", + "apoptosis", + "lipid&metabolism", + "cell&cycle" + ], + "metadata": [], + "question": "which genes are involved in the aging process", + "subquestions": null, + "task_id": "A1B97AFC3BB6C31EBB65B7E046A0A55E", + "usage": { + "chatgpt": 10445, + "gpt-4": 6328, + "gpt-4-turbo-preview": 5402 + }, + "user_id": 2 + }, + "document_id": "A1B97AFC3BB6C31EBB65B7E046A0A55E", + "task_id": "A1B97AFC3BB6C31EBB65B7E046A0A55E" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_01.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_01.json new file mode 100644 index 0000000..5d51bcf --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_01.json @@ -0,0 +1,130 @@ +{ + "created_at": "2024-04-01T22:21:34.595376Z", + "data": { + "amplify": false, + "answer": "1. A study assessing the impact of a rapamycin derivative on healthy aging in people (Mannick et al. 2014).\n2. A veterinary clinical trial testing rapamycin as a treatment for osteosarcoma (Paoloni et al. 2010).\n3. A veterinary clinical trial assessing the side effect profile and effects on age-associated cardiac function following 10 weeks of rapamycin treatment in healthy elderly dogs (Kaeberlein 2015).\n4. A study on the pharmacokinetic analysis of rapamycin treatment in healthy dogs (Larson et al. 2016).\n5. A study on the effects of rapamycin in aging mice (Johnson et al. 2015).\n6. A study on the effects of rapamycin in rodents (Johnson et al. 2013).\n7. A study on the effects of rapamycin on the replicative life of yeast (Reference [57]).\n8. A study on the effects of rapamycin in 20-month-old mice (Reference [58]).\n9. A study on the combination of rapamycin with other compounds for anti-aging activity (Reference [59]).", + "context": { + "00591f6a-0d6f-4993-ae6c-e9a8109a95ec": [ + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "text": "\n\nIntervention trials and cell-based monotherapy" + } + ], + "3043efd1-4b13-4300-b2a7-d1992c8d4e47": [ + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nRapamycin has been shown to be well tolerated in dogs, improves outcome in a glycogen storage disease model (Yi et al. 2014), and is currently being tested in veterinary clinical trials as a treatment for osteosarcoma (Paoloni et al. 2010).A veterinary clinical trial is underway to assess the side effect profile and effects on age-associated cardiac function following 10 weeks of rapamycin treatment in healthy elderly dogs (Kaeberlein 2015)." + }, + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nRapamycin is used clinically to prevent organ transplant rejection, for some forms of cancer, and to prevent restenosis in cardiac stents (Kaeberlein 2013b).Shortterm treatment with the rapamycin derivative RAD001 improves ageassociated decline in immune function, as measured by antibody response to an influenza vaccine, in healthy elderly people (Mannick et al. 2014)." + }, + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nTo date, only one study has been performed assessing the impact of a rapamycin derivative on healthy aging in people.In this trial, it was observed that 6 weeks of treatment with the rapamycin derivative RAD001 (everolimus) was sufficient to enhance function of the aged immune system, as assessed by response to an influenza vaccine (Mannick et al. 2014).This recapitulates what was observed in elderly mice (Chen et al. 2009), and suggests that at least some of the mechanisms by which rapamycin delays aging in mice work similarly in humans.Although both compounds have essentially identical biological activities, RAD001 was used in this study instead of rapamycin because the study was funded by Novartis, who holds the patent rights for RAD001 (rapamycin is now off patent and sold as a generic drug).The doses of RAD001 used in the human immune aging study were lower than those typically used to prevent organ transplant rejection and showed improved side effect profiles, although some adverse effects, including the presence of mouth sores in a subset of the patients, were noted." + }, + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nThis trial is designed to determine whether treatment with the drug rapamycin (see Table 1) can significantly reduce age-related disease and disability as well as mortality in middle-aged large dogs.The initial phase of this trial, which is in progress at the time of this writing, is intended to enroll at least 32 dogs 6 years of age or older and 40 lb in weight or greater.Each animal receives an initial veterinary exam and comprehensive blood work along with a cardiac exam including echocardiography (Fig. 3).Those dogs that do not present with any abnormalities or significant pre-existing health conditions are randomized into either placebo or rapamycin treatment groups for a 10-week treatment period.Initial rapamycin dosing regimens were determined, in part, based on pharmacokinetic analysis of rapamycin treatment in healthy dogs (Larson et al. 2016).After 10 weeks in the study, each dog receives another full exam and blood chemistry panel as well as repeat cardiac exam.The primary goals of this first phase are to establish appropriate dosing of rapamycin in the absence of significant adverse events and to determine whether similar improvements in cardiac function are achieved in aged dogs after 10 weeks of rapamycin treatment, as has been observed in laboratory mice (Dai et al. 2014;Flynn et al. 2013)." + }, + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nFig. 3 Design of the current short-term rapamycin intervention trial.Dogs must weigh at least 40 pounds and be at least 6 years old at time of entry into the study.If no significant pre-existing health conditions are detected at the first exam, dogs are randomized into either placebo or one of the rapamycin treatment groups.Red indicates the 10-week period during which the dogs receive either rapamycin or placebo.Dogs receive the same generic rapamycin (sirolimus) pill that is provided to human patients.Asterisk Serum and feces are collected at each appointment for future metabolomic and microbiome analyses and for quantitation of circulating rapamycin levels" + }, + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nPending the outcome of phase 1, we anticipate enrolling several hundred additional dogs with similar entry criteria into a longer-term, 3-5 year study, to carefully assess the extent to which rapamycin improves health and reduces mortality in middle-age companion dogs.In addition to cardiac function, assessments of multiple age-related phenotypes will be performed including measures of cognitive function, muscle function, kidney function, glucose homeostasis, and cancer incidence.Many of these parameters are beneficially impacted by rapamycin in aging mice (Johnson et al. 2015), and we predict that rapamycin will induce similar improvements in aging dogs." + }, + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nRapamycin is currently the most effective pharmacological intervention for extending lifespan and delaying a broad range of age-related functional declines in rodents (Johnson et al. 2013).However, the doses used clinically to prevent organ transplant rejection are associated with side effects, such as impaired wound healing, edema, elevated circulating triglycerides, impaired glucose homeostasis, gastrointestinal discomfort, and mouth ulcers (Augustine et al. 2007;de Oliveira et al. 2011).These adverse side effects would likely preclude long-term use of rapamycin at these levels in otherwise healthy people.With the possible exception of impaired glucose homeostasis (Lamming et al. 2012), these side effects have not been observed at doses that are associated with increased lifespan and healthspan in mice, however, raising the possibility that lower doses of this drug could promote healthy aging with minimal adverse effects." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Rapamycin\n\nRapamycin is a macrolide isolated from Streptomyces hygroscopicus, a bacteria from Pascua Island (Rapa Nui).It has functions as an antibiotic, an immune suppressant drug, and it is also proposed as a CRM.After the first studies, it was found that rapamycin could induce the extension of the replicative life of yeast through the inhibition of TOR signaling [57].This compound could extend the lifetime useful in 20-month-old mice in correlation with TOR activity [58].These studies were the basis of the research to determine the function of rapamycin as a CRM, due to its modulating properties over proteostasis.In addition, studies suggest that rapamycin can be combined with other compounds (metformin, losartan, statins, propranolol, and aspirin among others) to potentiate their anti-aging activity [59]." + } + ], + "7f23af74-95a3-46aa-bd61-629d2cfc2073": [ + { + "document_id": "7f23af74-95a3-46aa-bd61-629d2cfc2073", + "text": "Rapamycin\n\nRapamycin is a macrolide isolated from Streptomyces hygroscopicus, a bacteria from Pascua Island (Rapa Nui).It has functions as an antibiotic, an immune suppressant drug, and it is also proposed as a CRM.After the first studies, it was found that rapamycin could induce the extension of the replicative life of yeast through the inhibition of TOR signaling [57].This compound could extend the lifetime useful in 20-month-old mice in correlation with TOR activity [58].These studies were the basis of the research to determine the function of rapamycin as a CRM, due to its modulating properties over proteostasis.In addition, studies suggest that rapamycin can be combined with other compounds (metformin, losartan, statins, propranolol, and aspirin among others) to potentiate their anti-aging activity [59]." + } + ], + "7fc7babc-51be-4358-bae4-ca1058c36da7": [ + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "One out of the 25 FDA approved Breast cancer\ndrugs (Gemcitabine), was found in the top 20 drug list from LINCS from breast cancer stage I (dark magenta). As shown in Fig. 12, one drug out of 25 FDA approved Breast cancer drugs, Gemcitabine, was found as\nrepurposed drug from LINCS for breast cancer stage III. Letrozole (Breast cancer drug) has similar structure\n(greater than 60%) with Ruxolitinib (repurposed drug from LINCS) a drug for the treatment of intermediate or\nhigh-risk myelofibrosis (Fig. 13)." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "One out of the 25 FDA approved Breast\ncancer drugs (Palbociclib), was found in the top 20 drug list from LINCS from breast cancer stage II (deep pink). Scientific Reports | 6:20518 | DOI: 10.1038/srep20518\n\n13\nwww.nature.com/scientificreports/\n\nFigure 11. Highlighted target genes that physically interact with genes from the breast cancer stage\nII common network pattern and their corresponding repurposed drugs from LINCS, along with their\nstructurally similar Breast cancer drugs. As shown in Figs 16–17 two target genes (TOP2A and TYMS) are also involved in the Triple Negative pattern." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "Two of them (Gemcitabine and Palbociclib) are included in the list of the 25 known\nFDA-approved Breast cancer therapeutic drugs. We performed a Hypergeometric distribution test in order to\nfind the statistical significance of this drug overlapping. More precisely, LINCS_L1000 database is comprised\nfrom 20,413 chemical reagents. Twenty two out of twenty five breast cancer drugs are also included in LINCS\ndatabase. Finally, from the 105 drugs that were found from our analysis, the probability of finding two drugs to\noverlap with the Breast Cancer drugs in LINCS is 0.005471157, pointing out that there is statistical significance\nin their selection." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "Two from the 25 FDA\napproved Breast cancer drugs (Gemcitabine and Palbociclib), was found in the top 20 drug list from LINCS\nfrom Luminal A breast cancer (dark magenta and deep pink respectively)." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "18 two drugs out of 25 FDA approved Breast cancer drugs – Gemcitabine and Palbociclib –\nwere also found as repurposed drugs from LINCS for breast cancer Luminal A (Fig. 18). Two genes from the\nLuminal A network pattern physically interact with four genes that involved in Histone deacetylases class\n(HDAC1, HDAC2, HDAC3 and HDAC8), which are target genes of Vorinostat (repurposed drug from LINCS). Vorinostat is a member of a larger class of compounds that inhibit histone deacetylases (HDAC) and it is used\nto treat cutaneous T cell lymphoma (CTCL)." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "One out of the 25 FDA\napproved Breast cancer drugs (Gemcitabine), was found in the top 20 drug list from LINCS from breast cancer\nstage III (dark magenta). that was found from the drug repurposing analysis of HER2 pattern. It has similar structure - 75% with\nWZ-4002 repurposed drug, which is a novel mutant-selective inhibitor of EGFR. Finally, both Palbociclib\nand WZ-4002 are structurally similar to Dasatinib (more than 60%), which is a cancer drug used to treat\nacute lymphoblastic leukemia." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "Network pattern for each breast cancer subtype and the common interactions across Luminal A\nand Luminal B. As shown in Fig. 8, one drug out of 25 FDA approved Breast cancer drugs, Gemcitabine, was proposed as\nrepurposed drug by the LINCS for breast cancer stage I. Furthermore, Gemcitabine is quite similar (tanimoto31\nsimilarity greater than 80%) with Clofarabine and Kinetin-riboside (repurposed drugs from LINCS). Clofarabine\nis also an anti-cancer, antineoplastic chemotherapy drug and is classified as an antimetabolite." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "Hierarchical clustering using tanimoto similarity (Soergel\ndistance) was applied to each of the top 20 drug list from LINCS and the 25 known FDA-approved Breast cancer\ntherapeutic drugs (Supplementary Figs 54–61). LINCS Drug Names were transformed into ChemSpider IDs (see\nSupplementary Table 1)\nIn synopsis, the unique drugs for the breast cancer stages were 63 and for the breast cancer subtypes 58, as we\nhave located common drugs across them. Taking their union and removing the duplicates we conclude to a total\nof 105 repurposed drugs." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "13, is also\nstructurally similar (greater than 60%) with 6-(1,3-Benzodioxol-5-yl)-N-(cyclopentylmethyl)-4-quinazolinamine\n(repurposed small molecule from LINCS). As in breast cancer stages I and III one drug out of 25 FDA approved Breast cancer drugs – Gemcitabine – was\nfound as repurposed drug from LINCS for breast cancer stage IV (Fig. 14). A repurposed drug from LINCS –\nHomoharringtonine was found to be structurally similar with Everolimus and Vinblastine Breast cancer drugs\n(greater than 70%). On the other hand, as shown in Fig." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nRapamycin has serious side effects, particularly as an immunosuppressor, and thus it is not suitable as an antiaging drug.As in sirtuins, however, these studies highlight the road from basic discovery on the biology of aging to antiaging interventions.Further studies of the TOR pathway and of repressors more specific of its downstream signaling pathway are ongoing.Whether rapamycin produces a change in another parameter related to energy uptake or utilization is unknown, and determining which of its effects modulate lifespan is an important unsolved question.Like resveratrol, TOR has attracted considerable attention from the pharmaceutical industry, particularly in the context of cancer (Meric-Bernstam and Gonzalez-Angulo, 2009)." + } + ] + }, + "data_source": [], + "document_id": "8444FD3208D16364524ABE2CCAA9544D", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "rapamycin", + "dogs", + "osteosarcoma", + "cardiac&function", + "aging", + "immune&system", + "influenza&vaccine", + "lifespan", + "cancer", + "breast&cancer" + ], + "metadata": [], + "question": "List as many studies are you can that include rapamycin", + "subquestions": null, + "task_id": "8444FD3208D16364524ABE2CCAA9544D", + "usage": { + "chatgpt": 7060, + "gpt-4": 3910, + "gpt-4-turbo-preview": 2966 + }, + "user_id": 2 + }, + "document_id": "8444FD3208D16364524ABE2CCAA9544D", + "task_id": "8444FD3208D16364524ABE2CCAA9544D" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_02.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_02.json new file mode 100644 index 0000000..e80f554 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_02.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-04-02T09:32:25.786728Z", + "data": { + "amplify": false, + "answer": "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and their interactions with each other and the environment. Secondly, the effects of common genetic variations on longevity are small, requiring large studies for identification. Thirdly, human studies often face issues such as environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of social, economic, and cultural factors, which are unique to humans, can't be replicated in traditional model organisms, adding to the complexity.", + "context": { + "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7": [ + { + "document_id": "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7", + "text": "FUTURE DIRECTIONS: HIGHER RESOLUTION DATA VIA HIGHER THROUGHPUT ASSAYS\n\nOne inescapable conclusion of the aggregate results of genome-wide studies of aging to date (see summary Table 1) is that we have not come close to saturating the number of potentially lifespan-altering genes in any organism.This is in no small part because directly generating survival curves is a relatively time-consuming process in most model organisms using current methods.There are several possible ways to address this.One way that has been tried is by attempting to find surrogate phenotypes [72,73,126] that can be screened more rapidly, or even scored under selection.Another is mining candidates from the many whole-genome expression profiles.Results to date with these have been very fruitful, but have not suggested that these methods alone will rapidly saturate our search for lifespan-and healthspan-altering genes in tractable model organisms." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + } + ], + "4a27da1c-b184-47e8-bef2-de6435d7c3f5": [ + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + } + ], + "4ca8d070-8b58-4bd5-86be-127089b70324": [ + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "text": "\n\nThe aging process most certainly is under highly polygenic controls… This should not discourage us from pursuing a search for those loci which may be of profound importance to human aging as it ordinarily occurs in most human beings." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "606c59c5-5ae4-47e9-b3eb-58afa55669d1": [ + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + } + ], + "690a2ae6-962a-438c-91ca-60425a0c8d02": [ + { + "document_id": "690a2ae6-962a-438c-91ca-60425a0c8d02", + "text": "Accepted Article\n\n© 2013 The Authors Aging Cell © 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland over 90 years and 1,955 controls between 55 and 80 years did not reveal genome-wide significant loci (Newman et al., 2010) and neither did the analyses of all-cause mortality and survival free of major disease in this cohort (Walter et al., 2011).A smaller Dutch study of 403 nonagenarians and 1,670 controls younger than 65 years identified the APOE gene as a mortality locus (Deelen et al., 2011), which was confirmed in a German study of 763 long-lived individuals and 1,085 younger controls (Nebel et al., 2011) and a longitudinal study of 1,606 Danes showed that the effect size of this association increases at the highest ages (Jacobsen et al., 2010).Apparently, the influence of the common genetic variation on longevity is small which requires large meta-GWA studies for identification.Alternatively, rare genetic variants may play a more important role in longevity.Since the previous linkage studies showed contradictory results potentially due to heterogeneity in the longevity phenotype, it is expected that longevity is influenced by many private rare variants." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nSeveral explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nSecond, the largely negative findings of this and other studies contrast with the intriguing animal studies of longevity.Very large effects of single genes on lifespan have indeed been observed in laboratory animals, but humans often have several homologues of these genes which might significantly differ in function or compensate for mutated genes through redundant mechanisms (Kuningas et al., 2008).This could explain why our top findings did not include genes in these pathways found in animal models.Animal models also represent genetically homogenous populations and are exposed to controlled environmental influences.The lack of replication of animal model findings in humans suggests that the use of knockout animals may not provide the optimal approach to understanding the variation in survival in humans as interactions with environmental factors may obscure the associations and prevent the identification of loci in humans." + } + ], + "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed": [ + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nThe remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha ˜es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nResults from mutational analysis across eukaryote model organisms have shown unexpected conservation of genes and processes regulating aging.While unique properties exist within particular organisms that modulate these foundational networks, the conservation provides a tool to refine human genetic studies.As noted, GWAS for human longevity metrics suffer from large sample size requirements to obtain statistical resolution due to multiple hypothesis testing across the genome.Assuming that evolutionary genesets for longevity could be generated with confidence, an intersection of them with human variation data would increase the sensitivity of association studies.This would serve as a selective filter to refine the number of loci investigated for association in human populations.Similarly, such evolutionary filters could refine analysis of rare, unique variation within genome sequence data from extremely long-lived cohorts.A similar approach to refine human longevity GWAS used an intersection with age-related disease datasets.This 'disease-informed' GWAS helped refine candidates (iGWAS, Fortney et al., 2015), though, it should be noted that this particular strategy would further blur the distinction between aging and longevity as discussed above.The definition of gene sets from evolutionary experiments in longevity, across clades, would similarly empower detection of networks previously hidden under GWAS in human population analyses (Figure 3)." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "TRANSLATION OF LONGEVITY MODEL ORGANISMS AND CORE AGING PATHWAYS\n\nGenetic studies on lifespan have proven to be challenging.While longevity is a defining trait for a given species, the lifespan of individuals is of limited heritability, making analyses more difficult.Exceptional human life span, although a rare phenotype, is likely multifactorial; refined analyses are required to obtain statistically robust genomic signatures of longevity (Zhang et al., 2020) and these have proven elusive.Unlike laboratory models, the effect of environmental variance cannot be controlled in human studies, potentially masking purely biological aging mechanisms.Even laboratory models cannot replicate the complex \"environment\" of humans; it includes psychosocial, economic, and cultural factors, rather than strictly biological.These human-specific confounders are difficult or impossible to target in traditional model organisms.Despite these limitations, experimentally tractable model organisms have proven invaluable in deciphering the purely genetic contribution to lifespan, including genes and pathways conserved across the tree of life." + } + ], + "c7361625-831a-44a2-b04d-157a49d00c6a": [ + { + "document_id": "c7361625-831a-44a2-b04d-157a49d00c6a", + "text": "\n\nOur analyses show that it is extremely unlikely that there is a single gene harboring rare protein-altering variants shared by all supercentenarians but no controls.It is not surprising that a highly complex trait such as longevity is not explained by a single Mendelian gene." + } + ], + "efd5747f-9e8b-45e8-9e04-bb31131d44fa": [ + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "text": "\n\nWith modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "\n\nAlthough the models data set comprises all genes (to our knowledge) shown by the time of the latest update to statistically increase longevity or alter the aging process in a noticeable way, in the human data set we try to evaluate whether a given intervention is affecting the aging process itself or not.For example, many mutations may increase longevity by decreasing the incidence of specific diseases, rather than by altering the basic process of aging (de Magalhães et al ., 2005a(de Magalhães et al ., , 2005b)).Therefore, the human data set is not merely an extension of the work conducted in model organisms and of its bibliography, but a manually selected list of the most pertinent human aging candidate genes, each presented with a higher annotation level.We cite studies on whether the functions of aging-associated genes in model organisms are conserved in their human orthologues.Likewise, we cite flaws in previous studies based on new published observations, although we have a neutral stance on conflicting findings from different research groups.Our policy is to cite all conflicting reports and let visitors make their own decisions on how to interpret them.By contrast, each entry in GenAge model organisms has only one reference: the first publication reporting an association of the gene with longevity or aging.Moreover, one of the latest enhancements in the human data set was the inclusion of Gene Ontology annotation.Gene Ontology terms and annotation files were obtained from the Gene Ontology Consortium website (http://www.geneontology.org/ ) and provide an additional layer of description for the gene products in a cellular context (Ashburner et al ., 2000)." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "Conclusions and prospects\n\nOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + } + ] + }, + "data_source": [], + "document_id": "2B64FFA2A5559907DDEC720E3CC23553", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "GWAS", + "longevity", + "aging", + "human", + "gene", + "variants", + "lifespan", + "genome" + ], + "metadata": [], + "question": "Why is it so difficult to map gene loci that control aging in humans?", + "subquestions": null, + "task_id": "2B64FFA2A5559907DDEC720E3CC23553", + "usage": { + "chatgpt": 8006, + "gpt-4": 4826, + "gpt-4-turbo-preview": 3875 + }, + "user_id": 2 + }, + "document_id": "2B64FFA2A5559907DDEC720E3CC23553", + "task_id": "2B64FFA2A5559907DDEC720E3CC23553" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_03.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_03.json new file mode 100644 index 0000000..619e6ee --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_03.json @@ -0,0 +1,146 @@ +{ + "created_at": "2024-04-02T11:58:00.471434Z", + "data": { + "amplify": false, + "answer": "One would evaluate a candidate gene for mouse lifespan by conducting gene mapping methods and using mouse life span as a surrogate for aging. This involves studying the genetic contributions to mortality rates, conducting sex-specific analyses, and determining any changes in pathology associated with the loci. The study would involve a large population of mice, and the results would be compared to Mendelian expectations. Additionally, epigenetic predictors and differential methylation analysis could be used to assess the relationship between the gene and lifespan. The gene's influence on specific late-life diseases and its effect on the risk of these diseases would also be evaluated.", + "context": { + "0c6c0977-2cf9-4bbf-bc31-fe025f008089": [ + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "text": "Funding: See page 22\nPreprinted: 24 June 2021\nReceived: 03 November 2021\nAccepted: 01 April 2022\nPublished: 07 April 2022\nReviewing Editor: Joris Deelen,\nMax Planck Institute for Biology\nof Ageing, Germany\n Copyright Mozhui et al. This\narticle is distributed under the\nterms of the Creative Commons\nAttribution License, which\npermits unrestricted use and\nredistribution provided that the\noriginal author and source are\ncredited. Editor's evaluation\nThis article used three newly generated epigenetic predictors to test how they differ between\ngenetically diverse mice from the BXD family (by looking at metabolic traits and lifespan)." + }, + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "text": "Longevity data\nwas obtained from a parallel cohort of BXD mice housed in the same UTHSC colony, and members\nof this ‘longevity cohort’ were allowed to age until natural death (more detail on the longevity cohort\ncan be found in Roy et al. , 2021). Males were excluded and strain-by-diet lifespan summary statistics\nwere derived. Only strain-by-diet groups with five or more observations for lifespan were included in\nthe correlational analyses with the epigenetic predictors. Multivariable EWAS\nSite-by-site differential methylation analysis (EWAS) was performed on the 27,966 CpGs using a\nmultivariable regression model." + } + ], + "2464a084-1a11-44eb-8bce-4b344de049ff": [ + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "text": "Funding: See page 22\nPreprinted: 24 June 2021\nReceived: 03 November 2021\nAccepted: 01 April 2022\nPublished: 07 April 2022\nReviewing Editor: Joris Deelen,\nMax Planck Institute for Biology\nof Ageing, Germany\n Copyright Mozhui et al. This\narticle is distributed under the\nterms of the Creative Commons\nAttribution License, which\npermits unrestricted use and\nredistribution provided that the\noriginal author and source are\ncredited. Editor's evaluation\nThis article used three newly generated epigenetic predictors to test how they differ between\ngenetically diverse mice from the BXD family (by looking at metabolic traits and lifespan)." + }, + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "text": "Longevity data\nwas obtained from a parallel cohort of BXD mice housed in the same UTHSC colony, and members\nof this ‘longevity cohort’ were allowed to age until natural death (more detail on the longevity cohort\ncan be found in Roy et al. , 2021). Males were excluded and strain-by-diet lifespan summary statistics\nwere derived. Only strain-by-diet groups with five or more observations for lifespan were included in\nthe correlational analyses with the epigenetic predictors. Multivariable EWAS\nSite-by-site differential methylation analysis (EWAS) was performed on the 27,966 CpGs using a\nmultivariable regression model." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text":"Conclusions These results suggest a novel locus influencing survival in the B6/D2 genetic background, perhaps\nvia a metabolic disorder that emerges by 200 days of age in\nmale animals. Keywords\nPathology\n\nLongevity ␁ Lifespan ␁ Mouse ␁ Linkage ␁\n\nIntroduction\nLongevity, the quintessential complex trait, likely reflects\nall aspects of an organism’s life history. In humans, the\nestimated heritability of age at death is estimated at\n25–33 % [1]. Genetic contributions to mortality rates are\nthus of great interest and may aid in the understanding of\ndisease etiology and the process of aging itself [2]." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Leduc MS, Hageman RS, Meng Q et al (2010) Identification of\ngenetic determinants of IGF-1 levels and longevity among mouse\ninbred strains. Aging Cell 9(5):823–836. doi:10.1111/j.14749726.2010.00612.x\n10. Lang DH, Gerhard GS, Griffith JW et al (2010) Quantitative trait\nloci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin Exp Res 22(1):8–19\n11. Gelman R, Watson A, Bronson R et al (1988) Murine chromosomal\nregions\ncorrelated\nwith\nlongevity. Genetics\n118(4):693–704\n12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci\nassociated with life span exhibit sex-specific and epistatic effects." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Here, we have extended this analysis to search for\ngenotypes related to survival to the age of 800 days in a\npopulation of a reciprocal F2 cross between (B6) and (D2)\nmice. Since QTL for longevity in mice have shown strong\nsex specificity [10, 12], we conducted sex-specific analyses. In addition, we also determined whether there were\nany change in pathology changes associated with the loci\nthat showed frequency distortions with aging. To confirm\nthe associations of the loci of interest with longevity and\npathology, we performed replication analyses on a panel of\nBXD recombinant inbred strains." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Methods We examined a population of 1200 mice that\nwere F2 generation offspring of a 4-way reciprocal cross\nbetween C57BL6/J and DBA2/J strains. Animals were\nsacrificed at age 200, 500, or 800 days and genotyped at 96\nmarkers. The 800 days old cohort, which were the survivors of a much larger breeding group, were examined for\nenriched frequency of alleles that benefit survival and depletion of alleles that reduce survival. Results Loci on Chr 13 in males and on Chr X in females\nwere significantly distorted from Mendelian expectations,\neven after conservative correction for multiple testing." + } + ], + "4851405f-bb2b-4406-a218-ffe408d257f8": [ + { + "document_id": "4851405f-bb2b-4406-a218-ffe408d257f8", + "text": "Assessing epigenetic age in long-lived mice\n\nThe epigenetic-aging model was applied to the methylation profiles of long-lived mice and the age-matched controls not used for training (Additional file 2: Datasets used summary).Reductions in age were calculated by subtracting the epigenetic ages of the untreated, wild-type mice from those of the treated mice of the same genetic background.To assess the significance, we used an ANOVA for all 22-month-old mice or only 22-month-old UM-HET3 mice.We also compared the epigenetic ages between treatments with their agematched controls from the same genetic background using a t-test (Additional file 4: Treatment vs wild type stats)." + } + ], + "5b2055ca-65c0-49a5-a442-e4ea8d5e8efb": [ + { + "document_id": "5b2055ca-65c0-49a5-a442-e4ea8d5e8efb", + "text": "Editor's evaluation\n\nThis article used three newly generated epigenetic predictors to test how they differ between genetically diverse mice from the BXD family (by looking at metabolic traits and lifespan).The authors subsequently identified several quantitative trait loci for the different predictors, using linkage analysis, and performed transcriptome and proteome analyses of liver and adipose tissue.The described results provide some important new insights on the underlying biology of epigenetic mouse aging and may be used to inform future studies in other model organisms and humans focused on studying the relationship between epigenetic aging and metabolism." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "352(6291): p. aad0189. Liao, C.Y. , et al. , Genetic variation in the murine lifespan response to dietary restriction: from life extension to life\nshortening. Aging Cell, 2010. 9(1): p. 92-5. Johnson, M., Laboratory Mice and Rats. Mater. Methods, 2012. 2: p. 113. Fontaine, D.A. and D.B. Davis, Attention to Background Strain Is Essential for Metabolic Research: C57BL/6 and\nthe International Knockout Mouse Consortium. Diabetes, 2016. 65(1): p. 25-33. Simon, M.M. , et al. , A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol, 2013. 14(7): p. R82. Lilue, J., et al." + } + ], + "71cc1ce5-d23c-42cf-97b8-bb6110ed8d72": [ + { + "document_id": "71cc1ce5-d23c-42cf-97b8-bb6110ed8d72", + "text": "Materials and Methods\n\nStudy Design.Female mice of the long-lived F 1 hybrid strain C3B10RF1 were fed and maintained as described (7).Briefly, mice were weaned at 28 days, individually housed, given free access to water, and randomly assigned to study groups.Comparisons between five groups of mice were used to determine the effects of aging and CR on gene expression.Control young (7-month-old; n ϭ 3) and old (27-month-old; n ϭ 3) mice were fed 95 kcal of a semipurified control diet (Harlan Teklad, Madison, WI; no.TD94145) per week after weaning.Long-term CR (LT-CR) young (7-month-old; n ϭ 3) and old (27-month-old; n ϭ 3) mice were fed 53 kcal of a semipurified CR diet (Harlan Teklad; no.TD94146) per week after weaning.Short-term CR (ST-CR) mice were 34-monthold control mice that were switched to 80 kcal of CR diet for 2 weeks, followed by 53 kcal for 2 weeks (n ϭ 3).The effects of age on gene expression in control mice were determined by comparison between results from the young control and the old control groups.The effects of LT-CR on gene expression were determined by comparison between results from the young control and the young LT-CR groups, and from the old control and the old LT-CR groups.The effects of ST-CR were determined by comparison between results from the old control and the ST-CR groups.Mice were fasted for 48 h before killing.Mice were killed by cervical dislocation, and the livers were rapidly excised and flash frozen in liquid nitrogen.No signs of pathology were detected in any of the animals used.All animal use protocols were approved by the institutional animal use committee of the University of California, Riverside." + } + ], + "75813bc2-f0b5-400c-92d7-0958df97a04f": [ + { + "document_id": "75813bc2-f0b5-400c-92d7-0958df97a04f", + "text": "Accessing data resources in the mouse\nphenome database for genetic analysis of murine life span and health span. J.\nGerontol. A Biol. Sci. Med. Sci. 71 (2), 170–177. Brown, R.E. , Stanford, L., Schellinck, H.M., 2000. Developing standardized behavioral\ntests for knockout and mutant mice. ILAR J. 41 (3), 163–174. Bubier, J.A. , Jay, J.J., Baker, C.L. , Bergeson, S.E. , Ohno, H., Metten, P., Crabbe, J.C.,\nChesler, E.J. , 2014. Identification of a QTL in Mus musculus for alcohol preference,\nwithdrawal, and Ap3m2 expression using integrative functional genomics and precision genetics. Genetics 197 (4), 1377–1393. Burn, C.C. , 2008." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nOur own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nThe available dataset also provides examples in which genetic variants seem to influence the risk of specific late-life diseases.Figure 8-6, for example, shows longevity results for mice stratified by their inheritance at the 12th chromosome locus D12Mit167.This is a locus associated with differential longevity in both male and female mice, with the strongest effect (adjusted p < 0.01) seen in those mice living more than 657 days (Jackson et al., unpublished results).The longest-lived mice are those that inherit both the C57BL/6 allele from their mother and the C3H allele from their father; on average, they survive 93 days longer than siblings with the BALB plus C3H combination.Figure 8-6 shows that the D12Mit167, like the pair of loci illustrated in Figure 8-5, has significant and similar effects in mice dying of cancer (85 days) and in mice dying of non-neoplastic diseases (126 days).A more detailed analysis of the cancers, however, suggests that while lymphoma and hepatoma victims are equally protected by the favorable alleles (effect sizes of 93 and 167 days, respec- mice of two subgroups: those dying of the urinary syndrome MUS, and those dying of all other causes.The genetic analysis contrasts mice with both the C57BL/6 allele at D4Mit84 and the C3H allele at D9Mit110 to mice with any of the three other allele combinations.In the males dying of causes other than MUS, this allele pair is associated with a 170-day increment in longevity (post-hoc p < 0.00003).But for males that do die of MUS, the same allele combination is associated with a 187-day decline in mean life span (post-hoc p < 0.03).This effect is thus pleiotropic, in that these alleles accelerate death in mice susceptible to MUS, while postponing death for all other males in the population.Although these loci are associated with differential longevity in mice that do develop MUS, they do not have a significant effect on the chances that MUS will indeed occur (not shown).The risk of developing MUS seems to be under control of a separate locus on chromosome 6.As shown in the bottom panel of Figure 8-7, males that inherit the C3H allele at D6Mit268 are far more likely to develop MUS (28 percent risk) than are their brothers who receive the DBA/2 allele at this locus (7 percent risk; p = 0.012 by two-tailed Fisher's exact test)." + } + ], + "ce270796-8098-48e6-afe2-ad285a75bce2": [ + { + "document_id": "ce270796-8098-48e6-afe2-ad285a75bce2", + "text": "Previously, the methylation status of CpG sites within the genes Prima1, Hsf4,\nKcns1 was shown to qualify as a reliable predictor of\nchronological age of B6 mice.10 This same study also\nrevealed enhanced epigenetic aging of the D2 strain in\naccordance with its general reduced mean life span, supporting the possibility that the panel might also serve as a\nmarker for the biological age in mice. Applying this B6trained marker panel to our (congenic) experimental\nstrains, we observed that epigenetic age predictions correlated with chronological age in B6 (R2=0.93) and line A\nmice (R2=0.89)." + } + ], + "ce2c68bf-878d-460c-8d9b-d45ce3034ef7": [ + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "text": "34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with\nlongevity. Genetics 118, 693–704 (1988). [PubMed: 3163317]\n35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011). 36. Houtkooper RHet al.Mitonuclear protein imbalance as a conserved longevity mechanism. Nature497, 451–457 (2013). [PubMed: 23698443]\n37. Williams EGet al.An Evolutionarily conserved role for the aryl hydrocarbon receptor in the\nregulation of movement. PLOS Genet. 10, e1004673 (2014). [PubMed: 25255223]\n38. Lang DHet al.Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin. Exp. Res. 22, 8–19 (2010)." + } + ], + "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748": [ + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "For females, hairs of the congenic mice grew 31% faster, also highly significant (P =\n0.0006, 1-tailed). These results validated the presence of a gene in the differential region\naffecting FE. Discussion\nWe report the outcomes of a quantitative genetic study on aging and longevity in the mouse. We studied an extant series of recombinant inbred strains (ILSXISS) that have been used\nboth in DR aging studies as well as to study alcohol sensitivity (Williams et al. , 2004)." + } + ], + "efd5747f-9e8b-45e8-9e04-bb31131d44fa": [ + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "text": "FOURTH STEP: MEDICAL TESTING OF CANDIDATE DRUGS\n\nMany genes are common between fruit flies and mammals, but by no means all.Therefore, it is important to test biochemical pathways that work in fruit flies with mammals.Mice are the system of choice, as they have relatively short lifespans (2 -3 years) and a great deal is known of their genetics.Mortality rate measurements, like those studied in fruit flies, [10] might speed up mouse trials to just 6-12 months.Mouse trials would also help address issues of safety, such as liver and kidney toxicity, before going on to human trials." + } + ], + "f377428d-564d-4d5b-b86c-515b9325134f": [ + { + "document_id": "f377428d-564d-4d5b-b86c-515b9325134f", + "text": "Experimental Procedures\n\nMouse Breeding, Maintenance, and Longevity.Cdc42GAP Ϫ/Ϫ and p53 Ϫ/Ϫ mice were generated as previously described (6,35), and the mice used in the studies were mixed C57BL/6 ϩ/Ϫ 129/Sv inbred.Littermates of different genotypes were housed and fed freely with standard mouse chow over their life span in a pathogen-free environment and were monitored for vitality and longevity.Mice exhibiting extreme morbidity were euthanized and subjected to necropsy.All animal procedures were approved by the Institutional Animal Care and Use Committee at the Children's Hospital Research Foundation." + } + ] + }, + "data_source": [], + "document_id": "7163C22AFA8EE292590331B6E6B7B173", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "C57BL6/J&DBA2/J", + "F2&generation", + "mice", + "lifespan", + "longevity", + "genetic", + "alleles", + "chromosome&13&males", + "chromosome&X&females", + "Mendelian&expectations" + ], + "metadata": [], + "question": "How would one evaluate a candidate gene for mouse lifespan?", + "subquestions": null, + "task_id": "7163C22AFA8EE292590331B6E6B7B173", + "usage": { + "chatgpt": 7619, + "gpt-4": 5028, + "gpt-4-turbo-preview": 4003 + }, + "user_id": 2 + }, + "document_id": "7163C22AFA8EE292590331B6E6B7B173", + "task_id": "7163C22AFA8EE292590331B6E6B7B173" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_04.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_04.json new file mode 100644 index 0000000..a0ea882 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_04.json @@ -0,0 +1,154 @@ +{ + "created_at": "2024-04-02T12:00:22.254831Z", + "data": { + "amplify": false, + "answer": "Yes, significant quantitative trait loci (QTLs) have been found in aging studies. Three examples include:\n\n1. Lfdr1: This QTL was found on chromosome 7 and affects lifespan and fertility in response to dietary restriction. It also has suggestive effects on fuel efficiency (FE).\n\n2. Fedr1 and Fedr2: These QTLs were identified on chromosomes 9 and 15 respectively. They have significant effects on fuel efficiency in response to dietary restriction.\n\n3. QTLs associated with age-related thymic involution: These were identified in a study using C57BL/6 J X DBA/2 J (BXD) recombinant inbred (RI) strains of mice.", + "context": { + "0c6c0977-2cf9-4bbf-bc31-fe025f008089": [ + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "text": "Genetic associations for two biological age measures point\nto distinct aging phenotypes. Aging Cell 20:e13376. DOI: https://doi.org/10.1111/acel.13376, PMID:\n34038024\nLang DH, Gerhard GS, Griffith JW, Vogler GP, Vandenbergh DJ, Blizard DA, Stout JT, Lakoski JM, McClearn GE. 2010. Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clinical and Experimental Research 22:8–19. DOI: https://doi.org/10.1007/BF03324809, PMID:\n20305363\nLappalainen T. 2015. Functional genomics bridges the gap between quantitative genetics and molecular\nbiology. Genome Research 25:1427–1431." + } + ], + "1fb6e4db-79c1-49c9-a358-3414f6a674da": [ + { + "document_id": "1fb6e4db-79c1-49c9-a358-3414f6a674da", + "text": "Pharmacol Biochem Behav 81, 764–768. Hsu, H.C., Lu, L., Yi, N., Van Zant, G., Williams, R.W. & Mountz, J.D. (2007) Quantitative trait locus (QTL) mapping in aging systems. Methods Mol Biol 371, 321–348. Hurlin, P.J. & Huang, J. (2006) The MAX-interacting transcription\nfactor network. Semin Cancer Biol 16, 265–274. Jones, B.C. , Tarantino, L.M. , Rodriguez, L.A., Reed, C.L. , McClearn,\nG.E. , Plomin, R. & Erwin, V.G. (1999) Quantitative-trait loci analysis\nof cocaine-related behaviours and neurochemistry. Pharmacogenetics 9, 607–617. Jones, B.C. , Beard, J.L. , Gibson, J.N. , Unger, E.L., Allen, R.P. ,\nMcCarthy, K.A. & Earley, C.J." + } + ], + "2464a084-1a11-44eb-8bce-4b344de049ff": [ + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "text": "Genetic associations for two biological age measures point\nto distinct aging phenotypes. Aging Cell 20:e13376. DOI: https://doi.org/10.1111/acel.13376, PMID:\n34038024\nLang DH, Gerhard GS, Griffith JW, Vogler GP, Vandenbergh DJ, Blizard DA, Stout JT, Lakoski JM, McClearn GE. 2010. Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clinical and Experimental Research 22:8–19. DOI: https://doi.org/10.1007/BF03324809, PMID:\n20305363\nLappalainen T. 2015. Functional genomics bridges the gap between quantitative genetics and molecular\nbiology. Genome Research 25:1427–1431." + } + ], + "47c12133-5a30-45b9-bcb8-b96f00737f31": [ + { + "document_id": "47c12133-5a30-45b9-bcb8-b96f00737f31", + "text": "Interestingly, the correlation analysis indicates\nQTL Mapping in Aging Systems\n\n333\n\nFig. 5. Basic statistics provided by the WebQTL GeneNetwork website. The strain\ndistribution pattern (SDP) of the quantitative trait is presented in the basic statistics page\nof WebQTL in the following ways: (A) the raw data of the quantitative trait obtained\nfrom each BXD recombinant inbred (RI) strain, (B) data mean and distribution, (C) bar\ngraph showing the mean and variable of each strain, and (D) the normal probability plot\nof the SDP." + }, + { + "document_id": "47c12133-5a30-45b9-bcb8-b96f00737f31", + "text": "23\nQuantitative Trait Locus (QTL) Mapping in Aging\nSystems\nHui-Chen Hsu, Lu Lu, Nengjun Yi, Gary Van Zant, Robert W. Williams,\nand John D. Mountz\nSummary\nUnderstanding the genetic basis of the effects of aging on the decline in the immune\nresponse is an enormous undertaking. The most prominent age-related change in the\nimmune system is thymic involution. This chapter will focus on the use of C57BL/6 J X\nDBA/2 J (BXD) recombinant inbred (RI) strains of mice to map genetic loci associated\nwith age-related thymic involution in mice." + } + ], + "5b2055ca-65c0-49a5-a442-e4ea8d5e8efb": [ + { + "document_id": "5b2055ca-65c0-49a5-a442-e4ea8d5e8efb", + "text": "\n\nFor further prioritization, we converted the mouse QTL regions to the corresponding syntenic regions in the human genome and retrieved GWAS annotations for these intervals (Buniello et al., 2019).We specifically searched for the traits: epigenetic aging, longevity, age of menarche/menopause/puberty, Alzheimer's disease, and age-related cognitive decline and dementia.This highlighted five genes in Eaa11 and three genes in Eaa19 (Supplementary file 4c).We also identified a GWAS that found associations between variants near Myof-Cyp26a1 and human longevity (Yashin et al., 2018), and a meta-GWAS that found gene-level associations between Nkx2-3 and Cutc, and epigenetic aging (Supplementary file 4c; McCartney et al., 2021)." + } + ], + "5edf84d0-c2d9-45eb-91b9-c35743b6a463": [ + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "Jiang, C. and Zeng, Z. B. (1995). Multiple trait analysis of genetic mapping for quantitative\ntrait loci. Genetics 140, 1111–1127. Jin, W., Riley, R. M., Wolfinger, R. D.et al. (2001). The contributions of sex, genotype and age\nto transcriptional variance in Drosophila melanogaster. Nat Genet 29, 389–395. Kempermann, G., Chesler, E. J., Lu, L. et al. (2006). Natural variation and genetic covariance\nin adult hippocampal neurogenesis. Proc Natl Acad Sci U S A 103, 780–785. Kendziorski, C. M., Chen, M., Yuan, M. et al. (2006). Statistical methods for expression\nquantitative trait loci (eQTL) mapping. Biometrics 62, 19–27." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nHypothesis-free genome-wide approaches have also been undertaken.Genome-wide linkage scans reported evidence for linkage with longevity on chromosome 4q25 (Puca et al., 2001), 3p24-22, 9q31-34, and12q24 (Boyden &Kunkel, 2010).However, the evidence for these loci is still very weak as the results, obtained in centenarians and their families, could not be replicated in nonagenarian sibling pairs (Beekman et al., 2006) or have yet to be tested in other studies.A meta GWAS of survival to 90 years or older in 1836 cases and 1955 controls did not find any significant genome-wide associations (Newman et al., 2010).Thus far, hypothesis-free approaches have not identified any loci involved in longevity." + } + ], + "75e0ffe8-7675-4e11-be3e-880bfeb3dabd": [ + { + "document_id": "75e0ffe8-7675-4e11-be3e-880bfeb3dabd", + "text": "Abiola O, Angel JM, Avner P, Bachmanov AA, Belknap JK, Bennett B, et al. The nature and identification of quantitative trait loci: a community’s view. Nat Rev Genet. Nature Publishing Group; 2003; 4:\n911–916. https://doi.org/10.1038/nrg1206 PMID: 14634638\n\n18. Grupe A, Germer S, Usuka J, Aud D, Belknap JK, Klein RF, et al. In silico mapping of complex diseaserelated traits in mice. Science. American Association for the Advancement of Science; 2001; 292:\n1915–1918. https://doi.org/10.1126/science.1058889 PMID: 11397946\n\n19. Pletcher MT, McClurg P, Batalov S, Su AI, Barnes SW, Lagler E, et al." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\ncoid levels, etc.The mapping project should thus help to guide the search for human genes that regulate these interesting phenotypes and at the same time spark new investigations, in animal models, for the biochemical differences that mediate the genetic effects we detect.At the same time, the dataset that emerges should also allow us to test more general questions about the nature of aging and its genetic control.We may, for example, be able to identify QTLs that not only retard the development of one or more age-sensitive T-cell subsets, but also retard age-dependent changes in protein conformation, bone matrix turnover, and brain GFAP levels.Such a finding would imply that these changes are influenced, together, by a common biochemical pathway, and the corresponding QTLs would be excellent candidates for genes that regulate aging per se, rather than merely one among the many more agesensitive traits.In the same way, it will be of particular interest to determine if QTLs that regulate age-sensitive traits also are associated with differences in life span, and conversely if QTLs identified on the basis of longevity effects modify one (or nearly all?) of the age-sensitive traits in our test battery." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nThe strategy for mapping such quantitative trait loci (QTL) involves looking for preferential segregation of specific alleles or allele combina-tions in mice that differ in life span (or, more generally, any age-sensitive trait of interest).Our test population, called UM-HET3, consisted of a group of mice bred as the progeny of females of the (BALB/c × C57BL/6)F1 genotype and males of the (C3H/HeJ × DBA/2)F1 genotype.Mice bred in this way are, from a genetic perspective, all siblings; each shares a random half of its alleles with every other animal in the UM-HET3 population.The current set of analyses was conducted when genotype and longevity data were available from a group of 110 virgin males and 143 virgin females.The analytical method adjusted, by permutation testing, for Type I errors attributable to the simultaneous evaluation of multiple linkage hypotheses, and also included gender as a covariate to look for instances of sex-specific genetic effects.Because we had particular interest in regulation of late-life diseases rather than in causes of premature death, and because of evidence that genetic influences on mouse longevity were particularly strong when early deaths were not considered (Covelli et al., 1989), we repeated each analysis after exclusion of those animals dying before 657 days of age, i.e., the age at which 20 percent of the animals had already died." + } + ], + "9ac0b7e7-6294-4cfb-97e3-e5a4546af324": [ + { + "document_id": "9ac0b7e7-6294-4cfb-97e3-e5a4546af324", + "text": "The proportion of the phenotypic variance accounted for by\nthe QTL yield for Hbact and Hbrear was substantial and of the\nsame order of magnitude as that contributed by age. A small\nnumber of age-dependent QTL were found in the midst of\na majority of age-stable QTL (see discussion above). These\nage-sensitive loci point toward genes whose functions are\ncorrelated with important behavioral changes during aging." + } + ], + "9fed8fd1-fce5-4fc1-9911-05d312f88521": [ + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "text": "\n\nAgeing genes and pathways.Assessing the loci of interest for colocalisation with gene expression quantitative trait loci (eQTL), we find strong evidence (FDR SMR < 5%; P HEIDI > 1%; see \"Methods\") of cis-acting eQTL colocalisation for eight out of 10 loci.In total, we highlight 27 unique genes acting across 32 tissues, especially whole blood (12 genes) and the tibial nerve (7 genes) (Supplementary Data 5).In blood, higher expression levels of BCL3 and CKM (near APOE); CTC-510F12.2, ILF3, KANK2 and PDE4A (near LDLR); USP28 and ANKK1 (near ZW10); and CDKN2B are linked to an increase in multivariate ageing traits (i.e.improved survival), while the opposite is true for EXOC3L2 (near APOE), TTC12 (near ZW10), and FOXO3.For the multivariate signal near SLC4A7 we find colocalisation with expression of NEK10 (liver); for the signal near LPA we find colocalisation with expression of SLC22A1/A3 (multiple tissues) and MAP3K4 (pituitary); and for the signal near FGD6 we find colocalisation with expression of FGD6 itself (adipose/arterial).Including trans-acting eQTL from blood, while keeping the same thresholds for colocalisation, we additionally discover higher expression levels of FOXO3B colocalises with the life-extending signal near FOXO3.When we include genes which could not be tested for heterogeneity (N eQTL < 3), we identify one additional cis-acting and 49 additional trans-acting genes (of which 10 colocalise with the signal near LINC02513) (Table 2; Supplementary Data 5)." + } + ], + "c12e853e-4f0d-48f9-93af-15db9ad2dfae": [ + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "Jiang, C. and Zeng, Z. B. (1995). Multiple trait analysis of genetic mapping for quantitative\ntrait loci. Genetics 140, 1111–1127. Jin, W., Riley, R. M., Wolfinger, R. D.et al. (2001). The contributions of sex, genotype and age\nto transcriptional variance in Drosophila melanogaster. Nat Genet 29, 389–395. Kempermann, G., Chesler, E. J., Lu, L. et al. (2006). Natural variation and genetic covariance\nin adult hippocampal neurogenesis. Proc Natl Acad Sci U S A 103, 780–785. Kendziorski, C. M., Chen, M., Yuan, M. et al. (2006). Statistical methods for expression\nquantitative trait loci (eQTL) mapping. Biometrics 62, 19–27." + } + ], + "cb3f9967-9762-4a9b-96cb-0acccdc316d2": [ + { + "document_id": "cb3f9967-9762-4a9b-96cb-0acccdc316d2", + "text": "Quantitative trait loci (QTLs) can be identified in several ways, but is\nthere a definitive test of whether a candidate locus actually corresponds to a specific QTL? NIH-PA Author Manuscript\n\nMuch of the genetic variation that underlies disease susceptibility and morphology is complex\nand is governed by loci that have quantitative effects on the phenotype. Gene-gene and geneenvironment interactions are common and make these loci difficult to analyse. Here, we present\na community’s view on the steps that are necessary to identify genetic loci that govern\nquantitative traits, along with a set of interpretive guidelines." + } + ], + "d1f04d58-2589-4183-aee4-569820dae052": [ + { + "document_id": "d1f04d58-2589-4183-aee4-569820dae052", + "text": "QTL Analysis in Hematopoiesis\n\n47\n\n3\nQuantitative Trait Analysis in the Investigation\nof Function and Aging of Hematopoietic Stem Cells\nHans-Willem Snoeck\nSummary\nExtensive genetically determined quantitative variation exists in the number and function of hematopoietic stem cells in inbred mouse strains. Furthermore, aging of hematopoietic stem cells is genetically determined. Gene identification of quantitative trait loci\ninvolved in the regulation and aging of hematopoietic stem cells would provide novel\ninsights into regulatory mechanisms that are relevant in vivo and may be clinically important." + } + ], + "dbfe8986-e861-496f-a534-7bb9ca061ad6": [ + { + "document_id": "dbfe8986-e861-496f-a534-7bb9ca061ad6", + "text": "\n\nIn order to find the causal loci for heritable differences in transcript levels and possible interactions between age and genotype, we applied a two-time-point model.In this model, we used three factors-(1) relative age, (2) genotype (marker), and (3) the interaction between factors 1 and 2-to explain the differences in gene expression between RILs and age groups.With this mapping procedure, we found almost 900 genes that had an eQTL or gxa eQTL in developing and/or aging worms (P < 0.0001; Fig. 2).Almost half of these genes with heritable transcript differences were found to have a genotype-by-age effect (396 at P < 0.0001; Table 1) allocated to a specific marker, which we coined genotype-by-age expression-QTL ( gxa eQTL).One specific hotspot (trans-band) for gxa eQTL was found on chromosome IV for aging worms and a trans-band for eQTL on chromosome I was detected in developing worms (Fig. 2)." + } + ], + "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748": [ + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "NIH-PA Author Manuscript\n\nWe found three significant QTLs (genetic regions harboring genes controlling these various\naging traits, Supplementary Table 5). On chromosome 7, we found a QTL affecting lifespan\nand fertility after DR that we have named Lfdr1 for “longevity and fertility response to\ndietary restriction, QTL 1; this QTL also has suggestive effects on FE (Fig. 5D). Two QTLs\nhaving significant effects on FE were identified on chromosomes 9 and 15. These we have\nnamed Fedr1 and Fedr2, respectively, for “fuel efficiency response to dietary restriction”\nQTLs 1 and 2." + } + ], + "f041550e-5f2d-430e-8f46-15ebea6ca496": [ + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "text": "Quantitative trait locus (QTL) mapping in\naging systems. Methods in Molecular Biology (Clifton, NJ ). 2007; 371:321–348. Hunter KW, Crawford NPS. The future of mouse QTL mapping to diagnose disease in mice in the age\nof whole-genome association studies. Annual Review of Genetics. 2008; 42:131–141. Ito R, Robbins TW, Everitt BJ. Differential control over cocaine-seeking behavior by nucleus\naccumbens core and shell. Nature Neuroscience. 2004; 7:389–397. [PubMed: 15034590]\nKapp MB. Ethical and legal issues in research involving human subjects: do you want a piece of me? Journal of Clinical Pathology. 2006; 59:335–339." + } + ], + "f35e02a1-3314-4663-913f-38a3fc072aa8": [ + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "Jiang, C. and Zeng, Z. B. (1995). Multiple trait analysis of genetic mapping for quantitative\ntrait loci. Genetics 140, 1111–1127. Jin, W., Riley, R. M., Wolfinger, R. D.et al. (2001). The contributions of sex, genotype and age\nto transcriptional variance in Drosophila melanogaster. Nat Genet 29, 389–395. Kempermann, G., Chesler, E. J., Lu, L. et al. (2006). Natural variation and genetic covariance\nin adult hippocampal neurogenesis. Proc Natl Acad Sci U S A 103, 780–785. Kendziorski, C. M., Chen, M., Yuan, M. et al. (2006). Statistical methods for expression\nquantitative trait loci (eQTL) mapping. Biometrics 62, 19–27." + } + ] + }, + "data_source": [], + "document_id": "F609F53687E61A2158330F914066FE89", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "aging", + "QTL", + "quantitative&trait&loci", + "longevity", + "epigenetic&aging", + "Alzheimers&disease", + "cognitive&decline", + "dementia", + "GWAS", + "eQTL" + ], + "metadata": [], + "question": "Do we find significant quantitative trait loci in aging studies, if so give the top three examples with descriptions?", + "subquestions": null, + "task_id": "F609F53687E61A2158330F914066FE89", + "usage": { + "chatgpt": 7623, + "gpt-4": 5054, + "gpt-4-turbo-preview": 3989 + }, + "user_id": 2 + }, + "document_id": "F609F53687E61A2158330F914066FE89", + "task_id": "F609F53687E61A2158330F914066FE89" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_05.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_05.json new file mode 100644 index 0000000..7d5ca5e --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_05.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-04-02T12:04:11.979513Z", + "data": { + "amplify": false, + "answer": "The most fundamental human life expectancy extending geroscience studies include genetic and genomic research into longevity. These studies use both hypothesis-driven candidate gene approaches and explorative genome-wide analyses. They also involve the study of biomarkers related to aging, deep phenotyping, and the use of novel technologies like next-generation sequencing. Additionally, they examine the role of environmental factors, lifestyle, and heritability in human lifespan variation.", + "context": { + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "Introduction\n\nWith the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging." + } + ], + "1386c8ad-297d-48b1-aa34-41659a9f6544": [ + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + } + ], + "3043efd1-4b13-4300-b2a7-d1992c8d4e47": [ + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "Introduction\n\nGeroscience refers to research aimed at understanding the mechanisms of biological aging (Kennedy et al. 2014).A major goal of geroscience is to define the genetic, epigenetic, and environmental features that determine individual rates of aging.From a translational perspective, a further goal is to use this knowledge to develop interventions that can slow or delay aging in order to promote healthy longevity and increase healthspan, the period of life spent in good health free from chronic disease and disability (Burch et al. 2014;Pitt and Kaeberlein 2015)." + } + ], + "3bf70612-23e6-41b8-9b88-ce9ba23c1edf": [ + { + "document_id": "3bf70612-23e6-41b8-9b88-ce9ba23c1edf", + "text": "\nthe maximum human life span.Several avenues to studying aging have placed us on Department of Biology Massachusetts Institute of Technology the threshold of understanding basic underlying mechanisms.These approaches include the identification of Cambridge, Massachusetts 02139 key genes and pathways important in aging; genetic studies of heritable diseases that cause the appearance of premature aging in affected people; physiological ex-Introduction periments that relate the pace of aging to caloric intake; Is aging the final act in the script of developmental bioland advances in human genetics, as well as cell and ogy?The characteristic changes that are part and parcel molecular biology leading to an understanding of the of aging appear similar to developmentally regulated basis of many diseases of aging.Strikingly, single gene programs.But why would aging mechanisms have been mutations have been found to significantly extend the evolutionarily selected as advantageous?Indeed, evolife span in C. elegans, yeast, and, most recently, Drolutionary biologists might argue that aging occurs by sophila, suggesting that aging may be relatively simple, default due to the absence of selection in the postreproat least in these organisms.Further, the limited replicaductive phase of life.By this view, the aging process is tion potential of human cells in culture has been attribnot programmed, but, rather, the detritus of the absence uted to a specific mechanism (i.e., the shortening of of selection for maintenance (Medawar, 1952; Kirkwood, telomeric ends of chromosomes).An important chal- 1977).However, it is quite reasonable that any mechalenge is now to relate these recent findings to the more nisms that sprang up to slow or regulate the pace of complex case of human aging.aging would be selected, because lucky individualsIn this review, we will discuss several important mocould potentially give rise to more progeny.Therefore, lecular models of aging that come from current research.it is reasonable to suppose that life span extending pro-These are damage by reactive oxygen species (ROS) cesses have been selected and that these can be viewed generated by metabolism, genome instability, genetias an elaboration of development itself.In principle, cally programmed extension mechanisms, cell death, such extension mechanisms may act to slow or forestall and systemic aging.Questions to be posed include the deleterious changes in an organism that progressively following.What evidence exists for and against these lead to death.The life span of an organism, therefore, models?Can more than one of these models apply to is the sum of deleterious changes and counteracting aging of different tissues in humans-specifically do repair and maintenance mechanisms that respond to organs with continually dividing cells age by the same the damage (Figure 1).mechanism as organs that are postmitotic?Finally, is A priori, one imagines such longevity mechanisms to aging amenable to therapeutic intervention, and would be much less complex than those regulating embryonic such intervention be advisable?development.The spatial and temporal constraints on embryonic development are many, while requirements Oxidative Damage for longevity mechanisms might be much more specific One theory of aging proposes that ROS which are generif there were a single process (or a few processes) whose ated by metabolism cause cumulative damage over a breakdown is the limiting event in longevity (i.e., the lifetime (Harman, 1981).Roughly two to three percent Achilles heel).of oxygen taken up is chemically reduced by the addition Aging is defined when two criteria are met.First, the of single electrons, which are sequentially converted probability of death at any point in time increases with into ROS, including the superoxide anion, hydrogen perthe age of the organism.This statistical definition applies oxide, and the hydroxyl radical.ROS have been shown from yeast to mammals and reflects the progressive to cause molecular damage relatively indiscriminately nature of aging.Second, characteristic changes in pheto proteins, lipids, and nucleic acids.In addition, specific notype occur in all individuals over time due to the limdamage has been observed in the mitochondrial DNA, iting processes.which we consider below in Genome Instability.The phenotypic definition is equally general and is What is the evidence that oxidative damage causes useful in distinguishing the aging process itself from aging?One category of study that is supportive of this diseases of aging, such as cancer and heart disease.view involves animals transgenic for genes encoding Phenotypes of aging affect all of the individuals in a antioxidants.Transgenic Drosophila overexpressing both population, while diseases of aging affect only a subset.Cu/Zn SOD and catalase live 34% longer than controls Both impact on life span, but in different ways.For exam-(Orr and Sohal, 1994).A more recent study shows that ple, the many advances in medicine and public health expression of human SOD1 exclusively in Drosophila in this century have caused a large increase in the averadult motor neurons leads to a 40% extension in life age life span of humans in developed countries.Howspan (Parkes et al., 1998).Further experiments are necever, because these advances have not altered the aging essary to clarify the nature of this primary role of motor neurons in life span.Conversely, mice knocked out for either GPX1 (encoding glutathione peroxidase), SOD1," + }, + { + "document_id": "3bf70612-23e6-41b8-9b88-ce9ba23c1edf", + "text": "\n\nthe maximum human life span.Several avenues to studying aging have placed us on Department of Biology Massachusetts Institute of Technology the threshold of understanding basic underlying mechanisms.These approaches include the identification of Cambridge, Massachusetts 02139 key genes and pathways important in aging; genetic studies of heritable diseases that cause the appearance of premature aging in affected people; physiological ex-Introduction periments that relate the pace of aging to caloric intake; Is aging the final act in the script of developmental bioland advances in human genetics, as well as cell and ogy?The characteristic changes that are part and parcel molecular biology leading to an understanding of the of aging appear similar to developmentally regulated basis of many diseases of aging.Strikingly, single gene programs.But why would aging mechanisms have been mutations have been found to significantly extend the evolutionarily selected as advantageous?Indeed, evolife span in C. elegans, yeast, and, most recently, Drolutionary biologists might argue that aging occurs by sophila, suggesting that aging may be relatively simple, default due to the absence of selection in the postreproat least in these organisms.Further, the limited replicaductive phase of life.By this view, the aging process is tion potential of human cells in culture has been attribnot programmed, but, rather, the detritus of the absence uted to a specific mechanism (i.e., the shortening of of selection for maintenance (Medawar, 1952; Kirkwood, telomeric ends of chromosomes).An important chal- 1977).However, it is quite reasonable that any mechalenge is now to relate these recent findings to the more nisms that sprang up to slow or regulate the pace of complex case of human aging.aging would be selected, because lucky individuals" + } + ], + "555a1533-2905-4d91-a3b6-2fca3679ab02": [ + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nCurrently prevailing studies of genetic and biological origin of human health and longevity follow largely two approaches which focus on the aging-related diseases and on individuals with exceptionally long lives (Martin et al. 2007).This study provides de facto the rationale for a new approach.Specifically, Fig. 2 suggests that a promising strategy could be to focus on individuals who died prematurely.Studies of genetic profiles of short-lived subjects compared to those who aged more successfully (i.e., those who lived longer and perhaps healthier lives) can be a core of this strategy.Importantly, this strategy can be naturally implemented in longitudinal studies of aging and longevity by focusing on individuals who died first." + } + ], + "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4": [ + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "text": "\n\nT he average human life expectancy has been increasing for centuries 1 .Based on twin studies, the heritability of human lifespan has been estimated to be ~25%, although this estimate differs among studies 2 .On the other hand, the heritability of lifespan based on the correlation of the mid-parent (i.e., the average of the father and mother) and offspring difference between age at death and expected lifespan was estimated to be 12% 3 .A recent study has indicated that the different heritability estimates may be inflated due to assortative mating, leaving a true heritability that is below 10% 4 .The heritability of lifespan, estimated using the sibling relative risk, increases with age 5 and is assumed to be enriched in long-lived families, particularly when belonging to the 10% longest-lived of their generation 6 .To identify genetic associations with human lifespan, several genome-wide association (GWA) studies have been performed [7][8][9][10][11][12][13][14][15][16][17][18][19][20] .These studies have used a discrete (i.e., older cases versus younger controls) or a continuous phenotype (such as age at death of individuals or their parents).The selection of cases for the studies using a discrete longevity phenotype has been based on the survival to ages above 90 or 100 years or belonging to the top 10% or 1% of survivors in a population.Studies defining cases using a discrete longevity phenotype often need to rely on controls from more contemporary birth cohorts, because all others from the case birth cohorts have died before sample collection.Previous GWA studies have identified several genetic variants, but the only locus that has shown genome-wide significance (P ≤ 5 × 10 −8 ) in multiple independent meta-analyses of GWA studies is apolipoprotein E (APOE) 21 , where the ApoE ε4 variant is associated with lower odds of being a long-lived case." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "Introduction\n\nWorldwide human populations have shown an increase in mean life expectancy in the past two centuries (Oeppen & Vaupel, 2002).This is mainly because of environmental factors such as improved hygiene, nutrition, and health care.The large variation in healthy lifespan among the elderly has prompted research into the determinants of aging and lifespan regulation.The genetic contribution to human lifespan variation was estimated at 25-30% in twin studies (Gudmundsson et al., 2000;Skytthe et al., 2003;Hjelmborg et al., 2006).The most prominent genetic influence is observed in families in which the capacity to attain a long lifespan clusters (Perls et al., 2000;Schoenmaker et al., 2006).Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al., 2008;Terry et al., 2008), raising the question whether protective mechanisms against disease exist in long-lived subjects." + } + ], + "6005d141-8758-44b5-9baa-d553da68d167": [ + { + "document_id": "6005d141-8758-44b5-9baa-d553da68d167", + "text": "Introduction\n\nHuman life expectancies are increasing almost everywhere in the world where socio-economic circumstances are permissive (Tuljapurkar et al., 2000) and there is no evidence that a limit to life is anywhere near (Oeppen and Vaupel, 2002).While this increase in life span would prevent a proposed compression of morbidity (Fries, 1980), there is no evidence that higher average life spans are associated with an extension of the period of increased morbidity (Manton and Gu, 2001).On the contrary, older individuals have never been so healthy and further improvements in life style, environmental conditions and medical care are likely to help this trend to continue.Especially the medical sciences now seem poised to push the biological limits of longevity further by a number of innovations that seem to affect basic mechanisms of ageing and disease rather than merely alleviating its symptoms.While in the past medicine contributed mainly to public health advances by redu-cing infectious diseases, thereby helping infant mortality to decline, more recent developments hold promise for a more basic intervention in the processes that underlie age-related decline.An example is atherosclerosis, a common problem in ageing and, along with hypertension, the cause of most cardiovascular disease.Basic medical research has likely contributed significantly to the current dramatic decline in cardiovascular disease by actively intervening in some of its main risk factors, i.e., lipid levels and hypertension (Levi et al., 2002).However, one could question whether age-related diseases should be seen as separate from ageing.In this respect, ageing has been considered as a process of cellular degeneration and death universal to all or most species, increasing the risk of fatal disease in humans and other mammals.Would it be possible to define such a process and ultimately understand it in terms of the timedependent, coordinated action of the products of multiple genes interacting with the environment?If so, then ageing per se rather than the diseases associated with it, may offer a more logical starting point for further increasing healthy life expectancies through prevention and therapy.This is especially true now that we have a working draft of the human genome and are in a position to determine the functional significance of each gene as part of the dynamic network of all genes that ultimately determine the physiology of an organism.Termed 'Functional Genomics', this new discipline is now often called upon to solve the complex problems in biology, such as to understand functional control mechanisms and investigate the role that genotype and environment play in determining disease phenotypes.The question is then if this same approach would apply to ageing as a complex phenotype.What is ageing, how does it differ from its diametrical opposite, i.e., organismal development, and what role can functional genomics play in unraveling the basic causes of ageing and exploit such knowledge for developing new, rational strategies for extending healthy life span?" + } + ], + "6df20592-9856-49a6-8bf3-f6a701ff3b56": [ + { + "document_id": "6df20592-9856-49a6-8bf3-f6a701ff3b56", + "text": "Introduction\n\nAs a result of improvements in health care and living conditions over the past two centuries, the average human life expectancy has dramatically increased in many regions of the world [1].This major success reflects the great malleability of the ageing process.Unfortunately, for most people, ageing is accompanied with an increased risk of developing age-related illnesses/disabilities and frailty.Therefore new approaches are required to understand the genetic, cellular, and molecular factors controlling ageing to identify strategies to extend healthy life span." + } + ], + "79ae7122-3716-498b-9b9a-dd0960e33f99": [ + { + "document_id": "79ae7122-3716-498b-9b9a-dd0960e33f99", + "text": "\nThe search for the genetic determinants of extreme human longevity has been challenged by the phenotype's rarity and its nonspecific definition by investigators.To address these issues, we established a consortium of four studies of extreme longevity that contributed 2,070 individuals who survived to the oldest one percentile of survival for the 1900 U.S. birth year cohort.We conducted various analyses to discover longevity-associated variants (LAV) and characterized those LAVs that differentiate survival to extreme age at death (eSAVs) from those LAVs that become more frequent in centenarians because of mortality selection (eg, survival to younger years).The analyses identified new rare variants in chromosomes 4 and 7 associated with extreme survival and with reduced risk for cardiovascular disease and Alzheimer's disease.The results confirm the importance of studying truly rare survival to discover those combinations of common and rare variants associated with extreme longevity and longer health span." + }, + { + "document_id": "79ae7122-3716-498b-9b9a-dd0960e33f99", + "text": "\n\nThe search for the genetic determinants of extreme human longevity has been challenged by the phenotype's rarity and its nonspecific definition by investigators.To address these issues, we established a consortium of four studies of extreme longevity that contributed 2,070 individuals who survived to the oldest one percentile of survival for the 1900 U.S. birth year cohort.We conducted various analyses to discover longevity-associated variants (LAV) and characterized those LAVs that differentiate survival to extreme age at death (eSAVs) from those LAVs that become more frequent in centenarians because of mortality selection (eg, survival to younger years).The analyses identified new rare variants in chromosomes 4 and 7 associated with extreme survival and with reduced risk for cardiovascular disease and Alzheimer's disease.The results confirm the importance of studying truly rare survival to discover those combinations of common and rare variants associated with extreme longevity and longer health span." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + } + ], + "ae9d5a74-24c1-43f1-b514-5e3f10c91284": [ + { + "document_id": "ae9d5a74-24c1-43f1-b514-5e3f10c91284", + "text": "DESIGNS TO STUDY PARAMETERS OF HEALTHY AGEING, MORBIDITY, MORTALITY AND LONGEVITY\n\nHuman cohorts may vary considerably in their morbidity, mortality and longevity characteristics and yet they have shown a common increase in mean life expectancy in the past two centuries [5].This is mainly due to improved hygiene, nutrition and healthcare.There is a large variation in healthy lifespan among the elderly and remarkably exceptional longevity (EL) can be reached with a low degree of agerelated disability [6,7].Heritability studies comparing the concordance of lifespan in monozygous and dizygous twins estimated a 25 -30% genetic contribution to human lifespan variation [8 -11], which becomes increasingly important at higher ages.The most prominent genetic influence is present in families in which survival to high ages clusters [12,13].Unlike model systems where single-gene mutations have major life extension effects, human longevity is presumed to be a complex trait [14]." + }, + { + "document_id": "ae9d5a74-24c1-43f1-b514-5e3f10c91284", + "text": "INTRODUCTION\n\nGenomic studies into human longevity are inspired by the fact that, in animal models, healthy lifespan has proved to be remarkably plastic, and major pathways of lifespan regulation have been identified.Considerable lifespan extension has been induced in models as diverse as yeast, worms, fish, flies and rodents by applying genetic manipulation and dietary restriction (DR) (see [1] for review).Reduced activity of nutrient-sensing pathways such as insulin/insulin-like growth factor (IGF-1) signalling (IIS) and target of rapamycin (TOR) signalling mediated lifespan extension, and also the extension of lifespan by DR [2].An interesting observation from the perspective of human ageing is that, in rodents and monkeys, diets restricted in glucose, fat or protein uptake reduced or delayed the risk of cancer and metabolic disease, thus extending the healthspan of the animals [2].Following the discovery of genes and pathways involved in animal lifespan extension, human research has focused on the corresponding candidate human genes with genetic, genomic and epigenetic studies into ageing and longevity.The designs of these studies differ with respect to the selection of naturally occurring phenotypes and the study populations, which include population-based, patient-based, family-based and exposure-based cohorts." + }, + { + "document_id": "ae9d5a74-24c1-43f1-b514-5e3f10c91284", + "text": "GENETIC STUDIES OF HUMAN LONGEVITY\n\nGenetic and genomic studies into longevity have been performed based on a hypothesis, referred to as a candidate gene approach.Alternatively, explorative genome-wide analyses have been applied in which genetic variation and gene transcription across the complete genome are being studied for associations with longevity and related traits.Genetic studies into human disease and longevity include candidate gene approaches, genome-wide association studies (GWASs) and genome-wide linkage studies." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "Conclusions and prospects\n\nOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + } + ] + }, + "data_source": [], + "document_id": "9329B7F018681D21C6BF01A0ACF8A9FF", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "aging", + "longevity", + "genes", + "genetic", + "human", + "GWAS", + "APOE", + "IIS&mTOR", + "pathways", + "ROS" + ], + "metadata": [], + "question": "What are the most fundamental human life expectancy extending geroscience studies?", + "subquestions": null, + "task_id": "9329B7F018681D21C6BF01A0ACF8A9FF", + "usage": { + "chatgpt": 9018, + "gpt-4": 6435, + "gpt-4-turbo-preview": 5512 + }, + "user_id": 2 + }, + "document_id": "9329B7F018681D21C6BF01A0ACF8A9FF", + "task_id": "9329B7F018681D21C6BF01A0ACF8A9FF" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_06.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_06.json new file mode 100644 index 0000000..78b6bd7 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_06.json @@ -0,0 +1,140 @@ +{ + "created_at": "2024-04-02T12:10:07.521330Z", + "data": { + "amplify": false, + "answer": "The grand challenges related to aging research in genetics and genomics include:\n\n1. Lack of awareness and interest: Health professionals, governments, and researchers often lack awareness and interest in genetic diseases, which hinders progress.\n\n2. Limited funding and access to technology: Research funding is often limited, and access to high-end technology is not always available, which restricts the scope of research.\n\n3. Economic and health disparities: These disparities can affect the application and benefits of genomic medicine, making it less accessible to those in lower income brackets.\n\n4. Ethical, legal, and social issues: The interpretation, storage, and sharing of genetic data raise ethical and legal concerns, including issues of privacy and consent.\n\n5. Rapid advancements: The fast pace of developments in genetics and genomics can outpace the ability to address emerging ethical, legal, and social questions.\n\n6. Public knowledge and trust: There is a need to increase public knowledge about genomic research and address issues of trust in medicine and scientific research.\n\n7. Integration of genomics into public health: This is a major future challenge for healthcare systems, requiring dialogue with all stakeholders in society.", + "context": { + "16a19a42-1f27-43e4-a4df-46d5e9c81096": [ + { + "document_id": "16a19a42-1f27-43e4-a4df-46d5e9c81096", + "text": "\n\nThere is a great need for continuing efforts to increase public knowledge about genomic research.As individuals and communities from diverse social backgrounds become more aware of genomic research and the potential role of genetics in contributing to health outcomes, the public will hopefully be more informed about the implications of genomic research for personal medical care, public health and more broadly the public representation of diverse population groups based on genetic findings.This knowledge should reinforce the ability of potential participants to make informed choices about joining a genetic study.There are complicated issues underlying public trust in medicine as well as scientific and genetic research that must be addressed.Innovative strategies for public education and community engagement should take into account cultural settings and historical experiences that have contributed to distrust in the past." + } + ], + "64d87c52-1185-4080-8d06-134c32dae5fd": [ + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "text": "\n\nThe issues discussed in this section refl ect key current concerns, but, given the rapid advances in genetic and genomic research, new issues will continue to confront families in the next few years.For example, major advances in the developing area of neuropsychiatric genetics, studies of the heritable nature of psychiatric and other nervous system disorders, characterized at the molecular, cellular, or behavioral levels, will challenge family members to address the potential role genes play in the development of schizophrenia, bipolar, or affective disorders (Genomics Network, n.d.)." + }, + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "text": "Future Implications and Communication Research Directions\n\nGiven ever-expanding research on genetics and genomics, scholars interested in family interaction will be challenged to stay abreast of the implications for family disclosure and discussion of genetic health.We believe that the following issues will emerge as key concerns:" + } + ], + "855e497d-7305-4154-b395-283992ddc4d0": [ + { + "document_id": "855e497d-7305-4154-b395-283992ddc4d0", + "text": "Conclusion\n\nAfter more than four decades of working, genetics and genomic medicine still faces a considerable challenge to be addressed.Lack of awareness of health professionals and government, lack of interest of researcher on genetic diseases, limited research funding, limited access to high technology, low national health budget and low income family are seem to be the main obstacles to be overcome in implementation of genetics and genomic medicine.Despite these conditions, several research centers still managed to do some studies and few numbers of genetic testing.Several collaborations with countries abroad have been done to overcome some obstacles.Yet, Indonesia still has to accelerate this effort to be able to catch up its lag.Mentoring and collaborations are needed to enable Indonesia in doing so." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "Opportunities for Population-Based Research on Aging Human Subjects:\n\nPathology and Genetics" + } + ], + "9e513fea-5257-4887-9802-57d416f21dfc": [ + { + "document_id": "9e513fea-5257-4887-9802-57d416f21dfc", + "text": "Concluding remarks\n\nThe next decade will provide a window of opportunity to prepare health professionals, public health practitioners, the public and policy makers for the advent of genomics on health and health care.This will be a doable project but will require regional, national, European and global coordination on both the vertical and horizontal levels.We argue that there is an ethical obligation to prepare society to meet this challenge and to take up the opportunities provided by the science in a medically useful, effective, efficient, socially desirable and ethically justifiable manner.Here, health literacy, health communication and empowerment in managing risks are key for opening the doors to a truly beneficial Public Health Genomics practice.This can be facilitated by implementing ethical benchmarks and legal safeguards 70 such as respect for autonomy and social justice in the context of policy development." + }, + { + "document_id": "9e513fea-5257-4887-9802-57d416f21dfc", + "text": "\n\nClarifying the general conditions under which genomic knowledge can be put to best practice in the field of public health, paying particular consideration to the ethical, legal and social implications 12,17,35 is currently the most pressing task in Public Health Genomics.Aiming the application of genetic and molecular science to the promotion of health and disease prevention through the organised efforts of society, integral to its activities is a dialogue with all stakeholders in society, including industry, governments, health professionals and the general public. 18Thus, the integration of genomics into public health research, policy and practice is one of the major future challenges for our health-care systems. 36,37Expertise is already feasible and can be clustered and evaluated for a socially accountable use." + }, + { + "document_id": "9e513fea-5257-4887-9802-57d416f21dfc", + "text": "\n\nPublic health needs to prepare itself for the upcoming challenges, which derive from genomics.In this sense, it needs to strengthen the communication efforts among all sciences involved.Public health can serve as the umbrella, that spans the disciplines such as genetics, ethics, law and all other stakeholders." + } + ], + "9f21007a-1487-46d8-8e9e-cde8df4af6d5": [ + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "\n\nEconomic and health disparities related to genetics and genomics." + }, + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "\n\nCapabilities and limitations of current genetic/genomic technologies." + }, + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "\n\nIdentify ethical, legal, and social issues associated with genetic/genomic information." + }, + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "\n\nOngoing research contributing to improved understanding of the genetic/genomic influences on health." + }, + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "Economic and health disparities related to genetics and genomics. Integrate knowledge from psychology, history, politics, sociology and culture when delivering genetic and genomic care." + }, + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "\n\nEthical and legal issues surrounding genetic and genomic information and services." + } + ], + "a4e27158-1e54-4ee2-9cc1-049489a628bc": [ + { + "document_id": "a4e27158-1e54-4ee2-9cc1-049489a628bc", + "text": "\n\nDevelopments in genetics and genomics occur very rapidly and bring with them new ethical, legal and social questions that need swift, sensible and responsible responses (Pepper, 2011).Examples include next-generation sequencing, genetic cohort studies and biobanks, which have raised questions about data management, including quality of interpretation of data, data storage, data sharing, consent for re-use of data, as well as concerns about identifiability and privacy interests of those who provide samples (Kaye, 2012;Wolf, 2013;Pinxten and Howard, 2014).However, the rapidity of advancement poses difficulties for those who must determine the responses to these questions.They are often slow or even overtaken by further advancements.Ethical, legal and social-related challenges should be prioritised for policymakers, researchers, clinicians and public health practitioners to maximise the benefits of genomic and genetic applications while minimising the risk of harm to people (Geller et al., 2014).Any education strategy developed should therefore be dynamic." + } + ], + "af3d7cd3-40ec-4a86-a473-89f83da250e4": [ + { + "document_id": "af3d7cd3-40ec-4a86-a473-89f83da250e4", + "text": "Query 2. Perceptions of Genetics and Genomics\n\nAwareness of Genetic and Genomic Advancements." + } + ], + "be3e9fcb-5469-48eb-bc1b-118e58f82cc5": [ + { + "document_id": "be3e9fcb-5469-48eb-bc1b-118e58f82cc5", + "text": "\n\nIn addition, 4 scholarly commentaries in this issue provide insights into several current practical issues and developments in genetics and genomics.Feero and colleagues 11 describe advances in genomics science and explore many of the issues surrounding translation of these advances to routine \"personalized\" patient care.Offit 12 discusses the increasing availability of direct-to-consumer marketing of genomic and genetic testing and sounds an appropriately cautionary note about the need for standards, quality control, and appropriate regulation.Uhlmann and Guttmacher 13 present a useful collection of practical Internet genetics resources for clinicians and patients, including genetics information on specific diseases; guidelines for genetic testing; and educational resources to help clinicians integrate genetics into patient care.Ginsberg and colleagues 14 discuss the importance of centralized biorepositories for genetics and genomics research and empha-size the need to develop and implement standards for informed consent, informatics, and governance." + } + ], + "cb76344a-9307-4a44-b6b2-455b728bb249": [ + { + "document_id": "cb76344a-9307-4a44-b6b2-455b728bb249", + "text": "\n\nKey Themes Relevant To Genomic Research . . . . . . . . . . . . . . . . . . . . . . . . . . 3" + } + ], + "e8be2280-10e9-4b62-af14-0772947d2d7e": [ + { + "document_id": "e8be2280-10e9-4b62-af14-0772947d2d7e", + "text": "\n\nA first step is to define the challenges that stand in the way of realizing the promise of genomic medicine.These include addressing gaps in the oversight of genetic testing (including regulation of companies providing test interpretation services), ensuring that realistic claims are made in promotional materials for genetic testing, determining the appropriate role of new genomic technologies in patient care, ensuring the privacy of patients' genomic data, and improving insurance coverage and reimbursement for genetic services.The Secretary's Advisory Committee on Genetics, Health, and Society (SACGHS), on which two of us serve, advises the secretary of health and human services and reports on these issues." + }, + { + "document_id": "e8be2280-10e9-4b62-af14-0772947d2d7e", + "text": "\n\nHow can we maximize the benefits of these new developments and minimize the harms?How can we encourage patients' involvement and autonomy yet establish appropriate safeguards while avoiding inappropriate paternalism?How do we promote Preparing for a Consumer-Driven Genomic Age the understanding that interpretations of genomic information may evolve as research unravels the meaning of gene-gene and gene-environment interactions and the roles of noncoding DNA sequences, copy-number variants, epigenetic mechanisms, and behavioral factors in health and disease?" + } + ] + }, + "data_source": [], + "document_id": "8FE04B7D11697E4EF20F0147B082EC5B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "genetics&genomics", + "public&health", + "ethical&issues", + "legal&issues", + "genetic&testing", + "data&management", + "privacy", + "health&disparities", + "genomic&medicine", + "patient&care" + ], + "metadata": [], + "question": "List and describe the grand challenges related to aging research in genetics and genomics.", + "subquestions": null, + "task_id": "8FE04B7D11697E4EF20F0147B082EC5B", + "usage": { + "chatgpt": 3769, + "gpt-4": 2657, + "gpt-4-turbo-preview": 1711 + }, + "user_id": 2 + }, + "document_id": "8FE04B7D11697E4EF20F0147B082EC5B", + "task_id": "8FE04B7D11697E4EF20F0147B082EC5B" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_07.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_07.json new file mode 100644 index 0000000..aeb904c --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_07.json @@ -0,0 +1,134 @@ +{ + "created_at": "2024-04-02T12:30:28.749434Z", + "data": { + "amplify": false, + "answer": "Gene-by-drug effects (GxD) can modulate life expectancies and health by influencing how individuals respond to medications. Genetic variations can affect how drugs are absorbed, disseminated, metabolized, and excreted in the body, which can lead to differences in drug efficacy and the occurrence of adverse drug reactions. For example, a genetic variant in the HMG-CoA reductase gene can modify the LDL-C response to pravastatin. Additionally, certain genes are involved in DNA damage repair and oxidative stress, which can influence susceptibility to adverse drug effects. Therefore, understanding these genetic variations can help in personalizing treatment and potentially improving health outcomes.", + "context": { + "0bc591e0-bd1c-4c15-9e1e-3aa4499ad270": [ + { + "document_id": "0bc591e0-bd1c-4c15-9e1e-3aa4499ad270", + "text": "\n\nA supervised (pathway driven) approach was used to specifically query three general gene ontology (GO) areas of interest, namely xenobiotic metabolism, DNA damage repair, and oxidative stress-related genes (Table 1).These gene categories are hypothesized to play important roles in sex-and age-related susceptibility to adverse drug effects [18,30].Of the 122 genes included in the xenobiotic metabolism gene list in the Ingenuity Knowledge Base, 61 were differentially expressed.These included Cyp2d4, the rat ortholog of human gene CYP2D6, which is speculated to metabolize up to 25% of commonly prescribed drugs [31].Genes involved in DNA Damage Repair, derived from Ingenuity, were combined with the list by Wood et al. [32] to give 222 genes involved in DNA damage repair.Sixty-five of these genes (approximately 25%) were found to be differentially expressed in the liver.Oxidative Stress genes were defined by 68 genes included in \"response to oxidative stress\" (IPA) of which 23 genes were differentially expressed (Table 1)." + } + ], + "17cd95a4-6e8e-4696-8881-ea43fa80ccce": [ + { + "document_id": "17cd95a4-6e8e-4696-8881-ea43fa80ccce", + "text": "\n\nPharmacogenomics has advanced the field of drug-response assessment.For example, the first experiences with guiding vitamin K antagonist therapy with the aid of CYP2C9 (cytochrome P450, family 2, subfamily C, polypeptide 9) or VKORC1 (vitamin K epox- ide reductase complex, subunit 1) polymorphisms (93 ), and the use of cytochrome P450 polymorphisms for assessing clopidogrel response have entered US Food and Drug Administration recommendations (94 ).Disease prevention lags behind.Gene chips and modern sequencing approaches that allow largescale interrogation of the genome at the population level will generate novel hypotheses of disease causation.Furthermore, with the continuing drop in the costs of whole-genome sequencing, the practicing physician may soon be faced with having to comment on the disease risks of a patient's Ͼ4 ϫ 10 6 sequence variants before any clinical signs occur, a task that no certified genetic counselor could fulfill at present.With advent of GWASs, ethical and practical concerns of reporting genetic research results have become apparent.Initial efforts at defining rules of reporting large-scale association results and assessing the level of evidence also apply to nextgeneration large-scale genomics (95,96 ).Reports have suggested that on the consumer side, genomewide genetic profiling of employees of health and technology companies does not change anxiety symptoms, dietary fat intake, or exercise behavior (i.e., lifestyle factors) over a 6-month period (97 ); however, the association of genetic variation with risk and the dissection of objective markers of risk and risk factors that reside in the causal pathways of disease will need careful assessment before these approaches can enter clinical decision making (98 ).A data set containing 80 genes associated with coronary heart disease in GWASs was uploaded and overlaid onto the molecular networks developed from information contained in the Ingenuity Knowledge Base.Networks of Network Eligible Molecules were then algorithmically generated on the basis of their connectivity.The most substantially enriched network, as shown, comprises 36 genes, of which 20 are coronary heart disease genes." + } + ], + "5edf84d0-c2d9-45eb-91b9-c35743b6a463": [ + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "19.3.1 An environmental or pharmacogenetic basis for drug\nefficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many\nnon-genetic factors also influence the efficacy of medications, including the patient’s\nage, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit\njuice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the\nfirst-pass metabolism of many medications." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "Finally, it is possible that other\nmolecules (or drugs) might modulate the biological context within which the drug–\ntarget interaction takes place. Variation in any of the elements that control these\ntypes of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related\ncandidates. 19.3 PHARMACOGENETICS (PGx)\n\n519\n\n19.3.5 Using bioinformatics to gain understanding of adverse\ndrug reaction (ADR)\nOne of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "19.3 Pharmacogenetics (PGx)\nIt is well known that after exposure to a drug, almost any given cohort of patients show\na wide variety of responses. In an ideal situation, patients show a beneficial response\nto the therapy, although they may also show no response or a weak response, and\nperhaps most worryingly, they may experience an adverse drug reaction (ADR),\nwhich in extreme situations could lead to serious illness or even death. ADR is an\nincreasingly serious problem with a huge toll in lives and health-care costs every year." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "A good understanding of disease biology and effective chemistry is not the\nonly requirement for an efficacious drug; we also must understand how variation\nat the target affects drug action, and how variation in other genes affects the way\ndrugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the\ndrug development paradigm also faces some unique challenges; for example, the\nexquisite rarity of some adverse reactions makes collection of sufficient samples for\nwell-powered genetic analysis almost impossible." + } + ], + "c12e853e-4f0d-48f9-93af-15db9ad2dfae": [ + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "19.3.1 An environmental or pharmacogenetic basis for drug\nefficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many\nnon-genetic factors also influence the efficacy of medications, including the patient’s\nage, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit\njuice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the\nfirst-pass metabolism of many medications." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "Finally, it is possible that other\nmolecules (or drugs) might modulate the biological context within which the drug–\ntarget interaction takes place. Variation in any of the elements that control these\ntypes of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related\ncandidates. 19.3 PHARMACOGENETICS (PGx)\n\n519\n\n19.3.5 Using bioinformatics to gain understanding of adverse\ndrug reaction (ADR)\nOne of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "19.3 Pharmacogenetics (PGx)\nIt is well known that after exposure to a drug, almost any given cohort of patients show\na wide variety of responses. In an ideal situation, patients show a beneficial response\nto the therapy, although they may also show no response or a weak response, and\nperhaps most worryingly, they may experience an adverse drug reaction (ADR),\nwhich in extreme situations could lead to serious illness or even death. ADR is an\nincreasingly serious problem with a huge toll in lives and health-care costs every year." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "A good understanding of disease biology and effective chemistry is not the\nonly requirement for an efficacious drug; we also must understand how variation\nat the target affects drug action, and how variation in other genes affects the way\ndrugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the\ndrug development paradigm also faces some unique challenges; for example, the\nexquisite rarity of some adverse reactions makes collection of sufficient samples for\nwell-powered genetic analysis almost impossible." + } + ], + "cea13566-9d52-4423-9280-d46da486dd7f": [ + { + "document_id": "cea13566-9d52-4423-9280-d46da486dd7f", + "text": "Drug-Gene Interactions Predicting Efficacy\n\nIn 1 candidate gene study, a genetic variant in the HMG-CoA reductase gene, present in 6.7% of patients, modified the LDL-C response to pravastatin by 6.4 mg/dL. 244][247] However, these effect sizes are small and difficult to distinguish from random variation in individual patients.Indeed, the metformin finding is less important for its potential clinical applications than for the biological insight provided by this link between glucose control and a gene involved in the response to DNA damage. 245,246" + } + ], + "d2bbd79c-672b-4c18-8b37-717b9be32877": [ + { + "document_id": "d2bbd79c-672b-4c18-8b37-717b9be32877", + "text": "Nutrition and metabolism\n\nThe power of these new experimental protocols, comparing gene expression profiles to understand spontaneous differences in phenotype due to disease, was extended by inducing phenotypic differences using creative molecular intervention.The first experiments to manipulate phenotype in this way used drugs.A comparison of the gene expression of a drug-induced phenotype with that of the normal phenotype was brilliantly executed in a single study that simultaneously identified a mechanism for the regulation of sterol uptake in the intestine and a genetic disease, sitosterolemia [17 • ], mice were treated with a lipid-metabolism altering compound and the expression profiles of various tissues compared with normal mice using gene arrays.Differentially expressed genes were evaluated 'in silico,' and an unknown gene was found using bioinformatic tools to be homologous to the ATP-binding cassette (ABC) family of genes.Members of the ABC family include cellular cholesterol transport proteins.Defects in a member of this family (ABCA1) form the basis for the poor cholesterol delivery to high-density lipoprotein (HDL) that underlies Tangiers disease [18], another cholesterol-related disease [19].Through the use of a variety of in silico techniques, Berge et al. [17 •• ] concluded that the proteins produced from the newly discovered genes, ABCG5 and ABCG8, were responsible for the regulated reverse transport of newly absorbed cholesterol and phytosterols out of the apical surface of intestinal cells.Using public gene databases, a human homolog of the putative mouse transporter was identified, cloned and used to screen sitosterolemic humans.Dysfunctional mutations were found in these genes in all individuals suffering from sitosterolemia.Thus, individuals suffering from sitosterolemia lack the machinery responsible for the selective and controlled transport of cholesterol, and therefore hyperabsorb various sterols (including plant sterols).This study illustrated many of the strengths of genomic experimentation: the identification of phenotypically important genes using global differential gene expression analysis; querying internet databases to deduce structure/function relationships from sequence comparison; and the characterization of individual variation (polymorphism) linked to health.These findings have transformed our understanding of lipid absorption and metabolism, begging the question: how long would this knowledge have waited to be discovered without genomics?" + } + ], + "f35e02a1-3314-4663-913f-38a3fc072aa8": [ + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "19.3.1 An environmental or pharmacogenetic basis for drug\nefficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many\nnon-genetic factors also influence the efficacy of medications, including the patient’s\nage, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit\njuice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the\nfirst-pass metabolism of many medications." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "Finally, it is possible that other\nmolecules (or drugs) might modulate the biological context within which the drug–\ntarget interaction takes place. Variation in any of the elements that control these\ntypes of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related\ncandidates. 19.3 PHARMACOGENETICS (PGx)\n\n519\n\n19.3.5 Using bioinformatics to gain understanding of adverse\ndrug reaction (ADR)\nOne of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "19.3 Pharmacogenetics (PGx)\nIt is well known that after exposure to a drug, almost any given cohort of patients show\na wide variety of responses. In an ideal situation, patients show a beneficial response\nto the therapy, although they may also show no response or a weak response, and\nperhaps most worryingly, they may experience an adverse drug reaction (ADR),\nwhich in extreme situations could lead to serious illness or even death. ADR is an\nincreasingly serious problem with a huge toll in lives and health-care costs every year." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "A good understanding of disease biology and effective chemistry is not the\nonly requirement for an efficacious drug; we also must understand how variation\nat the target affects drug action, and how variation in other genes affects the way\ndrugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the\ndrug development paradigm also faces some unique challenges; for example, the\nexquisite rarity of some adverse reactions makes collection of sufficient samples for\nwell-powered genetic analysis almost impossible." + } + ], + "fca531d0-d45b-495f-a02c-fbd437617b20": [ + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "19.3.1 An environmental or pharmacogenetic basis for drug\nefficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many\nnon-genetic factors also influence the efficacy of medications, including the patient’s\nage, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit\njuice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the\nfirst-pass metabolism of many medications." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "Finally, it is possible that other\nmolecules (or drugs) might modulate the biological context within which the drug–\ntarget interaction takes place. Variation in any of the elements that control these\ntypes of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related\ncandidates. 19.3 PHARMACOGENETICS (PGx)\n\n519\n\n19.3.5 Using bioinformatics to gain understanding of adverse\ndrug reaction (ADR)\nOne of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "19.3 Pharmacogenetics (PGx)\nIt is well known that after exposure to a drug, almost any given cohort of patients show\na wide variety of responses. In an ideal situation, patients show a beneficial response\nto the therapy, although they may also show no response or a weak response, and\nperhaps most worryingly, they may experience an adverse drug reaction (ADR),\nwhich in extreme situations could lead to serious illness or even death. ADR is an\nincreasingly serious problem with a huge toll in lives and health-care costs every year." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "A good understanding of disease biology and effective chemistry is not the\nonly requirement for an efficacious drug; we also must understand how variation\nat the target affects drug action, and how variation in other genes affects the way\ndrugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the\ndrug development paradigm also faces some unique challenges; for example, the\nexquisite rarity of some adverse reactions makes collection of sufficient samples for\nwell-powered genetic analysis almost impossible." + } + ] + }, + "data_source": [], + "document_id": "56BFA4C5360F4028B70961B34F0F40D0", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "pharmacogenetics&PGx", + "gene-by-drug&effects&GxD", + "life&expectancies", + "health", + "adverse&drug&reaction&ADR", + "cytochrome&P-450&3A4", + "HMG-CoA&reductase&gene", + "LDL-C", + "metformin", + "CYP2C9" + ], + "metadata": [], + "question": "How do gene-by-drug effects (GxD) modulate life expectancies\nand health?", + "subquestions": null, + "task_id": "56BFA4C5360F4028B70961B34F0F40D0", + "usage": { + "chatgpt": 6822, + "gpt-4": 4211, + "gpt-4-turbo-preview": 3234 + }, + "user_id": 2 + }, + "document_id": "56BFA4C5360F4028B70961B34F0F40D0", + "task_id": "56BFA4C5360F4028B70961B34F0F40D0" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_08.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_08.json new file mode 100644 index 0000000..d9851e6 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_08.json @@ -0,0 +1,150 @@ +{ + "created_at": "2024-04-02T12:31:43.259395Z", + "data": { + "amplify": false, + "answer": "The study identified 10 genomic loci which influence healthspan, parental lifespan, and longevity. These include loci near FOXO3, SLC4A7, LINC02513, ZW10, and FGD6. The life-extending variant near FOXO3 is associated with a delay in the age at menarche, suggesting a possible sex-specific effect. However, the study did not perform sex-stratified analyses, so it's unclear if there are other sex-specific longevity-related genetic variants.", + "context": { + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nIn one case, a gene identified by mutation recovered from a genetic screen in the laboratory, methuselah, may have variants in natural populations.In particular, the common ATATC haplotype has a sharp geographic (north-south) cline in U.S. populations, which, intriguingly, is associated with an 18% difference in life span (97).It would be interesting to examine these natural populations for differences in their reproductive schedule.Extensive studies show that life span can be rapidly selected as an indirect outcome of artificial selection for age at reproduction.Samples from natural populations of Drosophila contain genetic variants that can be rapidly selected, within 15 generations, for 50% or greater differences in life span on the basis of choosing individuals that are reproductive at early versus later ages (93).Selection was reversible, indicating that these life history variants depended on existing gene combinations not new mutations.Among the genes that differed in quantitative expression between young-and old-selected lines were heat shock proteins, e.g., hsp 22 (60).An overarching conclusion from fly aging genetics is that stress resistance is coupled to longevity (94), as in C. elegans.Other gene candidates are being sought by QTL analysis and show complex interactions with gender and population density (17,115)." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Murabito JM, Yuan R, Lunetta KL (2012) The search for\nlongevity and healthy aging genes: insights from epidemiological\nstudies and samples of long-lived individuals. J Gerontol A Biol\nSci Med Sci 67(5):470–479. doi:10.1093/gerona/gls089\n20. Nuzhdin SV, Pasyukova EG, Dilda CL et al (1997) Sex-specific\nquantitative trait loci affecting longevity in Drosophila melanogaster. Proc Natl Acad Sci USA 94(18):9734–9739\n21. Gems D, Riddle DL (2000) Genetic, behavioral and environmental determinants of male longevity in Caenorhabditis elegans. Genetics 154(4):1597–1610\n\n123\n\n22." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + } + ], + "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4": [ + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "text": "\n\nOur study has several limitations.First, we did not analyse the sex and mitochondrial chromosomes, since we were unable to gather enough cohorts that could contribute to the analysis of these chromosomes.However, these chromosomes may harbour loci associated with longevity that we thus have missed.Second, although we included as many cohorts as possible, the sample size of our study is still relatively small (especially for the 99th percentile analysis) in comparison to GWA studies of age-related diseases, such as T2D and cardiovascular disease, and parental age at death 11,51,52 .Hence, this limited our power to detect loci with a low MAF (<1%) that contribute to longevity.Third, we did not perform sex-stratified analyses and may thus have missed sexspecific longevity-related genetic variants.The reason for this is that (1) we only identified a limited number of suggestive significant associations in our unstratified 90th and 99th percentile analyses, (2) our sample size is modest (especially when stratified by sex), and (3) thus far, there has been no report of any genomewide significant sex-specific longevity locus." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "690a2ae6-962a-438c-91ca-60425a0c8d02": [ + { + "document_id": "690a2ae6-962a-438c-91ca-60425a0c8d02", + "text": "\n\nPreviously, it has been suggested that genetic variation in the FOXO1 gene is specifically contributing to human female longevity (reviewed in Chung et al., 2010).However, at chromosome 13q14.11harboring the FOXO1 gene we found no evidence for linkage with female longevity (LOD<0.05)and at the gene position of FOXO1 we found no evidence for association in the females-only metaanalysis (p-values>0.042) in the GEHA Study.Potentially, the effect of this locus is not only influenced by gender but also by genetic background." + } + ], + "6b2dba7c-0249-448e-9e84-92de7088109b": [ + { + "document_id": "6b2dba7c-0249-448e-9e84-92de7088109b", + "text": ", 2003), to study GXE and\nconsequences of treatments as a function of age, diet, and sex (Fleet et al. , 2016; Philip et\nal. , 2010; Roy et al. , 2020; Sandoval-Sierra et al. , 2020; Williams et al. , 2016, 2020), gene\npleiotropy (Wang et al. , 2016a), and to test behavioral predictions based on differences in\nbrain architecture (Yang et al. , 2008). Author Manuscript\nAuthor Manuscript\n\nHere we summarize the current status of this resource with a focus on genetic structure, and\non the power and precision of mapping trait variance to loci and genes." + } + ], + "7f23af74-95a3-46aa-bd61-629d2cfc2073": [ + { + "document_id": "7f23af74-95a3-46aa-bd61-629d2cfc2073", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "The Height-Life Span Nexus\n\nSeveral observations and lines of experimentation have raised the issue of whether interindividual differences in aging rate are influenced by genes that modulate body size and early-life growth patterns.These include (a) the association between small stature and exceptional longevity in calorically restricted rodents (Yu et al., 1985), methionine-restricted rats (Orentreich et al., 1993), and mutant dwarf mice (Brown-Borg et al., 1996;Miller, 1999); and (b) the association between small body size and longer life span in natural populations of mice (Falconer et al., 1978), flies (Hillesheim and Stearns, 1992), dogs (Li et al., 1996), and, possibly, people (Samaras andStorms, 1992).The correlation in dogs is particularly striking: selective breeding for dogs of different body size has produced breeds varying in size from Chihuahua to Irish wolfhound.These breeds also vary greatly in mean longevity, from approximately 7 to 10.5 years, and the correlation between breed longevity and breed body weight (Miller, 1999) is a remarkable R 2 = 0.56.These differences are genetic and affect stature rather than obesity: no amount of overeating will convert a West Highland white terrier to a St. Bernard.The selective pressures applied were designed to create dogs of specific sizes and temperaments and were not intended to influence aging rate or life span.The clear implication is that the effects on longevity are pleiotropic, i.e., that genes selected for their effect on body size and conformation influenced life span as a side effect.It is of interest to note that the few analyses (Eigenmann et al., 1984(Eigenmann et al., , 1988) ) of the hormonal basis for interbreed differences in body size have shown that the genes in question influence levels of IGF-1, the most likely mediator of the life-span effects in the long-lived df/df and dw/dw mouse mutants.Could it be mere coincidence that long-lived mutant nematode worms (Kimura et al., 1997) also show mutations in genes related to insulin and IGF-1 receptors?" + } + ], + "9fed8fd1-fce5-4fc1-9911-05d312f88521": [ + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "text": "\n\nThe antagonistic pleiotropy and hyperfunction theories of ageing predict the presence of genetic variants important for growth and development in early life with deleterious effects towards the end of the reproductive window 19,20 .While we are unable to directly capture the genetic effects on individuals before age 40 due to the study design of our datasets, we found that the life-extending variant near FOXO3 is associated with a delay in the age at menarche and a decrease in intracranial volume and cognitive abilities.It thus appears that there are loci exhibiting antagonistic effects, although we are unable to discern whether this is due to true pleiotropy or due to linkage of causal variants within a region Genes which showed a significant effect (FDR < 5%) of gene expression on ageing traits are displayed here.Gene names are annotated with the direction of effect, where + andindicate whether the life-extending association of the locus is linked with higher or lower gene expression, respectively.Locus: nearest gene to lead variant in the multivariate analysis, Chr: chromosome, Position: base-pair position of lead variant (GRCh37), Cis-genes: genes in physical proximity (<500 kb) to the lead variant of the locus which colocalise with the multivariate signal, Trans-genes: genes located more than 500 kb from the lead variant of the locus." + }, + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "text": "\nAgeing phenotypes, such as years lived in good health (healthspan), total years lived (lifespan), and survival until an exceptional old age (longevity), are of interest to us all but require exceptionally large sample sizes to study genetically.Here we combine existing genome-wide association summary statistics for healthspan, parental lifespan, and longevity in a multivariate framework, increasing statistical power, and identify 10 genomic loci which influence all three phenotypes, of which five (near FOXO3, SLC4A7, LINC02513, ZW10, and FGD6) have not been reported previously at genome-wide significance.The majority of these 10 loci are associated with cardiovascular disease and some affect the expression of genes known to change their activity with age.In total, we implicate 78 genes, and find these to be enriched for ageing pathways previously highlighted in model organisms, such as the response to DNA damage, apoptosis, and homeostasis.Finally, we identify a pathway worthy of further study: haem metabolism." + }, + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "text": "\n\nHere, we assess the degree of genetic overlap between published GWAS of three different kinds of ageing phenotypeshealthspan, parental lifespan, and longevity (defined as survival to an age above the 90th percentile)-and perform a multivariate meta-analysis to identify genetic variants related to healthy ageing.We subsequently characterise the sex-and age-specific effects of loci which affect all three ageing traits and look up reported associations with age-related phenotypes and diseases.Finally, we link the observed signal in these loci to the expression of specific genes, including some that are currently studied in model organisms, and identify pathways involved in healthy ageing." + }, + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "text": "\n\nAgeing phenotypes, such as years lived in good health (healthspan), total years lived (lifespan), and survival until an exceptional old age (longevity), are of interest to us all but require exceptionally large sample sizes to study genetically.Here we combine existing genome-wide association summary statistics for healthspan, parental lifespan, and longevity in a multivariate framework, increasing statistical power, and identify 10 genomic loci which influence all three phenotypes, of which five (near FOXO3, SLC4A7, LINC02513, ZW10, and FGD6) have not been reported previously at genome-wide significance.The majority of these 10 loci are associated with cardiovascular disease and some affect the expression of genes known to change their activity with age.In total, we implicate 78 genes, and find these to be enriched for ageing pathways previously highlighted in model organisms, such as the response to DNA damage, apoptosis, and homeostasis.Finally, we identify a pathway worthy of further study: haem metabolism." + } + ], + "adf2d31e-e83d-47df-97af-3764e42aa80e": [ + { + "document_id": "adf2d31e-e83d-47df-97af-3764e42aa80e", + "text": "LongevityMap--human genetic variants associated with longevity\n\nVariation in human lifespan has been found to be 20-30% heritable, with increasing heritability at advanced ages (27).As next-generation sequencing and genome-wide approaches advance, so does the capacity for performing longevity association studies.To catalog the increasing volume of data in genetic studies of human longevity, we created LongevityMap (http://genomics.senescence.info/longevity/), a database of genes, gene variants and chromosomal locations associated with longevity (28).This differs from the GenAge database, which focuses mostly on data from model organisms and the few genes associated with human ageing (e.g.genes causing progeroid syndromes)." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nGenes/loci identified by genome-wide association studies of longevity and lifespan traits." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + } + ], + "ce2c68bf-878d-460c-8d9b-d45ce3034ef7": [ + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "text": "Put more simply: What is the strength of evidence in favor of GXE effects on\nlifespan? We ask if youthful adult body weight (~120 days) predicts lifespan. Is the change\nin body weight in adults in response to a HFD a causal predictor of lifespan? Finally,\nwe ask whether levels of classic serum metabolites or metabolic hormones measured in\nmiddle-age or old-age predict variation in lifespan? Our focus is both on overall effects and\non strain-specific difference in effect of diet on lifespan and weight gain, rather than on\nspecific genetic modifiers or loci of lifespan." + } + ], + "da4a9500-831f-48ab-acea-5ec7097276ed": [ + { + "document_id": "da4a9500-831f-48ab-acea-5ec7097276ed", + "text": "\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways." + } + ], + "db90a971-e55a-4ab0-a3b1-05908d6771a4": [ + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nStudies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches." + } + ] + }, + "data_source": [], + "document_id": "2FE689C771242014845BDF2008542DAA", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "FOXO3", + "APOE", + "AKT1", + "SLC4A7", + "LINC02513", + "ZW10", + "FGD6", + "CDKN2B", + "ANRIL", + "FOXO1" + ], + "metadata": [], + "question": "What loci and gene variants modulate normal variation in lifespan,\nand in which cases is this sex-specific (GxS)?", + "subquestions": null, + "task_id": "2FE689C771242014845BDF2008542DAA", + "usage": { + "chatgpt": 8066, + "gpt-4": 5645, + "gpt-4-turbo-preview": 4696 + }, + "user_id": 2 + }, + "document_id": "2FE689C771242014845BDF2008542DAA", + "task_id": "2FE689C771242014845BDF2008542DAA" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_09.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_09.json new file mode 100644 index 0000000..4346780 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_09.json @@ -0,0 +1,146 @@ +{ + "created_at": "2024-04-02T13:11:59.579145Z", + "data": { + "amplify": false, + "answer": "Rapamycin, an mTOR inhibitor, has been shown to extend the lifespan of various organisms, including mice, by delaying or reversing aging in multiple cell types. It does this by regulating rates of protein synthesis and energy utilization, which are processes associated with aging. In the hematopoietic system, rapamycin limits age-related increases in stem cells and biomarkers of aging, enhancing the performance of these cells. However, rapamycin also has potential unwanted metabolic effects, such as insulin resistance and glucose intolerance, though these effects are controversial and potentially reversible. The mTOR pathway, which rapamycin targets, integrates signals from insulin, cytokines, nutrients, oxygen, and mitogenic stimuli, and its regulation has implications for longevity and against the negative effects of aging. Rapamycin also induces autophagy, a process important for cellular homeostasis and damage prevention. Despite these benefits, the exact mechanisms by which rapamycin extends lifespan and whether it delays aging or affects specific diseases remain unclear.", + "context": { + "198bd45c-e3a3-4937-b83a-61914b64e43b": [ + { + "document_id": "198bd45c-e3a3-4937-b83a-61914b64e43b", + "text": "\n\nOne surprising result of our experiment was the relatively weak support for involvement of the insulin/insulin-like signaling (IIS) or target-of-rapamycin (TOR) pathways in the evolution of late-life performance.Mutations in genes within these pathways can alter life span and fertility in flies and other organisms (Partridge and Gems 2002); natural genetic variation in expression of IIS/TOR-pathway genes has been reported to predict agingrelated phenotypes (Nuzhdin et al. 2009), and natural clinal variation in the insulin receptor gene InR has been associated with variation in stress resistance and fecundity (Paaby et al. 2010).We therefore expected that some of these genes would contribute to the evolution of life span and late-life fecundity in our experiment.Only one gene previously annotated with the Gene Ontology biological function \"determination of adult life span\" (Cct1) was among the genes bearing the strongest signature of selection, no more than would be expected by chance (1/96 of the candidate genes that had some biological process annotation, compared to 116/10,792 of all genes with some biological-process annotation, χ [1] 2 = 0.002, P > 0.96).Genes annotated with the functions \"aging\" or \"determination of adult life span\" were also significantly underrepresented among differentially expressed genes (43/215 transcripts with these annotations had P < 0.05 for line or line-by-age effects, compared to 4488/13,258 of all annotated transcripts, χ [1] 2 = 18.1, P < 0.0001).Most of the genes we identified are therefore novel candidates for the regulation of life span and late-age performance." + } + ], + "3043efd1-4b13-4300-b2a7-d1992c8d4e47": [ + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "Rapamycin\n\nRapamycin has been shown to robustly increase lifespan in at least three different mouse strains and to improve healthspan measures including cognitive function, cardiac function, immune function, obesity, and cancer incidence (Johnson et al. 2015;Kaeberlein 2014)." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nmTOR activates the kinase S6K, which phosphorylates S6, inhibiting autophagy [92].Rapamycin can extend the life span of organisms from yeast to mammals in a dose-dependent manner [95].However, some data suggest that rapamycin has unwanted metabolic effects, including insulin resistance, hyperlipidemia, glucose intolerance, and hypophosphatemia; however, whether rapamycin is responsible for these effects remains controversial, and some of the effects are reversible [96,97].The mTOR pathway integrates different signals from insulin, cytokines, nutrients, oxygen, and mitogenic stimuli, and its regulation has important implications for longevity and against the negative effects of aging [92]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nThe molecular mechanisms that drive cellular senescence in proliferative and nonproliferative cells are being discovered.One of the metabolic pathways associated with aging is the growth-promoting mitogen/nutrient-sensing pathway, in which the target of rapamycin (mTOR) is considered a central signaling molecule that affects multiple cellular pathways associated with aging [137].In particular, mTOR participates in the transition of cells from quiescence to senescence [138]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Inductors of Autophagy and its Impact on Aging\n\nAutophagy has a role in homeostasis, which plays an essential role in the maintenance of cellular physiology and the prevention of cellular damage.Among the inducers of autophagy have been described the already-mentioned rapamycin, resveratrol, and polyamines; however, only polyamines have demonstrated results in clinical research in humans [65].It is known that these compounds can induce the canonical autophagy pathway, which includes inactivation of the mammalian objective of the rapamycin complex 1 (mTORC1), allowing phosphorylation and activation of the Unc-51 complex (Ulk1/2), where the cascade of the other members of the complex is subsequently activated, ULK as FIP200 and ATG13 [65]." + } + ], + "5030cbc8-e02c-4e3a-8cbc-0156ce123c99": [ + { + "document_id": "5030cbc8-e02c-4e3a-8cbc-0156ce123c99", + "text": "\n\nA third example illustrates that pharmacological targeting of pathways that have been implicated in promoting aging may also restore youthfulness at cellular and biochemical levels.Among the key regulators associated with interventions that extend life span is the enzyme mTOR, which senses cellular nutrient levels and in turn regulates rates of protein synthesis and energy utilization.Notably, administration of rapamycin, an mTOR inhibitor, starting at midlife can extend the life span of mice, suggesting that aging can be delayed or reversed in multiple cell types (Harrison et al., 2009).In the hematopoietic system, aging is associated with an increase in mTOR activation in stem cells and progenitors (Chen et al., 2009).Administration of rapamycin to old mice to inhibit mTOR not only limited the normal age-related increases in hematopoietic stem cells and biomarkers of aging in those cells, but also enhanced the performance of the stem cells to become as effective as young stem cells in heterochronic transplantation experiments (Chen et al., 2009) (Figure 1)." + } + ], + "6ee86c77-b359-45f1-bd54-b1cd9b260ae6": [ + { + "document_id": "6ee86c77-b359-45f1-bd54-b1cd9b260ae6", + "text": "Rapamycin inhibits TOR signalling to alter nDNA\ntranslation, inducing mitonuclear protein imbalance35, and increases\nlifespan in various species, including mice33. Rapamycin also\nincreased mean worm lifespan (by 16%)34 in a ubl-5-dependent manner, induced UPRmt, but not UPRER or heat shock response, and\nincreased respiration (Fig. 6a, c and Supplementary Fig. 9a). This\nwas associated with increased ATP levels, equal citrate synthase activity and altered nDNA/mtDNA oxidative phosphorylation protein\nratio (Fig. 6d, e). Additionally, rapamycin changed the balance\nbetween nDNA- and mtDNA-encoded oxidative phosphorylation\nsubunits in mouse hepatocytes in a dose dependent manner (Fig. 6f,\ng)." + }, + { + "document_id": "6ee86c77-b359-45f1-bd54-b1cd9b260ae6", + "text": "Zylbee, E., Vesco, C. & Penman, S. Selective inhibition of the synthesis of\nmitochondria-associated RNA by ethidium bromide. J. Mol. Biol. 44, 195–204\n(1969). 33. Harrison, D. E. et al. Rapamycin fed late in life extends lifespan in genetically\nheterogeneous mice. Nature 460, 392–395 (2009). 34. Robida-Stubbs, S. et al. TOR signaling and rapamycin influence longevity by\nregulating SKN-1/Nrf and DAF-16/FoxO. Cell Metab. 15, 713–724 (2012). 35. Zid, B. M. et al. 4E-BP extends lifespan upon dietary restriction by enhancing\nmitochondrial activity in Drosophila. Cell 139, 149–160 (2009). 36. Schulz, T. J. et al." + }, + { + "document_id": "6ee86c77-b359-45f1-bd54-b1cd9b260ae6", + "text": "a, Rapamycin (Rapa, 1 nM) extends worm lifespan in a\nubl-5-dependent manner; b, ubl-5-dependently induced UPRmt (hsp-6::GFP)\nbut not UPRER (hsp-4::GFP) (n 5 4). c–e, Rapamycin increased respiration\n(c, n 5 10) and ATP content but not citrate synthase activity (d, n 5 3) and\ninduced mitonuclear protein imbalance (e). f–h, In mouse hepatocytes,\nrapamycin induces mitonuclear protein imbalance (f, g) and induces UPRmt as\n\nshown at the protein (f, g, n 5 3), and transcriptional (h, n 5 8) level. i, Resveratrol (Resv, 25 mM) induced mitonuclear protein imbalance in mouse\nhepatocytes (n 5 4)." + } + ], + "7c2732db-ed6e-419a-8256-537b4dc68072": [ + { + "document_id": "7c2732db-ed6e-419a-8256-537b4dc68072", + "text": "\n\npivotal in this aspect providing molecular insights and having huge conceptual contributions in the field.Characterising the contribution of individual mutants in ageing is a continuously active and informative activity in the field.On top of these studies, genome-wide screens have provided insights on the role of evolutionarily conserved processes and signalling pathways in ageing such as nutrient response [17,18], protein translation, oxidative damage [19,20], mitochondrial function [21,22] and autophagy [22,23] opening new avenues for biogerontology research.Yeasts have proved informative and helped in understanding mechanisms of highly conserved pathways (from yeast to human) in physiology, health and disease such as the Target of Rapamycin (TOR) [24], glucose sensing (PKA) and stress response pathways (Sty1/p38) [25]." + } + ], + "7f23af74-95a3-46aa-bd61-629d2cfc2073": [ + { + "document_id": "7f23af74-95a3-46aa-bd61-629d2cfc2073", + "text": "\n\nmTOR activates the kinase S6K, which phosphorylates S6, inhibiting autophagy [92].Rapamycin can extend the life span of organisms from yeast to mammals in a dose-dependent manner [95].However, some data suggest that rapamycin has unwanted metabolic effects, including insulin resistance, hyperlipidemia, glucose intolerance, and hypophosphatemia; however, whether rapamycin is responsible for these effects remains controversial, and some of the effects are reversible [96,97].The mTOR pathway integrates different signals from insulin, cytokines, nutrients, oxygen, and mitogenic stimuli, and its regulation has important implications for longevity and against the negative effects of aging [92]." + }, + { + "document_id": "7f23af74-95a3-46aa-bd61-629d2cfc2073", + "text": "\n\nThe molecular mechanisms that drive cellular senescence in proliferative and nonproliferative cells are being discovered.One of the metabolic pathways associated with aging is the growth-promoting mitogen/nutrient-sensing pathway, in which the target of rapamycin (mTOR) is considered a central signaling molecule that affects multiple cellular pathways associated with aging [137].In particular, mTOR participates in the transition of cells from quiescence to senescence [138]." + }, + { + "document_id": "7f23af74-95a3-46aa-bd61-629d2cfc2073", + "text": "Inductors of Autophagy and its Impact on Aging\n\nAutophagy has a role in homeostasis, which plays an essential role in the maintenance of cellular physiology and the prevention of cellular damage.Among the inducers of autophagy have been described the already-mentioned rapamycin, resveratrol, and polyamines; however, only polyamines have demonstrated results in clinical research in humans [65].It is known that these compounds can induce the canonical autophagy pathway, which includes inactivation of the mammalian objective of the rapamycin complex 1 (mTORC1), allowing phosphorylation and activation of the Unc-51 complex (Ulk1/2), where the cascade of the other members of the complex is subsequently activated, ULK as FIP200 and ATG13 [65]." + } + ], + "844ab36b-9239-4d73-a61c-68f68acc4fd1": [ + { + "document_id": "844ab36b-9239-4d73-a61c-68f68acc4fd1", + "text": "Background\n\nGenetic, dietary and drug interventions can enhance longevity and suppress age-associated disease, such as cancer.Prominent genetic interventions that robustly extend longevity and healthspan in mammals include those that decrease growth hormone (GH) and insulin-like growth factor (IGF) signalling; for example, Ames dwarf mice live more than 50% longer than their wild-type siblings [1].These diminutive mice result from a point mutation in a gene (Prop1 df/df ) that drives development of the pituitary gland, so that mutant mice are deficient in specific hormones.The GH deficiency, in particular, has been shown to underlie their enhanced health span and extended lifespan.Ames mice are highly insulinsensitive, resistant to some stresses and the incidence of cancer is delayed [2][3][4].Dietary and drug interventions that extend lifespan include calorie restriction (CR) and the mTOR inhibitor rapamycin [5].Like the Ames dwarf mutation, CR and rapamycin also suppress and/ or delay the incidence of cancer [5][6][7].A detailed understanding of how these interventions exert their beneficial effects is essential to develop strategies to promote healthy aging in humans [8].Currently, these interventions are thought to exert their effects by related and interconnected effects on some or all of the following: genome stability, the epigenome, telomere attrition and/or function, protein quality control, mitochondrial function, nutrient sensing, cellular senescence, stem cell exhaustion, cellular stress responses and altered intercellular communication [9].Of note, the effects of longevity promoting interventions on the epigenome, a key determinant of cell phenotype, are poorly understood." + } + ], + "8a8bea99-d3b9-4109-88e4-ad459dcd7173": [ + { + "document_id": "8a8bea99-d3b9-4109-88e4-ad459dcd7173", + "text": "\n\nThe target of rapamycin (TOR) signaling pathway has also emerged as a major regulator of lifespan.TOR is a highly conserved kinase that transduces signals from nutrients to regulate cell size, cell growth, and metabolism (Martin & Hall, 2005).Genetic studies in yeast Saccharomyces cerevisiae have shown that reduced levels of nutrients, namely amino acids and sugars, can extend yeast lifespan through regulation of the TOR signaling pathway (Kaeberlein et al ., 2005;Powers et al ., 2006).In Drosophila , recent studies have shown that amino acid restriction, rather than 'calorie restriction', extends lifespan (Min & Tatar, 2006).In C. elegans , either inactivation of CeTOR/let-363 by RNAi, or mutations in Raptor/daf-15 , encoding a regulatory subunit of CeTOR, leads to lifespan extension (Vellai et al ., 2003;Jia et al ., 2004)." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nAs mentioned above, a number of genes regulating longevity also control growth and development.Some of these, such as the insulin/IGF1/GH pathway, have been suggested to play a role in the mechanisms of CR (Fig. 1).An emerging critical player is the target of rapamycin (TOR) signaling pathway, which involves both nutrient sensing and regulation of growth.Several genes in the TOR pathway, and the TOR gene itself, regulate longevity in flies (Kapahi et al., 2004) and both longevity and dauer diapause in worms (Jia et al., 2004).Strikingly, not only have genetic manipulations of the TOR gene extended lifespan in yeast and worms (Stanfel et al., 2009) but also feeding rapamycin (which inhibits TOR and is also known as sirolimus) to middle-aged mice significantly (9 -14%) increased lifespan (Harrison et al., 2009).Whether rapamycin is extending lifespan by delaying of aging or by affecting a specific disease, such as cancer, remains unclear.More recent studies show that starting rapamycin administration earlier in life does AGING GENES AS TARGETS FOR DRUG DISCOVERY not result in a significantly greater increase in lifespan (10 -18%) than that obtained in middle-aged mice (Miller et al., 2011)." + } + ], + "b1ffece8-f805-4d99-8e3b-402df309f1ed": [ + { + "document_id": "b1ffece8-f805-4d99-8e3b-402df309f1ed", + "text": "\n\nReplacement of the C/ebpα gene with C/ebpβ increases lifespan by 20% [35,36], and may alter the rate of aging [37], indicating that altering the isoform expression of these genes can affect lifespan.Moreover, the life-extending drug rapamycin may affect isoform ratios of C/ebpβ.Rapamycin has been shown to increase lifespan via the suppression of Mtor [38] which in turn controls the isoform ratios of C/ebpβ [39].Therefore, we speculate that rapamycin may in part exert its life extending effect through C/ebpβ." + } + ], + "c1df5fa6-1d3b-4085-9248-683c9666faa5": [ + { + "document_id": "c1df5fa6-1d3b-4085-9248-683c9666faa5", + "text": "\n\nThe genome-wide RNAi study conducted by the Ruvkun lab, authored by Hamilton et al. [88], identified a total of 89 additional aging genes with disparate functions including cell structure, cell surface proteins, cell signaling, cellular metabolism, and protein turnover.Of the 66 genes with previously known functions, 17 corresponded to various aspects of carbon metabolism, including citric acid cycle enzymes and subunits of complexes I, IV, and V of the ETC.Researchers also speculated that protein translation might play a role in lifespan regulation, based on the identification of iff-1 (T05G5.10),a gene that has homology to the translation initiation factor eIF5A.Other hits from this screen included two genes containing PH domains known to interact with phosphatidylinositol lipids, multiple G protein-coupled receptors, protein processing and degradation genes such as proteases and ubiquitin ligases/hydrolases, and chromatin modifying factors." + } + ], + "c89f6c23-d5ac-4352-9b82-2ba559b20c0b": [ + { + "document_id": "c89f6c23-d5ac-4352-9b82-2ba559b20c0b", + "text": "\n\nHow cellular processes that regulate aging impact genome stability also remain unclear.Compelling evidence now exists that in all eukaryotes, aging is regulated by conserved insulin/insulin-like growth factor (I-(IFG-1)) pathways and growth-signaling pathways regulated by the target of rapamycin (TOR) family of kinases (4).In general, experimental manipulations that upregulate these pathways promote aging, and manipulations that downregulate these pathways-including mutational inactivation or caloric restriction-extend life span and mitigate age-related pathologies.Downregulation of these pathways often leads to a reduction in oxidative stress and oxidative damage to DNA and other cellular constituents.For the most part, however, the relationship between aging and changes in oxidative damage downstream of alterations in growth-signaling pathways remains correlative rather than causal." + } + ], + "e397ac93-f115-4cee-8b87-59137a017985": [ + { + "document_id": "e397ac93-f115-4cee-8b87-59137a017985", + "text": "\n\nThe potential of interventional approaches targeted at aging has yet to be realized in part because aging is a complicated multisystem process that has remained enigmatic.However, research over the last two decades has led to significant excitement.One of the most striking findings is that it is possible to administer a clinically approved drug, rapamycin, to mice at 20 months of age and extend both their life span and health span (Harrison et al., 2009).Surprisingly, much of the recent success of aging research can be traced back to one of its simplest model organisms: yeast.Two of the major pathways studied in the context of aging and age-related disease are the sirtuin pathway and the TOR signaling pathway, and yeast was pivotal in their discovery." + } + ] + }, + "data_source": [], + "document_id": "32AFC12B71BD0478376834592459857C", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "rapamycin", + "mTOR", + "aging", + "lifespan", + "healthspan", + "cellular&bioenergetics", + "metabolic&effects", + "insulin&resistance", + "autophagy", + "TOR&signaling&pathway" + ], + "metadata": [], + "question": "what is the influence of rapamycin and aging on the in vivo physiological and metabolic states and cellular bioenergetic, and how do these relate to the rapamycin-induced health and lifespan modification for that genometype?", + "subquestions": null, + "task_id": "32AFC12B71BD0478376834592459857C", + "usage": { + "chatgpt": 8516, + "gpt-4": 4792, + "gpt-4-turbo-preview": 3850 + }, + "user_id": 2 + }, + "document_id": "32AFC12B71BD0478376834592459857C", + "task_id": "32AFC12B71BD0478376834592459857C" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_10.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_10.json new file mode 100644 index 0000000..2fd4921 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_10.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-04-02T13:14:53.880936Z", + "data": { + "amplify": false, + "answer": "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset Alzheimer's disease. The APOE gene is the strongest genetic risk factor for later onset Alzheimer's. The heritability of late-onset Alzheimer's disease (LOAD) is estimated to be ~60-80%, suggesting a large proportion of individual differences in LOAD risk is driven by genetics.", + "context": { + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nWe briefly comment on rare mutations that shorten life span through the early onset of diseases that are increasingly common during aging in the general population, e.g., familial forms of Alzheimer, breast cancer, coronary artery disease, type II diabetes, etc.The later onset forms of these diseases are associated with causes of death at later ages.A major question is what role the more common allelic variants of these same genes have in \"normal aging\".Although examination of this huge emerging topic goes beyond the present discussion, we may consider the example of Werner's syndrome, a rare autosomal recessive that causes adult onset progeria with a high incidence of cancer and atherosclerosis (70).The absence of Alzheimer-type dementia in Werner's syndrome illustrates the \"segmental\" nature of this and other progerias (70).Thus, heritable shortening of life span should not be considered as a simple acceleration of general aging processes.The Werner's lesion maps to a defective gene encoding a helicase and exonuclease, which also has several polymorphisms.In Japan, 1367Arg was associated with a lower risk of myocardial infarction (70), although it was not associated with longevity in Finland (14).In general, we know little of the genetic factors involved in frailty and morbidity at later ages, which are important to the geneenvironment interactions implied in the major longevity increase seen during the twentieth century." + } + ], + "0af83a97-18ef-47f4-9f0c-872633ca3414": [ + { + "document_id": "0af83a97-18ef-47f4-9f0c-872633ca3414", + "text": "\n\nIndicative diseases associated with the candidate aging genes" + } + ], + "213afab9-b2fb-40ed-abb7-d80853a0fbf3": [ + { + "document_id": "213afab9-b2fb-40ed-abb7-d80853a0fbf3", + "text": "D\n\nementia has an age-and sex-standardized prevalence of ~7.1% in Europeans 1 , with Alzheimer's disease (AD) being the most common form of dementia (50-70% of cases) 2 .AD is pathologically characterized by the presence of amyloid-beta plaques and tau neurofibrillary tangles in the brain 3 .Most patients are diagnosed with AD after the age of 65, termed late-onset AD (LOAD), while only 1% of AD cases have an early onset (before the age of 65) 3 .On the basis of twin studies, the heritability of LOAD is estimated to be ~60-80% (refs. 4,5 ), suggesting that a large proportion of individual differences in LOAD risk is driven by genetics.The heritability of LOAD is spread across many genetic variants; however, Zhang et al. 6 suggested that LOAD is more of an oligogenic than a polygenic disorder due to the large effects of APOE variants.Zhang et al. 6 and Holland et al. 7 predicted there to be ~100-10,000 causal variants contributing to LOAD; however, only a fraction have been identified.Increasing the sample size of genome-wide association studies (GWAS) will improve the statistical power to identify the missing causal variants and may highlight additional disease mechanisms.In combination with increasing the number of samples, it is beneficial to use different approaches to identify rare and private variation to help identify additional causal variants and increase understanding of disease mechanisms; however, we deem this to be out of the scope of the current analysis." + }, + { + "document_id": "213afab9-b2fb-40ed-abb7-d80853a0fbf3", + "text": "\nDementia has an age-and sex-standardized prevalence of ~7.1% in Europeans 1 , with Alzheimer's disease (AD) being the most common form of dementia (50-70% of cases) 2 .AD is pathologically characterized by the presence of amyloid-beta plaques and tau neurofibrillary tangles in the brain 3 .Most patients are diagnosed with AD after the age of 65, termed late-onset AD (LOAD), while only 1% of AD cases have an early onset (before the age of 65) 3 .On the basis of twin studies, the heritability of LOAD is estimated to be ~60-80% (refs. 4,5 ), suggesting that a large proportion of individual differences in LOAD risk is driven by genetics.The heritability of LOAD is spread across many genetic variants; however, Zhang et al. 6 suggested that LOAD is more of an oligogenic than a polygenic disorder due to the large effects of APOE variants.Zhang et al. 6 and Holland et al. 7 predicted there to be ~100-10,000 causal variants contributing to LOAD; however, only a fraction have been identified.Increasing the sample size of genome-wide association studies (GWAS) will improve the statistical power to identify the missing causal variants and may highlight additional disease mechanisms.In combination with increasing the number of samples, it is beneficial to use different approaches to identify rare and private variation to help identify additional causal variants and increase understanding of disease mechanisms; however, we deem this to be out of the scope of the current analysis.The largest previous GWAS of LOAD, identified 29 risk loci from 71,880 (46,613 proxy) cases and 383,378 (318,246 proxy) controls 8 .Our current study expands this to include 90,338 (46,613 proxy) cases and 1,036,225 (318,246 proxy) controls.The recruitment of LOAD cases can be difficult due to the late age of onset, so proxy cases can allow for the inclusion of younger individuals by estimating their risk of LOAD using parental status.Proxy cases and controls were defined on the basis of known parental LOAD status weighted by parental age (Supplementary Information).In the current study, we identified 38 loci, including seven loci that have not been reported previously.Functional follow-up analyses implicated tissues, cell types and genes of interest through tissue and cell type enrichment, colocalization and statistical fine-mapping.This study highlights microglia, immune cells and protein catabolism as relevant to LOAD, while identifying previously unidentified genes of potential interest. ResultsGenome-wide inferences.We performed meta-analysis on data from 13 cohorts, totaling 1,126,563 individuals (Supplementary" + } + ], + "38f806a9-f265-4854-b86b-38cf56b57dd8": [ + { + "document_id": "38f806a9-f265-4854-b86b-38cf56b57dd8", + "text": "Introduction\n\nAlzheimer's disease (AD) is a complex disorder and is the most common form of dementia [1].After age, family history is the single greatest risk factor for AD.AD can be classified into early and late onset forms.Mutations in three genes: PSEN1/2 and APP are known to cause early onset AD in an autosomal dominant manner [2,3].The majority of AD cases, however, are late onset (LOAD) and the APOE e4 allele is the strongest known genetic risk factor.Many additional genetic polymorphisms have been identified, though with substantially lower risk estimates [1,4,5,6,7,8,9,10].LOAD appears to be inherited and/or sporadic and there is evidence of a maternal inheritance pattern [11].Current estimates suggest that more than 20% of inherited LOAD cases are maternally inherited [12]." + } + ], + "3f41e709-4cf1-472b-b12b-804c6ebb07c9": [ + { + "document_id": "3f41e709-4cf1-472b-b12b-804c6ebb07c9", + "text": "INTRODUCTION\n\nMany common noninfectious diseases exhibit a more severe clinical presentation in older individuals.These diseases often exhibit complex etiology and can affect different tissues and cell types, with a wide spectrum of clinical outcomes.Prominent aging-associated neurodegenerative diseases are Alzheimer's disease (AD), Parkinson's disease (PD), and age-related macular degeneration (AMD), all of which can severely compromise the quality of life and have serious repercussions on both the individual and society at large.These late-onset diseases generally result from the interplay between multiple genetic susceptibility factors and environmental components.Sequencing of the human genome, cataloging of millions of single nucleotide polymorphisms (SNPs) together with the development of a map of common haplotypes, and technological innovations in genotyping are among the major milestones that are facilitating exploration of the genetic basis of common diseases (1,7,50).In the field of AMD genetics, these advances have led to the identification of several genetic susceptibility factors and enabled us to start dissecting the relationship between environmental risk factors and the genetic constitution of each individual (66,118,148).As a result, new opportunities are emerging for improved understanding of disease pathogenesis that may lead to better management and treatment of AMD.Clinical aspects of AMD are discussed only briefly (for a more in-depth discussion, see Reference 79)." + }, + { + "document_id": "3f41e709-4cf1-472b-b12b-804c6ebb07c9", + "text": "\nAging-associated neurodegenerative diseases significantly influence the quality of life of affected individuals.Genetic approaches, combined with genomic technology, have provided powerful insights into common late-onset diseases, such as age-related macular degeneration (AMD).Here, we discuss current findings on the genetics of AMD to highlight areas of rapid progress and new challenges.We also attempt to integrate available genetic and biochemical data with cellular pathways involved in aging to formulate an integrated model of AMD pathogenesis." + }, + { + "document_id": "3f41e709-4cf1-472b-b12b-804c6ebb07c9", + "text": "\n\nAging-associated neurodegenerative diseases significantly influence the quality of life of affected individuals.Genetic approaches, combined with genomic technology, have provided powerful insights into common late-onset diseases, such as age-related macular degeneration (AMD).Here, we discuss current findings on the genetics of AMD to highlight areas of rapid progress and new challenges.We also attempt to integrate available genetic and biochemical data with cellular pathways involved in aging to formulate an integrated model of AMD pathogenesis." + } + ], + "4c2f8dcb-02a1-4968-a117-bdf505cad02f": [ + { + "document_id": "4c2f8dcb-02a1-4968-a117-bdf505cad02f", + "text": "Genetics of Alzheimer Disease: Early-Onset AD\n\nIn the early to mid-1990s, genetic studies of AD focused on extended families with high burden of disease (two or more cases among first-degree relatives), and used linkage analysis of highly polymorphic genetic markers called short tandem repeats (STRs, or microsattelites) in order to identify genomic regions co-transmitting with disease in affected family members.This strategy, followed by \"fine mapping\"-the positional cloning of candidate genes-was used to identify genes and genetic variants contributing to AD risk.The first three genes known to cause AD were identified among families with multiple early-onset cases (age-at-onset <60 years): APP, encoding amyloid precursor protein [Goate et al., 1991], and PS1 and PS2, encoding presenilins I and II respectively [Levy-Lahad et al., 1995;Rogaev et al., 1995;Sherrington et al., 1995], each transmitting disease-causing variants in the predicted autosomal-dominant fashion." + }, + { + "document_id": "4c2f8dcb-02a1-4968-a117-bdf505cad02f", + "text": "\nAlzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia.With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets.Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately $20 genes with common variants contributing to LOAD risk.In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses.Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple lowfrequency and rare variant contributors to AD, and discuss ongoing efforts with next-generation sequencing studies to develop statistically well-powered and comprehensive genomic studies of AD.Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD." + }, + { + "document_id": "4c2f8dcb-02a1-4968-a117-bdf505cad02f", + "text": "\n\nAlzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia.With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets.Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately $20 genes with common variants contributing to LOAD risk.In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses.Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple lowfrequency and rare variant contributors to AD, and discuss ongoing efforts with next-generation sequencing studies to develop statistically well-powered and comprehensive genomic studies of AD.Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD." + } + ], + "6d98da1a-9964-4be7-bb67-47f829dcd2cf": [ + { + "document_id": "6d98da1a-9964-4be7-bb67-47f829dcd2cf", + "text": "Indeed, as\nage increases, there is an exponential increase in the incidence of\nAD, with a corresponding effect on healthcare costs and quality of\nlife. AD is a complex disease involving several genetic and environmental components (Hardy, 1997; Munoz & Feldman, 2000), and\n15% of patients have a genetic predisposition. Almost 100 candidate\ngenes are currently known to be involved in the development of AD,\nand only 4 (APP, PSEN1, PSEN2, APOE) in humans have been\nproven to play a direct role in AD pathogenesis (Thomas & Fenech,\n2007)." + } + ], + "70b52a1e-834b-43c0-9e6a-3010bc3a06ae": [ + { + "document_id": "70b52a1e-834b-43c0-9e6a-3010bc3a06ae", + "text": "T\n\nhe genetics of Alzheimer disease (AD) to date support an age-dependent dichotomous model whereby earlier age of disease onset (Ͻ60 years) is explained by 3 fully penetrant genes (APP [NCBI Entrez gene 351], PSEN1 [NCBI Entrez gene 5663], and PSEN2 [NCBI Entrez gene 5664]), whereas later age of disease onset (Ն65 years) representing most cases of AD has yet to be explained by a purely genetic model.The APOE gene (NCBI Entrez gene 348) is the strongest genetic risk factor for later onset, although it is neither sufficient nor necessary to explain all occurrences of disease.Numerous putative genetic risk alleles and genetic variants have been reported.Although all have relevance to biological mechanisms that may be associated with AD pathogenesis, they await replication in large representative populations.Genome-wide association studies have emerged as an increasingly effective tool for identifying genetic contributions to complex diseases and represent the next frontier for furthering our understanding of the underlying etiologic, biological, and pathologic mechanisms associated with chronic complex disorders.There have already been success stories for diseases such as macular degeneration and diabetes mellitus.Whether this will hold true for a genetically complex and heterogeneous disease such as AD is not known, although early reports are encouraging.This review considers recent publications from studies that have successfully applied genome-wide association methods to investigations of AD by taking advantage of the currently available high-throughput arrays, bioinformatics, and software advances.The inherent strengths, limitations, and challenges associated with study design issues in the context of AD are presented herein." + }, + { + "document_id": "70b52a1e-834b-43c0-9e6a-3010bc3a06ae", + "text": "\n\nArch Neurol.2008;65(3): 329-334 Alzheimer disease (AD) is the most common cause of dementia and the most prevalent neurodegenerative disorder associated with aging. 1 Alzheimer disease is a heterogeneous disorder with a complex etiology owing to genetic and environmental influences as causal or risk modifiers.The neuropathologic hallmarks of disease are extracellular amyloid plaques and intracellular neurofibrillary tangles of hyperphosphorylated tau protein. 2 Only 10% of AD cases occurring before 60 years of age (early-onset AD) are due to rare, fully penetrant (autosomal dominant) mutations in 3 genes: A precursor protein (APP) on chromosome 21, 3 presenilin 1 (PSEN1) on chromosome 14, 4 and presenilin 2 (PSEN2) on chromosome 1. 5,6In contrast, most cases of AD are later in onset (Ն 65 years of age) (late-onset AD), are nonfamilial, and are likely the result of highly prevalent genetic variants with low penetrance. 7To date, the only genetic risk factor for lateonset AD remains the apolipoprotein E gene (APOE), specifically the ε4 allele, which is moderately penetrant, accounting for up to 50% of cases. 8owever, a robust literature reports numerous putative genetic risk alleles and promising genetic variants.Recent reports from individual studies reveal significant associations with the sortilin-related receptor (SORL1 [NCBI Entrez gene 6653]) 9,10 and glycine-rich protein 2-associated binding protein 2 (GAB2 [NCBI Entrez gene 9846]) 11 on chromosome 11; death-associated protein kinase 1 (DAPK1 [NCBI Entrez gene 1612]), 12 ubiquilin 1 (UBQLN1 [NCBI Entrez gene 299798]), 13 and adenosine triphosphate-binding cassette transporter 1, subfamily A (ABCA1 [NCBI Entrez gene 19]), on chromosome 9 14 ; and low-density lipoprotein receptor-related protein 6 (LRP6 [NCBI Entrez gene 4040]) on chromosome 12. 15 All of these putative variants still lack replication in large representative populations but have relevance to neuropathologic mechanisms and pathways that may be associated with AD pathogenesis ( A large meta-analysis from the AlzGene database 16 17 All are associated with relevant biological mechanisms and pathways but await replication to further elucidate their utility as significant markers for AD." + } + ], + "7fee50dc-7172-4574-a3e7-4961060a655b": [ + { + "document_id": "7fee50dc-7172-4574-a3e7-4961060a655b", + "text": "Background\n\nAlzheimer's disease (AD) is the most common neurodegenerative disorder and the leading cause of dementia in the elderly [1].Diagnosis of AD is based on the presence of neurofibrillary tangles and amyloid plaques [2], and symptoms typically include memory loss and impaired cognitive ability.Although the pathological hallmarks associated with dementia-related symptoms in AD appear largely similar between both the early-onset and late-onset forms of the disease, their underlying etiologies contrast [3].Whereas early-onset AD is a familial autosomal dominant disorder caused by rare, highly penetrant mutations in one of a small set of genes (APP, PSEN1, and PSEN2), the more common late-onset form of the disease (accounting for 90-95 % of cases) occurs sporadically, and risk is determined by complex underlying mechanisms [3][4][5][6].Estimates based on twin concordance rates suggest heritability of late-onset AD is as high as 70 %, implicating major roles for genetic as well as non-genetic factors [6].Indeed, through candidate gene studies, as well as more recent genome-wide association studies (GWASs) and whole-exome sequencing, both common and rare variants associated with the late-onset form of AD have been identified [7][8][9][10][11].Collectively, however, common GWAS variants account for only a modest proportion (~30 %) of the underlying variance in disease susceptibility [12].Several environmental factors are also thought to play a role [5,6], yet exactly how these contribute to risk, onset, and progression remains poorly defined." + } + ], + "8275b075-735b-44dc-b549-32ee94dec32e": [ + { + "document_id": "8275b075-735b-44dc-b549-32ee94dec32e", + "text": "\nAlzheimer's disease is the most common type of dementia, and it is characterized by a decline in memory or other thinking skills.The greatest risk factor for Alzheimer's disease is advanced age.A recent genome-wide study identified a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11 is probably responsible for the association.The association of a protective haplotype with a 10-year delay in the onset of Alzheimer's disease and the identification of a CCL11 variant with possible functional roles in this association might allow the future development of immunomodulators with the potential to halve disease incidence." + }, + { + "document_id": "8275b075-735b-44dc-b549-32ee94dec32e", + "text": "\n\nAlzheimer's disease is the most common type of dementia, and it is characterized by a decline in memory or other thinking skills.The greatest risk factor for Alzheimer's disease is advanced age.A recent genome-wide study identified a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11 is probably responsible for the association.The association of a protective haplotype with a 10-year delay in the onset of Alzheimer's disease and the identification of a CCL11 variant with possible functional roles in this association might allow the future development of immunomodulators with the potential to halve disease incidence." + } + ], + "8881b5b0-fd7a-400d-9dd2-d4c3f9b012b4": [ + { + "document_id": "8881b5b0-fd7a-400d-9dd2-d4c3f9b012b4", + "text": "INTRODUCTION\n\nAlzheimer's disease (AD) is a common debilitating disorder with a prevalence that rises steeply with age from below 1% at 65 years to as high as 40% after the age of 90 [Bachman et al., 1992].Genes are known to play a role in the development of AD.Twin studies show heritabilities of around 60% [Bergem et al., 1997;Gatz et al., 1997].Indeed, variation in four genes has already been shown to cause rare forms of early-onset AD [the Amyloid Precursor Protein Gene (APP); Goate et al., 1991; Presenilin 1 (PS1); Sherrington et al., 1995; Presenilin 2 (PS2); Levy Lahad et al., 1995, Rogaev et al., 1995] or increase the general risk of disease development [Apolipoprotein E (APOE), Corder et al., 1993].As well as increasing disease susceptibility, APOE e4 alleles are associated with reduced age at onset (AAO) and appear to show their strongest effect below 70 years [Farrer et al., 1997].There is also evidence from both twin [Pedersen et al., 2001] and family studies [Tunstall et al., 2000;Li et al., 2002] that AAO in AD is heritable.Daw et al. [2000] have estimated that in addition to APOE, there are at least four loci with similar effect sizes, which contribute to AAO in AD." + } + ], + "8b03aabf-8965-42c9-a054-44592bd98e86": [ + { + "document_id": "8b03aabf-8965-42c9-a054-44592bd98e86", + "text": "Introduction\n\nAlzheimer's disease (AD), a devastating neurodegenerative disease, is the most common form of dementia among the elderly.Genetically, AD is a complex and multifactorial disease with the possible involvement of multiple genes.The rare early-onset form of the disease usually follows an autosomal-dominant inheritance pattern and to date three genes have been identified: amyloid precursor protein (APP) and presenilin 1 and 2 (PSEN1 and PSEN2).The common late-onset form of the disease is much more complex than the early-onset form and until recently the apolipoprotein E (APOE) gene was the only major genetic factor accounting for 20-29% of the risk for late-onset AD. 1,2 Recent large genome-wide association studies (GWAS) have identi-fied nine additional genes for late-onset AD, including CR1, BIN1, CLU (a.k.a.4][5][6][7] There is high heritability for AD risk (up to 80%), 8 but the total risk attributable to all confirmed loci is about 50%, indicating the presence of additional risk genes for late-onset AD." + } + ], + "c59757a9-deea-491e-a93c-3dfdb3d217f8": [ + { + "document_id": "c59757a9-deea-491e-a93c-3dfdb3d217f8", + "text": "\n\nNE OF EVERY 5 PERSONS AGED 65 years is predicted to develop Alzheimer disease (AD) in their lifetime, and genetic variants may play an important part in the development of the disease. 1 The apparent substantial heritability of late-onset AD 2 is inadequately explained by genetic variation within the well-replicated genes (apolipoprotein E [APOE; RefSeq NG_007084], presenilin-1 [PSEN1; RefSeq NG_007386], presenilin-2 [PSEN2; RefSeq NG_007381], and amyloid beta precursor protein [APP;" + } + ] + }, + "data_source": [], + "document_id": "6AD8F2432A4CDFAA63C74712C1165B0E", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE&e4", + "Alzheimers&disease", + "genome-wide&association&studies", + "amyloid-beta", + "tau&neurofibrillary&tangles", + "PSEN1", + "PSEN2", + "APP", + "late-onset&AD", + "early-onset&AD" + ], + "metadata": [], + "question": "Describe the genotypes related to alzheimers and dementia which have commonalities with those for aging?", + "subquestions": null, + "task_id": "6AD8F2432A4CDFAA63C74712C1165B0E", + "usage": { + "chatgpt": 10216, + "gpt-4": 5973, + "gpt-4-turbo-preview": 5048 + }, + "user_id": 2 + }, + "document_id": "6AD8F2432A4CDFAA63C74712C1165B0E", + "task_id": "6AD8F2432A4CDFAA63C74712C1165B0E" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/01.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/01.json new file mode 100644 index 0000000..5b9cc0f --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/01.json @@ -0,0 +1,402 @@ +{ + "created_at": "2024-05-31T23:02:19.686688Z", + "data": { + "amplify": false, + "answer": "The genes associated with diabetes mentioned in the text include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, HNFA4, LOC387761, KCNQ1, MTNR1B, GCKR, BACH2, CTNNB1, CREB5, PARD3B, PARD6G, PRKCZ, EBF1, GIGYF1, ADCY5, PROX1, DGKB-TMEM195, CRY2, FADS1, GLIS3, and C2CD4B.", + "context": { + "2bef9608-4bd6-4252-9fbd-2413b2cad4f8": [ + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "text": "\n\nTo see which other significant genes were likely to have a role in diabetes we looked at all variant sets with a significant glucose, HbA1c, or T2D association and examined whether they had associations with additional diabetes traits (p ≤ 0.0016, correcting for 32 sets tested).Damaging missense variants in PDX1 and PFAS, which significantly associated with HbA1c levels in our primary analysis, associated with T2D diagnosis using this threshold (Table 3 and Supplementary Table 14)." + }, + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "text": "Identification of genes with a biological role in diabetes. Variants in two genes, GCK and GIGYF1, significantly associated with glucose, HbA1c and T2D diagnosis, strongly suggesting a biological role in diabetes; GCK is involved in Mendelian forms of diabetes while GIGYF1 has not previously been implicated by genetics in the disease.Both GCK and GIGYF1 are located on chromosome 7 but are 56 Mb apart, strongly suggesting that these signals are independent; this independence was confirmed by conditional analysis (Supplementary Table 13).Two additional variant sets, HNF1A pLOF and TNRC6B pLOF, had genome-wide associations with both T2D diagnosis and HbA1c levels while G6PC2 damaging missense variants associated with decreased levels of both glucose and HbA1c but not T2D diagnosis (Table 3)." + } + ], + "2dade65a-5d31-4839-b2c9-4c6cd3056f58": [ + { + "document_id": "2dade65a-5d31-4839-b2c9-4c6cd3056f58", + "text": "\n\nOne obvious locus to consider is TCF7L2 in the context of type 2 diabetes.Common genetic variation located within the gene encoding transcription factor 7 like 2 (TCF7L2) has been consistently reported to be strongly associated with the disease.Such reports range from 2006, when we first published the association [3], to the recent transethnic meta-analysis GWAS of type 2 diabetes [4]." + } + ], + "31588831-61b3-4018-9962-bd6985c3061b": [ + { + "document_id": "31588831-61b3-4018-9962-bd6985c3061b", + "text": "\n\nTesting of these loci for association with T2D as a dichotomous trait in up to 40,655 cases and 87,022 nondiabetic controls demonstrated that the fasting glucose-raising alleles at seven loci (in or near ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 and the known T2D genes TCF7L2 and SLC30A8) are robustly associated (P < 5 × 10 −8 ) with increased risk of T2D (Table 2).The association of a highly correlated SNP in ADCY5 with T2D in partially overlapping samples is reported by our companion manuscript 29 .We found less significant T2D associations (P < 5 × 10 −3 ) for variants in or near CRY2, FADS1, GLIS3 and C2CD4B (Table 2).These data clearly show that loci with similar fasting glucose effect sizes may have very different T2D risk effects (see, for example, ADCY5 and MADD in Table 2)." + } + ], + "3c35547c-eb9b-470d-b74b-0f9a0529e965": [ + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "text": "\n\nAmong the confirmed and potential type 2 diabetes risk genes described in Tables 1 and 2, eight genes influence whole-body or peripheral insulin sensitivity: ADIPOQ (47, 52, 250 -257), AHSG (75, 258), CAPN10 (259 -264), ENPP1 (265)(266)(267)(268)(269)(270)(271), PPARG (272)(273)(274)(275)(276)(277)(278)(279)(280)(281)(282)(283), PPARGC1A (284,285), SREBF1 (65), and TCF7L2 (133,151,286,287)." + } + ], + "45c14654-f263-4031-9941-206d7b6a97f3": [ + { + "document_id": "45c14654-f263-4031-9941-206d7b6a97f3", + "text": "\n\nDespite identification of many putative causative genetic variants, few have generated credible susceptibility variants for type 2 diabetes.Indeed, the most important finding using linkage studies is the discovery that the alteration of TCF7L2 (TCF-4) gene expression or function (33) disrupts pancreatic islet function and results in enhanced risk of type 2 diabetes.Candidate gene studies have also reported many type 2 diabetes-associated loci and the coding variants in the nuclear receptor peroxisome proliferator-activated receptor-g (34), the potassium channel KCNJ11 (34), WFS1 (35), and HNF1B (TCF2) (36) are among the few that have been replicated (Table 2).Recently, there have been great advances in the analysis of associated variants in GWA and replication studies due to highthroughput genotyping technologies, the International HapMap Project, and the Human Genome Project.Type 2 susceptibility loci such as JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2, and ADCY5 (37,38) are among some of the established loci (Table 2).CDKN2A/B, CDKAL1, SLC30A8, IGF2BP2, HHEX/IDE, and FTO are other established susceptibility loci for diabetes (Table 2) (34,39,40).GWA studies have also identified the potassium voltage-gated channel KCNQ1 (32) as an associated gene variant for diabetes.A recent GWA study reporting a genetic variant with a strong association with insulin resistance, hyperinsulinemia, and type 2 diabetes, located adjacent to the insulin receptor substrate 1 (IRS1) gene, is the C allele of rs2943641 (41).Interestingly, the parental origin of the single nucleotide polymorphism is of importance because the allele that confers risk when paternally inherited is protected when maternally transmitted.GWA studies for glycemic traits have identified loci such as MTNR1B (42), GCK (glucokinase) (42), and GCKR (glucokinase receptor) (42); however, further investigation of genetic loci on glucose homeostasis and their impact on type 2 diabetes is needed.Indeed, a recent study by Soranzo et al. (42) using GWA studies identified ten genetic loci associated with HbA 1c .Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin may be associated with changes in levels of HbA 1c ." + } + ], + "4fe0a01d-3be8-4cd5-ac59-8b0ef085b20c": [ + { + "document_id": "4fe0a01d-3be8-4cd5-ac59-8b0ef085b20c", + "text": "\n\nG enome-wide association studies (GWAS) have iden- tified several type 2 diabetes mellitus (T2DM) susceptibility loci including CDKAL1, CDKN2B, IGF2BP2, HHEX, SLC30A8, PKN2, LOC387761 (1)(2)(3)(4)(5), and KCNQ1, which was recently identified by similar GWAS approach in two independent Japanese samples (6,7).Although these associations have been well replicated in Japanese populations (8), the role of these loci in other East Asian populations remains less clear.For example, a study in China by Wu et al. (9) did not find significant associations between single-nucleotide polymorphisms (SNPs) in IGF2BP2 and SLC30A8 with T2DM, whereas an association between SNPs at the HHEX locus and T2DM was reported among Chinese living in Shanghai, but not among Chinese in Beijing.Another study in Hong Kong Chinese (10) also did not find an association with SNPs at the IGF2BP2 locus; however, they reported an association between T2DM with SNPs at the HHEX and SLC30A8 loci." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "\n\nIn studies where overt T2D has been the phenotype the majority of associated polymorphisms have encoded proteins known to be involved in β-cell metabolism; for example TCF7L2, KCNJ11 and HHEX have shown robust association [170,171].This suggests that these genes could prove useful in predicting β-cell preservation during the course of T2D.The glucokinase gene (GCK) coding for the initial glucose-sensing step in the β-cell can have activating mutations causing hypoglycemia that might provide structural and functional models leading to drug targets for treating T2D [172].In the GoDARTs study, investigators examined the medication response of metformin and sulphonylurea based on the TCF7L2 variants mainly affecting the β-cell.The carriers of the at risk 'T' allele responded less well to sulphonylurea therapy than metformin [173].Also it is of significant public health interest that in the Diabetes Prevention Program, lifestyle modifications were shown to reduce the risk of diabetes conferred by risk variants of TCF7L2 at rs7093146, and in placebo participants who carried the homozygous risk genotype (TT), there was 80% higher risk for developing diabetes compared to the lifestyle intervention group carrying the same risk genotypes [35].These findings could herald significant future progress in the field of T2D pharmacogenomics, possibly leading to the development and use of agents tailored on the basis of genotype." + } + ], + "5d7a863d-1811-4eea-9fb0-fbc3067aa664": [ + { + "document_id": "5d7a863d-1811-4eea-9fb0-fbc3067aa664", + "text": "\n\nDespite sharing only 9 loci (among 26 and 17 total in the two analyses, respectively), the separate analyses both identified genes involved in diabetes-related biological functions, including \"glucose homeostasis,\" \"pancreas development\" and \"insulin secretion\" (Supplementary Tables 3 and 5).Three of the top eleven scoring genes in our independent replication analysis have verified causal links to T2D, as annotated in the OMIM 41 .These include genes encoding transcription factors TCF7L2 (TCF4), which has extensive evidence of being causal in T2D 61,62 , and HNF1B, which is a known cause of maturity onset diabetes of the young 63 .Other high-ranking candidate genes have been identified as therapeutic targets in T2D (for example, CTBP1 (ref.64) and LEP 65 ), and the high-scoring gene HHEX has recently been shown to play a key role in islet function 66 ." + } + ], + "7bd7a98f-955a-4988-8981-a0ff7ab6f7df": [ + { + "document_id": "7bd7a98f-955a-4988-8981-a0ff7ab6f7df", + "text": "\n\nSimilar findings to AMD are now unfolding with type 2 DM.Grant et al. (24) first reported on a variant of the gene TCF7L2, which has been linked to reduced beta cell function and poor insulin response to oral glucose loads (51).Since its first discovery, this gene has been widely confirmed in independent studies as a pivotal susceptibility marker for type 2 DM (23,(25)(26)(27)(28)40).Recently, 6 genome-wide SNP association studies have identified and replicated in separate stages several additional novel genes conferring susceptibility to type 2 DM (23,(25)(26)(27)(28)40) (Table 2).Interestingly, these loci primarily include genes involved in pancreatic beta cell development and function as opposed to insulin resistance-the current accepted mechanism for type 2 DM.This development casts doubt on our traditional pathophysiological modeling of the type 2 diabetic patient and underscores the need for genomic studies to further define pathobiological processes of complex traits." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "\n\nOf the 16 loci that have been associated with type 2 diabetes previously, [8][9][10][11][12][13][14][15] we showed that 11 -TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEXwere associated with an enhanced risk of future diabetes.Many of the variants that we genotyped appear to influence beta-cell function, possibly through effects on proliferation, regeneration, and apoptosis.There was a time-dependent increase in the BMI and a decrease in insulin sensitivity in the subjects from the Botnia study, an increase in insulin resistance that was reflected by an increase in insulin secretion.However, this increase was inadequate to compensate for the increase in insulin resistance in carriers with a high genetic risk, which resulted in a markedly impaired disposition index.Only variants in FTO were associated with an increased BMI.Both FTO and PPARG together with TCF7L2 and KCNJ11 predicted transition from impaired fasting glucose levels or impaired glucose tolerance to manifest diabetes, which suggests that a combination of increased obesity and insulin resistance with a deterioration in beta-cell function contribute to the manifestation of diabetes in these subjects.Collectively, our findings emphasize the critical role of inherited defects in beta-cell function for the development of type 2 diabetes." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Type 2 Diabetes\n\nCommon variants in 11 genes were significantly associated with the risk of future type 2 diabetes in the MPP cohort, including TCF7L2 (odds ratio, 1.30; P = 9.5×10 −13 ), PPARG (odds ratio, 1.20; P = 4.0×10 −4 ), FTO (odds ratio, 1.14; P = 9.2×10 −5 ), KCNJ11 (odds ratio, 1.13; P = 3.6×10 −4 ), NOTCH2 (odds ratio, 1.13; P = 0.02), WFS1 (odds ratio, 1.12; P = 0.001), CDKAL1 (odds ratio, 1.11; P = 0.004), IGF2BP2 (odds ratio, 1.10; P = 0.008), SLC30A8 (odds ratio, 1.10; P = 0.008), JAZF1 (odds ratio, 1.08; P = 0.03), and HHEX (odds ratio, 1.07; P = 0.03) (Table 2).Although these findings could not be fully replicated in the smaller Botnia study, there was little heterogeneity between the studies with respect to the risk conferred by different genotypes." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nTo date, more than 70 genes have been identified as involved in T2DM, primarily by association analysis [34].In addition, via GWAS arrays, more than 100 SNPs have been identified for T2DM [35].From the 50 novel loci associated with T2DM previously identified, more than 40 loci have been associated with T2DM-related traits, including fasting proinsulin, insulin and glucose (Table 1) [36][37][38][39].However, for T2DM-related traits, such as the HOMA index or pancreatic β cell function, there are virtually no published data examining the relationship between these traits or the genotype and environment interactions.Clinical investigations of some loci have suggested that the genetic components of T2DM risk act preferentially through β cell function [40].Among all 40 loci associated with T2DM-related traits, only transcription factor-7-like 2 (TCF7L2) was shown to clearly contribute to T2DM risk [41].Several studies in white European [42], Indian [43], Japanese [44], Mexican American [45] and West African [46] individuals have shown a strong association between TCF7L2 and T2DM.It is also noteworthy that these populations represent the major racial groups with a high prevalence of T2DM.In all populations, TCF7L2 showed a strong association, with the odds of developing T2DM increased by 30%-50% for each allele inherited.This finding indicates an approximately double odds ratio compared to most other diabetes susceptibility polymorphisms.TCF7L2 is a transcription factor involved in the Wnt signaling pathway that is ubiquitously expressed, and it has been observed that TCF7L2 risk alleles result in the overexpression of TCF7L2 in pancreatic β cells.This overexpression causes reduced nutrient-induced insulin secretion, which results in a direct predisposition to T2DM as well as an indirect predisposition via an increase in hepatic glucose production [47]." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Most Relevant T2DM Susceptibility Genes\n\nGene and environment interaction studies have shown a nice association between variants in peroxisome proliferator-activated receptor gamma (PPARG), TCF7L2 and fat mass and obesity-associated protein (FTO) genes, a Western dietary pattern and T2DM." + } + ], + "9b93b4eb-98c2-403f-aea2-6b24399501b8": [ + { + "document_id": "9b93b4eb-98c2-403f-aea2-6b24399501b8", + "text": "\n\nOne of these genes associated with type 2 diabetes is the insulin receptor substrate 1 (IRS1, OMIM association number, 147545) (Alharbi, Khan, Abotalib, & Al-Hakeem, 2014;Alharbi, Khan, Munshi et al., 2014;Brender et al., 2013;Brunetti, Chiefari, & Foti, 2014) and another is the C-C motif chemokine receptor5(CCR5, OMIM association number, 601373) (Balistreri et al., 2007;Mokubo et al., 2006;Muntinghe et al., 2009)." + } + ], + "a579db95-2a40-43ff-b237-d47f90aaf64f": [ + { + "document_id": "a579db95-2a40-43ff-b237-d47f90aaf64f", + "text": "Genes boosted in type 2 diabetes\n\nBefore the Wellcome Trust study, PPARG, KCNJ11, and TCF7L2 had all been identified as genes involved in type 2 diabetes through genome-wide association studies and replicated in follow-up studies (for review, see Bonnefond et al. 2010).The strongest candidate gene for type 2 diabetes, TCF7L2, was also the strongest signal seen in the Wellcome trust study, although the others were not so strong.However, the exact mechanism by which TCF7L2 acts was not entirely clear.In our analysis (Fig. 5), we find it directly connected to the b-catenin/WNT signaling pathway by its functional connection to CTNNB1, as well as to BACH2, a gene that has been repeatedly implicated in type 1 diabetes (e.g., Cooper et al. 2008;Madu et al. 2009), but which has not yet been linked to type 2 diabetes.BACH2 is among the genes most strongly boosted by network linkages, deriving additional signal from CREB5 and PARD3B, which both score highly in the GWAS data.PARD6G, PARD3B, and CDC42 are also emphasized by the method.Notably, these genes form a complex with PRKCZ (Koh et al. 2008), a variant of which correlates with type 2 diabetes in Han Chinese (Qin et al. 2008).EBF1, a known regulator of adipocyte differentiation (Akerblad et al. 2005) is also strongly boosted by the network, supporting a possible role in type 2 diabetes." + } + ], + "b978a189-6fbd-4791-8072-7db79f43746a": [ + { + "document_id": "b978a189-6fbd-4791-8072-7db79f43746a", + "text": "RESULTS-\n\nWe confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 ϫ 10 Ϫ12 Ͻ P unadjusted Ͻ 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted ϭ 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations." + }, + { + "document_id": "b978a189-6fbd-4791-8072-7db79f43746a", + "text": "\nOBJECTIVE-Recent genome-wide association studies have identified six novel genes for type 2 diabetes and obesity and confirmed TCF7L2 as the major type 2 diabetes gene to date in Europeans.However, the implications of these genes in Asians are unclear.RESEARCH DESIGN AND METHODS-We studied 13 associated single nucleotide polymorphisms from these genes in 3,041 patients with type 2 diabetes and 3,678 control subjects of Asian ancestry from Hong Kong and Korea. RESULTS-We confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 ϫ 10 Ϫ12 Ͻ P unadjusted Ͻ 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted ϭ 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations. CONCLUSIONS-Ourfindings support the important but differential contribution of these genetic variants to type 2 diabetes and obesity in Asians compared with Europeans.Diabetes 57: 2226-2233, 2008T ype 2 diabetes is a major health problem affecting more than 170 million people worldwide.In the next 20 years, Asia will be hit hardest, with the diabetic populations in India and China more than doubling (1).Type 2 diabetes is characterized by the presence of insulin resistance and pancreatic -cell dysfunction, resulting from the interaction of genetic and environmental factors.Until recently, few genes identified through linkage scans or the candidate gene approach have been confirmed to be associated with type 2 diabetes (e.g., PPARG, KCNJ11, CAPN10, and TCF7L2).Under the common variant-common disease hypothesis, several genome-wide association (GWA) studies on type 2 diabetes have been conducted in large-scale case-control samples.Six novel genes (SLC30A8, HHEX, CDKAL1, CDKN2A and CDKN2B, IGF2BP2, and FTO) with modest effect for type 2 diabetes (odds ratio [OR] 1.14 -1.20) had been reproducibly demonstrated in multiple populations of European ancestry.Moreover, TCF7L2 was shown to have the largest effect for type 2 diabetes (1.37) in the European populations to date (2-8).Although many of these genes may be implicated in the insulin production/secretion pathway (TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, and IGF2BP2) (6,9 -11), FTO is associated with type 2 diabetes through its regulation of adiposity (8,12,13).Moreover, two adjacent regions near CDKN2A/B are associated with type 2 diabetes and cardiovascular diseases risks, respectively (7,14 -16).Despite the consistent associations among Europeans, the contributions of these genetic variants in other ethnic groups are less clear.Given the differences in environmental factors (e.g., lifestyle), risk factor profiles (body composition and insulin secretion/resistance patterns), and genetic background (linkage disequilibrium pattern and risk allele frequencies) between Europeans and Asians, it is important to understand the role of these genes in Asians.A recent case-control study in 1,728 Japanese subjects revealed nominal association to type 2 diabetes for variants at the SLC30A8, HHEX, CDKAL1, CDKN2B, and FTO genes but not IGF2BP2 (17).In the present large-scale case-control replication study of 6,719 Asians, we aimed to test for the association of six novel genes from GWA studies and TCF7L2, which had the largest effect in Europeans, and their joint effects on type 2 diabetes risk and metabolic traits. RESEARCH DESIGN AND METHODSAll subjects were recruited from Hong Kong and Korea and of Asian ancestry.The subjects in the Hong Kong case-control study were of southern Han Chinese ancestry residing in Hong Kong.Participants for the case cohort consisting of 1,481 subjects with type 2 diabetes were selected from two" + } + ], + "bbb4af44-2659-4207-b9a1-0ff85d379a9f": [ + { + "document_id": "bbb4af44-2659-4207-b9a1-0ff85d379a9f", + "text": "\n\nOBJECTIVE-Common variants in PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 genes have been shown to be associated with type 2 diabetes in European populations by genome-wide association studies.We have studied the association of common variants in these eight genes with type 2 diabetes and related traits in Indians by combining the data from two independent case-control studies." + } + ], + "d9564b3c-efac-42ae-8e15-bf962c0a7a3c": [ + { + "document_id": "d9564b3c-efac-42ae-8e15-bf962c0a7a3c", + "text": "Introduction\n\nMany genes have been evaluated as candidates for T2D susceptibility.However, only variants in the TCF7L2, PPARG, KCNJ11 and HNFA4 genes have been extensively replicated in populations around the world, showing their indisputable association with T2D risk (Zeggini 2007).In the particular case of the HNF4A gene, it has been implicated in maturity-onset diabetes of the young type 1 (MODY 1) (Mitchell and Frayling 2002;Zhu et al. 2003).HNF4A is a member of the nuclear receptor super-family that plays a critical role in embryogenesis and metabolism, by regulating gene expression in pancreatic beta cells, liver and other tissues.The HNF4A gene is localized to chromosome 20q13, a region that has demonstrated evidence for linkage with T2D (Sladek et al. 1990;Ghosh et al. 1999).Several genetic studies, mainly in Caucasian and Asian populations, have provided evidence for the association of the variants in HNF4A with T2D (Ghosh et al. 1999;Silander et al. 2004;Winckler et al. 2005)." + } + ] + }, + "data_source": [ + { + "document_id": "bbb4af44-2659-4207-b9a1-0ff85d379a9f", + "section_type": "main", + "text": "\n\nOBJECTIVE-Common variants in PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 genes have been shown to be associated with type 2 diabetes in European populations by genome-wide association studies.We have studied the association of common variants in these eight genes with type 2 diabetes and related traits in Indians by combining the data from two independent case-control studies." + }, + { + "document_id": "5d7a863d-1811-4eea-9fb0-fbc3067aa664", + "section_type": "main", + "text": "\n\nDespite sharing only 9 loci (among 26 and 17 total in the two analyses, respectively), the separate analyses both identified genes involved in diabetes-related biological functions, including \"glucose homeostasis,\" \"pancreas development\" and \"insulin secretion\" (Supplementary Tables 3 and 5).Three of the top eleven scoring genes in our independent replication analysis have verified causal links to T2D, as annotated in the OMIM 41 .These include genes encoding transcription factors TCF7L2 (TCF4), which has extensive evidence of being causal in T2D 61,62 , and HNF1B, which is a known cause of maturity onset diabetes of the young 63 .Other high-ranking candidate genes have been identified as therapeutic targets in T2D (for example, CTBP1 (ref.64) and LEP 65 ), and the high-scoring gene HHEX has recently been shown to play a key role in islet function 66 ." + }, + { + "document_id": "1a93e25f-2a43-49e9-8450-03a57c93e613", + "section_type": "main", + "text": "Relation to human and rodent association and linkage studies\n\nRecently, a total of nine candidate genes for T2DM have been identified and replicated in humans through multi- [5][6][7][8][9][10][11].Interestingly, none of these genes shows a high score in our meta-analysis, although Pparg and Tcf7l2 are significant on the less restrictive 0.01 level.On the other hand, from the data we could infer that Fto and Hhex act in pancreatic islets indicated by the T2DM-GeneMiner result for these genes.Cdkal1 and Cdkn2a are not expressed in the transcriptional studies.These genes show very low expression levels or might be active in tissues not included in our study.Since our meta-analysis approach takes into account several data sets from DNA microarrays, our candidate genes have a bias towards transcripts whose expression is changed in the context of T2DM.Moreover, the gene variants from association studies may not result in altered gene expression and, for most SNPs found in association studies, there is a lack of functional information since the variation mostly occurs in non-coding regions of the genes.In order to correlate the T2DM genes with genetic variation we plotted the number of known SNPs for the genes [see Figure 2 in Additional file 1].No general tendency to highly variable genes is observable.Two genes of the candidate list show high variation, Pgcp (9,098 SNPs) and Sorbs1 (4,130).Particularly interesting is Pgcp, because it has not been related to T2DM before and its functional role is also undetermined." + }, + { + "document_id": "9b93b4eb-98c2-403f-aea2-6b24399501b8", + "section_type": "main", + "text": "\n\nOne of these genes associated with type 2 diabetes is the insulin receptor substrate 1 (IRS1, OMIM association number, 147545) (Alharbi, Khan, Abotalib, & Al-Hakeem, 2014;Alharbi, Khan, Munshi et al., 2014;Brender et al., 2013;Brunetti, Chiefari, & Foti, 2014) and another is the C-C motif chemokine receptor5(CCR5, OMIM association number, 601373) (Balistreri et al., 2007;Mokubo et al., 2006;Muntinghe et al., 2009)." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "\n\nOf the 16 loci that have been associated with type 2 diabetes previously, [8][9][10][11][12][13][14][15] we showed that 11 -TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEXwere associated with an enhanced risk of future diabetes.Many of the variants that we genotyped appear to influence beta-cell function, possibly through effects on proliferation, regeneration, and apoptosis.There was a time-dependent increase in the BMI and a decrease in insulin sensitivity in the subjects from the Botnia study, an increase in insulin resistance that was reflected by an increase in insulin secretion.However, this increase was inadequate to compensate for the increase in insulin resistance in carriers with a high genetic risk, which resulted in a markedly impaired disposition index.Only variants in FTO were associated with an increased BMI.Both FTO and PPARG together with TCF7L2 and KCNJ11 predicted transition from impaired fasting glucose levels or impaired glucose tolerance to manifest diabetes, which suggests that a combination of increased obesity and insulin resistance with a deterioration in beta-cell function contribute to the manifestation of diabetes in these subjects.Collectively, our findings emphasize the critical role of inherited defects in beta-cell function for the development of type 2 diabetes." + }, + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "section_type": "main", + "text": "\n\nAmong the confirmed and potential type 2 diabetes risk genes described in Tables 1 and 2, eight genes influence whole-body or peripheral insulin sensitivity: ADIPOQ (47, 52, 250 -257), AHSG (75, 258), CAPN10 (259 -264), ENPP1 (265)(266)(267)(268)(269)(270)(271), PPARG (272)(273)(274)(275)(276)(277)(278)(279)(280)(281)(282)(283), PPARGC1A (284,285), SREBF1 (65), and TCF7L2 (133,151,286,287)." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Type 2 Diabetes\n\nCommon variants in 11 genes were significantly associated with the risk of future type 2 diabetes in the MPP cohort, including TCF7L2 (odds ratio, 1.30; P = 9.5×10 −13 ), PPARG (odds ratio, 1.20; P = 4.0×10 −4 ), FTO (odds ratio, 1.14; P = 9.2×10 −5 ), KCNJ11 (odds ratio, 1.13; P = 3.6×10 −4 ), NOTCH2 (odds ratio, 1.13; P = 0.02), WFS1 (odds ratio, 1.12; P = 0.001), CDKAL1 (odds ratio, 1.11; P = 0.004), IGF2BP2 (odds ratio, 1.10; P = 0.008), SLC30A8 (odds ratio, 1.10; P = 0.008), JAZF1 (odds ratio, 1.08; P = 0.03), and HHEX (odds ratio, 1.07; P = 0.03) (Table 2).Although these findings could not be fully replicated in the smaller Botnia study, there was little heterogeneity between the studies with respect to the risk conferred by different genotypes." + }, + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "section_type": "main", + "text": "\n\nIn 2010, a meta-analysis of 21 genome-wide association studies performed by Dupuis and colleagues identified ADCY5, PROX1, GCK, GCKR, and DGKB/TMEM195 as new genetic loci for T2D susceptibility [22].Among these loci, DGKB/TMEM195, GCK, PROX1, and ADCY5 mainly affect -cell functions, whereas the locus mapped in GCKR shows a primary effect on insulin action [22].In the same year, another genome-wide association study by Qi and colleagues discovered new variants near RBMS1 and ITGB6 genes at 2q24, and these variants were found to affect glucose metabolism and insulin resistance [23].In addition, an expanded meta-analysis of existing GWAS by Voight and colleagues identified 12 new signals with a combined < 5 × 10 −8 , including BCL11A, ZBED3, KLF14, TP53INP1, TLE4, CENTD2, HMGA2, HNF1A, PRC1, ZFAND6, DUSP9, and KCNQ1 [24].HNF1A was previously recognized as the causal gene of MODY3 [62] and also harbored the common variant (G319S) that contributes to early-onset T2D [63,64].DUSP9, mapped on chromosome X, encodes a member of the family of mitogen-activated protein kinase phosphatase 4, MKP4, which is important in cell cycle regulation and plays pivotal roles in regulating insulin action [65][66][67]." + }, + { + "document_id": "b978a189-6fbd-4791-8072-7db79f43746a", + "section_type": "main", + "text": "RESULTS-\n\nWe confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 ϫ 10 Ϫ12 Ͻ P unadjusted Ͻ 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted ϭ 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations." + }, + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "section_type": "main", + "text": "\n\nBecause obesity is linked to the development of T2D, we adjusted for body mass index (BMI) in the regression and found that the association of these genes with diabetes-related traits remained significant (Supplementary Tables 17 and 18).We used the generalized linear mixed model implemented by SAIGE-Gene which accounts for relatedness and adjusts for unbalanced case-control ratios 16 to verify association of our variant sets of interest with glucose, HbA1c, and T2D diagnosis.SAIGE-Gene was run in the European ancestry population including related individuals (n = 398,574).Using the p-value thresholds previously employed, all associations were statistically significant using this method apart from the associations of TNRC6B pLOF with HbA1c (p = 6.85 × 10 -6 ) and T2D diagnosis (p = 4.77 × 10 -5 ) which were less significant (Supplementary Table 19)." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nTo date, more than 70 genes have been identified as involved in T2DM, primarily by association analysis [34].In addition, via GWAS arrays, more than 100 SNPs have been identified for T2DM [35].From the 50 novel loci associated with T2DM previously identified, more than 40 loci have been associated with T2DM-related traits, including fasting proinsulin, insulin and glucose (Table 1) [36][37][38][39].However, for T2DM-related traits, such as the HOMA index or pancreatic β cell function, there are virtually no published data examining the relationship between these traits or the genotype and environment interactions.Clinical investigations of some loci have suggested that the genetic components of T2DM risk act preferentially through β cell function [40].Among all 40 loci associated with T2DM-related traits, only transcription factor-7-like 2 (TCF7L2) was shown to clearly contribute to T2DM risk [41].Several studies in white European [42], Indian [43], Japanese [44], Mexican American [45] and West African [46] individuals have shown a strong association between TCF7L2 and T2DM.It is also noteworthy that these populations represent the major racial groups with a high prevalence of T2DM.In all populations, TCF7L2 showed a strong association, with the odds of developing T2DM increased by 30%-50% for each allele inherited.This finding indicates an approximately double odds ratio compared to most other diabetes susceptibility polymorphisms.TCF7L2 is a transcription factor involved in the Wnt signaling pathway that is ubiquitously expressed, and it has been observed that TCF7L2 risk alleles result in the overexpression of TCF7L2 in pancreatic β cells.This overexpression causes reduced nutrient-induced insulin secretion, which results in a direct predisposition to T2DM as well as an indirect predisposition via an increase in hepatic glucose production [47]." + }, + { + "document_id": "6b7c6ac7-208d-4942-af31-cc3c37252751", + "section_type": "main", + "text": "\n\nImportantly, our findings demonstrate that more than 50% of the genes in which genetic variants have been known to increase risk of T2DM showed altered expression in different tissues.The perturbation was highest, as expected, in pancreatic islets, where eight genes i.e.HHEX, HNF1B, KCNQ1, NOTCH2, TCF7L2, THADA, TSPAN8 and WFS1, showed aberrant expression.All of these genetic loci, apart from the less studied TSPAN8, have been implicated in pathways primarily involved in insulin secretion, cell proliferation and regeneration [30].Of note, genetic variants in the THADA and WFS1 have recently been shown to impair glucagon-like peptide-1stimulated insulin secretion [31,32].Furthermore, many of these loci have also shown effects on insulin sensitivity [33].In line with this, five genes, i.e.HNF1B, IRS1, KCNJ11, NOTCH2 and WFS1, were also differentially expressed in skeletal muscle.Of all T2DM genes, IRS1 seems to have a clear effect on insulin sensitivity; the T2DM-associated allele was associated with decreased IRS1 protein expression as well as reduced phosphatidylinositol-3-kinase-activity and insulin-stimulated glucose uptake in humans [12]." + }, + { + "document_id": "b978a189-6fbd-4791-8072-7db79f43746a", + "section_type": "abstract", + "text": "\nOBJECTIVE-Recent genome-wide association studies have identified six novel genes for type 2 diabetes and obesity and confirmed TCF7L2 as the major type 2 diabetes gene to date in Europeans.However, the implications of these genes in Asians are unclear.RESEARCH DESIGN AND METHODS-We studied 13 associated single nucleotide polymorphisms from these genes in 3,041 patients with type 2 diabetes and 3,678 control subjects of Asian ancestry from Hong Kong and Korea. RESULTS-We confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 ϫ 10 Ϫ12 Ͻ P unadjusted Ͻ 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted ϭ 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations. CONCLUSIONS-Ourfindings support the important but differential contribution of these genetic variants to type 2 diabetes and obesity in Asians compared with Europeans.Diabetes 57: 2226-2233, 2008T ype 2 diabetes is a major health problem affecting more than 170 million people worldwide.In the next 20 years, Asia will be hit hardest, with the diabetic populations in India and China more than doubling (1).Type 2 diabetes is characterized by the presence of insulin resistance and pancreatic -cell dysfunction, resulting from the interaction of genetic and environmental factors.Until recently, few genes identified through linkage scans or the candidate gene approach have been confirmed to be associated with type 2 diabetes (e.g., PPARG, KCNJ11, CAPN10, and TCF7L2).Under the common variant-common disease hypothesis, several genome-wide association (GWA) studies on type 2 diabetes have been conducted in large-scale case-control samples.Six novel genes (SLC30A8, HHEX, CDKAL1, CDKN2A and CDKN2B, IGF2BP2, and FTO) with modest effect for type 2 diabetes (odds ratio [OR] 1.14 -1.20) had been reproducibly demonstrated in multiple populations of European ancestry.Moreover, TCF7L2 was shown to have the largest effect for type 2 diabetes (1.37) in the European populations to date (2-8).Although many of these genes may be implicated in the insulin production/secretion pathway (TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, and IGF2BP2) (6,9 -11), FTO is associated with type 2 diabetes through its regulation of adiposity (8,12,13).Moreover, two adjacent regions near CDKN2A/B are associated with type 2 diabetes and cardiovascular diseases risks, respectively (7,14 -16).Despite the consistent associations among Europeans, the contributions of these genetic variants in other ethnic groups are less clear.Given the differences in environmental factors (e.g., lifestyle), risk factor profiles (body composition and insulin secretion/resistance patterns), and genetic background (linkage disequilibrium pattern and risk allele frequencies) between Europeans and Asians, it is important to understand the role of these genes in Asians.A recent case-control study in 1,728 Japanese subjects revealed nominal association to type 2 diabetes for variants at the SLC30A8, HHEX, CDKAL1, CDKN2B, and FTO genes but not IGF2BP2 (17).In the present large-scale case-control replication study of 6,719 Asians, we aimed to test for the association of six novel genes from GWA studies and TCF7L2, which had the largest effect in Europeans, and their joint effects on type 2 diabetes risk and metabolic traits. RESEARCH DESIGN AND METHODSAll subjects were recruited from Hong Kong and Korea and of Asian ancestry.The subjects in the Hong Kong case-control study were of southern Han Chinese ancestry residing in Hong Kong.Participants for the case cohort consisting of 1,481 subjects with type 2 diabetes were selected from two" + }, + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "section_type": "main", + "text": "\n\nTo see which other significant genes were likely to have a role in diabetes we looked at all variant sets with a significant glucose, HbA1c, or T2D association and examined whether they had associations with additional diabetes traits (p ≤ 0.0016, correcting for 32 sets tested).Damaging missense variants in PDX1 and PFAS, which significantly associated with HbA1c levels in our primary analysis, associated with T2D diagnosis using this threshold (Table 3 and Supplementary Table 14)." + }, + { + "document_id": "31588831-61b3-4018-9962-bd6985c3061b", + "section_type": "main", + "text": "Box 1: Genes nearest to loci associated with fasting diabetes-related quantitative traits\n\nThe DGKB-TMEM195 locus was recently reported to be associated with fasting glucose 24 ; here we report genome-wide significant replication of that finding and evaluate the genes mapping closest to the lead SNP in further detail.DGKB encodes the β (1 of 10) isotype of the catalytic domain of diacylglycerol kinase, which regulates the intracellular concentration of the second messenger diacylglycerol.In rat pancreatic islets, glucose increases diacylglycerol 49 , which activates protein kinase C (PKC) and thus potentiates insulin secretion 50 .TMEM195 encodes transmembrane protein 195, an integral membrane phosphoprotein highly expressed in liver.ADCY5 encodes adenylate cyclase 5, which catalyzes the generation of cAMP.Upon binding to its receptor in pancreatic beta cells, glucagon-like peptide 1 (GLP-1) induces cAMP-mediated activation of protein kinase A, transcription of the proinsulin gene and stimulation of insulin secretory processes 51 ." + }, + { + "document_id": "16e272af-f687-4261-99cf-8125a9e7cdc7", + "section_type": "main", + "text": "\n\nFigure2| effect sizes of the 11 common variants confirmed to be involved in type 2 diabetes risk.The x axis gives the year that published evidence reached the levels of statistical confidence that are now accepted as necessary for genetic association studies.CDKAL1, CDK5 regulatory subunitassociated protein 1-like 1; CDKN2, cyclin-dependent kinase inhibitor 2A; FTO, fat mass and obesity-associated; HHEX, haematopoietically expressed homeobox; IDE, insulin-degrading enzyme; IGF2BP2, insulin-like growth factor 2 mRNA-binding protein 2; KCNJ11, potassium inwardly-rectifying channel, subfamily J, member 11; PPARG, peroxisome proliferator-activated receptor-γ gene; SLC30A8, solute carrier family 30 (zinc transporter), member 8; TCF2, transcription factor 2, hepatic; TCF7L2, transcription factor 7-like 2 (T-cell specific, HMg-box); WFS1, Wolfram syndrome 1." + }, + { + "document_id": "5564cfa4-6a5c-4328-a0b6-5cd1cc0b2338", + "section_type": "main", + "text": "Box 1: Genes nearest to loci associated with fasting diabetes-related quantitative traits\n\nThe DGKB-TMEM195 locus was recently reported to be associated with fasting glucose 24 ; here we report genome-wide significant replication of that finding and evaluate the genes mapping closest to the lead SNP in further detail.DGKB encodes the β (1 of 10) isotype of the catalytic domain of diacylglycerol kinase, which regulates the intracellular concentration of the second messenger diacylglycerol.In rat pancreatic islets, glucose increases diacylglycerol 49 , which activates protein kinase C (PKC) and thus potentiates insulin secretion 50 .TMEM195 encodes transmembrane protein 195, an integral membrane phosphoprotein highly expressed in liver.ADCY5 encodes adenylate cyclase 5, which catalyzes the generation of cAMP.Upon binding to its receptor in pancreatic beta cells, glucagon-like peptide 1 (GLP-1) induces cAMP-mediated activation of protein kinase A, transcription of the proinsulin gene and stimulation of insulin secretory processes 51 ." + }, + { + "document_id": "9e3a4f4a-24d6-4a12-a798-ca654e225e7e", + "section_type": "main", + "text": "\n\nWhile the above findings show no evidence of association between relevant mitochondrial gene sets and T2D, these genes could still display causal associations with specific intermediate phenotypes linked to the disease.Support for this comes from reported mitochondrial dysfunction in insulin-resistant individuals [8].Therefore, we tested the same three gene sets described above for enrichment of associations with seven different glucose and insulin-related traits characteristic of T2D, using GWA metaanalyses of up to 46,186 non-diabetic individuals [37,38] (Soranzo N. et al., unpublished data).The quantitative traits analyzed include fasting levels of glucose and insulin, glucose and insulin levels 2 hours following a 75-gram oral glucose tolerance test, indices of b-cell function (HOMA-B) and insulin resistance (HOMA-IR) [49], and glycated hemoglobin levels (HbA 1C ), which reflect long-term plasma glucose concentrations (see Materials and Methods)." + }, + { + "document_id": "7bd7a98f-955a-4988-8981-a0ff7ab6f7df", + "section_type": "main", + "text": "\n\nSimilar findings to AMD are now unfolding with type 2 DM.Grant et al. (24) first reported on a variant of the gene TCF7L2, which has been linked to reduced beta cell function and poor insulin response to oral glucose loads (51).Since its first discovery, this gene has been widely confirmed in independent studies as a pivotal susceptibility marker for type 2 DM (23,(25)(26)(27)(28)40).Recently, 6 genome-wide SNP association studies have identified and replicated in separate stages several additional novel genes conferring susceptibility to type 2 DM (23,(25)(26)(27)(28)40) (Table 2).Interestingly, these loci primarily include genes involved in pancreatic beta cell development and function as opposed to insulin resistance-the current accepted mechanism for type 2 DM.This development casts doubt on our traditional pathophysiological modeling of the type 2 diabetic patient and underscores the need for genomic studies to further define pathobiological processes of complex traits." + }, + { + "document_id": "4fe0a01d-3be8-4cd5-ac59-8b0ef085b20c", + "section_type": "main", + "text": "\n\nG enome-wide association studies (GWAS) have iden- tified several type 2 diabetes mellitus (T2DM) susceptibility loci including CDKAL1, CDKN2B, IGF2BP2, HHEX, SLC30A8, PKN2, LOC387761 (1)(2)(3)(4)(5), and KCNQ1, which was recently identified by similar GWAS approach in two independent Japanese samples (6,7).Although these associations have been well replicated in Japanese populations (8), the role of these loci in other East Asian populations remains less clear.For example, a study in China by Wu et al. (9) did not find significant associations between single-nucleotide polymorphisms (SNPs) in IGF2BP2 and SLC30A8 with T2DM, whereas an association between SNPs at the HHEX locus and T2DM was reported among Chinese living in Shanghai, but not among Chinese in Beijing.Another study in Hong Kong Chinese (10) also did not find an association with SNPs at the IGF2BP2 locus; however, they reported an association between T2DM with SNPs at the HHEX and SLC30A8 loci." + }, + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "section_type": "main", + "text": "\n\nIn addition, these analyses highlighted notable biological connections between sets of genes within confirmed T2D-association regions.For example, HMGA2 emerges as a key transcriptional regulator of IGF2BP2 (refs.53,54).However, because Hmga/Hmg1c knockout mice are deficient in adipocyte differentiation 45 , and the IGF2BP2 risk allele is associated with reduced beta-cell function 55 , further work is required to establish the relevance of this regulatory Each point refers to a single T2D association signal, with colors denoting the strength of the association to either the x-axis variable (lefthand of each pair of plots) or y-axis variable (right-hand of each pair) (red, P < 10 −3 ; orange, 10 −3 < P < 10 −2 ; yellow, 0.01 < P < 0.05; green, 0.05 < P < 0.20; blue, P > 0.20).The two KCNQ1 associations are distinguished by the notation KCNQ1 for rs163184 and KCNQ1* for rs231362.The gene names associated with each signal have been chosen on the basis of proximity to the index SNP and should not be presumed to indicate causality." + }, + { + "document_id": "31588831-61b3-4018-9962-bd6985c3061b", + "section_type": "main", + "text": "\n\nTesting of these loci for association with T2D as a dichotomous trait in up to 40,655 cases and 87,022 nondiabetic controls demonstrated that the fasting glucose-raising alleles at seven loci (in or near ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 and the known T2D genes TCF7L2 and SLC30A8) are robustly associated (P < 5 × 10 −8 ) with increased risk of T2D (Table 2).The association of a highly correlated SNP in ADCY5 with T2D in partially overlapping samples is reported by our companion manuscript 29 .We found less significant T2D associations (P < 5 × 10 −3 ) for variants in or near CRY2, FADS1, GLIS3 and C2CD4B (Table 2).These data clearly show that loci with similar fasting glucose effect sizes may have very different T2D risk effects (see, for example, ADCY5 and MADD in Table 2)." + }, + { + "document_id": "5564cfa4-6a5c-4328-a0b6-5cd1cc0b2338", + "section_type": "main", + "text": "\n\nTesting of these loci for association with T2D as a dichotomous trait in up to 40,655 cases and 87,022 nondiabetic controls demonstrated that the fasting glucose-raising alleles at seven loci (in or near ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 and the known T2D genes TCF7L2 and SLC30A8) are robustly associated (P < 5 × 10 −8 ) with increased risk of T2D (Table 2).The association of a highly correlated SNP in ADCY5 with T2D in partially overlapping samples is reported by our companion manuscript 29 .We found less significant T2D associations (P < 5 × 10 −3 ) for variants in or near CRY2, FADS1, GLIS3 and C2CD4B (Table 2).These data clearly show that loci with similar fasting glucose effect sizes may have very different T2D risk effects (see, for example, ADCY5 and MADD in Table 2)." + }, + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "section_type": "main", + "text": "\n\nIn another important study, 12 loci, previously identified by GWAS as predictors of coronary heart disease (CHD) in the general population, were investigated in three CHD case-control studies of diabetic patients.Among them, five variants, rs4977574 (CDKN2A/2B), rs12526453 (PHACTR1), rs646776 (CELSR2-PSRC1-SORT1), rs2259816 (HNF1A), and rs11206510 (PCSK9), showed a significant association with the risk for CHD also in type 2 DM (43).Among the type 2 DM susceptibility genes investigated by GWAS, the transcription factor 7-like 2 gene (TCF7L2) has been identified as one of the most significant (73).TCF7L2 variants have been found to be associated with CVD in some (40,53), but not in all (74) reports, although the association between TCF7L2 risk alleles and CAD was not higher in diabetic individuals.Subsequent studies analyzed the association of three TCF7L2 variants (rs7903146, rs12255372, and rs11196205) with CAD in 1,650 patients that underwent coronary angiography, and found that these variants were more strongly associated with CAD in diabetic patients than in non-diabetics (54)." + }, + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "section_type": "main", + "text": "Other Association Studies of T2D\n\nAnother strong candidate gene for T2D is ABCC8, which encodes the sulfonylurea receptor (SUR1).This protein is the drug target for a widely used class of hypoglycemic medications, and the ABCC8 gene is also mutated in the monogenic disorder familial hyperinsulinism (168).ABCC8 carries a silent C → T polymorphism in exon 18 (T759T; also reported as \"exon 22\" or T761T), which has been associated with T2D in several populations (3,70,73,92), though not in others (3,63,64,77,103,149).The same gene also harbors an intronic cag → tag polymorphism at the -3 position (variably reported as \"intron 24\" or \"exon 16,\" depending on the gene orientation), with the preponderance of the evidence favoring the c allele as the one conferring risk (92,121), although other groups disagree (3, 70,77,135,149)." + }, + { + "document_id": "45c14654-f263-4031-9941-206d7b6a97f3", + "section_type": "main", + "text": "\n\nDespite identification of many putative causative genetic variants, few have generated credible susceptibility variants for type 2 diabetes.Indeed, the most important finding using linkage studies is the discovery that the alteration of TCF7L2 (TCF-4) gene expression or function (33) disrupts pancreatic islet function and results in enhanced risk of type 2 diabetes.Candidate gene studies have also reported many type 2 diabetes-associated loci and the coding variants in the nuclear receptor peroxisome proliferator-activated receptor-g (34), the potassium channel KCNJ11 (34), WFS1 (35), and HNF1B (TCF2) (36) are among the few that have been replicated (Table 2).Recently, there have been great advances in the analysis of associated variants in GWA and replication studies due to highthroughput genotyping technologies, the International HapMap Project, and the Human Genome Project.Type 2 susceptibility loci such as JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2, and ADCY5 (37,38) are among some of the established loci (Table 2).CDKN2A/B, CDKAL1, SLC30A8, IGF2BP2, HHEX/IDE, and FTO are other established susceptibility loci for diabetes (Table 2) (34,39,40).GWA studies have also identified the potassium voltage-gated channel KCNQ1 (32) as an associated gene variant for diabetes.A recent GWA study reporting a genetic variant with a strong association with insulin resistance, hyperinsulinemia, and type 2 diabetes, located adjacent to the insulin receptor substrate 1 (IRS1) gene, is the C allele of rs2943641 (41).Interestingly, the parental origin of the single nucleotide polymorphism is of importance because the allele that confers risk when paternally inherited is protected when maternally transmitted.GWA studies for glycemic traits have identified loci such as MTNR1B (42), GCK (glucokinase) (42), and GCKR (glucokinase receptor) (42); however, further investigation of genetic loci on glucose homeostasis and their impact on type 2 diabetes is needed.Indeed, a recent study by Soranzo et al. (42) using GWA studies identified ten genetic loci associated with HbA 1c .Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin may be associated with changes in levels of HbA 1c ." + }, + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "section_type": "main", + "text": "\n\nIn studies where overt T2D has been the phenotype the majority of associated polymorphisms have encoded proteins known to be involved in β-cell metabolism; for example TCF7L2, KCNJ11 and HHEX have shown robust association [170,171].This suggests that these genes could prove useful in predicting β-cell preservation during the course of T2D.The glucokinase gene (GCK) coding for the initial glucose-sensing step in the β-cell can have activating mutations causing hypoglycemia that might provide structural and functional models leading to drug targets for treating T2D [172].In the GoDARTs study, investigators examined the medication response of metformin and sulphonylurea based on the TCF7L2 variants mainly affecting the β-cell.The carriers of the at risk 'T' allele responded less well to sulphonylurea therapy than metformin [173].Also it is of significant public health interest that in the Diabetes Prevention Program, lifestyle modifications were shown to reduce the risk of diabetes conferred by risk variants of TCF7L2 at rs7093146, and in placebo participants who carried the homozygous risk genotype (TT), there was 80% higher risk for developing diabetes compared to the lifestyle intervention group carrying the same risk genotypes [35].These findings could herald significant future progress in the field of T2D pharmacogenomics, possibly leading to the development and use of agents tailored on the basis of genotype." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "Most Relevant T2DM Susceptibility Genes\n\nGene and environment interaction studies have shown a nice association between variants in peroxisome proliferator-activated receptor gamma (PPARG), TCF7L2 and fat mass and obesity-associated protein (FTO) genes, a Western dietary pattern and T2DM." + }, + { + "document_id": "2dade65a-5d31-4839-b2c9-4c6cd3056f58", + "section_type": "main", + "text": "\n\nOne obvious locus to consider is TCF7L2 in the context of type 2 diabetes.Common genetic variation located within the gene encoding transcription factor 7 like 2 (TCF7L2) has been consistently reported to be strongly associated with the disease.Such reports range from 2006, when we first published the association [3], to the recent transethnic meta-analysis GWAS of type 2 diabetes [4]." + }, + { + "document_id": "1a93e25f-2a43-49e9-8450-03a57c93e613", + "section_type": "main", + "text": "\n\nFor eighteen genes only limited functional information is available as a basis for assessing a possible relationship to T2DM: Ccrn4l, Serpina12, Htatip2, Mest, Pgcp, Tmsb4x, Angptl4, Mrpl33, Ndfip1, Yipf5, Tmem30a, Asnsd1, Oact5, Larp5, Thrsp, 1810015C04Rik, 2310003F16Rik, and 2610002J02Rik.High genetic variation is known for Pgcp in mouse.Serpina12, a target of Hnf4a, is massively changed in liver and 1810015C04Rik in pancreatic islets." + }, + { + "document_id": "5293f814-f4a7-48e0-b4e5-b1f13fdc8516", + "section_type": "main", + "text": "\n\nGlucagon receptor.The G 40 S variant has been associated with T2D in some but not all populations. 56sulin.Case-control studies have suggested an association between T2D and variation at a regulatory minisatellite upstream of the insulin gene.Unlike type 1 diabetes, susceptibility to T2D is associated with the larger class III alleles. 30To rule out the possibility of latent population substructure, Huxtable et al applied family-based association methods (using parent ± ospring trios ascertained via individuals with early-onset T2D) to con®rm this class III association and to show that the susceptibility eect is preferentially transmitted via the paternal allele. 31This ®ts neatly with evidence of maternal imprinting in this region during early development." + }, + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "section_type": "main", + "text": "\n\nWe also examined whether we detect associations for the 8 genes encoding T2D drug targets (GLP1R, IGF1R, PPARG, INSR, SLC5A2, DPP4, KCNJ11, ABCC8).Variant sets in three of these genes, DPP4, GLP1R and KCNJ11 significantly associated with either T2D diagnosis or HbA1c levels (p ≤ 0.003 correcting for 15 variant sets tested) and an additional 4 genes had a nominally significant association with T2D and/or HbA1c (Supplementary Figure 5 and Supplementary Table 27).Table 3. Genes and variant sets associated with multiple diabetes-related traits.Variant sets significant for at least one trait in our primary analysis that are also associated with additional diabetes traits (p ≤ 0.0016, 32 sets tested) are shown.Effect is shown in SD of transformed values or as an odds ratio (OR).www.nature.com/scientificreports/PheWAS of GIGYF1 pLOF reveals associations with cholesterol levels, hypothyroidism and complications of diabetes.The most significant novel associations were seen for GIGYF1 pLOF which associated with increased glucose and HbA1c levels as well as increased incidence of T2D diagnosis.To give additional insight into the biological roles of GIGYF1 we performed a phenome-wide association study (PheWAS) testing GIGYF1 pLOF for association with 142 quantitative traits and 262 ICD10-coded diagnoses (Fig. 3).GIGYF1 pLOF strongly associated with decreased levels of total cholesterol (p = 2.44 × 10 -12 , effect = − 0.61 SD) which was, in large part, driven by LDL cholesterol (p = 2.40 × 10 -10 , effect = − 0.56 SD) although an effect on HDL cholesterol was also observed (Table 4).To understand the extent to which this is influenced by the use of cholesterol-lowering medication in diabetics, we adjusted for medication use in the regression and also performed a separate analysis excluding those on cholesterol-lowering medication.The association between GIGYF1 pLOF and LDL cholesterol levels was significant in both analyses (Supplementary Table 28).GIGYF1 pLOF also associated with decreased grip strength and decreased peak expiratory flow.Notably, GIGYF1 pLOF also associated with increased levels of the kidney injury biomarker cystatin c (p = 6.65 × 10 -6 , effect = 0.36 SD) and increased diagnosis of urinary system disorders (p = 7.32 × 10 -5 , OR = 2.71) (Tables 4 and 5)." + }, + { + "document_id": "553ae95d-0a2b-4f2a-8123-da9a9e9e7a77", + "section_type": "main", + "text": "\n\nMinor susceptibility might operate in some populations from other genes, including insulin receptor substrate 1 ( IRS -1 ), adiponectin ( ACDC ) or ectonucleotide pyrophosphatase/phosphodiesterase 1 enzyme ( ENPP1 ) in a context of obesity or diabesity.• In genome scans of diabetic families, loci for T2DM have been found at several sites, including chromosomes 1q, 2q ( NIDDM1 ), 2p, 3q, 12q, 11q, 10q and 20.NIDDM1 has been identifi ed as coding for calpain 10, a non -lysosomal cysteine protease with actions at the mitochondria and plasma membrane, and also in pancreatic β -cell apoptosis.• In 2007, fi ve large genome -wide association studies in European descent populations have identifi ed new potential T2DM genes, including the Wnt signaling related transcription factors TCF7L2 and HHEX , the zinc transporter ZnT8 ( SLC30A8 ), the CDK5 regulatory subunit -associated protein 1 -like 1 ( CDKAL1 ) and a regulatory protein for IGF2 ( IGF2BP2 ).A consensus of close to 20 confi rmed T2DMsusceptibility loci to date provided novel insights into the biology of T2DM and glucose homeostasis, but individually with a relatively small genetic effect.Importantly, these genes implicate several pathways involved in β -cell development and function.• Compared with clinical risk factors alone, the inclusion of common genetic variants (at least those identifi ed to date) associated with the risk of T2DM has a small effect on the ability to predict future development of T2DM.At the individual level, however, a combined genotype score based on 15 risk alleles confers a 5 -8 fold increased risk of developing T2DM.Identifying the subgroups of individuals at higher risk is important to target these subjects with more effective preventative measures." + }, + { + "document_id": "752b2413-8c90-4af7-b65b-db429145b3bb", + "section_type": "abstract", + "text": "\nThe intersection of genome-wide association analyses with physiological and functional data indicates that variants regulating islet gene transcription influence type 2 diabetes (T2D) predisposition and glucose homeostasis.However, the specific genes through which these regulatory variants act remain poorly characterized.We generated expression quantitative trait locus (eQTL) data in 118 human islet samples using RNA-sequencing and highdensity genotyping.We identified fourteen loci at which cis-exon-eQTL signals overlapped active islet chromatin signatures and were coincident with established T2D and/or glycemic trait associations.At some, these data provide an experimental link between GWAS signals and biological candidates, such as DGKB and ADCY5.At others, the cis-signals implicate genes with no prior connection to islet biology, including WARS and ZMIZ1.At the ZMIZ1 locus, we show that perturbation of ZMIZ1 expression in human islets and beta-cells influences exocytosis and insulin secretion, highlighting a novel role for ZMIZ1 in the maintenance of glucose homeostasis.Together, these findings provide a significant advance in the mechanistic insights of T2D and glycemic trait association loci." + }, + { + "document_id": "d9564b3c-efac-42ae-8e15-bf962c0a7a3c", + "section_type": "main", + "text": "Introduction\n\nMany genes have been evaluated as candidates for T2D susceptibility.However, only variants in the TCF7L2, PPARG, KCNJ11 and HNFA4 genes have been extensively replicated in populations around the world, showing their indisputable association with T2D risk (Zeggini 2007).In the particular case of the HNF4A gene, it has been implicated in maturity-onset diabetes of the young type 1 (MODY 1) (Mitchell and Frayling 2002;Zhu et al. 2003).HNF4A is a member of the nuclear receptor super-family that plays a critical role in embryogenesis and metabolism, by regulating gene expression in pancreatic beta cells, liver and other tissues.The HNF4A gene is localized to chromosome 20q13, a region that has demonstrated evidence for linkage with T2D (Sladek et al. 1990;Ghosh et al. 1999).Several genetic studies, mainly in Caucasian and Asian populations, have provided evidence for the association of the variants in HNF4A with T2D (Ghosh et al. 1999;Silander et al. 2004;Winckler et al. 2005)." + }, + { + "document_id": "faa23996-65fc-4bc6-938a-c959e981d493", + "section_type": "main", + "text": "\n\nMost (71%) of the 1895 genes had minimal evidence linking them to a causal role in T2D pathogenesis (PCS < 0.05) (Additional file 4: Figure S3).However, 95% of T2D loci included at least one gene (median, 3) with PCS > 0.10, and at 70% of loci, there was at least one gene with PCS > 0.20 (Additional file 4: Figure S3).The top-scoring genes across the 101 loci (such as IRS1 [PCS = 0.69], SLC30A8 [PCS = 0.77], HNF1B [PCS = 0.54]) include many of the genes with the strongest prior claims for involvement in T2D risk, prior claims which arise in part from data used to generate the PCSs.For example, these genes each contain rare coding variants directly implicated in the development of T2D (or related conditions): these rare variants are independent of the common variant GWAS signals, but their relationship to diabetes is likely to have been captured through the semantic mapping.The PCS also highlighted several other highly scoring candidates with known causal roles in relation to diabetes and obesity such as MC4R (PCS = 0.43), WFS1 (0.41), ABCC8 (0.37), LEP (0.27), GCK (0.24) and HNF1A (0.23).At other loci, these analyses highlighted candidates that have received scant attention to date; for example, CENPW (PCS = 0.83) scored highly both in terms of semantic links to T2D-relevant processes and an adipose cis-eQTL linking the T2D GWAS SNP to CENPW expression [21]." + }, + { + "document_id": "a579db95-2a40-43ff-b237-d47f90aaf64f", + "section_type": "main", + "text": "Genes boosted in type 2 diabetes\n\nBefore the Wellcome Trust study, PPARG, KCNJ11, and TCF7L2 had all been identified as genes involved in type 2 diabetes through genome-wide association studies and replicated in follow-up studies (for review, see Bonnefond et al. 2010).The strongest candidate gene for type 2 diabetes, TCF7L2, was also the strongest signal seen in the Wellcome trust study, although the others were not so strong.However, the exact mechanism by which TCF7L2 acts was not entirely clear.In our analysis (Fig. 5), we find it directly connected to the b-catenin/WNT signaling pathway by its functional connection to CTNNB1, as well as to BACH2, a gene that has been repeatedly implicated in type 1 diabetes (e.g., Cooper et al. 2008;Madu et al. 2009), but which has not yet been linked to type 2 diabetes.BACH2 is among the genes most strongly boosted by network linkages, deriving additional signal from CREB5 and PARD3B, which both score highly in the GWAS data.PARD6G, PARD3B, and CDC42 are also emphasized by the method.Notably, these genes form a complex with PRKCZ (Koh et al. 2008), a variant of which correlates with type 2 diabetes in Han Chinese (Qin et al. 2008).EBF1, a known regulator of adipocyte differentiation (Akerblad et al. 2005) is also strongly boosted by the network, supporting a possible role in type 2 diabetes." + }, + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "section_type": "main", + "text": "Identification of genes with a biological role in diabetes. Variants in two genes, GCK and GIGYF1, significantly associated with glucose, HbA1c and T2D diagnosis, strongly suggesting a biological role in diabetes; GCK is involved in Mendelian forms of diabetes while GIGYF1 has not previously been implicated by genetics in the disease.Both GCK and GIGYF1 are located on chromosome 7 but are 56 Mb apart, strongly suggesting that these signals are independent; this independence was confirmed by conditional analysis (Supplementary Table 13).Two additional variant sets, HNF1A pLOF and TNRC6B pLOF, had genome-wide associations with both T2D diagnosis and HbA1c levels while G6PC2 damaging missense variants associated with decreased levels of both glucose and HbA1c but not T2D diagnosis (Table 3)." + }, + { + "document_id": "b1d09a6d-334a-48f4-b4ed-4754f398d046", + "section_type": "main", + "text": "\n\nThrough genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05).Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance.Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations.This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies.Functional analysis of these newly discovered loci will further improve our understanding of glycemic control." + }, + { + "document_id": "752b2413-8c90-4af7-b65b-db429145b3bb", + "section_type": "main", + "text": "\n\nThe intersection of genome-wide association analyses with physiological and functional data indicates that variants regulating islet gene transcription influence type 2 diabetes (T2D) predisposition and glucose homeostasis.However, the specific genes through which these regulatory variants act remain poorly characterized.We generated expression quantitative trait locus (eQTL) data in 118 human islet samples using RNA-sequencing and highdensity genotyping.We identified fourteen loci at which cis-exon-eQTL signals overlapped active islet chromatin signatures and were coincident with established T2D and/or glycemic trait associations.At some, these data provide an experimental link between GWAS signals and biological candidates, such as DGKB and ADCY5.At others, the cis-signals implicate genes with no prior connection to islet biology, including WARS and ZMIZ1.At the ZMIZ1 locus, we show that perturbation of ZMIZ1 expression in human islets and beta-cells influences exocytosis and insulin secretion, highlighting a novel role for ZMIZ1 in the maintenance of glucose homeostasis.Together, these findings provide a significant advance in the mechanistic insights of T2D and glycemic trait association loci." + } + ], + "document_id": "8909D2606E33C312F2ECC705FAF65CA2", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "TCF7L2&gene", + "PPARG&gene", + "KCNJ11&gene", + "SLC30A8&gene", + "HHEX&gene", + "CDKAL1&gene", + "CDKN2A&gene", + "IGF2BP2&gene", + "FTO&gene", + "WFS1&gene" + ], + "metadata": [ + { + "object": "he aim of this study was to ascertain the polymorphic markers profile of ADIPOQ, KCNJ11 and TCF7L2 genes in Kyrgyz population and to analyze the association of polymorphic markers and combinations of ADIPOQ gene's G276T locus, KCNJ11 gene's Glu23Lys locus and TCF7L2 gene's VS3C>T locus with type two diabetes T2D in Kyrgyz population", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab334669" + }, + { + "object": "TCF7L2 gene expression was determined using quantitative real-time RT-PCR. Treatment with curcumin significantly increased TCF7L2 gene expression while treatment with LPS decreased TCF7L2 gene expression.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab767034" + }, + { + "object": "Novel mutations were detected in ABCC8 and KCNJ11 gene in Chinese patients with congenital hyperinsulinism CHI. Hotspot mutations such as T1042Qfs*75, I1511K, E501K, G111R in ABCC8 gene, and R34H in KCNJ11 gene are predominantly responsible for Chinese CHI patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab535847" + }, + { + "object": "Description of a novel missense mutation of the WFS1 gene in exon 4 of WFS1 gene in two Italian siblings with Wolfram syndrome.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab225713" + }, + { + "object": "Our results indicate that the genetic variation in the FTO gene might be related to single metabolic disturbances. However, the FTO gene polymorphisms are not associated with the risk of MetS [metabolic syndrome ].", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab560870" + }, + { + "object": "Common variations in the FTO gene are associated with hip fracture risk in women and that FTO gene may help improve the predictive value of hip fracture risk.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab150499" + }, + { + "object": "CDKAL1 gene rs7756992 A/G polymorphism was significantly associated with T2DM. The person with G allele of CDKAL1 gene rs7756992 A/G polymorphism might be predisposed to T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab131436" + }, + { + "object": "This FTO gene variation might influence the baseline lipid oxidation in PCOS patients and might explain the impact of the FTO gene on body weight in PCOS.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab929792" + }, + { + "object": "functionally distinct alleles of the PPARG gene are positioned in different parts of the cell nucleus. This confirms the importance of nuclear architecture to the regulation of PPARG gene transcription, and thus to the fate of the adipose cell.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab307597" + }, + { + "object": "The PPARG gene has been reported to be strongly associated with type-2 diabetes, but the present study did not support the hypothesis that the PPARG gene may also play an important role in the development of schizophrenia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab847159" + } + ], + "question": "what genes are associated with diabetes?", + "subquestions": null, + "task_id": "8909D2606E33C312F2ECC705FAF65CA2", + "usage": { + "chatgpt": 10960, + "gpt-4": 6551, + "gpt-4-turbo-preview": 5611 + }, + "user_id": 2 + }, + "document_id": "8909D2606E33C312F2ECC705FAF65CA2", + "task_id": "8909D2606E33C312F2ECC705FAF65CA2" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/02.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/02.json new file mode 100644 index 0000000..d6e1df5 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/02.json @@ -0,0 +1,398 @@ +{ + "created_at": "2024-05-31T23:04:51.896429Z", + "data": { + "amplify": false, + "answer": "Genomics provides a comprehensive understanding of the genetic factors influencing the development and progression of diabetes. Nutritional genomics focuses on the interaction between genes and dietary patterns, which plays a crucial role in diabetes. By studying gene-nutrient interactions and genetic variations, researchers can develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. Furthermore, genomics can help identify how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake, potentially leading to diabetes. Thus, genomics offers a promising approach to understanding the nutritional factors of diabetes and developing personalized dietary interventions.", + "context": { + "069a62e0-e56a-46ab-9f93-c13a76a79989": [ + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + } + ], + "0da4d3d4-10d5-4a58-9e50-c1fa0b414427": [ + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "text": "\n\nenetic factors for many decades have been known to play a critical role in the etiology of diabetes, but it has been only recently that the specific genes have been identified.The identification of the underlying molecular genetics opens the possibility for understanding the genetic architecture of clinically defined categories of diabetes, new biological insights, new clinical insights, and new clinical applications.This article examines the new insights that have arisen from defining the etiological genes in monogenic diabetes and the predisposing polymorphisms in type 2 diabetes." + } + ], + "1907b52f-515b-447c-b7b3-0e37bf1ce8b7": [ + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "text": "\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + } + ], + "2a71b781-89fe-4055-bbb1-15aa226e1e3a": [ + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + } + ], + "3bde9884-e31d-4719-b42f-02dca25d6c08": [ + { + "document_id": "3bde9884-e31d-4719-b42f-02dca25d6c08", + "text": "\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner." + } + ], + "41ba5319-e77d-4838-8f50-e59fe86b94f8": [ + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + } + ], + "63752d7d-dfdd-48a2-9f39-e1672255a519": [ + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "text": "\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow." + } + ], + "64b63031-1024-43f9-8b27-0ada92829a7a": [ + { + "document_id": "64b63031-1024-43f9-8b27-0ada92829a7a", + "text": "\n\nIn recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management." + } + ], + "789097da-e961-4486-8c83-816626556b16": [ + { + "document_id": "789097da-e961-4486-8c83-816626556b16", + "text": "\n\nNonetheless, \"evidence\" for the genetics of diabetes risk is mounting, often at the expense of understanding the social context and determinants of the disease.Biogenetic views tend to trump sociological views in the diabetes research imaginary of consortium members.However, the genetic epidemiologists who make up part of the diabetes consortium are not ignorant of the effects of proper diet and adequate exercise. \"Take away the television and the automobile and diabetes would all but disappear,\" quipped the head of one lab.Neither are researchers unsympathetic to those who suffer from social inequality in the United States.Their career and intellectual interests lie in genetic explanations of diabetes, which, as I aim to show in this discussion, involves folding political and economic social relationships into biomedical discourse.In fact, the case of diabetes genetic epidemiology illustrates how, in spite of the sympathies of diabetes scientists, arrangements of racial inequality in the United States find their way into diabetes research publications and drug company promotional campaigns.To illustrate this phenomenon further, I present two tales from the field, one dealing with the naming of a publication article, the other with the marketing of a diabetes drug." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nThe aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nIt is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8]." + } + ], + "a83987ea-607c-4952-a1cc-69c6f193ba2a": [ + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + } + ], + "b3fa4d11-72b9-4e6f-9c28-39efdaded492": [ + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\n\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\n\nIn a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases." + } + ], + "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da": [ + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + } + ] + }, + "data_source": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "abstract", + "text": "\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "main", + "text": "\n\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "64b63031-1024-43f9-8b27-0ada92829a7a", + "section_type": "main", + "text": "\n\nIn recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "abstract", + "text": "\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + }, + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "section_type": "main", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "section_type": "main", + "text": "\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow." + }, + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "section_type": "main", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "main", + "text": "\n\nIn a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases." + }, + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "section_type": "main", + "text": "\n\nenetic factors for many decades have been known to play a critical role in the etiology of diabetes, but it has been only recently that the specific genes have been identified.The identification of the underlying molecular genetics opens the possibility for understanding the genetic architecture of clinically defined categories of diabetes, new biological insights, new clinical insights, and new clinical applications.This article examines the new insights that have arisen from defining the etiological genes in monogenic diabetes and the predisposing polymorphisms in type 2 diabetes." + }, + { + "document_id": "789097da-e961-4486-8c83-816626556b16", + "section_type": "main", + "text": "\n\nNonetheless, \"evidence\" for the genetics of diabetes risk is mounting, often at the expense of understanding the social context and determinants of the disease.Biogenetic views tend to trump sociological views in the diabetes research imaginary of consortium members.However, the genetic epidemiologists who make up part of the diabetes consortium are not ignorant of the effects of proper diet and adequate exercise. \"Take away the television and the automobile and diabetes would all but disappear,\" quipped the head of one lab.Neither are researchers unsympathetic to those who suffer from social inequality in the United States.Their career and intellectual interests lie in genetic explanations of diabetes, which, as I aim to show in this discussion, involves folding political and economic social relationships into biomedical discourse.In fact, the case of diabetes genetic epidemiology illustrates how, in spite of the sympathies of diabetes scientists, arrangements of racial inequality in the United States find their way into diabetes research publications and drug company promotional campaigns.To illustrate this phenomenon further, I present two tales from the field, one dealing with the naming of a publication article, the other with the marketing of a diabetes drug." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nIt is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8]." + }, + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "section_type": "main", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "section_type": "abstract", + "text": "\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "section_type": "main", + "text": "\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "section_type": "main", + "text": "\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "3bde9884-e31d-4719-b42f-02dca25d6c08", + "section_type": "main", + "text": "\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner." + }, + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "section_type": "abstract", + "text": "\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nThe aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM." + }, + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "section_type": "main", + "text": "\n\nIt is possible that there are genes that because of their known metabolic involvement are likely to interact with specific nutrients.For example, SLC30A8 which encodes a zinc transporter localized in secretory granules, interacted with dietary zinc to effect fasting insulin levels [132].However, the majority of GWAS variants have not shown interaction with environmental factors for effect on diabetes or related traits.Therefore, it is likely that prospective future studies will utilize improved assessment methods to increase power and avoid false interpretation [133,134].This could be enhanced by prioritizing variants that are most likely to have effects [135] or selective sampling according to extremes of the environmental factor could reduce the requirement for sample size [136].These and other strategies such as meta-analysis, nested case control and genotype-based studies have been recently reviewed [123,133] and the difficulties in measuring environmental exposures have been emphasized, including the application of analyses based on logistic regression [124] and problems with instruments such as physical activity questionnaires [137].Validated food frequency questionnaires are popular instruments for evaluation diabetes risk and are often used in conjunction with food analysis software [138,139].Similar methodology has been adapted to assess two predominant food consumption patterns by Prudent and Western [140], and demonstrated synergistic interaction with genotype and a less healthy Western dietary pattern in determining male risk for T2D by showing that the gene-diet interaction was higher in men with a high genetic risk score determined by a gene counting method [141].Also the effects of diet may predominate at specific developmental periods [142] suggesting that age and associated physiological changes are important as well as differences between genders.It has also been observed that homogeneity of an environmental factor such as physical activity in an Asian Indian study, may reduce ability to detect interaction, but could be solved by subgrouping by the level of activity [143], but increased recruitment would be needed to maintain power." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "INTRODUCTION\n\nThis research project grows out of interest in the genetics and genomics of complex diseases, particularly Type 1 Diabetes (T1D).The field of genomics has provided the first systematic approaches to discovering genes and cellular pathways underlying a number of diseases (Lander, 2011. ).My research is focused on SNP variants that occur in susceptibility regions for T1D." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "main", + "text": "Conclusions\n\nIn view of the overwhelming inconsistency observed in the results of genetic association studies of type 2 diabetes across the globe, it is pertinent to design the future studies in a way that neutralizes the confounding factors and provides useful results.It is equally important to curate the existing data and reanalyze it through advanced computational methods in the era of systems biology.Further, we need functional studies that complement the pace of genomic research.The post-genomic strategies are perplexed with practical difficulties; yet it is imperative to overcome those and conduct integrated genomic-metabolomic studies to derive meaningful outcomes of practical utility.These approaches may provide better insights into understanding the molecular mechanisms operating in the manifestation of the disease and may help in devising methods for prevention and/or treatment." + }, + { + "document_id": "9864689f-2c1e-4fb2-a621-f39d4c57f140", + "section_type": "main", + "text": "\n\nGenetic and epigenetic factors determine cell fate and function.Recent breakthroughs in genotyping technology have led to the identification of more than 20 loci associated with the risk of type 2 diabetes (Sambuy 2007;Zhao et al. 2009).However, all together these loci explain <5% of the genetic risk for diabetes.Epigenetic events have been implicated as contributing factors for metabolic diseases (Barker 1988;Kaput et al. 2007).Unhealthy diet and a sedentary lifestyle likely lead to epigenetic changes that can, in turn, contribute to the onset of diabetes (Kaput et al. 2007).At present, the underlying molecular mechanisms for disease progression remain to be elucidated." + }, + { + "document_id": "e9b48e14-aa0c-4331-a17d-82a7f424233c", + "section_type": "main", + "text": "\n\nThe public health genomics approach to type 2 diabetes.So, while exciting gene discoveries are being made, what can we do?The answer may lie in the relatively new field of public health genomics, \"a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health\" (12).Researchers, policymakers, and practitioners in public health genomics use populationbased data on genetic variation and gene-environment interactions to develop, implement, and evaluate evidencebased tools for improving health and preventing disease.They also apply systematic evidence-based knowledge synthesis and appraisal of the clinical validity and utility of genomic applications in health practice.Validated genomic information is then integrated into disease control and prevention programs (13)." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "main", + "text": "\n\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "abstract", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "section_type": "main", + "text": "\n\nProgress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions." + }, + { + "document_id": "41bc85bc-314f-4d92-9007-5d1571506ef3", + "section_type": "main", + "text": "Discussion\n\nThe goal of the present study was to understand whether metabolic factors affect the expression of the genes recently implicated in the development of type 2 diabetes for which there was little prior evidence of their potential role(s) in this disease.Although many additional SNPs have been identified in subsequent GWAS and meta-analyses [18], we focussed these studies on the genes identified in the first waves of GWAS, as these have been the subject of most follow-up studies to date.Specifically, we examined acute changes in expression of these genes in response to feeding and fasting and longer term changes in the expression of these genes in response to a diet high in fat and sugar, recognized as a critical environmental risk factor for type 2 diabetes." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "abstract", + "text": "\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nThus, studies performed during the last decade have provided strong evidence to support a diet-genome interaction as an important factor leading to the development of T2DM." + }, + { + "document_id": "ba7298cd-4d19-4f98-9a2a-5fb625aa0068", + "section_type": "main", + "text": "\n\nDiabetes is caused due to complex interaction between genetic and environmental factors, like poor life style, diet, physical inactivity and overweight.Genetic factors play a major role in causal of T2DM; however, identification and understanding of genetic factors were of great challenge.Genetic variation in the human genome exists in different forms; from single base pair to large structural variation.In recent times, as the technology has improved; SNP studies, large scale association studies, and next generation sequencing were carried out which helped in the better understanding of T2DM [3].Comparative genomic hybridization (CGH) technique has helped us know about copy number variation (CNVs) and its effect on human genome [4].Understanding the CNVs is critical for the proper study of disease-associated changes because segmental CNVs have been demonstrated in developmental disorders and susceptibility to disease [5,6].Therefore, analysis of CNVs at the whole-genome level is required to create a baseline of human genomic variation [7]." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "section_type": "main", + "text": "\n\nThis perspective changed with the success of the first genome-wide association studies for Type 2 diabetes in 2007 [15,16].These studies were made possible by: (i) the completion of first drafts of the human genome; (ii) the description of haplotypes ('hapmap'); (iii) the development of suitable technology (notably oligonucleotide arrays) to identify variants (single nucleotide polymorphisms); and (iv) the ability to obtain DNA from large populations (often tens of thousands) of healthy people and people with Type 2 diabetes.Given the central dogma of molecular biology, i.e. that information flows from genomic DNA through mRNA to proteins, and providing that robust account is taken of confounding factors, for example through population stratification and multiple testing, variants found more frequently in the Type 2 diabetes-affected population could reasonably be assumed to play a direct role in the disease process." + }, + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "section_type": "main", + "text": "\n\nAs estimated from the currently achieved genome coverage, the next generation of high-density SNP arrays is expected to provide about half a dozen novel type 2 diabetes risk loci in the near future using the same case-control setting.Alternative settings, such as correlational analyses with state-of-the-art measures for glucose-and incretin-stimulated insulin secretion, whole-body and tissue-specific insulin sensitivity, will probably further increase this number.Moreover, future studies on the role of copy number variants, with their obvious impact on gene dosage, could once more extend our appreciation of the genetic component of type 2 diabetes.Finally, taking into account that gene-environment interactions contribute to the development of type 2 diabetes (393, 394), well-de-fined intervention studies have a good potential to discover risk variants that remain cryptic in cross-sectional settings.The current emergence of diabetes-relevant genes susceptible to persistent and partly inheritable epigenetic regulations, i.e., DNA methylation and histone modifications, further underscores the importance of gene-environment interactions and the complexity of type 2 diabetes genetics (198,395,396).Because epigenetic modifications clearly affect gene expression, the establishment of diabetes-related gene expression profiles of metabolically relevant tissues or easily available surrogate \"tissues\", such as lymphocytes, could help identify novel candidate genes for type 2 diabetes." + } + ], + "document_id": "DD54A20CDF6D93EF18DE9FD00DD01191", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "mellitus", + "genomics", + "nutritional", + "factors", + "gene-nutrient", + "interactions", + "type&2", + "genetic", + "variants" + ], + "metadata": [ + { + "object": "rs2059806 of INSR was associated with both type 2 diabetes mellitus and type 2 diabetic nephropathy, while rs7212142 of mTOR was associated with type 2 diabetic nephropathy but not type 2 diabetes mellitus in a Chinese Han population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab687817" + }, + { + "object": "genotypes of methylenetetrahydrofolate reductase gene may be a risk factor for type 2 diabetes mellitus. interaction between genetic polymorphism and environmental factors increases the risk of type 2 diabetes mellitus", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab320805" + }, + { + "object": "Data confirm the association between the FTO first intron polymorphism and the presence of type 2 diabetes mellitus in the Slavonic Czech population. The same variant is likely to be associated with development of chronic complications of diabetes mellitus, especially with diabetic neuropathy and diabetic kidney disease in either T2DM or both T1DM and T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab173943" + }, + { + "object": "genetic association/nutrigenomic studies in population in South Korea: Data suggest that an SNP in BDNF rs6265 is negatively associated with type 2 diabetes; BDNF Val/Met and Met/Met variants rs6265 decrease risk for glucose intolerance and type 2 diabetes. Middle-aged individuals with BDNF Val/Val are prone to developing type 2 diabetes even with low energy intake and low protein intake.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab316682" + }, + { + "object": "show that ER and GR both have the ability to alter the genomic distribution of the FoxA1 pioneer factor. Single-molecule tracking experiments reveal a highly dynamic interaction of FoxA1 with chromatin in vivo; FoxA1 factor is not associated with footprints at its binding sites throughout the genome; findings support a model wherein interactions between transcription factors and pioneer factors are highly dynamic.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab704238" + }, + { + "object": "APOE and CETP TaqIB polymorphisms might not be the genetic risk factors for type 2 diabetes mellitus in Southern Thai population, however, APOE and CETP TaqIB polymorphisms were associated with serum lipids in healthy controls and type 2 diabetes mellitus, respectively.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab77338" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "The results of this meta-analysis support the hypothesis that RBP4 is a modest independent risk factor for gestational diabetes mellitus i.e., nonobese patients with gestational diabetes mellitus might express RBP4 at abnormal levels.The association between RBP4 rs3758539 polymorphism and gestational diabetes mellitus risk was not confirmed.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab860992" + }, + { + "object": "Study reports new variants, 1 near exon splice variant and 9 deep-intronic variants in ABCA4 and identifies splicing defects for 12 out of 19 variants. 4 deep-intronic variants create pseudo-exons or elongate the upstream exon. 8 noncanonical splice site NCSS variants cause a partial deletion or skipping of one or more exons in messenger RNAs. Among the 12 variants, 9 lead to stop codons predicting truncated proteins.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab747508" + }, + { + "object": "genetic association studies: Data suggest that an SNP in IGF2BP2 rs4402960 is associated with type 2 diabetes; IGF2BP2 may have genetic interactions with insulin-like growth factor II with a protective effect in male patients with type 1 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab316531" + } + ], + "question": "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabetes", + "subquestions": null, + "task_id": "DD54A20CDF6D93EF18DE9FD00DD01191", + "usage": { + "chatgpt": 5995, + "gpt-4": 4151, + "gpt-4-turbo-preview": 3211 + }, + "user_id": 2 + }, + "document_id": "DD54A20CDF6D93EF18DE9FD00DD01191", + "task_id": "DD54A20CDF6D93EF18DE9FD00DD01191" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/03.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/03.json new file mode 100644 index 0000000..1a43ffb --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/03.json @@ -0,0 +1,399 @@ +{ + "created_at": "2024-05-31T23:07:32.253629Z", + "data": { + "amplify": false, + "answer": "Genomics can be used to understand the nutritional factors of diabetes by studying nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This approach can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. Additionally, genomics can identify gene variants that interact with specific nutrients, providing insights into how diet and genetics interact to influence diabetes risk.", + "context": { + "069a62e0-e56a-46ab-9f93-c13a76a79989": [ + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + } + ], + "1907b52f-515b-447c-b7b3-0e37bf1ce8b7": [ + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "text": "\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + }, + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "text": "\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + } + ], + "3bde9884-e31d-4719-b42f-02dca25d6c08": [ + { + "document_id": "3bde9884-e31d-4719-b42f-02dca25d6c08", + "text": "\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner." + } + ], + "41ba5319-e77d-4838-8f50-e59fe86b94f8": [ + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + } + ], + "4d3330eb-acd0-4f72-aadf-b056d3c8b389": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "\n\nIt is possible that there are genes that because of their known metabolic involvement are likely to interact with specific nutrients.For example, SLC30A8 which encodes a zinc transporter localized in secretory granules, interacted with dietary zinc to effect fasting insulin levels [132].However, the majority of GWAS variants have not shown interaction with environmental factors for effect on diabetes or related traits.Therefore, it is likely that prospective future studies will utilize improved assessment methods to increase power and avoid false interpretation [133,134].This could be enhanced by prioritizing variants that are most likely to have effects [135] or selective sampling according to extremes of the environmental factor could reduce the requirement for sample size [136].These and other strategies such as meta-analysis, nested case control and genotype-based studies have been recently reviewed [123,133] and the difficulties in measuring environmental exposures have been emphasized, including the application of analyses based on logistic regression [124] and problems with instruments such as physical activity questionnaires [137].Validated food frequency questionnaires are popular instruments for evaluation diabetes risk and are often used in conjunction with food analysis software [138,139].Similar methodology has been adapted to assess two predominant food consumption patterns by Prudent and Western [140], and demonstrated synergistic interaction with genotype and a less healthy Western dietary pattern in determining male risk for T2D by showing that the gene-diet interaction was higher in men with a high genetic risk score determined by a gene counting method [141].Also the effects of diet may predominate at specific developmental periods [142] suggesting that age and associated physiological changes are important as well as differences between genders.It has also been observed that homogeneity of an environmental factor such as physical activity in an Asian Indian study, may reduce ability to detect interaction, but could be solved by subgrouping by the level of activity [143], but increased recruitment would be needed to maintain power." + } + ], + "63752d7d-dfdd-48a2-9f39-e1672255a519": [ + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "text": "\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow." + } + ], + "64b63031-1024-43f9-8b27-0ada92829a7a": [ + { + "document_id": "64b63031-1024-43f9-8b27-0ada92829a7a", + "text": "\n\nIn recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nThe aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nThus, studies performed during the last decade have provided strong evidence to support a diet-genome interaction as an important factor leading to the development of T2DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nIt is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8]." + } + ], + "a83987ea-607c-4952-a1cc-69c6f193ba2a": [ + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + } + ], + "b3fa4d11-72b9-4e6f-9c28-39efdaded492": [ + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\n\nIn a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases." + } + ], + "e9b48e14-aa0c-4331-a17d-82a7f424233c": [ + { + "document_id": "e9b48e14-aa0c-4331-a17d-82a7f424233c", + "text": "\n\nThe public health genomics approach to type 2 diabetes.So, while exciting gene discoveries are being made, what can we do?The answer may lie in the relatively new field of public health genomics, \"a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health\" (12).Researchers, policymakers, and practitioners in public health genomics use populationbased data on genetic variation and gene-environment interactions to develop, implement, and evaluate evidencebased tools for improving health and preventing disease.They also apply systematic evidence-based knowledge synthesis and appraisal of the clinical validity and utility of genomic applications in health practice.Validated genomic information is then integrated into disease control and prevention programs (13)." + } + ], + "f9b65334-56b7-43e9-9fda-b778c18c1c67": [ + { + "document_id": "f9b65334-56b7-43e9-9fda-b778c18c1c67", + "text": "Genomics for Type 2 Diabetes\n\nMany research studies have been carried out on genetic-based illness prediction.Incorporating machine learning approaches with genetic-based illness prediction could result in an accurate outcome.This has intensified the role of Artificial Intelligence (AI) in healthcare.It has been estimated that approximately $36 billion will be invested in AI by 2025 [48].Deep genomics through machine learning approaches has outperformed accuracy in predicting and diagnosing illnesses such as cancer with minimal inclusion of radiologists.It is desired to have sufficient biological knowledge to understand how genetics can help us predict various conditions and analyze each chromosome to identify the disease-causing gene.Pre-existing research studies have focused on genomics and gene interaction patterns of various persistent illnesses such as Alzheimer's, multiple cancers, and Parkinson's." + } + ] + }, + "data_source": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "abstract", + "text": "\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "section_type": "main", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "section_type": "abstract", + "text": "\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "section_type": "main", + "text": "\n\nIt is possible that there are genes that because of their known metabolic involvement are likely to interact with specific nutrients.For example, SLC30A8 which encodes a zinc transporter localized in secretory granules, interacted with dietary zinc to effect fasting insulin levels [132].However, the majority of GWAS variants have not shown interaction with environmental factors for effect on diabetes or related traits.Therefore, it is likely that prospective future studies will utilize improved assessment methods to increase power and avoid false interpretation [133,134].This could be enhanced by prioritizing variants that are most likely to have effects [135] or selective sampling according to extremes of the environmental factor could reduce the requirement for sample size [136].These and other strategies such as meta-analysis, nested case control and genotype-based studies have been recently reviewed [123,133] and the difficulties in measuring environmental exposures have been emphasized, including the application of analyses based on logistic regression [124] and problems with instruments such as physical activity questionnaires [137].Validated food frequency questionnaires are popular instruments for evaluation diabetes risk and are often used in conjunction with food analysis software [138,139].Similar methodology has been adapted to assess two predominant food consumption patterns by Prudent and Western [140], and demonstrated synergistic interaction with genotype and a less healthy Western dietary pattern in determining male risk for T2D by showing that the gene-diet interaction was higher in men with a high genetic risk score determined by a gene counting method [141].Also the effects of diet may predominate at specific developmental periods [142] suggesting that age and associated physiological changes are important as well as differences between genders.It has also been observed that homogeneity of an environmental factor such as physical activity in an Asian Indian study, may reduce ability to detect interaction, but could be solved by subgrouping by the level of activity [143], but increased recruitment would be needed to maintain power." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + }, + { + "document_id": "e9b48e14-aa0c-4331-a17d-82a7f424233c", + "section_type": "main", + "text": "\n\nThe public health genomics approach to type 2 diabetes.So, while exciting gene discoveries are being made, what can we do?The answer may lie in the relatively new field of public health genomics, \"a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health\" (12).Researchers, policymakers, and practitioners in public health genomics use populationbased data on genetic variation and gene-environment interactions to develop, implement, and evaluate evidencebased tools for improving health and preventing disease.They also apply systematic evidence-based knowledge synthesis and appraisal of the clinical validity and utility of genomic applications in health practice.Validated genomic information is then integrated into disease control and prevention programs (13)." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nIt is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8]." + }, + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "section_type": "abstract", + "text": "\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + }, + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "section_type": "main", + "text": "\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow." + }, + { + "document_id": "f9b65334-56b7-43e9-9fda-b778c18c1c67", + "section_type": "main", + "text": "Genomics for Type 2 Diabetes\n\nMany research studies have been carried out on genetic-based illness prediction.Incorporating machine learning approaches with genetic-based illness prediction could result in an accurate outcome.This has intensified the role of Artificial Intelligence (AI) in healthcare.It has been estimated that approximately $36 billion will be invested in AI by 2025 [48].Deep genomics through machine learning approaches has outperformed accuracy in predicting and diagnosing illnesses such as cancer with minimal inclusion of radiologists.It is desired to have sufficient biological knowledge to understand how genetics can help us predict various conditions and analyze each chromosome to identify the disease-causing gene.Pre-existing research studies have focused on genomics and gene interaction patterns of various persistent illnesses such as Alzheimer's, multiple cancers, and Parkinson's." + }, + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "section_type": "main", + "text": "\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + }, + { + "document_id": "64b63031-1024-43f9-8b27-0ada92829a7a", + "section_type": "main", + "text": "\n\nIn recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "section_type": "main", + "text": "\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "main", + "text": "\n\nIn a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases." + }, + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "section_type": "main", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + }, + { + "document_id": "3bde9884-e31d-4719-b42f-02dca25d6c08", + "section_type": "main", + "text": "\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nThe aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM." + }, + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "section_type": "main", + "text": "\n\nProgress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "main", + "text": "\n\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "section_type": "main", + "text": "\n\nThe availability of detailed information on gene × environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them." + }, + { + "document_id": "8f74252a-5ce1-4109-86b6-5b0228b23bba", + "section_type": "main", + "text": "\n\nThe clinical benefits of genomics: lessons from monogenic obesity and diabetes Thanks to their high penetrance, the alleles responsible for rare, monogenic forms of non-autoimmune diabetes and obesity were relatively easily identified through linkage analysis (reviewed in Owen and Hattersley 2001;O'Rahilly and Farooqi 2006).These discoveries have led to molecular classifications of disease with demonstrable prognostic and therapeutic relevance.For example, individuals with maturity onset diabetes of the young (MODY) due to mutations in HNF1A respond particularly well to treatment with sulfonylureas, whilst those with mutations in glucokinase (GCK) can often come off medication entirely given their relatively benign prognosis (Schnyder et al. 2005;Pearson et al. 2003)." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "abstract", + "text": "\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nGenome-wide interaction studies have potential to identify gene variants that influence diabetes risk that might not be detected using hypothesis-driven approaches.However, the statistical power limitations of such studies when applying conventional tests of interaction, combined with the challenges of identifying large cohort collections with appropriately characterized environmental, genetic, and phenotypic data, pose challenges that conventional genetic association studies do not face.Several methods have been developed to mitigate these challenges; among the most promising is the joint meta-analysis approach, which is derived from the model with two degrees of freedom popularized by Kraft et al. (45) and developed further by Manning et al. (46).Manning et al. (47) went on to apply the joint meta-analysis approach in a genome-wide study of 52 cohorts in which they tested for SNP main effects and interactions (with BMI) on fasting glucose and insulin levels.The analysis yielded novel experiment-wide association signals for main effects, but none was discovered for interactions." + }, + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "section_type": "main", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "section_type": "main", + "text": "Conclusions\n\nHow will sequencing genomes influence the health of people at risk for or affected with diabetes?The more complete understanding of the biological mechanisms underlying diabetes derived from these studies may lead to identification of novel drug targets.Individuals with variants in genes responsible for MODY or neonatal diabetes respond better to specific drugs [50,51], and sequencing may identify small numbers of individuals with combinations of rarer, more highly penetrant variants that respond better to specific therapeutic options.Although sets of known variants for type 2 diabetes do not add substantially to prediction of type 2 diabetes development in the overall population [52,53], identification of individuals at greater or lower genetic risk for diabetes within the overall population or in specific subgroups, such as younger onset or leaner individuals [54,55], could lead to better targeted health information and also allow identification of higher risk individuals leading to more efficient design of clinical trials for disease prevention." + }, + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "section_type": "main", + "text": "Future prospects\n\nWhilst the examples above provide interesting insights, it is clear that we are only at the beginning of mining the information generated by genome-wide association studies for Type 2 diabetes and other complex traits.work in human genetics, involving ever larger cohorts, meta-analyses and the search for rarer and more penetrant variants will in future be important to identify all of the heritable elements that control Type 2 diabetes risk; however, the useful deployment of this information for either disease prediction or the development of new therapies will require considerable further efforts at the cellular and molecular level to understand the function of the identified genes.Moreover, and although not the subject of this particular review, actions of single nucleotide polymorphisms through non-coding genes, e.g.mi-croRNAs and long non-coding RNAs, will require deeper investigation." + }, + { + "document_id": "063a0254-1d1b-4caa-b782-6a1fe4ebca0d", + "section_type": "main", + "text": "Genetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor γ (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a β cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18)." + }, + { + "document_id": "08858a32-d736-4d8d-a135-f86568152a81", + "section_type": "main", + "text": "\n\nWith further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients." + }, + { + "document_id": "41bc85bc-314f-4d92-9007-5d1571506ef3", + "section_type": "main", + "text": "\n\nIn summary, we have identified nutritional regulation of many of the newly found type 2 diabetes-associated genes.As these studies were performed with a relatively small number of samples, it should be noted that smaller changes in expression may also exist that we had insufficient power to detect.These data provide support for the involvement of these newly identified type 2 diabetes susceptibility genes in β-cell function and also suggest potential roles for many of them in peripheral tissues, notably in the brain and hypothalamus, highlighting the potential importance of neuronal regulation of metabolism and islet function to type 2 diabetes [38][39][40][41].Our study also highlights the tissue-specific regulation of these genes (changes in one or more tissues where the gene is expressed but not in all tissues), suggesting that the SNPs identified in the GWAS studies may need to be examined in the appropriate tissues and under several metabolic contexts [37].Indeed, recent studies aimed at identifying genetic variants that affect gene expression (eQTLs) have found varying effects of these SNPs on gene expression in different tissues, particularly for SNPs located within not between genes, and notably that the SNPs were more associated with expression of diabetesassociated genes in metabolically relevant tissues such as liver, adipose and muscle than in lymphocytes, which are sometimes used as a surrogate because they are easily accessible [80][81][82].The abundant regulation of these genes by nutritional status found in our study also suggests there are likely gene-diet interactions involving these SNPs [83] that may be a complicating factor in future human studies to assess the functional implications of the associated SNPs." + }, + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "section_type": "main", + "text": "\n\nWhat will be the clinical benefit of all this genetic knowledge beyond its use for prediction of the individual's type 2 diabetes risk?One major advantage of knowing an at-risk person's genotype could be to offer an individually tailored lifestyle intervention program to prevent or, at least, to significantly retard the onset of overt diabetes.This aim requires extensive future work to understand the interaction between risk genes and lifestyle modifications, such as diet (this research area is called nutrigenomics) and exercise regimens (this research area is called physiogenomics).In this regard, data from the Diabetes Prevention Program provided evidence that behavioral intervention can mitigate or even abolish the diabetes risk conferred by TCF7L2 or ENPP1, respectively (127,129).In the Finnish Diabetes Prevention Study, physical activity was shown to reduce the type 2 diabetes risk of PPARG risk allele carriers (387).Another advantage of the genetic knowledge could be to offer type 2 diabetic patients an individually tailored pharmacological therapy with currently available or newly developed, e.g., risk gene-targeting, antidiabetic drugs.Thus, future pharmacogenomic studies have to thoroughly investigate the interaction between risk genes and drugs.Understanding these interactions appears important also because it could help to reduce the therapeutical use of drugs (with their side effects) that are ineffective in certain genotypes." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "THE GENETICS OF TYPE 1 DIABETES\n\nThe study of the genome to map disease-susceptibility regions for T1D and other multifactorial diseases has been facilitated by recent advances in next generation DNA sequencing methods." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nThus, studies performed during the last decade have provided strong evidence to support a diet-genome interaction as an important factor leading to the development of T2DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nNutrient-or dietary pattern-gene interactions in the development of DM." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "INTRODUCTION\n\nThis research project grows out of interest in the genetics and genomics of complex diseases, particularly Type 1 Diabetes (T1D).The field of genomics has provided the first systematic approaches to discovering genes and cellular pathways underlying a number of diseases (Lander, 2011. ).My research is focused on SNP variants that occur in susceptibility regions for T1D." + }, + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "section_type": "main", + "text": "\n\nAs estimated from the currently achieved genome coverage, the next generation of high-density SNP arrays is expected to provide about half a dozen novel type 2 diabetes risk loci in the near future using the same case-control setting.Alternative settings, such as correlational analyses with state-of-the-art measures for glucose-and incretin-stimulated insulin secretion, whole-body and tissue-specific insulin sensitivity, will probably further increase this number.Moreover, future studies on the role of copy number variants, with their obvious impact on gene dosage, could once more extend our appreciation of the genetic component of type 2 diabetes.Finally, taking into account that gene-environment interactions contribute to the development of type 2 diabetes (393, 394), well-de-fined intervention studies have a good potential to discover risk variants that remain cryptic in cross-sectional settings.The current emergence of diabetes-relevant genes susceptible to persistent and partly inheritable epigenetic regulations, i.e., DNA methylation and histone modifications, further underscores the importance of gene-environment interactions and the complexity of type 2 diabetes genetics (198,395,396).Because epigenetic modifications clearly affect gene expression, the establishment of diabetes-related gene expression profiles of metabolically relevant tissues or easily available surrogate \"tissues\", such as lymphocytes, could help identify novel candidate genes for type 2 diabetes." + }, + { + "document_id": "9864689f-2c1e-4fb2-a621-f39d4c57f140", + "section_type": "main", + "text": "\n\nGenetic and epigenetic factors determine cell fate and function.Recent breakthroughs in genotyping technology have led to the identification of more than 20 loci associated with the risk of type 2 diabetes (Sambuy 2007;Zhao et al. 2009).However, all together these loci explain <5% of the genetic risk for diabetes.Epigenetic events have been implicated as contributing factors for metabolic diseases (Barker 1988;Kaput et al. 2007).Unhealthy diet and a sedentary lifestyle likely lead to epigenetic changes that can, in turn, contribute to the onset of diabetes (Kaput et al. 2007).At present, the underlying molecular mechanisms for disease progression remain to be elucidated." + } + ], + "document_id": "A9F8F600EC44B4FA08789ED3E990BE0D", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&genomics", + "nutrition", + "nutrient-gene&interactions", + "diabetes&mellitus", + "nutritional&genomics", + "gene&variants", + "epigenetic&modifications", + "GWAS", + "pharmacogenomics", + "personalized&medicine", + "machine&learning" + ], + "metadata": [ + { + "object": "Three loci with high mutation frequencies, the 138665410 FOXL2 gene variant, the 23862952 MYH6 gene variant, and the 71098693 HYDIN gene variant were found to be significantly associated with sporadic Atrial Septal Defect P<0.05; variants in FOXL2 and MYH6 were found in patients with isolated, sporadic Atrial Septal Defect P<5x10-4.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab953981" + }, + { + "object": "The results of this meta-analysis support the hypothesis that RBP4 is a modest independent risk factor for gestational diabetes mellitus i.e., nonobese patients with gestational diabetes mellitus might express RBP4 at abnormal levels.The association between RBP4 rs3758539 polymorphism and gestational diabetes mellitus risk was not confirmed.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab860992" + }, + { + "object": "We studied the association between retinoic acid receptor responder 2 rs17173608 and rs4721 gene polymorphisms and gestational diabetes mellitus. We found that RARRES2 rs4721 polymorphism increased the risk of gestational diabetes mellitus. RARRES2 rs17173608 polymorphism is not associated with gestational diabetes mellitus.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1013771" + }, + { + "object": "Data show that circulating ghrelin is high in situations of nutritional deficiency starvation and low in situations of nutritional plenty free access to food or total parenteral nutrition.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab191174" + }, + { + "object": "Data confirm the association between the FTO first intron polymorphism and the presence of type 2 diabetes mellitus in the Slavonic Czech population. The same variant is likely to be associated with development of chronic complications of diabetes mellitus, especially with diabetic neuropathy and diabetic kidney disease in either T2DM or both T1DM and T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab173943" + }, + { + "object": "Data suggest that subjects with point mutation 3243A>G in mtRNA-LeuUUR develop MIDD maternally inherited diabetes and deafness; as compared to patients with T1DM type 1 diabetes mellitus or early-onset T2DM type 2 diabetes mellitus matched for sex, age, duration of diabetes, such MIDD patients have highest rate of osteoporosis.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab211558" + }, + { + "object": "meta-analysis indicated that the risk allele of the GCK -30G>A polymorphism may increase gestational diabetes mellitus and type 2 diabetes mellitus risk in whites, whereas additional studies are needed to confirm the effect of this polymorphism on both diseases in Asians and Africans", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab478385" + }, + { + "object": "rs2059806 of INSR was associated with both type 2 diabetes mellitus and type 2 diabetic nephropathy, while rs7212142 of mTOR was associated with type 2 diabetic nephropathy but not type 2 diabetes mellitus in a Chinese Han population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab687817" + }, + { + "object": "The aim of this study was to examine the frequency of exocrine dysfunctions of the pancreas according to the level of fecal elastase-1 FE-1 in patients with diabetes mellitus, type 1 and diabetes mellitus, type 2.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab778488" + }, + { + "object": "Patellar tendon properties are not influenced by the MMP3 gene variants measured. Although MMP3 gene variants are associated with risk of tendon pathology, association is unlikely to be mediated via underlying tendon dimensional and functional properties.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab582593" + } + ], + "question": "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabetes", + "subquestions": null, + "task_id": "A9F8F600EC44B4FA08789ED3E990BE0D", + "usage": { + "chatgpt": 6443, + "gpt-4": 4073, + "gpt-4-turbo-preview": 3136 + }, + "user_id": 2 + }, + "document_id": "A9F8F600EC44B4FA08789ED3E990BE0D", + "task_id": "A9F8F600EC44B4FA08789ED3E990BE0D" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/04.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/04.json new file mode 100644 index 0000000..ef341f0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/04.json @@ -0,0 +1,404 @@ +{ + "created_at": "2024-05-31T23:10:07.733262Z", + "data": { + "amplify": false, + "answer": "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any association between the genes SH2B3 or ERBB3 and diabetes.", + "context": { + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "\n\nFigure 8 Molecular changes in the islets of patients with T2D mirror the processes altered in NOD mice.mRNA expression in human pancreatic islets from healthy individuals (n = 105) and those diagnosed with T2D (n = 14) was assessed through RNA-seq analysis. (a) Relationship between GLIS3 and MANF expression in healthy individuals (Spearman correlation P value = 0.043), individuals with T2D (Spearman correlation P value = 0.075) and all individuals (Spearman correlation P value = 0.028). (b-e) Expression of XRCC4 (b), LIG4 (c), H2AFX (d) and CDKN1A (e) in healthy islets as compared to i slets from patients withT2D (P values shown after multiple-testing correction).The median and interquartile range (IQR; box) are shown, with error bars indicating 1.5 times the IQR.Individual values are shown if beyond 1.5 times the IQR. (f) Relationship between H2AFX and LIG4 expression in human islets (Spearman correlation P value = 5 × 10 −9 )." + } + ], + "15524ac0-da3c-4c01-8ae2-1b8c901105ad": [ + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "text": "\n\nAll the genes involved in these pathways, as well as the genes involved in b-cells development and turnover, may be considered candidate genes for T2DM with predominant insulin deficiency." + } + ], + "1ef9a72d-b9ef-4955-a351-fca0175da3d1": [ + { + "document_id": "1ef9a72d-b9ef-4955-a351-fca0175da3d1", + "text": "\n\nOne method of searching for the cause of NIDDM is via the candidate gene approach.Possible candidates for NIDDM include genes involved in specifying pancreatic islet (3-cell phenotype and in directing fj-cell development and (3-cell responses of glucose-mediated insulin synthesis and secretion.The transcription factor islet-1 (Isl-1) has been shown to be a unique protein that binds to the mini-enhancer or Far-FLAT region (nucleotide -247 to -198) of the rat insulin I gene (7).Isl-1, a protein comprised of 349 residues (38 kD), is a member of the LIM/homeodomain family of proteins, named for the first three members described: lin-11, isl-1, and mec-3 (8,9).These proteins are comprised of three putative regulatory regions, two LIM domains (cysteine-rich motifs) in the amino terminus of the protein, a homeobox domain near the middle, and a glutamine-rich transcriptional activation domain at the carboxyl end (7,9).With the use of an antibody to Isl-1, expression was shown to be restricted to a subset of endocrine cells, including islets, neurons involved in autonomic and endocrine control, and selected other tissues in the adult rat (10)(11)(12)." + } + ], + "21368075-9e10-4260-b346-43b1029b3bf0": [ + { + "document_id": "21368075-9e10-4260-b346-43b1029b3bf0", + "text": "Results\n\nImpairment or alteration of the insulin-signaling pathway is a commonly recognized feature of type 2 diabetes.It is therefore notable that the IS-HD gene set (Dataset S4) was not detected to be significantly transcriptionally altered by application of either hypergeometric enrichmentt test, DEA or GSEA.In particular, applying GSEA to the transcriptional profile dataset of diabetic and normal glucose-tolerant skeletal muscle described in Mootha et al. [10] did not identify a significant level of alteration in the IS-HD gene set (p ¼ 0.536), while DEA produced a comparably weak enrichment score (p ¼ 0.607).The failure to detect a significant transcriptional alteration in IS-HD may be explained by a number of factors.The enrichment results depended on the specific choice of the IS-HD gene set, and it is possible that an alternatively defined insulin-signaling gene set would be determined as significantly enriched.Additionally, expression changes in a few critical genes in IS-HD may be sufficient to substantially alter insulin signaling, and running DEA on the large IS-HD set may miss the contributions from these few genes." + } + ], + "2715e261-b26c-46d6-918f-c6aa47688f0c": [ + { + "document_id": "2715e261-b26c-46d6-918f-c6aa47688f0c", + "text": "35\nABSTRACT 11\nA GENE EXPRESSION NETWORK MODEL OF TYPE 2 DIABETES\nESTABLISHES A RELATIONSHIP BETWEEN CELL CYCLE\nREGULATION IN ISLETS AND DIABETES SUSCEPTIBILITY\nMP Keller, YJ Choi, P Wang, DB Davis, ME Rabaglia, AT Oler, DS Stapleton,\nC Argmann, KL Schueler, S Edwards, HA Steinberg, EC Neto, R Klienhanz, S\nTurner, MK Hellerstein, EE Schadt, BS Yandell, C Kendziorski, and AD Attie\nDepts." + } + ], + "4322db2f-5f43-4fc0-8968-b24438a7d6b9": [ + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "text": "\n\nSecond, we performed an extensive manual curation according to a previously described b-cell-targeted annotation (Kutlu et al, 2003;Ortis et al, 2010).In partial agreement with the IPA, we found these genes to fall into three broad categories: (1) genes related to b-cell dysfunction and death, (2) genes potentially facilitating the adaptation of the pancreatic islets to the altered metabolic situation in T2D and (3) genes whose role in disease pathogenesis remains to be unearthed (Figure 6B).The adaptation-related gene category contains few metabolism-associated genes (e.g., HK1, FBP2; Figure 6B, right part, Figure 7) and many more genes involved in signal transduction or encoding hormones, growth factors (e.g., EGF, FGF1, IGF2/IGF2AS; Figure 7), or transcription factors involved in important regulatory networks (for instance, FOXA2/HNF3B, PAX4 and SOX6) (Figure 6B, right part, Figure 7).In the b-cell dysfunction and death category, there were hypomethylated genes related to DNA damage and oxidative stress (e.g., GSTP1, ALDH3B1; Figure 7), the endoplasmic reticulum (ER) stress response (NIBAN, PPP2R4, CHAC1), and apoptosis (CASP10, NR4A1, MADD; Figure 6B, left part, Figure 7).Some genes of interest from the highlighted categories are depicted in Figure 7. Their annotated functions provide possible explanations of how the epigenetic dysregulation of these genes in diabetic islets is connected to T2D pathogenesis.Numerous genes that were identified by our methylation profiling approach have been functionally implicated in insulin secretion.Examination of the available literature on the function of these genes revealed three aspects of insulin secretion with which they interfere: some of these genes influence the expression of the insulin gene, like MAPK1 and SOX6, or its post-translational maturation, like PPP2R4 (cf. Figure 7 and references therein).Others can deregulate the process of insulin secretion itself (SLC25A5, Ahuja et al, 2007;RALGDS, Ljubicic et al, 2009) or influence synthesis as well as secretion (vitronectin, Kaido et al, 2006).A third group of differentially methylated genes affects (i) signalling processes in the b-cell leading to insulin secretion or (ii) glucose homeostasis in b-cells, thereby modulating insulin response upon stimulation.GRB10 (Yamamoto et al, 2008), FBP2 and HK1 (Figure 7) are examples for these genes.Additional genes found in our study have been implicated in the b-cells' capability to secrete insulin, though the mechanisms have not yet been fully established.The putative functions of these genes indicate a potential epigenetic impact on insulin secretion at multiple levels, namely signalling, expression/synthesis and secretion." + } + ], + "647571cd-ff36-4be4-97c4-cd006d9bfbaf": [ + { + "document_id": "647571cd-ff36-4be4-97c4-cd006d9bfbaf", + "text": "\n\nIn summary, we have associated mutations in the SLC29A3 gene with diabetes mellitus in humans and the insulin signaling pathway in Drosophila.The mechanistic basis of these findings remains to be determined.This is strong evidence supporting the investment of resources to further investigate the role of SLC29A3 and its orthologs in diabetes and glucose metabolism in model systems." + }, + { + "document_id": "647571cd-ff36-4be4-97c4-cd006d9bfbaf", + "text": "DISCUSSION\n\nWe have identified mutations in the equilibrative nucleoside transporter 3 protein that are associated with an inherited syndrome of insulin-dependent DM, and provide prima facie evidence that the Drosophila ortholog of this protein interacts with the insulin signaling pathway.This is the first evidence that mutations in the human SLC29A3 gene can be associated with a diabetic phenotype." + } + ], + "6e80ed3b-2be6-4775-a3c5-89cb4ddc88ae": [ + { + "document_id": "6e80ed3b-2be6-4775-a3c5-89cb4ddc88ae", + "text": "\n\nThese observations taken together suggest that molecules involved in innate immunity could serve as candidate genes that determine the susceptibility of sensitive strains of mice to virusinduced diabetes.Interestingly, deficiency of the Tyk2 gene results in a reduced antiviral response 24 .In addition, the human TYK2 gene was mapped to the possible type 1 diabetes susceptibility locus 25 ." + } + ], + "7b7ce30c-f398-4b0e-bcb6-52f2644201fd": [ + { + "document_id": "7b7ce30c-f398-4b0e-bcb6-52f2644201fd", + "text": "\n\nA recent sequencing study provides an example of detection of rare variants in type 1 diabetes.Targeted sequencing in a series of candidate coding regions resulted in IFIH1 being identified as the causal gene in a region associated with type 1 diabetes by GWA studies (58).IFIH1 encodes a cytoplasmic helicase that mediates induction of the interferon response to viral RNA.The discovery of IFIH1 as a contributor to susceptibility to type 1 diabetes has strengthened the hypothesis (70) about a mechanism of disease pathogenesis involving virusgenetic interplay and raised type 1 interferon levels as a cofactor in -cell destruction.Nonetheless, it should be recognized that a component of the missing heritability (familial aggregation) in type 1 diabetes could well be due to unrecognized intra-familial environmental factors.Disease pathogenesis.Contemporary models of pathogenesis of type 1 diabetes support the involvement of two primary dramatis personae: the immune system and the -cell.The known and newly identified genetic risk factors for type 1 diabetes present exciting opportunities to build on to the current cast of disease mechanisms and networks.Most of the listed genes of interest (Table 2) and those in extended regions are assumed to regulate immune function.Some of these genes, however, may also have roles in the -cell (insulin being the most obvious example).Another gene, PTPN2, encoding a protein tyrosine phosphatase, was identified as affecting the risk for type 1 diabetes as well as for Crohn disease (47,71).PTPN2 is expressed in immune cells, and its expression is highly regulated by cytokines.However, PTPN2 is expressed also in -cells, where it modulates interferon (IFN)-␥ signal transduction and has been shown to regulate cytokineinduced apoptosis (72).Other candidate genes, such as NOS2A, IL1B, reactive oxygen species scavengers, and candidate genes, identified in large GWA studies of type 2 diabetes, have not been found to be significant contributors to the susceptibility of type 1 diabetes (73)." + } + ], + "7e816722-443f-463c-8a79-852752df28e6": [ + { + "document_id": "7e816722-443f-463c-8a79-852752df28e6", + "text": "Differential Expression Analyses of Type 1 Diabetes Mellitus Associated Genes\n\nFor the aforementioned 171 'novel' genes, we used t-test to compare ribonucleic acid expression signals in PBMCs or monocytes between type 1 diabetes mellitus patients and healthy controls.We found that 37 genes, including 21 non-HLA genes (e.g.FAM46B, OLFML3 and HIPK1), were differentially expressed between type 1 diabetes mellitus patients and controls (Table 2).For the differential expression study, the significance level of P < 5.0E-02 was used." + } + ], + "845adde7-823a-4bfc-9f5e-7082d2e26102": [ + { + "document_id": "845adde7-823a-4bfc-9f5e-7082d2e26102", + "text": "\n\nIn this study, we have correlated the function and genotype of human islets obtained from diabetic and nondiabetic (ND) donors.We have analyzed a panel of 14 gene variants robustly associated with T2D susceptibility identified by recent genetic association studies.We have identified four genetic variants that confer reduced b-cell exocytosis and six variants that interfere with insulin granule distribution.Based on these observations, we calculate a genetic risk score for islet dysfunction leading to T2D that involves decreased docking of insulin-containing secretory granules, impaired insulin exocytosis, and reduced insulin secretion." + } + ], + "8aee60c9-9bb4-4867-96c9-830c1e43c72e": [ + { + "document_id": "8aee60c9-9bb4-4867-96c9-830c1e43c72e", + "text": "\n\nAt present, insulin [15], glucokinase [16], amylin [17], mitochondrial DNA [18], and several transcriptional factors [19][20][21][22] are recognized as diabetogenic genes in pancreatic b-cells.In the present study we used the candidate gene approach in the examination of genomic variation in the a 1D and Kir6.2 channel genes in type 2 diabetic patients." + } + ], + "9fd49699-612f-48c0-b1d9-e01158472be6": [ + { + "document_id": "9fd49699-612f-48c0-b1d9-e01158472be6", + "text": "\n\nIn summary, we report AEIs that are consistent with type 2 diabetes-associated variation regulating the expression of cis-linked genes in human islets.For some of the genes where significant AEI was identified (e.g., SLC30A8, WFS1), there is strong evidence from human genetics that small changes in gene dosage may have significant consequences for the pancreatic b-cell.For other genes with significant AEI (e.g., ANPEP, HMG20A), their role is less well defined, and hence this study should provide a platform for further work examining the effects of carefully manipulating the expression of these genes in human islets." + } + ], + "e51e88b2-bea3-4ab7-858f-824f7d5ccbdd": [ + { + "document_id": "e51e88b2-bea3-4ab7-858f-824f7d5ccbdd", + "text": "\n\nResults.Pathway analysis of genes with differentially methylated promoters identified the top 3 enriched pathways as maturity onset diabetes of the young (MODY), type 2 diabetes, and Notch signaling.Several genes in these pathways are known to affect pancreatic development and insulin secretion." + } + ], + "e7bc9d83-6c3b-405c-a552-29874b927860": [ + { + "document_id": "e7bc9d83-6c3b-405c-a552-29874b927860", + "text": "The authors then used mouse liver and adipose expression\ndata from several mouse crosses to construct causal expression networks for the ERBB3 and\nRPS26 orthologs in the mouse. They then showed that ERBB3 is not associated with any\nknown Type I diabetes genes whereas RPS26 is associated a network of several genes that\nare part of the KEGG Type I diabetes pathway (Schadt et al. 2008). This type of analysis\ndemonstrates the power of combining human and mouse data with a network based\napproach that has been proposed for use in drug discovery (Schadt et al." + } + ], + "ebb49f39-ee30-4b32-959d-305276fd589e": [ + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "text": "\n\nIn conclusion, GWAS studies focusing on the causes of T2D have implicated islet dysfunction as a major contributing factor (18,71).By examining isolated islets for stress responses and cross-referencing gene hits with genes associated with glucose-stimulated insulin release in human populations with T2D, we identified 7 genes that may play a role in promoting or preventing islet decline in T2D.By further examining stress-induced expression changes in each of these genes, we identified 5 genes that stood out: F13a1 as a novel stress-inhibited gene in islets, Klhl6 and Pamr1 as induced genes specific to ER stress, Ripk2 as a broadly stress-induced gene, and Steap4 as an exceptionally cytokine-sensitive gene.These genes provide promising leads in elucidating islet stress responses and islet dysfunction during the development of T2D." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "text": "\nGenome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of Ͼ2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "text": "\n\nGenome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of Ͼ2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D." + } + ], + "faa23996-65fc-4bc6-938a-c959e981d493": [ + { + "document_id": "faa23996-65fc-4bc6-938a-c959e981d493", + "text": "\n\nFinally, several of the linking nodes introduced into this islet network through their PPI connections represent interesting candidates for a role in T2D pathogenesis, and there are several examples where external data provides validation of those assignments.An interesting example involves the gene GINS4 which maps at the ANK1 locus.Though this gene generated a low PCS [0.03] and was not included in the set of seed genes for this locus, GINS4 knock-down has an impact in a human beta-cell line [14].In addition, cyclin-dependent kinase 2 (CDK2) has been shown to influence beta-cell mass in a compensatory mechanism related to age-and diet-induced stress, connecting beta-cell dysfunction and progressive beta-cell mass deterioration [54].YHWAG is a member of the 14-3-3 family, known to be signalling hubs for beta-cell survival [55], and disruption of SMAD4 drives islet hypertrophy [56]." + } + ] + }, + "data_source": [ + { + "document_id": "7b7ce30c-f398-4b0e-bcb6-52f2644201fd", + "section_type": "main", + "text": "\n\nA recent sequencing study provides an example of detection of rare variants in type 1 diabetes.Targeted sequencing in a series of candidate coding regions resulted in IFIH1 being identified as the causal gene in a region associated with type 1 diabetes by GWA studies (58).IFIH1 encodes a cytoplasmic helicase that mediates induction of the interferon response to viral RNA.The discovery of IFIH1 as a contributor to susceptibility to type 1 diabetes has strengthened the hypothesis (70) about a mechanism of disease pathogenesis involving virusgenetic interplay and raised type 1 interferon levels as a cofactor in -cell destruction.Nonetheless, it should be recognized that a component of the missing heritability (familial aggregation) in type 1 diabetes could well be due to unrecognized intra-familial environmental factors.Disease pathogenesis.Contemporary models of pathogenesis of type 1 diabetes support the involvement of two primary dramatis personae: the immune system and the -cell.The known and newly identified genetic risk factors for type 1 diabetes present exciting opportunities to build on to the current cast of disease mechanisms and networks.Most of the listed genes of interest (Table 2) and those in extended regions are assumed to regulate immune function.Some of these genes, however, may also have roles in the -cell (insulin being the most obvious example).Another gene, PTPN2, encoding a protein tyrosine phosphatase, was identified as affecting the risk for type 1 diabetes as well as for Crohn disease (47,71).PTPN2 is expressed in immune cells, and its expression is highly regulated by cytokines.However, PTPN2 is expressed also in -cells, where it modulates interferon (IFN)-␥ signal transduction and has been shown to regulate cytokineinduced apoptosis (72).Other candidate genes, such as NOS2A, IL1B, reactive oxygen species scavengers, and candidate genes, identified in large GWA studies of type 2 diabetes, have not been found to be significant contributors to the susceptibility of type 1 diabetes (73)." + }, + { + "document_id": "9fd49699-612f-48c0-b1d9-e01158472be6", + "section_type": "main", + "text": "\n\nIn summary, we report AEIs that are consistent with type 2 diabetes-associated variation regulating the expression of cis-linked genes in human islets.For some of the genes where significant AEI was identified (e.g., SLC30A8, WFS1), there is strong evidence from human genetics that small changes in gene dosage may have significant consequences for the pancreatic b-cell.For other genes with significant AEI (e.g., ANPEP, HMG20A), their role is less well defined, and hence this study should provide a platform for further work examining the effects of carefully manipulating the expression of these genes in human islets." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "\n\nSecond, we performed an extensive manual curation according to a previously described b-cell-targeted annotation (Kutlu et al, 2003;Ortis et al, 2010).In partial agreement with the IPA, we found these genes to fall into three broad categories: (1) genes related to b-cell dysfunction and death, (2) genes potentially facilitating the adaptation of the pancreatic islets to the altered metabolic situation in T2D and (3) genes whose role in disease pathogenesis remains to be unearthed (Figure 6B).The adaptation-related gene category contains few metabolism-associated genes (e.g., HK1, FBP2; Figure 6B, right part, Figure 7) and many more genes involved in signal transduction or encoding hormones, growth factors (e.g., EGF, FGF1, IGF2/IGF2AS; Figure 7), or transcription factors involved in important regulatory networks (for instance, FOXA2/HNF3B, PAX4 and SOX6) (Figure 6B, right part, Figure 7).In the b-cell dysfunction and death category, there were hypomethylated genes related to DNA damage and oxidative stress (e.g., GSTP1, ALDH3B1; Figure 7), the endoplasmic reticulum (ER) stress response (NIBAN, PPP2R4, CHAC1), and apoptosis (CASP10, NR4A1, MADD; Figure 6B, left part, Figure 7).Some genes of interest from the highlighted categories are depicted in Figure 7. Their annotated functions provide possible explanations of how the epigenetic dysregulation of these genes in diabetic islets is connected to T2D pathogenesis.Numerous genes that were identified by our methylation profiling approach have been functionally implicated in insulin secretion.Examination of the available literature on the function of these genes revealed three aspects of insulin secretion with which they interfere: some of these genes influence the expression of the insulin gene, like MAPK1 and SOX6, or its post-translational maturation, like PPP2R4 (cf. Figure 7 and references therein).Others can deregulate the process of insulin secretion itself (SLC25A5, Ahuja et al, 2007;RALGDS, Ljubicic et al, 2009) or influence synthesis as well as secretion (vitronectin, Kaido et al, 2006).A third group of differentially methylated genes affects (i) signalling processes in the b-cell leading to insulin secretion or (ii) glucose homeostasis in b-cells, thereby modulating insulin response upon stimulation.GRB10 (Yamamoto et al, 2008), FBP2 and HK1 (Figure 7) are examples for these genes.Additional genes found in our study have been implicated in the b-cells' capability to secrete insulin, though the mechanisms have not yet been fully established.The putative functions of these genes indicate a potential epigenetic impact on insulin secretion at multiple levels, namely signalling, expression/synthesis and secretion." + }, + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "section_type": "main", + "text": "\n\nAll the genes involved in these pathways, as well as the genes involved in b-cells development and turnover, may be considered candidate genes for T2DM with predominant insulin deficiency." + }, + { + "document_id": "647571cd-ff36-4be4-97c4-cd006d9bfbaf", + "section_type": "main", + "text": "\n\nIn summary, we have associated mutations in the SLC29A3 gene with diabetes mellitus in humans and the insulin signaling pathway in Drosophila.The mechanistic basis of these findings remains to be determined.This is strong evidence supporting the investment of resources to further investigate the role of SLC29A3 and its orthologs in diabetes and glucose metabolism in model systems." + }, + { + "document_id": "e7bc9d83-6c3b-405c-a552-29874b927860", + "section_type": "main", + "text": "The authors then used mouse liver and adipose expression\ndata from several mouse crosses to construct causal expression networks for the ERBB3 and\nRPS26 orthologs in the mouse. They then showed that ERBB3 is not associated with any\nknown Type I diabetes genes whereas RPS26 is associated a network of several genes that\nare part of the KEGG Type I diabetes pathway (Schadt et al. 2008). This type of analysis\ndemonstrates the power of combining human and mouse data with a network based\napproach that has been proposed for use in drug discovery (Schadt et al." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "section_type": "main", + "text": "\n\nIn conclusion, GWAS studies focusing on the causes of T2D have implicated islet dysfunction as a major contributing factor (18,71).By examining isolated islets for stress responses and cross-referencing gene hits with genes associated with glucose-stimulated insulin release in human populations with T2D, we identified 7 genes that may play a role in promoting or preventing islet decline in T2D.By further examining stress-induced expression changes in each of these genes, we identified 5 genes that stood out: F13a1 as a novel stress-inhibited gene in islets, Klhl6 and Pamr1 as induced genes specific to ER stress, Ripk2 as a broadly stress-induced gene, and Steap4 as an exceptionally cytokine-sensitive gene.These genes provide promising leads in elucidating islet stress responses and islet dysfunction during the development of T2D." + }, + { + "document_id": "1ef9a72d-b9ef-4955-a351-fca0175da3d1", + "section_type": "main", + "text": "\n\nOne method of searching for the cause of NIDDM is via the candidate gene approach.Possible candidates for NIDDM include genes involved in specifying pancreatic islet (3-cell phenotype and in directing fj-cell development and (3-cell responses of glucose-mediated insulin synthesis and secretion.The transcription factor islet-1 (Isl-1) has been shown to be a unique protein that binds to the mini-enhancer or Far-FLAT region (nucleotide -247 to -198) of the rat insulin I gene (7).Isl-1, a protein comprised of 349 residues (38 kD), is a member of the LIM/homeodomain family of proteins, named for the first three members described: lin-11, isl-1, and mec-3 (8,9).These proteins are comprised of three putative regulatory regions, two LIM domains (cysteine-rich motifs) in the amino terminus of the protein, a homeobox domain near the middle, and a glutamine-rich transcriptional activation domain at the carboxyl end (7,9).With the use of an antibody to Isl-1, expression was shown to be restricted to a subset of endocrine cells, including islets, neurons involved in autonomic and endocrine control, and selected other tissues in the adult rat (10)(11)(12)." + }, + { + "document_id": "7e816722-443f-463c-8a79-852752df28e6", + "section_type": "main", + "text": "Differential Expression Analyses of Type 1 Diabetes Mellitus Associated Genes\n\nFor the aforementioned 171 'novel' genes, we used t-test to compare ribonucleic acid expression signals in PBMCs or monocytes between type 1 diabetes mellitus patients and healthy controls.We found that 37 genes, including 21 non-HLA genes (e.g.FAM46B, OLFML3 and HIPK1), were differentially expressed between type 1 diabetes mellitus patients and controls (Table 2).For the differential expression study, the significance level of P < 5.0E-02 was used." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "section_type": "abstract", + "text": "\nGenome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of Ͼ2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D." + }, + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "section_type": "main", + "text": "\n\nFigure 8 Molecular changes in the islets of patients with T2D mirror the processes altered in NOD mice.mRNA expression in human pancreatic islets from healthy individuals (n = 105) and those diagnosed with T2D (n = 14) was assessed through RNA-seq analysis. (a) Relationship between GLIS3 and MANF expression in healthy individuals (Spearman correlation P value = 0.043), individuals with T2D (Spearman correlation P value = 0.075) and all individuals (Spearman correlation P value = 0.028). (b-e) Expression of XRCC4 (b), LIG4 (c), H2AFX (d) and CDKN1A (e) in healthy islets as compared to i slets from patients withT2D (P values shown after multiple-testing correction).The median and interquartile range (IQR; box) are shown, with error bars indicating 1.5 times the IQR.Individual values are shown if beyond 1.5 times the IQR. (f) Relationship between H2AFX and LIG4 expression in human islets (Spearman correlation P value = 5 × 10 −9 )." + }, + { + "document_id": "845adde7-823a-4bfc-9f5e-7082d2e26102", + "section_type": "main", + "text": "\n\nIn this study, we have correlated the function and genotype of human islets obtained from diabetic and nondiabetic (ND) donors.We have analyzed a panel of 14 gene variants robustly associated with T2D susceptibility identified by recent genetic association studies.We have identified four genetic variants that confer reduced b-cell exocytosis and six variants that interfere with insulin granule distribution.Based on these observations, we calculate a genetic risk score for islet dysfunction leading to T2D that involves decreased docking of insulin-containing secretory granules, impaired insulin exocytosis, and reduced insulin secretion." + }, + { + "document_id": "faa23996-65fc-4bc6-938a-c959e981d493", + "section_type": "main", + "text": "\n\nFinally, several of the linking nodes introduced into this islet network through their PPI connections represent interesting candidates for a role in T2D pathogenesis, and there are several examples where external data provides validation of those assignments.An interesting example involves the gene GINS4 which maps at the ANK1 locus.Though this gene generated a low PCS [0.03] and was not included in the set of seed genes for this locus, GINS4 knock-down has an impact in a human beta-cell line [14].In addition, cyclin-dependent kinase 2 (CDK2) has been shown to influence beta-cell mass in a compensatory mechanism related to age-and diet-induced stress, connecting beta-cell dysfunction and progressive beta-cell mass deterioration [54].YHWAG is a member of the 14-3-3 family, known to be signalling hubs for beta-cell survival [55], and disruption of SMAD4 drives islet hypertrophy [56]." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "section_type": "main", + "text": "\n\nGenome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of Ͼ2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D." + }, + { + "document_id": "2715e261-b26c-46d6-918f-c6aa47688f0c", + "section_type": "main", + "text": "35\nABSTRACT 11\nA GENE EXPRESSION NETWORK MODEL OF TYPE 2 DIABETES\nESTABLISHES A RELATIONSHIP BETWEEN CELL CYCLE\nREGULATION IN ISLETS AND DIABETES SUSCEPTIBILITY\nMP Keller, YJ Choi, P Wang, DB Davis, ME Rabaglia, AT Oler, DS Stapleton,\nC Argmann, KL Schueler, S Edwards, HA Steinberg, EC Neto, R Klienhanz, S\nTurner, MK Hellerstein, EE Schadt, BS Yandell, C Kendziorski, and AD Attie\nDepts." + }, + { + "document_id": "21368075-9e10-4260-b346-43b1029b3bf0", + "section_type": "main", + "text": "Results\n\nImpairment or alteration of the insulin-signaling pathway is a commonly recognized feature of type 2 diabetes.It is therefore notable that the IS-HD gene set (Dataset S4) was not detected to be significantly transcriptionally altered by application of either hypergeometric enrichmentt test, DEA or GSEA.In particular, applying GSEA to the transcriptional profile dataset of diabetic and normal glucose-tolerant skeletal muscle described in Mootha et al. [10] did not identify a significant level of alteration in the IS-HD gene set (p ¼ 0.536), while DEA produced a comparably weak enrichment score (p ¼ 0.607).The failure to detect a significant transcriptional alteration in IS-HD may be explained by a number of factors.The enrichment results depended on the specific choice of the IS-HD gene set, and it is possible that an alternatively defined insulin-signaling gene set would be determined as significantly enriched.Additionally, expression changes in a few critical genes in IS-HD may be sufficient to substantially alter insulin signaling, and running DEA on the large IS-HD set may miss the contributions from these few genes." + }, + { + "document_id": "647571cd-ff36-4be4-97c4-cd006d9bfbaf", + "section_type": "main", + "text": "DISCUSSION\n\nWe have identified mutations in the equilibrative nucleoside transporter 3 protein that are associated with an inherited syndrome of insulin-dependent DM, and provide prima facie evidence that the Drosophila ortholog of this protein interacts with the insulin signaling pathway.This is the first evidence that mutations in the human SLC29A3 gene can be associated with a diabetic phenotype." + }, + { + "document_id": "8aee60c9-9bb4-4867-96c9-830c1e43c72e", + "section_type": "main", + "text": "\n\nAt present, insulin [15], glucokinase [16], amylin [17], mitochondrial DNA [18], and several transcriptional factors [19][20][21][22] are recognized as diabetogenic genes in pancreatic b-cells.In the present study we used the candidate gene approach in the examination of genomic variation in the a 1D and Kir6.2 channel genes in type 2 diabetic patients." + }, + { + "document_id": "6e80ed3b-2be6-4775-a3c5-89cb4ddc88ae", + "section_type": "main", + "text": "\n\nThese observations taken together suggest that molecules involved in innate immunity could serve as candidate genes that determine the susceptibility of sensitive strains of mice to virusinduced diabetes.Interestingly, deficiency of the Tyk2 gene results in a reduced antiviral response 24 .In addition, the human TYK2 gene was mapped to the possible type 1 diabetes susceptibility locus 25 ." + }, + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "section_type": "main", + "text": "Parallel transcriptional regulation in human islets\n\nTo determine whether the findings observed in mice were applicable to humans, we investigated whether the pathway identified in NOD mice also demonstrated genetic linkage to diabetes or glucose regulation traits in humans.GLIS3 polymorphisms have previously been associated with altered glucose regulation; we additionally identified nominally significant associations for MANF, XRCC4 and LIG4 polymorphisms (Supplementary Table 2).In an independent approach that takes into account environmental effects, we analyzed RNA-seq data from human pancreatic islets isolated from 119 donors, including 14 diagnosed with T2D 28 .To assess the validity of the Glis3-Manf relationship observed in mice, we investigated the relationship of these two genes in human islets.A trend toward reduced GLIS3 expression was observed in T2D islets, whereas MANF expression appeared unchanged (Supplementary Fig. 13).Critically, a significant positive relationship was observed between GLIS3 and MANF levels in human islets (Fig. 8a).Next, we investigated whether patients with T2D might exhibit reduced XRCC4 expression, analogous to the NOD polymorphisms.We found no change in XRCC4 expression in T2D islets (Fig. 8b); however, the levels of the obligate binding partner encoded by LIG4 were significantly reduced (Fig. 8c).In mice, Xrcc4 polymorphisms were associated with increased senescence; likewise, in patients with T2D, the levels of the senescence markers H2AFX (Fig. 8d) and CDKN1A (Fig. 8e) were increased.Finally, a direct relationship was observed between reduced LIG4 and increased H2AFX levels (Fig. 8f).Although the cause of coregulation cannot be assessed in ex vivo human islets, the parallel with NOD mice strongly supports a conservation of diabetes susceptibility mechanisms across species.3,500,000 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 Fluorescence" + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "section_type": "main", + "text": "\n\nWe previously reported that circulating levels of these cytokines were sufficient to reduce glucose-stimulated insulin release and increase cell death in islets from diabetes-prone mice but not heterozygous controls (12).To begin to identify the genes responsible for this effect, we conducted a microarray study of islets isolated from prediabetic BKS.Cg-m ϩ/ϩ Lepr db /J (db/db) mice and heterozygous controls to compare their responses to exposure to circulating levels of IL-1 and IL-6 at concentrations that mimic low-grade inflammation.The most cytokine-sensitive genes from the mouse islet microarray study were evaluated for associations with the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.GUARDIAN is a genome-wide association scan (GWAS) in Hispanic Americans, the largest US minority group and one at high risk of T2D (13).Participants in this study were monitored for glucose homeostasis measured by the frequently sampled intravenous glucose tolerance test (FSIVGTT) and the euglycemic clamp.Both FSIVGTTs and the euglycemic clamp methods yield underlying physiological, highly heritable parameters that are relevant to the risk of T2D (14,15)." + }, + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "section_type": "main", + "text": "\n\nIt has been hypothesized for a while that individual differences in insulin secretion capacity are predominantly determined by genetics (186,187).This is now clearly strengthened by the finding that, among the 27 confirmed (Table 1) and potential (Table 2) diabetes risk genes mentioned above, 18 genes affect -cell function, namely CAPN10 (188), CDC123/CAMK1D (189), CDKAL1 (166, 174, 190 -193), CDKN2A/B (34,167,193), ENPP1 (194), FOXO1 (77), HHEX (167,190,193,195,196), IGF2BP2 (34,166,167), JAZF1 (189), KCNJ11 (38,41,193), KCNQ1 (180,197), MTNR1B (181)(182)(183), PPARGC1A (198), SGK1 (79), SLC30A8 (34,166), TCF7L2 (129,134,138,160,193,199,200), TSPAN8/ LGR5 (189), and WFS1 (201)(202)(203).This was revealed by calculating fasting state-and oral glucose tolerance test (OGTT)-derived (plasma insulin-and C-peptide-based) surrogate indices for insulin secretion that do not allow further dissection of the aspects of -cell function affected, such as insulin maturation, glucose sensitivity, or incretin sensitivity.From these rough estimates of -cell function, pathomechanisms showing how these common gene variants impair -cell function were only proposed for the biological candidates KCNJ11, FOXO1, and SGK1, which have been well studied in vitro as well as in mice in vivo.KCNJ11 (potassium inwardly-rectifying channel, subfamily J, member 11; OMIM entry no.600937) encodes the pore-forming subunit Kir6.2 of the ATP-sensitive potassium channel of -cells, which couples glucose sensing with membrane depolarization and exocytosis of insulin granules.The best studied and confirmed diabetes risk variant E23K (rs5219) was shown in vitro to increase the probability of the channel's open state, to enhance its activity, and to impair its ATP sensitivity, thereby inhibiting -cell excitability and insulin release (204,205).Furthermore, the same variant was suggested to impair insulin secretion due to its enhanced response to the channel-ac-tivating effect of intracellular acyl coenzyme As, fatty acid metabolites known to be elevated in obese and type 2 diabetic subjects (206)." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "\n\nFor the first approach, we assessed whether the differentially methylated genes have any overlap or other association with known T2D risk genes.Then, we carried out an Ingenuity Pathway Analysis (IPA; Figure 6A) to identify pathways that are epigenetically affected in T2D islets according to our methylation profiling data.This was augmented by a manual search for the differentially methylated genes in scientific literature reporting on the general biology as well as T2D-related functions of these genes or the pathways they are part of (Figures 6 and 7).For the second approach, we knocked down expression of several genes by RNA interference and tested the functional consequence of their depletion in b-cells (Figure 8).For two selected genes, we explored their functional role more extensively in isolated b-cells and human islets (Figure 9)." + }, + { + "document_id": "e92427da-dee9-472f-bfa1-2e7bfa7de521", + "section_type": "main", + "text": "\n\nTo evaluate the effects of hyperglycemia or other metabolic consequences of DM per se on expression, we identified 12 genes altered in DM as compared with both nondiabetic groups but not as a function of family history (Table 4, which is published as supporting information on the PNAS web site).This included a 70-kDa heat-shock protein (HSP701A), which was decreased by 42% in DM and whose expression correlated inversely with fasting glucose for all subjects (r ϭ Ϫ0.77).Expression of a related HSP70 gene was previously found to be reduced in Caucasian diabetic subjects (20)." + }, + { + "document_id": "92eb0c69-5e98-41aa-9084-506e7f223b1a", + "section_type": "main", + "text": "\n\nIt is worth mentioning that in [132], a meta-analysis study was conducted, where a collection of gene expression datasets of pancreatic beta-cells, conditioned in an environment resembling T1D induced apoptosis, such as exposure to proinflammatory cytokines, in order to identify relevant and differentially expressed genes.The specific genes were then characterized according to their function and prior literature-based information to build temporal regulatory networks.Moreover, biological experiments were carried out revealing that inhibition of two of the most relevant genes (RIPK2 and ELF3), previously unknown in T1D literature, have a certain impact on apoptosis." + }, + { + "document_id": "18d88787-096b-4fc1-ad4e-3d1b1f3a90d9", + "section_type": "main", + "text": "\n\nFigure 2: The role of type 2 diabetes genes in insulin secretion Pancreatic β-cell genes associated with type 2 diabetes are in italics.G6P=glucose-6-phosphate. Adapted from Florez JC.Newly identifi ed loci highlight beta cell dysfunction as a key cause of type 2 diabetes: where are the insulin resistance genes?Diabetologia 2008; 51: 1100-10, by kind permission of the author and Springer Science + Business Media." + }, + { + "document_id": "845adde7-823a-4bfc-9f5e-7082d2e26102", + "section_type": "abstract", + "text": "\nThe majority of genetic risk variants for type 2 diabetes (T2D) affect insulin secretion, but the mechanisms through which they influence pancreatic islet function remain largely unknown.We functionally characterized human islets to determine secretory, biophysical, and ultrastructural features in relation to genetic risk profiles in diabetic and nondiabetic donors.Islets from donors with T2D exhibited impaired insulin secretion, which was more pronounced in lean than obese diabetic donors.We assessed the impact of 14 disease susceptibility variants on measures of glucose sensing, exocytosis, and structure.Variants near TCF7L2 and ADRA2A were associated with reduced glucose-induced insulin secretion, whereas susceptibility variants near ADRA2A, KCNJ11, KCNQ1, and TCF7L2 were associated with reduced depolarization-evoked insulin exocytosis.KCNQ1, ADRA2A, KCNJ11, HHEX/IDE, and SLC2A2 variants affected granule docking.We combined our results to create a novel genetic risk score for b-cell dysfunction that includes aberrant granule docking, decreased Ca 2+ sensitivity of exocytosis, and reduced insulin release.Individuals with a high risk score displayed an impaired response to intravenous glucose and deteriorating insulin secretion over time.Our results underscore the importance of defects in b-cell exocytosis in T2D and demonstrate the potential of cellular phenotypic characterization in the elucidation of complex genetic disorders." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "\n\nIt has been suggested that progressively occurring DNA methylation errors lead to diminished gene responsiveness to external stimuli and might thus contribute to the development of T2D (Gallou-Kabani and Junien, 2005).Our findings of prevalent promoter hypomethylation in T2D islets are indicative of active biological processes involved in adaptation to the diabetic environment as well as biological pathways associated with b-cell dysfunction and apoptosis (Figures 6B and 7).The functional relevance of some of the differentially methylated genes in b-cells was documented by screening for b-cell survival/death following RNAi and subsequent exposure to stresses relevant to T2D (Figure 8).Given the increased evidence that ER stress-induced apoptosis is one of the mechanisms of b-cell loss in T2D (Eizirik et al, 2008), it was of interest to further assess the biological functions of two putative ER stress-related genes that we found to be hypomethylated in T2D islets, namely NIBAN and CHAC1.We observed that these two genes are upregulated by synthetic ER stressors and by the more physiologically relevant saturated fatty acid palmitate in human islets, while knockdown of their expression by specific RNAi demonstrated their modulatory role in apoptosis (cf. Figure 9).While NIBAN protects against ER stress-induced apoptosis, CHAC1 seems to contribute to cell death.The hypomethylation observed at both genes could be explained by competing proapoptotic and antiapoptotic processes during ER stress response in diabetic islets.NIBAN is a negative regulator of translation initiation factor eIF2a (Sun et al, 2007).Therefore, its hypomethylation may indicate an attempt to re-establish ER homeostasis by reduction of protein synthesis (Eizirik et al, 2008).Pending the outcome of these attempts, ER stress-induced apoptosis may be triggered by CHAC1 and other proapoptotic genes." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "\n\nA recent study assessed gene expression in different islet cell types including the insulin-producing b-cells (Dorrell et al, 2011).A comparison showed that 240 of our 254 genes are covered by the microarray used by these authors.In all, 170 of these genes have a positive presence call in b-cells.This indicates that the majority of the genes we detected as differentially methylated in T2D islets are expressed in non-diabetic b-cells to a sufficient amount to be reliably detected by microarrays, that is, these are genes actively transcribed in b-cells." + }, + { + "document_id": "4a1a2496-1172-4262-8158-a3a96b80bcf4", + "section_type": "main", + "text": "\n\nStrikingly, three of the 10 candidate miRNA regulatory hubs in the T2D gene network were 59-shifted isomiRs: miR-375+1, miR-375-1, and miR-183-5p+1 (Fig. 4A).Moreover, all three of these were more significantly associated with T2D genes than their 59reference counterparts (Table S3 in File S2).This is particularly intriguing, given the already well-established role of 59-reference miR-375 in beta cell formation and function." + }, + { + "document_id": "70667239-7e12-494f-a6dd-5b1d073b5a56", + "section_type": "main", + "text": "\n\nNevertheless, taken together there is good evidence to propose that in human pancreas and in rodent pancreatic cell lines, steady state levels of insulin mRNA are lower from insulin genes linked to the class III VNTR alleles that for type 1 diabetes are dominantly protective.It is, however, difficult to explain how an approximately 30% reduction in insulin expression could explain the dominantly protective effect of class III VNTR alleles.Perhaps the pancreas is not the primary site of action of IDDM2-VNTRencoded predisposition to type 1 diabetes.In mice, the insulin gene is expressed transiently at birth in the thymus [30], presumably contributing to the normal state of non-responsiveness to insulin protein." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "\n\nThe analyses described above found only few common T2D candidate genes among the differentially methylated genes uncovered in this study.This could imply that T2D pathogenesis in islets is partially mediated by previously unappreciated genes.To decipher their roles in the context of T2D islets, as a first step we performed an IPA to determine which canonical pathways were overrepresented in our set of genes (Figure 6A).Inflammation-related processes were highly enriched, in particular the acute phase response and IL-8 signalling.Other enriched pathways, such as apoptosis and death receptor signalling, emphasise the role of b-cell loss in T2D.Enrichment for pathways involved in metabolism and internal and external cell structure (e.g., actin cytoskeleton and integrin signalling) may be indicative of altered islet function and architecture." + }, + { + "document_id": "41bc85bc-314f-4d92-9007-5d1571506ef3", + "section_type": "main", + "text": "Regulation of GWAS diabetes genes by glucose in pancreatic islets\n\nMany of the recently discovered type 2 diabetes genes have been suggested to affect the development and/or function of pancreatic islets [6].The function, growth and survival of β-cells can be regulated acutely and chronically by glucose [34].Thus, we examined whether the new type 2 diabetes susceptibility genes are regulated by overnight incubation in low (5 mM) or high (25 mM) glucose (Figure 5).Most genes were significantly or tended to be downregulated under conditions of high glucose.Cdkal1, Cdkn2a (Arf, P = 0.07), Ide, Jazf1, Camk1d, and Tspan8 (P = 0.06) expression levels were decreased ~50-60%.Meanwhile, the expression of Cdkn2b, Hhex (P = 0.10), Cdc123, Adamts9 (P = 0.09), and Thada were reduced 30-40%.To ensure the islets incubated in high glucose did not have globally decreased expression, we examined the expression of Txnip, which has been shown to be highly upregulated by glucose [35] and found that its expression was still significantly elevated in the islets cultured in high glucose (Figure 5).Mouse islets consist of β-cells and other cell types.Thus, the MIN6 β-cell line was also examined.We found that all the genes were expressed in this cell line (not shown), although this does not preclude that they also are expressed in other cell types within the islet." + }, + { + "document_id": "29d09d03-fd2f-48b3-a020-ea574d583dc4", + "section_type": "main", + "text": "\n\nThe majority of association studies has shown multiple gene loci for epigenetic regulation in these central mediators of type II diabetes, β-cells.Chen and colleagues characterized Ezh2 fl/fl mice and Cdkn2a −/− mice to reveal that an increased Ink4a and Arf expression in β-cells was linked to a reduced proliferative capacity.While Ezh2 levels declined throughout aging, INK4A levels increased.ChIP analysis uncovered that H3K27me3 occupancy regulating Ink4a and Ezh2 was declining with age, while H3K4me3 and histone acetylation at the Ink4a locus ascended in older mice.The authors concluded from their study that EZH2-dependent histone methylation and repression of the Ink4a/Arf locus are required for β-cell expansion [223,226].In a further study, the methylome of β cells was analyzed pancreatic islets from young and old mice using whole genome shotgun bisulfite sequencing (WGSBS).Overall, higher methylation rates (especially in CpGs with low methylation levels in youth), accompanied by a decline in replicative capacity, increased promoter methylation and decreased expression of cell cycle regulators were detected in \"healthy\" old β-cells.Intriguingly, this observation was associated with a functional improvement in aged murine and human islets [223,227]." + }, + { + "document_id": "787e2a2c-be24-4970-94b1-0f872a8cd684", + "section_type": "main", + "text": "\n\nWe screened our pediatric diabetes cohort with unknown etiology using Sanger sequencing.In mouse pancreatic β-cell lines (Min6 and SJ cells), we performed insulin secretion assay and quantitative RT-PCR to measure the β-cell function transfected with the detected HDAC4 variants and wild type.We carried out immunostaining and Western blot to investigate if the detected HDAC4 variants affect the cellular translocation and acetylation status of Forkhead box protein O1 (FoxO1) in the pancreatic β-cells." + }, + { + "document_id": "36858807-1395-4b2f-a3ee-e054f9b0149d", + "section_type": "main", + "text": "\n\nAs ER stress markers were not activated to potentially explain reduced insulin secretion, genes related to insulin secretion pathway were investigated using real-time-PCR, which revealed downregulation of the glucose-stimulated insulin secretion (GSIS) pathway and the glucose uptake pathway in RIN-m β-cells when compared to the control, indicating impairment of these pathways.mRNA levels by real-time PCR (Fig. 4c) showed a decrease in glucose transporter 2 (Glut2 [MIM: 138160]) to 54% compared to the control, p < 0.001.Pancreatic and duodenal homeobox 1 (Pdx1 [MIM: 600733]) was also suppressed to 85.7%, p = 0.01.On the other hand, the forkhead box protein A2 (Foxa2 [MIM: 600288]) mRNA level, which regulates PDX1, was unchanged, while the mRNA of glucokinase (Gck [MIM: 138079]), which phosphorylates glucose in the first step of the GSIS pathway in β-cells, was slightly elevated (11.5%, p = 0.008)." + }, + { + "document_id": "286480ca-0d7f-4a93-952b-2cf57292104d", + "section_type": "main", + "text": "\n\nIt is yet unclear, however, whether the decreased expression of Ica1 plays a functional role in the development (cause) or is merely an effect of diabetes.Interestingly, even though Ica1 (also known as Ica69) has been associated with diabetes in the human, mouse, and rat (4, 8 -10, 12, 16, 18, 19, 34), the Ica1 gene locus has not been previously identified as a risk locus for diabetes in either humans or in experimental models of diabetes, and this is the first time that this gene has been associated with a diabetes-related QTL." + }, + { + "document_id": "1dc0547a-1d61-4b27-b848-512875b52081", + "section_type": "main", + "text": "\n\nIt is yet unclear, however, whether the decreased expression of Ica1 plays a functional role in the development (cause) or is merely an effect of diabetes.Interestingly, even though Ica1 (also known as Ica69) has been associated with diabetes in the human, mouse, and rat (4, 8 -10, 12, 16, 18, 19, 34), the Ica1 gene locus has not been previously identified as a risk locus for diabetes in either humans or in experimental models of diabetes, and this is the first time that this gene has been associated with a diabetes-related QTL." + }, + { + "document_id": "e51e88b2-bea3-4ab7-858f-824f7d5ccbdd", + "section_type": "main", + "text": "\n\nResults.Pathway analysis of genes with differentially methylated promoters identified the top 3 enriched pathways as maturity onset diabetes of the young (MODY), type 2 diabetes, and Notch signaling.Several genes in these pathways are known to affect pancreatic development and insulin secretion." + }, + { + "document_id": "e92427da-dee9-472f-bfa1-2e7bfa7de521", + "section_type": "main", + "text": "\n\nGenes differentially expressed between control and diabetic subjects may reflect either the pathophysiology of insulin resistance (primary alterations) or secondary effects of hyperglycemia, hyperlipidemia, and other metabolic factors.To identify potentially primary expression changes associated with insulin resistance, we compared gene expression in FHϩ (nondiabetic but insulin resistant) and FHϪ controls.One hundred sixty-six genes were differentially expressed between FHϩ and FHϪ (P Ͻ 0.05) (Table 3, which is published as supporting information on the PNAS web site); 55 were common to both [FHϪ vs. DM] and [FHϪ vs. FHϩ] comparisons.No single gene remained differentially expressed after Benjamini-Hochberg multiple comparison testing.However, ontology classification analysis (17) revealed that 20S and 26S proteasome complexes were the top-ranked cellular component terms (Z 7.7 and 7.3); mitochondrion-linked genes were also overrepresented (Z 3.2).Cell structure (P ϭ 0.004), protein degradation (P ϭ 3.7 ϫ 10 Ϫ4 ), and energy generation (P ϭ 0.003) groups were represented to a greater extent than expected for random distribution; with multiple comparison testing, the protein degradation͞26S proteasome (P ϭ 1 ϫ 10 Ϫ5 ) group remained significant." + } + ], + "document_id": "A9F5CC0D31CE591D56814F3A276760E5", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "type&1&diabetes", + "type&2&diabetes", + "SH2B3", + "IFIH1", + "ERBB3", + "insulin", + "pancreatic&islets", + "gene&expression", + "mutations" + ], + "metadata": [ + { + "object": "We identified 32 compound heterozygous mutations and 9 homozygous mutations in IL10 receptor subunit alpha and 1 homozygous mutation in IL10 receptor subunit beta. Among these mutations, 10 novel mutations were identified, and 6 pathogenic mutations had been previously described. In patients with IL10 receptor subunit alpha mutations, c.301C>T p.R101RW and c.537 G>A p.T179T were the most common mutations.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007199" + }, + { + "object": "Data, including studies involving single-cell analysis, suggest that insulin-secreting cells exhibit 3 major states regarding unfolded protein response UPR: 1 low UPR and low insulin gene expression; 2 low UPR and high insulin gene expression; 3 high UPR and low insulin gene expression. The latter state promotes cell proliferation; UPR appears to mediate recovery from ER stress due to high insulin production.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab215528" + }, + { + "object": "Ten mutations were identified in five unrelated Chinese families and two sporadic patients with childhood, and adult hypophosphatasia including eight missense mutations and two frameshift mutations. Of which, four were novel: one frameshift mutation p.R138Pfsx45; three missense mutations p.C201R, p.V459A, p.C497S. No identical mutations and any other new ALPL mutations were found in unrelated 50 healthy controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab768168" + }, + { + "object": "Our aim was to identify VHL gene mutations in Argentinian patients who fulfilled the clinical criteria for type 1 VHL disease and in patients with VHL-associated manifestations. VHL mutations were detected in 16/19 84.2% patients in Group 1 and included: gross deletions 4/16; nonsense mutations 6/16; frameshift mutations 4/16; missense mutations 1/16; and splicing mutations 1/16. Three mutations were novel.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab550929" + }, + { + "object": "Data suggest IGT10 mice, diabetes type 2 model, exhibit 2 genetic defects: haploinsufficiency heterozygosity for null allele of insulin receptor Insr; splice-site mutation in protein phosphatase 2 regulatory subunit B alpha Ppp2r2a. Inheritance of either allele results in insulin resistance but not overt diabetes. Double heterozygosity leads to insulin resistance and diabetes type 2 without increase in body weight.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab203476" + }, + { + "object": "WFS1 and GJB2 mutations were identified in eight of 74 cases of Low-Frequency Sensorineural Hearing Loss. Four cases had heterozygous WFS1 mutations; one had a heterozygous WFS1 mutation and a heterozygous GJB2 mutation; and three cases had biallelic GJB2 mutations. Three cases with WFS1 mutations were sporadic; two of them were confirmed to be caused by a de novo mutation based on the genetic analysis of their parents.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1014986" + }, + { + "object": "Two patients harbored KRAS with codon 12 mutations; one harbored the gly12val mutation with a variation of leu597val in the BRAF exon 15 codon, the other harbored mutation in the BRAF exon 15 codon. One patient harbored a codon 117 mutation with a BRAF V600E mutation. The last patient harbored a NRAS exon 2 mutation with the GGT/GAT, V600G mutation in the BRAF exon 15 codon", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab978995" + }, + { + "object": "Findings extend the phenotype of insulin mutation carriers and suggest that insulin screening is warranted not only in neonatal diabetes, but also in maturity onset diabetes of the young and in selected cases of type 1 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab288509" + }, + { + "object": "CFTR genes from 46 African Americans and 356 Hispanic Americans were screened. 8 new mutations 1 missense mutation, 1 splice-site mutation & 6 frame-shift mutations & 21 distinct rare mutations not in the commercial mutation panels were identified.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab134892" + }, + { + "object": "4 different mutations including 1 novel mutation were identified. 4 had R225Q mutation, 3 had P224S mutation, 2 others had different frame-shift mutations. Identical gene mutations showed wide variation in supernumerary tooth formation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab10218" + } + ], + "question": "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?", + "subquestions": null, + "task_id": "A9F5CC0D31CE591D56814F3A276760E5", + "usage": { + "chatgpt": 8842, + "gpt-4": 5247, + "gpt-4-turbo-preview": 4320 + }, + "user_id": 2 + }, + "document_id": "A9F5CC0D31CE591D56814F3A276760E5", + "task_id": "A9F5CC0D31CE591D56814F3A276760E5" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/05.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/05.json new file mode 100644 index 0000000..0afbfb0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/05.json @@ -0,0 +1,400 @@ +{ + "created_at": "2024-05-31T23:11:51.586254Z", + "data": { + "amplify": false, + "answer": "Genetics plays a significant role in the emergence of diabetes. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus contribute to 50-60% of the genetic risk by affecting protein binding and antigen presentation to T cells. Other gene variants that modulate immune regulation, viral responses, environmental signals, and endocrine function also contribute to the disease. The risk for type 1 diabetes is strongly influenced by multiple genetic loci. In type 2 diabetes, many common variants, largely with small effect size, contribute to overall disease risk. Monogenic forms of diabetes, such as MODY, result from mutations in a single gene. Family history also plays a role, suggesting that genetics conveys more than heritable information; it probably includes non-genetic familial behaviors and norms.", + "context": { + "00591f6a-0d6f-4993-ae6c-e9a8109a95ec": [ + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "text": "A. Genetic Screening\n\nWe have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is ϳ10 -15 times greater.Although ϳ70% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3)." + }, + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "text": "II. THE GENETICS OF TYPE 1 DIABETES\n\nA comprehensive overview of genetic data in mouse and human is beyond the scope of this article.Instead, we will focus on how the various susceptibility genes and environmental triggers can fit in a mechanistic model for T1D etiology." + } + ], + "0da4d3d4-10d5-4a58-9e50-c1fa0b414427": [ + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "text": "\n\nThe relative prevalence of mutations causal for monogenic forms of diabetes suggests that mutations in -cellrelated processes are a more frequent cause of severe early-onset diabetes than those influencing insulin action (see above).Studies of the relative heritabilities of indexes of -cell function and insulin action in the general population also hint at a preponderance of -cell effects (52)." + } + ], + "30d5d1de-ab8a-4b12-be3f-dd4e07d44a01": [ + { + "document_id": "30d5d1de-ab8a-4b12-be3f-dd4e07d44a01", + "text": "\nIn 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention.Type 1 diabetes becomes clinically apparent after a preclinical period of varying length, during which autoimmune destruction reduces the mass of beta cells in the pancreatic islets to a level at which blood glucose levels can no longer be maintained in a physiologic range.The disease has two subtypes: 1A, which includes the common, immune-mediated forms of the disease; and 1B, which includes nonimmune forms.In this review, we focus on subtype 1A, which for simplicity will be referred to as type 1 diabetes.Although there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied. Gene tic S t udiesBecause of the uncertainty regarding the number and action of genes involved in type 1 diabetes, genetic studies have tended to focus on approaches that require few assumptions about the underlying model of disease risk.The two primary approaches have been linkage studies (using pairs of affected relatives, typically siblings) and association studies (using either case-control or family-based designs).Linkage studies using affected sibling pairs seek to identify regions of the genome that are shared" + } + ], + "516de7be-3cef-47ee-8338-199fb922bc6f": [ + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "text": "Environment\n\nThe second factor in Figure 1 is environmental aspects.An important concept is the diabetes genotype typically causes only a predisposition for glucose intolerance (note the terminology susceptibility gene was used in the preceding paragraphs).Whether one develops the diabetes phenotype depends on environmental factors, some obvious in how they act, others less so.For instance, the Nurses Health Survey showed positive associations between obesity and lack of physical activity in the development of type 2 diabetes (as expected), but also protection by not smoking and moderate alcohol intake (14).Already discussed, many studies have shown an association between TV watching, high calorie diets, and lack of physical activity with risk of diabetes, i.e., our modern lifestyle, so it is not surprising that there is an explosion in the incidence of diabetes worldwide." + } + ], + "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0": [ + { + "document_id": "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0", + "text": "The genetics of type 1 diabetes\n\nThere is a strong genetic risk to T1D.This is exemplified by (Redondo et al., 2001) who demonstrated a strong concordance of genetic inheritance (65%) and T1D susceptibility in monozygotic twin pairs.That is, when one sibling is afflicted, there is a high probability that the other twin will develop T1D by the age of 60 years.Additionally, autoantibody positivity and islet destruction was observed after a prospective long-term follow-up of monozygotic twins of patients with T1D, despite initial disease-discordance among the twins (Redondo et al., 2008)." + } + ], + "76ae2f09-af4d-422a-b939-625f0fe4ae1c": [ + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "Type 1 diabetes has unusual epidemiological features related to gender\n\nType 1 diabetes also displays unusual patterns of inheritance that may yield insights into etiology and provide clues to the best methods for analyzing genetic studies.The risk to the offspring is generally greater from a mother or father who was diagnosed at an early age (again suggesting that early-onset cases are more heavily genetically 'loaded').However, the risk of diabetes is approximately two to four times higher for a child whose father has type 1 diabetes than one whose mother is affected [see (52,53) and references therein].This parental difference is largely due to a low risk for offspring of mothers who were diagnosed at a later age (53).The difference could be explained by at least three different factors.First, the risk alleles could only be active when transmitted by the father (such as is seen in imprinting, where only one of the parental alleles is expressed).Alternatively, a maternal environmental factor during pregnancy could be protective.However, it is difficult to see how this protective effect would be restricted to mothers diagnosed at a later age, especially since the protective effect was unrelated to the mother's duration of diabetes or even diabetic status at delivery (53).Finally, mothers who are diagnosed at a later age could represent more 'environmental' cases of diabetes, and thus be less likely to pass on risk genes to their offspring." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "Type 1 diabetes is a genetic disease\n\nFamily studies have indicated that genetic factors are important determinants of type 1 diabetes risk.First, the risk to a sibling of an affected individual is approximately 6%, as compared with an average risk of 0.4% (depending on the population), or a relative increased risk of 15-fold (17).The increased risk to siblings is referred to as l s (18) and is one measure of the degree of familial clustering of the disease." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "\n\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered." + } + ], + "83a34294-d942-476f-be2f-ff8d7ec3dec4": [ + { + "document_id": "83a34294-d942-476f-be2f-ff8d7ec3dec4", + "text": "\n\nGenes affecting type 1 diabetes diagnosis age / A. Syreeni et al." + } + ], + "8d723c99-bd3c-43eb-9b31-14ee233c2ed4": [ + { + "document_id": "8d723c99-bd3c-43eb-9b31-14ee233c2ed4", + "text": "\n\nThus, the most likely scenario is that these genes are more poised for activation in the case group compared with the control group, contributing to various diabetes complications in the long term.This could be a consequence of the early exposure to hyperglycemia (measured by HbA 1c level), which is known to be associated with increased rates of long-term diabetes complications." + } + ], + "9240ab9b-c5bb-4475-ad2b-111843cb146a": [ + { + "document_id": "9240ab9b-c5bb-4475-ad2b-111843cb146a", + "text": "\n\nThe risk for T1D is strongly influenced by multiple genetic loci and environmental factors.The disease is heritable, with first-degree relatives of patients with T1D being at 15-fold greater risk for developing the condition than the general population." + } + ], + "92eb0c69-5e98-41aa-9084-506e7f223b1a": [ + { + "document_id": "92eb0c69-5e98-41aa-9084-506e7f223b1a", + "text": "Genetic Background and Environment\n\nBoth type 1 and 2 diabetes as well as other rare forms of diabetes that are directly inherited, including MODY and diabetes due to mutations in mitochondrial DNA, are caused by a combination of genetic and environmental risk factors.Unlike some traits, diabetes does not seem to be inherited in a simple pattern.Undoubtedly, however, some people are born prone to developing diabetes more so than others.Several epidemiological patterns suggest that environmental factors contribute to the etiology of T1D.Interestingly, the recent elevated number of T1D incidents projects a changing global environment, which acts either as initiator and/or accelerator of beta cell autoimmunity rather than variation in the gene pool.Several genetic factors are involved in the development of the disease [127].There is evidence that more than twenty regions of the genome are involved in the genetic susceptibility to T1D." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 1 Diabetes\n\nThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Genetics\n\nBoth type 1 and type 2 diabetes are polygenic diseases where many common variants, largely with small effect size, contribute to overall disease risk.Disease heritability (h 2 ), defined as sibling-relative risk, is 3 for type 2 diabetes and 15 for type 1 diabetes (17).The lifetime risk of developing type 2 diabetes is ;40% if one parent has type 2 diabetes and higher if the mother has the disease (18).The risk for type 1 diabetes is ;5% if a parent has type 1 diabetes and higher if the father has the disease (19).Maturity-onset diabetes of the young (MODY) is a monogenic disease and has a high h 2 of ;50 (20).Mutations in any 1 of 13 different individual genes have been identified to cause MODY (21), and a genetic diagnosis can be critical for selecting the most appropriate therapy.For example, children with mutations in KCJN11 causing MODY should be treated with sulfonylureas rather than insulin." + } + ], + "9cce7fe9-cb40-4e75-85bc-d8655c3343d6": [ + { + "document_id": "9cce7fe9-cb40-4e75-85bc-d8655c3343d6", + "text": "\n\nType 1 diabetes as well as type 2 diabetes shows a genetic predisposition, although only type 1 diabetes is HLA dependent [32,33,36,40]." + } + ], + "afb0bd31-df62-4a8d-8c20-9841e2d2dc4a": [ + { + "document_id": "afb0bd31-df62-4a8d-8c20-9841e2d2dc4a", + "text": "\n\nGenetic factors have an important role in the development of diabetes, with some forms of the disease resulting from mutations in a single gene.Others are multifactorial in origin.The monogenic forms of diabetes account for approximately 5% of cases and are caused by mutations in genes encoding insulin 3 , the insulin receptor 4 , the glycolytic enzyme glucokinase 5 , and the transcription factors hepatocyte nuclear factor-1α (HNF-1α), HNF-1β, HNF-4α, insulin promoter factor-1 and NeuroD1/BETA2 (refs 6-10).Mutations in maternally inherited mitochondrial genes can also cause diabetes, often in association with hearing loss 11 ." + } + ], + "d1449eee-d4ec-4886-87d1-835fb54a5f56": [ + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\n\nStudies [71][72][73][74] in Mexican and Asian populations have identified several mutations associated with type 2 diabetes in young people.The high prevalence of type 2 diabetes in the parents of young people diagnosed with type 2 diabetes could reflect a stronger genetic predisposition, even when monogenic diabetes is excluded.This hypothesis suggests that efforts to define genes that cause type 2 diabetes by linkage might be more powerful if focused on young adults with diabetes, raising the question of whether type 2 diabetes in older populations has a relatively smaller genetic contribution and a stronger environmental contribution. 66" + } + ], + "fa72cb33-e1e4-49ea-a72e-dd851225ee0b": [ + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "text": "\n\nWe found that the presence or absence of parental diabetes and the genotype score were independently associated with the risk of diabetes.This suggests that family history as a risk factor for diabetes conveys more than heritable genetic information; it probably includes nongenetic familial behaviors and norms.The lower relative risks for diabetes associated with observed parental diabetes as compared with those associated with self-reported family history (approximately 1.8 vs. approximately 2.2) support the contention that family history contains more risk information than is implied by inheritance of the diabetes phenotype alone.One of the limitations of our study is that the 18 SNPs we included are probably insufficient to account for the familial risk of diabetes.They account for a minority of diabetes heritability, and the SNP array platforms from which they were chosen capture only approximately 80% of common variants in Europeans.In addition, we have not considered structural variants that might confer a risk of diabetes.It is possible that the addition of rare risk alleles with large effects, or a much larger number of common risk alleles with small individual effects, could improve discrimination. 36Indeed, as many as 500 loci may underlie the genetic risk of type 2 diabetes. 16Also, we did not study interactions among genes or between genes and the environment that might alter the genetic risk in exposed persons.As more diabetes risk variants become known, their incorporation into the genotype score may explain more of the genetic risk implied by parental diabetes." + } + ] + }, + "data_source": [ + { + "document_id": "afb0bd31-df62-4a8d-8c20-9841e2d2dc4a", + "section_type": "main", + "text": "\n\nGenetic factors have an important role in the development of diabetes, with some forms of the disease resulting from mutations in a single gene.Others are multifactorial in origin.The monogenic forms of diabetes account for approximately 5% of cases and are caused by mutations in genes encoding insulin 3 , the insulin receptor 4 , the glycolytic enzyme glucokinase 5 , and the transcription factors hepatocyte nuclear factor-1α (HNF-1α), HNF-1β, HNF-4α, insulin promoter factor-1 and NeuroD1/BETA2 (refs 6-10).Mutations in maternally inherited mitochondrial genes can also cause diabetes, often in association with hearing loss 11 ." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Type 1 Diabetes\n\nThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "section_type": "main", + "text": "Type 1 diabetes is a genetic disease\n\nFamily studies have indicated that genetic factors are important determinants of type 1 diabetes risk.First, the risk to a sibling of an affected individual is approximately 6%, as compared with an average risk of 0.4% (depending on the population), or a relative increased risk of 15-fold (17).The increased risk to siblings is referred to as l s (18) and is one measure of the degree of familial clustering of the disease." + }, + { + "document_id": "8d723c99-bd3c-43eb-9b31-14ee233c2ed4", + "section_type": "main", + "text": "\n\nThus, the most likely scenario is that these genes are more poised for activation in the case group compared with the control group, contributing to various diabetes complications in the long term.This could be a consequence of the early exposure to hyperglycemia (measured by HbA 1c level), which is known to be associated with increased rates of long-term diabetes complications." + }, + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "section_type": "main", + "text": "Environment\n\nThe second factor in Figure 1 is environmental aspects.An important concept is the diabetes genotype typically causes only a predisposition for glucose intolerance (note the terminology susceptibility gene was used in the preceding paragraphs).Whether one develops the diabetes phenotype depends on environmental factors, some obvious in how they act, others less so.For instance, the Nurses Health Survey showed positive associations between obesity and lack of physical activity in the development of type 2 diabetes (as expected), but also protection by not smoking and moderate alcohol intake (14).Already discussed, many studies have shown an association between TV watching, high calorie diets, and lack of physical activity with risk of diabetes, i.e., our modern lifestyle, so it is not surprising that there is an explosion in the incidence of diabetes worldwide." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "section_type": "abstract", + "text": "\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered." + }, + { + "document_id": "30d5d1de-ab8a-4b12-be3f-dd4e07d44a01", + "section_type": "abstract", + "text": "\nIn 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention.Type 1 diabetes becomes clinically apparent after a preclinical period of varying length, during which autoimmune destruction reduces the mass of beta cells in the pancreatic islets to a level at which blood glucose levels can no longer be maintained in a physiologic range.The disease has two subtypes: 1A, which includes the common, immune-mediated forms of the disease; and 1B, which includes nonimmune forms.In this review, we focus on subtype 1A, which for simplicity will be referred to as type 1 diabetes.Although there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied. Gene tic S t udiesBecause of the uncertainty regarding the number and action of genes involved in type 1 diabetes, genetic studies have tended to focus on approaches that require few assumptions about the underlying model of disease risk.The two primary approaches have been linkage studies (using pairs of affected relatives, typically siblings) and association studies (using either case-control or family-based designs).Linkage studies using affected sibling pairs seek to identify regions of the genome that are shared" + }, + { + "document_id": "92eb0c69-5e98-41aa-9084-506e7f223b1a", + "section_type": "main", + "text": "Genetic Background and Environment\n\nBoth type 1 and 2 diabetes as well as other rare forms of diabetes that are directly inherited, including MODY and diabetes due to mutations in mitochondrial DNA, are caused by a combination of genetic and environmental risk factors.Unlike some traits, diabetes does not seem to be inherited in a simple pattern.Undoubtedly, however, some people are born prone to developing diabetes more so than others.Several epidemiological patterns suggest that environmental factors contribute to the etiology of T1D.Interestingly, the recent elevated number of T1D incidents projects a changing global environment, which acts either as initiator and/or accelerator of beta cell autoimmunity rather than variation in the gene pool.Several genetic factors are involved in the development of the disease [127].There is evidence that more than twenty regions of the genome are involved in the genetic susceptibility to T1D." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "section_type": "main", + "text": "\n\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Genetics\n\nBoth type 1 and type 2 diabetes are polygenic diseases where many common variants, largely with small effect size, contribute to overall disease risk.Disease heritability (h 2 ), defined as sibling-relative risk, is 3 for type 2 diabetes and 15 for type 1 diabetes (17).The lifetime risk of developing type 2 diabetes is ;40% if one parent has type 2 diabetes and higher if the mother has the disease (18).The risk for type 1 diabetes is ;5% if a parent has type 1 diabetes and higher if the father has the disease (19).Maturity-onset diabetes of the young (MODY) is a monogenic disease and has a high h 2 of ;50 (20).Mutations in any 1 of 13 different individual genes have been identified to cause MODY (21), and a genetic diagnosis can be critical for selecting the most appropriate therapy.For example, children with mutations in KCJN11 causing MODY should be treated with sulfonylureas rather than insulin." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "section_type": "main", + "text": "\n\nStudies [71][72][73][74] in Mexican and Asian populations have identified several mutations associated with type 2 diabetes in young people.The high prevalence of type 2 diabetes in the parents of young people diagnosed with type 2 diabetes could reflect a stronger genetic predisposition, even when monogenic diabetes is excluded.This hypothesis suggests that efforts to define genes that cause type 2 diabetes by linkage might be more powerful if focused on young adults with diabetes, raising the question of whether type 2 diabetes in older populations has a relatively smaller genetic contribution and a stronger environmental contribution. 66" + }, + { + "document_id": "83a34294-d942-476f-be2f-ff8d7ec3dec4", + "section_type": "main", + "text": "\n\nGenes affecting type 1 diabetes diagnosis age / A. Syreeni et al." + }, + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "section_type": "main", + "text": "\n\nThe relative prevalence of mutations causal for monogenic forms of diabetes suggests that mutations in -cellrelated processes are a more frequent cause of severe early-onset diabetes than those influencing insulin action (see above).Studies of the relative heritabilities of indexes of -cell function and insulin action in the general population also hint at a preponderance of -cell effects (52)." + }, + { + "document_id": "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0", + "section_type": "main", + "text": "The genetics of type 1 diabetes\n\nThere is a strong genetic risk to T1D.This is exemplified by (Redondo et al., 2001) who demonstrated a strong concordance of genetic inheritance (65%) and T1D susceptibility in monozygotic twin pairs.That is, when one sibling is afflicted, there is a high probability that the other twin will develop T1D by the age of 60 years.Additionally, autoantibody positivity and islet destruction was observed after a prospective long-term follow-up of monozygotic twins of patients with T1D, despite initial disease-discordance among the twins (Redondo et al., 2008)." + }, + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "section_type": "main", + "text": "\n\nWe found that the presence or absence of parental diabetes and the genotype score were independently associated with the risk of diabetes.This suggests that family history as a risk factor for diabetes conveys more than heritable genetic information; it probably includes nongenetic familial behaviors and norms.The lower relative risks for diabetes associated with observed parental diabetes as compared with those associated with self-reported family history (approximately 1.8 vs. approximately 2.2) support the contention that family history contains more risk information than is implied by inheritance of the diabetes phenotype alone.One of the limitations of our study is that the 18 SNPs we included are probably insufficient to account for the familial risk of diabetes.They account for a minority of diabetes heritability, and the SNP array platforms from which they were chosen capture only approximately 80% of common variants in Europeans.In addition, we have not considered structural variants that might confer a risk of diabetes.It is possible that the addition of rare risk alleles with large effects, or a much larger number of common risk alleles with small individual effects, could improve discrimination. 36Indeed, as many as 500 loci may underlie the genetic risk of type 2 diabetes. 16Also, we did not study interactions among genes or between genes and the environment that might alter the genetic risk in exposed persons.As more diabetes risk variants become known, their incorporation into the genotype score may explain more of the genetic risk implied by parental diabetes." + }, + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "section_type": "main", + "text": "II. THE GENETICS OF TYPE 1 DIABETES\n\nA comprehensive overview of genetic data in mouse and human is beyond the scope of this article.Instead, we will focus on how the various susceptibility genes and environmental triggers can fit in a mechanistic model for T1D etiology." + }, + { + "document_id": "fb7a24a3-9d72-49d7-93df-7a2f400f44c4", + "section_type": "main", + "text": "\n\nGenetics is one example of the 'other risk factors' involved in the pathogenesis of DR.Twin and epidemiological studies have strongly suggested a genetic component in the etiology of DR (6 -10), with heritability scores ranging from 27 to 52% in both type 1 and type 2 diabetes (7 -10).There is an increased risk of severe DR among family members of DR subjects (8,9) and in siblings of affected subjects (8,9).Furthermore, several studies have also shown a discrepant rate of the prevalence of DR among different racial ethnic groups in the US population, with a significantly higher prevalence observed among Hispanic, African-American and Chinese-American when compared with Caucasian populations (11).While these differences may partially be attributed to lifestyle factors, evidence from familial aggregation, ethnic differences and heritability clearly supports a genetic contribution in the etiology of DR." + }, + { + "document_id": "25481e34-2a45-4448-84f0-32c823cfcd03", + "section_type": "main", + "text": "\n\nMost cases of diabetes have multiple genetic and environmental causes and are classified according to the presumed pathophysiologic defectdautoimmune destruction of b-cells leading to insulin deficiency for type 1 diabetes and varying degrees of insulin resistance and deficiency for type 2 diabetes.In other words, the vast majority of diabetes is polygenic, and despite the growth in knowledge about the various genetic causes of diabetes in recent years, classification of individual cases into meaningful subtypes based on the underlying genetics has been difficult.On the other hand, genetic testing may be useful for the diagnosis of certain forms of diabetes caused by defects in a single gene, such as HNF1A mutations for maturityonset diabetes of the young (MODY) (39) and activating KCNJ11 mutations for neonatal diabetes (40), both of which are highly responsive to sulfonylurea therapy.These monogenic forms of diabetes account for ;1-2% of diabetes cases (41,42), and they typically present at a young age (,25 years) and follow an autosomal dominant pattern of inheritance.Targeted genotyping could also play a role in the diagnosis of type 2 diabetes in specific populations.For example, a rare missense variant in HNF1A (p.E508K) that increased the risk of diabetes fivefold was present among 2% in a study of Latinos in the southern U.S. with type 2 diabetes (20); additional studies are needed to determine whether this functional variant shares the sulfonylurearesponsiveness of the HNF1A variants that cause MODY." + }, + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "section_type": "main", + "text": "If an environmental contributor is near ubiquitous and the genetic\npredisposition common as well, interventions are most sensibly weighted towards\nenvironmental risk factor modification.\n Even here, though, there is room for further research, since the etiopathogenesis\nof type 2 diabetes may not be as well understood as some suggest. Specifically,\nChaufan implies that dietary intervention to prevent prenatal ‘programming’\nleading to susceptibility to develop type 2 diabetes (the fetal origins of adult onset\ndisease hypothesis) is as evidence-based as dietary management of the adult diabetic state. However, many questions remain in this area." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "section_type": "main", + "text": "Type 1 diabetes has unusual epidemiological features related to gender\n\nType 1 diabetes also displays unusual patterns of inheritance that may yield insights into etiology and provide clues to the best methods for analyzing genetic studies.The risk to the offspring is generally greater from a mother or father who was diagnosed at an early age (again suggesting that early-onset cases are more heavily genetically 'loaded').However, the risk of diabetes is approximately two to four times higher for a child whose father has type 1 diabetes than one whose mother is affected [see (52,53) and references therein].This parental difference is largely due to a low risk for offspring of mothers who were diagnosed at a later age (53).The difference could be explained by at least three different factors.First, the risk alleles could only be active when transmitted by the father (such as is seen in imprinting, where only one of the parental alleles is expressed).Alternatively, a maternal environmental factor during pregnancy could be protective.However, it is difficult to see how this protective effect would be restricted to mothers diagnosed at a later age, especially since the protective effect was unrelated to the mother's duration of diabetes or even diabetic status at delivery (53).Finally, mothers who are diagnosed at a later age could represent more 'environmental' cases of diabetes, and thus be less likely to pass on risk genes to their offspring." + }, + { + "document_id": "83a34294-d942-476f-be2f-ff8d7ec3dec4", + "section_type": "main", + "text": "\n\nGenome-wide search for genes affecting the age at diagnosis of type 1 diabetes." + }, + { + "document_id": "7b7ce30c-f398-4b0e-bcb6-52f2644201fd", + "section_type": "main", + "text": "CONCLUSION\n\nThe greatest genetic risk (both increased risk, susceptible, and decreased risk, protective) for type 1 diabetes is conferred by specific alleles, genotypes, and haplotypes of the HLA class II (and class I) genes.There are currently about 50 non-HLA region loci that also affect the type 1 diabetes risk.Many of the assumed functions of the non-HLA genes of interest suggest that variants at these loci act in concert on the adaptive and innate immune systems to initiate, magnify, and perpetuate -cell destruction.The clues that genetic studies provide will eventually help lead us to identify how -cell destruction is influenced by environmental factors.While there is extensive overlap between type 1 diabetes and other immune-mediated diseases, it appears that type 1 and type 2 diabetes are genetically distinct entities.These observations may suggest ways to help identify causal gene(s) and, ultimately, a set of disease-associated variants defined on specific haplotypes.Unlike other complex human diseases, relatively little familial clustering remains to be explained for type 1 diabetes.The remaining missing heritability for type 1 diabetes is likely to be explained by as yet unmapped common variants, rare variants, structural polymorphisms, and gene-gene and/or gene-environmental interactions, in which we can expect epigenetic effects to play a role.The examination of the type 1 diabetes genes and their pathways may reveal the earliest pathogenic mechanisms that result in the engagement of the innate and adaptive immune systems to produce massive -cell destruction and clinical disease.The resources established by the international T1DGC are available to the research community and provide a basis for future discovery of genes that regulate the earliest events in type 1 diabetes etiology-potential targets for intervention or biomarkers for monitoring the effects and outcomes of potential therapeutic agents." + }, + { + "document_id": "57d91713-225c-4c04-a9e7-e275588e2a68", + "section_type": "main", + "text": "Introduction\n\nClustering in families implicates a genetic component of diabetic nephropathy, but so far the specific genes underlying diabetic nephropathy remain largely unknown [1,2].Family studies have furthermore revealed that parental type 2 diabetes mellitus is associated with diabetic nephropathy in offspring with type 1 diabetes mellitus [3,4].A positive family history of type 2 diabetes mellitus has also been associated with cardiovascular disease [5] as well as markers of cardiovascular disease [6] in offspring with type 1 diabetes mellitus.Genetic variants or single-nucleotide polymorphisms (SNPs) predisposing to type 2 diabetes mellitus in the Finnish population have recently been identified in large-scale, genome-wide association studies [7,8].The question thus arises of whether these SNPs, which predispose to type 2 diabetes mellitus, also predispose to diabetic nephropathy and related complications in patients with type 1 diabetes mellitus.We therefore assessed the impact of a set of SNPs known to influence susceptibility to type 2 diabetes mellitus on diabetic nephropathy as well as diabetic retinopathy and cardiovascular disease in patients with type 1 diabetes mellitus." + }, + { + "document_id": "977994e6-80dc-4b82-9bb1-4a89455cd4da", + "section_type": "main", + "text": "Evidence for a genetic basis: family and twin studies of Type I diabetes\n\nWhat is the evidence that Type I diabetes has a genetic basis?The simplest evidence comes from the fact that the frequency of the disorder is higher in close relatives of diabetic patients than in the general population (note: the reference population in the discussion which follows are people of European ancestry, who have the highest prevalence of Type I diabetes).For example, the frequency of Type I diabetes in siblings of diabetics is about 6 % by age 30 [1], while the frequency in the general population is about 0.4 % by age 30 [2].Thus, Type I diabetes is about 6/0.4,i. e. 15 times more common in siblings of diabetic patients than in the general population.This ratio between frequency in siblings compared with the general population is referred to as l sib [3]." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "The proportion of diabetics t h a t will result from\nmating between genetic types can be predicted with\ncertainty, since the inheritance is known to be under\nthe control of a recessive gene with complete penetrance. Offspring t h a t will exhibit the diabetic syndrome can be distinguished from those t h a t will not,\nas early as 3 weeks after birth.\n Some disadvantages are equally apparent. Diabetic\nhomozygotes do not breed, and heterozygotes cannot\nbe distinguished from normals except b y progeny\ntesting." + }, + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "section_type": "main", + "text": "A. Genetic Screening\n\nWe have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is ϳ10 -15 times greater.Although ϳ70% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3)." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "Genetics of Diabetic Complications in Humans\n\nEpidemiologic studies have clearly established that only a subgroup of individuals with diabetes are at risk of nephropathy (2).To identify genetic determinants and candidate genes that confer susceptibility or progression for DNP in individuals with type 1 and type 2 diabetes, the National Institutes of Health established the ongoing Family Investigation of Nephropathy and Diabetes study consortium.The Family Investigation of Nephropathy and Diabetes is using Mapping by Admixture Linkage Disequilibrium and traditional affected and discordant sibling pair and relative pair analyses.Previous linkage analysis studies led to the mapping of several susceptibility loci for DNP on specific regions on chromosomes 3, 7, 9, 12, and 20 (14,15)." + }, + { + "document_id": "9cce7fe9-cb40-4e75-85bc-d8655c3343d6", + "section_type": "main", + "text": "\n\nType 1 diabetes as well as type 2 diabetes shows a genetic predisposition, although only type 1 diabetes is HLA dependent [32,33,36,40]." + }, + { + "document_id": "44cfaebc-d9de-4d25-8991-4b17d524ac6e", + "section_type": "main", + "text": "Introduction\n\nIn 1962, under the title \"Diabetes mellitus: A 'thrifty' genotype rendered detrimental by 'progress'?\" one of us published the suggestion that the basic defect in diabetes mellitus was a quick insulin trigger [I].This was an asset to our tribal, hunting-and-gathering ancestors, with their intermittent, sometimes feast-or-famine alimentation, since it should have minimized renal loss of precious glucose.Currently, however, it was hypothesized, the pattern of over-alimentation in the technologically advanced nations resulted in insulin levels that elicited the insulin antagonists popularized by Vallance-Owen and colleagues [2][3][4] , and the result was diabetes mellitus.The changing dietary patterns of Western Civilization had compromised a complex homeostatic mechanism.The paper was written before the clear distinction between type I and type II diabetes had been drawn, but in retrospect was directed at type II or non-insulin dependent diabetes (NIDDM).This quick insulin trigger was under a (still) poorly defined genetic control.Since too quick an insulin trigger might be as disadvantageous as too slow a trigger, it was suggested that this genetic control might take the form of a balanced polymorphism, by analogy with the polymorphisms for the sickle cell allele (ßs) then receiving so much attention.When other laboratories could not confirm Vallance-Owen's insulin antagonists (except in rare cases), the original physiological basis for the hypothesis collapsed.Although alternative \"balance\" hypotheses came to mind [5], they were neither as simple nor as intellectually satisfactory.However, the problem remained: why is the predisposition to NIDDM so frequent?Explanations based on the \"thrifty genotype\" hypothesis continue to be frequently invoked." + }, + { + "document_id": "30d5d1de-ab8a-4b12-be3f-dd4e07d44a01", + "section_type": "main", + "text": "I\n\nn 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "\n\nPresently, 48 other genomic regions, referred to as susceptibility regions, have been found to also confer susceptibility to T1D (Burren et al., 2011;Steck and Rewers, 2011;Yang et al., 2011;Bluestone et al. 2010;Poicot et al., 2010;Todd et al., 2010;Todd et al., 2007).But their contribution is minimal in comparison to the HLA locus (Gillespie, 2014).Also, research has shown that less than 10% of individuals with HLA-conferred diabetes susceptibility actually progress to clinical disease (Knip andSiljandera, 2008, Wenzlau et al., 2008).This implies that additional factors are needed to trigger and drive β-cell destruction in genetically predisposed persons (Knip and Siljandera, 2008).Environmental factors are believed to influence the expression of T1D.The reason being that in the case of identical twins, if one twin has T1D, the other twin only has it 30%-50% of the time, despite having the same genome.This means that other factors contribute to the prevalence or onset of this disease (Knip et al., 2005)." + }, + { + "document_id": "5293f814-f4a7-48e0-b4e5-b1f13fdc8516", + "section_type": "main", + "text": "\n\nA coherent synthesis of these data has yet to emerge but will inevitably include components of several of these competing, but not mutually exclusive, hypotheses.Indeed, there is evidence that models incorporating both genetic and environmental variation best explain the observed data. 28,32The observation that the risk of diabetes in modern societies with a lower rate of fetomaternal deprivation is increased at both extremes of birthweight (i.e.producing a U-shaped curve) suggests a schema capable of accommodating the insulin gene data. 33,34As with almost all human traits, the answer to the question `nature or nurture?' is almost certainly `both'." + }, + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "section_type": "main", + "text": "\n\nObserved increased risk in African Americans is likely to result from a combination of shared environmental and genetic factors.Although there are few published studies specifically investigating familial aggregation of type 2 diabetes in African-American families, Rotimi et al. (10) found that relatives of African-American probands with type 2 diabetes had a 2.95-fold (95% CI 1.55-5.62)higher prevalence of diabetes when compared with relatives of unaffected individuals.In the GENNID (Genetics of Noninsulin Dependent Diabetes Mellitus) African-American families, the majority of first-degree relatives of African-American individuals with type 2 diabetes had abnormal glucose tolerance (11), with 27% found to have undiagnosed diabetes and 31% impaired fasting glucose and/or impaired glucose tolerance." + }, + { + "document_id": "144c9105-3ce9-46cc-b9c6-cc14cf40e945", + "section_type": "main", + "text": "\n\nClearly genetics play an important role in the T1D disease process as both MZ and DZ twins have the same environmental exposures but different concordance rates and length to diagnosis of the second twin.Numerous genes have been associated with T1D, the most significant being the HLA region on chromosome 6 [6].More than 90% of type 1 diabetics carry HLA alleles DR3-DQ2 or DR4-DQ8 compared to no more than 40% of the general population [7].Alleles at HLA-DQB1 are known to be, in part, protective [8].Single nucleotide polymorphisms (SNPs) are also associated with T1D.A recent genome-wide association study of approximately 2,000 patients with each of 7 common, chronic diseases, including T1D, and 7,000 shared controls confirmed the association of SNPs in 5 previously identified regions with T1D and discovered 5 novel associations.However, the authors concluded that these regions, with the exception of the HLA on chromosome 6, confer only modest effects on T1D, and ''the association signals so far identified account for only a small proportion of overall familiality'' [9].These results suggest that additional genetic variants contribute to inheritance of T1D." + }, + { + "document_id": "d1f8656e-e58a-4461-b75b-89815b2c7369", + "section_type": "main", + "text": "\n\nA neat example of this kind of interplay relates to the control of birth weight (Figure 2).In developed societies, it has been shown that the relationship between birth weight and T2D risk is best described through a U-shaped curve (shown in exaggerated form in the figure), such that the future risk of T2D is highest in individuals with either low or high birth weight as compared with those of average birth weight.Both associations with the extremes of birth weight result from a mix of genetic and nongenetic effects.At the lower extreme, the association between low birth weight and later T2D risk reflects both the long-term programming effects of an adverse intrauterine environment (most likely mediated through epigenetic effects) 12 and the impact of a subset of T2D-risk variants, such as those at CDKAL1, which have a marked effect on the secretion of insulin in early life (a time at which insulin acts as a major influence on growth). 75At the other extreme, the association between high birth weight and later T2D risk is mediated, at least in part, by exposure to maternal diabetes during pregnancy 61,63 and by direct genetic effects, such as those of the T2D risk-variants at TCF7L2, where the dominant effect of allelic variation in the fetomaternal unit appears to be to promote maternal hyperglycemia (and consequent fetal macrosomia). 76his review highlights evidence to support the notion that individual predisposition to T2D and obesity reflects a complex mix of genetic, epigenetic, and environmental influences.Despite recent progress, the mechanisms driving these interactions remain poorly understood." + }, + { + "document_id": "08858a32-d736-4d8d-a135-f86568152a81", + "section_type": "main", + "text": "Genes\n\n2][43][44][45][46][47] Twin studies need to be considered carefully, however, as the intrauterine environments of dizygotic-twin (separate placentas), monozygotic-twin (60-70% share one placenta), and singleton pregnancies (one placenta without competition for maternal nutrients) will all be diff erent, and this can be a confounder in the inter pretation of eff ects. 44A large study from Sweden on familial risk of type 2 diabetes showed that the relative risks were highest in individuals with at least two aff ected siblings, irrespective of parental diabetes status. 42This fi nding suggests that a recessive pattern of inheritance from uncommon genetic defects, the sharing of similar intrauterine, postnatal, or both environments by siblings (eg, breastfeeding or bottle feeding or childhood nutrition), or a combination of these factors is important.9][50] A greater number of these loci are associated with impaired β-cell function (KCNJ11, TCF7L2, WFS1, HNF1B, SLC30A8, CDKAL1, IGF2BP2, CDKN2A, CDKN2B, NOTCH2, CAMK1D, THADA, KCNQ1, MTNR1B, GCKR, GCK, PROX1, SLC2A2, G6PC2, GLIS3, ADRA2A, and GIPR) than impaired insulin sensitivity (PPARG, IRS1, IGF1, FTO, and KLF14) or obesity (FTO). 38,48,50Of these, TCF7L2 is the strongest susceptibility locus for type 2 diabetes, being associated with β-cell dysfunction. 48Most patients with monogenic forms of diabetes also have gene defects that aff ect islet β-cell function. 51,52Nevertheless, only around 10% of the heritability of type 2 diabetes can be explained by susceptibility loci identifi ed so far, with each locus having a low eff ect size. 36The remaining heritability might be related to a large number of less common variants (allele frequency <5%) that are diffi cult to fi nd with current approaches of genome-wide association studies, and/or epigenetic phenomena." + }, + { + "document_id": "d1f8656e-e58a-4461-b75b-89815b2c7369", + "section_type": "main", + "text": "\n\nFirst, the fetal origins hypothesis established the notion of \"metabolic programming\" whereby nutritional and other exposures during early life generate long-term changes that later predispose to T2D and cardiovascular disease. 12This hypothesis builds on strong epidemiological data linking early life events to state art state art disease risk in late life, as seen, for example, in survivors of the Dutch \"Hunger Winter.\" 60 A growing body of data, from animal as well as human studies, has established that the molecular basis of programming involves altered DNA methylation. 61 second set of observations emerges from the longstanding follow-up of members of the Pima Native American community in Arizona, a population with an extremely high prevalence of T2D and obesity.The offspring of mothers who have T2D during pregnancy are at substantially higher risk of developing both T2D (45 vs. 1.4%) and obesity (58 vs. 17%) than are those born to women who are nondiabetic during pregnancy.61,62 Crucially, this difference is unlikely to completely reflect genetic transmission, as the distinction is preserved in children born to the same mother; that is, offspring born after the mother was diagnosed with T2D have higher rates of subsequent T2D and obesity than their siblings who arrived while their mother was nondiabetic.63 These findings suggest that the intrauterine environment is an important determinant of T2D and obesity predisposition, and they are broadly consistent with reports that the transmission of T2D and obesity is greater from mothers than from fathers.12,61 The increased risk of diabetes in female offspring of diabetic mothers clearly sets up the potential for an amplification of diabetes prevalence over successive generations." + }, + { + "document_id": "903e9615-c329-48be-9547-386a00f2dd94", + "section_type": "main", + "text": "\n\nDevelopmental Origins of Diabetes.Many Asian adults who experienced great hardship during wartime or civil unrest in early life are now experiencing marked changes in lifestyle.In addition, low birth weight and exposure to undernutrition in utero are common in some Asian populations, especially in India, where 30% of infants are underweight. 115Insults or stresses during the intrauterine period can lead to permanent changes in structure, metabolism, and physiology through altered expression of the genome without changes in the DNA codes, a process called epigenetics. 116These early life events may influence later susceptibility to diabetes, the metabolic syndrome, and cardiorenal diseases.Prospective studies from India have shown the impact of fetal undernutrition (often manifested as low birth weight) as well as overnutrition (eg, the infant of a mother with diabetes) on future risk of diabetes. 115In India, thinness in infancy and overweight at age 12 years was associated with increased risk of developing IGT or diabetes in young adulthood. 117 recent meta-analysis of 30 studies found a significant graded association between low birth weight and increased risk of type 2 diabetes. 118Low birth weight has also been found to predict diabetes and the metabolic syndrome in Asian adults and children, [119][120][121] thus lending support to the notion that fetal programming with exposure to poor nutrition in utero or during early childhood can promote a fatpreserving or thrifty phenotype.These metabolic changes predispose individuals to insulin resistance and reduced beta cell function.Positive energy balance in later life, caused by rapid westernization of diet and lifestyle, may then exaggerate accumulation of adiposity, particularly in the central depots. 122he 2-to 3-fold higher risk of gestational diabetes in Asian women than in their white counterparts also may contribute to the increasing epidemic of young-onset diabetes in Asia. 123Asian women with a history of gestational diabetes have a substantially increased risk of diabetes, while their offspring exhibit early features of the metabolic syndrome, thus setting up a vicious cycle of \"diabetes begetting diabetes. \"This combination of gestational diabetes, in utero nutritional imbalance, childhood obesity, and overnutrition in adulthood will continue to fuel the epidemic in Asian countries undergoing rapid nutritional transitions. 115enetic Susceptibility.Among lean, healthy individuals matched for age, BMI, waist circumference, birth weight, and current diet, Asians (especially those of Southeast Asian descent) had higher levels of postprandial glycemia and lower insulin sensitivity than whites in response to a 75-g carbohydrate load. 124These findings raise the possibility that Asians are more genetically susceptible to insulin resistance and diabetes than whites." + }, + { + "document_id": "789097da-e961-4486-8c83-816626556b16", + "section_type": "main", + "text": "\n\nAll these speculations may be utterly demolished the moment the precise etiologies of NIDDM [Non-Insulin-Dependent Diabetes Mellitus] become known.Until that time, however, devising fanciful hypotheses based on evolutionary principles offers an intellectual sweepstakes in which I invite you all to join. [Neel 1982:290] In perhaps his last written statement on the thrifty genotype hypothesis, Neel writes that there is \"no support to the notion that high frequency of NIDDM in reservation Amerindians might be due simply to an ethnic predisposition-rather, it must predominantly reflect lifestyle changes\" (Neel 1999:S3).In spite of this, many genetic epidemiologists argue that genetic differences explain rates of diabetes between different populations.For example, drawing on research with Mexicanos/as, one diabetes consortium member writes, \"there is strong evidence that Mexican Americans living in the barrio have considerably more Native Amerindian genetic admixture and as a result may have higher genetic susceptibility to diabetes\" (Stern 1999:S67). \"It smells and tastes like a thrifty gene in terms of its metabolic function,\" remarked one molecular biologist interested in the protein implicated in a genetic study of diabetes." + }, + { + "document_id": "9240ab9b-c5bb-4475-ad2b-111843cb146a", + "section_type": "main", + "text": "\n\nThe risk for T1D is strongly influenced by multiple genetic loci and environmental factors.The disease is heritable, with first-degree relatives of patients with T1D being at 15-fold greater risk for developing the condition than the general population." + } + ], + "document_id": "9892FB125B6B5D4C8FC4FDA6E1E25271", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&1&diabetes", + "genetic&risk", + "HLA", + "immune&function", + "environmental&factors", + "autoimmunity", + "gene&variants", + "epigenetic", + "insulin&gene", + "genetic&screening" + ], + "metadata": [ + { + "object": "The HLA-B*42, HLA-C*17, HLA-DPA1*03, and HLA-DPB1*105 genotypes were associated with allergic asthma and the HLA-B*48 genotype with the nonallergic phenotype. The presence of the haplotype HLA-DPA1*03 DQA*05 was associated with allergic asthma, and the presence of HLA-DPA1*03 and the absence of HLA-DQA*05 with nonallergic asthma.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab821120" + }, + { + "object": "In patients diagnosed with HLA-B27-related anterior uveitis cohort HLA-B27+1 and with HLA-B27- non related anterior uveitis cohort HLA-B27-, no significant differences were found regarding clinical characteristics between both cohorts with the exception of a higher frequency of recurrences in cohort HLA-B27+ and a higher frequency of chronic uveitis in cohort HLA-B27-.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab397404" + }, + { + "object": "HLA-B13:02, HLA-B38:02, HLA-B44:03, and HLA-B56:01 alleles were significantly increased in autistic subjects. HLA-B18:02 and HLA-B46:12 alleles were negatively associated with autism when compared to normal controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab356725" + }, + { + "object": "Haplotyping was done on 91 Southern Europe celiac patients. HLA-DR3-DQ2 without HLA-DR7-DQ2 was present in 62.6%, HLA-DR7-DQ2 without HLA-DR3-DQ2 was present in 16.5% and HLA-DR4-DQ8 without HLA-DQ2 was present in 3.3%.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab332478" + }, + { + "object": "The Sonora, Mexico HLA-DQ risk heterodimer proportion was 16.1% for HLA-DQ2 and 13.6% for HLA-DQ8, with an HLA-DQ2:HLA-DQ8 ratio of 1.2:1. The DQ8/DQ2 genotype represented a 1:14 risk for type 1 diabetes, whereas the DQ8/DQB1*0201 combination showed a 1:6 risk for celiac disease.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872942" + }, + { + "object": "In this study, molecular dynamics simulation was performed on the complexes of Top1 peptide with various HLA-DR subtypes divided into ATASSc-associated alleles HLA-DRB1*08:02, HLA-DRB1*11:01 and HLA-DRB1*11:04, suspected allele HLA-DRB5*01:02, and non-associated allele HLA-DRB1*01:01.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab404240" + }, + { + "object": "Data from pediatric patients with celiac disease CD in the Netherlands suggest that HLA-DQ2.2 HLA-DQA1/HLA-DQB1 is important HLA-type related to CD; the 6% of CD patients lacking 2 major diagnostic markers HLA-DQ2.5 and HLA-DQ8 carry HLA-DQ2.2.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab177145" + }, + { + "object": "The meta-analysis suggested that HLA-DRB1*15 and HLA-DRB1*15:01 polymorphisms might be associated with increased AA risk in Asians. IST might be more effective in HLA-DRB1*15+ and HLA-DRB1*15:01+ Asian patients with AA than in HLA-DRB1*15- and HLA-DRB1*15:01- Asian patients with AA.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab834223" + }, + { + "object": "The association of the HLA-A*24:02, HLA-B*39:01 and HLA-B*39:06 alleles with type 1 diabetes is restricted to specific HLA-DR/HLA-DQ haplotypes in Finns.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab782265" + }, + { + "object": "Data suggest HLA-DRB1*03 haplotype splits in African Americans into HLA-DRB1*03:01 which confers susceptibility to type 1 diabetes and HLA-DRB1*03:02 rarely observed in those with European ancestry which confers protection from type 1 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab203455" + } + ], + "question": "How does genetics influence the emergency of diabetes?", + "subquestions": null, + "task_id": "9892FB125B6B5D4C8FC4FDA6E1E25271", + "usage": { + "chatgpt": 8194, + "gpt-4": 4183, + "gpt-4-turbo-preview": 3246 + }, + "user_id": 2 + }, + "document_id": "9892FB125B6B5D4C8FC4FDA6E1E25271", + "task_id": "9892FB125B6B5D4C8FC4FDA6E1E25271" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/06.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/06.json new file mode 100644 index 0000000..d908030 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/06.json @@ -0,0 +1,400 @@ +{ + "created_at": "2024-05-31T23:15:26.855315Z", + "data": { + "amplify": false, + "answer": "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "context": { + "1cebda9b-a727-4bfd-8521-cf61903235cc": [ + { + "document_id": "1cebda9b-a727-4bfd-8521-cf61903235cc", + "text": "\n\nThe prevalence of diabetes mellitus worldwide is sobering; the International Diabetes Federation estimates that 415 million people have diabetes mellitus, with 90% of these individuals having type 2 diabetes mellitus (T2DM) 1 .T2DM occurs when pancreatic β-cells fail to release enough insulin to meet the demands of insulin-responsive tissues, which safely store and metabolize glucose.Driven by both genetic and environmental risk factors, T2DM is a complex, multifactorial disorder.Although the increasing prevalence of T2DM is driven by changes in our environment and mirrors the increase in obesity, the greater concordance for T2DM found in monozygotic compared with dizygotic twins has long provided evidence for a genetic component in T2DM risk 2 ." + } + ], + "4252d7ad-82de-480c-a801-9ed1c84fb968": [ + { + "document_id": "4252d7ad-82de-480c-a801-9ed1c84fb968", + "text": "\n\nIn the UK alone, nearly 1.8 million people are already recognized to have this disorder (consuming w5% of the total National Health Service budget), and the search is on to find the 'missing million' who are living with the condition but in whom the diagnosis has yet to be made. 3In the USA, the situation appears to be even more serious with some commentators predicting that one in every three Americans born in the year 2000 will go on to develop diabetes during their lifetime, bringing unprecedented costs in terms of healthcare dollars as well as human morbidity and mortality. 4The majority (w90%) of these cases will be type 2 in origin, reflecting a trend towards obesity and more sedentary lifestyles as the 'norm' rather than the exception in 'developed' societies.Indeed, the face of T2DM is changing, as a condition that was once considered the preserve of middle/old age is increasingly diagnosed in young adults and even children, reflecting the high rates of obesity (and, in particular, visceral adiposity) in these populations." + } + ], + "4d3330eb-acd0-4f72-aadf-b056d3c8b389": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "\n\nTable 1 lists the various subtypes of diabetes based on the classification suggested by the ADA [4]." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "\n\nThe ADA lists four subtypes of diabetes based on the clinical symptoms at time of presentation, [4] namely, Type 1 diabetes, Type 2 diabetes (T2D), gestational diabetes, and diabetes due to specific causes (genetic defects causing deficient insulin secretion or action, diseases of pancreas, use of certain drugs such as steroids, thiazides among others).Of these, T2D is the most prevalent (close to 90% of all cases) and is the major cause of morbidity and mortality in both developed and developing nations [1].At times it is difficult to assign a patient to a particular subtype due to the difference in conditions associated with hyperglycemia at the time of diagnosis [4,7].For example, a lady diagnosed with gestational diabetes mellitus during pregnancy is highly susceptible to develop T2D later.Therefore, other than proper treatment during and post pregnancy, a regular follow-up is required for stratifying disease risk, and for timely management before progression to another subtype.It is clear that the classification of diabetes may not be as simple as just categorizing it into any one of the four given subtypes due to its miscellaneous nature.Every case needs to be considered at the time of presentation, on the basis of the risk factors or underlying cause of hyperglycemia, the clinical symptoms, and disease prognosis." + } + ], + "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0": [ + { + "document_id": "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0", + "text": "Introduction\n\nGlobally, diabetes affects more than 400 million people (World Health Organization, 2016), with Type 1 (insulin-dependent) diabetes (T1D) accounting for up to 10 percent of cases (American Diabetes Association, 2009).In the United States, T1D occurs at a rate of 15-30 cases per 100,000 children aged 0-14 years annually (International Diabetes Foundation, 2017;Maahs et al., 2010), with similar prevalence in Canada, Europe, Australia, and New Zealand (Fig. 1) (Derraik et al., 2012;International Diabetes Foundation, 2017;Maahs et al., 2010).By contrast, the estimated incidence rate of T1D among Asians, South Americans, and Africans is below 15 cases per 100,000 children (Fig. 1) (International Diabetes Foundation, 2017;Maahs et al., 2010).The global incidence of T1D has been rising by 3-5% per annum over the past two decades, with a notable increase in children below 10 years of age (Diamond Project, 2006;Patterson et al., 2009)." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Animal Models\n\n9.2% in women and 9.8% in men, with approximately 347 million people suffering from the disease worldwide in 2008 (Danaei et al., 2011).There are several different classifications of diabetes, the most common being type 1 and type 2 diabetes." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nType 2 diabetes is the most common type of diabetes with prevalence in the United Kingdom of around 4%.It is most commonly diagnosed in middle-aged adults, although more recently the age of onset is decreasing with increasing levels of obesity (Pinhas-Hamiel and Zeitler, 2005).Indeed, although development of the disease shows high hereditability, the risk increases proportionally with body mass index (Lehtovirta et al., 2010).Type 2 diabetes is associated with insulin resistance, and a lack of appropriate compensation by the beta cells leads to a relative insulin deficiency.Insulin resistance can be improved by weight reduction and exercise (Solomon et al., 2008).If lifestyle intervention fails, there are a variety of drugs available to treat type 2 diabetes (Krentz et al., 2008), which can be divided into five main classes: drugs that stimulate insulin production from the beta cells (e.g.sulphonylureas), drugs that reduce hepatic glucose production (e.g.biguanides), drugs that delay carbohydrate uptake in the gut (e.g.a-glucosidase inhibitors), drugs that improve insulin action (e.g.thiazolidinediones) or drugs targeting the GLP-1 axis (e.g.GLP-1 receptor agonists or DPP-4 inhibitors)." + } + ], + "7d4a197e-3774-40a4-9897-ed7c71f213b6": [ + { + "document_id": "7d4a197e-3774-40a4-9897-ed7c71f213b6", + "text": "Introduction\n\nDiabetes impacts the lives of approximately 200 million people worldwide [1], with chronic complications including accelerated development of cardiovascular disease.Over 90% of cases are of type 2 diabetes (T2D), with the bulk of the remainder presenting with type 1 diabetes (T1D)." + } + ], + "961f88ba-2090-4904-942c-f0e014bbe53f": [ + { + "document_id": "961f88ba-2090-4904-942c-f0e014bbe53f", + "text": "Classification of Diabetes\n\nOn the basis of insulin deficiency, diabetes can be classified into the following types as follows." + } + ], + "9b93b4eb-98c2-403f-aea2-6b24399501b8": [ + { + "document_id": "9b93b4eb-98c2-403f-aea2-6b24399501b8", + "text": "| INTRODUCTION\n\nToday, more than 265 million people are affected across the world.It is estimated that by the year 2030 this number will reach 366 million people (about 4/4 percent of the world's population), and now the cause of death is more than 1.1 million per year (including 50% of the population under-70 years of age and 55% of women).On the other hand, given its negative effect on the economic growth of developing countries, it calls for universal mobilization to combat this disease (Bhattacharya, Dey, & Roy, 2007).Diabetes or diabetes mellitus is referred to as a heterogeneous group of metabolic disorders characterized by chronic hyperglycemia and carbohydrate, fat and protein metabolism disorders that result from a defect in the secretion of insulin, or impairment in its function, or both.Types of diabetes mellitus include type 1, type 2 diabetes and other kind of diabetes, but the two most common types of diabetes mellitus are type 1 and type 2, which are different in several aspects (Meshkani, Taghikhani, Mosapour et al., 2007).Type 1 diabetes has been identified with autoimmune destruction of pancreatic beta cells (insulin secreting cells) and accounts for about 5% of all diabetic people, while type 2 diabetes is a predominant disorder characterized by insulin resistance or a relative decline in insulin production, and accounts for about 90% of all types of diabetes mellitus (Meshkani, Taghikhani, Al-Kateb et al., 2007).Important factors that predispose a person to type 2 diabetes are multifactorial, including genetic factors and environments.However, its inheritance has certainly not been proven, but it is believed that first-degree relatives of diabetic patients have a higher chance to develop the disease.In this regard, recognizing gene polymorphisms of this disease seems to be necessary (Häring et al., 2014).Multiple genes have been studied in the pathogenesis of type 2 diabetes." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "CONCLUSIONS\n\nDiabetes is currently broadly classified as type 1, type 2, gestational, and a group of \"other specific syndromes. \"However, increasing evidence suggests that there are populations of individuals within these broad categories that have subtypes of disease with a well-defined etiology that may be clinically characterized (e.g., LADA, MODY).These developments suggest that perhaps, with more focused research in critical areas, we are approaching a point where it would be possible to categorize diabetes in a more precise manner that can inform individual treatment decisions." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 2 Diabetes\n\nIn the U.S., an estimated 95% of the nearly 30 million people living with diabetes have type 2 diabetes.An additional 86 million have prediabetes, putting them at high risk for developing type 2 diabetes (9).Among the demographic associations for type 2 diabetes are older age, race/ ethnicity, male sex, and socioeconomic status (9)." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 1 Diabetes\n\nBetween 2001 and 2009, there was a 21% increase in the number of youth with type 1 diabetes in the U.S. (7).Its prevalence is increasing at a rate of ;3% per year globally (8).Though diagnosis of type 1 diabetes frequently occurs in childhood, 84% of people living with type 1 diabetes are adults (9).Type 1 diabetes affects males and females equally (10) and decreases life expectancy by an estimated 13 years (11).An estimated 5-15% of adults diagnosed with type 2 diabetes actually have type 1 diabetes or latent autoimmune diabetes of adults (LADA) (12)." + } + ], + "ab32e261-658c-4a8b-94fc-857826b29f5a": [ + { + "document_id": "ab32e261-658c-4a8b-94fc-857826b29f5a", + "text": "\n\nBackground Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous.A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis." + } + ], + "b666545f-6a53-45de-8562-55d88fc6f7ee": [ + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "text": "\n\nDiabetes mellitus now affects ~8% of the world's adult population [1], including ~3 000 000 individuals in the UK (with a further 600 000 people affected but presently undiagnosed) [2].Of these cases, > 90% have Type 2 diabetes.Treatments of the complications of the disease, which range from stroke, blindness and kidney failure to lower limb amputations and cancer, presently consume ~10% of the National Health Service budget, some £14 bn per year [3].These figures are anticipated to increase further in the next 10 years, driven by increasingly sedentary lifestyles and increases in obesity; the collision between these 'environmental' factors and genetic susceptibility (see below) being the key underlying driver.Whilst existing treatments ameliorate the symptoms of the disease, notably hyperglyca-emia, none target the underlying molecular aetiology.In particular, no available treatments tackle the progressive and largely irreversible loss of insulin production [4] which, in the face of insulin resistance, underlies the progressive deterioration in glucose control.Reductions in b-cell mass [5,6] and dysfunction [7] both contribute to this gradual impairment in insulin release.Recent years have seen an increase in the view that the former may play a less important role than the latter, with a 2008 study by Rahier et al. [6] reporting that b-cell mass (and insulin content) in people with Type 2 diabetes was on average ~35% lower than that of healthy control subjects.However, this difference was only ~24% within 5 years of diagnosis, far below levels likely to lead to the symptoms of diabetes.Indeed, given our present inability to monitor b-cell mass prospectively over the course of the disease, it is conceivable that the differences observed post mortem between healthy individuals and those with Type 2 diabetes [5,6] may reflect an increased predisposition to diabetes in those born with a lower than average b-cell mass." + } + ], + "b72eb0d1-50e3-4def-94bc-abf77891f519": [ + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "text": "INTRODUCTION\n\nType 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 (1).The disease usually involves insulin resistance but is ultimately the result of pancreatic b cell failure, a sine qua non for disease development (2).In contrast, Type 1 diabetes (T1D) affects a smaller proportion of people with diabetes and is chiefly the result of pancreatic b cell destruction mediated by immune cells (3)." + } + ], + "ba7298cd-4d19-4f98-9a2a-5fb625aa0068": [ + { + "document_id": "ba7298cd-4d19-4f98-9a2a-5fb625aa0068", + "text": "Introduction\n\nDiabetes is a complex and heterogeneous disease with a staggering global impact and the most recent estimates indicate 346 million people worldwide suffer from this disease (WHO Diabetes Fact sheet No. 312, 2011).Type 2 diabetes mellitus (T2DM) is the most common form of diabetes, accounting for >90% of cases, and occurs when peripheral tissue insulin resistance accompanies insufficient b-cell insulin production.While >80% of diabetes deaths occur in low-and middle-income countries [1].India and China have the highest reported prevalence of diabetes with 65 and 98 million in 2013, respectively [2]." + } + ], + "ceab3d6d-62ca-459a-9a97-02a16d4dd193": [ + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "text": "\n\nThe disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition." + }, + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "text": "\nThe disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition." + } + ], + "eaca0f25-4a6b-4c0e-a6df-12e25060b169": [ + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "text": "\n\nIntroduction: Is Type 2 Diabetes a Genetic Disorder?According to the World Health Organization (WHO), approximately 350 million people worldwide have diabetes, and this disorder is likely to be the seventh leading cause of death in 2030.Diabetes is an economic burden on healthcare systems, especially in developing countries (World Health Organization, 2013)." + } + ] + }, + "data_source": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "CONCLUSIONS\n\nDiabetes is currently broadly classified as type 1, type 2, gestational, and a group of \"other specific syndromes. \"However, increasing evidence suggests that there are populations of individuals within these broad categories that have subtypes of disease with a well-defined etiology that may be clinically characterized (e.g., LADA, MODY).These developments suggest that perhaps, with more focused research in critical areas, we are approaching a point where it would be possible to categorize diabetes in a more precise manner that can inform individual treatment decisions." + }, + { + "document_id": "ba7298cd-4d19-4f98-9a2a-5fb625aa0068", + "section_type": "main", + "text": "Introduction\n\nDiabetes is a complex and heterogeneous disease with a staggering global impact and the most recent estimates indicate 346 million people worldwide suffer from this disease (WHO Diabetes Fact sheet No. 312, 2011).Type 2 diabetes mellitus (T2DM) is the most common form of diabetes, accounting for >90% of cases, and occurs when peripheral tissue insulin resistance accompanies insufficient b-cell insulin production.While >80% of diabetes deaths occur in low-and middle-income countries [1].India and China have the highest reported prevalence of diabetes with 65 and 98 million in 2013, respectively [2]." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Type 2 Diabetes\n\nIn the U.S., an estimated 95% of the nearly 30 million people living with diabetes have type 2 diabetes.An additional 86 million have prediabetes, putting them at high risk for developing type 2 diabetes (9).Among the demographic associations for type 2 diabetes are older age, race/ ethnicity, male sex, and socioeconomic status (9)." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "\n\nTable 1 lists the various subtypes of diabetes based on the classification suggested by the ADA [4]." + }, + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "section_type": "main", + "text": "\n\nThe disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition." + }, + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "section_type": "abstract", + "text": "\nThe disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "\n\nThe ADA lists four subtypes of diabetes based on the clinical symptoms at time of presentation, [4] namely, Type 1 diabetes, Type 2 diabetes (T2D), gestational diabetes, and diabetes due to specific causes (genetic defects causing deficient insulin secretion or action, diseases of pancreas, use of certain drugs such as steroids, thiazides among others).Of these, T2D is the most prevalent (close to 90% of all cases) and is the major cause of morbidity and mortality in both developed and developing nations [1].At times it is difficult to assign a patient to a particular subtype due to the difference in conditions associated with hyperglycemia at the time of diagnosis [4,7].For example, a lady diagnosed with gestational diabetes mellitus during pregnancy is highly susceptible to develop T2D later.Therefore, other than proper treatment during and post pregnancy, a regular follow-up is required for stratifying disease risk, and for timely management before progression to another subtype.It is clear that the classification of diabetes may not be as simple as just categorizing it into any one of the four given subtypes due to its miscellaneous nature.Every case needs to be considered at the time of presentation, on the basis of the risk factors or underlying cause of hyperglycemia, the clinical symptoms, and disease prognosis." + }, + { + "document_id": "9b93b4eb-98c2-403f-aea2-6b24399501b8", + "section_type": "main", + "text": "| INTRODUCTION\n\nToday, more than 265 million people are affected across the world.It is estimated that by the year 2030 this number will reach 366 million people (about 4/4 percent of the world's population), and now the cause of death is more than 1.1 million per year (including 50% of the population under-70 years of age and 55% of women).On the other hand, given its negative effect on the economic growth of developing countries, it calls for universal mobilization to combat this disease (Bhattacharya, Dey, & Roy, 2007).Diabetes or diabetes mellitus is referred to as a heterogeneous group of metabolic disorders characterized by chronic hyperglycemia and carbohydrate, fat and protein metabolism disorders that result from a defect in the secretion of insulin, or impairment in its function, or both.Types of diabetes mellitus include type 1, type 2 diabetes and other kind of diabetes, but the two most common types of diabetes mellitus are type 1 and type 2, which are different in several aspects (Meshkani, Taghikhani, Mosapour et al., 2007).Type 1 diabetes has been identified with autoimmune destruction of pancreatic beta cells (insulin secreting cells) and accounts for about 5% of all diabetic people, while type 2 diabetes is a predominant disorder characterized by insulin resistance or a relative decline in insulin production, and accounts for about 90% of all types of diabetes mellitus (Meshkani, Taghikhani, Al-Kateb et al., 2007).Important factors that predispose a person to type 2 diabetes are multifactorial, including genetic factors and environments.However, its inheritance has certainly not been proven, but it is believed that first-degree relatives of diabetic patients have a higher chance to develop the disease.In this regard, recognizing gene polymorphisms of this disease seems to be necessary (Häring et al., 2014).Multiple genes have been studied in the pathogenesis of type 2 diabetes." + }, + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "section_type": "main", + "text": "INTRODUCTION\n\nType 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 (1).The disease usually involves insulin resistance but is ultimately the result of pancreatic b cell failure, a sine qua non for disease development (2).In contrast, Type 1 diabetes (T1D) affects a smaller proportion of people with diabetes and is chiefly the result of pancreatic b cell destruction mediated by immune cells (3)." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Type 1 Diabetes\n\nBetween 2001 and 2009, there was a 21% increase in the number of youth with type 1 diabetes in the U.S. (7).Its prevalence is increasing at a rate of ;3% per year globally (8).Though diagnosis of type 1 diabetes frequently occurs in childhood, 84% of people living with type 1 diabetes are adults (9).Type 1 diabetes affects males and females equally (10) and decreases life expectancy by an estimated 13 years (11).An estimated 5-15% of adults diagnosed with type 2 diabetes actually have type 1 diabetes or latent autoimmune diabetes of adults (LADA) (12)." + }, + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "section_type": "main", + "text": "\n\nDiabetes mellitus now affects ~8% of the world's adult population [1], including ~3 000 000 individuals in the UK (with a further 600 000 people affected but presently undiagnosed) [2].Of these cases, > 90% have Type 2 diabetes.Treatments of the complications of the disease, which range from stroke, blindness and kidney failure to lower limb amputations and cancer, presently consume ~10% of the National Health Service budget, some £14 bn per year [3].These figures are anticipated to increase further in the next 10 years, driven by increasingly sedentary lifestyles and increases in obesity; the collision between these 'environmental' factors and genetic susceptibility (see below) being the key underlying driver.Whilst existing treatments ameliorate the symptoms of the disease, notably hyperglyca-emia, none target the underlying molecular aetiology.In particular, no available treatments tackle the progressive and largely irreversible loss of insulin production [4] which, in the face of insulin resistance, underlies the progressive deterioration in glucose control.Reductions in b-cell mass [5,6] and dysfunction [7] both contribute to this gradual impairment in insulin release.Recent years have seen an increase in the view that the former may play a less important role than the latter, with a 2008 study by Rahier et al. [6] reporting that b-cell mass (and insulin content) in people with Type 2 diabetes was on average ~35% lower than that of healthy control subjects.However, this difference was only ~24% within 5 years of diagnosis, far below levels likely to lead to the symptoms of diabetes.Indeed, given our present inability to monitor b-cell mass prospectively over the course of the disease, it is conceivable that the differences observed post mortem between healthy individuals and those with Type 2 diabetes [5,6] may reflect an increased predisposition to diabetes in those born with a lower than average b-cell mass." + }, + { + "document_id": "1cebda9b-a727-4bfd-8521-cf61903235cc", + "section_type": "main", + "text": "\n\nThe prevalence of diabetes mellitus worldwide is sobering; the International Diabetes Federation estimates that 415 million people have diabetes mellitus, with 90% of these individuals having type 2 diabetes mellitus (T2DM) 1 .T2DM occurs when pancreatic β-cells fail to release enough insulin to meet the demands of insulin-responsive tissues, which safely store and metabolize glucose.Driven by both genetic and environmental risk factors, T2DM is a complex, multifactorial disorder.Although the increasing prevalence of T2DM is driven by changes in our environment and mirrors the increase in obesity, the greater concordance for T2DM found in monozygotic compared with dizygotic twins has long provided evidence for a genetic component in T2DM risk 2 ." + }, + { + "document_id": "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0", + "section_type": "main", + "text": "Introduction\n\nGlobally, diabetes affects more than 400 million people (World Health Organization, 2016), with Type 1 (insulin-dependent) diabetes (T1D) accounting for up to 10 percent of cases (American Diabetes Association, 2009).In the United States, T1D occurs at a rate of 15-30 cases per 100,000 children aged 0-14 years annually (International Diabetes Foundation, 2017;Maahs et al., 2010), with similar prevalence in Canada, Europe, Australia, and New Zealand (Fig. 1) (Derraik et al., 2012;International Diabetes Foundation, 2017;Maahs et al., 2010).By contrast, the estimated incidence rate of T1D among Asians, South Americans, and Africans is below 15 cases per 100,000 children (Fig. 1) (International Diabetes Foundation, 2017;Maahs et al., 2010).The global incidence of T1D has been rising by 3-5% per annum over the past two decades, with a notable increase in children below 10 years of age (Diamond Project, 2006;Patterson et al., 2009)." + }, + { + "document_id": "ab32e261-658c-4a8b-94fc-857826b29f5a", + "section_type": "main", + "text": "\n\nBackground Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous.A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "Animal Models\n\n9.2% in women and 9.8% in men, with approximately 347 million people suffering from the disease worldwide in 2008 (Danaei et al., 2011).There are several different classifications of diabetes, the most common being type 1 and type 2 diabetes." + }, + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "section_type": "main", + "text": "\n\nIntroduction: Is Type 2 Diabetes a Genetic Disorder?According to the World Health Organization (WHO), approximately 350 million people worldwide have diabetes, and this disorder is likely to be the seventh leading cause of death in 2030.Diabetes is an economic burden on healthcare systems, especially in developing countries (World Health Organization, 2013)." + }, + { + "document_id": "4252d7ad-82de-480c-a801-9ed1c84fb968", + "section_type": "main", + "text": "\n\nIn the UK alone, nearly 1.8 million people are already recognized to have this disorder (consuming w5% of the total National Health Service budget), and the search is on to find the 'missing million' who are living with the condition but in whom the diagnosis has yet to be made. 3In the USA, the situation appears to be even more serious with some commentators predicting that one in every three Americans born in the year 2000 will go on to develop diabetes during their lifetime, bringing unprecedented costs in terms of healthcare dollars as well as human morbidity and mortality. 4The majority (w90%) of these cases will be type 2 in origin, reflecting a trend towards obesity and more sedentary lifestyles as the 'norm' rather than the exception in 'developed' societies.Indeed, the face of T2DM is changing, as a condition that was once considered the preserve of middle/old age is increasingly diagnosed in young adults and even children, reflecting the high rates of obesity (and, in particular, visceral adiposity) in these populations." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "\n\nType 2 diabetes is the most common type of diabetes with prevalence in the United Kingdom of around 4%.It is most commonly diagnosed in middle-aged adults, although more recently the age of onset is decreasing with increasing levels of obesity (Pinhas-Hamiel and Zeitler, 2005).Indeed, although development of the disease shows high hereditability, the risk increases proportionally with body mass index (Lehtovirta et al., 2010).Type 2 diabetes is associated with insulin resistance, and a lack of appropriate compensation by the beta cells leads to a relative insulin deficiency.Insulin resistance can be improved by weight reduction and exercise (Solomon et al., 2008).If lifestyle intervention fails, there are a variety of drugs available to treat type 2 diabetes (Krentz et al., 2008), which can be divided into five main classes: drugs that stimulate insulin production from the beta cells (e.g.sulphonylureas), drugs that reduce hepatic glucose production (e.g.biguanides), drugs that delay carbohydrate uptake in the gut (e.g.a-glucosidase inhibitors), drugs that improve insulin action (e.g.thiazolidinediones) or drugs targeting the GLP-1 axis (e.g.GLP-1 receptor agonists or DPP-4 inhibitors)." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "Background\n\nThe past few decades have shown a marked increase in the number of patients with diabetes rising from 151 million (4.6% of the global population) in 2000 to 463 million (9.3%) in 2019 [1].The risk of type 2 diabetes (T2DM), the most common type of diabetes, is modified by a strong interaction between environmental and genetic factors [2,3].T2DM is a multifactorial disease with a population-specific heritability (26% in the European population) [4].A number of common variants implicated in the pathogenesis and genetic architecture of T2DM have been identified so far, some of them also capable of modifying the pharmacologic response to antidiabetic drugs [5,6]." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "section_type": "main", + "text": "Introduction\n\nDiabetes is one of the most prevalent complex disorders with type 2 diabetes accounting for more than 90% of all diabetic cases.Hyperglycemia is the characteristic feature of this syndrome, which results from defective insulin secretion or action.The disease itself may not lead to death of the affected individual but being the major risk factor of macrovascular complications like coronary artery disease, cerebrovascular events and peripheral vascular disease, diabetes is an indirect cause of deaths due to such diseases.It is also responsible for disabilities such as diabetic nephropathy, diabetic neuropathy, diabetic retinopathy, skin complications, eye complications as well as mental illness.The International Diabetes Federation (IDF) 2015 reported an estimate of 415 million adults (20-79 years of age) worldwide to have diabetes in the year 2015, which is projected to reach 642 million by the year 2040.Diabetes has been a major public health concern in the 21st century (IDF 2015) among the worldwide countries/territories, particularly in China, India and USA, which show the alarmingly increasing prevalence (figure 1).India, in particular, is expected to have doubled its prevalence by 2040." + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "\n\nTHE GLOBAL BURDEN OF TYPE 2 DIABETES-The dynamics of the diabetes epidemic are changing rapidly.Once a disease of the West, type 2 diabetes has now spread to every country in the world.Once \"a disease of affluence,\" it is now increasingly common among the poor.Once an adult-onset disease almost unheard of in children, rising rates of childhood obesity have rendered it more common in the pediatric population, especially in certain ethnic groups.According to the International Diabetes Federation (1), diabetes affects at least 285 million people worldwide, and that number is expected to reach 438 million by the year 2030, with two-thirds of all diabetes cases occurring in low-to middle-income countries.The number of adults with impaired glucose tolerance will rise from 344 million in 2010 to an estimated 472 million by 2030." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "\n\nThere is a high degree of variability for prevalence of type 2 diabetes across the globe.East Asia, South Asia, and Australia have more adults with diabetes than any other region (153 million).North America and the Caribbean have the highest prevalence rate, with one in eight affected (8)." + }, + { + "document_id": "988d55c7-f831-4adb-94c0-6de4ebf4727b", + "section_type": "main", + "text": "\n\nIn Germany, type 2 diabetes shows increasing prevalence with 5-8 million people having some form of diabetes (prevalence: 6-10%).In an effort to identify causative genetic factors, we report here results of linkage studies in which we identified two type 2 diabetes loci.We elucidated potentially interacting regions by conditioning our sample on the positive linkage signals identified.Taken together, our results and the findings of other studies provide evidence for a complex metabolic syndrome locus on chromosome 1p36.13." + }, + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "section_type": "main", + "text": "\n\nof those initially classified may require revision [7] .The classical classification of diabetes as proposed by the American Diabetes Association (ADA) in 1997 as type 1, type 2, other types, and gestational diabetes mellitus (GDM) is still the most accepted classification and adopted by ADA [1] .Wilkin [8] proposed the accelerator hypothesis that argues \"type 1 and type 2 diabetes are the same disorder of insulin resistance set against different genetic backgrounds\" [9] .The difference between the two types relies on the tempo, the faster tempo reflecting the more susceptible genotype and earlier presentation in which obesity, and therefore, insulin resistance, is the center of the hypothesis.Other predictors of type 1 diabetes include increased height growth velocity [10,11] and impaired glucose sensitivity of β cells [12] .The implications of increased free radicals, oxidative stress, and many metabolic stressors in the development, pathogenesis and complications of diabetes mellitus [13-18] are very strong and well documented despite the inconsistency of the clinical trials using antioxidants in the treatment regimens of diabetes [19][20][21] .The female hormone 17-β estradiol acting through the estrogen receptor-α (ER-α) is essential for the development and preservation of pancreatic β cell function since it was clearly demonstrated that induced oxidative stress leads to β-cell destruction in ER-α knockout mouse.The ER-α receptor activity protects pancreatic islets against glucolipotoxicity and therefore prevents β-cell dysfunction [22] ." + }, + { + "document_id": "2e317f9d-c028-41b7-a99e-28da61db9970", + "section_type": "main", + "text": "Introduction\n\nDiabetes impacts approximately 200 million people worldwide [1], with microvascular and cardiovascular disease being the primary complications.Approximately 10% of cases are type 1 diabetes (T1D) sufferers, with ,3% increase in the incidence of T1D globally per year [2].It is expected that the incidence is 40% higher in 2010 than in 1998 [3].T1D is a clear example of a complex trait that results from the interplay between environmental and genetic factors.There are many lines of evidence that there is a strong genetic component to T1D, primarily due to the fact that T1D has high concordance among monozygotic twins [4] and runs strongly in families, together with a high sibling risk [5]." + }, + { + "document_id": "b9c9912f-0344-4945-adb1-fd038bed90ab", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes is a common complex disease characterised by deficient insulin secretion and decreased insulin sensitivity.In 2010, 285 million people worldwide were affected by type 2 diabetes [1], with 60% of them located in Asia [2,3].China now has the largest number of patients with diabetes in the world, with an estimated 92 million affected individuals, and an additional 150 million with impaired glucose tolerance [4]." + }, + { + "document_id": "f44149e0-d183-48c1-a937-729e7abd87f5", + "section_type": "main", + "text": "Background\n\nType 2 diabetes mellitus (T2D) is a phenotypic and genetically heterogeneous chronic disease [1] that represents 90% to 95% of all diabetes types; given its magnitude, it has become an increasingly important public health problem worldwide, occurring in ever-younger individuals [2].In México, the National Health Survey 2000 (ENSA 2000) showed a T2D prevalence of 7.5% in individuals 20 years and older [3]." + }, + { + "document_id": "15b5c53c-d153-4932-9d24-9864e92a601d", + "section_type": "main", + "text": "INTRODUCTION\n\nType 2 diabetes (T2D) is a complex disease characterized by insulin resistance and b-cell dysfunction.An estimated 630 million adults are expected to have T2D by 2045, 1 making it one of the fastest growing global health challenges of the 21st century.Genome-wide association studies (GWASs) have successfully identified more than 500 genomic loci to be associated with T2D, 2 although the majority of these are driven by common variants with small individual effects on T2D risk." + }, + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "section_type": "main", + "text": "TYPE 2 DIABETES MELLITUS\n\nThe global prevalence of diabetes in adults (20-79 years old) according to a report published in 2013 by the IDF was 8.3% (382 million people), with 14 million more men than women (198 million men vs 184 million women), the majority between the ages 40 and 59 years and the number is expected to rise beyond 592 million by 2035 with a 10.1% global prevalence.tissues.In addition to insulin resistance, the increased demand for insulin could not be met by the pancreatic β cells due to defects in the function of these cells [18] .On the contrary, insulin secretion decreases with the increased demand for insulin by time due to the gradual destruction of β cells [57] that could transform some of type 2 diabetes patients from being independent to become dependent on insulin.Most type 2 diabetes patients are not dependent on insulin where insulin secretion continues and insulin depletion rarely occurs." + }, + { + "document_id": "251d15dc-e1ec-4fea-8c29-b000f51a62cd", + "section_type": "main", + "text": "INTRODUCTION\n\nType 2 diabetes (T2D) is a complex metabolic disorder that accounts for 85%-95% of all cases of diabetes and afflicts hundreds of millions of people worldwide (http://www.diabetesatlas.org/content/diabetes).It is a leading cause of substantial morbidity and is characterized by defects in insulin sensitivity and secretion resulting from the progressive dysfunc-tion and loss of b cells in the pancreatic islets of Langerhans (Butler et al., 2007;Muoio and Newgard, 2008).Both genetic predisposition and environmental factors contribute to these islet defects.Islets constitute 1%-2% of human pancreatic mass (Joslin and Kahn, 2005) and are composed of five endocrine cell types that secrete different hormones: a cells (glucagon), b cells (insulin), d cells (somatostatin), PP cells (pancreatic polypeptide Y), and 3 cells (ghrelin).These cells sense changes in blood glucose concentration and respond by modulating the activity of multiple pathways, including insulin and glucagon secretion, to maintain glucose homeostasis (Joslin and Kahn, 2005).Several key transcription factors (TFs) that regulate these responses are known (Oliver-Krasinski and Stoffers, 2008).However, efforts to identify cis-regulatory elements upon which these and other factors act have been restricted primarily to promoter regions at specific loci (e.g., INS, PDX1) (Brink, 2003;Ohneda et al., 2000)." + }, + { + "document_id": "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f", + "section_type": "main", + "text": "\n\nType 2 diabetes affects more than 200 million individuals worldwide, and its prevalence is continuously increasing in many countries, including Japan.Although the precise mechanisms underlying the development and progression of type 2 diabetes have not been fully elucidated, a combination of multiple genetic and environmental factors is considered to contribute to the pathogenesis of the disease 1 ." + }, + { + "document_id": "ff69cd83-ab79-4c24-8bc5-fd9009aa259b", + "section_type": "main", + "text": "Background & Summary\n\nDiabetes is one of the fastest-growing health challenges of the 21 st century.The most common form of diabetes, type 2 diabetes (T2D), is a complex multifactorial disease which can lead to further severe health consequences such as cardiovascular diseases and premature death.In 2019, 463 million people worldwide were living with diabetes according to the International Diabetes Federation, and this number is expected to rise to 700 million by 2045 1 .Genome-wide association studies (GWAS) have made considerable progress in identifying genetic risk factors and in providing evidence for more in-depth understanding of the biological and pathological pathways underlying T2D.A recent study performed a meta-analysis of T2D across 32 GWAS of European ancestry participants and identified 243 genome-wide significant loci (403 distinct genetic variants) associated with T2D risk 2 .The summary statistics from this meta-analysis are publicly available; however, the GWAS results for each participating study, including EPIC-InterAct, cannot be acquired easily." + }, + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "section_type": "main", + "text": "\n\nDIABETES EPIDEMIC-The latest estimates from the Center for Disease Control and Prevention indicate that in 2010 approximately 26 million American adults had diabetes and 79 million had prediabetes (1).African Americans and other ethnic groups continue to suffer higher rates of diabetes than whites.Worldwide, diabetes affects 285 million adults (2).Type 2 diabetes accounts for ;95% of all cases.The exact reasons for the diabetes epidemic, and its predilection for certain ethnic groups, are unknown.However, interactions between genetic predisposition and environmental triggers (or accelerants) are generally presumed to underlie the etiology of diabetes (3-5) (Fig. 1).The best known environmental risk factors are dietary habits, physical inactivity, and obesity; interventions that ameliorate these risk factors prevent the development of type 2 diabetes (6,7)." + }, + { + "document_id": "d15b3490-241d-4766-8e3e-feb683503d1b", + "section_type": "main", + "text": "\n\nType 2 diabetes is one of the leading health problems in the United States, affecting approximately 21 million persons or almost 10% of the US adult population (1).Type 2 diabetes is nearly twice as prevalent among African Americans as among Caucasians (1)." + }, + { + "document_id": "7d4a197e-3774-40a4-9897-ed7c71f213b6", + "section_type": "main", + "text": "Introduction\n\nDiabetes impacts the lives of approximately 200 million people worldwide [1], with chronic complications including accelerated development of cardiovascular disease.Over 90% of cases are of type 2 diabetes (T2D), with the bulk of the remainder presenting with type 1 diabetes (T1D)." + }, + { + "document_id": "6a2d9ea5-7018-42fe-bed9-2c9c508531cb", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes mellitus (T2D) is a major chronic disease worldwide, affecting more than 300 million people.The greatest increase in the prevalence of T2D in the coming years is likely to be in Asia, home to half of the world's population with 3 billion people [1][2].It is estimated that in China alone, there are 100 million people with T2D [3]." + }, + { + "document_id": "961f88ba-2090-4904-942c-f0e014bbe53f", + "section_type": "main", + "text": "Classification of Diabetes\n\nOn the basis of insulin deficiency, diabetes can be classified into the following types as follows." + }, + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes (T2D) is a common disease with substantial and rapidly increasing global impact.While prevalence varies with age, sex and population, the global age-standardized adult diabetes prevalence is >9.2%, and an estimated >347 million adults have diabetes (1).Diabetes can be diagnosed based on the level of blood glucose after fasting or 2 h after an oral glucose challenge (2hGlu), or based on hemoglobin A1c (HbA1c), which provides a 3month average of blood glucose (2).In many individuals with T2D, insulin resistance coexists with obesity, adverse lipid profiles, high blood pressure and a proinflammatory state, each likely influenced by genetic and environmental factors (3).Progression to T2D is characterized by abnormalities in pancreatic islet β-cell function in the presence of insulin resistance (4), although these biological processes are only partially defined.Strong evidence for a genetic component exists for T2D risk, insulin secretion and insulin action (5,6)." + }, + { + "document_id": "ee21529b-bf7d-49ec-a21e-c52c9c7ff7e1", + "section_type": "main", + "text": "Symptomatic T1DM\n\nAccording to the International Diabetes Federation, 8.8% of the adult population worldwide has diabetes 14 .Of all individuals with diabetes, only 10-15% have T1DM; type 2 diabetes mellitus (T2DM) is the most common form.However, T1DM is the most com mon form of diabetes in children (<15 years of age), and >500,000 children are currently living with this condition globally." + }, + { + "document_id": "8857153e-a7be-45ee-84dd-14911bdd064a", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes (T2D) affects at least 6% of the world's population; the worldwide prevalence is expected to double by 2025 [1].T2D is a complex disorder that is characterized by hyperglycemia, which results from impaired pancreatic b cell function, decreased insulin action at target tissues, and increased glucose output by the liver [2].Both genetic and environmental factors contribute to the pathogenesis of T2D.The disease is considered to be a polygenic disorder in which each genetic variant confers a partial and additive effect.Only 5%-10% of T2D cases are due to single gene defects; these include maturity-onset diabetes of the young (MODY), insulin resistance syndromes, mitochondrial diabetes, and neonatal diabetes [3][4][5].Inherited variations have been identified from studies of monogenic diabetes, and have provided insights into b cell physiology, insulin release, and the action of insulin on target cells [6]." + } + ], + "document_id": "DF2A84CC99BAED8C3168AE12F76252A2", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&1&diabetes", + "type&2&diabetes", + "gestational&diabetes", + "LADA", + "MODY", + "insulin&resistance", + "pancreatic&beta&cells", + "autoimmune&destruction", + "insulin&deficiency", + "genetic&factors" + ], + "metadata": [ + { + "object": "rs2059806 of INSR was associated with both type 2 diabetes mellitus and type 2 diabetic nephropathy, while rs7212142 of mTOR was associated with type 2 diabetic nephropathy but not type 2 diabetes mellitus in a Chinese Han population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab687817" + }, + { + "object": "The genotype EE/EK/KK frequencies % for the CTRL group 38.2/50.2/11.6, Type 1 Diabetes 34.3/52.0/13.7, and Type 2 Diabetes 38.2/48.9/12.9 were in Hardy-Weinberg equilibrium and there were no significant differences. The minor allele frequencies MAF; K for CTRL 37.0%, Type 1 Diabetes 39.7%, and Type 2 Diabetes 37.4% were not different among the groups", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab818180" + }, + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "Data suggest IGT10 mice, diabetes type 2 model, exhibit 2 genetic defects: haploinsufficiency heterozygosity for null allele of insulin receptor Insr; splice-site mutation in protein phosphatase 2 regulatory subunit B alpha Ppp2r2a. Inheritance of either allele results in insulin resistance but not overt diabetes. Double heterozygosity leads to insulin resistance and diabetes type 2 without increase in body weight.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab203476" + }, + { + "object": "Sfrp5 may be concurrently associated with COPD [ chronic obstructive pulmonary disease ] and insulin resistance; insulin resistance may be associated with airway inflammation and airflow limitation. Sfrp5 may be involved in the development of COPD and may be the key link by which insulin resistance exerts its effects on airway inflammation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab702425" + }, + { + "object": "Data suggest a novel pathophysiological role of CD163 in type 2 diabetes; monocyte surface CD163 levels are significantly associated with insulin resistance in patients with type 2 diabetes; the association of insulin resistance with soluble CD163 levels is less significant. This study was conducted in Japan.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab202739" + }, + { + "object": "Decreased plasma ghrelin significantly associated with abdominal adiposity, hyperinsulinemia and insulin resistance in type 2 diabetic patients. Hyperinsulinemia with insulin resistance may suppress plasma ghrelin in type 2 diabetes mellitus.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab218455" + }, + { + "object": "results show an association between the AGER -374 T/A polymorphism & type 1 diabetes; the polymorphism was associated with diabetic nephropathy in both type 1 & type 2 diabetes & with sight-threatening retinopathy in type 1 diabetic patients", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab660185" + }, + { + "object": "polymorphism of IAPP gene in nondiabetic subjects and type 2 diabetes is associated with lower levels of LDL cholesterol; abnormalities of coding regions or 5'-UTR not associated with type 2 diabetes or gestational diabetes mellitus in Spanish population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab604559" + }, + { + "object": "The association of variants in IRS1 with type 2 diabetes and type 2 diabetes-related phenotypes and the differential expression of IRS1 in adipocytes and skeletal muscle suggest a role of this gene in the pathogenesis of type 2 diabetes in Pima Indians.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab782328" + } + ], + "question": "How many types of diabetes exist?", + "subquestions": null, + "task_id": "DF2A84CC99BAED8C3168AE12F76252A2", + "usage": { + "chatgpt": 6403, + "gpt-4": 4251, + "gpt-4-turbo-preview": 3329 + }, + "user_id": 2 + }, + "document_id": "DF2A84CC99BAED8C3168AE12F76252A2", + "task_id": "DF2A84CC99BAED8C3168AE12F76252A2" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_01.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_01.json new file mode 100644 index 0000000..b9417a9 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_01.json @@ -0,0 +1,150 @@ +{ + "created_at": "2024-05-31T23:02:19.686688Z", + "data": { + "amplify": false, + "answer": "The genes associated with diabetes mentioned in the text include PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, CDKAL1, IRS1, CCR5, FTO, NOTCH2, WFS1, JAZF1, ADIPOQ, AHSG, CAPN10, ENPP1, PPARGC1A, SREBF1, PDX1, PFAS, GCK, HNFA4, LOC387761, KCNQ1, MTNR1B, GCKR, BACH2, CTNNB1, CREB5, PARD3B, PARD6G, PRKCZ, EBF1, GIGYF1, ADCY5, PROX1, DGKB-TMEM195, CRY2, FADS1, GLIS3, and C2CD4B.", + "context": { + "2bef9608-4bd6-4252-9fbd-2413b2cad4f8": [ + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "text": "\n\nTo see which other significant genes were likely to have a role in diabetes we looked at all variant sets with a significant glucose, HbA1c, or T2D association and examined whether they had associations with additional diabetes traits (p ≤ 0.0016, correcting for 32 sets tested).Damaging missense variants in PDX1 and PFAS, which significantly associated with HbA1c levels in our primary analysis, associated with T2D diagnosis using this threshold (Table 3 and Supplementary Table 14)." + }, + { + "document_id": "2bef9608-4bd6-4252-9fbd-2413b2cad4f8", + "text": "Identification of genes with a biological role in diabetes. Variants in two genes, GCK and GIGYF1, significantly associated with glucose, HbA1c and T2D diagnosis, strongly suggesting a biological role in diabetes; GCK is involved in Mendelian forms of diabetes while GIGYF1 has not previously been implicated by genetics in the disease.Both GCK and GIGYF1 are located on chromosome 7 but are 56 Mb apart, strongly suggesting that these signals are independent; this independence was confirmed by conditional analysis (Supplementary Table 13).Two additional variant sets, HNF1A pLOF and TNRC6B pLOF, had genome-wide associations with both T2D diagnosis and HbA1c levels while G6PC2 damaging missense variants associated with decreased levels of both glucose and HbA1c but not T2D diagnosis (Table 3)." + } + ], + "2dade65a-5d31-4839-b2c9-4c6cd3056f58": [ + { + "document_id": "2dade65a-5d31-4839-b2c9-4c6cd3056f58", + "text": "\n\nOne obvious locus to consider is TCF7L2 in the context of type 2 diabetes.Common genetic variation located within the gene encoding transcription factor 7 like 2 (TCF7L2) has been consistently reported to be strongly associated with the disease.Such reports range from 2006, when we first published the association [3], to the recent transethnic meta-analysis GWAS of type 2 diabetes [4]." + } + ], + "31588831-61b3-4018-9962-bd6985c3061b": [ + { + "document_id": "31588831-61b3-4018-9962-bd6985c3061b", + "text": "\n\nTesting of these loci for association with T2D as a dichotomous trait in up to 40,655 cases and 87,022 nondiabetic controls demonstrated that the fasting glucose-raising alleles at seven loci (in or near ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 and the known T2D genes TCF7L2 and SLC30A8) are robustly associated (P < 5 × 10 −8 ) with increased risk of T2D (Table 2).The association of a highly correlated SNP in ADCY5 with T2D in partially overlapping samples is reported by our companion manuscript 29 .We found less significant T2D associations (P < 5 × 10 −3 ) for variants in or near CRY2, FADS1, GLIS3 and C2CD4B (Table 2).These data clearly show that loci with similar fasting glucose effect sizes may have very different T2D risk effects (see, for example, ADCY5 and MADD in Table 2)." + } + ], + "3c35547c-eb9b-470d-b74b-0f9a0529e965": [ + { + "document_id": "3c35547c-eb9b-470d-b74b-0f9a0529e965", + "text": "\n\nAmong the confirmed and potential type 2 diabetes risk genes described in Tables 1 and 2, eight genes influence whole-body or peripheral insulin sensitivity: ADIPOQ (47, 52, 250 -257), AHSG (75, 258), CAPN10 (259 -264), ENPP1 (265)(266)(267)(268)(269)(270)(271), PPARG (272)(273)(274)(275)(276)(277)(278)(279)(280)(281)(282)(283), PPARGC1A (284,285), SREBF1 (65), and TCF7L2 (133,151,286,287)." + } + ], + "45c14654-f263-4031-9941-206d7b6a97f3": [ + { + "document_id": "45c14654-f263-4031-9941-206d7b6a97f3", + "text": "\n\nDespite identification of many putative causative genetic variants, few have generated credible susceptibility variants for type 2 diabetes.Indeed, the most important finding using linkage studies is the discovery that the alteration of TCF7L2 (TCF-4) gene expression or function (33) disrupts pancreatic islet function and results in enhanced risk of type 2 diabetes.Candidate gene studies have also reported many type 2 diabetes-associated loci and the coding variants in the nuclear receptor peroxisome proliferator-activated receptor-g (34), the potassium channel KCNJ11 (34), WFS1 (35), and HNF1B (TCF2) (36) are among the few that have been replicated (Table 2).Recently, there have been great advances in the analysis of associated variants in GWA and replication studies due to highthroughput genotyping technologies, the International HapMap Project, and the Human Genome Project.Type 2 susceptibility loci such as JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2, and ADCY5 (37,38) are among some of the established loci (Table 2).CDKN2A/B, CDKAL1, SLC30A8, IGF2BP2, HHEX/IDE, and FTO are other established susceptibility loci for diabetes (Table 2) (34,39,40).GWA studies have also identified the potassium voltage-gated channel KCNQ1 (32) as an associated gene variant for diabetes.A recent GWA study reporting a genetic variant with a strong association with insulin resistance, hyperinsulinemia, and type 2 diabetes, located adjacent to the insulin receptor substrate 1 (IRS1) gene, is the C allele of rs2943641 (41).Interestingly, the parental origin of the single nucleotide polymorphism is of importance because the allele that confers risk when paternally inherited is protected when maternally transmitted.GWA studies for glycemic traits have identified loci such as MTNR1B (42), GCK (glucokinase) (42), and GCKR (glucokinase receptor) (42); however, further investigation of genetic loci on glucose homeostasis and their impact on type 2 diabetes is needed.Indeed, a recent study by Soranzo et al. (42) using GWA studies identified ten genetic loci associated with HbA 1c .Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin may be associated with changes in levels of HbA 1c ." + } + ], + "4fe0a01d-3be8-4cd5-ac59-8b0ef085b20c": [ + { + "document_id": "4fe0a01d-3be8-4cd5-ac59-8b0ef085b20c", + "text": "\n\nG enome-wide association studies (GWAS) have iden- tified several type 2 diabetes mellitus (T2DM) susceptibility loci including CDKAL1, CDKN2B, IGF2BP2, HHEX, SLC30A8, PKN2, LOC387761 (1)(2)(3)(4)(5), and KCNQ1, which was recently identified by similar GWAS approach in two independent Japanese samples (6,7).Although these associations have been well replicated in Japanese populations (8), the role of these loci in other East Asian populations remains less clear.For example, a study in China by Wu et al. (9) did not find significant associations between single-nucleotide polymorphisms (SNPs) in IGF2BP2 and SLC30A8 with T2DM, whereas an association between SNPs at the HHEX locus and T2DM was reported among Chinese living in Shanghai, but not among Chinese in Beijing.Another study in Hong Kong Chinese (10) also did not find an association with SNPs at the IGF2BP2 locus; however, they reported an association between T2DM with SNPs at the HHEX and SLC30A8 loci." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "\n\nIn studies where overt T2D has been the phenotype the majority of associated polymorphisms have encoded proteins known to be involved in β-cell metabolism; for example TCF7L2, KCNJ11 and HHEX have shown robust association [170,171].This suggests that these genes could prove useful in predicting β-cell preservation during the course of T2D.The glucokinase gene (GCK) coding for the initial glucose-sensing step in the β-cell can have activating mutations causing hypoglycemia that might provide structural and functional models leading to drug targets for treating T2D [172].In the GoDARTs study, investigators examined the medication response of metformin and sulphonylurea based on the TCF7L2 variants mainly affecting the β-cell.The carriers of the at risk 'T' allele responded less well to sulphonylurea therapy than metformin [173].Also it is of significant public health interest that in the Diabetes Prevention Program, lifestyle modifications were shown to reduce the risk of diabetes conferred by risk variants of TCF7L2 at rs7093146, and in placebo participants who carried the homozygous risk genotype (TT), there was 80% higher risk for developing diabetes compared to the lifestyle intervention group carrying the same risk genotypes [35].These findings could herald significant future progress in the field of T2D pharmacogenomics, possibly leading to the development and use of agents tailored on the basis of genotype." + } + ], + "5d7a863d-1811-4eea-9fb0-fbc3067aa664": [ + { + "document_id": "5d7a863d-1811-4eea-9fb0-fbc3067aa664", + "text": "\n\nDespite sharing only 9 loci (among 26 and 17 total in the two analyses, respectively), the separate analyses both identified genes involved in diabetes-related biological functions, including \"glucose homeostasis,\" \"pancreas development\" and \"insulin secretion\" (Supplementary Tables 3 and 5).Three of the top eleven scoring genes in our independent replication analysis have verified causal links to T2D, as annotated in the OMIM 41 .These include genes encoding transcription factors TCF7L2 (TCF4), which has extensive evidence of being causal in T2D 61,62 , and HNF1B, which is a known cause of maturity onset diabetes of the young 63 .Other high-ranking candidate genes have been identified as therapeutic targets in T2D (for example, CTBP1 (ref.64) and LEP 65 ), and the high-scoring gene HHEX has recently been shown to play a key role in islet function 66 ." + } + ], + "7bd7a98f-955a-4988-8981-a0ff7ab6f7df": [ + { + "document_id": "7bd7a98f-955a-4988-8981-a0ff7ab6f7df", + "text": "\n\nSimilar findings to AMD are now unfolding with type 2 DM.Grant et al. (24) first reported on a variant of the gene TCF7L2, which has been linked to reduced beta cell function and poor insulin response to oral glucose loads (51).Since its first discovery, this gene has been widely confirmed in independent studies as a pivotal susceptibility marker for type 2 DM (23,(25)(26)(27)(28)40).Recently, 6 genome-wide SNP association studies have identified and replicated in separate stages several additional novel genes conferring susceptibility to type 2 DM (23,(25)(26)(27)(28)40) (Table 2).Interestingly, these loci primarily include genes involved in pancreatic beta cell development and function as opposed to insulin resistance-the current accepted mechanism for type 2 DM.This development casts doubt on our traditional pathophysiological modeling of the type 2 diabetic patient and underscores the need for genomic studies to further define pathobiological processes of complex traits." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "\n\nOf the 16 loci that have been associated with type 2 diabetes previously, [8][9][10][11][12][13][14][15] we showed that 11 -TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEXwere associated with an enhanced risk of future diabetes.Many of the variants that we genotyped appear to influence beta-cell function, possibly through effects on proliferation, regeneration, and apoptosis.There was a time-dependent increase in the BMI and a decrease in insulin sensitivity in the subjects from the Botnia study, an increase in insulin resistance that was reflected by an increase in insulin secretion.However, this increase was inadequate to compensate for the increase in insulin resistance in carriers with a high genetic risk, which resulted in a markedly impaired disposition index.Only variants in FTO were associated with an increased BMI.Both FTO and PPARG together with TCF7L2 and KCNJ11 predicted transition from impaired fasting glucose levels or impaired glucose tolerance to manifest diabetes, which suggests that a combination of increased obesity and insulin resistance with a deterioration in beta-cell function contribute to the manifestation of diabetes in these subjects.Collectively, our findings emphasize the critical role of inherited defects in beta-cell function for the development of type 2 diabetes." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Type 2 Diabetes\n\nCommon variants in 11 genes were significantly associated with the risk of future type 2 diabetes in the MPP cohort, including TCF7L2 (odds ratio, 1.30; P = 9.5×10 −13 ), PPARG (odds ratio, 1.20; P = 4.0×10 −4 ), FTO (odds ratio, 1.14; P = 9.2×10 −5 ), KCNJ11 (odds ratio, 1.13; P = 3.6×10 −4 ), NOTCH2 (odds ratio, 1.13; P = 0.02), WFS1 (odds ratio, 1.12; P = 0.001), CDKAL1 (odds ratio, 1.11; P = 0.004), IGF2BP2 (odds ratio, 1.10; P = 0.008), SLC30A8 (odds ratio, 1.10; P = 0.008), JAZF1 (odds ratio, 1.08; P = 0.03), and HHEX (odds ratio, 1.07; P = 0.03) (Table 2).Although these findings could not be fully replicated in the smaller Botnia study, there was little heterogeneity between the studies with respect to the risk conferred by different genotypes." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nTo date, more than 70 genes have been identified as involved in T2DM, primarily by association analysis [34].In addition, via GWAS arrays, more than 100 SNPs have been identified for T2DM [35].From the 50 novel loci associated with T2DM previously identified, more than 40 loci have been associated with T2DM-related traits, including fasting proinsulin, insulin and glucose (Table 1) [36][37][38][39].However, for T2DM-related traits, such as the HOMA index or pancreatic β cell function, there are virtually no published data examining the relationship between these traits or the genotype and environment interactions.Clinical investigations of some loci have suggested that the genetic components of T2DM risk act preferentially through β cell function [40].Among all 40 loci associated with T2DM-related traits, only transcription factor-7-like 2 (TCF7L2) was shown to clearly contribute to T2DM risk [41].Several studies in white European [42], Indian [43], Japanese [44], Mexican American [45] and West African [46] individuals have shown a strong association between TCF7L2 and T2DM.It is also noteworthy that these populations represent the major racial groups with a high prevalence of T2DM.In all populations, TCF7L2 showed a strong association, with the odds of developing T2DM increased by 30%-50% for each allele inherited.This finding indicates an approximately double odds ratio compared to most other diabetes susceptibility polymorphisms.TCF7L2 is a transcription factor involved in the Wnt signaling pathway that is ubiquitously expressed, and it has been observed that TCF7L2 risk alleles result in the overexpression of TCF7L2 in pancreatic β cells.This overexpression causes reduced nutrient-induced insulin secretion, which results in a direct predisposition to T2DM as well as an indirect predisposition via an increase in hepatic glucose production [47]." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Most Relevant T2DM Susceptibility Genes\n\nGene and environment interaction studies have shown a nice association between variants in peroxisome proliferator-activated receptor gamma (PPARG), TCF7L2 and fat mass and obesity-associated protein (FTO) genes, a Western dietary pattern and T2DM." + } + ], + "9b93b4eb-98c2-403f-aea2-6b24399501b8": [ + { + "document_id": "9b93b4eb-98c2-403f-aea2-6b24399501b8", + "text": "\n\nOne of these genes associated with type 2 diabetes is the insulin receptor substrate 1 (IRS1, OMIM association number, 147545) (Alharbi, Khan, Abotalib, & Al-Hakeem, 2014;Alharbi, Khan, Munshi et al., 2014;Brender et al., 2013;Brunetti, Chiefari, & Foti, 2014) and another is the C-C motif chemokine receptor5(CCR5, OMIM association number, 601373) (Balistreri et al., 2007;Mokubo et al., 2006;Muntinghe et al., 2009)." + } + ], + "a579db95-2a40-43ff-b237-d47f90aaf64f": [ + { + "document_id": "a579db95-2a40-43ff-b237-d47f90aaf64f", + "text": "Genes boosted in type 2 diabetes\n\nBefore the Wellcome Trust study, PPARG, KCNJ11, and TCF7L2 had all been identified as genes involved in type 2 diabetes through genome-wide association studies and replicated in follow-up studies (for review, see Bonnefond et al. 2010).The strongest candidate gene for type 2 diabetes, TCF7L2, was also the strongest signal seen in the Wellcome trust study, although the others were not so strong.However, the exact mechanism by which TCF7L2 acts was not entirely clear.In our analysis (Fig. 5), we find it directly connected to the b-catenin/WNT signaling pathway by its functional connection to CTNNB1, as well as to BACH2, a gene that has been repeatedly implicated in type 1 diabetes (e.g., Cooper et al. 2008;Madu et al. 2009), but which has not yet been linked to type 2 diabetes.BACH2 is among the genes most strongly boosted by network linkages, deriving additional signal from CREB5 and PARD3B, which both score highly in the GWAS data.PARD6G, PARD3B, and CDC42 are also emphasized by the method.Notably, these genes form a complex with PRKCZ (Koh et al. 2008), a variant of which correlates with type 2 diabetes in Han Chinese (Qin et al. 2008).EBF1, a known regulator of adipocyte differentiation (Akerblad et al. 2005) is also strongly boosted by the network, supporting a possible role in type 2 diabetes." + } + ], + "b978a189-6fbd-4791-8072-7db79f43746a": [ + { + "document_id": "b978a189-6fbd-4791-8072-7db79f43746a", + "text": "RESULTS-\n\nWe confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 ϫ 10 Ϫ12 Ͻ P unadjusted Ͻ 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted ϭ 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations." + }, + { + "document_id": "b978a189-6fbd-4791-8072-7db79f43746a", + "text": "\nOBJECTIVE-Recent genome-wide association studies have identified six novel genes for type 2 diabetes and obesity and confirmed TCF7L2 as the major type 2 diabetes gene to date in Europeans.However, the implications of these genes in Asians are unclear.RESEARCH DESIGN AND METHODS-We studied 13 associated single nucleotide polymorphisms from these genes in 3,041 patients with type 2 diabetes and 3,678 control subjects of Asian ancestry from Hong Kong and Korea. RESULTS-We confirmed the associations of TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/CDKN2B, IGF2BP2, and FTO with risk for type 2 diabetes, with odds ratios ranging from 1.13 to 1.35 (1.3 ϫ 10 Ϫ12 Ͻ P unadjusted Ͻ 0.016).In addition, the A allele of rs8050136 at FTO was associated with increased BMI in the control subjects (P unadjusted ϭ 0.008).However, we did not observe significant association of any genetic variants with surrogate measures of insulin secretion or insulin sensitivity indexes in a subset of 2,662 control subjects.Compared with subjects carrying zero, one, or two risk alleles, each additional risk allele was associated with 17% increased risk, and there was an up to 3.3-fold increased risk for type 2 diabetes in those carrying eight or more risk alleles.Despite most of the effect sizes being similar between Asians and Europeans in the metaanalyses, the ethnic differences in risk allele frequencies in most of these genes lead to variable attributable risks in these two populations. CONCLUSIONS-Ourfindings support the important but differential contribution of these genetic variants to type 2 diabetes and obesity in Asians compared with Europeans.Diabetes 57: 2226-2233, 2008T ype 2 diabetes is a major health problem affecting more than 170 million people worldwide.In the next 20 years, Asia will be hit hardest, with the diabetic populations in India and China more than doubling (1).Type 2 diabetes is characterized by the presence of insulin resistance and pancreatic -cell dysfunction, resulting from the interaction of genetic and environmental factors.Until recently, few genes identified through linkage scans or the candidate gene approach have been confirmed to be associated with type 2 diabetes (e.g., PPARG, KCNJ11, CAPN10, and TCF7L2).Under the common variant-common disease hypothesis, several genome-wide association (GWA) studies on type 2 diabetes have been conducted in large-scale case-control samples.Six novel genes (SLC30A8, HHEX, CDKAL1, CDKN2A and CDKN2B, IGF2BP2, and FTO) with modest effect for type 2 diabetes (odds ratio [OR] 1.14 -1.20) had been reproducibly demonstrated in multiple populations of European ancestry.Moreover, TCF7L2 was shown to have the largest effect for type 2 diabetes (1.37) in the European populations to date (2-8).Although many of these genes may be implicated in the insulin production/secretion pathway (TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, and IGF2BP2) (6,9 -11), FTO is associated with type 2 diabetes through its regulation of adiposity (8,12,13).Moreover, two adjacent regions near CDKN2A/B are associated with type 2 diabetes and cardiovascular diseases risks, respectively (7,14 -16).Despite the consistent associations among Europeans, the contributions of these genetic variants in other ethnic groups are less clear.Given the differences in environmental factors (e.g., lifestyle), risk factor profiles (body composition and insulin secretion/resistance patterns), and genetic background (linkage disequilibrium pattern and risk allele frequencies) between Europeans and Asians, it is important to understand the role of these genes in Asians.A recent case-control study in 1,728 Japanese subjects revealed nominal association to type 2 diabetes for variants at the SLC30A8, HHEX, CDKAL1, CDKN2B, and FTO genes but not IGF2BP2 (17).In the present large-scale case-control replication study of 6,719 Asians, we aimed to test for the association of six novel genes from GWA studies and TCF7L2, which had the largest effect in Europeans, and their joint effects on type 2 diabetes risk and metabolic traits. RESEARCH DESIGN AND METHODSAll subjects were recruited from Hong Kong and Korea and of Asian ancestry.The subjects in the Hong Kong case-control study were of southern Han Chinese ancestry residing in Hong Kong.Participants for the case cohort consisting of 1,481 subjects with type 2 diabetes were selected from two" + } + ], + "bbb4af44-2659-4207-b9a1-0ff85d379a9f": [ + { + "document_id": "bbb4af44-2659-4207-b9a1-0ff85d379a9f", + "text": "\n\nOBJECTIVE-Common variants in PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 genes have been shown to be associated with type 2 diabetes in European populations by genome-wide association studies.We have studied the association of common variants in these eight genes with type 2 diabetes and related traits in Indians by combining the data from two independent case-control studies." + } + ], + "d9564b3c-efac-42ae-8e15-bf962c0a7a3c": [ + { + "document_id": "d9564b3c-efac-42ae-8e15-bf962c0a7a3c", + "text": "Introduction\n\nMany genes have been evaluated as candidates for T2D susceptibility.However, only variants in the TCF7L2, PPARG, KCNJ11 and HNFA4 genes have been extensively replicated in populations around the world, showing their indisputable association with T2D risk (Zeggini 2007).In the particular case of the HNF4A gene, it has been implicated in maturity-onset diabetes of the young type 1 (MODY 1) (Mitchell and Frayling 2002;Zhu et al. 2003).HNF4A is a member of the nuclear receptor super-family that plays a critical role in embryogenesis and metabolism, by regulating gene expression in pancreatic beta cells, liver and other tissues.The HNF4A gene is localized to chromosome 20q13, a region that has demonstrated evidence for linkage with T2D (Sladek et al. 1990;Ghosh et al. 1999).Several genetic studies, mainly in Caucasian and Asian populations, have provided evidence for the association of the variants in HNF4A with T2D (Ghosh et al. 1999;Silander et al. 2004;Winckler et al. 2005)." + } + ] + }, + "data_source": [], + "document_id": "8909D2606E33C312F2ECC705FAF65CA2", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "TCF7L2&gene", + "PPARG&gene", + "KCNJ11&gene", + "SLC30A8&gene", + "HHEX&gene", + "CDKAL1&gene", + "CDKN2A&gene", + "IGF2BP2&gene", + "FTO&gene", + "WFS1&gene" + ], + "metadata": [], + "question": "what genes are associated with diabetes?", + "subquestions": null, + "task_id": "8909D2606E33C312F2ECC705FAF65CA2", + "usage": { + "chatgpt": 10960, + "gpt-4": 6551, + "gpt-4-turbo-preview": 5611 + }, + "user_id": 2 + }, + "document_id": "8909D2606E33C312F2ECC705FAF65CA2", + "task_id": "8909D2606E33C312F2ECC705FAF65CA2" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_02.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_02.json new file mode 100644 index 0000000..e3021cb --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_02.json @@ -0,0 +1,146 @@ +{ + "created_at": "2024-05-31T23:04:51.896429Z", + "data": { + "amplify": false, + "answer": "Genomics provides a comprehensive understanding of the genetic factors influencing the development and progression of diabetes. Nutritional genomics focuses on the interaction between genes and dietary patterns, which plays a crucial role in diabetes. By studying gene-nutrient interactions and genetic variations, researchers can develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. Furthermore, genomics can help identify how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake, potentially leading to diabetes. Thus, genomics offers a promising approach to understanding the nutritional factors of diabetes and developing personalized dietary interventions.", + "context": { + "069a62e0-e56a-46ab-9f93-c13a76a79989": [ + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + } + ], + "0da4d3d4-10d5-4a58-9e50-c1fa0b414427": [ + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "text": "\n\nenetic factors for many decades have been known to play a critical role in the etiology of diabetes, but it has been only recently that the specific genes have been identified.The identification of the underlying molecular genetics opens the possibility for understanding the genetic architecture of clinically defined categories of diabetes, new biological insights, new clinical insights, and new clinical applications.This article examines the new insights that have arisen from defining the etiological genes in monogenic diabetes and the predisposing polymorphisms in type 2 diabetes." + } + ], + "1907b52f-515b-447c-b7b3-0e37bf1ce8b7": [ + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "text": "\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + } + ], + "2a71b781-89fe-4055-bbb1-15aa226e1e3a": [ + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + } + ], + "3bde9884-e31d-4719-b42f-02dca25d6c08": [ + { + "document_id": "3bde9884-e31d-4719-b42f-02dca25d6c08", + "text": "\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner." + } + ], + "41ba5319-e77d-4838-8f50-e59fe86b94f8": [ + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + } + ], + "63752d7d-dfdd-48a2-9f39-e1672255a519": [ + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "text": "\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow." + } + ], + "64b63031-1024-43f9-8b27-0ada92829a7a": [ + { + "document_id": "64b63031-1024-43f9-8b27-0ada92829a7a", + "text": "\n\nIn recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management." + } + ], + "789097da-e961-4486-8c83-816626556b16": [ + { + "document_id": "789097da-e961-4486-8c83-816626556b16", + "text": "\n\nNonetheless, \"evidence\" for the genetics of diabetes risk is mounting, often at the expense of understanding the social context and determinants of the disease.Biogenetic views tend to trump sociological views in the diabetes research imaginary of consortium members.However, the genetic epidemiologists who make up part of the diabetes consortium are not ignorant of the effects of proper diet and adequate exercise. \"Take away the television and the automobile and diabetes would all but disappear,\" quipped the head of one lab.Neither are researchers unsympathetic to those who suffer from social inequality in the United States.Their career and intellectual interests lie in genetic explanations of diabetes, which, as I aim to show in this discussion, involves folding political and economic social relationships into biomedical discourse.In fact, the case of diabetes genetic epidemiology illustrates how, in spite of the sympathies of diabetes scientists, arrangements of racial inequality in the United States find their way into diabetes research publications and drug company promotional campaigns.To illustrate this phenomenon further, I present two tales from the field, one dealing with the naming of a publication article, the other with the marketing of a diabetes drug." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nThe aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nIt is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8]." + } + ], + "a83987ea-607c-4952-a1cc-69c6f193ba2a": [ + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + } + ], + "b3fa4d11-72b9-4e6f-9c28-39efdaded492": [ + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\n\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way." + }, + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\n\nIn a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases." + } + ], + "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da": [ + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + } + ] + }, + "data_source": [], + "document_id": "DD54A20CDF6D93EF18DE9FD00DD01191", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "mellitus", + "genomics", + "nutritional", + "factors", + "gene-nutrient", + "interactions", + "type&2", + "genetic", + "variants" + ], + "metadata": [], + "question": "nutrition is a factor for diabetes. construct an abstract about how can genomics be use to better understand nutritional factors of diabetes", + "subquestions": null, + "task_id": "DD54A20CDF6D93EF18DE9FD00DD01191", + "usage": { + "chatgpt": 5995, + "gpt-4": 4151, + "gpt-4-turbo-preview": 3211 + }, + "user_id": 2 + }, + "document_id": "DD54A20CDF6D93EF18DE9FD00DD01191", + "task_id": "DD54A20CDF6D93EF18DE9FD00DD01191" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_03.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_03.json new file mode 100644 index 0000000..becabbc --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_03.json @@ -0,0 +1,147 @@ +{ + "created_at": "2024-05-31T23:07:32.253629Z", + "data": { + "amplify": false, + "answer": "Genomics can be used to understand the nutritional factors of diabetes by studying nutrient-gene interactions and how an individual's genetic makeup can affect nutrient metabolism and response to nutrient intake. This approach can help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, aiding in the prevention and delay of diabetes and its complications. Additionally, genomics can identify gene variants that interact with specific nutrients, providing insights into how diet and genetics interact to influence diabetes risk.", + "context": { + "069a62e0-e56a-46ab-9f93-c13a76a79989": [ + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + } + ], + "1907b52f-515b-447c-b7b3-0e37bf1ce8b7": [ + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "text": "\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + }, + { + "document_id": "1907b52f-515b-447c-b7b3-0e37bf1ce8b7", + "text": "\n\nGenomics has contributed to a better understanding of many disorders including diabetes.The following article looks at the ethical, social and legal consequences of genomic medicine and predictive genetic testing for diabetes.This is currently a field in its nascent stage and developing rapidly all over the world.The various ethical facets of genomic medicine in diabetes like its effects on patient physician relationship, risk communication, genetic counseling and familial factors are explored and elucidated from a clinical, ethical, social and legal perspective." + } + ], + "3bde9884-e31d-4719-b42f-02dca25d6c08": [ + { + "document_id": "3bde9884-e31d-4719-b42f-02dca25d6c08", + "text": "\n\nGenetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner." + } + ], + "41ba5319-e77d-4838-8f50-e59fe86b94f8": [ + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + } + ], + "4d3330eb-acd0-4f72-aadf-b056d3c8b389": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "\n\nIt is possible that there are genes that because of their known metabolic involvement are likely to interact with specific nutrients.For example, SLC30A8 which encodes a zinc transporter localized in secretory granules, interacted with dietary zinc to effect fasting insulin levels [132].However, the majority of GWAS variants have not shown interaction with environmental factors for effect on diabetes or related traits.Therefore, it is likely that prospective future studies will utilize improved assessment methods to increase power and avoid false interpretation [133,134].This could be enhanced by prioritizing variants that are most likely to have effects [135] or selective sampling according to extremes of the environmental factor could reduce the requirement for sample size [136].These and other strategies such as meta-analysis, nested case control and genotype-based studies have been recently reviewed [123,133] and the difficulties in measuring environmental exposures have been emphasized, including the application of analyses based on logistic regression [124] and problems with instruments such as physical activity questionnaires [137].Validated food frequency questionnaires are popular instruments for evaluation diabetes risk and are often used in conjunction with food analysis software [138,139].Similar methodology has been adapted to assess two predominant food consumption patterns by Prudent and Western [140], and demonstrated synergistic interaction with genotype and a less healthy Western dietary pattern in determining male risk for T2D by showing that the gene-diet interaction was higher in men with a high genetic risk score determined by a gene counting method [141].Also the effects of diet may predominate at specific developmental periods [142] suggesting that age and associated physiological changes are important as well as differences between genders.It has also been observed that homogeneity of an environmental factor such as physical activity in an Asian Indian study, may reduce ability to detect interaction, but could be solved by subgrouping by the level of activity [143], but increased recruitment would be needed to maintain power." + } + ], + "63752d7d-dfdd-48a2-9f39-e1672255a519": [ + { + "document_id": "63752d7d-dfdd-48a2-9f39-e1672255a519", + "text": "\n\nTo date, studies of diabetes have played a major role in shaping thinking about the genetic analysis of complex diseases.Based on trends in genomic information and technology, combined with the growing public health importance of diabetes, diabetes will likely continue to be an important arena in which methods will be pioneered and lessons learned.It is with great enthusiasm that we look forward to this effort, and with avid curiosity we await to see whether the lessons of today will be supported by the data of tomorrow." + } + ], + "64b63031-1024-43f9-8b27-0ada92829a7a": [ + { + "document_id": "64b63031-1024-43f9-8b27-0ada92829a7a", + "text": "\n\nIn recent years tremendous changes had occurred in the field of molecular genetics and personalized medicine especially on exploring novel genetic factors associated with complex diseases like T2D with the advancement of new and improved genetic techniques including the next generation sequencing (NGS).In this review, we summarize recent developments from studies on the genetic factors associated with the development of T2D in the Arab world published between 2015 and 2018, which were based on the latest available genetic technologies.Few such studies have been conducted in this region of the world.Therefore, our study will provide valuable contributions to advanced genetic research and a personalized approach to diabetes management." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\nDiabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide.Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease.The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved.Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, genediet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools.In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications.This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM.Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression," + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nThe aim of the present review was to provide insights regarding the role of nutrient-gene interactions in DM pathogenesis, prevention and treatment.In addition, we explored how an individual's genetic makeup can affect nutrient metabolism and the response to nutrient intake, potentially leading to DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nThus, studies performed during the last decade have provided strong evidence to support a diet-genome interaction as an important factor leading to the development of T2DM." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nIt is important to promote greater research in this field because these findings will provide a framework for the development of genotype-dependent food health promotion strategies and the design of dietetic approaches for the prevention and management of DM.This knowledge has begun to provide evidence where specific targeted nutritional advice, such as following a Mediterranean Diet, helps to decrease cardiovascular risk factors and stroke incidence in people with polymorphisms strongly associated with T2DM [8]." + } + ], + "a83987ea-607c-4952-a1cc-69c6f193ba2a": [ + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + }, + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes." + } + ], + "b3fa4d11-72b9-4e6f-9c28-39efdaded492": [ + { + "document_id": "b3fa4d11-72b9-4e6f-9c28-39efdaded492", + "text": "\n\nIn a nutshell, genomic and post-genomic approaches identified a large number of biomarkers to ponder over and explore further but we are yet to identify universally accepted biomarker which can be used for the successful management and prevention of type 2 diabetes.In order to understand environment related modifications of genetic susceptibility, it may be prudent to conduct studies with integrated genomic-metabolomic approach.It is also imperative to gather existing molecular genetic data and curate it into uniform format and analyze the same for understanding the present status of research.A few attempts were, however, made to develop type 2 diabetes informative databases.While the databases T2DGADB and T2D-DB are only a collection of publications related to type 2 diabetes genetic association studies, proteinprotein interactions and expression studies, T2D@ZJU is a comprehensive collection of pathway databases, protein-protein interaction databases, and literature (Yang et al. 2013).Further, T2D@ZJU is a user-friendly interface database that provides graphical output of information organized in networks.These attempts may provide basis for studying type 2 diabetes utilizing systems biology, which is a better approach for understanding complex genetic diseases." + } + ], + "e9b48e14-aa0c-4331-a17d-82a7f424233c": [ + { + "document_id": "e9b48e14-aa0c-4331-a17d-82a7f424233c", + "text": "\n\nThe public health genomics approach to type 2 diabetes.So, while exciting gene discoveries are being made, what can we do?The answer may lie in the relatively new field of public health genomics, \"a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health\" (12).Researchers, policymakers, and practitioners in public health genomics use populationbased data on genetic variation and gene-environment interactions to develop, implement, and evaluate evidencebased tools for improving health and preventing disease.They also apply systematic evidence-based knowledge synthesis and appraisal of the clinical validity and utility of genomic applications in health practice.Validated genomic information is then integrated into disease control and prevention programs (13)." + } + ], + "f9b65334-56b7-43e9-9fda-b778c18c1c67": [ + { + "document_id": "f9b65334-56b7-43e9-9fda-b778c18c1c67", + "text": "Genomics for Type 2 Diabetes\n\nMany research studies have been carried out on genetic-based illness prediction.Incorporating machine learning approaches with genetic-based illness prediction could result in an accurate outcome.This has intensified the role of Artificial Intelligence (AI) in healthcare.It has been estimated that approximately $36 billion will be invested in AI by 2025 [48].Deep genomics through machine learning approaches has outperformed accuracy in predicting and diagnosing illnesses such as cancer with minimal inclusion of radiologists.It is desired to have sufficient biological knowledge to understand how genetics can help us predict various conditions and analyze each chromosome to identify the disease-causing gene.Pre-existing research studies have focused on genomics and gene interaction patterns of various persistent illnesses such as Alzheimer's, multiple cancers, and Parkinson's." + } + ] + }, + "data_source": [], + "document_id": "A9F8F600EC44B4FA08789ED3E990BE0D", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&genomics", + "nutrition", + "nutrient-gene&interactions", + "diabetes&mellitus", + "nutritional&genomics", + "gene&variants", + "epigenetic&modifications", + "GWAS", + "pharmacogenomics", + "personalized&medicine", + "machine&learning" + ], + "metadata": [], + "question": "nutrition is a factor for diabetes. how can genomics be use to better understand nutritional factors of diabetes", + "subquestions": null, + "task_id": "A9F8F600EC44B4FA08789ED3E990BE0D", + "usage": { + "chatgpt": 6443, + "gpt-4": 4073, + "gpt-4-turbo-preview": 3136 + }, + "user_id": 2 + }, + "document_id": "A9F8F600EC44B4FA08789ED3E990BE0D", + "task_id": "A9F8F600EC44B4FA08789ED3E990BE0D" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_04.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_04.json new file mode 100644 index 0000000..e02ac80 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_04.json @@ -0,0 +1,152 @@ +{ + "created_at": "2024-05-31T23:10:07.733262Z", + "data": { + "amplify": false, + "answer": "Yes, the gene IFIH1 has been identified as contributing to susceptibility to type 1 diabetes. However, the text does not mention any association between the genes SH2B3 or ERBB3 and diabetes.", + "context": { + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "\n\nFigure 8 Molecular changes in the islets of patients with T2D mirror the processes altered in NOD mice.mRNA expression in human pancreatic islets from healthy individuals (n = 105) and those diagnosed with T2D (n = 14) was assessed through RNA-seq analysis. (a) Relationship between GLIS3 and MANF expression in healthy individuals (Spearman correlation P value = 0.043), individuals with T2D (Spearman correlation P value = 0.075) and all individuals (Spearman correlation P value = 0.028). (b-e) Expression of XRCC4 (b), LIG4 (c), H2AFX (d) and CDKN1A (e) in healthy islets as compared to i slets from patients withT2D (P values shown after multiple-testing correction).The median and interquartile range (IQR; box) are shown, with error bars indicating 1.5 times the IQR.Individual values are shown if beyond 1.5 times the IQR. (f) Relationship between H2AFX and LIG4 expression in human islets (Spearman correlation P value = 5 × 10 −9 )." + } + ], + "15524ac0-da3c-4c01-8ae2-1b8c901105ad": [ + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "text": "\n\nAll the genes involved in these pathways, as well as the genes involved in b-cells development and turnover, may be considered candidate genes for T2DM with predominant insulin deficiency." + } + ], + "1ef9a72d-b9ef-4955-a351-fca0175da3d1": [ + { + "document_id": "1ef9a72d-b9ef-4955-a351-fca0175da3d1", + "text": "\n\nOne method of searching for the cause of NIDDM is via the candidate gene approach.Possible candidates for NIDDM include genes involved in specifying pancreatic islet (3-cell phenotype and in directing fj-cell development and (3-cell responses of glucose-mediated insulin synthesis and secretion.The transcription factor islet-1 (Isl-1) has been shown to be a unique protein that binds to the mini-enhancer or Far-FLAT region (nucleotide -247 to -198) of the rat insulin I gene (7).Isl-1, a protein comprised of 349 residues (38 kD), is a member of the LIM/homeodomain family of proteins, named for the first three members described: lin-11, isl-1, and mec-3 (8,9).These proteins are comprised of three putative regulatory regions, two LIM domains (cysteine-rich motifs) in the amino terminus of the protein, a homeobox domain near the middle, and a glutamine-rich transcriptional activation domain at the carboxyl end (7,9).With the use of an antibody to Isl-1, expression was shown to be restricted to a subset of endocrine cells, including islets, neurons involved in autonomic and endocrine control, and selected other tissues in the adult rat (10)(11)(12)." + } + ], + "21368075-9e10-4260-b346-43b1029b3bf0": [ + { + "document_id": "21368075-9e10-4260-b346-43b1029b3bf0", + "text": "Results\n\nImpairment or alteration of the insulin-signaling pathway is a commonly recognized feature of type 2 diabetes.It is therefore notable that the IS-HD gene set (Dataset S4) was not detected to be significantly transcriptionally altered by application of either hypergeometric enrichmentt test, DEA or GSEA.In particular, applying GSEA to the transcriptional profile dataset of diabetic and normal glucose-tolerant skeletal muscle described in Mootha et al. [10] did not identify a significant level of alteration in the IS-HD gene set (p ¼ 0.536), while DEA produced a comparably weak enrichment score (p ¼ 0.607).The failure to detect a significant transcriptional alteration in IS-HD may be explained by a number of factors.The enrichment results depended on the specific choice of the IS-HD gene set, and it is possible that an alternatively defined insulin-signaling gene set would be determined as significantly enriched.Additionally, expression changes in a few critical genes in IS-HD may be sufficient to substantially alter insulin signaling, and running DEA on the large IS-HD set may miss the contributions from these few genes." + } + ], + "2715e261-b26c-46d6-918f-c6aa47688f0c": [ + { + "document_id": "2715e261-b26c-46d6-918f-c6aa47688f0c", + "text": "35\nABSTRACT 11\nA GENE EXPRESSION NETWORK MODEL OF TYPE 2 DIABETES\nESTABLISHES A RELATIONSHIP BETWEEN CELL CYCLE\nREGULATION IN ISLETS AND DIABETES SUSCEPTIBILITY\nMP Keller, YJ Choi, P Wang, DB Davis, ME Rabaglia, AT Oler, DS Stapleton,\nC Argmann, KL Schueler, S Edwards, HA Steinberg, EC Neto, R Klienhanz, S\nTurner, MK Hellerstein, EE Schadt, BS Yandell, C Kendziorski, and AD Attie\nDepts." + } + ], + "4322db2f-5f43-4fc0-8968-b24438a7d6b9": [ + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "text": "\n\nSecond, we performed an extensive manual curation according to a previously described b-cell-targeted annotation (Kutlu et al, 2003;Ortis et al, 2010).In partial agreement with the IPA, we found these genes to fall into three broad categories: (1) genes related to b-cell dysfunction and death, (2) genes potentially facilitating the adaptation of the pancreatic islets to the altered metabolic situation in T2D and (3) genes whose role in disease pathogenesis remains to be unearthed (Figure 6B).The adaptation-related gene category contains few metabolism-associated genes (e.g., HK1, FBP2; Figure 6B, right part, Figure 7) and many more genes involved in signal transduction or encoding hormones, growth factors (e.g., EGF, FGF1, IGF2/IGF2AS; Figure 7), or transcription factors involved in important regulatory networks (for instance, FOXA2/HNF3B, PAX4 and SOX6) (Figure 6B, right part, Figure 7).In the b-cell dysfunction and death category, there were hypomethylated genes related to DNA damage and oxidative stress (e.g., GSTP1, ALDH3B1; Figure 7), the endoplasmic reticulum (ER) stress response (NIBAN, PPP2R4, CHAC1), and apoptosis (CASP10, NR4A1, MADD; Figure 6B, left part, Figure 7).Some genes of interest from the highlighted categories are depicted in Figure 7. Their annotated functions provide possible explanations of how the epigenetic dysregulation of these genes in diabetic islets is connected to T2D pathogenesis.Numerous genes that were identified by our methylation profiling approach have been functionally implicated in insulin secretion.Examination of the available literature on the function of these genes revealed three aspects of insulin secretion with which they interfere: some of these genes influence the expression of the insulin gene, like MAPK1 and SOX6, or its post-translational maturation, like PPP2R4 (cf. Figure 7 and references therein).Others can deregulate the process of insulin secretion itself (SLC25A5, Ahuja et al, 2007;RALGDS, Ljubicic et al, 2009) or influence synthesis as well as secretion (vitronectin, Kaido et al, 2006).A third group of differentially methylated genes affects (i) signalling processes in the b-cell leading to insulin secretion or (ii) glucose homeostasis in b-cells, thereby modulating insulin response upon stimulation.GRB10 (Yamamoto et al, 2008), FBP2 and HK1 (Figure 7) are examples for these genes.Additional genes found in our study have been implicated in the b-cells' capability to secrete insulin, though the mechanisms have not yet been fully established.The putative functions of these genes indicate a potential epigenetic impact on insulin secretion at multiple levels, namely signalling, expression/synthesis and secretion." + } + ], + "647571cd-ff36-4be4-97c4-cd006d9bfbaf": [ + { + "document_id": "647571cd-ff36-4be4-97c4-cd006d9bfbaf", + "text": "\n\nIn summary, we have associated mutations in the SLC29A3 gene with diabetes mellitus in humans and the insulin signaling pathway in Drosophila.The mechanistic basis of these findings remains to be determined.This is strong evidence supporting the investment of resources to further investigate the role of SLC29A3 and its orthologs in diabetes and glucose metabolism in model systems." + }, + { + "document_id": "647571cd-ff36-4be4-97c4-cd006d9bfbaf", + "text": "DISCUSSION\n\nWe have identified mutations in the equilibrative nucleoside transporter 3 protein that are associated with an inherited syndrome of insulin-dependent DM, and provide prima facie evidence that the Drosophila ortholog of this protein interacts with the insulin signaling pathway.This is the first evidence that mutations in the human SLC29A3 gene can be associated with a diabetic phenotype." + } + ], + "6e80ed3b-2be6-4775-a3c5-89cb4ddc88ae": [ + { + "document_id": "6e80ed3b-2be6-4775-a3c5-89cb4ddc88ae", + "text": "\n\nThese observations taken together suggest that molecules involved in innate immunity could serve as candidate genes that determine the susceptibility of sensitive strains of mice to virusinduced diabetes.Interestingly, deficiency of the Tyk2 gene results in a reduced antiviral response 24 .In addition, the human TYK2 gene was mapped to the possible type 1 diabetes susceptibility locus 25 ." + } + ], + "7b7ce30c-f398-4b0e-bcb6-52f2644201fd": [ + { + "document_id": "7b7ce30c-f398-4b0e-bcb6-52f2644201fd", + "text": "\n\nA recent sequencing study provides an example of detection of rare variants in type 1 diabetes.Targeted sequencing in a series of candidate coding regions resulted in IFIH1 being identified as the causal gene in a region associated with type 1 diabetes by GWA studies (58).IFIH1 encodes a cytoplasmic helicase that mediates induction of the interferon response to viral RNA.The discovery of IFIH1 as a contributor to susceptibility to type 1 diabetes has strengthened the hypothesis (70) about a mechanism of disease pathogenesis involving virusgenetic interplay and raised type 1 interferon levels as a cofactor in -cell destruction.Nonetheless, it should be recognized that a component of the missing heritability (familial aggregation) in type 1 diabetes could well be due to unrecognized intra-familial environmental factors.Disease pathogenesis.Contemporary models of pathogenesis of type 1 diabetes support the involvement of two primary dramatis personae: the immune system and the -cell.The known and newly identified genetic risk factors for type 1 diabetes present exciting opportunities to build on to the current cast of disease mechanisms and networks.Most of the listed genes of interest (Table 2) and those in extended regions are assumed to regulate immune function.Some of these genes, however, may also have roles in the -cell (insulin being the most obvious example).Another gene, PTPN2, encoding a protein tyrosine phosphatase, was identified as affecting the risk for type 1 diabetes as well as for Crohn disease (47,71).PTPN2 is expressed in immune cells, and its expression is highly regulated by cytokines.However, PTPN2 is expressed also in -cells, where it modulates interferon (IFN)-␥ signal transduction and has been shown to regulate cytokineinduced apoptosis (72).Other candidate genes, such as NOS2A, IL1B, reactive oxygen species scavengers, and candidate genes, identified in large GWA studies of type 2 diabetes, have not been found to be significant contributors to the susceptibility of type 1 diabetes (73)." + } + ], + "7e816722-443f-463c-8a79-852752df28e6": [ + { + "document_id": "7e816722-443f-463c-8a79-852752df28e6", + "text": "Differential Expression Analyses of Type 1 Diabetes Mellitus Associated Genes\n\nFor the aforementioned 171 'novel' genes, we used t-test to compare ribonucleic acid expression signals in PBMCs or monocytes between type 1 diabetes mellitus patients and healthy controls.We found that 37 genes, including 21 non-HLA genes (e.g.FAM46B, OLFML3 and HIPK1), were differentially expressed between type 1 diabetes mellitus patients and controls (Table 2).For the differential expression study, the significance level of P < 5.0E-02 was used." + } + ], + "845adde7-823a-4bfc-9f5e-7082d2e26102": [ + { + "document_id": "845adde7-823a-4bfc-9f5e-7082d2e26102", + "text": "\n\nIn this study, we have correlated the function and genotype of human islets obtained from diabetic and nondiabetic (ND) donors.We have analyzed a panel of 14 gene variants robustly associated with T2D susceptibility identified by recent genetic association studies.We have identified four genetic variants that confer reduced b-cell exocytosis and six variants that interfere with insulin granule distribution.Based on these observations, we calculate a genetic risk score for islet dysfunction leading to T2D that involves decreased docking of insulin-containing secretory granules, impaired insulin exocytosis, and reduced insulin secretion." + } + ], + "8aee60c9-9bb4-4867-96c9-830c1e43c72e": [ + { + "document_id": "8aee60c9-9bb4-4867-96c9-830c1e43c72e", + "text": "\n\nAt present, insulin [15], glucokinase [16], amylin [17], mitochondrial DNA [18], and several transcriptional factors [19][20][21][22] are recognized as diabetogenic genes in pancreatic b-cells.In the present study we used the candidate gene approach in the examination of genomic variation in the a 1D and Kir6.2 channel genes in type 2 diabetic patients." + } + ], + "9fd49699-612f-48c0-b1d9-e01158472be6": [ + { + "document_id": "9fd49699-612f-48c0-b1d9-e01158472be6", + "text": "\n\nIn summary, we report AEIs that are consistent with type 2 diabetes-associated variation regulating the expression of cis-linked genes in human islets.For some of the genes where significant AEI was identified (e.g., SLC30A8, WFS1), there is strong evidence from human genetics that small changes in gene dosage may have significant consequences for the pancreatic b-cell.For other genes with significant AEI (e.g., ANPEP, HMG20A), their role is less well defined, and hence this study should provide a platform for further work examining the effects of carefully manipulating the expression of these genes in human islets." + } + ], + "e51e88b2-bea3-4ab7-858f-824f7d5ccbdd": [ + { + "document_id": "e51e88b2-bea3-4ab7-858f-824f7d5ccbdd", + "text": "\n\nResults.Pathway analysis of genes with differentially methylated promoters identified the top 3 enriched pathways as maturity onset diabetes of the young (MODY), type 2 diabetes, and Notch signaling.Several genes in these pathways are known to affect pancreatic development and insulin secretion." + } + ], + "e7bc9d83-6c3b-405c-a552-29874b927860": [ + { + "document_id": "e7bc9d83-6c3b-405c-a552-29874b927860", + "text": "The authors then used mouse liver and adipose expression\ndata from several mouse crosses to construct causal expression networks for the ERBB3 and\nRPS26 orthologs in the mouse. They then showed that ERBB3 is not associated with any\nknown Type I diabetes genes whereas RPS26 is associated a network of several genes that\nare part of the KEGG Type I diabetes pathway (Schadt et al. 2008). This type of analysis\ndemonstrates the power of combining human and mouse data with a network based\napproach that has been proposed for use in drug discovery (Schadt et al." + } + ], + "ebb49f39-ee30-4b32-959d-305276fd589e": [ + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "text": "\n\nIn conclusion, GWAS studies focusing on the causes of T2D have implicated islet dysfunction as a major contributing factor (18,71).By examining isolated islets for stress responses and cross-referencing gene hits with genes associated with glucose-stimulated insulin release in human populations with T2D, we identified 7 genes that may play a role in promoting or preventing islet decline in T2D.By further examining stress-induced expression changes in each of these genes, we identified 5 genes that stood out: F13a1 as a novel stress-inhibited gene in islets, Klhl6 and Pamr1 as induced genes specific to ER stress, Ripk2 as a broadly stress-induced gene, and Steap4 as an exceptionally cytokine-sensitive gene.These genes provide promising leads in elucidating islet stress responses and islet dysfunction during the development of T2D." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "text": "\nGenome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of Ͼ2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D." + }, + { + "document_id": "ebb49f39-ee30-4b32-959d-305276fd589e", + "text": "\n\nGenome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of Ͼ2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D." + } + ], + "faa23996-65fc-4bc6-938a-c959e981d493": [ + { + "document_id": "faa23996-65fc-4bc6-938a-c959e981d493", + "text": "\n\nFinally, several of the linking nodes introduced into this islet network through their PPI connections represent interesting candidates for a role in T2D pathogenesis, and there are several examples where external data provides validation of those assignments.An interesting example involves the gene GINS4 which maps at the ANK1 locus.Though this gene generated a low PCS [0.03] and was not included in the set of seed genes for this locus, GINS4 knock-down has an impact in a human beta-cell line [14].In addition, cyclin-dependent kinase 2 (CDK2) has been shown to influence beta-cell mass in a compensatory mechanism related to age-and diet-induced stress, connecting beta-cell dysfunction and progressive beta-cell mass deterioration [54].YHWAG is a member of the 14-3-3 family, known to be signalling hubs for beta-cell survival [55], and disruption of SMAD4 drives islet hypertrophy [56]." + } + ] + }, + "data_source": [], + "document_id": "A9F5CC0D31CE591D56814F3A276760E5", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "type&1&diabetes", + "type&2&diabetes", + "SH2B3", + "IFIH1", + "ERBB3", + "insulin", + "pancreatic&islets", + "gene&expression", + "mutations" + ], + "metadata": [], + "question": "Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?", + "subquestions": null, + "task_id": "A9F5CC0D31CE591D56814F3A276760E5", + "usage": { + "chatgpt": 8842, + "gpt-4": 5247, + "gpt-4-turbo-preview": 4320 + }, + "user_id": 2 + }, + "document_id": "A9F5CC0D31CE591D56814F3A276760E5", + "task_id": "A9F5CC0D31CE591D56814F3A276760E5" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_05.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_05.json new file mode 100644 index 0000000..3957957 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_05.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-05-31T23:11:51.586254Z", + "data": { + "amplify": false, + "answer": "Genetics plays a significant role in the emergence of diabetes. In type 1 diabetes, gene variants in the human leukocyte antigen (HLA) locus contribute to 50-60% of the genetic risk by affecting protein binding and antigen presentation to T cells. Other gene variants that modulate immune regulation, viral responses, environmental signals, and endocrine function also contribute to the disease. The risk for type 1 diabetes is strongly influenced by multiple genetic loci. In type 2 diabetes, many common variants, largely with small effect size, contribute to overall disease risk. Monogenic forms of diabetes, such as MODY, result from mutations in a single gene. Family history also plays a role, suggesting that genetics conveys more than heritable information; it probably includes non-genetic familial behaviors and norms.", + "context": { + "00591f6a-0d6f-4993-ae6c-e9a8109a95ec": [ + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "text": "A. Genetic Screening\n\nWe have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is ϳ10 -15 times greater.Although ϳ70% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3)." + }, + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "text": "II. THE GENETICS OF TYPE 1 DIABETES\n\nA comprehensive overview of genetic data in mouse and human is beyond the scope of this article.Instead, we will focus on how the various susceptibility genes and environmental triggers can fit in a mechanistic model for T1D etiology." + } + ], + "0da4d3d4-10d5-4a58-9e50-c1fa0b414427": [ + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "text": "\n\nThe relative prevalence of mutations causal for monogenic forms of diabetes suggests that mutations in -cellrelated processes are a more frequent cause of severe early-onset diabetes than those influencing insulin action (see above).Studies of the relative heritabilities of indexes of -cell function and insulin action in the general population also hint at a preponderance of -cell effects (52)." + } + ], + "30d5d1de-ab8a-4b12-be3f-dd4e07d44a01": [ + { + "document_id": "30d5d1de-ab8a-4b12-be3f-dd4e07d44a01", + "text": "\nIn 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention.Type 1 diabetes becomes clinically apparent after a preclinical period of varying length, during which autoimmune destruction reduces the mass of beta cells in the pancreatic islets to a level at which blood glucose levels can no longer be maintained in a physiologic range.The disease has two subtypes: 1A, which includes the common, immune-mediated forms of the disease; and 1B, which includes nonimmune forms.In this review, we focus on subtype 1A, which for simplicity will be referred to as type 1 diabetes.Although there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied. Gene tic S t udiesBecause of the uncertainty regarding the number and action of genes involved in type 1 diabetes, genetic studies have tended to focus on approaches that require few assumptions about the underlying model of disease risk.The two primary approaches have been linkage studies (using pairs of affected relatives, typically siblings) and association studies (using either case-control or family-based designs).Linkage studies using affected sibling pairs seek to identify regions of the genome that are shared" + } + ], + "516de7be-3cef-47ee-8338-199fb922bc6f": [ + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "text": "Environment\n\nThe second factor in Figure 1 is environmental aspects.An important concept is the diabetes genotype typically causes only a predisposition for glucose intolerance (note the terminology susceptibility gene was used in the preceding paragraphs).Whether one develops the diabetes phenotype depends on environmental factors, some obvious in how they act, others less so.For instance, the Nurses Health Survey showed positive associations between obesity and lack of physical activity in the development of type 2 diabetes (as expected), but also protection by not smoking and moderate alcohol intake (14).Already discussed, many studies have shown an association between TV watching, high calorie diets, and lack of physical activity with risk of diabetes, i.e., our modern lifestyle, so it is not surprising that there is an explosion in the incidence of diabetes worldwide." + } + ], + "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0": [ + { + "document_id": "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0", + "text": "The genetics of type 1 diabetes\n\nThere is a strong genetic risk to T1D.This is exemplified by (Redondo et al., 2001) who demonstrated a strong concordance of genetic inheritance (65%) and T1D susceptibility in monozygotic twin pairs.That is, when one sibling is afflicted, there is a high probability that the other twin will develop T1D by the age of 60 years.Additionally, autoantibody positivity and islet destruction was observed after a prospective long-term follow-up of monozygotic twins of patients with T1D, despite initial disease-discordance among the twins (Redondo et al., 2008)." + } + ], + "76ae2f09-af4d-422a-b939-625f0fe4ae1c": [ + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "Type 1 diabetes has unusual epidemiological features related to gender\n\nType 1 diabetes also displays unusual patterns of inheritance that may yield insights into etiology and provide clues to the best methods for analyzing genetic studies.The risk to the offspring is generally greater from a mother or father who was diagnosed at an early age (again suggesting that early-onset cases are more heavily genetically 'loaded').However, the risk of diabetes is approximately two to four times higher for a child whose father has type 1 diabetes than one whose mother is affected [see (52,53) and references therein].This parental difference is largely due to a low risk for offspring of mothers who were diagnosed at a later age (53).The difference could be explained by at least three different factors.First, the risk alleles could only be active when transmitted by the father (such as is seen in imprinting, where only one of the parental alleles is expressed).Alternatively, a maternal environmental factor during pregnancy could be protective.However, it is difficult to see how this protective effect would be restricted to mothers diagnosed at a later age, especially since the protective effect was unrelated to the mother's duration of diabetes or even diabetic status at delivery (53).Finally, mothers who are diagnosed at a later age could represent more 'environmental' cases of diabetes, and thus be less likely to pass on risk genes to their offspring." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "Type 1 diabetes is a genetic disease\n\nFamily studies have indicated that genetic factors are important determinants of type 1 diabetes risk.First, the risk to a sibling of an affected individual is approximately 6%, as compared with an average risk of 0.4% (depending on the population), or a relative increased risk of 15-fold (17).The increased risk to siblings is referred to as l s (18) and is one measure of the degree of familial clustering of the disease." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "text": "\n\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered." + } + ], + "83a34294-d942-476f-be2f-ff8d7ec3dec4": [ + { + "document_id": "83a34294-d942-476f-be2f-ff8d7ec3dec4", + "text": "\n\nGenes affecting type 1 diabetes diagnosis age / A. Syreeni et al." + } + ], + "8d723c99-bd3c-43eb-9b31-14ee233c2ed4": [ + { + "document_id": "8d723c99-bd3c-43eb-9b31-14ee233c2ed4", + "text": "\n\nThus, the most likely scenario is that these genes are more poised for activation in the case group compared with the control group, contributing to various diabetes complications in the long term.This could be a consequence of the early exposure to hyperglycemia (measured by HbA 1c level), which is known to be associated with increased rates of long-term diabetes complications." + } + ], + "9240ab9b-c5bb-4475-ad2b-111843cb146a": [ + { + "document_id": "9240ab9b-c5bb-4475-ad2b-111843cb146a", + "text": "\n\nThe risk for T1D is strongly influenced by multiple genetic loci and environmental factors.The disease is heritable, with first-degree relatives of patients with T1D being at 15-fold greater risk for developing the condition than the general population." + } + ], + "92eb0c69-5e98-41aa-9084-506e7f223b1a": [ + { + "document_id": "92eb0c69-5e98-41aa-9084-506e7f223b1a", + "text": "Genetic Background and Environment\n\nBoth type 1 and 2 diabetes as well as other rare forms of diabetes that are directly inherited, including MODY and diabetes due to mutations in mitochondrial DNA, are caused by a combination of genetic and environmental risk factors.Unlike some traits, diabetes does not seem to be inherited in a simple pattern.Undoubtedly, however, some people are born prone to developing diabetes more so than others.Several epidemiological patterns suggest that environmental factors contribute to the etiology of T1D.Interestingly, the recent elevated number of T1D incidents projects a changing global environment, which acts either as initiator and/or accelerator of beta cell autoimmunity rather than variation in the gene pool.Several genetic factors are involved in the development of the disease [127].There is evidence that more than twenty regions of the genome are involved in the genetic susceptibility to T1D." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 1 Diabetes\n\nThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Genetics\n\nBoth type 1 and type 2 diabetes are polygenic diseases where many common variants, largely with small effect size, contribute to overall disease risk.Disease heritability (h 2 ), defined as sibling-relative risk, is 3 for type 2 diabetes and 15 for type 1 diabetes (17).The lifetime risk of developing type 2 diabetes is ;40% if one parent has type 2 diabetes and higher if the mother has the disease (18).The risk for type 1 diabetes is ;5% if a parent has type 1 diabetes and higher if the father has the disease (19).Maturity-onset diabetes of the young (MODY) is a monogenic disease and has a high h 2 of ;50 (20).Mutations in any 1 of 13 different individual genes have been identified to cause MODY (21), and a genetic diagnosis can be critical for selecting the most appropriate therapy.For example, children with mutations in KCJN11 causing MODY should be treated with sulfonylureas rather than insulin." + } + ], + "9cce7fe9-cb40-4e75-85bc-d8655c3343d6": [ + { + "document_id": "9cce7fe9-cb40-4e75-85bc-d8655c3343d6", + "text": "\n\nType 1 diabetes as well as type 2 diabetes shows a genetic predisposition, although only type 1 diabetes is HLA dependent [32,33,36,40]." + } + ], + "afb0bd31-df62-4a8d-8c20-9841e2d2dc4a": [ + { + "document_id": "afb0bd31-df62-4a8d-8c20-9841e2d2dc4a", + "text": "\n\nGenetic factors have an important role in the development of diabetes, with some forms of the disease resulting from mutations in a single gene.Others are multifactorial in origin.The monogenic forms of diabetes account for approximately 5% of cases and are caused by mutations in genes encoding insulin 3 , the insulin receptor 4 , the glycolytic enzyme glucokinase 5 , and the transcription factors hepatocyte nuclear factor-1α (HNF-1α), HNF-1β, HNF-4α, insulin promoter factor-1 and NeuroD1/BETA2 (refs 6-10).Mutations in maternally inherited mitochondrial genes can also cause diabetes, often in association with hearing loss 11 ." + } + ], + "d1449eee-d4ec-4886-87d1-835fb54a5f56": [ + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\n\nStudies [71][72][73][74] in Mexican and Asian populations have identified several mutations associated with type 2 diabetes in young people.The high prevalence of type 2 diabetes in the parents of young people diagnosed with type 2 diabetes could reflect a stronger genetic predisposition, even when monogenic diabetes is excluded.This hypothesis suggests that efforts to define genes that cause type 2 diabetes by linkage might be more powerful if focused on young adults with diabetes, raising the question of whether type 2 diabetes in older populations has a relatively smaller genetic contribution and a stronger environmental contribution. 66" + } + ], + "fa72cb33-e1e4-49ea-a72e-dd851225ee0b": [ + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "text": "\n\nWe found that the presence or absence of parental diabetes and the genotype score were independently associated with the risk of diabetes.This suggests that family history as a risk factor for diabetes conveys more than heritable genetic information; it probably includes nongenetic familial behaviors and norms.The lower relative risks for diabetes associated with observed parental diabetes as compared with those associated with self-reported family history (approximately 1.8 vs. approximately 2.2) support the contention that family history contains more risk information than is implied by inheritance of the diabetes phenotype alone.One of the limitations of our study is that the 18 SNPs we included are probably insufficient to account for the familial risk of diabetes.They account for a minority of diabetes heritability, and the SNP array platforms from which they were chosen capture only approximately 80% of common variants in Europeans.In addition, we have not considered structural variants that might confer a risk of diabetes.It is possible that the addition of rare risk alleles with large effects, or a much larger number of common risk alleles with small individual effects, could improve discrimination. 36Indeed, as many as 500 loci may underlie the genetic risk of type 2 diabetes. 16Also, we did not study interactions among genes or between genes and the environment that might alter the genetic risk in exposed persons.As more diabetes risk variants become known, their incorporation into the genotype score may explain more of the genetic risk implied by parental diabetes." + } + ] + }, + "data_source": [], + "document_id": "9892FB125B6B5D4C8FC4FDA6E1E25271", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&1&diabetes", + "genetic&risk", + "HLA", + "immune&function", + "environmental&factors", + "autoimmunity", + "gene&variants", + "epigenetic", + "insulin&gene", + "genetic&screening" + ], + "metadata": [], + "question": "How does genetics influence the emergency of diabetes?", + "subquestions": null, + "task_id": "9892FB125B6B5D4C8FC4FDA6E1E25271", + "usage": { + "chatgpt": 8194, + "gpt-4": 4183, + "gpt-4-turbo-preview": 3246 + }, + "user_id": 2 + }, + "document_id": "9892FB125B6B5D4C8FC4FDA6E1E25271", + "task_id": "9892FB125B6B5D4C8FC4FDA6E1E25271" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_06.json b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_06.json new file mode 100644 index 0000000..81454c1 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/experts/experts_suga_06.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-05-31T23:15:26.855315Z", + "data": { + "amplify": false, + "answer": "There are primarily four types of diabetes: Type 1, Type 2, gestational diabetes, and diabetes due to specific causes. However, there are also subtypes within these broad categories, such as latent autoimmune diabetes of adults (LADA) and maturity onset diabetes of the young (MODY).", + "context": { + "1cebda9b-a727-4bfd-8521-cf61903235cc": [ + { + "document_id": "1cebda9b-a727-4bfd-8521-cf61903235cc", + "text": "\n\nThe prevalence of diabetes mellitus worldwide is sobering; the International Diabetes Federation estimates that 415 million people have diabetes mellitus, with 90% of these individuals having type 2 diabetes mellitus (T2DM) 1 .T2DM occurs when pancreatic β-cells fail to release enough insulin to meet the demands of insulin-responsive tissues, which safely store and metabolize glucose.Driven by both genetic and environmental risk factors, T2DM is a complex, multifactorial disorder.Although the increasing prevalence of T2DM is driven by changes in our environment and mirrors the increase in obesity, the greater concordance for T2DM found in monozygotic compared with dizygotic twins has long provided evidence for a genetic component in T2DM risk 2 ." + } + ], + "4252d7ad-82de-480c-a801-9ed1c84fb968": [ + { + "document_id": "4252d7ad-82de-480c-a801-9ed1c84fb968", + "text": "\n\nIn the UK alone, nearly 1.8 million people are already recognized to have this disorder (consuming w5% of the total National Health Service budget), and the search is on to find the 'missing million' who are living with the condition but in whom the diagnosis has yet to be made. 3In the USA, the situation appears to be even more serious with some commentators predicting that one in every three Americans born in the year 2000 will go on to develop diabetes during their lifetime, bringing unprecedented costs in terms of healthcare dollars as well as human morbidity and mortality. 4The majority (w90%) of these cases will be type 2 in origin, reflecting a trend towards obesity and more sedentary lifestyles as the 'norm' rather than the exception in 'developed' societies.Indeed, the face of T2DM is changing, as a condition that was once considered the preserve of middle/old age is increasingly diagnosed in young adults and even children, reflecting the high rates of obesity (and, in particular, visceral adiposity) in these populations." + } + ], + "4d3330eb-acd0-4f72-aadf-b056d3c8b389": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "\n\nTable 1 lists the various subtypes of diabetes based on the classification suggested by the ADA [4]." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "\n\nThe ADA lists four subtypes of diabetes based on the clinical symptoms at time of presentation, [4] namely, Type 1 diabetes, Type 2 diabetes (T2D), gestational diabetes, and diabetes due to specific causes (genetic defects causing deficient insulin secretion or action, diseases of pancreas, use of certain drugs such as steroids, thiazides among others).Of these, T2D is the most prevalent (close to 90% of all cases) and is the major cause of morbidity and mortality in both developed and developing nations [1].At times it is difficult to assign a patient to a particular subtype due to the difference in conditions associated with hyperglycemia at the time of diagnosis [4,7].For example, a lady diagnosed with gestational diabetes mellitus during pregnancy is highly susceptible to develop T2D later.Therefore, other than proper treatment during and post pregnancy, a regular follow-up is required for stratifying disease risk, and for timely management before progression to another subtype.It is clear that the classification of diabetes may not be as simple as just categorizing it into any one of the four given subtypes due to its miscellaneous nature.Every case needs to be considered at the time of presentation, on the basis of the risk factors or underlying cause of hyperglycemia, the clinical symptoms, and disease prognosis." + } + ], + "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0": [ + { + "document_id": "588bca6b-82c0-4ac1-9c7e-dc09af1d49b0", + "text": "Introduction\n\nGlobally, diabetes affects more than 400 million people (World Health Organization, 2016), with Type 1 (insulin-dependent) diabetes (T1D) accounting for up to 10 percent of cases (American Diabetes Association, 2009).In the United States, T1D occurs at a rate of 15-30 cases per 100,000 children aged 0-14 years annually (International Diabetes Foundation, 2017;Maahs et al., 2010), with similar prevalence in Canada, Europe, Australia, and New Zealand (Fig. 1) (Derraik et al., 2012;International Diabetes Foundation, 2017;Maahs et al., 2010).By contrast, the estimated incidence rate of T1D among Asians, South Americans, and Africans is below 15 cases per 100,000 children (Fig. 1) (International Diabetes Foundation, 2017;Maahs et al., 2010).The global incidence of T1D has been rising by 3-5% per annum over the past two decades, with a notable increase in children below 10 years of age (Diamond Project, 2006;Patterson et al., 2009)." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Animal Models\n\n9.2% in women and 9.8% in men, with approximately 347 million people suffering from the disease worldwide in 2008 (Danaei et al., 2011).There are several different classifications of diabetes, the most common being type 1 and type 2 diabetes." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nType 2 diabetes is the most common type of diabetes with prevalence in the United Kingdom of around 4%.It is most commonly diagnosed in middle-aged adults, although more recently the age of onset is decreasing with increasing levels of obesity (Pinhas-Hamiel and Zeitler, 2005).Indeed, although development of the disease shows high hereditability, the risk increases proportionally with body mass index (Lehtovirta et al., 2010).Type 2 diabetes is associated with insulin resistance, and a lack of appropriate compensation by the beta cells leads to a relative insulin deficiency.Insulin resistance can be improved by weight reduction and exercise (Solomon et al., 2008).If lifestyle intervention fails, there are a variety of drugs available to treat type 2 diabetes (Krentz et al., 2008), which can be divided into five main classes: drugs that stimulate insulin production from the beta cells (e.g.sulphonylureas), drugs that reduce hepatic glucose production (e.g.biguanides), drugs that delay carbohydrate uptake in the gut (e.g.a-glucosidase inhibitors), drugs that improve insulin action (e.g.thiazolidinediones) or drugs targeting the GLP-1 axis (e.g.GLP-1 receptor agonists or DPP-4 inhibitors)." + } + ], + "7d4a197e-3774-40a4-9897-ed7c71f213b6": [ + { + "document_id": "7d4a197e-3774-40a4-9897-ed7c71f213b6", + "text": "Introduction\n\nDiabetes impacts the lives of approximately 200 million people worldwide [1], with chronic complications including accelerated development of cardiovascular disease.Over 90% of cases are of type 2 diabetes (T2D), with the bulk of the remainder presenting with type 1 diabetes (T1D)." + } + ], + "961f88ba-2090-4904-942c-f0e014bbe53f": [ + { + "document_id": "961f88ba-2090-4904-942c-f0e014bbe53f", + "text": "Classification of Diabetes\n\nOn the basis of insulin deficiency, diabetes can be classified into the following types as follows." + } + ], + "9b93b4eb-98c2-403f-aea2-6b24399501b8": [ + { + "document_id": "9b93b4eb-98c2-403f-aea2-6b24399501b8", + "text": "| INTRODUCTION\n\nToday, more than 265 million people are affected across the world.It is estimated that by the year 2030 this number will reach 366 million people (about 4/4 percent of the world's population), and now the cause of death is more than 1.1 million per year (including 50% of the population under-70 years of age and 55% of women).On the other hand, given its negative effect on the economic growth of developing countries, it calls for universal mobilization to combat this disease (Bhattacharya, Dey, & Roy, 2007).Diabetes or diabetes mellitus is referred to as a heterogeneous group of metabolic disorders characterized by chronic hyperglycemia and carbohydrate, fat and protein metabolism disorders that result from a defect in the secretion of insulin, or impairment in its function, or both.Types of diabetes mellitus include type 1, type 2 diabetes and other kind of diabetes, but the two most common types of diabetes mellitus are type 1 and type 2, which are different in several aspects (Meshkani, Taghikhani, Mosapour et al., 2007).Type 1 diabetes has been identified with autoimmune destruction of pancreatic beta cells (insulin secreting cells) and accounts for about 5% of all diabetic people, while type 2 diabetes is a predominant disorder characterized by insulin resistance or a relative decline in insulin production, and accounts for about 90% of all types of diabetes mellitus (Meshkani, Taghikhani, Al-Kateb et al., 2007).Important factors that predispose a person to type 2 diabetes are multifactorial, including genetic factors and environments.However, its inheritance has certainly not been proven, but it is believed that first-degree relatives of diabetic patients have a higher chance to develop the disease.In this regard, recognizing gene polymorphisms of this disease seems to be necessary (Häring et al., 2014).Multiple genes have been studied in the pathogenesis of type 2 diabetes." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "CONCLUSIONS\n\nDiabetes is currently broadly classified as type 1, type 2, gestational, and a group of \"other specific syndromes. \"However, increasing evidence suggests that there are populations of individuals within these broad categories that have subtypes of disease with a well-defined etiology that may be clinically characterized (e.g., LADA, MODY).These developments suggest that perhaps, with more focused research in critical areas, we are approaching a point where it would be possible to categorize diabetes in a more precise manner that can inform individual treatment decisions." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 2 Diabetes\n\nIn the U.S., an estimated 95% of the nearly 30 million people living with diabetes have type 2 diabetes.An additional 86 million have prediabetes, putting them at high risk for developing type 2 diabetes (9).Among the demographic associations for type 2 diabetes are older age, race/ ethnicity, male sex, and socioeconomic status (9)." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 1 Diabetes\n\nBetween 2001 and 2009, there was a 21% increase in the number of youth with type 1 diabetes in the U.S. (7).Its prevalence is increasing at a rate of ;3% per year globally (8).Though diagnosis of type 1 diabetes frequently occurs in childhood, 84% of people living with type 1 diabetes are adults (9).Type 1 diabetes affects males and females equally (10) and decreases life expectancy by an estimated 13 years (11).An estimated 5-15% of adults diagnosed with type 2 diabetes actually have type 1 diabetes or latent autoimmune diabetes of adults (LADA) (12)." + } + ], + "ab32e261-658c-4a8b-94fc-857826b29f5a": [ + { + "document_id": "ab32e261-658c-4a8b-94fc-857826b29f5a", + "text": "\n\nBackground Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous.A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis." + } + ], + "b666545f-6a53-45de-8562-55d88fc6f7ee": [ + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "text": "\n\nDiabetes mellitus now affects ~8% of the world's adult population [1], including ~3 000 000 individuals in the UK (with a further 600 000 people affected but presently undiagnosed) [2].Of these cases, > 90% have Type 2 diabetes.Treatments of the complications of the disease, which range from stroke, blindness and kidney failure to lower limb amputations and cancer, presently consume ~10% of the National Health Service budget, some £14 bn per year [3].These figures are anticipated to increase further in the next 10 years, driven by increasingly sedentary lifestyles and increases in obesity; the collision between these 'environmental' factors and genetic susceptibility (see below) being the key underlying driver.Whilst existing treatments ameliorate the symptoms of the disease, notably hyperglyca-emia, none target the underlying molecular aetiology.In particular, no available treatments tackle the progressive and largely irreversible loss of insulin production [4] which, in the face of insulin resistance, underlies the progressive deterioration in glucose control.Reductions in b-cell mass [5,6] and dysfunction [7] both contribute to this gradual impairment in insulin release.Recent years have seen an increase in the view that the former may play a less important role than the latter, with a 2008 study by Rahier et al. [6] reporting that b-cell mass (and insulin content) in people with Type 2 diabetes was on average ~35% lower than that of healthy control subjects.However, this difference was only ~24% within 5 years of diagnosis, far below levels likely to lead to the symptoms of diabetes.Indeed, given our present inability to monitor b-cell mass prospectively over the course of the disease, it is conceivable that the differences observed post mortem between healthy individuals and those with Type 2 diabetes [5,6] may reflect an increased predisposition to diabetes in those born with a lower than average b-cell mass." + } + ], + "b72eb0d1-50e3-4def-94bc-abf77891f519": [ + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "text": "INTRODUCTION\n\nType 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 (1).The disease usually involves insulin resistance but is ultimately the result of pancreatic b cell failure, a sine qua non for disease development (2).In contrast, Type 1 diabetes (T1D) affects a smaller proportion of people with diabetes and is chiefly the result of pancreatic b cell destruction mediated by immune cells (3)." + } + ], + "ba7298cd-4d19-4f98-9a2a-5fb625aa0068": [ + { + "document_id": "ba7298cd-4d19-4f98-9a2a-5fb625aa0068", + "text": "Introduction\n\nDiabetes is a complex and heterogeneous disease with a staggering global impact and the most recent estimates indicate 346 million people worldwide suffer from this disease (WHO Diabetes Fact sheet No. 312, 2011).Type 2 diabetes mellitus (T2DM) is the most common form of diabetes, accounting for >90% of cases, and occurs when peripheral tissue insulin resistance accompanies insufficient b-cell insulin production.While >80% of diabetes deaths occur in low-and middle-income countries [1].India and China have the highest reported prevalence of diabetes with 65 and 98 million in 2013, respectively [2]." + } + ], + "ceab3d6d-62ca-459a-9a97-02a16d4dd193": [ + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "text": "\n\nThe disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition." + }, + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "text": "\nThe disease burden related to diabetes is high and rising in every country, fuelled by the global rise in the prevalence of obesity and unhealthy lifestyles.The latest estimates show a global prevalence of 382 million people with diabetes in 2013, expected to rise to 592 million by 2035.The aetiological classification of diabetes has now been widely accepted.Type 1 and type 2 diabetes are the two main types, with type 2 diabetes accounting for the majority (>85%) of total diabetes prevalence.Both forms of diabetes can lead to multisystem complications of microvascular endpoints, including retinopathy, nephropathy and neuropathy, and macrovascular endpoints including ischaemic heart disease, stroke and peripheral vascular disease.The premature morbidity, mortality, reduced life expectancy and financial and other costs of diabetes make it an important public health condition." + } + ], + "eaca0f25-4a6b-4c0e-a6df-12e25060b169": [ + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "text": "\n\nIntroduction: Is Type 2 Diabetes a Genetic Disorder?According to the World Health Organization (WHO), approximately 350 million people worldwide have diabetes, and this disorder is likely to be the seventh leading cause of death in 2030.Diabetes is an economic burden on healthcare systems, especially in developing countries (World Health Organization, 2013)." + } + ] + }, + "data_source": [], + "document_id": "DF2A84CC99BAED8C3168AE12F76252A2", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&1&diabetes", + "type&2&diabetes", + "gestational&diabetes", + "LADA", + "MODY", + "insulin&resistance", + "pancreatic&beta&cells", + "autoimmune&destruction", + "insulin&deficiency", + "genetic&factors" + ], + "metadata": [], + "question": "How many types of diabetes exist?", + "subquestions": null, + "task_id": "DF2A84CC99BAED8C3168AE12F76252A2", + "usage": { + "chatgpt": 6403, + "gpt-4": 4251, + "gpt-4-turbo-preview": 3329 + }, + "user_id": 2 + }, + "document_id": "DF2A84CC99BAED8C3168AE12F76252A2", + "task_id": "DF2A84CC99BAED8C3168AE12F76252A2" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_01.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_01.json new file mode 100644 index 0000000..4b91711 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_01.json @@ -0,0 +1,394 @@ +{ + "created_at": "2024-04-18T18:19:31.857649Z", + "data": { + "amplify": false, + "answer": "The onset of diabetes in mice can occur as early as 3 to 4 weeks of age, with blood sugar concentrations often elevating between 5 and 8 weeks. However, in some cases, hyperglycemia can develop at approximately 20 weeks of age.", + "context": { + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + } + ], + "1bf337a1-ffed-4199-a11f-c5a62df47980": [ + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + } + ], + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "52990c69-609c-448e-9f2c-36e1655ca6db": [ + { + "document_id": "52990c69-609c-448e-9f2c-36e1655ca6db", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nBB rats usually develop diabetes just after puberty and have similar incidence in males and females.Around 90% of rats develop diabetes between 8 and 16 weeks of age.The diabetic phenotype is quite severe, and the rats require insulin therapy for survival.Although the animals have insulitis with the presence of T cells, B cells, macrophages and NK cells, the animals are lymphopenic with a severe reduction in CD4 + T cells and a near absence of CD8 + T cells (Mordes et al., 2004).Lymphopenia is not a characteristic of type 1 diabetes in humans or NOD mice (Mordes et al., 2004) and is seen to be a disadvantage in using the BB as a model of type 1 diabetes in humans.Also, in contrast to NOD mice, the insulitis is not preceded by peri-insulitis.However, the model has been valuable in elucidating more about the genetics of type 1 diabetes (Wallis et al., 2009), and it has been suggested that it may be the preferable small animal model for islet transplantation tolerance induction (Mordes et al., 2004).In addition, BB rats have been used in intervention studies (Hartoft-Nielsen et al., 2009;Holmberg et al., 2011) and studies of diabetic neuropathy (Zhang et al., 2007)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAgeing likewise affects metabolic parameters in rodents.Analogous to what occurs in humans, the body weight of the C57BL/6J mouse, the most commonly used mouse strain for metabolic studies, increases with age, peaking at ~9 months 133 , and older C57BL/6J mice (22 months) have reduced lean mass and increased fat mass compared with young 3-month-old mice 134 .In both rats and mice, fasting glucose levels are mostly stable throughout life, but whereas glucose tolerance generally worsens with age in rats, mice are less affected [135][136][137][138][139][140] .In fact, 2-year-old male C57BL/6J mice were significantly more glucose tolerant than their 5-month-old counterparts 138 .Consistent with these findings, glucosestimulated insulin release from the pancreas decreases with age in rats, but not in mice 137,138 ." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "All mice h o m o z y g o u s for t h e d i a b e t e s\ngene (db/db) b e c o m e diabetic, t h e first d i s t i n g u i s h i n g\nf e a t u r e being a m a r k e d t e n d e n c y to o b e s i t y w i t h large\nf a t d e p o s i t i o n s o b s e r v e d in t h e a x i l l a r y a n d i n g u i n a l\nregions a t a b o u t 3 t o 4 weeks of age." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "In many of these diabetic mice\nblood sugar concentration tends to increase gradually\nbetween 5 and 12 weeks of age, after which it may rise\nsharply to over 500 rag/100 ml of blood almost overnight. The diabetic condition, thus, appears to develop\nin two phases, an early one when there is some regulation of blood sugar concentration, and a later stage\ncharacterized by a marked increase in hyperglycemia\nand a complete loss of metabolic control. A few exceptional diabetics, usually females, exhibit\na pattern similar to that shown in Fig. 3. Although\n16\n240\n\nD.L. COLEMANand K.P." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "of age; m o r e o f t e n this e l e v a t i o n occurs b e t w e e n 5\na n d 8 weeks. I n older d i a b e t i c mice b l o o d sugar\nc o n c e n t r a t i o n s g r e a t e r t h a n 600 m g / 1 0 0 m l are n o t\n\nu n c o m m o n ." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "I n older mice with blood sugar concentrations over 250 rag/100 ml, injections of up t o 100 units /\n100 g were completely ineffective in reducing blood sugar\nto normal levels. Continued treatment of young diabetic\nmice with daily injections of insulin, although controlling Mood sugar concentrations initially, did not prevent or delay either the obesity or the uncontrollable\nhigh blood sugar concentrations, which usually develop\nat about 6 to 8 weeks of age." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + } + ], + "c24330f7-9f82-404a-86d5-a16d814bb754": [ + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "Renal lesions in diabetic mouse models\n\nDb/db mice, which have a recessive mutation in the hypothalamic leptin receptor, develop obesity at 4 wk of age and type 2 diabetes at approximately 8 wk of age.In C57BL/6J background, the diabetes and the obesity are usually less severe than in the C57BL/KsJ background (44).Kidneys are generally enlarged in this mouse strain, and structural glomerular changes (e.g., diffuse glomerulosclerosis, GBM thickening) occur without evidence of tubulointerstitial disease (40).Glomerular lesions of the KK mice are characterized by diffuse and nodular mesangial sclerosis without evidence of tubular disease (45).The lack of reliable mouse models prompted the National Institute of Diabetes and Digestive and Kidney Diseases to fund a consortium for the development and phenotyping of new diabetic mouse models that would resemble closely human DNP." + } + ], + "c802cb60-1a15-4962-8e6d-f06608c00a54": [ + { + "document_id": "c802cb60-1a15-4962-8e6d-f06608c00a54", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "In many of these diabetic mice\nblood sugar concentration tends to increase gradually\nbetween 5 and 12 weeks of age, after which it may rise\nsharply to over 500 rag/100 ml of blood almost overnight. The diabetic condition, thus, appears to develop\nin two phases, an early one when there is some regulation of blood sugar concentration, and a later stage\ncharacterized by a marked increase in hyperglycemia\nand a complete loss of metabolic control.\n A few exceptional diabetics, usually females, exhibit\na pattern similar to that shown in Fig. 3. Although\n16\n240\n\nD.L. COLEMANand K.P." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "section_type": "main", + "text": "\n\nAgeing likewise affects metabolic parameters in rodents.Analogous to what occurs in humans, the body weight of the C57BL/6J mouse, the most commonly used mouse strain for metabolic studies, increases with age, peaking at ~9 months 133 , and older C57BL/6J mice (22 months) have reduced lean mass and increased fat mass compared with young 3-month-old mice 134 .In both rats and mice, fasting glucose levels are mostly stable throughout life, but whereas glucose tolerance generally worsens with age in rats, mice are less affected [135][136][137][138][139][140] .In fact, 2-year-old male C57BL/6J mice were significantly more glucose tolerant than their 5-month-old counterparts 138 .Consistent with these findings, glucosestimulated insulin release from the pancreas decreases with age in rats, but not in mice 137,138 ." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "All mice h o m o z y g o u s for t h e d i a b e t e s\ngene (db/db) b e c o m e diabetic, t h e first d i s t i n g u i s h i n g\nf e a t u r e being a m a r k e d t e n d e n c y to o b e s i t y w i t h large\nf a t d e p o s i t i o n s o b s e r v e d in t h e a x i l l a r y a n d i n g u i n a l\nregions a t a b o u t 3 t o 4 weeks of age." + }, + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "section_type": "main", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "of age; m o r e o f t e n this e l e v a t i o n occurs b e t w e e n 5\na n d 8 weeks. I n older d i a b e t i c mice b l o o d sugar\nc o n c e n t r a t i o n s g r e a t e r t h a n 600 m g / 1 0 0 m l are n o t\n\nu n c o m m o n ." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "I n older mice with blood sugar concentrations over 250 rag/100 ml, injections of up t o 100 units /\n100 g were completely ineffective in reducing blood sugar\nto normal levels. Continued treatment of young diabetic\nmice with daily injections of insulin, although controlling Mood sugar concentrations initially, did not prevent or delay either the obesity or the uncontrollable\nhigh blood sugar concentrations, which usually develop\nat about 6 to 8 weeks of age." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + }, + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "section_type": "main", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "\n\nBB rats usually develop diabetes just after puberty and have similar incidence in males and females.Around 90% of rats develop diabetes between 8 and 16 weeks of age.The diabetic phenotype is quite severe, and the rats require insulin therapy for survival.Although the animals have insulitis with the presence of T cells, B cells, macrophages and NK cells, the animals are lymphopenic with a severe reduction in CD4 + T cells and a near absence of CD8 + T cells (Mordes et al., 2004).Lymphopenia is not a characteristic of type 1 diabetes in humans or NOD mice (Mordes et al., 2004) and is seen to be a disadvantage in using the BB as a model of type 1 diabetes in humans.Also, in contrast to NOD mice, the insulitis is not preceded by peri-insulitis.However, the model has been valuable in elucidating more about the genetics of type 1 diabetes (Wallis et al., 2009), and it has been suggested that it may be the preferable small animal model for islet transplantation tolerance induction (Mordes et al., 2004).In addition, BB rats have been used in intervention studies (Hartoft-Nielsen et al., 2009;Holmberg et al., 2011) and studies of diabetic neuropathy (Zhang et al., 2007)." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33].\n Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nDiabetes-obesity syndromes in rodents" + }, + { + "document_id": "c802cb60-1a15-4962-8e6d-f06608c00a54", + "section_type": "main", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + }, + { + "document_id": "52990c69-609c-448e-9f2c-36e1655ca6db", + "section_type": "main", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "\n\nTo investigate the effects of genetic background variation on the measured traits, we also conducted a genetic cross using CAST as the diabetes-resistant strain (CAST cross).In the male BC1 progeny of this CAST cross, the onset of the hyperglycemia was slightly delayed compared to the B6 cross; 27% vs 45% of the male BC1 mice showed Ͼ300 mg/dl plasma glucose at 20 weeks.In the CAST cross the hyperglycemia was also maintained throughout the 30-week period studied.Therefore, the mean of three glucose measurements between 22 and 28 weeks of age for each BC1 progeny was used for genetic analysis.Body weights were measured at 24 weeks.At the end of the study (28 weeks), plasma insulin levels and nasal-anal lengths were measured, and five fat pads were dissected and weighed.In total, 95 male BC1 mice were collected and genotyped individually with 69 SSLP markers spaced through out the genome." + }, + { + "document_id": "a551b815-1d9d-4dae-a194-8f77e317b506", + "section_type": "main", + "text": "Diabetes monitoring\n\nCohorts of female mice were housed in an SPF facility and tested once a week for elevated urinary glucose (>110 mmol/L) using Diastix reagent strips (Bayer Australia, Ltd.) over a 300-d time course.Three consecutive elevated readings indicated the onset of diabetes.Pairwise comparisons of the diabetes incidence between mouse strains were done using the log-rank test." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Two of the mice had\nblood sugar concentrations only slightly above normal\nat the end of the 3 month period, while two others\nstabilized at the starting blood sugar concentrations.\n Weight gains of diabetic mice on this ration, were,\non the whole, variable but somewhat smaller than\nthose seen on the chow ration. However, those diabetic\nmice that showed the greatest decrease in rate of\nweight gain did not necessarily have the lowest blood\nsugar concentrations at the end of the treatment\n\nperiod." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "The diabetic mouse on the\nright weighs 50 per cent more t h a n the control mouse on the left and shows\ntypical f a t deposition\n\nwith age and concomitant elevations of blood sugar\nconcentration have been described previously [11]\nand will not be dealt with in detail here. Although\nthere are individual variations in the age of onset of\ndiabetes and the rate of increase in weight and blood\nsugar concentration, there is a general pattern, which\nis depicted in Fig. 2." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "They are probably typical of those\nfew mice that develop diabetes more slowly and do\nnot tax the pancreatic insulin supply as severely early\nin the course of the disease.\n Attempts at therapy. Attempts to keep the weight\nof diabetic mice within normal limits by total or\npartial food restriction resulted in premature deaths.\n After it was discovered that gluconeogenesis is greatly\nincreased in diabetic mice, attempts were made to\nregulate blood sugar levels and also weight gain by\nfeeding rations devoid of carbohydrate." + }, + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "section_type": "main", + "text": "\n\nM16 mouse: M16 mouse is a new model for obesity and type 2 diabetes which results from long-term selection for 3 to 6 wk weight gain from an Institute of Cancer Research, London, UK (ICR) base population.M16 mice exhibit early onset of obesity and are larger at all ages characterized by increased body fat percentage, fat cell size, fat cell numbers, and organ weights.These mice also exhibit hyperphagia, accompanied by moderate obesity, and are hyperinsulinaemic, hyperleptinaemic and hypercholesterolaemic relative to ICR.Both M16 males and females are hyperglycaemic relative to ICR, with 56 and 22 per cent higher fasted blood glucose levels at 8 wk of age.M16 mice represent an outbred animal model to facilitate gene discovery and pathway regulation controlling early onset polygenic obesity and type 2 diabetic phenotypes.Phenotypes prevalent in the M16 model, with obesity and diabesity exhibited at a young age, closely mirror current trends in human populations 36 ." + }, + { + "document_id": "38be907c-70ea-45f2-a8c1-7aed203a5256", + "section_type": "main", + "text": "Mice and Intervention Protocol\n\nProtocols were approved by the Rutgers University Institutional Care and Use Committee and followed federal and state laws.Five-week-old male C57BL/6J mice (10-20 g) were purchased from The Jackson Laboratory (Bar Harbor, ME) and fed a standard chow diet ad libitum (cat.no.5015; Purina) during their 1-week acclimatization period.Animals were housed, five per cage, with free access to water in a room with a temperature of 24 6 1°C and a 12:12-h light:dark cycle (7:00 A.M.-7:00 P.M.).At 6 weeks of age, oral glucose tolerance tests (OGTTs) were performed on 45 mice.The area under the curve (AUC) corresponding to the OGTT data from each mouse was calculated, and a mean AUC for each cage of five mice was determined.The nine cages were separated into three groups based on the average AUCs calculated for each cage so that each group of 15 mice would be similar at baseline with respect to oral glucose tolerance.This method of assignment was used as a way to normalize oral glucose tolerance at baseline and also keep mice in their original cage placements, as switching the animals around can sometimes lead to aggressive behavior in the new group.Mice were fed GP-SPI diet, SPI diet, or HFD (n = 15 mice/diet group) for a total of 13 weeks.The HFD group was used mainly as a control to monitor body weight gain and food intake between groups.Various end points were measured during the intervention period as described below.A second group of 5-week-old male C57BL/6J mice (10-20 g) (n = 10) was purchased at a later time to have an LFD cohort with which to compare body weights, food intake, and microbiome samples.These LFD-fed mice were similarly housed (five per cage) in the same experimental room and space.Mice were initially fed a regular chow diet ad libitum for 1 week and then switched to the LFD for 12 weeks with OGTT performed at the same intervals." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "section_type": "main", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + }, + { + "document_id": "8e92b2e3-b525-4c17-a0cb-5ca740a74c66", + "section_type": "main", + "text": "\n\nMice of the KK strain exhibit a multigenic syndrome of hyperphagia, moderate obesity, hyperinsulinemia, and hyperglycemia (Ikeda 1994;Nakamura andYamada 1963, 1967;Reddi and Camerini-Davalos 1988).Most KK males develop non-insulindependent diabetes after 4 months of age (Leiter and Herberg 1997).While KK females are much less diabetes prone, they do become obese.Previous analyses indicate that the inheritance of obesity and diabetes phenotypes in KK mice is multigenic (Nakamura and Yamada 1963;Reddi and Camerini-Davalos 1988).In the present study, we have searched for QTLs affecting male and female adiposity and related traits in an intercross between strains KK and B6." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "section_type": "main", + "text": "\n\nLeptin-receptor-deficient db/db mice on the C57BLKS/J background largely recapitulate the obesity phenotype of the ob/ob mouse.The nomenclature of db (that is, diabetic) stems from the original observation of marked hyperglycaemia in these mice.db/db mice are hyperphagic and have reduced energy expenditure, leading to early-onset obesity 195 .They are also hypothermic, have decreased linear growth owing to GH deficiency and are infertile 195 , and leptin levels in db/db mice are markedly elevated 205 .Hyperinsulinaemia can be detected as early as 10 days of age, and insulin levels continue to increase until 3 months of age.The hyperinsulinaemia is accompanied by hyperplasia and hypertrophy of the pancreatic β-cells.After 3 months, levels of insulin in db/db mice drop profoundly, which is concomitant with the atrophy of β-cells.Consequently, marked and sustained hyper glycaemia with blood glucose values >400 mg/dl promotes premature death around 5-8 months of age.However, the db/db model does not capture all the diabetic complications observed in the human disease.Vascular and retinal complications, for example, are rarely documented in db/db mice, likely because of the dramatically shortened lifespan.Notably, db/db mice on a C57BL/6J background exhibit only mild diabetic symptoms and a normal lifespan, despite marked obesity 78,79,195 ." + }, + { + "document_id": "7d5b12ef-7b17-4b49-8da2-1a4179601520", + "section_type": "main", + "text": "LEW.1AR1/Ztm-Iddm Rats\n\nIn this strain, type 1 diabetes develops at age 2 months as result of immune damage caused by heavy infiltration of the islets of Langerhans by B and T lymphocytes, macrophages and NK cells and beta cell destruction by apoptosis [85][86][87].The mutation in this strain resides in the Dock8 gene, which encodes a member of the DOCK180 protein superfamily of guanine nucleotide exchange factors that act as activators of Rac/Rho family GTPases [88]." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "section_type": "main", + "text": "\n\nTo achieve a slow pathogenesis of T2DM, young adult mice 284 or rats 285 are fed a high-fat or Western diet to elicit DIO and insulin resistance.Single or multiple injections with low-dose streptozotocin (~30-40 mg/kg intraperitoneally) then elicit partial loss of β-cells, which results in hypoinsulinaemia and hyperglycaemia.Protocols are being continuously refined and likely differ between species and even strains 283 .The HFD streptozotocin rat is sensitive to metformin, further demonstrating the utility of this model 285 .Downsides of streptozotocin treatment include liver and kidney toxicity and mild carcinogenic adverse effects (TABLE 1)." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "Renal lesions in diabetic mouse models\n\nDb/db mice, which have a recessive mutation in the hypothalamic leptin receptor, develop obesity at 4 wk of age and type 2 diabetes at approximately 8 wk of age.In C57BL/6J background, the diabetes and the obesity are usually less severe than in the C57BL/KsJ background (44).Kidneys are generally enlarged in this mouse strain, and structural glomerular changes (e.g., diffuse glomerulosclerosis, GBM thickening) occur without evidence of tubulointerstitial disease (40).Glomerular lesions of the KK mice are characterized by diffuse and nodular mesangial sclerosis without evidence of tubular disease (45).The lack of reliable mouse models prompted the National Institute of Diabetes and Digestive and Kidney Diseases to fund a consortium for the development and phenotyping of new diabetic mouse models that would resemble closely human DNP." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "section_type": "main", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "HV~MEI,: Studies with the Mutation, Diabetes\n\nalmost undetectable. Similarly, the activities of citrate\nlyase and glucose-6-phosphate dehydrogenase were\ngreatly decreased in these older diabetic as compared\n\nDiabetologia\n\nthe diabetic mice have attained m a x i m u m weight,\nafter which no further accumulation of adipose tissue\nis noted.\n\n Fig. 8." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "\n\nSummary of rodent models of type 1 diabetes" + }, + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "section_type": "main", + "text": "\n\nALS/Lt mouse: Alloxan susceptible (ALS) new mouse model is produced by inbreeding outbred CD-1 mice (a commercial stock of ICR mice from which inbred NSY and NON mouse are developed), with selection for susceptibility to alloxan (ALX), a generator of highly reactive oxygen free radicals and a potent betacell toxin.Initially, the type 2 diabetes predisposition of ALS mouse was recognized by congenic analysis of the yellow mutation (Ay) at the agouti locus on chromosome 2. Indeed, in ALS/Lt (a substrain maintained at Jackson Laboratory, Bar Habor) mice, hyperinsulinaemia and impaired glucose tolerance develop spontaneously between 6 and 8 wk of age in alloxan-untreated males.This mouse model with reduced ability to diffuse free radical stress is of obvious interest because free radical-mediated damage is implicated in the pathogenesis and complications of both type 1 and type 2 diabetes 62 ." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "I n the latter three,\nbody weights were stabilized at that seen when treatment was initiated. However, no actual weight losses\nwere seen and the relative obesity of these mice was\nstill apparent.\n Discussion\nThe marked tendency to obesity,\nactivities of several insulin-dependent\nthe degranulation of fl-cells of the islets\nobserved in the younger diabetic mice\n\nthe increased\nenzymes, and\nof Langerhans\nare quite con-\nVol. 3, 2Vo. 2, 1967\n\nD.L. COLEMAXand K.P. I-IuMM]~L:Studies with the Mutation, Diabetes\n\nsistent with the increased levels of circulating insulin\nfound in these mice." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "Results\n\nWe generated an F2 inter-cross between diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains, made genetically obese in response to the Lep ob mutation [24].The cross consisted of .500mice, evenly split between males and females.A comprehensive set of ,5000 genotype markers were used to genotype each F2 mouse (,2000 informative SNPs were used for analysis), and the expression levels of ,40 K transcripts (corresponding to 25,901 unique genes) were monitored in five tissues (adipose, liver, pancreatic islets, hypothalamus, and gastroc (gastrocnemius muscle)) that were harvested from each mouse at 10 weeks of age.In addition to gene expression, several key T2D-related traits were determined for each mouse.The medians, and 1st and 3rd quartiles for the following traits: body weight, the number of islets harvested per pancreas, HOMA, plasma insulin, glucose, triglyceride, and C-peptide are listed in Table 1." + }, + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "section_type": "main", + "text": "\n\nRodent models of diabetic retinopathy iii)" + } + ], + "document_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "db/db&mice", + "diabetes", + "onset", + "age", + "obesity", + "hyperglycemia", + "C57BL/KsJ", + "C57BL/6J", + "insulin&resistance", + "albuminuria" + ], + "metadata": [ + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "Data suggest that circulating IGF-1 levels are higher, insulin resistance is worse, and lean mass is higher in mice with obesity induced at earlier age modeling peripubertal-onset obesity as compared to older mice modeling adult-onset obesity.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab205540" + }, + { + "object": "We used young, leptin receptor deficient Db/Db mice to mimic the effect of diet and diabetes on adolescents. Db/Db and Control mice were fed either Western or Control diets, and were sacrificed at 3 months of age. Db/Db mice were obese, while only female mice developed diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1014541" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "Blockade of IL-27 significantly delayed the onset of diabetic splenocyte-transferred diabetes, while IL-27-treated diabetic splenocytes promoted the onset of autoimmune diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab103352" + }, + { + "object": "The mean age of Parkinsonism onset among LRRK2 G2385R carriers was 42.7 years old for early-onset compared to 74.3 for late-onset patients. LRRK2 G2385R mutation appears to be as prevalent among early-onset as late-onset patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab833283" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "Galectin-3 and S100A9 are overexpressed in Pancreatic cancer-associated diabetes tumors and mediate insulin resistance. Galectin-3 and S100A9 distinguish Pancreatic cancer-associated diabetes from type 2 diabetes in subjects with new-onset diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab310949" + }, + { + "object": "This study investigates the involvement of a 14-bp deletion polymorphism rs371194629 at the 3' untranslated region of HLA-G in the context of T1DM and age of onset.the deletion/deletion DEL/DEL genotype was found to be associated with an early age of onset P = 0.001, while the presence of the insertion allele INS was associated to a later age of onset of type I diabetes mellitus", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab944007" + }, + { + "object": "Data suggest that subjects with point mutation 3243A>G in mtRNA-LeuUUR develop MIDD maternally inherited diabetes and deafness; as compared to patients with T1DM type 1 diabetes mellitus or early-onset T2DM type 2 diabetes mellitus matched for sex, age, duration of diabetes, such MIDD patients have highest rate of osteoporosis.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab211558" + } + ], + "question": "What is the age range in mouse for the onset of diabetes?", + "subquestions": null, + "task_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "usage": { + "chatgpt": 6475, + "gpt-4": 4255, + "gpt-4-turbo-preview": 3283 + }, + "user_id": 2 + }, + "document_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "task_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_02.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_02.json new file mode 100644 index 0000000..c14ab82 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_02.json @@ -0,0 +1,205 @@ +{ + "created_at": "2024-03-25T10:53:10.812694Z", + "data": { + "amplify": false, + "answer": "Yes, the studies identified significant quantitative trait loci associated with both type 1 and type 2 diabetes. These include loci such as TCF7L2, DNER, SRR, HNF1A, KCNQ1, CDKN2A/CDKN2B, JAZF1, CDC123, CAMK1D, TSPAN8, LGR5, THADA, ADAMTS9, NOTCH2, and others. However, the significance of some loci varied across different populations and studies.", + "context": { + "0bcb68e6-4da3-457c-a68c-981b14d1ded8": [ + { + "document_id": "0bcb68e6-4da3-457c-a68c-981b14d1ded8", + "text": "\n\nAdditional large-scale meta-analysis predicated upon increased sample size, were carried out on existing datasets from the WTCCC [18] and the Genetics of Kidneys in Diabetes (GoKinD) study [69][70][71] plus control data derived from the National Institute of Mental Health.These investigators observed significant association of previously observed loci.Importantly, they did not observe evidence of new T1D loci reaching the threshold for genome-wide significance.Instead they re-analyzed the most nominally significant associated SNP in an independent British cohort of approximately 6000 cases, 7000 controls and in 2800 families, where they uncovered four additional loci, BACH2 (previously reported [67]), 10p15 harboring protein kinase C theta (PRKCQ), 15q24 harboring nine genes including the cathepsin H (CTSH), complement 1q (C1q), tumor necrosis factor related protein 6 (C1QTNF6) and somatostatin receptor 3 (SSTR3) genes.Table 1 summarizes the 16 T1D loci reported to date.An example of a tag-SNP that captures the association with T1D in each instance is highlighted together with its relative minor allele frequency in controls and what magnitude of risk or protection it confers.Key references regarding the role of each locus in the context of the disease are included and along with the chromosomal band where each locus resides, the main candidate gene (symbol and full name) is highlighted." + } + ], + "0de85e11-dcbb-4538-b043-ee18a30e9f14": [ + { + "document_id": "0de85e11-dcbb-4538-b043-ee18a30e9f14", + "text": "Detection of established loci\n\nWe explored the extent to which previously reported type 2 diabetes association signals could be detected in African-descent individuals.Based on the previously reported effect sizes and the effect allele frequency and sample size from our African meta-analysis, we had sufficient power (80%) to detect three signals (TCF7L2, DNER and SRR) at genome-wide significance (p < 2.5 × 10 −8 ) (ESM Table 2).Only the TCF7L2 variant reached genome-wide significance in our study, whereas both variants in DNER (rs1861612) and SRR (rs391300), originally discovered in Pima Indians and East Asians, respectively, had p > 0.1 (ESM Table 2)." + } + ], + "1c2f4eb9-5880-418a-be08-4c33ec3a8889": [ + { + "document_id": "1c2f4eb9-5880-418a-be08-4c33ec3a8889", + "text": "\n\nOn the basis of the combined stage 1-3 analyses, we found that six signals reached compelling levels of evidence (P ¼ 5.0 Â 10 -8 or better) for association with T2D (Table 2).As in all linkage disequilibrium (LD)-mapping approaches, characterization of the causal variants responsible, their effect sizes and the genes through which they act will require extensive resequencing and fine-mapping.However, on the basis of current evidence, we found that the most associated variants in each of these signals map to intron 1 of JAZF1, between CDC123 and CAMK1D, between TSPAN8 and LGR5, in exon 24 of THADA, near ADAMTS9 and in intron 5 of NOTCH2." + } + ], + "33c5de8c-7efc-41df-a540-22729d8b7d2c": [ + { + "document_id": "33c5de8c-7efc-41df-a540-22729d8b7d2c", + "text": "\n\nReplication study of newly identified type 1 diabetes risk loci" + } + ], + "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f": [ + { + "document_id": "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f", + "text": "\n\nAlthough these are considered to be loci convincingly associated with susceptibility to type 2 diabetes in populations of European descent, other genes related to susceptibility to the disease are probably still unidentified, particularly those for populations of other ancestries.In order to uncover genetic variants that increase the risk of type 2 diabetes, we conducted a genome-wide association study in Japanese individuals with type 2 diabetes and unrelated controls.We first genotyped 268,068 SNPs, which covered approximately 56% of common SNPs in the Japanese, in 194 individuals with type 2 diabetes and diabetic retinopathy (case 1) and in 1,558 controls (control 1) collected in the BioBank Japan.We compared the allele frequencies of 207,097 successfully genotyped SNPs and selected the 8,323 SNPs showing the lowest P values.We then attempted to genotype these 8,323 SNPs in 1,367 individuals with type 2 diabetes and diabetic retinopathy (case 2) and for 1,266 controls (control 2) (stage 2), and successfully obtained data for 6,731 SNPs (the P value distribution in the second test is shown in Supplementary Fig. 1a online).The results of principal component analysis 8 in the stage 1 and 2 samples and HapMap samples revealed that there was no evidence for population stratification between the case and control groups throughout the present tests (Supplementary Fig. 1b,c).We selected the 9 SNP loci showing P values o0.0001 (additive model in stage 2, Table 1) and genotyped a third set of cases and controls comprising 3,557 Japanese individuals with type 2 diabetes (cases 3,4,5) and 1,352 controls (controls 3,4).We evaluated the differences in the population structure among these three sets of case and two sets of control groups by Wright's F test.As the results indicated that there was no difference in the population structure among these groups (Supplementary Table 1b online), we combined these populations for the third test of case-control study.The third set of analysis identified the significant associations for six SNPs (Table 1), including the CDKAL1 locus at 6p22.3 (rs4712524, rs9295475 and rs9460546), the IGF2BP2 locus at 3q27.2 (rs6769511 and rs4376068) and the KCNQ1 locus at 11p15.5 (rs2283228).The remaining three SNPs (rs13259803, rs612774 and rs10836097) had P values of 40.05 in the third test and were not further examined.CDKAL1 and IGF2BP2 were previously reported as susceptibility genes for type 2 diabetes in the Japanese population 9 .Therefore, we focused on the KCNQ1 locus, which was highly associated with type 2 diabetes." + } + ], + "3a066437-9d88-46c7-bc55-9992728847a7": [ + { + "document_id": "3a066437-9d88-46c7-bc55-9992728847a7", + "text": "\n\nWe consider these data as an interesting preliminary result that surely requires additional independent studies including a higher number of patients in order to confirm and clarify the possible contribution of this locus to the development of T2DM complications." + } + ], + "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a": [ + { + "document_id": "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a", + "text": "DISCUSSION\n\nTaken together, our full second-stage approach and combined meta-analysis have revealed additional loci associated with type 1 diabetes.Clearly the risks are relatively modest compared with previously described associations, and it was only with this sample size at our disposal that we could we detect and establish these signals as true positives through an independent validation effort." + } + ], + "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc": [ + { + "document_id": "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc", + "text": "Identification of susceptibility loci\n\nThe degree of evidence for all reported T2D loci was quantified as follows: a locus with a logarithm of odds ratio (LOD) score of 3 or more was considered significant, a LOD score between 2.2 and 3 was considered suggestive and a LOD score between 1 and 2.2 was considered nominal.For T2D, only those loci were included that were significant at least once, or were suggestive in at least one study and at least nominal in two or more studies.The inclusion of the second category of loci was based on a study by Wiltshire et al. [72], in which it was postulated that locus counting is a useful additional tool for the evaluation of genome scan data for complex trait loci.We used the same two criteria to determine the loci from the five papers published on obesity since 2004 and combined these loci with those from Bell et al. [7].As obesity phenotypes, BMI, serum leptin levels, abdominal subcutaneous and visceral fat, and percentage body fat were included.All of these phenotypes were used as continuous quantitative traits, as well as with various cut-off levels." + } + ], + "4be1d780-404a-4826-ba06-80b2c15e705b": [ + { + "document_id": "4be1d780-404a-4826-ba06-80b2c15e705b", + "text": "\n\nToday, more than 100 loci for type 2 diabetes and glycemic traits have been identified through numerous GWA studies of common and rare variation in populations of diverse ancestral origins [31]; however, to date, very few GWA studies have been published in cohorts of Mexican ancestry.The first GWA study performed in a non-European cohort was published in 2007 and comprised 561 Mexican American type 2 diabetes cases and controls drawn from the Starr County Health Studies [32].Although no loci reached genome-wide significance, several loci identified in prior GWA studies in Europeans were replicated [32].This analysis was subsequently expanded (N = 1273) and meta-analyzed with a cohort from Mexico City (N = 1310) in 2011 [33,34].The most significant variants observed in this meta-analysis included known regions near HNF1A and KCNQ1.Top association signals were then meta-analyzed with the DIAGRAM and DIAGRAM+ datasets of European ancestry individuals, resulting in two regions reaching genome-wide significance: HNF1A and CDKN2A/CDKN2B (Table 1).Top association signals in both studies were annotated to explore their roles as expression quantitative trait loci (eQTL) in both adipose and muscle tissues, revealing a marked excess of transacting eQTL in top signals in both tissue types." + } + ], + "5293f814-f4a7-48e0-b4e5-b1f13fdc8516": [ + { + "document_id": "5293f814-f4a7-48e0-b4e5-b1f13fdc8516", + "text": "\n\n75±79 The main conclusion is that there is no major locus for T2D (analogous to HLA in type 1 diabetes).This is not surprising given the modest l s for T2D (approximately 3.5 in Europeans), imposing a limit on the magnitude of any single gene eect. 4Many scans have consequently been signi®cantly underpowered to detect the modest gene eects anticipated.Certainly, few T2D scans have reported linkages meeting the established criteria for genomewide signi®cance. 80This modest power, combined with the diversity of the pedigrees sampled and the analytical techniques used, means that the replication of positive ®ndings between data sets has been the exception rather than the rule." + } + ], + "711e3d33-a196-4072-bc31-ffaa6bb3efa0": [ + { + "document_id": "711e3d33-a196-4072-bc31-ffaa6bb3efa0", + "text": "Quantitative Trait Analysis\n\nExploration of putative T2DM variants with quantitative glycemic traits in a subset of African-American samples (n = 671 from the IRAS and IRASFS control samples, Table S5) revealed limited insight into the biological mechanism associated with T2DM risk.In addition, the five putative African-American T2DM susceptibility loci were tested for association with quantitative measures of glucose homeostasis in the European Caucasian population, in silico, by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC; [16]).These results did not provide further insight into the probable role these variants may have in disease susceptibility (Table S6).The most significantly associated SNP in African Americans, rs7560163, failed quality controls filters and was not included in analysis likely due to being monomorphic as seen in a representative Caucasian population from the HapMap project (Table S4)." + } + ], + "91d6996a-319d-461e-ae78-3c64a70832cc": [ + { + "document_id": "91d6996a-319d-461e-ae78-3c64a70832cc", + "text": "\n\nDiscovery of novel loci for T2D susceptibility.We tested for T2D association with ~27 million variants passing quality-control filters, ~21 million of which had a minor allele frequency (MAF) < 5%.Our meta-analysis identified variants at 231 loci reaching genomewide significance (P < 5 × 10 −8 ) in the BMI-unadjusted analysis (N eff 231,436) and 152 in the smaller (N eff 157,401) BMI-adjusted analysis.Of the 243 loci identified across these two analyses, 135 mapped outside regions previously implicated in T2D risk (Methods, Fig. 1 and Supplementary Table 2)." + } + ], + "ad88aed6-75ba-469d-b96b-7be4a65be8fc": [ + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "text": "\n\nGenetic studies performed since 2012 have identified many additional T2D loci based on risk alleles common in one population but less common in others.Studies in African Americans identified RND3-RBM43 (28), HLA-B and INS-IGF2 (29).Studies in South Asians identified TMEM163 (30) and SGCG (31).One locus, SLC16A11-SLC16A13, was simultaneously identified in Japanese and Mexican Americans (32,33), and studies in East Asians identified ANK1 (34), GRK5 and RASGRP1 (35), LEP and GPSM1 (32), and CCDC63 and C12orf51 (36).A study of individuals from Greenland identified TBC1D4 (37), and a sequencing-based study of Danes with follow-up in other Europeans identified MACF1 (38).Finally, the largest GWAS to date in American Indians identified DNER at near genome-wide significance (P = 6.6 × 10 −8 ) (39).Three of these studies imputed GWAS data using the 1000 Genomes Project sequence-based reference panels, providing better genome coverage (29,32,33,40).Taken together, these studies highlight the value of diverse populations, including founder and historically isolated populations, to detect risk loci." + } + ], + "b973bd17-aac9-4d68-8ac4-1c683165b68f": [ + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nFinally, a recent study identified additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs. 21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds.Thus, by combining data involving 10,128 samples, the authors found in the initial stages of the analysis highly associated variants (they followed only 69 signals out of over 2 million metaanalyzed SNPs) with P values Ͻ10 Ϫ4 in unknown loci, and 11 of these type 2 diabetes' associated SNPs were taken forward to further stages of analysis.Large stage replication testing allowed the detection of at least six previously unknown loci with robust evidence for association with type 2 diabetes." + }, + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nSurprisingly, data about previous published loci associated with type 2 diabetes were not sufficiently powerful to reach a significant P value in individual scans.For example, variants at SLC30A8 and PPARG were significantly associated with type 2 diabetes only when pooling all the GWAs data, whereas in a single genome scan (DGI), no gene showed a positive signal (P value: 0.92 and 0.83, respectively).Thus, this may suggest that GWAs are still underpowered to find SNPs with small effect size." + } + ], + "d86525a8-0a2f-44a8-b343-61a5df8d6e68": [ + { + "document_id": "d86525a8-0a2f-44a8-b343-61a5df8d6e68", + "text": "\nBackground: The two genome-wide association studies published by us and by the Wellcome Trust Case-Control Consortium (WTCCC) revealed a number of novel loci, but neither had the statistical power to elucidate all of the genetic components of type 1 diabetes risk, a task for which larger effective sample sizes are needed.Methods: We analysed data from two sources: (1) The previously published second stage of our study, with a total sample size of the two stages consisting of 1046 Canadian case-parent trios and 538 multiplex families with 929 affected offspring from the Type 1 Diabetes Genetics Consortium (T1DGC); (2) the Rapid Response 2 (RR2) project of the T1DGC, which genotyped 4417 individuals from 1062 non-overlapping families, including 2059 affected individuals (mostly sibling pairs) for the 1536 markers with the highest statistical significance for type 1 diabetes in the WTCCC results.Results: One locus, mapping to a linkage disequilibrium (LD) block at chr15q14, reached statistical significance by combining results from two markers (rs17574546 and rs7171171) in perfect LD with each other (r 2 = 1).We obtained a joint p value of 1.3610 26 , which exceeds by an order of magnitude the conservative threshold of 3.26610 25 obtained by correcting for the 1536 single nucleotide polymorphisms (SNPs) tested in our study.Meta-analysis with the original WTCCC genome-wide data produced a p value of 5.83610 29 .Conclusions: A novel type 1 diabetes locus was discovered.It involves RASGRP1, a gene known to play a crucial role in thymocyte differentiation and T cell receptor (TCR) signalling by activating the Ras signalling pathway." + } + ], + "dad48e98-2dcc-41ae-866a-139f5540a24c": [ + { + "document_id": "dad48e98-2dcc-41ae-866a-139f5540a24c", + "text": "\n\nFinally, we examined whether genes identified using our association studies were enriched within diabetes-related pathways.We collated a list of 42 genes to which 53 CpG sites associated with T2D traits (CS score ≥1.77, combined P < 0.017) mapped.Even in this small dataset, pathway analysis (Supplementary Material, Table S12) indicated significant enrichment in 31 pathways (Fisher's exact P < 0.05), including those related to circadian clock (P = 0.005), adipocytokine signaling (P = 0.009), leptin pathway (P = 0.023), HDL-mediated lipid transport (P = 0.031) and insulin signaling (P = 0.033)." + } + ], + "e88b610f-8afa-46f7-a03c-d7bd579a7496": [ + { + "document_id": "e88b610f-8afa-46f7-a03c-d7bd579a7496", + "text": "\n\nIn recent years, progress has been made in following up mechanistic studies of GWAS type 2 diabetes-association signals [6,7,9,[25][26][27][28][29][30], but challenges remain in sifting through the many associated variants at a locus to identify those influencing disease.We hypothesized that a common variant with modest effect underlies the association at the CDC123/CAMK1D locus and evaluated the location of high LD variants (r 2 $.7; n = 11) at the locus relative to known transcripts and to putative DNA regulatory elements.We identified two variants that overlapped putative islet and/or liver regulatory regions and none located in exons.We did not assess variants in lower LD (r 2 ,.7), and additional functional SNPs may exist at this locus acting through alternate functional mechanisms untested in the current study." + } + ], + "fdbabc3c-ec60-45ce-9f5c-683f745c4d00": [ + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "\n\nMeta-analysis results for T2D SNPs for insulin and glucose-related traits." + }, + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "A r t i c l e s\n\nBy combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P < 5 × 10 −8 .These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A).The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation.We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits." + } + ] + }, + "data_source": [], + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "TCF7L2", + "DNER", + "SRR", + "HNF1A", + "KCNQ1", + "CDKN2A", + "CDKN2B", + "JAZF1", + "CDC123", + "CAMK1D" + ], + "metadata": [ + { + "object": "We identified a Congenital long QT syndrome LQTS family harboring three compound mutations in different genes KCNQ1-R174C, hERG-E1039X and SCN5A-E428K. IKs-like, IKr-like, INa-like currents and the functional interaction between KCNQ1-R174C and hERG-E1039X channels were studied using patch-clamp.Expression of KCNQ1-R174C alone showed no IKs. Co-expression of KCNQ1-WT + KCNQ1-R174C caused a loss-of-function in IKs", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007244" + }, + { + "object": "Pancreatic cancer was induced in adult mice by the combination of KRASG12D overexpression and loss of Tp53 and Cdkn2a only if Cdkn2b was concomitantly inactivated. inactivation of both Cdkn2b and Cdkn2a was necessary for Rb phosphorylation and to encompass oncogene-induced cellular senescence.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab580373" + }, + { + "object": "Twenty-five different variants were identified in GCK gene 30 probands-61% of positivity, and 7 variants in HNF1A 10 probands-17% of positivity. Fourteen of them were novel 12- GCK /2- HNF1A . ACMG guidelines were able to classify a large portion of variants as pathogenic 36%- GCK /86%- HNF1A and likely pathogenic 44%- GCK /14%- HNF1A , with 16% 5/32 as uncertain significance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab977086" + }, + { + "object": "We found that CDKN2B was a virtual target of miR-15a-5p with potential binding sites in the 3'UTR of CDKN2B 77-83 bp. We also showed that miR-15a-5p could bind to the CDKN2B 3'UTR. The data revealed a negative regulatory role of miR-15a-5p in the apoptosis of smooth muscle cells via targeting CDKN2B, and showed that miR-15a-5p could be a novel therapeutic target of AAA.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1004682" + }, + { + "object": "For each gene and the four pathways in which they occurred, we tested whether pancreatic cancer PC patients overall or CDKN2A+ and CDKN2A- cases separately had an increased number of rare nonsynonymous variants. Overall, we identified 35 missense variants in PC patients, 14 in CDKN2A+ and 21 in CDKN2A- PC cases.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab300370" + }, + { + "object": "we investigated the effects of KCNQ1 A340E, a loss-of-function mutant. J343 mice bearing KCNQ1 A340E demonstrated a much higher 24-h intake of electrolytes potassium, sodium, and chloride. KCNQ1, therefore, is suggested to play a central role in electrolyte metabolism. KCNQ1 A340E, with the loss-of-function phenotype, may dysregulate electrolyte homeostasis", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1008629" + }, + { + "object": "Results show that C-FOS directly binds to rs7074440 TCF7L2. Its knockdown decreases TCF7L2 gene expression proving evidence that c-FOS protein regulates TCF7L2 through its binding to rs7074440.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab661049" + }, + { + "object": "This review provides an update of the latest research advances on JAZF1 and its regulatory network in T2 diabetes mellitus T2DM. The association between JAZF1 polymorphisms and T2DM is discussed as well. The information provided is of importance for guiding future studies as well as for the design of JAZF1-based T2DM therapy. [review]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab935511" + }, + { + "object": "JAZF1 was identified as a direct miR-1275 target. miR-1275 supresses migration and invasion of gastric cancer cells in vitro and in vivo, which was restored by JAZF1 overexpression. Moreover, JAZF1 was recognized as a direct regulator of Vimentin.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab434327" + }, + { + "object": "DNA hypermethylation of KCNQ1 promoter resulted in its downregulation in hepatocellular carcinoma HCC. Bioinformatic analysis indicated a regulatory role of KCNQ1 in the epithelial-to-mesenchymal transition process. Gain-of-function study showed that KCNQ1 exhibited remarkable inhibitory roles on tumor metastasis in vitro and in vivo.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab235904" + } + ], + "question": "Do we find significant quantitative trait loci in the studies for diabetes?", + "subquestions": null, + "task_id": "B7084C90C3CF93908B3FB34BBA00743B", + "usage": { + "chatgpt": 9426, + "gpt-4": 4931, + "gpt-4-turbo-preview": 3991 + }, + "user_id": 2 + }, + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "task_id": "B7084C90C3CF93908B3FB34BBA00743B" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_03.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_03.json new file mode 100644 index 0000000..ea08b61 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_03.json @@ -0,0 +1,189 @@ +{ + "created_at": "2024-03-25T14:00:01.668047Z", + "data": { + "amplify": false, + "answer": "Several environmental factors can affect mice and influence diabetes. These include the stress response, which can be greater in male mice and can confound feeding behavior, the unique gut microbiota of rodents from different production facilities, cage position, single versus group housing, the skill level of the researcher, ambient room temperature, and the type of cage bedding. Diet, particularly high-fat feeding, can also induce obesity and diabetes. Additionally, the intrauterine environment can impact the subsequent development of diabetes amongst offspring.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ae5d2bb-b09d-4646-922a-277188b53cbb": [ + { + "document_id": "0ae5d2bb-b09d-4646-922a-277188b53cbb", + "text": "\n\nIn these models, adult offspring of diabetic animals were noted to have normal development of the endocrine pancreas (Aerts et al., 1997;Ma et al., 2012).However, they develop glucose intolerance and impaired insulin response to glucose challenge, and display insulin resistance, mainly in the liver and muscle, highlighting the presence of both insulin resistance and b-cell dysfunction (Aerts et al., 1988;Holemans et al., 1991a,b).The key role of the intrauterine environment was demonstrated by a series of embryo transfer experiments, which showed that the diabetes risk in a low genetic risk strain can be substantially increased by the hyperglycaemic environment of a dam with a high genetic risk of diabetes (Gill-Randall et al., 2004)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nOther diet-induced rodent models of type 2 diabetes.Although rats and mice are the most commonly used models for studies of type 2 diabetes, other rodents have also been identified as useful models.These include the desert gerbil and the newly described Nile grass rat, both of which tend to develop obesity in captivity." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSince the obesity is induced by environmental manipulation rather than genes, it is thought to model the human situation more accurately than genetic models of obesityinduced diabetes.High fat feeding is often used in transgenic or knock-out models, which may not show an overt diabetic phenotype under normal conditions, but when the beta cells are 'pushed', the gene may be shown to be of importance.It should be noted that the background strain of the mice can determine the susceptibility to diet-induced metabolic changes, and thus, effects could be missed if a more resistant strain is used (Surwit et al., 1995;Bachmanov et al., 2001;Almind and Kahn, 2004).It has also been reported that there is heterogeneity of the response to high fat feeding within the inbred C57BL/6 strain, indicating that differential responses to a high-fat diet are not purely genetic (Burcelin et al., 2002)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "Other considerations and limitations\n\nA myriad of factors affect animal experiments.Men elicit a greater stress response in mice than women 292 , likely confounding feeding behaviour.Rodents from different production facilities (for example, Jackson Laboratory and Taconic) have unique gut microbiotas 293 , perhaps contributing to differences in their susceptibility to DIO and related diabetic complications 293 .Similarly, cage position within a rack of cages, single versus group housing, the skill level of the researcher, ambient room temperature or the type of cage bedding can all affect experimental outcomes." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nWe believe there are several factors that researchers should consider when conducting obesity and diabetes mellitus research in rodents (FIG.2).Although our list is by no means an exhaustive, it demonstrates the complexity and interconnectedness of the myriad of factors that can confound experimental outcomes.Although it is impossible to control for everything, researchers should accurately detail all experimental conditions and methods to allow for better interpretation of the results and, importantly, for better reproducibility." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nFigure2| Important experimental parameters and potential confounders of experimental outcomes in obesity and diabetes research and their interrelatedness.Countless factors influence experimental outcomes when using animal models, and what is enumerated here is by no means a complete list.This figure is one depiction of the multifactorial and interconnected genetic and environmental matrix that makes it virtually impossible to design the perfect experiment.For example, single-housing mice to obtain more accurate food intake data introduces a stress that in turn affects food intake.The severity of this stress response is both strain-specific and sex-dependent.What is important is to be aware of these challenges and to control for them in the most optimal manner.It is equally, if not more, important to accurately and comprehensively detail all experimental conditions in research papers, as these have bearing on the interpretation and reproducibility of the published results.DIO, diet-induced obesity." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAnother concern pertains to control mice.Compared with free-living mice in the wild, laboratory control mice with ad libitum access to food are sedentary, overweight, glucose intolerant and tend to die at a younger age 297 .Comparisons between mice with DIO and control mice might be analogous to investigating the genetic cause of obesity-resistance by comparing humans who are overweight or obese.This potential problem with control mice could explain why the use of DIO diets that have 40% to 60% of total energy from fat is so prevalent, as this might be necessary to achieve divergent weight gains.With free access to running wheels, C57BL/6J mice voluntarily run 5-10 km per day 298,299 .As is the case with humans 300 , mice get health benefits from regular physical activity including weight loss, decreased adiposity and improved insulin sensitivity 301,302 .Physical activity might also affect the epigenome over several generations 303 .An enriched physical and social cage environment alone improves leptin sensitivity and energy expenditure in mice, independent of physical activity 304,305 .Overall, these data suggest that with standard mouse husbandry, chow-fed laboratory mice are not the ideal healthy and lean control group for meaningful obesity research." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nTo better address these points, various animal models have been developed.For example, using HFD-T2DM male rats, the F1 female offspring showed reduced β cell area and insulin secretion, together with glucose intolerance, without changes in body weight [145].The islets of the F1 female offspring showed differential expression of many genes involved in Ca 2+ , mitogen-activated protein kinase and Wnt signaling, apoptosis and cell cycle regulation [145].Similarly, in pregnant C57BL6J mice, food deprivation resulted in β cell mass reduction and an increased risk of β cell failure in offspring [146]." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "They are probably typical of those\nfew mice that develop diabetes more slowly and do\nnot tax the pancreatic insulin supply as severely early\nin the course of the disease. Attempts at therapy. Attempts to keep the weight\nof diabetic mice within normal limits by total or\npartial food restriction resulted in premature deaths. After it was discovered that gluconeogenesis is greatly\nincreased in diabetic mice, attempts were made to\nregulate blood sugar levels and also weight gain by\nfeeding rations devoid of carbohydrate." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "The degree\nof dependence of adiposity, hyperglycemia, and islet\nhypertrophy on food consumption varies among these\nmice, but in all, the increase in islet volume and consequent fi-eell hyperplasia appears to be an effective\n\n247\n\nmeans of maintaining blood sugar concentrations at\nnear normal levels. I n contrast, neither the diabetic\nsand rat [5] nor the diabetic mouse has hypertrophied\nislets and neither effectively controls blood sugar levels." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "HV~MEI,: Studies with the Mutation, Diabetes\n\nalmost undetectable. Similarly, the activities of citrate\nlyase and glucose-6-phosphate dehydrogenase were\ngreatly decreased in these older diabetic as compared\n\nDiabetologia\n\nthe diabetic mice have attained m a x i m u m weight,\nafter which no further accumulation of adipose tissue\nis noted. Fig. 8." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Animal models of diabetes in pregnancy and the role of intrauterine environment\n\nAnother important field of diabetes research that has relied heavily on animal experimentation is the study of diabetes in pregnancy and the role of the intrauterine environment on the subsequent development of diabetes amongst offspring." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "obesity", + "insulin&resistance", + "glucose&intolerance", + "high-fat&diet", + "environmental&factors", + "mouse&models", + "genetic&background", + "intrauterine&environment", + "diet-induced&obesity" + ], + "metadata": [ + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "Data, including data from studies using knockout/transgenic mice, suggest that PrPC is involved in development of insulin resistance and obesity; PrPC knockout mice fed high-fat diet present all the symptoms associated with insulin resistance hyperglycemia, hyperinsulinemia, and obesity; transgenic mice overexpressing PrPC fed high-fat diet exhibit normal insulin sensitivity and reduced weight gain.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab215504" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "FNDC5 attenuates adipose tissue inflammation and insulin resistance via AMPK-mediated macrophage polarization in HFD-induced obesity. FNDC5 plays several beneficial roles in obesity and may be used as a therapeutic regimen for preventing inflammation and insulin resistance in obesity and diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab299408" + }, + { + "object": "WISP1 can be involved in glucose/lipid metabolism in obese youth, which may be modulated by IL-18. Increased WISP1 levels may be a risk factor of obesity and insulin resistance, and WISP1 has a potential therapeutic effect on insulin resistance in obese children and adolescents", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1017591" + }, + { + "object": "Obesity interacted with the TCF7L2-rs7903146 on Type 2 DiabetesT2D prevalence. Association of TCF7L2 polymorphism with T2D incidence was stronger in non-obese than in obese subjects. TCF7L2 predictive value was higher in non-obese subjects. We created obesity-specific genetic risk score with ten T2D-polymorphisms and demonstrated for the first time their higher strata-specific predictive value for T2D risk.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab541919" + }, + { + "object": "LCN-2 expression and serum levels could discriminate IGT from NGT and type 2 diabetes mellitus T2DMfrom IGT obese women and early predicting T2DM among obese women. While, LCN-2 expression level was the independent predictor of IGT in obese women. Combination of both LCN-2 expression and serum levels improved their diagnostic value in early detection of IGT and T2DM among obese women", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab445589" + }, + { + "object": "Gestational obesity and gestational diabetes mellitus may contribute to elevated serum chemerin. Serum chemerin in pregnancy was associated with insulin resistance and triglycerides. Chemerin gene may play a role both in obese and gestational diabetes mellitus patients", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321477" + }, + { + "object": "SGNE1 genetic variation does not contribute to obesity and common forms of Type 2 diabetes but may worsen glucose intolerance and insulin resistance, especially in the background of severe and early onset obesity", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab702715" + }, + { + "object": "our results indicate that UC-MSCs can alleviate insulin resistance in part via production of IL-6 that elicits M2 polarization. Additionally, human obesity and insulin resistance were associated with increased pro-inflammatory ATMs infiltration. Thus, MSCs may be a new treatment for obesity-related insulin resistance and T2D concerning macrophage polarized effects", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab560980" + } + ], + "question": "What environmental factors effect mice that also influence diabetes?", + "subquestions": null, + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "usage": { + "chatgpt": 6673, + "gpt-4": 3552, + "gpt-4-turbo-preview": 2599 + }, + "user_id": 2 + }, + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_04.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_04.json new file mode 100644 index 0000000..9a49068 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_04.json @@ -0,0 +1,191 @@ +{ + "created_at": "2024-03-25T14:02:33.179370Z", + "data": { + "amplify": false, + "answer": "1. Diabetes incidence study where mice were tested for diabetes monthly by blood glucose and weekly by urine assessment.\n2. Studies involving transgenic/knockout animal models of type 2 diabetes to study the role of genes and their effects on peripheral insulin action.\n3. Study involving male type-2 diabetic db/db mice and genetic control non-diabetic db/ mice.\n4. Study involving young adult mice fed a high-fat or Western diet to elicit DIO and insulin resistance.\n5. Study involving Akita mouse genotyping.\n6. Study involving 12-week-old male type 2 diabetic db/db mice and contemporary control wild-type mice.\n7. Study involving transgenic mice to create specific models of type 1 and type 2 diabetes.\n8. Study involving AKITA mice derived from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene.\n9. Study monitoring mice for the development of diabetes.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + } + ], + "42e06cda-627e-46f2-a289-c4c1fb6af8f2": [ + { + "document_id": "42e06cda-627e-46f2-a289-c4c1fb6af8f2", + "text": "Animal group and study design\n\nFirst, one set of animals comprising 12-week-old male type 2 diabetic db/db (C57BL/KsJ-db−/db−, n = 8) and contemporary control wild-type (C57BL/KsJ-db+/db−, n = 8) mice (Jackson Laboratories) were included in this study.Their weights and blood glucose levels were analysed to eliminate variation.Erectile functions of the animals were evaluated by the apomorphine-induced penile erection test, according to a previously described protocol (Pan et al. 2014).Afterwards, intracavernous pressure (ICP) investigations and histological measurements were applied to further confirm the results of the function tests.Then, all mice were sacrificed and the corpus cavernosum (CC) was collected from each mouse.Because the tissue of the CC is difficult to crush, we randomly collected the CCs from two mice and mixed them into one subgroup.As a result, four diabetic subgroups (DB groups) and four normal control subgroups (NC groups) were used for molecular measurements.Second, another set of animals, including three T2DMED and three normal control mice that were independent from the original set of animals, were included in the validation experiments using qRT-PCR.Third, another separate set of animals, including five T2DMED and five control mice, were used to verify one of the predicted targets, IGF-1, using ELISA.A luciferase reporter assay was performed to verify the binding of the differentially expressed miRNAs to the target gene IGF-1.All procedures were approved by the Institutional Animal Care and Use committee at Nanjing Medical University." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 1 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Knock-out and transgenic mice in diabetes research\n\nTransgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003)." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Genetically induced insulin-dependent diabetes\n\nAKITA mice.The AKITA mouse was derived in Akita, Japan from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene preventing correct processing of proinsulin.This causes an overload of misfolded proteins and subsequent ER stress.This results in a severe insulindependent diabetes starting from 3 to 4 weeks of age, which is characterized by hyperglycaemia, hypoinsulinaemia, polyuria and polydipsia.Untreated homozygotes rarely survive longer than 12 weeks.The lack of beta cell mass in this model makes it an alternative to streptozotocin-treated mice in transplantation studies (Mathews et al., 2002).It has also been used as a model of type 1 diabetic macrovascular disease (Zhou et al., 2011) and neuropathy (Drel et al., 2011).In addition, this model is commonly used to study potential alleviators of ER stress in the islets and in this respect models some of the pathology of type 2 diabetes (Chen et al., 2011)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nTo achieve a slow pathogenesis of T2DM, young adult mice 284 or rats 285 are fed a high-fat or Western diet to elicit DIO and insulin resistance.Single or multiple injections with low-dose streptozotocin (~30-40 mg/kg intraperitoneally) then elicit partial loss of β-cells, which results in hypoinsulinaemia and hyperglycaemia.Protocols are being continuously refined and likely differ between species and even strains 283 .The HFD streptozotocin rat is sensitive to metformin, further demonstrating the utility of this model 285 .Downsides of streptozotocin treatment include liver and kidney toxicity and mild carcinogenic adverse effects (TABLE 1)." + } + ], + "785df64a-ebbf-4dca-94dd-0ae27f7ac815": [ + { + "document_id": "785df64a-ebbf-4dca-94dd-0ae27f7ac815", + "text": "Materials and methods\n2.1 Mouse models\n2.1.1 Mouse strains\n2.1.2 Induction of type 1 diabetes\n8\n2.1.3 Insulin treatment on diabetic mice\n2.1.4 Akita mouse genotyping\n2.2 Characterization of diabetic nephropathy in mice\n2.2.1 Proteinuria measurement\n2.2.2 Glomerular cells quantification\n2.2.3 Methenamine silver staining quantification\n\n3. 4. 5. 6." + } + ], + "7e809821-000d-4fff-971d-264650e3612b": [ + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "text": "\n\nii) Rodent models of diabetic retinopathy" + } + ], + "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d": [ + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "text": "\n\nThere are some good reviews available in the literatures describing the transgenic/knockout animal models of type 2 diabetes [114][115][116][117][118] .The transgenic and knockout models are developed for studying the role of genes and their effects on peripheral insulin action such as insulin receptor, IRS-1, IRS-2, glucose transporter (GLUT 4), peroxisome proliferator activated receptor-g (PPAR-g) and tumour necrosis factor-a (TNF-a) as well as in insulin secretion such as GLUT-2, glucokinase (GK), islet amyloid polypeptide (IAPP) and GLP-1 and in hepatic glucose production (expression of PEPCK) associated with development of type 2 diabetes.Further, combination or double knockout mouse models including defect in insulin action and insulin secretion (e.g., IRS-1 +/-/GK +/-double knockout) have been produced which clearly illustrate the mechanisms associated with development of insulin resistance and beta cell dysfunction leading to overt hyperglycaemic state in human type 2 diabetes.These above genetically modified animals exhibit various phenotypic features of type 2 diabetes varying from mild to severe hyperglycaemia, insulin resistance, hyperinsulinaemia, impaired glucose tolerance and others as explained in detail elsewhere 6,9,[114][115][116][117][118] .Very recently, tissue specific knockout mouse models have been achieved, allowing further insight into the insulin action with respect to particular target tissues (muscle, adipose tissue and liver) associated with insulin resistance and type 2 diabetes 115,117,118 .The transgenic/knockout animals are currently used mostly for the mechanistic study in diabetes research and not usually recommended for screening programme as they are more complicated and costly." + } + ], + "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6": [ + { + "document_id": "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6", + "text": "Functional deficits refs\n\nNon-Alzheimer-disease mouse [71][72][73][74]76,78,81,85,87 and rat 59,75,77 ,79,95,97 Mouse [81][82][83][84][85] and rat 79,111 Cerebral effects of inducing diabetes or insulin resistance in normal rodents (that is, non-Alzheimer-disease rodent models) and in rodents genetically modified to accumulate amyloidβ in the brain (that is, rodent models of Alzheimer disease). Common intervetions to induce diabetic conditions in rodents included recessive mutations in the leptin gene (Lep; also known as Ob), defects in the leptin receptor (LEPR; also known as OB-R), diet and administration of streptozotocin. Rodents with pancratic overexpression of human amylin spontaneously develop both type 2 diabetes mellitus and dementia-like pathology." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 1 diabetes" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Introduction\n\nAnimal experimentation has a long history in the field of diabetes research.The aim of this article is to review the commonly used animal models and discuss the recent technological advances that are being employed in the discipline.The review is based on an extensive literature search using the terms rodent, mouse, rat, animal model, transgenics, knockout, diabetes and pathogenesis, in scientific journal databases such as MEDLINE ®.In addition, abstracts presented at meetings of Diabetes UK, the European Association for the Study of Diabetes and the American Diabetes Association over the last 5 years were examined in order to gain an appreciation of recent and ongoing research projects." + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "mouse", + "insulin", + "db/db", + "streptozotocin", + "AKITA", + "transgenic", + "knockout", + "glucose", + "tolerance" + ], + "metadata": [ + { + "object": "Hyperglycemia and blood pressure were similar between Trpc6 knockout and wild-type Akita mice, but knockout mice were more insulin resistant. In cultured podocytes, knockout of Trpc6 inhibited expression of the Irs2 and decreased insulin responsiveness. Data suggest that knockout of Trpc6 in Akita mice promotes insulin resistance and exacerbates glomerular disease independent of hyperglycemia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab367197" + }, + { + "object": "High levels of IP6K3 mRNA were found in myotubes and muscle tissues. Expression was elevated under diabetic, fasting, and disuse conditions in mouse skeletal muscles. Ip6k3-/- mice had lower blood glucose, less insulin, decreased fat, lower weight, increased plasma lactate, enhanced glucose tolerance, lower glucose during an insulin tolerance test, and reduced muscle Pdk4 expression. Ip6k3 deletion extended lifespan.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab348326" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "Mice overexpressing protein S showed significant improvements in blood glucose level, glucose tolerance, insulin sensitivity, and insulin secretion compared with wild-type counterparts. diabetic protein S transgenic mice developed significantly less severe diabetic glomerulosclerosis than controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab482040" + }, + { + "object": "Sequence difference between C57BL/6J and C57BL/6N strains of mice. Pmch knockout mice display decreased circulating glucose, abnormal glucose tolerance and increased oxygen consumption. N carries a private missense variant in this gene isoleucine to threonine. N mice display increased oxygen consumption, but higher circulating glucose levels and normal glucose tolerance compared to J.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab5150" + }, + { + "object": "Ghrl-/- and Ghsr-/- male mice studied after either 6 or 16 h of fasting had blood glucose concentrations comparable with those of controls following intraperitoneal glucose, or insulin tolerance tests, or after mixed nutrient meals. Collectively, our data provide strong evidence against a paracrine ghrelin-GHSR axis mediating insulin secretion or glucose tolerance in lean, chow-fed adult mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab322269" + }, + { + "object": "Patients with type 2 diabetes have significantly higher concentrations of plasma fetuin-B compared with normal glucose tolerance subjects and plasma fetuin-B is strongly associated with glucose and lipid metabolism, chronic inflammation and first-phase glucose-stimulated insulin secretion and insulin resistance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab584502" + }, + { + "object": "In wild-type mice, elevated bile acids impair hepatic insulin sensitivity by blunting the insulin suppression of hepatic glucose production. The impaired hepatic insulin sensitivity could not be attributed to TGR5 signaling, as TGR5 knockout mice exhibited a similar inhibition of insulin suppression of hepatic glucose production.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab406411" + }, + { + "object": "Gestational glucose tolerance in WT mice, but not Ffar2-/- mice improved while on antibiotics. Gestational glucose tolerance worsened in Ffar2-/- mice during a second pregnancy. Maternal Ffar2 expression had no effect on the growth rates and glucose and glucose tolerance in the offspring.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321474" + }, + { + "object": "ApoA-I is inversely associated with insulin resistance in patients with impaired glucose tolerance, and low apoA-I is an independent risk factor for impaired glucose tolerance IGT. These results indicate that apoA-I plays an important role in regulating insulin sensitivity and glucose metabolism in patients with IGT.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab76907" + } + ], + "question": "List mouse studies that involve insulin.", + "subquestions": null, + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "usage": { + "chatgpt": 5772, + "gpt-4": 4159, + "gpt-4-turbo-preview": 3223 + }, + "user_id": 2 + }, + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_05.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_05.json new file mode 100644 index 0000000..98933ac --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_05.json @@ -0,0 +1,390 @@ +{ + "created_at": "2024-04-18T18:24:11.260410Z", + "data": { + "amplify": false, + "answer": "Yes, there are phenotypes and genotypes associated with early onset diabetes in mice. The db/db mouse strain, which carries the diabetes (db) mutation, is one such genotype. These mice develop an abnormal and characteristic deposition of fat beginning at 3 to 4 weeks of age, which is an associated phenotype. The C57BL/Ks strain also exhibits early onset diabetes. The severity of the diabetes condition in these mice depends on the interaction of the mutant gene with the inbred background.", + "context": { + "1bf337a1-ffed-4199-a11f-c5a62df47980": [ + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nEffects of Inbred Background (Table 2).The syndrome produced in BL/Ks diabetes (db) mice, while similar in early development to that of BL/6 obese (ob) mice, has a more severe diabetes-like condition and a less pronounced obesity.However, both mutations when maintained on the same inbred background exhibit identical syndromes from 3 weeks of age on [9,21].Both diabetes and obese mice of the BL/Ks strain have the severe diabetes characterized by insulinopaenia and islet atrophy, whereas both mutations maintained on the BL/6 strain have mild diabetes characterized by islet hypertrophy and hyperplasia of the beta cells.Islet hypertrophy is either sustained or followed by atrophy depending on modifiers in the genetic background rather than the specific action of the mutant gene.The markedly different obesity-diabetes states exhibited when obese and diabetes mice are on different backgrounds points out the importance of strict genetic control in studies with all types of obese-hyperglycaemic mutants.Genetic studies [11] have shown that the modifiers leading to islet hypertrophy and well-compensated diabetes compatible with a near normal lifespan are dominant to those factors causing severe diabetes.Two other mutations, yellow and fat, cause similar diabetes-syndromes and yet have identical symptoms on both inbred backgrounds (Table 2).This may suggest that the primary insult caused by these mutations is not as severe as that for obese and diabetes and that this more gradual initiation of obesity permits the host genome to make a response (islet hypertrophy) compatible with life rather than islet atrophy, insulinopaenia, and life-shortening diabetes." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + } + ], + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "Results\n\nWe generated an F2 inter-cross between diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains, made genetically obese in response to the Lep ob mutation [24].The cross consisted of .500mice, evenly split between males and females.A comprehensive set of ,5000 genotype markers were used to genotype each F2 mouse (,2000 informative SNPs were used for analysis), and the expression levels of ,40 K transcripts (corresponding to 25,901 unique genes) were monitored in five tissues (adipose, liver, pancreatic islets, hypothalamus, and gastroc (gastrocnemius muscle)) that were harvested from each mouse at 10 weeks of age.In addition to gene expression, several key T2D-related traits were determined for each mouse.The medians, and 1st and 3rd quartiles for the following traits: body weight, the number of islets harvested per pancreas, HOMA, plasma insulin, glucose, triglyceride, and C-peptide are listed in Table 1." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Thus, there is a rich literature\nindicating strong genetic effects on glucose metabolism in\nthe B6 and D2 genetic background, and a male-specific\nform of diabetes is known to spontaneously occur in hybrids of this strain. Dental traits\nThe reported link between a Chr 13 locus and dental\nmalocclusions [46] might provide an alternative or additional explanation of the associations we observe. Dental\nmalocclusions were the only major male-specific cause of\ndeath we observed in this mouse population (20 % of\nmales that died before the 750-day phenotyping tests, 0 %\nof females)." + } + ], + "84b037c5-8e75-434f-aad1-d270257963f6": [ + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\n\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\n\nPolygenic basis of ''diabesity'' in mice: the interaction of obesity and diabetes genes Obesity-associated diabetes (''diabesity'') is due to interaction of genes causing obesity with diabetes genes.This conclusion is based on findings indicating that obesity is a necessary but not sufficient condition for the type 2 diabetes-like hyperglycaemia: Obese mice are insulin resistant and therefore more or less glucose intolerant, but in some strains such as C57BL/6J-ob/ob, insulin resistance is compensated by hyperinsulinemia and beta cell hyperplasia, and plasma glucose is only moderately elevated.Other models such as C57BLKS/J-db/db and NZO present overt diabetes mellitus as defined by a threshold of 16.6 mM (300 mg/dl) plasma glucose (Leiter et al. 1998); mice crossing this threshold usually exhibit progressive failure and subsequent apoptosis of beta cells.This type 2 diabetes-like condition is not due to the obesity-causing gene variants but to other genes in the genetic background of the strain, which cause obesity-associated diabetes.The severe and early onsetting diabetes of the C57BLKS/J-db/ db strain is due to the C57BLKS/J background, since mice carrying the db mutation on the C57BL/6J background are not diabetic (Stoehr et al. 2000).Conversely, C57BL/6Job/ob mice are normoglycemic, whereas introgression of the ob mutation into the C57BLKS/J background produced a severely diabetic strain (Coleman 1978).Furthermore, it has been shown that in crosses of lean, normoglycaemic strains with diabetic strains the lean strain can introduce variants that markedly aggravate the diabetic phenotype (Leiter et al. 1998;Plum et al. 2000)." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + } + ], + "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d": [ + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "text": "Spontaneous type 2 diabetic models\n\nSpontaneously diabetic animals of type 2 diabetes may be obtained from the animals with one or several genetic mutations transmitted from generation to generation (e.g., ob/ob, db/db mice) or by selected from non-diabetic outbred animals by repeated breeding over several generation [e.g., (GK) rat, Tsumara Suzuki Obese Diabetes (TSOD) mouse].These animals generally inherited diabetes either as single or multigene defects.The metabolic peculiarities result from single gene defect (monogenic) which may be due to dominant gene (e.g., Yellow obese or KK/A y mouse) or recessive gene (diabetic or db/db mouse, Zucker fatty rat) or it can be of polygenic origin [e.g., Kuo Kondo (KK) mouse, New Zealand obese (NZO) mouse] 13 .Type 2 diabetes occurring in majority of human being is a result of interaction between environmental and multiple gene defects though certain subtype of diabetes do also exist with well defined cause [i.e., maturity onset diabetes of youth (MODY) due to defect in glucokinase gene] and this single gene defects may cause type 2 diabetes only in few cases." + } + ], + "8e92b2e3-b525-4c17-a0cb-5ca740a74c66": [ + { + "document_id": "8e92b2e3-b525-4c17-a0cb-5ca740a74c66", + "text": "\n\nMice of the KK strain exhibit a multigenic syndrome of hyperphagia, moderate obesity, hyperinsulinemia, and hyperglycemia (Ikeda 1994;Nakamura andYamada 1963, 1967;Reddi and Camerini-Davalos 1988).Most KK males develop non-insulindependent diabetes after 4 months of age (Leiter and Herberg 1997).While KK females are much less diabetes prone, they do become obese.Previous analyses indicate that the inheritance of obesity and diabetes phenotypes in KK mice is multigenic (Nakamura and Yamada 1963;Reddi and Camerini-Davalos 1988).In the present study, we have searched for QTLs affecting male and female adiposity and related traits in an intercross between strains KK and B6." + } + ], + "acfbb3e9-6eeb-4541-bd1f-9f460de09958": [ + { + "document_id": "acfbb3e9-6eeb-4541-bd1f-9f460de09958", + "text": "We have previously shown that diabetes traits show strong\nheritability in an F2 intercross between the diabetes-resistant\nC57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob\nmouse strains. We assume that the disease phenotype is brought\nabout by a complex pattern of gene expression changes in key\ntissues [21,22]. However, we also recognize the complexity\ninherent in discriminating the gene expression changes that cause\ndiabetes from those that occur as a consequence of the disease. For\nexample, many genes are known to be responsive to elevated\nblood glucose levels [43]." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Key-words: Spontaneous Diabetes, Genotype : C57BL/\nK5-db, Diabetes in mice, Mutation: diabetes, Obesity,\nPrediabetes, Insulin in plasma, Insulin in pancreas." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Diabetologia 3, 238-248 (1967)\n\nStudies with the Mutation, Diabetes, in the Mouse*\nD . L . COT.EMA~ a n d I ~ T H A a I ~\n\nP. t I u M ~ L\n\nThe Jackson Laboratory, Bar Harbor, Maine\n\nSummary. The mutation, diabetes:,(db), t h a t occurred\nin the C57BL/Ks strain of mice is a unit autosomal recessive gene with full penetrance, and causes metabolic\ndisturbances in homozygous mice resembling diabetes\nmellitus in man." + } + ], + "c24330f7-9f82-404a-86d5-a16d814bb754": [ + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + } + ] + }, + "data_source": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33].\n Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + }, + { + "document_id": "acfbb3e9-6eeb-4541-bd1f-9f460de09958", + "section_type": "main", + "text": "We have previously shown that diabetes traits show strong\nheritability in an F2 intercross between the diabetes-resistant\nC57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob\nmouse strains. We assume that the disease phenotype is brought\nabout by a complex pattern of gene expression changes in key\ntissues [21,22]. However, we also recognize the complexity\ninherent in discriminating the gene expression changes that cause\ndiabetes from those that occur as a consequence of the disease. For\nexample, many genes are known to be responsive to elevated\nblood glucose levels [43]." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "Results\n\nWe generated an F2 inter-cross between diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains, made genetically obese in response to the Lep ob mutation [24].The cross consisted of .500mice, evenly split between males and females.A comprehensive set of ,5000 genotype markers were used to genotype each F2 mouse (,2000 informative SNPs were used for analysis), and the expression levels of ,40 K transcripts (corresponding to 25,901 unique genes) were monitored in five tissues (adipose, liver, pancreatic islets, hypothalamus, and gastroc (gastrocnemius muscle)) that were harvested from each mouse at 10 weeks of age.In addition to gene expression, several key T2D-related traits were determined for each mouse.The medians, and 1st and 3rd quartiles for the following traits: body weight, the number of islets harvested per pancreas, HOMA, plasma insulin, glucose, triglyceride, and C-peptide are listed in Table 1." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nDiabetes-obesity syndromes in rodents" + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Thus, there is a rich literature\nindicating strong genetic effects on glucose metabolism in\nthe B6 and D2 genetic background, and a male-specific\nform of diabetes is known to spontaneously occur in hybrids of this strain.\n Dental traits\nThe reported link between a Chr 13 locus and dental\nmalocclusions [46] might provide an alternative or additional explanation of the associations we observe. Dental\nmalocclusions were the only major male-specific cause of\ndeath we observed in this mouse population (20 % of\nmales that died before the 750-day phenotyping tests, 0 %\nof females)." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Key-words: Spontaneous Diabetes, Genotype : C57BL/\nK5-db, Diabetes in mice, Mutation: diabetes, Obesity,\nPrediabetes, Insulin in plasma, Insulin in pancreas." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "section_type": "abstract", + "text": "\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "section_type": "main", + "text": "\n\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Diabetologia 3, 238-248 (1967)\n\nStudies with the Mutation, Diabetes, in the Mouse*\nD . L . COT.EMA~ a n d I ~ T H A a I ~\n\nP. t I u M ~ L\n\nThe Jackson Laboratory, Bar Harbor, Maine\n\nSummary. The mutation, diabetes:,(db), t h a t occurred\nin the C57BL/Ks strain of mice is a unit autosomal recessive gene with full penetrance, and causes metabolic\ndisturbances in homozygous mice resembling diabetes\nmellitus in man." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "They are probably typical of those\nfew mice that develop diabetes more slowly and do\nnot tax the pancreatic insulin supply as severely early\nin the course of the disease.\n Attempts at therapy. Attempts to keep the weight\nof diabetic mice within normal limits by total or\npartial food restriction resulted in premature deaths.\n After it was discovered that gluconeogenesis is greatly\nincreased in diabetic mice, attempts were made to\nregulate blood sugar levels and also weight gain by\nfeeding rations devoid of carbohydrate." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "section_type": "main", + "text": "\n\nPolygenic basis of ''diabesity'' in mice: the interaction of obesity and diabetes genes Obesity-associated diabetes (''diabesity'') is due to interaction of genes causing obesity with diabetes genes.This conclusion is based on findings indicating that obesity is a necessary but not sufficient condition for the type 2 diabetes-like hyperglycaemia: Obese mice are insulin resistant and therefore more or less glucose intolerant, but in some strains such as C57BL/6J-ob/ob, insulin resistance is compensated by hyperinsulinemia and beta cell hyperplasia, and plasma glucose is only moderately elevated.Other models such as C57BLKS/J-db/db and NZO present overt diabetes mellitus as defined by a threshold of 16.6 mM (300 mg/dl) plasma glucose (Leiter et al. 1998); mice crossing this threshold usually exhibit progressive failure and subsequent apoptosis of beta cells.This type 2 diabetes-like condition is not due to the obesity-causing gene variants but to other genes in the genetic background of the strain, which cause obesity-associated diabetes.The severe and early onsetting diabetes of the C57BLKS/J-db/ db strain is due to the C57BLKS/J background, since mice carrying the db mutation on the C57BL/6J background are not diabetic (Stoehr et al. 2000).Conversely, C57BL/6Job/ob mice are normoglycemic, whereas introgression of the ob mutation into the C57BLKS/J background produced a severely diabetic strain (Coleman 1978).Furthermore, it has been shown that in crosses of lean, normoglycaemic strains with diabetic strains the lean strain can introduce variants that markedly aggravate the diabetic phenotype (Leiter et al. 1998;Plum et al. 2000)." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "section_type": "main", + "text": "Better Mouse Models. A key point to bear in mind in assessing the usefulness of mouse models is the relative plasticity displayed by rodents faced with gene deletions.Thus, differences between the penetrance of mutations in human genes linked to monogenic forms of diabetes, including maturity onset diabetes of the young (MODY), between humans and mice, are usually observed [114] with the mouse equivalents showing far less marked disturbances in glycemia or changes which are seen only after deletion of both alleles.This clearly reflects the limitations of the use of mice (weight ∼25 g, life expectancy ∼3 years) for comparisons with human subjects.Nonetheless, and although the phenotypes of the above murine models are thus often more subtle than the human counterparts, they remain useful models for the study of diabetes, allowing single-targeted gene deletions which are impossible in man.For example, human populations with different genetic backgrounds have different susceptibility to the R235W ZnT8 polymorphism.We should not, therefore, find surprising the results that different genetic backgrounds and different diet reveal different phenotypes in ZnT8 knockout models." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "main", + "text": "Renal lesions in diabetic mouse models\n\nDb/db mice, which have a recessive mutation in the hypothalamic leptin receptor, develop obesity at 4 wk of age and type 2 diabetes at approximately 8 wk of age.In C57BL/6J background, the diabetes and the obesity are usually less severe than in the C57BL/KsJ background (44).Kidneys are generally enlarged in this mouse strain, and structural glomerular changes (e.g., diffuse glomerulosclerosis, GBM thickening) occur without evidence of tubulointerstitial disease (40).Glomerular lesions of the KK mice are characterized by diffuse and nodular mesangial sclerosis without evidence of tubular disease (45).The lack of reliable mouse models prompted the National Institute of Diabetes and Digestive and Kidney Diseases to fund a consortium for the development and phenotyping of new diabetic mouse models that would resemble closely human DNP." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "section_type": "main", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + }, + { + "document_id": "f54c42a7-cba6-4d2c-b5a1-484d3ab107db", + "section_type": "abstract", + "text": "\nTo elucidate the genetic factors underlying non-insulindependent diabetes mellitus (NIDDM), we performed genomewide quantitative trait locus (QTL) analysis, using the Otsuka Long-Evans Tokushima Fatty (OLETF) rat.The OLETF rat is an excellent animal model of NIDDM because the features of the disease closely resemble human NIDDM.Genetic dissection with two kinds of F2 intercross progeny, from matings between the OLETF rat and non-diabetic control rats F344 or BN, allowed us to identify on Chromosome (Chr) 1 a major QTL associated with features of NIDDM that was common to both crosses.We also mapped two additional significant loci, on Chrs 7 and 14, in the (OLETF × F344)F 2 cross alone, and designated these three loci as Diabetes mellitus, OLETF type Dmo 1, Dmo2 and Dmo3 respectively.With regard to suggestive QTLs, we found loci on Chrs 10, 11, and 16 that were common to both crosses, as well as loci on Chrs 5 and 12 in the (OLETF × F344)F 2 cross and on Chrs 4 and 13 in the (OLETF × BN)F 2 cross.Our results showed that NIDDM in the OLETF rat is polygenic and demonstrated that different genetic backgrounds could affect ''fitness'' for QTLs and produce different phenotypic effects from the same locus. Microsatellite markers. Most markers were purchased from ResearchGenetics Inc.; some were synthesized here on the basis of information in public data bases and other reports (Du et al. 1996), and some were isolated directly in the manner described elsewhere (Bihoreau et al. 1997).Phenotyping.Measurements of body weight and oral glucose tolerance test (OGTT) were performed at 30 weeks of age.Each rat was not fed for 16 h before OGTT, and blood was taken (fasting glucose).Glucose solution (2g/kg body weight) was administered orally, and successively blood was collected at 30, 60, 90, 120 min (postprandial glucose).Plasma glucose was measured by a glucose oxidase method with Glucose-B Test Kit" + }, + { + "document_id": "e14d92cf-d1ff-4a75-beee-b3312defeffd", + "section_type": "main", + "text": "\n\nExperimental studies support epidemiological observations and have provided strong evidence for transmission of the obese and diabetic phenotype from parent to offspring through non-genetic mechanisms.Numerous studies in rodents have investigated the effects of maternal obesity obtained in response to high-fat (HF) only, or high-fat/high-sugar diet, before and/or throughout pregnancy and during lactation [32].Overnutrition and obesity in the F0 dam can also yield phenotypes in F2 and F3 generations [33,34].Despite the differences in diet composition, and length of maternal overnutrition, most of the studies showed increased offspring adiposity, insulin resistance, and finally development of poor glucose tolerance and T2D, which has been attributed to a combination of beta cell dysfunction [35] and insulin resistance [36][37][38].One must not forget that abnormalities in beta cell function are critical in defining the T2D risk, because T2D installs only when beta-cell function deteriorates and fails to compensate for insulin resistance in peripheral tissues [8].Prenatal and/or early postnatal exposure to undernutrition also causes increased adiposity and glucose intolerance/diabetes in the offspring (F1) [39,40] and reduction of the number and function of pancreatic islets [41].It also increased adiposity and glucose intolerance in the next (F2) generation [42,43].Moreover, if an undernutrition insult is sustained, there can be further propagation of metabolic phenotypes across many generations.When Wistar rats were subjected to 50% caloric restriction over 50 generations, offspring had fasting hyperinsulinemia, glucose intolerance, and increased adiposity.The impaired metabolic phenotype was not reversed by restoration of nutrition for two generations [44].In rat models of spontaneous diabetes, early beta cell alterations with decreased beta cell mass have been reported in fetuses from both spontaneously diabetic BB rats (T1D model) [45] and spontaneously diabetic GK rats (T2D model) [46].On evaluating the long-term consequences for the progeny in these models, IGT was observed in the offspring of mildly streptozotocin (STZ)-induced diabetic females due to lower insulin secretion in response to glucose, while insulin resistance was reported in the offspring of severely STZ-diabetic mothers [47][48][49].Glucose tolerance was also impaired in the offspring of normal mothers receiving glucose infusions during late gestation, and was associated with decreased glucose-induced insulin secretion [50].Since most of these models of diabetes in pregnancy have drawbacks (see discussion in [51]), we have proposed that embryo transfer experiments might represent a more relevant paradigm [52].When fertilized Wistar rat oocytes were transferred into diabetic GK female rats and the neonates were suckled by non-diabetic Wistar foster mothers, beta cell mass in the F1 offspring was decreased at fetal and adult ages, and impaired glucose tolerance was present at adult age (review in [51]).Control rats originating from Wistar oocyte transfer to normal Wistar females retained normal glucose tolerance.Therefore, maternal spontaneous diabetes shapes offspring beta cell mass and insulin secretion.Such a scenario is relevant to the GK rat model of spontaneous T2D [53] since the GK mothers are mildly hyperglycemic through their gestation and during the suckling period.This could represent one mechanism for initiation of pancreas programming in the F1 offspring of the first founders (F0), since the GK line is issued from intercrosses between females and males Wistar with borderline IGT but otherwise normal basal blood glucose level [53,54].This could also contribute to the lack of attenuation of the diabetic GK phenotype over time [53,54]." + }, + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "section_type": "main", + "text": "Spontaneous type 2 diabetic models\n\nSpontaneously diabetic animals of type 2 diabetes may be obtained from the animals with one or several genetic mutations transmitted from generation to generation (e.g., ob/ob, db/db mice) or by selected from non-diabetic outbred animals by repeated breeding over several generation [e.g., (GK) rat, Tsumara Suzuki Obese Diabetes (TSOD) mouse].These animals generally inherited diabetes either as single or multigene defects.The metabolic peculiarities result from single gene defect (monogenic) which may be due to dominant gene (e.g., Yellow obese or KK/A y mouse) or recessive gene (diabetic or db/db mouse, Zucker fatty rat) or it can be of polygenic origin [e.g., Kuo Kondo (KK) mouse, New Zealand obese (NZO) mouse] 13 .Type 2 diabetes occurring in majority of human being is a result of interaction between environmental and multiple gene defects though certain subtype of diabetes do also exist with well defined cause [i.e., maturity onset diabetes of youth (MODY) due to defect in glucokinase gene] and this single gene defects may cause type 2 diabetes only in few cases." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nTo better address these points, various animal models have been developed.For example, using HFD-T2DM male rats, the F1 female offspring showed reduced β cell area and insulin secretion, together with glucose intolerance, without changes in body weight [145].The islets of the F1 female offspring showed differential expression of many genes involved in Ca 2+ , mitogen-activated protein kinase and Wnt signaling, apoptosis and cell cycle regulation [145].Similarly, in pregnant C57BL6J mice, food deprivation resulted in β cell mass reduction and an increased risk of β cell failure in offspring [146]." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "abstract", + "text": "\nThe diabetes syndromes produced by the two single gene mutations, obese (ob), and diabetes (db) are identical when both genes are expressed on the same inbred background, whereas on different backgrounds the syndrome changes from a severeobesity, moderate-diabetes to a severe life-shortening diabetes.The same initial sequence of events occurs in both conditions.Increased secretion of insulin and hyperphagia is followed by moderate hyperglycaemia with a further compensatory increase in insulin secretion followed by an expansion of the beta-cell mass.On the BL/6 inbred background, hypertrophy and hyperplasia of the beta cells continues until hyperglycaemia is controlled, whereas on the BL/Ks background, beta cell expansion fails and islet atrophy occurs causing insulinopenia, marked hyperglycaemia, and severe diabetes.The data presented here suggest that hyperphagia, hyperinsulinaemia, or both, early in development trigger the abnormal sequence of metabolic events leading to the obesity-diabetes state.These primary events interact with unknown genetic modifiers to produce either a juvenile or maturity-onset type of diabetes.An understanding of the mode of action of these background modifiers influencing the severity of diabetes in mice should lead to a better understanding of the ways in which unknown genetic and environmental factors contribute to human diabetes." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "\n\nBecause hyperglycemia was detected in only a few animals in the colony of origin, and segregation in the early inbreeding experiments was consistent with a single recessive locus, it is conceivable that the hyperglycemia in TH mice is caused by a spontaneously arisen single gene mutation.However, in genetic crosses, a complex inheritance pattern emerges with multiple interacting genes determining the trait and susceptibility loci being contributed from both parental strains.This phenomenon has been observed in both the analysis of single gene obesity mutations (Suto et al., 1998;Leiter et al., 1999) and the analysis of polygenic obesity and diabetes (West et al., 1994;Leiter et al., 1998).This suggests that single gene mutations and QTLs affecting diabetes can manifest similarly and are equally challenging to study." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nThe diabetes syndromes produced by the two single gene mutations, obese (ob), and diabetes (db) are identical when both genes are expressed on the same inbred background, whereas on different backgrounds the syndrome changes from a severeobesity, moderate-diabetes to a severe life-shortening diabetes.The same initial sequence of events occurs in both conditions.Increased secretion of insulin and hyperphagia is followed by moderate hyperglycaemia with a further compensatory increase in insulin secretion followed by an expansion of the beta-cell mass.On the BL/6 inbred background, hypertrophy and hyperplasia of the beta cells continues until hyperglycaemia is controlled, whereas on the BL/Ks background, beta cell expansion fails and islet atrophy occurs causing insulinopenia, marked hyperglycaemia, and severe diabetes.The data presented here suggest that hyperphagia, hyperinsulinaemia, or both, early in development trigger the abnormal sequence of metabolic events leading to the obesity-diabetes state.These primary events interact with unknown genetic modifiers to produce either a juvenile or maturity-onset type of diabetes.An understanding of the mode of action of these background modifiers influencing the severity of diabetes in mice should lead to a better understanding of the ways in which unknown genetic and environmental factors contribute to human diabetes." + }, + { + "document_id": "39e48ed7-91ac-4062-b394-22606abe7e58", + "section_type": "main", + "text": "\n\nOur laboratory has modeled the genetics of obesityinduced type 2 diabetes in two mouse strains, diabetesresistant C57BL/6 (B6) mice and diabetes-susceptible BTBR T ?tf/J (BTBR) mice.When made morbidly obese by the leptin mutation (Lep ob/ob ), B6-ob/ob mice experience moderate and only transient hyperglycemia due to a large expansion of b-cell mass, resulting in a 20-50-fold increase in plasma insulin levels (Clee et al. 2005;Keller et al. 2008).In contrast, BTBR-ob/ob mice experience severe hyperglycemia due to a failure to increase their circulating insulin levels.An in vivo measure of cellular replication showed that B6-ob/ob mice experience an approximately threefold increase in islet cell proliferation, whereas BTBR-ob/ob mice do not increase islet cellular replication in response to obesity (Keller et al. 2008)." + }, + { + "document_id": "b3c2189b-270c-4b4a-9d40-cdc0dceebd9e", + "section_type": "main", + "text": "[PubMed: 1290452]\nPlum L, Kluge R, Giesen K, Altmuller J, Ortlepp JR, Joost HG. Type-2 diabetes-like hyperglycemia in\na backcross model of NZO and SJL mice: characterization of susceptibility locus on chromosome\n4 and its relationship with obesity. Diabetes. 2000; 49:1590–1596. [PubMed: 10969845]\n\nBrain Res. Author manuscript; available in PMC 2013 July 10.\n Boone et al.\n\n Page 9\n\nNIH-PA Author Manuscript\nNIH-PA Author Manuscript\nNIH-PA Author Manuscript\n\nRocha JL, Eisen EJ, Van Vleck LD, Pomp D. A large-sample QTL study in mice: II Body\ncomposition. Mamm Genome. 2004; 15:100–113. [PubMed: 15058381]\nSalinas A, Wilde JD, Maldve RE." + }, + { + "document_id": "c4c5c626-51f7-4b87-84a3-8323a9233ca1", + "section_type": "main", + "text": "\n\nMice homozygous for targeted disruption of the BLK gene have been generated and studied for 8 weeks with a focus on investigating the role of BLK in B-lymphocyte physiology (23).However, no phenotypes relevant to diabetes have been described for these mutants, and no phenotypic data are available with regard to responses to exposure to a diabetogenic environment such as a high-fat diet, or cross breeding with an insulinresistant strain.In light of our findings, further detailed studies are warranted to explore the phenotypes of global KO mice and/or  cell-specific knockouts, in the context of glucose homeostasis." + }, + { + "document_id": "785df64a-ebbf-4dca-94dd-0ae27f7ac815", + "section_type": "main", + "text": ", 2008) and specific genetic factors for predisposition to DN were\nrecently identified in several diabetic sibling studies (Bleyer et al. , 2008; Schelling et\nal.,2008; Tanaka et al. , 2005).\n Similar to humans, inbred strains of mice exhibit differences in their susceptibility to\ndiabetes, renal and cardiovascular diseases (Krolewski et al. , 1996). More recently,\ndifferential susceptibilities to DN have also been observed in well-defined strains of\n\n23" + }, + { + "document_id": "e14d92cf-d1ff-4a75-beee-b3312defeffd", + "section_type": "main", + "text": "\n\nThe heritability of the obese/diabetic paternal phenotype was confirmed by experimental approaches.Multiple animal studies have now demonstrated that offspring's metabolic phenotype is affected by paternal unbalanced diet.Female rats born to fathers on a HF diet had impaired pancreatic islet biology, insulin secretion and glucose tolerance in adulthood [105].The F1 offspring of male mice fed a HF diet exhibited the same obese phenotype as their fathers [99,106].The offspring metabolic phenotype can also be affected by paternal undernutrition.Male and female born to fathers fed a low protein and high sugar diet had increased hepatic expression of lipid biosynthetic genes [98].Offspring metabolic phenotype can also be affected by paternal diabetes.Paternal low-dose STZ-induced diabetes in mice was accompanied by insulitis and insulin secretion deficiency in their F1 offspring [107].Paternal T2D alone (i.e., without associated obesity) impairs early development of endocrine pancreas and adult tolerance du glucose in rat F1 offspring.This was previously suggested by our group using a spontaneous model of paternal T2D [46,108] (Figure 3).To our knowledge, the most comprehensive study to evaluate the transgenerational effects of paternal diabetes on offspring and the mechanisms that mediate these effects, has been provided by Wei et al. [109].Using a non-genetic diabetes mouse model (low dose of STZ combined to HF diet), this group showed that paternal diabetes did not alter body weight, fat mass, or energy intake in F1 offspring, but it induced fasting hyperglycemia, glucose intolerance and insulin insensitivity in the male offspring to an extent similar to that seen in their fathers.To determine the mechanisms of the glucose intolerance and insulin insensitivity observed in the F1 male offspring, Wei et al. performed genome-wide microarray analyses of their pancreatic islets.The expression of 402 genes was modified (97 up-regulated and 305 downregulated).A large proportion of these genes were related to insulin and glucose metabolism, including GTPase activity, GTP and ATP binding, sugar binding, and calcium binding.Wei et al. also found several differentially methylated loci in the F1 islets.The same group also asked whether the metabolic and epigenetic changes in the F1 generation can be passed to the next generation (F2 generation).For that purpose, they mated F1 diabetic males (F1-D) whose fathers were diabetic, with normal females, and then examined metabolic and epigenetic changes in their offspring (F2).The F2 generation also exhibited impaired glucose tolerance and decreased insulin sensitivity (but not fasting hyperglycemia).Examination of the methylation status for 10 regions distributed on different chromosomes that were most affected by paternal diabetes, showed that all of these regions were still significantly affected in the F2 generation.As the F1 animals received normal diet without any STZ treatment and their F2 offspring exhibited similar phenotypic and epigenetic changes, the observed effects of epigenetic inheritance are most likely attributable to the diabetes-associated physiological and metabolic conditions in F0 male founders." + }, + { + "document_id": "8e92b2e3-b525-4c17-a0cb-5ca740a74c66", + "section_type": "main", + "text": "\n\nMice of the KK strain exhibit a multigenic syndrome of hyperphagia, moderate obesity, hyperinsulinemia, and hyperglycemia (Ikeda 1994;Nakamura andYamada 1963, 1967;Reddi and Camerini-Davalos 1988).Most KK males develop non-insulindependent diabetes after 4 months of age (Leiter and Herberg 1997).While KK females are much less diabetes prone, they do become obese.Previous analyses indicate that the inheritance of obesity and diabetes phenotypes in KK mice is multigenic (Nakamura and Yamada 1963;Reddi and Camerini-Davalos 1988).In the present study, we have searched for QTLs affecting male and female adiposity and related traits in an intercross between strains KK and B6." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "section_type": "main", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "section_type": "main", + "text": "Genetic Crosses\n\nHyperglycemic male TH (ՆF7) mice were mated to normal female C57BL/6J (B6) or CAST/Ei (CAST) mice.The resulting F1 hybrid female mice were backcrossed to hyperglycemic male TH mice, and the offspring were referred to as backcross 1 (BC1) animals.Only male BC1 mice were used for the genetic study, since female mice do not develop hyperglycemia.Plasma glucose and insulin levels (nonfasted), body weights, nasal-anal lengths, and five fat pad weights (inguinal, epidydimal, mesenteric, retroperitoneal, and subscapular fat pads) were measured as phenotypic traits." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "section_type": "main", + "text": "Knock-out and transgenic mice in diabetes research\n\nTransgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003)." + }, + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "section_type": "main", + "text": "\n\nThese limitations support the increasing need of experimental systems to characterize the fundamental biological mechanisms responsible for diabetes inheritance and the function of risk genes.In the context of diabetes pathogenesis, in vitro systems are useful but often limited, in particular to assess glucose tolerance, insulin sensitivity, islet architecture and function and diabetes complications.The laboratory mouse provides a wide range of experimental models for diabetes gene discovery and for in vivo post-GWAS studies of diabetes that develops either spontaneously or following gene editing [5].The laboratory rat is also a powerful system to implement phenotyping methods required to record biological variables relevant to common chronic diseases.The rat is the preferred model to perform phenotyping procedures that are often technically challenging in mice or require the collection of large volumes of blood or organs.For these reasons, rat models of type 2 diabetes or hypertension have been successfully used to localise in the genome genes controlling endophenotypes relevant to these complex diseases.This review addresses strategies used to map the genetic determinants of physiological and molecular phenotypes relevant to type 2 diabetes pathogenesis and to characterize their biological function in vivo through examples derived from genetic and genomic research in the Goto-Kakizaki (GK) rat strain." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "section_type": "main", + "text": "\n\nEffects of Inbred Background (Table 2).The syndrome produced in BL/Ks diabetes (db) mice, while similar in early development to that of BL/6 obese (ob) mice, has a more severe diabetes-like condition and a less pronounced obesity.However, both mutations when maintained on the same inbred background exhibit identical syndromes from 3 weeks of age on [9,21].Both diabetes and obese mice of the BL/Ks strain have the severe diabetes characterized by insulinopaenia and islet atrophy, whereas both mutations maintained on the BL/6 strain have mild diabetes characterized by islet hypertrophy and hyperplasia of the beta cells.Islet hypertrophy is either sustained or followed by atrophy depending on modifiers in the genetic background rather than the specific action of the mutant gene.The markedly different obesity-diabetes states exhibited when obese and diabetes mice are on different backgrounds points out the importance of strict genetic control in studies with all types of obese-hyperglycaemic mutants.Genetic studies [11] have shown that the modifiers leading to islet hypertrophy and well-compensated diabetes compatible with a near normal lifespan are dominant to those factors causing severe diabetes.Two other mutations, yellow and fat, cause similar diabetes-syndromes and yet have identical symptoms on both inbred backgrounds (Table 2).This may suggest that the primary insult caused by these mutations is not as severe as that for obese and diabetes and that this more gradual initiation of obesity permits the host genome to make a response (islet hypertrophy) compatible with life rather than islet atrophy, insulinopaenia, and life-shortening diabetes." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "section_type": "main", + "text": "HV~MEI,: Studies with the Mutation, Diabetes\n\nalmost undetectable. Similarly, the activities of citrate\nlyase and glucose-6-phosphate dehydrogenase were\ngreatly decreased in these older diabetic as compared\n\nDiabetologia\n\nthe diabetic mice have attained m a x i m u m weight,\nafter which no further accumulation of adipose tissue\nis noted.\n\n Fig. 8." + } + ], + "document_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "db", + "diabetes", + "C57BL/Ks", + "obesity", + "insulin", + "hyperglycaemia", + "beta&cells", + "mutation", + "C57BLKS/J", + "NZO" + ], + "metadata": [ + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "We identified 32 compound heterozygous mutations and 9 homozygous mutations in IL10 receptor subunit alpha and 1 homozygous mutation in IL10 receptor subunit beta. Among these mutations, 10 novel mutations were identified, and 6 pathogenic mutations had been previously described. In patients with IL10 receptor subunit alpha mutations, c.301C>T p.R101RW and c.537 G>A p.T179T were the most common mutations.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007199" + }, + { + "object": "MicroRNA-26a miR-26a in pancreatic beta cells not only modulates insulin secretion and beta cell replication in an autocrine manner but also regulates peripheral insulin sensitivity in a paracrine manner through circulating exosomes. miR-26a is down-regulated in serum exosomes and islets of obese mice. miR-26a in beta cells alleviates obesity-induced insulin resistance and hyperinsulinemia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab483374" + }, + { + "object": "Ten mutations were identified in five unrelated Chinese families and two sporadic patients with childhood, and adult hypophosphatasia including eight missense mutations and two frameshift mutations. Of which, four were novel: one frameshift mutation p.R138Pfsx45; three missense mutations p.C201R, p.V459A, p.C497S. No identical mutations and any other new ALPL mutations were found in unrelated 50 healthy controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab768168" + }, + { + "object": "Two patients harbored KRAS with codon 12 mutations; one harbored the gly12val mutation with a variation of leu597val in the BRAF exon 15 codon, the other harbored mutation in the BRAF exon 15 codon. One patient harbored a codon 117 mutation with a BRAF V600E mutation. The last patient harbored a NRAS exon 2 mutation with the GGT/GAT, V600G mutation in the BRAF exon 15 codon", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab978995" + }, + { + "object": "Our aim was to identify VHL gene mutations in Argentinian patients who fulfilled the clinical criteria for type 1 VHL disease and in patients with VHL-associated manifestations. VHL mutations were detected in 16/19 84.2% patients in Group 1 and included: gross deletions 4/16; nonsense mutations 6/16; frameshift mutations 4/16; missense mutations 1/16; and splicing mutations 1/16. Three mutations were novel.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab550929" + }, + { + "object": "Data suggest IGT10 mice, diabetes type 2 model, exhibit 2 genetic defects: haploinsufficiency heterozygosity for null allele of insulin receptor Insr; splice-site mutation in protein phosphatase 2 regulatory subunit B alpha Ppp2r2a. Inheritance of either allele results in insulin resistance but not overt diabetes. Double heterozygosity leads to insulin resistance and diabetes type 2 without increase in body weight.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab203476" + }, + { + "object": "WFS1 and GJB2 mutations were identified in eight of 74 cases of Low-Frequency Sensorineural Hearing Loss. Four cases had heterozygous WFS1 mutations; one had a heterozygous WFS1 mutation and a heterozygous GJB2 mutation; and three cases had biallelic GJB2 mutations. Three cases with WFS1 mutations were sporadic; two of them were confirmed to be caused by a de novo mutation based on the genetic analysis of their parents.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1014986" + }, + { + "object": "Study revealed that the conserved HIF1alpha/PFKFB3 signaling pathway is activated by IAPP misfolded protein-driven stress in pancreatic beta-cells to trigger an adaptive protective metabolic response that slows beta-cell death at the expense of beta-cell function. This signaling pathway is activated in beta-cells in humans with type 2 diabetes providing a basis for slow beta-cell loss.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab748654" + }, + { + "object": "Study generated MafA and MafB double-knockout A0B0 mice in which MafB was specifically deleted from beta cells. As a result, the A0B0 mice became more vulnerable to diabetes under a high-fat diet treatment, with impaired islet formation and a decreased number of insulin+ beta cells because of increased beta-cell apoptosis, indicating MafB can take part in the maintenance of adult beta cells under certain pathologic...", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab742544" + } + ], + "question": "Are there phenotypes or genotypes associated with early onset diabetes in mouse?", + "subquestions": null, + "task_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "usage": { + "chatgpt": 8500, + "gpt-4": 4937, + "gpt-4-turbo-preview": 3964 + }, + "user_id": 2 + }, + "document_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "task_id": "C3F023A2C80BEF6F4CD95247A2F2D906" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_06.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_06.json new file mode 100644 index 0000000..372a368 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_06.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-04-18T18:30:30.982190Z", + "data": { + "amplify": false, + "answer": "Gene-environment interactions influence diabetes risk and progression by the interplay of genetic predisposition and environmental factors such as diet, physical activity, and lifestyle. Certain genetic variants may increase the risk of type 2 diabetes (T2D), but this risk can be modified by environmental factors. For instance, the adverse effect of some T2D-associated genetic variants may be attenuated by higher physical activity levels or a healthy lifestyle. Conversely, low physical activity and dietary factors characterizing a Western dietary pattern may augment the risk. Understanding these interactions can help in the development of personalized prevention strategies and treatments for T2D.", + "context": { + "1e3a2816-2a1f-41c3-88d6-03330f04652b": [ + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + } + ], + "2a7da18e-3756-45c5-b18c-a2231685fefd": [ + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002). Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + } + ], + "646689fd-501b-4b27-b8fa-dc098f613044": [ + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + } + ], + "8ab10856-5df7-4f76-897a-84e6f25cd3f5": [ + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + } + ], + "90015638-c92d-4506-95b5-b789f08d613a": [ + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + } + ], + "940283a4-b7e7-4bbe-ba34-c80c4717c15a": [ + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + } + ], + "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155": [ + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + } + ], + "b07d827c-136a-4938-b3f5-b1cde90a2332": [ + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + } + ], + "df542302-18b9-43c2-a421-cba1dba0b3be": [ + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + } + ] + }, + "data_source": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "section_type": "main", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "The Rationale for Studying Gene-Environment Interactions\n\nIt is often said that T2D is the consequence of geneenvironment interactions [17].Indeed, both the environment and the genome are involved in diabetes etiology, and there are many genetic and environmental risk factors for which very robust evidence of association exists.But when epidemiologists and statisticians discuss gene-environment interactions, they are usually referring to the synergistic relationship between the two exposures, and there is limited empirical evidence for such effects in the etiology of cardiometabolic disease.Indeed, in non-monogenic human obesity, a condition widely believed to result from a genetic predisposition triggered by exposure to adverse lifestyle factors, of the >200 human gene-lifestyle interaction studies reported since 1995, only a few examples of gene-environment interactions have been adequately replicated [18], and because these results are derived primarily from cross-sectional studies with little or no experimental validation, even those that have been robustly replicated may not represent causal interaction effects.The evidence base for T2D is thinner still.Nevertheless, other data support the existence of gene-environment interactions in complex disease, thus motivating the search for empirically defined interactions in T2D." + }, + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "section_type": "main", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "section_type": "main", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "section_type": "main", + "text": "\n\nThe availability of detailed information on gene × environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "abstract", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "GENETIC SUSCEPTIBILITY AND GENE-ENVIRONMENT INTERACTIONS-\n\nThe recent advent of genome-wide association studies (GWAS) has led to major advances in the identification of common genetic variants contributing to diabetes susceptibility (40).To date, at least 40 genetic loci have been convincingly associated with type 2 diabetes, but these loci confer only a modest effect size and do not add to the clinical prediction of diabetes beyond traditional risk factors, such as obesity, physical inactivity, unhealthy diet, and family history of diabetes.Many diabetes genes recently discovered through GWAS in Caucasian populations have been replicated in Asians; however, there were significant interethnic differences in the location and frequency of these risk alleles.For example, common variants of the TCF7L2 gene that are significantly associated with diabetes risk are present in 20-30% of Caucasian populations but only 3-5% of Asians (41,42).Conversely, a variant in the KCNQ1 gene associated with a 20-30% increased risk of diabetes in several Asian populations (43,44) is common in East Asians, but rare in Caucasians.It is intriguing that most diabetes susceptibility loci that have been identified are related to impaired b-cell function, whereas only a few (e.g., peroxisome proliferator-activated receptor-g, insulin receptor substrate 1, IGF-1, and GCKR) are associated with insulin resistance or fasting insulin, which points toward b-cell dysfunction as a primary defect for diabetes pathogenesis.It should be noted that most of the single nucleotide polymorphisms uncovered may not be the actual causal variants, which need to be pinpointed through fine-mapping, sequencing, and functional studies." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nSummary of key literature on gene-environment interactions in obesity and type 2 diabetes" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "d978c09f-53e0-4a69-bfa6-e15537f32ffb", + "section_type": "main", + "text": "Genomics and gene-environment interactions\n\nEven though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mellitus are more susceptible to T2DM than others, which suggests that individual differences in response to lifestyle interventions exist 76 .Substantial evidence from twin and family studies has suggested a genetic basis of T2DM 77 .Over the past decade, successive waves of T2DM genome-wide association studies have identified >100 robust association signals, demonstrating the complex polygenic nature of T2DM 5 .Most of these loci affect T2DM risk through primary effects on insulin secretion, and a minority act through reducing insulin action 78 .Individually, the common variants (minor allele frequency >5%) identified in these studies have only a modest effect on T2DM risk and collectively explain only a small portion (~20%) of observed T2DM heritability 5 .It has been hypothesized that lower-frequency variants could explain much of the remaining heritability 79 .However, results of a large-scale sequencing study from the GoT2D and T2D-GENES consortia, published in 2016, do not support such a hypothesis 5 .Genetic variants might help reveal possible aetiological mechanisms underlying T2DM development; however, the variants identified thus far have not enabled clinical prediction beyond that achieved with common clinical measurements, including age, BMI, fasting levels of glucose and dyslipidaemia.A study published in 2014 linked susceptibility variants to quantitative glycaemic traits and grouped these variants on the basis of their potential intermediate mechanisms in T2DM pathophysiology: four variants fitted a clear insulin resistance pattern; two reduced insulin secretion with fasting hyperglycaemia; nine reduced insulin secretion with normal fasting glycaemia; and one altered insulin processing 80 .Considering such evidence, the genetic architecture of T2DM is highly polygenic, and thus, substantially larger association studies are needed to identify most T2DM loci, which typically have small to modest effect sizes 81 ." + }, + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "section_type": "main", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "abstract", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nGene-nutrient or -dietary pattern interactions in the development of T2DM." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "abstract", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "section_type": "main", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002).\n Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + }, + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "section_type": "main", + "text": "Genes and enviromental factors in the development of type 2 diabetes\n\nThe susceptibility to the development of type 2 diabetes (T2DM) is determined by two factors: genetics and environment.The genetic background of T2DM is undoubtedly heterogeneous.Most patients with T2DM exhibit two different defects: the impairment of insulin secretion and decreased insulin sensitivity.This means that there are at least two groups of T2DM susceptibility genes.The substantial contribution of genetic factors to the development of diabetes has been known for many years.The important pieces of evidence for the role of genes are the results of twin studies showing higher concordance rate for T2DM among monozygotic twins (between 41% and 55%) in comparison to dizygotic twins (between 10% and 15%) [43,84].What is interesting, there are populations with extremely high prevalence of T2DM, for example Pima Indians, that can not be explained solely by environmental factors [117].Supporting evidence for the role of genes in development of T2DM include also familial clustering of diabetesrelated traits.It was shown that the level of insulin sensitivity in Caucasians is inherited and a low level is a poor prognostic factor that precedes the development of T2DM [68,69,115].Similar observations were published for other ethnic groups [9,36,60].Those facts underline the importance of genetic factors.However, it is well known that the incidence of T2DM is also associated with environmental factors.Increasing incidence of T2DM during the last few years with obvious links to lifestyle and diet points to the role of enviromental factors in the development of disease [80].The differences in the prevalence of T2DM in relative populations living in different geographical and cultural regions (for example Asians in Japan and USA) also support the role of non-genetic factors [27,125].The relations between genetic and eviromental factors in the development of T2DM may be complex.For instance, enviromental factors may be responsible for the initiation of b-cell damage or other metabolic abnormalities, while genes may regulate the rate of progression to overt diabetes.On the other hand, in some cases genetic factors may be nec-essary for environmental factors even to start processes leading to the development of the disease." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + }, + { + "document_id": "2a94ec9f-6fb6-4ce3-8e33-1a8859470be9", + "section_type": "main", + "text": "\n\nAn individual's risk of developing T2D is influenced by a combination of lifestyle, environmental, and genetic factors.Uncovering the genetic contributors to diabetes holds promise for clinical impact by revealing new therapeutic targets aimed at the molecular and cellular mechanisms that lead to disease.Genome-wide association studies performed during the past decade have uncovered more than 100 regions associated with T2D (5)(6)(7)(8)(9)(10)(11)(12).Although these studies have provided a better understanding of T2D genetics, the majority of identified variants fall outside protein-coding regions, leaving the molecular mechanism by which these variants confer altered disease risk obscure.Consequently, T2D genome-wide association studies have identified few loci with clear therapeutic potential." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nNutrient-or dietary pattern-gene interactions in the development of DM." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "abstract", + "text": "\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "9864689f-2c1e-4fb2-a621-f39d4c57f140", + "section_type": "main", + "text": "\n\nGenetic and epigenetic factors determine cell fate and function.Recent breakthroughs in genotyping technology have led to the identification of more than 20 loci associated with the risk of type 2 diabetes (Sambuy 2007;Zhao et al. 2009).However, all together these loci explain <5% of the genetic risk for diabetes.Epigenetic events have been implicated as contributing factors for metabolic diseases (Barker 1988;Kaput et al. 2007).Unhealthy diet and a sedentary lifestyle likely lead to epigenetic changes that can, in turn, contribute to the onset of diabetes (Kaput et al. 2007).At present, the underlying molecular mechanisms for disease progression remain to be elucidated." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + }, + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "section_type": "main", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "main", + "text": "\n\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "section_type": "main", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + }, + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "section_type": "main", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nWhy do we think GEIs cause type 2 diabetes?dTheevidence supporting the existence of gene-lifestyle interactions in type 2 diabetes comes primarily from 1) the pattern and distribution of diabetes across environmental settings and ethnic groups, 2) familybased intervention studies, in which response to interventions varies less between biologically related individuals than between unrelated individuals; and 3) animal studies in which genetic and environmental factors are experimentally manipulated to cause changes in the expression of metabolic phenotypes.A brief overview of pertinent literature from human studies is given below." + }, + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "section_type": "main", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "abstract", + "text": "\nA bs tr ac t\nBackgroundType 2 diabetes mellitus is thought to develop from an interaction between environmental and genetic factors.We examined whether clinical or genetic factors or both could predict progression to diabetes in two prospective cohorts. MethodsWe genotyped 16 single-nucleotide polymorphisms (SNPs) and examined clinical factors in 16,061 Swedish and 2770 Finnish subjects.Type 2 diabetes developed in 2201 (11.7%) of these subjects during a median follow-up period of 23.5 years.We also studied the effect of genetic variants on changes in insulin secretion and action over time. ResultsStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function.The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes, with a slight increase in the area under the receiveroperating-characteristic curve from 0.74 to 0.75; however, the magnitude of the increase was significant (P = 1.0×10 −4 ).The discriminative power of genetic risk factors improved with an increasing duration of follow-up, whereas that of clinical risk factors decreased. ConclusionsAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nEpidemiological studies have been the predominant source of literature on gene-lifestyle interactions in cardiovascular and metabolic disease.Dozens of casecontrol and cohort studies have been published since the late 1990s purporting to have identified gene-lifestyle interactions in type 2 diabetes or related quantitative metabolic traits.Until recently, however, most of these studies were small and often relied on imprecise estimates of environmental exposures and outcomes.These are prone to error and bias, and exposures may not be assessed at the time when they conveyed their effects; for example, the causative exposures may have occurred very early in life, perhaps even in utero.Moreover, the complexities of modeling interaction effects have forced geneticists to focus primarily on very simple models of interaction, whereas clinically relevant interaction effects likely involve multiple genetic and nongenetic biomarkers.In addition, barely a handful of studies have examined incident type 2 diabetes as an outcome, with most focusing on cross-sectional measures of glucose and others relying on analyses that include prevalent cases of diabetes; this may introduce labeling bias, where the recall of well-known diabetesassociated behaviors is less likely to be accurate in individuals recently diagnosed with disease than in those who have not been diagnosed with disease." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes (T2D) has developed into a major public health concern.While previously considered as a problem primarily for western populations, the disease is rapidly gaining global importance, as today around 285 million people are affected worldwide (IDF, 2009).Lifestyle and behavioural factors play an important role in determining T2D risk.For example, experimentally induced intrauterine growth retardation as well as nutrient restriction during pregnancy in rats have been shown to result in development of T2D in offspring (Inoue et al, 2009) while chronic high-fat diet in fathers programs b-cell dysfunction in female rat offspring (Ng et al, 2010).In humans, a reduced birth weight together with an accelerated growth in infancy has been associated with impaired glucose tolerance (IGT) in adulthood (Bhargava et al, 2004).The pancreatic islets of Langerhans are of central importance in the development of T2D.Under normal conditions, increasing blood glucose levels after a meal trigger insulin secretion from the pancreatic islet b-cells to regulate glucose homeostasis.b-Cell failure marks the irreversible deterioration of glucose tolerance (Cnop et al, 2007b;Tabak et al, 2009) and results in T2D (UKPDSG, 1995).The unbiased genome-wide search for T2D risk genes (Saxena et al, 2007;Scott et al, 2007;Sladek et al, 2007;Zeggini et al, 2007Zeggini et al, , 2008) ) has placed the insulinproducing b-cells at centre stage.These approaches have also inadvertently highlighted the complexity of the biological mechanisms critical to T2D development.Most T2D risk genes identified in these genome-wide association studies (GWAS) affect b-cell mass and/or function (Florez, 2008).While the majority of studies in the field have characterised diabetes aetiology on the basis of genetics, new findings suggest the potential involvement of epigenetic mechanisms in T2D as a crucial interface between the effects of genetic predisposition and environmental influences (Villeneuve and Natarajan, 2010).Epigenetic changes are heritable yet reversible modifications that occur without alterations in the primary DNA sequence.DNA methylation and histone modifications are the main molecular events that initiate and sustain epigenetic modifications.These modifications may therefore provide a link between the environment, that is, nutrition and lifestyle, and T2D but only few studies so far have documented aberrant DNA methylation events in T2D (Ling et al, 2008;Park et al, 2008)." + } + ], + "document_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&gene-environment&interactions", + "genetic", + "environmental", + "physical&activity", + "dietary&factors", + "GWAS", + "insulin&sensitivity", + "β-cell&dysfunction", + "PPARG", + "HNF1B" + ], + "metadata": [ + { + "object": "Data suggest that expression of Pparg can be regulated by dietary factors; expression of Pparg is down-regulated in preadipocytes by tannic acid, a form of tannins found in plant-based foods; Pparg appears to be a major factor in adipogenesis.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab206776" + }, + { + "object": "Circulating adiponectin increased in obese physically active participants >/=180 min/week compared to non-physically active counterparts, indicating that physical activity may mediate baseline adiponectin levels irrespective of the fat mass regulatory effect.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab141573" + }, + { + "object": "Upon stratifying the participants into tertiles by the Matsuda index, we observed an inhibitory relationship between the genetic risk score GRS and insulin secretion in low insulin sensitive but not in high insulin sensitive controls and treatment-naive Type 2 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab985500" + }, + { + "object": "The association of the FTO risk allele with the odds of obesity is attenuated by 27% in physically active adults, highlighting the importance of physical activity in particular in those genetically predisposed to obesity.[Meta-analysis]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab782259" + }, + { + "object": "Serum IGFBP-2 levels increase with age after the age of 50 years and evolve in parallel with insulin sensitivity. IGFBP-2 may therefore be a potential marker for insulin sensitivity. We further show that IGFBP-2 levels can predict mortality in this aging population. However, its predictive value for mortality can only be interpreted in relation to insulin sensitivity.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab699014" + }, + { + "object": "Our study validated the association between an FTO variant and BMI in Taiwanese individuals. In addition, individuals with TG and TT genotypes who were physically active had a decreased BMI. These results indicate that physical activity might be necessary to mitigate the deleterious effect of BMI among genetically susceptible Taiwanese individuals.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab566865" + }, + { + "object": "Irrespective of the genetic defect, adenoviral delivery of C5 improved insulin sensitivity in both C5cont and C5def mice, indicating an insulin-sensitizing function of C5.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab428686" + }, + { + "object": "there was a high heritability for IGF-I and IGFBP-3, but a low heritability for insulin secretion and insulin sensitivity in a group of elderly twins; in addition, study found a negative relationship between IGF-I and insulin sensitivity, which did not seem to be strongly genetically determined", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab887620" + }, + { + "object": "The authors showed that the expression of cysK is regulated by several genetic and environmental factors in addition to CysB: two genetic factors, OmpR and CysE, and lithium.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab785160" + }, + { + "object": "Production of nitric oxide NO within eNOS-positive NGC neurons increases after environmental perturbations, indicating a role for eNOS/NO in modulating environmentally appropriate levels of GA. Inhibition of NO production causes dysregulated behavioral arousal after exposure to environmental perturbation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab615356" + } + ], + "question": "How do gene-environment interactions influence diabetes risk and progression?", + "subquestions": null, + "task_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "usage": { + "chatgpt": 8576, + "gpt-4": 5471, + "gpt-4-turbo-preview": 4534 + }, + "user_id": 2 + }, + "document_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "task_id": "2CB17CD3F1D877A192793DBCA8F458FB" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_07.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_07.json new file mode 100644 index 0000000..19e2777 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_07.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-04-18T18:34:34.295904Z", + "data": { + "amplify": false, + "answer": "MicroRNAs and long noncoding RNAs (lncRNAs) are involved in diabetes. MicroRNAs modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs. They can regulate pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism. They also hold potential as diagnostic biomarkers and possible drug-targets for regulation of dysfunctional cell responses. LncRNAs are implicated in complications associated with diabetes, such as diabetic retinopathy and diabetic nephropathy. They can regulate cell proliferation, viability, migration, and the expression of pathological genes via post-transcriptional mechanisms.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "\n\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + } + ], + "18a35699-873a-4542-b35a-3a4a14edd628": [ + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "text": "\n\nPlatelets are key partaker in CVD and their involvement in the development of cardiovascular complications is strengthened in diabetes (148).Platelets play an important role in the pathophysiology of thrombosis and represent an important source of different RNA species, including pseudogenes, intronic transcripts, non-coding RNAs, and antisense transcripts (149,150).These molecules can be released by platelets through microvescicles, contributing to the horizontal transfer of molecular signals delivered through the bloodstream to specific sites of action (151).The downregulation of miR-223, miR-126, or 146a observed in diabetic and hyperglycemic patients (137,152) has been associated with increased platelet reactivity and aggregation (153,154).In line with these findings, silencing of miR-223 in mice caused a hyperreactive and hyperadhesive platelet phenotype, and was associated with calpain activation through the increased expression of beta1 integrin, kindlin-3, and factor XIII (153,155).Moreover, the modulation of the expression levels of platelet miRNAs can also be measured in plasma.In fact, plasma levels of miR-223 and miR-126 are decreased in diabetics (137,156).This leads to the upregulation of the P2Y12 receptor, as well as P-selectin, further contributing to platelet dysfunction (156).As a result of this interaction, activation level of platelets in type 2 DM is increased (149,156,157).Consistently with this, circulating miR-223 levels are independent predictors of high on-treatment platelet reactivity (158).Another interesting mechanism linking platelets and diabetes involves miR-103b, a platelet-derived biomarker proposed for the early diagnosis of type 2 DM, and the secreted frizzledrelated protein-4 (SFRP4), a potential biomarker of early β cell dysfunction and diabetes.In fact, platelet-derived miR-103b is able to downregulate SFRP4, whose expression levels are significantly increased in pancreatic islets and in the blood of patients with prediabetes or overt diabetes (159).These interesting results identify miR-103b as a novel potential marker of prediabetes and diabetes, and disclose a novel potential therapeutic target in type 2 DM." + }, + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "text": "\n\nIn vitro and in vivo studies concerning the mechanisms that are responsible for the endothelial dysfunction in diabetes demonstrated that, in the presence of high glucose concentrations, upregulation of miR-185 reduced the expression of the glutathione peroxidase-1 (GPx-1) gene, which encodes an enzyme that is important in the prevention of oxidative stress (129); instead upregulation of miR-34a and miR-204 contributed to endothelial cell senescence by impairing SIRT-1 expression and function (130,131).In the endothelium, miR-126 exerts proangiogenic, and anti-inflammatory activities.At a functional level, it enhances VEGF and fibroblast growth factor activities, contributing to vascular integrity and angiogenesis (132,133), recruits progenitor cells through the chemokine CXCL12 (134), while it suppresses inflammation by inhibiting TNF-α, ROS, and NADPH oxidase via HMGB1 (135).Consistently, miR-126 levels are down-regulated in both myocardial tissue and plasma from type 2 diabetic patients without any known anamnestic data for CVD (136,137), and in patients with CAD (138), suggesting that it could represent a new diagnostic marker for diabetes and CVD.Other studies in endothelial colony-forming cells, as well as in progenitor endothelial cells (EPCs) exposed to high glucose, demonstrated that miR-134 and miR-130a affected cell motility and apoptosis, respectively (139,140)." + } + ], + "2dc80127-89ba-47be-9e94-d90c2105be8d": [ + { + "document_id": "2dc80127-89ba-47be-9e94-d90c2105be8d", + "text": "\n\nNumerous recent reports have demonstrated abnormal expression of various miRNAs in renal, vascular and retinal cells under diabetic conditions, and in vivo models of related diabetic complications [8,[87][88][89][90][91]. Notably, the functional relevance of these miRNAs has been highlighted by the fact they target key genes associated with the progression of, or protection against, these complications.In particular, the role of miRNAs in diabetic nephropathy has been extensively studied, including in the actions of TGF-β related to fibrosis and other key renal outcomes in vitro and in vivo [8,[87][88][89][90].In diabetic retinopathy, several miRNAs have been reported to modulate the disease by targeting factors associated with angiogenesis, inflammation, and oxidant stress in RECs and in diabetic retinas [88,89].Reports have also implicated various miRNAs in the aberrant expression of genes associated with diabetic cardiomyopathy [88,91].In addition, effective in vivo targeting of miRNAs has now been demonstrated thanks to advances in nucleotide chemistry and the design of nuclease-resistant anti-miRNAs, which suggest future translational potential of miRNA-based therapies for human diabetic complications [8].Importantly, since miRNAs are stable in biological fluids such as urine and serum [8], they are being assessed in samples from various clinical cohorts as valuable biomarkers for the early detection of diabetic complications, for which there is a major unmet clinical need.It is clear that research in the field of miRNAs and diabetic complications will continue at a rapid pace." + } + ], + "34184c8d-b167-4ae8-bfce-01e18d78fe41": [ + { + "document_id": "34184c8d-b167-4ae8-bfce-01e18d78fe41", + "text": "Introduction\n\nDiabetes-related complications represent one of the most important health problems worldwide with dire social and economic projections (Cooper, 2012).One of the most important medical concerns of the diabetes epidemic is diabetic nephropathy (DN).Diabetic nephropathy is regarded as a prototypical disease of gene and environmental interactions because not all diabetic subjects with traditional risk factors develop clinically evident nephropathy, indicating a role for individual susceptibility.The majority (>85%) of GWAS-identified single nucleotide polymorphisms (SNPs) are located in the non-coding regions of the genome and thus their functional implication lies in identifying the target genes, cell types, and the mode of dysregulation caused by these non-coding SNPs (Maurano et al., 2012).Recent studies indicate that complex trait-causing variants localize to cell-type-specific, functionally important gene regulatory regions where they can disrupt or create transcription factor binding sites to alter transcript levels only in disease-target cell types (Ko and Susztak, 2013;Susztak, 2014).Several elements of the immune system including cytokines and resident chemokines, macrophage recruitment, T lymphocytes, and immune complex deposition have recently been associated with DN (Navarro-González and Mora-Fernández, 2008;Gaballa and Farag, 2013).Since renal cells are also capable of synthesizing pro-inflammatory cytokines such as tumor necrotic factor-alpha (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6), therefore, these cytokines acting in a paracrine or autocrine manner may induce significant effects leading to the development and progression of several renal disorders (Matoba et al., 2010;Pruijm et al., 2012;Shankar et al., 2011).The rationale of this study involved a concerted effort of genotyping, correlation and gene expression techniques involving three pro-inflammatory cytokine genes in the development and progression of DN as well as identification of high risk patients involving susceptibility or poor clinical outcome." + } + ], + "5d2fa6b9-8412-43cb-bc86-e9bcda73a4ef": [ + { + "document_id": "5d2fa6b9-8412-43cb-bc86-e9bcda73a4ef", + "text": "They also identified enrichment in coagulation and\ncomplement pathways, signaling pathways, tissue remodeling, and antigen presentation, including PI3K-Akt, Rap1,\nToll-like, and NOD-like. Sun et al. [25] studied diabetic retinopathy and identified four stress-inducible genes Rmb3,\nCirbp, Mt1, and Mt2 which commonly exist in most retinal\ncell types. Diabetes increases the inflammatory factor gene\nexpressions in retinal microglia and stimulates the immediate early gene expressions (IEGs) in retinal astrocytes. Van Zyl et al. [30] studied glaucoma cases and identified\nthe cell types that represent gene expressions implicated in\nglaucoma." + } + ], + "6011e960-6a6e-47fe-94f2-2c21c224fd25": [ + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "text": "\n\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3β isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies." + } + ], + "7e809821-000d-4fff-971d-264650e3612b": [ + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "text": "M A N U S C R I P T A C C E P T E D\n\nIn relation to the regulation of gene expression, the role of microRNAs (miRNAs) in diabetic retinopathy has been gaining more emphasis.miRNAs are non-coding small RNAs which modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs.miRNAs can be detected in serum (free, associated with proteins or within membrane-bound particles) (Weiland et al., 2012), vitreous (Ragusa et al., 2013) and aqueous (Dunmire et al., 2013).As reviewed by Mastropasqua et al., miRNAs hold considerable interest for diabetic retinopathy since they can regulate important pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism (Mastropasqua et al., 2014).Although based on a small patient sample, it has been reported that three separate miRNAs (miR-21, miR-181c, and miR-1179) in serum of patients with diabetic retinopathy have potential to be used as biomarkers for early detection of disease (Li et al., 2014;Qing et al., 2014).While this is still a growing research area, miRNAs hold considerable clinical potential in the diabetic retinopathy field, both as possible drug-targets for regulation of dysfunctional cell responses and as diagnostic biomarkers." + } + ], + "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753": [ + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "text": "Roles of lncRNAs in diabetic complications\n\nApart from being involved in major metabolic tissues during diabetes as discussed above, lncRNAs are implicated in complications associated with diabetes.Diabetic retinopathy is one of the common complications in diabetic patients, which leads to impaired or loss of vision.Altered expression of lncRNAs, namely MALAT1 [82,83] and MEG3 [84], are reported to be associated with diabetic retinopathy.In STZ-induced diabetic rats, the expression of MALAT1 is elevated in the endothelial cells of the retina and knockdown of MALAT1 ameliorates retinopathy in STZ-induced rats [82].The lncRNA, MEG3, was also found to be downregulated in the retina of STZ-induced diabetic mice and its in vitro knockdown in retinal endothelial cells was found to regulate cell proliferation, viability, and migration [84].Hyperglycemia as in diabetes causes upregulation of ANRIL levels in endothelial cells [85,86], and this elevates the levels of the PRC2 subunit, EZH2 that consequently promotes the expression of VEGF, a key promoter of angiogenesis [85].Another major complication associated with diabetes is diabetic nephropathy, and this is considered a major cause of end-stage renal disease and disability in diabetic patients [87].Recent studies show that lncRNAs play important roles in the development of diabetic nephropathy and accumulation of extracellular matrix (ECM) proteins.There is higher expression of the lncRNA, PVT1, during diabetic nephropathy, and this increase leads to increased fibrosis due to accumulation of ECM proteins in renal cells [88]; downregulation of PVT1 reduces ECM accumulation [88].LncRNA PVT1 is also a host to miR-1207-5p and this miRNA is shown to regulate the expression of fibronectin1 (FN1), plasminogen activator inhibitor-1 (PAI1), and transforming growth factor beta 1 (TGFβ1) [89].In renal tube injury during diabetes, the lncRNA, MIAT, is under-expressed, and this negatively correlates with creatinine and BUN levels in the serum of these subjects.It has been shown to regulate cell viability of proximal convoluted renal tubules [90].In diabetic nephropathic mice, the lncRNA, MGC, is increased in renal mesangial cells.Interestingly, this lncRNA harbours a cluster of approximately 40 miRNAs, and is regulated by the ER stress marker C/EBP homologous protein (CHOP) [91].In CHOP -deficient mice, there is decreased expression of the lncRNA, MGC, and the clustered miRNAs, and these mice have shown an improvement in diabetic nephropathy [91].Diabetic nephropathy is also associated with increased levels of lincRNA, Gm4419, and this exerts its action by interacting with NF-κβ.Knockdown of this lincRNA in renal mesangial cells lowers cellular proliferation and inhibits expression of NF-κβ in hyperglycemic states [92].The lncRNA, TUG1, that is upregulated in diabetic nephropathy acts as sponge for miR-377 and regulates PPAR-γ expression which further modulates the expression of FN1, collagen type IV alpha 1 chain (COL4A1), PAI1, and TGFβ1 in renal mesangial cells [93].Diabetic cardiomyopathy is a critical end-stage complication associated with diabetes.Several such cardiovascular complications and myocardial dysfunction in diabetic patients lead to heart failure [94].Differential expression analysis in cardiac tissue from normal and diabetic rats shows that the lncRNA, MALAT1, is upregulated during cardiomyopathy and knockdown of this lncRNA improves left ventricular systolic function by reducing myocardial inflammation in diabetic rats [95,96].Decreased expression of the lncRNA, H19, is also reported during diabetes [68,70], and this often results in decreased expression of the exonic miRNA, miR-675 [97,98].mir-675 directly targets the voltage-dependent anion channel 1 (VDAC1) which is involved in mitochondria-mediated apoptosis in the cardiac tissue during diabetes.H19 overexpression in diabetic rats reduces oxidative stress, apoptosis, and inflammation, and improves ventricle function [98].LncRNAs NONRATT021972 and uc.48+ are reported to be associated with diabetic neuropathic pain [99,100], and inhibition of both have been shown to alleviate such neuropathic pain by activating the P2X3 receptor.Impaired wound closure is a notable complication associated with diabetes and a recent report shows decreased levels of the lncRNA, Lethe in such impaired dorsal wounds of diabetic mice.This was demonstrated to be associated with increased ROS production, possibly through regulation of NOX2 expression [101]." + }, + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "text": "\n\nAll these suggest towards important roles of various lncRNAs in complications associated with diabetes and, therefore, assume importance to be studied in detail." + } + ], + "80e1b2af-be79-4d9b-852f-46bf3e23c963": [ + { + "document_id": "80e1b2af-be79-4d9b-852f-46bf3e23c963", + "text": "\n\nAn overall important consideration in study design is that similar to RNA, noncoding RNAs are tissue and cell specific [24,[77][78][79][80][81][82].Given that it is still unknown if pathogenic changes in AMD are localized to specific ocular tissues or systemic, one must take into consideration that potential biomarkers identified in the peripheral blood as \"disease associated\" may not reflect the disease mechanism occurring in the neural retina and/or RPE." + } + ], + "88dde947-5255-40e1-92d5-afde089b517b": [ + { + "document_id": "88dde947-5255-40e1-92d5-afde089b517b", + "text": "\n\nSkol et al. developed methods to study genomics and transcriptomics together to help discover genes that cause diabetic retinopathy.Genes involved in how cells respond to high blood sugar were first identified using cells grown in the lab.By comparing the activity of these genes in people with and without retinopathy the study identified genes associated with an increased risk of retinopathy in diabetes.In people with retinopathy, the activity of the folliculin gene (FLCN) increased more in response to high blood sugar.This was further verified with independent groups of people and using computer models to estimate the effect of different versions of the folliculin gene." + } + ], + "d23e9456-8ee8-46e0-9870-18ff69965c28": [ + { + "document_id": "d23e9456-8ee8-46e0-9870-18ff69965c28", + "text": "miRNAs in Kidney Disease and Diabetic Nephropathy\n\nDiabetic nephropathy is a progressive kidney disease and a major debilitating complication of both type 1 and type 2 diabetes that can lead to end-stage renal disease (ESRD) and related cardiovascular disorders.Absence or lower levels of particular miRNAs in the kidney compared with other organs may permit renal specific expression of target proteins that are important for kidney functions [45].Figure 4 depicts the connection between the role of miRNAs and kidney fibrosis.Altered expression of miRNAs causes renal fibrosis by inducing EMT, EndMT, and other fibrogenic stimuli.The accumulative effects of hyperglycaemia, inflammatory cytokines, proteinuria, ageing, high blood pressure, and hypoxia result into alteration of miRNAs expression profiles.The altered miRNAs level causes the initiation of such transition program in normal kidney, finally fibrosis.Some of the miRNAs that are more abundant in the kidney compared with other organs include miR-192, miR-194, miR-204, miR-215, and miR-216.A critical role of miRNA regulation in the progression of glomerular and tubular damage and the development of proteinuria been suggested by studies in mice with podocytespecific deletion of Dicer [46].There was a rapid progression of renal disease with initial development of albuminuria followed by pathological features of glomerulosclerosis and tubulointerstitial fibrosis.It is likely that these phenotypes are due to the global loss of miRNAs because of Dicer deletion, but, given multiple miRNAs and their myriad targets, the precise pathways responsible require identification.These investigators also identified specific miRNA changes, for example, the downregulation of the miR-30 family when Dicer was deleted.Of relevance, the miR-30 family was found to target connective tissue growth factor, a profibrotic molecule that is also downstream of transforming growth factor (TGF)- [47].Thus, the targets of these miRNAs may regulate critical glomerular and podocyte functions.These findings have also been complemented by an elegant study revealing a developmental role for the miR-30 family during pronephric kidney development in Xenopus [48].Sun et al. [49] identified five miRNAs (-192, -194, -204, -215, and -216) that were highly expressed in human and mouse kidney using miRNA microarray.A recent report using new proteomic approaches to profile and identify miRNA targets demonstrated that miR-NAs repress their targets at both the mRNA and translational levels and that the effects are mostly relatively mild [50].The role of miR-192 remains controversial and highlights the complex nature of miRNA research.Kato et al. [51] observed increased renal expression of miR-192 in streptozotocin-(STZ-) induced diabetes and in the db/db mouse and demonstrated that transforming growth factor (TGF-1) upregulated miR-192 in mesangial cells (MCs).miR-192 repressed the translation of Zeb2, a transcriptional repressor that binds to the E-box in the collagen 12 (col12) gene.They proposed that miR-192 repressed Zeb2 and resulted in increased col12 expression in vitro and contributed to increased collagen deposition in vivo.These data suggest a role for miR-192 in the development of the matrix accumulation observed in DN.It is interesting that the expression of miR-192 was increased by TGF- in mouse MCs (mesangial cells), whereas, conversely, the expression of its target, Zeb2, was decreased [51].This also paralleled the increased Col1 2 and TGF- expression [51].These results suggested that the increase in TGF- in vivo in diabetic glomeruli and in vitro in MCs can induce miR-192 expression, which can target and downregulate Zeb2 thereby to increase Col1 2.This is supported by the report showing that miR-192 is upregulated in human MCs treated with high glucose [51].TGF- induced downregulation of Zeb2 (via miR-192) and Zeb1 (via potentially another miRNA) can cooperate to enhance Col1 2 expression via de-repression at E-box elements [51].In contrast to the above, other reports suggest the relationship between miR-192 and renal fibrosis may be more complicated.Krupa et al. [52] identified two miRNAs in human renal biopsies, the expression of which differed by more than twofold between progressors and nonprogressors with respect to DN, the greatest change occurring in miR-192 which was significantly lower in patients with advanced DN, correlating with tubulointerstitial fibrosis and low glomerular filtration rate.They also reported, in contrast to the Kato et al. [51] study in MCs, that TGF-1 decreased expression of miR-192 in cultured proximal tubular cells (PTCs).These investigators concluded that a decrease in miR-192 is associated with increased renal fibrosis in vivo.Interestingly, connective tissue growth factor (CTGF) treatment also resulted in fibrogenesis but caused the induction of miR-192/215 and, consequently, decreased Zeb2 and increased E-cadherin.The contrasting findings above highlight the complex nature of miRNA research.Some of the differences may relate to models and/or experimental conditions; however, one often overlooked explanation is that some effects of miRNAs and inhibitors are likely to be indirect in nature.A recent report also showed that BMP6-induced miR-192 decreases the expression of Zeb1 in breast cancer cells [53].Thus, TGF- induced increase in the expression of key miRNAs (miR-192 and miR-200 family members) might coordinately downregulate E-box repressors Zeb1 and Zeb2 to increase Col12 expression in MCs related to the pathogenesis of DN.The proximal promoter of the Col1a2 gene responds to TGF- via smads and SP1.Conversely, the downregulation of Zeb1 and Zeb2 by TGF- via miR-200 family and miR-192 can affect upstream E-box regions.Because E-boxes are present in the upstream genomic regions of the miR-200 family, miR-200 family members may themselves be regulated by Zeb1 and Zeb2 [54].It is possible that the miR-200 family upregulated by TGF- or in diabetic glomeruli under early stages of the disease can also regulate collagen expression related to diabetic kidney disease by targeting and downregulating E-box repressors.miR-192 might initiate signaling from TGF- to upregulate miR-200 family members, which subsequently could amplify the signaling by further regulating themselves through down regulation of Ebox repressors.Such events could lead to progressive renal dysfunction under pathologic conditions such as diabetes, in which TGF- levels are enhanced.Conversely, there are several reports that miR-200 family members and miR-192 can be suppressed by TGF-, and this promotes epithelial-tomesenchymal transition (EMT) in cancer and other kidneyderived epithelial cell lines via subsequent upregulation of targets Zeb1 and Zeb2 to repress E-cadherin [54,55]." + } + ], + "e66846a6-1546-481b-baae-a55fc524c8af": [ + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "text": "\n\nDR. HARRINGTON: You mentioned Liu's data from China [abstract; Liu Z-H et al J Am Soc Nephrol 14:400A, 2003], which overwhelmed me.Apparently there are 182 genes whose expression is up-or down-regulated significantly in patients with diabetes.If I asked you to pick the \"top three\" genes other than the ACE polymorphisms, which three would you choose and why?DR.ADLER: Well, actually I didn't see all of their results nor did they report all 182.But I guess my favorite ones would be some that relate to the ROS pathway because this is an all-purpose pathway of cell injury fueled by a hyperglycemic environment; some that relate to podocyte structure to explain the development of proteinuria; and TGF-b, which is a master regulator of sclerosis and fibrosis." + } + ], + "ec62a4d9-2fe2-49b0-84d8-13b1597e2067": [ + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "IncRNAs and microRNAs\n\nFigure 1 | Emerging molecular mechanisms of diabetic nephropathy.Diabetic conditions induce the expression of growth factors such as transforming growth factor β1 and angiotensin II, cytokines and AGEs to promote inflammation, fibrosis and hypertrophy, which contribute to the progression of diabetic nephropathy.These factors stimulate various signal transduction mechanisms that activate downstream transcription factors.They can also affect DNA methylation and histone modifications, which result in increased chromatin accessibility to transcription factors near pathological genes in renal cells.Coordinated interactions between transcription factors and epigenetic mechanisms can increase the expression of not only coding RNAs, but also noncoding RNAs such as microRNAs and lncRNAs.Furthermore, microRNAs and lncRNAs can also increase the expression of pathological genes via post-transcriptional mechanisms.Notably, the induction of key coding genes and proteins, lncRNAs and microRNAs can also 'lock' open chromatin states to create persistent expression of genes, which could be one mechanism of metabolic memory.Abbreviations: AGE, advanced glycation end-product; lncRNA, long noncoding RNA." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "Key points\n\n■ Diabetic conditions induce inflammation, fibrosis and hypertrophy in renal cells through various cytokines and growth factors such as transforming growth factor β1, angiotensin II and platelet-derived growth factor ■ The engagement of cytokines and growth factors with their receptors triggers signal transduction cascades that result in the activation of transcription factors to increase expression of inflammatory and fibrotic genes ■ These signalling mechanisms affect epigenetic states-such as DNA methylation and chromatin histone modifications-to augment the expression of profibrotic and inflammatory genes, as well as noncoding RNAs ■ Noncoding RNAs that are induced by diabetic conditions can also promote the expression of pathological genes via various post-transcriptional and post-translational mechanisms ■ These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for 'metabolic memory' ■ Key epigenetic regulators, microRNAs and long noncoding RNAs could serve as new therapeutic targets for diabetic nephropathy" + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "\n| Diabetic nephropathy (DN), a severe microvascular complication frequently associated with both type 1 and type 2 diabetes mellitus, is a leading cause of renal failure.The condition can also lead to accelerated cardiovascular disease and macrovascular complications.Currently available therapies have not been fully efficacious in the treatment of DN, suggesting that further understanding of the molecular mechanisms underlying the pathogenesis of DN is necessary for the improved management of this disease.Although key signal transduction and gene regulation mechanisms have been identified, especially those related to the effects of hyperglycaemia, transforming growth factor β1 and angiotensin II, progress in functional genomics, high-throughput sequencing technology, epigenetics and systems biology approaches have greatly expanded our knowledge and uncovered new molecular mechanisms and factors involved in DN.These mechanisms include DNA methylation, chromatin histone modifications, novel transcripts and functional noncoding RNAs, such as microRNAs and long noncoding RNAs.In this Review, we discuss the significance of these emerging mechanisms, how they mediate the actions of growth factors to augment the expression of extracellular matrix and inflammatory genes associated with DN and their potential usefulness as diagnostic biomarkers or novel therapeutic targets for DN." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "\n\n| microRNAs relevant to the pathogenesis of diabetic nephropathy" + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "Review criteria\n\nA search for original published articles focusing on \"diabetic nephropathy\", \"signal transduction\", \"noncoding RNAs\", \"microRNAs\", \"long noncoding RNAs\", \"genetics\" and \"epigenetics\" was performed in MEDLINE and PubMed.All articles identified were English-language, full-text papers.We also searched the reference lists of identified articles for further relevant papers." + } + ] + }, + "data_source": [ + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "section_type": "main", + "text": "\n\nAll these suggest towards important roles of various lncRNAs in complications associated with diabetes and, therefore, assume importance to be studied in detail." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "main", + "text": "IncRNAs and microRNAs\n\nFigure 1 | Emerging molecular mechanisms of diabetic nephropathy.Diabetic conditions induce the expression of growth factors such as transforming growth factor β1 and angiotensin II, cytokines and AGEs to promote inflammation, fibrosis and hypertrophy, which contribute to the progression of diabetic nephropathy.These factors stimulate various signal transduction mechanisms that activate downstream transcription factors.They can also affect DNA methylation and histone modifications, which result in increased chromatin accessibility to transcription factors near pathological genes in renal cells.Coordinated interactions between transcription factors and epigenetic mechanisms can increase the expression of not only coding RNAs, but also noncoding RNAs such as microRNAs and lncRNAs.Furthermore, microRNAs and lncRNAs can also increase the expression of pathological genes via post-transcriptional mechanisms.Notably, the induction of key coding genes and proteins, lncRNAs and microRNAs can also 'lock' open chromatin states to create persistent expression of genes, which could be one mechanism of metabolic memory.Abbreviations: AGE, advanced glycation end-product; lncRNA, long noncoding RNA." + }, + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "section_type": "main", + "text": "M A N U S C R I P T A C C E P T E D\n\nIn relation to the regulation of gene expression, the role of microRNAs (miRNAs) in diabetic retinopathy has been gaining more emphasis.miRNAs are non-coding small RNAs which modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs.miRNAs can be detected in serum (free, associated with proteins or within membrane-bound particles) (Weiland et al., 2012), vitreous (Ragusa et al., 2013) and aqueous (Dunmire et al., 2013).As reviewed by Mastropasqua et al., miRNAs hold considerable interest for diabetic retinopathy since they can regulate important pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism (Mastropasqua et al., 2014).Although based on a small patient sample, it has been reported that three separate miRNAs (miR-21, miR-181c, and miR-1179) in serum of patients with diabetic retinopathy have potential to be used as biomarkers for early detection of disease (Li et al., 2014;Qing et al., 2014).While this is still a growing research area, miRNAs hold considerable clinical potential in the diabetic retinopathy field, both as possible drug-targets for regulation of dysfunctional cell responses and as diagnostic biomarkers." + }, + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "section_type": "main", + "text": "Roles of lncRNAs in diabetic complications\n\nApart from being involved in major metabolic tissues during diabetes as discussed above, lncRNAs are implicated in complications associated with diabetes.Diabetic retinopathy is one of the common complications in diabetic patients, which leads to impaired or loss of vision.Altered expression of lncRNAs, namely MALAT1 [82,83] and MEG3 [84], are reported to be associated with diabetic retinopathy.In STZ-induced diabetic rats, the expression of MALAT1 is elevated in the endothelial cells of the retina and knockdown of MALAT1 ameliorates retinopathy in STZ-induced rats [82].The lncRNA, MEG3, was also found to be downregulated in the retina of STZ-induced diabetic mice and its in vitro knockdown in retinal endothelial cells was found to regulate cell proliferation, viability, and migration [84].Hyperglycemia as in diabetes causes upregulation of ANRIL levels in endothelial cells [85,86], and this elevates the levels of the PRC2 subunit, EZH2 that consequently promotes the expression of VEGF, a key promoter of angiogenesis [85].Another major complication associated with diabetes is diabetic nephropathy, and this is considered a major cause of end-stage renal disease and disability in diabetic patients [87].Recent studies show that lncRNAs play important roles in the development of diabetic nephropathy and accumulation of extracellular matrix (ECM) proteins.There is higher expression of the lncRNA, PVT1, during diabetic nephropathy, and this increase leads to increased fibrosis due to accumulation of ECM proteins in renal cells [88]; downregulation of PVT1 reduces ECM accumulation [88].LncRNA PVT1 is also a host to miR-1207-5p and this miRNA is shown to regulate the expression of fibronectin1 (FN1), plasminogen activator inhibitor-1 (PAI1), and transforming growth factor beta 1 (TGFβ1) [89].In renal tube injury during diabetes, the lncRNA, MIAT, is under-expressed, and this negatively correlates with creatinine and BUN levels in the serum of these subjects.It has been shown to regulate cell viability of proximal convoluted renal tubules [90].In diabetic nephropathic mice, the lncRNA, MGC, is increased in renal mesangial cells.Interestingly, this lncRNA harbours a cluster of approximately 40 miRNAs, and is regulated by the ER stress marker C/EBP homologous protein (CHOP) [91].In CHOP -deficient mice, there is decreased expression of the lncRNA, MGC, and the clustered miRNAs, and these mice have shown an improvement in diabetic nephropathy [91].Diabetic nephropathy is also associated with increased levels of lincRNA, Gm4419, and this exerts its action by interacting with NF-κβ.Knockdown of this lincRNA in renal mesangial cells lowers cellular proliferation and inhibits expression of NF-κβ in hyperglycemic states [92].The lncRNA, TUG1, that is upregulated in diabetic nephropathy acts as sponge for miR-377 and regulates PPAR-γ expression which further modulates the expression of FN1, collagen type IV alpha 1 chain (COL4A1), PAI1, and TGFβ1 in renal mesangial cells [93].Diabetic cardiomyopathy is a critical end-stage complication associated with diabetes.Several such cardiovascular complications and myocardial dysfunction in diabetic patients lead to heart failure [94].Differential expression analysis in cardiac tissue from normal and diabetic rats shows that the lncRNA, MALAT1, is upregulated during cardiomyopathy and knockdown of this lncRNA improves left ventricular systolic function by reducing myocardial inflammation in diabetic rats [95,96].Decreased expression of the lncRNA, H19, is also reported during diabetes [68,70], and this often results in decreased expression of the exonic miRNA, miR-675 [97,98].mir-675 directly targets the voltage-dependent anion channel 1 (VDAC1) which is involved in mitochondria-mediated apoptosis in the cardiac tissue during diabetes.H19 overexpression in diabetic rats reduces oxidative stress, apoptosis, and inflammation, and improves ventricle function [98].LncRNAs NONRATT021972 and uc.48+ are reported to be associated with diabetic neuropathic pain [99,100], and inhibition of both have been shown to alleviate such neuropathic pain by activating the P2X3 receptor.Impaired wound closure is a notable complication associated with diabetes and a recent report shows decreased levels of the lncRNA, Lethe in such impaired dorsal wounds of diabetic mice.This was demonstrated to be associated with increased ROS production, possibly through regulation of NOX2 expression [101]." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "main", + "text": "\n\n| microRNAs relevant to the pathogenesis of diabetic nephropathy" + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "main", + "text": "Review criteria\n\nA search for original published articles focusing on \"diabetic nephropathy\", \"signal transduction\", \"noncoding RNAs\", \"microRNAs\", \"long noncoding RNAs\", \"genetics\" and \"epigenetics\" was performed in MEDLINE and PubMed.All articles identified were English-language, full-text papers.We also searched the reference lists of identified articles for further relevant papers." + }, + { + "document_id": "34184c8d-b167-4ae8-bfce-01e18d78fe41", + "section_type": "main", + "text": "Introduction\n\nDiabetes-related complications represent one of the most important health problems worldwide with dire social and economic projections (Cooper, 2012).One of the most important medical concerns of the diabetes epidemic is diabetic nephropathy (DN).Diabetic nephropathy is regarded as a prototypical disease of gene and environmental interactions because not all diabetic subjects with traditional risk factors develop clinically evident nephropathy, indicating a role for individual susceptibility.The majority (>85%) of GWAS-identified single nucleotide polymorphisms (SNPs) are located in the non-coding regions of the genome and thus their functional implication lies in identifying the target genes, cell types, and the mode of dysregulation caused by these non-coding SNPs (Maurano et al., 2012).Recent studies indicate that complex trait-causing variants localize to cell-type-specific, functionally important gene regulatory regions where they can disrupt or create transcription factor binding sites to alter transcript levels only in disease-target cell types (Ko and Susztak, 2013;Susztak, 2014).Several elements of the immune system including cytokines and resident chemokines, macrophage recruitment, T lymphocytes, and immune complex deposition have recently been associated with DN (Navarro-González and Mora-Fernández, 2008;Gaballa and Farag, 2013).Since renal cells are also capable of synthesizing pro-inflammatory cytokines such as tumor necrotic factor-alpha (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6), therefore, these cytokines acting in a paracrine or autocrine manner may induce significant effects leading to the development and progression of several renal disorders (Matoba et al., 2010;Pruijm et al., 2012;Shankar et al., 2011).The rationale of this study involved a concerted effort of genotyping, correlation and gene expression techniques involving three pro-inflammatory cytokine genes in the development and progression of DN as well as identification of high risk patients involving susceptibility or poor clinical outcome." + }, + { + "document_id": "2dc80127-89ba-47be-9e94-d90c2105be8d", + "section_type": "main", + "text": "\n\nNumerous recent reports have demonstrated abnormal expression of various miRNAs in renal, vascular and retinal cells under diabetic conditions, and in vivo models of related diabetic complications [8,[87][88][89][90][91]. Notably, the functional relevance of these miRNAs has been highlighted by the fact they target key genes associated with the progression of, or protection against, these complications.In particular, the role of miRNAs in diabetic nephropathy has been extensively studied, including in the actions of TGF-β related to fibrosis and other key renal outcomes in vitro and in vivo [8,[87][88][89][90].In diabetic retinopathy, several miRNAs have been reported to modulate the disease by targeting factors associated with angiogenesis, inflammation, and oxidant stress in RECs and in diabetic retinas [88,89].Reports have also implicated various miRNAs in the aberrant expression of genes associated with diabetic cardiomyopathy [88,91].In addition, effective in vivo targeting of miRNAs has now been demonstrated thanks to advances in nucleotide chemistry and the design of nuclease-resistant anti-miRNAs, which suggest future translational potential of miRNA-based therapies for human diabetic complications [8].Importantly, since miRNAs are stable in biological fluids such as urine and serum [8], they are being assessed in samples from various clinical cohorts as valuable biomarkers for the early detection of diabetic complications, for which there is a major unmet clinical need.It is clear that research in the field of miRNAs and diabetic complications will continue at a rapid pace." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "main", + "text": "Key points\n\n■ Diabetic conditions induce inflammation, fibrosis and hypertrophy in renal cells through various cytokines and growth factors such as transforming growth factor β1, angiotensin II and platelet-derived growth factor ■ The engagement of cytokines and growth factors with their receptors triggers signal transduction cascades that result in the activation of transcription factors to increase expression of inflammatory and fibrotic genes ■ These signalling mechanisms affect epigenetic states-such as DNA methylation and chromatin histone modifications-to augment the expression of profibrotic and inflammatory genes, as well as noncoding RNAs ■ Noncoding RNAs that are induced by diabetic conditions can also promote the expression of pathological genes via various post-transcriptional and post-translational mechanisms ■ These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for 'metabolic memory' ■ Key epigenetic regulators, microRNAs and long noncoding RNAs could serve as new therapeutic targets for diabetic nephropathy" + }, + { + "document_id": "d23e9456-8ee8-46e0-9870-18ff69965c28", + "section_type": "main", + "text": "miRNAs in Kidney Disease and Diabetic Nephropathy\n\nDiabetic nephropathy is a progressive kidney disease and a major debilitating complication of both type 1 and type 2 diabetes that can lead to end-stage renal disease (ESRD) and related cardiovascular disorders.Absence or lower levels of particular miRNAs in the kidney compared with other organs may permit renal specific expression of target proteins that are important for kidney functions [45].Figure 4 depicts the connection between the role of miRNAs and kidney fibrosis.Altered expression of miRNAs causes renal fibrosis by inducing EMT, EndMT, and other fibrogenic stimuli.The accumulative effects of hyperglycaemia, inflammatory cytokines, proteinuria, ageing, high blood pressure, and hypoxia result into alteration of miRNAs expression profiles.The altered miRNAs level causes the initiation of such transition program in normal kidney, finally fibrosis.Some of the miRNAs that are more abundant in the kidney compared with other organs include miR-192, miR-194, miR-204, miR-215, and miR-216.A critical role of miRNA regulation in the progression of glomerular and tubular damage and the development of proteinuria been suggested by studies in mice with podocytespecific deletion of Dicer [46].There was a rapid progression of renal disease with initial development of albuminuria followed by pathological features of glomerulosclerosis and tubulointerstitial fibrosis.It is likely that these phenotypes are due to the global loss of miRNAs because of Dicer deletion, but, given multiple miRNAs and their myriad targets, the precise pathways responsible require identification.These investigators also identified specific miRNA changes, for example, the downregulation of the miR-30 family when Dicer was deleted.Of relevance, the miR-30 family was found to target connective tissue growth factor, a profibrotic molecule that is also downstream of transforming growth factor (TGF)- [47].Thus, the targets of these miRNAs may regulate critical glomerular and podocyte functions.These findings have also been complemented by an elegant study revealing a developmental role for the miR-30 family during pronephric kidney development in Xenopus [48].Sun et al. [49] identified five miRNAs (-192, -194, -204, -215, and -216) that were highly expressed in human and mouse kidney using miRNA microarray.A recent report using new proteomic approaches to profile and identify miRNA targets demonstrated that miR-NAs repress their targets at both the mRNA and translational levels and that the effects are mostly relatively mild [50].The role of miR-192 remains controversial and highlights the complex nature of miRNA research.Kato et al. [51] observed increased renal expression of miR-192 in streptozotocin-(STZ-) induced diabetes and in the db/db mouse and demonstrated that transforming growth factor (TGF-1) upregulated miR-192 in mesangial cells (MCs).miR-192 repressed the translation of Zeb2, a transcriptional repressor that binds to the E-box in the collagen 12 (col12) gene.They proposed that miR-192 repressed Zeb2 and resulted in increased col12 expression in vitro and contributed to increased collagen deposition in vivo.These data suggest a role for miR-192 in the development of the matrix accumulation observed in DN.It is interesting that the expression of miR-192 was increased by TGF- in mouse MCs (mesangial cells), whereas, conversely, the expression of its target, Zeb2, was decreased [51].This also paralleled the increased Col1 2 and TGF- expression [51].These results suggested that the increase in TGF- in vivo in diabetic glomeruli and in vitro in MCs can induce miR-192 expression, which can target and downregulate Zeb2 thereby to increase Col1 2.This is supported by the report showing that miR-192 is upregulated in human MCs treated with high glucose [51].TGF- induced downregulation of Zeb2 (via miR-192) and Zeb1 (via potentially another miRNA) can cooperate to enhance Col1 2 expression via de-repression at E-box elements [51].In contrast to the above, other reports suggest the relationship between miR-192 and renal fibrosis may be more complicated.Krupa et al. [52] identified two miRNAs in human renal biopsies, the expression of which differed by more than twofold between progressors and nonprogressors with respect to DN, the greatest change occurring in miR-192 which was significantly lower in patients with advanced DN, correlating with tubulointerstitial fibrosis and low glomerular filtration rate.They also reported, in contrast to the Kato et al. [51] study in MCs, that TGF-1 decreased expression of miR-192 in cultured proximal tubular cells (PTCs).These investigators concluded that a decrease in miR-192 is associated with increased renal fibrosis in vivo.Interestingly, connective tissue growth factor (CTGF) treatment also resulted in fibrogenesis but caused the induction of miR-192/215 and, consequently, decreased Zeb2 and increased E-cadherin.The contrasting findings above highlight the complex nature of miRNA research.Some of the differences may relate to models and/or experimental conditions; however, one often overlooked explanation is that some effects of miRNAs and inhibitors are likely to be indirect in nature.A recent report also showed that BMP6-induced miR-192 decreases the expression of Zeb1 in breast cancer cells [53].Thus, TGF- induced increase in the expression of key miRNAs (miR-192 and miR-200 family members) might coordinately downregulate E-box repressors Zeb1 and Zeb2 to increase Col12 expression in MCs related to the pathogenesis of DN.The proximal promoter of the Col1a2 gene responds to TGF- via smads and SP1.Conversely, the downregulation of Zeb1 and Zeb2 by TGF- via miR-200 family and miR-192 can affect upstream E-box regions.Because E-boxes are present in the upstream genomic regions of the miR-200 family, miR-200 family members may themselves be regulated by Zeb1 and Zeb2 [54].It is possible that the miR-200 family upregulated by TGF- or in diabetic glomeruli under early stages of the disease can also regulate collagen expression related to diabetic kidney disease by targeting and downregulating E-box repressors.miR-192 might initiate signaling from TGF- to upregulate miR-200 family members, which subsequently could amplify the signaling by further regulating themselves through down regulation of Ebox repressors.Such events could lead to progressive renal dysfunction under pathologic conditions such as diabetes, in which TGF- levels are enhanced.Conversely, there are several reports that miR-200 family members and miR-192 can be suppressed by TGF-, and this promotes epithelial-tomesenchymal transition (EMT) in cancer and other kidneyderived epithelial cell lines via subsequent upregulation of targets Zeb1 and Zeb2 to repress E-cadherin [54,55]." + }, + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "section_type": "main", + "text": "\n\nPlatelets are key partaker in CVD and their involvement in the development of cardiovascular complications is strengthened in diabetes (148).Platelets play an important role in the pathophysiology of thrombosis and represent an important source of different RNA species, including pseudogenes, intronic transcripts, non-coding RNAs, and antisense transcripts (149,150).These molecules can be released by platelets through microvescicles, contributing to the horizontal transfer of molecular signals delivered through the bloodstream to specific sites of action (151).The downregulation of miR-223, miR-126, or 146a observed in diabetic and hyperglycemic patients (137,152) has been associated with increased platelet reactivity and aggregation (153,154).In line with these findings, silencing of miR-223 in mice caused a hyperreactive and hyperadhesive platelet phenotype, and was associated with calpain activation through the increased expression of beta1 integrin, kindlin-3, and factor XIII (153,155).Moreover, the modulation of the expression levels of platelet miRNAs can also be measured in plasma.In fact, plasma levels of miR-223 and miR-126 are decreased in diabetics (137,156).This leads to the upregulation of the P2Y12 receptor, as well as P-selectin, further contributing to platelet dysfunction (156).As a result of this interaction, activation level of platelets in type 2 DM is increased (149,156,157).Consistently with this, circulating miR-223 levels are independent predictors of high on-treatment platelet reactivity (158).Another interesting mechanism linking platelets and diabetes involves miR-103b, a platelet-derived biomarker proposed for the early diagnosis of type 2 DM, and the secreted frizzledrelated protein-4 (SFRP4), a potential biomarker of early β cell dysfunction and diabetes.In fact, platelet-derived miR-103b is able to downregulate SFRP4, whose expression levels are significantly increased in pancreatic islets and in the blood of patients with prediabetes or overt diabetes (159).These interesting results identify miR-103b as a novel potential marker of prediabetes and diabetes, and disclose a novel potential therapeutic target in type 2 DM." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "section_type": "main", + "text": "\n\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "section_type": "abstract", + "text": "\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + }, + { + "document_id": "80e1b2af-be79-4d9b-852f-46bf3e23c963", + "section_type": "main", + "text": "\n\nAn overall important consideration in study design is that similar to RNA, noncoding RNAs are tissue and cell specific [24,[77][78][79][80][81][82].Given that it is still unknown if pathogenic changes in AMD are localized to specific ocular tissues or systemic, one must take into consideration that potential biomarkers identified in the peripheral blood as \"disease associated\" may not reflect the disease mechanism occurring in the neural retina and/or RPE." + }, + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "section_type": "main", + "text": "\n\nDR. HARRINGTON: You mentioned Liu's data from China [abstract; Liu Z-H et al J Am Soc Nephrol 14:400A, 2003], which overwhelmed me.Apparently there are 182 genes whose expression is up-or down-regulated significantly in patients with diabetes.If I asked you to pick the \"top three\" genes other than the ACE polymorphisms, which three would you choose and why?DR.ADLER: Well, actually I didn't see all of their results nor did they report all 182.But I guess my favorite ones would be some that relate to the ROS pathway because this is an all-purpose pathway of cell injury fueled by a hyperglycemic environment; some that relate to podocyte structure to explain the development of proteinuria; and TGF-b, which is a master regulator of sclerosis and fibrosis." + }, + { + "document_id": "5d2fa6b9-8412-43cb-bc86-e9bcda73a4ef", + "section_type": "main", + "text": "They also identified enrichment in coagulation and\ncomplement pathways, signaling pathways, tissue remodeling, and antigen presentation, including PI3K-Akt, Rap1,\nToll-like, and NOD-like. Sun et al. [25] studied diabetic retinopathy and identified four stress-inducible genes Rmb3,\nCirbp, Mt1, and Mt2 which commonly exist in most retinal\ncell types. Diabetes increases the inflammatory factor gene\nexpressions in retinal microglia and stimulates the immediate early gene expressions (IEGs) in retinal astrocytes.\n Van Zyl et al. [30] studied glaucoma cases and identified\nthe cell types that represent gene expressions implicated in\nglaucoma." + }, + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "section_type": "main", + "text": "\n\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3β isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies." + }, + { + "document_id": "88dde947-5255-40e1-92d5-afde089b517b", + "section_type": "main", + "text": "\n\nSkol et al. developed methods to study genomics and transcriptomics together to help discover genes that cause diabetic retinopathy.Genes involved in how cells respond to high blood sugar were first identified using cells grown in the lab.By comparing the activity of these genes in people with and without retinopathy the study identified genes associated with an increased risk of retinopathy in diabetes.In people with retinopathy, the activity of the folliculin gene (FLCN) increased more in response to high blood sugar.This was further verified with independent groups of people and using computer models to estimate the effect of different versions of the folliculin gene." + }, + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "section_type": "abstract", + "text": "\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3β isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies." + }, + { + "document_id": "961f88ba-2090-4904-942c-f0e014bbe53f", + "section_type": "main", + "text": "\n\nDescription of some problems associated with diabetes and possible nanomedicine solutions." + }, + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "section_type": "main", + "text": "Newly Identified Genes Relevant in the Progression of Diabetic Nephropathy\n\nThe cellular events such as increased flux of polyols and hexosamines; generation of AGEs; increased activity of PKC, transforming growth factor-β-Smad-MAPK (mitogen-activated protein kinase) pathway and GTP-binding proteins; G1 cell cycle arrest associated with altered expression of cyclin kinases and their inhibitors; and generation of ROS are responsible for a final outcome of increased synthesis and deposition of ECM.The ROS, whether mitochondrial or cell membrane-derived, are also responsible for the activation of the renin-angiotensin system that eventually contributes to glomerular hyperfiltration and subsequent renal fibrosis (fig. 1) [71].In addition to these macromolecules, newly identified genes, such as RSOR/MIOX, Tim44 and Rap1b, may also be an integral part of the hyperglycemia-induced cytosolic and mitochondrial processes that culminate in the development of diabetic nephropathy [48][49][50][51][52][53][54][55]." + }, + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "section_type": "main", + "text": "\n\nIn vitro and in vivo studies concerning the mechanisms that are responsible for the endothelial dysfunction in diabetes demonstrated that, in the presence of high glucose concentrations, upregulation of miR-185 reduced the expression of the glutathione peroxidase-1 (GPx-1) gene, which encodes an enzyme that is important in the prevention of oxidative stress (129); instead upregulation of miR-34a and miR-204 contributed to endothelial cell senescence by impairing SIRT-1 expression and function (130,131).In the endothelium, miR-126 exerts proangiogenic, and anti-inflammatory activities.At a functional level, it enhances VEGF and fibroblast growth factor activities, contributing to vascular integrity and angiogenesis (132,133), recruits progenitor cells through the chemokine CXCL12 (134), while it suppresses inflammation by inhibiting TNF-α, ROS, and NADPH oxidase via HMGB1 (135).Consistently, miR-126 levels are down-regulated in both myocardial tissue and plasma from type 2 diabetic patients without any known anamnestic data for CVD (136,137), and in patients with CAD (138), suggesting that it could represent a new diagnostic marker for diabetes and CVD.Other studies in endothelial colony-forming cells, as well as in progenitor endothelial cells (EPCs) exposed to high glucose, demonstrated that miR-134 and miR-130a affected cell motility and apoptosis, respectively (139,140)." + }, + { + "document_id": "42e06cda-627e-46f2-a289-c4c1fb6af8f2", + "section_type": "main", + "text": "Discussion\n\nAs is known, several mechanisms, mainly related to the dysfunction of the endothelium and smooth muscles, have been proposed in the aetiology of T2DMED.In this study, the four differentially expressed miRNAs may also be involved in the regulation of the endothelium and smooth muscle function based on a literature review.Numerous studies have reported their function in pathophysiological processes, such as cellular development, differentiation, and apoptosis, which are all essential mechanisms of T2DMED (Beaumont et al. 2014;Girard et al. 2008;Komatsu et al. 2014;Lee et al. 2012;Liu et al. 2008;Shan et al. 2010;Sweetman et al. 2006).Importantly, miR-206 may be involved in diabetes-associated complications by contributing to high glucose-mediated apoptosis (Shan et al. 2010), and miR-133a has anti-apoptosis effects (Xu et al. 2007).In addition, miR-133a and miR-206 are muscle-specific miRNAs (Chen et al. 2012;Liu et al. 2008) and thus could regulate muscular cell functions, such as the augmentation of smooth muscle contraction by miR-133a (Chiba et al. 2009).Additionally, miR-18a could also increase vascular smooth muscle cell differentiation (Kee et al. 2014)." + }, + { + "document_id": "230022b2-931e-42ab-b100-5e9776483d1a", + "section_type": "main", + "text": "| DISCUSSION\n\nThis study examined retinas from WT and diabetic SD male rats to investigate the changes in a variety of retinal transcripts as a result of diabetes using RNA-seq.We identified a total of 118 DEGs, of which 72 were up-regulated and 46 were down-regulated.We also found 66 GO terms and 41 KEGG pathways which were significantly enriched by GO and KEGG analysis.Top 10 most down-regulated and up-regulated genes are listed in Tables 3 and 4, and were confirmed by qRT-PCR showed in Figure 4. Asb15 gene is the most up-regulated one we identified and confirmed.Asb15 is a member of Asb gene family; the family has been reported to be involved in cell proliferation and differentiation (Hancock et al., 1991;Kohroki et al., 2001;Liu et al., 2003).The presence of both Ankyrin repeat and suppressors of cytokine signaling (SOCS) box motifs are characters of members of Asb gene family (McDaneld, Hancock, & Moody, 2004).Member of SOCS family plays important roles in the negative regulation of signaling pathways (Kile & Alexander, 2001;Zhang et al., 2001).SOCS3 acts as a regulator of inflammation through inhibiting JAK/STAT pathway (Tamiya, Kashiwagi, & Takahashi, 2011).Down-regulating SOCS3-STAT3 can alleviate DR (Chen, Lv, & Gan, 2017;Jiang, Thaksan, & Bheemreddy, 2014;Ye & Steinle, 2015).Ladinin-1(Lad1), a largely uncharacterized protein to date, was found to be related to the proliferation and migration of breast cancer cells (Roth, Srivastava, & Lindzen, 2018).Cell proliferation and migration are processes of neovascularization.Neovascularization is the sign of PDR, which can lead to serious vision loss of patients.Fibroblast growth factor 2 (Fgf2) is a member of fibroblast growth factors (FGFs) family.FGFs and their receptors have important roles in cell proliferation, migration, differentiation, and survival (Saichaemchan, Ariyawutyakorn, & Varella-Garcia, 2016).FGF2 was found overexpression in the early stage of DR, and it can destroy the blood-retinal barrier (Yang et al., 2018).Hemoglobin alpha adult chain 1 (Hba-a1) is one of the hemoglobin genes.Hemoglobin plays an important role in neuronal respiration, oxidative stress, and response to injury (He et al., 2010;Poh, Yeo, Stohler, & Ong, 2012;Richter, Meurers, Zhu, Medvedeva, & Chesselet, 2009).Neuronal respiration is an important life activity of neuronal cells.Neurological injury is one of the performances of DR.Inositol monophosphatase domain containing 1 (Impad1) encodes gPAPP, which is a Golgi-resident nucleotide phosphatase that hydrolyzes phosphoadenosine phosphate (PAP), the by-product of sulfotransferase reactions, to AMP.AMP-activated protein kinase (AMPK) signaling pathway plays vital roles in the diabetes-induced retinal inflammation (Kubota, Ozawa, & Kurihara, 2011).RT1-Bb, RT1-Ba, belongs to RT1 complex, which is the major histocompatibility complex (MHC) of rat (Eberhard & Lutz, 2001).It is believed that the MHC region is vital because it plays an important role in diseases, such as autoimmune and infectious diseases, vascular diseases like DR, hematological and neurological diseases (John, 2005).Collagen type III alpha 1 chain (Col3a1) is a kind of type III collagen, mainly existing in the extracellular matrix.Lacking of type III collagen can destroy the structure of connective tissues (Cortini et al., 2017).According to previous researches, it is associated with the aneurysm.Retinal microaneurysm is the early performance of DR.Col3a1 was also found significantly changed in RNA-seq of human PDR fibrovascular membranes (Lam et al., 2017).αA-crystallin (Cryga) and αF-crystallin (Crygf) are members of crystallins, which were involved in different functions in various tissues (Clayton, Jeanny, Bower, & Errington, 1986;Head, Peter, & Clayton, 1991;Smolich, Tarkington, Saha, & Grainger, 1994).Knockout of αA-crystallin can inhibit ocular neovascularization (Xu, Bai, & Huang, 2015).More and more evidence indicated that inflammation (Adamis, 2002;Gologorsky, Thanos, & Vavvas, 2012) and neovascularization (Gardner & Davila, 2017;Nguyen et al., 2018) are important in the pathogenesis of DR.The results of the KEGG pathway significant enrichment analysis revealed two most enrichment items-cell adhesion molecules (CAMs) and PI3K-Akt signaling pathway.CAMs are proteins located on cell surface; the binding of CAMs to their receptors is important in the mediation of inflammatory and immune reactions (Golias et al., 2007).Previous studies have suggested that CAMs are important in the development of DR (Khalfaoui et al., 2009;Ugurlu et al., 2013) of insulin and is associated with DR neovascularization (Qin, Zhang, & Xu, 2015;Sasore, Reynolds, & Kennedy, 2014)." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "section_type": "abstract", + "text": "\n| Diabetic nephropathy (DN), a severe microvascular complication frequently associated with both type 1 and type 2 diabetes mellitus, is a leading cause of renal failure.The condition can also lead to accelerated cardiovascular disease and macrovascular complications.Currently available therapies have not been fully efficacious in the treatment of DN, suggesting that further understanding of the molecular mechanisms underlying the pathogenesis of DN is necessary for the improved management of this disease.Although key signal transduction and gene regulation mechanisms have been identified, especially those related to the effects of hyperglycaemia, transforming growth factor β1 and angiotensin II, progress in functional genomics, high-throughput sequencing technology, epigenetics and systems biology approaches have greatly expanded our knowledge and uncovered new molecular mechanisms and factors involved in DN.These mechanisms include DNA methylation, chromatin histone modifications, novel transcripts and functional noncoding RNAs, such as microRNAs and long noncoding RNAs.In this Review, we discuss the significance of these emerging mechanisms, how they mediate the actions of growth factors to augment the expression of extracellular matrix and inflammatory genes associated with DN and their potential usefulness as diagnostic biomarkers or novel therapeutic targets for DN." + }, + { + "document_id": "72aa5d47-336b-4e4f-8593-ee215b8891d2", + "section_type": "main", + "text": "\n\nThe current study takes an important first step towards this goal by identifying specific sets of genes whose expression accurately classifies patient samples with regard to diabetic neuropathy progression and by analysing their interactions within known cellular pathways.Identifying common elements in these complex networks will yield novel insights into disease pathogenesis, provide new therapeutic targets and identify potential diabetic neuropathy biomarkers.The genes identified in the current study confirm data gathered from experimental models of diabetes and provide a comprehensive picture of the expression of multiple targets in a single human tissue sample." + }, + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "section_type": "main", + "text": "\n\nFurthermore, the alpha kinase 1 gene (ALPK1) identified as a susceptibility gene for chronic kidney disease by GWAS [202] , was demonstrated in type 2 diabetes patients [203] .Three additional genes have been strongly correlated with this risk of diabetic retinopathy (DR) including the vascular endothelial growth receptor, aldose reductase and the receptor for advanced glycation products genes [204] where specific polymorphisms in these genes seem to increase the risk of DR development in diabetes patients [204] .A significant differential proteome (involving 56 out of 252 proteins) is evident that characterizes vitreous samples obtained from diabetes patients with the complication in comparison to diabetes patients without the complication and control individuals [205] .Interestingly, a large portion of these proteins (30 proteins) belong to the kallikrein-kinin, coagulation and complement systems including complement C3, complement factor 1, prothrombin, alpha-1antitrypsin and antithrombin III that are elevated in diabetic patients with retinopathy [205] .In addition, 2 single nucleotides polymorphisms in the human related B7-I gene seem to mediate podocyte injury in diabetic nephropathy [206] .Furthermore, increased concentration of the ligand of B7-1 correlates with the progression of end-stage renal disease (ESRD) in diabetes patients [206] .These results indicate that B7-I inhibition may serve as a potential target for diabetes nephropathy prevention and/or treatment.Recently, it was shown that direct correlation is evident between circulating levels of tumor necrosis factors 1 and 2 and increased risk of ESRD in American Indian patients [207] .The link between diabetes and proper bone development and health is evident.Studies using animal models with major significant reduction in insulin receptor (IR) in osteoprogenitor cells resulted in thin and rod-like weak bones with high risk of fractures [208] .Similar findings were observed in animal models with bone-specific IR knockdown animals which points to the central role of IR in the proper development of bones [208] .Type 2 diabetes is also associated with mitochondrial dysfunction in adipose tissues.Using knockout animal models of specific mitochondrial genes led to significant reduction in key electron transport complexes expression and eventually adipocytes death [209] .These animals exhibited Insulin resistance in addition to other complications that can potentially lead to cardiovascular disease [209] ." + }, + { + "document_id": "41fc22ce-f0dc-4d81-a2b5-14c563c7c767", + "section_type": "main", + "text": "Metabolism:\nA novel shared link between diabetes mellitus and Alzheimer’s disease. J. Diabetes\nRes. 2020:4981814. doi: 10.1155/2020/4981814\n\nLiu, C., Hu, J., Zhao, N., Wang, J., Wang, N., Cirrito, J. R., et al. (2017).\n Astrocytic LRP1 mediates brain abeta clearance and impacts amyloid deposition.\n J. Neurosci. 37, 4023–4031. doi: 10.1523/JNEUROSCI.3442-16.2017\n\nWainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, A., Knowles,\nD., Golan, D., et al. (2019). Opportunities and challenges for transcriptome-wide\nassociation studies. Nat. Genet. 51, 592–599. doi: 10.1038/s41588-019-0385-z\n\nLiu, Q., Trotter, J., Zhang, J., Peters, M. M., Cheng, H., Bao, J., et al. (2010)." + }, + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "section_type": "main", + "text": "\n\nI should underscore the fact that this discussion has been a simplified review of the relationships among glycemia, the RAS, histopathologic change, and the genetics of diabetic nephropathy, but its simplification allows us to underscore certain principles.In the redundant path of this biology, angiotensin II stimulates and interacts with a large number of other molecules.These are just a few of the major ones: glut-1, tumor necrosis factora (TNF-a), platelet-derived growth factor (PDGF), connective tissue growth factor (CTGF), basic fibroblast growth factor (bFGF), insulin-like growth factor-1 (IGF-1), advanced glycosylation end products (AGEs) (pentosidine), reactive oxygen species (ROS), oxidized low-density lipoprotein (LDL), vascular cell adhesion molecule (VCAM-1), osteopontin, NF-jB, RANTES (particularly in glomerular endothelial cells), and monocyte chemotactic protein (MCP).In closing, I'd like to leave you with the top 10 principles detailed by this discussion: (1) signaling systems, with their complexity and redundancy, are systems of great beauty, reflective of evolutionary order; (2) differentiated biologic tissues often use the same tools to achieve tissue-specific functions and express tissue-specific pathology; (3) diabetic nephropathy reflects cellular injury due to common biologic pathways manifested in different cell types/regions of the kidney; (4) the kidney's susceptibility to glomerulosclerosis and tubulointerstital fibrosis reflects the impact of the renal RAS and its interactions with other profibrotic molecular pathways; (5) defining these interactions and the downstream signaling mechanisms mediating them lays the foundation for discovering needed therapies beyond glycemic control and angiotensin II inhibition for the treatment of diabetic nephropathy; (6) signaling pathways downstream of angiotensin II represent prime targets for additional therapeutic interventions; (7) hypothesis-driven basic research on individual pathways has (and likely will continue to) shed light on the complexities of the pathologic interactions and the redundancies in the systems; (8) candidate gene studies are the genetic analogues of this type of hypothesis-driven basic research; (9) microarray and genomic scanning coupled with informatics technology offer the possibility of modeling these complex system interactions and hopefully will allow us to identify optimal targets for inhibition and/or up-regulation that can prevent progression and restore structure and function; and (10) given the redundancy and convergence of these pathways, the challenge will be in graded inhibition that will preserve salutary pathways, but inhibit deleterious ones." + }, + { + "document_id": "88dde947-5255-40e1-92d5-afde089b517b", + "section_type": "main", + "text": "\n\nIn this article, we identify genes whose expression responds differently to glucose in cells derived from T1D individuals with and without diabetic retinopathy.We show that one of these genes, folliculin (FLCN), is causally implicated in diabetic retinopathy based on results from genetic association testing and Mendelian randomization." + }, + { + "document_id": "e8dd8ca2-6fab-4acd-9b29-4e8583365d6d", + "section_type": "main", + "text": "Discussion\n\nRecent studies suggest inflammation to be an essential component of type 2 DM and its complications.We measured hs-CRP as a marker of inflammation in our diabetic cohort and found its levels to be significantly higher in diabetic patients as compared to controls and in nephropathy group as compared to diabetic subjects without nephropathy indicating inflammation to be a relevant factor in the pathogenesis of DN.Our results are consistent with an earlier study which has also reported increased hs-CRP levels in diabetics with proteinuria [18].Different inflammatory molecules, including pro-inflammatory cytokines have been proposed as critical factors in the development of microvascular diabetic complications, including nephropathy [19].It has been suggested that genetic variations in the genes encoding the inflammatory cytokines might confer susceptibility to DN by altering the function and/or expression of these cytokines.We investigated the association of genetic polymorphism(s) in inflammatory genes with the risk of diabetic nephropathy and whether co-occurrence of risk conferring variants of inflammatory genes were associated with increased risk of diabetic nephropathy in Asian Indian type 2 diabetic subjects.The key finding of our study was that polymorphisms in IL8, CCL2, CCR5, and MMP9 genes were associated with increased risk of nephropathy in Asian Indian type 2 diabetics and co-occurrence of specific risk genotypes of these genes conferred several fold greater risk of diabetic nephropathy." + }, + { + "document_id": "0951ba9d-bb8f-424b-b63f-16d94cb7166c", + "section_type": "main", + "text": "Page 43\n\nAuthor Manuscript\nAuthor Manuscript\nFig. 2 |. Main signalling pathways that regulate cardiac remodelling in the diabetic heart.\n\n Author Manuscript\nAuthor Manuscript\n\nThe systemic glucotoxicity (as a result of increased production of advanced glycation end\nproducts (AGEs)), lipotoxicity and angiotensin II (Ang II) production associated with type 2\ndiabetes mellitus induce the generation of reactive oxygen species (ROS) and reactive\nnitrogen species (RNS) by endothelial cells, resulting in decreased nitric oxide (NO)\nbioavailability." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "section_type": "abstract", + "text": "\nInsight into the molecular mechanisms that underlie the origin and progression of diabetic nephropathy remains limited in part because conventional research tools have restricted investigators to focus on single genes or isolated pathways.Microarray technologies provide opportunities for evaluating genetic factors and environmental effects at a genomic scale during the pathogenesis of diabetic nephropathy.Despite" + }, + { + "document_id": "230022b2-931e-42ab-b100-5e9776483d1a", + "section_type": "main", + "text": "Background:\n\nThe aim of this research was to investigate the retinal transcriptome changes in long-term streptozotocin (STZ)-induced rats' retinas using RNA sequencing (RNA-seq), to explore the molecular mechanisms of diabetic retinopathy (DR), and to identify novel targets for the treatment of DR by comparing the gene expression profile we obtained.Methods: In this study, 6 healthy male SD rats were randomly divided into wildtype (WT) group and streptozotocin (STZ)-induced group, 3 rats each group.After 6 months, 3 normal retina samples and 3 DM retina samples (2 retinas from the same rat were considered as 1 sample) were tested and differentially expressed genes (DEGs) were measured by RNA-seq technology.Then, we did Gene Ontology (GO) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and validated the results of RNA-seq through qRT-PCR.Results: A total of 118 DEGs were identified, of which 72 were up-regulated and 46 were down-regulated.The enriched GO terms showed that 3 most significant enrichment terms were binding (molecular function), cell part (cellular component), and biological regulation (biological process).The results of the KEGG pathway analysis revealed a significant enrichment in cell adhesion molecules, PI3K-Akt signaling pathway, and allograft rejection, etc. Conclusion: Our research has identified specific DEGs and also speculated their potential functions, which will provide novel targets to explore the molecular mechanisms of DR." + }, + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "section_type": "main", + "text": "Types of biomarkers include clinical, biochemical factors and molecular markers. Examples relevant to diabetic retinopathy include clinical factors (e.g.diabetes duration, obesity, smoking, ETDRS score, electroretinogram (ERGs) assessment; biochemical factors (e.g.HbA1c, lipoprotein related factors); and molecular factors (such as the results of GWAS analyses and miRNA profiles (discussed below).Cytokines, growth factors and/or hormones have been widely used, such as the case with adiponectin as an adipocyte-derived hormone that regulates glucose and lipid metabolism.Adiponectin has been shown to be significantly higher in T1D patients with severe diabetic retinopathy than in those without, even after adjustment for occurrence of microalbuminuria (Hadjadj et al., 2005).As retinopathy has multiple risk factors it is likely, as is increasingly used for cardiovascular disease and suggested for diabetic nephropathy (Elley et al., 2010;van Dieren et al., 2011;Vergouwe et al., 2010), and more recently for retinopathy (Harris Nwanyanwu et al., 2013) from genetic data (Sandholm et al., 2012;Williams et al., 2012).In terms of genetic association the diabetic retinopathy field is less advanced than that for nephropathy, although there have been a number of worthwhile studies (reviewed by (Kuo et al., 2014)).A genome-wide association study for diabetic retinopathy identified an association with a long intergenic non-coding RNA (LincRNA) sequence.LincRNAs are non-protein coding transcripts (>200 nucleotides in length) and the sequence called RP1-90L14 (adjacent to the CEP162 gene) has shown susceptibility to diabetic retinopathy (Awata et al., 2014).Interestingly, other LincRNAs are also being studied for their association with diabetic retinopathy such as MALAT1 (Yan et al., 2014) and MIAT (Yan et al., 2015).While some interesting leads are emerging, as yet there is no robust indication that diabetic retinopathy has a significant genetic component.Candidate gene and genome-wide studies may yet find genetic linkage to particular retinopathy phenotypes in T1D and T2D although both diabetes-types will need to be assessed separately in view of their distinct genetic architecture." + }, + { + "document_id": "72aa5d47-336b-4e4f-8593-ee215b8891d2", + "section_type": "main", + "text": "\n\nWe hypothesize that the genes identified in our classification models (Table 5) represent products or 'genetic biomarkers' of the biological networks involved in diabetic neuropathy onset and progression.This idea is reinforced by the fact that several of the genes have known associations with diabetes or diabetic complications.We are particularly interested in CST1, whose expression was increased by 10-fold in progressors.CST1, encoding a cysteine protease inhibitor, was initially implicated in gastric and colorectal tumourigenesis (Choi et al., 2009;Yoneda et al., 2009).Another member of this protein family, cystatin C (CST3), has been identified as a prime predictor of diabetic nephropathy progression (Shimizu et al., 2003;Taglieri et al., 2009).Although the CST1 gene product has not been investigated in the context of diabetic complications, it is detectable in saliva, tears and urine (Choi et al., 2009).To date, there are no definitive biomarkers of diabetic neuropathy progression easily accessed from body fluids, and we speculate that CST1 could prove to be an easily measureable biomarker for diabetic neuropathy." + }, + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "section_type": "main", + "text": "In vivo relevance\n\nWhat is the evidence that these pathways are relevant in vivo?In rats with streptozotocin-induced diabetes, glomerular 12/15-LO mRNA and protein were upregulated 1, 2, 3, and 4 months after diabetes induction as demonstrated by reverse transcription-polymerase chain reaction (RT-PCR) and by Western analysis and immunohistochemistry, respectively [14].Upstream of p38 MAPK is the signaling molecule MKK3/6, which is activated during the first 2 months in diabetic rats compared to controls [14].A similar pattern was observed for phospho-p38 MAPK and phospho-CREB.At 4 months, mesangial (and, parenthetically, podocyte) fibronectin accretion was increased; this phenomenon presumably contributes to mesangial expansion [14].I will loosely refer to this change as glomerulosclerosis.Thus, in diabetic rats, just as in mesangial cells and VSMCs in vitro, angiotensin II and high ambient glucose concentration activate a novel lipid-mediating signal transduction pathway, and in conjunction with MAPKs and transcription factors, lead to fibronectin synthesis; this process then accelerates renal disease." + }, + { + "document_id": "8f6c3be4-4598-4ae2-a7a8-8ea5a7a52794", + "section_type": "main", + "text": "Wnt signaling in diabetic nephropathy\n\nThe potential relevance of Wnt signaling in advanced DN was investigated in more detail.Mapping the respective genes found by each approach onto the canonical Wnt pathway was performed (KEGG [13] and Biocarta databases (BioCarta Pathways; http:// www.biocarta.com/genes/index.asp)).As shown in Fig. 4, and in line with previous findings, the CI-analysis identified a much larger fraction of the pathway as regulated than did the RMA analysis (23 versus 15 out of 27 genes, see Table S3 and Table S4).The potential downstream effects of this pathway on known Wnt target genes were then examined.Of the known Wnt target genes regulated on the microarray 15 of 15 were identified by CI while RMA identified 10 (Fig. 4 and Table S4).Matrix metalloproteinase 7 (MMP7) [14] showed the highest fold-change in Wnt-associated genes and was confirmed by RT-PCR on the cDNA used for the array analysis (DN 40.09623.88,LD: 1.061.73(p,0.05)) as well as on an independent cohort of patients with DN (DN: 6.4566.62;LD: 1.0060.79(p,0.05)) (Fig. 5a).The induction of MMP7 protein was verified by immunohistochemistry: MMP7 protein expression was strongly increased in the tubulo-interstitial compartment of patients with DN (Fig. 2 and Fig. 5b,c)" + }, + { + "document_id": "42e06cda-627e-46f2-a289-c4c1fb6af8f2", + "section_type": "main", + "text": "\n\nIn the past, many scientific studies were focused on ED in type 1 DM (Chitaley et al. 2009).However, there are more complicated but less comprehensive mechanisms in T2DMED (Chitaley 2009).The potential underlying mechanisms include hypogonadism, vascular dysfunction, veno-occlusive disorders, and others (Hidalgo-Tamola and Chitaley 2009).Some mechanisms, such as non-adrenergic and non-cholinergic dysfunction, are still debated in the pathogenesis of T2DMED (Chitaley et al. 2009).To our knowledge, only a few studies regarding of miRNA expression or function in DMED have been reported.Recently, miRNA expression was investigated in a murine model with vasculogenic ED induced by a long-term high fat diet (Barbery et al. 2015).Though accompanied with impaired glucose tolerance, this animal model could not fully represent the pathogenic processes of DMED.Instead, a classical genetic modified murine model with T2DMED was used in the present study, to investigate differentially expressed microRNAs.The bioinformatic analyses of differentially expressed miRNAs were further performed to detect whether these miRNAs played potential roles in the mechanisms of T2DMED." + }, + { + "document_id": "34184c8d-b167-4ae8-bfce-01e18d78fe41", + "section_type": "abstract", + "text": "\nGenetic variations in key inflammatory cytokines exacerbates the risk of diabetic nephropathy by influencing the gene expression.The address for the corresponding author was captured as affiliation for all authors.Please check if appropriate.Gene(2017)," + } + ], + "document_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "microRNAs", + "lncRNAs", + "diabetic&nephropathy", + "diabetic&retinopathy", + "TGF-β1", + "angiogenesis", + "fibrosis", + "inflammation", + "hyperglycemia" + ], + "metadata": [ + { + "object": "in this review, we focus on two microRNAs centrally involved in lung cancer progression. MicroRNA-21 promotes and microRNA-34 inhibits cancer progression. We elucidate here involved pathways and imbed these antagonistic microRNAs in a network of interactions, stressing their cancer microRNA biology, followed by experimental and bioinformatics analysis of such microRNAs and their targets", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab403726" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "Reporter assays reveal regulation by microRNA-339, microRNA-556, and, to a lesser extent, microRNA-10 and microRNA-199. MicroRNA-339 and microRNA-556 were further found to directly decrease Klotho protein expression in aging tissue.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab642566" + }, + { + "object": "after orthotopic lung transplantation, in the IL-17A KO group, less inflammation in the bronchovascular axis was observed and a non-significant trend towards less bronchovascular fibrosis, pleural/septal inflammation and fibrosis, and parenchymal inflammation and fibrosis when compared to WT mice", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab49527" + }, + { + "object": "*TFEB overexpression inhibits vascular inflammation in diabetic db/db mice. TFEB overexpression inhibits vascular inflammation in diabetic db/db mice .TFEB suppresses IKK activity to protect IkappaBalpha from degradation, thereby, inhibiting NF-kappaB p65 nuclear localization and attenuating vascular inflammation in endothelial cells of these mice. laminar shear stress induces TFEB through KLF2 which activates its pro...", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab7633" + }, + { + "object": "Data suggest that urine AQP5/creatinine ratio is significantly higher in patients with diabetic nephropathy than in control subjects, subjects diabetes, or subjects with nephropathy of unknown etiology; urine AQP5/creatinine ratio increases with stage of diabetic nephropathy; this biomarker may improve clinical models in distinguishing diabetic nephropathy from normal controls and subjects with type 2 diabetic alone.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab213643" + }, + { + "object": "Angiogenesis and hepatic fibrosis are mutually stimulatory, such that fibrosis requires angiogenesis and angiogenesis requires angiopoietin 1 from activated HSCs.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab72845" + }, + { + "object": "MicroRNA-625-3p was highly expressed in oral squamous cell carcinoma OSCC tissues. OSCC patients with T3+T4 stage had higher expression of microRNA-625-3p than those with T1+T2 stage. SCAI was identified as a target gene of microRNA-625-3p. ROC curve showed that microRNA-625-3p and SCAI exert certain values in diagnosing OSCC. MicroRNA-625-3p promoted migration of OSCC cells, which was reversed by SCAI knockdown.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab483708" + }, + { + "object": "Angiogenesis and hepatic fibrosis are mutually stimulatory, such that fibrosis requires angiogenesis and angiogenesis requires angiopoietin 1 from activated HSCs.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab72844" + }, + { + "object": "Hyperglycemia could induce pathological angiogenesis in subcutaneous Matrigel of diabetic rats, and Ang-1 could upregulate the expression of intercellular junction protein in subcutaneous Matrigel of diabetic rats and promote the integrity of neovascularization in the subcutaneous Matrigel of diabetic rats", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab367213" + } + ], + "question": "What non-coding RNAs are involved in diabetes, and what roles do they play?", + "subquestions": null, + "task_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "usage": { + "chatgpt": 13184, + "gpt-4": 7924, + "gpt-4-turbo-preview": 6969 + }, + "user_id": 2 + }, + "document_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "task_id": "7A3E5866E55FB9764BF9F70CFF63A333" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_08.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_08.json new file mode 100644 index 0000000..560c7aa --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_08.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-04-18T18:36:24.689995Z", + "data": { + "amplify": false, + "answer": "Gene-environment interactions influence diabetes risk and progression by the interplay of genetic predisposition and environmental factors such as diet, physical activity, and lifestyle. Certain genetic variants may increase the risk of type 2 diabetes (T2D), but this risk can be modified by environmental factors. For instance, the adverse effect of some T2D-associated genetic variants may be attenuated by higher physical activity levels or a healthy lifestyle. Conversely, low physical activity and dietary factors characterizing a Western dietary pattern may augment the risk. Understanding these interactions can help in the development of personalized prevention strategies and treatments for T2D.", + "context": { + "1e3a2816-2a1f-41c3-88d6-03330f04652b": [ + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + } + ], + "2a7da18e-3756-45c5-b18c-a2231685fefd": [ + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002). Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + } + ], + "646689fd-501b-4b27-b8fa-dc098f613044": [ + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + } + ], + "8ab10856-5df7-4f76-897a-84e6f25cd3f5": [ + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + } + ], + "90015638-c92d-4506-95b5-b789f08d613a": [ + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + } + ], + "940283a4-b7e7-4bbe-ba34-c80c4717c15a": [ + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + } + ], + "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155": [ + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + } + ], + "b07d827c-136a-4938-b3f5-b1cde90a2332": [ + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + } + ], + "df542302-18b9-43c2-a421-cba1dba0b3be": [ + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + } + ] + }, + "data_source": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "section_type": "main", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "The Rationale for Studying Gene-Environment Interactions\n\nIt is often said that T2D is the consequence of geneenvironment interactions [17].Indeed, both the environment and the genome are involved in diabetes etiology, and there are many genetic and environmental risk factors for which very robust evidence of association exists.But when epidemiologists and statisticians discuss gene-environment interactions, they are usually referring to the synergistic relationship between the two exposures, and there is limited empirical evidence for such effects in the etiology of cardiometabolic disease.Indeed, in non-monogenic human obesity, a condition widely believed to result from a genetic predisposition triggered by exposure to adverse lifestyle factors, of the >200 human gene-lifestyle interaction studies reported since 1995, only a few examples of gene-environment interactions have been adequately replicated [18], and because these results are derived primarily from cross-sectional studies with little or no experimental validation, even those that have been robustly replicated may not represent causal interaction effects.The evidence base for T2D is thinner still.Nevertheless, other data support the existence of gene-environment interactions in complex disease, thus motivating the search for empirically defined interactions in T2D." + }, + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "section_type": "main", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "section_type": "main", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "section_type": "main", + "text": "\n\nThe availability of detailed information on gene × environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "abstract", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "GENETIC SUSCEPTIBILITY AND GENE-ENVIRONMENT INTERACTIONS-\n\nThe recent advent of genome-wide association studies (GWAS) has led to major advances in the identification of common genetic variants contributing to diabetes susceptibility (40).To date, at least 40 genetic loci have been convincingly associated with type 2 diabetes, but these loci confer only a modest effect size and do not add to the clinical prediction of diabetes beyond traditional risk factors, such as obesity, physical inactivity, unhealthy diet, and family history of diabetes.Many diabetes genes recently discovered through GWAS in Caucasian populations have been replicated in Asians; however, there were significant interethnic differences in the location and frequency of these risk alleles.For example, common variants of the TCF7L2 gene that are significantly associated with diabetes risk are present in 20-30% of Caucasian populations but only 3-5% of Asians (41,42).Conversely, a variant in the KCNQ1 gene associated with a 20-30% increased risk of diabetes in several Asian populations (43,44) is common in East Asians, but rare in Caucasians.It is intriguing that most diabetes susceptibility loci that have been identified are related to impaired b-cell function, whereas only a few (e.g., peroxisome proliferator-activated receptor-g, insulin receptor substrate 1, IGF-1, and GCKR) are associated with insulin resistance or fasting insulin, which points toward b-cell dysfunction as a primary defect for diabetes pathogenesis.It should be noted that most of the single nucleotide polymorphisms uncovered may not be the actual causal variants, which need to be pinpointed through fine-mapping, sequencing, and functional studies." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nSummary of key literature on gene-environment interactions in obesity and type 2 diabetes" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "d978c09f-53e0-4a69-bfa6-e15537f32ffb", + "section_type": "main", + "text": "Genomics and gene-environment interactions\n\nEven though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mellitus are more susceptible to T2DM than others, which suggests that individual differences in response to lifestyle interventions exist 76 .Substantial evidence from twin and family studies has suggested a genetic basis of T2DM 77 .Over the past decade, successive waves of T2DM genome-wide association studies have identified >100 robust association signals, demonstrating the complex polygenic nature of T2DM 5 .Most of these loci affect T2DM risk through primary effects on insulin secretion, and a minority act through reducing insulin action 78 .Individually, the common variants (minor allele frequency >5%) identified in these studies have only a modest effect on T2DM risk and collectively explain only a small portion (~20%) of observed T2DM heritability 5 .It has been hypothesized that lower-frequency variants could explain much of the remaining heritability 79 .However, results of a large-scale sequencing study from the GoT2D and T2D-GENES consortia, published in 2016, do not support such a hypothesis 5 .Genetic variants might help reveal possible aetiological mechanisms underlying T2DM development; however, the variants identified thus far have not enabled clinical prediction beyond that achieved with common clinical measurements, including age, BMI, fasting levels of glucose and dyslipidaemia.A study published in 2014 linked susceptibility variants to quantitative glycaemic traits and grouped these variants on the basis of their potential intermediate mechanisms in T2DM pathophysiology: four variants fitted a clear insulin resistance pattern; two reduced insulin secretion with fasting hyperglycaemia; nine reduced insulin secretion with normal fasting glycaemia; and one altered insulin processing 80 .Considering such evidence, the genetic architecture of T2DM is highly polygenic, and thus, substantially larger association studies are needed to identify most T2DM loci, which typically have small to modest effect sizes 81 ." + }, + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "section_type": "main", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "abstract", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nGene-nutrient or -dietary pattern interactions in the development of T2DM." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "abstract", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "section_type": "main", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002).\n Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + }, + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "section_type": "main", + "text": "Genes and enviromental factors in the development of type 2 diabetes\n\nThe susceptibility to the development of type 2 diabetes (T2DM) is determined by two factors: genetics and environment.The genetic background of T2DM is undoubtedly heterogeneous.Most patients with T2DM exhibit two different defects: the impairment of insulin secretion and decreased insulin sensitivity.This means that there are at least two groups of T2DM susceptibility genes.The substantial contribution of genetic factors to the development of diabetes has been known for many years.The important pieces of evidence for the role of genes are the results of twin studies showing higher concordance rate for T2DM among monozygotic twins (between 41% and 55%) in comparison to dizygotic twins (between 10% and 15%) [43,84].What is interesting, there are populations with extremely high prevalence of T2DM, for example Pima Indians, that can not be explained solely by environmental factors [117].Supporting evidence for the role of genes in development of T2DM include also familial clustering of diabetesrelated traits.It was shown that the level of insulin sensitivity in Caucasians is inherited and a low level is a poor prognostic factor that precedes the development of T2DM [68,69,115].Similar observations were published for other ethnic groups [9,36,60].Those facts underline the importance of genetic factors.However, it is well known that the incidence of T2DM is also associated with environmental factors.Increasing incidence of T2DM during the last few years with obvious links to lifestyle and diet points to the role of enviromental factors in the development of disease [80].The differences in the prevalence of T2DM in relative populations living in different geographical and cultural regions (for example Asians in Japan and USA) also support the role of non-genetic factors [27,125].The relations between genetic and eviromental factors in the development of T2DM may be complex.For instance, enviromental factors may be responsible for the initiation of b-cell damage or other metabolic abnormalities, while genes may regulate the rate of progression to overt diabetes.On the other hand, in some cases genetic factors may be nec-essary for environmental factors even to start processes leading to the development of the disease." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + }, + { + "document_id": "2a94ec9f-6fb6-4ce3-8e33-1a8859470be9", + "section_type": "main", + "text": "\n\nAn individual's risk of developing T2D is influenced by a combination of lifestyle, environmental, and genetic factors.Uncovering the genetic contributors to diabetes holds promise for clinical impact by revealing new therapeutic targets aimed at the molecular and cellular mechanisms that lead to disease.Genome-wide association studies performed during the past decade have uncovered more than 100 regions associated with T2D (5)(6)(7)(8)(9)(10)(11)(12).Although these studies have provided a better understanding of T2D genetics, the majority of identified variants fall outside protein-coding regions, leaving the molecular mechanism by which these variants confer altered disease risk obscure.Consequently, T2D genome-wide association studies have identified few loci with clear therapeutic potential." + }, + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "section_type": "main", + "text": "\n\nNutrient-or dietary pattern-gene interactions in the development of DM." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "abstract", + "text": "\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "9864689f-2c1e-4fb2-a621-f39d4c57f140", + "section_type": "main", + "text": "\n\nGenetic and epigenetic factors determine cell fate and function.Recent breakthroughs in genotyping technology have led to the identification of more than 20 loci associated with the risk of type 2 diabetes (Sambuy 2007;Zhao et al. 2009).However, all together these loci explain <5% of the genetic risk for diabetes.Epigenetic events have been implicated as contributing factors for metabolic diseases (Barker 1988;Kaput et al. 2007).Unhealthy diet and a sedentary lifestyle likely lead to epigenetic changes that can, in turn, contribute to the onset of diabetes (Kaput et al. 2007).At present, the underlying molecular mechanisms for disease progression remain to be elucidated." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + }, + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "section_type": "main", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + }, + { + "document_id": "fd143578-73cd-4046-aecf-e546026c35ee", + "section_type": "main", + "text": "\n\nIntroduction: Genetic and environmental factors play an important role in susceptibility to type 2 diabetes mellitus (T2DM).Several genes have been implicated in the development of T2DM.Genetic variants of candidate genes are, therefore, prime targets for molecular analysis." + }, + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "section_type": "main", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + }, + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "section_type": "main", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "section_type": "main", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nWhy do we think GEIs cause type 2 diabetes?dTheevidence supporting the existence of gene-lifestyle interactions in type 2 diabetes comes primarily from 1) the pattern and distribution of diabetes across environmental settings and ethnic groups, 2) familybased intervention studies, in which response to interventions varies less between biologically related individuals than between unrelated individuals; and 3) animal studies in which genetic and environmental factors are experimentally manipulated to cause changes in the expression of metabolic phenotypes.A brief overview of pertinent literature from human studies is given below." + }, + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "section_type": "main", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "abstract", + "text": "\nA bs tr ac t\nBackgroundType 2 diabetes mellitus is thought to develop from an interaction between environmental and genetic factors.We examined whether clinical or genetic factors or both could predict progression to diabetes in two prospective cohorts. MethodsWe genotyped 16 single-nucleotide polymorphisms (SNPs) and examined clinical factors in 16,061 Swedish and 2770 Finnish subjects.Type 2 diabetes developed in 2201 (11.7%) of these subjects during a median follow-up period of 23.5 years.We also studied the effect of genetic variants on changes in insulin secretion and action over time. ResultsStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function.The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes, with a slight increase in the area under the receiveroperating-characteristic curve from 0.74 to 0.75; however, the magnitude of the increase was significant (P = 1.0×10 −4 ).The discriminative power of genetic risk factors improved with an increasing duration of follow-up, whereas that of clinical risk factors decreased. ConclusionsAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + }, + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "section_type": "main", + "text": "\n\nEpidemiological studies have been the predominant source of literature on gene-lifestyle interactions in cardiovascular and metabolic disease.Dozens of casecontrol and cohort studies have been published since the late 1990s purporting to have identified gene-lifestyle interactions in type 2 diabetes or related quantitative metabolic traits.Until recently, however, most of these studies were small and often relied on imprecise estimates of environmental exposures and outcomes.These are prone to error and bias, and exposures may not be assessed at the time when they conveyed their effects; for example, the causative exposures may have occurred very early in life, perhaps even in utero.Moreover, the complexities of modeling interaction effects have forced geneticists to focus primarily on very simple models of interaction, whereas clinically relevant interaction effects likely involve multiple genetic and nongenetic biomarkers.In addition, barely a handful of studies have examined incident type 2 diabetes as an outcome, with most focusing on cross-sectional measures of glucose and others relying on analyses that include prevalent cases of diabetes; this may introduce labeling bias, where the recall of well-known diabetesassociated behaviors is less likely to be accurate in individuals recently diagnosed with disease than in those who have not been diagnosed with disease." + }, + { + "document_id": "4322db2f-5f43-4fc0-8968-b24438a7d6b9", + "section_type": "main", + "text": "Introduction\n\nType 2 diabetes (T2D) has developed into a major public health concern.While previously considered as a problem primarily for western populations, the disease is rapidly gaining global importance, as today around 285 million people are affected worldwide (IDF, 2009).Lifestyle and behavioural factors play an important role in determining T2D risk.For example, experimentally induced intrauterine growth retardation as well as nutrient restriction during pregnancy in rats have been shown to result in development of T2D in offspring (Inoue et al, 2009) while chronic high-fat diet in fathers programs b-cell dysfunction in female rat offspring (Ng et al, 2010).In humans, a reduced birth weight together with an accelerated growth in infancy has been associated with impaired glucose tolerance (IGT) in adulthood (Bhargava et al, 2004).The pancreatic islets of Langerhans are of central importance in the development of T2D.Under normal conditions, increasing blood glucose levels after a meal trigger insulin secretion from the pancreatic islet b-cells to regulate glucose homeostasis.b-Cell failure marks the irreversible deterioration of glucose tolerance (Cnop et al, 2007b;Tabak et al, 2009) and results in T2D (UKPDSG, 1995).The unbiased genome-wide search for T2D risk genes (Saxena et al, 2007;Scott et al, 2007;Sladek et al, 2007;Zeggini et al, 2007Zeggini et al, , 2008) ) has placed the insulinproducing b-cells at centre stage.These approaches have also inadvertently highlighted the complexity of the biological mechanisms critical to T2D development.Most T2D risk genes identified in these genome-wide association studies (GWAS) affect b-cell mass and/or function (Florez, 2008).While the majority of studies in the field have characterised diabetes aetiology on the basis of genetics, new findings suggest the potential involvement of epigenetic mechanisms in T2D as a crucial interface between the effects of genetic predisposition and environmental influences (Villeneuve and Natarajan, 2010).Epigenetic changes are heritable yet reversible modifications that occur without alterations in the primary DNA sequence.DNA methylation and histone modifications are the main molecular events that initiate and sustain epigenetic modifications.These modifications may therefore provide a link between the environment, that is, nutrition and lifestyle, and T2D but only few studies so far have documented aberrant DNA methylation events in T2D (Ling et al, 2008;Park et al, 2008)." + } + ], + "document_id": "ADE056BF251274D15922CA6E7B5C3133", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&gene-environment&interactions", + "genetic", + "environmental", + "physical&activity", + "dietary&factors", + "GWAS", + "insulin&sensitivity", + "β-cell&dysfunction", + "PPARG", + "HNF1B" + ], + "metadata": [ + { + "object": "Data suggest that expression of Pparg can be regulated by dietary factors; expression of Pparg is down-regulated in preadipocytes by tannic acid, a form of tannins found in plant-based foods; Pparg appears to be a major factor in adipogenesis.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab206776" + }, + { + "object": "Circulating adiponectin increased in obese physically active participants >/=180 min/week compared to non-physically active counterparts, indicating that physical activity may mediate baseline adiponectin levels irrespective of the fat mass regulatory effect.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab141573" + }, + { + "object": "Upon stratifying the participants into tertiles by the Matsuda index, we observed an inhibitory relationship between the genetic risk score GRS and insulin secretion in low insulin sensitive but not in high insulin sensitive controls and treatment-naive Type 2 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab985500" + }, + { + "object": "The association of the FTO risk allele with the odds of obesity is attenuated by 27% in physically active adults, highlighting the importance of physical activity in particular in those genetically predisposed to obesity.[Meta-analysis]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab782259" + }, + { + "object": "Serum IGFBP-2 levels increase with age after the age of 50 years and evolve in parallel with insulin sensitivity. IGFBP-2 may therefore be a potential marker for insulin sensitivity. We further show that IGFBP-2 levels can predict mortality in this aging population. However, its predictive value for mortality can only be interpreted in relation to insulin sensitivity.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab699014" + }, + { + "object": "Our study validated the association between an FTO variant and BMI in Taiwanese individuals. In addition, individuals with TG and TT genotypes who were physically active had a decreased BMI. These results indicate that physical activity might be necessary to mitigate the deleterious effect of BMI among genetically susceptible Taiwanese individuals.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab566865" + }, + { + "object": "Irrespective of the genetic defect, adenoviral delivery of C5 improved insulin sensitivity in both C5cont and C5def mice, indicating an insulin-sensitizing function of C5.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab428686" + }, + { + "object": "there was a high heritability for IGF-I and IGFBP-3, but a low heritability for insulin secretion and insulin sensitivity in a group of elderly twins; in addition, study found a negative relationship between IGF-I and insulin sensitivity, which did not seem to be strongly genetically determined", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab887620" + }, + { + "object": "The authors showed that the expression of cysK is regulated by several genetic and environmental factors in addition to CysB: two genetic factors, OmpR and CysE, and lithium.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab785160" + }, + { + "object": "Production of nitric oxide NO within eNOS-positive NGC neurons increases after environmental perturbations, indicating a role for eNOS/NO in modulating environmentally appropriate levels of GA. Inhibition of NO production causes dysregulated behavioral arousal after exposure to environmental perturbation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab615356" + } + ], + "question": "How do gene-environment interactions influence diabetes risk and progression?", + "subquestions": null, + "task_id": "ADE056BF251274D15922CA6E7B5C3133", + "usage": { + "chatgpt": 8576, + "gpt-4": 5471, + "gpt-4-turbo-preview": 4534 + }, + "user_id": 2 + }, + "document_id": "ADE056BF251274D15922CA6E7B5C3133", + "task_id": "ADE056BF251274D15922CA6E7B5C3133" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_09.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_09.json new file mode 100644 index 0000000..0d3f71d --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_09.json @@ -0,0 +1,400 @@ +{ + "created_at": "2024-04-18T18:40:22.873037Z", + "data": { + "amplify": false, + "answer": "Yes, genetic predictors of diabetes complications can be identified. Studies have revealed several susceptibility loci for diabetic complications such as retinopathy and nephropathy. However, these genetic factors only explain a small proportion of the phenotypic variation observed in type 2 diabetes patients, indicating a need for the identification of more novel genetic risk factors.", + "context": { + "069a62e0-e56a-46ab-9f93-c13a76a79989": [ + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + } + ], + "091ab13a-1b8a-4849-b698-48db7b1a948f": [ + { + "document_id": "091ab13a-1b8a-4849-b698-48db7b1a948f", + "text": "\n\nA considerable amount of work has focused on dissecting the genetics of diabetes itself; however, fewer studies have been conducted on the molecular mechanisms leading to its specific complications such as DR.To identify susceptibility loci that are associated with T2D retinopathy in Taiwanese population, we conducted a genome-wide association study involving 749 T2D cases (174 with retinopathy and 575 without retinopathy) and 100 nondiabetic controls and identified 12 previously unknown susceptibility loci related to DR." + } + ], + "0da4d3d4-10d5-4a58-9e50-c1fa0b414427": [ + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "text": "\n\nProgress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions." + } + ], + "277be46c-4307-4738-972d-eb6efd9b175a": [ + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + } + ], + "3548bb7f-727c-4ccb-acc7-a97553b89992": [ + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "text": "\n\nRecent advances in GWAS have substantially improved our understanding of the pathophysiology of diabetes, but the currently identified genetic susceptibility loci are insufficient to explain differences in diabetes risk across different ethnic groups or the rapid rise in diabetes prevalence over the past several decades.Clinical utility of these loci in predicting future risk of diabetes is also limited." + } + ], + "45cdaf79-d881-43e6-8555-ff47f04ae3d4": [ + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\n\nConclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\n\nStudies show evidence of considerable genetic component predisposing to diabetic complications, explaining even around 50% of the risk of proliferative retinopathy [11].In the last few decades, genetic research including genome-wide association studies (GWAS), linkage analysis, and candidate gene approach has revealed several susceptibility loci for diabetic retinopathy and nephropathy (VEGF, CAT , FTO, UCP1, and INSR), and also macrovascular complications (ADIPOQ).Nevertheless, they explain only a small proportion of the phenotypic variation observed in T2DM patients [12][13][14][15][16][17], justifying a need for identification of novel genetic risk factors for T2DM complications and improvement of knowledge about molecular mechanisms underlying these comorbid conditions." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "Methods:\n\nWe performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\nBackground: Type 2 diabetes complications cause a serious emotional and economical burden to patients and healthcare systems globally.Management of both acute and chronic complications of diabetes, which dramatically impair the quality of patients' life, is still an unsolved issue in diabetes care, suggesting a need for early identification of individuals with high risk for developing diabetes complications. Methods:We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications. Results:The analysis revealed ten novel associations showing genome-wide significance, including rs1132787 (GYPA, OR = 2.71; 95% CI = 2.02-3.64)and diabetic neuropathy, rs2477088 (PDE4DIP, OR = 2.50; 95% CI = 1.87-3.34),rs4852954 (NAT8, OR = 2.27; 95% CI = 2.71-3.01),rs6032 (F5, OR = 2.12; 95% CI = 1.63-2.77),rs6935464 (RPS6KA2, OR = 2.25; 95% CI = 6.69-3.01)and macrovascular complications, rs3095447 (CCDC146, OR = 2.18; 95% CI = 1.66-2.87)and ophthalmic complications.By applying the targeted approach of previously reported susceptibility loci we managed to replicate three associations: MAPK14 (rs3761980, rs80028505) and diabetic neuropathy, APOL1 (rs136161) and diabetic nephropathy.Conclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "Discussion\n\nHere we present the results of the genome-wide association study for T2DM complications performed in a population of Latvia for the first time, revealing 10 susceptibility loci for T2DM complications, including diabetic neuropathy, macrovascular and ophthalmic complications.As in other reports aimed to identify the risk factors of T2DM complications [15,32], the control group of our study consisted of T2DM patients with no evidence of the complication type of interest instead of conventional healthy subjects, since the implementation of healthy controls would rather reveal genetic associations with the diagnosis of T2DM itself, not the T2DM complications." + } + ], + "50c72e55-b5fe-42a6-b837-64c28620a4c0": [ + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Conclusions\n\nAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + } + ], + "a7bad429-5f6a-464f-a666-f9cb1be60338": [ + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "text": "COMPLICATIONS\n\nIn addition to the genetic determinants of diabetes, several gene mutations and polymorphisms have been associated with the clinical complications of diabetes.The cumulative data on diabetes patients with a variety of micro-and macrovascular complications support the presence of strong genetic factors involved in the development of various complications [200] .A list of genes have been reported that are associated with diabetes complications including ACE and AKR1B1 in nephropathy, VEGF and AKRB1 in retinopathy and ADIPOQ and GLUL in cardiovascular diseases [200] ." + } + ], + "b666545f-6a53-45de-8562-55d88fc6f7ee": [ + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "text": "How do we identify the major 'culprits' at the implicated genome-wide association study loci? If population-based genetics, including genome-wide association studies, have allowed progress in the identification of Type 2 diabetes loci to be rapid over the past few years, progress towards determining which of the gene variants close to the implicated loci confer altered disease risk and how (at the molecular, cellular and whole body level) has lagged some way behind.Indeed, given the number of possible single nucleotide polymorphisms and genes, unravelling these questions represents a monumental challenge, requiring multiple, complementary approaches.Nonetheless, the rewards of success, in terms of new understanding of disease mechanisms and even the identification of new targets for therapeutic intervention, are likely to be great, potentially allowing the treatment of underlying disease aetiology in a personalized (stratified) manner." + } + ], + "cf022812-00a2-42ba-88fb-5c2014c86c43": [ + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "text": "\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + }, + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "text": "\n\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + } + ], + "eaca0f25-4a6b-4c0e-a6df-12e25060b169": [ + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "text": "\n\nConclusions and Future Directions GWAS and GWAS meta-analyses have by far been the most efficient way to identify new T2D genes (Figure 2), but their predictive value for future occurrence of T2D has been very limited compared to classic risk factors such as obesity and fasting glucose levels (Walford et al., 2014).Although it might be good news that our genome does not fully dictate our future, the knowledge of its specificities may help us to improve our health.Early genetic studies showed that the higher risk for T2D conferred by TCF7L2 variant can be reversed by lifestyle intervention (Florez et al., 2006), opening avenues for strategies targeted on genetically selected individuals with pre-diabetes.TCF7L2 has also been shown to be associated with a lower efficiency of oral sulfonylureas in newly diagnosed T2D patients (Pearson et al., 2007), but a more recent Danish study suggested that in contrast to clinical markers, all known T2D-associated variants do not significantly affect the time to prescription of the first drug after disease onset (Hornbak et al., 2014).In other words, frequent SNPs are not helpful to predict patients' futures, though the good use of genetic data may contribute to provide better care to newly diagnosed T2D patients who are currently all treated the same (with metformin)." + } + ], + "fa72cb33-e1e4-49ea-a72e-dd851225ee0b": [ + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "text": "Background\n\nMultiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus.We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + } + ] + }, + "data_source": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "section_type": "main", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "section_type": "main", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "\n\nConclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "\n\nStudies show evidence of considerable genetic component predisposing to diabetic complications, explaining even around 50% of the risk of proliferative retinopathy [11].In the last few decades, genetic research including genome-wide association studies (GWAS), linkage analysis, and candidate gene approach has revealed several susceptibility loci for diabetic retinopathy and nephropathy (VEGF, CAT , FTO, UCP1, and INSR), and also macrovascular complications (ADIPOQ).Nevertheless, they explain only a small proportion of the phenotypic variation observed in T2DM patients [12][13][14][15][16][17], justifying a need for identification of novel genetic risk factors for T2DM complications and improvement of knowledge about molecular mechanisms underlying these comorbid conditions." + }, + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "section_type": "main", + "text": "\n\nProgress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions." + }, + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "section_type": "main", + "text": "Background\n\nMultiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus.We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone." + }, + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "section_type": "abstract", + "text": "\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "Methods:\n\nWe performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Conclusions\n\nAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + }, + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "section_type": "main", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + }, + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "section_type": "main", + "text": "\n\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + }, + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "section_type": "main", + "text": "\n\nConclusions and Future Directions GWAS and GWAS meta-analyses have by far been the most efficient way to identify new T2D genes (Figure 2), but their predictive value for future occurrence of T2D has been very limited compared to classic risk factors such as obesity and fasting glucose levels (Walford et al., 2014).Although it might be good news that our genome does not fully dictate our future, the knowledge of its specificities may help us to improve our health.Early genetic studies showed that the higher risk for T2D conferred by TCF7L2 variant can be reversed by lifestyle intervention (Florez et al., 2006), opening avenues for strategies targeted on genetically selected individuals with pre-diabetes.TCF7L2 has also been shown to be associated with a lower efficiency of oral sulfonylureas in newly diagnosed T2D patients (Pearson et al., 2007), but a more recent Danish study suggested that in contrast to clinical markers, all known T2D-associated variants do not significantly affect the time to prescription of the first drug after disease onset (Hornbak et al., 2014).In other words, frequent SNPs are not helpful to predict patients' futures, though the good use of genetic data may contribute to provide better care to newly diagnosed T2D patients who are currently all treated the same (with metformin)." + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "\n\nRecent advances in GWAS have substantially improved our understanding of the pathophysiology of diabetes, but the currently identified genetic susceptibility loci are insufficient to explain differences in diabetes risk across different ethnic groups or the rapid rise in diabetes prevalence over the past several decades.Clinical utility of these loci in predicting future risk of diabetes is also limited." + }, + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "section_type": "main", + "text": "How do we identify the major 'culprits' at the implicated genome-wide association study loci? If population-based genetics, including genome-wide association studies, have allowed progress in the identification of Type 2 diabetes loci to be rapid over the past few years, progress towards determining which of the gene variants close to the implicated loci confer altered disease risk and how (at the molecular, cellular and whole body level) has lagged some way behind.Indeed, given the number of possible single nucleotide polymorphisms and genes, unravelling these questions represents a monumental challenge, requiring multiple, complementary approaches.Nonetheless, the rewards of success, in terms of new understanding of disease mechanisms and even the identification of new targets for therapeutic intervention, are likely to be great, potentially allowing the treatment of underlying disease aetiology in a personalized (stratified) manner." + }, + { + "document_id": "091ab13a-1b8a-4849-b698-48db7b1a948f", + "section_type": "main", + "text": "\n\nA considerable amount of work has focused on dissecting the genetics of diabetes itself; however, fewer studies have been conducted on the molecular mechanisms leading to its specific complications such as DR.To identify susceptibility loci that are associated with T2D retinopathy in Taiwanese population, we conducted a genome-wide association study involving 749 T2D cases (174 with retinopathy and 575 without retinopathy) and 100 nondiabetic controls and identified 12 previously unknown susceptibility loci related to DR." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Results\n\nStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "\n\nTo date, however, the improvement in predictive value of known genetic variants over that of classic clinical risk factors (BMI, family history, glucose) has proven minimal in type 2 diabetes." + }, + { + "document_id": "553ae95d-0a2b-4f2a-8123-da9a9e9e7a77", + "section_type": "main", + "text": "\n\nTwo more recent population -based studies using a longitudinal design with prospectively investigated cohorts have examined the predictive value of a genotype score in addition to common risk factors for prediction of T2DM [194,195] .Meigs et al. [194] reported that a genotype score based on 18 risk alleles predicted new cases of diabetes in the community but provided only a slightly better prediction of risk than knowledge of common clinical risk factors alone [195] .A similar conclusion was drawn in the paper by Lyssenko et al. [196] , along with an improved value of genetic factors with an increasing duration of follow -up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured.They also showed that β -cell function adjusted for insulin resistance (using the disposition index) was the strongest predictor of future diabetes, although subjects in the prediabetic stage presented with many features of insulin resistance.It is also noteworthy that many of the variants that were genotyped appear to infl uence β -cell function.The addition of DNA data to the clinical model improved not only the discriminatory power, but also the reclassifi cation of the subjects into different risk strategies.Identifying subgroups of the population at substantially different risk of disease is important to target these subgroups of individuals with more effective preventative measures.As more genetic variants are now identifi ed, tests with better predictive performance should become available with a valuable addition to clinical practice." + }, + { + "document_id": "5782c1a9-6ab1-4c66-b1e6-116ac6a0e50b", + "section_type": "main", + "text": "\n\nOver the past two years, there has been a spectacular change in the capacity to identify common genetic variants that contribute to predisposition to complex multifactorial phenotypes such as type 2 diabetes (T2D).The principal advance has been the ability to undertake surveys of genome-wide association in large study samples.Through these and related efforts, $20 common variants are now robustly implicated in T2D susceptibility.Current developments, for example in high-throughput resequencing, should help to provide a more comprehensive view of T2D susceptibility in the near future.Although additional investigation is needed to define the causal variants within these novel T2Dsusceptibility regions, to understand disease mechanisms and to effect clinical translation, these findings are already highlighting the predominant contribution of defects in pancreatic b-cell function to the development of T2D." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "abstract", + "text": "\nBackground: Type 2 diabetes complications cause a serious emotional and economical burden to patients and healthcare systems globally.Management of both acute and chronic complications of diabetes, which dramatically impair the quality of patients' life, is still an unsolved issue in diabetes care, suggesting a need for early identification of individuals with high risk for developing diabetes complications. Methods:We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications. Results:The analysis revealed ten novel associations showing genome-wide significance, including rs1132787 (GYPA, OR = 2.71; 95% CI = 2.02-3.64)and diabetic neuropathy, rs2477088 (PDE4DIP, OR = 2.50; 95% CI = 1.87-3.34),rs4852954 (NAT8, OR = 2.27; 95% CI = 2.71-3.01),rs6032 (F5, OR = 2.12; 95% CI = 1.63-2.77),rs6935464 (RPS6KA2, OR = 2.25; 95% CI = 6.69-3.01)and macrovascular complications, rs3095447 (CCDC146, OR = 2.18; 95% CI = 1.66-2.87)and ophthalmic complications.By applying the targeted approach of previously reported susceptibility loci we managed to replicate three associations: MAPK14 (rs3761980, rs80028505) and diabetic neuropathy, APOL1 (rs136161) and diabetic nephropathy.Conclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "f9b65334-56b7-43e9-9fda-b778c18c1c67", + "section_type": "main", + "text": "\n\nGenomic information associated with Type 2 diabetes." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Discussion\n\nOur study provides insight into the relative importance of clinical risk factors and those that are related to a panel of DNA variants associated with type 2 diabetes.Obesity was a strong risk factor for future diabetes, a risk that almost doubled in subjects with a family history of diabetes.However, the addition of data from genotyping of the known DNA variants to clinical risk factors (including a family history of diabetes) had a minimal, albeit statistically significant, effect on the prediction of future type 2 diabetes.Notably, the ability of genetic risk factors to predict future type 2 diabetes improved with an increasing duration of follow-up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "section_type": "main", + "text": "Discussion\n\nHere we present the results of the genome-wide association study for T2DM complications performed in a population of Latvia for the first time, revealing 10 susceptibility loci for T2DM complications, including diabetic neuropathy, macrovascular and ophthalmic complications.As in other reports aimed to identify the risk factors of T2DM complications [15,32], the control group of our study consisted of T2DM patients with no evidence of the complication type of interest instead of conventional healthy subjects, since the implementation of healthy controls would rather reveal genetic associations with the diagnosis of T2DM itself, not the T2DM complications." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "abstract", + "text": "\nA bs tr ac t\nBackgroundType 2 diabetes mellitus is thought to develop from an interaction between environmental and genetic factors.We examined whether clinical or genetic factors or both could predict progression to diabetes in two prospective cohorts. MethodsWe genotyped 16 single-nucleotide polymorphisms (SNPs) and examined clinical factors in 16,061 Swedish and 2770 Finnish subjects.Type 2 diabetes developed in 2201 (11.7%) of these subjects during a median follow-up period of 23.5 years.We also studied the effect of genetic variants on changes in insulin secretion and action over time. ResultsStrong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action.Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function.The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes, with a slight increase in the area under the receiveroperating-characteristic curve from 0.74 to 0.75; however, the magnitude of the increase was significant (P = 1.0×10 −4 ).The discriminative power of genetic risk factors improved with an increasing duration of follow-up, whereas that of clinical risk factors decreased. ConclusionsAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + }, + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "section_type": "main", + "text": "\n\nMajor consortia addressing the genetic basis of diabetes complications and associated traits" + }, + { + "document_id": "a5a0cd4f-8acf-4e89-9033-04f448dc0b15", + "section_type": "main", + "text": "CONCLUSIONS\n\nDuring the past several years, the identification of genetic risk factors for diabetic microvascular complications has improved.However, most of the studies were not fully powered for GWASs, with the exception of the GENIE study.Therefore, most of the results associated with the genetic risk factors were below the genome-wide significance threshold and inconsistent among studies.In addition, the definition of cases and controls differed, thereby introducing significant heterogeneity.Based on the findings reported, these genetic association results should be validated in other populations.In addition, a collaborative effort to harmonize phenotype definitions and to increase sample size is necessary." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "section_type": "main", + "text": "\n\nUntil recently, genome-wide linkage and candidate studies have been the main genetic epidemiological approaches to identifying the precise genetic variants underlying T2D heritability.These efforts confirmed only a few susceptibility variants, including those in PPARG, KCNJ11, WFS1, HNF1A, HNF1B, HNF4A, TCF7L2, and ADIPOQ (1,6,27,56,81,102).Recent genome-wide association studies (GWAS) have unveiled over 50 novel loci associated with T2D and more than 40 associated with T2D-related traits including fasting insulin, glucose, and proinsulin (16,48,57,82,87,97,105) (Table 1).Clinical investigations of some of the T2D loci, thus far, suggest that the genetic components of T2D risk act preferentially through β-cell function (20).This pattern may only be a function of case diagnostic criteria, which weigh heavily on parameters reflecting advanced stages of the disease.This notion is supported by the incomplete overlap of single-nucleotide polymorphisms (SNPs) contributing to variation in quantitative traits with those associated with overt T2D (20).With the exception of TCF7L2, most variants contribute modestly to T2D risk and together explain only a small proportion of the familial clustering of T2D, suggesting that many more loci await discovery (10,12,97)." + }, + { + "document_id": "9fd49699-612f-48c0-b1d9-e01158472be6", + "section_type": "main", + "text": "\n\nGenome-wide association studies (GWAS) have discovered germline genetic variation associated with type 2 diabetes risk (1)(2)(3)(4).One of the largest GWAS, involving DNA taken from individuals of European descent and conducted by the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium, identified 65 loci associated with type 2 diabetes risk (1).However, for most of these loci, the precise identity of the affected gene and the molecular mechanisms underpinning the altered risk are not known." + }, + { + "document_id": "41ba5319-e77d-4838-8f50-e59fe86b94f8", + "section_type": "main", + "text": "\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "063a0254-1d1b-4caa-b782-6a1fe4ebca0d", + "section_type": "main", + "text": "Genetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor γ (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a β cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18)." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "abstract", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "section_type": "main", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + }, + { + "document_id": "76ae2f09-af4d-422a-b939-625f0fe4ae1c", + "section_type": "main", + "text": "The future of type 1 diabetes genetics\n\nAfter more than two decades of work, type 1 diabetes is probably the best characterized of all common multigenic diseases.Thus far, the identified genetic risk factors have been plausible candidate genes with common variants that affect susceptibility.Of these, variation at HLA alone explains much of the risk to siblings (HLA provides a l s of 3.4 out of a total of 15, leaving a l s of 15/3.4 ¼ 4.4 to be explained), and INS and CTLA4 have also been identified as disease loci.What, then, is left to be done?First, many risk alleles remain undiscovered.Although their effect will be much weaker than is seen for HLA (and almost certainly weaker than for INS), they may identify genes or pathways that provide insight into etiology, pathogenesis, and perhaps even prevention or treatment.Each additional variant that is clearly proven to increase risk will also help to identify high-risk non-diabetic individuals who might participate in studies of prevention and, in turn, benefit from preventive interventions.These alleles might also be relevant to the genetics of diabetic complications (not discussed in this review), perhaps identifying patients who would benefit most from intensive treatment and monitoring." + }, + { + "document_id": "1ecd1047-39d1-44ea-b3a2-3d8472be3435", + "section_type": "main", + "text": "Genomic Analyses for Diabetes Risk\n\nGenes signifying increased risk for both type 1 and type 2 diabetes have been identified.Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes.Several T1D candidate genes for increased risk of developing type 1 diabetes have been suggested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood. 12Also, several candidate genes for increased risk of developing type 2 diabetes have been identified, including peroxisome proliferatoractivated receptor gamma (PPARγ2), angiotensin converting enzyme (ACE), methylene tetrahydrofolate reductase (MTHR), fatty acid binding protein-2 (FABP2), and fat mass and obesity associated gene (FTO). 13he conclusions of a \"Workshop on Metformin Pharmacogenomics,\" sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, were published in 2014. 14The meeting was intended to review metformin pharmacogenomics and identify both novel targets and more effective agents for diabetes.The idea behind the meeting was that understanding the genes and pathways that determine the response to metformin has the potential to reveal new drug targets for the treatment of diabetes.The group noted that there have been few genes associated with glycemic control by metformin, and the most reproducible associations have been in metformin transporter genes.They acknowledged that nongenetic factors also contribute to response to metformin and that broader system biology approaches will be required to model the combined effects of multiple gene variants and their interaction with nongenetic factors.They concluded that the overall challenge to the field of precision medicine as it relates to antidiabetes treatment is to identify the individualized factors that can lead to improved glycemic control." + }, + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "section_type": "main", + "text": "Future prospects\n\nWhilst the examples above provide interesting insights, it is clear that we are only at the beginning of mining the information generated by genome-wide association studies for Type 2 diabetes and other complex traits.work in human genetics, involving ever larger cohorts, meta-analyses and the search for rarer and more penetrant variants will in future be important to identify all of the heritable elements that control Type 2 diabetes risk; however, the useful deployment of this information for either disease prediction or the development of new therapies will require considerable further efforts at the cellular and molecular level to understand the function of the identified genes.Moreover, and although not the subject of this particular review, actions of single nucleotide polymorphisms through non-coding genes, e.g.mi-croRNAs and long non-coding RNAs, will require deeper investigation." + }, + { + "document_id": "7d4a197e-3774-40a4-9897-ed7c71f213b6", + "section_type": "abstract", + "text": "\nIt has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes.Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups.This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders." + }, + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "section_type": "main", + "text": "COMPLICATIONS\n\nIn addition to the genetic determinants of diabetes, several gene mutations and polymorphisms have been associated with the clinical complications of diabetes.The cumulative data on diabetes patients with a variety of micro-and macrovascular complications support the presence of strong genetic factors involved in the development of various complications [200] .A list of genes have been reported that are associated with diabetes complications including ACE and AKR1B1 in nephropathy, VEGF and AKRB1 in retinopathy and ADIPOQ and GLUL in cardiovascular diseases [200] ." + } + ], + "document_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&2&diabetes", + "genetic&predictors", + "diabetes&complications", + "GWAS", + "genome-wide&association&study", + "polygenic&score", + "susceptibility&loci", + "T2DM", + "genetic&variants", + "diabetic&neuropathy" + ], + "metadata": [ + { + "object": "rs2059806 of INSR was associated with both type 2 diabetes mellitus and type 2 diabetic nephropathy, while rs7212142 of mTOR was associated with type 2 diabetic nephropathy but not type 2 diabetes mellitus in a Chinese Han population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab687817" + }, + { + "object": "Data confirm the association between the FTO first intron polymorphism and the presence of type 2 diabetes mellitus in the Slavonic Czech population. The same variant is likely to be associated with development of chronic complications of diabetes mellitus, especially with diabetic neuropathy and diabetic kidney disease in either T2DM or both T1DM and T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab173943" + }, + { + "object": "Serum levels of APN and AdipoR1 are significantly lower in type 2 diabetes mellitus T2DM group and T2DM + macrovascular complications MVC group, showing lowest value in T2DM + MVC group. APN and AdipoR1 levels may influence glucose and lipid metabolism in T2DM patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab699512" + }, + { + "object": "this case control study showed that NET gene polymorphism G1287A, rs5569 was significantly associated with type 2 diabetes mellitus T2DM in North Indian male population where AG genotype and A allele was found to be protective against the risk of T2DM while the GG genotype and G allele were found to increase the risk of T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab928949" + }, + { + "object": "The results suggest that LEPR rs1327118 may be associated with elevated blood pressure and HDL-C levels in women with type 2 diabetes mellitus T2DM, and rs3806318 may be associated with T2DM and elevated blood pressure in men with T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab864916" + }, + { + "object": "of the five variants, SNP rs2236935T/C was significantly associated with type 2 diabetes mellitus T2DM in this study population; conclude that MAP4K4 gene is associated with T2DM in a Chinese Han population, and MAP4K4 gene variants may contribute to the risk toward the development of T2DM", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab545662" + }, + { + "object": "Study evaluated the associations between 6 SNPs in CDH13 and type 2 diabetes mellitus T2DM in a Han Chinese population. Results showed that the rs12596316 AG genotype was a risk genotype for the development of T2DM in the overdominant inheritance model; rs11646213, rs3865188, rs12444338, rs12051272, and rs7195409 had no observed associations with T2DM in terms of alleles, genotypes, and the various inheritance models.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab740648" + }, + { + "object": "data suggest a possible association of C332C-genotype of the glyoxalase 1 gene with diabetic neuropathy in type 2 diabetes, supporting the hypothesis that methylglyoxal might be an important mediator of diabetic neuropathy in type 2 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab202777" + }, + { + "object": "Compared with normal glucose tolerance NGT groups, the PTEN mRNA expression was significantly higher in Uyghur patients with mild type 2 diabetes mellitus T2DM groups; PTEN protein expression was upregulated in Uyghur patients with mild T2DM groups. PTEN methylation in T2DM patients was significantly lower than that in NGT groups. 2 CpG units demonstrated a significant difference between NGT and Uyghur patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab151151" + }, + { + "object": "Haplotype-based interaction between the PPARGC1A and UCP1 genes is associated with impaired fasting glucose IFG or type 2 diabetes mellitus T2DM among the residents of Henan province, China. Individuals with the haplotype AAG PPARGC1A gene and CTCG UCP1 gene have increased susceptibility to IFG or T2DM, while those with haplotype AAG PPARGC1A gene and CTCA UCP1 gene have a lower risk of IFG or T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab332396" + } + ], + "question": "Can we identify genetic predictors of diabetes complications?", + "subquestions": null, + "task_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "usage": { + "chatgpt": 6305, + "gpt-4": 3589, + "gpt-4-turbo-preview": 2668 + }, + "user_id": 2 + }, + "document_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "task_id": "0E3B1D23A525184EDA9AA62C618C9EC7" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_10.json b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_10.json new file mode 100644 index 0000000..c9762c0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/full_response/suga_resp_10.json @@ -0,0 +1,400 @@ +{ + "created_at": "2024-04-18T18:45:53.040778Z", + "data": { + "amplify": false, + "answer": "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "context": { + "063a0254-1d1b-4caa-b782-6a1fe4ebca0d": [ + { + "document_id": "063a0254-1d1b-4caa-b782-6a1fe4ebca0d", + "text": "Genetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor γ (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a β cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18)." + } + ], + "08858a32-d736-4d8d-a135-f86568152a81": [ + { + "document_id": "08858a32-d736-4d8d-a135-f86568152a81", + "text": "\n\nWith further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients." + } + ], + "183f165e-4d5c-4580-9aff-4e6b2e5a6463": [ + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "text": "Pharmacogenomics of Type 2 Diabetes\n\nWith the advent of GWAS, studies on the roles of inherited and acquired genetic variations in drug response have undergone an evolution from pharmacogenetics into pharmacogenomics, with a shift from the focus on individual candidate genes to GWAS [147].Clinically, it is often observed that even patients who receive similar antidiabetic regimens demonstrate large variability in drug disposition, glycemic response, tolerability, and incidence of adverse effects [148].This interindividual variability can be attributed to specific gene polymorphisms involved in the metabolism, transportation, and therapeutic mechanisms of oral antidiabetic drugs.Pharmacogenomics is on the agenda to explore feasible genetic testing to predict treatment outcome, so that appropriate steps could be taken to treat type 2 diabetes more efficiently." + } + ], + "277be46c-4307-4738-972d-eb6efd9b175a": [ + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + } + ], + "4d3330eb-acd0-4f72-aadf-b056d3c8b389": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genetics & genomics of T2D\n\n• Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D.However, only close to 10% heritability is explained by these variants.Other genetic variants, particularly those which are rare but with significant effects need to be identified.• Genetic variability is responsible for the difference in response to antidiabetic drugs seen across individuals." + } + ], + "4feda561-1914-404d-9092-3c629d5251bd": [ + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nDiabetes progression is a multifactorial process; however, pharmacogenetics seems to play an important role in understanding the different phenotypes and progression rates among diabetic patients.Genetic variants associated with decreased effect of a certain drug might explain why some individuals are more likely to experience glycemic deterioration on a given treatment.In the following sections, different genetic variants and their impact on treatment efficacy and outcome will be addressed." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nTo date, a number of genetic variants have been identified to be associated with response to antidiabetic drugs.Of these, some variants are present in either drug receptors or drug metabolizers as for OCT genes, KCNJ11, ABCC8, and CYP2C9.Other variants are known T2D susceptibility variants such as TCF7L2.To identify variants of importance for antiglycemic drug response, GWAS in large cohorts of patients with diabetes with detailed measures of pharmacotherapy are lacking.The pharmacologic management of patients with diabetes often involves drug classes other than antidiabetics.Pharmacogenetic studies on statin and antihypertensive treatment have reported several genetic variants associated with treatment response and adverse drug reactions [101,102].It therefore seems natural to conclude that the future perspectives in pharmacogenetics is to conduct genetic studies in large cohorts with wellphenotyped individuals, thorough data collection on baseline treatment, concomitant treatment, adherence to therapy as well as data collection on comorbidity and additional disease diagnoses.These types of pharmacogenetic studies may provide unique opportunities for future genotype-based treatment standards and may help in delaying or changing the slope of disease progression among patients with T2D." + } + ], + "50c72e55-b5fe-42a6-b837-64c28620a4c0": [ + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + } + ], + "516de7be-3cef-47ee-8338-199fb922bc6f": [ + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "text": "\n\nThus, specific answers are lacking as to the genetic basis for type 2 diabetes.Still, speculations can be made about what eventually will be found.It is almost certain the genetic basis for type 2 diabetes and other common metabolic diseases will be extremely complex-that a predisposition for the disease will require several genetic hits as opposed to just one.Also, it is generally assumed there will be many susceptibility genes for type 2 diabetes, with enormous variability in different families and ethnic groups.Not known is whether there will be a common form of type 2 diabetes, with any one or even a few susceptibility genes accounting for a sizeable percentage of affected persons.As such, identifying diabetes genes will be slow and difficult." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "Ta rge ted T r e atmen t a nd Pr e v en t ion\n\n4][75] In monogenic forms of diabetes, at least, genetic testing already drives the choice of therapy.For example, in patients who have maturity-onset diabetes of the young due to mutations in the gene encoding glucokinase (GCK), the hyperglycemia is mild and stable, the risk of complications is low, and dietary management is often sufficient.In contrast, in patients who have maturity-onset diabetes of the young due to mutations in HNF1A, the disease follows a more aggressive course, with a greater risk of severe complications, but is particularly responsive to the hypoglycemic effects of sulfonylureas. 62,73Most children with neonatal diabetes have mutations in KCNJ11 or ABCC8, adjacent genes that jointly encode the beta-cell ATP-sensitive potassium channel that mediates glucose-stimulated insulin secretion and is the target of sulfonylureas.In such children, treatment with sulfonylureas has proved more effective and convenient than the lifelong insulin therapy previously considered the default option. 74,75n children with severe obesity due to profound leptin deficiency, exogenous leptin therapy is lifesaving. 76s yet, there are insufficient genetic data to support management decisions for common forms of type 2 diabetes and obesity. 77Although the TCF7L2 genotype is associated with variation in the response to sulfonylurea treatment, 78 the effect is too modest to guide the care of individual patients.For the time being, the contribution of genetic information to therapy is most likely to come through the drug-discovery pipeline.Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physiological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32 and to characterize high-risk groups to enable more efficient clinical trials of agents designed to reduce the progression of type 2 diabetes or obesity or the risk of complications." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 2 Diabetes\n\nWhile a subset of genetic variants are linked to both type 1 and type 2 diabetes (42,43), the two diseases have a largely distinct genetic basis, which could be leveraged toward classification of diabetes (44).Genome-wide association studies have identified more than 130 genetic variants associated with type 2 diabetes, glucose levels, or insulin levels; however, these variants explain less than 15% of disease heritability (45)(46)(47).There are many possibilities for explaining the majority of type 2 diabetes heritability, including disease heterogeneity, gene-gene interactions, and epigenetics.Most type 2 variants are in noncoding genomic regions.Some variants, such as those in KCNQ1, show strong parent-of-origin effects (48).It is possible that children of mothers carrying KCNQ1 are born with a reduced functional b-cell mass and thereby are less able to increase their insulin secretion when exposed to insulin resistance (49).Another area of particular interest has been the search for rare variants protecting from type 2 diabetes, such as loss-of-function mutations in SLC30A8 (50), which could offer potential new drug targets for type 2 diabetes." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + } + ], + "ad88aed6-75ba-469d-b96b-7be4a65be8fc": [ + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "text": "\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 × 10 −8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes." + } + ], + "b00b9753-c198-4f8a-a8b9-dd5e94dc5896": [ + { + "document_id": "b00b9753-c198-4f8a-a8b9-dd5e94dc5896", + "text": "\n\nTogether, the findings from these studies were among the first to demonstrate that the genetic etiology of hyperglycemia may modulate response to hypoglycemia agents.Such results yielded strong implications for patient management and paved the way toward elucidating additional genetic factors that might influence drug response in the treatment of T2D." + } + ], + "c8c58fdf-06e3-4da4-a920-d5bcbcd18289": [ + { + "document_id": "c8c58fdf-06e3-4da4-a920-d5bcbcd18289", + "text": "A\n\nnumber of studies have implicated a genetic basis for type 2 diabetes (1).The discovery of monogenic forms of the disease underscored the phenotypic and genotypic heterogeneity, although monogenic forms account for only a few percent of the disease (1).Defining the genetic basis of the far more common polygenic form of the disease presents more difficulties (2,3).Nevertheless, some interesting results have recently emerged.A genome scan of Hispanic-American families (330 affected sib-pairs [ASPs]) found linkage to chromosome 2q37 (logarithm of odds [LOD] 4.15) (4), and the causative gene has been recently reported (5).A number of other genome scans in various racial groups have identified other putative susceptibility loci (6 -8).The largest genome-wide scan for type 2 diabetes loci reported to date studied 477 Finnish families (716 ASPs) and found evidence for linkage to chromosome 20q12-13.1(LOD 2.06 at D20S107) (9).Interestingly, similar results have been reported by at least three other groups (10 -12)." + } + ], + "f7072d9b-4e07-4541-bac7-13a25761f460": [ + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "text": "\n\nBecause more than one genetic mutation contributes to T1D, the differences that occur between individuals of different backgrounds (for instance, race and locality) may need to be considered in the design of treatments.Personalized medicine is about the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or in their response to a specific treatment (Blau and Liakopoulou, 2013;Timmeman, 2013).This will allow for a more accurate diagnosis per individual, and design of specific treatment plans including gene therapy." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + } + ] + }, + "data_source": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "abstract", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nDiabetes progression is a multifactorial process; however, pharmacogenetics seems to play an important role in understanding the different phenotypes and progression rates among diabetic patients.Genetic variants associated with decreased effect of a certain drug might explain why some individuals are more likely to experience glycemic deterioration on a given treatment.In the following sections, different genetic variants and their impact on treatment efficacy and outcome will be addressed." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "section_type": "main", + "text": "Pharmacogenomics of Type 2 Diabetes\n\nWith the advent of GWAS, studies on the roles of inherited and acquired genetic variations in drug response have undergone an evolution from pharmacogenetics into pharmacogenomics, with a shift from the focus on individual candidate genes to GWAS [147].Clinically, it is often observed that even patients who receive similar antidiabetic regimens demonstrate large variability in drug disposition, glycemic response, tolerability, and incidence of adverse effects [148].This interindividual variability can be attributed to specific gene polymorphisms involved in the metabolism, transportation, and therapeutic mechanisms of oral antidiabetic drugs.Pharmacogenomics is on the agenda to explore feasible genetic testing to predict treatment outcome, so that appropriate steps could be taken to treat type 2 diabetes more efficiently." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "section_type": "main", + "text": "Genetics & genomics of T2D\n\n• Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D.However, only close to 10% heritability is explained by these variants.Other genetic variants, particularly those which are rare but with significant effects need to be identified.• Genetic variability is responsible for the difference in response to antidiabetic drugs seen across individuals." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nTo date, a number of genetic variants have been identified to be associated with response to antidiabetic drugs.Of these, some variants are present in either drug receptors or drug metabolizers as for OCT genes, KCNJ11, ABCC8, and CYP2C9.Other variants are known T2D susceptibility variants such as TCF7L2.To identify variants of importance for antiglycemic drug response, GWAS in large cohorts of patients with diabetes with detailed measures of pharmacotherapy are lacking.The pharmacologic management of patients with diabetes often involves drug classes other than antidiabetics.Pharmacogenetic studies on statin and antihypertensive treatment have reported several genetic variants associated with treatment response and adverse drug reactions [101,102].It therefore seems natural to conclude that the future perspectives in pharmacogenetics is to conduct genetic studies in large cohorts with wellphenotyped individuals, thorough data collection on baseline treatment, concomitant treatment, adherence to therapy as well as data collection on comorbidity and additional disease diagnoses.These types of pharmacogenetic studies may provide unique opportunities for future genotype-based treatment standards and may help in delaying or changing the slope of disease progression among patients with T2D." + }, + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "section_type": "main", + "text": "\n\nThus, specific answers are lacking as to the genetic basis for type 2 diabetes.Still, speculations can be made about what eventually will be found.It is almost certain the genetic basis for type 2 diabetes and other common metabolic diseases will be extremely complex-that a predisposition for the disease will require several genetic hits as opposed to just one.Also, it is generally assumed there will be many susceptibility genes for type 2 diabetes, with enormous variability in different families and ethnic groups.Not known is whether there will be a common form of type 2 diabetes, with any one or even a few susceptibility genes accounting for a sizeable percentage of affected persons.As such, identifying diabetes genes will be slow and difficult." + }, + { + "document_id": "b00b9753-c198-4f8a-a8b9-dd5e94dc5896", + "section_type": "main", + "text": "\n\nTogether, the findings from these studies were among the first to demonstrate that the genetic etiology of hyperglycemia may modulate response to hypoglycemia agents.Such results yielded strong implications for patient management and paved the way toward elucidating additional genetic factors that might influence drug response in the treatment of T2D." + }, + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "section_type": "main", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + }, + { + "document_id": "08858a32-d736-4d8d-a135-f86568152a81", + "section_type": "main", + "text": "\n\nWith further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "\n\nBecause more than one genetic mutation contributes to T1D, the differences that occur between individuals of different backgrounds (for instance, race and locality) may need to be considered in the design of treatments.Personalized medicine is about the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or in their response to a specific treatment (Blau and Liakopoulou, 2013;Timmeman, 2013).This will allow for a more accurate diagnosis per individual, and design of specific treatment plans including gene therapy." + }, + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "section_type": "main", + "text": "Ta rge ted T r e atmen t a nd Pr e v en t ion\n\n4][75] In monogenic forms of diabetes, at least, genetic testing already drives the choice of therapy.For example, in patients who have maturity-onset diabetes of the young due to mutations in the gene encoding glucokinase (GCK), the hyperglycemia is mild and stable, the risk of complications is low, and dietary management is often sufficient.In contrast, in patients who have maturity-onset diabetes of the young due to mutations in HNF1A, the disease follows a more aggressive course, with a greater risk of severe complications, but is particularly responsive to the hypoglycemic effects of sulfonylureas. 62,73Most children with neonatal diabetes have mutations in KCNJ11 or ABCC8, adjacent genes that jointly encode the beta-cell ATP-sensitive potassium channel that mediates glucose-stimulated insulin secretion and is the target of sulfonylureas.In such children, treatment with sulfonylureas has proved more effective and convenient than the lifelong insulin therapy previously considered the default option. 74,75n children with severe obesity due to profound leptin deficiency, exogenous leptin therapy is lifesaving. 76s yet, there are insufficient genetic data to support management decisions for common forms of type 2 diabetes and obesity. 77Although the TCF7L2 genotype is associated with variation in the response to sulfonylurea treatment, 78 the effect is too modest to guide the care of individual patients.For the time being, the contribution of genetic information to therapy is most likely to come through the drug-discovery pipeline.Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physiological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32 and to characterize high-risk groups to enable more efficient clinical trials of agents designed to reduce the progression of type 2 diabetes or obesity or the risk of complications." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Type 2 Diabetes\n\nWhile a subset of genetic variants are linked to both type 1 and type 2 diabetes (42,43), the two diseases have a largely distinct genetic basis, which could be leveraged toward classification of diabetes (44).Genome-wide association studies have identified more than 130 genetic variants associated with type 2 diabetes, glucose levels, or insulin levels; however, these variants explain less than 15% of disease heritability (45)(46)(47).There are many possibilities for explaining the majority of type 2 diabetes heritability, including disease heterogeneity, gene-gene interactions, and epigenetics.Most type 2 variants are in noncoding genomic regions.Some variants, such as those in KCNQ1, show strong parent-of-origin effects (48).It is possible that children of mothers carrying KCNQ1 are born with a reduced functional b-cell mass and thereby are less able to increase their insulin secretion when exposed to insulin resistance (49).Another area of particular interest has been the search for rare variants protecting from type 2 diabetes, such as loss-of-function mutations in SLC30A8 (50), which could offer potential new drug targets for type 2 diabetes." + }, + { + "document_id": "c8c58fdf-06e3-4da4-a920-d5bcbcd18289", + "section_type": "main", + "text": "A\n\nnumber of studies have implicated a genetic basis for type 2 diabetes (1).The discovery of monogenic forms of the disease underscored the phenotypic and genotypic heterogeneity, although monogenic forms account for only a few percent of the disease (1).Defining the genetic basis of the far more common polygenic form of the disease presents more difficulties (2,3).Nevertheless, some interesting results have recently emerged.A genome scan of Hispanic-American families (330 affected sib-pairs [ASPs]) found linkage to chromosome 2q37 (logarithm of odds [LOD] 4.15) (4), and the causative gene has been recently reported (5).A number of other genome scans in various racial groups have identified other putative susceptibility loci (6 -8).The largest genome-wide scan for type 2 diabetes loci reported to date studied 477 Finnish families (716 ASPs) and found evidence for linkage to chromosome 20q12-13.1(LOD 2.06 at D20S107) (9).Interestingly, similar results have been reported by at least three other groups (10 -12)." + }, + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "section_type": "main", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + }, + { + "document_id": "063a0254-1d1b-4caa-b782-6a1fe4ebca0d", + "section_type": "main", + "text": "Genetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor γ (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a β cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18)." + }, + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "section_type": "abstract", + "text": "\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 × 10 −8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes." + }, + { + "document_id": "ce63119a-9a7b-4946-b1f5-bc8bfc4c10da", + "section_type": "main", + "text": "\n\nGenetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1" + }, + { + "document_id": "e2c1cfb0-9cfc-4a59-9df6-8599708b25ed", + "section_type": "main", + "text": "\n\nc With increasing efforts to map patients with T2D in etiological space using clinical and molecular phenotype, physiology, and genetics, it is likely that this increasingly granular view of T2D will lead to increasing precision therapeutic paradigms requiring evaluation and potential implementation.Genetic variation not only can capture etiological variation (i.e., genetic variants associated with diabetes risk) but also variation in drug pharmacokinetics (absorption, distribution, metabolism, and excretion [ADME]) and in drug action (pharmacodynamics)." + }, + { + "document_id": "d978c09f-53e0-4a69-bfa6-e15537f32ffb", + "section_type": "main", + "text": "Genomics and gene-environment interactions\n\nEven though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mellitus are more susceptible to T2DM than others, which suggests that individual differences in response to lifestyle interventions exist 76 .Substantial evidence from twin and family studies has suggested a genetic basis of T2DM 77 .Over the past decade, successive waves of T2DM genome-wide association studies have identified >100 robust association signals, demonstrating the complex polygenic nature of T2DM 5 .Most of these loci affect T2DM risk through primary effects on insulin secretion, and a minority act through reducing insulin action 78 .Individually, the common variants (minor allele frequency >5%) identified in these studies have only a modest effect on T2DM risk and collectively explain only a small portion (~20%) of observed T2DM heritability 5 .It has been hypothesized that lower-frequency variants could explain much of the remaining heritability 79 .However, results of a large-scale sequencing study from the GoT2D and T2D-GENES consortia, published in 2016, do not support such a hypothesis 5 .Genetic variants might help reveal possible aetiological mechanisms underlying T2DM development; however, the variants identified thus far have not enabled clinical prediction beyond that achieved with common clinical measurements, including age, BMI, fasting levels of glucose and dyslipidaemia.A study published in 2014 linked susceptibility variants to quantitative glycaemic traits and grouped these variants on the basis of their potential intermediate mechanisms in T2DM pathophysiology: four variants fitted a clear insulin resistance pattern; two reduced insulin secretion with fasting hyperglycaemia; nine reduced insulin secretion with normal fasting glycaemia; and one altered insulin processing 80 .Considering such evidence, the genetic architecture of T2DM is highly polygenic, and thus, substantially larger association studies are needed to identify most T2DM loci, which typically have small to modest effect sizes 81 ." + }, + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "section_type": "main", + "text": "\n\nRecent advances in GWAS have substantially improved our understanding of the pathophysiology of diabetes, but the currently identified genetic susceptibility loci are insufficient to explain differences in diabetes risk across different ethnic groups or the rapid rise in diabetes prevalence over the past several decades.Clinical utility of these loci in predicting future risk of diabetes is also limited." + }, + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "section_type": "abstract", + "text": "\nWith rapidly increasing prevalence, diabetes has become one of the major causes of mortality worldwide.According to the latest studies, genetic information makes substantial contributions towards the prediction of diabetes risk and individualized antidiabetic treatment.To date, approximately 70 susceptibility genes have been identified as being associated with type 2 diabetes (T2D) at a genome-wide significant level ( < 5×10 −8 ).However, all the genetic loci identified so far account for only about 10% of the overall heritability of T2D.In addition, how these novel susceptibility loci correlate with the pathophysiology of the disease remains largely unknown.This review covers the major genetic studies on the risk of T2D based on ethnicity and briefly discusses the potential mechanisms and clinical utility of the genetic information underlying T2D." + }, + { + "document_id": "a49c4251-7a66-44f1-9f95-0d6e8191a2ad", + "section_type": "main", + "text": "\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes." + }, + { + "document_id": "b29b3621-cdb5-4723-b771-8b48546241a5", + "section_type": "main", + "text": "\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes." + }, + { + "document_id": "f3b925cc-2556-4f30-809b-6bfe63a805b8", + "section_type": "main", + "text": "\n\nThe molecular mechanisms involved in the development of type 2 diabetes are poorly understood.Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia.We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8.Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect.The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes." + }, + { + "document_id": "b00b9753-c198-4f8a-a8b9-dd5e94dc5896", + "section_type": "main", + "text": "Conclusions\n\nPharmacogenetics research provides a means to better understand and improve on pharmacotherapy.However, pharmacogenetic studies of T2D therapies lag behind those for other complex diseases, despite the fact that pharmacologic interventions for T2D have been studied extensively at both the clinical and epidemiologic levels.Among the studies that have been conducted, several have identified variants that are potentially associated with differential response to anti-diabetes medications; these preliminary results are promising and warrant investigations in larger, well-designed cohorts to assess their potential roles in optimal drug selection and individualized pharmacotherapy in patients with T2D.At this time, larger, well-powered studies with clearly defined outcomes and utilizing a global approach are needed, as they will not only be more informative than extant candidate gene investigations, but will also be necessary to define the array of genetic variants that may underlie drug response.Such results will likely enable achievement of optimal glucose control, improvement of therapeutic efficacy, and reduction in risk of adverse drug events in at-risk patients, which together will lead to personalized treatment strategies for all individuals with T2D." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "Pharmacogenetics in disease progression\n\nOver the recent years, more than 90 susceptibility genes have been identified by genome-wide association studies (GWAS) [55][56][57][58].However, the knowledge of the potential interactions between T2D predisposing genetic variants and the efficacy of treatment of T2D is sparse.Identification of gene-treatment interactions is challenging and requires large sample sizes and sophisticated analytical methods.Furthermore, detailed information on lifestyle and compliance to treatment as well as a long follow-up period are necessary for analysis of pharmacogenomics in T2D." + }, + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "section_type": "main", + "text": "\n\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 × 10 −8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes." + }, + { + "document_id": "15524ac0-da3c-4c01-8ae2-1b8c901105ad", + "section_type": "abstract", + "text": "\nThe development of type 2 diabetes (T2DM) is determined by two factors: genetics and environment.The genetic background of T2DM is undoubtedly heterogeneous.Most patients with T2DM exhibit two different defects: the impairment of insulin secretion and decreased insulin sensitivity.This means that there are at least two pathophysiological pathways and at least two groups of genes that may be involved in the pathogenesis of T2DM.As far as genetic bacground of T2DM is concerned, the disease may be divided into two large groups: monogenic and polygenic forms.In this review, we present genes known to cause rare monogenic forms of diabetes with predominant insulin deficiency (MODY -maturity-onset diabetes of the young, MIDD -maternally inherited diabetes with deafness) and uncommon syndromes of severe insulin resistance.We also describe some of the main approaches used to identify genes involved in the more common forms of T2D and the reasons for the lack of spectacular success in this field.Although major genes for T2DM still await to be discovered, we have probably established a \"road map\" that we should follow." + }, + { + "document_id": "dcd88798-0248-45e0-8d45-8614c7697266", + "section_type": "main", + "text": "\n\ndiabetes (DoD) and poor glycemic control (2).Genetic factors are also implicated, with heritability of 52% for proliferative DR (PDR) (3,4).Several candidate gene and genome-wide association studies (GWAS) have been conducted (5)(6)(7)(8)(9)(10)(11).Although several polymorphisms have been suggested to be associated with DR, few have been convincingly replicated (10,(12)(13)(14)(15).There are several reasons why studies have not yielded consistent findings.The genetic effects are likely modest, and identification requires large sample sizes.Previous studies have not consistently accounted for the strongest two covariates, DoD and glycemic control.Liability threshold (LT) modeling is one way to incorporate these covariates while also increasing statistical power (16).Finally, previous genetic studies have largely examined individual variants.Techniques that examine top GWAS findings collectively for variants that cluster in biological networks based on known protein-protein interactions have the potential to identify variants where there is insufficient power to detect their individual effects." + }, + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "section_type": "main", + "text": "Genetic Predisposition\n\nThe fact that type 2 diabetes is a genetic disease is well known to clinicians by how it occurs in families, and by there being ethnic populations who are particularly high risk.The genetic link was clearly shown more than two decades ago by a famous study of identical twins in the U.K. that found essentially a 100% concordance rate for this diseaseif one twin developed type 2 diabetes, then the other one invariably developed it (9).However, this kind of study provides no insight into how genetics act in the disease.Is there a defective gene that directly impairs the glucose homeostasis system?Alternatively, does it cause insulin resistance or some other defect that acts indirectly by exceeding the capacity of an otherwise normal glucose homeostasis system to compensate?Also, are there one or many genetic defects that predispose to this disease?" + }, + { + "document_id": "2a71b781-89fe-4055-bbb1-15aa226e1e3a", + "section_type": "main", + "text": "\n\nDiabetes is a genetically complex multifactorial disease that requires sophisticated consideration of multigenic and phenotypic influences.As well as standard nonpara- metric methods, we used novel approaches to evaluate and identify locus heterogeneity.It has also proved productive to consider phenotypes such as age at type 2 diabetes onset and obesity, which may define a more homogeneous subgroup of families.A genome-wide scan of 247 African-American families has identified a locus on chromosome 6q and a region of 7p that apparently interacts with early-onset type 2 diabetes and low BMI, as target regions in the search for African-American type 2 diabetes susceptibility genes." + }, + { + "document_id": "2a94ec9f-6fb6-4ce3-8e33-1a8859470be9", + "section_type": "main", + "text": "\n\nAn individual's risk of developing T2D is influenced by a combination of lifestyle, environmental, and genetic factors.Uncovering the genetic contributors to diabetes holds promise for clinical impact by revealing new therapeutic targets aimed at the molecular and cellular mechanisms that lead to disease.Genome-wide association studies performed during the past decade have uncovered more than 100 regions associated with T2D (5)(6)(7)(8)(9)(10)(11)(12).Although these studies have provided a better understanding of T2D genetics, the majority of identified variants fall outside protein-coding regions, leaving the molecular mechanism by which these variants confer altered disease risk obscure.Consequently, T2D genome-wide association studies have identified few loci with clear therapeutic potential." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "section_type": "main", + "text": "\n\nThe purpose of this review is to summarize current knowledge of pharmacogenetics in T2D and provide a perspective on the relationships between human genetic variants, antidiabetic treatment, and disease progression.This topic is of utmost importance as an improved understanding of gene-treatment interactions may provide a basis for development of future individualized therapies and treatment guidelines." + }, + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "section_type": "main", + "text": "\n\nWith rapidly increasing prevalence, diabetes has become one of the major causes of mortality worldwide.According to the latest studies, genetic information makes substantial contributions towards the prediction of diabetes risk and individualized antidiabetic treatment.To date, approximately 70 susceptibility genes have been identified as being associated with type 2 diabetes (T2D) at a genome-wide significant level ( < 5×10 −8 ).However, all the genetic loci identified so far account for only about 10% of the overall heritability of T2D.In addition, how these novel susceptibility loci correlate with the pathophysiology of the disease remains largely unknown.This review covers the major genetic studies on the risk of T2D based on ethnicity and briefly discusses the potential mechanisms and clinical utility of the genetic information underlying T2D." + }, + { + "document_id": "3e53b34f-5bdf-43d5-9594-736cf83071db", + "section_type": "main", + "text": "\n\nTo extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent.We identified ten previously unreported T2D susceptibility loci, including two showing sex-differentiated association.Genomewide analyses of these data are consistent with a long tail of additional common variant loci explaining much of the variation in susceptibility to T2D.Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signaling and cell cycle regulation, in diabetes pathogenesis." + }, + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "section_type": "main", + "text": "DIABETES AND GENETICS\n\nDiabetes is a complex disease that involves a wide range of genetic and environmental factors.Over the past several years, many studies have focused on the elucidation of the wide spectrum of genes that played a role in the molecular mechanism of diabetes development [142][143][144] .However, despite the vast flow of genetic information including the identification of many gene mutations and a large array of single nucleotide polymorphisms (SNPs) in many genes involved in the metabolic pathways that affect blood glucose levels, the exact genetic mechanism of diabetes remains elusive [145,146] .Evidently, a major complication is the fact that a single gene mutation or polymorphism will not impose the same effect among different individuals within a population or different populations.This variation is directly or indirectly affected by the overall genetic background at the individual, family or population levels that are potentially further complicated by interaction with highly variable environmental modifier factors [147,148] ." + } + ], + "document_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&2&diabetes", + "pharmacogenetics", + "pharmacogenomics", + "GWAS", + "genetic&variants", + "OCT&genes", + "KCNJ11", + "ABCC8", + "CYP2C9", + "TCF7L2" + ], + "metadata": [ + { + "object": "The intrinsic clearance Vmax/Km values of all variants, with the exception of CYP2C9*2, CYP2C9*11, CYP2C9*23, CYP2C9*29, CYP2C9*34, CYP2C9*38, CYP2C9*44, CYP2C9*46 and CYP2C9*48, were significantly different from CYP2C9*1. CYP2C9*27, *40, *41, *47, *49, *51, *53, *54, *56 and N418T variant exhibited markedly larger values than CYP2C9*1.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab827642" + }, + { + "object": "genetic association studies in pediatric population in Japan: Data confirm that mutations in KCNJ11 or ABCC8 are associated with neonatal diabetes mellitus. Novel mutations were identified; 2 in KCNJ11 V64M, R201G and 6 in ABCC8 R216C, G832C, F1176L, A1263V, I196N, T229N. KCNJ11 = ATP-sensitive inward rectifier potassium channel-11; ABCC8 = ATP-binding cassette subfamily C member-8", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab316321" + }, + { + "object": "rs2059806 of INSR was associated with both type 2 diabetes mellitus and type 2 diabetic nephropathy, while rs7212142 of mTOR was associated with type 2 diabetic nephropathy but not type 2 diabetes mellitus in a Chinese Han population.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab687817" + }, + { + "object": "genetic association studies in population in Scotland: data suggest, in type 2 diabetes treated with sulfonylureas, 2 SNPs in CYP2C9 CYP2C9*2, R144C, rs1799853; CYP2C9*3, I359L, rs1057910 are associated with drug-induced hypoglycemia; an SNP in POR POR*28, A503V, rs1057868 is associated with better response to sulfonylureas. CYP2C9 = cytochrome P450 family 2 subfamily C member 9; POR = cytochrome p450 oxidoreductase", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab316392" + }, + { + "object": "Novel mutations were detected in ABCC8 and KCNJ11 gene in Chinese patients with congenital hyperinsulinism CHI. Hotspot mutations such as T1042Qfs*75, I1511K, E501K, G111R in ABCC8 gene, and R34H in KCNJ11 gene are predominantly responsible for Chinese CHI patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab535847" + }, + { + "object": "he aim of this study was to ascertain the polymorphic markers profile of ADIPOQ, KCNJ11 and TCF7L2 genes in Kyrgyz population and to analyze the association of polymorphic markers and combinations of ADIPOQ gene's G276T locus, KCNJ11 gene's Glu23Lys locus and TCF7L2 gene's VS3C>T locus with type two diabetes T2D in Kyrgyz population", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab334669" + }, + { + "object": "genetic variants in TCF7L2 confer a strong risk of future type 2 diabetes possibly mediated by altering expression of TCF7L2 in pancreatic islets [review]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab318653" + }, + { + "object": "Considering that CYP2C9*2 and CYP2C9*3 alleles have altered catalytic activities relative to CYP2C9*1, the present data suggest the need for pharmacogenetic studies to optimize drug dosages in different populations.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab155248" + }, + { + "object": "The association of variants in IRS1 with type 2 diabetes and type 2 diabetes-related phenotypes and the differential expression of IRS1 in adipocytes and skeletal muscle suggest a role of this gene in the pathogenesis of type 2 diabetes in Pima Indians.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab782328" + }, + { + "object": "trend for augmented exercise-induced IL6 release in type 2 diabetics; results also suggest that neither type 2 diabetic nor healthy skeletal muscle releases IL6 at rest,indicating that other organs contribute to elevated basal IL6 in type 2 diabetics", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab971647" + } + ], + "question": "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "subquestions": null, + "task_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "usage": { + "chatgpt": 7037, + "gpt-4": 4436, + "gpt-4-turbo-preview": 3522 + }, + "user_id": 2 + }, + "document_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "task_id": "C4C12C6896F2957844079BC4AFF8FF4B" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_01.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_01.json new file mode 100644 index 0000000..8720f97 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_01.json @@ -0,0 +1,142 @@ +{ + "created_at": "2024-04-18T18:19:31.857649Z", + "data": { + "amplify": false, + "answer": "The onset of diabetes in mice can occur as early as 3 to 4 weeks of age, with blood sugar concentrations often elevating between 5 and 8 weeks. However, in some cases, hyperglycemia can develop at approximately 20 weeks of age.", + "context": { + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + } + ], + "1bf337a1-ffed-4199-a11f-c5a62df47980": [ + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + } + ], + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "52990c69-609c-448e-9f2c-36e1655ca6db": [ + { + "document_id": "52990c69-609c-448e-9f2c-36e1655ca6db", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nBB rats usually develop diabetes just after puberty and have similar incidence in males and females.Around 90% of rats develop diabetes between 8 and 16 weeks of age.The diabetic phenotype is quite severe, and the rats require insulin therapy for survival.Although the animals have insulitis with the presence of T cells, B cells, macrophages and NK cells, the animals are lymphopenic with a severe reduction in CD4 + T cells and a near absence of CD8 + T cells (Mordes et al., 2004).Lymphopenia is not a characteristic of type 1 diabetes in humans or NOD mice (Mordes et al., 2004) and is seen to be a disadvantage in using the BB as a model of type 1 diabetes in humans.Also, in contrast to NOD mice, the insulitis is not preceded by peri-insulitis.However, the model has been valuable in elucidating more about the genetics of type 1 diabetes (Wallis et al., 2009), and it has been suggested that it may be the preferable small animal model for islet transplantation tolerance induction (Mordes et al., 2004).In addition, BB rats have been used in intervention studies (Hartoft-Nielsen et al., 2009;Holmberg et al., 2011) and studies of diabetic neuropathy (Zhang et al., 2007)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAgeing likewise affects metabolic parameters in rodents.Analogous to what occurs in humans, the body weight of the C57BL/6J mouse, the most commonly used mouse strain for metabolic studies, increases with age, peaking at ~9 months 133 , and older C57BL/6J mice (22 months) have reduced lean mass and increased fat mass compared with young 3-month-old mice 134 .In both rats and mice, fasting glucose levels are mostly stable throughout life, but whereas glucose tolerance generally worsens with age in rats, mice are less affected [135][136][137][138][139][140] .In fact, 2-year-old male C57BL/6J mice were significantly more glucose tolerant than their 5-month-old counterparts 138 .Consistent with these findings, glucosestimulated insulin release from the pancreas decreases with age in rats, but not in mice 137,138 ." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "All mice h o m o z y g o u s for t h e d i a b e t e s\ngene (db/db) b e c o m e diabetic, t h e first d i s t i n g u i s h i n g\nf e a t u r e being a m a r k e d t e n d e n c y to o b e s i t y w i t h large\nf a t d e p o s i t i o n s o b s e r v e d in t h e a x i l l a r y a n d i n g u i n a l\nregions a t a b o u t 3 t o 4 weeks of age." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "In many of these diabetic mice\nblood sugar concentration tends to increase gradually\nbetween 5 and 12 weeks of age, after which it may rise\nsharply to over 500 rag/100 ml of blood almost overnight. The diabetic condition, thus, appears to develop\nin two phases, an early one when there is some regulation of blood sugar concentration, and a later stage\ncharacterized by a marked increase in hyperglycemia\nand a complete loss of metabolic control. A few exceptional diabetics, usually females, exhibit\na pattern similar to that shown in Fig. 3. Although\n16\n240\n\nD.L. COLEMANand K.P." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "of age; m o r e o f t e n this e l e v a t i o n occurs b e t w e e n 5\na n d 8 weeks. I n older d i a b e t i c mice b l o o d sugar\nc o n c e n t r a t i o n s g r e a t e r t h a n 600 m g / 1 0 0 m l are n o t\n\nu n c o m m o n ." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "I n older mice with blood sugar concentrations over 250 rag/100 ml, injections of up t o 100 units /\n100 g were completely ineffective in reducing blood sugar\nto normal levels. Continued treatment of young diabetic\nmice with daily injections of insulin, although controlling Mood sugar concentrations initially, did not prevent or delay either the obesity or the uncontrollable\nhigh blood sugar concentrations, which usually develop\nat about 6 to 8 weeks of age." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + } + ], + "c24330f7-9f82-404a-86d5-a16d814bb754": [ + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + }, + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "Renal lesions in diabetic mouse models\n\nDb/db mice, which have a recessive mutation in the hypothalamic leptin receptor, develop obesity at 4 wk of age and type 2 diabetes at approximately 8 wk of age.In C57BL/6J background, the diabetes and the obesity are usually less severe than in the C57BL/KsJ background (44).Kidneys are generally enlarged in this mouse strain, and structural glomerular changes (e.g., diffuse glomerulosclerosis, GBM thickening) occur without evidence of tubulointerstitial disease (40).Glomerular lesions of the KK mice are characterized by diffuse and nodular mesangial sclerosis without evidence of tubular disease (45).The lack of reliable mouse models prompted the National Institute of Diabetes and Digestive and Kidney Diseases to fund a consortium for the development and phenotyping of new diabetic mouse models that would resemble closely human DNP." + } + ], + "c802cb60-1a15-4962-8e6d-f06608c00a54": [ + { + "document_id": "c802cb60-1a15-4962-8e6d-f06608c00a54", + "text":"In total, about\n360 male mice (10 for each strain) were fed with either a regular\nchow diet (CD) or a high-fat diet (HFD) to induce obesity and\nassociated metabolic stress. At 20 weeks of age, a test meal\nbolus was administered orally, and postprandial BAs and blood\nglucose levels were analyzed at three different time points (before\nand 30 or 60 min after gavage). Nine weeks later, the mice were\nsacrificed 4 h after feeding, a time point in which the main metabolic adaptive processes in response to BA-mediated food intake\nare captured." + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "db/db&mice", + "diabetes", + "onset", + "age", + "obesity", + "hyperglycemia", + "C57BL/KsJ", + "C57BL/6J", + "insulin&resistance", + "albuminuria" + ], + "metadata": [], + "question": "What is the age range in mouse for the onset of diabetes?", + "subquestions": null, + "task_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "usage": { + "chatgpt": 6475, + "gpt-4": 4255, + "gpt-4-turbo-preview": 3283 + }, + "user_id": 2 + }, + "document_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9", + "task_id": "75D95A4CEF90AC3DEAB5CD33E1C3DDD9" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_02.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_02.json new file mode 100644 index 0000000..c14ab82 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_02.json @@ -0,0 +1,205 @@ +{ + "created_at": "2024-03-25T10:53:10.812694Z", + "data": { + "amplify": false, + "answer": "Yes, the studies identified significant quantitative trait loci associated with both type 1 and type 2 diabetes. These include loci such as TCF7L2, DNER, SRR, HNF1A, KCNQ1, CDKN2A/CDKN2B, JAZF1, CDC123, CAMK1D, TSPAN8, LGR5, THADA, ADAMTS9, NOTCH2, and others. However, the significance of some loci varied across different populations and studies.", + "context": { + "0bcb68e6-4da3-457c-a68c-981b14d1ded8": [ + { + "document_id": "0bcb68e6-4da3-457c-a68c-981b14d1ded8", + "text": "\n\nAdditional large-scale meta-analysis predicated upon increased sample size, were carried out on existing datasets from the WTCCC [18] and the Genetics of Kidneys in Diabetes (GoKinD) study [69][70][71] plus control data derived from the National Institute of Mental Health.These investigators observed significant association of previously observed loci.Importantly, they did not observe evidence of new T1D loci reaching the threshold for genome-wide significance.Instead they re-analyzed the most nominally significant associated SNP in an independent British cohort of approximately 6000 cases, 7000 controls and in 2800 families, where they uncovered four additional loci, BACH2 (previously reported [67]), 10p15 harboring protein kinase C theta (PRKCQ), 15q24 harboring nine genes including the cathepsin H (CTSH), complement 1q (C1q), tumor necrosis factor related protein 6 (C1QTNF6) and somatostatin receptor 3 (SSTR3) genes.Table 1 summarizes the 16 T1D loci reported to date.An example of a tag-SNP that captures the association with T1D in each instance is highlighted together with its relative minor allele frequency in controls and what magnitude of risk or protection it confers.Key references regarding the role of each locus in the context of the disease are included and along with the chromosomal band where each locus resides, the main candidate gene (symbol and full name) is highlighted." + } + ], + "0de85e11-dcbb-4538-b043-ee18a30e9f14": [ + { + "document_id": "0de85e11-dcbb-4538-b043-ee18a30e9f14", + "text": "Detection of established loci\n\nWe explored the extent to which previously reported type 2 diabetes association signals could be detected in African-descent individuals.Based on the previously reported effect sizes and the effect allele frequency and sample size from our African meta-analysis, we had sufficient power (80%) to detect three signals (TCF7L2, DNER and SRR) at genome-wide significance (p < 2.5 × 10 −8 ) (ESM Table 2).Only the TCF7L2 variant reached genome-wide significance in our study, whereas both variants in DNER (rs1861612) and SRR (rs391300), originally discovered in Pima Indians and East Asians, respectively, had p > 0.1 (ESM Table 2)." + } + ], + "1c2f4eb9-5880-418a-be08-4c33ec3a8889": [ + { + "document_id": "1c2f4eb9-5880-418a-be08-4c33ec3a8889", + "text": "\n\nOn the basis of the combined stage 1-3 analyses, we found that six signals reached compelling levels of evidence (P ¼ 5.0 Â 10 -8 or better) for association with T2D (Table 2).As in all linkage disequilibrium (LD)-mapping approaches, characterization of the causal variants responsible, their effect sizes and the genes through which they act will require extensive resequencing and fine-mapping.However, on the basis of current evidence, we found that the most associated variants in each of these signals map to intron 1 of JAZF1, between CDC123 and CAMK1D, between TSPAN8 and LGR5, in exon 24 of THADA, near ADAMTS9 and in intron 5 of NOTCH2." + } + ], + "33c5de8c-7efc-41df-a540-22729d8b7d2c": [ + { + "document_id": "33c5de8c-7efc-41df-a540-22729d8b7d2c", + "text": "\n\nReplication study of newly identified type 1 diabetes risk loci" + } + ], + "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f": [ + { + "document_id": "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f", + "text": "\n\nAlthough these are considered to be loci convincingly associated with susceptibility to type 2 diabetes in populations of European descent, other genes related to susceptibility to the disease are probably still unidentified, particularly those for populations of other ancestries.In order to uncover genetic variants that increase the risk of type 2 diabetes, we conducted a genome-wide association study in Japanese individuals with type 2 diabetes and unrelated controls.We first genotyped 268,068 SNPs, which covered approximately 56% of common SNPs in the Japanese, in 194 individuals with type 2 diabetes and diabetic retinopathy (case 1) and in 1,558 controls (control 1) collected in the BioBank Japan.We compared the allele frequencies of 207,097 successfully genotyped SNPs and selected the 8,323 SNPs showing the lowest P values.We then attempted to genotype these 8,323 SNPs in 1,367 individuals with type 2 diabetes and diabetic retinopathy (case 2) and for 1,266 controls (control 2) (stage 2), and successfully obtained data for 6,731 SNPs (the P value distribution in the second test is shown in Supplementary Fig. 1a online).The results of principal component analysis 8 in the stage 1 and 2 samples and HapMap samples revealed that there was no evidence for population stratification between the case and control groups throughout the present tests (Supplementary Fig. 1b,c).We selected the 9 SNP loci showing P values o0.0001 (additive model in stage 2, Table 1) and genotyped a third set of cases and controls comprising 3,557 Japanese individuals with type 2 diabetes (cases 3,4,5) and 1,352 controls (controls 3,4).We evaluated the differences in the population structure among these three sets of case and two sets of control groups by Wright's F test.As the results indicated that there was no difference in the population structure among these groups (Supplementary Table 1b online), we combined these populations for the third test of case-control study.The third set of analysis identified the significant associations for six SNPs (Table 1), including the CDKAL1 locus at 6p22.3 (rs4712524, rs9295475 and rs9460546), the IGF2BP2 locus at 3q27.2 (rs6769511 and rs4376068) and the KCNQ1 locus at 11p15.5 (rs2283228).The remaining three SNPs (rs13259803, rs612774 and rs10836097) had P values of 40.05 in the third test and were not further examined.CDKAL1 and IGF2BP2 were previously reported as susceptibility genes for type 2 diabetes in the Japanese population 9 .Therefore, we focused on the KCNQ1 locus, which was highly associated with type 2 diabetes." + } + ], + "3a066437-9d88-46c7-bc55-9992728847a7": [ + { + "document_id": "3a066437-9d88-46c7-bc55-9992728847a7", + "text": "\n\nWe consider these data as an interesting preliminary result that surely requires additional independent studies including a higher number of patients in order to confirm and clarify the possible contribution of this locus to the development of T2DM complications." + } + ], + "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a": [ + { + "document_id": "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a", + "text": "DISCUSSION\n\nTaken together, our full second-stage approach and combined meta-analysis have revealed additional loci associated with type 1 diabetes.Clearly the risks are relatively modest compared with previously described associations, and it was only with this sample size at our disposal that we could we detect and establish these signals as true positives through an independent validation effort." + } + ], + "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc": [ + { + "document_id": "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc", + "text": "Identification of susceptibility loci\n\nThe degree of evidence for all reported T2D loci was quantified as follows: a locus with a logarithm of odds ratio (LOD) score of 3 or more was considered significant, a LOD score between 2.2 and 3 was considered suggestive and a LOD score between 1 and 2.2 was considered nominal.For T2D, only those loci were included that were significant at least once, or were suggestive in at least one study and at least nominal in two or more studies.The inclusion of the second category of loci was based on a study by Wiltshire et al. [72], in which it was postulated that locus counting is a useful additional tool for the evaluation of genome scan data for complex trait loci.We used the same two criteria to determine the loci from the five papers published on obesity since 2004 and combined these loci with those from Bell et al. [7].As obesity phenotypes, BMI, serum leptin levels, abdominal subcutaneous and visceral fat, and percentage body fat were included.All of these phenotypes were used as continuous quantitative traits, as well as with various cut-off levels." + } + ], + "4be1d780-404a-4826-ba06-80b2c15e705b": [ + { + "document_id": "4be1d780-404a-4826-ba06-80b2c15e705b", + "text": "\n\nToday, more than 100 loci for type 2 diabetes and glycemic traits have been identified through numerous GWA studies of common and rare variation in populations of diverse ancestral origins [31]; however, to date, very few GWA studies have been published in cohorts of Mexican ancestry.The first GWA study performed in a non-European cohort was published in 2007 and comprised 561 Mexican American type 2 diabetes cases and controls drawn from the Starr County Health Studies [32].Although no loci reached genome-wide significance, several loci identified in prior GWA studies in Europeans were replicated [32].This analysis was subsequently expanded (N = 1273) and meta-analyzed with a cohort from Mexico City (N = 1310) in 2011 [33,34].The most significant variants observed in this meta-analysis included known regions near HNF1A and KCNQ1.Top association signals were then meta-analyzed with the DIAGRAM and DIAGRAM+ datasets of European ancestry individuals, resulting in two regions reaching genome-wide significance: HNF1A and CDKN2A/CDKN2B (Table 1).Top association signals in both studies were annotated to explore their roles as expression quantitative trait loci (eQTL) in both adipose and muscle tissues, revealing a marked excess of transacting eQTL in top signals in both tissue types." + } + ], + "5293f814-f4a7-48e0-b4e5-b1f13fdc8516": [ + { + "document_id": "5293f814-f4a7-48e0-b4e5-b1f13fdc8516", + "text": "\n\n75±79 The main conclusion is that there is no major locus for T2D (analogous to HLA in type 1 diabetes).This is not surprising given the modest l s for T2D (approximately 3.5 in Europeans), imposing a limit on the magnitude of any single gene eect. 4Many scans have consequently been signi®cantly underpowered to detect the modest gene eects anticipated.Certainly, few T2D scans have reported linkages meeting the established criteria for genomewide signi®cance. 80This modest power, combined with the diversity of the pedigrees sampled and the analytical techniques used, means that the replication of positive ®ndings between data sets has been the exception rather than the rule." + } + ], + "711e3d33-a196-4072-bc31-ffaa6bb3efa0": [ + { + "document_id": "711e3d33-a196-4072-bc31-ffaa6bb3efa0", + "text": "Quantitative Trait Analysis\n\nExploration of putative T2DM variants with quantitative glycemic traits in a subset of African-American samples (n = 671 from the IRAS and IRASFS control samples, Table S5) revealed limited insight into the biological mechanism associated with T2DM risk.In addition, the five putative African-American T2DM susceptibility loci were tested for association with quantitative measures of glucose homeostasis in the European Caucasian population, in silico, by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC; [16]).These results did not provide further insight into the probable role these variants may have in disease susceptibility (Table S6).The most significantly associated SNP in African Americans, rs7560163, failed quality controls filters and was not included in analysis likely due to being monomorphic as seen in a representative Caucasian population from the HapMap project (Table S4)." + } + ], + "91d6996a-319d-461e-ae78-3c64a70832cc": [ + { + "document_id": "91d6996a-319d-461e-ae78-3c64a70832cc", + "text": "\n\nDiscovery of novel loci for T2D susceptibility.We tested for T2D association with ~27 million variants passing quality-control filters, ~21 million of which had a minor allele frequency (MAF) < 5%.Our meta-analysis identified variants at 231 loci reaching genomewide significance (P < 5 × 10 −8 ) in the BMI-unadjusted analysis (N eff 231,436) and 152 in the smaller (N eff 157,401) BMI-adjusted analysis.Of the 243 loci identified across these two analyses, 135 mapped outside regions previously implicated in T2D risk (Methods, Fig. 1 and Supplementary Table 2)." + } + ], + "ad88aed6-75ba-469d-b96b-7be4a65be8fc": [ + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "text": "\n\nGenetic studies performed since 2012 have identified many additional T2D loci based on risk alleles common in one population but less common in others.Studies in African Americans identified RND3-RBM43 (28), HLA-B and INS-IGF2 (29).Studies in South Asians identified TMEM163 (30) and SGCG (31).One locus, SLC16A11-SLC16A13, was simultaneously identified in Japanese and Mexican Americans (32,33), and studies in East Asians identified ANK1 (34), GRK5 and RASGRP1 (35), LEP and GPSM1 (32), and CCDC63 and C12orf51 (36).A study of individuals from Greenland identified TBC1D4 (37), and a sequencing-based study of Danes with follow-up in other Europeans identified MACF1 (38).Finally, the largest GWAS to date in American Indians identified DNER at near genome-wide significance (P = 6.6 × 10 −8 ) (39).Three of these studies imputed GWAS data using the 1000 Genomes Project sequence-based reference panels, providing better genome coverage (29,32,33,40).Taken together, these studies highlight the value of diverse populations, including founder and historically isolated populations, to detect risk loci." + } + ], + "b973bd17-aac9-4d68-8ac4-1c683165b68f": [ + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nFinally, a recent study identified additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs. 21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds.Thus, by combining data involving 10,128 samples, the authors found in the initial stages of the analysis highly associated variants (they followed only 69 signals out of over 2 million metaanalyzed SNPs) with P values Ͻ10 Ϫ4 in unknown loci, and 11 of these type 2 diabetes' associated SNPs were taken forward to further stages of analysis.Large stage replication testing allowed the detection of at least six previously unknown loci with robust evidence for association with type 2 diabetes." + }, + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nSurprisingly, data about previous published loci associated with type 2 diabetes were not sufficiently powerful to reach a significant P value in individual scans.For example, variants at SLC30A8 and PPARG were significantly associated with type 2 diabetes only when pooling all the GWAs data, whereas in a single genome scan (DGI), no gene showed a positive signal (P value: 0.92 and 0.83, respectively).Thus, this may suggest that GWAs are still underpowered to find SNPs with small effect size." + } + ], + "d86525a8-0a2f-44a8-b343-61a5df8d6e68": [ + { + "document_id": "d86525a8-0a2f-44a8-b343-61a5df8d6e68", + "text": "\nBackground: The two genome-wide association studies published by us and by the Wellcome Trust Case-Control Consortium (WTCCC) revealed a number of novel loci, but neither had the statistical power to elucidate all of the genetic components of type 1 diabetes risk, a task for which larger effective sample sizes are needed.Methods: We analysed data from two sources: (1) The previously published second stage of our study, with a total sample size of the two stages consisting of 1046 Canadian case-parent trios and 538 multiplex families with 929 affected offspring from the Type 1 Diabetes Genetics Consortium (T1DGC); (2) the Rapid Response 2 (RR2) project of the T1DGC, which genotyped 4417 individuals from 1062 non-overlapping families, including 2059 affected individuals (mostly sibling pairs) for the 1536 markers with the highest statistical significance for type 1 diabetes in the WTCCC results.Results: One locus, mapping to a linkage disequilibrium (LD) block at chr15q14, reached statistical significance by combining results from two markers (rs17574546 and rs7171171) in perfect LD with each other (r 2 = 1).We obtained a joint p value of 1.3610 26 , which exceeds by an order of magnitude the conservative threshold of 3.26610 25 obtained by correcting for the 1536 single nucleotide polymorphisms (SNPs) tested in our study.Meta-analysis with the original WTCCC genome-wide data produced a p value of 5.83610 29 .Conclusions: A novel type 1 diabetes locus was discovered.It involves RASGRP1, a gene known to play a crucial role in thymocyte differentiation and T cell receptor (TCR) signalling by activating the Ras signalling pathway." + } + ], + "dad48e98-2dcc-41ae-866a-139f5540a24c": [ + { + "document_id": "dad48e98-2dcc-41ae-866a-139f5540a24c", + "text": "\n\nFinally, we examined whether genes identified using our association studies were enriched within diabetes-related pathways.We collated a list of 42 genes to which 53 CpG sites associated with T2D traits (CS score ≥1.77, combined P < 0.017) mapped.Even in this small dataset, pathway analysis (Supplementary Material, Table S12) indicated significant enrichment in 31 pathways (Fisher's exact P < 0.05), including those related to circadian clock (P = 0.005), adipocytokine signaling (P = 0.009), leptin pathway (P = 0.023), HDL-mediated lipid transport (P = 0.031) and insulin signaling (P = 0.033)." + } + ], + "e88b610f-8afa-46f7-a03c-d7bd579a7496": [ + { + "document_id": "e88b610f-8afa-46f7-a03c-d7bd579a7496", + "text": "\n\nIn recent years, progress has been made in following up mechanistic studies of GWAS type 2 diabetes-association signals [6,7,9,[25][26][27][28][29][30], but challenges remain in sifting through the many associated variants at a locus to identify those influencing disease.We hypothesized that a common variant with modest effect underlies the association at the CDC123/CAMK1D locus and evaluated the location of high LD variants (r 2 $.7; n = 11) at the locus relative to known transcripts and to putative DNA regulatory elements.We identified two variants that overlapped putative islet and/or liver regulatory regions and none located in exons.We did not assess variants in lower LD (r 2 ,.7), and additional functional SNPs may exist at this locus acting through alternate functional mechanisms untested in the current study." + } + ], + "fdbabc3c-ec60-45ce-9f5c-683f745c4d00": [ + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "\n\nMeta-analysis results for T2D SNPs for insulin and glucose-related traits." + }, + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "A r t i c l e s\n\nBy combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P < 5 × 10 −8 .These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A).The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation.We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits." + } + ] + }, + "data_source": [], + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "TCF7L2", + "DNER", + "SRR", + "HNF1A", + "KCNQ1", + "CDKN2A", + "CDKN2B", + "JAZF1", + "CDC123", + "CAMK1D" + ], + "metadata": [ + { + "object": "We identified a Congenital long QT syndrome LQTS family harboring three compound mutations in different genes KCNQ1-R174C, hERG-E1039X and SCN5A-E428K. IKs-like, IKr-like, INa-like currents and the functional interaction between KCNQ1-R174C and hERG-E1039X channels were studied using patch-clamp.Expression of KCNQ1-R174C alone showed no IKs. Co-expression of KCNQ1-WT + KCNQ1-R174C caused a loss-of-function in IKs", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007244" + }, + { + "object": "Pancreatic cancer was induced in adult mice by the combination of KRASG12D overexpression and loss of Tp53 and Cdkn2a only if Cdkn2b was concomitantly inactivated. inactivation of both Cdkn2b and Cdkn2a was necessary for Rb phosphorylation and to encompass oncogene-induced cellular senescence.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab580373" + }, + { + "object": "Twenty-five different variants were identified in GCK gene 30 probands-61% of positivity, and 7 variants in HNF1A 10 probands-17% of positivity. Fourteen of them were novel 12- GCK /2- HNF1A . ACMG guidelines were able to classify a large portion of variants as pathogenic 36%- GCK /86%- HNF1A and likely pathogenic 44%- GCK /14%- HNF1A , with 16% 5/32 as uncertain significance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab977086" + }, + { + "object": "We found that CDKN2B was a virtual target of miR-15a-5p with potential binding sites in the 3'UTR of CDKN2B 77-83 bp. We also showed that miR-15a-5p could bind to the CDKN2B 3'UTR. The data revealed a negative regulatory role of miR-15a-5p in the apoptosis of smooth muscle cells via targeting CDKN2B, and showed that miR-15a-5p could be a novel therapeutic target of AAA.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1004682" + }, + { + "object": "For each gene and the four pathways in which they occurred, we tested whether pancreatic cancer PC patients overall or CDKN2A+ and CDKN2A- cases separately had an increased number of rare nonsynonymous variants. Overall, we identified 35 missense variants in PC patients, 14 in CDKN2A+ and 21 in CDKN2A- PC cases.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab300370" + }, + { + "object": "we investigated the effects of KCNQ1 A340E, a loss-of-function mutant. J343 mice bearing KCNQ1 A340E demonstrated a much higher 24-h intake of electrolytes potassium, sodium, and chloride. KCNQ1, therefore, is suggested to play a central role in electrolyte metabolism. KCNQ1 A340E, with the loss-of-function phenotype, may dysregulate electrolyte homeostasis", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1008629" + }, + { + "object": "Results show that C-FOS directly binds to rs7074440 TCF7L2. Its knockdown decreases TCF7L2 gene expression proving evidence that c-FOS protein regulates TCF7L2 through its binding to rs7074440.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab661049" + }, + { + "object": "This review provides an update of the latest research advances on JAZF1 and its regulatory network in T2 diabetes mellitus T2DM. The association between JAZF1 polymorphisms and T2DM is discussed as well. The information provided is of importance for guiding future studies as well as for the design of JAZF1-based T2DM therapy. [review]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab935511" + }, + { + "object": "JAZF1 was identified as a direct miR-1275 target. miR-1275 supresses migration and invasion of gastric cancer cells in vitro and in vivo, which was restored by JAZF1 overexpression. Moreover, JAZF1 was recognized as a direct regulator of Vimentin.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab434327" + }, + { + "object": "DNA hypermethylation of KCNQ1 promoter resulted in its downregulation in hepatocellular carcinoma HCC. Bioinformatic analysis indicated a regulatory role of KCNQ1 in the epithelial-to-mesenchymal transition process. Gain-of-function study showed that KCNQ1 exhibited remarkable inhibitory roles on tumor metastasis in vitro and in vivo.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab235904" + } + ], + "question": "Do we find significant quantitative trait loci in the studies for diabetes?", + "subquestions": null, + "task_id": "B7084C90C3CF93908B3FB34BBA00743B", + "usage": { + "chatgpt": 9426, + "gpt-4": 4931, + "gpt-4-turbo-preview": 3991 + }, + "user_id": 2 + }, + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "task_id": "B7084C90C3CF93908B3FB34BBA00743B" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_03.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_03.json new file mode 100644 index 0000000..ea08b61 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_03.json @@ -0,0 +1,189 @@ +{ + "created_at": "2024-03-25T14:00:01.668047Z", + "data": { + "amplify": false, + "answer": "Several environmental factors can affect mice and influence diabetes. These include the stress response, which can be greater in male mice and can confound feeding behavior, the unique gut microbiota of rodents from different production facilities, cage position, single versus group housing, the skill level of the researcher, ambient room temperature, and the type of cage bedding. Diet, particularly high-fat feeding, can also induce obesity and diabetes. Additionally, the intrauterine environment can impact the subsequent development of diabetes amongst offspring.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ae5d2bb-b09d-4646-922a-277188b53cbb": [ + { + "document_id": "0ae5d2bb-b09d-4646-922a-277188b53cbb", + "text": "\n\nIn these models, adult offspring of diabetic animals were noted to have normal development of the endocrine pancreas (Aerts et al., 1997;Ma et al., 2012).However, they develop glucose intolerance and impaired insulin response to glucose challenge, and display insulin resistance, mainly in the liver and muscle, highlighting the presence of both insulin resistance and b-cell dysfunction (Aerts et al., 1988;Holemans et al., 1991a,b).The key role of the intrauterine environment was demonstrated by a series of embryo transfer experiments, which showed that the diabetes risk in a low genetic risk strain can be substantially increased by the hyperglycaemic environment of a dam with a high genetic risk of diabetes (Gill-Randall et al., 2004)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nOther diet-induced rodent models of type 2 diabetes.Although rats and mice are the most commonly used models for studies of type 2 diabetes, other rodents have also been identified as useful models.These include the desert gerbil and the newly described Nile grass rat, both of which tend to develop obesity in captivity." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSince the obesity is induced by environmental manipulation rather than genes, it is thought to model the human situation more accurately than genetic models of obesityinduced diabetes.High fat feeding is often used in transgenic or knock-out models, which may not show an overt diabetic phenotype under normal conditions, but when the beta cells are 'pushed', the gene may be shown to be of importance.It should be noted that the background strain of the mice can determine the susceptibility to diet-induced metabolic changes, and thus, effects could be missed if a more resistant strain is used (Surwit et al., 1995;Bachmanov et al., 2001;Almind and Kahn, 2004).It has also been reported that there is heterogeneity of the response to high fat feeding within the inbred C57BL/6 strain, indicating that differential responses to a high-fat diet are not purely genetic (Burcelin et al., 2002)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "Other considerations and limitations\n\nA myriad of factors affect animal experiments.Men elicit a greater stress response in mice than women 292 , likely confounding feeding behaviour.Rodents from different production facilities (for example, Jackson Laboratory and Taconic) have unique gut microbiotas 293 , perhaps contributing to differences in their susceptibility to DIO and related diabetic complications 293 .Similarly, cage position within a rack of cages, single versus group housing, the skill level of the researcher, ambient room temperature or the type of cage bedding can all affect experimental outcomes." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nWe believe there are several factors that researchers should consider when conducting obesity and diabetes mellitus research in rodents (FIG.2).Although our list is by no means an exhaustive, it demonstrates the complexity and interconnectedness of the myriad of factors that can confound experimental outcomes.Although it is impossible to control for everything, researchers should accurately detail all experimental conditions and methods to allow for better interpretation of the results and, importantly, for better reproducibility." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nFigure2| Important experimental parameters and potential confounders of experimental outcomes in obesity and diabetes research and their interrelatedness.Countless factors influence experimental outcomes when using animal models, and what is enumerated here is by no means a complete list.This figure is one depiction of the multifactorial and interconnected genetic and environmental matrix that makes it virtually impossible to design the perfect experiment.For example, single-housing mice to obtain more accurate food intake data introduces a stress that in turn affects food intake.The severity of this stress response is both strain-specific and sex-dependent.What is important is to be aware of these challenges and to control for them in the most optimal manner.It is equally, if not more, important to accurately and comprehensively detail all experimental conditions in research papers, as these have bearing on the interpretation and reproducibility of the published results.DIO, diet-induced obesity." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAnother concern pertains to control mice.Compared with free-living mice in the wild, laboratory control mice with ad libitum access to food are sedentary, overweight, glucose intolerant and tend to die at a younger age 297 .Comparisons between mice with DIO and control mice might be analogous to investigating the genetic cause of obesity-resistance by comparing humans who are overweight or obese.This potential problem with control mice could explain why the use of DIO diets that have 40% to 60% of total energy from fat is so prevalent, as this might be necessary to achieve divergent weight gains.With free access to running wheels, C57BL/6J mice voluntarily run 5-10 km per day 298,299 .As is the case with humans 300 , mice get health benefits from regular physical activity including weight loss, decreased adiposity and improved insulin sensitivity 301,302 .Physical activity might also affect the epigenome over several generations 303 .An enriched physical and social cage environment alone improves leptin sensitivity and energy expenditure in mice, independent of physical activity 304,305 .Overall, these data suggest that with standard mouse husbandry, chow-fed laboratory mice are not the ideal healthy and lean control group for meaningful obesity research." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nTo better address these points, various animal models have been developed.For example, using HFD-T2DM male rats, the F1 female offspring showed reduced β cell area and insulin secretion, together with glucose intolerance, without changes in body weight [145].The islets of the F1 female offspring showed differential expression of many genes involved in Ca 2+ , mitogen-activated protein kinase and Wnt signaling, apoptosis and cell cycle regulation [145].Similarly, in pregnant C57BL6J mice, food deprivation resulted in β cell mass reduction and an increased risk of β cell failure in offspring [146]." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "They are probably typical of those\nfew mice that develop diabetes more slowly and do\nnot tax the pancreatic insulin supply as severely early\nin the course of the disease. Attempts at therapy. Attempts to keep the weight\nof diabetic mice within normal limits by total or\npartial food restriction resulted in premature deaths. After it was discovered that gluconeogenesis is greatly\nincreased in diabetic mice, attempts were made to\nregulate blood sugar levels and also weight gain by\nfeeding rations devoid of carbohydrate." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "The degree\nof dependence of adiposity, hyperglycemia, and islet\nhypertrophy on food consumption varies among these\nmice, but in all, the increase in islet volume and consequent fi-eell hyperplasia appears to be an effective\n\n247\n\nmeans of maintaining blood sugar concentrations at\nnear normal levels. I n contrast, neither the diabetic\nsand rat [5] nor the diabetic mouse has hypertrophied\nislets and neither effectively controls blood sugar levels." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "HV~MEI,: Studies with the Mutation, Diabetes\n\nalmost undetectable. Similarly, the activities of citrate\nlyase and glucose-6-phosphate dehydrogenase were\ngreatly decreased in these older diabetic as compared\n\nDiabetologia\n\nthe diabetic mice have attained m a x i m u m weight,\nafter which no further accumulation of adipose tissue\nis noted. Fig. 8." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Animal models of diabetes in pregnancy and the role of intrauterine environment\n\nAnother important field of diabetes research that has relied heavily on animal experimentation is the study of diabetes in pregnancy and the role of the intrauterine environment on the subsequent development of diabetes amongst offspring." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "obesity", + "insulin&resistance", + "glucose&intolerance", + "high-fat&diet", + "environmental&factors", + "mouse&models", + "genetic&background", + "intrauterine&environment", + "diet-induced&obesity" + ], + "metadata": [ + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "Data, including data from studies using knockout/transgenic mice, suggest that PrPC is involved in development of insulin resistance and obesity; PrPC knockout mice fed high-fat diet present all the symptoms associated with insulin resistance hyperglycemia, hyperinsulinemia, and obesity; transgenic mice overexpressing PrPC fed high-fat diet exhibit normal insulin sensitivity and reduced weight gain.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab215504" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "FNDC5 attenuates adipose tissue inflammation and insulin resistance via AMPK-mediated macrophage polarization in HFD-induced obesity. FNDC5 plays several beneficial roles in obesity and may be used as a therapeutic regimen for preventing inflammation and insulin resistance in obesity and diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab299408" + }, + { + "object": "WISP1 can be involved in glucose/lipid metabolism in obese youth, which may be modulated by IL-18. Increased WISP1 levels may be a risk factor of obesity and insulin resistance, and WISP1 has a potential therapeutic effect on insulin resistance in obese children and adolescents", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1017591" + }, + { + "object": "Obesity interacted with the TCF7L2-rs7903146 on Type 2 DiabetesT2D prevalence. Association of TCF7L2 polymorphism with T2D incidence was stronger in non-obese than in obese subjects. TCF7L2 predictive value was higher in non-obese subjects. We created obesity-specific genetic risk score with ten T2D-polymorphisms and demonstrated for the first time their higher strata-specific predictive value for T2D risk.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab541919" + }, + { + "object": "LCN-2 expression and serum levels could discriminate IGT from NGT and type 2 diabetes mellitus T2DMfrom IGT obese women and early predicting T2DM among obese women. While, LCN-2 expression level was the independent predictor of IGT in obese women. Combination of both LCN-2 expression and serum levels improved their diagnostic value in early detection of IGT and T2DM among obese women", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab445589" + }, + { + "object": "Gestational obesity and gestational diabetes mellitus may contribute to elevated serum chemerin. Serum chemerin in pregnancy was associated with insulin resistance and triglycerides. Chemerin gene may play a role both in obese and gestational diabetes mellitus patients", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321477" + }, + { + "object": "SGNE1 genetic variation does not contribute to obesity and common forms of Type 2 diabetes but may worsen glucose intolerance and insulin resistance, especially in the background of severe and early onset obesity", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab702715" + }, + { + "object": "our results indicate that UC-MSCs can alleviate insulin resistance in part via production of IL-6 that elicits M2 polarization. Additionally, human obesity and insulin resistance were associated with increased pro-inflammatory ATMs infiltration. Thus, MSCs may be a new treatment for obesity-related insulin resistance and T2D concerning macrophage polarized effects", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab560980" + } + ], + "question": "What environmental factors effect mice that also influence diabetes?", + "subquestions": null, + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "usage": { + "chatgpt": 6673, + "gpt-4": 3552, + "gpt-4-turbo-preview": 2599 + }, + "user_id": 2 + }, + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_04.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_04.json new file mode 100644 index 0000000..9a49068 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_04.json @@ -0,0 +1,191 @@ +{ + "created_at": "2024-03-25T14:02:33.179370Z", + "data": { + "amplify": false, + "answer": "1. Diabetes incidence study where mice were tested for diabetes monthly by blood glucose and weekly by urine assessment.\n2. Studies involving transgenic/knockout animal models of type 2 diabetes to study the role of genes and their effects on peripheral insulin action.\n3. Study involving male type-2 diabetic db/db mice and genetic control non-diabetic db/ mice.\n4. Study involving young adult mice fed a high-fat or Western diet to elicit DIO and insulin resistance.\n5. Study involving Akita mouse genotyping.\n6. Study involving 12-week-old male type 2 diabetic db/db mice and contemporary control wild-type mice.\n7. Study involving transgenic mice to create specific models of type 1 and type 2 diabetes.\n8. Study involving AKITA mice derived from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene.\n9. Study monitoring mice for the development of diabetes.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + } + ], + "42e06cda-627e-46f2-a289-c4c1fb6af8f2": [ + { + "document_id": "42e06cda-627e-46f2-a289-c4c1fb6af8f2", + "text": "Animal group and study design\n\nFirst, one set of animals comprising 12-week-old male type 2 diabetic db/db (C57BL/KsJ-db−/db−, n = 8) and contemporary control wild-type (C57BL/KsJ-db+/db−, n = 8) mice (Jackson Laboratories) were included in this study.Their weights and blood glucose levels were analysed to eliminate variation.Erectile functions of the animals were evaluated by the apomorphine-induced penile erection test, according to a previously described protocol (Pan et al. 2014).Afterwards, intracavernous pressure (ICP) investigations and histological measurements were applied to further confirm the results of the function tests.Then, all mice were sacrificed and the corpus cavernosum (CC) was collected from each mouse.Because the tissue of the CC is difficult to crush, we randomly collected the CCs from two mice and mixed them into one subgroup.As a result, four diabetic subgroups (DB groups) and four normal control subgroups (NC groups) were used for molecular measurements.Second, another set of animals, including three T2DMED and three normal control mice that were independent from the original set of animals, were included in the validation experiments using qRT-PCR.Third, another separate set of animals, including five T2DMED and five control mice, were used to verify one of the predicted targets, IGF-1, using ELISA.A luciferase reporter assay was performed to verify the binding of the differentially expressed miRNAs to the target gene IGF-1.All procedures were approved by the Institutional Animal Care and Use committee at Nanjing Medical University." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 1 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Knock-out and transgenic mice in diabetes research\n\nTransgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003)." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Genetically induced insulin-dependent diabetes\n\nAKITA mice.The AKITA mouse was derived in Akita, Japan from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene preventing correct processing of proinsulin.This causes an overload of misfolded proteins and subsequent ER stress.This results in a severe insulindependent diabetes starting from 3 to 4 weeks of age, which is characterized by hyperglycaemia, hypoinsulinaemia, polyuria and polydipsia.Untreated homozygotes rarely survive longer than 12 weeks.The lack of beta cell mass in this model makes it an alternative to streptozotocin-treated mice in transplantation studies (Mathews et al., 2002).It has also been used as a model of type 1 diabetic macrovascular disease (Zhou et al., 2011) and neuropathy (Drel et al., 2011).In addition, this model is commonly used to study potential alleviators of ER stress in the islets and in this respect models some of the pathology of type 2 diabetes (Chen et al., 2011)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nTo achieve a slow pathogenesis of T2DM, young adult mice 284 or rats 285 are fed a high-fat or Western diet to elicit DIO and insulin resistance.Single or multiple injections with low-dose streptozotocin (~30-40 mg/kg intraperitoneally) then elicit partial loss of β-cells, which results in hypoinsulinaemia and hyperglycaemia.Protocols are being continuously refined and likely differ between species and even strains 283 .The HFD streptozotocin rat is sensitive to metformin, further demonstrating the utility of this model 285 .Downsides of streptozotocin treatment include liver and kidney toxicity and mild carcinogenic adverse effects (TABLE 1)." + } + ], + "785df64a-ebbf-4dca-94dd-0ae27f7ac815": [ + { + "document_id": "785df64a-ebbf-4dca-94dd-0ae27f7ac815", + "text": "Materials and methods\n2.1 Mouse models\n2.1.1 Mouse strains\n2.1.2 Induction of type 1 diabetes\n8\n2.1.3 Insulin treatment on diabetic mice\n2.1.4 Akita mouse genotyping\n2.2 Characterization of diabetic nephropathy in mice\n2.2.1 Proteinuria measurement\n2.2.2 Glomerular cells quantification\n2.2.3 Methenamine silver staining quantification\n\n3. 4. 5. 6." + } + ], + "7e809821-000d-4fff-971d-264650e3612b": [ + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "text": "\n\nii) Rodent models of diabetic retinopathy" + } + ], + "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d": [ + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "text": "\n\nThere are some good reviews available in the literatures describing the transgenic/knockout animal models of type 2 diabetes [114][115][116][117][118] .The transgenic and knockout models are developed for studying the role of genes and their effects on peripheral insulin action such as insulin receptor, IRS-1, IRS-2, glucose transporter (GLUT 4), peroxisome proliferator activated receptor-g (PPAR-g) and tumour necrosis factor-a (TNF-a) as well as in insulin secretion such as GLUT-2, glucokinase (GK), islet amyloid polypeptide (IAPP) and GLP-1 and in hepatic glucose production (expression of PEPCK) associated with development of type 2 diabetes.Further, combination or double knockout mouse models including defect in insulin action and insulin secretion (e.g., IRS-1 +/-/GK +/-double knockout) have been produced which clearly illustrate the mechanisms associated with development of insulin resistance and beta cell dysfunction leading to overt hyperglycaemic state in human type 2 diabetes.These above genetically modified animals exhibit various phenotypic features of type 2 diabetes varying from mild to severe hyperglycaemia, insulin resistance, hyperinsulinaemia, impaired glucose tolerance and others as explained in detail elsewhere 6,9,[114][115][116][117][118] .Very recently, tissue specific knockout mouse models have been achieved, allowing further insight into the insulin action with respect to particular target tissues (muscle, adipose tissue and liver) associated with insulin resistance and type 2 diabetes 115,117,118 .The transgenic/knockout animals are currently used mostly for the mechanistic study in diabetes research and not usually recommended for screening programme as they are more complicated and costly." + } + ], + "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6": [ + { + "document_id": "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6", + "text": "Functional deficits refs\n\nNon-Alzheimer-disease mouse [71][72][73][74]76,78,81,85,87 and rat 59,75,77 ,79,95,97 Mouse [81][82][83][84][85] and rat 79,111 Cerebral effects of inducing diabetes or insulin resistance in normal rodents (that is, non-Alzheimer-disease rodent models) and in rodents genetically modified to accumulate amyloidβ in the brain (that is, rodent models of Alzheimer disease). Common intervetions to induce diabetic conditions in rodents included recessive mutations in the leptin gene (Lep; also known as Ob), defects in the leptin receptor (LEPR; also known as OB-R), diet and administration of streptozotocin. Rodents with pancratic overexpression of human amylin spontaneously develop both type 2 diabetes mellitus and dementia-like pathology." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 1 diabetes" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Introduction\n\nAnimal experimentation has a long history in the field of diabetes research.The aim of this article is to review the commonly used animal models and discuss the recent technological advances that are being employed in the discipline.The review is based on an extensive literature search using the terms rodent, mouse, rat, animal model, transgenics, knockout, diabetes and pathogenesis, in scientific journal databases such as MEDLINE ®.In addition, abstracts presented at meetings of Diabetes UK, the European Association for the Study of Diabetes and the American Diabetes Association over the last 5 years were examined in order to gain an appreciation of recent and ongoing research projects." + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "mouse", + "insulin", + "db/db", + "streptozotocin", + "AKITA", + "transgenic", + "knockout", + "glucose", + "tolerance" + ], + "metadata": [ + { + "object": "Hyperglycemia and blood pressure were similar between Trpc6 knockout and wild-type Akita mice, but knockout mice were more insulin resistant. In cultured podocytes, knockout of Trpc6 inhibited expression of the Irs2 and decreased insulin responsiveness. Data suggest that knockout of Trpc6 in Akita mice promotes insulin resistance and exacerbates glomerular disease independent of hyperglycemia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab367197" + }, + { + "object": "High levels of IP6K3 mRNA were found in myotubes and muscle tissues. Expression was elevated under diabetic, fasting, and disuse conditions in mouse skeletal muscles. Ip6k3-/- mice had lower blood glucose, less insulin, decreased fat, lower weight, increased plasma lactate, enhanced glucose tolerance, lower glucose during an insulin tolerance test, and reduced muscle Pdk4 expression. Ip6k3 deletion extended lifespan.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab348326" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "Mice overexpressing protein S showed significant improvements in blood glucose level, glucose tolerance, insulin sensitivity, and insulin secretion compared with wild-type counterparts. diabetic protein S transgenic mice developed significantly less severe diabetic glomerulosclerosis than controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab482040" + }, + { + "object": "Sequence difference between C57BL/6J and C57BL/6N strains of mice. Pmch knockout mice display decreased circulating glucose, abnormal glucose tolerance and increased oxygen consumption. N carries a private missense variant in this gene isoleucine to threonine. N mice display increased oxygen consumption, but higher circulating glucose levels and normal glucose tolerance compared to J.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab5150" + }, + { + "object": "Ghrl-/- and Ghsr-/- male mice studied after either 6 or 16 h of fasting had blood glucose concentrations comparable with those of controls following intraperitoneal glucose, or insulin tolerance tests, or after mixed nutrient meals. Collectively, our data provide strong evidence against a paracrine ghrelin-GHSR axis mediating insulin secretion or glucose tolerance in lean, chow-fed adult mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab322269" + }, + { + "object": "Patients with type 2 diabetes have significantly higher concentrations of plasma fetuin-B compared with normal glucose tolerance subjects and plasma fetuin-B is strongly associated with glucose and lipid metabolism, chronic inflammation and first-phase glucose-stimulated insulin secretion and insulin resistance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab584502" + }, + { + "object": "In wild-type mice, elevated bile acids impair hepatic insulin sensitivity by blunting the insulin suppression of hepatic glucose production. The impaired hepatic insulin sensitivity could not be attributed to TGR5 signaling, as TGR5 knockout mice exhibited a similar inhibition of insulin suppression of hepatic glucose production.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab406411" + }, + { + "object": "Gestational glucose tolerance in WT mice, but not Ffar2-/- mice improved while on antibiotics. Gestational glucose tolerance worsened in Ffar2-/- mice during a second pregnancy. Maternal Ffar2 expression had no effect on the growth rates and glucose and glucose tolerance in the offspring.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321474" + }, + { + "object": "ApoA-I is inversely associated with insulin resistance in patients with impaired glucose tolerance, and low apoA-I is an independent risk factor for impaired glucose tolerance IGT. These results indicate that apoA-I plays an important role in regulating insulin sensitivity and glucose metabolism in patients with IGT.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab76907" + } + ], + "question": "List mouse studies that involve insulin.", + "subquestions": null, + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "usage": { + "chatgpt": 5772, + "gpt-4": 4159, + "gpt-4-turbo-preview": 3223 + }, + "user_id": 2 + }, + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32" +} diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_05.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_05.json new file mode 100644 index 0000000..fafa914 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_05.json @@ -0,0 +1,138 @@ +{ + "created_at": "2024-04-18T18:24:11.260410Z", + "data": { + "amplify": false, + "answer": "Yes, there are phenotypes and genotypes associated with early onset diabetes in mice. The db/db mouse strain, which carries the diabetes (db) mutation, is one such genotype. These mice develop an abnormal and characteristic deposition of fat beginning at 3 to 4 weeks of age, which is an associated phenotype. The C57BL/Ks strain also exhibits early onset diabetes. The severity of the diabetes condition in these mice depends on the interaction of the mutant gene with the inbred background.", + "context": { + "1bf337a1-ffed-4199-a11f-c5a62df47980": [ + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "text": "\n\nSubsequently, genetic dissection of the diabetes-associated traits in the male BC1 progeny obtained from a cross between (normal B6 female ϫ diabetic TH male)F1 female and diabetic TH male mice (B6 cross) was carried out.Because of the sexual dimorphism, with respect to NIDDM onset, we used diabetic TH male mice as breeders to ensure the presence of a mutant allele(s) and targeted our genetic dissection using only male BC1 progeny.In male BC1 mice hyperglycemia developed at approximately 20 weeks of age and was sustained through a 30-week period studied.Based on these data, we measured plasma glucose levels three times in biweekly intervals (to minimize phenotyping error) between 20 and 26 weeks of age, and the mean of the three measurements was used for genetic analysis.Body weights were measured at 20 weeks.At the end of the study (26 weeks), plasma insulin levels and nasal-anal lengths were measured, and the five regional fat pads were dissected and weighed from a subset of 133 mice.In total, 206 male BC1 mice were collected, and individual mice were genotyped with 92 SSLP markers at approximately 20-cM intervals (covering ϳ96% of the genome)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nEffects of Inbred Background (Table 2).The syndrome produced in BL/Ks diabetes (db) mice, while similar in early development to that of BL/6 obese (ob) mice, has a more severe diabetes-like condition and a less pronounced obesity.However, both mutations when maintained on the same inbred background exhibit identical syndromes from 3 weeks of age on [9,21].Both diabetes and obese mice of the BL/Ks strain have the severe diabetes characterized by insulinopaenia and islet atrophy, whereas both mutations maintained on the BL/6 strain have mild diabetes characterized by islet hypertrophy and hyperplasia of the beta cells.Islet hypertrophy is either sustained or followed by atrophy depending on modifiers in the genetic background rather than the specific action of the mutant gene.The markedly different obesity-diabetes states exhibited when obese and diabetes mice are on different backgrounds points out the importance of strict genetic control in studies with all types of obese-hyperglycaemic mutants.Genetic studies [11] have shown that the modifiers leading to islet hypertrophy and well-compensated diabetes compatible with a near normal lifespan are dominant to those factors causing severe diabetes.Two other mutations, yellow and fat, cause similar diabetes-syndromes and yet have identical symptoms on both inbred backgrounds (Table 2).This may suggest that the primary insult caused by these mutations is not as severe as that for obese and diabetes and that this more gradual initiation of obesity permits the host genome to make a response (islet hypertrophy) compatible with life rather than islet atrophy, insulinopaenia, and life-shortening diabetes." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe animal models available for diabetes research (Table 1) are most often more like maturityonset diabetes in man.Obesity is a consistent factor and insulinopaenia is rare.However, the time of gene expression at about two weeks of age is within the time period of juvenile expression.The severity and clinical course of the diabetes produced depends on the interaction of the mutant gene with the inbred background rather than the action of the gene itself.Thus on one inbred background a well-compensated, maturity onset type diabetes, compatible with near normal life is observed whereas on another inbred background the syndrome presents as a juvenile-type diabetes with insulinopaenia, islet cell degeneration, marked hyperglycaemia, some ketosis and a much shortened lifespan.Unfortunately, vascular, retinal and the other complications of diabetes are not seen consistently in these rodent syndromes.It seems that the severely diabetic animal either does not live long enough to develop these complications or that rodents are particularly resistant to those complications that commonly afflict human diabetics.Several comprehensive bibliographies and excellent reviews of the various studies carried out with each of these syndromes in animals have been published [2,3,19,30,31,32].This presentation will be restricted primarily to the research undertaken by my colleagues and myself with the two mouse mutations; diabetes (db), and obese (ob).Both mutations have been extensively studied by numerous investigators in attempts to define the primary lesion causing the syndrome.As yet, the primary defect remains illusive, although several possibilities are becoming increasingly plausible in the light of current research.Although the metabolic abnormalities associated with both obese and diabetes have many similarities with regard to the overall progression of the obesity-diabetes state, the documentation of two single genes on separate chromosomes makes it unlikely that the two syndromes are caused by the same primary lesion.However, the marked similarity between the two mutants when maintained on the same genetic background implies that the defects may occur in the same metabolic pathway." + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + }, + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nThe Diabetes (db) .Mouse (Chromosome 4).Diabetes (db), an autosomal recessive mutation, occurred in the C57BL/KsJ (BL/Ks) inbred strain and on this background is characterized by obesity, hyperphagia, and a severe diabetes with marked hyperglycaemia [7,22].Increased plasma insulin concentration is observed as early as 10 days of age [10].The concentration of insulin peaks at 6 to 10 times normal by 2 to 3 months of age then drops precipitously to near normal levels.Prior to the fall in plasma insulin concentration, the most consistent morphological feature of the islets of Langerhans appears to be hyperplasia and hypertrophy of the beta cells in an attempt to produce sufficient insulin to control blood glucose concentration at physiological levels.The drop in plasma insulin concentration is concomitant with islet atrophy and rapidly rising blood glucose concentrations that remain over 400 mg per 100 ml until death at 5 to 8 months [7].Compared with other obesity mutants the diabetic condition is more severe and the lifespan is markedly decreased." + } + ], + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nDiabetes-related clinical traits for 275 B6XBTBR-ob/ ob F2 male mice at 10 weeks of age." + }, + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "Results\n\nWe generated an F2 inter-cross between diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains, made genetically obese in response to the Lep ob mutation [24].The cross consisted of .500mice, evenly split between males and females.A comprehensive set of ,5000 genotype markers were used to genotype each F2 mouse (,2000 informative SNPs were used for analysis), and the expression levels of ,40 K transcripts (corresponding to 25,901 unique genes) were monitored in five tissues (adipose, liver, pancreatic islets, hypothalamus, and gastroc (gastrocnemius muscle)) that were harvested from each mouse at 10 weeks of age.In addition to gene expression, several key T2D-related traits were determined for each mouse.The medians, and 1st and 3rd quartiles for the following traits: body weight, the number of islets harvested per pancreas, HOMA, plasma insulin, glucose, triglyceride, and C-peptide are listed in Table 1." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Thus, there is a rich literature\nindicating strong genetic effects on glucose metabolism in\nthe B6 and D2 genetic background, and a male-specific\nform of diabetes is known to spontaneously occur in hybrids of this strain. Dental traits\nThe reported link between a Chr 13 locus and dental\nmalocclusions [46] might provide an alternative or additional explanation of the associations we observe. Dental\nmalocclusions were the only major male-specific cause of\ndeath we observed in this mouse population (20 % of\nmales that died before the 750-day phenotyping tests, 0 %\nof females)." + } + ], + "84b037c5-8e75-434f-aad1-d270257963f6": [ + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\n\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\n\nPolygenic basis of ''diabesity'' in mice: the interaction of obesity and diabetes genes Obesity-associated diabetes (''diabesity'') is due to interaction of genes causing obesity with diabetes genes.This conclusion is based on findings indicating that obesity is a necessary but not sufficient condition for the type 2 diabetes-like hyperglycaemia: Obese mice are insulin resistant and therefore more or less glucose intolerant, but in some strains such as C57BL/6J-ob/ob, insulin resistance is compensated by hyperinsulinemia and beta cell hyperplasia, and plasma glucose is only moderately elevated.Other models such as C57BLKS/J-db/db and NZO present overt diabetes mellitus as defined by a threshold of 16.6 mM (300 mg/dl) plasma glucose (Leiter et al. 1998); mice crossing this threshold usually exhibit progressive failure and subsequent apoptosis of beta cells.This type 2 diabetes-like condition is not due to the obesity-causing gene variants but to other genes in the genetic background of the strain, which cause obesity-associated diabetes.The severe and early onsetting diabetes of the C57BLKS/J-db/ db strain is due to the C57BLKS/J background, since mice carrying the db mutation on the C57BL/6J background are not diabetic (Stoehr et al. 2000).Conversely, C57BL/6Job/ob mice are normoglycemic, whereas introgression of the ob mutation into the C57BLKS/J background produced a severely diabetic strain (Coleman 1978).Furthermore, it has been shown that in crosses of lean, normoglycaemic strains with diabetic strains the lean strain can introduce variants that markedly aggravate the diabetic phenotype (Leiter et al. 1998;Plum et al. 2000)." + }, + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\nObesity-associated diabetes (''diabesity'') in mouse strains is characterized by severe insulin resistance, hyperglycaemia and progressive failure, and loss of beta cells.This condition is observed in inbred obese mouse strains such as the New Zealand Obese (NZO/HlLt and NZO/HlBomDife) or the TALLYHO/JngJ mouse.In lean strains such as C57BLKS/J, BTBR T?tf/J or DBA/2 J carrying diabetes susceptibility genes (''diabetes susceptible'' background), it can be induced by introgression of the obesity-causing mutations Lep \\ob[ (ob) or Lepr \\db[ (db).Outcross populations of these models have been employed in the genome-wide search for mouse diabetes genes, and have led to positional cloning of the strong candidates Pctp, Tbc1d1, Zfp69, and Ifi202b (NZO-derived obesity) and Sorcs1, Lisch-like, Tomosyn-2, App, Tsc2, and Ube2l6 (obesity caused by the ob or db mutation).Some of these genes have been shown to play a role in the regulation of the human glucose or lipid metabolism.Thus, dissection of the genetic basis of obesity and diabetes in mouse models can identify regulatory mechanisms that are relevant for the human disease." + } + ], + "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d": [ + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "text": "Spontaneous type 2 diabetic models\n\nSpontaneously diabetic animals of type 2 diabetes may be obtained from the animals with one or several genetic mutations transmitted from generation to generation (e.g., ob/ob, db/db mice) or by selected from non-diabetic outbred animals by repeated breeding over several generation [e.g., (GK) rat, Tsumara Suzuki Obese Diabetes (TSOD) mouse].These animals generally inherited diabetes either as single or multigene defects.The metabolic peculiarities result from single gene defect (monogenic) which may be due to dominant gene (e.g., Yellow obese or KK/A y mouse) or recessive gene (diabetic or db/db mouse, Zucker fatty rat) or it can be of polygenic origin [e.g., Kuo Kondo (KK) mouse, New Zealand obese (NZO) mouse] 13 .Type 2 diabetes occurring in majority of human being is a result of interaction between environmental and multiple gene defects though certain subtype of diabetes do also exist with well defined cause [i.e., maturity onset diabetes of youth (MODY) due to defect in glucokinase gene] and this single gene defects may cause type 2 diabetes only in few cases." + } + ], + "8e92b2e3-b525-4c17-a0cb-5ca740a74c66": [ + { + "document_id": "8e92b2e3-b525-4c17-a0cb-5ca740a74c66", + "text": "\n\nMice of the KK strain exhibit a multigenic syndrome of hyperphagia, moderate obesity, hyperinsulinemia, and hyperglycemia (Ikeda 1994;Nakamura andYamada 1963, 1967;Reddi and Camerini-Davalos 1988).Most KK males develop non-insulindependent diabetes after 4 months of age (Leiter and Herberg 1997).While KK females are much less diabetes prone, they do become obese.Previous analyses indicate that the inheritance of obesity and diabetes phenotypes in KK mice is multigenic (Nakamura and Yamada 1963;Reddi and Camerini-Davalos 1988).In the present study, we have searched for QTLs affecting male and female adiposity and related traits in an intercross between strains KK and B6." + } + ], + "acfbb3e9-6eeb-4541-bd1f-9f460de09958": [ + { + "document_id": "acfbb3e9-6eeb-4541-bd1f-9f460de09958", + "text": "We have previously shown that diabetes traits show strong\nheritability in an F2 intercross between the diabetes-resistant\nC57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob\nmouse strains. We assume that the disease phenotype is brought\nabout by a complex pattern of gene expression changes in key\ntissues [21,22]. However, we also recognize the complexity\ninherent in discriminating the gene expression changes that cause\ndiabetes from those that occur as a consequence of the disease. For\nexample, many genes are known to be responsive to elevated\nblood glucose levels [43]." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Although the early onset of diabetes in db mice\ncoincides with t h a t in juvenile diabetes in man, the\nsymptoms of obesity and elevated serum insulin are\nmore suggestive of the pattern of development observed in the maturity-onset type of diabetes. As yet,\nnone of the lesions associated with advanced diabetes\nin humans such as retinopathies, cardiovascular and\nkidney lesions have been observed, possibly because\nof the early onset of the diabetes and the relatively\nrapid deterioration and death of these mice." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Key-words: Spontaneous Diabetes, Genotype : C57BL/\nK5-db, Diabetes in mice, Mutation: diabetes, Obesity,\nPrediabetes, Insulin in plasma, Insulin in pancreas." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Results\nAll mice homozygous for the trait, diabetes (db),\ndevelop an abnormal and characteristic deposition of\nfat beginning at 3 to 4 weeks of age, making their early\nidentification possible. The difference in size and\nappearance of litter-mate 6-week old mice, one normal\nand one diabetic, is shown in Fig. 1. Weight increases\n\nFig. 1. C57BL/Ks-db litter-mates a t 6 weeks." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Diabetologia 3, 238-248 (1967)\n\nStudies with the Mutation, Diabetes, in the Mouse*\nD . L . COT.EMA~ a n d I ~ T H A a I ~\n\nP. t I u M ~ L\n\nThe Jackson Laboratory, Bar Harbor, Maine\n\nSummary. The mutation, diabetes:,(db), t h a t occurred\nin the C57BL/Ks strain of mice is a unit autosomal recessive gene with full penetrance, and causes metabolic\ndisturbances in homozygous mice resembling diabetes\nmellitus in man." + } + ], + "c24330f7-9f82-404a-86d5-a16d814bb754": [ + { + "document_id": "c24330f7-9f82-404a-86d5-a16d814bb754", + "text": "\n\nTo screen for genes that show correlation with different phenotypic outcome in diabetic mouse models, we used the cross-sectional design and performed microarray analysis on 24-wk-old STZ-treated and db/db mice with established renal pathology.In parallel with the functional genomics characterization, each individual mouse underwent a detailed renal phenotype analysis.Mice that were treated with low doses of STZ developed diabetes and moderately severe albuminuria (twice the control).In mice with C57B6/J background, the mesangial changes were mild or absent.Mice with 129SvJ genetic background developed significant glomerular changes.However, these were not significantly different from the agematched controls (K.Sharma, K. Susztak, and E.P. Bo ¨ttinger, unpublished observations).The db/db mice became insulin resistant and developed diabetes at approximately 8 wk of age.Albuminuria was detected as early as 3 to 4 wk after the development of hyperglycemia.The glomerular histology was characterized by severe diffuse mesangial expansion, as previously reported (49)." + } + ] + }, + "data_source": [], + "document_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "db", + "diabetes", + "C57BL/Ks", + "obesity", + "insulin", + "hyperglycaemia", + "beta&cells", + "mutation", + "C57BLKS/J", + "NZO" + ], + "metadata": [], + "question": "Are there phenotypes or genotypes associated with early onset diabetes in mouse?", + "subquestions": null, + "task_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "usage": { + "chatgpt": 8500, + "gpt-4": 4937, + "gpt-4-turbo-preview": 3964 + }, + "user_id": 2 + }, + "document_id": "C3F023A2C80BEF6F4CD95247A2F2D906", + "task_id": "C3F023A2C80BEF6F4CD95247A2F2D906" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_06.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_06.json new file mode 100644 index 0000000..50efdb4 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_06.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-04-18T18:30:30.982190Z", + "data": { + "amplify": false, + "answer": "Gene-environment interactions influence diabetes risk and progression by the interplay of genetic predisposition and environmental factors such as diet, physical activity, and lifestyle. Certain genetic variants may increase the risk of type 2 diabetes (T2D), but this risk can be modified by environmental factors. For instance, the adverse effect of some T2D-associated genetic variants may be attenuated by higher physical activity levels or a healthy lifestyle. Conversely, low physical activity and dietary factors characterizing a Western dietary pattern may augment the risk. Understanding these interactions can help in the development of personalized prevention strategies and treatments for T2D.", + "context": { + "1e3a2816-2a1f-41c3-88d6-03330f04652b": [ + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + } + ], + "2a7da18e-3756-45c5-b18c-a2231685fefd": [ + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002). Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + } + ], + "646689fd-501b-4b27-b8fa-dc098f613044": [ + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + } + ], + "8ab10856-5df7-4f76-897a-84e6f25cd3f5": [ + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + } + ], + "90015638-c92d-4506-95b5-b789f08d613a": [ + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + } + ], + "940283a4-b7e7-4bbe-ba34-c80c4717c15a": [ + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + } + ], + "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155": [ + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + } + ], + "b07d827c-136a-4938-b3f5-b1cde90a2332": [ + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + } + ], + "df542302-18b9-43c2-a421-cba1dba0b3be": [ + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + } + ] + }, + "data_source": [], + "document_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&gene-environment&interactions", + "genetic", + "environmental", + "physical&activity", + "dietary&factors", + "GWAS", + "insulin&sensitivity", + "β-cell&dysfunction", + "PPARG", + "HNF1B" + ], + "metadata": [], + "question": "How do gene-environment interactions influence diabetes risk and progression?", + "subquestions": null, + "task_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "usage": { + "chatgpt": 8576, + "gpt-4": 5471, + "gpt-4-turbo-preview": 4534 + }, + "user_id": 2 + }, + "document_id": "2CB17CD3F1D877A192793DBCA8F458FB", + "task_id": "2CB17CD3F1D877A192793DBCA8F458FB" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_07.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_07.json new file mode 100644 index 0000000..93414e3 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_07.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-04-18T18:34:34.295904Z", + "data": { + "amplify": false, + "answer": "MicroRNAs and long noncoding RNAs (lncRNAs) are involved in diabetes. MicroRNAs modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs. They can regulate pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism. They also hold potential as diagnostic biomarkers and possible drug-targets for regulation of dysfunctional cell responses. LncRNAs are implicated in complications associated with diabetes, such as diabetic retinopathy and diabetic nephropathy. They can regulate cell proliferation, viability, migration, and the expression of pathological genes via post-transcriptional mechanisms.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "\n\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + }, + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop À / À mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression." + } + ], + "18a35699-873a-4542-b35a-3a4a14edd628": [ + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "text": "\n\nPlatelets are key partaker in CVD and their involvement in the development of cardiovascular complications is strengthened in diabetes (148).Platelets play an important role in the pathophysiology of thrombosis and represent an important source of different RNA species, including pseudogenes, intronic transcripts, non-coding RNAs, and antisense transcripts (149,150).These molecules can be released by platelets through microvescicles, contributing to the horizontal transfer of molecular signals delivered through the bloodstream to specific sites of action (151).The downregulation of miR-223, miR-126, or 146a observed in diabetic and hyperglycemic patients (137,152) has been associated with increased platelet reactivity and aggregation (153,154).In line with these findings, silencing of miR-223 in mice caused a hyperreactive and hyperadhesive platelet phenotype, and was associated with calpain activation through the increased expression of beta1 integrin, kindlin-3, and factor XIII (153,155).Moreover, the modulation of the expression levels of platelet miRNAs can also be measured in plasma.In fact, plasma levels of miR-223 and miR-126 are decreased in diabetics (137,156).This leads to the upregulation of the P2Y12 receptor, as well as P-selectin, further contributing to platelet dysfunction (156).As a result of this interaction, activation level of platelets in type 2 DM is increased (149,156,157).Consistently with this, circulating miR-223 levels are independent predictors of high on-treatment platelet reactivity (158).Another interesting mechanism linking platelets and diabetes involves miR-103b, a platelet-derived biomarker proposed for the early diagnosis of type 2 DM, and the secreted frizzledrelated protein-4 (SFRP4), a potential biomarker of early β cell dysfunction and diabetes.In fact, platelet-derived miR-103b is able to downregulate SFRP4, whose expression levels are significantly increased in pancreatic islets and in the blood of patients with prediabetes or overt diabetes (159).These interesting results identify miR-103b as a novel potential marker of prediabetes and diabetes, and disclose a novel potential therapeutic target in type 2 DM." + }, + { + "document_id": "18a35699-873a-4542-b35a-3a4a14edd628", + "text": "\n\nIn vitro and in vivo studies concerning the mechanisms that are responsible for the endothelial dysfunction in diabetes demonstrated that, in the presence of high glucose concentrations, upregulation of miR-185 reduced the expression of the glutathione peroxidase-1 (GPx-1) gene, which encodes an enzyme that is important in the prevention of oxidative stress (129); instead upregulation of miR-34a and miR-204 contributed to endothelial cell senescence by impairing SIRT-1 expression and function (130,131).In the endothelium, miR-126 exerts proangiogenic, and anti-inflammatory activities.At a functional level, it enhances VEGF and fibroblast growth factor activities, contributing to vascular integrity and angiogenesis (132,133), recruits progenitor cells through the chemokine CXCL12 (134), while it suppresses inflammation by inhibiting TNF-α, ROS, and NADPH oxidase via HMGB1 (135).Consistently, miR-126 levels are down-regulated in both myocardial tissue and plasma from type 2 diabetic patients without any known anamnestic data for CVD (136,137), and in patients with CAD (138), suggesting that it could represent a new diagnostic marker for diabetes and CVD.Other studies in endothelial colony-forming cells, as well as in progenitor endothelial cells (EPCs) exposed to high glucose, demonstrated that miR-134 and miR-130a affected cell motility and apoptosis, respectively (139,140)." + } + ], + "2dc80127-89ba-47be-9e94-d90c2105be8d": [ + { + "document_id": "2dc80127-89ba-47be-9e94-d90c2105be8d", + "text": "\n\nNumerous recent reports have demonstrated abnormal expression of various miRNAs in renal, vascular and retinal cells under diabetic conditions, and in vivo models of related diabetic complications [8,[87][88][89][90][91]. Notably, the functional relevance of these miRNAs has been highlighted by the fact they target key genes associated with the progression of, or protection against, these complications.In particular, the role of miRNAs in diabetic nephropathy has been extensively studied, including in the actions of TGF-β related to fibrosis and other key renal outcomes in vitro and in vivo [8,[87][88][89][90].In diabetic retinopathy, several miRNAs have been reported to modulate the disease by targeting factors associated with angiogenesis, inflammation, and oxidant stress in RECs and in diabetic retinas [88,89].Reports have also implicated various miRNAs in the aberrant expression of genes associated with diabetic cardiomyopathy [88,91].In addition, effective in vivo targeting of miRNAs has now been demonstrated thanks to advances in nucleotide chemistry and the design of nuclease-resistant anti-miRNAs, which suggest future translational potential of miRNA-based therapies for human diabetic complications [8].Importantly, since miRNAs are stable in biological fluids such as urine and serum [8], they are being assessed in samples from various clinical cohorts as valuable biomarkers for the early detection of diabetic complications, for which there is a major unmet clinical need.It is clear that research in the field of miRNAs and diabetic complications will continue at a rapid pace." + } + ], + "34184c8d-b167-4ae8-bfce-01e18d78fe41": [ + { + "document_id": "34184c8d-b167-4ae8-bfce-01e18d78fe41", + "text": "Introduction\n\nDiabetes-related complications represent one of the most important health problems worldwide with dire social and economic projections (Cooper, 2012).One of the most important medical concerns of the diabetes epidemic is diabetic nephropathy (DN).Diabetic nephropathy is regarded as a prototypical disease of gene and environmental interactions because not all diabetic subjects with traditional risk factors develop clinically evident nephropathy, indicating a role for individual susceptibility.The majority (>85%) of GWAS-identified single nucleotide polymorphisms (SNPs) are located in the non-coding regions of the genome and thus their functional implication lies in identifying the target genes, cell types, and the mode of dysregulation caused by these non-coding SNPs (Maurano et al., 2012).Recent studies indicate that complex trait-causing variants localize to cell-type-specific, functionally important gene regulatory regions where they can disrupt or create transcription factor binding sites to alter transcript levels only in disease-target cell types (Ko and Susztak, 2013;Susztak, 2014).Several elements of the immune system including cytokines and resident chemokines, macrophage recruitment, T lymphocytes, and immune complex deposition have recently been associated with DN (Navarro-González and Mora-Fernández, 2008;Gaballa and Farag, 2013).Since renal cells are also capable of synthesizing pro-inflammatory cytokines such as tumor necrotic factor-alpha (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6), therefore, these cytokines acting in a paracrine or autocrine manner may induce significant effects leading to the development and progression of several renal disorders (Matoba et al., 2010;Pruijm et al., 2012;Shankar et al., 2011).The rationale of this study involved a concerted effort of genotyping, correlation and gene expression techniques involving three pro-inflammatory cytokine genes in the development and progression of DN as well as identification of high risk patients involving susceptibility or poor clinical outcome." + } + ], + "5d2fa6b9-8412-43cb-bc86-e9bcda73a4ef": [ + { + "document_id": "5d2fa6b9-8412-43cb-bc86-e9bcda73a4ef", + "text": "They also identified enrichment in coagulation and\ncomplement pathways, signaling pathways, tissue remodeling, and antigen presentation, including PI3K-Akt, Rap1,\nToll-like, and NOD-like. Sun et al. [25] studied diabetic retinopathy and identified four stress-inducible genes Rmb3,\nCirbp, Mt1, and Mt2 which commonly exist in most retinal\ncell types. Diabetes increases the inflammatory factor gene\nexpressions in retinal microglia and stimulates the immediate early gene expressions (IEGs) in retinal astrocytes. Van Zyl et al. [30] studied glaucoma cases and identified\nthe cell types that represent gene expressions implicated in\nglaucoma." + } + ], + "6011e960-6a6e-47fe-94f2-2c21c224fd25": [ + { + "document_id": "6011e960-6a6e-47fe-94f2-2c21c224fd25", + "text": "\n\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3β isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies." + } + ], + "7e809821-000d-4fff-971d-264650e3612b": [ + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "text": "M A N U S C R I P T A C C E P T E D\n\nIn relation to the regulation of gene expression, the role of microRNAs (miRNAs) in diabetic retinopathy has been gaining more emphasis.miRNAs are non-coding small RNAs which modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs.miRNAs can be detected in serum (free, associated with proteins or within membrane-bound particles) (Weiland et al., 2012), vitreous (Ragusa et al., 2013) and aqueous (Dunmire et al., 2013).As reviewed by Mastropasqua et al., miRNAs hold considerable interest for diabetic retinopathy since they can regulate important pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism (Mastropasqua et al., 2014).Although based on a small patient sample, it has been reported that three separate miRNAs (miR-21, miR-181c, and miR-1179) in serum of patients with diabetic retinopathy have potential to be used as biomarkers for early detection of disease (Li et al., 2014;Qing et al., 2014).While this is still a growing research area, miRNAs hold considerable clinical potential in the diabetic retinopathy field, both as possible drug-targets for regulation of dysfunctional cell responses and as diagnostic biomarkers." + } + ], + "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753": [ + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "text": "Roles of lncRNAs in diabetic complications\n\nApart from being involved in major metabolic tissues during diabetes as discussed above, lncRNAs are implicated in complications associated with diabetes.Diabetic retinopathy is one of the common complications in diabetic patients, which leads to impaired or loss of vision.Altered expression of lncRNAs, namely MALAT1 [82,83] and MEG3 [84], are reported to be associated with diabetic retinopathy.In STZ-induced diabetic rats, the expression of MALAT1 is elevated in the endothelial cells of the retina and knockdown of MALAT1 ameliorates retinopathy in STZ-induced rats [82].The lncRNA, MEG3, was also found to be downregulated in the retina of STZ-induced diabetic mice and its in vitro knockdown in retinal endothelial cells was found to regulate cell proliferation, viability, and migration [84].Hyperglycemia as in diabetes causes upregulation of ANRIL levels in endothelial cells [85,86], and this elevates the levels of the PRC2 subunit, EZH2 that consequently promotes the expression of VEGF, a key promoter of angiogenesis [85].Another major complication associated with diabetes is diabetic nephropathy, and this is considered a major cause of end-stage renal disease and disability in diabetic patients [87].Recent studies show that lncRNAs play important roles in the development of diabetic nephropathy and accumulation of extracellular matrix (ECM) proteins.There is higher expression of the lncRNA, PVT1, during diabetic nephropathy, and this increase leads to increased fibrosis due to accumulation of ECM proteins in renal cells [88]; downregulation of PVT1 reduces ECM accumulation [88].LncRNA PVT1 is also a host to miR-1207-5p and this miRNA is shown to regulate the expression of fibronectin1 (FN1), plasminogen activator inhibitor-1 (PAI1), and transforming growth factor beta 1 (TGFβ1) [89].In renal tube injury during diabetes, the lncRNA, MIAT, is under-expressed, and this negatively correlates with creatinine and BUN levels in the serum of these subjects.It has been shown to regulate cell viability of proximal convoluted renal tubules [90].In diabetic nephropathic mice, the lncRNA, MGC, is increased in renal mesangial cells.Interestingly, this lncRNA harbours a cluster of approximately 40 miRNAs, and is regulated by the ER stress marker C/EBP homologous protein (CHOP) [91].In CHOP -deficient mice, there is decreased expression of the lncRNA, MGC, and the clustered miRNAs, and these mice have shown an improvement in diabetic nephropathy [91].Diabetic nephropathy is also associated with increased levels of lincRNA, Gm4419, and this exerts its action by interacting with NF-κβ.Knockdown of this lincRNA in renal mesangial cells lowers cellular proliferation and inhibits expression of NF-κβ in hyperglycemic states [92].The lncRNA, TUG1, that is upregulated in diabetic nephropathy acts as sponge for miR-377 and regulates PPAR-γ expression which further modulates the expression of FN1, collagen type IV alpha 1 chain (COL4A1), PAI1, and TGFβ1 in renal mesangial cells [93].Diabetic cardiomyopathy is a critical end-stage complication associated with diabetes.Several such cardiovascular complications and myocardial dysfunction in diabetic patients lead to heart failure [94].Differential expression analysis in cardiac tissue from normal and diabetic rats shows that the lncRNA, MALAT1, is upregulated during cardiomyopathy and knockdown of this lncRNA improves left ventricular systolic function by reducing myocardial inflammation in diabetic rats [95,96].Decreased expression of the lncRNA, H19, is also reported during diabetes [68,70], and this often results in decreased expression of the exonic miRNA, miR-675 [97,98].mir-675 directly targets the voltage-dependent anion channel 1 (VDAC1) which is involved in mitochondria-mediated apoptosis in the cardiac tissue during diabetes.H19 overexpression in diabetic rats reduces oxidative stress, apoptosis, and inflammation, and improves ventricle function [98].LncRNAs NONRATT021972 and uc.48+ are reported to be associated with diabetic neuropathic pain [99,100], and inhibition of both have been shown to alleviate such neuropathic pain by activating the P2X3 receptor.Impaired wound closure is a notable complication associated with diabetes and a recent report shows decreased levels of the lncRNA, Lethe in such impaired dorsal wounds of diabetic mice.This was demonstrated to be associated with increased ROS production, possibly through regulation of NOX2 expression [101]." + }, + { + "document_id": "7ebf3dcf-0e9a-44d7-bd1c-1c49004d0753", + "text": "\n\nAll these suggest towards important roles of various lncRNAs in complications associated with diabetes and, therefore, assume importance to be studied in detail." + } + ], + "80e1b2af-be79-4d9b-852f-46bf3e23c963": [ + { + "document_id": "80e1b2af-be79-4d9b-852f-46bf3e23c963", + "text": "\n\nAn overall important consideration in study design is that similar to RNA, noncoding RNAs are tissue and cell specific [24,[77][78][79][80][81][82].Given that it is still unknown if pathogenic changes in AMD are localized to specific ocular tissues or systemic, one must take into consideration that potential biomarkers identified in the peripheral blood as \"disease associated\" may not reflect the disease mechanism occurring in the neural retina and/or RPE." + } + ], + "88dde947-5255-40e1-92d5-afde089b517b": [ + { + "document_id": "88dde947-5255-40e1-92d5-afde089b517b", + "text": "\n\nSkol et al. developed methods to study genomics and transcriptomics together to help discover genes that cause diabetic retinopathy.Genes involved in how cells respond to high blood sugar were first identified using cells grown in the lab.By comparing the activity of these genes in people with and without retinopathy the study identified genes associated with an increased risk of retinopathy in diabetes.In people with retinopathy, the activity of the folliculin gene (FLCN) increased more in response to high blood sugar.This was further verified with independent groups of people and using computer models to estimate the effect of different versions of the folliculin gene." + } + ], + "d23e9456-8ee8-46e0-9870-18ff69965c28": [ + { + "document_id": "d23e9456-8ee8-46e0-9870-18ff69965c28", + "text": "miRNAs in Kidney Disease and Diabetic Nephropathy\n\nDiabetic nephropathy is a progressive kidney disease and a major debilitating complication of both type 1 and type 2 diabetes that can lead to end-stage renal disease (ESRD) and related cardiovascular disorders.Absence or lower levels of particular miRNAs in the kidney compared with other organs may permit renal specific expression of target proteins that are important for kidney functions [45].Figure 4 depicts the connection between the role of miRNAs and kidney fibrosis.Altered expression of miRNAs causes renal fibrosis by inducing EMT, EndMT, and other fibrogenic stimuli.The accumulative effects of hyperglycaemia, inflammatory cytokines, proteinuria, ageing, high blood pressure, and hypoxia result into alteration of miRNAs expression profiles.The altered miRNAs level causes the initiation of such transition program in normal kidney, finally fibrosis.Some of the miRNAs that are more abundant in the kidney compared with other organs include miR-192, miR-194, miR-204, miR-215, and miR-216.A critical role of miRNA regulation in the progression of glomerular and tubular damage and the development of proteinuria been suggested by studies in mice with podocytespecific deletion of Dicer [46].There was a rapid progression of renal disease with initial development of albuminuria followed by pathological features of glomerulosclerosis and tubulointerstitial fibrosis.It is likely that these phenotypes are due to the global loss of miRNAs because of Dicer deletion, but, given multiple miRNAs and their myriad targets, the precise pathways responsible require identification.These investigators also identified specific miRNA changes, for example, the downregulation of the miR-30 family when Dicer was deleted.Of relevance, the miR-30 family was found to target connective tissue growth factor, a profibrotic molecule that is also downstream of transforming growth factor (TGF)- [47].Thus, the targets of these miRNAs may regulate critical glomerular and podocyte functions.These findings have also been complemented by an elegant study revealing a developmental role for the miR-30 family during pronephric kidney development in Xenopus [48].Sun et al. [49] identified five miRNAs (-192, -194, -204, -215, and -216) that were highly expressed in human and mouse kidney using miRNA microarray.A recent report using new proteomic approaches to profile and identify miRNA targets demonstrated that miR-NAs repress their targets at both the mRNA and translational levels and that the effects are mostly relatively mild [50].The role of miR-192 remains controversial and highlights the complex nature of miRNA research.Kato et al. [51] observed increased renal expression of miR-192 in streptozotocin-(STZ-) induced diabetes and in the db/db mouse and demonstrated that transforming growth factor (TGF-1) upregulated miR-192 in mesangial cells (MCs).miR-192 repressed the translation of Zeb2, a transcriptional repressor that binds to the E-box in the collagen 12 (col12) gene.They proposed that miR-192 repressed Zeb2 and resulted in increased col12 expression in vitro and contributed to increased collagen deposition in vivo.These data suggest a role for miR-192 in the development of the matrix accumulation observed in DN.It is interesting that the expression of miR-192 was increased by TGF- in mouse MCs (mesangial cells), whereas, conversely, the expression of its target, Zeb2, was decreased [51].This also paralleled the increased Col1 2 and TGF- expression [51].These results suggested that the increase in TGF- in vivo in diabetic glomeruli and in vitro in MCs can induce miR-192 expression, which can target and downregulate Zeb2 thereby to increase Col1 2.This is supported by the report showing that miR-192 is upregulated in human MCs treated with high glucose [51].TGF- induced downregulation of Zeb2 (via miR-192) and Zeb1 (via potentially another miRNA) can cooperate to enhance Col1 2 expression via de-repression at E-box elements [51].In contrast to the above, other reports suggest the relationship between miR-192 and renal fibrosis may be more complicated.Krupa et al. [52] identified two miRNAs in human renal biopsies, the expression of which differed by more than twofold between progressors and nonprogressors with respect to DN, the greatest change occurring in miR-192 which was significantly lower in patients with advanced DN, correlating with tubulointerstitial fibrosis and low glomerular filtration rate.They also reported, in contrast to the Kato et al. [51] study in MCs, that TGF-1 decreased expression of miR-192 in cultured proximal tubular cells (PTCs).These investigators concluded that a decrease in miR-192 is associated with increased renal fibrosis in vivo.Interestingly, connective tissue growth factor (CTGF) treatment also resulted in fibrogenesis but caused the induction of miR-192/215 and, consequently, decreased Zeb2 and increased E-cadherin.The contrasting findings above highlight the complex nature of miRNA research.Some of the differences may relate to models and/or experimental conditions; however, one often overlooked explanation is that some effects of miRNAs and inhibitors are likely to be indirect in nature.A recent report also showed that BMP6-induced miR-192 decreases the expression of Zeb1 in breast cancer cells [53].Thus, TGF- induced increase in the expression of key miRNAs (miR-192 and miR-200 family members) might coordinately downregulate E-box repressors Zeb1 and Zeb2 to increase Col12 expression in MCs related to the pathogenesis of DN.The proximal promoter of the Col1a2 gene responds to TGF- via smads and SP1.Conversely, the downregulation of Zeb1 and Zeb2 by TGF- via miR-200 family and miR-192 can affect upstream E-box regions.Because E-boxes are present in the upstream genomic regions of the miR-200 family, miR-200 family members may themselves be regulated by Zeb1 and Zeb2 [54].It is possible that the miR-200 family upregulated by TGF- or in diabetic glomeruli under early stages of the disease can also regulate collagen expression related to diabetic kidney disease by targeting and downregulating E-box repressors.miR-192 might initiate signaling from TGF- to upregulate miR-200 family members, which subsequently could amplify the signaling by further regulating themselves through down regulation of Ebox repressors.Such events could lead to progressive renal dysfunction under pathologic conditions such as diabetes, in which TGF- levels are enhanced.Conversely, there are several reports that miR-200 family members and miR-192 can be suppressed by TGF-, and this promotes epithelial-tomesenchymal transition (EMT) in cancer and other kidneyderived epithelial cell lines via subsequent upregulation of targets Zeb1 and Zeb2 to repress E-cadherin [54,55]." + } + ], + "e66846a6-1546-481b-baae-a55fc524c8af": [ + { + "document_id": "e66846a6-1546-481b-baae-a55fc524c8af", + "text": "\n\nDR. HARRINGTON: You mentioned Liu's data from China [abstract; Liu Z-H et al J Am Soc Nephrol 14:400A, 2003], which overwhelmed me.Apparently there are 182 genes whose expression is up-or down-regulated significantly in patients with diabetes.If I asked you to pick the \"top three\" genes other than the ACE polymorphisms, which three would you choose and why?DR.ADLER: Well, actually I didn't see all of their results nor did they report all 182.But I guess my favorite ones would be some that relate to the ROS pathway because this is an all-purpose pathway of cell injury fueled by a hyperglycemic environment; some that relate to podocyte structure to explain the development of proteinuria; and TGF-b, which is a master regulator of sclerosis and fibrosis." + } + ], + "ec62a4d9-2fe2-49b0-84d8-13b1597e2067": [ + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "IncRNAs and microRNAs\n\nFigure 1 | Emerging molecular mechanisms of diabetic nephropathy.Diabetic conditions induce the expression of growth factors such as transforming growth factor β1 and angiotensin II, cytokines and AGEs to promote inflammation, fibrosis and hypertrophy, which contribute to the progression of diabetic nephropathy.These factors stimulate various signal transduction mechanisms that activate downstream transcription factors.They can also affect DNA methylation and histone modifications, which result in increased chromatin accessibility to transcription factors near pathological genes in renal cells.Coordinated interactions between transcription factors and epigenetic mechanisms can increase the expression of not only coding RNAs, but also noncoding RNAs such as microRNAs and lncRNAs.Furthermore, microRNAs and lncRNAs can also increase the expression of pathological genes via post-transcriptional mechanisms.Notably, the induction of key coding genes and proteins, lncRNAs and microRNAs can also 'lock' open chromatin states to create persistent expression of genes, which could be one mechanism of metabolic memory.Abbreviations: AGE, advanced glycation end-product; lncRNA, long noncoding RNA." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "Key points\n\n■ Diabetic conditions induce inflammation, fibrosis and hypertrophy in renal cells through various cytokines and growth factors such as transforming growth factor β1, angiotensin II and platelet-derived growth factor ■ The engagement of cytokines and growth factors with their receptors triggers signal transduction cascades that result in the activation of transcription factors to increase expression of inflammatory and fibrotic genes ■ These signalling mechanisms affect epigenetic states-such as DNA methylation and chromatin histone modifications-to augment the expression of profibrotic and inflammatory genes, as well as noncoding RNAs ■ Noncoding RNAs that are induced by diabetic conditions can also promote the expression of pathological genes via various post-transcriptional and post-translational mechanisms ■ These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for 'metabolic memory' ■ Key epigenetic regulators, microRNAs and long noncoding RNAs could serve as new therapeutic targets for diabetic nephropathy" + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "\n| Diabetic nephropathy (DN), a severe microvascular complication frequently associated with both type 1 and type 2 diabetes mellitus, is a leading cause of renal failure.The condition can also lead to accelerated cardiovascular disease and macrovascular complications.Currently available therapies have not been fully efficacious in the treatment of DN, suggesting that further understanding of the molecular mechanisms underlying the pathogenesis of DN is necessary for the improved management of this disease.Although key signal transduction and gene regulation mechanisms have been identified, especially those related to the effects of hyperglycaemia, transforming growth factor β1 and angiotensin II, progress in functional genomics, high-throughput sequencing technology, epigenetics and systems biology approaches have greatly expanded our knowledge and uncovered new molecular mechanisms and factors involved in DN.These mechanisms include DNA methylation, chromatin histone modifications, novel transcripts and functional noncoding RNAs, such as microRNAs and long noncoding RNAs.In this Review, we discuss the significance of these emerging mechanisms, how they mediate the actions of growth factors to augment the expression of extracellular matrix and inflammatory genes associated with DN and their potential usefulness as diagnostic biomarkers or novel therapeutic targets for DN." + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "\n\n| microRNAs relevant to the pathogenesis of diabetic nephropathy" + }, + { + "document_id": "ec62a4d9-2fe2-49b0-84d8-13b1597e2067", + "text": "Review criteria\n\nA search for original published articles focusing on \"diabetic nephropathy\", \"signal transduction\", \"noncoding RNAs\", \"microRNAs\", \"long noncoding RNAs\", \"genetics\" and \"epigenetics\" was performed in MEDLINE and PubMed.All articles identified were English-language, full-text papers.We also searched the reference lists of identified articles for further relevant papers." + } + ] + }, + "data_source": [], + "document_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "microRNAs", + "lncRNAs", + "diabetic&nephropathy", + "diabetic&retinopathy", + "TGF-β1", + "angiogenesis", + "fibrosis", + "inflammation", + "hyperglycemia" + ], + "metadata": [], + "question": "What non-coding RNAs are involved in diabetes, and what roles do they play?", + "subquestions": null, + "task_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "usage": { + "chatgpt": 13184, + "gpt-4": 7924, + "gpt-4-turbo-preview": 6969 + }, + "user_id": 2 + }, + "document_id": "7A3E5866E55FB9764BF9F70CFF63A333", + "task_id": "7A3E5866E55FB9764BF9F70CFF63A333" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_08.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_08.json new file mode 100644 index 0000000..878a503 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_08.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-04-18T18:36:24.689995Z", + "data": { + "amplify": false, + "answer": "Gene-environment interactions influence diabetes risk and progression by the interplay of genetic predisposition and environmental factors such as diet, physical activity, and lifestyle. Certain genetic variants may increase the risk of type 2 diabetes (T2D), but this risk can be modified by environmental factors. For instance, the adverse effect of some T2D-associated genetic variants may be attenuated by higher physical activity levels or a healthy lifestyle. Conversely, low physical activity and dietary factors characterizing a Western dietary pattern may augment the risk. Understanding these interactions can help in the development of personalized prevention strategies and treatments for T2D.", + "context": { + "1e3a2816-2a1f-41c3-88d6-03330f04652b": [ + { + "document_id": "1e3a2816-2a1f-41c3-88d6-03330f04652b", + "text": "\n\nAdditional evidence supporting a potentially important role for environmental modulation of genetic risk was found in previous population studies.For example, although some of the GWASidentified T2D loci could be replicated successfully in various populations (e.g., CDKAL1, HHEX, IGF2BP2, TCF7L2 and SLC30A8), more genetic variants have been identified only in some specific populations [26].T2D risk alleles showed extreme directional differentiation between different populations compared with other common diseases [29].Different T2D loci and loci frequencies across different populations may reflect the adaptation to the local environments and diets along with human migration [30].Therefore, the interplay between gene and environment leads to a more complex pathogenesis of T2D and related traits.These hypotheses are strongly supported by a number of recent GxE studies [7,11,31,32].For example, Qi et al. [31] generated a genetic risk score (GRS) using ten GWAS-identified SNPs and observed a significant interaction between the Western dietary pattern and GRS in the Health Professionals Follow-Up Study.The Western dietary pattern was only positively associated with risk of T2D among men with a high GRS, but not with low GRS subjects.Another large meta-analysis of 14 cohort studies [32] revealed that dietary whole-grain intake potentially interacted with one GCKR variant (rs780094) for fasting insulin in individuals of European descent.Greater whole-grain intake was associated with a smaller reduction of fasting insulin in individuals with the insulin-raising allele of rs780094, compared to the non-risk allele." + } + ], + "2a7da18e-3756-45c5-b18c-a2231685fefd": [ + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "Gene–exercise interaction in type 2 diabetes\nWhen studying gene–environment interaction on the quantitative traits that\nunderlie diabetes, the power to detect interaction is highly dependent on the precision with which non-genetic exposures are measured (Wareham et al 2002). Achievement of optimal glycaemic control is the focus of traditional treatment\nparadigms. Regular exercise, both aerobic (walking, jogging, or cycling) and resistance (weightlifting) training results in increased glucose uptake and insulin sensitivity and is a primary modality used in the treatment of type 2 diabetes patients\n(Sigal et al 2007)." + } + ], + "559a3a15-da15-4132-a8b5-5401bfe770ef": [ + { + "document_id": "559a3a15-da15-4132-a8b5-5401bfe770ef", + "text": "Gene-Environment Interaction\n\nEvidence from the epidemiology of T2D overwhelmingly supports a strong environmental influence interacting with genetic predisposition in a synergistic fashion as has been recently reviewed [123], however current state-of-the-art methods for measuring environmental effects lack precision and can result in changes in statistical power to detect interaction [123,124].Since lifestyle factors are important in preventing diabetes [125,126], interaction of gene variants with measures of dietary intake and exercise have been selected for studies on gene-environment interaction.For example, HNF1B (rs 4430796) was shown to interact with exercise; low levels of activity enhanced the risk of T2D in association with absence of the risk allele, but there was no protective effect of exercise when the allele was present.It follows that subgrouping by genotype may serve to enhance risk prediction while considering gene-environment interaction as has been done for exercise [127].Also lifestyle including exercise modified the effect of a CDKN2A/B variant on 2-hour glucose levels in the Diabetes Prevention Program [128] but was not confirmed in the HERITAGE study using different measurements and phenotypes involving insulin sensitivity and β-cell function [129].The pro12ala PPARG variant also interacts with physical activity for effect on 2-hour glucose levels [130], which was confirmed in the smaller HERITAGE study [129].In addition, a relationship of dietary fat intake with plasma insulin and BMI differs by the pro12ala PPARG genotype [131]." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "\n\nA person's risk of type 2 diabetes or obesity reflects the joint effects of genetic predisposition and relevant environmental exposures.Efforts to determine whether these genetic and environmental components of risk interact (in the statistical sense that joint effects cannot be predicted from main effects alone) 70 face challenges associated with measuring relevant exposures (diet and physical activity being notoriously difficult to estimate) and the effect of imprecision on statistical power. 71Although claims that statistical interactions reflect shared mechanisms (i.e., that the interacting factors act through the same pathways) are probably overstated, understanding the relative contributions of genetic and environmental components to risk is important.After all, environmental factors can be modified more readily than genetic factors.Genetic discoveries have provided a molecular basis for the clinically useful classification of monogenic forms of diabetes and obesity. 3,4Will the same be true for the common forms of these conditions?Probably not: as far as the common variants are concerned, each patient with diabetes or obesity has an individual \"barcode\" of susceptibility alleles and protective alleles across many loci.It is possible to show that the genetic profiles of lean subjects with type 2 diabetes and obese subjects with type 2 diabetes are not identical, but these differences appear to be inadequate for clinically useful subclassification. 22,72f efforts to uncover less prevalent, higher-penetrance alleles are successful, more precise classification of disease subtypes may become possible, particularly if genetic data can be integrated with clinical and biochemical information.For example, in persons presenting with diabetes in early adulthood, there are several possible diagnoses: various subtypes of maturity-onset diabetes of the young or mitochondrial diabetes, for example, as well as type 1 or type 2 diabetes.Assigning the correct diagnosis has both prognostic and therapeutic benefits for the patient (Table 3)." + } + ], + "646689fd-501b-4b27-b8fa-dc098f613044": [ + { + "document_id": "646689fd-501b-4b27-b8fa-dc098f613044", + "text": "Genes, environment, and development of type 2 diabetes\n\nGenes and the environment together are important determinants of insulin resistance and β-cell dysfunction (fi gure 2).Because changes in the gene pool cannot account for the rapid increase in prevalence of type 2 diabetes in recent decades, environmental changes are essential to understanding of the epidemic." + } + ], + "8ab10856-5df7-4f76-897a-84e6f25cd3f5": [ + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nType 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors.Since the advent of genomewide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D.Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it.However, there still remain inconsistencies warranting further investigation.Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "Gene and Environment Selection\n\nEnvironmental factors selected for recent G × E interactions studies continue to be the established modifiable risk factors for T2D such as obesity, physical activity, dietary fat, and carbohydrate quality as well as measures of pre-and post-uterine environment.The genetic factors selected, however, have shifted from biological candidates based on functional evidence to genome-wide established loci for T2D or related traits (Table 1).This approach may improve power to detect and strengthen causal inference for an interaction (49).Focusing on established T2D loci may also further our understanding of their functional role in disease development in addition to their public health relevance in the context of genetic risk modification (13)." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nWe have seen considerable progress in our understanding of the role that both environment and genetics play in the development of T2D.Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate changes in certain lifestyle factors.Our recent approach to studies of G × E interactions in T2D has gained considerable advantage over previous approaches, but it is clearly not optimal.Lack of statistical power and measurement error for environmental factors will continue to challenge our efforts to characterize G × E interactions.Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS.Agnostic approaches to the discovery of G × E interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges.Nevertheless, large collaborative efforts have the potential to uncover true G × E interactions, which will enhance our understanding of the interplays between genes and environment in the etiology of T2D." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "\n\nThe purpose of the present review is to summarize recent epidemiological approaches and progress pertaining to gene-environment (G × E) interactions potentially implicated in the pathogenesis of T2D and its related traits.We also discuss continuing challenges, evolving approaches, and recommendations for future efforts in this field." + }, + { + "document_id": "8ab10856-5df7-4f76-897a-84e6f25cd3f5", + "text": "FUTURE PERSPECTIVES\n\nContinued investment in studies of G × E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G × E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G × E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "Gene-Nutrient or Dietary Pattern Interactions in The Development of T2DM\n\nRecently, several studies have demonstrated the significant effects of genotype by environment interactions on T2DM [48,49].However, further clarification of the role of these interactions at the genome-wide level could help predict disease risk more accurately and facilitate the development of dietary recommendations to improve prevention and treatment.Moreover, it would be very interesting to identify the specific dietary factors that are the most influential in the variation of a given T2DM-related phenotype and to what extent these dietary factors contribute to the phenotypic variation (Table 2).In particular, the dietary factors considered are macro-and micronutrients, foods and type of diets.A recent review present evidence on the dietary environment and genetics as risk factors for T2DM [50]. * Adiponectin (ADIPOQ)." + } + ], + "90015638-c92d-4506-95b5-b789f08d613a": [ + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "text": "Introduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene  environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4]." + } + ], + "940283a4-b7e7-4bbe-ba34-c80c4717c15a": [ + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + }, + { + "document_id": "940283a4-b7e7-4bbe-ba34-c80c4717c15a", + "text": "\n\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases." + } + ], + "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155": [ + { + "document_id": "95a5a00b-9cf4-4988-bc6c-9df0e8e1b155", + "text": "\n\nPredisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype.Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors.Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1) optimize targeting of health interventions to persons most likely to respond well to them, 2) improve cost-and health-effectiveness of existing preventive and treatment paradigms; 3) reduce unnecessary adverse consequences of interventions; 4) increase patient adherence to health practitioners' recommendations; and 5) identify novel interventions that are beneficial only in a defined genetic subgroup of the population.In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and genetreatment interactions in type 2 diabetes.We discuss the tried, tested, and oftenfailed approaches to investigating genelifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future.We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes.Here we describe the rationale and evidence concerning GEIs and gene-treatment interactions in type 2 diabetes, provide an interpretation of current findings and strategies, and offer a view for their future translation." + } + ], + "b07d827c-136a-4938-b3f5-b1cde90a2332": [ + { + "document_id": "b07d827c-136a-4938-b3f5-b1cde90a2332", + "text": "\n\nT2DM results from the contribution of many genes [10] , many environmental factors [11] , and the interactions among those genetic and environmental factors.Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant proportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influence the disease risk.Florez et al. [13] reported that response to the Diabetes Prevention Program lifestyle intervention did not differ by genotype groups at TCF7L2 rs7903146 [13] .A more recent report from the Diabetes Prevention Program [14] showed that among 10 of the recently identified diabetes susceptibility polymorphisms (single nucleotide polymorphisms, SNPs), only CDKN2A/B rs10811661 was shown to marginally modify the effect of the lifestyle intervention on diabetes risk reduction.Similarly, the study of Brito et al. [15] reported that among 17 of the diabetes SNPs, only HNF1B rs4430796 significantly interacted with physical activity to influence impaired glucose tolerance risk and incident diabetes." + } + ], + "df542302-18b9-43c2-a421-cba1dba0b3be": [ + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "text": "Gene-Environment\n\nInteractions.An risk of developing T2D is the product of interaction between the individual's genetic constitution and the environment inhabited by the individual.Whilst the contribution of genetic factors to disease risk is relatively easy to quantify, the impact of environmental exposure is less easily measured in a clinical setting.Nevertheless, efforts have been made to study the interactions between some of the known susceptibility loci for T2D and the environment, and these findings may be useful for the development of prediction models and tailoring clinical treatment for T2D [122,123].For example, for carriers of the risk allele for TCF7L2, diets of low glycaemic load [124,125] and a more intensive lifestyle modification regime (versus that recommended for nonrisk carriers) [61,62,126,127] have been shown to reduce the risk of T2D.Meaningful studies for gene-environment interactions will require samples of sufficient size to increase statistical power [128] and accurate methods for measuring environmental exposure, for example, the use of metabolomics to identify and assess metabolic characteristics, changes, and phenotypes in response to the environment, diet, lifestyle, and pathophysiological states.This information will allow the generation of better risk prediction models and personalisation/stratification of treatment, the holy grail of GWAS." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nOther aspects that have been overlooked in large GWAS on T2DM relate to environmental effects such as diet, physical activity, and stresses, which may affect gene expression.For example, fish oil may stimulate PPARG in much the same fashion as the thiazolidinedione class of drugs; however, studies on the interaction of the PPARG variant with dietary components have not been performed.The spectacular rise in the incidence of diabetes among Pima Indians and other populations as they adopt Western diets and lifestyles dramatically demonstrates the key role of the environment [12].Consequently, it could be expected that the effect of a common gene variant among populations that have very different diets and exercise habits might be totally different, thus explaining some instances of lack of replication. [4].Another variable that influences the statistical and real association of an SNP with a disease or response to a diet is epigenetic interaction.Epigenesis is the study of heritable changes in gene function that occur without a change in the DNA sequence, such as DNA methylation and chromatin remodeling.Both mechanisms can affect gene expression by altering the accessibility of DNA to regulatory proteins or complexes such as transcription factors, and they can be influenced by certain nutrients and by overall caloric intake.Thus, it can be expected that long-term exposure to certain diets could produce permanent epigenetic changes in the genome [7]." + } + ] + }, + "data_source": [], + "document_id": "ADE056BF251274D15922CA6E7B5C3133", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "T2D&gene-environment&interactions", + "genetic", + "environmental", + "physical&activity", + "dietary&factors", + "GWAS", + "insulin&sensitivity", + "β-cell&dysfunction", + "PPARG", + "HNF1B" + ], + "metadata": [], + "question": "How do gene-environment interactions influence diabetes risk and progression?", + "subquestions": null, + "task_id": "ADE056BF251274D15922CA6E7B5C3133", + "usage": { + "chatgpt": 8576, + "gpt-4": 5471, + "gpt-4-turbo-preview": 4534 + }, + "user_id": 2 + }, + "document_id": "ADE056BF251274D15922CA6E7B5C3133", + "task_id": "ADE056BF251274D15922CA6E7B5C3133" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_09.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_09.json new file mode 100644 index 0000000..1bece07 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_09.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-04-18T18:40:22.873037Z", + "data": { + "amplify": false, + "answer": "Yes, genetic predictors of diabetes complications can be identified. Studies have revealed several susceptibility loci for diabetic complications such as retinopathy and nephropathy. However, these genetic factors only explain a small proportion of the phenotypic variation observed in type 2 diabetes patients, indicating a need for the identification of more novel genetic risk factors.", + "context": { + "069a62e0-e56a-46ab-9f93-c13a76a79989": [ + { + "document_id": "069a62e0-e56a-46ab-9f93-c13a76a79989", + "text": "\n\nResearchers are expanding our understanding of genetic risk factors for diabetes through ongoing discoveries.Genetic variants associated with increased susceptibility to type 2 diabetes, a disease that affects more than 200 million people worldwide, have been identified (NHGRI & NIDDK, 2007).Such discoveries accelerate efforts to understand genetic contributions to chronic illness, as well as facilitate greater investigation of how these genetic factors interact with each other and with lifestyle factors.Ultimately, once the association of these variants with diabetes are confirmed, genetic tests may be utilized to identify (even before escalating blood sugars) those individuals, like Vanessa, who may be able to delay or prevent diabetes with healthy lifestyle decisions and behaviors.Information to assist nurses in this challenge is available in a toolkit \"Your Game Plan for Preventing Type 2 Diabetes\" (Your Game Plan, n.d.).Would you have known whether or not genetic testing was available for Vanessa?If you had said no to this question but could have explained the progress currently being made in understanding diabetes, Vanessa would have had access to the best care possible today." + } + ], + "091ab13a-1b8a-4849-b698-48db7b1a948f": [ + { + "document_id": "091ab13a-1b8a-4849-b698-48db7b1a948f", + "text": "\n\nA considerable amount of work has focused on dissecting the genetics of diabetes itself; however, fewer studies have been conducted on the molecular mechanisms leading to its specific complications such as DR.To identify susceptibility loci that are associated with T2D retinopathy in Taiwanese population, we conducted a genome-wide association study involving 749 T2D cases (174 with retinopathy and 575 without retinopathy) and 100 nondiabetic controls and identified 12 previously unknown susceptibility loci related to DR." + } + ], + "0da4d3d4-10d5-4a58-9e50-c1fa0b414427": [ + { + "document_id": "0da4d3d4-10d5-4a58-9e50-c1fa0b414427", + "text": "\n\nProgress toward wider use of genetic testing in the prediction of type 2 diabetes and its complications will require three developments.The first involves identification of a growing number of risk variants that, collectively, deliver greater predictive and discriminative performance than the subset thus far known.The second involves understanding how genetic information can be combined with other conventional risk factors (and possibly with non-DNA-based biomarkers, as these emerge) to provide a more accurate assessment of individual risk.It should be kept in mind that susceptibility genotype information will not be orthogonal to those traditional factors, since several of them (such as ethnicity, family history, and BMI) capture overlapping genetic information.The third development will be evidence that imparting such information results in clinically meaningful differences in individual behavior or provides a more rational basis for therapeutic or preventative interventions." + } + ], + "277be46c-4307-4738-972d-eb6efd9b175a": [ + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + } + ], + "3548bb7f-727c-4ccb-acc7-a97553b89992": [ + { + "document_id": "3548bb7f-727c-4ccb-acc7-a97553b89992", + "text": "\n\nRecent advances in GWAS have substantially improved our understanding of the pathophysiology of diabetes, but the currently identified genetic susceptibility loci are insufficient to explain differences in diabetes risk across different ethnic groups or the rapid rise in diabetes prevalence over the past several decades.Clinical utility of these loci in predicting future risk of diabetes is also limited." + } + ], + "45cdaf79-d881-43e6-8555-ff47f04ae3d4": [ + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\n\nConclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\n\nStudies show evidence of considerable genetic component predisposing to diabetic complications, explaining even around 50% of the risk of proliferative retinopathy [11].In the last few decades, genetic research including genome-wide association studies (GWAS), linkage analysis, and candidate gene approach has revealed several susceptibility loci for diabetic retinopathy and nephropathy (VEGF, CAT , FTO, UCP1, and INSR), and also macrovascular complications (ADIPOQ).Nevertheless, they explain only a small proportion of the phenotypic variation observed in T2DM patients [12][13][14][15][16][17], justifying a need for identification of novel genetic risk factors for T2DM complications and improvement of knowledge about molecular mechanisms underlying these comorbid conditions." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "Methods:\n\nWe performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "\nBackground: Type 2 diabetes complications cause a serious emotional and economical burden to patients and healthcare systems globally.Management of both acute and chronic complications of diabetes, which dramatically impair the quality of patients' life, is still an unsolved issue in diabetes care, suggesting a need for early identification of individuals with high risk for developing diabetes complications. Methods:We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications. Results:The analysis revealed ten novel associations showing genome-wide significance, including rs1132787 (GYPA, OR = 2.71; 95% CI = 2.02-3.64)and diabetic neuropathy, rs2477088 (PDE4DIP, OR = 2.50; 95% CI = 1.87-3.34),rs4852954 (NAT8, OR = 2.27; 95% CI = 2.71-3.01),rs6032 (F5, OR = 2.12; 95% CI = 1.63-2.77),rs6935464 (RPS6KA2, OR = 2.25; 95% CI = 6.69-3.01)and macrovascular complications, rs3095447 (CCDC146, OR = 2.18; 95% CI = 1.66-2.87)and ophthalmic complications.By applying the targeted approach of previously reported susceptibility loci we managed to replicate three associations: MAPK14 (rs3761980, rs80028505) and diabetic neuropathy, APOL1 (rs136161) and diabetic nephropathy.Conclusions: Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions.Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations." + }, + { + "document_id": "45cdaf79-d881-43e6-8555-ff47f04ae3d4", + "text": "Discussion\n\nHere we present the results of the genome-wide association study for T2DM complications performed in a population of Latvia for the first time, revealing 10 susceptibility loci for T2DM complications, including diabetic neuropathy, macrovascular and ophthalmic complications.As in other reports aimed to identify the risk factors of T2DM complications [15,32], the control group of our study consisted of T2DM patients with no evidence of the complication type of interest instead of conventional healthy subjects, since the implementation of healthy controls would rather reveal genetic associations with the diagnosis of T2DM itself, not the T2DM complications." + } + ], + "50c72e55-b5fe-42a6-b837-64c28620a4c0": [ + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + } + ], + "80500e0d-0e39-4e46-bb60-8721f4f512c0": [ + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "text": "Conclusions\n\nAs compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes.The value of genetic factors increased with an increasing duration of follow-up." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + } + ], + "a7bad429-5f6a-464f-a666-f9cb1be60338": [ + { + "document_id": "a7bad429-5f6a-464f-a666-f9cb1be60338", + "text": "COMPLICATIONS\n\nIn addition to the genetic determinants of diabetes, several gene mutations and polymorphisms have been associated with the clinical complications of diabetes.The cumulative data on diabetes patients with a variety of micro-and macrovascular complications support the presence of strong genetic factors involved in the development of various complications [200] .A list of genes have been reported that are associated with diabetes complications including ACE and AKR1B1 in nephropathy, VEGF and AKRB1 in retinopathy and ADIPOQ and GLUL in cardiovascular diseases [200] ." + } + ], + "b666545f-6a53-45de-8562-55d88fc6f7ee": [ + { + "document_id": "b666545f-6a53-45de-8562-55d88fc6f7ee", + "text": "How do we identify the major 'culprits' at the implicated genome-wide association study loci? If population-based genetics, including genome-wide association studies, have allowed progress in the identification of Type 2 diabetes loci to be rapid over the past few years, progress towards determining which of the gene variants close to the implicated loci confer altered disease risk and how (at the molecular, cellular and whole body level) has lagged some way behind.Indeed, given the number of possible single nucleotide polymorphisms and genes, unravelling these questions represents a monumental challenge, requiring multiple, complementary approaches.Nonetheless, the rewards of success, in terms of new understanding of disease mechanisms and even the identification of new targets for therapeutic intervention, are likely to be great, potentially allowing the treatment of underlying disease aetiology in a personalized (stratified) manner." + } + ], + "cf022812-00a2-42ba-88fb-5c2014c86c43": [ + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "text": "\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + }, + { + "document_id": "cf022812-00a2-42ba-88fb-5c2014c86c43", + "text": "\n\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized." + } + ], + "eaca0f25-4a6b-4c0e-a6df-12e25060b169": [ + { + "document_id": "eaca0f25-4a6b-4c0e-a6df-12e25060b169", + "text": "\n\nConclusions and Future Directions GWAS and GWAS meta-analyses have by far been the most efficient way to identify new T2D genes (Figure 2), but their predictive value for future occurrence of T2D has been very limited compared to classic risk factors such as obesity and fasting glucose levels (Walford et al., 2014).Although it might be good news that our genome does not fully dictate our future, the knowledge of its specificities may help us to improve our health.Early genetic studies showed that the higher risk for T2D conferred by TCF7L2 variant can be reversed by lifestyle intervention (Florez et al., 2006), opening avenues for strategies targeted on genetically selected individuals with pre-diabetes.TCF7L2 has also been shown to be associated with a lower efficiency of oral sulfonylureas in newly diagnosed T2D patients (Pearson et al., 2007), but a more recent Danish study suggested that in contrast to clinical markers, all known T2D-associated variants do not significantly affect the time to prescription of the first drug after disease onset (Hornbak et al., 2014).In other words, frequent SNPs are not helpful to predict patients' futures, though the good use of genetic data may contribute to provide better care to newly diagnosed T2D patients who are currently all treated the same (with metformin)." + } + ], + "fa72cb33-e1e4-49ea-a72e-dd851225ee0b": [ + { + "document_id": "fa72cb33-e1e4-49ea-a72e-dd851225ee0b", + "text": "Background\n\nMultiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus.We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + } + ] + }, + "data_source": [], + "document_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&2&diabetes", + "genetic&predictors", + "diabetes&complications", + "GWAS", + "genome-wide&association&study", + "polygenic&score", + "susceptibility&loci", + "T2DM", + "genetic&variants", + "diabetic&neuropathy" + ], + "metadata": [], + "question": "Can we identify genetic predictors of diabetes complications?", + "subquestions": null, + "task_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "usage": { + "chatgpt": 6305, + "gpt-4": 3589, + "gpt-4-turbo-preview": 2668 + }, + "user_id": 2 + }, + "document_id": "0E3B1D23A525184EDA9AA62C618C9EC7", + "task_id": "0E3B1D23A525184EDA9AA62C618C9EC7" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_10.json b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_10.json new file mode 100644 index 0000000..fe3b2cf --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/diabetes/suga_resp_10.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-04-18T18:45:53.040778Z", + "data": { + "amplify": false, + "answer": "The varying efficacy of diabetes treatments among individuals can be attributed to genetic variants present in drug receptors or drug metabolizers, such as OCT genes, KCNJ11, ABCC8, and CYP2C9. These genetic variants can influence the metabolism, transportation, and therapeutic mechanisms of antidiabetic drugs, leading to differences in drug disposition, glycemic response, tolerability, and incidence of adverse effects. Additionally, gene-gene, gene-environment, and gene-treatment interactions may also contribute to the variation in disease progression and response to therapy.", + "context": { + "063a0254-1d1b-4caa-b782-6a1fe4ebca0d": [ + { + "document_id": "063a0254-1d1b-4caa-b782-6a1fe4ebca0d", + "text": "Genetics and pharmacogenomics\n\nWe are at the dawn of the age of pharmacogenomics and personalized medicine and ever closer to achieving the \"$1,000 genome. \"What does this mean for diabetes?Forward genetic approaches (i.e., starting from phenotype and identifying the genetic cause) to dissecting mendelian forms of diabetes have been hugely successful in identifying a small subset of diabetic patients in whom rare, highly penetrant mutations of a single gene cause their diabetes (13).While common variants of these genes that make a small contribution to polygenic diabetes may also exist (13), the variants causing monogenic diabetes have limited utility in pharmacogenetics due to their low allele frequency.The vast majority of type 2 diabetes patients have polygenetic forms of the disease that typically also require a permissive environment (e.g., obesity, sedentary lifestyle, advancing age, etc.) to be penetrant.Each locus contributes a small amount of risk (odds ratios typically ranging from 1.1- to 1.5-fold), so large cohorts are needed to identify the at-risk alleles.Some of the loci identified to date include transcription factor 7-like 2 (TCF7L2) (14), calpain 10 (CAPN10) (15), peroxisome proliferator-activated receptor γ (PPARG) (16), and potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) (17).However, the pace of gene identification is increasing due to the availability of large-scale databases of genetic variation and advances in genotyping technology.A recent genome-wide study identified solute carrier family 30, member 8 (SLC30A8), a β cell Zn transporter, and two other genomic regions as additional diabetes risk loci (18)." + } + ], + "08858a32-d736-4d8d-a135-f86568152a81": [ + { + "document_id": "08858a32-d736-4d8d-a135-f86568152a81", + "text": "\n\nWith further progress in unravelling the pathogenic roles of genes and epigenomic phenomena in type 2 diabetes, pharmacogenomic and pharmacoepigenomic studies might eventually yield treatment choices that can be personalised for individual patients." + } + ], + "183f165e-4d5c-4580-9aff-4e6b2e5a6463": [ + { + "document_id": "183f165e-4d5c-4580-9aff-4e6b2e5a6463", + "text": "Pharmacogenomics of Type 2 Diabetes\n\nWith the advent of GWAS, studies on the roles of inherited and acquired genetic variations in drug response have undergone an evolution from pharmacogenetics into pharmacogenomics, with a shift from the focus on individual candidate genes to GWAS [147].Clinically, it is often observed that even patients who receive similar antidiabetic regimens demonstrate large variability in drug disposition, glycemic response, tolerability, and incidence of adverse effects [148].This interindividual variability can be attributed to specific gene polymorphisms involved in the metabolism, transportation, and therapeutic mechanisms of oral antidiabetic drugs.Pharmacogenomics is on the agenda to explore feasible genetic testing to predict treatment outcome, so that appropriate steps could be taken to treat type 2 diabetes more efficiently." + } + ], + "277be46c-4307-4738-972d-eb6efd9b175a": [ + { + "document_id": "277be46c-4307-4738-972d-eb6efd9b175a", + "text": "Future directions\n\nDelays in identifying genetic variants that are robustly associated with differences in individual predisposition to the complications of diabetes, have constrained progress towards a mechanistic understanding of these conditions.Some approaches to overcome these limitations are outlined in Figure 4." + } + ], + "4d3330eb-acd0-4f72-aadf-b056d3c8b389": [ + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes." + }, + { + "document_id": "4d3330eb-acd0-4f72-aadf-b056d3c8b389", + "text": "Genetics & genomics of T2D\n\n• Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D.However, only close to 10% heritability is explained by these variants.Other genetic variants, particularly those which are rare but with significant effects need to be identified.• Genetic variability is responsible for the difference in response to antidiabetic drugs seen across individuals." + } + ], + "4feda561-1914-404d-9092-3c629d5251bd": [ + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nDiabetes progression is a multifactorial process; however, pharmacogenetics seems to play an important role in understanding the different phenotypes and progression rates among diabetic patients.Genetic variants associated with decreased effect of a certain drug might explain why some individuals are more likely to experience glycemic deterioration on a given treatment.In the following sections, different genetic variants and their impact on treatment efficacy and outcome will be addressed." + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nThe aim of this study was to summarize current knowledge and provide perspectives on the relationships between human genetic variants, type 2 diabetes, antidiabetic treatment, and disease progression.Type 2 diabetes is a complex disease with clear-cut diagnostic criteria and treatment guidelines.Yet, the interindividual response to therapy and slope of disease progression varies markedly among patients with type 2 diabetes.Gene-gene, gene-environment, and gene-treatment interactions may explain some of the variation in disease progression.Several genetic variants have been suggested to be associated with response to antidiabetic drugs.Some are present in drug receptors or drug metabolizers (OCT genes, KCNJ11, ABCC8, and CYP2C9).Numerous type 2 diabetes risk variants have been identified, but genetic risk score models applying these variants have failed to identify 'disease progressors' among patients with diabetes.Although genetic risk scores are based on a few known loci and only explain a fraction of the heritability of type 2 diabetes, it seems that the genes responsible for the development of diabetes may not be the same driving disease progression after the diagnosis has been made.Pharmacogenetic interactions explain some of the interindividual variation in responses to antidiabetic treatment and may provide the foundation for future genotype-based treatment standards.Pharmacogenetics and Genomics 25:475-484" + }, + { + "document_id": "4feda561-1914-404d-9092-3c629d5251bd", + "text": "\n\nTo date, a number of genetic variants have been identified to be associated with response to antidiabetic drugs.Of these, some variants are present in either drug receptors or drug metabolizers as for OCT genes, KCNJ11, ABCC8, and CYP2C9.Other variants are known T2D susceptibility variants such as TCF7L2.To identify variants of importance for antiglycemic drug response, GWAS in large cohorts of patients with diabetes with detailed measures of pharmacotherapy are lacking.The pharmacologic management of patients with diabetes often involves drug classes other than antidiabetics.Pharmacogenetic studies on statin and antihypertensive treatment have reported several genetic variants associated with treatment response and adverse drug reactions [101,102].It therefore seems natural to conclude that the future perspectives in pharmacogenetics is to conduct genetic studies in large cohorts with wellphenotyped individuals, thorough data collection on baseline treatment, concomitant treatment, adherence to therapy as well as data collection on comorbidity and additional disease diagnoses.These types of pharmacogenetic studies may provide unique opportunities for future genotype-based treatment standards and may help in delaying or changing the slope of disease progression among patients with T2D." + } + ], + "50c72e55-b5fe-42a6-b837-64c28620a4c0": [ + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "text": "\n\nGenetic determinants of diabetes and metabolic syndromes." + } + ], + "516de7be-3cef-47ee-8338-199fb922bc6f": [ + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "text": "\n\nThus, specific answers are lacking as to the genetic basis for type 2 diabetes.Still, speculations can be made about what eventually will be found.It is almost certain the genetic basis for type 2 diabetes and other common metabolic diseases will be extremely complex-that a predisposition for the disease will require several genetic hits as opposed to just one.Also, it is generally assumed there will be many susceptibility genes for type 2 diabetes, with enormous variability in different families and ethnic groups.Not known is whether there will be a common form of type 2 diabetes, with any one or even a few susceptibility genes accounting for a sizeable percentage of affected persons.As such, identifying diabetes genes will be slow and difficult." + } + ], + "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec": [ + { + "document_id": "5d1d5baa-75f4-42d5-8e4c-fb038a71bbec", + "text": "Ta rge ted T r e atmen t a nd Pr e v en t ion\n\n4][75] In monogenic forms of diabetes, at least, genetic testing already drives the choice of therapy.For example, in patients who have maturity-onset diabetes of the young due to mutations in the gene encoding glucokinase (GCK), the hyperglycemia is mild and stable, the risk of complications is low, and dietary management is often sufficient.In contrast, in patients who have maturity-onset diabetes of the young due to mutations in HNF1A, the disease follows a more aggressive course, with a greater risk of severe complications, but is particularly responsive to the hypoglycemic effects of sulfonylureas. 62,73Most children with neonatal diabetes have mutations in KCNJ11 or ABCC8, adjacent genes that jointly encode the beta-cell ATP-sensitive potassium channel that mediates glucose-stimulated insulin secretion and is the target of sulfonylureas.In such children, treatment with sulfonylureas has proved more effective and convenient than the lifelong insulin therapy previously considered the default option. 74,75n children with severe obesity due to profound leptin deficiency, exogenous leptin therapy is lifesaving. 76s yet, there are insufficient genetic data to support management decisions for common forms of type 2 diabetes and obesity. 77Although the TCF7L2 genotype is associated with variation in the response to sulfonylurea treatment, 78 the effect is too modest to guide the care of individual patients.For the time being, the contribution of genetic information to therapy is most likely to come through the drug-discovery pipeline.Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physiological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention), 32 and to characterize high-risk groups to enable more efficient clinical trials of agents designed to reduce the progression of type 2 diabetes or obesity or the risk of complications." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Type 2 Diabetes\n\nWhile a subset of genetic variants are linked to both type 1 and type 2 diabetes (42,43), the two diseases have a largely distinct genetic basis, which could be leveraged toward classification of diabetes (44).Genome-wide association studies have identified more than 130 genetic variants associated with type 2 diabetes, glucose levels, or insulin levels; however, these variants explain less than 15% of disease heritability (45)(46)(47).There are many possibilities for explaining the majority of type 2 diabetes heritability, including disease heterogeneity, gene-gene interactions, and epigenetics.Most type 2 variants are in noncoding genomic regions.Some variants, such as those in KCNQ1, show strong parent-of-origin effects (48).It is possible that children of mothers carrying KCNQ1 are born with a reduced functional b-cell mass and thereby are less able to increase their insulin secretion when exposed to insulin resistance (49).Another area of particular interest has been the search for rare variants protecting from type 2 diabetes, such as loss-of-function mutations in SLC30A8 (50), which could offer potential new drug targets for type 2 diabetes." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nAfter consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY.The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications.The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes.These types of information are necessary to develop the tools to predict response to-and side effects of-therapeutic approaches for diabetes in patient populations." + } + ], + "ad88aed6-75ba-469d-b96b-7be4a65be8fc": [ + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "text": "\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 × 10 −8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF ≤ 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total ∼88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes." + } + ], + "b00b9753-c198-4f8a-a8b9-dd5e94dc5896": [ + { + "document_id": "b00b9753-c198-4f8a-a8b9-dd5e94dc5896", + "text": "\n\nTogether, the findings from these studies were among the first to demonstrate that the genetic etiology of hyperglycemia may modulate response to hypoglycemia agents.Such results yielded strong implications for patient management and paved the way toward elucidating additional genetic factors that might influence drug response in the treatment of T2D." + } + ], + "c8c58fdf-06e3-4da4-a920-d5bcbcd18289": [ + { + "document_id": "c8c58fdf-06e3-4da4-a920-d5bcbcd18289", + "text": "A\n\nnumber of studies have implicated a genetic basis for type 2 diabetes (1).The discovery of monogenic forms of the disease underscored the phenotypic and genotypic heterogeneity, although monogenic forms account for only a few percent of the disease (1).Defining the genetic basis of the far more common polygenic form of the disease presents more difficulties (2,3).Nevertheless, some interesting results have recently emerged.A genome scan of Hispanic-American families (330 affected sib-pairs [ASPs]) found linkage to chromosome 2q37 (logarithm of odds [LOD] 4.15) (4), and the causative gene has been recently reported (5).A number of other genome scans in various racial groups have identified other putative susceptibility loci (6 -8).The largest genome-wide scan for type 2 diabetes loci reported to date studied 477 Finnish families (716 ASPs) and found evidence for linkage to chromosome 20q12-13.1(LOD 2.06 at D20S107) (9).Interestingly, similar results have been reported by at least three other groups (10 -12)." + } + ], + "f7072d9b-4e07-4541-bac7-13a25761f460": [ + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "text": "\n\nBecause more than one genetic mutation contributes to T1D, the differences that occur between individuals of different backgrounds (for instance, race and locality) may need to be considered in the design of treatments.Personalized medicine is about the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or in their response to a specific treatment (Blau and Liakopoulou, 2013;Timmeman, 2013).This will allow for a more accurate diagnosis per individual, and design of specific treatment plans including gene therapy." + } + ], + "fcf8fb37-20cf-491c-96f8-04a5621812a2": [ + { + "document_id": "fcf8fb37-20cf-491c-96f8-04a5621812a2", + "text": "\n\nGenetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?" + } + ] + }, + "data_source": [], + "document_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "type&2&diabetes", + "pharmacogenetics", + "pharmacogenomics", + "GWAS", + "genetic&variants", + "OCT&genes", + "KCNJ11", + "ABCC8", + "CYP2C9", + "TCF7L2" + ], + "metadata": [], + "question": "What are the genetic bases for the varying efficacy of diabetes treatments among individuals?", + "subquestions": null, + "task_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "usage": { + "chatgpt": 7037, + "gpt-4": 4436, + "gpt-4-turbo-preview": 3522 + }, + "user_id": 2 + }, + "document_id": "C4C12C6896F2957844079BC4AFF8FF4B", + "task_id": "C4C12C6896F2957844079BC4AFF8FF4B" +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/responses/general/answer_relevancy.json b/gnqa/paper1_eval/src/data/responses/general/answer_relevancy.json new file mode 100644 index 0000000..11a09d1 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/answer_relevancy.json @@ -0,0 +1,7 @@ +A1: Air pollution is the most cited environmental factor for the onset of asthma. + +The given answer is: "Air pollution is the most cited environmental factor for the onset of asthma." + +Question: What is the most cited environmental factor for the onset of asthma? + +The answer is committal as it provides a specific and direct response to the question. Therefore, the noncommittal value is 0. diff --git a/gnqa/paper1_eval/src/data/responses/general/answer_relevancy.md b/gnqa/paper1_eval/src/data/responses/general/answer_relevancy.md new file mode 100644 index 0000000..d0001a5 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/answer_relevancy.md @@ -0,0 +1,111 @@ +# Answer Relevancy + +## 1 + +### Prompt 1 +Generate 3 questions from the answer [Air pollution is the most cited environmental factor for the onset of asthma.] + +#### Generated questions + +1. What is the most cited environmental factor for the onset of asthma? +1. Which factor is most commonly linked to the development of asthma? +1. What is the leading cause of asthma in relation to the environment? + +### Prompt 2 + +Generate a question for the given answer and Identify if answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal and 0 if the answer is committal. A noncommittal answer is one that is evasive, vague, or ambiguous. For example, "I don't know" or "I'm not sure" are noncommittal answers. The given answer is [Air pollution is the most cited environmental factor for the onset of asthma.] + + +### Response +The given answer is: "Air pollution is the most cited environmental factor for the onset of asthma." + +Question: What is the most cited environmental factor for the onset of asthma? + +The answer is committal as it provides a specific and direct response to the question. Therefore, the noncommittal value is 0. + +# 2 + +### Prompt +Generate a question for the given answer and Identify if answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal and 0 if the answer is committal. A noncommittal answer is one that is evasive, vague, or ambiguous. For example, "I don't know" or "I'm not sure" are noncommittal answers. The given answer is [Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.] + +### Response + +## 3 + +### Prompt + +Generate a question for the given answer and Identify if answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal and 0 if the answer is committal. A noncommittal answer is one that is evasive, vague, or ambiguous. For example, "I don't know" or "I'm not sure" are noncommittal answers. The given answer is [] + +### Response + + +## 4 + +### Prompt + +Generate a question for the given answer and Identify if answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal and 0 if the answer is committal. A noncommittal answer is one that is evasive, vague, or ambiguous. For example, "I don't know" or "I'm not sure" are noncommittal answers. The given answer is [] + +### Response + + +## 5 + +### Prompt + +Generate a question for the given answer and Identify if answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal and 0 if the answer is committal. A noncommittal answer is one that is evasive, vague, or ambiguous. For example, "I don't know" or "I'm not sure" are noncommittal answers. The given answer is [] + +### Response + + + +## 6 + +### Prompt + +Generate a question for the given answer and Identify if answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal and 0 if the answer is committal. A noncommittal answer is one that is evasive, vague, or ambiguous. For example, "I don't know" or "I'm not sure" are noncommittal answers. The given answer is [] + +### Response + + + +## 7 + +### Prompt + +Generate a question for the given answer and Identify if answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal and 0 if the answer is committal. A noncommittal answer is one that is evasive, vague, or ambiguous. For example, "I don't know" or "I'm not sure" are noncommittal answers. The given answer is [] + +### Response + + + + + +## 8 + +### Prompt + +Generate a question for the given answer and Identify if answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal and 0 if the answer is committal. A noncommittal answer is one that is evasive, vague, or ambiguous. For example, "I don't know" or "I'm not sure" are noncommittal answers. The given answer is [] + +### Response + + + +## 9 + +### Prompt + +Generate a question for the given answer and Identify if answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal and 0 if the answer is committal. A noncommittal answer is one that is evasive, vague, or ambiguous. For example, "I don't know" or "I'm not sure" are noncommittal answers. The given answer is [] + +### Response + + + +## 10 + +### Prompt + +Generate a question for the given answer and Identify if answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal and 0 if the answer is committal. A noncommittal answer is one that is evasive, vague, or ambiguous. For example, "I don't know" or "I'm not sure" are noncommittal answers. The given answer is [] + +### Response + + diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/01.json b/gnqa/paper1_eval/src/data/responses/general/experts/01.json new file mode 100644 index 0000000..d8912cf --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/01.json @@ -0,0 +1,408 @@ +{ + "created_at": "2024-05-31T13:08:08.864950Z", + "data": { + "amplify": false, + "answer": "To determine which gene in your QTL is causal for the trait, you can follow several steps. First, narrow down the QTL interval to a reasonable size. Then, sort through the genes within the interval to identify potential Quantitative Trait Genes (QTGs). This can be challenging as more than one gene may be involved and the function of some genes may be unknown. You can use techniques like LCMS to establish causal relationships between DNA loci and transcripts. Once potential QTGs are identified, you need to prove that a particular gene is involved in the quantitative trait under study. This can be done by complementation of a QTL, which can be achieved in several ways. You can also use mediation analysis to identify the causal gene between the genetic variant and the trait-of-interest. Finally, investigate whether the expression of these genes correlates with the phenotype(s) of interest. If the gene expression strongly correlates with the QTL phenotype, this further strengthens the assumption that this gene might be causal.", + "context": { + "07d409f4-91f9-4701-bb39-1421845a6321": [ + { + "document_id": "07d409f4-91f9-4701-bb39-1421845a6321", + "text": "Prior belief or knowledge about the number of true causal and true\nindependent links that might be expected in a typical QTL, depending on the study\ndesign, should be considered to safeguard against high false-positive rates (low\npositive predictive values). In studies that involve mapping gene expression (eQTL),\nprotein (pQTL) or metabolite (mQTL) traits, information about co-localization of\nQTL and genes that are functionally linked to the trait provides information about\nthe likelihood of causal links." + } + ], + "1a041a89-4da8-4ad5-b241-da36df917930": [ + { + "document_id": "1a041a89-4da8-4ad5-b241-da36df917930", + "text": "\n\nThe next step is to investigate whether the expression of these genes correlates with the phenotype(s) of interest.This would suggest a chain of causality: a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest.To do this, we created a correlation matrix between all genes within a QTL with a cis-eQTL in any brain tissue as well as the phenotypes that contributed to the QTL (Supplementary Table S6).Any gene with a cis-eQTL and a significantly correlated expression was considered a good candidate.If the gene only had a cis-eQTL and correlation in a single brain region, then it suggested that this brain region might also be of interest for the phenotype (adding another link to this chain)." + } + ], + "33814fad-d831-46f5-b41f-ff31626a82ca": [ + { + "document_id": "33814fad-d831-46f5-b41f-ff31626a82ca", + "text": "One possible approach to facilitate this endeavor is to identify quantitative trait loci\n(QTL) that contribute to the phenotype and consequently unravel the candidate\ngenes within these loci. Each proposed candidate locus contains multiple genes and,\ntherefore, further analysis is required to choose plausible candidate genes. One of\nsuch methods is to use comparative genomics in order to narrow down the QTL to a\nregion containing only a few genes. We illustrate this strategy by applying it to\ngenetic findings regarding physical activity (PA) in mice and human." + } + ], + "4049da4d-c7cf-4e30-9a21-c77609fad23d": [ + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "text": "Network analyses\nWe now have two QTL, and we have picked potentially interesting genes within each, but now\nwe want to build up more evidence for which gene in our QTL interval is causal. The first, and\nmost obvious way, is to see what genes our trait of interest correlates with, in tissues that we\nexpect to be related to the trait. We calculated the Spearman’s correlation between the trait\nBXD_17850 and all probes with expression data in T helper cells (GN319)." + } + ], + "47c12133-5a30-45b9-bcb8-b96f00737f31": [ + { + "document_id": "47c12133-5a30-45b9-bcb8-b96f00737f31", + "text": "Another\napproach to help to determine if a gene located near the mapped QTL would\nhave effects to influence the quantitative trait will be to use genetically engineered mice to determine if altering the expression of a candidate gene will alter\nthe phenotype of interest (38). However, it is possible that a quantitative trait is\na combined effect of multiple genes located near the QTL (39)." + } + ], + "547ce63b-5178-45cb-ae07-12ae66aa2967": [ + { + "document_id": "547ce63b-5178-45cb-ae07-12ae66aa2967", + "text": "With a known QTL and a\nbody of evidence suggesting possible roles for the affected gene,\nphenotypes can be predicted that may be modulated as a result\nof this sequence variation. If this phenotype is of interest, it\ncan be directly measured and a traditional ‘forward’ QTL analysis carried out to confirm the prediction. Such an approach is\nextremely attractive when the enormous cost and time required\nfor phenotyping a large panel is considered." + } + ], + "581f83bc-3521-4cb3-ad3c-d905a90ecc29": [ + { + "document_id": "581f83bc-3521-4cb3-ad3c-d905a90ecc29", + "text": "The first\nstep is to narrow down the list of\ncandidate causal genes within a\nFig\n1. Interval\nmapping\nof\noviduct\ngross\npathology\nacross\nthe\nBXD\nstrains\n\nQuantitative Trait Locus (QTL)—a\nreveals\na\nQTL\non\ndistal\nChr\n3. The\nL RS\nvalues\nare\nplotted\nin\nblue\nacross\nthe\n\nchromosomal region containing\ngenome\nand\nmeasure\nthe\nstrength\nof\nthe\nassociation\nbetween\n\nsequence variants strongly\nchromosome\nand\nMb\nposition\n(top\nand\nbottom\nX-‐axis,\nrespectively)\nand\n\nassociated with phenotypic\nphenotype\nexpression. Allele\ncontribution\nis\nshown\nby\nthe\nred\n(C57BL/6J)\n\nand\ngreen\n(DBA/2J)\nlines. Red\nand\ngrey\nhorizontal\nlines\nindicate\ngenome-‐\nvariation." + } + ], + "5a56fa6d-9e77-4b95-a836-04d0fa31ee2c": [ + { + "document_id": "5a56fa6d-9e77-4b95-a836-04d0fa31ee2c", + "text": "A special case is the\ncorrelation of the target phenotype with the expression of the\npriorized gene(s) (RNA or protein amounts). This refers to\ncolocalization of the QTL of the target phenotype with the\neQTL position. Correlation can also be examined between the\ntarget QTL phenotype and expression of all genes in the QTL\ninterval. If the gene expression strongly correlates with the\nQTL phenotype, this further strengthens the assumption that\nthis gene might be causal (see Note 12). For performing a correlation analysis:\n–\n\nGo to the Trait Overview Page, as described in step 3, point\n1." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "QTL mapping of traits in mouse cohorts often ends up with a genetic locus, composed of a list of candidate\ngenes. Several studies proposed the use of mediation analysis to identify the causal gene (mediator) between\nthe genetic variant (independent variable) and the trait-of-interest (dependent variable) (Figure 1.4B) [7, 47,\n61, 77]. Mediation analysis can be used either on gene expression levels to identify the regulatory mechanisms\n[7, 47, 61], or on phenotypic traits to discover the potential causal drivers contributing to the phenotypic\nvariances [77] (Figure 1.4C upper)." + } + ], + "7a451204-390c-4ff2-8a1d-b4de62b73503": [ + { + "document_id": "7a451204-390c-4ff2-8a1d-b4de62b73503", + "text": "1a). Second-generation offspring are then\nphenotyped and genotyped, and linkage analysis is carried out to identify a region that is\nassociated with the trait1. This approach has led to the identification of thousands of quantitative trait loci (QTLs) for\nvarious phenotypes and diseases. However, each QTL region is large, often tens of\nmegabases, and contains hundreds of genes. The process of identifying the causal variant\nand the gene involved is therefore difficult and costly. Of the thousands of QTLs identified,\nonly a small fraction of genes has been identified. NIH-PA Author Manuscript\n\n© 2012 Macmillan Publishers Limited." + } + ], + "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d": [ + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "text": "Network analyses\nWe now have two QTL, and we have picked potentially interesting genes within each, but now\nwe want to build up more evidence for which gene in our QTL interval is causal. The first, and\nmost obvious way, is to see what genes our trait of interest correlates with, in tissues that we\nexpect to be related to the trait. We calculated the Spearman’s correlation between the trait\nBXD_17850 and all probes with expression data in T helper cells (GN319)." + } + ], + "7d866915-9d92-4401-8340-ffdef457debe": [ + { + "document_id": "7d866915-9d92-4401-8340-ffdef457debe", + "text": "10 JUNE 2016 • VOL 352 ISSUE 6291\n\naad0189-5\nR ES E A RC H | R E S EA R C H A R T I C LE\n\nSolving QTLs: Finding the quantitative\ntrait gene\nFor cis-QTLs, the causal factors can be quickly\nidentified: With few exceptions, they will be driven by variants within the gene itself or immediately adjacent. For trans-QTLs, mQTLs, and\ncQTLs, the identification of the causal quantitative trait gene (QTG) is challenging due to the\nwidth of the QTLs." + } + ], + "95b99c09-c336-44fd-b378-f41991edb3aa": [ + { + "document_id": "95b99c09-c336-44fd-b378-f41991edb3aa", + "text": "Once the QTL interval is reduced to a reasonable size,\nthe next step in the process involves sorting through the\ngenes within the interval and attempting to determine\nwhich is the QTG. This step is daunting because more than\none gene may be involved and the function of some genes\nwithin the interval may be unknown. Until recently, this\nstep emphasized the detection of polymorphisms within\ncoding sequence (reviewed in Korstanje and Paigen, 2002\nand Glazier et al. 2002); for a polymorphism that produces\nan amino acid substitution, one can often infer and then\ntest for a functional consequence." + } + ], + "abea3dd4-9492-4a2b-8904-b8052e384785": [ + { + "document_id": "abea3dd4-9492-4a2b-8904-b8052e384785", + "text": "To understand the genetic networks that underlie\nquantitative variation in the trait, it is also very important to\ndiscover genes whose expression is correlated with the trait\nafter accounting for the known effects of the QTL on the\ntrait. Many of these genes may have expression that is\nassociated with QTL genotype, and would therefore be\nidentified as important via the tests described above. Other\n\ngenes, however, may have expression values that are correlated with the trait but unassociated with genotype at the\nQTL." + }, + { + "document_id": "abea3dd4-9492-4a2b-8904-b8052e384785", + "text": "The\napproach is motivated by the fact that a research project is\noften focused on a specific classical quantitative trait. If a\nmajor QTL for this classical trait has been identified, it is\noften desirable to test whether this QTL is also associated\nwith the transcription level of any genes, which will provide clues as to which genes belong to the pathway that the\nQTL uses to modulate the classical trait." + } + ], + "d1f04d58-2589-4183-aee4-569820dae052": [ + { + "document_id": "d1f04d58-2589-4183-aee4-569820dae052", + "text": "Confirmation of Candidate Genes\nThe next step is to prove that a particular gene is involved in the quantitative trait\nunder study. This is done by complementation of a QTL, which can be achieved in\nseveral ways (9–11,40). In principle, transgenic complementation is the most straightforward. This approach has been used successfully to demonstrate that Pla2g2a was\nthe correct candidate gene for Mom1, a modifier of the apcmin allele that causes\nadenomatous polyposis coli (41)." + } + ], + "da485354-fcdc-49b8-9a41-0f673610156a": [ + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "So, how do you go about planning and performing a QTL study, and how\ndo you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbred\nstrains that have a markedly different trait. One can now look up many different\ntraits of inbred mice online at the Mouse Phenome Database (http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). However, the trait you may\nwant to study may not be present in wild type mice, so you may want to cross\na mutant (or genetically engineered) strain onto several inbred strains." + } + ], + "f041550e-5f2d-430e-8f46-15ebea6ca496": [ + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "text": "Along with correlations, this tool also derives new traits representing the\nprincipal components (Figure 2d). The user can add these principal components to their Trait\nCollection and proceed to perform QTL mapping, as in the case of a single trait QTL\nmapping. The R/QTL (Broman et al. 2003) and R/CAPE (Tyler et al. 2013) packages can be\nused for deeper analysis of epistasis and pleiotropy for multiple traits and multiple\nregulatory loci. Prioritizing Candidate Genes\n7\n\nAuthor Manuscript\n\nFollowing the identification of a significant QTL, focus shifts to identifying the\nparticular gene(s) that cause the QTL." + } + ], + "f4e26cf0-d214-41bf-b392-9c63a903b0b8": [ + { + "document_id": "f4e26cf0-d214-41bf-b392-9c63a903b0b8", + "text": "The investigators\nfirst identified all QTLs associated with\na classical phenotype and then winnowed the list of potentially associated\ngene-expression traits on the basis of\ntheir correlation or eQTL overlap with\nthe phenotype of interest. Candidate\ngenes then were ranked by applying\n\nthe LCMS technique, which uses the\neQTL data to establish causal relationships between DNA loci and transcripts as well as between transcripts\nand phenotypes and finally identifies\na model that best fits the data." + } + ], + "ff35f4c8-b78b-4dad-9aa8-1bb16479872d": [ + { + "document_id": "ff35f4c8-b78b-4dad-9aa8-1bb16479872d", + "text": "The goal of QTL mapping is clearly the\nidentification and eventual confirmation of candidate genes\n(QTGs) underlying the phenotype. The evidence required for\nsuch confirmation has engendered much discussion (Complex\nTrait Consortium 2003; Glazier et al. 2002) and is likely to vary\ndepending on the nature of the trait and specific resources\navailable to pin down underlying genes (e.g. availability of\nknock-in or knock-outs, specific antibodies, siRNA, etc.). The\npaucity of QTGs meeting such multifaceted standards is testament to the difficulty of narrowing the confidence interval\nsufficiently to identify and test suitable candidate genes (Flint\net al." + } + ] + }, + "data_source": [ + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "section_type": "main", + "text": "So, how do you go about planning and performing a QTL study, and how\ndo you identify the responsible gene within a QTL that you have identified?\n Generally, one starts by performing a strain survey to find two parental inbred\nstrains that have a markedly different trait. One can now look up many different\ntraits of inbred mice online at the Mouse Phenome Database (http://phenome.\n jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). However, the trait you may\nwant to study may not be present in wild type mice, so you may want to cross\na mutant (or genetically engineered) strain onto several inbred strains." + }, + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "section_type": "main", + "text": "Network analyses\nWe now have two QTL, and we have picked potentially interesting genes within each, but now\nwe want to build up more evidence for which gene in our QTL interval is causal. The first, and\nmost obvious way, is to see what genes our trait of interest correlates with, in tissues that we\nexpect to be related to the trait. We calculated the Spearman’s correlation between the trait\nBXD_17850 and all probes with expression data in T helper cells (GN319)." + }, + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "section_type": "main", + "text": "Network analyses\nWe now have two QTL, and we have picked potentially interesting genes within each, but now\nwe want to build up more evidence for which gene in our QTL interval is causal. The first, and\nmost obvious way, is to see what genes our trait of interest correlates with, in tissues that we\nexpect to be related to the trait. We calculated the Spearman’s correlation between the trait\nBXD_17850 and all probes with expression data in T helper cells (GN319)." + }, + { + "document_id": "47c12133-5a30-45b9-bcb8-b96f00737f31", + "section_type": "main", + "text": "Another\napproach to help to determine if a gene located near the mapped QTL would\nhave effects to influence the quantitative trait will be to use genetically engineered mice to determine if altering the expression of a candidate gene will alter\nthe phenotype of interest (38). However, it is possible that a quantitative trait is\na combined effect of multiple genes located near the QTL (39)." + }, + { + "document_id": "7a451204-390c-4ff2-8a1d-b4de62b73503", + "section_type": "main", + "text": "1a). Second-generation offspring are then\nphenotyped and genotyped, and linkage analysis is carried out to identify a region that is\nassociated with the trait1.\n This approach has led to the identification of thousands of quantitative trait loci (QTLs) for\nvarious phenotypes and diseases. However, each QTL region is large, often tens of\nmegabases, and contains hundreds of genes. The process of identifying the causal variant\nand the gene involved is therefore difficult and costly. Of the thousands of QTLs identified,\nonly a small fraction of genes has been identified.\n\n NIH-PA Author Manuscript\n\n© 2012 Macmillan Publishers Limited." + }, + { + "document_id": "7d866915-9d92-4401-8340-ffdef457debe", + "section_type": "main", + "text": "10 JUNE 2016 • VOL 352 ISSUE 6291\n\naad0189-5\nR ES E A RC H | R E S EA R C H A R T I C LE\n\nSolving QTLs: Finding the quantitative\ntrait gene\nFor cis-QTLs, the causal factors can be quickly\nidentified: With few exceptions, they will be driven by variants within the gene itself or immediately adjacent. For trans-QTLs, mQTLs, and\ncQTLs, the identification of the causal quantitative trait gene (QTG) is challenging due to the\nwidth of the QTLs." + }, + { + "document_id": "f4e26cf0-d214-41bf-b392-9c63a903b0b8", + "section_type": "main", + "text": "The investigators\nfirst identified all QTLs associated with\na classical phenotype and then winnowed the list of potentially associated\ngene-expression traits on the basis of\ntheir correlation or eQTL overlap with\nthe phenotype of interest. Candidate\ngenes then were ranked by applying\n\nthe LCMS technique, which uses the\neQTL data to establish causal relationships between DNA loci and transcripts as well as between transcripts\nand phenotypes and finally identifies\na model that best fits the data." + }, + { + "document_id": "95b99c09-c336-44fd-b378-f41991edb3aa", + "section_type": "main", + "text": "Once the QTL interval is reduced to a reasonable size,\nthe next step in the process involves sorting through the\ngenes within the interval and attempting to determine\nwhich is the QTG. This step is daunting because more than\none gene may be involved and the function of some genes\nwithin the interval may be unknown. Until recently, this\nstep emphasized the detection of polymorphisms within\ncoding sequence (reviewed in Korstanje and Paigen, 2002\nand Glazier et al. 2002); for a polymorphism that produces\nan amino acid substitution, one can often infer and then\ntest for a functional consequence." + }, + { + "document_id": "abea3dd4-9492-4a2b-8904-b8052e384785", + "section_type": "main", + "text": "To understand the genetic networks that underlie\nquantitative variation in the trait, it is also very important to\ndiscover genes whose expression is correlated with the trait\nafter accounting for the known effects of the QTL on the\ntrait. Many of these genes may have expression that is\nassociated with QTL genotype, and would therefore be\nidentified as important via the tests described above. Other\n\ngenes, however, may have expression values that are correlated with the trait but unassociated with genotype at the\nQTL." + }, + { + "document_id": "d1f04d58-2589-4183-aee4-569820dae052", + "section_type": "main", + "text": "Confirmation of Candidate Genes\nThe next step is to prove that a particular gene is involved in the quantitative trait\nunder study. This is done by complementation of a QTL, which can be achieved in\nseveral ways (9–11,40). In principle, transgenic complementation is the most straightforward. This approach has been used successfully to demonstrate that Pla2g2a was\nthe correct candidate gene for Mom1, a modifier of the apcmin allele that causes\nadenomatous polyposis coli (41)." + }, + { + "document_id": "547ce63b-5178-45cb-ae07-12ae66aa2967", + "section_type": "main", + "text": "With a known QTL and a\nbody of evidence suggesting possible roles for the affected gene,\nphenotypes can be predicted that may be modulated as a result\nof this sequence variation. If this phenotype is of interest, it\ncan be directly measured and a traditional ‘forward’ QTL analysis carried out to confirm the prediction. Such an approach is\nextremely attractive when the enormous cost and time required\nfor phenotyping a large panel is considered." + }, + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "section_type": "main", + "text": "QTL mapping of traits in mouse cohorts often ends up with a genetic locus, composed of a list of candidate\ngenes. Several studies proposed the use of mediation analysis to identify the causal gene (mediator) between\nthe genetic variant (independent variable) and the trait-of-interest (dependent variable) (Figure 1.4B) [7, 47,\n61, 77]. Mediation analysis can be used either on gene expression levels to identify the regulatory mechanisms\n[7, 47, 61], or on phenotypic traits to discover the potential causal drivers contributing to the phenotypic\nvariances [77] (Figure 1.4C upper)." + }, + { + "document_id": "581f83bc-3521-4cb3-ad3c-d905a90ecc29", + "section_type": "main", + "text": "The first\nstep is to narrow down the list of\ncandidate causal genes within a\nFig\n1.\n Interval\nmapping\nof\noviduct\ngross\npathology\nacross\nthe\nBXD\nstrains\n\nQuantitative Trait Locus (QTL)—a\nreveals\na\nQTL\non\ndistal\nChr\n3.\n The\nL RS\nvalues\nare\nplotted\nin\nblue\nacross\nthe\n\nchromosomal region containing\ngenome\nand\nmeasure\nthe\nstrength\nof\nthe\nassociation\nbetween\n\nsequence variants strongly\nchromosome\nand\nMb\nposition\n(top\nand\nbottom\nX-‐axis,\nrespectively)\nand\n\nassociated with phenotypic\nphenotype\nexpression.\n Allele\ncontribution\nis\nshown\nby\nthe\nred\n(C57BL/6J)\n\nand\ngreen\n(DBA/2J)\nlines.\n Red\nand\ngrey\nhorizontal\nlines\nindicate\ngenome-‐\nvariation." + }, + { + "document_id": "1a041a89-4da8-4ad5-b241-da36df917930", + "section_type": "main", + "text": "\n\nThe next step is to investigate whether the expression of these genes correlates with the phenotype(s) of interest.This would suggest a chain of causality: a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest.To do this, we created a correlation matrix between all genes within a QTL with a cis-eQTL in any brain tissue as well as the phenotypes that contributed to the QTL (Supplementary Table S6).Any gene with a cis-eQTL and a significantly correlated expression was considered a good candidate.If the gene only had a cis-eQTL and correlation in a single brain region, then it suggested that this brain region might also be of interest for the phenotype (adding another link to this chain)." + }, + { + "document_id": "ff35f4c8-b78b-4dad-9aa8-1bb16479872d", + "section_type": "main", + "text": "The goal of QTL mapping is clearly the\nidentification and eventual confirmation of candidate genes\n(QTGs) underlying the phenotype. The evidence required for\nsuch confirmation has engendered much discussion (Complex\nTrait Consortium 2003; Glazier et al. 2002) and is likely to vary\ndepending on the nature of the trait and specific resources\navailable to pin down underlying genes (e.g. availability of\nknock-in or knock-outs, specific antibodies, siRNA, etc.). The\npaucity of QTGs meeting such multifaceted standards is testament to the difficulty of narrowing the confidence interval\nsufficiently to identify and test suitable candidate genes (Flint\net al." + }, + { + "document_id": "abea3dd4-9492-4a2b-8904-b8052e384785", + "section_type": "main", + "text": "The\napproach is motivated by the fact that a research project is\noften focused on a specific classical quantitative trait. If a\nmajor QTL for this classical trait has been identified, it is\noften desirable to test whether this QTL is also associated\nwith the transcription level of any genes, which will provide clues as to which genes belong to the pathway that the\nQTL uses to modulate the classical trait." + }, + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "section_type": "main", + "text": "Along with correlations, this tool also derives new traits representing the\nprincipal components (Figure 2d). The user can add these principal components to their Trait\nCollection and proceed to perform QTL mapping, as in the case of a single trait QTL\nmapping. The R/QTL (Broman et al. 2003) and R/CAPE (Tyler et al. 2013) packages can be\nused for deeper analysis of epistasis and pleiotropy for multiple traits and multiple\nregulatory loci.\n Prioritizing Candidate Genes\n7\n\nAuthor Manuscript\n\nFollowing the identification of a significant QTL, focus shifts to identifying the\nparticular gene(s) that cause the QTL." + }, + { + "document_id": "cb3f9967-9762-4a9b-96cb-0acccdc316d2", + "section_type": "main", + "text": "Quantitative trait loci (QTLs) can be identified in several ways, but is\nthere a definitive test of whether a candidate locus actually corresponds to a specific QTL?\n\n NIH-PA Author Manuscript\n\nMuch of the genetic variation that underlies disease susceptibility and morphology is complex\nand is governed by loci that have quantitative effects on the phenotype. Gene-gene and geneenvironment interactions are common and make these loci difficult to analyse. Here, we present\na community’s view on the steps that are necessary to identify genetic loci that govern\nquantitative traits, along with a set of interpretive guidelines." + }, + { + "document_id": "47c12133-5a30-45b9-bcb8-b96f00737f31", + "section_type": "main", + "text":"Thus, simply\naltering one gene may not necessarily provide a comprehensive link of the\ncandidate genes with the quantitative trait, and in some cases, a false-positive\nresult may even be obtained using the QTL analysis approach. Ideally, one\nFig. 8. Quantitative trait locus (QTL) Marker regression analysis. (A) Marker regression report provides the loci in the BXD data set that show associations with the entered\nthymic involution G1 values from BXD RI strains of mice. All loci listed in this report\nexhibited an LRS value that is greater than the suggestive linkage value." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "section_type": "main", + "text": "One can apply the method of quantitative trait locus (QTL) mapping\nto identify the chromosomal region (locus) of a gene, or genes, that have\nan effect on a trait. This mapping is the first step in the identification of the\nresponsible gene by a method that is referred to as positional cloning. In this\nchapter, the focus will be on the use of QTL mapping to identify genes for\ncomplex traits in mice; although, QTL mapping can be applied to any experimental system in which there is meiotic recombination and different inbred\nstrains are available." + }, + { + "document_id": "07d409f4-91f9-4701-bb39-1421845a6321", + "section_type": "main", + "text": "Prior belief or knowledge about the number of true causal and true\nindependent links that might be expected in a typical QTL, depending on the study\ndesign, should be considered to safeguard against high false-positive rates (low\npositive predictive values). In studies that involve mapping gene expression (eQTL),\nprotein (pQTL) or metabolite (mQTL) traits, information about co-localization of\nQTL and genes that are functionally linked to the trait provides information about\nthe likelihood of causal links." + }, + { + "document_id": "b3e8c6d4-fc8b-4a1c-b6d8-7c0252101571", + "section_type": "main", + "text": "Often, the first step in analysis of new trait\ndata is single-marker regression across all chromosomes. A hypothetical QTL is evaluated at\nthe location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott,\n1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill\nand Doerge, 1994)." + }, + { + "document_id": "2c6178fe-c05a-42e6-aafb-7408592dcc50", + "section_type": "main", + "text": "Often, the first step in analysis of new trait\ndata is single-marker regression across all chromosomes. A hypothetical QTL is evaluated at\nthe location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott,\n1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill\nand Doerge, 1994)." + }, + { + "document_id": "9a882703-e0ff-4bac-b11a-d99284bf7f6c", + "section_type": "main", + "text": "Often, the first step in analysis of new trait\ndata is single-marker regression across all chromosomes. A hypothetical QTL is evaluated at\nthe location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott,\n1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill\nand Doerge, 1994)." + }, + { + "document_id": "8b4276be-c77e-4e80-a5bb-54e9ff75d2ba", + "section_type": "main", + "text": "QTL mapping requires a few essential steps: initially, the trait must be measured\nin the parental (or progenitor) inbred strains that were used to create the GRP that will be\nused for the study before culminating studies in the RILs (i.e. BXD mice). Since the\nindividuals in GRP have polymorphic genes (i.e. genes that exist in multiple forms), there\nis a high potential for distinctive strains to exhibit differences in phenotype. Once a\ndifferential phenotype is established in the parents and the RILs, the next step is to\ndetermine the heritability of the variation in the trait being measured." + }, + { + "document_id": "33814fad-d831-46f5-b41f-ff31626a82ca", + "section_type": "main", + "text": "One possible approach to facilitate this endeavor is to identify quantitative trait loci\n(QTL) that contribute to the phenotype and consequently unravel the candidate\ngenes within these loci. Each proposed candidate locus contains multiple genes and,\ntherefore, further analysis is required to choose plausible candidate genes. One of\nsuch methods is to use comparative genomics in order to narrow down the QTL to a\nregion containing only a few genes. We illustrate this strategy by applying it to\ngenetic findings regarding physical activity (PA) in mice and human." + }, + { + "document_id": "d1f04d58-2589-4183-aee4-569820dae052", + "section_type": "main", + "text": "This would be acceptable evidence that\na particular gene is indeed responsible for the quantitative trait. Further confirmation\nof the QTL can be achieved by quantitative complementation, where the effect of a\nQTL is assessed in the context of a deficient allele of a candidate gene on the same\ngenetic background.\n Gene identification of QTL should be distinguished from identification of the quantitative trait nucleotide (QTN). The latter is a daunting task, since SNPs are so frequent." + }, + { + "document_id": "d3b364c4-bdd3-4c7c-8b3f-e27bd3460c37", + "section_type": "main", + "text": "For each of the QTL intervals, there are often three or\nmore candidate genes (e.g. , Cyrba4, genes labeled gene X and\ngene Y in Figure 12). It is therefore necessary to evaluate the\nrelative merits of candidates." + }, + { + "document_id": "c2efeeee-f71a-4292-8240-80a4518f820d", + "section_type": "main", + "text": "The method uses two pieces of information: mapping data from crosses that\ninvolve more than two inbred strains and sequence variants in the progenitor strains within the interval\ncontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that any\nsequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals except\nat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis." + }, + { + "document_id": "0950746d-90b5-484d-853d-70026e85c9ce", + "section_type": "main", + "text": "Some of this analysis software is available on the\nWebQTL Web site (http://www.genenetwork.org/home). While\nthe authors of these initial studies generated their own expression data, data for other experiments are becoming increasingly\navailable in expression databases such as NCBI GEO (http://\nwww.ncbi.nlm.nih.gov/geo/). This approach is a powerful one\nand is likely to become a common one to use for QTL studies.\n\n Causative gene identification\nOnce strong candidates are identified, it is crucial to test them." + }, + { + "document_id": "624ba3ed-0965-4451-a5e1-2150b68ae1b3", + "section_type": "main", + "text": "Some of this analysis software is available on the\nWebQTL Web site (http://www.genenetwork.org/home). While\nthe authors of these initial studies generated their own expression data, data for other experiments are becoming increasingly\navailable in expression databases such as NCBI GEO (http://\nwww.ncbi.nlm.nih.gov/geo/). This approach is a powerful one\nand is likely to become a common one to use for QTL studies.\n\n Causative gene identification\nOnce strong candidates are identified, it is crucial to test them." + }, + { + "document_id": "a64778cd-bff8-43dd-b5a3-d608ab8f4828", + "section_type": "main", + "text": "The method uses two pieces of information: mapping data from crosses that\ninvolve more than two inbred strains and sequence variants in the progenitor strains within the interval\ncontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that any\nsequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals except\nat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis." + }, + { + "document_id": "1a041a89-4da8-4ad5-b241-da36df917930", + "section_type": "main", + "text": "Candidate Causal Genes within Novel QTL\n\nWe concentrated on a subset of six novel QTL that contained less than 100 genes.These QTLs are more amenable to finding plausible candidate genes using bioinformatic methods.After reducing the likelihood of finding false positives, these large QTLs are more likely to be due to two or more variants in different genes both contributing to the phenotype.The advantage of families of isogenic strains of mice, such as the BXD, is that more strains could be phenotyped, reducing the size of these QTL regions and allowing for greater precision.S4)" + }, + { + "document_id": "eb90c74a-60f0-4485-b1b9-bb6665469828", + "section_type": "main", + "text": "A major goal is to identify which,\namong a set of candidate genes, are the most likely regulators of trait variation. These\nmethods are applied in an effort to identify multiple-QTL regulatory models for large\ngroups of genetically co-expressed genes, and to extrapolate the consequences of this\ngenetic variation on phenotypes observed across levels of biological scale through the\nevaluation of vertex coverage. This approach is furthermore applied to definitions of\nhomology-based gene sets, and the incorporation of categorical data such as known\ngene pathways." + }, + { + "document_id": "d8993417-3a27-4000-b693-6cb4662b9f80", + "section_type": "main", + "text": "This is useful, since it clearly shows that a variant in the eQTL region has a regulatory effect.\n Therefore, genes with a cis-eQTL are interesting candidate genes.\n The next step is to investigate whether the expression of these genes correlates with the\nphenotype(s) of interest. This would suggest a chain of causality: a variant within a gene\ncauses a change in its expression, and the expression of that gene correlates with expression\nof a phenotypic trait of interest." + }, + { + "document_id": "d0deb53b-7286-4fd0-9188-b7b9f366fd76", + "section_type": "main", + "text": "This is useful, since it clearly shows that a variant in the eQTL region has a regulatory effect.\n Therefore, genes with a cis-eQTL are interesting candidate genes.\n The next step is to investigate whether the expression of these genes correlates with the\nphenotype(s) of interest. This would suggest a chain of causality: a variant within a gene\ncauses a change in its expression, and the expression of that gene correlates with expression\nof a phenotypic trait of interest." + }, + { + "document_id": "835a094d-9c2b-4686-8725-d3c4123175b0", + "section_type": "main", + "text": "This poses a serious challenge, and\nto date, only a small handful of genes have been definitively identified for complex traits.\n Our own efforts to identify a causal gene were stymied by the compound nature of QTLs\nand the high gene density in Qrr1, and in Vol8a. Furthermore, it is now becoming clear\nthat in addition to the canonical candidate genes, there are multiple spliced variants,\nmicroRNAs, and epigenetic factors to be considered.\n With what appears to be an increasingly complex genomic landscape, it is now all\nthe more necessary to apply the multipronged approach taken by systems genetics." + }, + { + "document_id": "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce", + "section_type": "main", + "text":"These candidate genes are then sequenced in the two parental inbred\nstrains looking for sequence di¡erences in coding or regulatory regions.\n After ¢ne mapping the QTL interval and shortening the list of plausible\ncandidate polymorphisms, the major challenge remains proving de¢nitively\nwhich nucleotide polymorphism underlies the QTL. The most direct proof\nwould be replacing one strain’s allele with another strain’s allele (creating a\nFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mouse\ngenerations are depicted." + }, + { + "document_id": "f253e087-e030-40a8-8400-3b6bf50c1fd6", + "section_type": "main", + "text":"These candidate genes are then sequenced in the two parental inbred\nstrains looking for sequence di¡erences in coding or regulatory regions.\n After ¢ne mapping the QTL interval and shortening the list of plausible\ncandidate polymorphisms, the major challenge remains proving de¢nitively\nwhich nucleotide polymorphism underlies the QTL. The most direct proof\nwould be replacing one strain’s allele with another strain’s allele (creating a\nFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mouse\ngenerations are depicted." + }, + { + "document_id": "5a56fa6d-9e77-4b95-a836-04d0fa31ee2c", + "section_type": "main", + "text": "A special case is the\ncorrelation of the target phenotype with the expression of the\npriorized gene(s) (RNA or protein amounts). This refers to\ncolocalization of the QTL of the target phenotype with the\neQTL position. Correlation can also be examined between the\ntarget QTL phenotype and expression of all genes in the QTL\ninterval. If the gene expression strongly correlates with the\nQTL phenotype, this further strengthens the assumption that\nthis gene might be causal (see Note 12).\n For performing a correlation analysis:\n–\n\nGo to the Trait Overview Page, as described in step 3, point\n1." + } + ], + "document_id": "EFB8B9EF07428DA8D36EFCB6B06F9161", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "QTL", + "gene", + "trait", + "phenotype", + "eQTL", + "expression", + "cis-eQTL", + "quantitative&trait&locus", + "QTG", + "correlation" + ], + "metadata": [ + { + "object": "Transient overexpression of WRKY79 in protoplasts results in up-regulation of Gene:542165, Gene:541974, Gene:100274033, Gene:542688, Gene:542150, Gene:542151, Gene:100273457, Gene:100285509, Gene:103626248, Gene:103646045, Gene:100217270, Gene:100279981, Gene:100281950, Gene:542476, Gene:542369, Gene:100281950, and Gene:542260.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab969966" + }, + { + "object": "DNA sequencing demonstrated that in the absence of ectopic PAF53 expression, cells demonstrated unique means of surviving; including recombination or the utilization of alternative reading frames. We never observed a clone in which one PAF53 gene is expressed, unless there was also ectopic expression In the absence of ectopic gene expression, the gene products of both endogenous genes were expressed, irrespective of wheth", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab236437" + }, + { + "object": "SF3B2 is a critical determinant of AR-V7 expression and is correlated with aggressive cancer phenotypes. Pladienolide B, an inhibitor of a splicing modulator of the SF3b complex, suppressed the growth of tumors addicted to high SF3B2 expression. SF3B2 is a critical determinant of RNA splicing and gene expression patterns and controls the expression of key genes associated with CRPC progression, such as AR-V7.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab702217" + }, + { + "object": "These tumor samples express CD44 protein at low rather than high levels. There is no correlation between CLDN3 gene expression and protein expression in these CPTAC samples; hence, the claudin-low subtype defined by gene expression is not the same group of tumors as that defined by low expression of CLDN3 protein.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab928122" + }, + { + "object": "expression studies revealed inverse correlation of KLF1, BCL11A reduced with gamma-globin gene expression increased in patients showing KLF1 gene mutations, thus indicating the role of KLF1 gene in regulating the gamma-globin gene expression.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab278866" + }, + { + "object": "During early zebrafish embryonic development, p63 binds to enhancers associated to neural plate-expressing genes, where it limits Sox3 binding and neural gene expression. p63 binds enhancers associated to epidermis-expressing genes when they are in a non-accessible chromatin state, leading to its opening and epidermal gene expression.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab243624" + }, + { + "object": "Study observed elevated EA2 gene expression in the subcutaneous compared to that in the visceral human adipose tissue. EA2 gene expression negatively correlated with adiponectin and chemerin in visceral adipose tissue, and positively correlated with TNF-alpha in subcutaneous adipose tissue. EA2 gene expression was significantly downregulated during differentiation of preadipocytes in vitro.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab745216" + }, + { + "object": "Study indicate that the observed level of FHIT promoter methylation was not enough to suppress gene expression in non-small cell lung cancer NSCLC. Lack of negative correlation between FHIT expression and methylation, or positive correlation between gene expression and immunoexpression suggest the role of another molecular mechanisms regulating FHIT expression on mRNA and protein levels in NSCLC patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab744476" + }, + { + "object": "Correlation analyses showed that 5hmC enrichment in gene body is positively associated with gene expression level in mouse kidney. Moreover, ischemia reperfusion IR injury-associated genes both up- and down-regulated genes during renal IR injury in mouse kidney exhibit significantly higher 5hmC enrichment in their gene body regions when compared to those un-changed genes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab157853" + }, + { + "object": "LAG-3 expression was correlated with expression of PD-1 on TILs and expression of PD-L1 on tumor cells. Higher expression of LAG-3 on TILs was significantly correlated with higher expression of PD-1 on TILs and higher expression of PD-L1 on tumor cells.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab444259" + } + ], + "question": "How do I determine which gene in my QTL is causal for the trait?", + "subquestions": null, + "task_id": "EFB8B9EF07428DA8D36EFCB6B06F9161", + "usage": { + "chatgpt": 4765, + "gpt-4": 3573, + "gpt-4-turbo-preview": 2599 + }, + "user_id": 2 + }, + "document_id": "EFB8B9EF07428DA8D36EFCB6B06F9161", + "task_id": "EFB8B9EF07428DA8D36EFCB6B06F9161" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/02.json b/gnqa/paper1_eval/src/data/responses/general/experts/02.json new file mode 100644 index 0000000..ba248fa --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/02.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-06-01T14:20:28.998327Z", + "data": { + "amplify": false, + "answer": "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.", + "context": { + "1ac23aae-2a51-4846-81ec-6db70ab6e5a4": [ + { + "document_id": "1ac23aae-2a51-4846-81ec-6db70ab6e5a4", + "text": "\n\nY chromosome in peripheral blood cells increases with age in men (6) and is correlated with increased risk of cancer mortality and Alzheimer's disease (6,7).X chromosome mosaicism in women also increases with age (8), as does autosomal mosaicism in both sexes (9,10).Recent studies have shown that the prevalence of age-related mosaic abnormalities is greater in men than women (9,10); however, mechanisms underlying the sex differences observed in chromosomal mosaicism in humans are unknown." + } + ], + "20b466c6-004b-484f-96a1-c1b4651bc856": [ + { + "document_id": "20b466c6-004b-484f-96a1-c1b4651bc856", + "text": "\n\nRecent reports suggested a role of Y chromosome loss in risk for all-cause mortality and common age-related disease such as cancer, Alzheimer disease as well as severe atherosclerosis [12][13][14][15][16][17][18][19][20].Building on such reports, we aimed to evaluate the contribution of male Y chromosome mosaicism to the risk for late-stage AMD." + } + ], + "3f72832b-fad9-4d38-aed8-d22e5bd12a22": [ + { + "document_id": "3f72832b-fad9-4d38-aed8-d22e5bd12a22", + "text": "Box 1. Sex-specific cytonuclear interactions\n\nSeveral predictions about the nature of cytonuclear conflicts follow from the patterns of chromosomal inheritance (Table I).In a mated pair of animals, mtDNA is co-transmitted with half of the autosomal genes, two-thirds of the X-linked genes and none of the Y-linked genes [76].This predicts that, relative to the autosomal case, positive nuclear-mitochondrial interactions are more likely to evolve for X-linked loci whereas deleterious interactions between Y-linked genes and mtDNA should accumulate (or cannot be purged efficiently)." + } + ], + "4ad6da14-56a3-48ab-a587-42761ceac238": [ + { + "document_id": "4ad6da14-56a3-48ab-a587-42761ceac238", + "text": "\n\nIn addition to genetic data, the 9p Network Cohort dataset also lists the gender for all 719 individuals.Of these individuals, 406 individuals are female and 313 are male, indicating a female bias (Binomial test p ¼ 0.0006).This result was surprising considering that no female bias has been previously reported in 9p deletion and duplication syndromes.A possible explanation for the significant bias in the 9p Network Cohort dataset is the XY sex reversal phenotype, which is commonly observed in individuals with 9p deletion syndrome.This phenotype could lead to individuals with XY sex chromosomes being listed in the dataset as having a female gender.To further examine this hypothesis, we subset our dataset to include only the 236 individuals whose sex chromosomes are listed in their genetic information.For this much smaller subset, 125 individuals had female sex chromosomes and 111 had male sex chromosomes, indicating no significant sex bias (Binomial test p ¼ 0.4).We also found no significant gender bias in this group (Binomial test p ¼ 0.2), although we did confirm that four of the individuals with XY sex chromosomes had a gender of female.This comparison suggests that the XY sex reversal phenotype may be responsible for a female gender bias, but not a sex bias, in 9p deletion and duplication syndrome cohorts." + } + ], + "6910b508-6d25-4804-9e47-3590b57aa061": [ + { + "document_id": "6910b508-6d25-4804-9e47-3590b57aa061", + "text": "\n\nDuplicated variants with multiple alternative alleles and variants in sex chromosomes X and Y" + } + ], + "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa": [ + { + "document_id": "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa", + "text": "\n\nAutosome-One of the numbered, or nonsex, chromosomes (1 through 22).X and Y are the sex chromosomes." + } + ], + "7d451e79-b698-4744-aeb2-ff319f430d96": [ + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "text": "\n\nGiven such a high abundance of young male-biased genes, we asked whether their parental genes are also male-biased.We found that fewer parental genes of X-linked male-biased duplicates were also male-biased (20%, 2/10) compared to the parental genes of autosomal young male-biased duplicates (32%, 12/37).These data, despite the small sample sizes and being statistically not significant, may suggest that compared to autosomal young genes, X-linked young genes more often evolved novel male-biased expression.However, as the majority of young genes are the result of intrachromosomal duplication events, the pattern might also reflect the fact that X-linked old genes are less likely to be male-biased." + }, + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "text": "\n\nA slight excess of X-linked female-biased genes was also detected (Fig. 2).Although most of them are old, a few recently arose on the X chromosome over 4 to 6 Myr in the common ancestor of the D. melanogaster and D. simulans clade (branch 5).This can be interpreted in the context of the dominance model of the sexual antagonism hypothesis.In this case, a dominant, X-linked gene that is favorable to females but disadvantageous for males can become fixed.The slow accumulation of female-biased genes in the X reflects an overall low rate of female gene origination, either due to a small dominance effect (the degree of dominance h!1/2), or a minor disadvantageous effect on males (the ratio of fitness effects of male relative to female k!0) along with a favorable effect on females (Vicoso and Charlesworth 2006, Equation 10)." + }, + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "text": "\n\nRegarding the second step in the evolution of male-biased genes, namely X!A transposition, sexual antagonism favorable for autosomal fixation (Vicoso and Charlesworth 2006) and/or MSCI (Lifschytz and Lindsley 1972;Betran et al. 2002) may play a role in this process.On the other hand, the within-chromosomal duplication rate is higher than the between-chromosomal duplication rate (Emerson et al. 2008), which may contribute to the slow pace of X!A transposition." + }, + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "text": "\n\nIt has been observed that male-biased genes in Drosophila are overrepresented on autosomes (Parisi et al. 2003;Ranz et al. 2003).Consistent with this result, a dynamic process that can explain the nonrandom autosomal distribution has also been observed, in which autosomal new genes with X-linked parental genes are often male-biased.Specifically, a significant excess of autosomal testisexpressed retrogenes were identified as RNA-duplicates of X-linked parental genes (Betran et al. 2002).Recently, similar X!A gene traffic was observed in the DNA-level duplication and relocation data set of the Drosophila genus (Vibranovski et al. 2009b), and was further confirmed for DNA-level duplications in the D. pseudoobscura neo-X chromosome (Meisel et al. 2009).In addition, selective extinction of neo-X linked male-biased genes also occurred in D. pseudoobscura (Sturgill et al. 2007).These three lines of genome-wide investigation support a common pattern of outof-X traffic for male-biased genes, resulting in an enrichment of these genes on autosomes in the long term." + }, + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "text": "\n\nIt has been reported that the initial manifestations of new gene emergence, namely polymorphic duplicates, occur at a lower frequency on the X chromosome, thus indicating that these duplicates are subject to stronger purifying selection (Emerson et al. 2008).Therefore, the excessive fixation of X-linked duplicates might not occur via neutral processes.Positive selection could have facilitated the fixation of X-linked young genes in addition to driving their subsequent sequence evolution." + } + ], + "96cb840e-747f-4849-8354-e8764aa0a1ce": [ + { + "document_id": "96cb840e-747f-4849-8354-e8764aa0a1ce", + "text": "\n\nOccasionally, Y chromosome DNA is detected in the maternal plasma, and the fetus appears to have female genitalia on sonographic examination.The underlying mechanisms for this include a twin demise, a maternal disorder of sexual differentiation, such as Swyer syndrome, or that the mother has undergone a bone marrow or solid organ transplant from a male donor (Bianchi, 2018;Hartwig, Ambye, Sorensen, & Jorgensen, 2017)." + } + ], + "9a5c3e73-8270-400f-8a2d-4f36b757188c": [ + { + "document_id": "9a5c3e73-8270-400f-8a2d-4f36b757188c", + "text": "Because\nof the differences in sex chromosome number, the sexunmatched comparison contains internal controls, i.e. ,\nin this comparison, genes on the X-chromosome and\nY-chromosome (but not those on the autosomes) should\nshow copy number imbalances reflective of a single copy\nchange. We showed that the sample that is not sexmatched had readily detectable differences in aCGH\nsignals for genes on the X and Y chromosomes. No such\npatterns were evident for the autosomes of the sex\nunmatched individuals or for the sex chromosomes of the\nsex matched samples." + } + ], + "af3d7cd3-40ec-4a86-a473-89f83da250e4": [ + { + "document_id": "af3d7cd3-40ec-4a86-a473-89f83da250e4", + "text": "Sex chromosome:\n\nThe X or Y chromosome in human beings that determines the sex of an individual.Females have two X chromosomes in diploid cells; males have an X and a Y chromosome.The sex chromosomes comprise the 23rd chromosome pair in a karyotype.See also: autosome Sex-linked: Traits or diseases associated with the X or Y chromosome; generally seen in males." + }, + { + "document_id": "af3d7cd3-40ec-4a86-a473-89f83da250e4", + "text": "\n\nX chromosome: One of the two sex chromosomes, X and Y. See also: Y chromosome, sex chromosome Y chromosome: One of the two sex chromosomes, X and Y. See also; X chromosome, sex chromosome" + } + ], + "b04f2221-de28-4c4b-893e-9da982ff864c": [ + { + "document_id": "b04f2221-de28-4c4b-893e-9da982ff864c", + "text": "The male heterogamety (XY) is the most\ncommon reported system, but many species\nhave female heterogamety (ZW), and more\noccasionally, multiple chromosome systems\n\n(Almeida-Toledo and Foresti, 2001; Devlin\nand Nagahama, 2002; Penman and Piferrer,\n2008). Given the low resolution of optical microscopy to differentiate sex chromosomes in\nfish, researchers have looked for an alternative\nin the tenfold longer meiotic chromosomes to\ndetect mispairing tracts at the synaptonemal\ncomplex as an indication of the sex differentiated region with variable success." + }, + { + "document_id": "b04f2221-de28-4c4b-893e-9da982ff864c", + "text": "The exclusive female\nconstitution of gynogenetic genomes provides\ninformation on the SD system, especially in a\nXX/XY system, where all female progenies are\nexpected. If ZZ/ZW is the underlying system,\nmale offspring always will be present, but the\ninterpretation is more complex and will depend\non the distance of the SD region to centromere\nand on the viability of WW offspring (Devlin\nand Nagahama, 2002; Penman and Piferrer,\n2008). Induced triploids, on the other hand, are\nconstituted by the combination of two female\nand one male genomes (Piferrer et al." + } + ], + "ef2c8463-5169-46aa-938b-7d04ea8da6b7": [ + { + "document_id": "ef2c8463-5169-46aa-938b-7d04ea8da6b7", + "text": "\n\nThe existence of a maternally silenced X-linked imprinted locus playing a role in social cognition could explain why males (X m Y) are more vulnerable to disorders of social cognition such as autism spectrum disorders than are females (X m X p ).The absence of the expression of this gene would not lead to autism itself, but would eliminate a putative protective factor, making an individual more susceptible to the effects of other ASD-predisposing genetic mutations or environmental factors." + } + ], + "f051ad23-572d-4302-8dda-4d992aeaeb1a": [ + { + "document_id": "f051ad23-572d-4302-8dda-4d992aeaeb1a", + "text": "\n\nWhen meiosis takes place, a pair of chromosomes may fail to separate properly, creating a sperm or egg that has either two copies or no copy of a specific chromosome.This is a sporadic event and it is called nondisjunction.Nondisjunction can lead to an extra chromosome, called trisomy, or a missing chromosome, called monosomy (GHR, 2008l).Down syndrome is an example of trisomy.Individuals who have Down syndrome have an extra chromosome number 21. Turner syndrome is an example of monosomy.Girls who have Turner syndrome have only one X chromosome.This causes them to have short stature and be infertile (NHGRI, 2008l)." + }, + { + "document_id": "f051ad23-572d-4302-8dda-4d992aeaeb1a", + "text": "X-Linked Inheritance\n\nX-linked genetic disorders (also called sex-linked) are caused by gene mutations on the X chromosome.Most often X-linked genetic disorders are seen in males.Males inherit the X chromosome from their mother and the Y chromosome from their father.Because males have only one X chromosome, if they inherit a gene mutation on the X chromosome from their mother, they will have the disorder.Examples of X-linked genetic disorders occurring in males include hemophilia and Duchenne muscular dystrophy (GHR, 2008o)." + } + ] + }, + "data_source": [ + { + "document_id": "af3d7cd3-40ec-4a86-a473-89f83da250e4", + "section_type": "main", + "text": "Sex chromosome:\n\nThe X or Y chromosome in human beings that determines the sex of an individual.Females have two X chromosomes in diploid cells; males have an X and a Y chromosome.The sex chromosomes comprise the 23rd chromosome pair in a karyotype.See also: autosome Sex-linked: Traits or diseases associated with the X or Y chromosome; generally seen in males." + }, + { + "document_id": "b04f2221-de28-4c4b-893e-9da982ff864c", + "section_type": "main", + "text": "The male heterogamety (XY) is the most\ncommon reported system, but many species\nhave female heterogamety (ZW), and more\noccasionally, multiple chromosome systems\n\n(Almeida-Toledo and Foresti, 2001; Devlin\nand Nagahama, 2002; Penman and Piferrer,\n2008). Given the low resolution of optical microscopy to differentiate sex chromosomes in\nfish, researchers have looked for an alternative\nin the tenfold longer meiotic chromosomes to\ndetect mispairing tracts at the synaptonemal\ncomplex as an indication of the sex differentiated region with variable success." + }, + { + "document_id": "af3d7cd3-40ec-4a86-a473-89f83da250e4", + "section_type": "main", + "text": "\n\nX chromosome: One of the two sex chromosomes, X and Y. See also: Y chromosome, sex chromosome Y chromosome: One of the two sex chromosomes, X and Y. See also; X chromosome, sex chromosome" + }, + { + "document_id": "96cb840e-747f-4849-8354-e8764aa0a1ce", + "section_type": "main", + "text": "\n\nOccasionally, Y chromosome DNA is detected in the maternal plasma, and the fetus appears to have female genitalia on sonographic examination.The underlying mechanisms for this include a twin demise, a maternal disorder of sexual differentiation, such as Swyer syndrome, or that the mother has undergone a bone marrow or solid organ transplant from a male donor (Bianchi, 2018;Hartwig, Ambye, Sorensen, & Jorgensen, 2017)." + }, + { + "document_id": "9a5c3e73-8270-400f-8a2d-4f36b757188c", + "section_type": "main", + "text": "Because\nof the differences in sex chromosome number, the sexunmatched comparison contains internal controls, i.e. ,\nin this comparison, genes on the X-chromosome and\nY-chromosome (but not those on the autosomes) should\nshow copy number imbalances reflective of a single copy\nchange. We showed that the sample that is not sexmatched had readily detectable differences in aCGH\nsignals for genes on the X and Y chromosomes. No such\npatterns were evident for the autosomes of the sex\nunmatched individuals or for the sex chromosomes of the\nsex matched samples." + }, + { + "document_id": "b04f2221-de28-4c4b-893e-9da982ff864c", + "section_type": "main", + "text": "The exclusive female\nconstitution of gynogenetic genomes provides\ninformation on the SD system, especially in a\nXX/XY system, where all female progenies are\nexpected. If ZZ/ZW is the underlying system,\nmale offspring always will be present, but the\ninterpretation is more complex and will depend\non the distance of the SD region to centromere\nand on the viability of WW offspring (Devlin\nand Nagahama, 2002; Penman and Piferrer,\n2008). Induced triploids, on the other hand, are\nconstituted by the combination of two female\nand one male genomes (Piferrer et al." + }, + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "section_type": "main", + "text": "\n\nGiven such a high abundance of young male-biased genes, we asked whether their parental genes are also male-biased.We found that fewer parental genes of X-linked male-biased duplicates were also male-biased (20%, 2/10) compared to the parental genes of autosomal young male-biased duplicates (32%, 12/37).These data, despite the small sample sizes and being statistically not significant, may suggest that compared to autosomal young genes, X-linked young genes more often evolved novel male-biased expression.However, as the majority of young genes are the result of intrachromosomal duplication events, the pattern might also reflect the fact that X-linked old genes are less likely to be male-biased." + }, + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "section_type": "main", + "text": "\n\nA slight excess of X-linked female-biased genes was also detected (Fig. 2).Although most of them are old, a few recently arose on the X chromosome over 4 to 6 Myr in the common ancestor of the D. melanogaster and D. simulans clade (branch 5).This can be interpreted in the context of the dominance model of the sexual antagonism hypothesis.In this case, a dominant, X-linked gene that is favorable to females but disadvantageous for males can become fixed.The slow accumulation of female-biased genes in the X reflects an overall low rate of female gene origination, either due to a small dominance effect (the degree of dominance h!1/2), or a minor disadvantageous effect on males (the ratio of fitness effects of male relative to female k!0) along with a favorable effect on females (Vicoso and Charlesworth 2006, Equation 10)." + }, + { + "document_id": "4ad6da14-56a3-48ab-a587-42761ceac238", + "section_type": "main", + "text": "\n\nIn addition to genetic data, the 9p Network Cohort dataset also lists the gender for all 719 individuals.Of these individuals, 406 individuals are female and 313 are male, indicating a female bias (Binomial test p ¼ 0.0006).This result was surprising considering that no female bias has been previously reported in 9p deletion and duplication syndromes.A possible explanation for the significant bias in the 9p Network Cohort dataset is the XY sex reversal phenotype, which is commonly observed in individuals with 9p deletion syndrome.This phenotype could lead to individuals with XY sex chromosomes being listed in the dataset as having a female gender.To further examine this hypothesis, we subset our dataset to include only the 236 individuals whose sex chromosomes are listed in their genetic information.For this much smaller subset, 125 individuals had female sex chromosomes and 111 had male sex chromosomes, indicating no significant sex bias (Binomial test p ¼ 0.4).We also found no significant gender bias in this group (Binomial test p ¼ 0.2), although we did confirm that four of the individuals with XY sex chromosomes had a gender of female.This comparison suggests that the XY sex reversal phenotype may be responsible for a female gender bias, but not a sex bias, in 9p deletion and duplication syndrome cohorts." + }, + { + "document_id": "6910b508-6d25-4804-9e47-3590b57aa061", + "section_type": "main", + "text": "\n\nDuplicated variants with multiple alternative alleles and variants in sex chromosomes X and Y" + }, + { + "document_id": "1ac23aae-2a51-4846-81ec-6db70ab6e5a4", + "section_type": "main", + "text": "\n\nY chromosome in peripheral blood cells increases with age in men (6) and is correlated with increased risk of cancer mortality and Alzheimer's disease (6,7).X chromosome mosaicism in women also increases with age (8), as does autosomal mosaicism in both sexes (9,10).Recent studies have shown that the prevalence of age-related mosaic abnormalities is greater in men than women (9,10); however, mechanisms underlying the sex differences observed in chromosomal mosaicism in humans are unknown." + }, + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "section_type": "main", + "text": "\n\nRegarding the second step in the evolution of male-biased genes, namely X!A transposition, sexual antagonism favorable for autosomal fixation (Vicoso and Charlesworth 2006) and/or MSCI (Lifschytz and Lindsley 1972;Betran et al. 2002) may play a role in this process.On the other hand, the within-chromosomal duplication rate is higher than the between-chromosomal duplication rate (Emerson et al. 2008), which may contribute to the slow pace of X!A transposition." + }, + { + "document_id": "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa", + "section_type": "main", + "text": "\n\nAutosome-One of the numbered, or nonsex, chromosomes (1 through 22).X and Y are the sex chromosomes." + }, + { + "document_id": "20b466c6-004b-484f-96a1-c1b4651bc856", + "section_type": "main", + "text": "\n\nRecent reports suggested a role of Y chromosome loss in risk for all-cause mortality and common age-related disease such as cancer, Alzheimer disease as well as severe atherosclerosis [12][13][14][15][16][17][18][19][20].Building on such reports, we aimed to evaluate the contribution of male Y chromosome mosaicism to the risk for late-stage AMD." + }, + { + "document_id": "f051ad23-572d-4302-8dda-4d992aeaeb1a", + "section_type": "main", + "text": "\n\nWhen meiosis takes place, a pair of chromosomes may fail to separate properly, creating a sperm or egg that has either two copies or no copy of a specific chromosome.This is a sporadic event and it is called nondisjunction.Nondisjunction can lead to an extra chromosome, called trisomy, or a missing chromosome, called monosomy (GHR, 2008l).Down syndrome is an example of trisomy.Individuals who have Down syndrome have an extra chromosome number 21. Turner syndrome is an example of monosomy.Girls who have Turner syndrome have only one X chromosome.This causes them to have short stature and be infertile (NHGRI, 2008l)." + }, + { + "document_id": "ef2c8463-5169-46aa-938b-7d04ea8da6b7", + "section_type": "main", + "text": "\n\nThe existence of a maternally silenced X-linked imprinted locus playing a role in social cognition could explain why males (X m Y) are more vulnerable to disorders of social cognition such as autism spectrum disorders than are females (X m X p ).The absence of the expression of this gene would not lead to autism itself, but would eliminate a putative protective factor, making an individual more susceptible to the effects of other ASD-predisposing genetic mutations or environmental factors." + }, + { + "document_id": "3f72832b-fad9-4d38-aed8-d22e5bd12a22", + "section_type": "main", + "text": "Box 1. Sex-specific cytonuclear interactions\n\nSeveral predictions about the nature of cytonuclear conflicts follow from the patterns of chromosomal inheritance (Table I).In a mated pair of animals, mtDNA is co-transmitted with half of the autosomal genes, two-thirds of the X-linked genes and none of the Y-linked genes [76].This predicts that, relative to the autosomal case, positive nuclear-mitochondrial interactions are more likely to evolve for X-linked loci whereas deleterious interactions between Y-linked genes and mtDNA should accumulate (or cannot be purged efficiently)." + }, + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "section_type": "main", + "text": "\n\nIt has been observed that male-biased genes in Drosophila are overrepresented on autosomes (Parisi et al. 2003;Ranz et al. 2003).Consistent with this result, a dynamic process that can explain the nonrandom autosomal distribution has also been observed, in which autosomal new genes with X-linked parental genes are often male-biased.Specifically, a significant excess of autosomal testisexpressed retrogenes were identified as RNA-duplicates of X-linked parental genes (Betran et al. 2002).Recently, similar X!A gene traffic was observed in the DNA-level duplication and relocation data set of the Drosophila genus (Vibranovski et al. 2009b), and was further confirmed for DNA-level duplications in the D. pseudoobscura neo-X chromosome (Meisel et al. 2009).In addition, selective extinction of neo-X linked male-biased genes also occurred in D. pseudoobscura (Sturgill et al. 2007).These three lines of genome-wide investigation support a common pattern of outof-X traffic for male-biased genes, resulting in an enrichment of these genes on autosomes in the long term." + }, + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "section_type": "main", + "text": "\n\nIt has been reported that the initial manifestations of new gene emergence, namely polymorphic duplicates, occur at a lower frequency on the X chromosome, thus indicating that these duplicates are subject to stronger purifying selection (Emerson et al. 2008).Therefore, the excessive fixation of X-linked duplicates might not occur via neutral processes.Positive selection could have facilitated the fixation of X-linked young genes in addition to driving their subsequent sequence evolution." + }, + { + "document_id": "f051ad23-572d-4302-8dda-4d992aeaeb1a", + "section_type": "main", + "text": "X-Linked Inheritance\n\nX-linked genetic disorders (also called sex-linked) are caused by gene mutations on the X chromosome.Most often X-linked genetic disorders are seen in males.Males inherit the X chromosome from their mother and the Y chromosome from their father.Because males have only one X chromosome, if they inherit a gene mutation on the X chromosome from their mother, they will have the disorder.Examples of X-linked genetic disorders occurring in males include hemophilia and Duchenne muscular dystrophy (GHR, 2008o)." + }, + { + "document_id": "ef2c8463-5169-46aa-938b-7d04ea8da6b7", + "section_type": "main", + "text": "X Chromosome and Turner Syndrome\n\nThe existence on the X chromosome of imprinted gene(s) with a role in social cognition was first suggested by Skuse et al. 36 Such a gene could play a role in ASD susceptibility.The hyposthesis of Skuse et al. derives from studies of patients with Turner syndrome (TS), who are monosomic for all or part of the X chromosome.Using a socialcognition questionnaire, Skuse et al. found that females monosomic for the paternal X chromosome (X p 0) score significantly better on social adjustment and verbal skills than females monosomic for the maternal X (X m 0).Therefore, the investigators hypothesized that there is an imprinted gene on the X chromosome, expressed from the paternal X and silenced on the maternal X.To date, no such imprinted gene on the human X chromosome has been identified.The known murine X-linked imprinted genes do not have orthologues in humans. 92Notably, TS patients do have an increased risk of autism.In a series of TS patients, 5 of 150 (3%) were diagnosed with autism by ICD-10 criteria. 93This is five times higher than the 0.6% risk for the general population and 25 times higher than the 0.12% risk for XX females." + }, + { + "document_id": "af3d7cd3-40ec-4a86-a473-89f83da250e4", + "section_type": "main", + "text": "\n\nRecessive gene: A gene, which will be expressed only if there are 2 identical copies or, for a male, if one copy is present on the X chromosome." + }, + { + "document_id": "f4dd6a1d-062b-42bc-8e22-83fcb3135578", + "section_type": "main", + "text": "\n\nThe most widely studied age-related chromosomal abnormality that gives rise to somatic genome mosaicism, reported early in the 1970s from studying metaphases from human blood lymphocytes (Jacobs et al., 1963) and bone marrow (Pierre and Hoagland, 1972), is mosaic loss of the Y chromosome (LOY) in males during aging, which has now been widely confirmed with more advanced technology.LOY is defined as a lowerthan-expected abundance of DNA from the Y chromosome with a certain threshold of detection, for example, as 10% or more of affected cells (Dumanski et al., 2016).In a recent study of 205,011 men from the UK Biobank, LOY was found to affect from 2.5% of men at age 40 to 43.6% at age 70, which makes it the most common de novo somatic mutation over the human lifetime (Thompson et al., 2019).LOY frequency has been associated with a shorter lifespan, a higher risk of cancer, smoking, Alzheimer's disease, cardiovascular disease, diabetes, immune deficiencies, and other age-related diseases (Dumanski et al., 2016;Loftfield et al., 2018;Thompson et al., 2019).LOY has a genetic component, and in the aforementioned UK Biobank study, more than 150 autosomal genetic determinants of LOY were identified in the male cohort.LOY is most likely a general biomarker for genome instability in somatic cells.Indeed, the loci found to be genetically associated with LOY in males were themselves genetically associated, in a female cohort, with female cancers (breast, ovarian, and endometrial cancer) and age at natural menopause (Thompson et al., 2019).Of note, early menopause has been genetically associated with DNA damage response (DDR) genes (Day et al., 2015).Based on these results, it is tempting to speculate that the association of LOY with a diverse series of age-related pathologies points toward a causal role of somatic mutations in aging and age-related disease." + }, + { + "document_id": "81c3edc4-f625-45f2-bf78-e49faf118c88", + "section_type": "main", + "text": "\n\nHow Many Inherited Disease Genes are There in the Human Genome?" + }, + { + "document_id": "516fb027-d7ef-481b-95b2-89c25f4e4f8d", + "section_type": "main", + "text": "\n\nUsing the more advanced FISH-based methods, a dramatically more severe picture of aneuploidy levels was obtained.For example, up to 15-20% of aged human oocytes have chromosomal abnormalities, mainly aneuploidy [17] .In comparison, paternal age only causes a modest increase in the frequency of sex chromosomal aneuploidy in sperm cells [18] .Interestingly, this is the other way around for small DNA mutations, such as basepair substitutions.Virtually all genetic diseases based on point mutations are inherited from the father, most like-ly because such small mutations can arise through replication errors and sperm cells undergo many more rounds of replication than oocytes [19] .Indeed, the so-called 'paternal age effect', as observed first by Weinberg in achondroplasia, indicates that the high incidence of sporadic genetic diseases found among the youngest children in a family may reflect accelerating mutagenesis in sperms as men age [19] ." + }, + { + "document_id": "4ad6da14-56a3-48ab-a587-42761ceac238", + "section_type": "main", + "text": "\n\nWhile DSDs have been reported in 9p deletion and duplication syndromes, we identify for the first time a significant gender bias in the full cohort with an enrichment for females.Among those with available sex chromosome information, we found individuals in the cohort with a gender of female and a sex chromosome complement of XY as expected in some DSDs.To make this a comprehensive study of phenotypes and genes in 9p deletion and duplication syndromes, we performed a meta-analysis of phenotypes observed in 9p deletion and duplication syndromes and found shared, similar, mirrored, and differing phenotypes.Several gene features were also considered for prioritization including constraint, enrichment for deletions/duplications in NDDs, and prior established disease associations.These are useful resources for the assessment of 9p-related structural variations.Recently developed genomic technologies are revolutionizing the way we assess syndromes with complex structural variations.We applied several of these technologies in this study to an individual with a complex 9p deletion, duplication, and associated translocation.We found that the classical karyotype is essential, that either a microarray or short-read WGS is critical to identify the mosaic duplication, and that long-read sequencing is the only technology able to resolve the intricate complexities of this variation." + }, + { + "document_id": "4ba4d5e0-cb28-433d-8e9f-b09779e9d429", + "section_type": "main", + "text": "\n\nAutosomes -All of the chromosomes except for the sex chromosomes and the mitochondrial chromosome." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "section_type": "main", + "text": "In 1967, Lubs (20) discovered\nexcessive genetic material extending beyond the low arm of the X chromosome in affected males. Diagnosis was originally based on cytogenetic analysis of metaphase spreads, but less than 60% of the affected cells in affected\nindividuals showed a positive result. With this variability in the test, the carrier\nstatus of individuals could not be determined. Interpretation of the result is\nfurther complicated by the presence of other fragile sites in the same region\nof the X chromosome." + }, + { + "document_id": "bf11c54e-7cc4-4fe2-97b0-70c464263846", + "section_type": "main", + "text": "\n\nAlthough abnormalities of the X chromosome have been linked to premature ovarian failure (20,21), it is not surprising that we did not identify a signal on the X chromosome (crude LOD score 0).Premature ovarian failure, defined as a decline in ovarian function by age 40 years, only occurs in approximately 1% of women in the general population.Because our sample was not enriched for women with early menopause, there were only 29 women with the onset of natural menopause at age Յ40 years in our sample.Thus we did not have the power to detect significant linkage to chromosome X.Furthermore, the largest Framingham families were selected for inclusion in the genome scan.Women with early decline in ovarian function might have difficulty with fertility and hence might be underrepresented in our sample." + }, + { + "document_id": "f051ad23-572d-4302-8dda-4d992aeaeb1a", + "section_type": "main", + "text": "...an unexpected change in the structure of DNA can sometimes cause harm to the body. division process by which egg and sperm are formed.During the cell division process of meiosis, there is a reduction in the number of chromosomes that results in egg and sperm cells that contain 23 chromosomes, or half of the usual number of 46.Egg and sperm cells are called haploid cells because they have a single copy of each chromosome instead of the usual two copies (GHR, 2008i)." + }, + { + "document_id": "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa", + "section_type": "main", + "text": "X-Linked Inheritance\n\nMore complicated patterns emerge if a disease mutation is present in a gene on the X chromosome.If a mutation is dominant, then a mother with the mutation (who herself should have the disease) has a 50% chance of passing the mutation to an offspring, who in turn will have the disease.In contrast, a father with the mutation (who himself should have the disease) has a 50% chance of passing the mutation to a daughter because he passes an X chromosome to her, but he cannot transmit the mutation to a son because he passes a Y chromosome to him.Thus, the inheritance of disease depends on sex.This is X-linked dominant inheritance (Figure 11).An example of an X-linked dominant disorder is Rett syndrome.If a mutation is recessive, then a mother with the mutation (who should be a healthy carrier) has a 50% chance of passing the mutation to an offspring.A daughter who inherits the mutation will be a carrier, whereas a son who inherits the mutation will have the disease because he has only a single X chromosome and has no normal gene copy to counteract the mutant gene copy.A father with the mutation (who should have the disease) has a 50% chance of passing the mutation to a daughter, who will be a carrier, but cannot transmit the mutation to a son.The only way a daughter can have the disease is if she inherits mutant gene copies from both parents.This is X-linked recessive inheritance (Figure 11).Such diseases are much more likely to affect men than women.Classic examples of X-linked recessive disorders include red-green color blindness and hemophilia." + }, + { + "document_id": "20b466c6-004b-484f-96a1-c1b4651bc856", + "section_type": "main", + "text": "\n\nMosaic loss of Y chromosome (mLOY) in peripheral blood is the most common acquired mutation in the process of normal aging in men, affecting about 1.8% of the genetic material in the human genome [12].The prevalence of mLOY increases with age and can exceed 20% in male populations older than 80 years [13].Furthermore, the occurrence of mLOY is strongly correlated with smoking behaviour [14].Current smokers have a more than fourfold increased risk for mLOY [13], although this effect seems to be transient as smoking cessation can result in normal mLOY levels after several years [14,15]." + }, + { + "document_id": "76f1e8d2-15bf-4ce6-9cd0-2ab889c23664", + "section_type": "main", + "text": "\n\nBackground: Turner syndrome (TS) is caused by the absence or fragmentation of the second sex chromosome.An increased risk of diabetes mellitus (DM) has consistently been noted, but the specific phenotype and genetic etiology of this trait are unknown." + }, + { + "document_id": "e913e8b9-7a8a-4a5e-9794-a947d94654a5", + "section_type": "main", + "text": "Marsupial Chromosomes\n\nMarsupials are famous for their low diploid numbers and large chromosomes, which offered cytologists optimal material for many classic studies of chromosome structure and behavior and of the effects of radiation.The karyotype is highly conserved across even distantly related groups.Classic work identified two modes of chromosome number (49,115), one of which, a 2n = 14 karyotype, was found to have identical G-band patterns across species in several families, including South American families (110).A fierce debate arose about whether the ancestral marsupial shared this low-diploid-number, large-chromosome karyotype, because some of the earliest offshoots in South America have a larger number of chromosomes, and interstitial telomere sequences suggested recent Robertsonian fusions to engender the lower number that is basic to Australidelphia (123).However, these sequences may be repeats that have accumulated at the centromeres and do not necessarily represent fusion points (88)." + }, + { + "document_id": "b014e368-d0d5-4eff-a9af-abd4a4ed6d29", + "section_type": "main", + "text": "\n\nSeveral observations suggest that genetic factors could predispose to both the general baseline and age-related elevation in aneuploidy conceptions.A recent genome-wide screen for new meiotic genes in mouse oocytes revealed hundreds of genes, whose depletion by RNAi affected chromosome segregation (Pfender et al., 2015).This suggests that conducting refined analyses in human oocytes and population-based studies may yet yield new molecular targets.Studies in mice suggest that heterozygosity of SMC1β, a conserved meiosis-specific cohesin subunit, predisposes to aneuploidy (Murdoch et al., 2013).Deletion of both copies of SMC1β predisposes to agerelated loss of bivalent structures and therefore to aneuploidy in mouse oocytes (Hodges et al., 2005).The haploinsufficiency studies are important because they suggest dosage sensitivity.This is particularly relevant in human populations where complete deletions (homozygous) of gene activities are relatively rare and usually only found in consanguineous families (O'Driscoll, 2008)." + }, + { + "document_id": "b014e368-d0d5-4eff-a9af-abd4a4ed6d29", + "section_type": "main", + "text": "\n\nSince the discovery that aneuploidy is the major cause of congenital disorders (Jacobs and Strong, 1959;Jacobs et al., 1959;Lejeune, Gautier, and Turpin, 1959;Ford et al., 1959a,b), most our knowledge has derived from population-based studies of foetal losses and rare live births.Maternal age is the major factor that influences aneuploidy, giving rise to the characteristic J curve (Erickson, 1978;Hassold and Hunt, 2001; Fig. 1A).However, individual chromosomes follow different age-dependent curves (Nagaoka et al., 2012;Franasiak et al., 2014a, b;Fig. 1B) suggesting that both chromosome-specific as well as general cellular factors conspire to shape the segregation efficiency in human oocytes." + }, + { + "document_id": "b014e368-d0d5-4eff-a9af-abd4a4ed6d29", + "section_type": "main", + "text": "Introduction\n\nHuman conceptions are afflicted by an extraordinary rate of chromosome errors, and the majority derive from the oocyte (Hassold and Hunt, 2001).In natural conceptions that reach clinical recognition, 35% of human pregnancies are aneuploid.The rate observed in preimplantation embryos is substantially higher, in part because aneuploid embryos have poor developmental potential and are selected against during the peri-implantation stages and throughout foetal life (Capalbo et al., 2014).In natural conception, more than 90% are of meiotic origin and the majority are caused by errors in meiosis I (Hassold and Hunt, 2001;Gabriel et al., 2011).In reproductive aged women, 20-30% of occytes (and up to 70% of oocytes in advanced maternal age (AMA) women) are aneuploid, while just 1-8% of spermatozoa are afflicted (Lu et al., 2012;Wang et al., 2012).In sperm, the incidence of aneuploidy is independent of paternal age (Erickson, 1978;Hassold and Hunt, 2001;Lu et al., 2012;Wang et al., 2012).The analyses of aneuploidy in miscarriages have been invaluable for our appreciation of the serious consequences chromosomal imbalances have for embryonic and foetal development, since a much higher incidence and wider range and representation of chromosomes are detected compared to subsequent developmental stages, including live births (Hassold et al., 1980;Zaragoza et al., 1994)." + }, + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "section_type": "main", + "text": "\n\nThe processes of surveillance and searching for inheritance patterns may lead family members to believe that a genetic condition is transmitted through males or through females (Featherstone et al., 2006), resulting in predictions regarding who might be affected or included in conversations on the topic.Because of these assumptions, family members may ignore the importance of opposite gender relatives who may be carriers, such as in the case of HBOC." + }, + { + "document_id": "02b1c922-a9cf-470d-b036-52c367fc1ca9", + "section_type": "main", + "text": "\n\nAnalogous to the post-natal occurrence of somatic mutations, we previously demonstrated a similar phenomenon, termed selfish spermatogonial selection, that occurs in the testes of adult men as they age.However, because the testis contains germ cells that, upon fertilization, will carry the genetic information across generations, this process has important reproductive implications, being associated with an increased prevalence of pathogenic DNMs in the next generation.Despite the relatively low average human germline point mutation rate of ∼1.2 × 10 −8 per nucleotide per generation (Kong et al. 2012;Goldmann et al. 2016;Jonsson et al. 2017), specific \"selfish\" DNMs in FGFR2, FGFR3, HRAS, PTPN11, and RET are observed up to 1000-fold more frequently in offspring (Goriely and Wilkie 2012).These pathogenic mutations, which cause developmental disorders that show an extreme paternal bias in origin and an epidemiological paternal age-effect (collectively referred to as PAE disorders; e.g., achondroplasia; Apert, Costello, and Noonan syndromes; multiple endocrine neoplasia type 2a/b), are identical (or allelic) to oncogenic driver mutations in tumors (Goriely and Wilkie 2012).We have proposed that although they arise at the normal background rate in male germline stem cells (spermatogonia), selfish mutations alter the behavior of spermatogonia within the testis.In a process akin to oncogenesis, these gain-of-function mutations provide a selective advantage that may involve increasing the rate of symmetrical divisions of the mutant spermatogonia (Qin et al. 2007;Choi et al. 2008Choi et al. , 2012;;Giannoulatou et al. 2013;Yoon et al. 2013;Martin et al. 2014), leading to their clonal expansion over time, which results in increased apparent mutation levels in sperm with age (Goriely and Wilkie 2012;Maher et al. 2014)." + }, + { + "document_id": "f3c57cf2-da42-4833-ab8d-99517f987aea", + "section_type": "main", + "text": "\n\nChromosome copy number changes in the polar bodies and the corresponding cleavage stage embryos of 30 embryos predicted to have one or more aneuploidies of maternal meiotic origin." + } + ], + "document_id": "34A6BD721632631DF6D97BCA1D315B0A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "X&chromosome", + "Y&chromosome", + "male", + "female", + "sex&chromosomes", + "autosome", + "nondisjunction", + "trisomy", + "monosomy", + "X-linked&inheritance" + ], + "metadata": [ + { + "object": "Male schizophrenia subjects had more anterior cingulate cortex DEK protein expression compared to male controls. Female schizophrenia subjects had less DEK protein expression compared to female controls. Finally, while there were no differences in DEK protein expression between control males and control females, males with schizophrenia had higher DEK protein expression compared to females with schizophrenia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab468329" + }, + { + "object": "Study found robust hypersocial behavior in the dyadic interaction test in both PSD95+/- males and females. Additionally, male PSD95+/- mice exhibited higher levels of aggression and territoriality, while female PSD95+/- mice showed increased vocalization upon exposure to an anesthetized female mouse. Both male and female PSD95+/- mice revealed mild hypoactivity in the open field but no obvious motor deficit.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab741605" + }, + { + "object": "Data suggest expression of Ptger2/Ptgs2 prostaglandin-endoperoxide synthase 2 is induced in cumulus cells of females sired by males with Y-chromosome long-arm deletion; paternal genes on Y-chromosome are involved indirectly in female reproduction.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab203335" + }, + { + "object": "Data suggest expression of Ptgs2/Ptger2 prostaglandin E receptor 2 is induced in cumulus cells of females sired by males with Y-chromosome long-arm deletion; thus, paternal genes on Y-chromosome are involved indirectly in female reproduction.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab203337" + }, + { + "object": "Lay summary Stress decreased vertical activity VA in female but not male rats while shock stress SS decreased serum BDNF in female but not male rats. VA was positively correlated with serum BDNF for female rats. These findings suggest sex differences in response to stress.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab445381" + }, + { + "object": "Data identify Tudor domain containing protein 5-like Tdrd5l, which is expressed 17-fold higher in ovaries lacking Sxl. Additionally, Tdrd5l plays an important role in males as male flies that are mutant for this gene cannot make sperm properly and thus are less fertile. Tdrd5l promotes male identity in the germline and it can shift the germ cell developmental program from female to male.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab177945" + }, + { + "object": "Aortic UCP1 content was greater in females than males and its deletion improved ex vivo aortic vasomotor function in females only. Constitutive UCP1 content in BAT was similar between females and males and loss of UCP1 did not abolish sex differences in insulin sensitivity. Metabolic disruptions caused by UCP1 ablation did not appear to be contingent upon increased oxidative stress in mice under normal dietary conditions.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab75747" + }, + { + "object": "There was strong AR expression within the central core region of the suprachiasmatic nucleus of both XYM genetic and gonadal male and XXM genetic female, gonadal male groups. AR expression in the gonadal males was significantly higher than in females.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab889111" + }, + { + "object": "ALPL in both male and female human liver data covaries extremely well with the unlinked PPP2R4 gene about 0.7 in both e.g., GSE9588 Human Liver Normal Mar11 Females. In females best linkage to rs6535513 on Chr 4 at 85.253280 with -logP of 6.56 nothing cis. In males best linkage to rs4664151 on Chr 2 at 154.336956 with -logP 7.55.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab167" + }, + { + "object": "Disruption of Ser2 resulted in dominant heritable male sterility. Wild-type females mated with Ser2-deficient males laid eggs normally, but the eggs did not hatch. The authors detected no differences in other reproductive behaviors in the mutant males. These results support the conclusion that Ser2 gene is necessary for male reproductive success in diverse lepidopterans.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab233385" + } + ], + "question": "Why do males have two Y chromosomes and females only one?", + "subquestions": null, + "task_id": "34A6BD721632631DF6D97BCA1D315B0A", + "usage": { + "chatgpt": 6105, + "gpt-4": 3430, + "gpt-4-turbo-preview": 2504 + }, + "user_id": 2 + }, + "document_id": "34A6BD721632631DF6D97BCA1D315B0A", + "task_id": "34A6BD721632631DF6D97BCA1D315B0A" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/03.json b/gnqa/paper1_eval/src/data/responses/general/experts/03.json new file mode 100644 index 0000000..8de2a7c --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/03.json @@ -0,0 +1,406 @@ +{ + "created_at": "2024-06-01T14:22:47.136754Z", + "data": { + "amplify": false, + "answer": "GeneNetwork utilizes datasets containing phenotypes and genotypes for QTL mapping analysis. These datasets may include genetic, genomic, and phenotypic data. The data are often formatted and normalized, and come with metadata. The database also includes data on complex phenotypes ranging from gene expression to behavior in standard inbred strains, and panels of mouse recombinant inbred strains. Rat and Arabidopsis populations are also represented in the database.", + "context": { + "071b4686-f5c4-4759-a038-14d79a45dac7": [ + { + "document_id": "071b4686-f5c4-4759-a038-14d79a45dac7", + "text": "The project also provides online analysis tools to allow\nidentification of correlations within its data set. GeneNetwork (http://www.genenetwork.org), encompassing WebQTL, is a database of\ngenotypes and complex phenotypes ranging from gene expression to behaviour in standard\ninbred strains, and six panels of mouse recombinant inbred strains including the two largest\nsets (BXD and LXS) of approximately 80 strains each. Rat and Arabidopsis populations are\nalso represented. Approximately 1500 phenotypes spanning the 25 year history of these\nstrains are incorporated in this public resource, many of which were retrieved from the\nliterature." + } + ], + "0e6c370f-b514-4551-b6ed-9cc72e6f6b75": [ + { + "document_id": "0e6c370f-b514-4551-b6ed-9cc72e6f6b75", + "text": "GN spares the\nuser most of these problem. Data are formatted and normalized, and usually come with good\nmetadata (often in the form of links to more information). This greatly simplifies QTL and\neQTL analysis, candidate gene discovery, coexpression analysis, and hypothesis testing [3,\n10]." + }, + { + "document_id": "0e6c370f-b514-4551-b6ed-9cc72e6f6b75", + "text": "Suitable for quantitative\ngenetics (QTL mapping) and systems genetics, including correlation and\nnetwork analysis to compare associations between tissues and between\nother rodent or human data sets\n\nDescription and usage\n\n[32]\n\n[31]\n\n[30]\n\n[11]\n\nReferences\n\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential\nusage." + } + ], + "2a92d7b5-946c-4a22-a4b9-26e950b0f757": [ + { + "document_id": "2a92d7b5-946c-4a22-a4b9-26e950b0f757", + "text": "Bioinformatics\nAll of the genetic analyses were carried out in GeneNetwork, which\nis an open source bioinformatics resource for systems genetics that\nexists as both a repository for genetic, genomic and phenotypic\ndata together with a suite of statistical programs for data analysis that includes mapping and evaluating QTLs, examining phenotype/genotype correlations and building interaction networks. QTL mapping\nThe QTL mapping module of GeneNetwork was used to identify\nQTLs for hippocampal morphometry and radial maze trait data. This\nmodule enables interval mapping, composite interval mapping and\na pairwise scan option to identify epistatic effects." + } + ], + "389bdbf3-0224-4edb-a4fb-71a54971ba66": [ + { + "document_id": "389bdbf3-0224-4edb-a4fb-71a54971ba66", + "text": "There\nare four options for QTL mapping on the GeneNetwork website: interval\nmapping, marker regression analysis, composite interval mapping, and pairscan analysis. In this case, interval mapping was used to compute linkage\nmaps for the entire genome. The log of odds (LOD) score was used to\nassert that a causal relation exists between a chromosomal location and a\nphenotypic variant, such as Gsto1 expression variation." + } + ], + "3df1bffa-3d23-4b6b-9d59-6ef8b0001f48": [ + { + "document_id": "3df1bffa-3d23-4b6b-9d59-6ef8b0001f48", + "text": "Webqtl is an online database [110] of linked datasets, including genotype and expression\ndata, covering multiple species including mouse, macaque monkey, rat, drosophila,\narabidopsis, plants and humans [60]. While this tool cannot be used to calculate eQTLs, it\ncan be used to find and visualize eQTLs in different species, strains and tissues. It can\nperform single- and multiple-interval QTL mapping of up to 100 selected traits. Users can\nalso upload their own trait data for populations included in the database. It can also calculate\nand display trait-correlation matrices and network graphs (also for up to 100 traits)." + } + ], + "43407486-b9c2-487b-b19c-b605c4d201c6": [ + { + "document_id": "43407486-b9c2-487b-b19c-b605c4d201c6", + "text": "GN spares the\nuser most of these problem. Data are formatted and normalized, and usually come with good\nmetadata (often in the form of links to more information). This greatly simplifies QTL and\neQTL analysis, candidate gene discovery, coexpression analysis, and hypothesis testing [3,\n10]." + }, + { + "document_id": "43407486-b9c2-487b-b19c-b605c4d201c6", + "text": "Suitable for quantitative\ngenetics (QTL mapping) and systems genetics, including correlation and\nnetwork analysis to compare associations between tissues and between\nother rodent or human data sets\n\nDescription and usage\n\n[32]\n\n[31]\n\n[30]\n\n[11]\n\nReferences\n\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential\nusage." + } + ], + "516cc395-4e7c-4371-9444-24edb56a7233": [ + { + "document_id": "516cc395-4e7c-4371-9444-24edb56a7233", + "text": "QTL MAPPING AND QTG DISCOVERY IN THE RCC\nA variety of statistical methods and tools have been developed for QTL mapping and\nimplemented in free software for public use. These methods are well suited for simple\nbackcross and F2 RCC populations. R/qtl9,39 was developed for identification of\nQTLs and higher order modeling. Another Web-based tool, GeneNetwork or\nWebQTL (GeneNetwork.org),40 was developed for QTL mapping and to explore\nassociations between variants, molecular traits (e.g. , gene expression), and higher order\nphenotypes (e.g. , behavior) and facilitate QTG identification." + } + ], + "550c099f-88d0-483f-865a-01ef7362e2be": [ + { + "document_id": "550c099f-88d0-483f-865a-01ef7362e2be", + "text": "This enables gene expression\ncorrelation and interval mapping, candidate gene searches and multitrait analyses. Each exported dataset was subject to an interval mapping analysis,\nwhich uses GeneNetwork’s embedded MapManager software\n(Manly et al . 2001) to perform Haley–Knott regression. Empirical P values were derived using 1000 permutations using the incorporated\npermutation feature of WebQTL. The peak of each statistically\nsignificant (P -value <0.05) or suggestive (P -value <0.63) (Lander\n& Kruglyak 1995) QTL was determined based on empirical P values (Doerge & Churchill 1996). A one-LOD drop-off was used\nto determine the QTL confidence interval about each peak." + } + ], + "581f83bc-3521-4cb3-ad3c-d905a90ecc29": [ + { + "document_id": "581f83bc-3521-4cb3-ad3c-d905a90ecc29", + "text": "The peak linkage value\nand position was databased in GeneNetwork and users\ncan rapidly retrieve and view these mapping results for\nany probe set. Any of the QTL maps can also be rapidly\nregenerated using the same Haley-Knott methods, again\nusing functions imbedded in GeneNetwork. GeneNetwork also enable a search for epistatic interactions (pair\nscanning function) and composite interval mapping with\ncontrol for a single marker. Data quality control\n\nWe used two simple but effective methods to confirm\ncorrect sample identification of all data entered into\nGeneNetwork." + } + ], + "5bd8262b-b2cd-4098-a494-ede168941a9a": [ + { + "document_id": "5bd8262b-b2cd-4098-a494-ede168941a9a", + "text": "QTL analysis\nAll QTL mapping for phenotypes was performed using the WebQTL software module of the\n\n170\n\nGeneNetwork (www.genenetwork.org) [34]. Interval mapping to evaluate potential QTLs was\ncalculated from the likelihood ratio statistics (LRS) as the software’s default measurement of\nthe association between differences in traits and differences in particular genotype markers. Another common measure score, the log of the odds (LOD) ratio, can be converted from the\nLRS (LRS/4.61). Suggestive and significant LRS values were determined by applying 1000\n\n175\n\npermutations." + } + ], + "80eb54fe-0d83-4300-9fba-e17ce5d1e5b4": [ + { + "document_id": "80eb54fe-0d83-4300-9fba-e17ce5d1e5b4", + "text": "Unlike interval-specific haplotype analysis, which is most useful for narrowing a QTL shared by\nmultiple crosses, genome-wide haplotype analysis\nrequires only phenotype information from many inbred\nstrains and can effectively narrow a QTL identified in\nonly one experimental cross [36]. After narrowing the QTL to an interval that is !5 Mb\nusing these bioinformatics techniques or classical experimental methods, strain-specific sequence and gene\nexpression comparisons are effective for focusing on a\nfew strong candidate genes (Figure 7)." + } + ], + "86b86235-b7a8-4dfc-be13-d119dc31b377": [ + { + "document_id": "86b86235-b7a8-4dfc-be13-d119dc31b377", + "text": "We considered QTL intervals that achieved genome-wide\nsignificance for one phenotype, and genome-wide suggestive for\nothers, as highest priority for candidate gene analysis. The January 2017 BXD genotype file was used4 . Updated linear mixed model mapping algorithms are now\navailable on GeneNetwork 25 (Sloan et al. , 2016), that account for\nkinship among strains. These new algorithms include GEMMA\n(Zhou and Stephens, 2012), pyLMM6 (Sul et al. , 2016), and\nR/qtl27 ." + } + ], + "9b2a48a0-f85e-4104-944f-0c47a3b03a9b": [ + { + "document_id": "9b2a48a0-f85e-4104-944f-0c47a3b03a9b", + "text": "The peak linkage value\nand position was databased in GeneNetwork and users\ncan rapidly retrieve and view these mapping results for\nany probe set. Any of the QTL maps can also be rapidly\nregenerated using the same Haley-Knott methods, again\nusing functions imbedded in GeneNetwork. GeneNetwork also enable a search for epistatic interactions (pair\nscanning function) and composite interval mapping with\ncontrol for a single marker. Data quality control\n\nWe used two simple but effective methods to confirm\ncorrect sample identification of all data entered into\nGeneNetwork." + } + ], + "a4508fb3-c66b-4526-b2a2-a327505d085a": [ + { + "document_id": "a4508fb3-c66b-4526-b2a2-a327505d085a", + "text": "There\nare four options for QTL mapping on the GeneNetwork website: interval\nmapping, marker regression analysis, composite interval mapping, and pairscan analysis. In this case, interval mapping was used to compute linkage\nmaps for the entire genome. The log of odds (LOD) score was used to\nassert that a causal relation exists between a chromosomal location and a\nphenotypic variant, such as Gsto1 expression variation." + } + ], + "b5c36c1e-458e-4009-818e-9c0c2ee23e45": [ + { + "document_id": "b5c36c1e-458e-4009-818e-9c0c2ee23e45", + "text": "eQTL mapping\n\nQTL mapping was performed with GeneNetwork, an online bioinformatics resource\nfeaturing tools for systems genetic and complex trait analysis [9, 35]. QTL mapping\ninvolves entering VMB and CP iron data (strain means and SEM) as quantitative traits; the\nsoftware generates whole-genome interval maps for each trait. The interval maps graphically\nillustrate phenotype–genotype associations as peaks (QTL) indicating the strength of\nassociation between genomic polymorphisms and the quantitative trait throughout the\ngenome." + } + ], + "baacd740-efc8-42f2-af22-6f5ac9710900": [ + { + "document_id": "baacd740-efc8-42f2-af22-6f5ac9710900", + "text": "Genetic Mapping\nIn this study we utilize GeneNetwork, a database containing phenotypes and genotypes,\nand also serves as an analysis engine for quantitative trait locus (QTL) mapping, genetic\ncorrelations, and phenome-wide association studies (PheWAS) (Sloan et al. , 2016; Mulligan et\nal. , 2017; Watson and Ashbrook, 2020). QTL analysis involves connecting phenotype data with\ngenotype data to examine genetic variation in traits controlled by multiple genes and their\ninteraction with the environment (also called complex traits)(Lynch et al. , 1998; Myles and\nWayne, 2008; Goddard et al. , 2016)." + } + ], + "beb7a242-21fe-4a66-8b44-7f228c0d3640": [ + { + "document_id": "beb7a242-21fe-4a66-8b44-7f228c0d3640", + "text": "Once the resulting record set of the\nquery is returned, it can be further restricted by selecting\nrelevant records based on attached annotations before forwarding it for further analysis. To map genetic loci associated with mRNA abundance or\ntrait phenotypes, any one of the three QTL mapping functions currently employed by GeneNetwork's WebQTL\nmodule can be used. These are 1. interval mapping, 2. single-marker regression, or 3. composite mapping [29,30]." + } + ], + "e70f7c61-1734-4048-8a79-382e9b381686": [ + { + "document_id": "e70f7c61-1734-4048-8a79-382e9b381686", + "text": "genenetwork.org/) a set of 3795 markers. Linkage is reported with\ngenome-wide significance levels based on 2000 permutation tests. Two types of QTL mapping analyses–simple mapping using the Haley–\nKnott regression equation, and composite interval mapping–were\nutilized in this study. Simple interval mapping was performed to\nillustrate the significance of any QTLs that regulate the TID. As a\nsecondary analysis, composite interval mapping which controlled for\nthe influence of Tyrp1 was also performed with the goal of identifying\nany secondary QTLs that may have been masked by the major QTL on\nChr 4." + } + ] + }, + "data_source": [ + { + "document_id": "baacd740-efc8-42f2-af22-6f5ac9710900", + "section_type": "main", + "text": "Genetic Mapping\nIn this study we utilize GeneNetwork, a database containing phenotypes and genotypes,\nand also serves as an analysis engine for quantitative trait locus (QTL) mapping, genetic\ncorrelations, and phenome-wide association studies (PheWAS) (Sloan et al. , 2016; Mulligan et\nal. , 2017; Watson and Ashbrook, 2020). QTL analysis involves connecting phenotype data with\ngenotype data to examine genetic variation in traits controlled by multiple genes and their\ninteraction with the environment (also called complex traits)(Lynch et al. , 1998; Myles and\nWayne, 2008; Goddard et al. , 2016)." + }, + { + "document_id": "550c099f-88d0-483f-865a-01ef7362e2be", + "section_type": "main", + "text": "This enables gene expression\ncorrelation and interval mapping, candidate gene searches and multitrait analyses.\n Each exported dataset was subject to an interval mapping analysis,\nwhich uses GeneNetwork’s embedded MapManager software\n(Manly et al . 2001) to perform Haley–Knott regression. Empirical P values were derived using 1000 permutations using the incorporated\npermutation feature of WebQTL. The peak of each statistically\nsignificant (P -value <0.05) or suggestive (P -value <0.63) (Lander\n& Kruglyak 1995) QTL was determined based on empirical P values (Doerge & Churchill 1996). A one-LOD drop-off was used\nto determine the QTL confidence interval about each peak." + }, + { + "document_id": "beb7a242-21fe-4a66-8b44-7f228c0d3640", + "section_type": "main", + "text": "Once the resulting record set of the\nquery is returned, it can be further restricted by selecting\nrelevant records based on attached annotations before forwarding it for further analysis.\n\n To map genetic loci associated with mRNA abundance or\ntrait phenotypes, any one of the three QTL mapping functions currently employed by GeneNetwork's WebQTL\nmodule can be used. These are 1. interval mapping, 2. single-marker regression, or 3. composite mapping [29,30]." + }, + { + "document_id": "86b86235-b7a8-4dfc-be13-d119dc31b377", + "section_type": "main", + "text": "We considered QTL intervals that achieved genome-wide\nsignificance for one phenotype, and genome-wide suggestive for\nothers, as highest priority for candidate gene analysis.\n The January 2017 BXD genotype file was used4 .\n Updated linear mixed model mapping algorithms are now\navailable on GeneNetwork 25 (Sloan et al. , 2016), that account for\nkinship among strains. These new algorithms include GEMMA\n(Zhou and Stephens, 2012), pyLMM6 (Sul et al. , 2016), and\nR/qtl27 ." + }, + { + "document_id": "516cc395-4e7c-4371-9444-24edb56a7233", + "section_type": "main", + "text": "QTL MAPPING AND QTG DISCOVERY IN THE RCC\nA variety of statistical methods and tools have been developed for QTL mapping and\nimplemented in free software for public use. These methods are well suited for simple\nbackcross and F2 RCC populations. R/qtl9,39 was developed for identification of\nQTLs and higher order modeling. Another Web-based tool, GeneNetwork or\nWebQTL (GeneNetwork.org),40 was developed for QTL mapping and to explore\nassociations between variants, molecular traits (e.g. , gene expression), and higher order\nphenotypes (e.g. , behavior) and facilitate QTG identification." + }, + { + "document_id": "3df1bffa-3d23-4b6b-9d59-6ef8b0001f48", + "section_type": "main", + "text": "Webqtl is an online database [110] of linked datasets, including genotype and expression\ndata, covering multiple species including mouse, macaque monkey, rat, drosophila,\narabidopsis, plants and humans [60]. While this tool cannot be used to calculate eQTLs, it\ncan be used to find and visualize eQTLs in different species, strains and tissues. It can\nperform single- and multiple-interval QTL mapping of up to 100 selected traits. Users can\nalso upload their own trait data for populations included in the database. It can also calculate\nand display trait-correlation matrices and network graphs (also for up to 100 traits)." + }, + { + "document_id": "e70f7c61-1734-4048-8a79-382e9b381686", + "section_type": "main", + "text": "genenetwork.org/) a set of 3795 markers. Linkage is reported with\ngenome-wide significance levels based on 2000 permutation tests.\n Two types of QTL mapping analyses–simple mapping using the Haley–\nKnott regression equation, and composite interval mapping–were\nutilized in this study. Simple interval mapping was performed to\nillustrate the significance of any QTLs that regulate the TID. As a\nsecondary analysis, composite interval mapping which controlled for\nthe influence of Tyrp1 was also performed with the goal of identifying\nany secondary QTLs that may have been masked by the major QTL on\nChr 4." + }, + { + "document_id": "2a92d7b5-946c-4a22-a4b9-26e950b0f757", + "section_type": "main", + "text": "Bioinformatics\nAll of the genetic analyses were carried out in GeneNetwork, which\nis an open source bioinformatics resource for systems genetics that\nexists as both a repository for genetic, genomic and phenotypic\ndata together with a suite of statistical programs for data analysis that includes mapping and evaluating QTLs, examining phenotype/genotype correlations and building interaction networks.\n\n QTL mapping\nThe QTL mapping module of GeneNetwork was used to identify\nQTLs for hippocampal morphometry and radial maze trait data. This\nmodule enables interval mapping, composite interval mapping and\na pairwise scan option to identify epistatic effects." + }, + { + "document_id": "0e6c370f-b514-4551-b6ed-9cc72e6f6b75", + "section_type": "main", + "text": "Suitable for quantitative\ngenetics (QTL mapping) and systems genetics, including correlation and\nnetwork analysis to compare associations between tissues and between\nother rodent or human data sets\n\nDescription and usage\n\n[32]\n\n[31]\n\n[30]\n\n[11]\n\nReferences\n\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential\nusage." + }, + { + "document_id": "43407486-b9c2-487b-b19c-b605c4d201c6", + "section_type": "main", + "text": "Suitable for quantitative\ngenetics (QTL mapping) and systems genetics, including correlation and\nnetwork analysis to compare associations between tissues and between\nother rodent or human data sets\n\nDescription and usage\n\n[32]\n\n[31]\n\n[30]\n\n[11]\n\nReferences\n\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential\nusage." + }, + { + "document_id": "071b4686-f5c4-4759-a038-14d79a45dac7", + "section_type": "main", + "text": "The project also provides online analysis tools to allow\nidentification of correlations within its data set.\n GeneNetwork (http://www.genenetwork.org), encompassing WebQTL, is a database of\ngenotypes and complex phenotypes ranging from gene expression to behaviour in standard\ninbred strains, and six panels of mouse recombinant inbred strains including the two largest\nsets (BXD and LXS) of approximately 80 strains each. Rat and Arabidopsis populations are\nalso represented. Approximately 1500 phenotypes spanning the 25 year history of these\nstrains are incorporated in this public resource, many of which were retrieved from the\nliterature." + }, + { + "document_id": "581f83bc-3521-4cb3-ad3c-d905a90ecc29", + "section_type": "main", + "text": "The peak linkage value\nand position was databased in GeneNetwork and users\ncan rapidly retrieve and view these mapping results for\nany probe set. Any of the QTL maps can also be rapidly\nregenerated using the same Haley-Knott methods, again\nusing functions imbedded in GeneNetwork. GeneNetwork also enable a search for epistatic interactions (pair\nscanning function) and composite interval mapping with\ncontrol for a single marker.\n Data quality control\n\nWe used two simple but effective methods to confirm\ncorrect sample identification of all data entered into\nGeneNetwork." + }, + { + "document_id": "9b2a48a0-f85e-4104-944f-0c47a3b03a9b", + "section_type": "main", + "text": "The peak linkage value\nand position was databased in GeneNetwork and users\ncan rapidly retrieve and view these mapping results for\nany probe set. Any of the QTL maps can also be rapidly\nregenerated using the same Haley-Knott methods, again\nusing functions imbedded in GeneNetwork. GeneNetwork also enable a search for epistatic interactions (pair\nscanning function) and composite interval mapping with\ncontrol for a single marker.\n Data quality control\n\nWe used two simple but effective methods to confirm\ncorrect sample identification of all data entered into\nGeneNetwork." + }, + { + "document_id": "5bd8262b-b2cd-4098-a494-ede168941a9a", + "section_type": "main", + "text": "QTL analysis\nAll QTL mapping for phenotypes was performed using the WebQTL software module of the\n\n170\n\nGeneNetwork (www.genenetwork.org) [34]. Interval mapping to evaluate potential QTLs was\ncalculated from the likelihood ratio statistics (LRS) as the software’s default measurement of\nthe association between differences in traits and differences in particular genotype markers.\n Another common measure score, the log of the odds (LOD) ratio, can be converted from the\nLRS (LRS/4.61). Suggestive and significant LRS values were determined by applying 1000\n\n175\n\npermutations." + }, + { + "document_id": "389bdbf3-0224-4edb-a4fb-71a54971ba66", + "section_type": "main", + "text": "There\nare four options for QTL mapping on the GeneNetwork website: interval\nmapping, marker regression analysis, composite interval mapping, and pairscan analysis. In this case, interval mapping was used to compute linkage\nmaps for the entire genome. The log of odds (LOD) score was used to\nassert that a causal relation exists between a chromosomal location and a\nphenotypic variant, such as Gsto1 expression variation." + }, + { + "document_id": "a4508fb3-c66b-4526-b2a2-a327505d085a", + "section_type": "main", + "text": "There\nare four options for QTL mapping on the GeneNetwork website: interval\nmapping, marker regression analysis, composite interval mapping, and pairscan analysis. In this case, interval mapping was used to compute linkage\nmaps for the entire genome. The log of odds (LOD) score was used to\nassert that a causal relation exists between a chromosomal location and a\nphenotypic variant, such as Gsto1 expression variation." + }, + { + "document_id": "80eb54fe-0d83-4300-9fba-e17ce5d1e5b4", + "section_type": "main", + "text": "Unlike interval-specific haplotype analysis, which is most useful for narrowing a QTL shared by\nmultiple crosses, genome-wide haplotype analysis\nrequires only phenotype information from many inbred\nstrains and can effectively narrow a QTL identified in\nonly one experimental cross [36].\n After narrowing the QTL to an interval that is !5 Mb\nusing these bioinformatics techniques or classical experimental methods, strain-specific sequence and gene\nexpression comparisons are effective for focusing on a\nfew strong candidate genes (Figure 7)." + }, + { + "document_id": "7dc4230d-c0a3-484b-9fb4-04d5ff09956b", + "section_type": "main", + "text": "Unlike interval-specific haplotype analysis, which is most useful for narrowing a QTL shared by\nmultiple crosses, genome-wide haplotype analysis\nrequires only phenotype information from many inbred\nstrains and can effectively narrow a QTL identified in\nonly one experimental cross [36].\n After narrowing the QTL to an interval that is !5 Mb\nusing these bioinformatics techniques or classical experimental methods, strain-specific sequence and gene\nexpression comparisons are effective for focusing on a\nfew strong candidate genes (Figure 7)." + }, + { + "document_id": "1b31c086-dbd1-4b0d-8b51-c33b074b8e9d", + "section_type": "main", + "text": "Genotyping and QTL mapping\nQTL and eQTL mapping was performed using GeneNetwork http://www.genenetwork.org and a standardized set\nof 3795 genotyped markers (mapping algorithm and genotypes described at http://www.genenetwork.org/dbdoc/\nBXDGeno.html; genotypes downloadable as a text file\nfrom\nhttp://www.genenetwork.org/genotypes/\nBXD.geno). Residuals from the model described above\n(Trait 10701) were simple interval mapped using a modified Haley-Knott algorithm [36,37], weighted by the\nwithin strain variances. Genome-wide significance was\ncalculated by comparing the best likelihood ratio statistic\nof the original data set with the distribution of highest LRS\ncomputed for 10,000 permutations." + }, + { + "document_id": "9d225f6f-e434-45a7-b199-f3a09eda1d04", + "section_type": "main", + "text": "Next, we used GeneNetwork2, an online analysis tool and data repository containing\nlegacy SNP and transcriptome datasets to explore gene regulatory networks (Chesler et al. 2004; Mulligan et al.\n 2017). We conducted both eQTL and PheQTL-eQTL network analysis using several BXD RI gene expression\ndatasets from multiple brain regions (datasets documented in Supplementary Information) and using the\nentirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2 [BXDPublish; GN602]." + }, + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "section_type": "main", + "text": "Once the data is normalized appropriately (in our case, no normalization was required), the QTL\ncan be mapped. To do this, select the mapping tools drop down window (Figure 6). There are\nthree methods to choose from, GEMMA, Haley-Knott Regression, and R/qtl (Figure 6). Genomewide Efficient Mixed Model Analysis (GEMMA; github.com/genetics-statistics/GEMMA; (Zhou\nand Stephens, 2012) is a multivariate linear mixed model mapping tool that is used to map\nphenotypes with SNPs with a correction for kinship or any other covariate of interest. This\nability to account for covariates is highly useful, but also this increases the time taken for\ncomputations." + }, + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "section_type": "main", + "text": "Once the data is normalized appropriately (in our case, no normalization was required), the QTL\ncan be mapped. To do this, select the mapping tools drop down window (Figure 6). There are\nthree methods to choose from, GEMMA, Haley-Knott Regression, and R/qtl (Figure 6). Genomewide Efficient Mixed Model Analysis (GEMMA; github.com/genetics-statistics/GEMMA; (Zhou\nand Stephens, 2012) is a multivariate linear mixed model mapping tool that is used to map\nphenotypes with SNPs with a correction for kinship or any other covariate of interest. This\nability to account for covariates is highly useful, but also this increases the time taken for\ncomputations." + }, + { + "document_id": "8dad24f7-b658-44fa-af65-6f33db69c15a", + "section_type": "main", + "text": "The values were analysed by using\nthe software program MapManager QTX (KF Manley,\nhttp://www.mapmanger.org) [20] and WebQTL (http://\nwww.webqtl.org) [15, 16] in order to perform a genomewide search for mapping QTL. In this case, the user is not\nrequired to discriminate between ‘B’ and ‘D’ phenotypes.\n Rather, the quantitative phenotypic data for each RI\nstrain serve as the starting point for analysis. This results\nin statistics that are essentially two-tailed, more conservative than may be warranted in some situations with\nextreme differences between parental lines." + }, + { + "document_id": "89fdce49-cd76-446e-bc47-9484071f9d3e", + "section_type": "main", + "text": "GeneNetwork and WebQTL are our group’s first attempts to embrace these\nnew opportunities (Wang et al. 2003) and to generate\nan appropriate research environment that combines\ndata sets, statistical resources, and summaries of\nfindings—a knowledgebase (www.genenetwork.org).\n Mapping traits will become far easier; cloning allelic\nvariants for molecular and cellular phenotypes will\nprogress from difficult to trivial as it already has for\nmost cis-QTL with high LOD scores." + }, + { + "document_id": "18d12255-3cc6-415b-bd30-ff94bb087813", + "section_type": "main", + "text": "These estimates were uploaded to GeneNetwork (genenetwork.org;\nhttp://gn2.genenetwork.org; GN IDs 21497-21517) (Mulligan et al. , 2017; Parker et al. , 2017; Sloan et al. ,\n2016), and quantitative trait loci (QTL) were mapped.\n 2.14. QTL mapping\nQTL mapping allows the identification of linkage between any region of the genome, and a phenotype of\ninterest. The fast linear regression equations of Haley and Knott (Haley and Knott, 1992) were used for\ninitial QTL mapping. Using 5000 permutations of the phenotypes, genome-wide significant (p < 0.05), and\nsuggestive (p < 0.63) thresholds were calculated within GeneNetwork." + }, + { + "document_id": "4439ac39-e421-482f-9aa9-9ad11fa641c1", + "section_type": "main", + "text": "WebQTL is the primary module in the GeneNetwork online resource (www.genenetwork.org),\nand provides a powerful environment to analyze\ntraits controlled by genetic variants (Chesler et al.\n 2004; Wang et al. 2003). It includes data from many\n\n485\n\nFig. 2. Complexity of eQTL data. The graph shows a threedimensional schematic view of the high dimensionality of\nthe eQTL data set generated from the BXH/HXB RI strain\npanel (Hubner et al 2005; unpublished)." + }, + { + "document_id": "0e6c370f-b514-4551-b6ed-9cc72e6f6b75", + "section_type": "main", + "text": "GN spares the\nuser most of these problem. Data are formatted and normalized, and usually come with good\nmetadata (often in the form of links to more information). This greatly simplifies QTL and\neQTL analysis, candidate gene discovery, coexpression analysis, and hypothesis testing [3,\n10]." + }, + { + "document_id": "43407486-b9c2-487b-b19c-b605c4d201c6", + "section_type": "main", + "text": "GN spares the\nuser most of these problem. Data are formatted and normalized, and usually come with good\nmetadata (often in the form of links to more information). This greatly simplifies QTL and\neQTL analysis, candidate gene discovery, coexpression analysis, and hypothesis testing [3,\n10]." + }, + { + "document_id": "85ee9743-b34d-4d49-9017-d7d2e5d4b996", + "section_type": "main", + "text": "1 The\n\n2\n3\n4\n\nIntroduction\n\nModern high-throughput technologies generate large amounts of genomic, transcriptomic, proteomic and metabolomic data. However, existing open source web-based tools for QTL analysis, such as webQTL\n[358] and QTLNetwork [377], are not easily extendable to different settings and computationally scalable for whole genome analyses. xQTL\nworkbench makes it easy to analyse large and complex datasets using\nstate-of-the-art QTL mapping tools and to apply these methods to millions of phenotypes using parallelized ‘Big Data’ solutions [342]." + }, + { + "document_id": "516cc395-4e7c-4371-9444-24edb56a7233", + "section_type": "main", + "text": "In this section, we will\nfocus mainly on QTL analysis performed in F2 mice using the R package R/qtl. For a\nreview of GeneNetwork tools and functions, see Ref. 41.\n A variety of analytical methodologies are available in the R/qtl package, including,\ne.g. , composite interval mapping or Haley-Knott regression (see Ref. 42 for discussion).\n The “scanone” function in R/qtl is used to calculate log of the odds (LOD) scores. Permutation analysis (perm ¼ 1000) is used to establish the significance threshold for each\nphenotype (P < .05). Additive and/or interactive covariates can be added to the model\n(e.g." + }, + { + "document_id": "99eb95e6-f439-453e-b90f-4752f1b66d0b", + "section_type": "main", + "text": "able to estimate the quality of the several thousand\nQTL results that each data set typically produces.\n This direct replication clearly shows that many\neQTL, particularly cis-acting QTL, are high-quality,\nreplicable observations and that eQTL data sets are a\nvaluable means of understanding gene expression\nrelationships.\n Using our data, researchers without the luxury of\na confirmatory F2 data set can estimate the fraction\nof QTL in a similar RI data set that are likely to also\nbe observed in a relatively small F2 data set, and they\ncan select significance thresholds that reflect desired\nvalues of this fraction." + }, + { + "document_id": "bbf4a07f-b30d-4bd6-ba32-16ad470231b1", + "section_type": "main", + "text": "Genetic dissection of gene expression\n\n2.2.4\n\nDensity of the genetic grid in QTL analysis The computational\ndemand of QTL mapping can be decreased by using a sparser genetic grid\nfor a genome scan. Most of the currently used QTL mapping strategies are\nbased on interval mapping where QTL are evaluated at regular intervals\n(e.g. 1 cM) on the genetic map. In a situation where markers are fully informative Coffman et al. (2003) suggest that a genome scan using single marker\ninformation can be equally or even more powerful than analyses based on\nflanking markers. We evaluated three alternatives." + }, + { + "document_id": "8bb7e3b1-bdb0-4c54-a916-6424237616da", + "section_type": "main", + "text": "Expression QTLs Mapping\nSince we had not any co-segregated genetical marker, a simple query in related gene\nexpression database in GeneNetwrok resources was done to find the most biologically\nrelated genes to our candidate genes. We used the MDC/CAS/ICL Kidney 230A (Apr05)\nMAS5 database for above the purpose (for more information about this population reader\nconsult WebQtl site http://www.webqtl.org/). Using publicly available data on gene\nexpression, SNP linkage maps and all the related software’s freely available at WebQTL\nserver (www.genenetwork.org), we ran eQTL mapping to get insights into systems\ngenetics of candidate genes." + }, + { + "document_id": "f0bf9619-6bb9-41c7-9d2b-51d9b650d5b2", + "section_type": "main", + "text": "The raw microarray data is available from the Gene Expression\nOmnibus (GSE14563) as well as from WebQTL (Wang et al. 2003).\n MDP QTL Mapping\nHigh density single nucleotide polymorphism (SNP) data was used to perform eQTL mapping\nin the MDP (McClurg et al. 2007). Association mapping was carried out using FastMap (Gatti\net al. 2009) as detailed above. Population structure was identified using a PCA plot of the SNP\ndata and two major strata were identified; C57BL/6J, C57BL/10J, C57BLKS/J, C57BR/cdJ &\nC57L/J were in one stratum and the remaining strains were in the other." + }, + { + "document_id": "2845fea0-7cf7-4bb8-915e-ff13c41f0176", + "section_type": "main", + "text": "QTL mapping was performed using web-based complex\ntrait analysis (www. genenetwork.org) which uses QTL reaper software. A single marker regression\nacross all chromosomes was performed where a hypothetical QTL was evaluated at the location of\n8222 informative markers. At a single chromosomal level, interval mapping evaluates potential\nQTL at regular intervals and estimates the significance at each location with a graphical\nrepresentation of the likelihood ratio statistic (LRS). A permutation test establishes genome-wide\nsignificance criteria of 5% for the trait.\n Correlation analysis and gene network construction." + }, + { + "document_id": "2e0bbb7b-45cd-4208-b2f0-e229df86d8ff", + "section_type": "main", + "text": "Genetical genomics analysis\nQuantitative trait locus (QTL) mapping was performed for the\nsaline and ethanol treated RMA datasets, as well as the saline vs\nethanol S-score dataset, using a subset of informative microsatellite\nand SNP markers that have been used to genotype the BXD\nfamily [37,38], and are available from GeneNetwork (genenetwork.org/genotypes/BXD.geno). Linkage between genotypes and\nexpression phenotypes was assessed by performing Haley-Knott\nregression using R/qtl [39]. Genome-wide adjusted p-values were\nderived using distributions of maximum LOD scores obtained\nfrom 1,000 permutations of each probe-set’s expression data." + }, + { + "document_id": "bbd1d762-faab-409d-9243-bc94023e16c0", + "section_type": "main", + "text": "WebQTL contains\ncomprehensive, manually curated, publicly available data\nfor phenotypic and gene expression profiling of a number\nof RI and F2 crosses in both mice and rats along with the\ndense genetic marker maps for these strains. These data\ncan be used to search for correlations between the phenotypes, gene expression, and genetic markers, that is, to\nperform in silico genotype-phenotype association analysis. The inherent significance of the defined reference genetic populations, such as BXD RI strains, is in the ability\nto connect historical data generated in many laboratories\nto the exact genetic map of each strain." + }, + { + "document_id": "cc4fd4f5-b5b8-419e-9631-2df633d53570", + "section_type": "main", + "text": "QTL mapping was carried out using simple and\ncomposite interval mapping in GeneNetwork (http://\nwww.genenetwork.org). Candidate genes in QTL regions\nwere ranked using PGMapper. SNP genotypes of candidate genes were verified directly using PCR amplification and sequencing." + }, + { + "document_id": "b5c36c1e-458e-4009-818e-9c0c2ee23e45", + "section_type": "main", + "text": "eQTL mapping\n\nQTL mapping was performed with GeneNetwork, an online bioinformatics resource\nfeaturing tools for systems genetic and complex trait analysis [9, 35]. QTL mapping\ninvolves entering VMB and CP iron data (strain means and SEM) as quantitative traits; the\nsoftware generates whole-genome interval maps for each trait. The interval maps graphically\nillustrate phenotype–genotype associations as peaks (QTL) indicating the strength of\nassociation between genomic polymorphisms and the quantitative trait throughout the\ngenome." + }, + { + "document_id": "6b5ae9e0-ea61-45e2-9b6d-663b532c1a81", + "section_type": "main", + "text": "An automated QTL mapping strategy needs to rely strictly on\nstatistical measures to highlight candidate regions because manual\ninspection of QTL results across the genome for individual traits,\nwhich is common in standard QTL mapping, is not feasible for\nevery individual gene transcript. In this study, we will apply various\n\n© The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org\n\n2383\nÖ.Carlborg et al.\n\n standard QTL mapping scenarios to analyse data from one of the\nfirst publicly available genetical genomics datasets (Chesler et al. ,\n2005)." + } + ], + "document_id": "39076B38EDAF24ECEEB91924D370F4AD", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "GeneNetwork", + "QTL", + "mapping", + "genotype", + "phenotype", + "interval&mapping", + "composite&interval&mapping", + "marker®ression", + "eQTL", + "haplotype" + ], + "metadata": [ + { + "object": "The genotype GG group had higher consumption of Remifentanil than the genotype AA group P<0.05, but the genotype AG group was not different from the genotype AA and GG groups P>0.05. The analepsia time, autonomous respiratory recovery time, and orientation recovery time in the genotype GG group were longer than in the genotype AA group P<0.05, but the genotype AG group was not different from the genotype AA and GG.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab818259" + }, + { + "object": "We showed that Rheumatoid was more likely with the AA genotype compared with the AG genotype of SNP rs2977537, and with the TT genotype, or the GG genotype compared with the GT genotype of rs2929973, and with the AA genotype or GG genotype vs the AG genotype of rs2977530", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1013556" + }, + { + "object": "APOE genotype and haplotype distributions differ significantly along the age classes Genotype: p=0.014; Haplotype: p=0.005 with APOE*epsilon4 genotype status and haplotype displaying negative association Genotype: O.R.=0.377, p=0.002, Haplotype: O.R.=0.447, p=0.005", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab77498" + }, + { + "object": "LTA4H genotype predicted survival of HIV-uninfected patients, with TT-genotype patients significantly more likely to survive tuberculous meningitis than CC-genotype patients. LTA4H genotype and HIV infection influence pretreatment inflammatory phenotype and survival from tuberculous meningitis. LTA4H genotype may predict adjunctive corticosteroid responsiveness in HIV-uninfected individuals.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab464785" + }, + { + "object": "A haplotype block across a 24-kb region within the TOX2 gene reached genome-wide significance in haplotype-block-based regional heritability mapping. Single-SNP- and haplotype-based association tests demonstrated that five of nine genotyped SNPs and two haplotypes within this block were significantly associated with major depressive disorder.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab17193" + }, + { + "object": "Apa1 Aa genotype compared to AA genotype had odds ratios of 1.65, 1.79 and 1.64 respectively p > 0.05. In TMJ-ID women versus healthy women Aa genotype had 2.06 fold p = 0.15 odds compared to AA genotype. In TMJ-ID women versus healthy women Aa genotype had 2.06 fold p = 0.15 odds compared to AA genotype. our results do not confirm susceptibility of VDR polymorphisms to TMJ-ID/TMJOA", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab76039" + }, + { + "object": "DICER rs3742330 AG+GG genotype was associated with more advanced T stage compared to AA genotype P=0.009. More patients with XPO5 rs2257082 CC genotype had poorly differentiated tumors compared with CT+TT genotype carriers.., carriers of RAN rs14035 CC genotype had higher three-year OS rate than carriers of CT+TT genotype adjusted HR 3.174; 95% CI 1.010, 9.973; P=0.048.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab229028" + }, + { + "object": "The antiproteinuric response to olmesartan by genotype was: genotype TT: 76.8%; genotype MM: 67.3%; genotype MT: 65.8%, significantly higher P<.05 for genotype TT compared to genotypes MM and MT.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab780501" + }, + { + "object": "A significant QTL for intraocular pressure IOP in 10-13 months-old BXD family mice maps to proximal Chr 5 15 to 18 Mb near Gnai1 Record ID 12309. Data by Monica Jablonski and Lu Lu. Gnai1 is a cis eQTL in eye LRS of 18.6. Other candidates include Cacna2d1 and Pclo and both are also cis eQTLs. Hgf is also a candidate. This is the same region as the eye weight QTL possibly Hgf of Zhou and Williams 1999. This QTL is not significant at younger ages.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab130" + }, + { + "object": "The antiproteinuric response to olmesartan by genotype was: DD: 71.4%; genotype ID: 60.6%, genotype II: 34.8%, significantly higher P<.05 for genotype DD compared to genotypes ID and II, and also P<.05 for genotype ID compared to II.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab780500" + } + ], + "question": "what type of dataset is useful for qtl mapping analysis in genenetwork2?", + "subquestions": null, + "task_id": "39076B38EDAF24ECEEB91924D370F4AD", + "usage": { + "chatgpt": 5013, + "gpt-4": 3398, + "gpt-4-turbo-preview": 2412 + }, + "user_id": 2 + }, + "document_id": "39076B38EDAF24ECEEB91924D370F4AD", + "task_id": "39076B38EDAF24ECEEB91924D370F4AD" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/04.json b/gnqa/paper1_eval/src/data/responses/general/experts/04.json new file mode 100644 index 0000000..2f84ab1 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/04.json @@ -0,0 +1,392 @@ +{ + "created_at": "2024-06-01T14:24:19.558227Z", + "data": { + "amplify": false, + "answer": "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "context": { + "30eabd29-2f48-459a-b162-bd90d99f1411": [ + { + "document_id": "30eabd29-2f48-459a-b162-bd90d99f1411", + "text": "\n\nA number of additional ethical implications must be considered.Associating financial investments with the prevention of disease, especially where reproductive decisions are involved, requires sensitivity, caution, and ethical rigor.Funding decisions based on imputed cost-savings must not result in implicit pressure on individuals to violate personal ethics to reduce financial burden on society.As discussions regarding prenatal testing have demonstrated, 35 is risk that \"routinization\" of testing may lead to social or medical expectations of testing in all eligible individuals.These expectations, if linked with financial incentives for the health system, could risk applying implicit pressure on serious, and potentially irreversible, personal decisions.Such expectations, if applied at the population level, could risk becoming normalized, compromising the values of informed consent and individual autonomy." + }, + { + "document_id": "30eabd29-2f48-459a-b162-bd90d99f1411", + "text": "\n\nWith regard to pregnancies affected by a genetic condition identified through population carrier screening, we modeled the decision to terminate affected pregnancies conservatively (0.50).This is despite the literature suggesting rates above 0.90 for elective TOP for conditions such as Down syndrome 33 and SMA. 34We recognize this issue is controversial, and that laws and ethical positions vary considerably between countries/ jurisdictions.Variations in population attitudes based on age, religion, and other factors, as well as the criticality of preserving individual choice, were acknowledged in adopting this highly conservative estimate." + } + ], + "56cf7be3-8c73-498d-b48f-8d99592b0213": [ + { + "document_id": "56cf7be3-8c73-498d-b48f-8d99592b0213", + "text": "\n\nThe use of genetic testing from pre-conception through adulthood is expanding rapidly.As a result of this expansion, new ethical issues are emerging related to genetic testing and informed consent.These new issues create ethical challenges for nurses and all healthcare providers.Currently expanding areas include newborn screening and genetic testing of children.These new ethical challenges will be described below." + }, + { + "document_id": "56cf7be3-8c73-498d-b48f-8d99592b0213", + "text": "The use of genetic testing from pre-conception through adulthood is expanding rapidly. Psychological risks for parents who are carriers may include parental guilt." + } + ], + "64d87c52-1185-4080-8d06-134c32dae5fd": [ + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "text": "\n\nEthnic and cultural backgrounds may also play a role in the decisions that families make regarding prenatal testing.Moyer et al. (1999) concluded that Caucasian women more often undergo prenatal diagnoses than African American or Asian women, or Latinas.Furthermore, Awwad et al. (2008) found American couples less inclined to involve extended relatives in the prenatal decision-making process than Native Palestinian couples.Both of these examples clearly indicate that cultural differences can impact the ways in which families negotiate prenatal decisions.Further research needs to investigate how different families engage in such discussions and decision-making processes, especially as prenatal testing becomes more common and better able to predict or prevent a wider range of genetic conditions.Tightly closed ethnic groups remain at high risk of serving as carriers for genetic mutations, but the management of this possibility varies greatly.For example, some Ashkenazi Jewish groups use screening for mutations for Tay-Sachs disease (TSD) as the basis for rabbinical marriage advice; whereas, children born to Amish families in Pennsylvania more often present with glutaric aciduria type 1 (GA1) but, given their beliefs, parents tend not to accept prenatal testing because of the implication of abortion (McKusick, 2000)." + }, + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "text": "\n\nResearchers studying factors that contribute toward a couple's choice to undergo prenatal testing have determined that partners base their decision upon several factors, including, but not limited to: parental beliefs about abortion, attitudes regarding disability and their \"perceptions of the usefulness of having the information revealed by genetic tests\" (Moyer et al., 1999, p. 522).Abortion beliefs constitute a key issue in the decision-making process.Even though a majority of parents receiving abnormal prenatal test results terminate their pregnancies (Redlinger-Grosse, Bernhardt, Berg, Muenke, & Biesecker, 2002), Moyer et al. noted that, when asked, more families reported that they would make use of prenatal testing than would be willing to terminate a pregnancy.The decision to continue or terminate a pregnancy after prenatal testing Downloaded by [University of the Sunshine Coast] at 10:32 05 August 2017 comprises a joint decision between both parents (e.g., Awwad et al., 2008;Beeson & Golbus, 1985); however, the nature of the conversations leading to the decision and the involvement of extended family members in the decisionmaking process remains highly understudied." + }, + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "text": "The Genetic Divide(s) and Communication\n\nThe ability of scientists to \"map\" disease through several generations (Collins, 1999) raises practical and ethical issues of access to resulting opportunities and creates family communication challenges.Currently, prenatal testing for chromosomal diseases has become increasingly common (Moyer et al., 1999).Options such as pre-implantation genetic diagnosis (PGD) can identify over 1,250 disease-related mutations creating an opportunity for parents to select unaffected embryos for implantation in the womb (R. M. Green, 2008).Test results provide potential parents with information that may lead to decisions involving intervention in the genetic makeup of future children.Although some families welcome such options, others may be unable or unwilling to consider such procedures, due to fi nancial concerns or moral/ethical/religious beliefs." + } + ], + "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa": [ + { + "document_id": "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa", + "text": "Privacy Issues\n\nFinally, privacy issues should be seriously considered when the use of genetic testing is contemplated, especially with respect to whole-genome sequencing of healthy people.It is an unanswered question under what circumstances, to what extent, and by what means genetic data should be incorporated into the medical record.Although easy access to such data could be helpful to providers in improving patient care, it remains to be seen how other parties (eg, insurance companies) might act on the data in ways that do not benefit patients.The US Congress acted to prohibit discrimination by employers and health insurers on the basis of genetic testing with the Genetic Information Nondiscrimination Act in 2008, but further safeguards will undoubtedly be needed as the health implications of genetic data become clearer." + } + ], + "782103fd-2cb6-44c8-9b39-d82430d335c9": [ + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "text": "\n\nThe ethical evaluation of genetic testing in children is traditionally based on the balance of clinical benefits and risks (American Society of Human Genetics Board of Directors and the American College of Medical Genetics All correspondence concerning this article should be addressed to Benjamin Wilfond, MD, Treuman Katz Center for Pediatric Bioethics, Seattle Children's Hospital, Metropolitan Park West M/S: MPW 8-2, 1100 Olive Way, Room 876, Seattle WA 98101, USA.E-mail: benjamin.wilfond@seattlechildrens.org Board of Directors, 1995;Andrews, Fullerton, Holtzman, & Motolsky, 1994;Clarke, 1994;Wertz, Fanos, & Reilly, 1994).In the early 1990s, when there were only scant data about children who had received genetic tests results, the presumption was to give greater weight to the potential risks and to restrict testing.However, this criterion is not necessarily consistent with the general practice of respecting broad parental discretion in health care decisionmaking for and on behalf of their children.In general, parents are the presumed decision makers for their children and their decisions are respected unless they are abusive or neglectful (Buchanan & Brock, 1989;Goldstein, Freud, & Solnit, 1979;Ross, 1998).The tension between assessments of benefits and risks made by health care providers and policy makers, and the procedural respect owed to parental authority will be clearly tested as the ability to conduct and interpret whole-genome sequencing and related technologies gain in momentum." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "text": "Ethical Considerations in Developing Policy for ''Comprehensive'' Genomic Testing\n\nIn the near future, genomic testing is likely to become more accessible and will provide both information about the risks of common conditions such as heart disease, diabetes, and hypertension as well as predictions about individual responses to specific pharmaceuticals and other medical therapies (Aspinall & Hamermesh, 2007).Over time, the number and range of conditions for which such testing is available is likely to expand to include more behavioral traits, ranging from information about anxiety and depression, to attention and addiction (Rothstein, 2005)." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "text": "\nObjective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical benefits and risks.However, this focus can be inconsistent with the general practice of respecting parental decision-making about their children's health care.We argue that respect for parental decision-making should play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions to use emerging genomic technologies.Methods Genomic testing involves the examination of thousands of DNA markers spanning genes throughout the genome and their interrelationships, yielding virtually limitless interpretations.We presume that parents and providers should proceed cautiously in applying genomic testing in children, as we explore how genomic testing will stress the fault lines of the traditional ethical analysis.Results Empirical data about the psychosocial risks and benefits of genetic testing of children do not reveal serious harms, yet virtually no such data exist yet about genomic testing.Unless empirical social and behavioral data indicate that genomic testing is highly likely to cause serious harms to the children, parental decisions to obtain comprehensive genomic testing in their children should be respected.Once comprehensive genomic testing of children becomes routine, resultant information may be more easily integrated by families than anticipated.Conclusions Research on the social and behavioral impact of comprehensive genomic testing on children and their families is needed to further inform parents, clinicians, and policy makers." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "text": "\n\nObjective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical benefits and risks.However, this focus can be inconsistent with the general practice of respecting parental decision-making about their children's health care.We argue that respect for parental decision-making should play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions to use emerging genomic technologies.Methods Genomic testing involves the examination of thousands of DNA markers spanning genes throughout the genome and their interrelationships, yielding virtually limitless interpretations.We presume that parents and providers should proceed cautiously in applying genomic testing in children, as we explore how genomic testing will stress the fault lines of the traditional ethical analysis.Results Empirical data about the psychosocial risks and benefits of genetic testing of children do not reveal serious harms, yet virtually no such data exist yet about genomic testing.Unless empirical social and behavioral data indicate that genomic testing is highly likely to cause serious harms to the children, parental decisions to obtain comprehensive genomic testing in their children should be respected.Once comprehensive genomic testing of children becomes routine, resultant information may be more easily integrated by families than anticipated.Conclusions Research on the social and behavioral impact of comprehensive genomic testing on children and their families is needed to further inform parents, clinicians, and policy makers." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "text": "\n\nTo the extent that ''personal meaning'' gains wider acceptance as a legitimate criterion for expanding the availability of new tests and applications of genomic technology, the current policies and practices of restricting some genetic testing of children and mandating other tests will need to be reevaluated.There will be some parents who will find the information that becomes available through new technologies and data useful in shaping their parenting practices, while others will be more skeptical of their value.These disparate parental judgments may be independent of professional assessments of clinical validity and utility.Extrapolating from the empirical data about predictive genetic testing of children in at-risk families discussed earlier, we speculate that once comprehensive genomic testing of children becomes routine, the information may be more easily integrated by families than might be predicted.This is not meant to imply that whatever information parents want about their children should be provided carte blanche.Clearly, education and counseling will be crucial to ensure that families understand the limitations of the information.However, restrictions and mandates should be based on a criterion of risk of serious harm (Diekema, 2004).Given the lack of data confirming harm and the related data that indicate children may fare better than anticipated, such restrictions and mandates cannot be justified.Policies and practices will also need to clarify the role of the older adolescent in the decision-making process, although the issues related to balancing and assessing parental and adolescent interests and preferences goes beyond the focus of this article.This is also not meant to ignore the professional and moral obligation to educate parents and to help parents make good decisions on behalf of their children.It is morally appropriate for providers to strongly recommend particular tests in infancy and young childhood (i.e., PKU testing), and to strongly discourage other tests (e.g., ApoE testing of children for adult onset Alzheimer disease and heart disease because ApoE is not predictive but only provides an increased relative risk and has limited sensitivity and specificity) (Roberts, Cupples, Relkin, Whitehouse, & Green, 2005).Selective and directive recommendations are a routine aspect of pediatric practice.However, it will become increasingly important for professional organizations to begin to reconcile their support for mandatory genetic testing for some conditions and their support for restrictions for other conditions with the broad discretion that parents have and need in the health care arena in order to promote their children's well-being." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "text": "\n\nWhat limits should be imposed, if any, need to be determined prior to commercial feasibility.In this article, we consider how genetic testing decisions for children have been made traditionally and how the anticipation of comprehensive genomic testing in the near future will stress the fault lines of traditional approaches.The potential for comprehensive genomic testing in children could shift the equilibrium towards expanding or reducing parental discretion, and forces us to reexamine the evidence for our genetic testing policies and practices.We will highlight specific domains where further empirical social and behavioral research is necessary to inform policy and practice." + } + ], + "93dc581e-5e45-48b4-b82f-35e32d7bd58e": [ + { + "document_id": "93dc581e-5e45-48b4-b82f-35e32d7bd58e", + "text": "\n\nPrenatal genetics is largely practiced by maternal-fetal medicine specialists due to severe deficiency in the number of qualified clinical geneticists.Recent years have witnessed a tremendous growth in the demand for chorionic villous sampling and amniocentesis for the diagnosis of single gene disorders.At KFSHRC alone, the number of prenatal samples that are tested for single gene disorders has increased from 5 in 2004 to 250 in 2013.Therapeutic abortion is permitted by law if performed within 120 days from the time of fertilization in order to comply with the Islamic view of the timing of ensoulment (Alkuraya and Kilani 2001).However, the approved indication for the procedure, which is \"severe malformation\", must be authorized by three attending-level physicians.The definition of \"severe\" is left to the discretion of the medical team after consulting with the family.For example, intellectual disability is a common indication for many therapeutic abortion procedures.Contrary to commonly held views, we have shown that early prenatal diagnosis is the method of choice for couples who had one or more children with single gene disorders, as long as they are provided with a culturally sensitive genetic counseling that addresses their religious and cultural concerns (Alkuraya and Kilani 2001).Nearly 45% of these couples opt for early prenatal diagnosis compared to 35% who choose preimplantation genetic diagnosis (PGD) (Alkuraya 2013a).PGD is available freely at KFSHRC but is also provided by the private sector.Noninvasive prenatal screening using cell-free fetal DNA in maternal blood is quickly becoming integrated in prenatal care.KFSHRC offers this test routinely to all pregnant women regardless of their perceived risk and the MOH is considering making this test available throughout its vast network of hospitals and medical centers." + } + ], + "9f21007a-1487-46d8-8e9e-cde8df4af6d5": [ + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "\n\nSocial and psychological implications of accessing genetic services and information." + } + ], + "a4b0655d-895c-4368-9401-ee2903b15d42": [ + { + "document_id": "a4b0655d-895c-4368-9401-ee2903b15d42", + "text": "\n\nA corollary of the predictive power of genetic information is the limited ability to prevent or treat many conditions with significant genetic factors involved.Indeed, virtually all of the complex ethical and legal issues relevant to genetic testing would disappear if there were effective preventions or treatments available for genetic conditions.The ability to predict future disease in conjunction with a limited ability to do much about it has important social and psychological implications that must be addressed in conducting genetic research." + } + ], + "b0b60080-2338-411b-bc44-1f5626a3c442": [ + { + "document_id": "b0b60080-2338-411b-bc44-1f5626a3c442", + "text": "\n\nInterpretations of the literature will likely mirror the priorities and evaluative tendencies of the reader.Are you willing to accept the overall trends in genetic and genomic testing evaluation and to trust that the existing clinical approaches will apply informed consent appropriately while identifying and supporting the rare individual who has a serious adverse response to the testing?If so, you might advocate that attention be turned more toward other issues relevant to the effective implementation of genetic and genomic testing.Or do you feel a strong need to understand in more detail the possible psychosocial harms of the testing, particularly the subtler impacts or responses of individuals who do not fit the norm?In that case, you would likely encourage renewed and innovative efforts to study the psychosocial consequences of the receipt of risk information from genetic and genomic testing." + } + ], + "f7fe5d02-ee7c-4ec2-b6c4-ca9aa5efb41f": [ + { + "document_id": "f7fe5d02-ee7c-4ec2-b6c4-ca9aa5efb41f", + "text": "\n\nOther social issues require our attention if genomic medicine is to benefit our patients.How should genetic tests be regulated?What, if any, are the appropriate uses of direct-to-consumer marketing of genetic tests?The Internet has recently had a proliferation of genetic-testing sites that feature claims grounded in greed and pseudoscience, rather than in data or reality.How will health care providers and the public distinguish between these and responsible testing services, whether they are available through the Internet or in the hospital?" + } + ], + "f97246cb-7a71-409b-bb1a-dd01a5ef5c5e": [ + { + "document_id": "f97246cb-7a71-409b-bb1a-dd01a5ef5c5e", + "text": "Environmental Factors\n\nAs widespread use of genetic testing increases, it is the responsibility of the medical community to ensure its equitable use across socioeconomic and cultural spectrums." + } + ] + }, + "data_source": [ + { + "document_id": "30eabd29-2f48-459a-b162-bd90d99f1411", + "section_type": "main", + "text": "\n\nA number of additional ethical implications must be considered.Associating financial investments with the prevention of disease, especially where reproductive decisions are involved, requires sensitivity, caution, and ethical rigor.Funding decisions based on imputed cost-savings must not result in implicit pressure on individuals to violate personal ethics to reduce financial burden on society.As discussions regarding prenatal testing have demonstrated, 35 is risk that \"routinization\" of testing may lead to social or medical expectations of testing in all eligible individuals.These expectations, if linked with financial incentives for the health system, could risk applying implicit pressure on serious, and potentially irreversible, personal decisions.Such expectations, if applied at the population level, could risk becoming normalized, compromising the values of informed consent and individual autonomy." + }, + { + "document_id": "56cf7be3-8c73-498d-b48f-8d99592b0213", + "section_type": "main", + "text": "\n\nThe use of genetic testing from pre-conception through adulthood is expanding rapidly.As a result of this expansion, new ethical issues are emerging related to genetic testing and informed consent.These new issues create ethical challenges for nurses and all healthcare providers.Currently expanding areas include newborn screening and genetic testing of children.These new ethical challenges will be described below." + }, + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "section_type": "main", + "text": "\n\nResearchers studying factors that contribute toward a couple's choice to undergo prenatal testing have determined that partners base their decision upon several factors, including, but not limited to: parental beliefs about abortion, attitudes regarding disability and their \"perceptions of the usefulness of having the information revealed by genetic tests\" (Moyer et al., 1999, p. 522).Abortion beliefs constitute a key issue in the decision-making process.Even though a majority of parents receiving abnormal prenatal test results terminate their pregnancies (Redlinger-Grosse, Bernhardt, Berg, Muenke, & Biesecker, 2002), Moyer et al. noted that, when asked, more families reported that they would make use of prenatal testing than would be willing to terminate a pregnancy.The decision to continue or terminate a pregnancy after prenatal testing Downloaded by [University of the Sunshine Coast] at 10:32 05 August 2017 comprises a joint decision between both parents (e.g., Awwad et al., 2008;Beeson & Golbus, 1985); however, the nature of the conversations leading to the decision and the involvement of extended family members in the decisionmaking process remains highly understudied." + }, + { + "document_id": "30eabd29-2f48-459a-b162-bd90d99f1411", + "section_type": "main", + "text": "\n\nWith regard to pregnancies affected by a genetic condition identified through population carrier screening, we modeled the decision to terminate affected pregnancies conservatively (0.50).This is despite the literature suggesting rates above 0.90 for elective TOP for conditions such as Down syndrome 33 and SMA. 34We recognize this issue is controversial, and that laws and ethical positions vary considerably between countries/ jurisdictions.Variations in population attitudes based on age, religion, and other factors, as well as the criticality of preserving individual choice, were acknowledged in adopting this highly conservative estimate." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "section_type": "main", + "text": "\n\nThe ethical evaluation of genetic testing in children is traditionally based on the balance of clinical benefits and risks (American Society of Human Genetics Board of Directors and the American College of Medical Genetics All correspondence concerning this article should be addressed to Benjamin Wilfond, MD, Treuman Katz Center for Pediatric Bioethics, Seattle Children's Hospital, Metropolitan Park West M/S: MPW 8-2, 1100 Olive Way, Room 876, Seattle WA 98101, USA.E-mail: benjamin.wilfond@seattlechildrens.org Board of Directors, 1995;Andrews, Fullerton, Holtzman, & Motolsky, 1994;Clarke, 1994;Wertz, Fanos, & Reilly, 1994).In the early 1990s, when there were only scant data about children who had received genetic tests results, the presumption was to give greater weight to the potential risks and to restrict testing.However, this criterion is not necessarily consistent with the general practice of respecting broad parental discretion in health care decisionmaking for and on behalf of their children.In general, parents are the presumed decision makers for their children and their decisions are respected unless they are abusive or neglectful (Buchanan & Brock, 1989;Goldstein, Freud, & Solnit, 1979;Ross, 1998).The tension between assessments of benefits and risks made by health care providers and policy makers, and the procedural respect owed to parental authority will be clearly tested as the ability to conduct and interpret whole-genome sequencing and related technologies gain in momentum." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "section_type": "main", + "text": "Ethical Considerations in Developing Policy for ''Comprehensive'' Genomic Testing\n\nIn the near future, genomic testing is likely to become more accessible and will provide both information about the risks of common conditions such as heart disease, diabetes, and hypertension as well as predictions about individual responses to specific pharmaceuticals and other medical therapies (Aspinall & Hamermesh, 2007).Over time, the number and range of conditions for which such testing is available is likely to expand to include more behavioral traits, ranging from information about anxiety and depression, to attention and addiction (Rothstein, 2005)." + }, + { + "document_id": "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa", + "section_type": "main", + "text": "Privacy Issues\n\nFinally, privacy issues should be seriously considered when the use of genetic testing is contemplated, especially with respect to whole-genome sequencing of healthy people.It is an unanswered question under what circumstances, to what extent, and by what means genetic data should be incorporated into the medical record.Although easy access to such data could be helpful to providers in improving patient care, it remains to be seen how other parties (eg, insurance companies) might act on the data in ways that do not benefit patients.The US Congress acted to prohibit discrimination by employers and health insurers on the basis of genetic testing with the Genetic Information Nondiscrimination Act in 2008, but further safeguards will undoubtedly be needed as the health implications of genetic data become clearer." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "section_type": "abstract", + "text": "\nObjective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical benefits and risks.However, this focus can be inconsistent with the general practice of respecting parental decision-making about their children's health care.We argue that respect for parental decision-making should play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions to use emerging genomic technologies.Methods Genomic testing involves the examination of thousands of DNA markers spanning genes throughout the genome and their interrelationships, yielding virtually limitless interpretations.We presume that parents and providers should proceed cautiously in applying genomic testing in children, as we explore how genomic testing will stress the fault lines of the traditional ethical analysis.Results Empirical data about the psychosocial risks and benefits of genetic testing of children do not reveal serious harms, yet virtually no such data exist yet about genomic testing.Unless empirical social and behavioral data indicate that genomic testing is highly likely to cause serious harms to the children, parental decisions to obtain comprehensive genomic testing in their children should be respected.Once comprehensive genomic testing of children becomes routine, resultant information may be more easily integrated by families than anticipated.Conclusions Research on the social and behavioral impact of comprehensive genomic testing on children and their families is needed to further inform parents, clinicians, and policy makers." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "section_type": "main", + "text": "\n\nObjective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical benefits and risks.However, this focus can be inconsistent with the general practice of respecting parental decision-making about their children's health care.We argue that respect for parental decision-making should play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions to use emerging genomic technologies.Methods Genomic testing involves the examination of thousands of DNA markers spanning genes throughout the genome and their interrelationships, yielding virtually limitless interpretations.We presume that parents and providers should proceed cautiously in applying genomic testing in children, as we explore how genomic testing will stress the fault lines of the traditional ethical analysis.Results Empirical data about the psychosocial risks and benefits of genetic testing of children do not reveal serious harms, yet virtually no such data exist yet about genomic testing.Unless empirical social and behavioral data indicate that genomic testing is highly likely to cause serious harms to the children, parental decisions to obtain comprehensive genomic testing in their children should be respected.Once comprehensive genomic testing of children becomes routine, resultant information may be more easily integrated by families than anticipated.Conclusions Research on the social and behavioral impact of comprehensive genomic testing on children and their families is needed to further inform parents, clinicians, and policy makers." + }, + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "section_type": "main", + "text": "The Genetic Divide(s) and Communication\n\nThe ability of scientists to \"map\" disease through several generations (Collins, 1999) raises practical and ethical issues of access to resulting opportunities and creates family communication challenges.Currently, prenatal testing for chromosomal diseases has become increasingly common (Moyer et al., 1999).Options such as pre-implantation genetic diagnosis (PGD) can identify over 1,250 disease-related mutations creating an opportunity for parents to select unaffected embryos for implantation in the womb (R. M. Green, 2008).Test results provide potential parents with information that may lead to decisions involving intervention in the genetic makeup of future children.Although some families welcome such options, others may be unable or unwilling to consider such procedures, due to fi nancial concerns or moral/ethical/religious beliefs." + }, + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "section_type": "main", + "text": "\n\nEthnic and cultural backgrounds may also play a role in the decisions that families make regarding prenatal testing.Moyer et al. (1999) concluded that Caucasian women more often undergo prenatal diagnoses than African American or Asian women, or Latinas.Furthermore, Awwad et al. (2008) found American couples less inclined to involve extended relatives in the prenatal decision-making process than Native Palestinian couples.Both of these examples clearly indicate that cultural differences can impact the ways in which families negotiate prenatal decisions.Further research needs to investigate how different families engage in such discussions and decision-making processes, especially as prenatal testing becomes more common and better able to predict or prevent a wider range of genetic conditions.Tightly closed ethnic groups remain at high risk of serving as carriers for genetic mutations, but the management of this possibility varies greatly.For example, some Ashkenazi Jewish groups use screening for mutations for Tay-Sachs disease (TSD) as the basis for rabbinical marriage advice; whereas, children born to Amish families in Pennsylvania more often present with glutaric aciduria type 1 (GA1) but, given their beliefs, parents tend not to accept prenatal testing because of the implication of abortion (McKusick, 2000)." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "section_type": "main", + "text": "\n\nTo the extent that ''personal meaning'' gains wider acceptance as a legitimate criterion for expanding the availability of new tests and applications of genomic technology, the current policies and practices of restricting some genetic testing of children and mandating other tests will need to be reevaluated.There will be some parents who will find the information that becomes available through new technologies and data useful in shaping their parenting practices, while others will be more skeptical of their value.These disparate parental judgments may be independent of professional assessments of clinical validity and utility.Extrapolating from the empirical data about predictive genetic testing of children in at-risk families discussed earlier, we speculate that once comprehensive genomic testing of children becomes routine, the information may be more easily integrated by families than might be predicted.This is not meant to imply that whatever information parents want about their children should be provided carte blanche.Clearly, education and counseling will be crucial to ensure that families understand the limitations of the information.However, restrictions and mandates should be based on a criterion of risk of serious harm (Diekema, 2004).Given the lack of data confirming harm and the related data that indicate children may fare better than anticipated, such restrictions and mandates cannot be justified.Policies and practices will also need to clarify the role of the older adolescent in the decision-making process, although the issues related to balancing and assessing parental and adolescent interests and preferences goes beyond the focus of this article.This is also not meant to ignore the professional and moral obligation to educate parents and to help parents make good decisions on behalf of their children.It is morally appropriate for providers to strongly recommend particular tests in infancy and young childhood (i.e., PKU testing), and to strongly discourage other tests (e.g., ApoE testing of children for adult onset Alzheimer disease and heart disease because ApoE is not predictive but only provides an increased relative risk and has limited sensitivity and specificity) (Roberts, Cupples, Relkin, Whitehouse, & Green, 2005).Selective and directive recommendations are a routine aspect of pediatric practice.However, it will become increasingly important for professional organizations to begin to reconcile their support for mandatory genetic testing for some conditions and their support for restrictions for other conditions with the broad discretion that parents have and need in the health care arena in order to promote their children's well-being." + }, + { + "document_id": "f97246cb-7a71-409b-bb1a-dd01a5ef5c5e", + "section_type": "main", + "text": "Environmental Factors\n\nAs widespread use of genetic testing increases, it is the responsibility of the medical community to ensure its equitable use across socioeconomic and cultural spectrums." + }, + { + "document_id": "93dc581e-5e45-48b4-b82f-35e32d7bd58e", + "section_type": "main", + "text": "\n\nPrenatal genetics is largely practiced by maternal-fetal medicine specialists due to severe deficiency in the number of qualified clinical geneticists.Recent years have witnessed a tremendous growth in the demand for chorionic villous sampling and amniocentesis for the diagnosis of single gene disorders.At KFSHRC alone, the number of prenatal samples that are tested for single gene disorders has increased from 5 in 2004 to 250 in 2013.Therapeutic abortion is permitted by law if performed within 120 days from the time of fertilization in order to comply with the Islamic view of the timing of ensoulment (Alkuraya and Kilani 2001).However, the approved indication for the procedure, which is \"severe malformation\", must be authorized by three attending-level physicians.The definition of \"severe\" is left to the discretion of the medical team after consulting with the family.For example, intellectual disability is a common indication for many therapeutic abortion procedures.Contrary to commonly held views, we have shown that early prenatal diagnosis is the method of choice for couples who had one or more children with single gene disorders, as long as they are provided with a culturally sensitive genetic counseling that addresses their religious and cultural concerns (Alkuraya and Kilani 2001).Nearly 45% of these couples opt for early prenatal diagnosis compared to 35% who choose preimplantation genetic diagnosis (PGD) (Alkuraya 2013a).PGD is available freely at KFSHRC but is also provided by the private sector.Noninvasive prenatal screening using cell-free fetal DNA in maternal blood is quickly becoming integrated in prenatal care.KFSHRC offers this test routinely to all pregnant women regardless of their perceived risk and the MOH is considering making this test available throughout its vast network of hospitals and medical centers." + }, + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "section_type": "main", + "text": "\n\nSocial and psychological implications of accessing genetic services and information." + }, + { + "document_id": "a4b0655d-895c-4368-9401-ee2903b15d42", + "section_type": "main", + "text": "\n\nA corollary of the predictive power of genetic information is the limited ability to prevent or treat many conditions with significant genetic factors involved.Indeed, virtually all of the complex ethical and legal issues relevant to genetic testing would disappear if there were effective preventions or treatments available for genetic conditions.The ability to predict future disease in conjunction with a limited ability to do much about it has important social and psychological implications that must be addressed in conducting genetic research." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "section_type": "main", + "text": "\n\nWhat limits should be imposed, if any, need to be determined prior to commercial feasibility.In this article, we consider how genetic testing decisions for children have been made traditionally and how the anticipation of comprehensive genomic testing in the near future will stress the fault lines of traditional approaches.The potential for comprehensive genomic testing in children could shift the equilibrium towards expanding or reducing parental discretion, and forces us to reexamine the evidence for our genetic testing policies and practices.We will highlight specific domains where further empirical social and behavioral research is necessary to inform policy and practice." + }, + { + "document_id": "b0b60080-2338-411b-bc44-1f5626a3c442", + "section_type": "main", + "text": "\n\nInterpretations of the literature will likely mirror the priorities and evaluative tendencies of the reader.Are you willing to accept the overall trends in genetic and genomic testing evaluation and to trust that the existing clinical approaches will apply informed consent appropriately while identifying and supporting the rare individual who has a serious adverse response to the testing?If so, you might advocate that attention be turned more toward other issues relevant to the effective implementation of genetic and genomic testing.Or do you feel a strong need to understand in more detail the possible psychosocial harms of the testing, particularly the subtler impacts or responses of individuals who do not fit the norm?In that case, you would likely encourage renewed and innovative efforts to study the psychosocial consequences of the receipt of risk information from genetic and genomic testing." + }, + { + "document_id": "f7fe5d02-ee7c-4ec2-b6c4-ca9aa5efb41f", + "section_type": "main", + "text": "\n\nOther social issues require our attention if genomic medicine is to benefit our patients.How should genetic tests be regulated?What, if any, are the appropriate uses of direct-to-consumer marketing of genetic tests?The Internet has recently had a proliferation of genetic-testing sites that feature claims grounded in greed and pseudoscience, rather than in data or reality.How will health care providers and the public distinguish between these and responsible testing services, whether they are available through the Internet or in the hospital?" + }, + { + "document_id": "56cf7be3-8c73-498d-b48f-8d99592b0213", + "section_type": "main", + "text": "The use of genetic testing from pre-conception through adulthood is expanding rapidly. Psychological risks for parents who are carriers may include parental guilt." + }, + { + "document_id": "3992d979-8089-49a5-b0f1-84d04eaf79ad", + "section_type": "main", + "text": "\n\nAttitudes Toward Genetics Research and Testing" + }, + { + "document_id": "a4e27158-1e54-4ee2-9cc1-049489a628bc", + "section_type": "main", + "text": "Return of Genetic Results to an Individual or Family\n\nOne of the most pertinent ethical challenges in genomics care and research relates to whether, when and which genetic results ought to be fed back to patients or research participants.In section 3.1 some considerations about the consent process in relation to incidental findings are detailed and this issue in relation to governance is addressed.The ongoing development of genomic tools has led to a significant decrease in the cost of running large diagnostic and research platforms resulting in the generation of a large volume of data for each individual, including potentially important clinical information about susceptibility to selected conditions that were not originally screened for (in the case of a diagnostic test) or investigated (in the case of research).The question is whether and when such unsolicited results should be shared with patients and participants." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "section_type": "main", + "text": "\n\nThere is also a more fundamental criticism towards these normative claims against pediatric genetic testing.Both deviate from the moral position that parents should have the authority to decide which medical interventions are appropriate for their children (McConkie-Rosell & Spiridigliozzi, 2004;Pelias, 2006;Rhodes, 2006;Robertson & Savulescu, 2001).It is not necessary to argue that parental authority is limitless or unconstrained for this consideration to gain moral traction; it is only necessary to show that genetic testing is consistent with the types of health care decisions that typically belong to parents (Ross, 1998).Further, respecting parental authority does not imply that providers should refrain from making explicit directive recommendations to parents about health care decisions." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "section_type": "main", + "text": "Conclusion\n\nWhile it is important to acknowledge potential harms when developing policy, one of the lessons of our recent genetic testing social history is that it has been neither the ''best of times, nor the worst of times. ''To date, the positive impact on population-based clinical practice has been less than imagined, but many concerns about adverse sideeffects have also turned out to be overstated.Respect for parental decision-making implies that the primary justification to restrict parents from obtaining genomic data would be that the harms clearly outweigh the benefits.Given that such data are lacking, the presumption should be to respect parental discretion.Parents will need advice and guidance about the potential benefits and limitations of such information, and health care providers should be proactive about engaging parents in these discussions." + }, + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "section_type": "main", + "text": "\n\nPsychosocial issues and impact of genetic/genomic information on individual and the family (such as emotional distress, discrimination)." + }, + { + "document_id": "1f5f2923-ca25-496c-b70e-5d15825c5575", + "section_type": "main", + "text": "\n\nA number of professional healthcare organizations have voiced concern about the clinical validity and the clinical utility of PG and PGM testing 12,26,27 and have developed position statements on DTC marketing that address the performance characteristics of the tests and the ethical, legal, and social implications (ELSI) of these technologies.Overall, there is broad agreement among the organizations that companies offering DTC PG and PGM testing should comply with existing practice and ethical standards of genetic testing.All agree that basic elements of informed consent for predisposition testing should include:" + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "section_type": "main", + "text": "Ethical Considerations for Comprehensive Genomic Testing in Children\n\nOnce testing an individual's entire genome becomes feasible, interest in using this technology with children can be anticipated.There are already proposals, based primarily on technical feasibility and potential public interest, for expanding NBS to include conditions for which early and effective treatments are not yet available (Alexander & van Dyck, 2006).Health-related information from comprehensive genomic testing in children raises the same concerns about clinical benefits and risks that have been associated with ''traditional'' genetic testing.However, the range of health information will be much broader and will include information about adult onset conditions and carrier status.The concerns about how parents will use this information and how it will impact children's self-identity, selfconcept, social and behavioral functioning, and lifestyle choices need to be empirically studied.Child health psychologists, in particular, have much to contribute to this process in light of their background and training in child development, clinical assessment, and the relationship between health and behavior." + }, + { + "document_id": "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa", + "section_type": "main", + "text": "Clinical, Social, and Ethical Implications\n\nThere are 2 methods by which genetic testing can occur: specific gene sequencing or genotyping arranged by providers for patients with clinical diagnoses for which the likelihood of a genetic cause is high (ie, risk prediction) or for patients for whom the appropriateness of a specific treatment is being evaluated (ie, pharmacogenetics), and direct-toconsumer genome-wide SNP genotyping services.In either case, although there may be no immediate physical harm for a patient in undergoing genetic testing, which typically involves only swabbing of the inside of a cheek, collection of saliva, or drawing of a blood sample, there are important long-term consequences to consider.Specific gene testing often occurs at the discretion of the provider rather than the patient (although it should not occur without the patient's permission).Such testing may be informative because the presence of particular mutations may have diagnostic and therapeutic implications.For example, the finding of a BRCA1 or BRCA2 mutation that indicates increased risk of breast cancer may result in a management plan (made jointly by the provider and patient) in which the patient chooses to undergo prophylactic mastectomy.The finding of a mutation that augurs heightened risk of sudden cardiac death in a cardiomyopathy patient may result in the provider and patient opting for the placement of an implantable cardioverter-defibrillator.Typically, these sorts of decisions are driven by the presence of mutations that, on the basis of prior research, are likely to have large clinical effects.However, this is not always the case, and the premature use of a genetic test may carry risks.In 1 example, a company marketed a test for a variant in the KIF6 gene that initial research studies had found to predict patient response to statin therapy.Many providers used the test, presumably to help decide whether to prescribe statins to patients.Subsequent larger studies failed to replicate the KIF6 association with statin response, undermining the validity of the indication for the marketed test and suggesting that use of the test may have adversely affected patient management (if a provider had chosen not to prescribe a statin to a patient who otherwise met guidelines for statin therapy)." + }, + { + "document_id": "df1cc001-06bb-4070-84ed-dc48d12395fc", + "section_type": "main", + "text": "\n\nIn clinical practice, genetic tests based on the ana lysis of genetic material (typically chromosomes, DNA or RNA) are carried out in the context of: diagnostic testing for genetic conditions, carrier testing for autosomal or X-linked recessive conditions and presymptomatic testing for autosomal dominant conditions.In addition, prenatal diagnosis of a fetus at risk of a genetic condition is available for many disorders.It is strongly recommended that appropriate counseling accompanies all such testing to enable patients to make informed decisions about whether to accept or decline such a test.For example, the European guidelines for presymptomatic testing developed as part of the EuroGentest project [5] emphasize the need for pre-and post-test counseling by trained health professionals to enable patients to determine whether the test is appropriate for them in the context of their own beliefs, values and lifestyle.European guidance on prenatal testing [9] includes the same requirements.To achieve this, an individualized approach to each patient is required.However, an ethical question can be raised by this requirement; if a patient does not wish to have counseling, is this simply an expression of their individual choice and should the health professional insist?Patients may feel that they have given sufficient thought to the decision over years or even decades [10], while the health professional who is offering an intervention in the form of a test has a responsibility to ensure as far as possible (within the boundaries of professional practice) that the intervention PersPective Skirton, Jackson, Goldsmith & O'Connor causes no harm to the patient [11].While the need for informed consent is paramount in the health professional's perception of ethical practice, evidence suggests that the public place more emphasis on the access to appropriate information [12], which is of course one component (alongside voluntariness and capacity of the patient to make a decision) of informed consent [13].This does, however, emphasize the expectation of patients that health professionals are knowledgeable about both genetic and genomic testing offered within the health service [14,15], and they may also expect them to understand health-related tests offered by private companies." + }, + { + "document_id": "35e7b535-f3ed-4de4-a323-f1880a5873c2", + "section_type": "main", + "text": "\n\nIn addition to considering the effectiveness and the cost-effectiveness of stratified-screening programs, there are additional organizational, ethical, legal and social considerations before risk-tailored screening can be translated into policy and practice.It is not known how the public and professionals will respond to genetic testing.Would it be acceptable to health professionals, policy-makers and the public to have eligibility for screening based on absolute risk that is dependent on genetic profile in addition to age and possibly other environmental and lifestyle risk factors?Would it be acceptable to offer more sensitive and more expensive screening technology, such as MRI, instead of mammography for breast cancer screening, to those at high risk?How would the workforce be trained to understand genetic profiles and to communicate the test results and the management options effectively to the patients?A major organizational challenge will be to incorporate the advances of the rapidly evolving fields of genomics and the changes in environmental and lifestyle risk factors over an individual's lifetime into a dynamic risk estimation tool.How would the professionals organizing the screening programs and the public react to changing a bsolute risk levels? (HEALTH-F2-2009-223175).The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials d iscussed in the manuscript apart from those disclosed." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "section_type": "main", + "text": "\n\nThere is also an inconsistency between the restrictions regarding genetic testing of children and the policies permitting prenatal testing for these same conditions.Although parents are discouraged from testing their young children for adult onset conditions, pregnant women are allowed to test their fetus, and providers may be reluctant to discourage them from doing so out of respect for reproductive freedom.In the prenatal context, providers are traditionally ''nondirective'' and ''offer options,'' rather than explicitly recommending which tests to undergo, or what actions to take based on the results.Consider, then, an expectant couple who seeks prenatal testing for Huntington disease (HD; an autosomal dominant cause of early-onset dementia) because one partner carries the gene associated with HD.Although it was historically assumed that parents would test a fetus for a condition like HD and then terminate an affected pregnancy (International Huntington Association [IHA] and the World Federation of Neurology [WFN] Research Group on Huntington's Chorea, 1994), a small number of parents do not terminate at-risk fetuses (Simpson et al., 2002).In light of current pediatric practice that proscribes testing of children, prenatal testing is the only option for parents who really want to know if their child has inherited the risk for HD, even though the medical risks of amniocentesis are greater (and therefore less desirable) than collecting a blood sample from a small child." + }, + { + "document_id": "56cf7be3-8c73-498d-b48f-8d99592b0213", + "section_type": "main", + "text": "\n\nFurthermore, many genetic conditions are still difficult to treat or prevent, which means that the information gained from newborn screening may be of limited value in terms of treatment.Given these concerns, the American Academy of Pediatrics ( 2001) noted \"detailed counseling, informed consent and confidentiality should be key aspects of the genetic testing process, particularly when the benefits are uncertain\" (p.2)." + }, + { + "document_id": "b0b60080-2338-411b-bc44-1f5626a3c442", + "section_type": "main", + "text": "\n\nA different interpretation of the systematic reviews would likely encourage more research on psychosocial impacts of genetic and genomic testing.One could argue that it is risky to overgeneralize given the many limitations to the evidence base.Furthermore, there are enough data showing that people are influenced by such testing, even if more subtly than is detected with many general, validated measures, to justify concern that we may be missing important implications of applied genetic and genomic testing.These factors might be revealed with broader use of qualitative methods, improved condition-specific measures in quantitative studies, greater attention to diverse study samples, and efforts to understand subpopulations or outliers who might be at higher risk." + }, + { + "document_id": "f6baaabe-5856-4be5-8fe5-cd2b935ebacf", + "section_type": "main", + "text": "\n\nEthically, it is not reasonable to screen for certain genetic diseases while being unable to treat or effectively manage already diagnosed patients.A targeted screening and prevention strategy toward high-risk families at risk to have another affected child can be adopted to avoid this possible fact." + }, + { + "document_id": "a4e27158-1e54-4ee2-9cc1-049489a628bc", + "section_type": "main", + "text": "\n\nOn the other hand, from a legal and ethical point of view information that could influence an individual's health or alter the course of a disease should not be withheld.In an under-resourced setting, however, the feeding back of an incidental genomic diagnosis with specific health implications to an individual who does not have access to relevant health care services to treat such a condition further seems unethical.Where possible however, steps could for example be taken around career and family planning.In contrast to the situation where genomic information may have positive health benefits to those who have access to treatment, the same information will not be helpful to those who do not, and may create anxiety and result in social ostracism and stigmatisation and therefore affect their quality of life negatively.The inclusion of a question in the consent form that requires participants to indicate whether they wish to be informed of incidental findings needs to be debated (De Vries et al., 2012aVries et al., , 2012b))." + }, + { + "document_id": "b0b60080-2338-411b-bc44-1f5626a3c442", + "section_type": "main", + "text": "General Considerations for Assessing the Psychosocial Impacts\n\nG enetic and genomic applications are diverse, and generalizing about the psychosocial harms of testing in these areas is challenging.At least four interrelated factors about genetic and genomic testing must be understood.The first regards the characteristics of the genetic variants themselves, including penetrance (the likelihood of developing a health condition when the variant is present) and expressivity (the range of severity in the health outcome when the variant is present).These bear on what risk information would be conveyed" + }, + { + "document_id": "56cf7be3-8c73-498d-b48f-8d99592b0213", + "section_type": "main", + "text": "Preventing Genetic Discrimination\n\nGenetic discrimination was identified early on in the Human Genome Project by the Ethical, Legal, and Social Implications program at the National Human Genome Research Institute as an ethical issue that needed to be addressed before the benefits of the Human Genome Project could be fully implemented.Although many are hopeful about the use of genetic information to improve health and combat disease, many are concerned about the potential for misuse, involving, for example, insurance and employment discrimination.Individual concerns include worries that genetic information may be used to deny or limit insurance coverage or to determine who is hired or fired.There is concern voiced that some insurers may choose not to insure people who are healthy but genetically pre-disposed to future disease onset (National Human Genome Research Institute, 2007)." + }, + { + "document_id": "f051ad23-572d-4302-8dda-4d992aeaeb1a", + "section_type": "main", + "text": "\n\nGenetic testing is now used in prenatal, pediatric, and adult populations.Prenatal genetic testing is used to screen for and diagnose genetic conditions, such as Down syndrome.Carrier testing helps to identify people who carry one copy of a gene mutation that, when present in two copies, causes a genetic disorder, such as cystic fibrosis.Carrier testing is offered to people who come from certain ethnic groups that have an increased risk of specific genetic disorders, such as Tay-Sachs disease among Ashkenazi Jewish populations.When both parents are tested, the test gives information about a couple's chance of having a child with a genetic disorder (GHR, 2008n)." + }, + { + "document_id": "68c109d7-cfef-4a50-8f22-f0b16a5cb52c", + "section_type": "main", + "text": "\n\nGenetic diseases are sometimes shocking and may cause substantial disability and even death in infant [27].The prenatal finding of genetic diseases permits parents to take choices about whether to continue with the pregnancy, or to permit initial diagnosis and probable treatment in utero or at birth.Whereas earlier methods to prenatal diagnosis could place the pregnancy at danger, new approaches utilizing genomic technology can aspect directly at the DNA of the fetus from a motherly body fluid test, without growing the risk of miscarriage." + }, + { + "document_id": "936ddcae-95ca-496a-9ef0-182a6aa62a33", + "section_type": "main", + "text": "incidental findings in children\n\n4][25] However, these recommendations can be inconsistent with the general practice of respecting parental decision making about their children's health, and questions have been raised about the sustainability of these standards in an era of comprehensive genomic testing. 26One of these recent policy statements noted \"results from genetic testing of a child may have implications for the parents and other family members.Health-care providers have an obligation to inform parents and the child, when appropriate, about these potential implications.\" 24 This statement suggests an important consideration in the era of genomic medicine because after sequencing a child for a primary indication it becomes relatively easy for a laboratory to report a limited number of variants for conditions that could be medically important to that child's future or to the rest of the family." + } + ], + "document_id": "F7FF28704C5239FB329F508530F982CC", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "Tay-Sachs&disease", + "glutaric&aciduria&type&1", + "Ashkenazi&Jewish", + "Amish", + "Down&syndrome", + "SMA", + "PGD", + "KFSHRC", + "Genetic&Information&Nondiscrimination&Act", + "Islamic" + ], + "metadata": [ + { + "object": "we describe patients with craniosynostosis and Noonan syndrome due to de novo mutations in PTPN11 and patients with craniosynostosis and CFC syndrome due to de novo mutations in BRAF or KRAS. All of these patients had cranial deformities in addition to the typical phenotypes of CFC syndrome and Noonan syndrome.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1002469" + }, + { + "object": "Gain-of-function mutations in the PCNA domain of CDKN1C have been reported as the genetic basis of various growth-retarded syndromes including IMAGe syndrome, Russell Silver syndrome as well as a novel undergrowth syndrome that additionally exhibited early adulthood onset diabetes. {review]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab310461" + }, + { + "object": "analysis of SALL4 defects and associated syndromes including Okihiro syndrome Duane-radial ray syndrome, acro-renal-ocular syndrome and description of the clinical distinctions with similar phenotypes caused by other gene defects", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab69830" + }, + { + "object": "we describe the phenotype of a patient with Varadi syndrome who is homozygous for a previously reported mutation in TCTN1 NM_001082538.2:c.342-2A>G, p.Gly115Lysfs*8 and suggest that allelic disorders linked to TCTN1 include Varadi syndrome, in addition to Joubert syndrome and Meckel-Gruber syndrome.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1002562" + }, + { + "object": "We identified a HUWE1 mutation in an affected male with Juberg-Marsidi and Brooks syndromes from the original family reported by Juberg and Marsidi; it is evident the syndrome does not result from a mutation in ATRX as reported in the literature. Additionally, the data indicated that Juberg-Marsidi syndrome and Brooks syndromes are allelic having the same HUWE1 mutation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007295" + }, + { + "object": "The dermatological features of Costello syndrome, a Ras dysregulation syndrome, share many features with cutaneous paraneoplastic syndromes. This may provide further insight into the role of Ras signalling in cutaneous paraneoplastic syndromes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab800527" + }, + { + "object": "The BCSIL gene mutation is responsible for GRACILE syndrome, Bjornstad syndrome and complex III deficiency. Bjomstad syndrome is characterized by sensorineural hearing loss and abnormal flat twisted hair shafts. The case is GRACILE syndrome with Bjomstad phenotype in neonatal period due to BCSL1 gene mutation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab785878" + }, + { + "object": "We suggest that patients with Perrault syndrome are screened for variants in RMND1 along side the known Perrault syndrome genes.Renal phenotypes in women with Perrault syndrome features may indicate the causative variant is in RMND1 but the absence of renal dysfunction should not preclude RMND1 screening", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1014038" + }, + { + "object": "Three Smad genes, sma-2, sma-3 and sma-4, are all required for signal transduction, suggesting that the functional complex could be a heterotrimer.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab954432" + }, + { + "object": "CHD7 mutations have also been found in some patients with Kallmann syndrome, hypogonadotrophic hypogonadism, and anosmia, and we discuss the overlap between this syndrome and CHARGE syndrome.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab137452" + } + ], + "question": "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "subquestions": null, + "task_id": "F7FF28704C5239FB329F508530F982CC", + "usage": { + "chatgpt": 6791, + "gpt-4": 4502, + "gpt-4-turbo-preview": 3567 + }, + "user_id": 2 + }, + "document_id": "F7FF28704C5239FB329F508530F982CC", + "task_id": "F7FF28704C5239FB329F508530F982CC" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/05.json b/gnqa/paper1_eval/src/data/responses/general/experts/05.json new file mode 100644 index 0000000..e4a5968 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/05.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-06-01T14:26:03.986157Z", + "data": { + "amplify": false, + "answer": "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "context": { + "03110c8a-1232-40c2-8380-c9feb8b6468c": [ + { + "document_id": "03110c8a-1232-40c2-8380-c9feb8b6468c", + "text": "\n\nGene editing has gained considerable interest with the identification of the CRISPR-Cas9 system, 27 which allows for a targeted modification in the DNA sequence of an organism.Researchers can utilize their knowledge of the basic biology of the gene and its protein function to precisely change the DNA sequence, thus altering the protein function of the gene and allowing for edits to stay within the species.Researchers at the University of Missouri used the CRISPR-Cas9 system to modify the CD163 gene such that the PRRS virus is not able to replicate inside the pig. 28This slight modification of the swine genome through gene editing keeps the pigs from succumbing to PRRS which has an annual estimated loss to the United States swine industry of over $660 million per year.Despite this benefit, given the public's concerns over food safety, it is likely that approval for such technology is years away in the US, Canada and Europe.However, in some cultures, there is a wide range of non-livestock species that are consumed.Therefore, it is conceivable that these countries and cultures may be open to transgenic/gene edited livestock.They may see the importance of useful gene editing which may lead to approval and consumption of reasonable genetically edited animal products such as those with modifications that are already found in nature or those that offer a substantial welfare benefit to society." + } + ], + "1942712a-a39d-44f7-9b2d-609926374cbd": [ + { + "document_id": "1942712a-a39d-44f7-9b2d-609926374cbd", + "text": "\n\nAs a researcher who has devoted an entire career since 1994 to the development of genome editing tools and methods, I have been amazed by the rapid progress in the field over the last few years.Considering the widespread use of the tools, I am sure that the pace will continue to accelerate.Indeed, programmable nucleases, may eventually enable humans-products of evolution-to become masters of evolution.delivered preassembled recombinant Cas9-guide RNA ribonucleoproteins (RNPs) into animal embryos 6,9 and plant 11 and mammalian cells [73][74][75] .Indeed, Cas9 RNPs were rapidly turned over in cells 73 , reducing off-target effects and mosaicism in gene-edited organisms 11 .Cas9 RNPs can be delivered into cells by various methods, including microinjection 6,9 , electroporation 73 , lipofection 74 and protein transduction 75 .Importantly-and unlike in conventional gene therapy, where therapeutic genes are delivered via plasmids or viral vectors-Cas9 RNP delivery does not involve the use of exogenous DNA; host innate immune responses against foreign DNA are not elicited, and undesired integration of foreign DNA into the host genome is avoided." + } + ], + "33f1abde-a821-483b-b8b4-785f499db09d": [ + { + "document_id": "33f1abde-a821-483b-b8b4-785f499db09d", + "text": "\n\nIn comparison to a transgenic approach, a gene editing technique such as CRISPR-Cas9 offers the advantage that gene-edited crops are not considered genetically modified organism (GMO) in some countries, such as the US, where the demand for natural food colorants such as anthocyanins is high.Indeed, the use of GMO crops as a source of natural pigments may be inconsistent with consumer interests.However, carrot cultivars engineered with either the transgenic or gene editing approach have not been reported so far, but their development is possible." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nThe notable accuracy and versatility of CRISPR-Cas for genome editing also opened the door to its use in preclinical and translational settings.In the latter case, CRISPR in vivo gene editing has led to several proof-of-concept studies that would have been unachievable without it, as in the first ever correction of inherited pathogenic mutations linked to degenerative disease in a living organism [22] and even shown to be possible in human embryos [23,24].It also has great potential in the field of precision medicine as large-scale population DNA sequencing studies have provided vast amounts of information linking particular diseases with specific genetic mutations which could, in theory, be targeted through CRISPR [25,26].This could be used during the identification and validation of potential DNA targets during the development of personalised drug or cell therapies, which will require the generation of engineered cell lines and/or animal models.Techniques such as HDR-mediated gene targeting are too labour intensive, with low targeting efficiencies and long times necessary for their establishment, and consequently are not ideally suited for drug discovery purposes.Conversely, CRISPR-Cas has been proven to be efficient for editing virtually any kind of cell line, from primary immune cells to induced pluripotent stem cells (iPSCs) [27,28].Additionally, CRISPR can also be used for functional screening in the development of combined inhibitory therapy aimed at strengthening the efficiency of targeted therapeutics.An example of the latter is shown in a study where a variation of the technology known as CRISPR interference (CRISPRi) was used in genome-wide scale to identify different survival pathways used by cancer cells after oncogene inactivation and allowing the identification of successful combination therapies [29].In terms of translational applications, the overall safety of CRISPR genome editing in humans will require long-term scrutiny before its adoption in the clinic.Nonetheless, a number of CRISPR-based clinical trials are currently in progress, including studies focused on targeting patients' own T cells in order to improve the immune response towards some forms of malignant cancer [30,31], and others aimed at correcting pathogenic mutations in the hematopoietic cells of patients with beta-thalassemia and sickle cell disease [32]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Caveats and Ethical Concerns of CRISPR-Cas Applications\n\nDespite the presence of both a PAM sequence and a specific gRNA, the CRISPR-Cas9 system is not infallible.In fact, DSBs can occur at different sites in the genome, potentially causing so-called \"off-target\" effects.This eventuality remains to date the biggest concern in the field, as possible undesirable modifications must be properly identified and followed in order to guarantee safety for medical purposes.Nevertheless, there is still little evidence of the biological consequence of Cas9 off-target effects.Two recent studies describe new methods to investigate potential off-target effects in both mammals and plants [33,34].In both cases, whole-genome sequencing revealed that selective nucleotide changes, such as conversion of an adenine to a guanine, caused off-target occurrence very rarely, with a frequency comparable to the one of spontaneous mutations.However, substitution of a cytosine with a thymidine was linked to a sizable number of off-target mutations.This newly acquired information adds to the plethora of studies conducted on the safety of CRISPR, which altogether highlight the need for the establishment of clinical standards for the future use of genome-editing techniques in the clinic.Despite this and other technical challenges still ahead for CRISPR genome editing, the pace at which this technology has developed in recent years suggests many of these concerns could be addressed soon, as long as proper ethical guidelines and regulatory mechanisms are established." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Conclusions\n\nThere is no reason to doubt that the development of CRISPR-Cas genome editing represents an unprecedented breakthrough in modern science, as it has potential applications in a wide array of disciplines ranging from agriculture, zoology and renewable energy to biomedicine and synthetic biology.This powerful tool holds promise for further elucidating the molecular causes of ageing by allowing scientists to probe genetic and epigenetic pathways with a level of sophistication that was unattainable just a few years ago.It will allow so in traditional animal and cell models of ageing, but it will also drastically accelerate the generation of refined versions of those models or even allow the development of new research approaches in non-model organisms.Moreover, CRISPR-based genome editing is already having a significant impact in research aiming to understand the cellular and molecular origins of age-related diseases, as well as developing potential treatments against them.The application of CRISPR-Cas gene editing for the treatment of age-related diseases is not over the horizon yet, as it will require the identification of causative genes and their role under a variety of contexts that could be as diverse as the ageing process is across individuals.However, CRISPR-Cas might also hold the key for solving such conundrum, as it has opened the way for achieving true personalised medicine by providing both the precision and scalability required for conducting genome-wide functional screens during the refinement of drug-and cell-based therapies for age-related diseases." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nSince its discovery, CRISPR-Cas technology has ignited a biological revolution by providing a highly versatile platform that allows fast and efficient genome editing in an ever-growing list of organisms.In this chapter we will first describe the most recent advances in the development and application of the CRISPR-Cas platform in biomedical research.Then we will discuss the most recent and notable basic research applications of this technology in the study of the molecular causes of ageing.Finally, we will review how CRISPR-Cas has been used for creating new models for the study of age-related diseases, as well as for manipulating diseaseassociated gene pathways." + } + ], + "50c72e55-b5fe-42a6-b837-64c28620a4c0": [ + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "text": "Caveats of advanced genome editing tools\n\nOff-target effects.The DNA-binding domains of ZFNs and TALENs need to be very specific for the target site to avoid off-target cleavage, which results in unwanted mutations and potentially cytotoxic effects [27].CRISPR/Cas9 is also known to generate off-target alterations, albeit apparently at low incidence [28,29], since mispairing is allowed between the guide RNA and the genomic DNA.Nonetheless, caution is required in their design and use.Some strategies involving the optimization of the guide RNA/Cas9 include using of software tools to predict potential off-target sites (http://omictools.com/crispr-cas9-Figure1: Genome editing methodologies which can be applied to human pluripotent stem cells.Homologous recombination (HR), or the more advanced tools such as zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) or clustered regularly interspaced short palindromic repeat (CRISPR)/Cas system can be applied to human pluripotent stem cells (hPSCs) either to 1) create naturally occurring mutations or 2) repair a mutation to generate isogenic controls in hPSCs, to understand the function of a gene of interest.c1268-p1.html),truncating the guide RNA (<20 nucleotides) to decrease off-target mutagenesis [30], lowering the dosage of guide RNA and Cas9 plasmids, and decreasing the number of mismatches between the guide RNA and the genomic DNA.A \"double nick\" system with Cas9 nickase, which contains a single inactive catalytic domain, may also be used [31e33]." + } + ], + "52480703-5353-4e55-a06b-110fd59db3a6": [ + { + "document_id": "52480703-5353-4e55-a06b-110fd59db3a6", + "text": "CRISPR screening technologies\n\nThe discovery of CRISPR-Cas9 as a sequence-specific programmable nuclease democratized gene editing and fueled progress in forward genetic screening [20 , 66] .Genetic screens using Cas9 with a pooled singleguide RNA (sgRNA) library allow the interrogation of seemingly all genes in a genome in a single experiment [96 , 97] [null] .Engineered Cas9 variants further extend the versatility of forward genetic screening.Catalytically inactive Cas9 (dCas9) fused with chromatin effector domains permit specific activation (CRISPRa) or inhibition (CRISPRi) of gene expression [37 , 54] .Recently developed and emerging technologies -base editors, prime editors, and Cas transposases -are beginning to enable new types of genetic screens with directed, controlled, and on demand mutations by allowing the creation of user specified modifications, such as single base conversion, deletions, and insertions [4 , 42 , 58] ." + } + ], + "801c9288-70c9-4d14-b8bc-13ee6708803a": [ + { + "document_id": "801c9288-70c9-4d14-b8bc-13ee6708803a", + "text": "\n\nComing on the heels of engineered nucleases, CRISPR-Cas9 tools have accelerated the pace of genomic research by permitting highly efficient knockouts or edits of virtually any gene in cells or model organisms.Multiple CRISPR-Cas9-based clinical trials are in progress or are expected to begin soon.Although Cas9engineered cells haven't yet demonstrated efficacy at scale, early trial results suggest that such cells are stable and don't cause acute adverse reactions in humans.Long-term safety is yet to be determined.Current applications largely focus on single-gene disorders for which gene editing can be carried out ex vivo on appropriate cells, such as bone marrow hematopoietic stem cells in the case of sickle cell anemia.Exploration is under way to develop delivery systems that can target the gene-editing apparatus to the appropriate tissue in vivo." + }, + { + "document_id": "801c9288-70c9-4d14-b8bc-13ee6708803a", + "text": "\n\nOver the past 8 years, CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 (CRISPR-associated protein 9) technologies have emerged as accessible and adaptable tools for studying and altering genomes. 5RISPR-Cas9 can be used to induce genome edits by creating targeted DNA breaks that trigger site-specific DNA repair.In nextgeneration formats, it can also control the transcriptional output of genes or alter genome sequences using a process of nucleotide base editing that does not require repair of DNA breaks.As these technologies continue to mature, it will become increasingly possible to alter cellular genomes efficiently and accurately." + } + ], + "a7f21808-dce3-4110-8e7c-ceb2437e72ff": [ + { + "document_id": "a7f21808-dce3-4110-8e7c-ceb2437e72ff", + "text": "\n\nThe type II CRISPR-Cas9 systems, repurposed from prokaryotic adaptive immune responses, are now widely used for targeted genome modifications in plants, animals, and human cells (Kim et al. 2014;Woo et al. 2015;Zuris et al. 2015).In particular, Cas9 nucleases have shown promise for gene and cell therapy (Maeder and Gersbach 2016).Typically, these nucleases are expressed or delivered in vivo using plasmid DNA or viruses (Yin et al. 2014;Ran et al. 2015).However, plasmid DNA delivery is often inefficient, especially in vivo, and can cause integration of small plasmid fragments degraded by endogenous nucleases at on-target and offtarget sites in the genome (Kim et al. 2014).Viral delivery of Cas9 can be highly efficient in vivo (Ran et al. 2015;Long et al. 2016;Nelson et al. 2016;Tabebordbar et al. 2016), but may be hampered by antibodies or T cells induced against the protein (Shankar et al. 2007;Calcedo et al. 2015;Chew et al. 2016).We and others have shown that preassembled Cas9 ribonucleoproteins (RNPs) can be delivered to human primary and stem cells and mice to modify target genes (Kim et al. 2014;Schumann et al. 2015;Zuris et al. 2015).Cas9 RNPs are rapidly turned over in cells, reducing off-target effects.Furthermore, Cas9 RNPs are unlikely to be limited by host immune systems because they function and disappear before the generation of antibodies and T cells directed against them.Currently, despite these advantages of RNPs, the difficult delivery of Cas9 RNPs in vivo limits its utility for therapeutic applications (Zuris et al. 2015).Here, we show that in vivo genome editing of an wild-type gene, whose up-regulation is responsible for pathogenesis, could be a new therapeutic modality for the treatment of nongenetic degenerative diseases.Our ultimate goal is to harness Cas9 RNPs for a clinical application of therapeutic genome surgery in patients with AMD." + } + ], + "ac00c552-7514-49d4-9e90-ab01c22472ae": [ + { + "document_id": "ac00c552-7514-49d4-9e90-ab01c22472ae", + "text": "\n\nClustered regularly interspaced short palindromic repeat (CRISPR)-Cas nucleases have revolutionized the field of gene editing and have tremendous application in the field of molecular medicine [98][99][100][101][102].Despite a significant surge in CRISPR/Cas9mediated genome editing in various disease models, the progress in the field of AD has lagged behind substantially.We believe that genome editing can significantly improve the development of AD models and also create novel opportunities for the development of the next generation precision targeted AD gene and stem cell therapies.Since there are several excellent review articles on CRISPR/Cas9-mediated genome editing, here we will limit our focus on select recent articles that are noteworthy.CRISPR/Cas9 system can be engineered to either activate transcription (gain-of-function) or achieve gene silencing (Loss-of-function).Dahlman et al. have developed a CRISPR-based system that uses catalytically active Cas9 and distinct single guide (sgRNA) constructs to activate and knockout different genes in the same cell [103].Konermann et al. have used structure-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci [104].Using crystallographic studies, they have engineered a combination of sgRNA2.0,NLS-dCas9-VP64 and MS2-p65-HSF1 to develop one of the most effective transcription activation system." + } + ], + "b72eb0d1-50e3-4def-94bc-abf77891f519": [ + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "text": "Limitations of CRISPR-Cas9\n\nCRISPR provides a simple and easy tool not only for in vitro use but potentially also for in vivo genome editing.However, there are limitations and downsides to this approach.First, and despite considerable improvements in the technology, the risk of the offtarget effect remains and must be considered carefully.Second, DSB may lead to wide-ranging deletions or recombination events involving the on-target site (204).Third, in cycling cells, DNA double strand breaks caused by Cas9 cleavage may trigger a P53 response leading to apoptosis and enrichment for potentially oncogenic P53-deficient cells (205,206).Fourth, subjects may generate antibodies to Cas9, potentially limiting gene therapies (207,208)." + }, + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "text": "\n\nGenome editing tools that target the desired genomic region and allow for variants to be altered (e.g. from risk to protective), or for more substantial changes to be made (e.g. the deletion of a longer stretch of DNA harbouring a number of variants) and can help to answer each of these questions.These technologies are evolving rapidly (Figure 1 and Table 2).The most recently developed of these, Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) technology, originally developed by Doudna, Charpentier and their colleagues (72,73) and Zhang and his colleagues (50) has become a widely used tool for this purpose.Engineered CRISPR/Cas9 technology uses a guide RNA (gRNA) to direct CRISPR-associated endonuclease (Cas) to the target DNA and generate a double strand DNA break.Correction of a mutation or variant in the target DNA sequence can then be carried out by homology-directed DNA repair (HDR) with a donor template.Since its discovery eight years ago, CRISPR technology has evolved quickly to be a critical part of the molecular biologist's toolbox." + } + ], + "c3ae2186-ef48-46a5-b214-dc944366df8f": [ + { + "document_id": "c3ae2186-ef48-46a5-b214-dc944366df8f", + "text": "INTRODUCTION\n\nGenome editing technologies based on the clustered regularly interspaced short palindromic repeats (CRISPR)-associated endonuclease Cas9 enable rapid and efficient modification of endogenous genes in a variety of cell types, allowing for analysis of gene function in many organs in vivo.CRISPR-Cas9 induces DNA double strand breaks (DSBs) at single-guide RNA (sgRNA)-specific loci in the genome, which are repaired through either non-homologous end-joining (NHEJ) or homology-directed repair (HDR) pathways.While NHEJ introduces unpredictable pattern of insertion or deletion (indel) mutations, HDR directs a precise recombination event between a homologous DNA donor template and the damaged DNA site (Cong et al., 2013;Cox et al., 2015;Doudna and Charpentier, 2014;Heidenreich and Zhang, 2016;Jinek et al., 2012;Mali et al., 2013;Sander and Joung, 2014;Wang et al., 2013;Yang et al., 2013).Thus, HDR can be used to precisely introduce sequence insertions, deletions or mutations by encoding the desired changes in the donor template DNA." + } + ], + "d14e93b5-01de-4208-8255-baae7898a7bb": [ + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "text": "\nCRISPR technology has rapidly changed the face of biological research, such that precise genome editing has now become routine for many labs within several years of its initial development.What makes CRISPR/Cas9 so revolutionary is the ability to target a protein (Cas9) to an exact genomic locus, through designing a specific short complementary nucleotide sequence, that together with a common scaffold sequence, constitute the guide RNA bridging the protein and the DNA.Wild-type Cas9 cleaves both DNA strands at its target sequence, but this protein can also be modified to exert many other functions.For instance, by attaching an activation domain to catalytically inactive Cas9 and targeting a promoter region, it is possible to stimulate the expression of a specific endogenous gene.In principle, any genomic region can be targeted, and recent efforts have successfully generated pooled guide RNA libraries for coding and regulatory regions of human, mouse and Drosophila genomes with high coverage, thus facilitating functional phenotypic screening.In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening." + }, + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "text": "\n\nCRISPR technology has rapidly changed the face of biological research, such that precise genome editing has now become routine for many labs within several years of its initial development.What makes CRISPR/Cas9 so revolutionary is the ability to target a protein (Cas9) to an exact genomic locus, through designing a specific short complementary nucleotide sequence, that together with a common scaffold sequence, constitute the guide RNA bridging the protein and the DNA.Wild-type Cas9 cleaves both DNA strands at its target sequence, but this protein can also be modified to exert many other functions.For instance, by attaching an activation domain to catalytically inactive Cas9 and targeting a promoter region, it is possible to stimulate the expression of a specific endogenous gene.In principle, any genomic region can be targeted, and recent efforts have successfully generated pooled guide RNA libraries for coding and regulatory regions of human, mouse and Drosophila genomes with high coverage, thus facilitating functional phenotypic screening.In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening." + }, + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "text": "\n\nThe recent development of clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 for experimental purposes has dismantled the perception that genome editing technology is off-limits for screening in mammalian systems (Heintze et al., 2013).Since this system employs the basic principle of Watson-Crick base pairing for gene targeting, generation of libraries with whole-genome target coverage is relatively easy and cost-effective.For instance, simple protocols are available to synthesize pooled lentiviral libraries by in silico design of oligonucleotides, which can then be cloned, packaged and delivered to cells by viral transduction (Paddison et al., 2004;LeProust et al., 2010).Similarly, the generation of arrayed libraries can be achieved by following protocols originally developed for arrayed shRNA library production that have been in use for a number of years (Moffat et al., 2006).All in all, the stage is set for CRISPR to make an enormous impact on genomic screening and thus scientific discovery in the coming years, and recent demonstrations of this system have shown great promise (Shalem et al., 2015).However, a number of technical challenges must be addressed in order to maximize the benefit of this technology.In this review, we will discuss current applications of CRISPR in functional genomics and provide a perspective on future developments in this area." + } + ], + "e5cf067c-8be0-4b0a-b376-7882cdc9d96c": [ + { + "document_id": "e5cf067c-8be0-4b0a-b376-7882cdc9d96c", + "text": "Genome editing for crop improvement\n\nReports of CRISPR-Cas9-based genome editing first appeared in 2013 (Cong et al., 2013;Feng et al., 2013;Mao et al., 2013).Since then, genome editing technologies have proven to be powerful and efficient tools for the improvement of many crop species.At present, genome editing has been widely used to introduce/modify agronomically important traits, such as increased yield, improved nutritional quality, and resistance to biotic and abiotic stresses, in multiple crops, including rice, wheat, maize, tomato, and potato (Lu et al., 2017;Soyk et al., 2017;Tang et al., 2017;D'Ambrosio et al., 2018;Ye et al., 2018;Miao et al., 2019;Zhang et al., 2019;Zhong et al., 2019;Butt et al., 2020;Zhang et al., 2020c;Li et al., 2021b;Zhan et al., 2021).CRISPR-Cas-based genome editing has been extended to targeted mutagenesis, base editing, and precisely targeted gene/allele replacement or tagging in plants.mportantly, using CRISPR-Cas9 technology, transgenes present in the genomes of genome-edited plants can be removed by chromosomal segregation via a simple self-pollination or hybridization step.Gene editing technologies continue to be developed and utilized (Mao et al., 2013;Lu and Zhu, 2017;Lu et al., 2020)." + } + ] + }, + "data_source": [ + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "section_type": "main", + "text": "Limitations of CRISPR-Cas9\n\nCRISPR provides a simple and easy tool not only for in vitro use but potentially also for in vivo genome editing.However, there are limitations and downsides to this approach.First, and despite considerable improvements in the technology, the risk of the offtarget effect remains and must be considered carefully.Second, DSB may lead to wide-ranging deletions or recombination events involving the on-target site (204).Third, in cycling cells, DNA double strand breaks caused by Cas9 cleavage may trigger a P53 response leading to apoptosis and enrichment for potentially oncogenic P53-deficient cells (205,206).Fourth, subjects may generate antibodies to Cas9, potentially limiting gene therapies (207,208)." + }, + { + "document_id": "801c9288-70c9-4d14-b8bc-13ee6708803a", + "section_type": "main", + "text": "\n\nComing on the heels of engineered nucleases, CRISPR-Cas9 tools have accelerated the pace of genomic research by permitting highly efficient knockouts or edits of virtually any gene in cells or model organisms.Multiple CRISPR-Cas9-based clinical trials are in progress or are expected to begin soon.Although Cas9engineered cells haven't yet demonstrated efficacy at scale, early trial results suggest that such cells are stable and don't cause acute adverse reactions in humans.Long-term safety is yet to be determined.Current applications largely focus on single-gene disorders for which gene editing can be carried out ex vivo on appropriate cells, such as bone marrow hematopoietic stem cells in the case of sickle cell anemia.Exploration is under way to develop delivery systems that can target the gene-editing apparatus to the appropriate tissue in vivo." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Caveats and Ethical Concerns of CRISPR-Cas Applications\n\nDespite the presence of both a PAM sequence and a specific gRNA, the CRISPR-Cas9 system is not infallible.In fact, DSBs can occur at different sites in the genome, potentially causing so-called \"off-target\" effects.This eventuality remains to date the biggest concern in the field, as possible undesirable modifications must be properly identified and followed in order to guarantee safety for medical purposes.Nevertheless, there is still little evidence of the biological consequence of Cas9 off-target effects.Two recent studies describe new methods to investigate potential off-target effects in both mammals and plants [33,34].In both cases, whole-genome sequencing revealed that selective nucleotide changes, such as conversion of an adenine to a guanine, caused off-target occurrence very rarely, with a frequency comparable to the one of spontaneous mutations.However, substitution of a cytosine with a thymidine was linked to a sizable number of off-target mutations.This newly acquired information adds to the plethora of studies conducted on the safety of CRISPR, which altogether highlight the need for the establishment of clinical standards for the future use of genome-editing techniques in the clinic.Despite this and other technical challenges still ahead for CRISPR genome editing, the pace at which this technology has developed in recent years suggests many of these concerns could be addressed soon, as long as proper ethical guidelines and regulatory mechanisms are established." + }, + { + "document_id": "03110c8a-1232-40c2-8380-c9feb8b6468c", + "section_type": "main", + "text": "\n\nGene editing has gained considerable interest with the identification of the CRISPR-Cas9 system, 27 which allows for a targeted modification in the DNA sequence of an organism.Researchers can utilize their knowledge of the basic biology of the gene and its protein function to precisely change the DNA sequence, thus altering the protein function of the gene and allowing for edits to stay within the species.Researchers at the University of Missouri used the CRISPR-Cas9 system to modify the CD163 gene such that the PRRS virus is not able to replicate inside the pig. 28This slight modification of the swine genome through gene editing keeps the pigs from succumbing to PRRS which has an annual estimated loss to the United States swine industry of over $660 million per year.Despite this benefit, given the public's concerns over food safety, it is likely that approval for such technology is years away in the US, Canada and Europe.However, in some cultures, there is a wide range of non-livestock species that are consumed.Therefore, it is conceivable that these countries and cultures may be open to transgenic/gene edited livestock.They may see the importance of useful gene editing which may lead to approval and consumption of reasonable genetically edited animal products such as those with modifications that are already found in nature or those that offer a substantial welfare benefit to society." + }, + { + "document_id": "801c9288-70c9-4d14-b8bc-13ee6708803a", + "section_type": "main", + "text": "\n\nOver the past 8 years, CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 (CRISPR-associated protein 9) technologies have emerged as accessible and adaptable tools for studying and altering genomes. 5RISPR-Cas9 can be used to induce genome edits by creating targeted DNA breaks that trigger site-specific DNA repair.In nextgeneration formats, it can also control the transcriptional output of genes or alter genome sequences using a process of nucleotide base editing that does not require repair of DNA breaks.As these technologies continue to mature, it will become increasingly possible to alter cellular genomes efficiently and accurately." + }, + { + "document_id": "ac00c552-7514-49d4-9e90-ab01c22472ae", + "section_type": "main", + "text": "\n\nClustered regularly interspaced short palindromic repeat (CRISPR)-Cas nucleases have revolutionized the field of gene editing and have tremendous application in the field of molecular medicine [98][99][100][101][102].Despite a significant surge in CRISPR/Cas9mediated genome editing in various disease models, the progress in the field of AD has lagged behind substantially.We believe that genome editing can significantly improve the development of AD models and also create novel opportunities for the development of the next generation precision targeted AD gene and stem cell therapies.Since there are several excellent review articles on CRISPR/Cas9-mediated genome editing, here we will limit our focus on select recent articles that are noteworthy.CRISPR/Cas9 system can be engineered to either activate transcription (gain-of-function) or achieve gene silencing (Loss-of-function).Dahlman et al. have developed a CRISPR-based system that uses catalytically active Cas9 and distinct single guide (sgRNA) constructs to activate and knockout different genes in the same cell [103].Konermann et al. have used structure-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci [104].Using crystallographic studies, they have engineered a combination of sgRNA2.0,NLS-dCas9-VP64 and MS2-p65-HSF1 to develop one of the most effective transcription activation system." + }, + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "section_type": "abstract", + "text": "\nCRISPR technology has rapidly changed the face of biological research, such that precise genome editing has now become routine for many labs within several years of its initial development.What makes CRISPR/Cas9 so revolutionary is the ability to target a protein (Cas9) to an exact genomic locus, through designing a specific short complementary nucleotide sequence, that together with a common scaffold sequence, constitute the guide RNA bridging the protein and the DNA.Wild-type Cas9 cleaves both DNA strands at its target sequence, but this protein can also be modified to exert many other functions.For instance, by attaching an activation domain to catalytically inactive Cas9 and targeting a promoter region, it is possible to stimulate the expression of a specific endogenous gene.In principle, any genomic region can be targeted, and recent efforts have successfully generated pooled guide RNA libraries for coding and regulatory regions of human, mouse and Drosophila genomes with high coverage, thus facilitating functional phenotypic screening.In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening." + }, + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "section_type": "main", + "text": "\n\nCRISPR technology has rapidly changed the face of biological research, such that precise genome editing has now become routine for many labs within several years of its initial development.What makes CRISPR/Cas9 so revolutionary is the ability to target a protein (Cas9) to an exact genomic locus, through designing a specific short complementary nucleotide sequence, that together with a common scaffold sequence, constitute the guide RNA bridging the protein and the DNA.Wild-type Cas9 cleaves both DNA strands at its target sequence, but this protein can also be modified to exert many other functions.For instance, by attaching an activation domain to catalytically inactive Cas9 and targeting a promoter region, it is possible to stimulate the expression of a specific endogenous gene.In principle, any genomic region can be targeted, and recent efforts have successfully generated pooled guide RNA libraries for coding and regulatory regions of human, mouse and Drosophila genomes with high coverage, thus facilitating functional phenotypic screening.In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening." + }, + { + "document_id": "e5cf067c-8be0-4b0a-b376-7882cdc9d96c", + "section_type": "main", + "text": "Genome editing for crop improvement\n\nReports of CRISPR-Cas9-based genome editing first appeared in 2013 (Cong et al., 2013;Feng et al., 2013;Mao et al., 2013).Since then, genome editing technologies have proven to be powerful and efficient tools for the improvement of many crop species.At present, genome editing has been widely used to introduce/modify agronomically important traits, such as increased yield, improved nutritional quality, and resistance to biotic and abiotic stresses, in multiple crops, including rice, wheat, maize, tomato, and potato (Lu et al., 2017;Soyk et al., 2017;Tang et al., 2017;D'Ambrosio et al., 2018;Ye et al., 2018;Miao et al., 2019;Zhang et al., 2019;Zhong et al., 2019;Butt et al., 2020;Zhang et al., 2020c;Li et al., 2021b;Zhan et al., 2021).CRISPR-Cas-based genome editing has been extended to targeted mutagenesis, base editing, and precisely targeted gene/allele replacement or tagging in plants.mportantly, using CRISPR-Cas9 technology, transgenes present in the genomes of genome-edited plants can be removed by chromosomal segregation via a simple self-pollination or hybridization step.Gene editing technologies continue to be developed and utilized (Mao et al., 2013;Lu and Zhu, 2017;Lu et al., 2020)." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Conclusions\n\nThere is no reason to doubt that the development of CRISPR-Cas genome editing represents an unprecedented breakthrough in modern science, as it has potential applications in a wide array of disciplines ranging from agriculture, zoology and renewable energy to biomedicine and synthetic biology.This powerful tool holds promise for further elucidating the molecular causes of ageing by allowing scientists to probe genetic and epigenetic pathways with a level of sophistication that was unattainable just a few years ago.It will allow so in traditional animal and cell models of ageing, but it will also drastically accelerate the generation of refined versions of those models or even allow the development of new research approaches in non-model organisms.Moreover, CRISPR-based genome editing is already having a significant impact in research aiming to understand the cellular and molecular origins of age-related diseases, as well as developing potential treatments against them.The application of CRISPR-Cas gene editing for the treatment of age-related diseases is not over the horizon yet, as it will require the identification of causative genes and their role under a variety of contexts that could be as diverse as the ageing process is across individuals.However, CRISPR-Cas might also hold the key for solving such conundrum, as it has opened the way for achieving true personalised medicine by providing both the precision and scalability required for conducting genome-wide functional screens during the refinement of drug-and cell-based therapies for age-related diseases." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nThe notable accuracy and versatility of CRISPR-Cas for genome editing also opened the door to its use in preclinical and translational settings.In the latter case, CRISPR in vivo gene editing has led to several proof-of-concept studies that would have been unachievable without it, as in the first ever correction of inherited pathogenic mutations linked to degenerative disease in a living organism [22] and even shown to be possible in human embryos [23,24].It also has great potential in the field of precision medicine as large-scale population DNA sequencing studies have provided vast amounts of information linking particular diseases with specific genetic mutations which could, in theory, be targeted through CRISPR [25,26].This could be used during the identification and validation of potential DNA targets during the development of personalised drug or cell therapies, which will require the generation of engineered cell lines and/or animal models.Techniques such as HDR-mediated gene targeting are too labour intensive, with low targeting efficiencies and long times necessary for their establishment, and consequently are not ideally suited for drug discovery purposes.Conversely, CRISPR-Cas has been proven to be efficient for editing virtually any kind of cell line, from primary immune cells to induced pluripotent stem cells (iPSCs) [27,28].Additionally, CRISPR can also be used for functional screening in the development of combined inhibitory therapy aimed at strengthening the efficiency of targeted therapeutics.An example of the latter is shown in a study where a variation of the technology known as CRISPR interference (CRISPRi) was used in genome-wide scale to identify different survival pathways used by cancer cells after oncogene inactivation and allowing the identification of successful combination therapies [29].In terms of translational applications, the overall safety of CRISPR genome editing in humans will require long-term scrutiny before its adoption in the clinic.Nonetheless, a number of CRISPR-based clinical trials are currently in progress, including studies focused on targeting patients' own T cells in order to improve the immune response towards some forms of malignant cancer [30,31], and others aimed at correcting pathogenic mutations in the hematopoietic cells of patients with beta-thalassemia and sickle cell disease [32]." + }, + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "section_type": "main", + "text": "\n\nThe recent development of clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 for experimental purposes has dismantled the perception that genome editing technology is off-limits for screening in mammalian systems (Heintze et al., 2013).Since this system employs the basic principle of Watson-Crick base pairing for gene targeting, generation of libraries with whole-genome target coverage is relatively easy and cost-effective.For instance, simple protocols are available to synthesize pooled lentiviral libraries by in silico design of oligonucleotides, which can then be cloned, packaged and delivered to cells by viral transduction (Paddison et al., 2004;LeProust et al., 2010).Similarly, the generation of arrayed libraries can be achieved by following protocols originally developed for arrayed shRNA library production that have been in use for a number of years (Moffat et al., 2006).All in all, the stage is set for CRISPR to make an enormous impact on genomic screening and thus scientific discovery in the coming years, and recent demonstrations of this system have shown great promise (Shalem et al., 2015).However, a number of technical challenges must be addressed in order to maximize the benefit of this technology.In this review, we will discuss current applications of CRISPR in functional genomics and provide a perspective on future developments in this area." + }, + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "section_type": "main", + "text": "\n\nGenome editing tools that target the desired genomic region and allow for variants to be altered (e.g. from risk to protective), or for more substantial changes to be made (e.g. the deletion of a longer stretch of DNA harbouring a number of variants) and can help to answer each of these questions.These technologies are evolving rapidly (Figure 1 and Table 2).The most recently developed of these, Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) technology, originally developed by Doudna, Charpentier and their colleagues (72,73) and Zhang and his colleagues (50) has become a widely used tool for this purpose.Engineered CRISPR/Cas9 technology uses a guide RNA (gRNA) to direct CRISPR-associated endonuclease (Cas) to the target DNA and generate a double strand DNA break.Correction of a mutation or variant in the target DNA sequence can then be carried out by homology-directed DNA repair (HDR) with a donor template.Since its discovery eight years ago, CRISPR technology has evolved quickly to be a critical part of the molecular biologist's toolbox." + }, + { + "document_id": "52480703-5353-4e55-a06b-110fd59db3a6", + "section_type": "main", + "text": "CRISPR screening technologies\n\nThe discovery of CRISPR-Cas9 as a sequence-specific programmable nuclease democratized gene editing and fueled progress in forward genetic screening [20 , 66] .Genetic screens using Cas9 with a pooled singleguide RNA (sgRNA) library allow the interrogation of seemingly all genes in a genome in a single experiment [96 , 97] [null] .Engineered Cas9 variants further extend the versatility of forward genetic screening.Catalytically inactive Cas9 (dCas9) fused with chromatin effector domains permit specific activation (CRISPRa) or inhibition (CRISPRi) of gene expression [37 , 54] .Recently developed and emerging technologies -base editors, prime editors, and Cas transposases -are beginning to enable new types of genetic screens with directed, controlled, and on demand mutations by allowing the creation of user specified modifications, such as single base conversion, deletions, and insertions [4 , 42 , 58] ." + }, + { + "document_id": "c3ae2186-ef48-46a5-b214-dc944366df8f", + "section_type": "main", + "text": "INTRODUCTION\n\nGenome editing technologies based on the clustered regularly interspaced short palindromic repeats (CRISPR)-associated endonuclease Cas9 enable rapid and efficient modification of endogenous genes in a variety of cell types, allowing for analysis of gene function in many organs in vivo.CRISPR-Cas9 induces DNA double strand breaks (DSBs) at single-guide RNA (sgRNA)-specific loci in the genome, which are repaired through either non-homologous end-joining (NHEJ) or homology-directed repair (HDR) pathways.While NHEJ introduces unpredictable pattern of insertion or deletion (indel) mutations, HDR directs a precise recombination event between a homologous DNA donor template and the damaged DNA site (Cong et al., 2013;Cox et al., 2015;Doudna and Charpentier, 2014;Heidenreich and Zhang, 2016;Jinek et al., 2012;Mali et al., 2013;Sander and Joung, 2014;Wang et al., 2013;Yang et al., 2013).Thus, HDR can be used to precisely introduce sequence insertions, deletions or mutations by encoding the desired changes in the donor template DNA." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nSince its discovery, CRISPR-Cas technology has ignited a biological revolution by providing a highly versatile platform that allows fast and efficient genome editing in an ever-growing list of organisms.In this chapter we will first describe the most recent advances in the development and application of the CRISPR-Cas platform in biomedical research.Then we will discuss the most recent and notable basic research applications of this technology in the study of the molecular causes of ageing.Finally, we will review how CRISPR-Cas has been used for creating new models for the study of age-related diseases, as well as for manipulating diseaseassociated gene pathways." + }, + { + "document_id": "1942712a-a39d-44f7-9b2d-609926374cbd", + "section_type": "main", + "text": "\n\nAs a researcher who has devoted an entire career since 1994 to the development of genome editing tools and methods, I have been amazed by the rapid progress in the field over the last few years.Considering the widespread use of the tools, I am sure that the pace will continue to accelerate.Indeed, programmable nucleases, may eventually enable humans-products of evolution-to become masters of evolution.delivered preassembled recombinant Cas9-guide RNA ribonucleoproteins (RNPs) into animal embryos 6,9 and plant 11 and mammalian cells [73][74][75] .Indeed, Cas9 RNPs were rapidly turned over in cells 73 , reducing off-target effects and mosaicism in gene-edited organisms 11 .Cas9 RNPs can be delivered into cells by various methods, including microinjection 6,9 , electroporation 73 , lipofection 74 and protein transduction 75 .Importantly-and unlike in conventional gene therapy, where therapeutic genes are delivered via plasmids or viral vectors-Cas9 RNP delivery does not involve the use of exogenous DNA; host innate immune responses against foreign DNA are not elicited, and undesired integration of foreign DNA into the host genome is avoided." + }, + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "section_type": "main", + "text": "Caveats of advanced genome editing tools\n\nOff-target effects.The DNA-binding domains of ZFNs and TALENs need to be very specific for the target site to avoid off-target cleavage, which results in unwanted mutations and potentially cytotoxic effects [27].CRISPR/Cas9 is also known to generate off-target alterations, albeit apparently at low incidence [28,29], since mispairing is allowed between the guide RNA and the genomic DNA.Nonetheless, caution is required in their design and use.Some strategies involving the optimization of the guide RNA/Cas9 include using of software tools to predict potential off-target sites (http://omictools.com/crispr-cas9-Figure1: Genome editing methodologies which can be applied to human pluripotent stem cells.Homologous recombination (HR), or the more advanced tools such as zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) or clustered regularly interspaced short palindromic repeat (CRISPR)/Cas system can be applied to human pluripotent stem cells (hPSCs) either to 1) create naturally occurring mutations or 2) repair a mutation to generate isogenic controls in hPSCs, to understand the function of a gene of interest.c1268-p1.html),truncating the guide RNA (<20 nucleotides) to decrease off-target mutagenesis [30], lowering the dosage of guide RNA and Cas9 plasmids, and decreasing the number of mismatches between the guide RNA and the genomic DNA.A \"double nick\" system with Cas9 nickase, which contains a single inactive catalytic domain, may also be used [31e33]." + }, + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "section_type": "main", + "text": "\n\nIn addition to gene knock-out and HDR repair, genome-wide pooled CRISPR-Cas9 libraries have been used to systematically delete genes responsible for diverse phenotypes.Recent studies have shown that such loss-of-function screens using libraries comprising tens of thousands of sgRNAs can be used to identify genes involved in tumour growth and metastasis (171).In the diabetes field, similar approaches have also been used recently to identify key insulin gene regulators (172) and the genes involving in auto-immune killing of b cell transplants (173).Screens based on transcriptional interference (CRISPRi) and activation (CRISPRa) have also harnessed Cas9-based technologies for use in genome-wide studies (59,174).In addition, recent improvements in lentiviral library generation and propagation, as well as large-scale DNA and RNA synthesis, have allowed CRISPR-Cas9 technology to be exploited across multiple model platforms (59,(175)(176)(177)(178)." + }, + { + "document_id": "a7f21808-dce3-4110-8e7c-ceb2437e72ff", + "section_type": "main", + "text": "\n\nThe type II CRISPR-Cas9 systems, repurposed from prokaryotic adaptive immune responses, are now widely used for targeted genome modifications in plants, animals, and human cells (Kim et al. 2014;Woo et al. 2015;Zuris et al. 2015).In particular, Cas9 nucleases have shown promise for gene and cell therapy (Maeder and Gersbach 2016).Typically, these nucleases are expressed or delivered in vivo using plasmid DNA or viruses (Yin et al. 2014;Ran et al. 2015).However, plasmid DNA delivery is often inefficient, especially in vivo, and can cause integration of small plasmid fragments degraded by endogenous nucleases at on-target and offtarget sites in the genome (Kim et al. 2014).Viral delivery of Cas9 can be highly efficient in vivo (Ran et al. 2015;Long et al. 2016;Nelson et al. 2016;Tabebordbar et al. 2016), but may be hampered by antibodies or T cells induced against the protein (Shankar et al. 2007;Calcedo et al. 2015;Chew et al. 2016).We and others have shown that preassembled Cas9 ribonucleoproteins (RNPs) can be delivered to human primary and stem cells and mice to modify target genes (Kim et al. 2014;Schumann et al. 2015;Zuris et al. 2015).Cas9 RNPs are rapidly turned over in cells, reducing off-target effects.Furthermore, Cas9 RNPs are unlikely to be limited by host immune systems because they function and disappear before the generation of antibodies and T cells directed against them.Currently, despite these advantages of RNPs, the difficult delivery of Cas9 RNPs in vivo limits its utility for therapeutic applications (Zuris et al. 2015).Here, we show that in vivo genome editing of an wild-type gene, whose up-regulation is responsible for pathogenesis, could be a new therapeutic modality for the treatment of nongenetic degenerative diseases.Our ultimate goal is to harness Cas9 RNPs for a clinical application of therapeutic genome surgery in patients with AMD." + }, + { + "document_id": "1942712a-a39d-44f7-9b2d-609926374cbd", + "section_type": "main", + "text": "Genome editing comes of age\n\nJin-Soo Kim 1,2 Genome editing harnesses programmable nucleases to cut and paste genetic information in a targeted manner in living cells and organisms.Here, I review the development of programmable nucleases, including zinc finger nucleases (ZFNs), TAL (transcription-activator-like) effector nucleases (TALENs) and CRISPR (cluster of regularly interspaced palindromic repeats)-Cas9 (CRISPR-associated protein 9) RNA-guided endonucleases (RGENs).I specifically highlight the key advances that set the foundation for the rapid and widespread implementation of CRISPR-Cas9 genome editing approaches that has revolutionized the field." + }, + { + "document_id": "1942712a-a39d-44f7-9b2d-609926374cbd", + "section_type": "main", + "text": "\n\nThe ability to genetically modify living cells and organisms is a fundamental tool for biological research, but achieving highly specific targeted changes has been technically demanding.Genome editing has been recently democratized by the development of RGENs (see Glossary in Box 1), repurposed from the type II CRISPR-Cas9 prokaryotic adaptive immune system 1 .Unlike other programmable nucleases, namely ZFNs and TALENs, whose target specificities are determined by modifying their DNA-binding domains, CRISPR-Cas9 can be customized by replacing guide RNAs, making the system much more affordable and scalable.Cas9 nucleases have been successfully used for modifying genomes in human cells [2][3][4][5] , animals [6][7][8][9] and plants 10,11 , heralding the age of genome editing.Furthermore, Cas9 or guide RNAs have been linked to various effector proteins to enable targeted gene regulation 12,13 and epigenome modifications 14,15 .It is worth noting, however, that many of these feats had been demonstrated previously using other nucleases or DNA-binding proteins 1,16 .In this Perspective, I shed light on early genome editing platforms that laid the groundwork for the widespread use of CRISPR-Cas9 in research and medicine (Fig. 1)." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nGenome editing has always been a challenging area to provide more efficient ways to create a meaningful change in the genome.Today, the CRISPR (clustered regularly interspaced short palindromic repeat) restoration system is considered as one of the suitable and promising options for genome editing.Compared to the previous systems, CRISPR can deactivate or eliminate a gene without interfering with intracellular mechanisms.The system could be used in the treatment of diseases and in related research by identifying the performance of defective genes in these diseases.CRISPR seems to have more potential and applications compared to previous systems.Among these applications, we can note the use of CRISPR in understanding complex genetic and epigenetic conditions such as aging or cancer.The complex interactions between several genetic and epigenetic mechanisms that characterize aging pose significant challenges to scientists attempting to understand this phenomenon and its causes and still constitute a barrier to a better understanding of aging and the ability to develop effective application of CRISPR-cas to aging research." + }, + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "section_type": "main", + "text": "Cas9\n\nDue to its simplicity and adaptability, CRISPR has rapidly become the most popular genome editing tool available for the mammalian genome (50,63).Because NHEJ DNA repair often introduces unwanted indels at the Cas9 cutting site, CRISPR has been used to knock-out genes by introducing frameshift mutations, resulting in protein depletion (156,157).In the diabetes field, CRISPR has also been adopted to study several genes in b cell lines and in human ES-derived b cells (21,151,158,159) as well as in animals (160,161)." + }, + { + "document_id": "e2d1d559-d48f-4e57-8372-04d31f0f9da3", + "section_type": "main", + "text": "\n\nSome believe genome editing tools provide the best imaginable technology for mutating the germline.Indeed it is hard at the moment to imagine what could be better.Nevertheless there are remaining challenges.We need to improve efficiency of editing within a given population of cells (destined for SCNT) and in the zygote and overcome mosaicism.In our work with zygotes we regularly achieve 30 % editing frequency with delivery of editors-ZFN, TALEN and CRISPR/ Cas9-to the cytoplasm of livestock.We should aspire to at least [50 % and why not frequencies approaching or even achieving 100 %." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nThe application of CRISPR-Cas in epigenome editing is currently in its infancy.However, the technique holds significant promise for providing clarity to the myriad of epigenetic mechanisms that may impact on the ageing process.In this regard, it must be noted that in comparison to other hallmarks of ageing, the range of discrepancies observed across model species is the broadest in the case of some epigenetic alterations.This prevents the use of many of the most time-cost efficient in vivo models of ageing such as yeast, worms and flies, as they can even lack some of those alterations.Therefore, the use of CRISPR-Cas for the fast and efficient generation of in vitro and in vivo models of higher species will prove invaluable for studying epigenetic mechanisms of ageing that are of relevance to humans." + }, + { + "document_id": "33f1abde-a821-483b-b8b4-785f499db09d", + "section_type": "main", + "text": "\n\nIn comparison to a transgenic approach, a gene editing technique such as CRISPR-Cas9 offers the advantage that gene-edited crops are not considered genetically modified organism (GMO) in some countries, such as the US, where the demand for natural food colorants such as anthocyanins is high.Indeed, the use of GMO crops as a source of natural pigments may be inconsistent with consumer interests.However, carrot cultivars engineered with either the transgenic or gene editing approach have not been reported so far, but their development is possible." + }, + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "section_type": "main", + "text": "\n\nApplications of gene/genome editing tools." + }, + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "section_type": "main", + "text": "\n\nKey characteristics in CRISPR and siRNA technologies." + }, + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "section_type": "main", + "text": "CRISPR/Cas9 Screening\n\nA growing number of published studies have utilized CRISPR technology for screening (see Table 3 for a comparison).CRISPR For an overview of key differences between siRNA and CRISPR technologies, please see Taylor and Woodcock (2015)." + }, + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "section_type": "main", + "text": "\n\nFinally, CRISPR screening has become a possibility in 3D models, tissues and whole organisms (Platt et al., 2014;Chen et al., 2015).The generation of a Cre-dependent Cas9 knockin mouse enables the manipulation of genes in specific tissues, for instance by viral or non-viral delivery of sgRNA to the brain or other tissues.Importantly, this technology for the first time enables complex studies of acute modulation of brainspecific phenotypes, which will be key to develop a more thorough understanding of neuronal diseases.Using tissuespecific expression systems, it is thus possible to target a functionalized protein to any location within a whole organism.This truly is a new age in functional genomics." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "The Molecular Basis of CRISPR-Cas Technology and Its Variants\n\nDuring the last decade, a more robust system in terms of targeting efficiency and ease of design was developed and rapidly became the most widely used gene-editing technique in the life sciences.CRISPR stands for clustered regularly interspaced short palindromic repeat DNA sequences.These short repeat elements were first observed in E. coli in 1987 and were later determined to be part of the bacterial adaptive immune system [9].However, the first concrete experimental evidence of the potential widespread application of CRISPR came with the demonstration that following viral infection, bacteria could integrate specific sequences of the viral genome into their own.These sequences would then be used by bacteria to produce short RNAs able to recognise the viral DNA in subsequent infections and guide the Cas9 nuclease to it.The RNA/Cas9 complex would then induce a DSB in the viral DNA, disabling it [10].This defence mechanism can be easily exploited in an experimental set-up, where short RNA sequences (around 20 base pairs), named gRNA (guide RNA), can be designed to bind any determined DNA sequence in virtually any kind of cell.gRNAs then become complexed to the Cas9 enzyme and will dictate the specificity of its enzymatic action, which in turn will lead to the generation of a DSB in the targeted genome." + }, + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "section_type": "main", + "text": "\n\nIn view of the above, genome editing tools need to be carefully selected.The newly developed nCas9-RT holds great potential: 1.The nCas9 nicks the DNA rather than induces DSB and therefore avoids indel formation at the cutting site; 2. The use of pegRNA, which is a combination of gRNA, reverse transcription template and primer-binding sites, increases the specificity of target DNA binding hence reduces off-targets (62); 3.While multiplex pegRNAs could target various variants including SNPs, deletions or insertions without separating DNA donors as templates, it is possible the nCas9-RT will be able to convert all variants at once.This new technique, however, is still in early development, and its editing efficiency and sideeffects remain to be seen." + }, + { + "document_id": "f28111d5-fe88-4668-8699-f02f907af80a", + "section_type": "main", + "text": "\n\n146 Genome studies and molecular genetics Features of Cas9-mediated gene editing methods in wheat.The top graph shows the percentages of on-target mutagenesis using the different methods across independent experiments as defined by their target genes or genotypes [39 ,42 ,43].Features include the method of delivery of the Cas9 and gRNA, the stable or transient expression of the system and the possibility of nuclear DNA integration.The target genes used in each method are shown, although the detailed homoeolog specificity of each experiment is not always reported.The number of different hexaploid (6x) bread wheat and tetraploid (4x) pasta wheat varieties used for each method is also shown in parenthesis.Abbreviations: Transiently expressing CRISPR/Cas9 DNA (TECCDNA) or RNA (TECCRNA), ribonucleoprotein (RNP).Original publications for stable transformation [39 ,42 ], TECCDNA [42 ,43], TECCRNA [42 ] and RNP [43].15, 2017, 367-378.effectively between disciplines and appreciate the potential of genomics and field-based research to complement each other. 'Reaping the benefits' [57] of the latest genomic developments will ultimately depend on our success in translating this knowledge into improved wheat cultivars for farmers and consumers." + }, + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "section_type": "main", + "text": "Conclusion and Outlook\n\nOverall, the use of CRISPR based methods in high-throughput functional genomics screening is still in its infancy.The first pooled libraries show encouraging results, but many technical considerations need to be explored for the development of arrayed libraries.The generation of large-scale libraries is possible not only for human and mouse, but virtually any organism.In the past, siRNA libraries have mostly focused on Drosophila, C. elegans, human, mouse, and rat genomes, though in principle has always been possible to design and produce libraries for other organisms as well.It is uncertain which model organisms will be targeted with whole genome or focused libraries using CRISPR as the availability of whole-genome sequence information expands." + }, + { + "document_id": "429abfc1-f628-48ff-bfe8-f7be6d1419a8", + "section_type": "main", + "text": "Effective protocols that exist for gene editing use\nguide RNA in combination with the enzyme recombinase\n(CRISPR/Cas9), zinc finger nuclease, or zinc finger protein in combination with a nuclease (TALEN) (Gupta and\nMusunuru, 2014). Although these methods can be applied\neasily to livestock species, the mouse still has the unequal\nadvantage of a short generation interval, which shortens the\ntime for testing the effects of the introduced mutation several\nfold." + }, + { + "document_id": "ac00c552-7514-49d4-9e90-ab01c22472ae", + "section_type": "main", + "text": "\n\nAs compared to the complete gene knockout mouse models, CRISPR/Cas9 based gene editing provides only a partial knockout in a fraction of targeted cells.Therefore, it is important to improve the current gene editing efficiency of the CRISPR/Cas9 systems.Another caveat is that using a ubiquitous promoter to drive the expression of CRISPR/Cas9 can lead to gene editing in the non-target cells.This can be achieved using a highly tissue specific promoter to drive the expression of CRISPR/Cas9.However, despite utilizing a tissue specific promoter, it is still not desirable to have constitutive expression of CRISPR/Cas9 as it may lead to off-target effects.This potential drawback can be overcome by utilizing a conditional expression system wherein the expression levels as well as the duration of CRISPR/Cas9 can be tightly regulated.This has been recently demonstrated by de Solis et al. who have developed a doxycycline-inducible AAV based system for gene editing [118].Their strategy involved generating two separate AAV/DJ vectors such that the vector harbors a TRE Tight promoter driving the expression of CRISPR/Cas9 while the second vector contains a U6 promoter driving Tet2 sgRNA and a CMV promoter driving the expression of rtTA (Tet-On Advanced and an IRES driven GFP.Surprisingly, their results indicate doxycycline-inducible expression of CRISPR but Tet2 gene editing in a doxycycline independent manner due to leakiness.To overcome the issue of leakiness, they have significantly modified their vectors by utilizing a combination of hybrid H1/TO promoter to drive the expression of Tet2-sgRNA and a CMV promoter controlling the expression of TetR in frame with a self-cleaving P2A sequence followed by a GFP ORF fused to a KASH domain.In this system in the absence of doxycycline, TetR binds to H1/TO promoter and represses the gRNA transcription.However, addition of doxycycline inhibits TetR binding and induces gRNA expression.This system allowed doxycycline dependent genome editing of Tet2 in N2A cells in vitro.Besides, doxycycline inducible system there are several other inducible systems available including rapamycin, mifepristone, tamoxifen, and ecdysone inducible systems that can be engineered to overcome the leakiness of the dinducible system." + }, + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "section_type": "main", + "text": "From Genome Editing to Genome Functionalization\n\nThe Cas9 protein has been engineered to obtain various properties that range from transcriptional repression to endogenous gene tagging (Table 1).In a more simplified view, Cas9 can be seen as the adaptor between the target sequence and a variety of functions.This reveals the most powerful concept of CRISPR technology: the ability to target a function to an exact genomic position.With this view in mind, it is conceivable to be able to design a minimal Cas9 protein with all extraneous regions deleted so that the protein simply binds the target DNA, and this would provide the most basic possible template for protein engineering.To date, successful CRISPR-based genome functionalization techniques have been based on fusing one or several functional domain to full-length catalytically inactive Cas9 (dCas9), which binds to the target locus but does not cleave the DNA.An important experimental consideration to take into account when following these approaches is that different sgRNAs must be designed for each functionalization in order to target the correct genomic features and achieve the desired output.For instance, transcriptional regulation requires sgRNAs that target promoter or regulatory regions, whereas sgRNAs used for knockouts most commonly target exons.Furthermore, the location of targeting within an individual gene can have a significant impact on the functional effect of the resulting mutation.For example, when using wild-type Cas9, targeting a coding region corresponding to a functional protein domain has been shown to be result in loss-of-function even for in-frame mutations, compared to exclusively targeting early exon regions, which often require frameshift mutations to achieve loss-of-function (Shi et al., 2015).Deliberately targeting certain gene regions can be used for achieving specific outcomes, such as knocking out a specific splice variant." + }, + { + "document_id": "9a12db75-1efa-46b1-9da4-d2fc8d828f42", + "section_type": "main", + "text": "\n\nIn addition to DNA-targeting CRISPR systems, RNA-targeting Cas9 enzymes are also available, such as CasRx which showed robust knockdown of gene expression (Konermann et al., 2018).Interestingly, CasRx can also be used to target pre-mRNA to manipulate alternative splicing et al., 2018).Deregulation of alternative splicing has been implicated in the aging process (Li et al., 2017) and observed in several ageassociated diseases such as amyotrophic lateral sclerosis and Alzheimer's disease (Lin et al., 1998;Spillantini et al., 1998;Glatz et al., 2006).In regards to AMD, Allikmets et al. (1997) have shown that a point mutation (G5196A) in the Stargardt disease gene ABCA4, eliminates a 5 donor splice site and increases the risk of AMD.However, a subsequent GWAS study with larger cohorts could not confirm this association between ABCA4 and AMD (Fritsche et al., 2016).Overall, the association of AMD pathophysiology with alternative splicing regulation remains unclear and CasRx technology could facilitate research in this understudied area.In summary, recent development of CRISPR/Cas technology has greatly expanded the toolbox to carry out functional study of AMD-associated genes, providing new tools that can modulate gene expression by targeting at the DNA level, RNA level as well as the splicing variants." + }, + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "section_type": "main", + "text": "\n\nManipulation of hESC/iPSC cells via CRISPR-Cas9 technology provides a platform for the correction of genomic mutations not only in diabetes but in other disease fields as well (276)(277)(278)(279).Through CRISPR-mediated HDR and base editing, it is possible to correct the vast majority of genetic variants, if not all.Conversion of GWAS-identified non-coding variants has not been conducted/documented in the diabetes field, but it seems inevitable that such work will be carried out in the near future given its importance in basic research and potential clinical application.Variants identified by GWAS are often clustered in the genome (134).Although an individual variant may change transcription factor binding on its own, neighbouring risk variants might cooperate to change the transcriptional landscape of local chromatin and thus the activity of the enhancer cluster leading to changes in the expression of multiple genes whose aggregate effect is to impair b cell function.Hence, multiplex genome-editing needs to be carried out to convert multiple risk variants into protective (non-risk) variants in hESC or iPSC cells.In this case, the off-target effects brought by multiplex gRNAs may have a large impact on the rest of the genome and raise major concerns." + } + ], + "document_id": "38E097866214E3EEFE346FB836ABF345", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "CRISPR-Cas9", + "gene&editing", + "off-target&effects", + "genome", + "clinical&trials", + "agriculture", + "biomedicine", + "precision&medicine", + "transgenic", + "ethical&concerns" + ], + "metadata": [ + { + "object": "Genome-wide association analyses in 22,981 participants 2280 shingles cases from the electronic Medical Records and Genomics Network identified a genomic region in the combined and European ancestry groups that has an age of onset effect reaching genome-wide significance region tags the non-coding gene HCP5 HLA Complex P5.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab319975" + }, + { + "object": "Study show that CXCL9 and CXCL10 are overexpressed in skin of HPV16 E7 transgenic mice when compared with non-transgenic animals. Immune cell infiltration to E7 transgenic skin is a consequence of epidermal hyperplasia, and that hyperplasia induces CXCL9 and CXCL10 production to recruit a subset of CXCR3+ T cells, promoting rejection of grafted E7 transgenic skin depleted of immunosuppressive lymphocytes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab749071" + }, + { + "object": "shRNAs targeting MSTN were expressed in muscles of transgenic sheep. MSTN expression was inhibited in muscle tissues of transgenics compared with controls. Moreover, transgenic sheep showed a tendency to faster increase in body weight than control sheep.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab705362" + }, + { + "object": "ompounding a previously described Bmi1-transgene and Pten-deficiency prostate cancer mouse model with the Ezh2 transgene did not enhance tumour progression or drive metastasis formation. In conclusion, we here report the generation of a wildtype Ezh2 overexpression mouse model that allows for intravital surveillance of tissues with activated transgene", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab546432" + }, + { + "object": "Using a series of transgenic constructs with various Alk1 genomic fragments joined to a reporter, it seems a 9.2-kb genomic fragment including the 2.7-kb promoter region & the whole intron 2 is sufficient for arterial endothelium-specific expression.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab988637" + }, + { + "object": "Data including data from studies using transgenic plants or cells from transgenic plants suggest crucial role for UreG in nickel delivery for urease multimerization/activation. These studies were conducted using recombinant Arabidopsis thaliana proteins expressed in transgenic Nicotiana benthamiana cloned cells or hydroponic plants. UreG = urease accessory protein UreG", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab173373" + }, + { + "object": "TaWRKY33 transgenic Arabidopsis lines showed lower rates of water loss than TaWRKY1 transgenic Arabidopsis lines and wild type plants during dehydration. Most importantly, TaWRKY33 transgenic lines exhibited enhanced tolerance to heat stress. [WRKY33]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab766040" + }, + { + "object": "Study tracked the post-weaning development of a motor phenotype that arose in mice after random insertion of a transgene into the genome. The transgene was later found to have disrupted the beta-IV spectrin gene, which was confirmed by reduced expression of betaIV spectrin protein in brain homogenates. The motor phenotype was recessive, occurring in 20% of L25+/-xL25+/- progeny.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab752806" + }, + { + "object": "eight of 11 Sox10 genomic elements direct reporter gene expression in transgenic zebrafish similar to patterns observed in transgenic mice, despite an absence of observable sequence conservation between mice and zebrafish.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab252134" + }, + { + "object": "More oligodendrocytes and less demyelination were observed after SCI in p35 transgenic mice than in controls which did not carry the p35 transgene. Motor function recovered more in the cre/p35 transgenic mice than in the control cre mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab501036" + } + ], + "question": "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "subquestions": null, + "task_id": "38E097866214E3EEFE346FB836ABF345", + "usage": { + "chatgpt": 9104, + "gpt-4": 6248, + "gpt-4-turbo-preview": 5340 + }, + "user_id": 2 + }, + "document_id": "38E097866214E3EEFE346FB836ABF345", + "task_id": "38E097866214E3EEFE346FB836ABF345" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/06.json b/gnqa/paper1_eval/src/data/responses/general/experts/06.json new file mode 100644 index 0000000..894ee69 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/06.json @@ -0,0 +1,398 @@ +{ + "created_at": "2024-06-01T14:27:38.978700Z", + "data": { + "amplify": false, + "answer": "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "context": { + "395a51ba-bd2a-4160-8396-b13a3bf762ff": [ + { + "document_id": "395a51ba-bd2a-4160-8396-b13a3bf762ff", + "text": "\n\nPrimate and mouse centromeric DNA contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B.In humans, the CENP-B box is present on every chromosome except for the Y chromosome [20].It is paradoxically necessary for formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres, and mice lacking CENP-B are viable and fertile [21,22]." + } + ], + "3dfe0ec3-b3a6-4a08-8929-e54cab3ec262": [ + { + "document_id": "3dfe0ec3-b3a6-4a08-8929-e54cab3ec262", + "text": "Box 3 Mechanism of homologous recombination and end joining\n\nThe severe phenotype of the mouse mutants and the highly cancer-prone human syndromes highlight the importance of homologous recombination.Mouse KU mutants display sensitivity to agents that lead to breaks in DNA, and have immunological problems because the KU proteins are involved in V(D)J recombination of antibody gene sequences.In addition, these mutants display poor development, several features of premature ageing and increased apoptosis of postmitotic neurons in the developing brain.Mice with defects in DNA-PK cs (SCID mice) display a similar but generally milder phenotype.In contrast, XRCC4-and ligase IV-knockout mice seem more severe, with late embryonic lethality resulting from massive ATM-and p53-dependent neuronal apoptosis 33,38 ." + }, + { + "document_id": "3dfe0ec3-b3a6-4a08-8929-e54cab3ec262", + "text": "\n\nCells in G1 have only the homologous chromosome for recombination repair.However, this may be difficult to find in the complex genome.Moreover, it is potentially dangerous as a template for repair as it may lead to homozygosity for recessive mutations.As an alternative, the end-joining reaction simply links ends of a DSB together, without any template, using the end-binding KU70/80 complex and DNA-PK cs , followed by ligation by XRCC4-ligase4 (reviewed by 27,33; see the right panel of the figure, stages V-VII).The function of KU70/80 might involve end protection and approximating the ends, in addition to a signalling function by DNA-PK cs .End joining may be further facilitated when the ends are still held together through nucleosomes or other structures.End joining is sometimes associated with gain or loss of a few nucleotides if internal microhomologies are used for annealing before sealing.This implies the involvement of DNA polymerases and/or nucleases.Note that the KU complex is also involved in telomere metabolism 27,62 .found to be lethal 34 .Inactivation of ATR by itself is inviable already at the blastocyst stage.Inactivation of BRCA1 and BRCA2 in mice is also embryonically lethal; cell lines display defects in homologous recombination [35][36][37] ." + }, + { + "document_id": "3dfe0ec3-b3a6-4a08-8929-e54cab3ec262", + "text": "371\n\nA tentative scenario for the homologousrecombination reaction is depicted in the left panel of the figure.To promote strand invasion into homologous sequences, the 5፱-3፱ exonuclease activity of the RAD50/MRE11/NBS1 complex (also a substrate for ATM phosphorylation) exposes both 3፱ ends 30 (I).RPA facilitates assembly of a RAD51 nucleoprotein filament that probably includes RAD51-related proteins XRCC2, XRCC3, RAD51B, C and D. RAD52 stimulates filament assembly (II).RAD51 has, like its Escherichia coli RecA counterpart, the ability to exchange the single strand with the same sequence from a double-stranded DNA molecule.Correct positioning of the sister chromatids by cohesins probably facilitates the identification of a homologous sequence.A candidate for the complex chromatin transactions associated with these DNA gymnastics is RAD54, a member of the SWI/SNF family of DNA-dependent ATPases.After identification of the identical sister chromatid sequence, the intact double-stranded copy is used as a template to properly heal the broken ends by DNA synthesis (III).Finally, the so-called Hollidayjunctions are resolved by resolvases 27,33,60 (IV).Homologous recombination involves the simultaneous action of large numbers of the same molecules, which are found to be concentrated in radiation-induced nuclear foci.These depend on, and also include, the BRCA1 and BRCA2 proteins 36 .Recent evidence implicates BRCA2 directly or indirectly in nuclear translocation of RAD51 (ref.61)." + } + ], + "748cfe7e-e4f2-453f-8575-50dfe84e2538": [ + { + "document_id": "748cfe7e-e4f2-453f-8575-50dfe84e2538", + "text": "\n\nThis picture poses more questions than it seeks to answer.Is the grouping of the regions by product rather than by type of region correct?Given that the recombina- tion fraction between HLA-A and HLA-B is of the order of .08%,and that this is likely to represent a distance of at least hundreds of thousands of nucleotides, how are the pieces put together over such relatively long distances?Is it possible that regions of the DNA loop out, so that transcripts can be made directly from noncon- tiguous DNA sequences, the loops being held in place by small RNAs as suggested for the control of splicing by Steitz, and her colleagues [24] and by others [25]?If these small RNAs are coded for well outside the HLA region, does this provide a mechanism for control of expression of products by unlinked genes, as may be the case for one of the constituent polypeptides of the HLA-DR product?What might be the nature of the signals that control which of a multiple set of alternative regions is expressed by any given chromosome?" + } + ], + "7a451204-390c-4ff2-8a1d-b4de62b73503": [ + { + "document_id": "7a451204-390c-4ff2-8a1d-b4de62b73503", + "text": "Mamm Genome. 2006; 17:220–229. [PubMed: 16518689]\n72. Romanoski CE, et al. Systems genetics analysis of gene-by-environment interactions in human\ncells. Am J Hum Genet. 2010; 86:399–410. [PubMed: 20170901]\n73. Myers S, Freeman C, Auton A, Donnelly P, McVean G. A common sequence motif associated\nwith recombination hot spots and genome instability in humans. Nature Genet. 2008; 40:1124–\n1129. [PubMed: 19165926]\n74. Myers S, et al. Drive against hotspot motifs in primates implicates the PRDM9 gene in meiotic\nrecombination. Science. 2010; 327:876–879. [PubMed: 20044541]\n75. Cordell HJ. Detecting gene-gene interactions that underlie human diseases. Nature Rev Genet. 2009; 10:392–404." + } + ], + "8604652e-2477-4552-8f43-f5f19e421df2": [ + { + "document_id": "8604652e-2477-4552-8f43-f5f19e421df2", + "text": "Classification of common conserved sequences in mammalian\nintergenic regions. Hum. Mol. Genet. 2002, 11, 669–674. 25. Zhu, L.; Swergold, G.D.; Seldin, M.F. Examination of sequence homology between human\nchromosome 20 and the mouse genome: Intense conservation of many genomic elements. Hum. Genet. 2003, 113, 60–70. 26. Pevzner, P.; Tesler, G. Human and mouse genomic sequences reveal extensive breakpoint reuse in\nmammalian evolution. Proc. Natl. Acad. Sci. USA 2003, 100, 7672–7677. 27. Christmann, R.B. ; Sampaio-Barros, P.; Stifano, G.; Borges, C.L. ; de Carvalho, C.R. ; Kairalla, R.;\nParra, E.R. ; Spira, A.; Simms, R.; Capellozzi, V.L. ; et al." + } + ], + "9d82958a-45b0-4f1d-b765-38d018e4b140": [ + { + "document_id": "9d82958a-45b0-4f1d-b765-38d018e4b140", + "text": "\n\na The table lists proteins in which mutations have been shown to increase homologous recombination (HR), gross chromosomal rearrangements (GCRs), chromosomal instability (CIN), sister chromatid exchanges (SCEs), tri-nucleotide repeat expansions and contractions (TNR), telomere fusions (Tel fusion), or fragile telomeres (Tel fragility).A phenotype inside brackets ([ ]) indicates that it is caused by overexpression of the protein.For further details and references see Supplementary Table1.Abbreviations: DSB, double-strand break; PCNA, proliferating cell nuclear antigen; RFC, replication factor C complex; SCF, Skp1-Cdc53/Cullin-F-box." + }, + { + "document_id": "9d82958a-45b0-4f1d-b765-38d018e4b140", + "text": "\n\nFigure 3 Intermediates and chromosome structural alterations, as observed by different techniques. (a) Replication fork stalling, as monitored by 2D-gel electrophoresis and Southern analysis in yeast (for details about the technique, see Reference 161). (b) Slower human replication forks covering shorter DNA synthesis tracks, as determined by incorporation of IdU and CldU via DNA combing (52), which permits visualization of the process of replication on DNA fibers. (c) Accumulation of double-strand breaks (DSBs) or replicative stress, as inferred by γH2AX foci or by γH2AX pan staining, respectively, in human cells. (d ) DSBs or ssDNA (single-stranded DNA) gaps as seen directly by nuclear \"comet tails\" via single-cell electrophoresis assays in human cells (52). (e) Sister-chromatid exchanges (SCEs), as determined by Giemsa staining in human cells (207). ( f ) Hyper-recombination, as determined by colony sectoring in yeast (5). ( g) Gross chromosomal rearrangements (GCRs), as determined by spectral karyotyping in mouse cells (118). (h) Translocations, as visualized by pulse-field gel electrophoresis in yeast (168). (i ) Fragile sites, as detected by mitotic spreads in human cells (109). ( j) Telomere fusions, as determined by CO-FISH (chromosome-orientation fluorescent in situ hybridization) in mouse cells (124). (k) Anaphase bridges, presumably resulting from unfinished replication, dicentric chromosomes, and sister-chromatid nondisjunction, as detected by fluorescence microscopy in mouse cells.Arrows indicate the specific structural alterations referred to in each panel; in panel h, closed and open arrows indicate the position where the translocated or missing parental chromosome migrate or should migrate, respectively.When necessary, a normal control is shown on top of the panel, with the exception of panel a, which is shown on the left.Detailed description of each technique can be found in the references provided.Photos are from the laboratories of A. Nussenzweig ( g), A. Losada (k), M. Blasco ( j), L. Tora (i ), and ours (all others).Abbreviations: HR, homologous recombination; NHEJ, nonhomologous end-joining." + } + ], + "9ee491f4-5f16-4cb2-b803-54f2fdee1dba": [ + { + "document_id": "9ee491f4-5f16-4cb2-b803-54f2fdee1dba", + "text": "\n\nIn humans, the pericentromeric region of chromosome 9 is densely packed with segmental genomic duplications (segdups) and is prone to microdeletions and microduplications. 5In order to evaluate this region for microdeletions and microduplications in family T, we screened genomic DNA from affected individual II-7 by arrayCGH with the Nimblegen HD2 platform with the previously described CHP-SKN sample 6 as the reference.Data were normalized and CNVs were called by identifying regions where Z-scores consistently deviated from the diploid mean.At 9q21.11, a genomic duplication of ~270 kb was apparent in the genomic DNA of II-7 (Figure 1D).The Genomic duplications may or may not be in tandem with their parent segment and may be either in the same or inverted orientation. 7We developed primers that would uniquely amplify genomic DNA with the duplication under each of these conditions.Forward (5 0 -CCCAGCAGA AGCAATGGTGGTAGCC-3 0 ) and reverse (5 0 -GGTGGTGAA TCCAAAAACACAAGAACAAAGTC-3 0 ) primers diagnostic for a tandem inverted duplication (Figure 2A) yielded products of expected size in family T relatives with hearing loss, but yielded no product in unaffected family T relatives (Figure 2B).Genotypes of all 58 participating relatives in family T indicated that the tandem inverted duplication was coinherited with hearing loss.The duplication spans approximately positions 71,705,804 to 71,974,823 (hg19) on chromosome 9 for a size of ~269,023 bp.The duplication includes the entire locus for the tight junction protein TJP2, which spans positions 71,788,971 to 71,870,124 (hg19)." + } + ], + "ab0a3234-c3b3-46be-8954-01eda9bc962e": [ + { + "document_id": "ab0a3234-c3b3-46be-8954-01eda9bc962e", + "text": "Chromosomal context of human NORs\n\nHuman NORs are positioned on the short arms of the acrocentric chromosomes that still remain unsequenced and thus missing from the current human genome draft, GRCh38.p7.Seeking an understanding of the chromosomal context of human NORs and to identify potential NOR regulatory elements, my laboratory has begun to characterize the sequences on both proximal (centromeric) and distal (telomeric) sides of the rDNA arrays (Fig. 3A; Floutsakou et al. 2013).Building on earlier reports of sequences distal and proximal to the rDNA array on HSA21 and HSA22, respectively (Worton et al. 1988;Sakai et al. 1995;Gonzalez and Sylvester 1997), 207 kb of sequence immediately proximal and 379 kb distal to rDNA arrays have been reported recently (Floutsakou et al. 2013).Consensus proximal junction (PJ) and distal junction (DJ) sequences were constructed mostly from chromosome 21 BACs (bacterial artificial chromosomes).Comparison of these sequences with BACs and cosmids derived from the other acrocentrics revealed that the PJ and DJ sequences are, respectively, ∼95% and 99% identical between all five acrocentric chromosomes.Conservation of DJ sequences among the acrocentrics is consistent with frequent recombination between the rDNA arrays on each of the acrocentric chromosomes (Worton et al. 1988).However, conservation of PJ sequences suggests that there must also be frequent recombination events in the interval between the centromere and rDNA arrays.Proximal sequences are almost entirely segmentally duplicated, similar to the regions bordering centromeres.Consequently, they are unlikely to contain any specific elements that would regulate the activity of the linked NOR.In contrast, the distal sequence is predominantly unique to the acrocentric short arms and is dominated by a very large inverted repeat.Each arm of the inverted repeat is >100 kb, and they share an average sequence identity of 80%.There is a large (∼40-kb) block of a 48base-pair (bp) satellite repeat, CER, at the distal end of the DJ (Fig. 3A).CER blocks are found distal to the rDNA on all acrocentric chromosomes, with additional pericentromeric blocks on chromosomes 14 and 22. Finally, there are two blocks of a novel 138-bp tandem repeat, ACRO138, present within the DJ." + }, + { + "document_id": "ab0a3234-c3b3-46be-8954-01eda9bc962e", + "text": "\n\nThe conservation of DJ sequence between the five human acrocentric chromosomes provides a unique opportunity to visualize NORs by FISH.Whereas the rDNA content of NORs can vary greatly, probing of human metaphase chromosome spreads with a DJ BAC results in signal that is consistent between NORs (Floutsakou et al. 2013).Using this probing scheme, it was observed that in most human cell lines analyzed, including multiple primary lines, at least one and sometimes as many as four of the NORs present have very little or no detectable rDNA (C van Vuuren and B McStay, unpubl. ).Many studies have used silver staining of metaphase spreads prepared from stimulated human peripheral blood lymphocytes to determine how many NORs are active in normal human cells.The number of active NORs ranges from seven to 10, with an average of eight (Heliot et al. 2000).Possibly, NORs with low rDNA content are active but fall below a detection threshold in silver staining.At this point, it is worth considering the distribution of active versus silent rDNA repeats in humans and other mammals.If 50% of rDNA repeats are truly repressed, there are insufficient \"silent\" NORs to house them.We must conclude that active NORs are a mosaic of active and silent repeats." + } + ], + "b04f2221-de28-4c4b-893e-9da982ff864c": [ + { + "document_id": "b04f2221-de28-4c4b-893e-9da982ff864c", + "text": "However, excluding some cases, recombination\nsuppression occurs in a small genomic tract\nwhere these genes are located, and it does\nnot extend over most of the sex chromosome\npair, as occurs in mammals and birds (Bergero\nand Charlesworth, 2009). It is not clear if this\nsuppression occurs by the presence of inversions or as a modulation of the recombination\nmechanism itself, but both could be involved\n(Bergero and Charlesworth, 2009). Evidence of\nrecombination in the SD region in sex reversal\nindividuals supports the second hypothesis." + } + ], + "d4fb56e4-06ab-4c01-b7a0-a193c4a40800": [ + { + "document_id": "d4fb56e4-06ab-4c01-b7a0-a193c4a40800", + "text": "\n\nOrthologous chromosomes between baboon and human" + } + ], + "da485354-fcdc-49b8-9a41-0f673610156a": [ + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "Lichter P, Cremer T, Borden J, Manuelidis L, Ward DC (1988) Delineation of\nindividual human chromosomes in metaphase and interphase cells by in situ suppression hybridization using recombinant DNA libraries. Hum Genet 80:224–234\n3. Jang W, Yonescu R, Knutsen T, Brown T, Reppert T, Sirotkin K, Schuler GD, Ried\nT, Kirsch IR (2006) Linking the human cytogenetic map with nucleotide sequence:\nthe CCAP clone set. Cancer Genet Cytogenet 168:89–97\n4." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "Nature\nGenet 1:222–225\n55. Foote S, Vollrath D, Hilton A, Page DC (1992) The human Y chromosome: overlapping DNA clones spanning the euchromatic region. Science 258:60–66\n56. Chumakov IM, Rigault P, Le Gall I et al (1995) A YAC contig map of the human\ngenome. Nature 377:175–297\n57. Hudson TJ, Stein LD, Gerety SS et al (1995) An STS-based map of the human\ngenome. Science 270:1945–1954\n58. Coffey AJ, Roberts RG, Green ED et al (1992) Construction of a 2.6-Mb contig in\nyeast artificial chromosomes spanning the human dystrophin gene using an STSbased approach. Genomics 12:474–484\n59." + } + ], + "e4541c0c-53fb-4c2c-b550-40728c356549": [ + { + "document_id": "e4541c0c-53fb-4c2c-b550-40728c356549", + "text": "\n\nFigure 4 Schematic depiction of proposed mechanisms for observed intrachromosomal rearrangements.The blue and red arrows indicate the orientation of the integrated plasmid loci and the recovered mouse sequences, respectively, on the original non-rearranged chromosome (left column).All four combinations are given for an arbitrarily orientated chromosome (green line).The middle column shows how two breakpoints (lightning signs) could lead to the inversion or deletion of the encompassed chromosomal sequence (yellow-orange dual tone line) and result in a recoverable mutation in the right column.The last row indicates the two options for a transposition, in which either the transgene locus or the recovered mouse sequence is copied or excised (as indicated by the pink and light blue arrows) and integrates in the breakpoint at the other location." + }, + { + "document_id": "e4541c0c-53fb-4c2c-b550-40728c356549", + "text": "\n\nAs mentioned above, by taking into account that for a genome rearrangement to be detected, the 5Ј plasmid sequence of the breakpoint in lacZ must remain intact and end immediately in front of the recovered mouse sequence, the simplest intrachromosomal mutation that could have taken place was inferred (Fig. 4).Rearrangements with breakpoints in the mouse genome on either site of the integrated plasmid concatamer, but with reversely orientated sequences, could be inversions (Fig. 4).Rearrangements in the direction of the integrated plasmids, proximal for chromosome 3 and distal for chromosome 4 (Fig. 3), with similarly orientated breakpoints in the mouse genome, could be deletions (Fig. 4).Rearrangements in the reverse direction of the integrated plasmids, with reversely orientated mouse sequences, are more complicated and might be owing to transpositions (Fig. 4).According to these schemes, half of the intrachromosomal rearrangements would have been inversions, whereas deletions and transpositions each made up one fourth (Fig. 3).Alternatively, these rearrangements could be explained by translocations involving the transgene clusters integrated on either the homolog or the other chromosome." + } + ], + "f08c0391-2d72-491c-a472-5db71bf11ac8": [ + { + "document_id": "f08c0391-2d72-491c-a472-5db71bf11ac8", + "text": "\n\nFIGURE 3. Telomere arrays of chicken and human chromosomes: the chicken genome contains more telomere sequence than the human genome.Chicken (a) and human (b) metaphase chromosomes and interphase cells hybridized with a telomeric sequence-peptide nucleic acid (PNA)-fluorescein probe.Human and chicken slide preparations were processed, and images were captured using the same parameters.Qualitatively, the telomere-positive fluorescent signals (white spots) from chicken cells and chromosomes have greater intensity than those of human (4′,6 diamidino-2-phenylindole, DAPI counterstain)." + } + ], + "f4762690-64e9-4f6d-9031-c249dc4a6d85": [ + { + "document_id": "f4762690-64e9-4f6d-9031-c249dc4a6d85", + "text": "\n\nIn a previous study on the accumulation of spontaneous genome rearrangements in normal mice with aging, we discovered that 50% of the events were intrachromosomal, i.e., large deletions or inversions [22].In contrast, in this present study most of the rearrangements resulted from inter-chromosomal recombination, in both the Ercc1-mutant and control animals (Table 3).Previously, we used lacZ-plasmid line 60 mice with integration sites on Chromosomes 3 and 4, while in the present study line 30 mice were used with a single integration site on Chromosome 11.This indicates that the relative frequency of translocations is founder line specific and could be due to the position of the lacZ-plasmid cluster on the chromosome.Indeed, the chromosomal integration sites in line 60 mice are in the E1 region of Chromosome 3 (half way along the chromosome) and the C5 region of Chromosome 4 (two-thirds of the way along the chromosome) [22], while the integration site of founder line 30 (used in this study) is on the centromeric tip of Chromosome 11 (region A1-A2; not shown).The proximal location on Chromosome 11 prevents the detection of all but relatively small intra-chromosomal recombinations; larger events would lead to loss of the centromere and, therefore, the entire chromosome.If the orientation of the integration site in line 30, which is currently unknown, is towards the centromere, transpositions and inversions towards the distal end are the only detectable large intra-chromosomal rearrangements (for a detailed explanation of the different chromosomal events that can occur at the lacZ locus, see [22])." + } + ] + }, + "data_source": [ + { + "document_id": "f4762690-64e9-4f6d-9031-c249dc4a6d85", + "section_type": "main", + "text": "\n\nIn a previous study on the accumulation of spontaneous genome rearrangements in normal mice with aging, we discovered that 50% of the events were intrachromosomal, i.e., large deletions or inversions [22].In contrast, in this present study most of the rearrangements resulted from inter-chromosomal recombination, in both the Ercc1-mutant and control animals (Table 3).Previously, we used lacZ-plasmid line 60 mice with integration sites on Chromosomes 3 and 4, while in the present study line 30 mice were used with a single integration site on Chromosome 11.This indicates that the relative frequency of translocations is founder line specific and could be due to the position of the lacZ-plasmid cluster on the chromosome.Indeed, the chromosomal integration sites in line 60 mice are in the E1 region of Chromosome 3 (half way along the chromosome) and the C5 region of Chromosome 4 (two-thirds of the way along the chromosome) [22], while the integration site of founder line 30 (used in this study) is on the centromeric tip of Chromosome 11 (region A1-A2; not shown).The proximal location on Chromosome 11 prevents the detection of all but relatively small intra-chromosomal recombinations; larger events would lead to loss of the centromere and, therefore, the entire chromosome.If the orientation of the integration site in line 30, which is currently unknown, is towards the centromere, transpositions and inversions towards the distal end are the only detectable large intra-chromosomal rearrangements (for a detailed explanation of the different chromosomal events that can occur at the lacZ locus, see [22])." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "section_type": "main", + "text": "Lichter P, Cremer T, Borden J, Manuelidis L, Ward DC (1988) Delineation of\nindividual human chromosomes in metaphase and interphase cells by in situ suppression hybridization using recombinant DNA libraries. Hum Genet 80:224–234\n3. Jang W, Yonescu R, Knutsen T, Brown T, Reppert T, Sirotkin K, Schuler GD, Ried\nT, Kirsch IR (2006) Linking the human cytogenetic map with nucleotide sequence:\nthe CCAP clone set. Cancer Genet Cytogenet 168:89–97\n4." + }, + { + "document_id": "9d82958a-45b0-4f1d-b765-38d018e4b140", + "section_type": "main", + "text": "\n\na The table lists proteins in which mutations have been shown to increase homologous recombination (HR), gross chromosomal rearrangements (GCRs), chromosomal instability (CIN), sister chromatid exchanges (SCEs), tri-nucleotide repeat expansions and contractions (TNR), telomere fusions (Tel fusion), or fragile telomeres (Tel fragility).A phenotype inside brackets ([ ]) indicates that it is caused by overexpression of the protein.For further details and references see Supplementary Table1.Abbreviations: DSB, double-strand break; PCNA, proliferating cell nuclear antigen; RFC, replication factor C complex; SCF, Skp1-Cdc53/Cullin-F-box." + }, + { + "document_id": "395a51ba-bd2a-4160-8396-b13a3bf762ff", + "section_type": "main", + "text": "\n\nPrimate and mouse centromeric DNA contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B.In humans, the CENP-B box is present on every chromosome except for the Y chromosome [20].It is paradoxically necessary for formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres, and mice lacking CENP-B are viable and fertile [21,22]." + }, + { + "document_id": "3dfe0ec3-b3a6-4a08-8929-e54cab3ec262", + "section_type": "main", + "text": "Box 3 Mechanism of homologous recombination and end joining\n\nThe severe phenotype of the mouse mutants and the highly cancer-prone human syndromes highlight the importance of homologous recombination.Mouse KU mutants display sensitivity to agents that lead to breaks in DNA, and have immunological problems because the KU proteins are involved in V(D)J recombination of antibody gene sequences.In addition, these mutants display poor development, several features of premature ageing and increased apoptosis of postmitotic neurons in the developing brain.Mice with defects in DNA-PK cs (SCID mice) display a similar but generally milder phenotype.In contrast, XRCC4-and ligase IV-knockout mice seem more severe, with late embryonic lethality resulting from massive ATM-and p53-dependent neuronal apoptosis 33,38 ." + }, + { + "document_id": "7a451204-390c-4ff2-8a1d-b4de62b73503", + "section_type": "main", + "text": "Mamm Genome. 2006; 17:220–229. [PubMed: 16518689]\n72. Romanoski CE, et al. Systems genetics analysis of gene-by-environment interactions in human\ncells. Am J Hum Genet. 2010; 86:399–410. [PubMed: 20170901]\n73. Myers S, Freeman C, Auton A, Donnelly P, McVean G. A common sequence motif associated\nwith recombination hot spots and genome instability in humans. Nature Genet. 2008; 40:1124–\n1129. [PubMed: 19165926]\n74. Myers S, et al. Drive against hotspot motifs in primates implicates the PRDM9 gene in meiotic\nrecombination. Science. 2010; 327:876–879. [PubMed: 20044541]\n75. Cordell HJ. Detecting gene-gene interactions that underlie human diseases. Nature Rev Genet.\n 2009; 10:392–404." + }, + { + "document_id": "d4fb56e4-06ab-4c01-b7a0-a193c4a40800", + "section_type": "main", + "text": "\n\nOrthologous chromosomes between baboon and human" + }, + { + "document_id": "748cfe7e-e4f2-453f-8575-50dfe84e2538", + "section_type": "main", + "text": "\n\nThis picture poses more questions than it seeks to answer.Is the grouping of the regions by product rather than by type of region correct?Given that the recombina- tion fraction between HLA-A and HLA-B is of the order of .08%,and that this is likely to represent a distance of at least hundreds of thousands of nucleotides, how are the pieces put together over such relatively long distances?Is it possible that regions of the DNA loop out, so that transcripts can be made directly from noncon- tiguous DNA sequences, the loops being held in place by small RNAs as suggested for the control of splicing by Steitz, and her colleagues [24] and by others [25]?If these small RNAs are coded for well outside the HLA region, does this provide a mechanism for control of expression of products by unlinked genes, as may be the case for one of the constituent polypeptides of the HLA-DR product?What might be the nature of the signals that control which of a multiple set of alternative regions is expressed by any given chromosome?" + }, + { + "document_id": "e4541c0c-53fb-4c2c-b550-40728c356549", + "section_type": "main", + "text": "\n\nFigure 4 Schematic depiction of proposed mechanisms for observed intrachromosomal rearrangements.The blue and red arrows indicate the orientation of the integrated plasmid loci and the recovered mouse sequences, respectively, on the original non-rearranged chromosome (left column).All four combinations are given for an arbitrarily orientated chromosome (green line).The middle column shows how two breakpoints (lightning signs) could lead to the inversion or deletion of the encompassed chromosomal sequence (yellow-orange dual tone line) and result in a recoverable mutation in the right column.The last row indicates the two options for a transposition, in which either the transgene locus or the recovered mouse sequence is copied or excised (as indicated by the pink and light blue arrows) and integrates in the breakpoint at the other location." + }, + { + "document_id": "f08c0391-2d72-491c-a472-5db71bf11ac8", + "section_type": "main", + "text": "\n\nFIGURE 3. Telomere arrays of chicken and human chromosomes: the chicken genome contains more telomere sequence than the human genome.Chicken (a) and human (b) metaphase chromosomes and interphase cells hybridized with a telomeric sequence-peptide nucleic acid (PNA)-fluorescein probe.Human and chicken slide preparations were processed, and images were captured using the same parameters.Qualitatively, the telomere-positive fluorescent signals (white spots) from chicken cells and chromosomes have greater intensity than those of human (4′,6 diamidino-2-phenylindole, DAPI counterstain)." + }, + { + "document_id": "e4541c0c-53fb-4c2c-b550-40728c356549", + "section_type": "main", + "text": "\n\nAs mentioned above, by taking into account that for a genome rearrangement to be detected, the 5Ј plasmid sequence of the breakpoint in lacZ must remain intact and end immediately in front of the recovered mouse sequence, the simplest intrachromosomal mutation that could have taken place was inferred (Fig. 4).Rearrangements with breakpoints in the mouse genome on either site of the integrated plasmid concatamer, but with reversely orientated sequences, could be inversions (Fig. 4).Rearrangements in the direction of the integrated plasmids, proximal for chromosome 3 and distal for chromosome 4 (Fig. 3), with similarly orientated breakpoints in the mouse genome, could be deletions (Fig. 4).Rearrangements in the reverse direction of the integrated plasmids, with reversely orientated mouse sequences, are more complicated and might be owing to transpositions (Fig. 4).According to these schemes, half of the intrachromosomal rearrangements would have been inversions, whereas deletions and transpositions each made up one fourth (Fig. 3).Alternatively, these rearrangements could be explained by translocations involving the transgene clusters integrated on either the homolog or the other chromosome." + }, + { + "document_id": "ab0a3234-c3b3-46be-8954-01eda9bc962e", + "section_type": "main", + "text": "Chromosomal context of human NORs\n\nHuman NORs are positioned on the short arms of the acrocentric chromosomes that still remain unsequenced and thus missing from the current human genome draft, GRCh38.p7.Seeking an understanding of the chromosomal context of human NORs and to identify potential NOR regulatory elements, my laboratory has begun to characterize the sequences on both proximal (centromeric) and distal (telomeric) sides of the rDNA arrays (Fig. 3A; Floutsakou et al. 2013).Building on earlier reports of sequences distal and proximal to the rDNA array on HSA21 and HSA22, respectively (Worton et al. 1988;Sakai et al. 1995;Gonzalez and Sylvester 1997), 207 kb of sequence immediately proximal and 379 kb distal to rDNA arrays have been reported recently (Floutsakou et al. 2013).Consensus proximal junction (PJ) and distal junction (DJ) sequences were constructed mostly from chromosome 21 BACs (bacterial artificial chromosomes).Comparison of these sequences with BACs and cosmids derived from the other acrocentrics revealed that the PJ and DJ sequences are, respectively, ∼95% and 99% identical between all five acrocentric chromosomes.Conservation of DJ sequences among the acrocentrics is consistent with frequent recombination between the rDNA arrays on each of the acrocentric chromosomes (Worton et al. 1988).However, conservation of PJ sequences suggests that there must also be frequent recombination events in the interval between the centromere and rDNA arrays.Proximal sequences are almost entirely segmentally duplicated, similar to the regions bordering centromeres.Consequently, they are unlikely to contain any specific elements that would regulate the activity of the linked NOR.In contrast, the distal sequence is predominantly unique to the acrocentric short arms and is dominated by a very large inverted repeat.Each arm of the inverted repeat is >100 kb, and they share an average sequence identity of 80%.There is a large (∼40-kb) block of a 48base-pair (bp) satellite repeat, CER, at the distal end of the DJ (Fig. 3A).CER blocks are found distal to the rDNA on all acrocentric chromosomes, with additional pericentromeric blocks on chromosomes 14 and 22. Finally, there are two blocks of a novel 138-bp tandem repeat, ACRO138, present within the DJ." + }, + { + "document_id": "b04f2221-de28-4c4b-893e-9da982ff864c", + "section_type": "main", + "text": "However, excluding some cases, recombination\nsuppression occurs in a small genomic tract\nwhere these genes are located, and it does\nnot extend over most of the sex chromosome\npair, as occurs in mammals and birds (Bergero\nand Charlesworth, 2009). It is not clear if this\nsuppression occurs by the presence of inversions or as a modulation of the recombination\nmechanism itself, but both could be involved\n(Bergero and Charlesworth, 2009). Evidence of\nrecombination in the SD region in sex reversal\nindividuals supports the second hypothesis." + }, + { + "document_id": "3dfe0ec3-b3a6-4a08-8929-e54cab3ec262", + "section_type": "main", + "text": "\n\nCells in G1 have only the homologous chromosome for recombination repair.However, this may be difficult to find in the complex genome.Moreover, it is potentially dangerous as a template for repair as it may lead to homozygosity for recessive mutations.As an alternative, the end-joining reaction simply links ends of a DSB together, without any template, using the end-binding KU70/80 complex and DNA-PK cs , followed by ligation by XRCC4-ligase4 (reviewed by 27,33; see the right panel of the figure, stages V-VII).The function of KU70/80 might involve end protection and approximating the ends, in addition to a signalling function by DNA-PK cs .End joining may be further facilitated when the ends are still held together through nucleosomes or other structures.End joining is sometimes associated with gain or loss of a few nucleotides if internal microhomologies are used for annealing before sealing.This implies the involvement of DNA polymerases and/or nucleases.Note that the KU complex is also involved in telomere metabolism 27,62 .found to be lethal 34 .Inactivation of ATR by itself is inviable already at the blastocyst stage.Inactivation of BRCA1 and BRCA2 in mice is also embryonically lethal; cell lines display defects in homologous recombination [35][36][37] ." + }, + { + "document_id": "9d82958a-45b0-4f1d-b765-38d018e4b140", + "section_type": "main", + "text": "\n\nFigure 3 Intermediates and chromosome structural alterations, as observed by different techniques. (a) Replication fork stalling, as monitored by 2D-gel electrophoresis and Southern analysis in yeast (for details about the technique, see Reference 161). (b) Slower human replication forks covering shorter DNA synthesis tracks, as determined by incorporation of IdU and CldU via DNA combing (52), which permits visualization of the process of replication on DNA fibers. (c) Accumulation of double-strand breaks (DSBs) or replicative stress, as inferred by γH2AX foci or by γH2AX pan staining, respectively, in human cells. (d ) DSBs or ssDNA (single-stranded DNA) gaps as seen directly by nuclear \"comet tails\" via single-cell electrophoresis assays in human cells (52). (e) Sister-chromatid exchanges (SCEs), as determined by Giemsa staining in human cells (207). ( f ) Hyper-recombination, as determined by colony sectoring in yeast (5). ( g) Gross chromosomal rearrangements (GCRs), as determined by spectral karyotyping in mouse cells (118). (h) Translocations, as visualized by pulse-field gel electrophoresis in yeast (168). (i ) Fragile sites, as detected by mitotic spreads in human cells (109). ( j) Telomere fusions, as determined by CO-FISH (chromosome-orientation fluorescent in situ hybridization) in mouse cells (124). (k) Anaphase bridges, presumably resulting from unfinished replication, dicentric chromosomes, and sister-chromatid nondisjunction, as detected by fluorescence microscopy in mouse cells.Arrows indicate the specific structural alterations referred to in each panel; in panel h, closed and open arrows indicate the position where the translocated or missing parental chromosome migrate or should migrate, respectively.When necessary, a normal control is shown on top of the panel, with the exception of panel a, which is shown on the left.Detailed description of each technique can be found in the references provided.Photos are from the laboratories of A. Nussenzweig ( g), A. Losada (k), M. Blasco ( j), L. Tora (i ), and ours (all others).Abbreviations: HR, homologous recombination; NHEJ, nonhomologous end-joining." + }, + { + "document_id": "8604652e-2477-4552-8f43-f5f19e421df2", + "section_type": "main", + "text": "Classification of common conserved sequences in mammalian\nintergenic regions. Hum. Mol. Genet. 2002, 11, 669–674.\n 25. Zhu, L.; Swergold, G.D.; Seldin, M.F. Examination of sequence homology between human\nchromosome 20 and the mouse genome: Intense conservation of many genomic elements. Hum. Genet.\n 2003, 113, 60–70.\n 26. Pevzner, P.; Tesler, G. Human and mouse genomic sequences reveal extensive breakpoint reuse in\nmammalian evolution. Proc. Natl. Acad. Sci. USA 2003, 100, 7672–7677.\n 27. Christmann, R.B. ; Sampaio-Barros, P.; Stifano, G.; Borges, C.L. ; de Carvalho, C.R. ; Kairalla, R.;\nParra, E.R. ; Spira, A.; Simms, R.; Capellozzi, V.L. ; et al." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "section_type": "main", + "text": "Nature\nGenet 1:222–225\n55. Foote S, Vollrath D, Hilton A, Page DC (1992) The human Y chromosome: overlapping DNA clones spanning the euchromatic region. Science 258:60–66\n56. Chumakov IM, Rigault P, Le Gall I et al (1995) A YAC contig map of the human\ngenome. Nature 377:175–297\n57. Hudson TJ, Stein LD, Gerety SS et al (1995) An STS-based map of the human\ngenome. Science 270:1945–1954\n58. Coffey AJ, Roberts RG, Green ED et al (1992) Construction of a 2.6-Mb contig in\nyeast artificial chromosomes spanning the human dystrophin gene using an STSbased approach. Genomics 12:474–484\n59." + }, + { + "document_id": "9ee491f4-5f16-4cb2-b803-54f2fdee1dba", + "section_type": "main", + "text": "\n\nIn humans, the pericentromeric region of chromosome 9 is densely packed with segmental genomic duplications (segdups) and is prone to microdeletions and microduplications. 5In order to evaluate this region for microdeletions and microduplications in family T, we screened genomic DNA from affected individual II-7 by arrayCGH with the Nimblegen HD2 platform with the previously described CHP-SKN sample 6 as the reference.Data were normalized and CNVs were called by identifying regions where Z-scores consistently deviated from the diploid mean.At 9q21.11, a genomic duplication of ~270 kb was apparent in the genomic DNA of II-7 (Figure 1D).The Genomic duplications may or may not be in tandem with their parent segment and may be either in the same or inverted orientation. 7We developed primers that would uniquely amplify genomic DNA with the duplication under each of these conditions.Forward (5 0 -CCCAGCAGA AGCAATGGTGGTAGCC-3 0 ) and reverse (5 0 -GGTGGTGAA TCCAAAAACACAAGAACAAAGTC-3 0 ) primers diagnostic for a tandem inverted duplication (Figure 2A) yielded products of expected size in family T relatives with hearing loss, but yielded no product in unaffected family T relatives (Figure 2B).Genotypes of all 58 participating relatives in family T indicated that the tandem inverted duplication was coinherited with hearing loss.The duplication spans approximately positions 71,705,804 to 71,974,823 (hg19) on chromosome 9 for a size of ~269,023 bp.The duplication includes the entire locus for the tight junction protein TJP2, which spans positions 71,788,971 to 71,870,124 (hg19)." + }, + { + "document_id": "3dfe0ec3-b3a6-4a08-8929-e54cab3ec262", + "section_type": "main", + "text": "371\n\nA tentative scenario for the homologousrecombination reaction is depicted in the left panel of the figure.To promote strand invasion into homologous sequences, the 5፱-3፱ exonuclease activity of the RAD50/MRE11/NBS1 complex (also a substrate for ATM phosphorylation) exposes both 3፱ ends 30 (I).RPA facilitates assembly of a RAD51 nucleoprotein filament that probably includes RAD51-related proteins XRCC2, XRCC3, RAD51B, C and D. RAD52 stimulates filament assembly (II).RAD51 has, like its Escherichia coli RecA counterpart, the ability to exchange the single strand with the same sequence from a double-stranded DNA molecule.Correct positioning of the sister chromatids by cohesins probably facilitates the identification of a homologous sequence.A candidate for the complex chromatin transactions associated with these DNA gymnastics is RAD54, a member of the SWI/SNF family of DNA-dependent ATPases.After identification of the identical sister chromatid sequence, the intact double-stranded copy is used as a template to properly heal the broken ends by DNA synthesis (III).Finally, the so-called Hollidayjunctions are resolved by resolvases 27,33,60 (IV).Homologous recombination involves the simultaneous action of large numbers of the same molecules, which are found to be concentrated in radiation-induced nuclear foci.These depend on, and also include, the BRCA1 and BRCA2 proteins 36 .Recent evidence implicates BRCA2 directly or indirectly in nuclear translocation of RAD51 (ref.61)." + }, + { + "document_id": "ab0a3234-c3b3-46be-8954-01eda9bc962e", + "section_type": "main", + "text": "\n\nThe conservation of DJ sequence between the five human acrocentric chromosomes provides a unique opportunity to visualize NORs by FISH.Whereas the rDNA content of NORs can vary greatly, probing of human metaphase chromosome spreads with a DJ BAC results in signal that is consistent between NORs (Floutsakou et al. 2013).Using this probing scheme, it was observed that in most human cell lines analyzed, including multiple primary lines, at least one and sometimes as many as four of the NORs present have very little or no detectable rDNA (C van Vuuren and B McStay, unpubl. ).Many studies have used silver staining of metaphase spreads prepared from stimulated human peripheral blood lymphocytes to determine how many NORs are active in normal human cells.The number of active NORs ranges from seven to 10, with an average of eight (Heliot et al. 2000).Possibly, NORs with low rDNA content are active but fall below a detection threshold in silver staining.At this point, it is worth considering the distribution of active versus silent rDNA repeats in humans and other mammals.If 50% of rDNA repeats are truly repressed, there are insufficient \"silent\" NORs to house them.We must conclude that active NORs are a mosaic of active and silent repeats." + }, + { + "document_id": "e4541c0c-53fb-4c2c-b550-40728c356549", + "section_type": "main", + "text": "\n\nOne possible explanation for the high number of genome rearrangements observed in this present study is that some or even most of the events scored by us as genome rearrangements are artifacts of the procedure applied to recover the mutant plasmids (Fig. 1).Although it is impossible to completely rule this out, we have addressed the possibility of artifacts extensively in a previous paper in which various control experiments had been performed on plasmids grown in E. coli, mixed with nontransgenic mouse genomic DNA, and mock-rescued into E. coli.Such experiments generally indicated significantly lower mutation frequencies in E. coli than in the mouse and no evidence for genome rearrangements as indicated by a mouse sequence at a lacZ breakpoint (Dolle ´et al. 1999b).Also, enhanced instability caused by the artificial nature of the lacZ-plasmid cluster in the mouse genome is unlikely to be responsible for the observed mutations.Indeed, neither the mutation frequencies nor their spectra are dramatically different from results reported with endogenous reporter genes such as HPRT, APRT, or HLA.Mutation frequencies at these loci were generally in the same range as our own values and also indicated a significant fraction of all mutations caused by genome rearrangements (Grist Significance between age groups within organs for genome rearrangements using the Wilcoxon rank sum test.et al. 1992;Dempsey et al. 1993;Stambrook et al. 1996;Albertini 2001).In general, mutation frequencies at HPRT were among the lowest, possibly because of selection against HPRT mutant lymphocytes in vivo.Interestingly, although virtually all results obtained with HPRT and other endogenous reporters involved lymphocytes, in a study using human kidney cells, significantly higher mutation frequencies were found, that is, up to ∼4 ן 10 4מ , than in lymphocytes (Martin et al. 1996;Colgin et al. 2002).This could reflect a significantly lower selection pressure operating on kidney cells than in lymphocytes.About 15% of these HPRT mutations were genome rearrangements such as deletions.Based on the 44-kb target size of HPRT, a similar extrapolation as performed for the lacZ-reporter gene resulted in up to four genome rearrangements per kidney cell, which might be an underestimate owing to the lethality of such events at this X-linked locus.Preliminary data on the same lacZ-reporter construct, but now integrated as a single copy transgene, in Drosophila show a similar or even higher frequency of genome rearrangements, also indicating that the concatamer of constructs in the current mouse model is not intrinsically less stable than a single copy transgene.Finally, the observed organ specificities and age-related increase make it highly unlikely that a significant fraction of the mutants scored in our system as genome rearrangements are artifacts." + }, + { + "document_id": "ab37ae93-c6dd-41a2-a9d0-35666249c057", + "section_type": "main", + "text": "\n\nUnfortunately, flanking regions of 80 bp in length, that could be synthesized as oligonucleotide primers and used in a one-step PCR strategy as in S. cerevisiae (Baudin et al. 1993;Lorenz et al. 1995), appear to be insufficient to allow efficient homologous recombination in U. maydis (A. Brachmann, unpublished).Therefore we designed primers to amplify flanking regions for homologous recombination that are between 0.8 and 1 kb in length.Flanking sequences of this length have been shown to be sufficient to generate gene disruption mutants in eight different cases in two wild type strains each.The rate of homologous integration varied between 3% and 40%, with an average of 13% (P.Becht and M. Feldbru¨gge, unpublished).Using the SfiI sites that are introduced by PCR, the flanking regions can be combined with any gene replacement insert from our collection." + }, + { + "document_id": "bd0f30e8-81e1-4553-bf88-762bc49197a3", + "section_type": "main", + "text": "\n\nEven with a large amount of human genomic DNA surrounding the repeat, the third characteristic (range of amplifications) remains moderate in our models, in the mice carrying 45 CAG in the AR YAC (44) and in the transgenic mice carrying 78 CAG in the DRPLA gene (45).In all CAG repeat models, the range of amplification is smaller in mice and there is often a tendency towards contraction after female transmissions.Using a large repeat surrounded by extensive human genomic flanking sequences, we obtained a higher range of expansions, and CTG repeat instability was remarkably similar in its characteristics and dynamics to the CTG repeat instability observed in DM patients.However, even with > 300 CTG, the largest amplification observed in a single generation was 60 CTG.Enlargements of several hundred repeats (or 'big jumps'), which are observed in DM families, were not observed in mice.If intergenerational instability results from the mosaicism observed in the germline, with enlargement of the CTG repeat throughout life, then the lower level of amplification in mice may result from their shorter reproductive life-span, as suggested previously (45).Alternatively, the mechanisms involved in trinucleotide repeat instability may act at a greater repeat length in mice than in humans.The DNA repair system may be more efficient and the repeat size threshold for 'big jumps' may be higher in mice.We found a negative correlation between the size of the repeat and the range of expansions after male but not after female transmission.Therefore, we will continue to breed DM300 transgenic females to determine the extent to which the repeat can be expanded in mouse and whether a threshold can be reached to obtain big jumps in amplification." + }, + { + "document_id": "f0db8a37-76fc-4eaf-a667-4d2422ecc32f", + "section_type": "main", + "text": "\n\nFigure 1.The density of interspersed repeats and processed pseudogenes in (a) the mouse and (b) the human genomes.Pseudogene and the repeats are grouped according to the G þ C content of the surrounding 100-kb DNA.TRENDS in Genetics" + }, + { + "document_id": "9588738f-b0d2-4b37-9554-f0699a66c4fb", + "section_type": "main", + "text": "[PMID: 19426536]\nWong AC, Shkolny D, Dorman A, Willingham D, Roe BA,\nMcDermid HE. Two novel human RAB genes with near\nidentical sequence each map to a telomere-associated region:\nthe subtelomeric region of 22q13.3 and the ancestral telomere\nband 2q13. Genomics 1999; 59:326-34. [PMID: 10444334]\nMah N, Stoehr H, Schulz HL, White K, Weber BH.\n Identification of a novel retina-specific gene located in a\nsubtelomeric region with polymorphic distribution among\nmultiple human chromosomes. Biochim Biophys Acta 2001;\n1522:167-74. [PMID: 11779631]\nMalone K, Sohocki MM, Sullivan LS, Daiger SP. Identifying\nand mapping novel retinal-expressed ESTs from humans. Mol\nVis 1999; 5:5." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "Proc Natl Acad Sci U S A 102, 4795–4800.\n Martin, J., Han, C., Gordon, L. A. et al. (2004). The sequence and analysis of duplication-rich\nhuman chromosome 16. Nature 432, 988–994.\n Mattick, J. S. (2004). RNA regulation: a new genetics? Nat Rev Genet 5, 316–323.\n Mayor, C., Brudno, M., Schwartz, J. R. et al. (2000). VISTA: visualizing global DNA sequence\nalignments of arbitrary length. Bioinformatics 16, 1046–1047.\n McDonald, J. H. and Kreitman, M. (1991). Adaptive protein evolution at the Adh locus in\nDrosophila. Nature 351, 652–654.\n Miles, C., Elgar, G., Coles, E. et al. (1998)." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "Proc Natl Acad Sci U S A 102, 4795–4800.\n Martin, J., Han, C., Gordon, L. A. et al. (2004). The sequence and analysis of duplication-rich\nhuman chromosome 16. Nature 432, 988–994.\n Mattick, J. S. (2004). RNA regulation: a new genetics? Nat Rev Genet 5, 316–323.\n Mayor, C., Brudno, M., Schwartz, J. R. et al. (2000). VISTA: visualizing global DNA sequence\nalignments of arbitrary length. Bioinformatics 16, 1046–1047.\n McDonald, J. H. and Kreitman, M. (1991). Adaptive protein evolution at the Adh locus in\nDrosophila. Nature 351, 652–654.\n Miles, C., Elgar, G., Coles, E. et al. (1998)." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "Proc Natl Acad Sci U S A 102, 4795–4800.\n Martin, J., Han, C., Gordon, L. A. et al. (2004). The sequence and analysis of duplication-rich\nhuman chromosome 16. Nature 432, 988–994.\n Mattick, J. S. (2004). RNA regulation: a new genetics? Nat Rev Genet 5, 316–323.\n Mayor, C., Brudno, M., Schwartz, J. R. et al. (2000). VISTA: visualizing global DNA sequence\nalignments of arbitrary length. Bioinformatics 16, 1046–1047.\n McDonald, J. H. and Kreitman, M. (1991). Adaptive protein evolution at the Adh locus in\nDrosophila. Nature 351, 652–654.\n Miles, C., Elgar, G., Coles, E. et al. (1998)." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "Proc Natl Acad Sci U S A 102, 4795–4800.\n Martin, J., Han, C., Gordon, L. A. et al. (2004). The sequence and analysis of duplication-rich\nhuman chromosome 16. Nature 432, 988–994.\n Mattick, J. S. (2004). RNA regulation: a new genetics? Nat Rev Genet 5, 316–323.\n Mayor, C., Brudno, M., Schwartz, J. R. et al. (2000). VISTA: visualizing global DNA sequence\nalignments of arbitrary length. Bioinformatics 16, 1046–1047.\n McDonald, J. H. and Kreitman, M. (1991). Adaptive protein evolution at the Adh locus in\nDrosophila. Nature 351, 652–654.\n Miles, C., Elgar, G., Coles, E. et al. (1998)." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "section_type": "main", + "text": "Kim UJ, Shizuya H, de Jong, PJ, Birren B, and Simon MI (1992) Stable propagation of cosmid sized human DNA inserts in an F factor based vector. Nucleic Acids\nRes 20:1083–1085\n17. Hoskins RA, Nelson CR, Berman BP et al (2000) A BAC-based physical map of\nthe major autosomes of Drosophila melanogaster. Science 287:2271–2274\n18. Morton NE. (1991) Parameters of the human genome Proc Natl Acad Sci USA\n88:7474–6\n19. International Human Genome Sequencing Consortium (2001) Initial sequencing\nand analysis of the human genome. Nature 409:860–921\n20." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "(2004) were selected, from chromosome\n21, on the basis of a simple threshold identity in man to mouse alignment, and also\non the ability to PCR amplify homologous sequences from 14 mammalian species.\n 134\n\nCH 6 COMPARATIVE GENOMICS\n\nConsequently, these sequences should represent the subset of CNGs that both have\nthe highest nucleotide identity and are the most constrained through mammalian\nevolution. Ironically, a whole-genome analysis of non-coding conservation has since\nshown that human chromosome 21 is the only autosome devoid of so-called ultraconserved elements (Bejerano et al. , 2004)." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "(2004) were selected, from chromosome\n21, on the basis of a simple threshold identity in man to mouse alignment, and also\non the ability to PCR amplify homologous sequences from 14 mammalian species.\n 134\n\nCH 6 COMPARATIVE GENOMICS\n\nConsequently, these sequences should represent the subset of CNGs that both have\nthe highest nucleotide identity and are the most constrained through mammalian\nevolution. Ironically, a whole-genome analysis of non-coding conservation has since\nshown that human chromosome 21 is the only autosome devoid of so-called ultraconserved elements (Bejerano et al. , 2004)." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "(2004) were selected, from chromosome\n21, on the basis of a simple threshold identity in man to mouse alignment, and also\non the ability to PCR amplify homologous sequences from 14 mammalian species.\n 134\n\nCH 6 COMPARATIVE GENOMICS\n\nConsequently, these sequences should represent the subset of CNGs that both have\nthe highest nucleotide identity and are the most constrained through mammalian\nevolution. Ironically, a whole-genome analysis of non-coding conservation has since\nshown that human chromosome 21 is the only autosome devoid of so-called ultraconserved elements (Bejerano et al. , 2004)." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "(2004) were selected, from chromosome\n21, on the basis of a simple threshold identity in man to mouse alignment, and also\non the ability to PCR amplify homologous sequences from 14 mammalian species.\n 134\n\nCH 6 COMPARATIVE GENOMICS\n\nConsequently, these sequences should represent the subset of CNGs that both have\nthe highest nucleotide identity and are the most constrained through mammalian\nevolution. Ironically, a whole-genome analysis of non-coding conservation has since\nshown that human chromosome 21 is the only autosome devoid of so-called ultraconserved elements (Bejerano et al. , 2004)." + }, + { + "document_id": "bd0f30e8-81e1-4553-bf88-762bc49197a3", + "section_type": "main", + "text": "\n\nIn all mouse models generated so far, the mutability of the CAG/CTG repeat appears to be strongly correlated with the size of the repeat but also with the presence of human flanking sequences.Long repeats (>100 CAG/CTG) are very unstable in mice (40,41,46); however, human flanking sequences seem to be necessary to reproduce instability for moderate amplifications such as 55 CTG in our mice, 45 CAG in the YAC carrying the SBMA gene or 78 CAG in the cosmid carrying the DRPLA gene (39,44,45).It has been observed that, for the CAG repeat involved in Huntington's disease (HD), the 48 repeats carried by a 4.6 kb fragment of human genomic flanking DNA are moderately unstable in transgenic mice, with 2% of meioses resulting in repeat changes.Interestingly, this 48 CAG repeat shows a similar frequency of mutation in knock-in experiments and a larger repeat of 109 CAG has a higher mutation frequency (73%) (46).These results also demonstrate the determinant effect of the size of the repeat for trinucleotide repeat mutability.In addition, comparison of these knock-in models with transgenic mice carrying stable 79 CAG repeats (37) suggests that, to some extent, the mouse hd cis-sequences allow some mutability of the CAG repeat.Such mutability probably depends on cross-species conservation of sequences and/or functional elements (like origin of replication) involved in the instability mechanisms.This crossspecies conservation may differ for the various loci involved in trinucleotide diseases." + }, + { + "document_id": "e074ba47-cd7a-4bb2-8bcb-9a15da69cc2d", + "section_type": "main", + "text": "Effect of SNPs overlapping p53-RE half-sites\nUsing the p53-REs as a test case, we sought to assess the impact of human non-coding\nsingle nucleotide polymorphisms (SNPs) on the p53-RE transactivation capability. To do\nthis, using the UCSC genome browser [99], we made an intersection of 199 validated\np53-REs and human non-coding SNPs. There were 36 non-coding SNPs overlapping\nwith a known validated p53-RE (Table 2). Of these 33 overlapped with dimers, out of\nwhich 10 SNPs were predicted to impact the transactivation capacity by our predictor." + }, + { + "document_id": "ab0a3234-c3b3-46be-8954-01eda9bc962e", + "section_type": "main", + "text": "\n\nFigure 3.The chromosomal context of human NORs located on acrocentric short arms. (A) Schematic human acrocentric chromosome short arm showing the NOR (rDNA array), expanded below into rDNA repeats, and the PJ (orange) and DJ (green) regions.The DJ region is further expanded to show the location of inverted repeats (light green arrows), DJ promoters and transcripts, Acro138 repeat blocks (red), and CER satellite (blue). (B) Cartoonshowing the transition from normal nucleolar organization to segregated nucleolar organization in response to AMD treatment or the introduction of rDNA double-strand breaks (DSBs).rDNA (red) retreats from the nucleolar interior (black) to the nucleolar periphery, forming caps adjacent to DJ sequences (green) that are embedded in PNH (dark blue)(Floutsakou et al. 2013;van Sluis and McStay 2015)." + }, + { + "document_id": "7a7773ed-2548-4297-86ad-b7ce115448e0", + "section_type": "main", + "text": "At the recombination joint points formed at the site of deletion, the IS-elements (or other transposable genetic elements), or\nrepeated sequences have been found in different species of bac-\n\nG. B. Smirnov\n\nteria (13, 45). This means that the integrations of genetic material and deletions are facilitated by the listed types of nucleotide\nsequences forming the preferable recombination sites." + }, + { + "document_id": "ad14b0c4-2a38-411b-9bb1-cacf9203f29d", + "section_type": "main", + "text": "At the recombination joint points formed at the site of deletion, the IS-elements (or other transposable genetic elements), or\nrepeated sequences have been found in different species of bac-\n\nG. B. Smirnov\n\nteria (13, 45). This means that the integrations of genetic material and deletions are facilitated by the listed types of nucleotide\nsequences forming the preferable recombination sites." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "section_type": "main", + "text": "Shao Z, Zhao H, Giver L, Arnold FH (1998) Random-priming in vitro recombination: an effective tool for directed evolution. Nucleic Acids Res 26:\n681–683\n18. Volkov AA, Shao Z, Arnold FH (1999) Recombination and chimeragenesis by in\nvitro heteroduplex formation and in vivo repair. Nucleic Acids Res 27:e18\n19. Voigt CA, Martinez C, Wang ZG, Mayo SL, Arnold FH (2002) Protein building\nblocks preserved by recombination. Nat Struct Biol 9:553–558\n20. Ostermeier M, Shim JH, Benkovic SJ (1999) A combinatorial approach to hybrid\nenzymes independent of DNA homology. Nat Biotechnol 17:1205–1209\n21." + } + ], + "document_id": "575BE8FB36E8D520760A31B2CAE92034", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "CENP-B&box", + "human¢romeres", + "recombination", + "chromosome&11", + "Ercc1-mutant", + "lacZ-plasmid", + "inversions", + "translocations", + "NORs", + "rDNA" + ], + "metadata": [ + { + "object": "we show that Wnt5a rapidly represses rDNA gene transcription in breast cancer cells and generates a chromatin state with reduced transcription of rDNA by RNA polymerase I Pol I. These effects were specifically dependent on Dishevelled1 DVL1, which accumulates in nucleolar organizer regions NORs and binds to rDNA regions of the chromosome.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1013349" + }, + { + "object": "W22A, W22K, W22D, W22Y, and W22F substitutions were made in Munc13-1. The GFP-tagged constructs were expressed in Neuro-2a cells. Their membrane translocation in response to phorbol ester was observed in live cells by confocal microscopy. Munc13-1 translocated to the plasma membrane, the C1 domain translocated to internal membranes in response to phorbol ester. Trp-588 is important for ligand binding and translocation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab997956" + }, + { + "object": "results suggest that histone H1 represses recombination at the rDNA by a mechanism that is independent of the recombination pathways regulated by Sir2", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab669454" + }, + { + "object": "during AID-induced class switch recombination, UNG in association with recombination factors may facilitate the stabilization of the S-S synapse to facilitate efficient recombination.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab243376" + }, + { + "object": "Study found that HIF1alpha overexpression led to an enhanced betacatenin nuclear translocation, while betacatenin silencing inhibited betacatenin nuclear translocation. The enhanced betacatenin nuclear translocation induced resulted in an enhanced cell proliferation and cell invasion, an altered cell cycle distribution, decreased apoptosis, and improved nonhomologous end joining repair under normal and irradiation cond...", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab741909" + }, + { + "object": "Beckwith-Wiedemann syndrome patients that inherited a maternal translocation or inversion of chromosome 11 also demonstrated reduced expression of the growth suppressing imprinted gene, CDKN1C.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab98104" + }, + { + "object": "The amount of multiprotein complex UAF determines the rDNA copy number that is stably maintained. UAF ensures rDNA production not only by rDNA transcription activation but also by its copy-number maintenance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab779628" + }, + { + "object": "Here, recombinant fowlicidin-2 was successfully produced by E. coli recombinant expression system.The recombinant peptide exhibited high antibacterial activity against the Gram-positive and Gram-negative bacteria, and even drug-resistant strains.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab338954" + }, + { + "object": "Our results suggest that macroscopic rate of UvrD monomer translocation is not limited by each ATPase cycle but by a slow step pause in each translocation cycle that occurs after four to five rapid 1 nt translocation steps.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab563146" + }, + { + "object": "this study shows that Sox2 is expressed in activated B cells, but almost exclusively in those that have undergone IgH class switch recombination, enforced expression of Sox2 in splenic B cells severely inhibits AID expression and IgH class switch recombination, and that deletion of Sox2 increases the frequency of IgH:c-Myc translocations", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab949995" + } + ], + "question": "What about recombination in human centromeres?", + "subquestions": null, + "task_id": "575BE8FB36E8D520760A31B2CAE92034", + "usage": { + "chatgpt": 8855, + "gpt-4": 5918, + "gpt-4-turbo-preview": 4935 + }, + "user_id": 2 + }, + "document_id": "575BE8FB36E8D520760A31B2CAE92034", + "task_id": "575BE8FB36E8D520760A31B2CAE92034" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/07.json b/gnqa/paper1_eval/src/data/responses/general/experts/07.json new file mode 100644 index 0000000..007dd4d --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/07.json @@ -0,0 +1,394 @@ +{ + "created_at": "2024-06-01T14:29:54.405039Z", + "data": { + "amplify": false, + "answer": "The text mentions the application of site-specific recombinase technology, which allows investigators to engineer genes in the mouse that will allow for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. However, it does not provide specific details about recombination in the human genome.", + "context": { + "081924f4-cdcc-4fce-9223-744c6ecffe4e": [ + { + "document_id": "081924f4-cdcc-4fce-9223-744c6ecffe4e", + "text": "Genome Res, 2011, 21: 1769–1776\nMattick JS, Dinger ME. The extent of functionality in the human\ngenome. HUGO J, 2013, 7, doi:10.1186/1877-6566-1187-1182\nENCODE Project Consortium, Bernstein BE, Birney E, Dunham I,\nGreen ED, Gunter C, Snyder M. An integrated encyclopedia of DNA\nelements in the human genome. Nature, 2012, 489: 57–74\nPheasant M, Mattick JS. Raising the estimate of functional human\nsequences. Genome Res, 2007, 17: 1245–1253\nHu T, Long M, Yuan D, Zhu Z, Huang Y, Huang S. The genetic\nequidistance result, misreading by the molecular clock and neutral\ntheory and reinterpretation nearly half of a century later." + } + ], + "33814fad-d831-46f5-b41f-ff31626a82ca": [ + { + "document_id": "33814fad-d831-46f5-b41f-ff31626a82ca", + "text": "This approach enables, on the one hand, studying the process of\nmammalian evolution and, on the other hand, translational studies using model\norganisms of complex human phenotypes. Detection of regions conserved between\ndistant species points to high functional importance of these fragments of the DNA\nsequence. Human and mouse developmental lines diverged about 75 million years ago, and\never since evolutionary forces shaped the two genotypes in a different manner\n(Waterston et al. , 2002). Nevertheless, the extent of the changes is, however, small\nenough for conservation of local gene order (Waterston et al. , 2002)." + } + ], + "3cafb9e7-b3d9-4e8e-a727-da79282d2b14": [ + { + "document_id": "3cafb9e7-b3d9-4e8e-a727-da79282d2b14", + "text": "First, the human and mouse genome projects\nelucidated the sequences of over 20,000 genes [Lander et al. ,\n2001; Venter et al. , 2001], and most are expressed in the CNS. The availability of gene sequences has allowed rapid analysis of\ncandidate human disease and disorder genes and the isolation of\nthe mouse homologues. Second, the application of site-specific\nrecombinase technology provides investigators with the opportunity to engineer genes in the mouse that will allow for the\ndeletion, insertion, inversion, or exchange of chromosomal\nDNA with high fidelity (for review see Branda and Dymechi,\n2004]." + } + ], + "5edf84d0-c2d9-45eb-91b9-c35743b6a463": [ + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "In some cases, structural variations, such as copy number polymorphisms,\nexist (Feuk et al. , 2006); however, because of the nature of the genome assembly\nprocess, these will invariably be collapsed into a single contig that does not reflect\nthe natural sequence. To address the technical challenges of whole-genome assembly,\nthe human genome is released as defined ‘builds’ on a quarterly basis (Lander et al. ,\n2001; reviewed in Chapter 4). The increasing complexity of processes that map\ndata to the genome implicitly involves some lag in availability of the most current\nsequence assembly." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "In\npractical terms, this has meant that we acquire many fragments, from a few hundred\nbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human\ngenome, two unfinished ‘draft’ sequences were produced by different methods, one\nby the International Human Genome Sequencing Consortium (IHGSC) and one by\nCelera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001)." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + } + ], + "74f148ef-696c-4e25-80e5-1d44ae70540e": [ + { + "document_id": "74f148ef-696c-4e25-80e5-1d44ae70540e", + "text": "\nTHE HUMAN GENOME PROJECT IS generating vast amounts of new information at breakneck speed and causing a fundamental shift in disease research.Now with the availability of a nearly complete, high-accuracy sequence of the mouse genome (7), a new and powerful paradigm for biomedical research is established.The remarkable similarity of mouse and human genomes, in both synteny and sequence, unconditionally validates the mouse as an exceptional model organism for understanding human biology.The discovery among inbred mouse strains of defined regions of high and low genomic variation inherited primarily from two ancestral Mus subspecies (6) holds great promise to make mapping and positional cloning more rapid and feasible.Haplotype maps of inbred mouse strains combined with sophisticated delineation of their phenotypic variation and gene expression patterns will enable complex trait analysis on an unprecedented scale.This issue of Journal of Applied Physiology highlights inbred strain surveys exploring phenotypic variation in drug responses [see Crabbe et al. (1) and Watters et al. (8) in this issue].These mouse initiatives demonstrate a viable, cost-effective alternative to human research requiring family studies, population linkage analysis, or genome-wide genotyping on a multitude of individuals for association mapping." + } + ], + "81c3edc4-f625-45f2-bf78-e49faf118c88": [ + { + "document_id": "81c3edc4-f625-45f2-bf78-e49faf118c88", + "text": "\n\nHow Many Genes are There in the Human Genome?" + } + ], + "b1656249-5f62-428f-8b71-7549cc2886ff": [ + { + "document_id": "b1656249-5f62-428f-8b71-7549cc2886ff", + "text": "\n\nThe Landscape of Human Genome Variation" + } + ], + "c12e853e-4f0d-48f9-93af-15db9ad2dfae": [ + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "In some cases, structural variations, such as copy number polymorphisms,\nexist (Feuk et al. , 2006); however, because of the nature of the genome assembly\nprocess, these will invariably be collapsed into a single contig that does not reflect\nthe natural sequence. To address the technical challenges of whole-genome assembly,\nthe human genome is released as defined ‘builds’ on a quarterly basis (Lander et al. ,\n2001; reviewed in Chapter 4). The increasing complexity of processes that map\ndata to the genome implicitly involves some lag in availability of the most current\nsequence assembly." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "In\npractical terms, this has meant that we acquire many fragments, from a few hundred\nbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human\ngenome, two unfinished ‘draft’ sequences were produced by different methods, one\nby the International Human Genome Sequencing Consortium (IHGSC) and one by\nCelera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001)." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + } + ], + "da485354-fcdc-49b8-9a41-0f673610156a": [ + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "Science 291:1304–\n1351\n3. Lander ES et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921\n4. Engle LJ, Simpson CL, Landers JE (2006) Using high-throughput SNP technologies to study cancer. Oncogene 25:1594–1601\n5. Elston RC, Anne Spence M (2006) Advances in statistical human genetics over the\nlast 25 years. Stat Med 25:3049–3080\n6. Larson GP et al (2005) Genetic linkage of prostate cancer risk to the chromosome\n3 region bearing FHIT. Cancer Res 65:805–814\n7. Botstein D, Risch N (2003) Discovering genotypes underlying human phenotypes:\npast successes for mendelian disease, future approaches for complex disease." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "McPherson JD, Marra M, Hillier L et al (2001) A physical map of the human\ngenome. Nature 409:934–941\n13. Burke DT, Carle GF, Olson MV. (1987) Cloning of large segments of exogenous\nDNA into yeast by means of artificial chromosome vectors. Science 236:806–812\n14. Fleischmann RD, Adams MD, White O et al (1995) Whole-genome random\nsequencing and assembly of Haemophilus influenzae Rd Science 269:496–512\n15. Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the\nflowering plant Arabidopsis thaliana. Nature 408:796–815\n16." + } + ], + "e17ef791-e77a-486b-a3c1-c7f037fa530c": [ + { + "document_id": "e17ef791-e77a-486b-a3c1-c7f037fa530c", + "text": "\n\nT he human genome has been cracked wide open in recent years and is spilling many of its secrets.More than 100 genome wide association studies have been conducted for scores of hu man diseases, identifying hun dreds of polymorphisms that are widely seen to influence disease risk.After many years in which the study of complex human traits was mired in false claims and methodologic inconsistencies, ge nomics has brought not only com prehensive representation of com mon variation but also welcome rigor in the interpretation of sta tistical evidence.Researchers now know how to properly account for most of the multiple hypothesis testing involved in mining the ge nome for associations, and most reported associations reflect real biologic causation.But do they matter?" + } + ], + "f35e02a1-3314-4663-913f-38a3fc072aa8": [ + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "In some cases, structural variations, such as copy number polymorphisms,\nexist (Feuk et al. , 2006); however, because of the nature of the genome assembly\nprocess, these will invariably be collapsed into a single contig that does not reflect\nthe natural sequence. To address the technical challenges of whole-genome assembly,\nthe human genome is released as defined ‘builds’ on a quarterly basis (Lander et al. ,\n2001; reviewed in Chapter 4). The increasing complexity of processes that map\ndata to the genome implicitly involves some lag in availability of the most current\nsequence assembly." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "In\npractical terms, this has meant that we acquire many fragments, from a few hundred\nbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human\ngenome, two unfinished ‘draft’ sequences were produced by different methods, one\nby the International Human Genome Sequencing Consortium (IHGSC) and one by\nCelera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001)." + } + ], + "fca531d0-d45b-495f-a02c-fbd437617b20": [ + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "In some cases, structural variations, such as copy number polymorphisms,\nexist (Feuk et al. , 2006); however, because of the nature of the genome assembly\nprocess, these will invariably be collapsed into a single contig that does not reflect\nthe natural sequence. To address the technical challenges of whole-genome assembly,\nthe human genome is released as defined ‘builds’ on a quarterly basis (Lander et al. ,\n2001; reviewed in Chapter 4). The increasing complexity of processes that map\ndata to the genome implicitly involves some lag in availability of the most current\nsequence assembly." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "In\npractical terms, this has meant that we acquire many fragments, from a few hundred\nbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human\ngenome, two unfinished ‘draft’ sequences were produced by different methods, one\nby the International Human Genome Sequencing Consortium (IHGSC) and one by\nCelera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001)." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + } + ] + }, + "data_source": [ + { + "document_id": "81c3edc4-f625-45f2-bf78-e49faf118c88", + "section_type": "main", + "text": "\n\nHow Many Genes are There in the Human Genome?" + }, + { + "document_id": "3cafb9e7-b3d9-4e8e-a727-da79282d2b14", + "section_type": "main", + "text": "First, the human and mouse genome projects\nelucidated the sequences of over 20,000 genes [Lander et al. ,\n2001; Venter et al. , 2001], and most are expressed in the CNS.\n The availability of gene sequences has allowed rapid analysis of\ncandidate human disease and disorder genes and the isolation of\nthe mouse homologues. Second, the application of site-specific\nrecombinase technology provides investigators with the opportunity to engineer genes in the mouse that will allow for the\ndeletion, insertion, inversion, or exchange of chromosomal\nDNA with high fidelity (for review see Branda and Dymechi,\n2004]." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "In some cases, structural variations, such as copy number polymorphisms,\nexist (Feuk et al. , 2006); however, because of the nature of the genome assembly\nprocess, these will invariably be collapsed into a single contig that does not reflect\nthe natural sequence. To address the technical challenges of whole-genome assembly,\nthe human genome is released as defined ‘builds’ on a quarterly basis (Lander et al. ,\n2001; reviewed in Chapter 4). The increasing complexity of processes that map\ndata to the genome implicitly involves some lag in availability of the most current\nsequence assembly." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "In some cases, structural variations, such as copy number polymorphisms,\nexist (Feuk et al. , 2006); however, because of the nature of the genome assembly\nprocess, these will invariably be collapsed into a single contig that does not reflect\nthe natural sequence. To address the technical challenges of whole-genome assembly,\nthe human genome is released as defined ‘builds’ on a quarterly basis (Lander et al. ,\n2001; reviewed in Chapter 4). The increasing complexity of processes that map\ndata to the genome implicitly involves some lag in availability of the most current\nsequence assembly." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "In some cases, structural variations, such as copy number polymorphisms,\nexist (Feuk et al. , 2006); however, because of the nature of the genome assembly\nprocess, these will invariably be collapsed into a single contig that does not reflect\nthe natural sequence. To address the technical challenges of whole-genome assembly,\nthe human genome is released as defined ‘builds’ on a quarterly basis (Lander et al. ,\n2001; reviewed in Chapter 4). The increasing complexity of processes that map\ndata to the genome implicitly involves some lag in availability of the most current\nsequence assembly." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "In some cases, structural variations, such as copy number polymorphisms,\nexist (Feuk et al. , 2006); however, because of the nature of the genome assembly\nprocess, these will invariably be collapsed into a single contig that does not reflect\nthe natural sequence. To address the technical challenges of whole-genome assembly,\nthe human genome is released as defined ‘builds’ on a quarterly basis (Lander et al. ,\n2001; reviewed in Chapter 4). The increasing complexity of processes that map\ndata to the genome implicitly involves some lag in availability of the most current\nsequence assembly." + }, + { + "document_id": "e17ef791-e77a-486b-a3c1-c7f037fa530c", + "section_type": "main", + "text": "\n\nT he human genome has been cracked wide open in recent years and is spilling many of its secrets.More than 100 genome wide association studies have been conducted for scores of hu man diseases, identifying hun dreds of polymorphisms that are widely seen to influence disease risk.After many years in which the study of complex human traits was mired in false claims and methodologic inconsistencies, ge nomics has brought not only com prehensive representation of com mon variation but also welcome rigor in the interpretation of sta tistical evidence.Researchers now know how to properly account for most of the multiple hypothesis testing involved in mining the ge nome for associations, and most reported associations reflect real biologic causation.But do they matter?" + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "In\npractical terms, this has meant that we acquire many fragments, from a few hundred\nbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human\ngenome, two unfinished ‘draft’ sequences were produced by different methods, one\nby the International Human Genome Sequencing Consortium (IHGSC) and one by\nCelera Genomics (CG).\n The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001)." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "In\npractical terms, this has meant that we acquire many fragments, from a few hundred\nbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human\ngenome, two unfinished ‘draft’ sequences were produced by different methods, one\nby the International Human Genome Sequencing Consortium (IHGSC) and one by\nCelera Genomics (CG).\n The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001)." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "In\npractical terms, this has meant that we acquire many fragments, from a few hundred\nbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human\ngenome, two unfinished ‘draft’ sequences were produced by different methods, one\nby the International Human Genome Sequencing Consortium (IHGSC) and one by\nCelera Genomics (CG).\n The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001)." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "In\npractical terms, this has meant that we acquire many fragments, from a few hundred\nbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human\ngenome, two unfinished ‘draft’ sequences were produced by different methods, one\nby the International Human Genome Sequencing Consortium (IHGSC) and one by\nCelera Genomics (CG).\n The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001)." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "section_type": "main", + "text": "Science 291:1304–\n1351\n3. Lander ES et al (2001) Initial sequencing and analysis of the human genome.\n Nature 409:860–921\n4. Engle LJ, Simpson CL, Landers JE (2006) Using high-throughput SNP technologies to study cancer. Oncogene 25:1594–1601\n5. Elston RC, Anne Spence M (2006) Advances in statistical human genetics over the\nlast 25 years. Stat Med 25:3049–3080\n6. Larson GP et al (2005) Genetic linkage of prostate cancer risk to the chromosome\n3 region bearing FHIT. Cancer Res 65:805–814\n7. Botstein D, Risch N (2003) Discovering genotypes underlying human phenotypes:\npast successes for mendelian disease, future approaches for complex disease." + }, + { + "document_id": "081924f4-cdcc-4fce-9223-744c6ecffe4e", + "section_type": "main", + "text": "Genome Res, 2011, 21: 1769–1776\nMattick JS, Dinger ME. The extent of functionality in the human\ngenome. HUGO J, 2013, 7, doi:10.1186/1877-6566-1187-1182\nENCODE Project Consortium, Bernstein BE, Birney E, Dunham I,\nGreen ED, Gunter C, Snyder M. An integrated encyclopedia of DNA\nelements in the human genome. Nature, 2012, 489: 57–74\nPheasant M, Mattick JS. Raising the estimate of functional human\nsequences. Genome Res, 2007, 17: 1245–1253\nHu T, Long M, Yuan D, Zhu Z, Huang Y, Huang S. The genetic\nequidistance result, misreading by the molecular clock and neutral\ntheory and reinterpretation nearly half of a century later." + }, + { + "document_id": "b1656249-5f62-428f-8b71-7549cc2886ff", + "section_type": "main", + "text": "\n\nThe Landscape of Human Genome Variation" + }, + { + "document_id": "33814fad-d831-46f5-b41f-ff31626a82ca", + "section_type": "main", + "text": "This approach enables, on the one hand, studying the process of\nmammalian evolution and, on the other hand, translational studies using model\norganisms of complex human phenotypes. Detection of regions conserved between\ndistant species points to high functional importance of these fragments of the DNA\nsequence.\n Human and mouse developmental lines diverged about 75 million years ago, and\never since evolutionary forces shaped the two genotypes in a different manner\n(Waterston et al. , 2002). Nevertheless, the extent of the changes is, however, small\nenough for conservation of local gene order (Waterston et al. , 2002)." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "section_type": "main", + "text": "McPherson JD, Marra M, Hillier L et al (2001) A physical map of the human\ngenome. Nature 409:934–941\n13. Burke DT, Carle GF, Olson MV. (1987) Cloning of large segments of exogenous\nDNA into yeast by means of artificial chromosome vectors. Science 236:806–812\n14. Fleischmann RD, Adams MD, White O et al (1995) Whole-genome random\nsequencing and assembly of Haemophilus influenzae Rd Science 269:496–512\n15. Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the\nflowering plant Arabidopsis thaliana. Nature 408:796–815\n16." + }, + { + "document_id": "74f148ef-696c-4e25-80e5-1d44ae70540e", + "section_type": "abstract", + "text": "\nTHE HUMAN GENOME PROJECT IS generating vast amounts of new information at breakneck speed and causing a fundamental shift in disease research.Now with the availability of a nearly complete, high-accuracy sequence of the mouse genome (7), a new and powerful paradigm for biomedical research is established.The remarkable similarity of mouse and human genomes, in both synteny and sequence, unconditionally validates the mouse as an exceptional model organism for understanding human biology.The discovery among inbred mouse strains of defined regions of high and low genomic variation inherited primarily from two ancestral Mus subspecies (6) holds great promise to make mapping and positional cloning more rapid and feasible.Haplotype maps of inbred mouse strains combined with sophisticated delineation of their phenotypic variation and gene expression patterns will enable complex trait analysis on an unprecedented scale.This issue of Journal of Applied Physiology highlights inbred strain surveys exploring phenotypic variation in drug responses [see Crabbe et al. (1) and Watters et al. (8) in this issue].These mouse initiatives demonstrate a viable, cost-effective alternative to human research requiring family studies, population linkage analysis, or genome-wide genotyping on a multitude of individuals for association mapping." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + }, + { + "document_id": "937fe28b-dbaf-422b-a2de-9ffeafd94172", + "section_type": "main", + "text": "High copy number repeat sequences\n\nThe HGP revealed that repeat sequences account for at least 50 per cent of the human genome sequence.These repeats may be classified as (i) transposon-derived repeats, (ii) partially retroposed copies of genes (referred to as processed pseudogenes), (iii) simple sequence repeats, (iv) blocks of tandemly repeated sequences at centromeres, telomeres and the short arms of acrocentric chromosomes and (v) segmental duplications (SDs) or low copy number repeats." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "6.7 Challenges and future directions\nThere has been great progress in understanding the biology and functions encoded\nby the human genome since the first draft of a reference sequence was produced in\n2001 (Lander et al. , 2001; (Venter et al. , 2001), and much of this insight has been\ngained by comparison both within and between genomes. However, as with many scientific endeavours, more questions arise with each increment in understanding." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "6.7 Challenges and future directions\nThere has been great progress in understanding the biology and functions encoded\nby the human genome since the first draft of a reference sequence was produced in\n2001 (Lander et al. , 2001; (Venter et al. , 2001), and much of this insight has been\ngained by comparison both within and between genomes. However, as with many scientific endeavours, more questions arise with each increment in understanding." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "6.7 Challenges and future directions\nThere has been great progress in understanding the biology and functions encoded\nby the human genome since the first draft of a reference sequence was produced in\n2001 (Lander et al. , 2001; (Venter et al. , 2001), and much of this insight has been\ngained by comparison both within and between genomes. However, as with many scientific endeavours, more questions arise with each increment in understanding." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "6.7 Challenges and future directions\nThere has been great progress in understanding the biology and functions encoded\nby the human genome since the first draft of a reference sequence was produced in\n2001 (Lander et al. , 2001; (Venter et al. , 2001), and much of this insight has been\ngained by comparison both within and between genomes. However, as with many scientific endeavours, more questions arise with each increment in understanding." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "After the publication of the publicly available human genome draft in 2001, the\nIHGSC undertook the arduous task of ‘finishing’: producing a genome sequence\ncovering 99 per cent of the euchromatic regions sequenced to an accuracy of 99.99\nper cent. On 14 April 2003, the IHGSC announced that this target had been reached;\nleaving less than 400 persistent gaps where highly repetitive sequences evaded current sequencing technology. A steady trickle of papers in the journal Nature has\nmarked the emergence of each finished human chromosome sequence, along with\nthe annotation describing its notable features." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "After the publication of the publicly available human genome draft in 2001, the\nIHGSC undertook the arduous task of ‘finishing’: producing a genome sequence\ncovering 99 per cent of the euchromatic regions sequenced to an accuracy of 99.99\nper cent. On 14 April 2003, the IHGSC announced that this target had been reached;\nleaving less than 400 persistent gaps where highly repetitive sequences evaded current sequencing technology. A steady trickle of papers in the journal Nature has\nmarked the emergence of each finished human chromosome sequence, along with\nthe annotation describing its notable features." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "After the publication of the publicly available human genome draft in 2001, the\nIHGSC undertook the arduous task of ‘finishing’: producing a genome sequence\ncovering 99 per cent of the euchromatic regions sequenced to an accuracy of 99.99\nper cent. On 14 April 2003, the IHGSC announced that this target had been reached;\nleaving less than 400 persistent gaps where highly repetitive sequences evaded current sequencing technology. A steady trickle of papers in the journal Nature has\nmarked the emergence of each finished human chromosome sequence, along with\nthe annotation describing its notable features." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "After the publication of the publicly available human genome draft in 2001, the\nIHGSC undertook the arduous task of ‘finishing’: producing a genome sequence\ncovering 99 per cent of the euchromatic regions sequenced to an accuracy of 99.99\nper cent. On 14 April 2003, the IHGSC announced that this target had been reached;\nleaving less than 400 persistent gaps where highly repetitive sequences evaded current sequencing technology. A steady trickle of papers in the journal Nature has\nmarked the emergence of each finished human chromosome sequence, along with\nthe annotation describing its notable features." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "6.2.3 A varied landscape\nIn probably every measure that has been made of the human genome sequence, it has\nbeen found to be far from homogeneous. We have already touched on the distinction\nbetween heterochromatic regions that perform roles in the packaging and segregation\nof chromosomes, from the remaining (euchromatic) regions. Throughout the rest of\nthe euchromatic genome, there is considerable variation in gene density (the number\nof genes per unit sequence), IRE content, nucleotide and dinucleotide frequency, and\nthe observed rates of genetic recombination, nucleotide substitution, insertions and\ndeletions." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "6.2.3 A varied landscape\nIn probably every measure that has been made of the human genome sequence, it has\nbeen found to be far from homogeneous. We have already touched on the distinction\nbetween heterochromatic regions that perform roles in the packaging and segregation\nof chromosomes, from the remaining (euchromatic) regions. Throughout the rest of\nthe euchromatic genome, there is considerable variation in gene density (the number\nof genes per unit sequence), IRE content, nucleotide and dinucleotide frequency, and\nthe observed rates of genetic recombination, nucleotide substitution, insertions and\ndeletions." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "6.2.3 A varied landscape\nIn probably every measure that has been made of the human genome sequence, it has\nbeen found to be far from homogeneous. We have already touched on the distinction\nbetween heterochromatic regions that perform roles in the packaging and segregation\nof chromosomes, from the remaining (euchromatic) regions. Throughout the rest of\nthe euchromatic genome, there is considerable variation in gene density (the number\nof genes per unit sequence), IRE content, nucleotide and dinucleotide frequency, and\nthe observed rates of genetic recombination, nucleotide substitution, insertions and\ndeletions." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "6.2.3 A varied landscape\nIn probably every measure that has been made of the human genome sequence, it has\nbeen found to be far from homogeneous. We have already touched on the distinction\nbetween heterochromatic regions that perform roles in the packaging and segregation\nof chromosomes, from the remaining (euchromatic) regions. Throughout the rest of\nthe euchromatic genome, there is considerable variation in gene density (the number\nof genes per unit sequence), IRE content, nucleotide and dinucleotide frequency, and\nthe observed rates of genetic recombination, nucleotide substitution, insertions and\ndeletions." + }, + { + "document_id": "0ecf5586-f80d-4b5e-8687-5a0d92423597", + "section_type": "main", + "text": "The precision and the power in human genetics will improve greatly over the\nnext several decades as full genome sequences, better human disease phenotyping, and\nelectronic health records are merged at the scale of millions of subjects and whole\nnations. Therefore, we need to revamp experimental genetic resources in an era flooded\nin GWAS hits. How are new and old mouse resources best repositioned to help deliver on\nthe still unmet and much more integrative promises of predictive genetics and\npersonalized precision health care?\n\n 25\nbioRxiv preprint doi: https://doi.org/10.1101/672097; this version posted July 8, 2019." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "section_type": "main", + "text": "Resequencing\nCompletion of a single version of the human genome (2,3) has now provided\nthe substrates for direct comparison of individuals in both health and disease.\n Ideally, to better understand the genetic contributions to severe diseases, one\nwould obtain the entire human genome sequence for all disease-carrying individuals for comparison to unaffected control groups. While these complete\ndata sets are not readily obtainable today, a strategy that is currently approachable is the re-sequencing of a large set of appropriate candidate genes in\nindividuals with a given disease to screen for potential causative/susceptibility\nalleles." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "The interplay between the adaptive benefits introduced by mutations and natural selection shapes the genome into\nunique patterns of genetic variations in different regions. Therefore, investigating\nthe functional roles of these genetic variations provides a great opportunity for understanding complex common diseases, such as cancer. The compilation of human\n\nBioinformatics for Geneticists, Second Edition." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "The interplay between the adaptive benefits introduced by mutations and natural selection shapes the genome into\nunique patterns of genetic variations in different regions. Therefore, investigating\nthe functional roles of these genetic variations provides a great opportunity for understanding complex common diseases, such as cancer. The compilation of human\n\nBioinformatics for Geneticists, Second Edition." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "The interplay between the adaptive benefits introduced by mutations and natural selection shapes the genome into\nunique patterns of genetic variations in different regions. Therefore, investigating\nthe functional roles of these genetic variations provides a great opportunity for understanding complex common diseases, such as cancer. The compilation of human\n\nBioinformatics for Geneticists, Second Edition." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "The interplay between the adaptive benefits introduced by mutations and natural selection shapes the genome into\nunique patterns of genetic variations in different regions. Therefore, investigating\nthe functional roles of these genetic variations provides a great opportunity for understanding complex common diseases, such as cancer. The compilation of human\n\nBioinformatics for Geneticists, Second Edition." + } + ], + "document_id": "1A879F7DD77C0462CC12FB20F7D14486", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "human&genome", + "recombination", + "genes", + "CNS", + "site-specific&recombinase", + "structural&variations", + "copy&number&polymorphisms", + "genome&assembly", + "genome&wide&association&studies", + "polymorphisms" + ], + "metadata": [ + { + "object": "Transient overexpression of WRKY79 in protoplasts results in up-regulation of Gene:542165, Gene:541974, Gene:100274033, Gene:542688, Gene:542150, Gene:542151, Gene:100273457, Gene:100285509, Gene:103626248, Gene:103646045, Gene:100217270, Gene:100279981, Gene:100281950, Gene:542476, Gene:542369, Gene:100281950, and Gene:542260.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab969966" + }, + { + "object": "CAT 419 C/T gene polymorphism was not informative, -89 A/T was associated with risk, and 389 C/T conferred protection against vitiligo along with AT haplotype. VDR BsmI, ApaI, and TaqI gene polymorphisms were not associated with vitiligo in Northwestern Mexicans suggesting a role for CAT gene polymorphisms in vitiligo susceptibility in the Mexican population and a lack of association with VDR gene polymorphisms.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab122773" + }, + { + "object": "P2Y1 and P2Y12 genes were polymorphic in a Korean population; 3 intronic P2Y12 polymorphisms i-139C>T, i-744T>C, i-801insA were in complete linkage disequilibrium but not with the c.52C>T polymorphism; platelet aggregation in response to ADP associated with c.52C>T polymorphism but not with the 3 intronic polymorphisms or the P2Y1 c.1622A>T polymorphism", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab576406" + }, + { + "object": "Uniform Mu insertion results in up-regulation of cytokinin synthesis genes and down-regulation of cytokinin degradation genes. The protein binds to Gene:103632693, Gene:100502174, Gene:100283866, Gene:542044, and Gene:100037786.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab983367" + }, + { + "object": "No relationship was found between the studied polymorphisms 14094 ACE gene, rs1800469 gene TGFbeta1, GNB3 gene rs5443, rs5186 AGTR1 gene and the occurrence of primary vesicoureteral reflux. TT genotype polymorphism rs5443 of the GNB3 gene may be a protective factor for improved renal function in patients with primary vesicoureteral reflux.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab530514" + }, + { + "object": "There was no association between the extended psychosis phenotype and BDNF rs6265/COMT rs4680 polymorphisms. The lack of an association between different expression levels of the extended psychosis phenotype and the BDNF rs6265/ COMT rs4680 polymorphism might be related to sample characteristics, underlying gene-gene, gene-environment and gene-environment-gene interactions.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab888369" + }, + { + "object": "In general, the meta-analysis found no marked association between the IL-10-1082G/A gene polymorphism and HIV-1 susceptibility, IL-10-529C/A gene polymorphism might lead to a decreased risk of HIV-1 infection, and IL-10-819C/T gene polymorphism might lead to an increased risk of HIV-1 infection.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab393616" + }, + { + "object": "When rs13154178 gene polymorphism was compared with AA homozygous individuals, fasting blood glucose levels were significantly higher in carriers of either polymorphism than in those with no polymorphism. We suggest rs13154178 gene polymorphism may lead to gestational diabetes mellitus in the Turkish society.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1015662" + }, + { + "object": "Study confirmed the presence of a gene-environment interaction between the rs1801516 polymorphism and radiation exposure in carcinogenesis, whereas no association was found between the rs1801516 polymorphism and cancer risk for individuals without radiation exposure. The heterogeneity observed in the meta-analysis of individuals with radiation exposure might be due to gene-ethnicity or gene-gene interactions.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab737660" + }, + { + "object": "Genome-wide associations P < 5 x 10-8 were found at the PCSK9 gene, the APOB gene, theLPL gene, the APOA1-APOA5 locus, the LIPC gene, the CETP gene, the LDLR gene, and the APOE locus.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab320109" + } + ], + "question": "What about recombination in the human genome?", + "subquestions": null, + "task_id": "1A879F7DD77C0462CC12FB20F7D14486", + "usage": { + "chatgpt": 4864, + "gpt-4": 3728, + "gpt-4-turbo-preview": 2745 + }, + "user_id": 2 + }, + "document_id": "1A879F7DD77C0462CC12FB20F7D14486", + "task_id": "1A879F7DD77C0462CC12FB20F7D14486" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/08.json b/gnqa/paper1_eval/src/data/responses/general/experts/08.json new file mode 100644 index 0000000..aea3464 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/08.json @@ -0,0 +1,415 @@ +{ + "created_at": "2024-06-01T14:31:40.882988Z", + "data": { + "amplify": false, + "answer": "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "context": { + "0e6c370f-b514-4551-b6ed-9cc72e6f6b75": [ + { + "document_id": "0e6c370f-b514-4551-b6ed-9cc72e6f6b75", + "text": "The hierarchical organization of GN’s main Select and Search menu is\nsimple and makes it relatively easy to find relevant data sets (Fig. 1). To get data, after\nopening the browser, select the most appropriate Species from the dropdown menu. For an\nopen-ended search of phenotypes you can also select All Species at the bottom of the menu. The next steps are to select the Group, Type, and Data Set from the drop-down menus. For\nmany groups, a combination of phenotypes, genotypes, and molecular data are available." + } + ], + "4049da4d-c7cf-4e30-9a21-c77609fad23d": [ + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "text": "GeneNetwork contains data from a\nwide range of species, from humans to soybeans, but most of the available phenotypic data is\nfrom mice. Within the mouse dataset there are groups of families, crosses, non-genetic\ngroupings, and individual data. The type of dataset must be selected after defining the species\nand sample population. While genotypes, mRNA, methylated DNA, protein, metagenomic, and\n2\nbioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint\n(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. metabolome datasets are available (i.e." + } + ], + "43407486-b9c2-487b-b19c-b605c4d201c6": [ + { + "document_id": "43407486-b9c2-487b-b19c-b605c4d201c6", + "text": "The hierarchical organization of GN’s main Select and Search menu is\nsimple and makes it relatively easy to find relevant data sets (Fig. 1). To get data, after\nopening the browser, select the most appropriate Species from the dropdown menu. For an\nopen-ended search of phenotypes you can also select All Species at the bottom of the menu. The next steps are to select the Group, Type, and Data Set from the drop-down menus. For\nmany groups, a combination of phenotypes, genotypes, and molecular data are available." + } + ], + "47a15e69-dc83-452e-95d8-c605e61f43c0": [ + { + "document_id": "47a15e69-dc83-452e-95d8-c605e61f43c0", + "text": "Search and Data Retrieval\nPoint your browser to www.genenetwork.org. This brings you by default to\nthe Search page, from which you can retrieve data from many GN data sets. We will focus on the default data set, defined by Species: Mouse, Group: BXD,\nType: Whole Brain, Database: INIA Brain mRNA M430 (Apr05) PDNN\nEnter “Kcnj*” into the ALL or ANY field and click the Search button. Note\nthe location and annotation of available potassium channel genes in the Search\nResults page that opens. Use the browser Back button to return to previous page." + } + ], + "638b3811-7054-4788-a42d-2ccc7bfce1c7": [ + { + "document_id": "638b3811-7054-4788-a42d-2ccc7bfce1c7", + "text": "Add\ninformation on data provenance by giving details in Investigation, Protocols and ProtocolApplications\n\nCustomize Customize ‘my’ XGAP database with extended variants of Trait and Subject. In the online XGAP demonstrator, Probe traits have a\nsequence and genome location and Strain subjects have parent strains and (in)breeding method. Describe extensions using MOLGENIS\nlanguage and the generator automatically changes XGAP database software to your research\nUpload\n\nUpload data from measurement devices, public databases, collaborating XGAP databases, or a public XGAP repository with community\ndata." + }, + { + "document_id": "638b3811-7054-4788-a42d-2ccc7bfce1c7", + "text": "However, a suitable and customizable integration of\nthese elements to support high throughput genotype-tophenotype experiments is still needed [34]: dbGaP, GeneNetwork and the model organism databases are\ndesigned as international repositories and not to serve\nas general data infrastructure for individual projects;\nmany of the existing bespoke data models are too complicated and specialized, hard to integrate between profiling technologies, or lack software support to easily\nconnect to new analysis tools; and customization of the\nexisting infrastructures dbGaP, GeneNetwork or other\ninternational repositories [35,36] or assembly of Bioconductor and generic model organism database components to suit particular experimental designs, organisms\nand biotechnologies still requires many minor and\nsometimes major manual changes in the software code\nthat go beyond what individual lab bioinformaticians\ncan or should do, and result in duplicated efforts\nbetween labs if attempted." + } + ], + "75813bc2-f0b5-400c-92d7-0958df97a04f": [ + { + "document_id": "75813bc2-f0b5-400c-92d7-0958df97a04f", + "text": ", 2014; see Section 9). GeneNetwork is a database that enables searching for ∼4000 phenotypes from multiple studies in the BXD, HXB, and in other recombinant inbred rodent families, as well as in other model organisms\nand even humans (Mulligan et al. , 2017). GeneNetwork employed a\nsomewhat different strategy than MPD in that it did not rely solely on\nresearchers submitting their data. Instead the database operators extracted the data from the scientific literature and integrated them into a\nuniform format (Chesler et al. , 2003)." + } + ], + "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d": [ + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "text": "GeneNetwork contains data from a\nwide range of species, from humans to soybeans, but most of the available phenotypic data is\nfrom mice. Within the mouse dataset there are groups of families, crosses, non-genetic\ngroupings, and individual data. The type of dataset must be selected after defining the species\nand sample population. While genotypes, mRNA, methylated DNA, protein, metagenomic, and\n2\nbioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint\n(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. metabolome datasets are available (i.e." + } + ], + "85ee9743-b34d-4d49-9017-d7d2e5d4b996": [ + { + "document_id": "85ee9743-b34d-4d49-9017-d7d2e5d4b996", + "text": "However, a suitable and customizable integration of these elements\nto support high throughput genotype-to-phenotype experiments is still\nneeded[340]: dbGaP, GeneNetwork and the model organism databases\nare designed as international repositories and not to serve as general\ndata infrastructure for individual projects; many of the existing bespoke\ndata models are too complicated and specialized, hard to integrate between profiling technologies, or lack software support to easily connect\nto new analysis tools; and customization of the existing infrastructures\ndbGaP, GeneNetwork or other international repositories[384, 154] or\nassembly of Bioconductor and generic model organism database components to suit particular experimental designs, organisms and biotechnologies still requires many minor and sometimes major manual changes\n38\n2.1." + } + ], + "92fa8f50-2923-41a1-812b-32d931c71684": [ + { + "document_id": "92fa8f50-2923-41a1-812b-32d931c71684", + "text": "All data presented in this paper were deposited in the online database\nGeneNetwork (www.genenetwork.org), an open web resource that contains\ngenotypic, gene expression, and phenotypic data from several genetic reference\npopulations of multiple species (e.g. mouse, rat and human) and various cell\ntypes and tissues.35;36 It provides a valuable tool to integrate gene networks and\nphenotypic traits, and also allows cross-cell type and cross-species comparative\ngene expression and eQTL analyses." + } + ], + "d2f9c5cf-835c-450a-bb42-a2454a99e058": [ + { + "document_id": "d2f9c5cf-835c-450a-bb42-a2454a99e058", + "text": "There is a good chance that you will be able to apply these new\ntechniques to specific problems, even while you read. If you have a computer with an\nInternet connection—so much the better, and you can read and work along at the same time. This short review and primer will take you on a tour of a web site called GeneNetwork that\nembeds many large data sets that are relevant to studies of behavioral variation. GeneNetwork is an unusual site because it contains a coherent \"universe\" of data, as well as\nmany powerful analytic tools." + } + ], + "dbe5a781-3561-48cb-9f63-cfb4f3246434": [ + { + "document_id": "dbe5a781-3561-48cb-9f63-cfb4f3246434", + "text": "The GeneNetwork database provides open access\nto BXD and other RI strain derived microarray data, single nucleotide polymorphism (SNP) data,\nand phenotypic data for quantitative trait loci analysis and gene expression correlation analyses. Gene expression data were exported for manually selected probes in the PDNN hippocampus\ndatabase (Hippocampus Consortium M430v2), and the PDNN whole brain database (INIA Brain\nmRNA M430). The Hippocampus database was chosen as one of the most elaborate brain databases,\nas well as most highly recommended dataset on GeneNetwork itself (http://www.genenetwork.org/\nwebqtl/main.py?FormID=sharinginfo&GN_AccessionId=112)." + } + ], + "f041550e-5f2d-430e-8f46-15ebea6ca496": [ + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "text": "2016) and can\nalso be accessed in GeneNetwork by entering Record ID 18494 in the Get Any\nspace on the Search page and clicking on the Search button. Alternatively, enter\ndata by hand into the designated boxes provided by GeneNetwork. These latter\noptions also allow for the inclusion of trait variance. It is a good idea to name\nthe trait in the box provided. Then click Next, and manually enter the data for\neach RI strain, F1, and founder strain. 3\n\nAuthor Manuscript\n\nAfter entering the data, click on the blue plus sign button called Add." + }, + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "text": "To submit multiple phenotypes at the same\ntime, select the option for Batch Submission under the Home tab. This allows\nusers to submit up to 100 traits for analysis by GeneNetwork. Here, select BXD\nas the cross or RI set to analyze from the first pull-down menu. The phenotype\nfile should follow the format described in the Sample text (http://\ngenenetwork.org/sample.txt). After uploading the appropriate file using the\nBrowse button, enter a name for the file in the Dataset space. The data will be\nstored in the GeneNetwork server for 24 hours. Click Next." + }, + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "text": "Author Manuscript\n\nMaterials\nHere we will provide detailed instructions for using GeneNetwork along with some\n“worked” examples taken from the recent study of intravenous cocaine self-administration\nby Dickson et al. (2016) in BXD RI mice. A complete overview of GeneNetwork is beyond\nthe scope of this protocol, but is extensively covered in elsewhere (see Mulligan et al. 2016;\nWilliams & Mulligan 2012 for excellent reviews on GeneNetwork). A computer with an internet connection and current web browser. See the GeneNetwork.org\nsite for information on supported browser versions. Author Manuscript\n\nMethod\nEntering Data\n\nAuthor Manuscript\n\n1\n\nLink to http://www.genenetwork.org." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "\n\nSpecies in GenAge model organisms" + } + ], + "f9b2eeba-5f93-49c1-8828-311f0797d9e3": [ + { + "document_id": "f9b2eeba-5f93-49c1-8828-311f0797d9e3", + "text": "Data are reviewed before entry in\nGeneNetwork by the senior author. Phenotypes are currently split into 15 broad\nphenotypic categories (Supplementary Data 1). Phenome curation and description\nwas initiated by R.W.W. and Dr Elissa Chesler in 2002 by literature review and data\nextraction. The early work is described briefly in Chesler et al.51,52. Most work over\nthe past 5 years has been performed by two of the coauthors (R.W.W. and\nM.K.M.). We have used a controlled vocabulary and set of rules described here\n(http://www.genenetwork.org/faq.html#Q-22)." + } + ], + "fa8bba46-ce94-439a-a676-35187a3abcbf": [ + { + "document_id": "fa8bba46-ce94-439a-a676-35187a3abcbf", + "text": "9) To bring your data to GeneWeaver,\nclick on the GeneWeaver icon, making sure to be previously\nlogin to your GeneWeaver account. You will be brought to the\nGeneSet upload page with the Genes Uploaded and the\nGeneweaver Analysis Platform\n\n139\n\nFig. 5 Default settings at GeneNetwork.org are set to search “Mouse”, “Phenotypes”, from among the “BXD\nPublished Phenotypes” data set. Here the term nociception was searched for\n\nFig. 6 The search results page in GeneNetwork showing the 33 records retrieved from the phenotype search\nfor nociception." + }, + { + "document_id": "fa8bba46-ce94-439a-a676-35187a3abcbf", + "text": "Users may also share their data with other users selectively,\nmake it public, or keep it restricted to a private account. Data can be\nimported by users, uploading their gene set data directly or exporting to GeneWeaver from within another online resource such as\nNeuro Informatics Framework (NIF) [8], Grappa [9], Mouse\nPhenome Database (MPD) [10] or GeneNetwork [11]. These datasets can then be added to your collection to be analyzed together\nwith other gene sets retrieved from the GeneWeaver database. To begin a GeneWeaver analysis a user must collect “GeneSets”\ntogether in a “Project”." + }, + { + "document_id": "fa8bba46-ce94-439a-a676-35187a3abcbf", + "text": "Alternatively the spreadsheet can be saved as a .txt file\nand uploaded by clicking on “Switch to file upload.” Once\ncomplete click on upload GeneSet. 7. Once completed you are taken to the GeneSet detail page. If\nthere are errors in your uploaded data you can correct them by\nclicking on “Edit”. 8. Use the Add Selected to Project, and create a new project, e.g. “Chronic Cocaine”. 9. Now using the Search function populate this project with additional gene sets related to this study trying Queries such as\n“Cocaine Addiction”, “Chronic Cocaine”." + } + ] + }, + "data_source": [ + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "section_type": "main", + "text": "2016) and can\nalso be accessed in GeneNetwork by entering Record ID 18494 in the Get Any\nspace on the Search page and clicking on the Search button. Alternatively, enter\ndata by hand into the designated boxes provided by GeneNetwork. These latter\noptions also allow for the inclusion of trait variance. It is a good idea to name\nthe trait in the box provided. Then click Next, and manually enter the data for\neach RI strain, F1, and founder strain.\n 3\n\nAuthor Manuscript\n\nAfter entering the data, click on the blue plus sign button called Add." + }, + { + "document_id": "75813bc2-f0b5-400c-92d7-0958df97a04f", + "section_type": "main", + "text": ", 2014; see Section 9).\n GeneNetwork is a database that enables searching for ∼4000 phenotypes from multiple studies in the BXD, HXB, and in other recombinant inbred rodent families, as well as in other model organisms\nand even humans (Mulligan et al. , 2017). GeneNetwork employed a\nsomewhat different strategy than MPD in that it did not rely solely on\nresearchers submitting their data. Instead the database operators extracted the data from the scientific literature and integrated them into a\nuniform format (Chesler et al. , 2003)." + }, + { + "document_id": "638b3811-7054-4788-a42d-2ccc7bfce1c7", + "section_type": "main", + "text": "Add\ninformation on data provenance by giving details in Investigation, Protocols and ProtocolApplications\n\nCustomize Customize ‘my’ XGAP database with extended variants of Trait and Subject. In the online XGAP demonstrator, Probe traits have a\nsequence and genome location and Strain subjects have parent strains and (in)breeding method. Describe extensions using MOLGENIS\nlanguage and the generator automatically changes XGAP database software to your research\nUpload\n\nUpload data from measurement devices, public databases, collaborating XGAP databases, or a public XGAP repository with community\ndata." + }, + { + "document_id": "fa8bba46-ce94-439a-a676-35187a3abcbf", + "section_type": "main", + "text": "9) To bring your data to GeneWeaver,\nclick on the GeneWeaver icon, making sure to be previously\nlogin to your GeneWeaver account. You will be brought to the\nGeneSet upload page with the Genes Uploaded and the\nGeneweaver Analysis Platform\n\n139\n\nFig. 5 Default settings at GeneNetwork.org are set to search “Mouse”, “Phenotypes”, from among the “BXD\nPublished Phenotypes” data set. Here the term nociception was searched for\n\nFig. 6 The search results page in GeneNetwork showing the 33 records retrieved from the phenotype search\nfor nociception." + }, + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "section_type": "main", + "text": "To submit multiple phenotypes at the same\ntime, select the option for Batch Submission under the Home tab. This allows\nusers to submit up to 100 traits for analysis by GeneNetwork. Here, select BXD\nas the cross or RI set to analyze from the first pull-down menu. The phenotype\nfile should follow the format described in the Sample text (http://\ngenenetwork.org/sample.txt). After uploading the appropriate file using the\nBrowse button, enter a name for the file in the Dataset space. The data will be\nstored in the GeneNetwork server for 24 hours. Click Next." + }, + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "section_type": "main", + "text": "Author Manuscript\n\nMaterials\nHere we will provide detailed instructions for using GeneNetwork along with some\n“worked” examples taken from the recent study of intravenous cocaine self-administration\nby Dickson et al. (2016) in BXD RI mice. A complete overview of GeneNetwork is beyond\nthe scope of this protocol, but is extensively covered in elsewhere (see Mulligan et al. 2016;\nWilliams & Mulligan 2012 for excellent reviews on GeneNetwork).\n A computer with an internet connection and current web browser. See the GeneNetwork.org\nsite for information on supported browser versions.\n\n Author Manuscript\n\nMethod\nEntering Data\n\nAuthor Manuscript\n\n1\n\nLink to http://www.genenetwork.org." + }, + { + "document_id": "0e6c370f-b514-4551-b6ed-9cc72e6f6b75", + "section_type": "main", + "text": "The hierarchical organization of GN’s main Select and Search menu is\nsimple and makes it relatively easy to find relevant data sets (Fig. 1). To get data, after\nopening the browser, select the most appropriate Species from the dropdown menu. For an\nopen-ended search of phenotypes you can also select All Species at the bottom of the menu.\n The next steps are to select the Group, Type, and Data Set from the drop-down menus. For\nmany groups, a combination of phenotypes, genotypes, and molecular data are available." + }, + { + "document_id": "43407486-b9c2-487b-b19c-b605c4d201c6", + "section_type": "main", + "text": "The hierarchical organization of GN’s main Select and Search menu is\nsimple and makes it relatively easy to find relevant data sets (Fig. 1). To get data, after\nopening the browser, select the most appropriate Species from the dropdown menu. For an\nopen-ended search of phenotypes you can also select All Species at the bottom of the menu.\n The next steps are to select the Group, Type, and Data Set from the drop-down menus. For\nmany groups, a combination of phenotypes, genotypes, and molecular data are available." + }, + { + "document_id": "fa8bba46-ce94-439a-a676-35187a3abcbf", + "section_type": "main", + "text": "Users may also share their data with other users selectively,\nmake it public, or keep it restricted to a private account. Data can be\nimported by users, uploading their gene set data directly or exporting to GeneWeaver from within another online resource such as\nNeuro Informatics Framework (NIF) [8], Grappa [9], Mouse\nPhenome Database (MPD) [10] or GeneNetwork [11]. These datasets can then be added to your collection to be analyzed together\nwith other gene sets retrieved from the GeneWeaver database.\n To begin a GeneWeaver analysis a user must collect “GeneSets”\ntogether in a “Project”." + }, + { + "document_id": "638b3811-7054-4788-a42d-2ccc7bfce1c7", + "section_type": "main", + "text": "However, a suitable and customizable integration of\nthese elements to support high throughput genotype-tophenotype experiments is still needed [34]: dbGaP, GeneNetwork and the model organism databases are\ndesigned as international repositories and not to serve\nas general data infrastructure for individual projects;\nmany of the existing bespoke data models are too complicated and specialized, hard to integrate between profiling technologies, or lack software support to easily\nconnect to new analysis tools; and customization of the\nexisting infrastructures dbGaP, GeneNetwork or other\ninternational repositories [35,36] or assembly of Bioconductor and generic model organism database components to suit particular experimental designs, organisms\nand biotechnologies still requires many minor and\nsometimes major manual changes in the software code\nthat go beyond what individual lab bioinformaticians\ncan or should do, and result in duplicated efforts\nbetween labs if attempted." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "\n\nSpecies in GenAge model organisms" + }, + { + "document_id": "fa8bba46-ce94-439a-a676-35187a3abcbf", + "section_type": "main", + "text": "Alternatively the spreadsheet can be saved as a .txt file\nand uploaded by clicking on “Switch to file upload.” Once\ncomplete click on upload GeneSet.\n 7. Once completed you are taken to the GeneSet detail page. If\nthere are errors in your uploaded data you can correct them by\nclicking on “Edit”.\n 8. Use the Add Selected to Project, and create a new project, e.g.\n “Chronic Cocaine”.\n 9. Now using the Search function populate this project with additional gene sets related to this study trying Queries such as\n“Cocaine Addiction”, “Chronic Cocaine”." + }, + { + "document_id": "85ee9743-b34d-4d49-9017-d7d2e5d4b996", + "section_type": "main", + "text": "However, a suitable and customizable integration of these elements\nto support high throughput genotype-to-phenotype experiments is still\nneeded[340]: dbGaP, GeneNetwork and the model organism databases\nare designed as international repositories and not to serve as general\ndata infrastructure for individual projects; many of the existing bespoke\ndata models are too complicated and specialized, hard to integrate between profiling technologies, or lack software support to easily connect\nto new analysis tools; and customization of the existing infrastructures\ndbGaP, GeneNetwork or other international repositories[384, 154] or\nassembly of Bioconductor and generic model organism database components to suit particular experimental designs, organisms and biotechnologies still requires many minor and sometimes major manual changes\n38\n2.1." + }, + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "section_type": "main", + "text": "GeneNetwork contains data from a\nwide range of species, from humans to soybeans, but most of the available phenotypic data is\nfrom mice. Within the mouse dataset there are groups of families, crosses, non-genetic\ngroupings, and individual data. The type of dataset must be selected after defining the species\nand sample population. While genotypes, mRNA, methylated DNA, protein, metagenomic, and\n2\nbioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint\n(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.\n\n metabolome datasets are available (i.e." + }, + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "section_type": "main", + "text": "GeneNetwork contains data from a\nwide range of species, from humans to soybeans, but most of the available phenotypic data is\nfrom mice. Within the mouse dataset there are groups of families, crosses, non-genetic\ngroupings, and individual data. The type of dataset must be selected after defining the species\nand sample population. While genotypes, mRNA, methylated DNA, protein, metagenomic, and\n2\nbioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint\n(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.\n\n metabolome datasets are available (i.e." + }, + { + "document_id": "92fa8f50-2923-41a1-812b-32d931c71684", + "section_type": "main", + "text": "All data presented in this paper were deposited in the online database\nGeneNetwork (www.genenetwork.org), an open web resource that contains\ngenotypic, gene expression, and phenotypic data from several genetic reference\npopulations of multiple species (e.g. mouse, rat and human) and various cell\ntypes and tissues.35;36 It provides a valuable tool to integrate gene networks and\nphenotypic traits, and also allows cross-cell type and cross-species comparative\ngene expression and eQTL analyses." + }, + { + "document_id": "f9b2eeba-5f93-49c1-8828-311f0797d9e3", + "section_type": "main", + "text": "Data are reviewed before entry in\nGeneNetwork by the senior author. Phenotypes are currently split into 15 broad\nphenotypic categories (Supplementary Data 1). Phenome curation and description\nwas initiated by R.W.W. and Dr Elissa Chesler in 2002 by literature review and data\nextraction. The early work is described briefly in Chesler et al.51,52. Most work over\nthe past 5 years has been performed by two of the coauthors (R.W.W. and\nM.K.M.). We have used a controlled vocabulary and set of rules described here\n(http://www.genenetwork.org/faq.html#Q-22)." + }, + { + "document_id": "d2f9c5cf-835c-450a-bb42-a2454a99e058", + "section_type": "main", + "text": "There is a good chance that you will be able to apply these new\ntechniques to specific problems, even while you read. If you have a computer with an\nInternet connection—so much the better, and you can read and work along at the same time.\n This short review and primer will take you on a tour of a web site called GeneNetwork that\nembeds many large data sets that are relevant to studies of behavioral variation.\n GeneNetwork is an unusual site because it contains a coherent \"universe\" of data, as well as\nmany powerful analytic tools." + }, + { + "document_id": "47a15e69-dc83-452e-95d8-c605e61f43c0", + "section_type": "main", + "text": "Search and Data Retrieval\nPoint your browser to www.genenetwork.org. This brings you by default to\nthe Search page, from which you can retrieve data from many GN data sets.\n We will focus on the default data set, defined by Species: Mouse, Group: BXD,\nType: Whole Brain, Database: INIA Brain mRNA M430 (Apr05) PDNN\nEnter “Kcnj*” into the ALL or ANY field and click the Search button. Note\nthe location and annotation of available potassium channel genes in the Search\nResults page that opens.\n Use the browser Back button to return to previous page." + }, + { + "document_id": "dbe5a781-3561-48cb-9f63-cfb4f3246434", + "section_type": "main", + "text": "The GeneNetwork database provides open access\nto BXD and other RI strain derived microarray data, single nucleotide polymorphism (SNP) data,\nand phenotypic data for quantitative trait loci analysis and gene expression correlation analyses.\n Gene expression data were exported for manually selected probes in the PDNN hippocampus\ndatabase (Hippocampus Consortium M430v2), and the PDNN whole brain database (INIA Brain\nmRNA M430). The Hippocampus database was chosen as one of the most elaborate brain databases,\nas well as most highly recommended dataset on GeneNetwork itself (http://www.genenetwork.org/\nwebqtl/main.py?FormID=sharinginfo&GN_AccessionId=112)." + }, + { + "document_id": "4edf9e5c-915d-4e38-b48f-2a0b82132bd0", + "section_type": "main", + "text": "Then, users can, with a single\nmouse-click, send these variables to the BNW network building\ninterface and start network modeling. The applications of BNW\nmay go beyond systems genetics as it can be used as a general webbased engine for causal inference in various databases.\n References\n1. The Genomes Project, C (2015) A global reference for human genetic variation. Nature\n526:68–74\n2. Visscher PM, Brown MA, McCarthy MI, Yang\nJ (2012) Five years of GWAS discovery. Am\nJ Hum Genet 90:7–24\n3." + }, + { + "document_id": "638b3811-7054-4788-a42d-2ccc7bfce1c7", + "section_type": "main", + "text": "The software behind the GUI checks the\nrelationships between subjects, traits, and data elements\nSwertz et al. Genome Biology 2010, 11:R27\nhttp://genomebiology.com/2010/11/3/R27\n\nso no ‘orphaned’ data are loaded into the database - for\nexample, genetic fingerprint data cannot be added\nbefore all information is uploaded on the markers and\nsubjects involved. Standard paths through the data\nupload process are employed to ensure that only complete and valid data are uploaded and to provide a consistent user experience.\n Biologists can use the graphical user interface to navigate and retrieve available data for analysis." + }, + { + "document_id": "bec58804-181a-4683-8e51-0ec6d381da69", + "section_type": "main", + "text": "3, 2008\n\nAnother approach to helping researchers integrate data obtained\nat different levels and in different organisms is GeneNetwork,1\na Web site and resource (www.genenetwork.org) that provides\n1\nGeneNetwork is sponsored by different grants, including grants from INIA and a Human\nBrain Project funded jointly by NIAAA, the National Institute on Drug Abuse, and the\nNational Institute of Mental Health.\n\n ROBERT W. WILLIAMS, PH.D., is a professor, and LU LU,\nM.D. , is an associate professor in the Department of Anatomy\nand Neurobiology, University of Tennessee Health Science\nCenter, Memphis, Tennessee." + }, + { + "document_id": "9d225f6f-e434-45a7-b199-f3a09eda1d04", + "section_type": "main", + "text": "GeneNetwork2 (www.genenetwork.org/) is an online data repository and tool for analyzing thousands\nof historical gene expression, physiological, and behavioral traits in the BXD recombinant inbred panel that\nsegregates C57BL/6J and DBA/2J alleles (Chesler et al. 2004; Mulligan et al. 2017).\n METHODS\nMice\nAll experiments were conducted in accordance with the NIH Guidelines for the Use of Laboratory Animals\nand were approved by the Institutional Animal Care and Use Committee at Boston University (AN-15403)." + }, + { + "document_id": "d8993417-3a27-4000-b693-6cb4662b9f80", + "section_type": "main", + "text": "The GeneNetwork.org (http://www.genenetwork.org/,\naccessed on 2 February 2022) website allows this combination of FAIR data and reproducible\ngenomes, meaning that research teams can now go back to previous datasets and reanalyse\nthem with new data and new tools. Every new dataset adds exponentially to the number of\npossible connections. In this paper, we will reanalyse drug and addiction related data from\nover a decade ago, using new genometypes for the BXD family of murine strains, as well\nas new statistical tools, showing that we can identify new quantitative trait loci (QTLs),\nresulting in highly plausible candidate genes." + }, + { + "document_id": "d0deb53b-7286-4fd0-9188-b7b9f366fd76", + "section_type": "main", + "text": "The GeneNetwork.org (http://www.genenetwork.org/,\naccessed on 2 February 2022) website allows this combination of FAIR data and reproducible\ngenomes, meaning that research teams can now go back to previous datasets and reanalyse\nthem with new data and new tools. Every new dataset adds exponentially to the number of\npossible connections. In this paper, we will reanalyse drug and addiction related data from\nover a decade ago, using new genometypes for the BXD family of murine strains, as well\nas new statistical tools, showing that we can identify new quantitative trait loci (QTLs),\nresulting in highly plausible candidate genes." + }, + { + "document_id": "beb7a242-21fe-4a66-8b44-7f228c0d3640", + "section_type": "main", + "text": "By\nintegrating datasets from an unsequenced crop plant (barley) in a database that has been designed for an animal\nmodel species (mouse) with well established genome\nsequence, we prove the importance of the concept and\npractice of modular development and interoperability of\nsoftware engineering for biological data sets.\n\n Availability and requirements\nGeneNetwork usage conditions and limitations are available from here [58]. Online tutorial accompanying this\n\nPage 9 of 11\n(page number not for citation purposes)\nBMC Genetics 2008, 9:73\n\nmanuscript can be either viewed or downloaded from the\n[59]." + }, + { + "document_id": "d2f9c5cf-835c-450a-bb42-a2454a99e058", + "section_type": "main", + "text": "Web services such as GeneNetwork and its\ncompanions—GeneWeaver (Baker et al. , 2012), WebGestalt (Zhang et al. , 2005), DAVID\n(Huang et al. , 2009a; Huang et al. , 2009b), and the Allen Brain Atlas (Lein et al. , 2007)—\ncan now be used as virtual and free laboratories to test specific biological hypothesis, or they\ncan be used to generate new ideas ab initio.\n\n Acknowledgments\nNIH-PA Author Manuscript\n\nWe would like to thank the Center for Integrative and Translational Genomics for graciously supporting the BXD\ncolony at the University of Tennessee Health Science Center." + }, + { + "document_id": "23dcf284-7c19-4335-91e1-50c3b85e6bad", + "section_type": "main", + "text": "The Mouse\nGenome Database (MGD) has structured their mouse genomic data in terms of the Mammalian Phenotype Ontology\n[10]. Similarly, the Rat Genome Database (RGD) [11] also\ndeveloped a phenome database, integrated with its genomic\ndata. In humans, the GeneNetwork (WebQTL) provides a\ndatabase of complex traits with mappings to quantitative trait\nloci [12]. And several studies have focused on integrating\nhuman phenome and genome resources. For example, Butte\net al. created a large-scale phenome–genome network by\nintegrating the Unified Medical Language System with human\nmicroarray gene expression data [13]; and Aerts et al." + }, + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "section_type": "main", + "text": "The authors of any related manuscript (or the lab group who gathered\nthe data) are shown, as well as the title and links to the published paper (Figure 4C). There is\nalso a button to add the trait to a collection (see below; Figure 4D), and to view this trait in the\n4\nbioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint\n(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.\n\n earlier version of GeneNetwork, GN1 (Figure 4E)." + }, + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "section_type": "main", + "text": "The authors of any related manuscript (or the lab group who gathered\nthe data) are shown, as well as the title and links to the published paper (Figure 4C). There is\nalso a button to add the trait to a collection (see below; Figure 4D), and to view this trait in the\n4\nbioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint\n(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.\n\n earlier version of GeneNetwork, GN1 (Figure 4E)." + }, + { + "document_id": "638b3811-7054-4788-a42d-2ccc7bfce1c7", + "section_type": "main", + "text": "This option enables upload of whole lists of traits and\nsubjects from a simple tab-delimited format (3), which\ncan easily be produced with Excel or R; MOLGENIS\nautomatically generates online documentation describing\nthe expected format (4). Subsequently, the protocol\napplications involved can be added with the resulting\nraw data (for example, genetic fingerprints, expression\nprofiles) and processed data (for example, normalized\nprofiles, QTL profiles, metabolic networks). These data\ncan be uploaded, again using the common tab-delimited\nformat or custom parsers (5) that bioinformaticians can\n‘plug-in’ for specific file formats (for example, Affymetrix CEL files)." + }, + { + "document_id": "638b3811-7054-4788-a42d-2ccc7bfce1c7", + "section_type": "main", + "text": "They can\nuse the advanced search options (6) to find certain\ntraits, subjects, or data. Using menu option ‘file|download’ (7) they can download visible/selected (8) data as\ntab-delimited files to analyze them in third party software. Bioinformaticians can ‘plug-in’ a custom-built\nscreen (see ‘customization’ section) that allows processing of selected data inside the GUI, for example, visualizing a correlation matrix as a graph (9) without the\nadditional steps of downloading data and uploading it\ninto another tool. Biologists can create link-outs to\nrelated information, for example, to probes in GeneNetwork.org (not shown)." + }, + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "section_type": "main", + "text": "If you have chosen a\nrecombinant inbred set, your data will be displayed in a form where you can\n\nCurr Protoc Neurosci. Author manuscript; available in PMC 2018 April 10.\n Parker et al.\n\n Page 5\n\nAuthor Manuscript\n\nconfirm and/or edit them. GeneNetwork provides sample data so that you can\nensure you have the correct format." + }, + { + "document_id": "4a34fec8-ff56-4ec0-b51c-c21c130e53dd", + "section_type": "main", + "text": "The data are stored in a SQL-based database, and a web interface\n(http://genomics.cnr.berkeley.edu/BarleyTag/unigene result.pl) was developed to\naid in searching the results from the database. Its availability will facilitate making\ndetailed comparisons of the protein and DNA data available for these plant species.\n Queries can be performed using various options, including species, percent identity, length of a match, sequence type (CDS or EST), or by key word. The database\nwill be continuously updated as additional sequence information becomes available." + }, + { + "document_id": "e17b5b05-4676-4b3d-a625-74d453c342bd", + "section_type": "main", + "text": "The data are stored in a SQL-based database, and a web interface\n(http://genomics.cnr.berkeley.edu/BarleyTag/unigene result.pl) was developed to\naid in searching the results from the database. Its availability will facilitate making\ndetailed comparisons of the protein and DNA data available for these plant species.\n Queries can be performed using various options, including species, percent identity, length of a match, sequence type (CDS or EST), or by key word. The database\nwill be continuously updated as additional sequence information becomes available." + }, + { + "document_id": "fa8bba46-ce94-439a-a676-35187a3abcbf", + "section_type": "main", + "text": "If you cannot find the\ncorrect identifier or your identifier is not supported try converting at a website such as NIAID’s DAVID website (https://\ndavid.ncifcrf.gov/) which has a nice ID conversion tool [26].\n\n Acknowledgements\nGeneWeaver is currently supported by NIH AA18776 jointly\nfunded by NIAAA/NIDA.\n References\n1. Smith CL, Eppig JT (2012) The Mammalian\nPhenotype Ontology as a unifying standard for\nexperimental and high-throughput phenotyping data. Mamm Genome 23(9–10):653–668.\n doi:10.1007/s00335-012-9421-3\n2." + }, + { + "document_id": "85ee9743-b34d-4d49-9017-d7d2e5d4b996", + "section_type": "main", + "text": "This option enables upload of whole lists of traits\nand subjects from a simple tab-delimited format (3), which can easily\nbe produced with Excel or R; MOLGENIS automatically generates online documentation describing the expected format (4). Subsequently,\nthe protocol applications involved can be added with the resulting raw\ndata (for example, genetic fingerprints, expression profiles) and processed data (for example, normalized profiles, QTL profiles, metabolic\nnetworks). These data can be uploaded, again using the common tabdelimited format or custom parsers (5) that bioinformaticians can ‘plugin’ for specific file formats (for example, Affymetrix CEL files)." + }, + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "section_type": "main", + "text": "BASIC PROTOCOL TITLE: Genetic mapping and\nsystems genetics using GeneNetwork\nIntroductory paragraph\nGeneNetwork (www.genenetwork.org) is a free online resource for systems genetics that\nstores and analyzes behavioral phenotypes, physiological phenotypes, and large gene\nexpression data-sets with matched genomic data for numerous species, including mice.\n GeneNetwork can analyze a variety of mouse mapping populations, (including F2\n\nCurr Protoc Neurosci. Author manuscript; available in PMC 2018 April 10.\n Parker et al." + }, + { + "document_id": "bb5ed347-0f54-431a-a125-97b9d762b003", + "section_type": "main", + "text": "GeneNetwork’s WebQTL provides a direct link to the\nUniversity of California, Santa Cruz Genome Browser (URL\n\nThe UCSC Genome Browser also provides links to the\nNational Center for Biotechnology Information resources\nThe Journal of Undergraduate Neuroscience Education (JUNE), Fall 2009, 8(1):A26-A31\n\nsuch as Entrez Gene and PUBMED (URLs in References).\n These resources allow the students to discover more\ninformation about their highly expressed gene including its\nnucleotide and amino acid sequence, as well as find\narticles about their gene that provide a deeper intellectual\ninvolvement in this exercise.\n Our website has already been populated with some of\nthese materials http://mdcune.psych.ucla.edu/." + } + ], + "document_id": "FA1E32391509D1EEAEBB70D3014C444A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "GeneNetwork", + "Record&ID&18494", + "Search&page", + "Add", + "Trait", + "RI&strain", + "F1", + "founder&strain", + "Batch&Submission", + "GeneWeaver", + "GeneSet", + "Project", + "Cocaine&Addiction", + "Chronic&Cocaine", + "Species", + "Mouse", + "BXD", + "HXB", + "Phenotypes", + "genotypes", + "mRNA", + "methylated&DNA", + "protein", + "metagenomic", + "metabolome" + ], + "metadata": [ + { + "object": "Both ANXA11 G38R protein and ANXA11 D40G protein showed a shorter half-life than ANXA11 wild type protein, while there was no difference between ANXA11 G38R protein and ANXA11 D40G protein. There was no visible insoluble substance in the NP-40 lysates for ANXA11 wild type protein, ANXA11 G38R protein and ANXA11 D40G protein. G38R and D40G mutations reduce the stability of ANXA11 protein.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab106261" + }, + { + "object": "We showed that Rheumatoid was more likely with the AA genotype compared with the AG genotype of SNP rs2977537, and with the TT genotype, or the GG genotype compared with the GT genotype of rs2929973, and with the AA genotype or GG genotype vs the AG genotype of rs2977530", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1013556" + }, + { + "object": "mRNA and protein expression levels of DNMT3b were upregulated in genotype 1b and 3a HCV-infected hepatocellular carcinoma patients as compared to control. DNMT3b mRNA levels did not change in genotypes 2a, 3, and 4, but were upregulated at the protein level by genotype 1b, 2a, and 3a. No differences were seen for genotypes 5 and 7.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab503048" + }, + { + "object": "The genotype GG group had higher consumption of Remifentanil than the genotype AA group P<0.05, but the genotype AG group was not different from the genotype AA and GG groups P>0.05. The analepsia time, autonomous respiratory recovery time, and orientation recovery time in the genotype GG group were longer than in the genotype AA group P<0.05, but the genotype AG group was not different from the genotype AA and GG.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab818259" + }, + { + "object": "plasma exposure resulted in expression of unfolded protein response UPR proteins such as glucoserelated protein 78 GRP78, protein kinase R PKRlike ER kinase PERK, and inositolrequiring enzyme 1 IRE1. Elevated expression of spliced Xbox binding protein 1 XBP1 and CCAAT/enhancerbinding protein homologous protein CHOP further confirmed that ROS generatedby NTGP induces apoptosis through the ER stress", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab599086" + }, + { + "object": "MST3 protein coats lipid droplets in mouse liver cells from mice fed a high-fat diet. MST3 fully colocalized with ADRP, the main LD-coating protein in mouse liver. No MST3 protein was detected in the cytosolic fraction. High mRNA and protein expression of MST3 was also found in organs that do not accumulate significant amounts of intracellular LDs.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab504219" + }, + { + "object": "ID1 protein and mRNA expression decreased during myoblast differentiation. Lactacystin reversed the decrease in ID1 protein but not in ID1 mRNA expression, but cycloheximide prevented this reversal. Direct incubation of ID1 protein with proteasomes from myoblasts did not show differentiation stage-associated degradation of ID1 protein. Ubiquitinated ID1 protein was not detected in lactacystin-treated myoblasts", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab369968" + }, + { + "object": "plasma exposure resulted in expression of unfolded protein response UPR proteins such as glucoserelated protein 78 GRP78, protein kinase R PKRlike ER kinase PERK, and inositolrequiring enzyme 1 IRE1. Elevated expression of spliced Xbox binding protein 1 XBP1 and CCAAT/enhancerbinding protein homologous protein CHOP further confirmed that ROS generatedby NTGP induces apoptosis through the ER stress", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab599087" + }, + { + "object": "For the MYF5 gene, the C5084T and T5127A SNP genotypes were significantly associated with carcass traits of pigeons. Within those two SNPs, the BB genotype showed relatively higher trait association values than those of AA or AB genotypes. No significant association was observed between the KLF15 SNP genotypes and carcass traits.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab300762" + }, + { + "object": "For the MYF5 gene, the C5084T and T5127A SNP genotypes were significantly associated with carcass traits of pigeons. Within those two SNPs, the BB genotype showed relatively higher trait association values than those of AA or AB genotypes. No significant association was observed between the KLF15 SNP genotypes and carcass traits.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab300761" + } + ], + "question": "How can I add a new species to the GeneNetwork database?", + "subquestions": null, + "task_id": "FA1E32391509D1EEAEBB70D3014C444A", + "usage": { + "chatgpt": 5344, + "gpt-4": 3726, + "gpt-4-turbo-preview": 2725 + }, + "user_id": 2 + }, + "document_id": "FA1E32391509D1EEAEBB70D3014C444A", + "task_id": "FA1E32391509D1EEAEBB70D3014C444A" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/09.json b/gnqa/paper1_eval/src/data/responses/general/experts/09.json new file mode 100644 index 0000000..966d755 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/09.json @@ -0,0 +1,394 @@ +{ + "created_at": "2024-06-01T14:33:22.252190Z", + "data": { + "amplify": false, + "answer": "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "context": { + "0381d0d4-717f-470b-a8d4-562475cf58bb": [ + { + "document_id": "0381d0d4-717f-470b-a8d4-562475cf58bb", + "text": "Oxidative stress and mitochondrial DNA\n\nNot long after it was discovered that mitochondria have their own genetic apparatus, Harman proposed that mitochondria play a central role in the free radical theory of aging [16].This idea was developed further by Miquel et al. [330], and the notion that mtDNA mutagenesis played a role in aging took hold.The phenotypical importance of mutations in mtDNA was demonstrated by Wallace et al. [331] and Holt et al. [332], who first showed that Leber's hereditary optic neuropathy and mitochondrial myopathies were caused by mtDNA mutations (reviewed in [333]).Because mtDNA is so close to the site of mitochondrial ROS production, it is exposed to considerably higher oxidative stress, resulting in 3-fold higher levels of DNA oxidative damage (the previously quoted 20-fold figure is apparently due to an isolation artifact [334,335]).In the 1990s a series of papers reported that the frequency of mitochondrial DNA deletions increases dramatically with age, being essentially undetectable in young individuals and reaching levels as high as 2% of mtDNA in old individuals.This age-related increase in mtDNA deletions was found in organisms as diverse as worms, mice, and humans (reviewed in [24,336]).The same is also true with mtDNA point mutations [337,338].Certain mtDNA polymorphisms have been found in increased frequency in centenarians, implying a protective effect during aging [339][340][341].Similar protective effects of mtDNA polymorphisms have been reported for the age-related neurodegenerative condition, Parkinson's disease [342]." + } + ], + "21d2cb60-92ab-4fbb-a3a1-85d3424881c1": [ + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "text": "\n\nVariation in the structure and function of mitochondria underlies variation in organismal energetics broadly (Seebacher et al., 2010) and evidence for the importance of mitochondrial function in the evolution of natural populations continues to accumulate (Ballard and Melvin, 2010;Glanville et al., 2012;Hicks et al., 2012;Kurbalija Novičić et al., 2015).For example, variation in mitochondrial DNA sequences (mtDNA) can determine whole-organism metabolism, i.e., the rate at which organisms process energy from their environment, a phenomenon widespread across animal taxa (Arnqvist et al., 2010;Ballard et al., 2007;Ballard and Pichaud, 2014;Havird et al., 2019;Hood et al., 2018;James et al., 2016;Wolff et al., 2014).Specifically, mtDNA sequence variants are linked to functional metabolic differences in fish (Chapdelaine et al., 2020;Flight et al., 2011;Healy et al., 2019), birds (Scott et al., 2011), and mammals (Fontanillas et al., 2005), including humans (Amo and Brand, 2007;Dato et al., 2004;Niemi et al., 2003;Tranah et al., 2011).These mtDNA variants are often correlated with environmental factors such as temperature and altitude (Storz et al., 2010).However, other studies attempting to link mitochondrial function to mitochondrial DNA (mtDNA) sequence variation or environmental factors have offered mixed reports (Amo and Brand, 2007;Flight et al., 2011;Fontanillas et al., 2005;Hicks et al., 2012)." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "text": "\n\nThe results here point to several potentially fruitful research directions.We have identified how nonsynonymous mutations in the mitochondrial genome associate with variation in whole-organism metabolism (including CytB, ND1, ND5 and ND6).A next step will be to characterize the molecular details of how these changes affect molecular function.It would also be beneficial to describe how variation in cellular oxygen consumption rate scales up to determine whole-organism metabolic rate across a range of temperatures, thus identifying potential mismatches across levels of organization that may impact organismal performance (Gangloff and Telemeco, 2018).While the interconnected processes that shape organismal and population-level responses to environmental variation do not lend themselves to simple narratives, and many molecular processes interact to produce the emergent ecotypic divergences at the phenotypic level, it is clear that the mitochondria play a central role even as that role may change across populations and ecological contexts (Fig. 1).Research within well-characterized natural systems, such as these garter snake populations, can offer illustrative case studies of how mitochondria respond to their environments, and thus impact physiological pathways and evolutionary patterns, creating variation in life histories and aging." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "text": "\n\nDespite the complexities underlying observed variation in mitochondrial function, recent work has demonstrated examples of how evolution and plasticity in mitochondrial function across populations within a species can shape life histories.For example, evidence from Drosophila has demonstrated the effect of temperature on components of the ETC and has linked mtDNA variants to metabolic thermosensitivity (Pichaud et al., 2012), to differences in whole-organism metabolic rates (Kurbalija Novičić et al., 2015), and to fitness-related traits (Ballard et al., 2007;Pichaud et al., 2011;Pichaud et al., 2010).In general, studies in birds and mammals demonstrate that mitochondria of longer-lived species are more efficient in ATP production, produce less reactive oxygen species, and demonstrate increased antioxidant capacities (Barja and Herrero, 2000;Ku et al., 1993;Lambert et al., 2007).While some studies in lizards and snakes demonstrate a similar pattern (Olsson et al., 2008;Robert et al., 2007), the extent to which these results are generalizable across vertebrate taxa is not yet known.The diversity of life-history traits and immense variation in longevity demonstrated by reptiles, both within and among species, make these taxa ideal candidates for understanding how variation in mitochondrial physiology drives this variation in whole-organism traits (reviewed in Hoekstra et al., 2019).Such work has moved to the forefront with a recent focus on the ecological and evolutionary significance of aging processes in wild populations (reviewed in Nussey et al., 2013;Fletcher and Selman, 2015;Gaillard and Lemaître, 2020)." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "text": "\n\nOver evolutionary time, differential mortality rates are a selective force in shaping genetic structure.This results in divergence of a variety of physiological networks that shape, ultimately, patterns of aging and longevity in different habitats (Monaghan et al., 2008;Stojković et al., 2017).Such selective pressures can have differential effects on the nuclear and mitochondrial genomes (McKenzie et al., 2019;Wolff et al., 2014).Genetic variation in the mitochondrial genome is known to drive mitochondrial function in many species (Ballard and Melvin, 2010;McKenzie et al., 2019;Novelletto et al., 2016) and we find this in our system as well.Whole organism metabolic rate varies with the mitochondrial genome haplogroups we identified in this study.T. elegans individuals with the introgressed T. sirtalis mitochondrial genome had the lowest metabolic rate and had 68 amino acid changes in the ETC genes relative to the T. elegans mitochondrial genomes.As species divergence are a continuation of population divergence, this introgression provides additional insight into how genetic variation can alter mitochondrial function.Whether the lower metabolic rate in our snakes with the introgressed mitochondrial genome is due to the fixed amino acid changes between the species or a mismatch between the coadapted nuclear and mitochondrially-encoded ETC proteins that could alter function of the mitochondria (Burton et al., 2013;Haenel, 2017;Rawson and Burton, 2002;Toews et al., 2014;Wolff et al., 2014) will require further comparisons to T. sirtalis individuals." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "text": "\n\nBuilding on previous work in this system, the current study tests three primary hypotheses about how variation in mtDNA and mitochondrial function relate to variation in life-history traits and aging within this system (Fig. 1): (1) First, we test whether rates of cellular oxygen consumption in isolated immune cells exhibit patterns that are consistent with the hypothesis that cellular processes drive whole-organism senescence and aging, and if these patterns differ between the SA and FA ecotypes and between sexes.By measuring basal, ATP-production associated, and maximal rates of cellular oxygen consumption, we further test for evidence that phenotypic divergence is dependent on a specific aspect of oxidative phosphorylation within immune cells.The energetics of these cells are particularly important given their essential role in modulating disease and infection, important factors contributing to senescence (Metcalf et al., 2019).We predict that SA snakes will maintain levels of cellular oxygen consumption across age, whereas the FA snakes will show a decline with age, especially in ATP-associated rates, possibly due to continual degradation of electron transport chain functionality from accumulating oxidative damage and reduced DNA repair mechanisms (Robert and Bronikowski, 2010;Schwartz and Bronikowski, 2013). ( 2) Second, we expand our mitochondrial genomics dataset to quantify mtDNA genetic structure across the landscape and test whether mtDNA haplotypes, and alleles at a nonsynonymous SNP in the Cytochrome B (CytB) gene correlate with aging ecotypes. (3) Third, we test the hypothesis that variation in mtDNA correlates with whole-organism variation in metabolic rates, suggesting a pathway linking mitochondrial genetic variation in mtDNA to whole-organism energetics.We first test whether different haplotypes differ in resting metabolic rate.Then, we test the effects of the nonsynonymous SNP in CytB on resting metabolic rate.The CytB gene encodes a component of complex III of the ETC, and was previously found to segregate between these life-history ecotypes (Schwartz et al., 2015).This SNP results in an amino acid substitution from isoleucine (aliphatic, hydrophobic) to threonine (hydrophilic) on a region that comes into close contact with a nuclear-encoded subunit (Schwartz et al., 2015).We combine previously published and new data on whole-organism resting metabolic rates (oxygen consumption) to test for the effects of this nonsynonymous mutation in three populations where we find heterogeneity at this nucleotide, thus allowing us to disentangle the effects of shared environment (population) from sequence variation (SNP).We predict that this SNP will correlate with variation in whole-organism metabolic rate, demonstrating a putatively adaptive difference between the derived and ancestral sequence.By utilizing this integrative data setfrom genes to organelles to whole organisms to populationsin a known life-history context, we are able to test hypotheses across levels of organization to provide a more complete picture of the complicated story of mitochondria and life history (Havird et al., 2019)." + } + ], + "253fad94-3be6-4362-b56f-f00c9c5705e6": [ + { + "document_id": "253fad94-3be6-4362-b56f-f00c9c5705e6", + "text": "mtDNA Diversity\n\nUnlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage.It is unclear what advantage a uniparental mtDNA transmission confers, but one possibility is to minimize the number of distinct genomes to maximize the efficiency of a multi-genomic system (Hill et al. 2019).In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca and O'Farrell 2012; Rojansky et al. 2016).Paternal mitochondria from sperms that enter into the egg during fertilization are actively and selectively eliminated via mitophagy through two E3 ligases, PARKIN, and MUL1 (Rojansky et al. 2016).PARKIN and MUL1 serve redundant purposes, and mitophagy becomes insufficient to eliminate paternal mtDNA only in the absence of both (Rojansky et al. 2016).Even though oocytes have at least a thousand-fold more mitochondria than a sperm cell (Rojansky et al. 2016) and heteroplasmy levels would be very low if paternal mtDNA were to contaminate the embryo, the results can still be non-trivial.However, challenging this notion, a recent study provides evidence of potential paternal transmission (Luo et al. 2018), but awaits further corroborating studies (Lutz-Bonengel and Parson 2019)." + }, + { + "document_id": "253fad94-3be6-4362-b56f-f00c9c5705e6", + "text": "\n\nMtDNA exhibit a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms (van Oven and Kayser 2009; Wallace 1999; Wallace and Chalkia 2013).In fact, the co-evolution of the mitonuclear genomes has been proposed to be driven by mtDNA mutations that select for compensatory changes in the nuclear genome (Havird and Sloan 2016).Populations that share similar mtDNA polymorphisms can be clustered into distinct haplogroups that are designated using all letters of the alphabet (i.e., A through Z).The mtDNA haplogroups represent major branch points on the mitochondrial phylogenetic tree that have strong regional ties around the globe, thus supporting the concept of a 'mitochondrial eve' (Wallace 1999).Haplogroups present inherently different mitonuclear interactions (Zaidi and Makova 2019), which eventually affect the aging process (Wolff et al. 2016).For example, one haplogroup commonly found in Ashkenazi Jews can interact with a specific enrichment of an amino acid sequence in complex I, and result in altered susceptibility to type 2 diabetes mellitus (Gershoni et al. 2014).The effect of mitonuclear compatibility on lifespan is influenced by environmental cues in flies (Drummond et al. 2019).It is unclear if mitonuclear compatibility is invariable throughout an organism's life, or antagonistically pleiotropic during aging, making it a difficult moving target to understand." + } + ], + "2f39f55f-2604-49d4-9589-0e1403b84d7a": [ + { + "document_id": "2f39f55f-2604-49d4-9589-0e1403b84d7a", + "text": "\n\nBackground: The accumulation of mitochondrial DNA (mtDNA) mutations, and the reduction of mtDNA copy number, both disrupt mitochondrial energetics, and may contribute to aging and age-associated phenotypes.However, there are few genetic and epidemiological studies on the spectra of blood mtDNA heteroplasmies, and the distribution of mtDNA copy numbers in different age groups and their impact on age-related phenotypes.In this work, we used whole-genome sequencing data of isolated peripheral blood mononuclear cells (PBMCs) from the UK10K project to investigate in parallel mtDNA heteroplasmy and copy number in 1511 women, between 17 and 85 years old, recruited in the TwinsUK cohorts." + } + ], + "4a17ce5c-55df-4aa0-a664-f6a03238d332": [ + { + "document_id": "4a17ce5c-55df-4aa0-a664-f6a03238d332", + "text": "Discussion\n\nTwo significant questions are raised by the findings that mitochondrial DNA can integrate into the nucleus.Firstly, is this an extraordinarily rare event or is it occurring continually and at high frequency?Secondly, can such an event have pathological consequences to the organism?" + } + ], + "4f010a74-a9b4-4538-94f7-ae8f35c8b96e": [ + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "text": "Phylogeny\n\nThe mtDNA is maternally inherited (120) by offspring through the oocyte cytoplasm; namely, the mother transmits her mtDNAs to all of her offspring, and her daughters transmit their mtDNAs to the next generation.This is the consequence of the fact that the mature oocyte such as mouse (304) or bovine (144) contains lOO-1,000 times more mtDNA than is found in somatic cells.Hence, the few sperm mtDNAs that enter the egg (130) have little effect on the genotype.The maternal inheritance results in sequentially diverged mtDNA polymorphism of modern human, as shown in Figure 2. The polymorphism derives from the combinations of small deletions and additions of <14 bp in noncoding region and base substitutions including some point mutations in coding region." + }, + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "text": "\n\nThere have been few reports on distinct correlation between mitochondrial morphology and human aging, except changes in number and size of mitochondria associated with age.Concerning the gross structure of mitochondria, the overwhelming importance of the cell nucleus in mitochondrial biogenesis should be noted, because the major parts of mitochondrial proteins are encoded by nuclear genes that are stable during life with the efficient repair mechanism for nDNA." + }, + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "text": "\n\nEarly data on DNA polymorphism detected by restriction endonuclease (263) have suggested that the evolutionary change of mtDNA in higher animals occurs mainly by nucleotide substitution rather than by deletion and insertion.The mtDNA nucleotide sequence evolves 6-17 times faster than comparable nuclear DNA gene sequences (51,52,405).Rapid evolution of mtDNA of higher primates including human, 0.02 base substitutions per site per million years, was calculated from the restriction map of mtDNA (51).Because orthodox recombination mechanism appears to be absent in mtDNA (128), germline mutation seems to go down to posterity as maternal inheritance from our common ancestor (57)." + } + ], + "612a70c6-2f42-492f-9f23-0d5e9296919e": [ + { + "document_id": "612a70c6-2f42-492f-9f23-0d5e9296919e", + "text": "\n\nA number of conclusions may be drawn from these results.Firstly, the data begin to answer the question of how closely mtDNA replication is kept in synchrony with nuclear DNA replication: it would appear to be regulated not by direct coupling to the nuclear DNA replication, but rather by the cell mass to be serviced by mitochondria." + } + ], + "65c8287b-eb19-437a-b9ca-5aaa8664d429": [ + { + "document_id": "65c8287b-eb19-437a-b9ca-5aaa8664d429", + "text": "\n\nIt may be that high mtDNA levels are indeed indicative of compromised mitochondria, but that the underlying defects are unrelated to alterations in the DNA sequence.Alternatively, elevated quantities of mtDNA might be associated with increased metabolic requirements of the embryo, rather than organelles of suboptimal function.It is possible that embryos produced by older oocytes are under some form of stress and therefore have larger energy requirements.Functional experiments will be required to address these questions.Whatever the underlying basis, the current study has unequivocally demonstrated that female reproductive aging is associated with changes in the mtDNA content at the blastocyst stage." + } + ], + "67ec2631-aa17-436e-800b-1bc046fb5b19": [ + { + "document_id": "67ec2631-aa17-436e-800b-1bc046fb5b19", + "text": "\n\nAge-associated alterations of the mitochondrial genome occur in several different species; however, their physiological relevance remains unclear.The age-associated changes of mitochondrial DNA (mtDNA) include nucleotide point mutations and modifications, as well as deletions.In this review, we summarize the current literature on age-associated mtDNA mutations and deletions and comment on their abundance.A clear need exists for a more thorough evaluation of the total damage to the mitochondrial genome that accumulates in aged tissues.᭧ 1997 Elsevier Science Inc." + } + ], + "8a9fe1bc-7fa3-40ee-ade0-9a498bcf9def": [ + { + "document_id": "8a9fe1bc-7fa3-40ee-ade0-9a498bcf9def", + "text": "Mitochondrial genetics\n\nOne underexplored avenue for determining maternal risk for preterm birth involves the influence of the mitochondrial genome.The high mutation rate of mito chondrial DNA (mtDNA), together with the fact that most of its encoded proteins are evolutionarily con served, allowing for the selection of neutral or beneficial variants, has generated interest in defining human mtDNA variations and their roles in human biology [58]." + } + ], + "aa942230-9a43-4b5f-90d9-96d364861a57": [ + { + "document_id": "aa942230-9a43-4b5f-90d9-96d364861a57", + "text": "\n\nClearly, as mitochondrial metabolic and genetic therapies advance for treating mitochondrial disease, they will also be available to enhance the personal lives of others.However, mitochondrial genetic variation appears to have been one of the primary factors that permitted our ancestors to adapt to new environments, survive adverse conditions, and multiple throughout the globe.Is it possible that by taking over control of individual mtDNA variation, we might also be setting our species on the road to functional decline and ultimately extinction?" + }, + { + "document_id": "aa942230-9a43-4b5f-90d9-96d364861a57", + "text": "Mitochondrial therapeutics and performance enhancement\n\nIt is now clear that not all mtDNA variation is deleterious.Indeed, about 25% of all ancient mtDNA variation appears to have caused functional mitochondrial changes and thus been adaptive.Those mtDNA variants that are adapted to warm climates have mtDNA variants that result in tightly coupled OXPHOS, thus maximizing ATP output and minimizing heat production.The presence of these mtDNAs permits maximum muscle performance but also predispose sedentary individuals that consume excess calories to multiple problems.They would be prone to be overweight and their mitochondria would generate excessive ROS, thus making them susceptible to a variety of degenerative diseases, cancer and premature aging.Partially uncoupled mitochondria generate more heat, but at the expense of ATP production.Individual's with these variants are better able to tolerate the cold, and are less prone to obesity.They also generate less ROS making then resistant to degenerative diseases and aging.Finally, the mitochondria are why we breathe.Hence, mitochondrial variation might be an important factor in individual predisposition to altitude sickness." + } + ], + "b0af29ac-0997-416d-907a-6caba940536d": [ + { + "document_id": "b0af29ac-0997-416d-907a-6caba940536d", + "text": "\n\nHuman mtDNA codes for 13 essential polypeptide components of the mitochondrial oxidative phosphorylation (OXPHOS) system.mtDNA undergoes strict maternal inheritance, resulting in the absence of bi-parental recombination (Elson et al., 2001) and has a high mutation rate (Tuppen et al., 2010).As such, the evolution of mtDNA is characterised by the emergence of distinct lineages (or haplogroups) (Hernstadt et al., 2002).This results in high levels of mtDNA variation at the population level despite its rather small size, which is also illustrated by the large number of sub-haplogroups (van Oven and Kayser, 2009).Africa" + } + ] + }, + "data_source": [ + { + "document_id": "253fad94-3be6-4362-b56f-f00c9c5705e6", + "section_type": "main", + "text": "mtDNA Diversity\n\nUnlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage.It is unclear what advantage a uniparental mtDNA transmission confers, but one possibility is to minimize the number of distinct genomes to maximize the efficiency of a multi-genomic system (Hill et al. 2019).In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca and O'Farrell 2012; Rojansky et al. 2016).Paternal mitochondria from sperms that enter into the egg during fertilization are actively and selectively eliminated via mitophagy through two E3 ligases, PARKIN, and MUL1 (Rojansky et al. 2016).PARKIN and MUL1 serve redundant purposes, and mitophagy becomes insufficient to eliminate paternal mtDNA only in the absence of both (Rojansky et al. 2016).Even though oocytes have at least a thousand-fold more mitochondria than a sperm cell (Rojansky et al. 2016) and heteroplasmy levels would be very low if paternal mtDNA were to contaminate the embryo, the results can still be non-trivial.However, challenging this notion, a recent study provides evidence of potential paternal transmission (Luo et al. 2018), but awaits further corroborating studies (Lutz-Bonengel and Parson 2019)." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "section_type": "main", + "text": "\n\nVariation in the structure and function of mitochondria underlies variation in organismal energetics broadly (Seebacher et al., 2010) and evidence for the importance of mitochondrial function in the evolution of natural populations continues to accumulate (Ballard and Melvin, 2010;Glanville et al., 2012;Hicks et al., 2012;Kurbalija Novičić et al., 2015).For example, variation in mitochondrial DNA sequences (mtDNA) can determine whole-organism metabolism, i.e., the rate at which organisms process energy from their environment, a phenomenon widespread across animal taxa (Arnqvist et al., 2010;Ballard et al., 2007;Ballard and Pichaud, 2014;Havird et al., 2019;Hood et al., 2018;James et al., 2016;Wolff et al., 2014).Specifically, mtDNA sequence variants are linked to functional metabolic differences in fish (Chapdelaine et al., 2020;Flight et al., 2011;Healy et al., 2019), birds (Scott et al., 2011), and mammals (Fontanillas et al., 2005), including humans (Amo and Brand, 2007;Dato et al., 2004;Niemi et al., 2003;Tranah et al., 2011).These mtDNA variants are often correlated with environmental factors such as temperature and altitude (Storz et al., 2010).However, other studies attempting to link mitochondrial function to mitochondrial DNA (mtDNA) sequence variation or environmental factors have offered mixed reports (Amo and Brand, 2007;Flight et al., 2011;Fontanillas et al., 2005;Hicks et al., 2012)." + }, + { + "document_id": "253fad94-3be6-4362-b56f-f00c9c5705e6", + "section_type": "main", + "text": "\n\nMtDNA exhibit a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms (van Oven and Kayser 2009; Wallace 1999; Wallace and Chalkia 2013).In fact, the co-evolution of the mitonuclear genomes has been proposed to be driven by mtDNA mutations that select for compensatory changes in the nuclear genome (Havird and Sloan 2016).Populations that share similar mtDNA polymorphisms can be clustered into distinct haplogroups that are designated using all letters of the alphabet (i.e., A through Z).The mtDNA haplogroups represent major branch points on the mitochondrial phylogenetic tree that have strong regional ties around the globe, thus supporting the concept of a 'mitochondrial eve' (Wallace 1999).Haplogroups present inherently different mitonuclear interactions (Zaidi and Makova 2019), which eventually affect the aging process (Wolff et al. 2016).For example, one haplogroup commonly found in Ashkenazi Jews can interact with a specific enrichment of an amino acid sequence in complex I, and result in altered susceptibility to type 2 diabetes mellitus (Gershoni et al. 2014).The effect of mitonuclear compatibility on lifespan is influenced by environmental cues in flies (Drummond et al. 2019).It is unclear if mitonuclear compatibility is invariable throughout an organism's life, or antagonistically pleiotropic during aging, making it a difficult moving target to understand." + }, + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "section_type": "main", + "text": "Phylogeny\n\nThe mtDNA is maternally inherited (120) by offspring through the oocyte cytoplasm; namely, the mother transmits her mtDNAs to all of her offspring, and her daughters transmit their mtDNAs to the next generation.This is the consequence of the fact that the mature oocyte such as mouse (304) or bovine (144) contains lOO-1,000 times more mtDNA than is found in somatic cells.Hence, the few sperm mtDNAs that enter the egg (130) have little effect on the genotype.The maternal inheritance results in sequentially diverged mtDNA polymorphism of modern human, as shown in Figure 2. The polymorphism derives from the combinations of small deletions and additions of <14 bp in noncoding region and base substitutions including some point mutations in coding region." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "section_type": "main", + "text": "\n\nThe results here point to several potentially fruitful research directions.We have identified how nonsynonymous mutations in the mitochondrial genome associate with variation in whole-organism metabolism (including CytB, ND1, ND5 and ND6).A next step will be to characterize the molecular details of how these changes affect molecular function.It would also be beneficial to describe how variation in cellular oxygen consumption rate scales up to determine whole-organism metabolic rate across a range of temperatures, thus identifying potential mismatches across levels of organization that may impact organismal performance (Gangloff and Telemeco, 2018).While the interconnected processes that shape organismal and population-level responses to environmental variation do not lend themselves to simple narratives, and many molecular processes interact to produce the emergent ecotypic divergences at the phenotypic level, it is clear that the mitochondria play a central role even as that role may change across populations and ecological contexts (Fig. 1).Research within well-characterized natural systems, such as these garter snake populations, can offer illustrative case studies of how mitochondria respond to their environments, and thus impact physiological pathways and evolutionary patterns, creating variation in life histories and aging." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "section_type": "main", + "text": "\n\nDespite the complexities underlying observed variation in mitochondrial function, recent work has demonstrated examples of how evolution and plasticity in mitochondrial function across populations within a species can shape life histories.For example, evidence from Drosophila has demonstrated the effect of temperature on components of the ETC and has linked mtDNA variants to metabolic thermosensitivity (Pichaud et al., 2012), to differences in whole-organism metabolic rates (Kurbalija Novičić et al., 2015), and to fitness-related traits (Ballard et al., 2007;Pichaud et al., 2011;Pichaud et al., 2010).In general, studies in birds and mammals demonstrate that mitochondria of longer-lived species are more efficient in ATP production, produce less reactive oxygen species, and demonstrate increased antioxidant capacities (Barja and Herrero, 2000;Ku et al., 1993;Lambert et al., 2007).While some studies in lizards and snakes demonstrate a similar pattern (Olsson et al., 2008;Robert et al., 2007), the extent to which these results are generalizable across vertebrate taxa is not yet known.The diversity of life-history traits and immense variation in longevity demonstrated by reptiles, both within and among species, make these taxa ideal candidates for understanding how variation in mitochondrial physiology drives this variation in whole-organism traits (reviewed in Hoekstra et al., 2019).Such work has moved to the forefront with a recent focus on the ecological and evolutionary significance of aging processes in wild populations (reviewed in Nussey et al., 2013;Fletcher and Selman, 2015;Gaillard and Lemaître, 2020)." + }, + { + "document_id": "4a17ce5c-55df-4aa0-a664-f6a03238d332", + "section_type": "main", + "text": "Discussion\n\nTwo significant questions are raised by the findings that mitochondrial DNA can integrate into the nucleus.Firstly, is this an extraordinarily rare event or is it occurring continually and at high frequency?Secondly, can such an event have pathological consequences to the organism?" + }, + { + "document_id": "612a70c6-2f42-492f-9f23-0d5e9296919e", + "section_type": "main", + "text": "\n\nA number of conclusions may be drawn from these results.Firstly, the data begin to answer the question of how closely mtDNA replication is kept in synchrony with nuclear DNA replication: it would appear to be regulated not by direct coupling to the nuclear DNA replication, but rather by the cell mass to be serviced by mitochondria." + }, + { + "document_id": "b0af29ac-0997-416d-907a-6caba940536d", + "section_type": "main", + "text": "\n\nHuman mtDNA codes for 13 essential polypeptide components of the mitochondrial oxidative phosphorylation (OXPHOS) system.mtDNA undergoes strict maternal inheritance, resulting in the absence of bi-parental recombination (Elson et al., 2001) and has a high mutation rate (Tuppen et al., 2010).As such, the evolution of mtDNA is characterised by the emergence of distinct lineages (or haplogroups) (Hernstadt et al., 2002).This results in high levels of mtDNA variation at the population level despite its rather small size, which is also illustrated by the large number of sub-haplogroups (van Oven and Kayser, 2009).Africa" + }, + { + "document_id": "2f39f55f-2604-49d4-9589-0e1403b84d7a", + "section_type": "main", + "text": "\n\nBackground: The accumulation of mitochondrial DNA (mtDNA) mutations, and the reduction of mtDNA copy number, both disrupt mitochondrial energetics, and may contribute to aging and age-associated phenotypes.However, there are few genetic and epidemiological studies on the spectra of blood mtDNA heteroplasmies, and the distribution of mtDNA copy numbers in different age groups and their impact on age-related phenotypes.In this work, we used whole-genome sequencing data of isolated peripheral blood mononuclear cells (PBMCs) from the UK10K project to investigate in parallel mtDNA heteroplasmy and copy number in 1511 women, between 17 and 85 years old, recruited in the TwinsUK cohorts." + }, + { + "document_id": "0381d0d4-717f-470b-a8d4-562475cf58bb", + "section_type": "main", + "text": "Oxidative stress and mitochondrial DNA\n\nNot long after it was discovered that mitochondria have their own genetic apparatus, Harman proposed that mitochondria play a central role in the free radical theory of aging [16].This idea was developed further by Miquel et al. [330], and the notion that mtDNA mutagenesis played a role in aging took hold.The phenotypical importance of mutations in mtDNA was demonstrated by Wallace et al. [331] and Holt et al. [332], who first showed that Leber's hereditary optic neuropathy and mitochondrial myopathies were caused by mtDNA mutations (reviewed in [333]).Because mtDNA is so close to the site of mitochondrial ROS production, it is exposed to considerably higher oxidative stress, resulting in 3-fold higher levels of DNA oxidative damage (the previously quoted 20-fold figure is apparently due to an isolation artifact [334,335]).In the 1990s a series of papers reported that the frequency of mitochondrial DNA deletions increases dramatically with age, being essentially undetectable in young individuals and reaching levels as high as 2% of mtDNA in old individuals.This age-related increase in mtDNA deletions was found in organisms as diverse as worms, mice, and humans (reviewed in [24,336]).The same is also true with mtDNA point mutations [337,338].Certain mtDNA polymorphisms have been found in increased frequency in centenarians, implying a protective effect during aging [339][340][341].Similar protective effects of mtDNA polymorphisms have been reported for the age-related neurodegenerative condition, Parkinson's disease [342]." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "section_type": "main", + "text": "\n\nOver evolutionary time, differential mortality rates are a selective force in shaping genetic structure.This results in divergence of a variety of physiological networks that shape, ultimately, patterns of aging and longevity in different habitats (Monaghan et al., 2008;Stojković et al., 2017).Such selective pressures can have differential effects on the nuclear and mitochondrial genomes (McKenzie et al., 2019;Wolff et al., 2014).Genetic variation in the mitochondrial genome is known to drive mitochondrial function in many species (Ballard and Melvin, 2010;McKenzie et al., 2019;Novelletto et al., 2016) and we find this in our system as well.Whole organism metabolic rate varies with the mitochondrial genome haplogroups we identified in this study.T. elegans individuals with the introgressed T. sirtalis mitochondrial genome had the lowest metabolic rate and had 68 amino acid changes in the ETC genes relative to the T. elegans mitochondrial genomes.As species divergence are a continuation of population divergence, this introgression provides additional insight into how genetic variation can alter mitochondrial function.Whether the lower metabolic rate in our snakes with the introgressed mitochondrial genome is due to the fixed amino acid changes between the species or a mismatch between the coadapted nuclear and mitochondrially-encoded ETC proteins that could alter function of the mitochondria (Burton et al., 2013;Haenel, 2017;Rawson and Burton, 2002;Toews et al., 2014;Wolff et al., 2014) will require further comparisons to T. sirtalis individuals." + }, + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "section_type": "main", + "text": "\n\nThere have been few reports on distinct correlation between mitochondrial morphology and human aging, except changes in number and size of mitochondria associated with age.Concerning the gross structure of mitochondria, the overwhelming importance of the cell nucleus in mitochondrial biogenesis should be noted, because the major parts of mitochondrial proteins are encoded by nuclear genes that are stable during life with the efficient repair mechanism for nDNA." + }, + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "section_type": "main", + "text": "\n\nEarly data on DNA polymorphism detected by restriction endonuclease (263) have suggested that the evolutionary change of mtDNA in higher animals occurs mainly by nucleotide substitution rather than by deletion and insertion.The mtDNA nucleotide sequence evolves 6-17 times faster than comparable nuclear DNA gene sequences (51,52,405).Rapid evolution of mtDNA of higher primates including human, 0.02 base substitutions per site per million years, was calculated from the restriction map of mtDNA (51).Because orthodox recombination mechanism appears to be absent in mtDNA (128), germline mutation seems to go down to posterity as maternal inheritance from our common ancestor (57)." + }, + { + "document_id": "65c8287b-eb19-437a-b9ca-5aaa8664d429", + "section_type": "main", + "text": "\n\nIt may be that high mtDNA levels are indeed indicative of compromised mitochondria, but that the underlying defects are unrelated to alterations in the DNA sequence.Alternatively, elevated quantities of mtDNA might be associated with increased metabolic requirements of the embryo, rather than organelles of suboptimal function.It is possible that embryos produced by older oocytes are under some form of stress and therefore have larger energy requirements.Functional experiments will be required to address these questions.Whatever the underlying basis, the current study has unequivocally demonstrated that female reproductive aging is associated with changes in the mtDNA content at the blastocyst stage." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "section_type": "main", + "text": "\n\nBuilding on previous work in this system, the current study tests three primary hypotheses about how variation in mtDNA and mitochondrial function relate to variation in life-history traits and aging within this system (Fig. 1): (1) First, we test whether rates of cellular oxygen consumption in isolated immune cells exhibit patterns that are consistent with the hypothesis that cellular processes drive whole-organism senescence and aging, and if these patterns differ between the SA and FA ecotypes and between sexes.By measuring basal, ATP-production associated, and maximal rates of cellular oxygen consumption, we further test for evidence that phenotypic divergence is dependent on a specific aspect of oxidative phosphorylation within immune cells.The energetics of these cells are particularly important given their essential role in modulating disease and infection, important factors contributing to senescence (Metcalf et al., 2019).We predict that SA snakes will maintain levels of cellular oxygen consumption across age, whereas the FA snakes will show a decline with age, especially in ATP-associated rates, possibly due to continual degradation of electron transport chain functionality from accumulating oxidative damage and reduced DNA repair mechanisms (Robert and Bronikowski, 2010;Schwartz and Bronikowski, 2013). ( 2) Second, we expand our mitochondrial genomics dataset to quantify mtDNA genetic structure across the landscape and test whether mtDNA haplotypes, and alleles at a nonsynonymous SNP in the Cytochrome B (CytB) gene correlate with aging ecotypes. (3) Third, we test the hypothesis that variation in mtDNA correlates with whole-organism variation in metabolic rates, suggesting a pathway linking mitochondrial genetic variation in mtDNA to whole-organism energetics.We first test whether different haplotypes differ in resting metabolic rate.Then, we test the effects of the nonsynonymous SNP in CytB on resting metabolic rate.The CytB gene encodes a component of complex III of the ETC, and was previously found to segregate between these life-history ecotypes (Schwartz et al., 2015).This SNP results in an amino acid substitution from isoleucine (aliphatic, hydrophobic) to threonine (hydrophilic) on a region that comes into close contact with a nuclear-encoded subunit (Schwartz et al., 2015).We combine previously published and new data on whole-organism resting metabolic rates (oxygen consumption) to test for the effects of this nonsynonymous mutation in three populations where we find heterogeneity at this nucleotide, thus allowing us to disentangle the effects of shared environment (population) from sequence variation (SNP).We predict that this SNP will correlate with variation in whole-organism metabolic rate, demonstrating a putatively adaptive difference between the derived and ancestral sequence.By utilizing this integrative data setfrom genes to organelles to whole organisms to populationsin a known life-history context, we are able to test hypotheses across levels of organization to provide a more complete picture of the complicated story of mitochondria and life history (Havird et al., 2019)." + }, + { + "document_id": "aa942230-9a43-4b5f-90d9-96d364861a57", + "section_type": "main", + "text": "\n\nClearly, as mitochondrial metabolic and genetic therapies advance for treating mitochondrial disease, they will also be available to enhance the personal lives of others.However, mitochondrial genetic variation appears to have been one of the primary factors that permitted our ancestors to adapt to new environments, survive adverse conditions, and multiple throughout the globe.Is it possible that by taking over control of individual mtDNA variation, we might also be setting our species on the road to functional decline and ultimately extinction?" + }, + { + "document_id": "aa942230-9a43-4b5f-90d9-96d364861a57", + "section_type": "main", + "text": "Mitochondrial therapeutics and performance enhancement\n\nIt is now clear that not all mtDNA variation is deleterious.Indeed, about 25% of all ancient mtDNA variation appears to have caused functional mitochondrial changes and thus been adaptive.Those mtDNA variants that are adapted to warm climates have mtDNA variants that result in tightly coupled OXPHOS, thus maximizing ATP output and minimizing heat production.The presence of these mtDNAs permits maximum muscle performance but also predispose sedentary individuals that consume excess calories to multiple problems.They would be prone to be overweight and their mitochondria would generate excessive ROS, thus making them susceptible to a variety of degenerative diseases, cancer and premature aging.Partially uncoupled mitochondria generate more heat, but at the expense of ATP production.Individual's with these variants are better able to tolerate the cold, and are less prone to obesity.They also generate less ROS making then resistant to degenerative diseases and aging.Finally, the mitochondria are why we breathe.Hence, mitochondrial variation might be an important factor in individual predisposition to altitude sickness." + }, + { + "document_id": "67ec2631-aa17-436e-800b-1bc046fb5b19", + "section_type": "main", + "text": "\n\nAge-associated alterations of the mitochondrial genome occur in several different species; however, their physiological relevance remains unclear.The age-associated changes of mitochondrial DNA (mtDNA) include nucleotide point mutations and modifications, as well as deletions.In this review, we summarize the current literature on age-associated mtDNA mutations and deletions and comment on their abundance.A clear need exists for a more thorough evaluation of the total damage to the mitochondrial genome that accumulates in aged tissues.᭧ 1997 Elsevier Science Inc." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "section_type": "main", + "text": "\n\nWorking within a generalized framework that integrates the environmental pressures driving the evolution of metabolic rates and lifehistories can provide insight into how these traits are integrated.In Fig. 1A we present such a framework.Central to this framework, and the best place to start our investigation of these complex interactions across scales and across lifespans, is the mitochondrion.In the cells of eukaryotes, the overwhelming majority of energy substrate (adenosine triphosphate; ATP) to sustain life's functions is produced in mitochondria via oxidative phosphorylation in the electron transport chain (reviewed in Hood et al., 2018;Solaini et al., 2010).Yet these mitochondria must carefully balance their energetic and oxygen demands, their capacity to meet cellular need for ATP, and their production of potentially toxic and harmful byproducts (Barja, 2002;Barja, 2004;Harmon, 1956;Lee and Wei, 2012;Muller et al., 2007).On one hand, energy flow through mitochondria can determine the pace of life of an individual, with implications for lifespan and 'rate of living'.On the other hand, the production of damaging reactive oxygen species (ROS) molecules is implicated in reduced performance and decline during old age, leading to the free radical theory of aging (or oxidative stress theory of aging; Balaban et al., 2005;Barja, 2002;Lyons and Kozak, 2019;Robert et al., 2007;Speakman, 2005).In addition, mitochondria possess a genome that encodes core peptides to electron transport chain (ETC) protein complexes along with nuclear-encoded mitochondrial proteins (Rand et al., 2004;Sunnucks et al., 2017;Wolff et al., 2014).This maternally inherited mitochondrial genome is subject to extensive replication across its lifetime and is linked to aging phenotypes independent of the production of damage-causing reactive oxygen species (Pinto and Moraes, 2015;Seo and Leeuwenburgh, 2015).For these reasons, any variation in mitochondrial function, especially if it is sexor age-dependent, will have ramifications for the evolution of life histories (Dowling and Simmons, 2009;Finkel and Holbrook, 2000;Fletcher et al., 2013;Wikelski and Ricklefs, 2001;Wolff et al., 2016)." + }, + { + "document_id": "67ec2631-aa17-436e-800b-1bc046fb5b19", + "section_type": "abstract", + "text": "\nAge-associated alterations of the mitochondrial genome occur in several different species; however, their physiological relevance remains unclear.The age-associated changes of mitochondrial DNA (mtDNA) include nucleotide point mutations and modifications, as well as deletions.In this review, we summarize the current literature on age-associated mtDNA mutations and deletions and comment on their abundance.A clear need exists for a more thorough evaluation of the total damage to the mitochondrial genome that accumulates in aged tissues.᭧ 1997 Elsevier Science Inc." + }, + { + "document_id": "8a9fe1bc-7fa3-40ee-ade0-9a498bcf9def", + "section_type": "main", + "text": "Mitochondrial genetics\n\nOne underexplored avenue for determining maternal risk for preterm birth involves the influence of the mitochondrial genome.The high mutation rate of mito chondrial DNA (mtDNA), together with the fact that most of its encoded proteins are evolutionarily con served, allowing for the selection of neutral or beneficial variants, has generated interest in defining human mtDNA variations and their roles in human biology [58]." + }, + { + "document_id": "fd5edd5b-25d5-41ef-b9ad-7599905b844f", + "section_type": "main", + "text": "\n\nMitochondrial DNA (mtDNA) rearrangements have been shown to accumulate with age in the post-mitotic tissues of a variety of animals and have been hypothesized to result in the age-related decline of mitochondrial bioenergetics leading to tissue and organ failure.Caloric restriction in rodents has been shown to extend life span supporting an association between bioenergetics and senescence.In the present study, we use full length mtDNA amplification by long-extension polymerase chain reaction (LX-PCR) to demonstrate that mice accumulate a wide variety of mtDNA rearrangements with age in post mitotic tissues.Similarly, using an alternative PCR strategy, we have found that 2-4 kb minicircles containing the origin of heavy-strand replication accumulate with age in heart but not brain.Analysis of mtDNA structure and conformation by Southern blots of unrestricted DNA resolved by field inversion gel electrophoresis have revealed that the brain mtDNAs of young animals contain the traditional linear, nicked, and supercoiled mtDNAs while old animals accumulate substantial levels of a slower migrating species we designate age-specific mtDNAs.In old caloric restricted animals, a wide variety of rearranged mtDNAs can be detected by LX-PCR in post mitotic tissues, but Southern blots of unrestricted DNA reveals a marked reduction in the levels of the agespecific mtDNA species.These observations confirm that mtDNA mutations accumulate with age in mice and suggest that caloric restriction impedes this progress." + }, + { + "document_id": "2f39f55f-2604-49d4-9589-0e1403b84d7a", + "section_type": "main", + "text": "\n\nHowever, no studies have investigated whether the effects of age on the two mitochondrial characteristics are independent, as it is possible that age can affect mtDNA copy number through age-related heteroplasmy changes or vice versa.In this study, we demonstrated that age was independently associated with mtDNA copy number and heteroplasmy.Furthermore, compared to previous studies, we also included WBC count and platelet count as covariates in the regression model to adjust for potential bias caused by blood cell contaminations.Mitochondrial biogenesis has been proposed as a marker of many age-related health outcomes or even the aging process itself [58].Our results suggested that both mtDNA heteroplasmy and copy number should be included to establish this relationship.Mitochondrial mutations that occur early in life can clonally expand to cause mitochondrial dysfunction and further contribute to aging through a number of potential mechanisms including decreased oxidative capacity and energy production capacity, but also nuclear signaling and transcriptional dysregulation [59][60][61][62][63].In addition, decreased mtDNA copy number may also lead to decreased energy production and/or decreased mitochondrial gene expression [57,64].Maintaining both mtDNA quality and quantity together may help to counteract or slow down the aging process." + }, + { + "document_id": "1152aa3c-a9df-4745-b262-97c03ccf0e1a", + "section_type": "main", + "text": "\n\nWe next examined whether aging influenced behavior of the mice and added 6-month-old and 12-month-old mice to the experiments.The interaction between nDNA and mtDNA seemed to be more pronounced in older mice, as the difference in the slopes of the learning curves of H and H mtDNA N mice was amplified with age (Fig. 1c).Mitochondrial decay has been associated with memory loss and particularly with age-dependent cognitive impairment 4,7 . ).All effects were significant, including the double interaction of nDNA, mtDNA and age (P < .01).For all age groups, the transfer of mtDNA to the parental strains resulted in fewer steps taken. (c) Number of holes explored.All effects were significant including the double interaction (P < 0.0007), with congenic strains exploring fewer holes." + }, + { + "document_id": "fd5edd5b-25d5-41ef-b9ad-7599905b844f", + "section_type": "abstract", + "text": "\nMitochondrial DNA (mtDNA) rearrangements have been shown to accumulate with age in the post-mitotic tissues of a variety of animals and have been hypothesized to result in the age-related decline of mitochondrial bioenergetics leading to tissue and organ failure.Caloric restriction in rodents has been shown to extend life span supporting an association between bioenergetics and senescence.In the present study, we use full length mtDNA amplification by long-extension polymerase chain reaction (LX-PCR) to demonstrate that mice accumulate a wide variety of mtDNA rearrangements with age in post mitotic tissues.Similarly, using an alternative PCR strategy, we have found that 2-4 kb minicircles containing the origin of heavy-strand replication accumulate with age in heart but not brain.Analysis of mtDNA structure and conformation by Southern blots of unrestricted DNA resolved by field inversion gel electrophoresis have revealed that the brain mtDNAs of young animals contain the traditional linear, nicked, and supercoiled mtDNAs while old animals accumulate substantial levels of a slower migrating species we designate age-specific mtDNAs.In old caloric restricted animals, a wide variety of rearranged mtDNAs can be detected by LX-PCR in post mitotic tissues, but Southern blots of unrestricted DNA reveals a marked reduction in the levels of the agespecific mtDNA species.These observations confirm that mtDNA mutations accumulate with age in mice and suggest that caloric restriction impedes this progress." + }, + { + "document_id": "5d133558-fc58-42c7-8407-b3e734e8db9c", + "section_type": "abstract", + "text": "\nQuantitative information on the cell-to-cell distribution of all possible mitochondrial DNA (mtDNA) mutations in young and aged tissues is needed to assess the relevance of these mutations to the aging process.In the present study, we used PCR amplification of full-length mitochondrial genomes from single cells to scan human cardiomyocytes for all possible large deletions in mtDNA.Analysis of more than 350 individual cells that were derived from three middleaged and four centenarian donors demonstrates that while most of the cells contain no deletions, in certain cardiomyocytes a significant portion of the mtDNA molecules carried one particular deletion.Different affected cells contained different deletions.Although similar numbers of cells were screened for each donor, these deletion-rich cells were found only in the hearts of old donors, where they occurred at a frequency of up to one in seven cells.These initial observations demonstrate the efficiency of the method and indicate that mitochondrial mutations have the potential to play an important role in human myocardial aging." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "section_type": "main", + "text": "\n\nAs a pacesetter for physiological processes, variation in metabolic rate can determine the shape of energetic trade-offs and thereby drive variation in life-history traits.In turn, such variation in metabolic performance and life-histories can have profound consequences for lifespan and lifetime fitness.Thus, the extent to which metabolic rate variation is due to phenotypic plasticity or fixed genetic differences among individuals or populations is likely to be shaped by natural selection.Here, we first present a generalized framework describing the central role of mitochondria in processes linking environmental, genomic, physiological, and aging variation.We then present a test of these relationships in an exemplary system: populations of garter snakes (Thamnophis elegans) exhibiting contrasting life-history strategiesfast-growing, early-reproducing, and fast-aging (FA) versus slow-growing, late-reproducing, and slow-aging (SA).Previous work has characterized divergences in mitochondrial function, reactive oxygen species processing, and whole-organism metabolic rate between these contrasting life-history ecotypes.Here, we report new data on cellular respiration and mitochondrial genomics and synthesize these results with previous work.We test hypotheses about the causes and implications of mitochondrial genome variation within this generalized framework.First, we demonstrate that snakes of the FA ecotype increase cellular metabolic rate across their lifespan, while the opposite pattern holds for SA snakes, implying that reduced energetic throughput is associated with a longer life.Second, we show that variants in mitochondrial genomes are segregating across the landscape in a manner suggesting selection on the physiological consequences of this variation in habitats varying in temperature, food availability, and rates of predation.Third, we demonstrate functional variation in whole-organism metabolic rate related to these mitochondrial genome sequence variants.With this synthesis of numerous datasets, we are able to further characterize how variation across levels of biological organization interact within this generalized framework and how this has resulted in the emergence of distinct life-history ecotypes that vary in their rates of aging and lifespan." + }, + { + "document_id": "fd5edd5b-25d5-41ef-b9ad-7599905b844f", + "section_type": "main", + "text": "\n\nIt was previously reported that no substantive changes accumulate in the structure of the mitochondrial genome with age in either fibroblasts or Drosophila melanogaster (17,18).This was determined through analysis by Southern blot of uniquely restricted mitochondrial DNA.However, recent studies have shown that complex mtDNA rearrangements associated with human disease (19)(20)(21) can be 'masked' through restriction digestion (22).In addition, Southern analysis of unrestricted mtDNAs from senescent human skeletal muscle has revealed the accumulation of additional mtDNA species not found in young tissues which migrate with the same mobility as deleted mtDNAs (10).Hence, detection of age-specific mtDNA rearrangements requires application of appropriate methodologies." + }, + { + "document_id": "2f39f55f-2604-49d4-9589-0e1403b84d7a", + "section_type": "main", + "text": "\n\nAging is commonly characterized as a time-dependent progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death [14].One important factor in aging is the accumulation of DNA damage over time [15].mtDNA has been considered a major target of aging-associated mutation accumulation, possibly because it experiences higher oxidative damages, more turnover, and has lower replication fidelity compared to nuclear DNA (nDNA) [16][17][18].Mice carrying elevated mtDNA mutation burden present premature signs of aging including hair loss, kyphosis, and premature death (lifespan shortened by up to 50%) [19,20].In human studies, mtDNA heteroplasmy incidence increases with age [21][22][23], while lower mtDNA copy number has been reported in aged populations [12,24].Ding et al. reported an trend of increased heteroplasmies and decreased mtDNA copy number with age in their study population [25].However, previous studies were limited in one or more ways: i) limited power in detecting low-to-medium frequency heteroplasmies in blood due to low sequencing depth; ii) relatively small sample sizes, limiting statistical power; iii) small age range; iv) whole blood as the source of DNA, which contains several sources of contaminants for mtDNA analysis; and/or v) assessing either mtDNA mutation or copy number, but not both in the same biological samples.Thus, it is largely unknown whether the impacts of age on mtDNA mutation burden and on copy number are independent from each other." + }, + { + "document_id": "b547b680-8602-4a15-8d91-6a6d3ffa19d2", + "section_type": "main", + "text": "\n\nIn the present study, myocardium was found to contain approximately twice the number of mtDNA genomes per diploid nucleus as skeletal muscle (6970 versus 3650, P = 0.006).This is in keeping with an earlier study (10) that used Southern hybridisation.This ®nding accords with a greater reliance on aerobic ATP production by the myocardium than by skeletal muscle.The mtDNA copy number in myocardium and skeletal muscle was found to remain unchanged over a 10 decade timespan in the tissues we studied (Figs 3 and 4), similar to the previous ®ndings on rat heart (12)." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nAging is a complex process as a time-dependent progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death [74], and as we described above, aging is highly associated with mtDNA mutations; in fact heteroplasmy incidence increases with age, while lower mtDNA copy number has been reported in aged populations as well as mitochondria morphology, abundance, and oxidative phosphorylation activity [75,76].Interestingly, in aging the significant amount of these mutations converges in sites that encode structural subunits of the ETC such as complexes I and III [77], leading to OxPhos uncoupling and mitochondrial dysfunction in aged population.Since there are several limitations to study mitochondrial metabolism in human samples, in this section we briefly described the implications of mitochondrial metabolism for aging in the most studied and high energy demand human tissues, such as skeletal muscle, heart, and brain." + }, + { + "document_id": "ddc57e64-2b93-41e5-baac-6bdb52e7b6e6", + "section_type": "main", + "text": "\n\nIt is not known how mtDNA deletions accumulate during aging.Although the smaller size of partially-deleted molecules suggested early on that they could have a replicative advantage (5,6), direct evidence of this phenomenon has been lacking.In most cases, partially-deleted mtDNAs (DmtDNAs) contain the same number of replication origins as the wildtype genome but they can be up to 50% shorter (7).We have previously shown that cells harboring homoplasmic levels of DmtDNA repopulated their organelles with mtDNA faster than cells containing wild-type mitochondrial genomes (8).In these cells, however, there was no competition between mutated and wild-type genomes, as they were present in a homoplasmic state.Therefore, we could not rule out that differences in mtDNA repopulation were due to different metabolic states of these cells.In the present study, we addressed this issue by studying heteroplasmic cells.Our results showed that mtDNA with large deletions, but not with pathogenic point mutations, repopulates organelles signi®cantly faster than wild-type genomes in the same cell, particularly during relaxed copy number control." + }, + { + "document_id": "ddc57e64-2b93-41e5-baac-6bdb52e7b6e6", + "section_type": "main", + "text": "\n\nAlthough there may be important differences between postmitotic tissues and our culture cell system, the observation of heteroplasmy ¯uctuations during rapid mtDNA repopulation allows us to draw some conclusions regarding the molecular aspect of differential repopulation rates.Our results are in agreement with previous in situ hybridization experiments that showed that most age-related mtDNA deletions in muscle are caused by clonal expansion of deletions (36,37).In muscle, mitochondria with defective function are stimulated to proliferate, and that may increase mtDNA replication, mimicking a relaxed copy number control situation.It also strengthened the view that age-related mtDNA deletions are probably generated at random but their levels gradually increase with time.Our results also raise the possibility that the accumulation of DmtDNAs may be accelerated by metabolic or environmental changes leading to either a transient reduction in mtDNA levels or a relaxation in copy number control." + }, + { + "document_id": "2f39f55f-2604-49d4-9589-0e1403b84d7a", + "section_type": "abstract", + "text": "\nBackground: The accumulation of mitochondrial DNA (mtDNA) mutations, and the reduction of mtDNA copy number, both disrupt mitochondrial energetics, and may contribute to aging and age-associated phenotypes.However, there are few genetic and epidemiological studies on the spectra of blood mtDNA heteroplasmies, and the distribution of mtDNA copy numbers in different age groups and their impact on age-related phenotypes.In this work, we used whole-genome sequencing data of isolated peripheral blood mononuclear cells (PBMCs) from the UK10K project to investigate in parallel mtDNA heteroplasmy and copy number in 1511 women, between 17 and 85 years old, recruited in the TwinsUK cohorts.Results: We report a high prevalence of pathogenic mtDNA heteroplasmies in this population.We also find an increase in mtDNA heteroplasmies with age (β = 0.011, P = 5.77e-6), and showed that, on average, individuals aged 70-years or older had 58.5% more mtDNA heteroplasmies than those under 40-years old.Conversely, mtDNA copy number decreased by an average of 0.4 copies per year (β = −0.395,P = 0.0097).Multiple regression analyses also showed that age had independent effects on mtDNA copy number decrease and heteroplasmy accumulation.Finally, mtDNA copy number was positively associated with serum bicarbonate level (P = 4.46e-5), and inversely correlated with white blood cell count (P = 0.0006).Moreover, the aggregated heteroplasmy load was associated with blood apolipoprotein B level (P = 1.33e-5), linking the accumulation of mtDNA mutations to age-related physiological markers.Conclusions: Our population-based study indicates that both mtDNA quality and quantity are influenced by age.An open question for the future is whether interventions that would contribute to maintain optimal mtDNA copy number and prevent the expansion of heteroplasmy could promote healthy aging." + }, + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "section_type": "main", + "text": "DNA genotype during development\n\nRelatively rapid turnover of mtDNA in cells was documented (129).The half-life of mtDNA was 6.7 days in heart, 9.4 days in liver, 10.4 days in kidney, and 31 days in brain in adult rats, while a half-life of heart nDNA was -30 days.Accompanying mtDNA turnover, replicative advantage either to mutant or to wild-type mitochondrial genome has been reported on the germline point mutations." + }, + { + "document_id": "63308275-a453-415d-8814-6f2932148ecd", + "section_type": "main", + "text": "\n\nIn this study, we have taken advantage of recent developments in high-throughput DNA sequencing to assemble one of the largest ancient mitochondrial DNA (mtDNA) datasets to date, consisting of a total of nearly 300,000 nucleotides of unique sequence data from 18 individual samples.By exploiting permafrost-preserved hair shaft material as a source of ancient DNA (3), we present five newly sequenced Siberian woolly mammoth mtDNA genomes (Fig. 1).In combination with the 13 previously published (3-7), these make it possible to scan for signs of natural selection along the mitochondrial genome and allow further investigation of the population structure discovered in past studies (1,8), including the inference of a more precise evolutionary time scale.Analysis of the combined dataset indicates a deep temporal split between the two clades (I and II).This observation, coupled with statistical analysis of the temporal distribution of the 14 C ages of these and previously identified members of the two mammoth clades (1), suggests that, although they are apparently sympatric, clade II vanished from Siberia long before clade I." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "section_type": "abstract", + "text": "\nAs a pacesetter for physiological processes, variation in metabolic rate can determine the shape of energetic trade-offs and thereby drive variation in life-history traits.In turn, such variation in metabolic performance and life-histories can have profound consequences for lifespan and lifetime fitness.Thus, the extent to which metabolic rate variation is due to phenotypic plasticity or fixed genetic differences among individuals or populations is likely to be shaped by natural selection.Here, we first present a generalized framework describing the central role of mitochondria in processes linking environmental, genomic, physiological, and aging variation.We then present a test of these relationships in an exemplary system: populations of garter snakes (Thamnophis elegans) exhibiting contrasting life-history strategiesfast-growing, early-reproducing, and fast-aging (FA) versus slow-growing, late-reproducing, and slow-aging (SA).Previous work has characterized divergences in mitochondrial function, reactive oxygen species processing, and whole-organism metabolic rate between these contrasting life-history ecotypes.Here, we report new data on cellular respiration and mitochondrial genomics and synthesize these results with previous work.We test hypotheses about the causes and implications of mitochondrial genome variation within this generalized framework.First, we demonstrate that snakes of the FA ecotype increase cellular metabolic rate across their lifespan, while the opposite pattern holds for SA snakes, implying that reduced energetic throughput is associated with a longer life.Second, we show that variants in mitochondrial genomes are segregating across the landscape in a manner suggesting selection on the physiological consequences of this variation in habitats varying in temperature, food availability, and rates of predation.Third, we demonstrate functional variation in whole-organism metabolic rate related to these mitochondrial genome sequence variants.With this synthesis of numerous datasets, we are able to further characterize how variation across levels of biological organization interact within this generalized framework and how this has resulted in the emergence of distinct life-history ecotypes that vary in their rates of aging and lifespan. \"Sometimes reality is too complex.Stories give it form.\"" + }, + { + "document_id": "aa942230-9a43-4b5f-90d9-96d364861a57", + "section_type": "main", + "text": "\n\nAll of these factors and numerous others are areas that influence our daily lives.Consequently, some individuals may wish to change their energetic phenotype by changing their mtDNA genotype.If some people will undergo surgery to change their appearance, there will certainly be some who will submit to mtDNA alterations to change their life style, appearance, and physical performance.For example, changing a single mtDNA nucleotide of a high performance athlete to increase mitochondrial ATP production through altered OXPHOS coupling could increase performance by several percent and mean the difference between Olympic immortality versus obscurity.Since such a change would be undetectable by any reasonable standard screening procedure.Why wouldn't a competitive athlete take advantage of such an opportunity?" + }, + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "section_type": "main", + "text": "\n\nThese results strongly urged the researchers' attention on mtDNA mutations and, as a result, bridged over the distance between the biochemical findings and the molecular biology of mtDNA." + } + ], + "document_id": "8CC70421A233A4B1F89A6701731F7017", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "mtDNA", + "mitochondrial&DNA", + "nuclear&DNA", + "heredity", + "haplogroups", + "mitochondrial&genome", + "oxidative&phosphorylation", + "OXPHOS", + "mutation&rate", + "mitophagy" + ], + "metadata": [ + { + "object": "Carriers of hemochromatosis gene HFE 845A and 187G alleles have significantly higher mitochondrial DNA mtDNA levels than noncarriers, but mtDNA declines among all individuals on study during 48 weeks on uninterrupted antiretroviral therapy ART. Increased cellular mtDNA content may represent a compensatory response to mitochondrial stress that is influenced by iron-loading HFE variants.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab120751" + }, + { + "object": "We also assessed mitochondrial DNA mtDNA content, citrate synthase activity, oxidative lesions to protein and mtDNA i.e., carbonyls and the abundance of mtDNA4834 deletion, and the mitochondrial transcription factor A TFAM binding to specific mtDNA regions.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab998500" + }, + { + "object": "Mitochondrial mtDNA haplogroups show an influence on serum levels of catalase among osteoarthritis patients. Carriers of mtDNA haplogroup J show higher serum levels than non-J carriers.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab494822" + }, + { + "object": "We determined mitochondrial DNA mtDNA and ACTN3 genotypes in Finnish elite endurance n = 52 and sprint n = 89 athletes, and found that the frequencies of mtDNA haplogroups differed significantly between the two groups", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1002772" + }, + { + "object": "In mutated HMI1 the wild-type mitochondrial DNA is fragmented and loss of the wild-type mitochondrial genome is caused by this fragmentation of the mitochondrial DNA.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab396367" + }, + { + "object": "The T790M mutation rate was 8.4% in overall patients. The T790M mutation was more frequent in patients with brain metastasis 30.0% . We found that post-TKI tyrosine kinase inhibitors samples 42.8% were associated with a higher T790M mutation rate. Subgroup analysis showed that the duration of TKI therapy for 6 to 10 months 66.6% and >10 months 75.0% were also associated with higher T790M mutation rate.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab878154" + }, + { + "object": "Study revealed that Irc3 - a dsDNA dependent ATPase of the Superfamily II- is essential for yeast mtDNA maintenance. irc3Delta mutant yeast cells accumulated double-stranded breaks in mtDNA and lose the wild-type mitochondrial genome in the course of a few generations of growth on glucose-containing media. These results demonstrate that Irc3 is a mitochondrial branch migration enzyme.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab748584" + }, + { + "object": "human ClpX, a novel mtDNA regulator, maintains mtDNA nucleoid distribution through TFAM function as a chaperone rather than as a protease and its involvement in mtDNA segregation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab364328" + }, + { + "object": "Here, we identified the E3 ubiquitin ligase HUWE1 as a key inducing factor in AMBRA1-mediated mitophagy, a process that takes place independently of the main mitophagy receptors. Furthermore, we show that mitophagy function of AMBRA1 is post-translationally controlled, upon HUWE1 activity, by a positive phosphorylation on its serine 1014.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab339502" + }, + { + "object": "MGME1-mediated mtDNA processing is essential for faithful mitochondrial genome replication and might be required for intramolecular recombination of mtDNA.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab480937" + } + ], + "question": "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "subquestions": null, + "task_id": "8CC70421A233A4B1F89A6701731F7017", + "usage": { + "chatgpt": 9175, + "gpt-4": 5434, + "gpt-4-turbo-preview": 4489 + }, + "user_id": 2 + }, + "document_id": "8CC70421A233A4B1F89A6701731F7017", + "task_id": "8CC70421A233A4B1F89A6701731F7017" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/10.json b/gnqa/paper1_eval/src/data/responses/general/experts/10.json new file mode 100644 index 0000000..5ce6b0a --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/10.json @@ -0,0 +1,384 @@ +{ + "created_at": "2024-06-01T14:34:54.821428Z", + "data": { + "amplify": false, + "answer": "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute. It is a publicly available web resource that contains automatically annotated genomes and integrates data from a wide range of biological research sources. Launched in 1999, the Ensembl database was the first to provide a window on the draft genome, curating the results of a series of computational analyses. It provides comprehensive information about the human genome including variants like SNPs, insertions, deletions, and somatic mutations for several species. Ensembl also offers tools for data retrieval and analysis.", + "context": { + "1a93e25f-2a43-49e9-8450-03a57c93e613": [ + { + "document_id": "1a93e25f-2a43-49e9-8450-03a57c93e613", + "text": "Annotation, preprocessing and categorization of data\n\nWe used Ensembl (version 39) as the annotation reference database.Homology between human and mouse genes was derived via BioMart.The total number of genes under study comprises 15,277 Ensembl mouse genes representing the union of the homologue genes from all data sources.An overview about the T2DM specific datasets is given in Table 1." + } + ], + "5edf84d0-c2d9-45eb-91b9-c35743b6a463": [ + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried\nout and the way the results are displayed. 4.4.1 Ensembl\nEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome\nTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard\net al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a\nwindow on the draft genome, curating the results of a series of computational analyses." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence\nassemblies as its starting point, but it is now based upon NCBI assemblies. The\nEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify ‘confirmed’ genes that are\ncomputationally predicted (by the GENSCAN gene prediction program) and also\nsupported by a significant BLAST match to one or more expressed sequences or\nproteins. Ensembl also identifies the positions of known human genes from public\nsequence database entries, usually using GENEWISE to predict their exon structures." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "Data retrieval is extremely well catered for in Ensembl, with text searches of all\ndatabase entries, BLAST searches of all sequences archived, and the availability of bulk\ndownloads of all Ensembl data and even software source code. Ensembl annotation\ncan also be viewed interactively on one’s local machine with the Apollo viewer (Lewis\net al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome Browser\nThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;\nit, too, provides annotation of the NCBI assemblies, and it displays a similar array of\nfeatures, including confirmed genes from Ensembl." + } + ], + "c12e853e-4f0d-48f9-93af-15db9ad2dfae": [ + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried\nout and the way the results are displayed. 4.4.1 Ensembl\nEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome\nTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard\net al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a\nwindow on the draft genome, curating the results of a series of computational analyses." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence\nassemblies as its starting point, but it is now based upon NCBI assemblies. The\nEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify ‘confirmed’ genes that are\ncomputationally predicted (by the GENSCAN gene prediction program) and also\nsupported by a significant BLAST match to one or more expressed sequences or\nproteins. Ensembl also identifies the positions of known human genes from public\nsequence database entries, usually using GENEWISE to predict their exon structures." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "Data retrieval is extremely well catered for in Ensembl, with text searches of all\ndatabase entries, BLAST searches of all sequences archived, and the availability of bulk\ndownloads of all Ensembl data and even software source code. Ensembl annotation\ncan also be viewed interactively on one’s local machine with the Apollo viewer (Lewis\net al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome Browser\nThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;\nit, too, provides annotation of the NCBI assemblies, and it displays a similar array of\nfeatures, including confirmed genes from Ensembl." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "Ensembl provides a DAS reference\nserver giving access to a wide range of specialist annotations of the human\ngenome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy a\nhypothesis (‘top-down’ data mining), or to interrogate a database in order to\ngenerate new hypotheses based on rigorous statistical correlations (‘bottom-up’\ndata mining). Domain (protein) A region of special biological interest within a single protein\nsequence." + } + ], + "f35e02a1-3314-4663-913f-38a3fc072aa8": [ + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried\nout and the way the results are displayed. 4.4.1 Ensembl\nEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome\nTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard\net al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a\nwindow on the draft genome, curating the results of a series of computational analyses." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence\nassemblies as its starting point, but it is now based upon NCBI assemblies. The\nEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify ‘confirmed’ genes that are\ncomputationally predicted (by the GENSCAN gene prediction program) and also\nsupported by a significant BLAST match to one or more expressed sequences or\nproteins. Ensembl also identifies the positions of known human genes from public\nsequence database entries, usually using GENEWISE to predict their exon structures." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "Data retrieval is extremely well catered for in Ensembl, with text searches of all\ndatabase entries, BLAST searches of all sequences archived, and the availability of bulk\ndownloads of all Ensembl data and even software source code. Ensembl annotation\ncan also be viewed interactively on one’s local machine with the Apollo viewer (Lewis\net al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome Browser\nThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;\nit, too, provides annotation of the NCBI assemblies, and it displays a similar array of\nfeatures, including confirmed genes from Ensembl." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "Ensembl provides a DAS reference\nserver giving access to a wide range of specialist annotations of the human\ngenome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy a\nhypothesis (‘top-down’ data mining), or to interrogate a database in order to\ngenerate new hypotheses based on rigorous statistical correlations (‘bottom-up’\ndata mining). Domain (protein) A region of special biological interest within a single protein\nsequence." + } + ], + "f7072d9b-4e07-4541-bac7-13a25761f460": [ + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "text": "Ensembl\n\nEnsembl is a publicly available web resource that contains automatically annotated genomes.It is integrated with other available biological databases like Jasper for binding motifs.It is a much larger web resource than T1Dbase, and contains general information about the human genome including variants.These include SNPs, insertions, deletions and somatic mutations (Alterations in DNA that occur after conception, meaning that they are not inherited) for several species.Data from Ensembl can be accessed in a number of ways.The names of all the SNPs that occur in the T1D susceptibility regions can be collected from Ensembl using the Biomart tool (Kinsella et al., 2011).To achieve this, the coordinates of the T1D regions obtained from T1Dbase are uploaded to the biomart query page which allows one to search the genome browser and retrieve data like the names, chromosomal positions, and genic positions (referred to as \"consequence to transcript\", in Ensembl) of the SNPs.The SNP genic positions tell if a SNP is located within a gene, adjacent to a gene or whether they occur in inter-genic positions between gene coding regions, as well as the particular genes in which they are located." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "text": "Advantages of Ensembl:\n\nThere is a number of advantages to using Ensembl. (i) It is a larger web resource than T1Dbase and integrates data from a wide range of biological research sources into its database.Therefore, available information is quite comprehensive. (ii) Genic positions for 99% of the variants obtained from T1Dbase could be retrieved. (iii) Ensembl contains quality checks for genetic variants in its variation pipeline.A variant is flagged as failed if certain quality criteria are not met, for instance if none of the variant alleles match the reference allele of the variant.Generally, Ensembl was found to give more detailed information regarding the genic positions of variants compared to T1Dbase." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "text": "\n\nInformation about genes, including gene names, chromosomal coordinates, biotype (coding or non-coding), and number of splice variants, can also be retrieved from Ensembl." + } + ], + "fa8bba46-ce94-439a-a676-35187a3abcbf": [ + { + "document_id": "fa8bba46-ce94-439a-a676-35187a3abcbf", + "text": "doi:10.1093/nar/gkp858\nCunningham F, Amode MR, Barrell D, Beal K,\nBillis K, Brent S, Carvalho-Silva D, Clapham\nP, Coates G, Fitzgerald S, Gil L, Giron CG,\nGordon L, Hourlier T, Hunt SE, Janacek SH,\nJohnson N, Juettemann T, Kahari AK, Keenan\nS, Martin FJ, Maurel T, McLaren W, Murphy\nDN, Nag R, Overduin B, Parker A, Patricio\nM, Perry E, Pignatelli M, Riat HS, Sheppard\nD, Taylor K, Thormann A, Vullo A, Wilder\nSP, Zadissa A, Aken BL, Birney E, Harrow J,\nKinsella R, Muffato M, Ruffier M, Searle SM,\nSpudich G, Trevanion SJ, Yates A, Zerbino\nDR, Flicek P (2015) Ensembl 2015." + } + ], + "fca531d0-d45b-495f-a02c-fbd437617b20": [ + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried\nout and the way the results are displayed. 4.4.1 Ensembl\nEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome\nTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard\net al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a\nwindow on the draft genome, curating the results of a series of computational analyses." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence\nassemblies as its starting point, but it is now based upon NCBI assemblies. The\nEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify ‘confirmed’ genes that are\ncomputationally predicted (by the GENSCAN gene prediction program) and also\nsupported by a significant BLAST match to one or more expressed sequences or\nproteins. Ensembl also identifies the positions of known human genes from public\nsequence database entries, usually using GENEWISE to predict their exon structures." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "Data retrieval is extremely well catered for in Ensembl, with text searches of all\ndatabase entries, BLAST searches of all sequences archived, and the availability of bulk\ndownloads of all Ensembl data and even software source code. Ensembl annotation\ncan also be viewed interactively on one’s local machine with the Apollo viewer (Lewis\net al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome Browser\nThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;\nit, too, provides annotation of the NCBI assemblies, and it displays a similar array of\nfeatures, including confirmed genes from Ensembl." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "Ensembl provides a DAS reference\nserver giving access to a wide range of specialist annotations of the human\ngenome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy a\nhypothesis (‘top-down’ data mining), or to interrogate a database in order to\ngenerate new hypotheses based on rigorous statistical correlations (‘bottom-up’\ndata mining). Domain (protein) A region of special biological interest within a single protein\nsequence." + } + ] + }, + "data_source": [ + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried\nout and the way the results are displayed.\n\n 4.4.1 Ensembl\nEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome\nTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard\net al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a\nwindow on the draft genome, curating the results of a series of computational analyses." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried\nout and the way the results are displayed.\n\n 4.4.1 Ensembl\nEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome\nTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard\net al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a\nwindow on the draft genome, curating the results of a series of computational analyses." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried\nout and the way the results are displayed.\n\n 4.4.1 Ensembl\nEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome\nTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard\net al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a\nwindow on the draft genome, curating the results of a series of computational analyses." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried\nout and the way the results are displayed.\n\n 4.4.1 Ensembl\nEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome\nTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard\net al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a\nwindow on the draft genome, curating the results of a series of computational analyses." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "Ensembl\n\nEnsembl is a publicly available web resource that contains automatically annotated genomes.It is integrated with other available biological databases like Jasper for binding motifs.It is a much larger web resource than T1Dbase, and contains general information about the human genome including variants.These include SNPs, insertions, deletions and somatic mutations (Alterations in DNA that occur after conception, meaning that they are not inherited) for several species.Data from Ensembl can be accessed in a number of ways.The names of all the SNPs that occur in the T1D susceptibility regions can be collected from Ensembl using the Biomart tool (Kinsella et al., 2011).To achieve this, the coordinates of the T1D regions obtained from T1Dbase are uploaded to the biomart query page which allows one to search the genome browser and retrieve data like the names, chromosomal positions, and genic positions (referred to as \"consequence to transcript\", in Ensembl) of the SNPs.The SNP genic positions tell if a SNP is located within a gene, adjacent to a gene or whether they occur in inter-genic positions between gene coding regions, as well as the particular genes in which they are located." + }, + { + "document_id": "fa8bba46-ce94-439a-a676-35187a3abcbf", + "section_type": "main", + "text": "doi:10.1093/nar/gkp858\nCunningham F, Amode MR, Barrell D, Beal K,\nBillis K, Brent S, Carvalho-Silva D, Clapham\nP, Coates G, Fitzgerald S, Gil L, Giron CG,\nGordon L, Hourlier T, Hunt SE, Janacek SH,\nJohnson N, Juettemann T, Kahari AK, Keenan\nS, Martin FJ, Maurel T, McLaren W, Murphy\nDN, Nag R, Overduin B, Parker A, Patricio\nM, Perry E, Pignatelli M, Riat HS, Sheppard\nD, Taylor K, Thormann A, Vullo A, Wilder\nSP, Zadissa A, Aken BL, Birney E, Harrow J,\nKinsella R, Muffato M, Ruffier M, Searle SM,\nSpudich G, Trevanion SJ, Yates A, Zerbino\nDR, Flicek P (2015) Ensembl 2015." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "Advantages of Ensembl:\n\nThere is a number of advantages to using Ensembl. (i) It is a larger web resource than T1Dbase and integrates data from a wide range of biological research sources into its database.Therefore, available information is quite comprehensive. (ii) Genic positions for 99% of the variants obtained from T1Dbase could be retrieved. (iii) Ensembl contains quality checks for genetic variants in its variation pipeline.A variant is flagged as failed if certain quality criteria are not met, for instance if none of the variant alleles match the reference allele of the variant.Generally, Ensembl was found to give more detailed information regarding the genic positions of variants compared to T1Dbase." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence\nassemblies as its starting point, but it is now based upon NCBI assemblies. The\nEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify ‘confirmed’ genes that are\ncomputationally predicted (by the GENSCAN gene prediction program) and also\nsupported by a significant BLAST match to one or more expressed sequences or\nproteins. Ensembl also identifies the positions of known human genes from public\nsequence database entries, usually using GENEWISE to predict their exon structures." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence\nassemblies as its starting point, but it is now based upon NCBI assemblies. The\nEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify ‘confirmed’ genes that are\ncomputationally predicted (by the GENSCAN gene prediction program) and also\nsupported by a significant BLAST match to one or more expressed sequences or\nproteins. Ensembl also identifies the positions of known human genes from public\nsequence database entries, usually using GENEWISE to predict their exon structures." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence\nassemblies as its starting point, but it is now based upon NCBI assemblies. The\nEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify ‘confirmed’ genes that are\ncomputationally predicted (by the GENSCAN gene prediction program) and also\nsupported by a significant BLAST match to one or more expressed sequences or\nproteins. Ensembl also identifies the positions of known human genes from public\nsequence database entries, usually using GENEWISE to predict their exon structures." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence\nassemblies as its starting point, but it is now based upon NCBI assemblies. The\nEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify ‘confirmed’ genes that are\ncomputationally predicted (by the GENSCAN gene prediction program) and also\nsupported by a significant BLAST match to one or more expressed sequences or\nproteins. Ensembl also identifies the positions of known human genes from public\nsequence database entries, usually using GENEWISE to predict their exon structures." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "Another\ngrowing area of activity is in cataloguing the genetic variation present in human\npopulations as Ensembl reflects the progress of the International Haplotype Map\nProject (Thorisson et al. , 2005).\n More speculative data, such as GENSCAN-predicted exons that have not been\nincorporated into Ensembl-confirmed genes, may also be viewed. This means that\nthe display can be used as a workbench for the user to develop personalized annotation." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "Another\ngrowing area of activity is in cataloguing the genetic variation present in human\npopulations as Ensembl reflects the progress of the International Haplotype Map\nProject (Thorisson et al. , 2005).\n More speculative data, such as GENSCAN-predicted exons that have not been\nincorporated into Ensembl-confirmed genes, may also be viewed. This means that\nthe display can be used as a workbench for the user to develop personalized annotation." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "Another\ngrowing area of activity is in cataloguing the genetic variation present in human\npopulations as Ensembl reflects the progress of the International Haplotype Map\nProject (Thorisson et al. , 2005).\n More speculative data, such as GENSCAN-predicted exons that have not been\nincorporated into Ensembl-confirmed genes, may also be viewed. This means that\nthe display can be used as a workbench for the user to develop personalized annotation." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "Another\ngrowing area of activity is in cataloguing the genetic variation present in human\npopulations as Ensembl reflects the progress of the International Haplotype Map\nProject (Thorisson et al. , 2005).\n More speculative data, such as GENSCAN-predicted exons that have not been\nincorporated into Ensembl-confirmed genes, may also be viewed. This means that\nthe display can be used as a workbench for the user to develop personalized annotation." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "Data retrieval is extremely well catered for in Ensembl, with text searches of all\ndatabase entries, BLAST searches of all sequences archived, and the availability of bulk\ndownloads of all Ensembl data and even software source code. Ensembl annotation\ncan also be viewed interactively on one’s local machine with the Apollo viewer (Lewis\net al. , 2002; http://www.fruitfly.org/annot/apollo/).\n\n 4.4.2 The UCSC Human Genome Browser\nThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;\nit, too, provides annotation of the NCBI assemblies, and it displays a similar array of\nfeatures, including confirmed genes from Ensembl." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "Data retrieval is extremely well catered for in Ensembl, with text searches of all\ndatabase entries, BLAST searches of all sequences archived, and the availability of bulk\ndownloads of all Ensembl data and even software source code. Ensembl annotation\ncan also be viewed interactively on one’s local machine with the Apollo viewer (Lewis\net al. , 2002; http://www.fruitfly.org/annot/apollo/).\n\n 4.4.2 The UCSC Human Genome Browser\nThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;\nit, too, provides annotation of the NCBI assemblies, and it displays a similar array of\nfeatures, including confirmed genes from Ensembl." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "Data retrieval is extremely well catered for in Ensembl, with text searches of all\ndatabase entries, BLAST searches of all sequences archived, and the availability of bulk\ndownloads of all Ensembl data and even software source code. Ensembl annotation\ncan also be viewed interactively on one’s local machine with the Apollo viewer (Lewis\net al. , 2002; http://www.fruitfly.org/annot/apollo/).\n\n 4.4.2 The UCSC Human Genome Browser\nThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;\nit, too, provides annotation of the NCBI assemblies, and it displays a similar array of\nfeatures, including confirmed genes from Ensembl." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "Data retrieval is extremely well catered for in Ensembl, with text searches of all\ndatabase entries, BLAST searches of all sequences archived, and the availability of bulk\ndownloads of all Ensembl data and even software source code. Ensembl annotation\ncan also be viewed interactively on one’s local machine with the Apollo viewer (Lewis\net al. , 2002; http://www.fruitfly.org/annot/apollo/).\n\n 4.4.2 The UCSC Human Genome Browser\nThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;\nit, too, provides annotation of the NCBI assemblies, and it displays a similar array of\nfeatures, including confirmed genes from Ensembl." + }, + { + "document_id": "429abfc1-f628-48ff-bfe8-f7be6d1419a8", + "section_type": "main", + "text": "Zerbino, D. R., Achuthan, P., Akanni, W., Amode, M. R., Barrell,\nD., Bhai, J., Billis, K., Cummins, C., Gall, A., Girón, C. G., Gil,\nL., Gordon, L., Haggerty, L., Haskell, E., Hourlier, T., Izuogu, O.\nG., Janacek, S. H., Juettemann, T., To, J. K., Laird, M. R., Lavidas, I., Liu, Z., Loveland, J. E., Maurel, T., McLaren, W., Moore,\nB., Mudge, J., Murphy, D. N., Newman, V., Nuhn, M., Ogeh, D.,\nOng, C. K., Parker, A., Patricio, M., Riat, H. S., Schuilenburg,\nH., Sheppard, D., Sparrow, H., Taylor, K., Thormann, A., Vullo,\nA., Walts, B., Zadissa, A., Frankish, A., Hunt, S. E., Kostadima,\nM., Langridge, N., Martin, F. J., Muffato, M., Perry, E., Ruffier,\nM., Staines, D. M., Trevanion, S. J., Aken, B. L., Cunningham,\nF., Yates, A., and Flicek, P.: Ensembl 2018, Nucl." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "gov/mapview/) evolved to allow graphical depictions of, and comparisons between,\na wide range of genetic and physical maps in parallel with NCBI draft and finished sequence contigs. The locations of genes, markers, and SNPs are indicated\non the assembled sequences. As with Ensembl, there is a NCBI analysis protocol\nwhich aims to predict gene structures based upon EST and mRNA alignments with\nthe draft genome. This is carried out by a program called Acembly (unpublished;\nhttp://www.ncbi.nlm.nih.gov/IEB/Research/Acembly/index.html), which aims to\nderive gene structure from these alignments alone." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "gov/mapview/) evolved to allow graphical depictions of, and comparisons between,\na wide range of genetic and physical maps in parallel with NCBI draft and finished sequence contigs. The locations of genes, markers, and SNPs are indicated\non the assembled sequences. As with Ensembl, there is a NCBI analysis protocol\nwhich aims to predict gene structures based upon EST and mRNA alignments with\nthe draft genome. This is carried out by a program called Acembly (unpublished;\nhttp://www.ncbi.nlm.nih.gov/IEB/Research/Acembly/index.html), which aims to\nderive gene structure from these alignments alone." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "gov/mapview/) evolved to allow graphical depictions of, and comparisons between,\na wide range of genetic and physical maps in parallel with NCBI draft and finished sequence contigs. The locations of genes, markers, and SNPs are indicated\non the assembled sequences. As with Ensembl, there is a NCBI analysis protocol\nwhich aims to predict gene structures based upon EST and mRNA alignments with\nthe draft genome. This is carried out by a program called Acembly (unpublished;\nhttp://www.ncbi.nlm.nih.gov/IEB/Research/Acembly/index.html), which aims to\nderive gene structure from these alignments alone." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "gov/mapview/) evolved to allow graphical depictions of, and comparisons between,\na wide range of genetic and physical maps in parallel with NCBI draft and finished sequence contigs. The locations of genes, markers, and SNPs are indicated\non the assembled sequences. As with Ensembl, there is a NCBI analysis protocol\nwhich aims to predict gene structures based upon EST and mRNA alignments with\nthe draft genome. This is carried out by a program called Acembly (unpublished;\nhttp://www.ncbi.nlm.nih.gov/IEB/Research/Acembly/index.html), which aims to\nderive gene structure from these alignments alone." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "B., Ching, K. A., Batalov, S. et al. (2001). A comparison of the Celera and\nEnsembl predicted gene sets reveals little overlap in novel genes. Cell 106, 413–415.\n Hubbard, T., Barker, D., Birney, E. et al. (2002). The Ensembl genome database project. Nucleic\nAcids Res 30, 38–41.\n Huson, D. H., Reinert, K., Kravitz, S. A. et al. (2001). Design of a compartmentalized shotgun\nassembler for the human genome. Bioinformatics 17 Suppl 1, S132–139.\n Huynen, M. A. and Bork, P. (1998). Measuring genome evolution. Proc Natl Acad Sci U S A\n95, 5849–5856.\n Ideker, T., Galitski, T. and Hood, L. (2001)." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "B., Ching, K. A., Batalov, S. et al. (2001). A comparison of the Celera and\nEnsembl predicted gene sets reveals little overlap in novel genes. Cell 106, 413–415.\n Hubbard, T., Barker, D., Birney, E. et al. (2002). The Ensembl genome database project. Nucleic\nAcids Res 30, 38–41.\n Huson, D. H., Reinert, K., Kravitz, S. A. et al. (2001). Design of a compartmentalized shotgun\nassembler for the human genome. Bioinformatics 17 Suppl 1, S132–139.\n Huynen, M. A. and Bork, P. (1998). Measuring genome evolution. Proc Natl Acad Sci U S A\n95, 5849–5856.\n Ideker, T., Galitski, T. and Hood, L. (2001)." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "B., Ching, K. A., Batalov, S. et al. (2001). A comparison of the Celera and\nEnsembl predicted gene sets reveals little overlap in novel genes. Cell 106, 413–415.\n Hubbard, T., Barker, D., Birney, E. et al. (2002). The Ensembl genome database project. Nucleic\nAcids Res 30, 38–41.\n Huson, D. H., Reinert, K., Kravitz, S. A. et al. (2001). Design of a compartmentalized shotgun\nassembler for the human genome. Bioinformatics 17 Suppl 1, S132–139.\n Huynen, M. A. and Bork, P. (1998). Measuring genome evolution. Proc Natl Acad Sci U S A\n95, 5849–5856.\n Ideker, T., Galitski, T. and Hood, L. (2001)." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "B., Ching, K. A., Batalov, S. et al. (2001). A comparison of the Celera and\nEnsembl predicted gene sets reveals little overlap in novel genes. Cell 106, 413–415.\n Hubbard, T., Barker, D., Birney, E. et al. (2002). The Ensembl genome database project. Nucleic\nAcids Res 30, 38–41.\n Huson, D. H., Reinert, K., Kravitz, S. A. et al. (2001). Design of a compartmentalized shotgun\nassembler for the human genome. Bioinformatics 17 Suppl 1, S132–139.\n Huynen, M. A. and Bork, P. (1998). Measuring genome evolution. Proc Natl Acad Sci U S A\n95, 5849–5856.\n Ideker, T., Galitski, T. and Hood, L. (2001)." + }, + { + "document_id": "1a93e25f-2a43-49e9-8450-03a57c93e613", + "section_type": "main", + "text": "Annotation, preprocessing and categorization of data\n\nWe used Ensembl (version 39) as the annotation reference database.Homology between human and mouse genes was derived via BioMart.The total number of genes under study comprises 15,277 Ensembl mouse genes representing the union of the homologue genes from all data sources.An overview about the T2DM specific datasets is given in Table 1." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "section_type": "main", + "text": "\n\nInformation about genes, including gene names, chromosomal coordinates, biotype (coding or non-coding), and number of splice variants, can also be retrieved from Ensembl." + }, + { + "document_id": "046184a9-f062-4da2-9900-641aab9468e1", + "section_type": "main", + "text": "Electronic-Database Information\n\nURLs for data presented herein are as follows: Center for Medical Genetics, http://research.marshfieldclinic.org/genetics/Ensembl Genome Browser, http://www.ensembl.org/Harvard Partners Genome Center, http://www.hpcgg.org/Sequence/human.htmlOnline Mendelian Inheritance in Man (OMIM), http://www .ncbi.nlm.nih.gov/Omim/(forcandidate genes related to the chromosome 12 region of interest) Unified Database for Human Genome Mapping, The, http:// genecards.weizmann.ac.il/udb/" + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "section_type": "main", + "text": "Ensembl provides a DAS reference\nserver giving access to a wide range of specialist annotations of the human\ngenome (for more detail, see http://www.ensembl.org/das/).\n Data mining The ability to query very large databases in order to satisfy a\nhypothesis (‘top-down’ data mining), or to interrogate a database in order to\ngenerate new hypotheses based on rigorous statistical correlations (‘bottom-up’\ndata mining).\n Domain (protein) A region of special biological interest within a single protein\nsequence." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "section_type": "main", + "text": "Ensembl provides a DAS reference\nserver giving access to a wide range of specialist annotations of the human\ngenome (for more detail, see http://www.ensembl.org/das/).\n Data mining The ability to query very large databases in order to satisfy a\nhypothesis (‘top-down’ data mining), or to interrogate a database in order to\ngenerate new hypotheses based on rigorous statistical correlations (‘bottom-up’\ndata mining).\n Domain (protein) A region of special biological interest within a single protein\nsequence." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "Ensembl provides a DAS reference\nserver giving access to a wide range of specialist annotations of the human\ngenome (for more detail, see http://www.ensembl.org/das/).\n Data mining The ability to query very large databases in order to satisfy a\nhypothesis (‘top-down’ data mining), or to interrogate a database in order to\ngenerate new hypotheses based on rigorous statistical correlations (‘bottom-up’\ndata mining).\n Domain (protein) A region of special biological interest within a single protein\nsequence." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "section_type": "main", + "text": "Ensembl provides a DAS reference\nserver giving access to a wide range of specialist annotations of the human\ngenome (for more detail, see http://www.ensembl.org/das/).\n Data mining The ability to query very large databases in order to satisfy a\nhypothesis (‘top-down’ data mining), or to interrogate a database in order to\ngenerate new hypotheses based on rigorous statistical correlations (‘bottom-up’\ndata mining).\n Domain (protein) A region of special biological interest within a single protein\nsequence." + }, + { + "document_id": "f2f55df4-7e90-4600-90a4-fa30a4c91c5f", + "section_type": "main", + "text": "\n\n*The number of Ensembl genes per megabases.Genome Biology 2003, 4:R74" + }, + { + "document_id": "82fcaf77-adf7-47f4-8ebd-6b7a9df8d73e", + "section_type": "main", + "text": "\n\nURLs.Ensembl: http://www.ensembl.org;British 1958 Birth Cohort: http:// www.b58cgene.sgul.ac.uk/;T1DBase: http://t1dbase.org(and UK mirror site, http://dil.t1dbase.org);Stata: http://www.stata.com/;R: http://www.r-project.org/; rpart: http://cran.r-project.org/;D. Clayton's software: http://www-gene.cimr.cam.ac.uk/clayton/software/;Haploview: http://www.broad.mit.edu/mpg/haploview/; gbrowse: http://www.gmod.org/;T1DBase PosterPages: https:// dil.t1dbase.org/page/PosterAdhocAccession codes.All genes are referred to by their HUGO symbol, except for Tenr on 4q27 (Entrez GeneID 132612, alias FLJ32741) and DEXI on 16p13 (Entrez GeneID 28955, alias MYLE)." + }, + { + "document_id": "e2a02184-d59a-4884-b67e-67209b9b9ae2", + "section_type": "main", + "text": "\n\n. ENIGMA Consortium, http://enigma.loni.ucla.edu;eqtl.uchicago.edu,http://eqtl.uchicago.edu/cgi-bin/gbrowse/eqtl/;SNAP, http://www.broadinstitute.org/mpg/snap/;GeneCruiser, http:// genecruiser.broadinstitute.org/genecruiser3/. : Supplementary information is available on the Nature Genetics website.Research was funded by the US National Institute on Aging (NIA; N01-AG-12100), with contributions from the National Eye Institute (NEI), the National Institute on Deafness and Other Communication Disorders (NIDCD), the US National Heart, Lung, and Blood Institute (NHLBI), the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association) and the Althingi (the Icelandic Parliament)." + }, + { + "document_id": "a4e9db98-b007-49f5-bcbd-ce0f78cbff1f", + "section_type": "main", + "text": "Thierry-Mieg D, Thierry-Mieg J: AceView: a comprehensive cDNAsupported gene and transcripts annotation. Genome Biol 2006,\n7(Suppl 1):S12.\n 28. Kuhn RM, Karolchik D, Zweig AS, Wang T, Smith KE, Rosenbloom KR, Rhead\nB, Raney BJ, Pohl A, Pheasant M, et al: The UCSC genome browser\ndatabase: update 2009. Nucleic Acids Res 2009, 37(suppl 1):D755–D761.\n 29. The EPC: A User’s guide to the encyclopedia of DNA elements (ENCODE).\n PLoS Biol 2011, 9(4):e1001046.\n 30. Frazer KA, Pachter L, Poliakov A, Rubin EM, Dubchak I: VISTA:\ncomputational tools for comparative genomics. Nucleic Acids Res 2004,\n32(suppl 2):W273–W279.\n 31." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "section_type": "main", + "text": "The Ensembl and UCSC sites now display at least 16 vertebrate genome\nassemblies; these can either be viewed directly or aligned against the human genome.\n Cross-species data can be assessed at several levels. Comparison of DNA similarity between (vertebrate) genomes is termed ‘phylogenetic footprinting’ (Susens and\nBorgmeyer, 2001; see Chapter 6 for a detailed review of this approach)." + } + ], + "document_id": "FB39925222D82C39F4BBAA833135ACA5", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "Ensembl", + "EBI", + "Wellcome&Trust&Sanger&Institute", + "genome", + "computational&analyses", + "T1Dbase", + "SNPs", + "Biomart", + "NCBI", + "GENSCAN" + ], + "metadata": [ + { + "object": "1443823_s_at: short probe set - potential SNPs could affect mapping result; 1427465_at: 3 SNPs in target area affect the hybridization of 5 probes; 1434893_at: 6 SNPs in target area could affect the hybridization of 7 probes; 1455136_at generate true cisQTL even 3 SNPs in target area affect mapping accuracy of 4 probes - BUT probes without any SNPs reveal the presence of an eQTL.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab43" + }, + { + "object": "We discovered two genome-wide significant SNPs. The first was novel and near ISG20. The second was in TRIOBP, a gene previously associated with prelingual nonsyndromic hearing loss. Motivated by our TRIOBP results, we also looked at exons in known hearing loss genes, and identified two additional SNPs, rs2877561 in ILDR1 and rs9493672 in EYA4 at a significance threshold adjusted for number of SNPs in those regions.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1003104" + }, + { + "object": "We here reviewed published data on single nucleotide polymorphisms SNPs in HIF1A in various diseases; in total, 34 SNPs were tested for an association with 49 phenotypes, and the results were visualized using the Cytoscape software. Among all collected polymorphisms 16 SNPs showed significant associations with 40 different phenotypes, including six SNPs associated with 14 cancer types", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1006971" + }, + { + "object": "Genome-wide association analyses in 22,981 participants 2280 shingles cases from the electronic Medical Records and Genomics Network identified a genomic region in the combined and European ancestry groups that has an age of onset effect reaching genome-wide significance region tags the non-coding gene HCP5 HLA Complex P5.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab319975" + }, + { + "object": "We identified 89 single nucleotide polymorphisms SNPs and 11 DNA insertion-deletions InDels, of which 70 SNPs and 8 InDels were found in rhg1, 9 SNPs were found in Rhg4, and 10 SNPs and 3 InDels were found in SHMT.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007215" + }, + { + "object": "PNPLA3 is associated with liver enzymes in populations of Mexican American ancestry. In the PNPLA3 gene, single-nucleotide polymorphisms SNPs rs4823173 rs2896019 and rs2281135 were significantly associated with aspartate aminotransferase Although not genome-wide significant, the same SNPs were the top hits for alanine aminotransferase. The strong correlation for these SNPs indicated a single hit in the PNPLA3 gene.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab602807" + }, + { + "object": "TH2B is a unique histone variant that plays a key role in the histone-to-protamine packing of the male genome and guides genome-wide chromatin transitions that both precede and follow transmission of the male genome to the egg.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab771863" + }, + { + "object": "variation in GGTA1 of 8 commercial pig populations; 17 SNPs were detected: 11 in intronic regions & 6 in the 3' untranslated region; no SNPs change the encoded protein; but 8 of the SNPs may alter GGTA1 transcriptional regulation & pre-mRNA splicing", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab992182" + }, + { + "object": "Study surveyed the non-synonymous SNPs of DNASE1L2: 19 SNPs originating from frameshift/nonsense mutations found in DNASE1L2 resulted in loss of function of the enzyme. Thus, the present findings suggest that each of the minor alleles for these SNPs may serve as one of genetic risk factors for parakeratotic skin diseases such as psoriasis, even though they lack a worldwide genetic distribution.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab752673" + }, + { + "object": "Our results show the significance of ABCB1 SNPs on GO response in AML and warrants the need to investigate this in other cohorts. Once validated, ABCB1-SNPs in conjunction with CD33-SNPs can open up opportunities to personalize GO-therapy.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab562151" + } + ], + "question": "what is ensembl?", + "subquestions": null, + "task_id": "FB39925222D82C39F4BBAA833135ACA5", + "usage": { + "chatgpt": 5858, + "gpt-4": 3829, + "gpt-4-turbo-preview": 2871 + }, + "user_id": 2 + }, + "document_id": "FB39925222D82C39F4BBAA833135ACA5", + "task_id": "FB39925222D82C39F4BBAA833135ACA5" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_01.json b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_01.json new file mode 100644 index 0000000..523fff5 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_01.json @@ -0,0 +1,156 @@ +{ + "created_at": "2024-05-31T13:08:08.864950Z", + "data": { + "amplify": false, + "answer": "To determine which gene in your QTL is causal for the trait, you can follow several steps. First, narrow down the QTL interval to a reasonable size. Then, sort through the genes within the interval to identify potential Quantitative Trait Genes (QTGs). This can be challenging as more than one gene may be involved and the function of some genes may be unknown. You can use techniques like LCMS to establish causal relationships between DNA loci and transcripts. Once potential QTGs are identified, you need to prove that a particular gene is involved in the quantitative trait under study. This can be done by complementation of a QTL, which can be achieved in several ways. You can also use mediation analysis to identify the causal gene between the genetic variant and the trait-of-interest. Finally, investigate whether the expression of these genes correlates with the phenotype(s) of interest. If the gene expression strongly correlates with the QTL phenotype, this further strengthens the assumption that this gene might be causal.", + "context": { + "07d409f4-91f9-4701-bb39-1421845a6321": [ + { + "document_id": "07d409f4-91f9-4701-bb39-1421845a6321", + "text": "Prior belief or knowledge about the number of true causal and true\nindependent links that might be expected in a typical QTL, depending on the study\ndesign, should be considered to safeguard against high false-positive rates (low\npositive predictive values). In studies that involve mapping gene expression (eQTL),\nprotein (pQTL) or metabolite (mQTL) traits, information about co-localization of\nQTL and genes that are functionally linked to the trait provides information about\nthe likelihood of causal links." + } + ], + "1a041a89-4da8-4ad5-b241-da36df917930": [ + { + "document_id": "1a041a89-4da8-4ad5-b241-da36df917930", + "text": "\n\nThe next step is to investigate whether the expression of these genes correlates with the phenotype(s) of interest.This would suggest a chain of causality: a variant within a gene causes a change in its expression, and the expression of that gene correlates with expression of a phenotypic trait of interest.To do this, we created a correlation matrix between all genes within a QTL with a cis-eQTL in any brain tissue as well as the phenotypes that contributed to the QTL (Supplementary Table S6).Any gene with a cis-eQTL and a significantly correlated expression was considered a good candidate.If the gene only had a cis-eQTL and correlation in a single brain region, then it suggested that this brain region might also be of interest for the phenotype (adding another link to this chain)." + } + ], + "33814fad-d831-46f5-b41f-ff31626a82ca": [ + { + "document_id": "33814fad-d831-46f5-b41f-ff31626a82ca", + "text": "One possible approach to facilitate this endeavor is to identify quantitative trait loci\n(QTL) that contribute to the phenotype and consequently unravel the candidate\ngenes within these loci. Each proposed candidate locus contains multiple genes and,\ntherefore, further analysis is required to choose plausible candidate genes. One of\nsuch methods is to use comparative genomics in order to narrow down the QTL to a\nregion containing only a few genes. We illustrate this strategy by applying it to\ngenetic findings regarding physical activity (PA) in mice and human." + } + ], + "4049da4d-c7cf-4e30-9a21-c77609fad23d": [ + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "text": "Network analyses\nWe now have two QTL, and we have picked potentially interesting genes within each, but now\nwe want to build up more evidence for which gene in our QTL interval is causal. The first, and\nmost obvious way, is to see what genes our trait of interest correlates with, in tissues that we\nexpect to be related to the trait. We calculated the Spearman’s correlation between the trait\nBXD_17850 and all probes with expression data in T helper cells (GN319)." + } + ], + "47c12133-5a30-45b9-bcb8-b96f00737f31": [ + { + "document_id": "47c12133-5a30-45b9-bcb8-b96f00737f31", + "text": "Another\napproach to help to determine if a gene located near the mapped QTL would\nhave effects to influence the quantitative trait will be to use genetically engineered mice to determine if altering the expression of a candidate gene will alter\nthe phenotype of interest (38). However, it is possible that a quantitative trait is\na combined effect of multiple genes located near the QTL (39)." + } + ], + "547ce63b-5178-45cb-ae07-12ae66aa2967": [ + { + "document_id": "547ce63b-5178-45cb-ae07-12ae66aa2967", + "text": "With a known QTL and a\nbody of evidence suggesting possible roles for the affected gene,\nphenotypes can be predicted that may be modulated as a result\nof this sequence variation. If this phenotype is of interest, it\ncan be directly measured and a traditional ‘forward’ QTL analysis carried out to confirm the prediction. Such an approach is\nextremely attractive when the enormous cost and time required\nfor phenotyping a large panel is considered." + } + ], + "581f83bc-3521-4cb3-ad3c-d905a90ecc29": [ + { + "document_id": "581f83bc-3521-4cb3-ad3c-d905a90ecc29", + "text": "The first\nstep is to narrow down the list of\ncandidate causal genes within a\nFig\n1. Interval\nmapping\nof\noviduct\ngross\npathology\nacross\nthe\nBXD\nstrains\n\nQuantitative Trait Locus (QTL)—a\nreveals\na\nQTL\non\ndistal\nChr\n3. The\nL RS\nvalues\nare\nplotted\nin\nblue\nacross\nthe\n\nchromosomal region containing\ngenome\nand\nmeasure\nthe\nstrength\nof\nthe\nassociation\nbetween\n\nsequence variants strongly\nchromosome\nand\nMb\nposition\n(top\nand\nbottom\nX-‐axis,\nrespectively)\nand\n\nassociated with phenotypic\nphenotype\nexpression. Allele\ncontribution\nis\nshown\nby\nthe\nred\n(C57BL/6J)\n\nand\ngreen\n(DBA/2J)\nlines. Red\nand\ngrey\nhorizontal\nlines\nindicate\ngenome-‐\nvariation." + } + ], + "5a56fa6d-9e77-4b95-a836-04d0fa31ee2c": [ + { + "document_id": "5a56fa6d-9e77-4b95-a836-04d0fa31ee2c", + "text": "A special case is the\ncorrelation of the target phenotype with the expression of the\npriorized gene(s) (RNA or protein amounts). This refers to\ncolocalization of the QTL of the target phenotype with the\neQTL position. Correlation can also be examined between the\ntarget QTL phenotype and expression of all genes in the QTL\ninterval. If the gene expression strongly correlates with the\nQTL phenotype, this further strengthens the assumption that\nthis gene might be causal (see Note 12). For performing a correlation analysis:\n–\n\nGo to the Trait Overview Page, as described in step 3, point\n1." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "QTL mapping of traits in mouse cohorts often ends up with a genetic locus, composed of a list of candidate\ngenes. Several studies proposed the use of mediation analysis to identify the causal gene (mediator) between\nthe genetic variant (independent variable) and the trait-of-interest (dependent variable) (Figure 1.4B) [7, 47,\n61, 77]. Mediation analysis can be used either on gene expression levels to identify the regulatory mechanisms\n[7, 47, 61], or on phenotypic traits to discover the potential causal drivers contributing to the phenotypic\nvariances [77] (Figure 1.4C upper)." + } + ], + "7a451204-390c-4ff2-8a1d-b4de62b73503": [ + { + "document_id": "7a451204-390c-4ff2-8a1d-b4de62b73503", + "text": "1a). Second-generation offspring are then\nphenotyped and genotyped, and linkage analysis is carried out to identify a region that is\nassociated with the trait1. This approach has led to the identification of thousands of quantitative trait loci (QTLs) for\nvarious phenotypes and diseases. However, each QTL region is large, often tens of\nmegabases, and contains hundreds of genes. The process of identifying the causal variant\nand the gene involved is therefore difficult and costly. Of the thousands of QTLs identified,\nonly a small fraction of genes has been identified. NIH-PA Author Manuscript\n\n© 2012 Macmillan Publishers Limited." + } + ], + "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d": [ + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "text": "Network analyses\nWe now have two QTL, and we have picked potentially interesting genes within each, but now\nwe want to build up more evidence for which gene in our QTL interval is causal. The first, and\nmost obvious way, is to see what genes our trait of interest correlates with, in tissues that we\nexpect to be related to the trait. We calculated the Spearman’s correlation between the trait\nBXD_17850 and all probes with expression data in T helper cells (GN319)." + } + ], + "7d866915-9d92-4401-8340-ffdef457debe": [ + { + "document_id": "7d866915-9d92-4401-8340-ffdef457debe", + "text": "10 JUNE 2016 • VOL 352 ISSUE 6291\n\naad0189-5\nR ES E A RC H | R E S EA R C H A R T I C LE\n\nSolving QTLs: Finding the quantitative\ntrait gene\nFor cis-QTLs, the causal factors can be quickly\nidentified: With few exceptions, they will be driven by variants within the gene itself or immediately adjacent. For trans-QTLs, mQTLs, and\ncQTLs, the identification of the causal quantitative trait gene (QTG) is challenging due to the\nwidth of the QTLs." + } + ], + "95b99c09-c336-44fd-b378-f41991edb3aa": [ + { + "document_id": "95b99c09-c336-44fd-b378-f41991edb3aa", + "text": "Once the QTL interval is reduced to a reasonable size,\nthe next step in the process involves sorting through the\ngenes within the interval and attempting to determine\nwhich is the QTG. This step is daunting because more than\none gene may be involved and the function of some genes\nwithin the interval may be unknown. Until recently, this\nstep emphasized the detection of polymorphisms within\ncoding sequence (reviewed in Korstanje and Paigen, 2002\nand Glazier et al. 2002); for a polymorphism that produces\nan amino acid substitution, one can often infer and then\ntest for a functional consequence." + } + ], + "abea3dd4-9492-4a2b-8904-b8052e384785": [ + { + "document_id": "abea3dd4-9492-4a2b-8904-b8052e384785", + "text": "To understand the genetic networks that underlie\nquantitative variation in the trait, it is also very important to\ndiscover genes whose expression is correlated with the trait\nafter accounting for the known effects of the QTL on the\ntrait. Many of these genes may have expression that is\nassociated with QTL genotype, and would therefore be\nidentified as important via the tests described above. Other\n\ngenes, however, may have expression values that are correlated with the trait but unassociated with genotype at the\nQTL." + }, + { + "document_id": "abea3dd4-9492-4a2b-8904-b8052e384785", + "text": "The\napproach is motivated by the fact that a research project is\noften focused on a specific classical quantitative trait. If a\nmajor QTL for this classical trait has been identified, it is\noften desirable to test whether this QTL is also associated\nwith the transcription level of any genes, which will provide clues as to which genes belong to the pathway that the\nQTL uses to modulate the classical trait." + } + ], + "d1f04d58-2589-4183-aee4-569820dae052": [ + { + "document_id": "d1f04d58-2589-4183-aee4-569820dae052", + "text": "Confirmation of Candidate Genes\nThe next step is to prove that a particular gene is involved in the quantitative trait\nunder study. This is done by complementation of a QTL, which can be achieved in\nseveral ways (9–11,40). In principle, transgenic complementation is the most straightforward. This approach has been used successfully to demonstrate that Pla2g2a was\nthe correct candidate gene for Mom1, a modifier of the apcmin allele that causes\nadenomatous polyposis coli (41)." + } + ], + "da485354-fcdc-49b8-9a41-0f673610156a": [ + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "So, how do you go about planning and performing a QTL study, and how\ndo you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbred\nstrains that have a markedly different trait. One can now look up many different\ntraits of inbred mice online at the Mouse Phenome Database (http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). However, the trait you may\nwant to study may not be present in wild type mice, so you may want to cross\na mutant (or genetically engineered) strain onto several inbred strains." + } + ], + "f041550e-5f2d-430e-8f46-15ebea6ca496": [ + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "text": "Along with correlations, this tool also derives new traits representing the\nprincipal components (Figure 2d). The user can add these principal components to their Trait\nCollection and proceed to perform QTL mapping, as in the case of a single trait QTL\nmapping. The R/QTL (Broman et al. 2003) and R/CAPE (Tyler et al. 2013) packages can be\nused for deeper analysis of epistasis and pleiotropy for multiple traits and multiple\nregulatory loci. Prioritizing Candidate Genes\n7\n\nAuthor Manuscript\n\nFollowing the identification of a significant QTL, focus shifts to identifying the\nparticular gene(s) that cause the QTL." + } + ], + "f4e26cf0-d214-41bf-b392-9c63a903b0b8": [ + { + "document_id": "f4e26cf0-d214-41bf-b392-9c63a903b0b8", + "text": "The investigators\nfirst identified all QTLs associated with\na classical phenotype and then winnowed the list of potentially associated\ngene-expression traits on the basis of\ntheir correlation or eQTL overlap with\nthe phenotype of interest. Candidate\ngenes then were ranked by applying\n\nthe LCMS technique, which uses the\neQTL data to establish causal relationships between DNA loci and transcripts as well as between transcripts\nand phenotypes and finally identifies\na model that best fits the data." + } + ], + "ff35f4c8-b78b-4dad-9aa8-1bb16479872d": [ + { + "document_id": "ff35f4c8-b78b-4dad-9aa8-1bb16479872d", + "text": "The goal of QTL mapping is clearly the\nidentification and eventual confirmation of candidate genes\n(QTGs) underlying the phenotype. The evidence required for\nsuch confirmation has engendered much discussion (Complex\nTrait Consortium 2003; Glazier et al. 2002) and is likely to vary\ndepending on the nature of the trait and specific resources\navailable to pin down underlying genes (e.g. availability of\nknock-in or knock-outs, specific antibodies, siRNA, etc.). The\npaucity of QTGs meeting such multifaceted standards is testament to the difficulty of narrowing the confidence interval\nsufficiently to identify and test suitable candidate genes (Flint\net al." + } + ] + }, + "data_source": [], + "document_id": "EFB8B9EF07428DA8D36EFCB6B06F9161", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "QTL", + "gene", + "trait", + "phenotype", + "eQTL", + "expression", + "cis-eQTL", + "quantitative&trait&locus", + "QTG", + "correlation" + ], + "metadata": [], + "question": "How do I determine which gene in my QTL is causal for the trait?", + "subquestions": null, + "task_id": "EFB8B9EF07428DA8D36EFCB6B06F9161", + "usage": { + "chatgpt": 4765, + "gpt-4": 3573, + "gpt-4-turbo-preview": 2599 + }, + "user_id": 2 + }, + "document_id": "EFB8B9EF07428DA8D36EFCB6B06F9161", + "task_id": "EFB8B9EF07428DA8D36EFCB6B06F9161" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_02.json b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_02.json new file mode 100644 index 0000000..c9ab451 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_02.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-06-01T14:20:28.998327Z", + "data": { + "amplify": false, + "answer": "Actually, males have one Y chromosome and one X chromosome, while females have two X chromosomes. This combination determines the sex of an individual.", + "context": { + "1ac23aae-2a51-4846-81ec-6db70ab6e5a4": [ + { + "document_id": "1ac23aae-2a51-4846-81ec-6db70ab6e5a4", + "text": "\n\nY chromosome in peripheral blood cells increases with age in men (6) and is correlated with increased risk of cancer mortality and Alzheimer's disease (6,7).X chromosome mosaicism in women also increases with age (8), as does autosomal mosaicism in both sexes (9,10).Recent studies have shown that the prevalence of age-related mosaic abnormalities is greater in men than women (9,10); however, mechanisms underlying the sex differences observed in chromosomal mosaicism in humans are unknown." + } + ], + "20b466c6-004b-484f-96a1-c1b4651bc856": [ + { + "document_id": "20b466c6-004b-484f-96a1-c1b4651bc856", + "text": "\n\nRecent reports suggested a role of Y chromosome loss in risk for all-cause mortality and common age-related disease such as cancer, Alzheimer disease as well as severe atherosclerosis [12][13][14][15][16][17][18][19][20].Building on such reports, we aimed to evaluate the contribution of male Y chromosome mosaicism to the risk for late-stage AMD." + } + ], + "3f72832b-fad9-4d38-aed8-d22e5bd12a22": [ + { + "document_id": "3f72832b-fad9-4d38-aed8-d22e5bd12a22", + "text": "Box 1. Sex-specific cytonuclear interactions\n\nSeveral predictions about the nature of cytonuclear conflicts follow from the patterns of chromosomal inheritance (Table I).In a mated pair of animals, mtDNA is co-transmitted with half of the autosomal genes, two-thirds of the X-linked genes and none of the Y-linked genes [76].This predicts that, relative to the autosomal case, positive nuclear-mitochondrial interactions are more likely to evolve for X-linked loci whereas deleterious interactions between Y-linked genes and mtDNA should accumulate (or cannot be purged efficiently)." + } + ], + "4ad6da14-56a3-48ab-a587-42761ceac238": [ + { + "document_id": "4ad6da14-56a3-48ab-a587-42761ceac238", + "text": "\n\nIn addition to genetic data, the 9p Network Cohort dataset also lists the gender for all 719 individuals.Of these individuals, 406 individuals are female and 313 are male, indicating a female bias (Binomial test p ¼ 0.0006).This result was surprising considering that no female bias has been previously reported in 9p deletion and duplication syndromes.A possible explanation for the significant bias in the 9p Network Cohort dataset is the XY sex reversal phenotype, which is commonly observed in individuals with 9p deletion syndrome.This phenotype could lead to individuals with XY sex chromosomes being listed in the dataset as having a female gender.To further examine this hypothesis, we subset our dataset to include only the 236 individuals whose sex chromosomes are listed in their genetic information.For this much smaller subset, 125 individuals had female sex chromosomes and 111 had male sex chromosomes, indicating no significant sex bias (Binomial test p ¼ 0.4).We also found no significant gender bias in this group (Binomial test p ¼ 0.2), although we did confirm that four of the individuals with XY sex chromosomes had a gender of female.This comparison suggests that the XY sex reversal phenotype may be responsible for a female gender bias, but not a sex bias, in 9p deletion and duplication syndrome cohorts." + } + ], + "6910b508-6d25-4804-9e47-3590b57aa061": [ + { + "document_id": "6910b508-6d25-4804-9e47-3590b57aa061", + "text": "\n\nDuplicated variants with multiple alternative alleles and variants in sex chromosomes X and Y" + } + ], + "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa": [ + { + "document_id": "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa", + "text": "\n\nAutosome-One of the numbered, or nonsex, chromosomes (1 through 22).X and Y are the sex chromosomes." + } + ], + "7d451e79-b698-4744-aeb2-ff319f430d96": [ + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "text": "\n\nGiven such a high abundance of young male-biased genes, we asked whether their parental genes are also male-biased.We found that fewer parental genes of X-linked male-biased duplicates were also male-biased (20%, 2/10) compared to the parental genes of autosomal young male-biased duplicates (32%, 12/37).These data, despite the small sample sizes and being statistically not significant, may suggest that compared to autosomal young genes, X-linked young genes more often evolved novel male-biased expression.However, as the majority of young genes are the result of intrachromosomal duplication events, the pattern might also reflect the fact that X-linked old genes are less likely to be male-biased." + }, + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "text": "\n\nA slight excess of X-linked female-biased genes was also detected (Fig. 2).Although most of them are old, a few recently arose on the X chromosome over 4 to 6 Myr in the common ancestor of the D. melanogaster and D. simulans clade (branch 5).This can be interpreted in the context of the dominance model of the sexual antagonism hypothesis.In this case, a dominant, X-linked gene that is favorable to females but disadvantageous for males can become fixed.The slow accumulation of female-biased genes in the X reflects an overall low rate of female gene origination, either due to a small dominance effect (the degree of dominance h!1/2), or a minor disadvantageous effect on males (the ratio of fitness effects of male relative to female k!0) along with a favorable effect on females (Vicoso and Charlesworth 2006, Equation 10)." + }, + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "text": "\n\nRegarding the second step in the evolution of male-biased genes, namely X!A transposition, sexual antagonism favorable for autosomal fixation (Vicoso and Charlesworth 2006) and/or MSCI (Lifschytz and Lindsley 1972;Betran et al. 2002) may play a role in this process.On the other hand, the within-chromosomal duplication rate is higher than the between-chromosomal duplication rate (Emerson et al. 2008), which may contribute to the slow pace of X!A transposition." + }, + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "text": "\n\nIt has been observed that male-biased genes in Drosophila are overrepresented on autosomes (Parisi et al. 2003;Ranz et al. 2003).Consistent with this result, a dynamic process that can explain the nonrandom autosomal distribution has also been observed, in which autosomal new genes with X-linked parental genes are often male-biased.Specifically, a significant excess of autosomal testisexpressed retrogenes were identified as RNA-duplicates of X-linked parental genes (Betran et al. 2002).Recently, similar X!A gene traffic was observed in the DNA-level duplication and relocation data set of the Drosophila genus (Vibranovski et al. 2009b), and was further confirmed for DNA-level duplications in the D. pseudoobscura neo-X chromosome (Meisel et al. 2009).In addition, selective extinction of neo-X linked male-biased genes also occurred in D. pseudoobscura (Sturgill et al. 2007).These three lines of genome-wide investigation support a common pattern of outof-X traffic for male-biased genes, resulting in an enrichment of these genes on autosomes in the long term." + }, + { + "document_id": "7d451e79-b698-4744-aeb2-ff319f430d96", + "text": "\n\nIt has been reported that the initial manifestations of new gene emergence, namely polymorphic duplicates, occur at a lower frequency on the X chromosome, thus indicating that these duplicates are subject to stronger purifying selection (Emerson et al. 2008).Therefore, the excessive fixation of X-linked duplicates might not occur via neutral processes.Positive selection could have facilitated the fixation of X-linked young genes in addition to driving their subsequent sequence evolution." + } + ], + "96cb840e-747f-4849-8354-e8764aa0a1ce": [ + { + "document_id": "96cb840e-747f-4849-8354-e8764aa0a1ce", + "text": "\n\nOccasionally, Y chromosome DNA is detected in the maternal plasma, and the fetus appears to have female genitalia on sonographic examination.The underlying mechanisms for this include a twin demise, a maternal disorder of sexual differentiation, such as Swyer syndrome, or that the mother has undergone a bone marrow or solid organ transplant from a male donor (Bianchi, 2018;Hartwig, Ambye, Sorensen, & Jorgensen, 2017)." + } + ], + "9a5c3e73-8270-400f-8a2d-4f36b757188c": [ + { + "document_id": "9a5c3e73-8270-400f-8a2d-4f36b757188c", + "text": "Because\nof the differences in sex chromosome number, the sexunmatched comparison contains internal controls, i.e. ,\nin this comparison, genes on the X-chromosome and\nY-chromosome (but not those on the autosomes) should\nshow copy number imbalances reflective of a single copy\nchange. We showed that the sample that is not sexmatched had readily detectable differences in aCGH\nsignals for genes on the X and Y chromosomes. No such\npatterns were evident for the autosomes of the sex\nunmatched individuals or for the sex chromosomes of the\nsex matched samples." + } + ], + "af3d7cd3-40ec-4a86-a473-89f83da250e4": [ + { + "document_id": "af3d7cd3-40ec-4a86-a473-89f83da250e4", + "text": "Sex chromosome:\n\nThe X or Y chromosome in human beings that determines the sex of an individual.Females have two X chromosomes in diploid cells; males have an X and a Y chromosome.The sex chromosomes comprise the 23rd chromosome pair in a karyotype.See also: autosome Sex-linked: Traits or diseases associated with the X or Y chromosome; generally seen in males." + }, + { + "document_id": "af3d7cd3-40ec-4a86-a473-89f83da250e4", + "text": "\n\nX chromosome: One of the two sex chromosomes, X and Y. See also: Y chromosome, sex chromosome Y chromosome: One of the two sex chromosomes, X and Y. See also; X chromosome, sex chromosome" + } + ], + "b04f2221-de28-4c4b-893e-9da982ff864c": [ + { + "document_id": "b04f2221-de28-4c4b-893e-9da982ff864c", + "text": "The male heterogamety (XY) is the most\ncommon reported system, but many species\nhave female heterogamety (ZW), and more\noccasionally, multiple chromosome systems\n\n(Almeida-Toledo and Foresti, 2001; Devlin\nand Nagahama, 2002; Penman and Piferrer,\n2008). Given the low resolution of optical microscopy to differentiate sex chromosomes in\nfish, researchers have looked for an alternative\nin the tenfold longer meiotic chromosomes to\ndetect mispairing tracts at the synaptonemal\ncomplex as an indication of the sex differentiated region with variable success." + }, + { + "document_id": "b04f2221-de28-4c4b-893e-9da982ff864c", + "text": "The exclusive female\nconstitution of gynogenetic genomes provides\ninformation on the SD system, especially in a\nXX/XY system, where all female progenies are\nexpected. If ZZ/ZW is the underlying system,\nmale offspring always will be present, but the\ninterpretation is more complex and will depend\non the distance of the SD region to centromere\nand on the viability of WW offspring (Devlin\nand Nagahama, 2002; Penman and Piferrer,\n2008). Induced triploids, on the other hand, are\nconstituted by the combination of two female\nand one male genomes (Piferrer et al." + } + ], + "ef2c8463-5169-46aa-938b-7d04ea8da6b7": [ + { + "document_id": "ef2c8463-5169-46aa-938b-7d04ea8da6b7", + "text": "\n\nThe existence of a maternally silenced X-linked imprinted locus playing a role in social cognition could explain why males (X m Y) are more vulnerable to disorders of social cognition such as autism spectrum disorders than are females (X m X p ).The absence of the expression of this gene would not lead to autism itself, but would eliminate a putative protective factor, making an individual more susceptible to the effects of other ASD-predisposing genetic mutations or environmental factors." + } + ], + "f051ad23-572d-4302-8dda-4d992aeaeb1a": [ + { + "document_id": "f051ad23-572d-4302-8dda-4d992aeaeb1a", + "text": "\n\nWhen meiosis takes place, a pair of chromosomes may fail to separate properly, creating a sperm or egg that has either two copies or no copy of a specific chromosome.This is a sporadic event and it is called nondisjunction.Nondisjunction can lead to an extra chromosome, called trisomy, or a missing chromosome, called monosomy (GHR, 2008l).Down syndrome is an example of trisomy.Individuals who have Down syndrome have an extra chromosome number 21. Turner syndrome is an example of monosomy.Girls who have Turner syndrome have only one X chromosome.This causes them to have short stature and be infertile (NHGRI, 2008l)." + }, + { + "document_id": "f051ad23-572d-4302-8dda-4d992aeaeb1a", + "text": "X-Linked Inheritance\n\nX-linked genetic disorders (also called sex-linked) are caused by gene mutations on the X chromosome.Most often X-linked genetic disorders are seen in males.Males inherit the X chromosome from their mother and the Y chromosome from their father.Because males have only one X chromosome, if they inherit a gene mutation on the X chromosome from their mother, they will have the disorder.Examples of X-linked genetic disorders occurring in males include hemophilia and Duchenne muscular dystrophy (GHR, 2008o)." + } + ] + }, + "data_source": [], + "document_id": "34A6BD721632631DF6D97BCA1D315B0A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "X&chromosome", + "Y&chromosome", + "male", + "female", + "sex&chromosomes", + "autosome", + "nondisjunction", + "trisomy", + "monosomy", + "X-linked&inheritance" + ], + "metadata": [], + "question": "Why do males have two Y chromosomes and females only one?", + "subquestions": null, + "task_id": "34A6BD721632631DF6D97BCA1D315B0A", + "usage": { + "chatgpt": 6105, + "gpt-4": 3430, + "gpt-4-turbo-preview": 2504 + }, + "user_id": 2 + }, + "document_id": "34A6BD721632631DF6D97BCA1D315B0A", + "task_id": "34A6BD721632631DF6D97BCA1D315B0A" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_03.json b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_03.json new file mode 100644 index 0000000..494fe08 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_03.json @@ -0,0 +1,154 @@ +{ + "created_at": "2024-06-01T14:22:47.136754Z", + "data": { + "amplify": false, + "answer": "GeneNetwork utilizes datasets containing phenotypes and genotypes for QTL mapping analysis. These datasets may include genetic, genomic, and phenotypic data. The data are often formatted and normalized, and come with metadata. The database also includes data on complex phenotypes ranging from gene expression to behavior in standard inbred strains, and panels of mouse recombinant inbred strains. Rat and Arabidopsis populations are also represented in the database.", + "context": { + "071b4686-f5c4-4759-a038-14d79a45dac7": [ + { + "document_id": "071b4686-f5c4-4759-a038-14d79a45dac7", + "text": "The project also provides online analysis tools to allow\nidentification of correlations within its data set. GeneNetwork (http://www.genenetwork.org), encompassing WebQTL, is a database of\ngenotypes and complex phenotypes ranging from gene expression to behaviour in standard\ninbred strains, and six panels of mouse recombinant inbred strains including the two largest\nsets (BXD and LXS) of approximately 80 strains each. Rat and Arabidopsis populations are\nalso represented. Approximately 1500 phenotypes spanning the 25 year history of these\nstrains are incorporated in this public resource, many of which were retrieved from the\nliterature." + } + ], + "0e6c370f-b514-4551-b6ed-9cc72e6f6b75": [ + { + "document_id": "0e6c370f-b514-4551-b6ed-9cc72e6f6b75", + "text": "GN spares the\nuser most of these problem. Data are formatted and normalized, and usually come with good\nmetadata (often in the form of links to more information). This greatly simplifies QTL and\neQTL analysis, candidate gene discovery, coexpression analysis, and hypothesis testing [3,\n10]." + }, + { + "document_id": "0e6c370f-b514-4551-b6ed-9cc72e6f6b75", + "text": "Suitable for quantitative\ngenetics (QTL mapping) and systems genetics, including correlation and\nnetwork analysis to compare associations between tissues and between\nother rodent or human data sets\n\nDescription and usage\n\n[32]\n\n[31]\n\n[30]\n\n[11]\n\nReferences\n\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential\nusage." + } + ], + "2a92d7b5-946c-4a22-a4b9-26e950b0f757": [ + { + "document_id": "2a92d7b5-946c-4a22-a4b9-26e950b0f757", + "text": "Bioinformatics\nAll of the genetic analyses were carried out in GeneNetwork, which\nis an open source bioinformatics resource for systems genetics that\nexists as both a repository for genetic, genomic and phenotypic\ndata together with a suite of statistical programs for data analysis that includes mapping and evaluating QTLs, examining phenotype/genotype correlations and building interaction networks. QTL mapping\nThe QTL mapping module of GeneNetwork was used to identify\nQTLs for hippocampal morphometry and radial maze trait data. This\nmodule enables interval mapping, composite interval mapping and\na pairwise scan option to identify epistatic effects." + } + ], + "389bdbf3-0224-4edb-a4fb-71a54971ba66": [ + { + "document_id": "389bdbf3-0224-4edb-a4fb-71a54971ba66", + "text": "There\nare four options for QTL mapping on the GeneNetwork website: interval\nmapping, marker regression analysis, composite interval mapping, and pairscan analysis. In this case, interval mapping was used to compute linkage\nmaps for the entire genome. The log of odds (LOD) score was used to\nassert that a causal relation exists between a chromosomal location and a\nphenotypic variant, such as Gsto1 expression variation." + } + ], + "3df1bffa-3d23-4b6b-9d59-6ef8b0001f48": [ + { + "document_id": "3df1bffa-3d23-4b6b-9d59-6ef8b0001f48", + "text": "Webqtl is an online database [110] of linked datasets, including genotype and expression\ndata, covering multiple species including mouse, macaque monkey, rat, drosophila,\narabidopsis, plants and humans [60]. While this tool cannot be used to calculate eQTLs, it\ncan be used to find and visualize eQTLs in different species, strains and tissues. It can\nperform single- and multiple-interval QTL mapping of up to 100 selected traits. Users can\nalso upload their own trait data for populations included in the database. It can also calculate\nand display trait-correlation matrices and network graphs (also for up to 100 traits)." + } + ], + "43407486-b9c2-487b-b19c-b605c4d201c6": [ + { + "document_id": "43407486-b9c2-487b-b19c-b605c4d201c6", + "text": "GN spares the\nuser most of these problem. Data are formatted and normalized, and usually come with good\nmetadata (often in the form of links to more information). This greatly simplifies QTL and\neQTL analysis, candidate gene discovery, coexpression analysis, and hypothesis testing [3,\n10]." + }, + { + "document_id": "43407486-b9c2-487b-b19c-b605c4d201c6", + "text": "Suitable for quantitative\ngenetics (QTL mapping) and systems genetics, including correlation and\nnetwork analysis to compare associations between tissues and between\nother rodent or human data sets\n\nDescription and usage\n\n[32]\n\n[31]\n\n[30]\n\n[11]\n\nReferences\n\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential\nusage." + } + ], + "516cc395-4e7c-4371-9444-24edb56a7233": [ + { + "document_id": "516cc395-4e7c-4371-9444-24edb56a7233", + "text": "QTL MAPPING AND QTG DISCOVERY IN THE RCC\nA variety of statistical methods and tools have been developed for QTL mapping and\nimplemented in free software for public use. These methods are well suited for simple\nbackcross and F2 RCC populations. R/qtl9,39 was developed for identification of\nQTLs and higher order modeling. Another Web-based tool, GeneNetwork or\nWebQTL (GeneNetwork.org),40 was developed for QTL mapping and to explore\nassociations between variants, molecular traits (e.g. , gene expression), and higher order\nphenotypes (e.g. , behavior) and facilitate QTG identification." + } + ], + "550c099f-88d0-483f-865a-01ef7362e2be": [ + { + "document_id": "550c099f-88d0-483f-865a-01ef7362e2be", + "text": "This enables gene expression\ncorrelation and interval mapping, candidate gene searches and multitrait analyses. Each exported dataset was subject to an interval mapping analysis,\nwhich uses GeneNetwork’s embedded MapManager software\n(Manly et al . 2001) to perform Haley–Knott regression. Empirical P values were derived using 1000 permutations using the incorporated\npermutation feature of WebQTL. The peak of each statistically\nsignificant (P -value <0.05) or suggestive (P -value <0.63) (Lander\n& Kruglyak 1995) QTL was determined based on empirical P values (Doerge & Churchill 1996). A one-LOD drop-off was used\nto determine the QTL confidence interval about each peak." + } + ], + "581f83bc-3521-4cb3-ad3c-d905a90ecc29": [ + { + "document_id": "581f83bc-3521-4cb3-ad3c-d905a90ecc29", + "text": "The peak linkage value\nand position was databased in GeneNetwork and users\ncan rapidly retrieve and view these mapping results for\nany probe set. Any of the QTL maps can also be rapidly\nregenerated using the same Haley-Knott methods, again\nusing functions imbedded in GeneNetwork. GeneNetwork also enable a search for epistatic interactions (pair\nscanning function) and composite interval mapping with\ncontrol for a single marker. Data quality control\n\nWe used two simple but effective methods to confirm\ncorrect sample identification of all data entered into\nGeneNetwork." + } + ], + "5bd8262b-b2cd-4098-a494-ede168941a9a": [ + { + "document_id": "5bd8262b-b2cd-4098-a494-ede168941a9a", + "text": "QTL analysis\nAll QTL mapping for phenotypes was performed using the WebQTL software module of the\n\n170\n\nGeneNetwork (www.genenetwork.org) [34]. Interval mapping to evaluate potential QTLs was\ncalculated from the likelihood ratio statistics (LRS) as the software’s default measurement of\nthe association between differences in traits and differences in particular genotype markers. Another common measure score, the log of the odds (LOD) ratio, can be converted from the\nLRS (LRS/4.61). Suggestive and significant LRS values were determined by applying 1000\n\n175\n\npermutations." + } + ], + "80eb54fe-0d83-4300-9fba-e17ce5d1e5b4": [ + { + "document_id": "80eb54fe-0d83-4300-9fba-e17ce5d1e5b4", + "text": "Unlike interval-specific haplotype analysis, which is most useful for narrowing a QTL shared by\nmultiple crosses, genome-wide haplotype analysis\nrequires only phenotype information from many inbred\nstrains and can effectively narrow a QTL identified in\nonly one experimental cross [36]. After narrowing the QTL to an interval that is !5 Mb\nusing these bioinformatics techniques or classical experimental methods, strain-specific sequence and gene\nexpression comparisons are effective for focusing on a\nfew strong candidate genes (Figure 7)." + } + ], + "86b86235-b7a8-4dfc-be13-d119dc31b377": [ + { + "document_id": "86b86235-b7a8-4dfc-be13-d119dc31b377", + "text": "We considered QTL intervals that achieved genome-wide\nsignificance for one phenotype, and genome-wide suggestive for\nothers, as highest priority for candidate gene analysis. The January 2017 BXD genotype file was used4 . Updated linear mixed model mapping algorithms are now\navailable on GeneNetwork 25 (Sloan et al. , 2016), that account for\nkinship among strains. These new algorithms include GEMMA\n(Zhou and Stephens, 2012), pyLMM6 (Sul et al. , 2016), and\nR/qtl27 ." + } + ], + "9b2a48a0-f85e-4104-944f-0c47a3b03a9b": [ + { + "document_id": "9b2a48a0-f85e-4104-944f-0c47a3b03a9b", + "text": "The peak linkage value\nand position was databased in GeneNetwork and users\ncan rapidly retrieve and view these mapping results for\nany probe set. Any of the QTL maps can also be rapidly\nregenerated using the same Haley-Knott methods, again\nusing functions imbedded in GeneNetwork. GeneNetwork also enable a search for epistatic interactions (pair\nscanning function) and composite interval mapping with\ncontrol for a single marker. Data quality control\n\nWe used two simple but effective methods to confirm\ncorrect sample identification of all data entered into\nGeneNetwork." + } + ], + "a4508fb3-c66b-4526-b2a2-a327505d085a": [ + { + "document_id": "a4508fb3-c66b-4526-b2a2-a327505d085a", + "text": "There\nare four options for QTL mapping on the GeneNetwork website: interval\nmapping, marker regression analysis, composite interval mapping, and pairscan analysis. In this case, interval mapping was used to compute linkage\nmaps for the entire genome. The log of odds (LOD) score was used to\nassert that a causal relation exists between a chromosomal location and a\nphenotypic variant, such as Gsto1 expression variation." + } + ], + "b5c36c1e-458e-4009-818e-9c0c2ee23e45": [ + { + "document_id": "b5c36c1e-458e-4009-818e-9c0c2ee23e45", + "text": "eQTL mapping\n\nQTL mapping was performed with GeneNetwork, an online bioinformatics resource\nfeaturing tools for systems genetic and complex trait analysis [9, 35]. QTL mapping\ninvolves entering VMB and CP iron data (strain means and SEM) as quantitative traits; the\nsoftware generates whole-genome interval maps for each trait. The interval maps graphically\nillustrate phenotype–genotype associations as peaks (QTL) indicating the strength of\nassociation between genomic polymorphisms and the quantitative trait throughout the\ngenome." + } + ], + "baacd740-efc8-42f2-af22-6f5ac9710900": [ + { + "document_id": "baacd740-efc8-42f2-af22-6f5ac9710900", + "text": "Genetic Mapping\nIn this study we utilize GeneNetwork, a database containing phenotypes and genotypes,\nand also serves as an analysis engine for quantitative trait locus (QTL) mapping, genetic\ncorrelations, and phenome-wide association studies (PheWAS) (Sloan et al. , 2016; Mulligan et\nal. , 2017; Watson and Ashbrook, 2020). QTL analysis involves connecting phenotype data with\ngenotype data to examine genetic variation in traits controlled by multiple genes and their\ninteraction with the environment (also called complex traits)(Lynch et al. , 1998; Myles and\nWayne, 2008; Goddard et al. , 2016)." + } + ], + "beb7a242-21fe-4a66-8b44-7f228c0d3640": [ + { + "document_id": "beb7a242-21fe-4a66-8b44-7f228c0d3640", + "text": "Once the resulting record set of the\nquery is returned, it can be further restricted by selecting\nrelevant records based on attached annotations before forwarding it for further analysis. To map genetic loci associated with mRNA abundance or\ntrait phenotypes, any one of the three QTL mapping functions currently employed by GeneNetwork's WebQTL\nmodule can be used. These are 1. interval mapping, 2. single-marker regression, or 3. composite mapping [29,30]." + } + ], + "e70f7c61-1734-4048-8a79-382e9b381686": [ + { + "document_id": "e70f7c61-1734-4048-8a79-382e9b381686", + "text": "genenetwork.org/) a set of 3795 markers. Linkage is reported with\ngenome-wide significance levels based on 2000 permutation tests. Two types of QTL mapping analyses–simple mapping using the Haley–\nKnott regression equation, and composite interval mapping–were\nutilized in this study. Simple interval mapping was performed to\nillustrate the significance of any QTLs that regulate the TID. As a\nsecondary analysis, composite interval mapping which controlled for\nthe influence of Tyrp1 was also performed with the goal of identifying\nany secondary QTLs that may have been masked by the major QTL on\nChr 4." + } + ] + }, + "data_source": [], + "document_id": "39076B38EDAF24ECEEB91924D370F4AD", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "GeneNetwork", + "QTL", + "mapping", + "genotype", + "phenotype", + "interval&mapping", + "composite&interval&mapping", + "marker®ression", + "eQTL", + "haplotype" + ], + "metadata": [], + "question": "what type of dataset is useful for qtl mapping analysis in genenetwork2?", + "subquestions": null, + "task_id": "39076B38EDAF24ECEEB91924D370F4AD", + "usage": { + "chatgpt": 5013, + "gpt-4": 3398, + "gpt-4-turbo-preview": 2412 + }, + "user_id": 2 + }, + "document_id": "39076B38EDAF24ECEEB91924D370F4AD", + "task_id": "39076B38EDAF24ECEEB91924D370F4AD" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_04.json b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_04.json new file mode 100644 index 0000000..6fe6d7f --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_04.json @@ -0,0 +1,140 @@ +{ + "created_at": "2024-06-01T14:24:19.558227Z", + "data": { + "amplify": false, + "answer": "The ethical considerations surrounding prenatal genetic testing and selective termination of pregnancies based on genetic factors include the potential for implicit pressure on individuals to violate personal ethics to reduce financial burden on society, the risk of routinization of testing leading to social or medical expectations of testing in all eligible individuals, and the potential compromise of values of informed consent and individual autonomy. There are also cultural and religious beliefs to consider, as well as the potential psychological impact on parents who may feel guilt if they are carriers of genetic conditions. Furthermore, the decision to terminate a pregnancy based on genetic factors is a joint decision between parents, and the involvement of extended family members in this process varies greatly across different cultures.", + "context": { + "30eabd29-2f48-459a-b162-bd90d99f1411": [ + { + "document_id": "30eabd29-2f48-459a-b162-bd90d99f1411", + "text": "\n\nA number of additional ethical implications must be considered.Associating financial investments with the prevention of disease, especially where reproductive decisions are involved, requires sensitivity, caution, and ethical rigor.Funding decisions based on imputed cost-savings must not result in implicit pressure on individuals to violate personal ethics to reduce financial burden on society.As discussions regarding prenatal testing have demonstrated, 35 is risk that \"routinization\" of testing may lead to social or medical expectations of testing in all eligible individuals.These expectations, if linked with financial incentives for the health system, could risk applying implicit pressure on serious, and potentially irreversible, personal decisions.Such expectations, if applied at the population level, could risk becoming normalized, compromising the values of informed consent and individual autonomy." + }, + { + "document_id": "30eabd29-2f48-459a-b162-bd90d99f1411", + "text": "\n\nWith regard to pregnancies affected by a genetic condition identified through population carrier screening, we modeled the decision to terminate affected pregnancies conservatively (0.50).This is despite the literature suggesting rates above 0.90 for elective TOP for conditions such as Down syndrome 33 and SMA. 34We recognize this issue is controversial, and that laws and ethical positions vary considerably between countries/ jurisdictions.Variations in population attitudes based on age, religion, and other factors, as well as the criticality of preserving individual choice, were acknowledged in adopting this highly conservative estimate." + } + ], + "56cf7be3-8c73-498d-b48f-8d99592b0213": [ + { + "document_id": "56cf7be3-8c73-498d-b48f-8d99592b0213", + "text": "\n\nThe use of genetic testing from pre-conception through adulthood is expanding rapidly.As a result of this expansion, new ethical issues are emerging related to genetic testing and informed consent.These new issues create ethical challenges for nurses and all healthcare providers.Currently expanding areas include newborn screening and genetic testing of children.These new ethical challenges will be described below." + }, + { + "document_id": "56cf7be3-8c73-498d-b48f-8d99592b0213", + "text": "The use of genetic testing from pre-conception through adulthood is expanding rapidly. Psychological risks for parents who are carriers may include parental guilt." + } + ], + "64d87c52-1185-4080-8d06-134c32dae5fd": [ + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "text": "\n\nEthnic and cultural backgrounds may also play a role in the decisions that families make regarding prenatal testing.Moyer et al. (1999) concluded that Caucasian women more often undergo prenatal diagnoses than African American or Asian women, or Latinas.Furthermore, Awwad et al. (2008) found American couples less inclined to involve extended relatives in the prenatal decision-making process than Native Palestinian couples.Both of these examples clearly indicate that cultural differences can impact the ways in which families negotiate prenatal decisions.Further research needs to investigate how different families engage in such discussions and decision-making processes, especially as prenatal testing becomes more common and better able to predict or prevent a wider range of genetic conditions.Tightly closed ethnic groups remain at high risk of serving as carriers for genetic mutations, but the management of this possibility varies greatly.For example, some Ashkenazi Jewish groups use screening for mutations for Tay-Sachs disease (TSD) as the basis for rabbinical marriage advice; whereas, children born to Amish families in Pennsylvania more often present with glutaric aciduria type 1 (GA1) but, given their beliefs, parents tend not to accept prenatal testing because of the implication of abortion (McKusick, 2000)." + }, + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "text": "\n\nResearchers studying factors that contribute toward a couple's choice to undergo prenatal testing have determined that partners base their decision upon several factors, including, but not limited to: parental beliefs about abortion, attitudes regarding disability and their \"perceptions of the usefulness of having the information revealed by genetic tests\" (Moyer et al., 1999, p. 522).Abortion beliefs constitute a key issue in the decision-making process.Even though a majority of parents receiving abnormal prenatal test results terminate their pregnancies (Redlinger-Grosse, Bernhardt, Berg, Muenke, & Biesecker, 2002), Moyer et al. noted that, when asked, more families reported that they would make use of prenatal testing than would be willing to terminate a pregnancy.The decision to continue or terminate a pregnancy after prenatal testing Downloaded by [University of the Sunshine Coast] at 10:32 05 August 2017 comprises a joint decision between both parents (e.g., Awwad et al., 2008;Beeson & Golbus, 1985); however, the nature of the conversations leading to the decision and the involvement of extended family members in the decisionmaking process remains highly understudied." + }, + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "text": "The Genetic Divide(s) and Communication\n\nThe ability of scientists to \"map\" disease through several generations (Collins, 1999) raises practical and ethical issues of access to resulting opportunities and creates family communication challenges.Currently, prenatal testing for chromosomal diseases has become increasingly common (Moyer et al., 1999).Options such as pre-implantation genetic diagnosis (PGD) can identify over 1,250 disease-related mutations creating an opportunity for parents to select unaffected embryos for implantation in the womb (R. M. Green, 2008).Test results provide potential parents with information that may lead to decisions involving intervention in the genetic makeup of future children.Although some families welcome such options, others may be unable or unwilling to consider such procedures, due to fi nancial concerns or moral/ethical/religious beliefs." + } + ], + "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa": [ + { + "document_id": "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa", + "text": "Privacy Issues\n\nFinally, privacy issues should be seriously considered when the use of genetic testing is contemplated, especially with respect to whole-genome sequencing of healthy people.It is an unanswered question under what circumstances, to what extent, and by what means genetic data should be incorporated into the medical record.Although easy access to such data could be helpful to providers in improving patient care, it remains to be seen how other parties (eg, insurance companies) might act on the data in ways that do not benefit patients.The US Congress acted to prohibit discrimination by employers and health insurers on the basis of genetic testing with the Genetic Information Nondiscrimination Act in 2008, but further safeguards will undoubtedly be needed as the health implications of genetic data become clearer." + } + ], + "782103fd-2cb6-44c8-9b39-d82430d335c9": [ + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "text": "\n\nThe ethical evaluation of genetic testing in children is traditionally based on the balance of clinical benefits and risks (American Society of Human Genetics Board of Directors and the American College of Medical Genetics All correspondence concerning this article should be addressed to Benjamin Wilfond, MD, Treuman Katz Center for Pediatric Bioethics, Seattle Children's Hospital, Metropolitan Park West M/S: MPW 8-2, 1100 Olive Way, Room 876, Seattle WA 98101, USA.E-mail: benjamin.wilfond@seattlechildrens.org Board of Directors, 1995;Andrews, Fullerton, Holtzman, & Motolsky, 1994;Clarke, 1994;Wertz, Fanos, & Reilly, 1994).In the early 1990s, when there were only scant data about children who had received genetic tests results, the presumption was to give greater weight to the potential risks and to restrict testing.However, this criterion is not necessarily consistent with the general practice of respecting broad parental discretion in health care decisionmaking for and on behalf of their children.In general, parents are the presumed decision makers for their children and their decisions are respected unless they are abusive or neglectful (Buchanan & Brock, 1989;Goldstein, Freud, & Solnit, 1979;Ross, 1998).The tension between assessments of benefits and risks made by health care providers and policy makers, and the procedural respect owed to parental authority will be clearly tested as the ability to conduct and interpret whole-genome sequencing and related technologies gain in momentum." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "text": "Ethical Considerations in Developing Policy for ''Comprehensive'' Genomic Testing\n\nIn the near future, genomic testing is likely to become more accessible and will provide both information about the risks of common conditions such as heart disease, diabetes, and hypertension as well as predictions about individual responses to specific pharmaceuticals and other medical therapies (Aspinall & Hamermesh, 2007).Over time, the number and range of conditions for which such testing is available is likely to expand to include more behavioral traits, ranging from information about anxiety and depression, to attention and addiction (Rothstein, 2005)." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "text": "\nObjective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical benefits and risks.However, this focus can be inconsistent with the general practice of respecting parental decision-making about their children's health care.We argue that respect for parental decision-making should play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions to use emerging genomic technologies.Methods Genomic testing involves the examination of thousands of DNA markers spanning genes throughout the genome and their interrelationships, yielding virtually limitless interpretations.We presume that parents and providers should proceed cautiously in applying genomic testing in children, as we explore how genomic testing will stress the fault lines of the traditional ethical analysis.Results Empirical data about the psychosocial risks and benefits of genetic testing of children do not reveal serious harms, yet virtually no such data exist yet about genomic testing.Unless empirical social and behavioral data indicate that genomic testing is highly likely to cause serious harms to the children, parental decisions to obtain comprehensive genomic testing in their children should be respected.Once comprehensive genomic testing of children becomes routine, resultant information may be more easily integrated by families than anticipated.Conclusions Research on the social and behavioral impact of comprehensive genomic testing on children and their families is needed to further inform parents, clinicians, and policy makers." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "text": "\n\nObjective Ethical evaluation of genetic testing in children is traditionally based on balancing clinical benefits and risks.However, this focus can be inconsistent with the general practice of respecting parental decision-making about their children's health care.We argue that respect for parental decision-making should play a larger role in shaping pediatric genetic testing practices, and play a similar role regarding decisions to use emerging genomic technologies.Methods Genomic testing involves the examination of thousands of DNA markers spanning genes throughout the genome and their interrelationships, yielding virtually limitless interpretations.We presume that parents and providers should proceed cautiously in applying genomic testing in children, as we explore how genomic testing will stress the fault lines of the traditional ethical analysis.Results Empirical data about the psychosocial risks and benefits of genetic testing of children do not reveal serious harms, yet virtually no such data exist yet about genomic testing.Unless empirical social and behavioral data indicate that genomic testing is highly likely to cause serious harms to the children, parental decisions to obtain comprehensive genomic testing in their children should be respected.Once comprehensive genomic testing of children becomes routine, resultant information may be more easily integrated by families than anticipated.Conclusions Research on the social and behavioral impact of comprehensive genomic testing on children and their families is needed to further inform parents, clinicians, and policy makers." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "text": "\n\nTo the extent that ''personal meaning'' gains wider acceptance as a legitimate criterion for expanding the availability of new tests and applications of genomic technology, the current policies and practices of restricting some genetic testing of children and mandating other tests will need to be reevaluated.There will be some parents who will find the information that becomes available through new technologies and data useful in shaping their parenting practices, while others will be more skeptical of their value.These disparate parental judgments may be independent of professional assessments of clinical validity and utility.Extrapolating from the empirical data about predictive genetic testing of children in at-risk families discussed earlier, we speculate that once comprehensive genomic testing of children becomes routine, the information may be more easily integrated by families than might be predicted.This is not meant to imply that whatever information parents want about their children should be provided carte blanche.Clearly, education and counseling will be crucial to ensure that families understand the limitations of the information.However, restrictions and mandates should be based on a criterion of risk of serious harm (Diekema, 2004).Given the lack of data confirming harm and the related data that indicate children may fare better than anticipated, such restrictions and mandates cannot be justified.Policies and practices will also need to clarify the role of the older adolescent in the decision-making process, although the issues related to balancing and assessing parental and adolescent interests and preferences goes beyond the focus of this article.This is also not meant to ignore the professional and moral obligation to educate parents and to help parents make good decisions on behalf of their children.It is morally appropriate for providers to strongly recommend particular tests in infancy and young childhood (i.e., PKU testing), and to strongly discourage other tests (e.g., ApoE testing of children for adult onset Alzheimer disease and heart disease because ApoE is not predictive but only provides an increased relative risk and has limited sensitivity and specificity) (Roberts, Cupples, Relkin, Whitehouse, & Green, 2005).Selective and directive recommendations are a routine aspect of pediatric practice.However, it will become increasingly important for professional organizations to begin to reconcile their support for mandatory genetic testing for some conditions and their support for restrictions for other conditions with the broad discretion that parents have and need in the health care arena in order to promote their children's well-being." + }, + { + "document_id": "782103fd-2cb6-44c8-9b39-d82430d335c9", + "text": "\n\nWhat limits should be imposed, if any, need to be determined prior to commercial feasibility.In this article, we consider how genetic testing decisions for children have been made traditionally and how the anticipation of comprehensive genomic testing in the near future will stress the fault lines of traditional approaches.The potential for comprehensive genomic testing in children could shift the equilibrium towards expanding or reducing parental discretion, and forces us to reexamine the evidence for our genetic testing policies and practices.We will highlight specific domains where further empirical social and behavioral research is necessary to inform policy and practice." + } + ], + "93dc581e-5e45-48b4-b82f-35e32d7bd58e": [ + { + "document_id": "93dc581e-5e45-48b4-b82f-35e32d7bd58e", + "text": "\n\nPrenatal genetics is largely practiced by maternal-fetal medicine specialists due to severe deficiency in the number of qualified clinical geneticists.Recent years have witnessed a tremendous growth in the demand for chorionic villous sampling and amniocentesis for the diagnosis of single gene disorders.At KFSHRC alone, the number of prenatal samples that are tested for single gene disorders has increased from 5 in 2004 to 250 in 2013.Therapeutic abortion is permitted by law if performed within 120 days from the time of fertilization in order to comply with the Islamic view of the timing of ensoulment (Alkuraya and Kilani 2001).However, the approved indication for the procedure, which is \"severe malformation\", must be authorized by three attending-level physicians.The definition of \"severe\" is left to the discretion of the medical team after consulting with the family.For example, intellectual disability is a common indication for many therapeutic abortion procedures.Contrary to commonly held views, we have shown that early prenatal diagnosis is the method of choice for couples who had one or more children with single gene disorders, as long as they are provided with a culturally sensitive genetic counseling that addresses their religious and cultural concerns (Alkuraya and Kilani 2001).Nearly 45% of these couples opt for early prenatal diagnosis compared to 35% who choose preimplantation genetic diagnosis (PGD) (Alkuraya 2013a).PGD is available freely at KFSHRC but is also provided by the private sector.Noninvasive prenatal screening using cell-free fetal DNA in maternal blood is quickly becoming integrated in prenatal care.KFSHRC offers this test routinely to all pregnant women regardless of their perceived risk and the MOH is considering making this test available throughout its vast network of hospitals and medical centers." + } + ], + "9f21007a-1487-46d8-8e9e-cde8df4af6d5": [ + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "\n\nSocial and psychological implications of accessing genetic services and information." + } + ], + "a4b0655d-895c-4368-9401-ee2903b15d42": [ + { + "document_id": "a4b0655d-895c-4368-9401-ee2903b15d42", + "text": "\n\nA corollary of the predictive power of genetic information is the limited ability to prevent or treat many conditions with significant genetic factors involved.Indeed, virtually all of the complex ethical and legal issues relevant to genetic testing would disappear if there were effective preventions or treatments available for genetic conditions.The ability to predict future disease in conjunction with a limited ability to do much about it has important social and psychological implications that must be addressed in conducting genetic research." + } + ], + "b0b60080-2338-411b-bc44-1f5626a3c442": [ + { + "document_id": "b0b60080-2338-411b-bc44-1f5626a3c442", + "text": "\n\nInterpretations of the literature will likely mirror the priorities and evaluative tendencies of the reader.Are you willing to accept the overall trends in genetic and genomic testing evaluation and to trust that the existing clinical approaches will apply informed consent appropriately while identifying and supporting the rare individual who has a serious adverse response to the testing?If so, you might advocate that attention be turned more toward other issues relevant to the effective implementation of genetic and genomic testing.Or do you feel a strong need to understand in more detail the possible psychosocial harms of the testing, particularly the subtler impacts or responses of individuals who do not fit the norm?In that case, you would likely encourage renewed and innovative efforts to study the psychosocial consequences of the receipt of risk information from genetic and genomic testing." + } + ], + "f7fe5d02-ee7c-4ec2-b6c4-ca9aa5efb41f": [ + { + "document_id": "f7fe5d02-ee7c-4ec2-b6c4-ca9aa5efb41f", + "text": "\n\nOther social issues require our attention if genomic medicine is to benefit our patients.How should genetic tests be regulated?What, if any, are the appropriate uses of direct-to-consumer marketing of genetic tests?The Internet has recently had a proliferation of genetic-testing sites that feature claims grounded in greed and pseudoscience, rather than in data or reality.How will health care providers and the public distinguish between these and responsible testing services, whether they are available through the Internet or in the hospital?" + } + ], + "f97246cb-7a71-409b-bb1a-dd01a5ef5c5e": [ + { + "document_id": "f97246cb-7a71-409b-bb1a-dd01a5ef5c5e", + "text": "Environmental Factors\n\nAs widespread use of genetic testing increases, it is the responsibility of the medical community to ensure its equitable use across socioeconomic and cultural spectrums." + } + ] + }, + "data_source": [], + "document_id": "F7FF28704C5239FB329F508530F982CC", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "Tay-Sachs&disease", + "glutaric&aciduria&type&1", + "Ashkenazi&Jewish", + "Amish", + "Down&syndrome", + "SMA", + "PGD", + "KFSHRC", + "Genetic&Information&Nondiscrimination&Act", + "Islamic" + ], + "metadata": [], + "question": "What are the ethical considerations surrounding prenatal genetic testing and the selective termination of pregnancies based on genetic factors?", + "subquestions": null, + "task_id": "F7FF28704C5239FB329F508530F982CC", + "usage": { + "chatgpt": 6791, + "gpt-4": 4502, + "gpt-4-turbo-preview": 3567 + }, + "user_id": 2 + }, + "document_id": "F7FF28704C5239FB329F508530F982CC", + "task_id": "F7FF28704C5239FB329F508530F982CC" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_05.json b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_05.json new file mode 100644 index 0000000..8273f71 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_05.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-06-01T14:26:03.986157Z", + "data": { + "amplify": false, + "answer": "The potential benefits of gene editing technologies like CRISPR-Cas9 include the ability to modify genes for the treatment of diseases, improvement of crop species, and the development of personalized drug or cell therapies. It can also be used for functional screening in the development of therapies and for the study of molecular causes of ageing. However, there are risks associated with these technologies. These include off-target effects, which can lead to unwanted mutations, and the potential for wide-ranging deletions or recombination events. There's also a risk of triggering a P53 response leading to apoptosis in cycling cells, and the potential for subjects to generate antibodies to Cas9, which could limit gene therapies. Furthermore, the long-term safety of CRISPR genome editing in humans is yet to be determined.", + "context": { + "03110c8a-1232-40c2-8380-c9feb8b6468c": [ + { + "document_id": "03110c8a-1232-40c2-8380-c9feb8b6468c", + "text": "\n\nGene editing has gained considerable interest with the identification of the CRISPR-Cas9 system, 27 which allows for a targeted modification in the DNA sequence of an organism.Researchers can utilize their knowledge of the basic biology of the gene and its protein function to precisely change the DNA sequence, thus altering the protein function of the gene and allowing for edits to stay within the species.Researchers at the University of Missouri used the CRISPR-Cas9 system to modify the CD163 gene such that the PRRS virus is not able to replicate inside the pig. 28This slight modification of the swine genome through gene editing keeps the pigs from succumbing to PRRS which has an annual estimated loss to the United States swine industry of over $660 million per year.Despite this benefit, given the public's concerns over food safety, it is likely that approval for such technology is years away in the US, Canada and Europe.However, in some cultures, there is a wide range of non-livestock species that are consumed.Therefore, it is conceivable that these countries and cultures may be open to transgenic/gene edited livestock.They may see the importance of useful gene editing which may lead to approval and consumption of reasonable genetically edited animal products such as those with modifications that are already found in nature or those that offer a substantial welfare benefit to society." + } + ], + "1942712a-a39d-44f7-9b2d-609926374cbd": [ + { + "document_id": "1942712a-a39d-44f7-9b2d-609926374cbd", + "text": "\n\nAs a researcher who has devoted an entire career since 1994 to the development of genome editing tools and methods, I have been amazed by the rapid progress in the field over the last few years.Considering the widespread use of the tools, I am sure that the pace will continue to accelerate.Indeed, programmable nucleases, may eventually enable humans-products of evolution-to become masters of evolution.delivered preassembled recombinant Cas9-guide RNA ribonucleoproteins (RNPs) into animal embryos 6,9 and plant 11 and mammalian cells [73][74][75] .Indeed, Cas9 RNPs were rapidly turned over in cells 73 , reducing off-target effects and mosaicism in gene-edited organisms 11 .Cas9 RNPs can be delivered into cells by various methods, including microinjection 6,9 , electroporation 73 , lipofection 74 and protein transduction 75 .Importantly-and unlike in conventional gene therapy, where therapeutic genes are delivered via plasmids or viral vectors-Cas9 RNP delivery does not involve the use of exogenous DNA; host innate immune responses against foreign DNA are not elicited, and undesired integration of foreign DNA into the host genome is avoided." + } + ], + "33f1abde-a821-483b-b8b4-785f499db09d": [ + { + "document_id": "33f1abde-a821-483b-b8b4-785f499db09d", + "text": "\n\nIn comparison to a transgenic approach, a gene editing technique such as CRISPR-Cas9 offers the advantage that gene-edited crops are not considered genetically modified organism (GMO) in some countries, such as the US, where the demand for natural food colorants such as anthocyanins is high.Indeed, the use of GMO crops as a source of natural pigments may be inconsistent with consumer interests.However, carrot cultivars engineered with either the transgenic or gene editing approach have not been reported so far, but their development is possible." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nThe notable accuracy and versatility of CRISPR-Cas for genome editing also opened the door to its use in preclinical and translational settings.In the latter case, CRISPR in vivo gene editing has led to several proof-of-concept studies that would have been unachievable without it, as in the first ever correction of inherited pathogenic mutations linked to degenerative disease in a living organism [22] and even shown to be possible in human embryos [23,24].It also has great potential in the field of precision medicine as large-scale population DNA sequencing studies have provided vast amounts of information linking particular diseases with specific genetic mutations which could, in theory, be targeted through CRISPR [25,26].This could be used during the identification and validation of potential DNA targets during the development of personalised drug or cell therapies, which will require the generation of engineered cell lines and/or animal models.Techniques such as HDR-mediated gene targeting are too labour intensive, with low targeting efficiencies and long times necessary for their establishment, and consequently are not ideally suited for drug discovery purposes.Conversely, CRISPR-Cas has been proven to be efficient for editing virtually any kind of cell line, from primary immune cells to induced pluripotent stem cells (iPSCs) [27,28].Additionally, CRISPR can also be used for functional screening in the development of combined inhibitory therapy aimed at strengthening the efficiency of targeted therapeutics.An example of the latter is shown in a study where a variation of the technology known as CRISPR interference (CRISPRi) was used in genome-wide scale to identify different survival pathways used by cancer cells after oncogene inactivation and allowing the identification of successful combination therapies [29].In terms of translational applications, the overall safety of CRISPR genome editing in humans will require long-term scrutiny before its adoption in the clinic.Nonetheless, a number of CRISPR-based clinical trials are currently in progress, including studies focused on targeting patients' own T cells in order to improve the immune response towards some forms of malignant cancer [30,31], and others aimed at correcting pathogenic mutations in the hematopoietic cells of patients with beta-thalassemia and sickle cell disease [32]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Caveats and Ethical Concerns of CRISPR-Cas Applications\n\nDespite the presence of both a PAM sequence and a specific gRNA, the CRISPR-Cas9 system is not infallible.In fact, DSBs can occur at different sites in the genome, potentially causing so-called \"off-target\" effects.This eventuality remains to date the biggest concern in the field, as possible undesirable modifications must be properly identified and followed in order to guarantee safety for medical purposes.Nevertheless, there is still little evidence of the biological consequence of Cas9 off-target effects.Two recent studies describe new methods to investigate potential off-target effects in both mammals and plants [33,34].In both cases, whole-genome sequencing revealed that selective nucleotide changes, such as conversion of an adenine to a guanine, caused off-target occurrence very rarely, with a frequency comparable to the one of spontaneous mutations.However, substitution of a cytosine with a thymidine was linked to a sizable number of off-target mutations.This newly acquired information adds to the plethora of studies conducted on the safety of CRISPR, which altogether highlight the need for the establishment of clinical standards for the future use of genome-editing techniques in the clinic.Despite this and other technical challenges still ahead for CRISPR genome editing, the pace at which this technology has developed in recent years suggests many of these concerns could be addressed soon, as long as proper ethical guidelines and regulatory mechanisms are established." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Conclusions\n\nThere is no reason to doubt that the development of CRISPR-Cas genome editing represents an unprecedented breakthrough in modern science, as it has potential applications in a wide array of disciplines ranging from agriculture, zoology and renewable energy to biomedicine and synthetic biology.This powerful tool holds promise for further elucidating the molecular causes of ageing by allowing scientists to probe genetic and epigenetic pathways with a level of sophistication that was unattainable just a few years ago.It will allow so in traditional animal and cell models of ageing, but it will also drastically accelerate the generation of refined versions of those models or even allow the development of new research approaches in non-model organisms.Moreover, CRISPR-based genome editing is already having a significant impact in research aiming to understand the cellular and molecular origins of age-related diseases, as well as developing potential treatments against them.The application of CRISPR-Cas gene editing for the treatment of age-related diseases is not over the horizon yet, as it will require the identification of causative genes and their role under a variety of contexts that could be as diverse as the ageing process is across individuals.However, CRISPR-Cas might also hold the key for solving such conundrum, as it has opened the way for achieving true personalised medicine by providing both the precision and scalability required for conducting genome-wide functional screens during the refinement of drug-and cell-based therapies for age-related diseases." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nSince its discovery, CRISPR-Cas technology has ignited a biological revolution by providing a highly versatile platform that allows fast and efficient genome editing in an ever-growing list of organisms.In this chapter we will first describe the most recent advances in the development and application of the CRISPR-Cas platform in biomedical research.Then we will discuss the most recent and notable basic research applications of this technology in the study of the molecular causes of ageing.Finally, we will review how CRISPR-Cas has been used for creating new models for the study of age-related diseases, as well as for manipulating diseaseassociated gene pathways." + } + ], + "50c72e55-b5fe-42a6-b837-64c28620a4c0": [ + { + "document_id": "50c72e55-b5fe-42a6-b837-64c28620a4c0", + "text": "Caveats of advanced genome editing tools\n\nOff-target effects.The DNA-binding domains of ZFNs and TALENs need to be very specific for the target site to avoid off-target cleavage, which results in unwanted mutations and potentially cytotoxic effects [27].CRISPR/Cas9 is also known to generate off-target alterations, albeit apparently at low incidence [28,29], since mispairing is allowed between the guide RNA and the genomic DNA.Nonetheless, caution is required in their design and use.Some strategies involving the optimization of the guide RNA/Cas9 include using of software tools to predict potential off-target sites (http://omictools.com/crispr-cas9-Figure1: Genome editing methodologies which can be applied to human pluripotent stem cells.Homologous recombination (HR), or the more advanced tools such as zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) or clustered regularly interspaced short palindromic repeat (CRISPR)/Cas system can be applied to human pluripotent stem cells (hPSCs) either to 1) create naturally occurring mutations or 2) repair a mutation to generate isogenic controls in hPSCs, to understand the function of a gene of interest.c1268-p1.html),truncating the guide RNA (<20 nucleotides) to decrease off-target mutagenesis [30], lowering the dosage of guide RNA and Cas9 plasmids, and decreasing the number of mismatches between the guide RNA and the genomic DNA.A \"double nick\" system with Cas9 nickase, which contains a single inactive catalytic domain, may also be used [31e33]." + } + ], + "52480703-5353-4e55-a06b-110fd59db3a6": [ + { + "document_id": "52480703-5353-4e55-a06b-110fd59db3a6", + "text": "CRISPR screening technologies\n\nThe discovery of CRISPR-Cas9 as a sequence-specific programmable nuclease democratized gene editing and fueled progress in forward genetic screening [20 , 66] .Genetic screens using Cas9 with a pooled singleguide RNA (sgRNA) library allow the interrogation of seemingly all genes in a genome in a single experiment [96 , 97] [null] .Engineered Cas9 variants further extend the versatility of forward genetic screening.Catalytically inactive Cas9 (dCas9) fused with chromatin effector domains permit specific activation (CRISPRa) or inhibition (CRISPRi) of gene expression [37 , 54] .Recently developed and emerging technologies -base editors, prime editors, and Cas transposases -are beginning to enable new types of genetic screens with directed, controlled, and on demand mutations by allowing the creation of user specified modifications, such as single base conversion, deletions, and insertions [4 , 42 , 58] ." + } + ], + "801c9288-70c9-4d14-b8bc-13ee6708803a": [ + { + "document_id": "801c9288-70c9-4d14-b8bc-13ee6708803a", + "text": "\n\nComing on the heels of engineered nucleases, CRISPR-Cas9 tools have accelerated the pace of genomic research by permitting highly efficient knockouts or edits of virtually any gene in cells or model organisms.Multiple CRISPR-Cas9-based clinical trials are in progress or are expected to begin soon.Although Cas9engineered cells haven't yet demonstrated efficacy at scale, early trial results suggest that such cells are stable and don't cause acute adverse reactions in humans.Long-term safety is yet to be determined.Current applications largely focus on single-gene disorders for which gene editing can be carried out ex vivo on appropriate cells, such as bone marrow hematopoietic stem cells in the case of sickle cell anemia.Exploration is under way to develop delivery systems that can target the gene-editing apparatus to the appropriate tissue in vivo." + }, + { + "document_id": "801c9288-70c9-4d14-b8bc-13ee6708803a", + "text": "\n\nOver the past 8 years, CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 (CRISPR-associated protein 9) technologies have emerged as accessible and adaptable tools for studying and altering genomes. 5RISPR-Cas9 can be used to induce genome edits by creating targeted DNA breaks that trigger site-specific DNA repair.In nextgeneration formats, it can also control the transcriptional output of genes or alter genome sequences using a process of nucleotide base editing that does not require repair of DNA breaks.As these technologies continue to mature, it will become increasingly possible to alter cellular genomes efficiently and accurately." + } + ], + "a7f21808-dce3-4110-8e7c-ceb2437e72ff": [ + { + "document_id": "a7f21808-dce3-4110-8e7c-ceb2437e72ff", + "text": "\n\nThe type II CRISPR-Cas9 systems, repurposed from prokaryotic adaptive immune responses, are now widely used for targeted genome modifications in plants, animals, and human cells (Kim et al. 2014;Woo et al. 2015;Zuris et al. 2015).In particular, Cas9 nucleases have shown promise for gene and cell therapy (Maeder and Gersbach 2016).Typically, these nucleases are expressed or delivered in vivo using plasmid DNA or viruses (Yin et al. 2014;Ran et al. 2015).However, plasmid DNA delivery is often inefficient, especially in vivo, and can cause integration of small plasmid fragments degraded by endogenous nucleases at on-target and offtarget sites in the genome (Kim et al. 2014).Viral delivery of Cas9 can be highly efficient in vivo (Ran et al. 2015;Long et al. 2016;Nelson et al. 2016;Tabebordbar et al. 2016), but may be hampered by antibodies or T cells induced against the protein (Shankar et al. 2007;Calcedo et al. 2015;Chew et al. 2016).We and others have shown that preassembled Cas9 ribonucleoproteins (RNPs) can be delivered to human primary and stem cells and mice to modify target genes (Kim et al. 2014;Schumann et al. 2015;Zuris et al. 2015).Cas9 RNPs are rapidly turned over in cells, reducing off-target effects.Furthermore, Cas9 RNPs are unlikely to be limited by host immune systems because they function and disappear before the generation of antibodies and T cells directed against them.Currently, despite these advantages of RNPs, the difficult delivery of Cas9 RNPs in vivo limits its utility for therapeutic applications (Zuris et al. 2015).Here, we show that in vivo genome editing of an wild-type gene, whose up-regulation is responsible for pathogenesis, could be a new therapeutic modality for the treatment of nongenetic degenerative diseases.Our ultimate goal is to harness Cas9 RNPs for a clinical application of therapeutic genome surgery in patients with AMD." + } + ], + "ac00c552-7514-49d4-9e90-ab01c22472ae": [ + { + "document_id": "ac00c552-7514-49d4-9e90-ab01c22472ae", + "text": "\n\nClustered regularly interspaced short palindromic repeat (CRISPR)-Cas nucleases have revolutionized the field of gene editing and have tremendous application in the field of molecular medicine [98][99][100][101][102].Despite a significant surge in CRISPR/Cas9mediated genome editing in various disease models, the progress in the field of AD has lagged behind substantially.We believe that genome editing can significantly improve the development of AD models and also create novel opportunities for the development of the next generation precision targeted AD gene and stem cell therapies.Since there are several excellent review articles on CRISPR/Cas9-mediated genome editing, here we will limit our focus on select recent articles that are noteworthy.CRISPR/Cas9 system can be engineered to either activate transcription (gain-of-function) or achieve gene silencing (Loss-of-function).Dahlman et al. have developed a CRISPR-based system that uses catalytically active Cas9 and distinct single guide (sgRNA) constructs to activate and knockout different genes in the same cell [103].Konermann et al. have used structure-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci [104].Using crystallographic studies, they have engineered a combination of sgRNA2.0,NLS-dCas9-VP64 and MS2-p65-HSF1 to develop one of the most effective transcription activation system." + } + ], + "b72eb0d1-50e3-4def-94bc-abf77891f519": [ + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "text": "Limitations of CRISPR-Cas9\n\nCRISPR provides a simple and easy tool not only for in vitro use but potentially also for in vivo genome editing.However, there are limitations and downsides to this approach.First, and despite considerable improvements in the technology, the risk of the offtarget effect remains and must be considered carefully.Second, DSB may lead to wide-ranging deletions or recombination events involving the on-target site (204).Third, in cycling cells, DNA double strand breaks caused by Cas9 cleavage may trigger a P53 response leading to apoptosis and enrichment for potentially oncogenic P53-deficient cells (205,206).Fourth, subjects may generate antibodies to Cas9, potentially limiting gene therapies (207,208)." + }, + { + "document_id": "b72eb0d1-50e3-4def-94bc-abf77891f519", + "text": "\n\nGenome editing tools that target the desired genomic region and allow for variants to be altered (e.g. from risk to protective), or for more substantial changes to be made (e.g. the deletion of a longer stretch of DNA harbouring a number of variants) and can help to answer each of these questions.These technologies are evolving rapidly (Figure 1 and Table 2).The most recently developed of these, Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) technology, originally developed by Doudna, Charpentier and their colleagues (72,73) and Zhang and his colleagues (50) has become a widely used tool for this purpose.Engineered CRISPR/Cas9 technology uses a guide RNA (gRNA) to direct CRISPR-associated endonuclease (Cas) to the target DNA and generate a double strand DNA break.Correction of a mutation or variant in the target DNA sequence can then be carried out by homology-directed DNA repair (HDR) with a donor template.Since its discovery eight years ago, CRISPR technology has evolved quickly to be a critical part of the molecular biologist's toolbox." + } + ], + "c3ae2186-ef48-46a5-b214-dc944366df8f": [ + { + "document_id": "c3ae2186-ef48-46a5-b214-dc944366df8f", + "text": "INTRODUCTION\n\nGenome editing technologies based on the clustered regularly interspaced short palindromic repeats (CRISPR)-associated endonuclease Cas9 enable rapid and efficient modification of endogenous genes in a variety of cell types, allowing for analysis of gene function in many organs in vivo.CRISPR-Cas9 induces DNA double strand breaks (DSBs) at single-guide RNA (sgRNA)-specific loci in the genome, which are repaired through either non-homologous end-joining (NHEJ) or homology-directed repair (HDR) pathways.While NHEJ introduces unpredictable pattern of insertion or deletion (indel) mutations, HDR directs a precise recombination event between a homologous DNA donor template and the damaged DNA site (Cong et al., 2013;Cox et al., 2015;Doudna and Charpentier, 2014;Heidenreich and Zhang, 2016;Jinek et al., 2012;Mali et al., 2013;Sander and Joung, 2014;Wang et al., 2013;Yang et al., 2013).Thus, HDR can be used to precisely introduce sequence insertions, deletions or mutations by encoding the desired changes in the donor template DNA." + } + ], + "d14e93b5-01de-4208-8255-baae7898a7bb": [ + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "text": "\nCRISPR technology has rapidly changed the face of biological research, such that precise genome editing has now become routine for many labs within several years of its initial development.What makes CRISPR/Cas9 so revolutionary is the ability to target a protein (Cas9) to an exact genomic locus, through designing a specific short complementary nucleotide sequence, that together with a common scaffold sequence, constitute the guide RNA bridging the protein and the DNA.Wild-type Cas9 cleaves both DNA strands at its target sequence, but this protein can also be modified to exert many other functions.For instance, by attaching an activation domain to catalytically inactive Cas9 and targeting a promoter region, it is possible to stimulate the expression of a specific endogenous gene.In principle, any genomic region can be targeted, and recent efforts have successfully generated pooled guide RNA libraries for coding and regulatory regions of human, mouse and Drosophila genomes with high coverage, thus facilitating functional phenotypic screening.In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening." + }, + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "text": "\n\nCRISPR technology has rapidly changed the face of biological research, such that precise genome editing has now become routine for many labs within several years of its initial development.What makes CRISPR/Cas9 so revolutionary is the ability to target a protein (Cas9) to an exact genomic locus, through designing a specific short complementary nucleotide sequence, that together with a common scaffold sequence, constitute the guide RNA bridging the protein and the DNA.Wild-type Cas9 cleaves both DNA strands at its target sequence, but this protein can also be modified to exert many other functions.For instance, by attaching an activation domain to catalytically inactive Cas9 and targeting a promoter region, it is possible to stimulate the expression of a specific endogenous gene.In principle, any genomic region can be targeted, and recent efforts have successfully generated pooled guide RNA libraries for coding and regulatory regions of human, mouse and Drosophila genomes with high coverage, thus facilitating functional phenotypic screening.In this review, we will highlight recent developments in the area of CRISPR-based functional genomics and discuss potential future directions, with a special focus on mammalian cell systems and arrayed library screening." + }, + { + "document_id": "d14e93b5-01de-4208-8255-baae7898a7bb", + "text": "\n\nThe recent development of clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 for experimental purposes has dismantled the perception that genome editing technology is off-limits for screening in mammalian systems (Heintze et al., 2013).Since this system employs the basic principle of Watson-Crick base pairing for gene targeting, generation of libraries with whole-genome target coverage is relatively easy and cost-effective.For instance, simple protocols are available to synthesize pooled lentiviral libraries by in silico design of oligonucleotides, which can then be cloned, packaged and delivered to cells by viral transduction (Paddison et al., 2004;LeProust et al., 2010).Similarly, the generation of arrayed libraries can be achieved by following protocols originally developed for arrayed shRNA library production that have been in use for a number of years (Moffat et al., 2006).All in all, the stage is set for CRISPR to make an enormous impact on genomic screening and thus scientific discovery in the coming years, and recent demonstrations of this system have shown great promise (Shalem et al., 2015).However, a number of technical challenges must be addressed in order to maximize the benefit of this technology.In this review, we will discuss current applications of CRISPR in functional genomics and provide a perspective on future developments in this area." + } + ], + "e5cf067c-8be0-4b0a-b376-7882cdc9d96c": [ + { + "document_id": "e5cf067c-8be0-4b0a-b376-7882cdc9d96c", + "text": "Genome editing for crop improvement\n\nReports of CRISPR-Cas9-based genome editing first appeared in 2013 (Cong et al., 2013;Feng et al., 2013;Mao et al., 2013).Since then, genome editing technologies have proven to be powerful and efficient tools for the improvement of many crop species.At present, genome editing has been widely used to introduce/modify agronomically important traits, such as increased yield, improved nutritional quality, and resistance to biotic and abiotic stresses, in multiple crops, including rice, wheat, maize, tomato, and potato (Lu et al., 2017;Soyk et al., 2017;Tang et al., 2017;D'Ambrosio et al., 2018;Ye et al., 2018;Miao et al., 2019;Zhang et al., 2019;Zhong et al., 2019;Butt et al., 2020;Zhang et al., 2020c;Li et al., 2021b;Zhan et al., 2021).CRISPR-Cas-based genome editing has been extended to targeted mutagenesis, base editing, and precisely targeted gene/allele replacement or tagging in plants.mportantly, using CRISPR-Cas9 technology, transgenes present in the genomes of genome-edited plants can be removed by chromosomal segregation via a simple self-pollination or hybridization step.Gene editing technologies continue to be developed and utilized (Mao et al., 2013;Lu and Zhu, 2017;Lu et al., 2020)." + } + ] + }, + "data_source": [], + "document_id": "38E097866214E3EEFE346FB836ABF345", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "CRISPR-Cas9", + "gene&editing", + "off-target&effects", + "genome", + "clinical&trials", + "agriculture", + "biomedicine", + "precision&medicine", + "transgenic", + "ethical&concerns" + ], + "metadata": [], + "question": "What are the potential benefits and risks associated with gene editing technologies like CRISPR-Cas9?", + "subquestions": null, + "task_id": "38E097866214E3EEFE346FB836ABF345", + "usage": { + "chatgpt": 9104, + "gpt-4": 6248, + "gpt-4-turbo-preview": 5340 + }, + "user_id": 2 + }, + "document_id": "38E097866214E3EEFE346FB836ABF345", + "task_id": "38E097866214E3EEFE346FB836ABF345" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_06.json b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_06.json new file mode 100644 index 0000000..01f3600 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_06.json @@ -0,0 +1,146 @@ +{ + "created_at": "2024-06-01T14:27:38.978700Z", + "data": { + "amplify": false, + "answer": "Human centromeres contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B. This motif is present on every chromosome except for the Y chromosome. It is necessary for the formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres. Mice lacking CENP-B are viable and fertile.", + "context": { + "395a51ba-bd2a-4160-8396-b13a3bf762ff": [ + { + "document_id": "395a51ba-bd2a-4160-8396-b13a3bf762ff", + "text": "\n\nPrimate and mouse centromeric DNA contain the CENP-B box, a 17-bp motif that is bound by the centromere protein CENP-B.In humans, the CENP-B box is present on every chromosome except for the Y chromosome [20].It is paradoxically necessary for formation of de novo centromeres on artificial chromosomes, but it is not essential for the formation of neocentromeres, and mice lacking CENP-B are viable and fertile [21,22]." + } + ], + "3dfe0ec3-b3a6-4a08-8929-e54cab3ec262": [ + { + "document_id": "3dfe0ec3-b3a6-4a08-8929-e54cab3ec262", + "text": "Box 3 Mechanism of homologous recombination and end joining\n\nThe severe phenotype of the mouse mutants and the highly cancer-prone human syndromes highlight the importance of homologous recombination.Mouse KU mutants display sensitivity to agents that lead to breaks in DNA, and have immunological problems because the KU proteins are involved in V(D)J recombination of antibody gene sequences.In addition, these mutants display poor development, several features of premature ageing and increased apoptosis of postmitotic neurons in the developing brain.Mice with defects in DNA-PK cs (SCID mice) display a similar but generally milder phenotype.In contrast, XRCC4-and ligase IV-knockout mice seem more severe, with late embryonic lethality resulting from massive ATM-and p53-dependent neuronal apoptosis 33,38 ." + }, + { + "document_id": "3dfe0ec3-b3a6-4a08-8929-e54cab3ec262", + "text": "\n\nCells in G1 have only the homologous chromosome for recombination repair.However, this may be difficult to find in the complex genome.Moreover, it is potentially dangerous as a template for repair as it may lead to homozygosity for recessive mutations.As an alternative, the end-joining reaction simply links ends of a DSB together, without any template, using the end-binding KU70/80 complex and DNA-PK cs , followed by ligation by XRCC4-ligase4 (reviewed by 27,33; see the right panel of the figure, stages V-VII).The function of KU70/80 might involve end protection and approximating the ends, in addition to a signalling function by DNA-PK cs .End joining may be further facilitated when the ends are still held together through nucleosomes or other structures.End joining is sometimes associated with gain or loss of a few nucleotides if internal microhomologies are used for annealing before sealing.This implies the involvement of DNA polymerases and/or nucleases.Note that the KU complex is also involved in telomere metabolism 27,62 .found to be lethal 34 .Inactivation of ATR by itself is inviable already at the blastocyst stage.Inactivation of BRCA1 and BRCA2 in mice is also embryonically lethal; cell lines display defects in homologous recombination [35][36][37] ." + }, + { + "document_id": "3dfe0ec3-b3a6-4a08-8929-e54cab3ec262", + "text": "371\n\nA tentative scenario for the homologousrecombination reaction is depicted in the left panel of the figure.To promote strand invasion into homologous sequences, the 5፱-3፱ exonuclease activity of the RAD50/MRE11/NBS1 complex (also a substrate for ATM phosphorylation) exposes both 3፱ ends 30 (I).RPA facilitates assembly of a RAD51 nucleoprotein filament that probably includes RAD51-related proteins XRCC2, XRCC3, RAD51B, C and D. RAD52 stimulates filament assembly (II).RAD51 has, like its Escherichia coli RecA counterpart, the ability to exchange the single strand with the same sequence from a double-stranded DNA molecule.Correct positioning of the sister chromatids by cohesins probably facilitates the identification of a homologous sequence.A candidate for the complex chromatin transactions associated with these DNA gymnastics is RAD54, a member of the SWI/SNF family of DNA-dependent ATPases.After identification of the identical sister chromatid sequence, the intact double-stranded copy is used as a template to properly heal the broken ends by DNA synthesis (III).Finally, the so-called Hollidayjunctions are resolved by resolvases 27,33,60 (IV).Homologous recombination involves the simultaneous action of large numbers of the same molecules, which are found to be concentrated in radiation-induced nuclear foci.These depend on, and also include, the BRCA1 and BRCA2 proteins 36 .Recent evidence implicates BRCA2 directly or indirectly in nuclear translocation of RAD51 (ref.61)." + } + ], + "748cfe7e-e4f2-453f-8575-50dfe84e2538": [ + { + "document_id": "748cfe7e-e4f2-453f-8575-50dfe84e2538", + "text": "\n\nThis picture poses more questions than it seeks to answer.Is the grouping of the regions by product rather than by type of region correct?Given that the recombina- tion fraction between HLA-A and HLA-B is of the order of .08%,and that this is likely to represent a distance of at least hundreds of thousands of nucleotides, how are the pieces put together over such relatively long distances?Is it possible that regions of the DNA loop out, so that transcripts can be made directly from noncon- tiguous DNA sequences, the loops being held in place by small RNAs as suggested for the control of splicing by Steitz, and her colleagues [24] and by others [25]?If these small RNAs are coded for well outside the HLA region, does this provide a mechanism for control of expression of products by unlinked genes, as may be the case for one of the constituent polypeptides of the HLA-DR product?What might be the nature of the signals that control which of a multiple set of alternative regions is expressed by any given chromosome?" + } + ], + "7a451204-390c-4ff2-8a1d-b4de62b73503": [ + { + "document_id": "7a451204-390c-4ff2-8a1d-b4de62b73503", + "text": "Mamm Genome. 2006; 17:220–229. [PubMed: 16518689]\n72. Romanoski CE, et al. Systems genetics analysis of gene-by-environment interactions in human\ncells. Am J Hum Genet. 2010; 86:399–410. [PubMed: 20170901]\n73. Myers S, Freeman C, Auton A, Donnelly P, McVean G. A common sequence motif associated\nwith recombination hot spots and genome instability in humans. Nature Genet. 2008; 40:1124–\n1129. [PubMed: 19165926]\n74. Myers S, et al. Drive against hotspot motifs in primates implicates the PRDM9 gene in meiotic\nrecombination. Science. 2010; 327:876–879. [PubMed: 20044541]\n75. Cordell HJ. Detecting gene-gene interactions that underlie human diseases. Nature Rev Genet. 2009; 10:392–404." + } + ], + "8604652e-2477-4552-8f43-f5f19e421df2": [ + { + "document_id": "8604652e-2477-4552-8f43-f5f19e421df2", + "text": "Classification of common conserved sequences in mammalian\nintergenic regions. Hum. Mol. Genet. 2002, 11, 669–674. 25. Zhu, L.; Swergold, G.D.; Seldin, M.F. Examination of sequence homology between human\nchromosome 20 and the mouse genome: Intense conservation of many genomic elements. Hum. Genet. 2003, 113, 60–70. 26. Pevzner, P.; Tesler, G. Human and mouse genomic sequences reveal extensive breakpoint reuse in\nmammalian evolution. Proc. Natl. Acad. Sci. USA 2003, 100, 7672–7677. 27. Christmann, R.B. ; Sampaio-Barros, P.; Stifano, G.; Borges, C.L. ; de Carvalho, C.R. ; Kairalla, R.;\nParra, E.R. ; Spira, A.; Simms, R.; Capellozzi, V.L. ; et al." + } + ], + "9d82958a-45b0-4f1d-b765-38d018e4b140": [ + { + "document_id": "9d82958a-45b0-4f1d-b765-38d018e4b140", + "text": "\n\na The table lists proteins in which mutations have been shown to increase homologous recombination (HR), gross chromosomal rearrangements (GCRs), chromosomal instability (CIN), sister chromatid exchanges (SCEs), tri-nucleotide repeat expansions and contractions (TNR), telomere fusions (Tel fusion), or fragile telomeres (Tel fragility).A phenotype inside brackets ([ ]) indicates that it is caused by overexpression of the protein.For further details and references see Supplementary Table1.Abbreviations: DSB, double-strand break; PCNA, proliferating cell nuclear antigen; RFC, replication factor C complex; SCF, Skp1-Cdc53/Cullin-F-box." + }, + { + "document_id": "9d82958a-45b0-4f1d-b765-38d018e4b140", + "text": "\n\nFigure 3 Intermediates and chromosome structural alterations, as observed by different techniques. (a) Replication fork stalling, as monitored by 2D-gel electrophoresis and Southern analysis in yeast (for details about the technique, see Reference 161). (b) Slower human replication forks covering shorter DNA synthesis tracks, as determined by incorporation of IdU and CldU via DNA combing (52), which permits visualization of the process of replication on DNA fibers. (c) Accumulation of double-strand breaks (DSBs) or replicative stress, as inferred by γH2AX foci or by γH2AX pan staining, respectively, in human cells. (d ) DSBs or ssDNA (single-stranded DNA) gaps as seen directly by nuclear \"comet tails\" via single-cell electrophoresis assays in human cells (52). (e) Sister-chromatid exchanges (SCEs), as determined by Giemsa staining in human cells (207). ( f ) Hyper-recombination, as determined by colony sectoring in yeast (5). ( g) Gross chromosomal rearrangements (GCRs), as determined by spectral karyotyping in mouse cells (118). (h) Translocations, as visualized by pulse-field gel electrophoresis in yeast (168). (i ) Fragile sites, as detected by mitotic spreads in human cells (109). ( j) Telomere fusions, as determined by CO-FISH (chromosome-orientation fluorescent in situ hybridization) in mouse cells (124). (k) Anaphase bridges, presumably resulting from unfinished replication, dicentric chromosomes, and sister-chromatid nondisjunction, as detected by fluorescence microscopy in mouse cells.Arrows indicate the specific structural alterations referred to in each panel; in panel h, closed and open arrows indicate the position where the translocated or missing parental chromosome migrate or should migrate, respectively.When necessary, a normal control is shown on top of the panel, with the exception of panel a, which is shown on the left.Detailed description of each technique can be found in the references provided.Photos are from the laboratories of A. Nussenzweig ( g), A. Losada (k), M. Blasco ( j), L. Tora (i ), and ours (all others).Abbreviations: HR, homologous recombination; NHEJ, nonhomologous end-joining." + } + ], + "9ee491f4-5f16-4cb2-b803-54f2fdee1dba": [ + { + "document_id": "9ee491f4-5f16-4cb2-b803-54f2fdee1dba", + "text": "\n\nIn humans, the pericentromeric region of chromosome 9 is densely packed with segmental genomic duplications (segdups) and is prone to microdeletions and microduplications. 5In order to evaluate this region for microdeletions and microduplications in family T, we screened genomic DNA from affected individual II-7 by arrayCGH with the Nimblegen HD2 platform with the previously described CHP-SKN sample 6 as the reference.Data were normalized and CNVs were called by identifying regions where Z-scores consistently deviated from the diploid mean.At 9q21.11, a genomic duplication of ~270 kb was apparent in the genomic DNA of II-7 (Figure 1D).The Genomic duplications may or may not be in tandem with their parent segment and may be either in the same or inverted orientation. 7We developed primers that would uniquely amplify genomic DNA with the duplication under each of these conditions.Forward (5 0 -CCCAGCAGA AGCAATGGTGGTAGCC-3 0 ) and reverse (5 0 -GGTGGTGAA TCCAAAAACACAAGAACAAAGTC-3 0 ) primers diagnostic for a tandem inverted duplication (Figure 2A) yielded products of expected size in family T relatives with hearing loss, but yielded no product in unaffected family T relatives (Figure 2B).Genotypes of all 58 participating relatives in family T indicated that the tandem inverted duplication was coinherited with hearing loss.The duplication spans approximately positions 71,705,804 to 71,974,823 (hg19) on chromosome 9 for a size of ~269,023 bp.The duplication includes the entire locus for the tight junction protein TJP2, which spans positions 71,788,971 to 71,870,124 (hg19)." + } + ], + "ab0a3234-c3b3-46be-8954-01eda9bc962e": [ + { + "document_id": "ab0a3234-c3b3-46be-8954-01eda9bc962e", + "text": "Chromosomal context of human NORs\n\nHuman NORs are positioned on the short arms of the acrocentric chromosomes that still remain unsequenced and thus missing from the current human genome draft, GRCh38.p7.Seeking an understanding of the chromosomal context of human NORs and to identify potential NOR regulatory elements, my laboratory has begun to characterize the sequences on both proximal (centromeric) and distal (telomeric) sides of the rDNA arrays (Fig. 3A; Floutsakou et al. 2013).Building on earlier reports of sequences distal and proximal to the rDNA array on HSA21 and HSA22, respectively (Worton et al. 1988;Sakai et al. 1995;Gonzalez and Sylvester 1997), 207 kb of sequence immediately proximal and 379 kb distal to rDNA arrays have been reported recently (Floutsakou et al. 2013).Consensus proximal junction (PJ) and distal junction (DJ) sequences were constructed mostly from chromosome 21 BACs (bacterial artificial chromosomes).Comparison of these sequences with BACs and cosmids derived from the other acrocentrics revealed that the PJ and DJ sequences are, respectively, ∼95% and 99% identical between all five acrocentric chromosomes.Conservation of DJ sequences among the acrocentrics is consistent with frequent recombination between the rDNA arrays on each of the acrocentric chromosomes (Worton et al. 1988).However, conservation of PJ sequences suggests that there must also be frequent recombination events in the interval between the centromere and rDNA arrays.Proximal sequences are almost entirely segmentally duplicated, similar to the regions bordering centromeres.Consequently, they are unlikely to contain any specific elements that would regulate the activity of the linked NOR.In contrast, the distal sequence is predominantly unique to the acrocentric short arms and is dominated by a very large inverted repeat.Each arm of the inverted repeat is >100 kb, and they share an average sequence identity of 80%.There is a large (∼40-kb) block of a 48base-pair (bp) satellite repeat, CER, at the distal end of the DJ (Fig. 3A).CER blocks are found distal to the rDNA on all acrocentric chromosomes, with additional pericentromeric blocks on chromosomes 14 and 22. Finally, there are two blocks of a novel 138-bp tandem repeat, ACRO138, present within the DJ." + }, + { + "document_id": "ab0a3234-c3b3-46be-8954-01eda9bc962e", + "text": "\n\nThe conservation of DJ sequence between the five human acrocentric chromosomes provides a unique opportunity to visualize NORs by FISH.Whereas the rDNA content of NORs can vary greatly, probing of human metaphase chromosome spreads with a DJ BAC results in signal that is consistent between NORs (Floutsakou et al. 2013).Using this probing scheme, it was observed that in most human cell lines analyzed, including multiple primary lines, at least one and sometimes as many as four of the NORs present have very little or no detectable rDNA (C van Vuuren and B McStay, unpubl. ).Many studies have used silver staining of metaphase spreads prepared from stimulated human peripheral blood lymphocytes to determine how many NORs are active in normal human cells.The number of active NORs ranges from seven to 10, with an average of eight (Heliot et al. 2000).Possibly, NORs with low rDNA content are active but fall below a detection threshold in silver staining.At this point, it is worth considering the distribution of active versus silent rDNA repeats in humans and other mammals.If 50% of rDNA repeats are truly repressed, there are insufficient \"silent\" NORs to house them.We must conclude that active NORs are a mosaic of active and silent repeats." + } + ], + "b04f2221-de28-4c4b-893e-9da982ff864c": [ + { + "document_id": "b04f2221-de28-4c4b-893e-9da982ff864c", + "text": "However, excluding some cases, recombination\nsuppression occurs in a small genomic tract\nwhere these genes are located, and it does\nnot extend over most of the sex chromosome\npair, as occurs in mammals and birds (Bergero\nand Charlesworth, 2009). It is not clear if this\nsuppression occurs by the presence of inversions or as a modulation of the recombination\nmechanism itself, but both could be involved\n(Bergero and Charlesworth, 2009). Evidence of\nrecombination in the SD region in sex reversal\nindividuals supports the second hypothesis." + } + ], + "d4fb56e4-06ab-4c01-b7a0-a193c4a40800": [ + { + "document_id": "d4fb56e4-06ab-4c01-b7a0-a193c4a40800", + "text": "\n\nOrthologous chromosomes between baboon and human" + } + ], + "da485354-fcdc-49b8-9a41-0f673610156a": [ + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "Lichter P, Cremer T, Borden J, Manuelidis L, Ward DC (1988) Delineation of\nindividual human chromosomes in metaphase and interphase cells by in situ suppression hybridization using recombinant DNA libraries. Hum Genet 80:224–234\n3. Jang W, Yonescu R, Knutsen T, Brown T, Reppert T, Sirotkin K, Schuler GD, Ried\nT, Kirsch IR (2006) Linking the human cytogenetic map with nucleotide sequence:\nthe CCAP clone set. Cancer Genet Cytogenet 168:89–97\n4." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "Nature\nGenet 1:222–225\n55. Foote S, Vollrath D, Hilton A, Page DC (1992) The human Y chromosome: overlapping DNA clones spanning the euchromatic region. Science 258:60–66\n56. Chumakov IM, Rigault P, Le Gall I et al (1995) A YAC contig map of the human\ngenome. Nature 377:175–297\n57. Hudson TJ, Stein LD, Gerety SS et al (1995) An STS-based map of the human\ngenome. Science 270:1945–1954\n58. Coffey AJ, Roberts RG, Green ED et al (1992) Construction of a 2.6-Mb contig in\nyeast artificial chromosomes spanning the human dystrophin gene using an STSbased approach. Genomics 12:474–484\n59." + } + ], + "e4541c0c-53fb-4c2c-b550-40728c356549": [ + { + "document_id": "e4541c0c-53fb-4c2c-b550-40728c356549", + "text": "\n\nFigure 4 Schematic depiction of proposed mechanisms for observed intrachromosomal rearrangements.The blue and red arrows indicate the orientation of the integrated plasmid loci and the recovered mouse sequences, respectively, on the original non-rearranged chromosome (left column).All four combinations are given for an arbitrarily orientated chromosome (green line).The middle column shows how two breakpoints (lightning signs) could lead to the inversion or deletion of the encompassed chromosomal sequence (yellow-orange dual tone line) and result in a recoverable mutation in the right column.The last row indicates the two options for a transposition, in which either the transgene locus or the recovered mouse sequence is copied or excised (as indicated by the pink and light blue arrows) and integrates in the breakpoint at the other location." + }, + { + "document_id": "e4541c0c-53fb-4c2c-b550-40728c356549", + "text": "\n\nAs mentioned above, by taking into account that for a genome rearrangement to be detected, the 5Ј plasmid sequence of the breakpoint in lacZ must remain intact and end immediately in front of the recovered mouse sequence, the simplest intrachromosomal mutation that could have taken place was inferred (Fig. 4).Rearrangements with breakpoints in the mouse genome on either site of the integrated plasmid concatamer, but with reversely orientated sequences, could be inversions (Fig. 4).Rearrangements in the direction of the integrated plasmids, proximal for chromosome 3 and distal for chromosome 4 (Fig. 3), with similarly orientated breakpoints in the mouse genome, could be deletions (Fig. 4).Rearrangements in the reverse direction of the integrated plasmids, with reversely orientated mouse sequences, are more complicated and might be owing to transpositions (Fig. 4).According to these schemes, half of the intrachromosomal rearrangements would have been inversions, whereas deletions and transpositions each made up one fourth (Fig. 3).Alternatively, these rearrangements could be explained by translocations involving the transgene clusters integrated on either the homolog or the other chromosome." + } + ], + "f08c0391-2d72-491c-a472-5db71bf11ac8": [ + { + "document_id": "f08c0391-2d72-491c-a472-5db71bf11ac8", + "text": "\n\nFIGURE 3. Telomere arrays of chicken and human chromosomes: the chicken genome contains more telomere sequence than the human genome.Chicken (a) and human (b) metaphase chromosomes and interphase cells hybridized with a telomeric sequence-peptide nucleic acid (PNA)-fluorescein probe.Human and chicken slide preparations were processed, and images were captured using the same parameters.Qualitatively, the telomere-positive fluorescent signals (white spots) from chicken cells and chromosomes have greater intensity than those of human (4′,6 diamidino-2-phenylindole, DAPI counterstain)." + } + ], + "f4762690-64e9-4f6d-9031-c249dc4a6d85": [ + { + "document_id": "f4762690-64e9-4f6d-9031-c249dc4a6d85", + "text": "\n\nIn a previous study on the accumulation of spontaneous genome rearrangements in normal mice with aging, we discovered that 50% of the events were intrachromosomal, i.e., large deletions or inversions [22].In contrast, in this present study most of the rearrangements resulted from inter-chromosomal recombination, in both the Ercc1-mutant and control animals (Table 3).Previously, we used lacZ-plasmid line 60 mice with integration sites on Chromosomes 3 and 4, while in the present study line 30 mice were used with a single integration site on Chromosome 11.This indicates that the relative frequency of translocations is founder line specific and could be due to the position of the lacZ-plasmid cluster on the chromosome.Indeed, the chromosomal integration sites in line 60 mice are in the E1 region of Chromosome 3 (half way along the chromosome) and the C5 region of Chromosome 4 (two-thirds of the way along the chromosome) [22], while the integration site of founder line 30 (used in this study) is on the centromeric tip of Chromosome 11 (region A1-A2; not shown).The proximal location on Chromosome 11 prevents the detection of all but relatively small intra-chromosomal recombinations; larger events would lead to loss of the centromere and, therefore, the entire chromosome.If the orientation of the integration site in line 30, which is currently unknown, is towards the centromere, transpositions and inversions towards the distal end are the only detectable large intra-chromosomal rearrangements (for a detailed explanation of the different chromosomal events that can occur at the lacZ locus, see [22])." + } + ] + }, + "data_source": [], + "document_id": "575BE8FB36E8D520760A31B2CAE92034", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "CENP-B&box", + "human¢romeres", + "recombination", + "chromosome&11", + "Ercc1-mutant", + "lacZ-plasmid", + "inversions", + "translocations", + "NORs", + "rDNA" + ], + "metadata": [], + "question": "What about recombination in human centromeres?", + "subquestions": null, + "task_id": "575BE8FB36E8D520760A31B2CAE92034", + "usage": { + "chatgpt": 8855, + "gpt-4": 5918, + "gpt-4-turbo-preview": 4935 + }, + "user_id": 2 + }, + "document_id": "575BE8FB36E8D520760A31B2CAE92034", + "task_id": "575BE8FB36E8D520760A31B2CAE92034" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_07.json b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_07.json new file mode 100644 index 0000000..ad4369d --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_07.json @@ -0,0 +1,142 @@ +{ + "created_at": "2024-06-01T14:29:54.405039Z", + "data": { + "amplify": false, + "answer": "The text mentions the application of site-specific recombinase technology, which allows investigators to engineer genes in the mouse that will allow for the deletion, insertion, inversion, or exchange of chromosomal DNA with high fidelity. However, it does not provide specific details about recombination in the human genome.", + "context": { + "081924f4-cdcc-4fce-9223-744c6ecffe4e": [ + { + "document_id": "081924f4-cdcc-4fce-9223-744c6ecffe4e", + "text": "Genome Res, 2011, 21: 1769–1776\nMattick JS, Dinger ME. The extent of functionality in the human\ngenome. HUGO J, 2013, 7, doi:10.1186/1877-6566-1187-1182\nENCODE Project Consortium, Bernstein BE, Birney E, Dunham I,\nGreen ED, Gunter C, Snyder M. An integrated encyclopedia of DNA\nelements in the human genome. Nature, 2012, 489: 57–74\nPheasant M, Mattick JS. Raising the estimate of functional human\nsequences. Genome Res, 2007, 17: 1245–1253\nHu T, Long M, Yuan D, Zhu Z, Huang Y, Huang S. The genetic\nequidistance result, misreading by the molecular clock and neutral\ntheory and reinterpretation nearly half of a century later." + } + ], + "33814fad-d831-46f5-b41f-ff31626a82ca": [ + { + "document_id": "33814fad-d831-46f5-b41f-ff31626a82ca", + "text": "This approach enables, on the one hand, studying the process of\nmammalian evolution and, on the other hand, translational studies using model\norganisms of complex human phenotypes. Detection of regions conserved between\ndistant species points to high functional importance of these fragments of the DNA\nsequence. Human and mouse developmental lines diverged about 75 million years ago, and\never since evolutionary forces shaped the two genotypes in a different manner\n(Waterston et al. , 2002). Nevertheless, the extent of the changes is, however, small\nenough for conservation of local gene order (Waterston et al. , 2002)." + } + ], + "3cafb9e7-b3d9-4e8e-a727-da79282d2b14": [ + { + "document_id": "3cafb9e7-b3d9-4e8e-a727-da79282d2b14", + "text": "First, the human and mouse genome projects\nelucidated the sequences of over 20,000 genes [Lander et al. ,\n2001; Venter et al. , 2001], and most are expressed in the CNS. The availability of gene sequences has allowed rapid analysis of\ncandidate human disease and disorder genes and the isolation of\nthe mouse homologues. Second, the application of site-specific\nrecombinase technology provides investigators with the opportunity to engineer genes in the mouse that will allow for the\ndeletion, insertion, inversion, or exchange of chromosomal\nDNA with high fidelity (for review see Branda and Dymechi,\n2004]." + } + ], + "5edf84d0-c2d9-45eb-91b9-c35743b6a463": [ + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "In some cases, structural variations, such as copy number polymorphisms,\nexist (Feuk et al. , 2006); however, because of the nature of the genome assembly\nprocess, these will invariably be collapsed into a single contig that does not reflect\nthe natural sequence. To address the technical challenges of whole-genome assembly,\nthe human genome is released as defined ‘builds’ on a quarterly basis (Lander et al. ,\n2001; reviewed in Chapter 4). The increasing complexity of processes that map\ndata to the genome implicitly involves some lag in availability of the most current\nsequence assembly." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "In\npractical terms, this has meant that we acquire many fragments, from a few hundred\nbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human\ngenome, two unfinished ‘draft’ sequences were produced by different methods, one\nby the International Human Genome Sequencing Consortium (IHGSC) and one by\nCelera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001)." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + } + ], + "74f148ef-696c-4e25-80e5-1d44ae70540e": [ + { + "document_id": "74f148ef-696c-4e25-80e5-1d44ae70540e", + "text": "\nTHE HUMAN GENOME PROJECT IS generating vast amounts of new information at breakneck speed and causing a fundamental shift in disease research.Now with the availability of a nearly complete, high-accuracy sequence of the mouse genome (7), a new and powerful paradigm for biomedical research is established.The remarkable similarity of mouse and human genomes, in both synteny and sequence, unconditionally validates the mouse as an exceptional model organism for understanding human biology.The discovery among inbred mouse strains of defined regions of high and low genomic variation inherited primarily from two ancestral Mus subspecies (6) holds great promise to make mapping and positional cloning more rapid and feasible.Haplotype maps of inbred mouse strains combined with sophisticated delineation of their phenotypic variation and gene expression patterns will enable complex trait analysis on an unprecedented scale.This issue of Journal of Applied Physiology highlights inbred strain surveys exploring phenotypic variation in drug responses [see Crabbe et al. (1) and Watters et al. (8) in this issue].These mouse initiatives demonstrate a viable, cost-effective alternative to human research requiring family studies, population linkage analysis, or genome-wide genotyping on a multitude of individuals for association mapping." + } + ], + "81c3edc4-f625-45f2-bf78-e49faf118c88": [ + { + "document_id": "81c3edc4-f625-45f2-bf78-e49faf118c88", + "text": "\n\nHow Many Genes are There in the Human Genome?" + } + ], + "b1656249-5f62-428f-8b71-7549cc2886ff": [ + { + "document_id": "b1656249-5f62-428f-8b71-7549cc2886ff", + "text": "\n\nThe Landscape of Human Genome Variation" + } + ], + "c12e853e-4f0d-48f9-93af-15db9ad2dfae": [ + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "In some cases, structural variations, such as copy number polymorphisms,\nexist (Feuk et al. , 2006); however, because of the nature of the genome assembly\nprocess, these will invariably be collapsed into a single contig that does not reflect\nthe natural sequence. To address the technical challenges of whole-genome assembly,\nthe human genome is released as defined ‘builds’ on a quarterly basis (Lander et al. ,\n2001; reviewed in Chapter 4). The increasing complexity of processes that map\ndata to the genome implicitly involves some lag in availability of the most current\nsequence assembly." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "In\npractical terms, this has meant that we acquire many fragments, from a few hundred\nbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human\ngenome, two unfinished ‘draft’ sequences were produced by different methods, one\nby the International Human Genome Sequencing Consortium (IHGSC) and one by\nCelera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001)." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + } + ], + "da485354-fcdc-49b8-9a41-0f673610156a": [ + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "Science 291:1304–\n1351\n3. Lander ES et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921\n4. Engle LJ, Simpson CL, Landers JE (2006) Using high-throughput SNP technologies to study cancer. Oncogene 25:1594–1601\n5. Elston RC, Anne Spence M (2006) Advances in statistical human genetics over the\nlast 25 years. Stat Med 25:3049–3080\n6. Larson GP et al (2005) Genetic linkage of prostate cancer risk to the chromosome\n3 region bearing FHIT. Cancer Res 65:805–814\n7. Botstein D, Risch N (2003) Discovering genotypes underlying human phenotypes:\npast successes for mendelian disease, future approaches for complex disease." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "McPherson JD, Marra M, Hillier L et al (2001) A physical map of the human\ngenome. Nature 409:934–941\n13. Burke DT, Carle GF, Olson MV. (1987) Cloning of large segments of exogenous\nDNA into yeast by means of artificial chromosome vectors. Science 236:806–812\n14. Fleischmann RD, Adams MD, White O et al (1995) Whole-genome random\nsequencing and assembly of Haemophilus influenzae Rd Science 269:496–512\n15. Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the\nflowering plant Arabidopsis thaliana. Nature 408:796–815\n16." + } + ], + "e17ef791-e77a-486b-a3c1-c7f037fa530c": [ + { + "document_id": "e17ef791-e77a-486b-a3c1-c7f037fa530c", + "text": "\n\nT he human genome has been cracked wide open in recent years and is spilling many of its secrets.More than 100 genome wide association studies have been conducted for scores of hu man diseases, identifying hun dreds of polymorphisms that are widely seen to influence disease risk.After many years in which the study of complex human traits was mired in false claims and methodologic inconsistencies, ge nomics has brought not only com prehensive representation of com mon variation but also welcome rigor in the interpretation of sta tistical evidence.Researchers now know how to properly account for most of the multiple hypothesis testing involved in mining the ge nome for associations, and most reported associations reflect real biologic causation.But do they matter?" + } + ], + "f35e02a1-3314-4663-913f-38a3fc072aa8": [ + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "In some cases, structural variations, such as copy number polymorphisms,\nexist (Feuk et al. , 2006); however, because of the nature of the genome assembly\nprocess, these will invariably be collapsed into a single contig that does not reflect\nthe natural sequence. To address the technical challenges of whole-genome assembly,\nthe human genome is released as defined ‘builds’ on a quarterly basis (Lander et al. ,\n2001; reviewed in Chapter 4). The increasing complexity of processes that map\ndata to the genome implicitly involves some lag in availability of the most current\nsequence assembly." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "In\npractical terms, this has meant that we acquire many fragments, from a few hundred\nbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human\ngenome, two unfinished ‘draft’ sequences were produced by different methods, one\nby the International Human Genome Sequencing Consortium (IHGSC) and one by\nCelera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001)." + } + ], + "fca531d0-d45b-495f-a02c-fbd437617b20": [ + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "In some cases, structural variations, such as copy number polymorphisms,\nexist (Feuk et al. , 2006); however, because of the nature of the genome assembly\nprocess, these will invariably be collapsed into a single contig that does not reflect\nthe natural sequence. To address the technical challenges of whole-genome assembly,\nthe human genome is released as defined ‘builds’ on a quarterly basis (Lander et al. ,\n2001; reviewed in Chapter 4). The increasing complexity of processes that map\ndata to the genome implicitly involves some lag in availability of the most current\nsequence assembly." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "In\npractical terms, this has meant that we acquire many fragments, from a few hundred\nbases to a few hundred kilobases in length, of a genome that must then be assembled computationally to produce a continuous sequence. In the case of the human\ngenome, two unfinished ‘draft’ sequences were produced by different methods, one\nby the International Human Genome Sequencing Consortium (IHGSC) and one by\nCelera Genomics (CG). The IHGSC began with a BAC (bacterial artificial chromosome) clone-based physical map of the genome (IHGSC, 2001)." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + } + ] + }, + "data_source": [], + "document_id": "1A879F7DD77C0462CC12FB20F7D14486", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "human&genome", + "recombination", + "genes", + "CNS", + "site-specific&recombinase", + "structural&variations", + "copy&number&polymorphisms", + "genome&assembly", + "genome&wide&association&studies", + "polymorphisms" + ], + "metadata": [], + "question": "What about recombination in the human genome?", + "subquestions": null, + "task_id": "1A879F7DD77C0462CC12FB20F7D14486", + "usage": { + "chatgpt": 4864, + "gpt-4": 3728, + "gpt-4-turbo-preview": 2745 + }, + "user_id": 2 + }, + "document_id": "1A879F7DD77C0462CC12FB20F7D14486", + "task_id": "1A879F7DD77C0462CC12FB20F7D14486" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_08.json b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_08.json new file mode 100644 index 0000000..10a7cd2 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_08.json @@ -0,0 +1,163 @@ +{ + "created_at": "2024-06-01T14:31:40.882988Z", + "data": { + "amplify": false, + "answer": "The text does not provide specific instructions on how to add a new species to the GeneNetwork database. It is recommended to contact the administrators or operators of the GeneNetwork database for guidance on this process.", + "context": { + "0e6c370f-b514-4551-b6ed-9cc72e6f6b75": [ + { + "document_id": "0e6c370f-b514-4551-b6ed-9cc72e6f6b75", + "text": "The hierarchical organization of GN’s main Select and Search menu is\nsimple and makes it relatively easy to find relevant data sets (Fig. 1). To get data, after\nopening the browser, select the most appropriate Species from the dropdown menu. For an\nopen-ended search of phenotypes you can also select All Species at the bottom of the menu. The next steps are to select the Group, Type, and Data Set from the drop-down menus. For\nmany groups, a combination of phenotypes, genotypes, and molecular data are available." + } + ], + "4049da4d-c7cf-4e30-9a21-c77609fad23d": [ + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "text": "GeneNetwork contains data from a\nwide range of species, from humans to soybeans, but most of the available phenotypic data is\nfrom mice. Within the mouse dataset there are groups of families, crosses, non-genetic\ngroupings, and individual data. The type of dataset must be selected after defining the species\nand sample population. While genotypes, mRNA, methylated DNA, protein, metagenomic, and\n2\nbioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint\n(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. metabolome datasets are available (i.e." + } + ], + "43407486-b9c2-487b-b19c-b605c4d201c6": [ + { + "document_id": "43407486-b9c2-487b-b19c-b605c4d201c6", + "text": "The hierarchical organization of GN’s main Select and Search menu is\nsimple and makes it relatively easy to find relevant data sets (Fig. 1). To get data, after\nopening the browser, select the most appropriate Species from the dropdown menu. For an\nopen-ended search of phenotypes you can also select All Species at the bottom of the menu. The next steps are to select the Group, Type, and Data Set from the drop-down menus. For\nmany groups, a combination of phenotypes, genotypes, and molecular data are available." + } + ], + "47a15e69-dc83-452e-95d8-c605e61f43c0": [ + { + "document_id": "47a15e69-dc83-452e-95d8-c605e61f43c0", + "text": "Search and Data Retrieval\nPoint your browser to www.genenetwork.org. This brings you by default to\nthe Search page, from which you can retrieve data from many GN data sets. We will focus on the default data set, defined by Species: Mouse, Group: BXD,\nType: Whole Brain, Database: INIA Brain mRNA M430 (Apr05) PDNN\nEnter “Kcnj*” into the ALL or ANY field and click the Search button. Note\nthe location and annotation of available potassium channel genes in the Search\nResults page that opens. Use the browser Back button to return to previous page." + } + ], + "638b3811-7054-4788-a42d-2ccc7bfce1c7": [ + { + "document_id": "638b3811-7054-4788-a42d-2ccc7bfce1c7", + "text": "Add\ninformation on data provenance by giving details in Investigation, Protocols and ProtocolApplications\n\nCustomize Customize ‘my’ XGAP database with extended variants of Trait and Subject. In the online XGAP demonstrator, Probe traits have a\nsequence and genome location and Strain subjects have parent strains and (in)breeding method. Describe extensions using MOLGENIS\nlanguage and the generator automatically changes XGAP database software to your research\nUpload\n\nUpload data from measurement devices, public databases, collaborating XGAP databases, or a public XGAP repository with community\ndata." + }, + { + "document_id": "638b3811-7054-4788-a42d-2ccc7bfce1c7", + "text": "However, a suitable and customizable integration of\nthese elements to support high throughput genotype-tophenotype experiments is still needed [34]: dbGaP, GeneNetwork and the model organism databases are\ndesigned as international repositories and not to serve\nas general data infrastructure for individual projects;\nmany of the existing bespoke data models are too complicated and specialized, hard to integrate between profiling technologies, or lack software support to easily\nconnect to new analysis tools; and customization of the\nexisting infrastructures dbGaP, GeneNetwork or other\ninternational repositories [35,36] or assembly of Bioconductor and generic model organism database components to suit particular experimental designs, organisms\nand biotechnologies still requires many minor and\nsometimes major manual changes in the software code\nthat go beyond what individual lab bioinformaticians\ncan or should do, and result in duplicated efforts\nbetween labs if attempted." + } + ], + "75813bc2-f0b5-400c-92d7-0958df97a04f": [ + { + "document_id": "75813bc2-f0b5-400c-92d7-0958df97a04f", + "text": ", 2014; see Section 9). GeneNetwork is a database that enables searching for ∼4000 phenotypes from multiple studies in the BXD, HXB, and in other recombinant inbred rodent families, as well as in other model organisms\nand even humans (Mulligan et al. , 2017). GeneNetwork employed a\nsomewhat different strategy than MPD in that it did not rely solely on\nresearchers submitting their data. Instead the database operators extracted the data from the scientific literature and integrated them into a\nuniform format (Chesler et al. , 2003)." + } + ], + "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d": [ + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "text": "GeneNetwork contains data from a\nwide range of species, from humans to soybeans, but most of the available phenotypic data is\nfrom mice. Within the mouse dataset there are groups of families, crosses, non-genetic\ngroupings, and individual data. The type of dataset must be selected after defining the species\nand sample population. While genotypes, mRNA, methylated DNA, protein, metagenomic, and\n2\nbioRxiv preprint doi: https://doi.org/10.1101/2020.12.23.424047; this version posted December 24, 2020. The copyright holder for this preprint\n(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. metabolome datasets are available (i.e." + } + ], + "85ee9743-b34d-4d49-9017-d7d2e5d4b996": [ + { + "document_id": "85ee9743-b34d-4d49-9017-d7d2e5d4b996", + "text": "However, a suitable and customizable integration of these elements\nto support high throughput genotype-to-phenotype experiments is still\nneeded[340]: dbGaP, GeneNetwork and the model organism databases\nare designed as international repositories and not to serve as general\ndata infrastructure for individual projects; many of the existing bespoke\ndata models are too complicated and specialized, hard to integrate between profiling technologies, or lack software support to easily connect\nto new analysis tools; and customization of the existing infrastructures\ndbGaP, GeneNetwork or other international repositories[384, 154] or\nassembly of Bioconductor and generic model organism database components to suit particular experimental designs, organisms and biotechnologies still requires many minor and sometimes major manual changes\n38\n2.1." + } + ], + "92fa8f50-2923-41a1-812b-32d931c71684": [ + { + "document_id": "92fa8f50-2923-41a1-812b-32d931c71684", + "text": "All data presented in this paper were deposited in the online database\nGeneNetwork (www.genenetwork.org), an open web resource that contains\ngenotypic, gene expression, and phenotypic data from several genetic reference\npopulations of multiple species (e.g. mouse, rat and human) and various cell\ntypes and tissues.35;36 It provides a valuable tool to integrate gene networks and\nphenotypic traits, and also allows cross-cell type and cross-species comparative\ngene expression and eQTL analyses." + } + ], + "d2f9c5cf-835c-450a-bb42-a2454a99e058": [ + { + "document_id": "d2f9c5cf-835c-450a-bb42-a2454a99e058", + "text": "There is a good chance that you will be able to apply these new\ntechniques to specific problems, even while you read. If you have a computer with an\nInternet connection—so much the better, and you can read and work along at the same time. This short review and primer will take you on a tour of a web site called GeneNetwork that\nembeds many large data sets that are relevant to studies of behavioral variation. GeneNetwork is an unusual site because it contains a coherent \"universe\" of data, as well as\nmany powerful analytic tools." + } + ], + "dbe5a781-3561-48cb-9f63-cfb4f3246434": [ + { + "document_id": "dbe5a781-3561-48cb-9f63-cfb4f3246434", + "text": "The GeneNetwork database provides open access\nto BXD and other RI strain derived microarray data, single nucleotide polymorphism (SNP) data,\nand phenotypic data for quantitative trait loci analysis and gene expression correlation analyses. Gene expression data were exported for manually selected probes in the PDNN hippocampus\ndatabase (Hippocampus Consortium M430v2), and the PDNN whole brain database (INIA Brain\nmRNA M430). The Hippocampus database was chosen as one of the most elaborate brain databases,\nas well as most highly recommended dataset on GeneNetwork itself (http://www.genenetwork.org/\nwebqtl/main.py?FormID=sharinginfo&GN_AccessionId=112)." + } + ], + "f041550e-5f2d-430e-8f46-15ebea6ca496": [ + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "text": "2016) and can\nalso be accessed in GeneNetwork by entering Record ID 18494 in the Get Any\nspace on the Search page and clicking on the Search button. Alternatively, enter\ndata by hand into the designated boxes provided by GeneNetwork. These latter\noptions also allow for the inclusion of trait variance. It is a good idea to name\nthe trait in the box provided. Then click Next, and manually enter the data for\neach RI strain, F1, and founder strain. 3\n\nAuthor Manuscript\n\nAfter entering the data, click on the blue plus sign button called Add." + }, + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "text": "To submit multiple phenotypes at the same\ntime, select the option for Batch Submission under the Home tab. This allows\nusers to submit up to 100 traits for analysis by GeneNetwork. Here, select BXD\nas the cross or RI set to analyze from the first pull-down menu. The phenotype\nfile should follow the format described in the Sample text (http://\ngenenetwork.org/sample.txt). After uploading the appropriate file using the\nBrowse button, enter a name for the file in the Dataset space. The data will be\nstored in the GeneNetwork server for 24 hours. Click Next." + }, + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "text": "Author Manuscript\n\nMaterials\nHere we will provide detailed instructions for using GeneNetwork along with some\n“worked” examples taken from the recent study of intravenous cocaine self-administration\nby Dickson et al. (2016) in BXD RI mice. A complete overview of GeneNetwork is beyond\nthe scope of this protocol, but is extensively covered in elsewhere (see Mulligan et al. 2016;\nWilliams & Mulligan 2012 for excellent reviews on GeneNetwork). A computer with an internet connection and current web browser. See the GeneNetwork.org\nsite for information on supported browser versions. Author Manuscript\n\nMethod\nEntering Data\n\nAuthor Manuscript\n\n1\n\nLink to http://www.genenetwork.org." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "\n\nSpecies in GenAge model organisms" + } + ], + "f9b2eeba-5f93-49c1-8828-311f0797d9e3": [ + { + "document_id": "f9b2eeba-5f93-49c1-8828-311f0797d9e3", + "text": "Data are reviewed before entry in\nGeneNetwork by the senior author. Phenotypes are currently split into 15 broad\nphenotypic categories (Supplementary Data 1). Phenome curation and description\nwas initiated by R.W.W. and Dr Elissa Chesler in 2002 by literature review and data\nextraction. The early work is described briefly in Chesler et al.51,52. Most work over\nthe past 5 years has been performed by two of the coauthors (R.W.W. and\nM.K.M.). We have used a controlled vocabulary and set of rules described here\n(http://www.genenetwork.org/faq.html#Q-22)." + } + ], + "fa8bba46-ce94-439a-a676-35187a3abcbf": [ + { + "document_id": "fa8bba46-ce94-439a-a676-35187a3abcbf", + "text": "9) To bring your data to GeneWeaver,\nclick on the GeneWeaver icon, making sure to be previously\nlogin to your GeneWeaver account. You will be brought to the\nGeneSet upload page with the Genes Uploaded and the\nGeneweaver Analysis Platform\n\n139\n\nFig. 5 Default settings at GeneNetwork.org are set to search “Mouse”, “Phenotypes”, from among the “BXD\nPublished Phenotypes” data set. Here the term nociception was searched for\n\nFig. 6 The search results page in GeneNetwork showing the 33 records retrieved from the phenotype search\nfor nociception." + }, + { + "document_id": "fa8bba46-ce94-439a-a676-35187a3abcbf", + "text": "Users may also share their data with other users selectively,\nmake it public, or keep it restricted to a private account. Data can be\nimported by users, uploading their gene set data directly or exporting to GeneWeaver from within another online resource such as\nNeuro Informatics Framework (NIF) [8], Grappa [9], Mouse\nPhenome Database (MPD) [10] or GeneNetwork [11]. These datasets can then be added to your collection to be analyzed together\nwith other gene sets retrieved from the GeneWeaver database. To begin a GeneWeaver analysis a user must collect “GeneSets”\ntogether in a “Project”." + }, + { + "document_id": "fa8bba46-ce94-439a-a676-35187a3abcbf", + "text": "Alternatively the spreadsheet can be saved as a .txt file\nand uploaded by clicking on “Switch to file upload.” Once\ncomplete click on upload GeneSet. 7. Once completed you are taken to the GeneSet detail page. If\nthere are errors in your uploaded data you can correct them by\nclicking on “Edit”. 8. Use the Add Selected to Project, and create a new project, e.g. “Chronic Cocaine”. 9. Now using the Search function populate this project with additional gene sets related to this study trying Queries such as\n“Cocaine Addiction”, “Chronic Cocaine”." + } + ] + }, + "data_source": [], + "document_id": "FA1E32391509D1EEAEBB70D3014C444A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "GeneNetwork", + "Record&ID&18494", + "Search&page", + "Add", + "Trait", + "RI&strain", + "F1", + "founder&strain", + "Batch&Submission", + "GeneWeaver", + "GeneSet", + "Project", + "Cocaine&Addiction", + "Chronic&Cocaine", + "Species", + "Mouse", + "BXD", + "HXB", + "Phenotypes", + "genotypes", + "mRNA", + "methylated&DNA", + "protein", + "metagenomic", + "metabolome" + ], + "metadata": [], + "question": "How can I add a new species to the GeneNetwork database?", + "subquestions": null, + "task_id": "FA1E32391509D1EEAEBB70D3014C444A", + "usage": { + "chatgpt": 5344, + "gpt-4": 3726, + "gpt-4-turbo-preview": 2725 + }, + "user_id": 2 + }, + "document_id": "FA1E32391509D1EEAEBB70D3014C444A", + "task_id": "FA1E32391509D1EEAEBB70D3014C444A" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_09.json b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_09.json new file mode 100644 index 0000000..fc81f81 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_09.json @@ -0,0 +1,142 @@ +{ + "created_at": "2024-06-01T14:33:22.252190Z", + "data": { + "amplify": false, + "answer": "Mitochondrial DNA (mtDNA) is inherited solely from the maternal lineage, unlike nuclear DNA which requires both paternal and maternal contributions. This uniparental transmission of mtDNA is ensured by complex mechanisms that eliminate paternal mitochondria from sperm during fertilization. mtDNA also exhibits a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms. These polymorphisms can be clustered into distinct haplogroups that represent major branch points on the mitochondrial phylogenetic tree. In contrast, nuclear DNA undergoes bi-parental recombination.", + "context": { + "0381d0d4-717f-470b-a8d4-562475cf58bb": [ + { + "document_id": "0381d0d4-717f-470b-a8d4-562475cf58bb", + "text": "Oxidative stress and mitochondrial DNA\n\nNot long after it was discovered that mitochondria have their own genetic apparatus, Harman proposed that mitochondria play a central role in the free radical theory of aging [16].This idea was developed further by Miquel et al. [330], and the notion that mtDNA mutagenesis played a role in aging took hold.The phenotypical importance of mutations in mtDNA was demonstrated by Wallace et al. [331] and Holt et al. [332], who first showed that Leber's hereditary optic neuropathy and mitochondrial myopathies were caused by mtDNA mutations (reviewed in [333]).Because mtDNA is so close to the site of mitochondrial ROS production, it is exposed to considerably higher oxidative stress, resulting in 3-fold higher levels of DNA oxidative damage (the previously quoted 20-fold figure is apparently due to an isolation artifact [334,335]).In the 1990s a series of papers reported that the frequency of mitochondrial DNA deletions increases dramatically with age, being essentially undetectable in young individuals and reaching levels as high as 2% of mtDNA in old individuals.This age-related increase in mtDNA deletions was found in organisms as diverse as worms, mice, and humans (reviewed in [24,336]).The same is also true with mtDNA point mutations [337,338].Certain mtDNA polymorphisms have been found in increased frequency in centenarians, implying a protective effect during aging [339][340][341].Similar protective effects of mtDNA polymorphisms have been reported for the age-related neurodegenerative condition, Parkinson's disease [342]." + } + ], + "21d2cb60-92ab-4fbb-a3a1-85d3424881c1": [ + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "text": "\n\nVariation in the structure and function of mitochondria underlies variation in organismal energetics broadly (Seebacher et al., 2010) and evidence for the importance of mitochondrial function in the evolution of natural populations continues to accumulate (Ballard and Melvin, 2010;Glanville et al., 2012;Hicks et al., 2012;Kurbalija Novičić et al., 2015).For example, variation in mitochondrial DNA sequences (mtDNA) can determine whole-organism metabolism, i.e., the rate at which organisms process energy from their environment, a phenomenon widespread across animal taxa (Arnqvist et al., 2010;Ballard et al., 2007;Ballard and Pichaud, 2014;Havird et al., 2019;Hood et al., 2018;James et al., 2016;Wolff et al., 2014).Specifically, mtDNA sequence variants are linked to functional metabolic differences in fish (Chapdelaine et al., 2020;Flight et al., 2011;Healy et al., 2019), birds (Scott et al., 2011), and mammals (Fontanillas et al., 2005), including humans (Amo and Brand, 2007;Dato et al., 2004;Niemi et al., 2003;Tranah et al., 2011).These mtDNA variants are often correlated with environmental factors such as temperature and altitude (Storz et al., 2010).However, other studies attempting to link mitochondrial function to mitochondrial DNA (mtDNA) sequence variation or environmental factors have offered mixed reports (Amo and Brand, 2007;Flight et al., 2011;Fontanillas et al., 2005;Hicks et al., 2012)." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "text": "\n\nThe results here point to several potentially fruitful research directions.We have identified how nonsynonymous mutations in the mitochondrial genome associate with variation in whole-organism metabolism (including CytB, ND1, ND5 and ND6).A next step will be to characterize the molecular details of how these changes affect molecular function.It would also be beneficial to describe how variation in cellular oxygen consumption rate scales up to determine whole-organism metabolic rate across a range of temperatures, thus identifying potential mismatches across levels of organization that may impact organismal performance (Gangloff and Telemeco, 2018).While the interconnected processes that shape organismal and population-level responses to environmental variation do not lend themselves to simple narratives, and many molecular processes interact to produce the emergent ecotypic divergences at the phenotypic level, it is clear that the mitochondria play a central role even as that role may change across populations and ecological contexts (Fig. 1).Research within well-characterized natural systems, such as these garter snake populations, can offer illustrative case studies of how mitochondria respond to their environments, and thus impact physiological pathways and evolutionary patterns, creating variation in life histories and aging." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "text": "\n\nDespite the complexities underlying observed variation in mitochondrial function, recent work has demonstrated examples of how evolution and plasticity in mitochondrial function across populations within a species can shape life histories.For example, evidence from Drosophila has demonstrated the effect of temperature on components of the ETC and has linked mtDNA variants to metabolic thermosensitivity (Pichaud et al., 2012), to differences in whole-organism metabolic rates (Kurbalija Novičić et al., 2015), and to fitness-related traits (Ballard et al., 2007;Pichaud et al., 2011;Pichaud et al., 2010).In general, studies in birds and mammals demonstrate that mitochondria of longer-lived species are more efficient in ATP production, produce less reactive oxygen species, and demonstrate increased antioxidant capacities (Barja and Herrero, 2000;Ku et al., 1993;Lambert et al., 2007).While some studies in lizards and snakes demonstrate a similar pattern (Olsson et al., 2008;Robert et al., 2007), the extent to which these results are generalizable across vertebrate taxa is not yet known.The diversity of life-history traits and immense variation in longevity demonstrated by reptiles, both within and among species, make these taxa ideal candidates for understanding how variation in mitochondrial physiology drives this variation in whole-organism traits (reviewed in Hoekstra et al., 2019).Such work has moved to the forefront with a recent focus on the ecological and evolutionary significance of aging processes in wild populations (reviewed in Nussey et al., 2013;Fletcher and Selman, 2015;Gaillard and Lemaître, 2020)." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "text": "\n\nOver evolutionary time, differential mortality rates are a selective force in shaping genetic structure.This results in divergence of a variety of physiological networks that shape, ultimately, patterns of aging and longevity in different habitats (Monaghan et al., 2008;Stojković et al., 2017).Such selective pressures can have differential effects on the nuclear and mitochondrial genomes (McKenzie et al., 2019;Wolff et al., 2014).Genetic variation in the mitochondrial genome is known to drive mitochondrial function in many species (Ballard and Melvin, 2010;McKenzie et al., 2019;Novelletto et al., 2016) and we find this in our system as well.Whole organism metabolic rate varies with the mitochondrial genome haplogroups we identified in this study.T. elegans individuals with the introgressed T. sirtalis mitochondrial genome had the lowest metabolic rate and had 68 amino acid changes in the ETC genes relative to the T. elegans mitochondrial genomes.As species divergence are a continuation of population divergence, this introgression provides additional insight into how genetic variation can alter mitochondrial function.Whether the lower metabolic rate in our snakes with the introgressed mitochondrial genome is due to the fixed amino acid changes between the species or a mismatch between the coadapted nuclear and mitochondrially-encoded ETC proteins that could alter function of the mitochondria (Burton et al., 2013;Haenel, 2017;Rawson and Burton, 2002;Toews et al., 2014;Wolff et al., 2014) will require further comparisons to T. sirtalis individuals." + }, + { + "document_id": "21d2cb60-92ab-4fbb-a3a1-85d3424881c1", + "text": "\n\nBuilding on previous work in this system, the current study tests three primary hypotheses about how variation in mtDNA and mitochondrial function relate to variation in life-history traits and aging within this system (Fig. 1): (1) First, we test whether rates of cellular oxygen consumption in isolated immune cells exhibit patterns that are consistent with the hypothesis that cellular processes drive whole-organism senescence and aging, and if these patterns differ between the SA and FA ecotypes and between sexes.By measuring basal, ATP-production associated, and maximal rates of cellular oxygen consumption, we further test for evidence that phenotypic divergence is dependent on a specific aspect of oxidative phosphorylation within immune cells.The energetics of these cells are particularly important given their essential role in modulating disease and infection, important factors contributing to senescence (Metcalf et al., 2019).We predict that SA snakes will maintain levels of cellular oxygen consumption across age, whereas the FA snakes will show a decline with age, especially in ATP-associated rates, possibly due to continual degradation of electron transport chain functionality from accumulating oxidative damage and reduced DNA repair mechanisms (Robert and Bronikowski, 2010;Schwartz and Bronikowski, 2013). ( 2) Second, we expand our mitochondrial genomics dataset to quantify mtDNA genetic structure across the landscape and test whether mtDNA haplotypes, and alleles at a nonsynonymous SNP in the Cytochrome B (CytB) gene correlate with aging ecotypes. (3) Third, we test the hypothesis that variation in mtDNA correlates with whole-organism variation in metabolic rates, suggesting a pathway linking mitochondrial genetic variation in mtDNA to whole-organism energetics.We first test whether different haplotypes differ in resting metabolic rate.Then, we test the effects of the nonsynonymous SNP in CytB on resting metabolic rate.The CytB gene encodes a component of complex III of the ETC, and was previously found to segregate between these life-history ecotypes (Schwartz et al., 2015).This SNP results in an amino acid substitution from isoleucine (aliphatic, hydrophobic) to threonine (hydrophilic) on a region that comes into close contact with a nuclear-encoded subunit (Schwartz et al., 2015).We combine previously published and new data on whole-organism resting metabolic rates (oxygen consumption) to test for the effects of this nonsynonymous mutation in three populations where we find heterogeneity at this nucleotide, thus allowing us to disentangle the effects of shared environment (population) from sequence variation (SNP).We predict that this SNP will correlate with variation in whole-organism metabolic rate, demonstrating a putatively adaptive difference between the derived and ancestral sequence.By utilizing this integrative data setfrom genes to organelles to whole organisms to populationsin a known life-history context, we are able to test hypotheses across levels of organization to provide a more complete picture of the complicated story of mitochondria and life history (Havird et al., 2019)." + } + ], + "253fad94-3be6-4362-b56f-f00c9c5705e6": [ + { + "document_id": "253fad94-3be6-4362-b56f-f00c9c5705e6", + "text": "mtDNA Diversity\n\nUnlike the nuclear genome, which requires both paternal and maternal contributions, mtDNA is inherited solely from the maternal lineage.It is unclear what advantage a uniparental mtDNA transmission confers, but one possibility is to minimize the number of distinct genomes to maximize the efficiency of a multi-genomic system (Hill et al. 2019).In fact, humans have developed complex, redundant mechanisms to ensure uniparental inheritance of mtDNA (DeLuca and O'Farrell 2012; Rojansky et al. 2016).Paternal mitochondria from sperms that enter into the egg during fertilization are actively and selectively eliminated via mitophagy through two E3 ligases, PARKIN, and MUL1 (Rojansky et al. 2016).PARKIN and MUL1 serve redundant purposes, and mitophagy becomes insufficient to eliminate paternal mtDNA only in the absence of both (Rojansky et al. 2016).Even though oocytes have at least a thousand-fold more mitochondria than a sperm cell (Rojansky et al. 2016) and heteroplasmy levels would be very low if paternal mtDNA were to contaminate the embryo, the results can still be non-trivial.However, challenging this notion, a recent study provides evidence of potential paternal transmission (Luo et al. 2018), but awaits further corroborating studies (Lutz-Bonengel and Parson 2019)." + }, + { + "document_id": "253fad94-3be6-4362-b56f-f00c9c5705e6", + "text": "\n\nMtDNA exhibit a higher mutation rate than nuclear DNA, leading to significant population-level mtDNA polymorphisms (van Oven and Kayser 2009; Wallace 1999; Wallace and Chalkia 2013).In fact, the co-evolution of the mitonuclear genomes has been proposed to be driven by mtDNA mutations that select for compensatory changes in the nuclear genome (Havird and Sloan 2016).Populations that share similar mtDNA polymorphisms can be clustered into distinct haplogroups that are designated using all letters of the alphabet (i.e., A through Z).The mtDNA haplogroups represent major branch points on the mitochondrial phylogenetic tree that have strong regional ties around the globe, thus supporting the concept of a 'mitochondrial eve' (Wallace 1999).Haplogroups present inherently different mitonuclear interactions (Zaidi and Makova 2019), which eventually affect the aging process (Wolff et al. 2016).For example, one haplogroup commonly found in Ashkenazi Jews can interact with a specific enrichment of an amino acid sequence in complex I, and result in altered susceptibility to type 2 diabetes mellitus (Gershoni et al. 2014).The effect of mitonuclear compatibility on lifespan is influenced by environmental cues in flies (Drummond et al. 2019).It is unclear if mitonuclear compatibility is invariable throughout an organism's life, or antagonistically pleiotropic during aging, making it a difficult moving target to understand." + } + ], + "2f39f55f-2604-49d4-9589-0e1403b84d7a": [ + { + "document_id": "2f39f55f-2604-49d4-9589-0e1403b84d7a", + "text": "\n\nBackground: The accumulation of mitochondrial DNA (mtDNA) mutations, and the reduction of mtDNA copy number, both disrupt mitochondrial energetics, and may contribute to aging and age-associated phenotypes.However, there are few genetic and epidemiological studies on the spectra of blood mtDNA heteroplasmies, and the distribution of mtDNA copy numbers in different age groups and their impact on age-related phenotypes.In this work, we used whole-genome sequencing data of isolated peripheral blood mononuclear cells (PBMCs) from the UK10K project to investigate in parallel mtDNA heteroplasmy and copy number in 1511 women, between 17 and 85 years old, recruited in the TwinsUK cohorts." + } + ], + "4a17ce5c-55df-4aa0-a664-f6a03238d332": [ + { + "document_id": "4a17ce5c-55df-4aa0-a664-f6a03238d332", + "text": "Discussion\n\nTwo significant questions are raised by the findings that mitochondrial DNA can integrate into the nucleus.Firstly, is this an extraordinarily rare event or is it occurring continually and at high frequency?Secondly, can such an event have pathological consequences to the organism?" + } + ], + "4f010a74-a9b4-4538-94f7-ae8f35c8b96e": [ + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "text": "Phylogeny\n\nThe mtDNA is maternally inherited (120) by offspring through the oocyte cytoplasm; namely, the mother transmits her mtDNAs to all of her offspring, and her daughters transmit their mtDNAs to the next generation.This is the consequence of the fact that the mature oocyte such as mouse (304) or bovine (144) contains lOO-1,000 times more mtDNA than is found in somatic cells.Hence, the few sperm mtDNAs that enter the egg (130) have little effect on the genotype.The maternal inheritance results in sequentially diverged mtDNA polymorphism of modern human, as shown in Figure 2. The polymorphism derives from the combinations of small deletions and additions of <14 bp in noncoding region and base substitutions including some point mutations in coding region." + }, + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "text": "\n\nThere have been few reports on distinct correlation between mitochondrial morphology and human aging, except changes in number and size of mitochondria associated with age.Concerning the gross structure of mitochondria, the overwhelming importance of the cell nucleus in mitochondrial biogenesis should be noted, because the major parts of mitochondrial proteins are encoded by nuclear genes that are stable during life with the efficient repair mechanism for nDNA." + }, + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "text": "\n\nEarly data on DNA polymorphism detected by restriction endonuclease (263) have suggested that the evolutionary change of mtDNA in higher animals occurs mainly by nucleotide substitution rather than by deletion and insertion.The mtDNA nucleotide sequence evolves 6-17 times faster than comparable nuclear DNA gene sequences (51,52,405).Rapid evolution of mtDNA of higher primates including human, 0.02 base substitutions per site per million years, was calculated from the restriction map of mtDNA (51).Because orthodox recombination mechanism appears to be absent in mtDNA (128), germline mutation seems to go down to posterity as maternal inheritance from our common ancestor (57)." + } + ], + "612a70c6-2f42-492f-9f23-0d5e9296919e": [ + { + "document_id": "612a70c6-2f42-492f-9f23-0d5e9296919e", + "text": "\n\nA number of conclusions may be drawn from these results.Firstly, the data begin to answer the question of how closely mtDNA replication is kept in synchrony with nuclear DNA replication: it would appear to be regulated not by direct coupling to the nuclear DNA replication, but rather by the cell mass to be serviced by mitochondria." + } + ], + "65c8287b-eb19-437a-b9ca-5aaa8664d429": [ + { + "document_id": "65c8287b-eb19-437a-b9ca-5aaa8664d429", + "text": "\n\nIt may be that high mtDNA levels are indeed indicative of compromised mitochondria, but that the underlying defects are unrelated to alterations in the DNA sequence.Alternatively, elevated quantities of mtDNA might be associated with increased metabolic requirements of the embryo, rather than organelles of suboptimal function.It is possible that embryos produced by older oocytes are under some form of stress and therefore have larger energy requirements.Functional experiments will be required to address these questions.Whatever the underlying basis, the current study has unequivocally demonstrated that female reproductive aging is associated with changes in the mtDNA content at the blastocyst stage." + } + ], + "67ec2631-aa17-436e-800b-1bc046fb5b19": [ + { + "document_id": "67ec2631-aa17-436e-800b-1bc046fb5b19", + "text": "\n\nAge-associated alterations of the mitochondrial genome occur in several different species; however, their physiological relevance remains unclear.The age-associated changes of mitochondrial DNA (mtDNA) include nucleotide point mutations and modifications, as well as deletions.In this review, we summarize the current literature on age-associated mtDNA mutations and deletions and comment on their abundance.A clear need exists for a more thorough evaluation of the total damage to the mitochondrial genome that accumulates in aged tissues.᭧ 1997 Elsevier Science Inc." + } + ], + "8a9fe1bc-7fa3-40ee-ade0-9a498bcf9def": [ + { + "document_id": "8a9fe1bc-7fa3-40ee-ade0-9a498bcf9def", + "text": "Mitochondrial genetics\n\nOne underexplored avenue for determining maternal risk for preterm birth involves the influence of the mitochondrial genome.The high mutation rate of mito chondrial DNA (mtDNA), together with the fact that most of its encoded proteins are evolutionarily con served, allowing for the selection of neutral or beneficial variants, has generated interest in defining human mtDNA variations and their roles in human biology [58]." + } + ], + "aa942230-9a43-4b5f-90d9-96d364861a57": [ + { + "document_id": "aa942230-9a43-4b5f-90d9-96d364861a57", + "text": "\n\nClearly, as mitochondrial metabolic and genetic therapies advance for treating mitochondrial disease, they will also be available to enhance the personal lives of others.However, mitochondrial genetic variation appears to have been one of the primary factors that permitted our ancestors to adapt to new environments, survive adverse conditions, and multiple throughout the globe.Is it possible that by taking over control of individual mtDNA variation, we might also be setting our species on the road to functional decline and ultimately extinction?" + }, + { + "document_id": "aa942230-9a43-4b5f-90d9-96d364861a57", + "text": "Mitochondrial therapeutics and performance enhancement\n\nIt is now clear that not all mtDNA variation is deleterious.Indeed, about 25% of all ancient mtDNA variation appears to have caused functional mitochondrial changes and thus been adaptive.Those mtDNA variants that are adapted to warm climates have mtDNA variants that result in tightly coupled OXPHOS, thus maximizing ATP output and minimizing heat production.The presence of these mtDNAs permits maximum muscle performance but also predispose sedentary individuals that consume excess calories to multiple problems.They would be prone to be overweight and their mitochondria would generate excessive ROS, thus making them susceptible to a variety of degenerative diseases, cancer and premature aging.Partially uncoupled mitochondria generate more heat, but at the expense of ATP production.Individual's with these variants are better able to tolerate the cold, and are less prone to obesity.They also generate less ROS making then resistant to degenerative diseases and aging.Finally, the mitochondria are why we breathe.Hence, mitochondrial variation might be an important factor in individual predisposition to altitude sickness." + } + ], + "b0af29ac-0997-416d-907a-6caba940536d": [ + { + "document_id": "b0af29ac-0997-416d-907a-6caba940536d", + "text": "\n\nHuman mtDNA codes for 13 essential polypeptide components of the mitochondrial oxidative phosphorylation (OXPHOS) system.mtDNA undergoes strict maternal inheritance, resulting in the absence of bi-parental recombination (Elson et al., 2001) and has a high mutation rate (Tuppen et al., 2010).As such, the evolution of mtDNA is characterised by the emergence of distinct lineages (or haplogroups) (Hernstadt et al., 2002).This results in high levels of mtDNA variation at the population level despite its rather small size, which is also illustrated by the large number of sub-haplogroups (van Oven and Kayser, 2009).Africa" + } + ] + }, + "data_source": [], + "document_id": "8CC70421A233A4B1F89A6701731F7017", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "mtDNA", + "mitochondrial&DNA", + "nuclear&DNA", + "heredity", + "haplogroups", + "mitochondrial&genome", + "oxidative&phosphorylation", + "OXPHOS", + "mutation&rate", + "mitophagy" + ], + "metadata": [], + "question": "Describe the role of mitochondrial DNA in heredity and how it differs from nuclear DNA.", + "subquestions": null, + "task_id": "8CC70421A233A4B1F89A6701731F7017", + "usage": { + "chatgpt": 9175, + "gpt-4": 5434, + "gpt-4-turbo-preview": 4489 + }, + "user_id": 2 + }, + "document_id": "8CC70421A233A4B1F89A6701731F7017", + "task_id": "8CC70421A233A4B1F89A6701731F7017" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_10.json b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_10.json new file mode 100644 index 0000000..72e4a7a --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/experts/expert_general_10.json @@ -0,0 +1,132 @@ +{ + "created_at": "2024-06-01T14:34:54.821428Z", + "data": { + "amplify": false, + "answer": "Ensembl is a joint project between the EBI and the Wellcome Trust Sanger Institute. It is a publicly available web resource that contains automatically annotated genomes and integrates data from a wide range of biological research sources. Launched in 1999, the Ensembl database was the first to provide a window on the draft genome, curating the results of a series of computational analyses. It provides comprehensive information about the human genome including variants like SNPs, insertions, deletions, and somatic mutations for several species. Ensembl also offers tools for data retrieval and analysis.", + "context": { + "1a93e25f-2a43-49e9-8450-03a57c93e613": [ + { + "document_id": "1a93e25f-2a43-49e9-8450-03a57c93e613", + "text": "Annotation, preprocessing and categorization of data\n\nWe used Ensembl (version 39) as the annotation reference database.Homology between human and mouse genes was derived via BioMart.The total number of genes under study comprises 15,277 Ensembl mouse genes representing the union of the homologue genes from all data sources.An overview about the T2DM specific datasets is given in Table 1." + } + ], + "5edf84d0-c2d9-45eb-91b9-c35743b6a463": [ + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried\nout and the way the results are displayed. 4.4.1 Ensembl\nEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome\nTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard\net al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a\nwindow on the draft genome, curating the results of a series of computational analyses." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence\nassemblies as its starting point, but it is now based upon NCBI assemblies. The\nEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify ‘confirmed’ genes that are\ncomputationally predicted (by the GENSCAN gene prediction program) and also\nsupported by a significant BLAST match to one or more expressed sequences or\nproteins. Ensembl also identifies the positions of known human genes from public\nsequence database entries, usually using GENEWISE to predict their exon structures." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "Data retrieval is extremely well catered for in Ensembl, with text searches of all\ndatabase entries, BLAST searches of all sequences archived, and the availability of bulk\ndownloads of all Ensembl data and even software source code. Ensembl annotation\ncan also be viewed interactively on one’s local machine with the Apollo viewer (Lewis\net al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome Browser\nThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;\nit, too, provides annotation of the NCBI assemblies, and it displays a similar array of\nfeatures, including confirmed genes from Ensembl." + } + ], + "c12e853e-4f0d-48f9-93af-15db9ad2dfae": [ + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried\nout and the way the results are displayed. 4.4.1 Ensembl\nEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome\nTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard\net al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a\nwindow on the draft genome, curating the results of a series of computational analyses." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence\nassemblies as its starting point, but it is now based upon NCBI assemblies. The\nEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify ‘confirmed’ genes that are\ncomputationally predicted (by the GENSCAN gene prediction program) and also\nsupported by a significant BLAST match to one or more expressed sequences or\nproteins. Ensembl also identifies the positions of known human genes from public\nsequence database entries, usually using GENEWISE to predict their exon structures." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "Data retrieval is extremely well catered for in Ensembl, with text searches of all\ndatabase entries, BLAST searches of all sequences archived, and the availability of bulk\ndownloads of all Ensembl data and even software source code. Ensembl annotation\ncan also be viewed interactively on one’s local machine with the Apollo viewer (Lewis\net al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome Browser\nThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;\nit, too, provides annotation of the NCBI assemblies, and it displays a similar array of\nfeatures, including confirmed genes from Ensembl." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "Ensembl provides a DAS reference\nserver giving access to a wide range of specialist annotations of the human\ngenome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy a\nhypothesis (‘top-down’ data mining), or to interrogate a database in order to\ngenerate new hypotheses based on rigorous statistical correlations (‘bottom-up’\ndata mining). Domain (protein) A region of special biological interest within a single protein\nsequence." + } + ], + "f35e02a1-3314-4663-913f-38a3fc072aa8": [ + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried\nout and the way the results are displayed. 4.4.1 Ensembl\nEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome\nTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard\net al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a\nwindow on the draft genome, curating the results of a series of computational analyses." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence\nassemblies as its starting point, but it is now based upon NCBI assemblies. The\nEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify ‘confirmed’ genes that are\ncomputationally predicted (by the GENSCAN gene prediction program) and also\nsupported by a significant BLAST match to one or more expressed sequences or\nproteins. Ensembl also identifies the positions of known human genes from public\nsequence database entries, usually using GENEWISE to predict their exon structures." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "Data retrieval is extremely well catered for in Ensembl, with text searches of all\ndatabase entries, BLAST searches of all sequences archived, and the availability of bulk\ndownloads of all Ensembl data and even software source code. Ensembl annotation\ncan also be viewed interactively on one’s local machine with the Apollo viewer (Lewis\net al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome Browser\nThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;\nit, too, provides annotation of the NCBI assemblies, and it displays a similar array of\nfeatures, including confirmed genes from Ensembl." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "Ensembl provides a DAS reference\nserver giving access to a wide range of specialist annotations of the human\ngenome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy a\nhypothesis (‘top-down’ data mining), or to interrogate a database in order to\ngenerate new hypotheses based on rigorous statistical correlations (‘bottom-up’\ndata mining). Domain (protein) A region of special biological interest within a single protein\nsequence." + } + ], + "f7072d9b-4e07-4541-bac7-13a25761f460": [ + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "text": "Ensembl\n\nEnsembl is a publicly available web resource that contains automatically annotated genomes.It is integrated with other available biological databases like Jasper for binding motifs.It is a much larger web resource than T1Dbase, and contains general information about the human genome including variants.These include SNPs, insertions, deletions and somatic mutations (Alterations in DNA that occur after conception, meaning that they are not inherited) for several species.Data from Ensembl can be accessed in a number of ways.The names of all the SNPs that occur in the T1D susceptibility regions can be collected from Ensembl using the Biomart tool (Kinsella et al., 2011).To achieve this, the coordinates of the T1D regions obtained from T1Dbase are uploaded to the biomart query page which allows one to search the genome browser and retrieve data like the names, chromosomal positions, and genic positions (referred to as \"consequence to transcript\", in Ensembl) of the SNPs.The SNP genic positions tell if a SNP is located within a gene, adjacent to a gene or whether they occur in inter-genic positions between gene coding regions, as well as the particular genes in which they are located." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "text": "Advantages of Ensembl:\n\nThere is a number of advantages to using Ensembl. (i) It is a larger web resource than T1Dbase and integrates data from a wide range of biological research sources into its database.Therefore, available information is quite comprehensive. (ii) Genic positions for 99% of the variants obtained from T1Dbase could be retrieved. (iii) Ensembl contains quality checks for genetic variants in its variation pipeline.A variant is flagged as failed if certain quality criteria are not met, for instance if none of the variant alleles match the reference allele of the variant.Generally, Ensembl was found to give more detailed information regarding the genic positions of variants compared to T1Dbase." + }, + { + "document_id": "f7072d9b-4e07-4541-bac7-13a25761f460", + "text": "\n\nInformation about genes, including gene names, chromosomal coordinates, biotype (coding or non-coding), and number of splice variants, can also be retrieved from Ensembl." + } + ], + "fa8bba46-ce94-439a-a676-35187a3abcbf": [ + { + "document_id": "fa8bba46-ce94-439a-a676-35187a3abcbf", + "text": "doi:10.1093/nar/gkp858\nCunningham F, Amode MR, Barrell D, Beal K,\nBillis K, Brent S, Carvalho-Silva D, Clapham\nP, Coates G, Fitzgerald S, Gil L, Giron CG,\nGordon L, Hourlier T, Hunt SE, Janacek SH,\nJohnson N, Juettemann T, Kahari AK, Keenan\nS, Martin FJ, Maurel T, McLaren W, Murphy\nDN, Nag R, Overduin B, Parker A, Patricio\nM, Perry E, Pignatelli M, Riat HS, Sheppard\nD, Taylor K, Thormann A, Vullo A, Wilder\nSP, Zadissa A, Aken BL, Birney E, Harrow J,\nKinsella R, Muffato M, Ruffier M, Searle SM,\nSpudich G, Trevanion SJ, Yates A, Zerbino\nDR, Flicek P (2015) Ensembl 2015." + } + ], + "fca531d0-d45b-495f-a02c-fbd437617b20": [ + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "But the four sites are not equivalent; there are important distinctions between them in terms of the data analysed, the analyses carried\nout and the way the results are displayed. 4.4.1 Ensembl\nEnsembl is a joint project between the EBI (http://www.ebi.ac.uk/) and the Wellcome\nTrust Sanger Institute (http://www.sanger.ac.uk/). The Ensembl database (Hubbard\net al. , 2002; http://www.ensembl.org/), launched in 1999, was the first to provide a\nwindow on the draft genome, curating the results of a series of computational analyses." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "Until January 2002 (Release 3.26.1), Ensembl used the UCSC draft sequence\nassemblies as its starting point, but it is now based upon NCBI assemblies. The\nEnsembl analysis pipeline consists of a rule-based system designed to mimic decisions made by a human annotator. The idea is to identify ‘confirmed’ genes that are\ncomputationally predicted (by the GENSCAN gene prediction program) and also\nsupported by a significant BLAST match to one or more expressed sequences or\nproteins. Ensembl also identifies the positions of known human genes from public\nsequence database entries, usually using GENEWISE to predict their exon structures." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "Data retrieval is extremely well catered for in Ensembl, with text searches of all\ndatabase entries, BLAST searches of all sequences archived, and the availability of bulk\ndownloads of all Ensembl data and even software source code. Ensembl annotation\ncan also be viewed interactively on one’s local machine with the Apollo viewer (Lewis\net al. , 2002; http://www.fruitfly.org/annot/apollo/). 4.4.2 The UCSC Human Genome Browser\nThe UCSC Human Genome Browser (UCSC) bears many similarities to Ensembl;\nit, too, provides annotation of the NCBI assemblies, and it displays a similar array of\nfeatures, including confirmed genes from Ensembl." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "Ensembl provides a DAS reference\nserver giving access to a wide range of specialist annotations of the human\ngenome (for more detail, see http://www.ensembl.org/das/). Data mining The ability to query very large databases in order to satisfy a\nhypothesis (‘top-down’ data mining), or to interrogate a database in order to\ngenerate new hypotheses based on rigorous statistical correlations (‘bottom-up’\ndata mining). Domain (protein) A region of special biological interest within a single protein\nsequence." + } + ] + }, + "data_source": [], + "document_id": "FB39925222D82C39F4BBAA833135ACA5", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "Ensembl", + "EBI", + "Wellcome&Trust&Sanger&Institute", + "genome", + "computational&analyses", + "T1Dbase", + "SNPs", + "Biomart", + "NCBI", + "GENSCAN" + ], + "metadata": [], + "question": "what is ensembl?", + "subquestions": null, + "task_id": "FB39925222D82C39F4BBAA833135ACA5", + "usage": { + "chatgpt": 5858, + "gpt-4": 3829, + "gpt-4-turbo-preview": 2871 + }, + "user_id": 2 + }, + "document_id": "FB39925222D82C39F4BBAA833135ACA5", + "task_id": "FB39925222D82C39F4BBAA833135ACA5" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/gen_resp01.json b/gnqa/paper1_eval/src/data/responses/general/gen_resp01.json new file mode 100644 index 0000000..49d5475 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/gen_resp01.json @@ -0,0 +1,136 @@ +{ + "created_at": "2024-04-01T11:43:32.011648Z", + "data": { + "amplify": false, + "answer": "Air pollution is the most cited environmental factor for the onset of asthma.", + "context": { + "0df24827-df9f-46ee-a43d-dc5465c62f0d": [ + { + "document_id": "0df24827-df9f-46ee-a43d-dc5465c62f0d", + "text": "INTRODUCTION\n\nAsthma is a chronic disease of the airways defined by its symptoms, which include reversible airflow obstruction, inflammation, and bronchial hyperresponsiveness.The ancient Egyptians, Greeks, and Romans made reference to the symptoms of asthma, and today the disease is estimated to affect 235-334 million people worldwide (44,53)." + }, + { + "document_id": "0df24827-df9f-46ee-a43d-dc5465c62f0d", + "text": "\n\nThe atopic triad.Perhaps the most widely recognized pattern of co-occurrence is the one of asthma, atopic dermatitis (eczema), and allergic rhinitis (hay fever), which together are referred to as the atopic triad and characteristically present clinically in a temporal sequence known as the atopic march.Within this sequence, atopic dermatitis is typically the first component to manifest, with approximately 20-30% of individuals with mild disease and 70% of those with severe disease going on to develop asthma.Individuals who undergo this distinctive sequence of disease progression frequently exhibit a more severe and persistent phenotype, with increased risk of allergen sensitization." + } + ], + "14cad5a7-e53a-4ab8-9d4f-8f0b827ae427": [ + { + "document_id": "14cad5a7-e53a-4ab8-9d4f-8f0b827ae427", + "text": "\n\nClinically, asthma is characterized by episodes of coughing, chest tightness, wheezing, dyspnea, or sputum production.Often, asthma sufferers experience a combination of these symptoms, or some symptoms more than others.Pulmonary breathing tests typically demonstrate variable airway obstruction and hyperreactivity, but may be normal, even in patients with severe and uncontrolled disease [8].Thus, the diagnosis of asthma, which is based on general clinical symptoms and variable lung function testing, is non-specific and heavily dependent on clinical history.Within the \"umbrella\" diagnosis of asthma there exists a diverse array of differing clinical phenotypes [9].For example, childhood asthma is often associated with personal and parental atopic diseases (i.e., atopic dermatitis, food allergy, eosinophilic esophagitis, allergic rhinitis), viral infections, and tobacco smoke exposure [10].Alternatively, adult-onset asthma is less associated with atopic disease [11,12], but more associated with female sex [13], sinus disease [14], and preceding respiratory infections such as pneumonia [15].In addition, adult-onset disease is often of higher severity [12,16] with a faster and more persistent decline in lung function [17].Moreover, although severe patients are found in every demographic and age group, the most common phenotype is an adult female that is older and obese [18]." + }, + { + "document_id": "14cad5a7-e53a-4ab8-9d4f-8f0b827ae427", + "text": "Introduction\n\nAn estimated 9% of children and 6% of adults in the United States have asthma [1].The total number of asthma sufferers worldwide is estimated to be over 300 million, with an additional 100 million expected to develop asthma by 2025 [2][3][4][5].Developed countries are the most affected, with some of the highest rates found in the United Kingdom, Australia, New Zealand and the Republic of Ireland [3].Asthma prevalence is rising significantly in developing countries in transition to a more Western lifestyle [3].In 2007, the cost of disease in the United States was estimated to be $56 billion in relation to medical expenses, missed days of work, and early deaths [1].The rate of asthma deaths has likely plateaued, but is still as high as 250,000 per year worldwide [6].Morbidity and mortality are particularly high in ethnic minorities living below or near the poverty line, and African American children had a death rate 10 times that of non-Hispanic white children in 2015 [7].Thus, asthma is a costly, growing health problem associated with high morbidity and mortality." + } + ], + "2a7da18e-3756-45c5-b18c-a2231685fefd": [ + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "Getting accurate estimates\nof exposures is difficult, whether this is air pollution or toxins in our food and\ndrink, but these are important questions. Rutter: That is an important point. From the twin study data it is clear that\nenvironmental effects account for quite a lot of the variance on all the multifactorial disorders. Yet the kinds of measures that are used aren’t terribly solid. They\ninclude broad thing such as socio-economic status (SES). Even where there are\ngood measures the care taken in testing for environmental mediation is usually\npoor." + }, + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "Bronchiolitis, a disease\nthat happens in the first year of life in many infants, is strongly associated with\nsubsequent asthma. We ascertained it in the first years of life and have been following these people to age 25 now. For the people who had bronchiolitis and now\nhave asthma, their parents recall much better that they had bronchiolitis than those\nwho don’t have asthma now. It is at least twice more. Extraordinarily, some of\nthese latter parents don’t recall that they took their child to the doctor in the fi rst\nyear of life." + }, + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "If you arrive in the USA when\nyou are young you have almost the same prevalence of asthma as an adult as those\nwho are born in the USA and who are not Mexican. But if you arrive at older ages\nyou have less asthma. If you arrive at the age of 20 you have the same asthma risk\nas those born in Mexico (Eldeirawi et al 2005). Kotb: This is extremely interesting. There is a relationship between depression\nand the immune system. This especially applies to natural killer (NK) cells, which\nare the main cells that fight cancers." + }, + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "A colleague of mine in\nGeorgia found this may have a protective effect against later development of\nasthma (Ownby et al 2002). Martinez: We find significantly decreased likelihood of asthma if you have a dog\nin a home, but not if you have a cat. The reason for this is not that I hate cats,\nwhich I do, but most likely because cats are stealth hunters, and they have to be\nvery clean. Dogs are collective hunters and they don’t care if they smell." + } + ], + "443efea1-ffe7-446e-b2fb-37d8ec3cb74a": [ + { + "document_id": "443efea1-ffe7-446e-b2fb-37d8ec3cb74a", + "text": "; Guffey, S.E. Investigation into pedestrian exposure to near-vehicle exhaust emissions. Environ. Health\n2009, 8, 13. [CrossRef] [PubMed]\nOur World in Data.org. 2017. Available online: https://ourworldindata.org/data-review-air-pollution-deaths (accessed on\n10 January 2022). Pope, C.A. , III. Respiratory disease associated with community air pollution and a steel mill, Utah Valley. Am. J. Public Health\n1989, 79, 623–628. [CrossRef] [PubMed]\nPope, C.A. , III. What do epidemiologic findings tell us about the health effects of environmental aerosols? J. Aerosol. Med. 2000,\n13, 335–354. [CrossRef] [PubMed]\nPope, C.A. , III." + } + ], + "58714c13-954b-46b3-bd0e-69ccadd9dc6a": [ + { + "document_id": "58714c13-954b-46b3-bd0e-69ccadd9dc6a", + "text": "Case for Support BBSRC Grant Application September 2005\n“Integrative Analysis of the Genetic Factors behind Asthma and Atopic Dermatitis”\n\nPart I: Research Proposal\nBackground\nA\nIntroduction of topic of research and its academic and wider context\nAsthma is the most common disease of childhood, and affects one child in seven in the United\nKingdom. Atopic Dermatitis (AD, eczema) affects similar numbers of children. About 60% of children with\nsevere AD will have concomitant asthma. Treatments for both diseases are unsatisfactory. Abandonment of\northodox medical therapy for AD is common in many families who have children with the disease." + } + ], + "8b4276be-c77e-4e80-a5bb-54e9ff75d2ba": [ + { + "document_id": "8b4276be-c77e-4e80-a5bb-54e9ff75d2ba", + "text": "This is most common during the rainy\nseason when aerosols are created, which results in repeated inhalation of Bp [43, 44]. Environmental sampling studies reveal there is a positive association between the\nprevalence of disease and the degree of environmental contamination [7]. In addition to\nenvironmental factors, data suggests that host factors play an important role in mounting\nan immune response against infectious diseases [45] such as melioidosis. While healthy\npersons can contract melioidosis, most patients in endemic regions have an underlying\npredisposition [28], which suggests that the immunological status of the patient can\ninfluence disease initiation and progression [15]." + } + ], + "98d443c7-8d99-4139-a27d-e447b0f6630f": [ + { + "document_id": "98d443c7-8d99-4139-a27d-e447b0f6630f", + "text": "Sensitivity analysis\n\nWe did two sets of post-hoc sensitivity analyses to assess the effects of potential poor recall of age of onset among individuals with adult-onset asthma, and the effects of misclassification of COPD as asthma among the adultonset cases, even with exclusion of cases with a reported diagnosis of COPD, emphysema, or chronic bronchitis.First, to assure that the adult-onset cases did not include a significant proportion of childhood-onset asthma in which symptoms remitted in early life but then relapsed in adulthood, we replaced adult-onset cases with increasing proportions of randomly selected childhood-onset cases, and then tested for association at the two most significant childhood onset-specific loci.This procedure was repeated 20 times for each proportion to quantify the sampling variability (appendix pp 7-8).Second, we did two analyses in which we removed either individuals with ages of asthma onset between 46 and 65 years or adult-onset cases and controls with FEV₁/FVC <0•70.For each, we compared p values and ORs with the GWAS including all adult-onset cases (appendix pp 8-9)." + }, + { + "document_id": "98d443c7-8d99-4139-a27d-e447b0f6630f", + "text": "\n\nWe used data for British white individuals from UK Biobank data release July 19, 2017. 8We extracted disease status (asthma, allergic rhinitis, atopic dermatitis, food allergy, chronic obstructive pulmonary disease (COPD), emphysema, and chronic bronchitis), age of on set of asthma, and sex from self-reported question naires and hospital records (International Classification of Diseases 10th revision [ICD-10] codes) by querying our in-house protected UK Biobank database server. 9For our main case analysis, we included individuals who self-reported that they had doctor-diagnosed asthma.Further details of our research approach are provided in the appendix (pp 4-7)." + } + ], + "a1c91fbe-9f6c-45fe-af9a-46c162d340ed": [ + { + "document_id": "a1c91fbe-9f6c-45fe-af9a-46c162d340ed", + "text": "; Guffey, S.E. Investigation into pedestrian exposure to near-vehicle exhaust emissions. Environ. Health\n2009, 8, 13. [CrossRef] [PubMed]\nOur World in Data.org. 2017. Available online: https://ourworldindata.org/data-review-air-pollution-deaths (accessed on\n10 January 2022). Pope, C.A. , III. Respiratory disease associated with community air pollution and a steel mill, Utah Valley. Am. J. Public Health\n1989, 79, 623–628. [CrossRef] [PubMed]\nPope, C.A. , III. What do epidemiologic findings tell us about the health effects of environmental aerosols? J. Aerosol. Med. 2000,\n13, 335–354. [CrossRef] [PubMed]\nPope, C.A. , III." + } + ], + "c449650e-a0ac-4023-b3c8-82cf3463b0f3": [ + { + "document_id": "c449650e-a0ac-4023-b3c8-82cf3463b0f3", + "text": "8 The\nsocio-ecologic framework posits that various aspects of a child’s environment directly and indirectly impact the\nchild’s health and development.9 Drawing on this framework, Beck and colleagues10 examined several biologic,\nsocial and ecologic variables to provide a greater understanding of factors influencing asthma-related hospital\nreadmissions for black children compared to their white counterparts. The study revealed that black children\nwere over two times as likely to be readmitted for an asthma-related illness compared to white children; this\nresulted from significant differences in almost every socio-ecologic variable measured, including disease\nmanagement practices and access to primary care." + }, + { + "document_id": "c449650e-a0ac-4023-b3c8-82cf3463b0f3", + "text": "Specific Aims\nAsthma is the most common chronic pediatric medical condition in the United States, with a prevalence\nover 9.6% in children under 18 years of age.1, 2 Low-income, urban children incur a disproportionate share of\nasthma prevalence and morbidity;2-4 13% of children living below the poverty threshold are diagnosed with\nasthma compared to 8% of non-poor (>200% poverty),3 and poverty is associated with higher rates of asthma\nattacks.1 Living in an urban area confers additional risk for asthma and increased ED utilization.4, 5\nImplementation of the National Asthma Education and Prevention Program’s (NAEPP) Guidelines has\ncontributed to reductions in asthma morbidity and mortality rates, and these guidelines emphasize establishing\na partnership between healthcare providers and patients/families to promote effective asthma management.6\nThe NAEPP expert panel states, “building a partnership requires that clinicians promote open\ncommunication and ensure that patients have a basic and accurate foundation of knowledge about asthma…”\n(p.124),6 yet care partnerships also require that the patient/parent effectively communicate issues such as\nemerging symptoms or response to medications." + }, + { + "document_id": "c449650e-a0ac-4023-b3c8-82cf3463b0f3", + "text": "Vital & health statistics Series 3, Analytical and epidemiological studies. 2012(35):1-58. CDC. Current Asthma Prevalence. https://www.cdc.gov/asthma/most_recent_data.htm. 2015. Updated\nJune 2017. Accessed March 9, 2018. Northridge J, Ramirez OF, Stingone JA, Claudio L. The role of housing type and housing quality in\nurban children with asthma. Journal of urban health : bulletin of the New York Academy of Medicine. 2010;87(2):211-224. Flores G, Snowden-Bridon C, Torres S, et al. Urban minority children with asthma: substantial\nmorbidity, compromised quality and access to specialists, and the importance of poverty and specialty\ncare." + }, + { + "document_id": "c449650e-a0ac-4023-b3c8-82cf3463b0f3", + "text": "Asthma Prevalence and Disparities\nAsthma is the most common chronic pediatric medical condition in the United States,1 affecting an\nestimated 6.2 million children annually.2 Poorly controlled pediatric asthma contributes to over 700,000 visits a\nyear to emergency departments (ED).1 Children living in impoverished, urban settings are disproportionately\naffected by asthma,3 and the disparate impact of asthma is even worse among black and Latino children, and\nchildren whose parents have limited English proficiency (LEP) in these urban low-income areas.4-6 A 2017\nlongitudinal study revealed that black race and Latino ethnicity are significantly associated with worse asthma\noutcomes including 1) asthma knowledge, 2) asthma-related quality of life, 3) asthma severity, and\n4) asthma control." + }, + { + "document_id": "c449650e-a0ac-4023-b3c8-82cf3463b0f3", + "text": "The Journal of asthma : official journal of the Association for the Care of Asthma. 2017:16. Inkelas M, Garro N, McQuaid EL, Ortega AN. Race/ethnicity, language, and asthma care: findings from\na 4-state survey. Annals of allergy, asthma & immunology : official publication of the American College\nof Allergy, Asthma, & Immunology. 2008;100(2):120-127. National Asthma Education and Prevention Program. Expert Panel Report 3: Guidelines for the\nDiagnosis and Management of Asthma Bethesda, MD: National Institutes of Health, National Heart,\nLung, and Blood Institute; 2007. Publication no. 08-045.1. NIH Consensus Group. Video report: What is mHealth?" + }, + { + "document_id": "c449650e-a0ac-4023-b3c8-82cf3463b0f3", + "text": "Contact PD/PI: Coker, Tumaini Rucker\n\nINTRODUCTION TO APPLICATION\nResearch Plan Overview\nChildhood asthma is the most common pediatric medical condition in the United States, and\ndisproportionately affects children living in low-income, urban settings. Many low-income, urban families rely on\nemergency department (ED) services as their source for sick care for their child. This is often due to not having\na primary care provider or sufficient access to their primary care provider for asthma management." + } + ] + }, + "data_source": [], + "document_id": "1BB877C826E934EA87131E2F63B68EEC", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "asthma", + "environmental&factors", + "urban", + "poverty", + "air&pollution", + "atopic&dermatitis", + "allergic&rhinitis", + "bronchiolitis", + "socio-economic&status", + "dogs" + ], + "metadata": [], + "question": "What is the most cited environmental factor for the onset of asthma.", + "subquestions": null, + "task_id": "1BB877C826E934EA87131E2F63B68EEC", + "usage": { + "chatgpt": 6004, + "gpt-4": 4357, + "gpt-4-turbo-preview": 3350 + }, + "user_id": 2 + }, + "document_id": "1BB877C826E934EA87131E2F63B68EEC", + "task_id": "1BB877C826E934EA87131E2F63B68EEC" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/gen_resp02.json b/gnqa/paper1_eval/src/data/responses/general/gen_resp02.json new file mode 100644 index 0000000..20982f0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/gen_resp02.json @@ -0,0 +1,152 @@ +{ + "created_at": "2024-04-01T11:57:23.158088Z", + "data": { + "amplify": false, + "answer": "Apoptosis, also known as cell suicide or programmed cell death, is a biological process in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism. It is characterized by a sequence of well-defined events resulting in cell destruction and is necessary for normal cell turnover. It is also essential to various other biological processes.", + "context": { + "05346fc4-57eb-424f-8c9f-cc144e600cb6": [ + { + "document_id": "05346fc4-57eb-424f-8c9f-cc144e600cb6", + "text": "\n\nApoptosis, or controlled cell death [62], is another major stressed-cell response, and was also represented in our results (Fig. 9e).A large body of direct evidence points to apoptosis as one of the main routes of RPE degeneration in AMD [63].Induction of apoptosis upon stress is dictated by the action of master regulator p53, and it was recently shown that aging increases the activity of p53 in RPE cells and the likelihood for apoptotic cell death [64].Consistent with this evidence, we found association with pathways in Transcriptional regulation by TP53 group (Fig. 9d).In particular, Regulation of TP53 activity through methylation was among the top pathway in our association analysis (Table 1), suggesting that p53 modification by methylation and the closely related histone modifications [Protein lysine methyltransferases (PKMTs) methylate histone lysine in Fig. 9e] play important roles in RPE apoptosis regulation.In the intrinsic apoptotic pathway induced by oxidative stress, cytochrome c is released from mitochondria into the cytosol, binding and activating caspases, the main proteases central to apoptotic action.We found association in pathways involving 'inhibitor of apoptosis' (IAP) and its negative regulator 'second mitochondrial activator of caspases' (SMAC) [65], which suggests that disruption to regulatory mechanisms preventing apoptosis in RPE cells may play roles in AMD." + } + ], + "2186130e-2523-4fcc-a52f-fc2bdd986230": [ + { + "document_id": "2186130e-2523-4fcc-a52f-fc2bdd986230", + "text": "Apoptosis\n\nPersistent DNA damage" + } + ], + "2715e261-b26c-46d6-918f-c6aa47688f0c": [ + { + "document_id": "2715e261-b26c-46d6-918f-c6aa47688f0c", + "text": "42\nABSTRACT 18\nA MODULARIZED MODEL OF APOPTOSIS\nHA Harrington, KHo, Sk Ghosh, KC Tung , CY Kao, and B Aguda\nImperial College London, Courant Institute of Mathematical Sciences New York\nUniversity, University of Texas at Arlington, University of Texas Southwestern\nMedical Center, Mathematical Biosciences Institute, and Department of\nMathematics, The Ohio State University Columbus, OH, USA\nBackground: One of the key physiological mechanisms employed by the cell\n(during development and for maintenance of homeostasis) in multi-cellular\norganism is apoptosis, which is characterized by a sequence of well-defined\nevents resulting in cell destruction." + } + ], + "2dfc2b82-b8eb-4e73-957a-0ea8a4401a84": [ + { + "document_id": "2dfc2b82-b8eb-4e73-957a-0ea8a4401a84", + "text": "14\nApoptosis is caused by the activation of the caspase cascade, which is\ninitiated by two signaling routes (stress-induced death and death-domain\nreceptor-induced death) (Domen 2001). This process can be prevented by antiapoptotic molecules, such as Bcl-2 (Domen and Weissman 2000). Direct\nevidence for the involvement of apoptosis in HSC number regulation came from\nthe findings that overexpression of the anti-apoptotic gene bcl-2 led to increased\nnumbers of Thy-1.1low, Sca-1+, c-kit+, Lin- cells, a population with long-term\nmulti-lineage repopulation potential (Domen et al. 2000)." + }, + { + "document_id": "2dfc2b82-b8eb-4e73-957a-0ea8a4401a84", + "text": "Several lines of evidence have indicated that apoptosis acts as an\nimportant regulator of stem cells. First of all, expression of some apoptosisrelated genes were detected in human and/or murine HSCs (Domen 2001). Secondly, targeted disruption of some of these genes in null and dominant\nnegative mutant mice interfered with normal apoptotic processes in HSCs. For\nexample, overexpression of Bcl-2, a negative regulator of apoptosis, increased\nnot only the numbers and competitive repopulation capabilities of HSCs, but also\nthe resistance of HSCs to apoptosis induced by ionizing radiation (Domen and\nWeissman 2003)." + } + ], + "3c78c2be-0bd2-4954-bb47-8b48f6125ed7": [ + { + "document_id": "3c78c2be-0bd2-4954-bb47-8b48f6125ed7", + "text": "Apoptosis\n\nCell suicide, or apoptosis, is a well-studied biological phenomenon in multicellular organisms that allows specific cells to be removed during the development of complex tissues, or potentially dangerous damaged cells to be destroyed for the benefit of the whole organism.The lack of an apparent evolutionary benefit for such a process in a single-celled organism initially caused controversy about the presence of an apoptotic pathway in yeast.Today, however, a number of yeast orthologues to mammalian apoptosis genes have been discovered and apoptotic-like cell death has been linked to mating, colony formation, and aging (Buttner et al. 2006;Eisenberg et al. 2007;Frohlich et al. 2007).With respect to aging, both replicatively and chronologically aged cells that die have increased ROS and display apoptotic phenotypes (Fabrizio et al. 2004a;Herker et al. 2004;Laun et al. 2001)." + }, + { + "document_id": "3c78c2be-0bd2-4954-bb47-8b48f6125ed7", + "text": "\n\nThe importance of apoptosis in yeast aging has yet to be fully characterized.At the very least, yeast apoptosis provides a useful pathway for studying genetic interactions for age-related diseases that affect humans, such as cancer.Readers interested in further information related to yeast apoptosis are referred to several in-depth reviews (Buttner et al. 2006;Eisenberg et al. 2007;Frohlich et al. 2007)." + } + ], + "489539fd-f7c5-44eb-bb58-5fc19d50a7cf": [ + { + "document_id": "489539fd-f7c5-44eb-bb58-5fc19d50a7cf", + "text": "Early redistribution of plasma membrane phosphatidylserine is a general\nfeature of apoptosis regardless of the initiating stimulus: inhibition by overexpression of\nBcl-2 and Abl. J Exp Med 182: 1545-56. Mathew CG (2006). Fanconi anaemia genes and susceptibility to cancer. Oncogene 25:\n5875-84. McBride MW, Carr FJ, Graham D, Anderson NH, Clark JS, Lee WK et al (2003). Microarray analysis of rat chromosome 2 congenic strains. Hypertension 41: 847-53. Merino-Trigo A, Kerr MC, Houghton F, Lindberg A, Mitchell C, Teasdale RD et al\n(2004)." + } + ], + "516fb027-d7ef-481b-95b2-89c25f4e4f8d": [ + { + "document_id": "516fb027-d7ef-481b-95b2-89c25f4e4f8d", + "text": "\n\nWhen a cell harbors such severe DNA damage that it is beyond repair, it is disposed of through apoptosis.Alternatively, DNA damage can induce cellular senescence, the irreversible cessation of mitosis.Both processes are critically dependent on p53, which is known as the guardian of the genome [3] .DNA damage may also trigger autophagy, a cellular catabolic process that maintains homeostasis [4] .It should be noted that under normal conditions cells are rarely exposed to very high doses of DNAdamaging agents, which may be the explanation why we do not age and die because we run out of cells.However, aging is associated with some atrophy [1] and it is conceivable that at older ages bursts of DNA damage, for example from free radical reactions associated with inflammation, do occur and give rise to an increasingly high rate of apoptosis or cellular senescence.While there is some evidence for increased apoptosis and cellular senescence at old age, it is doubtful that under normal conditions this would lead to a significant loss of functional cells." + } + ], + "5c814c02-7157-40db-968d-98ac062744d6": [ + { + "document_id": "5c814c02-7157-40db-968d-98ac062744d6", + "text": "\n\nApoptosis, or programmed cell death, literally eliminates cells at risk for neoplastic transformation.Senescence, by contrast, permanently arrests their growth.Both processes are controlled by the p53 tumor suppressor protein (Amundson, Myers, & Fornace, 1998;Bringold & Serrano, 2000;Hickman, Moroni, & Helin, 2002;Itahana, Dimri, & Campisi, 2001).p53 is a transcriptional regulator that both transactivates and transrepresses target genes in response to stress (Prives & Hall, 1999;Ryan, Phillips, & Voudsen, 2001).These target genes, in turn, stimulate DNA repair, transient cell cycle arrest, permanent cell cycle arrest (senescence) or cell death (apoptosis), depending on cell type, degree and type of damage, and other variables.In contrast, cells that lack normal p53 regulation or function -for example, tumor cells -tend to die in response to telomere dysfunction.Some normal human cells, on the other hand, undergo a senescence growth arrest.In either case, when present, p53 is crucial for mediating the cellular response to telomere dysfunction (Yaswen & Stampfer, 2002) (Fig. 4)." + } + ], + "667ac3eb-7d19-4359-98b7-e76871637910": [ + { + "document_id": "667ac3eb-7d19-4359-98b7-e76871637910", + "text": "Cell death, and in particular\napoptosis, can be caused by a number of mechanisms including\nloss of growth factors and excitotoxicity (e.g. , Bhutta and Anand,\n2002; Nikolić et al. , 2013). It is of interest therefore, that proximal\nto the region of the QTL there are several genes that are related\nto growth factors including the latent transforming growth factor\nprotein 2 (ltbp2), placental growth factor (pgf), and transforming\ngrowth factor beta (Tgf beta)." + } + ], + "6f38cfff-88f1-4333-bc97-293200855bbf": [ + { + "document_id": "6f38cfff-88f1-4333-bc97-293200855bbf", + "text": "\n\nApoptosis-related gene expression profiles" + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nApoptosis.Programmed death of cells during embryogenesis and metamorphosis or during cell turnover in adult tissues." + } + ], + "9c266a06-68f9-4e25-8de4-87d8ee02d929": [ + { + "document_id": "9c266a06-68f9-4e25-8de4-87d8ee02d929", + "text": "14\nApoptosis is caused by the activation of the caspase cascade, which is\ninitiated by two signaling routes (stress-induced death and death-domain\nreceptor-induced death) (Domen 2001). This process can be prevented by antiapoptotic molecules, such as Bcl-2 (Domen and Weissman 2000). Direct\nevidence for the involvement of apoptosis in HSC number regulation came from\nthe findings that overexpression of the anti-apoptotic gene bcl-2 led to increased\nnumbers of Thy-1.1low, Sca-1+, c-kit+, Lin- cells, a population with long-term\nmulti-lineage repopulation potential (Domen et al. 2000)." + }, + { + "document_id": "9c266a06-68f9-4e25-8de4-87d8ee02d929", + "text": "Several lines of evidence have indicated that apoptosis acts as an\nimportant regulator of stem cells. First of all, expression of some apoptosisrelated genes were detected in human and/or murine HSCs (Domen 2001). Secondly, targeted disruption of some of these genes in null and dominant\nnegative mutant mice interfered with normal apoptotic processes in HSCs. For\nexample, overexpression of Bcl-2, a negative regulator of apoptosis, increased\nnot only the numbers and competitive repopulation capabilities of HSCs, but also\nthe resistance of HSCs to apoptosis induced by ionizing radiation (Domen and\nWeissman 2003)." + } + ], + "a68762fb-d3d0-4589-80a2-24ad1fca73a9": [ + { + "document_id": "a68762fb-d3d0-4589-80a2-24ad1fca73a9", + "text": "\n\nFraction of cells displaying apoptosis" + } + ], + "b47e2055-8573-46ac-aec5-c2697df4d4b9": [ + { + "document_id": "b47e2055-8573-46ac-aec5-c2697df4d4b9", + "text": "\n\nIt has been known that mitochondria play a central role in the life and death of cells (Kroemer & Reed, 2000).Apoptosis was observed in developmentally arrested embryos by 72 h, but not at 24 h after FCCP treatment, despite considerable telomere attrition at this early stage, suggesting that telomere attrition occurs prior to apoptosis and may serve as an intermediate step between mitochondrial dysfunction and apoptosis.These results also suggest that telomere shortening may signal apoptosis (Lee et al ., 1998;Karlseder et al ., 1999)." + } + ], + "d05f2105-e665-426c-8a7b-1ee57c89f23d": [ + { + "document_id": "d05f2105-e665-426c-8a7b-1ee57c89f23d", + "text": "Cell Death\n\nA form of programmed cell death, apoptosis is necessary for normal cell turnover and is essential to a plethora of other biological processes.Apoptosis can be executed via Bcl-2 activation of caspases, via signals from the death receptor on the plasma membrane, or via induction by granzyme B secreted from cytotoxic T cells (Tc cells) [35].Endonucleases and proteases are activated by active caspases, eventually leading to the death of the cell.With age, however, apoptotic activity changes.In heart [36], kidney [37], skeletal muscle [38], and Tc cells [39], increased apoptosis has been reported, perhaps contributing to loss of cellularity in these tissues.This escalation across various tissues may be attributed to the increased production of free radicals [40] and furthermore exacerbated by the accumulation of DNA damage in the aged cells [41].As the risk increases for cells to turn cancerous and dysfunctional with advancing age, increased apoptosis in aged cells is argued to be a defense strategy.In other tissues, such as the colon, apoptosis appears to decrease with age perhaps contributing to the accumulation of senescent cells and age-associated carcinogenesis [42]." + } + ], + "d54db58c-5e5f-4552-a0cb-4e27787aee00": [ + { + "document_id": "d54db58c-5e5f-4552-a0cb-4e27787aee00", + "text": "\n\nThe regulation and execution of apoptosis in endothelial cells is a complex process involving paracrine factors, membrane receptors, interaction of pro-and anti-apoptotic factors and cysteinyl aspartate-specific proteases (caspases).Recent studies suggest that in aging there is an imbalance in the expression of pro-and anti-apoptotic genes resulting in an enhanced apoptosis in the myocardium (19), central nervous system (24), skeletal muscle (10), lung (33), and liver (2,33).Yet, age-related alterations in the expression of pro-and anti-apoptotic genes in coronary arteries have not been elucidated." + } + ], + "e4eca0fc-d0bc-45fa-87e6-4958ebdf66f0": [ + { + "document_id": "e4eca0fc-d0bc-45fa-87e6-4958ebdf66f0", + "text": "Apoptosis modulating genes\n\nApopotosis or programmed cell death is associated with alterations in cell morphology, particularly the nucleus, with endonucleatytic cleavage of DNA into nucleosomal length fragments.Apoptosis may result from withdrawal of growth signals." + } + ] + }, + "data_source": [], + "document_id": "80493D7D624E399B7931DC3737018190", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "apoptosis", + "cell", + "death", + "p53", + "caspases", + "Bcl-2", + "DNA", + "damage", + "senescence", + "aging" + ], + "metadata": [], + "question": "What is apoptosis?", + "subquestions": null, + "task_id": "80493D7D624E399B7931DC3737018190", + "usage": { + "chatgpt": 5468, + "gpt-4": 3741, + "gpt-4-turbo-preview": 2788 + }, + "user_id": 2 + }, + "document_id": "80493D7D624E399B7931DC3737018190", + "task_id": "80493D7D624E399B7931DC3737018190" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/gen_resp03.json b/gnqa/paper1_eval/src/data/responses/general/gen_resp03.json new file mode 100644 index 0000000..3c7935f --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/gen_resp03.json @@ -0,0 +1,156 @@ +{ + "created_at": "2024-04-01T11:59:12.696644Z", + "data": { + "amplify": false, + "answer": "For mammals, DNA can be extracted using a DNA Isolation Kit for Mammalian Blood, following the manufacturer's instructions. This process typically involves mixing blood with lysis buffer and Proteinase K, followed by the addition of isopropanol and Inhibitor Removal Buffer. The DNA is then washed, centrifuged, and the supernatant discarded. The remaining pellet, which contains the purified genomic DNA, is diluted in TE buffer.\n\nFor plants, the mixed alkyl trimethyl ammonium bromide (MATAB) procedure can be used. This involves grinding plant material in liquid nitrogen, incubating it in extraction buffer, and then purifying it with chloroform:isoamylalcohol. The DNA extracts are then precipitated with isopropanol and resuspended in a buffer. The extracts are further purified on anion exchange columns.", + "context": { + "05c26b4c-cc56-49bc-914e-066ba0f05e97": [ + { + "document_id": "05c26b4c-cc56-49bc-914e-066ba0f05e97", + "text": "DNA and RNA extraction of tissues\n\nGenomic DNA was extracted from frozen placentae (n ϭ 3/group) and liver (n ϭ 9/group) using a modified version of an established protocol (28,29).Total RNA was extracted from the remaining tissue using TRIzol, as per the manufacturer's instructions (Invitrogen Canada Inc).Genomic DNA and RNA purity and concentration were assessed using spectrophotometric anal-ysis, and integrity was verified using agarose gel [1% (wt/vol)] electrophoresis." + } + ], + "147b69a0-1397-4b1a-aa01-fa310677edb9": [ + { + "document_id": "147b69a0-1397-4b1a-aa01-fa310677edb9", + "text": "Taxon Sampling and DNA Extractions\n\nWe extracted DNA from 72 pinned specimens from the National Museum of Natural History (NMNH) Entomology collection for this study.We plucked middle legs from the pinned bees using a pair of sterilized forceps and washed the tissue in 95% ethanol to remove dust, pollen, and other forms of accumulated debris on the bee legs.After evaporation of the ethanol (by drying the tissue on a clean Kimwipe ™ ), the samples were placed in a freezer for several hours.DNA was then extracted destructively by grinding the frozen tissue with a sterile pestle, using a DNeasy Blood and TissueKit (Qiagen, Valencia, CA, USA) and following the manufacturer's protocol, except the DNA was eluted in 130μL ddH 2 O instead of the supplied buffer.We ran 10μL of each extract for 60 min at 100 volt on 1.5% agarose SB (sodium borate) gels, to estimate size of the genomic DNA." + } + ], + "1c1f2541-c4ff-407a-b541-0e7859f5b49a": [ + { + "document_id": "1c1f2541-c4ff-407a-b541-0e7859f5b49a", + "text": "DNA extraction\n\nDNA was extracted from PBMCs using the QIAamp DNA Mini kit (Qiagen, CA, USA), following the manufacturer's instructions for the spin protocol.The DNA was eluted in 60 μl of AE elution buffer and stored at -20°C.The concentration and quality of the DNA was assessed with the Qubit dsDNA HS Assay (Invitrogen, Eugene, OR, USA)." + } + ], + "27b471ec-acc3-4624-9050-57516328da07": [ + { + "document_id": "27b471ec-acc3-4624-9050-57516328da07", + "text": "Methods\n\nLaboratory procedures.We initially screened 107 ancient samples (Supplementary Data 1) in dedicated clean facilities at the ancient DNA lab of Jilin University, China, following published protocols for DNA extraction and library preparation 36,37 .Prior to sampling, we wiped all skeletal elements with 5% bleach and irradiated with UV-light for 30 min from each side.We drilled teeth to obtain fine powder using a dental drill (Dremel, USA).We sampled the dense part of petrous bones around the cochlea by first removing the outer part using the sandblaster (Renfert, Germany), and then grinding the clean inner part into fine powder with the mixer mill (Retsch, Germany).We digested the powder (50-100 mg) in 900 μl 0.5 M EDTA (Sigma-Aldrich), 16.7 μl of Proteinase K (Sigma-Aldrich), and 83.3 μl ddH 2 O (Thermo Fisher, USA) at 37 °C for 18 h.Then we transferred the supernatant to a MinElute silica spin column (QIAGEN, Germany) after fully mixed with the 13 ml custom binding buffer [5 M guanidine hydrochloride (MW 95.53), 40% Isopropanol, 90 mM Sodium Acetate (3 M), and 0.05% Tween-20] followed by two washes with PE buffer (80% ethanol).Then we eluted the DNA with 100 μl TET buffer (QIAGEN, Germany)." + } + ], + "3bde9884-e31d-4719-b42f-02dca25d6c08": [ + { + "document_id": "3bde9884-e31d-4719-b42f-02dca25d6c08", + "text": "DNA Extraction\n\nAfter blood was drawn into EDTA tubes, genomic DNA was extracted using a DNA Isolation Kit for Mammalian Blood Kit (Roche Applied Science, Indianapolis, IN, USA) according to the manufacturer's recommendations.Briefly, 300 μl of whole blood from each sample was mixed with 200 μl of lysis buffer (50 mM Tris pH 8.0, 100 mM EDTA, 100 mM NaCl, 1% SDS) and 40 μl of Proteinase K, followed by addition of 100 μl of isoproponal and 500 μl of Inhibitor Removal Buffer (5M guanidine-HCl, 20 mM Tris-HCl pH 6.6).The DNA was washed with a buffer (20 mM NaCl; 2 mM Tris-HCl; pH 7.5), centrifuged twice at 2000 rpm, washed using cold 70% ethanol and centrifuged at 3000 rpm.The supernatant was discarded and the pellet containing purified genomic DNA was diluted in TE buffer (1 mM EDTA; 10 mM Tris-HCl, pH 7.5) to a concentration of approximately 50 ng/μl." + } + ], + "58f36772-b82e-437e-a5dd-2442277089f5": [ + { + "document_id": "58f36772-b82e-437e-a5dd-2442277089f5", + "text": "Genomic DNA extraction\n\nLeukocytes were isolated from 5-ml peripheral blood samples.DNA was prepared by phenol extraction and chloroform extraction followed by isopropanol precipitation, washed with ethanol, and air-dried.Tris-EDTA buffer pH 8.0 was used to dissolve the final genomic DNA product." + } + ], + "5b4350f1-779d-4763-a0e1-23008db25633": [ + { + "document_id": "5b4350f1-779d-4763-a0e1-23008db25633", + "text": "\n\nThe pulled down DNA fragments were extracted and purified using phenolchloroform extraction/ethanol precipitation.The samples were stored at -20 °C until use." + } + ], + "752b2413-8c90-4af7-b65b-db429145b3bb": [ + { + "document_id": "752b2413-8c90-4af7-b65b-db429145b3bb", + "text": "DNA extraction for genotyping\n\nFor the majority of samples, DNA was extracted from either spleen or the exocrine fraction of the islet isolation using the Tissue DNA Purification Kit according to manufacturer's instructions on an automated Maxwell 16 system (both Promega, USA).When no other tissue was available, DNA was extracted from human islets using the Trizol fraction remaining after extraction of RNA (see above).To precipitate the DNA, 300μl 100% ethanol was added to the thawed solution.This mixture was incubated at room temperature for a minimum of 30 minutes.DNA was then pelleted by centrifugation at 4,000 x g for 5 minutes at 4°C.After removing the supernatant, the pellet was twice washed with 0.1M trisodium citrate (Sigma Aldrich, UK) in 10% ethanol and left at room temperature for 30 minutes, followed by another wash step with 75% ethanol.After the final wash step, pellets were air-dried for 10 minutes to remove residual ethanol and re-suspended in a minimum of 100 μL 8mM NaOH (Sigma Aldrich).Extracted DNA was stored at -20°C before further use." + } + ], + "9292750d-3941-465c-8e2c-bb041f6bea0b": [ + { + "document_id": "9292750d-3941-465c-8e2c-bb041f6bea0b", + "text": "DNA extraction\n\nTissue samples were incubated at 50°C overnight with shaking in DNA extraction buffer (100 mM NaCl, 10 mM Tris.HCl pH8, 25 mM EDTA, 0.5% (w/v) SDS), containing 200 μg/ml proteinase K. DNA was isolated by two rounds of phenol:chloroform extraction, followed by RNAse A treatment, precipitation in absolute ethanol containing 10% (v/v) sodium acetate (3 M, pH 5.2), and resuspended in 100 μl nuclease-free water (Ambion, Austin, TX, USA) or using salting out method followed by purification with Qiagen blood and tissue kit (Qiagen, Mississauga, ON, USA).DNA was stored at -20°C." + } + ], + "9605f23b-0620-4c0c-8f38-d9e0171e7e64": [ + { + "document_id": "9605f23b-0620-4c0c-8f38-d9e0171e7e64", + "text": "Methods\n\nHuman DNA samples DNA was extracted from human patient tissue samples acquired from the University of Minnesota Tissue Procurement Facility from BioNet (IRB#0805E32181).See Supplemental Table S4 for patient data.Briefly, 2 mg of tissue was digested overnight at 55°C on a rotating platform in 710 mL of digest buffer (1 M Tris at pH 8.0, 1 mM EDTA, 13 SSC, 1% SDS, 1 Mm NaCl, 10 mg/mL Proteinase K).Following digest, DNA was purified using phenolchloroform-isoamyl alcohol (Life Sciences) isolation protocol." + } + ], + "9981a933-8fdf-4107-a6fd-3f9ef71f5d08": [ + { + "document_id": "9981a933-8fdf-4107-a6fd-3f9ef71f5d08", + "text": "3.2.2 Isolation of genomic DNA\nGenomic DNA was isolated from frozen liver tissue. The isolation was conducted using the\nQiagen DNeasy Blood & Tissue Kit (Qiagen) according to the manufacturer’s protocol. DNA concentration was evaluated photometrically at a wavelength of 260 nm using\nthe FusionTM Universal Microplate Analyzer. For nucleic acid quantification, the Beer-Lambert\n(A = ε * b * c) equation is modified to use an extinction coefficient with units of M-1 cm-1." + } + ], + "a4e27158-1e54-4ee2-9cc1-049489a628bc": [ + { + "document_id": "a4e27158-1e54-4ee2-9cc1-049489a628bc", + "text": "\n\nMost typically, DNA is extracted from blood samples, dried blood spots, buccal swabs, saliva, tissue and even urine and stool samples.In forensic science, other sources have been validated e.g.bone, tooth pulp, dandruff and others." + } + ], + "c10ff8e0-81ff-4ac2-b1cc-2fdc89640166": [ + { + "document_id": "c10ff8e0-81ff-4ac2-b1cc-2fdc89640166", + "text": "DNA isolation\n\nHigh-molecular weight DNAs was isolated from the samples by organic solvent extraction method, followed by precipitation in cold ethanol [14]." + } + ], + "c6b165b1-a39e-4278-9615-8285c1999e7e": [ + { + "document_id": "c6b165b1-a39e-4278-9615-8285c1999e7e", + "text": "Genomic DNA extraction\n\nDNA from MEF cultures or mouse liver was isolated by phenol/chloroform extraction, as described [11]." + } + ], + "da485354-fcdc-49b8-9a41-0f673610156a": [ + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "DNA is\nusually recovered from cells by methods that include cell rupture but that\nprevent the DNA from fragmenting by mechanical shearing. This is generally undertaken in the presence of EDTA, which chelates the magnesium ions\nneeded as cofactors for enzymes that degrade DNA, termed DNase. Ideally,\ncell walls, if present, should be digested enzymatically (e.g. , lysozyme in the\nbacteria or bacterial cell). In addition the cell membrane should be solubilized\nusing detergent." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "DNA solutions can be stored frozen,\nalthough repeated freezing and thawing tends to damage long DNA molecules\nby shearing. A flow diagram summarizing the extraction of DNA is given in\nFig. 1.2. The above-described procedure is suitable for total cellular DNA. If the DNA from a specific organelle or viral particle is needed, it is best to\nisolate the organelle or virus before extracting its DNA, because the recovery\nof a particular type of DNA from a mixture is usually rather difficult." + } + ], + "f0849937-dc25-42f4-a512-99783761674d": [ + { + "document_id": "f0849937-dc25-42f4-a512-99783761674d", + "text": "Genomic DNA extraction\n\nGenomic DNA was extracted by the mixed alkyl trimethyl ammonium bromide (MATAB) procedure.Briefly, 250 mg of plant material was ground in liquid nitrogen and immediately incubated in 2 ml of pre-warmed extraction buffer (100 mM Tris-HCl, pH 8, containing 20 mM EDTA, 1.4 M NaCl, 2% (w/v) MATAB, 1% (w/v) PEG6000 (polyethylene glycol), 0.5% (w/v) sodium sulfite, 20% (w/v) Igepal CA630, 20% (w/v) lithium dodecyl sulfate, and 20% (w/v) sodium deoxycholate) at 74 °C for 20 min.After purification with 2 ml of chloroform:isoamylalcohol (24:1, v/v), DNA extracts were precipitated with 1.6 ml of isopropanol then resuspended in 1 ml of buffer (50 mM Tris-HCl, pH 8, containing 10 mM EDTA and 0.7 M NaCl).The extracts were purified on anion exchange columns (QIAGEN-tip 20) following the manufacturer's instructions (QIAGEN, Valencia, CA)." + } + ], + "f9002547-db31-4f9e-abc1-7aace5c8ea18": [ + { + "document_id": "f9002547-db31-4f9e-abc1-7aace5c8ea18", + "text": "DNA extraction and enzymatic digestion\n\nTotal DNA was isolated from whole blood and separated blood subtypes using a Qiagen DNeasy Blood & Tissue Kit following the manufacturer instructions.After extraction, DNA was quantified by NanoDrop (Thermo Scientific NanoDrop products, Wilmington, DE).The isolated genomic DNA was enzymatically digested according to previously described method.Briefly, DNA (3 μg) was first denatured by heating at 95 °C for 5 min and then chilling on ice for 2 min.Then, 1/10 volume of S1 nuclease buffer (30 mM CH 3 COONa, pH 4.6, 280 mM NaCl, 1 mM ZnSO 4 ) and 100 units of S1 nuclease were added before the mixture (20 μL) was incubated at 37 °C for 16 h.Subsequently, after 1/10 volume of alkaline phosphatase buffer (50 mM Tris-HCl, 10 mM MgCl 2 , pH 9.0), 0.002 units of venom phosphodiesterase I, and 10 units of alkaline phosphatase were added, the solution was incubated at 37 °C for an additional 4 h followed by extraction with an equal volume of chloroform for twice.The aqueous layer was collected and lyophilized to dryness and then reconstituted in 100 μL water.About 30 μL of the obtained samples were then subjected to liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) analysis." + } + ], + "fabfccb1-4ba7-47b4-8415-941742ddea40": [ + { + "document_id": "fabfccb1-4ba7-47b4-8415-941742ddea40", + "text": "\n\nThe conventional DNA extraction procedure involved the homogenization of single D. magna in 400 l of sperm lysis buffer (100 mM Tris-HCl, pH 8; 500 mM NaCl; 10 mM ethylenediaminetetraacetic acid [EDTA], pH 8; 1% SDS; 2% mercaptoethanol) followed by RNase treatment (40 g, 37ЊC for 1.5 h).The DNA was then extracted in phenol (pH 8) and chloroform:isoamyl alcohol (1:1).The DNA was finally precipitated by two volumes of ice-cold ethanol in the presence of 3 M sodium acetate (1/10 of the DNA volume) and was incubated at Ϫ80ЊC overnight.Precipitated DNA was harvested by centrifugation, dried in air, and the final pellet dissolved in sterile analytic grade water." + } + ], + "fd5edd5b-25d5-41ef-b9ad-7599905b844f": [ + { + "document_id": "fd5edd5b-25d5-41ef-b9ad-7599905b844f", + "text": "DNA extraction and quantification\n\nDNA was extracted from whole organs by standard techniques (34) with emphasis on minimizing shearing or nicking of DNA as nicked DNA has been shown to be refractory to LX-PCR (35).DNA from the brain was extracted from the right hemisphere.Extracted DNA was resuspended in 10 mM Tris 1 mM EDTA (pH 8) (TE) and stored at 4_C.A number of samples were normalized for mtDNA content by dot blotting and hybridization with digoxigenin-labeled full-length mtDNA and densitometry.In cases where mtDNA quantification was not carried out, the DNAs were normalized by A 260 of total DNA." + } + ] + }, + "data_source": [], + "document_id": "D5884AA974550493E54BFAD47AAEB737", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "DNA", + "extraction", + "phenol", + "chloroform", + "ethanol", + "precipitation", + "Qiagen", + "EDTA", + "Proteinase&K", + "isopropanol" + ], + "metadata": [], + "question": "How would one extract the DNA, from say, mammals or plants?", + "subquestions": null, + "task_id": "D5884AA974550493E54BFAD47AAEB737", + "usage": { + "chatgpt": 6218, + "gpt-4": 4090, + "gpt-4-turbo-preview": 3185 + }, + "user_id": 2 + }, + "document_id": "D5884AA974550493E54BFAD47AAEB737", + "task_id": "D5884AA974550493E54BFAD47AAEB737" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/gen_resp04.json b/gnqa/paper1_eval/src/data/responses/general/gen_resp04.json new file mode 100644 index 0000000..8497fd1 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/gen_resp04.json @@ -0,0 +1,150 @@ +{ + "created_at": "2024-04-01T12:01:45.671040Z", + "data": { + "amplify": false, + "answer": "A genetic marker is a measurable polymorphic sequence of DNA whose chromosomal location is known. They often have no known functional significance but are used as pointers to a particular chromosomal location. They are used to track the inheritance of genes and can be a gene or some section of DNA with no known function.", + "context": { + "0a30029f-fa8f-49e3-9a68-82d1a8ae3157": [ + { + "document_id": "0a30029f-fa8f-49e3-9a68-82d1a8ae3157", + "text": "Using genetic markers, the pattern of inheritance can be tracked through\nfamilies. For example, by analyzing a marker linked to the eye color gene\nin several generations, it is possible to determine from which grandparents a\nchild has inherited its eye color alleles. More importantly, finding a marker\nlinked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance." + } + ], + "0c80320f-bca2-4f46-858e-bd3fba2f67a2": [ + { + "document_id": "0c80320f-bca2-4f46-858e-bd3fba2f67a2", + "text": "\n\nIt is well known, however, that not all genomic markers are independent (Frazer et al., 2007).Genetic variation is often inherited in contiguous segments of DNA, such that there tends to be correlation between the inheritance of alleles at markers close to each other on the same chromosome.This genetic correlation is called linkage disequilibrium (LD), and, as a result, the effective number of independent tests (M eff ) conducted is less than the total number of markers (M).By effective number of tests, we mean the number of independent tests that would have to be conducted to lead to a null distribution for the minimum P-values that was approximately the same as that obtained when conducting tests that are necessarily correlated due to LD." + } + ], + "32338b01-15af-4ec9-9bc4-e9c58b53068e": [ + { + "document_id": "32338b01-15af-4ec9-9bc4-e9c58b53068e", + "text": "Genetic\nmapping is a powerful strategy that exploits genomic information to dissect complex traits into Mendelian loci\n(quantitative trait loci or QTL) and identifies genetic\n* Correspondence: marioenrico.pe@sssup.it\n1\nInstitute of Life Sciences, Scuola Superiore Sant’Anna, Pisa, Italy\nFull list of author information is available at the end of the article\n\ndeterminants that may lead to crop improvement. As\nmarker density ceases to be a limiting factor [3], our\nability to discover specific genetic determinants in a\nsingle mapping study depends upon the availability of\npopulations with high genetic diversity and recombination density [4]." + } + ], + "7a7773ed-2548-4297-86ad-b7ce115448e0": [ + { + "document_id": "7a7773ed-2548-4297-86ad-b7ce115448e0", + "text": "This capacity allows samples to be placed into\nmeaningful genetic groups that reflect evolutionary relationships (more stable, lower diversity markers), while simultaneously permitting high levels of strain resolution (high diversity\nmarkers). From a clinical perspective, markers that accurately\nreflect broad evolutionary relationships are valuable for comparing the genetic similarity of an isolate to isolates on a regional\nor global scale, whereas high-resolution markers are valuable\nfor detailed epidemiological tracking in an outbreak. Variable-number tandem repeats (VNTRs) are genetic markers that can span a range of variability and, therefore, can capture\ngenetic relationships on multiple scales (18–19)." + } + ], + "835a094d-9c2b-4686-8725-d3c4123175b0": [ + { + "document_id": "835a094d-9c2b-4686-8725-d3c4123175b0", + "text": "Identifying the genetic loci that modulate a trait based on correlation between\nvariation in phenotype and variation in genotype is the essence of genetic mapping. This\nfirst involves systematically genotyping a genetically diverse population using\nmicrosatellite or SNP markers. The phenotype of interest is then measured and its\nvariability in the population assessed. A statistical test is then carried out to identify\nchromosomal regions that segregate with the trait and show linkage with the trait, i.e. ,\n\n3\nidentify genetic regions that have the same genotype among individuals with similar trait\nvalues but differ between individuals with dissimilar trait values." + } + ], + "83a4ab87-f4a5-40b9-9297-5a3596e3636f": [ + { + "document_id": "83a4ab87-f4a5-40b9-9297-5a3596e3636f", + "text": "Using genetic markers, the pattern of inheritance can be tracked through\nfamilies. For example, by analyzing a marker linked to the eye color gene\nin several generations, it is possible to determine from which grandparents a\nchild has inherited its eye color alleles. More importantly, finding a marker\nlinked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance." + } + ], + "8b95c7a6-0ca5-445e-8776-14d1e6550fa0": [ + { + "document_id": "8b95c7a6-0ca5-445e-8776-14d1e6550fa0", + "text": "Genetic variation\n\nFor decades researchers used single markers to elucidate clinal differentiation and spatial variation in allele frequencies.This approach revealed multiple markers with variation that tracked the clines, including some with the same allele at higher frequency at the same latitude in the Northern and Southern hemispheres.Examples include alcohol dehydrogenase (Adh), a-glycerol-3-phosphate dehydrogenase (Gpdh), glucose-6-phosphate dehydrogenase (G6pd), esterase-6 (Est-6), octanol dehydrogenase (Odh), and 6-phosphogluconate dehydrogenase (Pgd) [30][31][32][33] (Table 1).Perhaps the most heavily explored locus in D. melanogaster has been Adh, the first step in the ethanol detoxification pathway.The Adh-F allele encodes high catalytic activity of ADH, but this increase in activity trades off with enzyme stability at higher temperatures [34,35].Unsurprisingly, the Adh-F allele is found at a higher frequency in cooler high-latitude populations, and differentiation has occurred in parallel along clines in" + } + ], + "92fa8f50-2923-41a1-812b-32d931c71684": [ + { + "document_id": "92fa8f50-2923-41a1-812b-32d931c71684", + "text": "In the case of\ngenetic markers, this easily runs in the several hundreds to thousands. Moreover,\nthe optimal subset of markers is heavily dependent on how these markers are\ncombined, i.e. dependent on the optimal Boolean function . Altogether, one\nfrequently has to rely on greedy search strategies that easily get stuck in local\noptima or near exhaustive searches that are computationally too expensive,\nespecially when employed in permutation procedures required to assess statistical\nsignificance. Our solution to this problem hinges upon two observations." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "GENE MAPPING\n\nThe opportunity to merge advances in molecular genetic technology with advances in statistical techniques expanded in earnest with the development of DNA markers such as restriction fragment length polymorphisms (Lander and Botstein, 1989).Research exploded in the past decade with the continued refinement of molecular technology yielding a variety of DNA markers-e.g., short tandem repeats (STRs) or microsatellites; variable number of tandem repeats (VNTRs); single nucleotide polymorpohisms (SNPs), and gene expression microarrays or gene chips.A genetic marker is a measurable polymorphic sequence of DNA whose chromosomal location is known.Markers often have no known functional significance but are used as pointers to a particular chromosomal location.The logic of gene mapping technology is simple: Determine if there is a relationship between variability in a phenotype and variability in an anonymous DNA marker of known chromosomal location.If there is a relationship, it is taken as evidence that there is a gene that influences the trait at or near the marker." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "Genetic drift. Genetic changes in populations caused by random phenomena rather than by selection.Genetic marker.A segment of DNA with an identifiable physical location on a chromosome whose inheritance can be followed.A marker can be a gene, or it can be some section of DNA with no known function." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nBiological characteristics indicating initial resiliency or susceptibility of an organism include genetic profiles.As noted above, genetic markers need to have a high prevalence in the population and have a reasonably strong effect on common population health outcomes, or have an interaction effect with other health-affecting mechanisms, to be candidates for inclusion in population studies.At the moment, the only known genetic marker of clear value in a population survey is the apolipoprotein E gene (APOE), although this is likely to change in the very near future.APOE allele status is clearly related to a number of major health outcomes in older populations which are reasonably well measured in population surveys: mortality, heart disease, and cognitive functioning (Albert et al., 1995b;Corder et al., 1993;Evans et al., 1997;Ewbank, 1997;Hofman et al., 1997;Hyman et al., 1996;Luc et al., 1994;Saunders et al., 1993).Both the prevalence of alleles indicating higher risk and the size of the effect are large enough to be of importance in explaining variability in currently studied health outcomes.APOE allele status has been shown to have independent effects on health outcomes and to interact with other life circumstances such as sex and race in its effect on health outcomes (Jarvik et al., 1995;Maestre et al., 1995;Payami et al., 1992).Incorporation of information on this genetic indicator could lead to increased knowledge of the interactive mechanisms of this genetic marker and other social and behavioral variables and thus clarify some of the mechanisms leading to population differentials in cognition, heart disease, and mortality." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nAs described by Hermalin (1999), if genetic markers are modeled as part of an individual's physiological structure, they can provide controls for predisposing factors that affect more proximate mid-level markers of function as well as downstream health outcomes.This potential benefit of genetic information-i.e., its power in explicating the black box of Figure 11-1-may outweigh, or at least precede, its near-term potential for discovering genetic links to chronic disease.As discussed by Weiss (1998b), the situation with chronic disease differs from single locus disorders that are inherited following well-identified Mendelian rules.In general, we cannot expect to find relationships that are even as straightforward as the APOE links to cardiovascular and Alzheimer's disease.Variation across populations, difficulty in identifying a small enough area on the chromosome to search for disease-associated genes, and the problems inherent in identifying continuous outcomes with particular genes may limit finding the connections." + } + ], + "ad14b0c4-2a38-411b-9bb1-cacf9203f29d": [ + { + "document_id": "ad14b0c4-2a38-411b-9bb1-cacf9203f29d", + "text": "This capacity allows samples to be placed into\nmeaningful genetic groups that reflect evolutionary relationships (more stable, lower diversity markers), while simultaneously permitting high levels of strain resolution (high diversity\nmarkers). From a clinical perspective, markers that accurately\nreflect broad evolutionary relationships are valuable for comparing the genetic similarity of an isolate to isolates on a regional\nor global scale, whereas high-resolution markers are valuable\nfor detailed epidemiological tracking in an outbreak. Variable-number tandem repeats (VNTRs) are genetic markers that can span a range of variability and, therefore, can capture\ngenetic relationships on multiple scales (18–19)." + } + ], + "b04f2221-de28-4c4b-893e-9da982ff864c": [ + { + "document_id": "b04f2221-de28-4c4b-893e-9da982ff864c", + "text":"These variations provide a species the ability of adapting\nto the environment change (Liu and Cordes,\n2004). DNA markers are among the most powerful tools for revealing genetic variations in\norganisms. Historically, many different types of markers have been used for aquaculture studies\n\nFunctional Genomics in Aquaculture, First Edition. Edited by Marco Saroglia and Zhanjiang (John) Liu. ␂\nC 2012 John Wiley & Sons, Inc. Published 2012 by John Wiley & Sons, Inc.\n\n41\n42\n\nFunctional Genomics in Aquaculture\n\nTable 2.1\n\nA summary of characteristics of various molecular markers used in aquaculture species." + }, + { + "document_id": "b04f2221-de28-4c4b-893e-9da982ff864c", + "text": "For instance,\nmapping of a trait or a phenotype would require polymorphic DNA markers such as microsatellites (SSRs) or single nucleotide polymorphisms (SNPs); expression profiling would\nrequire genome annotation information; microarray design would require sequence information of genes, etc. The objective of this chapter is to provide a general review of genomic\nresources needed, and currently present for\naquaculture species, for functional genomics\nstudies. Polymorphic DNA Markers\nThe key factor behind the significant differences at the level of individuals, species,\nand higher order of taxonomic groups is genetic variation (polymorphism)." + } + ], + "cbc03a11-fe9c-4b54-b290-bd24c1447607": [ + { + "document_id": "cbc03a11-fe9c-4b54-b290-bd24c1447607", + "text": "Functional genomics:\n\nThe study of genes, their resulting proteins, and the role played by the proteins in the biochemical processes of the body.Gene: A unit of inheritance; a working subunit of DNA.Each of the 20 000 to 25 000 genes in the body contains the code for a specific product, typically a protein such as an enzyme.Gene expression: The process by which the coded information of a gene is translated into the structures present and operating in the cell (either proteins or ribonucleic acids).Gene markers: Landmarks for a target gene, either detectable traits that are inherited along with the gene or distinctive segments of DNA.Gene map: A description of the relative positions of genes on a chromosome and the distance between them.Genetic counseling: A short-term educational counseling process for individuals and families who have a genetic disease or who are at risk for such a disease.Genetic counseling provides patients with information about their condition and helps them make informed decisions.Genetic linkage maps: DNA maps that assign relative chromosomal locations to genetic landmarks-either genes for known traits or distinctive sequences of DNA (ie, genetic markers)-on the basis of how frequently they are inherited together.Genetic testing: Examining a sample of blood or other body fluid or tissue for biochemical, chromosomal, or genetic markers that indicate the presence or absence of genetic disease.Genetics: The scientific study of heredity, how particular qualities or traits are transmitted from parents to offspring.Genome: All the genetic material in the chromosomes of a particular organism.Genome-wide: Descriptor that indicates that the entire breadth of the genome has been examined in a study (eg, a linkage or association study).Genome-wide studies do not resequence the entire genome but type (an increasingly large set of) markers distributed throughout the genome.Genomics: A \"scaled-up\" version of the science of genetics that investigates the structure and function of large sections of the genome simultaneously.Genotype: The actual genes carried by an individual (as distinct from phenotype-ie, the physical, bodily characteristics into which genes are translated).Haplotype: A way of denoting the collective genotype of a number of closely linked loci on a chromosome.Heritability (h 2 ): For any trait, the proportion of the phenotypic variability resulting from genetic variance.Note that heritability does not indicate the degree to which a trait is \"genetic. \"Nor does a high h 2 mean that the trait cannot be influenced by environment.A heritability significantly Ͼ0, however, can provide a rationale for further genetic and genomic study of a trait of interest.Heterozygous: Possessing 2 different sequences (ie, genotypes) of a particular gene, 1 inherited from each parent.High-throughput genotyping: In contrast to the older labor-and time-intensive genotyping methods, high-throughput genotyping makes use of robots, computers, and other evolving technologies, thus enabling laboratories to type up to hundreds of thousands of polymorphisms in many samples in a relatively short period of time.Homozygous: Possessing 2 identical sequences of a particular gene, 1 inherited from each parent.Interaction: The differing effect of 1 independent variable on the dependent variable, depending on the particular level of another independent variable.For example, there would be an interaction between the factors sex and treatment if the effect of treatment was not the same for male and female subjects in a drug trial.Linkage analysis: A gene-hunting technique that traces patterns of heredity in large, high-risk families in an attempt to locate a disease-causing gene mutation by identifying traits that are coinherited with it.Linkage disequilibrium: Two alleles at different loci that occur together on the same chromosome more often than would be predicted by chance alone.It is a measure of cosegregation of alleles in a population." + } + ], + "d0d6c5d6-36c6-45f1-9107-cef95df83bb3": [ + { + "document_id": "d0d6c5d6-36c6-45f1-9107-cef95df83bb3", + "text": "Source: Kearsey and Pooni (1996). Genetic maps consist of a series of markers or identifiable features at known, or perhaps\nbest described as estimated, locations on the genome (see Figure 9). For some discrete traits, simple Mendelian inheritance is followed and the phenotype has\na one to one correspondence with the genes controlling it. These are so called morphological\nmarkers, which were then related to continuous or quantitative traits of interest. Examples are\nshape, colour, size or height in particular varieties of peas, as studied by Mendel. For another\nexample, see Appendix A.2." + } + ], + "d333b766-b7e4-4ab5-96a8-50a8a1d805f1": [ + { + "document_id": "d333b766-b7e4-4ab5-96a8-50a8a1d805f1", + "text": "Genomic markers used in linkage mapping have evolved from\nrestriction fragment length polymorphisms (RFLPs) to microsatellites (simple sequence repeat\npolymorphisms; SSRPs), to single-nucleotide polymorphisms (SNPs), with the more modern\nmarkers exhibiting higher frequencies in the genome (thus ensuring fuller coverage). Linkage\nmapping of a trait is in fact the demonstration of linkage between the phenotype and a genomic\nmarker, followed by an inference of linkage between the genomic marker and the responsible\nDNA variant. Transitive logic ties the phenotype with the DNA variant, which is of course the\npoint of the exercise. See Fig." + } + ], + "e8397443-575a-4645-b161-59862203f7b4": [ + { + "document_id": "e8397443-575a-4645-b161-59862203f7b4", + "text": "However, because of time constraints it is often more\npracticable to choose an appropriate mapping population that is already available\nthrough the current stock centers. Plant species chosen for study will depend\nlargely on the availability of suitable plant resources. Obtain appropriate mapping population information to include information on\nmarkers/genotypes (see Note 4). A marker is an identifying factor; a gene or other\nDNA of known location that is used to track the inheritance and so on of other\ngenes whose exact location is not yet known." + } + ], + "f9f8f648-bd40-49e9-8aee-c341faa43290": [ + { + "document_id": "f9f8f648-bd40-49e9-8aee-c341faa43290", + "text":"The closer two genes are together on a chromosome, the\n\nless likely it is for a recombination event to occur between the two, causing a non-random association. This is the basis for genetic linkage. The development of genetic markers allowed the theory of linkage disequilibrium (LD) to be used\nin mapping genes. Genetic markers are speci\nc genetic di␛erences between species or cultivars, and\ngenetic linkage of these markers to particular morphological traits can allow genetic markers to be used\nto represent the gene of interest (Collard et al. , 2005)." + } + ] + }, + "data_source": [], + "document_id": "90CA41EEE653DCE5B03FBDDF9E1E82BF", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "genetic&marker", + "DNA", + "chromosome", + "phenotype", + "genotype", + "SNP", + "microsatellite", + "linkage&disequilibrium", + "quantitative&trait&loci", + "gene&mapping" + ], + "metadata": [], + "question": "What is a genetic marker?", + "subquestions": null, + "task_id": "90CA41EEE653DCE5B03FBDDF9E1E82BF", + "usage": { + "chatgpt": 5874, + "gpt-4": 4652, + "gpt-4-turbo-preview": 3673 + }, + "user_id": 2 + }, + "document_id": "90CA41EEE653DCE5B03FBDDF9E1E82BF", + "task_id": "90CA41EEE653DCE5B03FBDDF9E1E82BF" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/gen_resp05.json b/gnqa/paper1_eval/src/data/responses/general/gen_resp05.json new file mode 100644 index 0000000..c807f03 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/gen_resp05.json @@ -0,0 +1,156 @@ +{ + "created_at": "2024-04-01T12:02:57.687420Z", + "data": { + "amplify": false, + "answer": "The process of finding a genetic marker followed by a quantitative trait loci (QTL) involves several steps. First, a population is developed for genetic mapping. This population can be a segregating population or a permanent population. The population is then genotyped using molecular markers. Next, the population is phenotyped for an interested trait. QTL analysis is then conducted using statistical procedures to find markers linked to the QTL. This involves single-marker regression across all chromosomes, where a hypothetical QTL is evaluated at the location of each marker locus. The significance of that QTL is estimated from a likelihood ratio statistic. A permutation test is then conducted to establish genome-wide significance criteria for the trait. The result is a list of marker loci that show a significant association with the trait. These loci are most likely to be near QTLs. The goal of QTL mapping is to identify regions of the genome that harbor genes relevant to a specified trait.", + "context": { + "0265286c-7bac-4ae3-831c-5bf5a4f758c6": [ + { + "document_id": "0265286c-7bac-4ae3-831c-5bf5a4f758c6", + "text": "This is an open access article distributed under the Creative Commons Attribution License,\nwhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction\nThe association between a complex phenotypic trait and\ngenetic markers on the chromosomes can be detected\nthrough statistical analysis, leading to the identification of\nquantitative trait loci (QTL)—regions of the chromosomes\nthat appear to be associated with the phenotype. Quantitative\ntrait loci (QTL) are expected to be associated with the genes\ncontrolling some aspects of the phenotype." + } + ], + "07d409f4-91f9-4701-bb39-1421845a6321": [ + { + "document_id": "07d409f4-91f9-4701-bb39-1421845a6321", + "text": "Nowadays many\ndifferent cost-efficient genotyping solutions (including sequencing and Single\nNucleotide Polymorphisms arrays) have opened the way to systematic genome-wide\nfine mapping of quantitative traits (Quantitative Trait Locus or QTL mapping). The process of QTL mapping (Figure 1) consists in searching for genome regions that influence the value of a given trait. For example, identifying a QTL for\nplant height means finding a DNA region at which the plants that carry a certain\nallele tend to be significantly higher or lower than those carrying another allele." + } + ], + "29f5af5f-8dc7-4e53-b0fa-66d37317a3f4": [ + { + "document_id": "29f5af5f-8dc7-4e53-b0fa-66d37317a3f4", + "text": "QTLs are regions within the\ngenome whose genetic variation modulates quantitatively a phenotype characteristic of\nthe particular trait under study (Lynch and Walsh, 1998). Determining the association\nbetween variations in specific disease phenotypes or a trait, with variations in genotypes\nof a reference population can be used to locate a QTL. One of the methods used for\nmapping QTLs associated with complex traits is genetic markers-trait association. Genetic markers associated with certain loci can be inherited in linkage disequilibrium. Generating populations with linked loci in disequilibrium is achieved though either\ncrosses between inbred lines, or use of the out-bred populations." + } + ], + "2c6178fe-c05a-42e6-aafb-7408592dcc50": [ + { + "document_id": "2c6178fe-c05a-42e6-aafb-7408592dcc50", + "text": "Often, the first step in analysis of new trait\ndata is single-marker regression across all chromosomes. A hypothetical QTL is evaluated at\nthe location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott,\n1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill\nand Doerge, 1994)." + } + ], + "33814fad-d831-46f5-b41f-ff31626a82ca": [ + { + "document_id": "33814fad-d831-46f5-b41f-ff31626a82ca", + "text": "One possible approach to facilitate this endeavor is to identify quantitative trait loci\n(QTL) that contribute to the phenotype and consequently unravel the candidate\ngenes within these loci. Each proposed candidate locus contains multiple genes and,\ntherefore, further analysis is required to choose plausible candidate genes. One of\nsuch methods is to use comparative genomics in order to narrow down the QTL to a\nregion containing only a few genes. We illustrate this strategy by applying it to\ngenetic findings regarding physical activity (PA) in mice and human." + } + ], + "3c69df9d-414a-420b-a513-ca3860662d57": [ + { + "document_id": "3c69df9d-414a-420b-a513-ca3860662d57", + "text": "Elucidation of the molecular basis of these traits has proven\ndifficult as they are under the control of multiple genes and\ngenetic loci. The standard approach to gene identification\ninvolves mapping by linkage analysis in experimental crosses,\nand this has led to the localization in the rat genome of\nhundreds of quantitative trait loci (QTLs) underlying trait\nvariation (68). We refer to these loci as physiological quantitative trait loci (pQTLs)." + } + ], + "561145bb-7fe6-4941-9f02-5e6c73839100": [ + { + "document_id": "561145bb-7fe6-4941-9f02-5e6c73839100", + "text": "\n\nOften, the first step in analysis of new trait data is single-marker regression across all chromosomes.A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992).For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).By default, it returns a list of marker loci that show greater than sugges-tive association with the trait according to standard criteria (Lander and Kruglyak, 1995), but it will also accept user-defined criteria.Local maxima in the LRS in this list identify loci that are most likely to be near QTLs.WebQTL provides this list within a few seconds." + } + ], + "8b4276be-c77e-4e80-a5bb-54e9ff75d2ba": [ + { + "document_id": "8b4276be-c77e-4e80-a5bb-54e9ff75d2ba", + "text": "QTLs can be identified through their genetic\nlinkage to visible marker loci with genotypes that can be readily classified [94, 97]. As\nsuch, markers that are genetically linked quantitative trait will segregate more often with\ntrait values, whereas unlinked markers will lack an association with the phenotype [94,\n98]. The principal goal of a QTL analysis is to identify all QTLs linked to a trait and\ndiscern whether phenotypic differences are mainly due to a few loci with large effects, or\nmany loci with small effects [98]." + } + ], + "8ec43c84-e565-4b47-a07a-0ddd99da6728": [ + { + "document_id": "8ec43c84-e565-4b47-a07a-0ddd99da6728", + "text": "This is an open access article distributed under the Creative Commons Attribution License,\nwhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction\nThe association between a complex phenotypic trait and\ngenetic markers on the chromosomes can be detected\nthrough statistical analysis, leading to the identification of\nquantitative trait loci (QTL)—regions of the chromosomes\nthat appear to be associated with the phenotype. Quantitative\ntrait loci (QTL) are expected to be associated with the genes\ncontrolling some aspects of the phenotype." + } + ], + "8fb56fda-e1a2-4407-acb2-9a5983861202": [ + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "The basic principle of classic QTL is trait segregation along with the\nmarkers and necessitated the availability of two or more genetically different\nlines corresponding with the phenotypic trait. Markers like single nucleotide\npolymorphisms (SNPs) and microsatellites are used for genotypic distinctions\n(Vignal et al. , 2002). QTL mapping is achieved in four basic steps; the first one is the measurement\nof variation for a trait in the individuals. It is a prerequisite to have the traits\nthat show phenotypic variability among the individuals (inbred strains)." + } + ], + "9161eaca-9841-4097-8dcd-4ea73ae81188": [ + { + "document_id": "9161eaca-9841-4097-8dcd-4ea73ae81188", + "text": "\n\nOften, the first step in analysis of new trait data is single-marker regression across all chromosomes.A hypothetical QTL is evaluated at the location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott, 1992).For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill and Doerge, 1994).By default, it returns a list of marker loci that show greater than sugges-tive association with the trait according to standard criteria (Lander and Kruglyak, 1995), but it will also accept user-defined criteria.Local maxima in the LRS in this list identify loci that are most likely to be near QTLs.WebQTL provides this list within a few seconds." + } + ], + "9a882703-e0ff-4bac-b11a-d99284bf7f6c": [ + { + "document_id": "9a882703-e0ff-4bac-b11a-d99284bf7f6c", + "text": "Often, the first step in analysis of new trait\ndata is single-marker regression across all chromosomes. A hypothetical QTL is evaluated at\nthe location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott,\n1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill\nand Doerge, 1994)." + } + ], + "ae202e58-4233-4abe-9231-c17f802e8d61": [ + { + "document_id": "ae202e58-4233-4abe-9231-c17f802e8d61", + "text": "Quantitative Trait Locus (QTL) mapping\nTo map QTL, we used 934 AXB/BXA genetic informative markers obtained from http://www. genenetwork.org. For all the in vitro measurements and gene expression linkage analysis, a\ngenome-wide scan was performed using R/qtl [57]. Significance of QTL logarithm-of-odds\n(LOD) scores was assessed using 1000 permutations of the phenotype data [114] and the corresponding p-values reported. For the cellular phenotypes, QTL significance was reported at a\ngenome-wide threshold corresponding to p < 0.05." + } + ], + "b034070a-267b-428e-8d6b-bda2b1727b51": [ + { + "document_id": "b034070a-267b-428e-8d6b-bda2b1727b51", + "text": "Typically one may obtain a location known to derive from only one of the two\nparent strains that contains a chromosomal region that correlates with a trait of interest. Since the actual gene and gene product will frequently remain unknown, the region is\nreferred to as quantitative trait locus (QTL), and is simply named for the trait itself\n(Alberts & Schughart, 2010). Growing sets of strain-dependent marker locations in\nestablished RI strains are continually updated in online repositories." + } + ], + "b078162f-a48d-405b-b2cf-3559fc3338c8": [ + { + "document_id": "b078162f-a48d-405b-b2cf-3559fc3338c8", + "text": "By definition, a\nquantitative trait locus is a chromosomal region that contains a gene, or genes, that\nregulate a portion of the genetic variation for a particular phenotype (Wehner et al. 2001). The goal of QTL mapping is to identify regions of the genome that harbour\ngenes relevant to a specified trait. QTL map locations are commonly determined by\ninitial screening of mice with specific genetic characteristics, such as recombinant\ninbred strains, the F2 of two inbred strains, or recombinant congenic strains (Flint\n2003)." + } + ], + "b3e8c6d4-fc8b-4a1c-b6d8-7c0252101571": [ + { + "document_id": "b3e8c6d4-fc8b-4a1c-b6d8-7c0252101571", + "text": "Often, the first step in analysis of new trait\ndata is single-marker regression across all chromosomes. A hypothetical QTL is evaluated at\nthe location of each marker locus, and the significance of that QTL is estimated from a likelihood ratio statistic (LRS) (Haley and Knott,\n1992). For this analysis, WebQTL automatically does a permutation test to establish genomewide significance criteria for the trait (Churchill\nand Doerge, 1994)." + } + ], + "d0d6c5d6-36c6-45f1-9107-cef95df83bb3": [ + { + "document_id": "d0d6c5d6-36c6-45f1-9107-cef95df83bb3", + "text": "QTL linkage studies are conducted in order to map a region or regions of the genome which\naffect a continuous or quantitative trait. In agriculture, as soon as markers linked to QTL are\nfound for economically important traits, these markers can be used for selecting individuals\nin breeding programmes. In human studies, the aim is often to identify markers indicating\ndisease susceptibility. Current techniques for measuring markers are usually relatively slow\nand laborious. Newer DNA technology, such as SNP or single nucleotide polymorphisms\n(Kwok, 2001b; Patil et al." + } + ], + "eae7406a-efdd-46af-b2e2-7868ce150157": [ + { + "document_id": "eae7406a-efdd-46af-b2e2-7868ce150157", + "text": "Genomic regions linked to complex traits can be identified by genetic mapping\nand quantitative trait locus (QTL) analysis (Shehzad and Okuno 2014). 7\nQTL mapping\nQTL mapping with molecular markers is the first strategy in genetic studies. In plant\nbreeding, QTL mapping is an essential step required for marker-assisted selection\n(Mohan et al. 1997; Shehzad and Okuno 2014). The fundamental idea underlying QTL\nanalysis is to associate genotype and phenotype in a population exhibiting a genetic\nvariation (Broman and Sen 2009)." + }, + { + "document_id": "eae7406a-efdd-46af-b2e2-7868ce150157", + "text": "Four steps of QTL mapping are (1) development a\n\nW\n\npopulation, (2) genotyping the population using molecular markers, (3) phenotyping the\npopulation for an interested trait, and (4) QTL analysis using statistical procedures to find\n\nIE\n\nmarkers linked to the QTL (Bernardo 2002). PR\nEV\n\nPopulations used for genetic mapping can be a segregating population (F2 and\nbackcross) or a permanent population (double haploids or recombinant inbred lines). Recombinant inbred lines (RILs) are developed by selfing of individual progenies of the\nF2 plants until homozygosity is achieved (F7-F8)." + } + ], + "eb90c74a-60f0-4485-b1b9-bb6665469828": [ + { + "document_id": "eb90c74a-60f0-4485-b1b9-bb6665469828", + "text": "This\ntool allows systems genetic analysis of single genes or small sets of genes using a bottom-up\napproach. relations define quantitative trait loci (QTLs). Because the marker is not typically the\nactual site of the polymorphism, interpolative methods have been developed to estimate\nthe distance of the QTL from the marker and the strength of the association. Using\nmultiple-regression and model-fitting methods, the true complexity of the phenotypic\nvariation can be modeled through the consideration of multiple loci and environmental\nfactors as predictors [13]." + } + ] + }, + "data_source": [], + "document_id": "241CA981DE08EC38C6A145D452CF6615", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "QTL&mapping", + "molecular&markers", + "genetic&variation", + "phenotype", + "genotype", + "quantitative&trait&loci", + "statistical&analysis", + "genetic&linkage", + "single-marker®ression", + "likelihood&ratio&statistic" + ], + "metadata": [], + "question": "Explain the process of finding a genetic marker followed by a quantitative trait loci.", + "subquestions": null, + "task_id": "241CA981DE08EC38C6A145D452CF6615", + "usage": { + "chatgpt": 5221, + "gpt-4": 3525, + "gpt-4-turbo-preview": 2564 + }, + "user_id": 2 + }, + "document_id": "241CA981DE08EC38C6A145D452CF6615", + "task_id": "241CA981DE08EC38C6A145D452CF6615" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/gen_resp06.json b/gnqa/paper1_eval/src/data/responses/general/gen_resp06.json new file mode 100644 index 0000000..88fcd65 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/gen_resp06.json @@ -0,0 +1,140 @@ +{ + "created_at": "2024-04-01T12:04:13.564651Z", + "data": { + "amplify": false, + "answer": "1. Start with an initial linkage or association: This is the first step in a genetic study where a trait or disease is linked to a specific region of the genome.\n\n2. Perform Laboratory Genotyping: This involves determining the genetic variation of an individual by examining their DNA sequence using biological assays and comparing it to a standard sequence.\n\n3. Use Genomic Sequence: The role of genomic sequence is crucial at every stage. It serves as a common thread that ties all the stages together.\n\n4. Establish True Orthology: Determine the true orthology between genes in different species. If two genes are truly orthologous, their evolution closely follows patterns of speciation.\n\n5. Apply Bioinformatics: Use bioinformatics tools and techniques to analyze the data. This can be applied to sequence data from any collection of organisms.\n\n6. Understand Genome Structure and Content: Provide an overview of genome structure and content to provide a context for subsequent discussions.\n\n7. Improve DNA Sequencing Methods: Constantly work on improving DNA sequencing methods with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000.\n\n8. Interpret the Information: The major challenge ahead is the interpretation of this information. Understand how our genes interact with each other, and how the environment contributes to the development of health and disease.\n\n9. Consider the Implications: Understand the individual and societal implications of knowing our genome sequence.\n\n10. Keep Up with Technological Advances: With further technological advances, other previously unimaginable research approaches will become real.", + "context": { + "1f5f2923-ca25-496c-b70e-5d15825c5575": [ + { + "document_id": "1f5f2923-ca25-496c-b70e-5d15825c5575", + "text": "\n\nTo overcome the lack of phenotypic information in the 1000 Genomes Project, The ClinSeq Project was developed to pilot large-scale genome sequencing for research in genomic medicine at the National Institutes of Health Clinical Research Center in Bethesda, MD. 40 The study seeks to enroll 1000 individuals who will be evaluated for personal health status and family history.The project aims to:" + } + ], + "58714c13-954b-46b3-bd0e-69ccadd9dc6a": [ + { + "document_id": "58714c13-954b-46b3-bd0e-69ccadd9dc6a", + "text": "We (Hein, Schierup and Wiuf) have published a\n300 page book on molecular population genetics titled “Gene Genealogies, Sequence Variation and Evolution” Oxford\nUniversity Press, and are presently developing a tutorial in association mapping that we hope to publish as a booklet in\n2006 and are also involved in a very large EU collaboration (Holland, Denmark, Iceland and UK) to find susceptibility\ngenes for breast and prostate cancer. In comparative genomics, the most fundamental investigation is to find genes in a pair of aligned genomes." + } + ], + "5edf84d0-c2d9-45eb-91b9-c35743b6a463": [ + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "Key bioinformatic steps to\ntake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stage\n\nregions in man (see Chapter 5). Similar issues also exist in the establishment of\ntrue orthology between genes in different species, where one is identified to play a\nrole in a disease model. If two genes are truly orthologous, their evolution closely\nfollows patterns of speciation (Fitch, 2000)." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text":"In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily on\nBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes\n2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\n\n␂\nC\n106\n\nCH 6 COMPARATIVE GENOMICS\n\nquestions of relevance to human genetics. We begin, in Section 6.2 by presenting an\noverview of genome structure and content, providing a context for the subsequent\ndiscussions." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text":"This fully indexed but semi-intelligible\n\nBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes\n2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\n\n␂\nC\n4\n\nCH 1 BIOINFORMATICS CHALLENGES FOR THE GENETICIST\n\n‘book of life’ immediately began to serve as a valuable framework for integration of\ngenetic and biological data. However, knowledge of the genome sequence did not\nimmediately clarify the nature and structure of human genetic variation." + } + ], + "96f13e8e-633e-4728-853f-81ffbad6c58a": [ + { + "document_id": "96f13e8e-633e-4728-853f-81ffbad6c58a", + "text": "\n\nMethods for DNA sequencing are constantly being improved, with the ultimate goal of sequencing a human genome in a single day for a cost of about US $1,000, an end that appears to be in sight (Hayden, 2014).In the very near future, whole-genome sequencing will be routinely available for clinical purposes, perhaps even beginning at birth.The major challenge ahead is the interpretation of this information.How do our genes interact with each other, and how does the environment contribute to the development of health and disease?What are the individual and societal implications of knowing our genome sequence?The answers to these and other important questions will unfold in the years ahead.Thus, we are truly in an era where precision medicine may soon become a reality." + } + ], + "a83987ea-607c-4952-a1cc-69c6f193ba2a": [ + { + "document_id": "a83987ea-607c-4952-a1cc-69c6f193ba2a", + "text": "\n\nCharacteristics of genotyping and sequencing technologies" + } + ], + "c12e853e-4f0d-48f9-93af-15db9ad2dfae": [ + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "Key bioinformatic steps to\ntake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stage\n\nregions in man (see Chapter 5). Similar issues also exist in the establishment of\ntrue orthology between genes in different species, where one is identified to play a\nrole in a disease model. If two genes are truly orthologous, their evolution closely\nfollows patterns of speciation (Fitch, 2000)." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text":"In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily on\nBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes\n2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\n\n␂\nC\n106\n\nCH 6 COMPARATIVE GENOMICS\n\nquestions of relevance to human genetics. We begin, in Section 6.2 by presenting an\noverview of genome structure and content, providing a context for the subsequent\ndiscussions." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + } + ], + "e074ba47-cd7a-4bb2-8bcb-9a15da69cc2d": [ + { + "document_id": "e074ba47-cd7a-4bb2-8bcb-9a15da69cc2d", + "text": "Introduction\n\nSince the first human genome was sequenced at an estimated cost of $150 million,\nseveral advanced high-throughput techniques – some with lower costs - have come up. At\nthe same time, this resulted in a data deluge and a critical need to connect the\nheterogeneous sequencing data and associated annotations – structural and functional with the basic tenets of biology or molecular basis of development and disease." + } + ], + "f35e02a1-3314-4663-913f-38a3fc072aa8": [ + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "Key bioinformatic steps to\ntake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stage\n\nregions in man (see Chapter 5). Similar issues also exist in the establishment of\ntrue orthology between genes in different species, where one is identified to play a\nrole in a disease model. If two genes are truly orthologous, their evolution closely\nfollows patterns of speciation (Fitch, 2000)." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text":"In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily on\nBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes\n2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\n\n␂\nC\n106\n\nCH 6 COMPARATIVE GENOMICS\n\nquestions of relevance to human genetics. We begin, in Section 6.2 by presenting an\noverview of genome structure and content, providing a context for the subsequent\ndiscussions." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + } + ], + "f8659e89-3f2f-4c83-8069-f015862b7377": [ + { + "document_id": "f8659e89-3f2f-4c83-8069-f015862b7377", + "text": "\n\nAmple time was allotted to answer questions and a copy of \"A Guide to Your Genome\" (National Human Genome Research Institute 2007) was provided to further assist participants' understanding and ability to communicate results with family members or others." + } + ], + "f8be7949-8fa0-4730-9143-caa6161bf463": [ + { + "document_id": "f8be7949-8fa0-4730-9143-caa6161bf463", + "text": "\n\nWhether within 10 or 12 (or 8) years, such inexpensive sequencing will change both research and clinical care, and progress does not need to wait even that long.The National Human Genome Research Institute (NHGRI) plans to focus a significant portion of the sequencing capacity that it supports on medical sequencing.For instance, the NHGRI and the National Cancer Institute are actively considering a Human Cancer Genome Project, 22 which would use DNA sequencing and a host of other genome technologies to gather information about the mutations and functional abnormalities found in multiple samples from many major types of cancer.Medical sequencing should also provide important insight into many other diseases.For example, sequencing all exons in X-linked mental retardation syndromes may reveal much about their etiology.Sequencing candidate genes in the extremes of the distribution of quantitative traits should also reveal much of importance about common diseases, such as coronary atherosclerosis. 23With further technological advances, other previously unimaginable research approaches will become real." + } + ], + "fca531d0-d45b-495f-a02c-fbd437617b20": [ + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "Key bioinformatic steps to\ntake a genetic study from an initial linkage or association to laboratory genotyping are illustrated. The reader should note the role of genomic sequence as a common thread through every stage\n\nregions in man (see Chapter 5). Similar issues also exist in the establishment of\ntrue orthology between genes in different species, where one is identified to play a\nrole in a disease model. If two genes are truly orthologous, their evolution closely\nfollows patterns of speciation (Fitch, 2000)." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text":"In general terms, the approaches we describe can be applied to sequence data from any collection of organisms, but our emphasis here is primarily on\nBioinformatics for Geneticists, Second Edition. Edited by Michael R. Barnes\n2007 John Wiley & Sons, Ltd ISBN 978-0-470-02619-9 (HB) ISBN 978-0-470-02620-5 (PB)\n\n␂\nC\n106\n\nCH 6 COMPARATIVE GENOMICS\n\nquestions of relevance to human genetics. We begin, in Section 6.2 by presenting an\noverview of genome structure and content, providing a context for the subsequent\ndiscussions." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "4\nAssembling a View of the\nHuman Genome\nColin A. M. Semple\nBioinformatics, MRC Human Genetics Unit, Edinburgh EH4 2XU, UK\n\n4.1 Introduction\nThe miraculous birth of the draft human genome sequence took place against\nthe odds. It was only made possible by parallel revolutions in the technologies\nused to produce, store and analyse the sequence data, and by the development of\nnew, large-scale consortia to organize and obtain funding for the work (Watson,\n1990). The initial flood of human sequence has subsided as the sequencing centres have sequenced genomes from other mammalian orders and beyond." + } + ] + }, + "data_source": [], + "document_id": "B756E99E279768E5604363F2A725E6BE", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "genetic", + "sequencing", + "genome", + "bioinformatics", + "human", + "genes", + "disease", + "research", + "technology", + "data" + ], + "metadata": [], + "question": "Create a how-to guide for genetic sequencing.", + "subquestions": null, + "task_id": "B756E99E279768E5604363F2A725E6BE", + "usage": { + "chatgpt": 5646, + "gpt-4": 3422, + "gpt-4-turbo-preview": 2434 + }, + "user_id": 2 + }, + "document_id": "B756E99E279768E5604363F2A725E6BE", + "task_id": "B756E99E279768E5604363F2A725E6BE" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/gen_resp07.json b/gnqa/paper1_eval/src/data/responses/general/gen_resp07.json new file mode 100644 index 0000000..01cb1c3 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/gen_resp07.json @@ -0,0 +1,134 @@ +{ + "created_at": "2024-04-01T12:07:20.647660Z", + "data": { + "amplify": false, + "answer": "The length of telomeres is significant as it is associated with aging, disease, and mortality. Longer telomeres are generally associated with better health outcomes, including protection from age-related diseases, better cognitive function, and healthier lipid profiles. Shorter telomeres, on the other hand, have been linked to higher risks of heart disease, infection-related death, and other diseases. Telomere length can also be influenced by factors such as stress, with high-stress situations potentially leading to faster telomere shortening and premature aging.", + "context": { + "1ac23aae-2a51-4846-81ec-6db70ab6e5a4": [ + { + "document_id": "1ac23aae-2a51-4846-81ec-6db70ab6e5a4", + "text": "\n\nIn birds, where erythrocyte telomere length (ETL) is measured, the majority of species sampled have shown no sex difference (36).Nonetheless, bird telomere dynamics are complex and, as with humans, may be affected by environment and stress.For example, a longitudinal study of black-tailed gulls (Larus crassitostris) over 2-5 years found no correlation between ETL and age or sex.Rather, ETL attrition was correlated with reduced food availability and environmental stressors (55).In a captive zebra finch (Taeniopygia guttata) population, male and female mean telomere length decreased with increasing age of the animals, but did differ between sexes (56).As these examples illustrate, the relationship between telomere length, lifespan, and sex is likely to be complex in other vertebrates." + }, + { + "document_id": "1ac23aae-2a51-4846-81ec-6db70ab6e5a4", + "text": "\n\nComparative studies of age-related telomere attrition in other species also reveal a variety of patterns.Barrett and Richardson (36) recently summarized the comparative data available on sex differences in telomere length.They found a strong correlation between male-biased mortality and either shorter telomeres or greater telomere attrition in males across bird and mammal taxa.However, telomere length did not differ between males and females in species where females are shorter-lived than males (36), suggesting that telomere shortening is not associated with species-specific longevity in a simple linear fashion.These studies generally suffer from relatively small sample sizes and are largely cross-sectional.Further, the use of diverse assays, different tissues (eg, leukocytes in mammals vs erythrocytes in birds), and lack of standardized benchmarks for accuracy makes comparisons between studies difficult." + }, + { + "document_id": "1ac23aae-2a51-4846-81ec-6db70ab6e5a4", + "text": "\n\nIn some organisms, there is no clear relationship between telomere length and lifespan.Age-related telomere attrition could not be detected in Daphnia pulex (57) or sea urchin species (Strongylocentrotus franciscanus and Lytechinus variegatus) (58).Studies in C. elegans examining natural variation in telomere length and experimentally manipulated telomere length detect no correlation with lifespan (59,60), and in Drosophila, which uses a telomerase-independent mechanism for telomere maintenance, there is a similar lack of correlation between longevity and telomere length (61).Similarly, data on sex differences in age-related telomere shortening are mixed.For example, in the ant species Lasius niger, the rate of telomere shortening is more rapid in short-lived males compared to longer-lived females.But, mean telomere length does not differ between the two types of females, queens and workers, despite the fact that queens live much longer than workers (up to 28 years vs 2-3 months) (62).These findings suggest that the question of how telomere shortening affects aging across species and how sex affects telomere attrition rates are complex." + } + ], + "5a8540de-d034-4dc4-b08b-e96e22f47ff8": [ + { + "document_id": "5a8540de-d034-4dc4-b08b-e96e22f47ff8", + "text": "\n\nWith new methodologies to assess relative telomere length by Q-PCR, studies were designed to address the impact of telomere length on aging, aging associated pathologies, and mortality.One such study has correlated shorter leukocyte telomere lengths at age 60 with a three times higher risk of heart disease and an eightfold increase in risk of infection-related death (36), thereby associating measured relative cellular aging with disease and life expectancy.In a similar way, chronic stress was shown to correlate with short leukocyte telomere length, a phenomenon attributed to higher levels of oxidative stress at the cellular level (70).More recent studies have linked telomere length in smooth muscle cells with senescence and disease severity in patients with atherosclerosis (141,150).Leukocyte telomere length was also short in a cohort of similar patients and associated with a higher risk of developing occult cardiovascular disease (71).More data are needed to understand and validate the use of leukocyte telomere length as a biomarker for cardiovascular and other diseases." + } + ], + "5e6ad994-9cad-4b8b-903d-2d5c350e25dc": [ + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nShortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19]." + } + ], + "99445b90-1950-4299-815d-e912c5ae06ac": [ + { + "document_id": "99445b90-1950-4299-815d-e912c5ae06ac", + "text": "\n\nNew research has indicated how social factors, such as subordination, may translate into biological effects (epel et al. 2004;Chae et al. 2014).In a now classic study, epel et al. ( 2004) examined the telomere lengths of fifty-eight healthy premenopausal women who either had a healthy child (n = 19) or were giving care to a chronically ill child (n = 39. )They measured perceived stress, years of caregiving, telomere length, and oxidative stress.They found highly statistically significant differences in telomere length between women taking care of chronically ill children and those who had healthy children.They found highly statistically significant negative correlations between telomere length and perceived stress and years of caregiving.Telomerase activity had highly statistically significant negative correlations with perceived stress and years of caregiving.Oxidative stress was highly positively correlated with perceived stress and years of caregiving.They concluded that the telomere length shortening was equivalent to 9 to 17 years of aging in the high stress group.Telomere length is considered a biomarker of aging (Finch and Kirkwood 2000).Thus, this study showed that caregiver stress had essentially aged these women 9 to 17 years compared to women who had healthy children." + } + ], + "c9fda811-1e12-480c-b432-987fa1d24fce": [ + { + "document_id": "c9fda811-1e12-480c-b432-987fa1d24fce", + "text": "\n\nThe single, consistent predictor of the rate of telomere attrition shown in multiple adult and the few child longitudinal studies is the baseline measurement of telomere length at the start of each study.This suggests the importance of understanding predictors of telomere length prior to adulthood, as it determines in part the rate of change (Revesz et al. 2014a, b;Nordfjall et al. 2009).Moreover, longitudinal studies in adults have had found that telomere attrition rate is dependent on baseline telomere length independent of any phenotypic predictors of shortening, such as disease or demographic variables (Nordfjall et al. 2009), attesting to the importance of studies to evaluate risk factors for shortening prior to adulthood." + }, + { + "document_id": "c9fda811-1e12-480c-b432-987fa1d24fce", + "text": "\n\nRates of decline in childhood may be particularly relevant for later chronic disease risk as shorter telomere length has been implicated in disease progression through exposure to cellular senescence, inflammatory cytokines and adipocyte hypertrophy (Raschenberger et al. 2015;Willeit et al. 2014;Monickaraj et al. 2012;Fyhrquist et al. 2013)." + }, + { + "document_id": "c9fda811-1e12-480c-b432-987fa1d24fce", + "text": "\n\nAdult studies have also found a negative correlation with baseline telomere length, suggesting a negative feedback regulation of leukocyte telomere length (Farzaneh-Far et al. 2010;Aviv et al. 2009;Epel et al. 2008;Nordfjall et al. 2009).It is possible that while our follow-up period was shorter than Shalev et al. 2013 and adult studies, which had a minimum of 5 year intervals with the exception of Puterman et al. (2015) who followed for a one-year time period, there may be biological regulation of telomere length at 4 and 5 years of age such that shorter telomeres are more robustly maintained, whereas longer telomeres have greater rates of decline, over a short period of one year.It is unlikely that this relationship is due to assay error or regression to the mean given the consistency of our findings across studies.We have had similar findings of longer telomeres having greater rates of decline and shorter telomeres being maintained in our different studies (Farzaneh-Far et al. 2010;Epel et al. 2008;Puterman et al. 2015)." + }, + { + "document_id": "c9fda811-1e12-480c-b432-987fa1d24fce", + "text": "\n\nWe found primarily maintenance and lengthening from 4 to 5 years of age in children, with minimal telomere attrition, indicating that most of the telomere loss happens in the first 4 years, plateauing by age 4. Lastly, we found close to 10 % of the variance in rate of change in children shared by mothers.While some of this shared variance is genetic, there are likely environmental factors that need to be further identified that impact rate of telomere length change." + }, + { + "document_id": "c9fda811-1e12-480c-b432-987fa1d24fce", + "text": "\n\nAbstract Telomeres are the protective complexes at the end of chromosomes, required for genomic stability.Little is known about predictors of attrition in young children or the relationship between parental and child patterns of telomere change.Telomere length was assessed twice over one year, at 4 and at 5 years of age, in Latino preschool children (n = 77) and their mothers (n = 70) in whole blood leukocytes.Maternal and child rates of attrition during the same time period were compared in 70 mother-child pairs.More children showed lengthened telomeres over one year compared to their mothers and very few children showed attrition (2.6 %).Approximately 31 % of children and 16 % of mothers displayed lengthening over one year while 66 % of children showed maintenance in contrast with 74 % of mothers.The strongest predictor for child telomere length change was child's baseline telomere length (r = −0.61,p < 0.01).Maternal rate of change was associated with child rate of change (r = 0.33, p < 0.01).After controlling for child baseline telomere length, the relationship between child and maternal rate of change trended towards significance (Coeff = 0.20, 95 % CI −0.03 to 0.43; p = 0.08)." + } + ], + "ca76f85d-9f72-4e15-8ba9-3bf94308c449": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "text": "\n\nBlackburn and Epel, a health psychologist who did original research on how specific lifestyle and psychological habits can protect telomeres, published The Telomere Effect (Blackburn & Epel, 2017), in which they suggested that individuals with shorter telomeres developed diseases earlier in life (a shorter \"disease span\").What follows is the evidence from these authors, their colleagues, and other researchers describing how length of telomeres contributes to mind-body connection and healthy longevity." + } + ], + "eea4020b-1e14-4af9-9d67-f75d1802fdcd": [ + { + "document_id": "eea4020b-1e14-4af9-9d67-f75d1802fdcd", + "text": "\n\nAs early as at the time of birth, each of the 92 telomeres of the human genome has its own characteristic length.Additionally, each telomere shortens by its individual attrition rate.In general, longer telomeres at birth are associated with higher age-dependent attrition rates and vice versa.Overall, telomere shortening appears more dynamic in males." + }, + { + "document_id": "eea4020b-1e14-4af9-9d67-f75d1802fdcd", + "text": "\n\nIn conclusion, a combination of overall and chromosomespecifi c shorter telomeres and more pronounced age-dependent telomere erosion could be observed in males.There is a prospective clinical study strongly suggesting that longer telomeres decrease the risk of dying (Cawthon et al., 2003).With this in mind, the telomere length discrepancies between the sexes may indeed be a factor infl uencing the differences in their life expectancy." + }, + { + "document_id": "eea4020b-1e14-4af9-9d67-f75d1802fdcd", + "text": "\n\nIn every chromosome a linear decline of telomere length with age was observed, being more pronounced in men independent of the examined chromosome arm.This might suggest that telomere length on single chromosome arms may be infl uenced by the same factors which determine overall telomere length." + }, + { + "document_id": "eea4020b-1e14-4af9-9d67-f75d1802fdcd", + "text": "\n\nS. Mayer a S. Brüderlein a S. Perner a I. Waibel a A. Holdenried a N. Ciloglu a C. Hasel a T. Mattfeldt a K.V. Nielsen b P. Möller a a Institute of Pathology, University of Ulm, Ulm (Germany); b DakoCytomation A/S, Glostrup (Denmark) follow uniformity.In previous studies, sex-specifi c differences in telomere length and attrition rate of men and women were found (Benetos et al., 2001;Cawthon et al., 2003;Nawrot et al., 2004), suggesting gender differences in behavior of telomeres.In individual chromosome arms, telomere length was also shown not to be homogeneous (Lansdorp et al., 1996;Benn, 1997;Martens et al., 1998;Surralles et al., 1999;Hao and Tan, 2001;Londono-Vallejo et al., 2001;Graakjaer et al., 2003), some telomeres being signifi cantly shorter, others longer than the average length." + }, + { + "document_id": "eea4020b-1e14-4af9-9d67-f75d1802fdcd", + "text": "\n\nTo date, these characteristics in telomere lengths could not be set in a biological context, as only a few groups have provided detailed information about chromosome-specifi c patterns of telomere distribution (Lansdorp et al., 1996;Graakjaer et al., 2003).Whether accumulation of short telomeres (Martens et al., 2000;Londono-Vallejo et al., 2001) or rather the shortest telomere of one specifi c chromosome arm (Hemann et al., 2001) elicits senescence, remains an open question so far." + }, + { + "document_id": "eea4020b-1e14-4af9-9d67-f75d1802fdcd", + "text": "\n\nIn recent literature, there are hints that the average telomere length may be higher in women and that their annual shortening rate may be somewhat lower (Vaziri et al., 1993;Rufer et al., 1998;Jeanclos et al., 2000), but these reported differences failed to reach statistical signifi cance except for one study (Jeanclos et al., 2000).Here, we provide compelling evidence that this is indeed the case." + }, + { + "document_id": "eea4020b-1e14-4af9-9d67-f75d1802fdcd", + "text": "\n\nIt is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo.In individuals, short telomeres are considered to be a sign of advanced age.Cawthon and coworkers (2003) showed that telomere shortening in humans likely contributes to mortality, supporting the hypothesis that they might act as a mitotic clock (Allsopp et al., 1992).Telomere length dynamics, however, does not seem to Abstract.During aging, telomeres are gradually shortened, eventually leading to cellular senescence.By T/C-FISH (telomere/centromere-FISH), we investigated human telomere length differences on single chromosome arms of 205 individuals in different age groups and sexes.For all chromosome arms, we found a linear correlation between telomere length and donor age.Generally, males had shorter telomeres and higher attrition rates.Every chromosome arm had its individual age-specifi c telomere length and erosion pattern, resulting in an unexpected heterogeneity in chromosomespecifi c regression lines.This differential erosion pattern, however, does not seem to be accidental, since we found a correlation between average telomere length of single chromosome arms in newborns and their annual attrition rate.Apart from the above-mentioned sex-specifi c discrepancies, chromosome arm-specifi c telomere lengths were strikingly similar in men and women.This implies a mechanism that arm specifi cally regulates the telomere length independent of gender, thus leading to interchromosomal telomere variations." + } + ], + "f3610ccc-2831-42f6-a3d3-1a0feeba4902": [ + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\n\nShortening of the telomeres at the ends of chromosomes has been associated with age-related disease and mortality [16][17][18].A recent study identified a common haplotype of four SNPs in the human telomerase reverse transcriptase gene (hTERT) that is enriched in centenarians and associated with longer telomere length [19].It was also shown that centenarians and their offspring maintain longer telomeres compared with controls and that longer telomeres are associated with protection from age-related diseases, better cognitive function and lipid profiles of healthy ageing [19]." + } + ] + }, + "data_source": [], + "document_id": "F91D61921F6212793795027C274D0E7E", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "telomeres", + "telomere&length", + "DNA", + "chromosomes", + "aging", + "senescence", + "leukocyte", + "telomerase", + "oxidative&stress", + "centenarians" + ], + "metadata": [], + "question": "What is the significance of the length of telomeres?", + "subquestions": null, + "task_id": "F91D61921F6212793795027C274D0E7E", + "usage": { + "chatgpt": 7797, + "gpt-4": 4400, + "gpt-4-turbo-preview": 3459 + }, + "user_id": 2 + }, + "document_id": "F91D61921F6212793795027C274D0E7E", + "task_id": "F91D61921F6212793795027C274D0E7E" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/gen_resp08.json b/gnqa/paper1_eval/src/data/responses/general/gen_resp08.json new file mode 100644 index 0000000..6914fd7 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/gen_resp08.json @@ -0,0 +1,142 @@ +{ + "created_at": "2024-04-01T12:09:26.502626Z", + "data": { + "amplify": false, + "answer": "The traits are determined by the combination of genes from both the sperm and the egg. This process involves meiosis, where each gamete (sperm and egg) contributes one chromosome to each pair, resulting in a zygote with a full complement of 23 chromosome pairs. The process of recombination or crossing over, where similar DNA sequences from the paired chromosomes swap genetic material, also plays a crucial role in determining the traits of the offspring. This results in a shuffling of genetic material and contributes to the genetic variation seen among offspring.", + "context": { + "2e22b52d-1a13-48d8-aab8-01a1840aa749": [ + { + "document_id": "2e22b52d-1a13-48d8-aab8-01a1840aa749", + "text": "\n\nSelection could occur at multiple levels, from germ cell generation and propagation to fertilization and early embryonic growth.Chromosomal abnormalities, including aneuploidy, were found in 10-20% of spermatozoa and oocytes (20) and in the cleaved embryo, with a 21% rate of abnormalities in preimplantation embryos (21).These findings led to a model for natural selection against chromosome abnormalities (21).Selection extends to the end of gestation: Only approximately 30% of all conceptions result in a live birth, with more than half of aborted fetuses containing chromosomal abnormalities (22), a number likely to be an underestimate because of technological limitations in measuring all possible mutations.But even in the very small fraction of germ cell duos that survive this withering genome attack and result in a live birth, a number of severe de novo mutations will still be found (23).The data on gross chromosomal alterations suggest that overall, mutation frequency early in life is very high.The functional consequence, however, is limited because of selection.Somewhat surprisingly, this picture points toward an initial decline in genomic alterations, allowing the adult individual to acquire a somatic genome optimally equipped to provide function." + } + ], + "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce": [ + { + "document_id": "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce", + "text": "The phenotype of\nthe F1 hybrids is compared to those of the parental inbred strains to reveal\ndominance or semi-dominance relationships between the alleles that a¡ect the\nphenotype. Phenotypic di¡erences between reciprocal F1 hybrids indicate that\none or more of the following factors may a¡ect the trait: (1) sex linkage (X- or Ylinked traits), (2) genomic imprinting of QTLs that a¡ect the phenotype, (3)\nprenatal maternal e¡ects (e¡ects of intrauterine environment), and/or (4)\npostnatal maternal or paternal e¡ects (e¡ects of maternal and/or paternal\nparenting behaviour on o¡spring)." + } + ], + "606c59c5-5ae4-47e9-b3eb-58afa55669d1": [ + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "text": "Sex brings harmful alleles together into the\nsame genetic background, allowing selection to more efficiently purge them from\nthe population and potentially producing some offspring that are fitter than either\nparent. However, the benefit of recombining deleterious mutations may depend on the\nnature of the epistatic interactions between them. The mutational deterministic hypothesis\n(Kondrashov 1988) depends partly on this epistasis." + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "text": "In most plants and animals, sex\nis a necessary component of reproduction, and the question for evolutionary biologists\nis why reproductive mechanisms have evolved that way. In one of the experiments\ndescribed next, evolutionary geneticists have nevertheless devised a way to compare\nevolution with and without recombination in the obligately sexual fruit fly." + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "text": "This disparity in investment is the basis for the twofold cost: asexual\nfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. But\nsex usually also involves the basic process of physical recombination: the breakage and\nreunion of two different DNA or RNA molecules. Of these two processes, recombination\nis clearly the more widespread feature of sexual reproduction. A variety of reproductive\nsystems, such as selfing and automixis, involve recombination but not outcrossing. In\ncontrast, relatively few reproductive systems have outcrossing without recombination." + } + ], + "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa": [ + { + "document_id": "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa", + "text": "\n\nCrossing over-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes cross over one another.Crossing over results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.This process is also known as meiotic recombination." + }, + { + "document_id": "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa", + "text": "\n\nThe reason for the rarity of these mutations is natural selection: If the mutations result in disorders that decrease health and reproductive fitness, they will eventually be eliminated from a population.In exceptional cases, mutations may cause both beneficial and detrimental consequences, resulting in opposing forces of positive selection and negative selection that may cause the mutations to be preserved at nonrare frequencies in a population.For example, the HbS mutation in the HBB gene (which produces the β subunit of hemoglobin) causes sickle cell disease when present in both alleles, a detrimental consequence, but protects against malaria when present in 1 allele, a beneficial consequence, ensuring that the mutation persists in populations in areas of the world where malaria is endemic.Genes are passed from parents to offspring via the process of meiosis by which gametes, the egg cells in the mother and the sperm cells in the father, are generated.Ordinarily, each cell has 23 pairs of chromosomes; the gametes have 23 unpaired chromosomes.In meiosis, the 23 pairs are split so that each gamete receives 1 chromosome from each pair (Figures 8 and 9).Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full complement of 23 chromosome pairs restored.If all goes well, the zygote gives rise to a live offspring." + }, + { + "document_id": "6c0eb981-977a-42f5-a3b1-136e1ccfc5aa", + "text": "\n\nRecombination (meiotic recombination)-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes recombine with one another.Recombination results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.Also known as crossing over." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nIn the generation of gametes, crossing over regularly occurs, and genetic information is swapped between members of a chromosome pair.That doesn't matter within inbred animals, because the swapped parts are identical.In an F 1 animal, however, the chromosomes of a particular pair are genetically different, one each having come from each parent.Each gamete produced will be unique, as will be each F 2 zygote formed by uniting of the gametes from two F 1 parents.An F 2 group thus provides for expression of some genetic variability.This variability is limited to the allelic differences existing between the parent strains of the F 1 s, so that another F 2 , derived from different inbred strains, will express different genetic differences." + } + ], + "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed": [ + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "text": "Sex brings harmful alleles together into the\nsame genetic background, allowing selection to more efficiently purge them from\nthe population and potentially producing some offspring that are fitter than either\nparent. However, the benefit of recombining deleterious mutations may depend on the\nnature of the epistatic interactions between them. The mutational deterministic hypothesis\n(Kondrashov 1988) depends partly on this epistasis." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "text": "In most plants and animals, sex\nis a necessary component of reproduction, and the question for evolutionary biologists\nis why reproductive mechanisms have evolved that way. In one of the experiments\ndescribed next, evolutionary geneticists have nevertheless devised a way to compare\nevolution with and without recombination in the obligately sexual fruit fly." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "text": "This disparity in investment is the basis for the twofold cost: asexual\nfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. But\nsex usually also involves the basic process of physical recombination: the breakage and\nreunion of two different DNA or RNA molecules. Of these two processes, recombination\nis clearly the more widespread feature of sexual reproduction. A variety of reproductive\nsystems, such as selfing and automixis, involve recombination but not outcrossing. In\ncontrast, relatively few reproductive systems have outcrossing without recombination." + } + ], + "b014e368-d0d5-4eff-a9af-abd4a4ed6d29": [ + { + "document_id": "b014e368-d0d5-4eff-a9af-abd4a4ed6d29", + "text": "\n\nAberrant recombination patterns on chromosomes that have missegregated have also been identified as an important factor, in both male and female gametes (Table I).This is because recombination together with cohesion of sister chromatids establish the unique 'bivalent' chromosome structure where homologous partner chromosomes are tethered together, a configuration that is critical for their accurate segregation in meiosis I (Fig. 2A).The remarkable feature is that recombination occurs in foetal oocytes whereas chromosome segregation takes place decades later (Fig. 2A).Since mammalian oocytes are arrested at the G2/M transition (or dictyate stage), this raises the intriguing question of how the bivalent is maintained until the meiotic divisions." + } + ], + "b04f2221-de28-4c4b-893e-9da982ff864c": [ + { + "document_id": "b04f2221-de28-4c4b-893e-9da982ff864c", + "text": "Traditionally, it has been agreed that the\nfinal sex of an individual (phenotypic sex)\ndepends on two sequential processes: the sex\ndetermination system of the species and the\ngonad differentiation process (Valenzuela,\n2008). However, recently, these two seemingly\ndistinct processes are viewed as part of a general process leading to gonad formation and\nsex ratios (Sarre et al. , 2004; Quinn et al. , 2011;\nUller and Helanterä, 2011)." + }, + { + "document_id": "b04f2221-de28-4c4b-893e-9da982ff864c", + "text": "However, we expect that\nonly at this level, the most significant contributions brought by integrating epigenetics will be\nmade. Concluding Remarks and Future\nProspects\nFish sex ratios are the result of a complex interaction between genetic, biochemical, and environmental interactions. The ultimate result\nof these interactions at the individual level is\ngender: male or female. However, at the population level, the combination of sex determination and differentiation sets the sex ratio. In\nturn, sex ratios define the reproductive capacity\nof populations and, if sex growth dimorphism\nexists, also the growth characteristics, something very important in an aquaculture context." + } + ], + "dcc71b11-5668-4274-9f35-d9b7f01695a2": [ + { + "document_id": "dcc71b11-5668-4274-9f35-d9b7f01695a2", + "text": "Obehav is, in turn, influenced by offspring genes\nand environment (Ogene and Oenvir respectively). Hence, indirect genetic effects (blue arrows)\nand direct genetic effects (red arrow) are important influencers of behaviour. B) Parentoffspring conflict theory predicts that parental resource investment and offspring solicitation\nbehaviours are influenced by the fitness benefit to a focal individual (O), cost to a social\npartner such as a sibling (S1 and S2) or parent (P), and by their coefficient of relatedness\n(black arrows). 42\nFigure 2: Genomic imprinting can result in divergent phenotypes from the same\ngenotype. A) A paternally imprinted gene, i.e. maternally expressed." + } + ], + "e7030862-fb3c-48cc-bbd1-e30ac5ed5864": [ + { + "document_id": "e7030862-fb3c-48cc-bbd1-e30ac5ed5864", + "text": "Because of the small contribution, through the sperm, of\nthe paternal transcriptome to the fertilized zygote, and because of the stronger maternal contribution\nto child rearing in most model organisms, parental effects are typically thought of as synonymous with\nmaternal effects, although true paternal effects are known to exist (Rando, 2012). Maternal effects have been shown to be important during embryonic development, leading to\ndifferences in the birth weight of mice depending on the genotype of the mother (Cowley et al. ,\n1989; Wolf et al. , 2011)." + }, + { + "document_id": "e7030862-fb3c-48cc-bbd1-e30ac5ed5864", + "text": "Therefore, the resulting phenotypic patterns lag a generation\nbehind the genetic transmission of the causal variants. The most well-studied parental genetic effects\nare caused by deposition of maternal transcripts into the egg prior to fertilization, resulting in\ndifferences in early embryonic development depending on the genotype of the mother. Certain genes\nhave also been shown to respond to maternal influence after birth through genetically defined\nmaternal behaviors (Weaver et al. , 2004)." + } + ], + "f253e087-e030-40a8-8400-3b6bf50c1fd6": [ + { + "document_id": "f253e087-e030-40a8-8400-3b6bf50c1fd6", + "text": "The phenotype of\nthe F1 hybrids is compared to those of the parental inbred strains to reveal\ndominance or semi-dominance relationships between the alleles that a¡ect the\nphenotype. Phenotypic di¡erences between reciprocal F1 hybrids indicate that\none or more of the following factors may a¡ect the trait: (1) sex linkage (X- or Ylinked traits), (2) genomic imprinting of QTLs that a¡ect the phenotype, (3)\nprenatal maternal e¡ects (e¡ects of intrauterine environment), and/or (4)\npostnatal maternal or paternal e¡ects (e¡ects of maternal and/or paternal\nparenting behaviour on o¡spring)." + } + ], + "f9d93e23-292c-44b3-8f27-dc1b4d6b494f": [ + { + "document_id": "f9d93e23-292c-44b3-8f27-dc1b4d6b494f", + "text": "It was believed by many that for each trait variant we should expect to find a\ncorresponding genetic change, or „gene for‟ that trait. Through historical happenstance the\nrelationship between genes and traits was set up and treated as if it were one-to-one. But the\nproduction of a trait involves not only genes, but also their interactions with each other and the\nenvironment, and chance." + } + ] + }, + "data_source": [], + "document_id": "4FD9E20567FA6D4E536E3BA72E3CB09F", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "sperm", + "egg", + "traits", + "genetic", + "recombination", + "meiosis", + "chromosomes", + "mutations", + "selection", + "zygote" + ], + "metadata": [], + "question": "Once a sperm combines with an egg, what determines how traits are passed onto the resulting lifeform?", + "subquestions": null, + "task_id": "4FD9E20567FA6D4E536E3BA72E3CB09F", + "usage": { + "chatgpt": 5281, + "gpt-4": 3689, + "gpt-4-turbo-preview": 2717 + }, + "user_id": 2 + }, + "document_id": "4FD9E20567FA6D4E536E3BA72E3CB09F", + "task_id": "4FD9E20567FA6D4E536E3BA72E3CB09F" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/gen_resp09.json b/gnqa/paper1_eval/src/data/responses/general/gen_resp09.json new file mode 100644 index 0000000..0c70fd3 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/gen_resp09.json @@ -0,0 +1,154 @@ +{ + "created_at": "2024-04-01T12:11:33.748564Z", + "data": { + "amplify": false, + "answer": "Genetic tracing is matrilineal due to the inheritance of mitochondrial DNA (mtDNA), which is passed from mother to all her children without any admixture from the father. This allows for a clear lineage tracing through the maternal line. In contrast, Y-DNA is passed from father to son, allowing for patrilineal tracing, but it does not provide information about the genetic contributions of other ancestors in a family tree.", + "context": { + "0376df42-5df7-4966-8f0d-e254b86ae743": [ + { + "document_id": "0376df42-5df7-4966-8f0d-e254b86ae743", + "text": "distinguishing prenatal\nfrom postnatal maternal effects, see below). Maternal effects can\naccount for a large proportion of phenotypic variance, especially\nduring early life, and for some traits explain more variation than\ndirect genetic effects [33, 97, 99, 100, 102–115]. However, maternal and offspring genotype are correlated (i.e. half their genes are\nshared), and in inbred lines they are fully confounded, thus separating the effects of their respective genotypes is difficult. To remove\nthis confounding effect cross-fostering has been used, both in the\nlaboratory and in the field [119, 131]." + } + ], + "0a30029f-fa8f-49e3-9a68-82d1a8ae3157": [ + { + "document_id": "0a30029f-fa8f-49e3-9a68-82d1a8ae3157", + "text": "Using genetic markers, the pattern of inheritance can be tracked through\nfamilies. For example, by analyzing a marker linked to the eye color gene\nin several generations, it is possible to determine from which grandparents a\nchild has inherited its eye color alleles. More importantly, finding a marker\nlinked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance." + } + ], + "0e27d15f-e4a2-4902-b4a4-1e72c4202346": [ + { + "document_id": "0e27d15f-e4a2-4902-b4a4-1e72c4202346", + "text": "\n\nAlthough autosomal SNPs are commonly used as genetic markers to infer ancestry or race/ethnicity membership, haploid such as mitochondria, Y-DNA, and X-lined markers are also important to provide separate stories of ancestry of individuals from paternal and maternal sides [42,43].Therefore, genetic structure created due to autosomal markers could be different from those of lineage markers (often influenced by political, social, and migration history of individuals/populations).mitochondrial DNA or mtDNA haploid is the maternally inherited mitochondrial genome (mtDNA) [44].All children inherit mtDNA from their mother, with no admixture from the father.Like Y-line DNA, mtDNA is passed intact from one generation to the next but through maternal line." + }, + { + "document_id": "0e27d15f-e4a2-4902-b4a4-1e72c4202346", + "text": "\n\na) Autosomal DNA (testing both sexes) markers: autosomal DNA tests utilize DNA from the 22 pairs of autosomal chromosomes.Autosomal DNA is inherited from both parents.Autosomal testing provides percentages of ethnicity using autosomal DNA SNP test (i.e., ancestry informative markers), and it is the most commonly used test to infer ancestry across diploid genome.b) Y-DNA or Y-SNPs (paternal line testing) markers: a haploid Y-DNA is the paternally inherited non-recombining portion of the Y chromosome, and it tests only for males.The Y-DNA testing tests the Y chromosome which is passed intact from father to son with no DNA from the mother.Y-DNA testing can then be used to trace direct paternal line.Y-DNA remains the same in each generation, allowing us to compare surname from different regions to see if we are from the same family.Y-line testing does not indicate anything about the contributions of the other ancestors in a family tree.In other words, you could be 3/4th Native American, with only the direct paternal line being European, and this test would tell you nothing at all about those other three Native lines.When testing the Y-chromosome, there are two types of tests, short tandem repeat (STR) and SNP markers.STR tests are best for recent ancestry while SNP tests tell about more ancient ancestry.c) Mitochondrial DNA (maternal line testing) markers:" + } + ], + "14a15ff3-706d-44be-aca5-4bad24a5e4ec": [ + { + "document_id": "14a15ff3-706d-44be-aca5-4bad24a5e4ec", + "text": "\n\nAdditional information about past breeding practices can be gleaned by quantifying the number of reproductive males and females in a population.This can be achieved by comparing levels of genetic diversity between sex chromosomes, autosomes and mtDNA 99 .In cattle, for example, gene flow from aurochs is evident in the autosomes but is absent in mtDNA 41 .This has been interpreted as a management strategy that may have involved allowing insemination of domesticated females by wild bulls 41,100 .In horses, a comparison of the levels of diversity of the Y chromosome and the autosomal chromosomes demonstrated that some cultures allowed fewer males to breed and instead selected specific stallion bloodlines 55 .This male-oriented breeding strategy was not practised by the Romans and only became increasingly prominent in the past 1,000 years as a result of the growing influence of Oriental stallions (Arabian, Persian and Turkmen) 101 ." + } + ], + "2420b221-94fa-40ac-8bfd-55e90d7c1c23": [ + { + "document_id": "2420b221-94fa-40ac-8bfd-55e90d7c1c23", + "text": "\n\nDr Ring: What makes the maternal gene so peculiar compared to the paternal?Dr Cookson: If you look in the epidemiologic sense, many studies show that there is increased risk of allergic disease if the mother is affected.However, very few studies have actually set out to test that formally and most of them might suffer from some sort of selection bias because the mother is more likely to be aware of her symptoms and feel guilty, and so on.It is very difficult to explain.Is it genomic imprinting, where the gene is only active when transmitted through the mother?I do not think all of these genes would be imprinted, though it is possible.It also seems that there are effects of the maternal phenotype.The maternal phenotype, if the mother is affected or unaffected, determines the strength of the maternal effect.Again, if a gene was imprinted, you would not expect maternal phenotype to be important.So, I think that this has something to do with maternal/fetal interaction, either through the placenta or shortly after birth.There is the issue of immune conflict between mother and child.At the same time, the mother is trying to prime the infant's immune system." + } + ], + "25622783-ac42-479d-8698-905a7523c38a": [ + { + "document_id": "25622783-ac42-479d-8698-905a7523c38a", + "text": "Genetic and Genomic Discovery Using Family Studies\n\nIngrid B. Borecki, PhD; Michael A. Province, PhD G enetic studies traditionally have been performed on sets of related individuals, that is, families.Mendel's early studies in sweet peas (Pisum sativum) on the inheritance patterns of discrete traits from parents with specific mating types to offspring has shed light on the basic mechanisms of inheritance, including the fundamental laws of segregation of discrete factors (genes) from parents to offspring and the cosegregation of genes that are closely located on a chromosome (linkage).The distribution of traits within families exhibited mathematical segregation ratios in offspring from known mating types.These expected segregation ratios have been used as an important discovery tool in the study of human diseases in pedigrees, providing evidence for a multitude of single-gene disorders.Furthermore, in some cases, trait cosegregation with genetic markers with known positions provides mapping information that enables localization and, ultimately, identification of the relevant causative gene." + } + ], + "46f190d1-f784-45cd-be09-d43a27ec4063": [ + { + "document_id": "46f190d1-f784-45cd-be09-d43a27ec4063", + "text": "In fact, this idea has been pursued before in the\ncontext of signatures of reproductive isolation and shown to reveal\npatterns consistent with epistatic gene interactions that arise in the\nshape of Dobzhansky-Muller incompatibilities [10,11]. In contrast to the mouse data, the available human genotypes\nwere derived from outbred, ethnically distinct populations. In this\ncase pairs of functionally interacting genes can be detected\nfollowing a slightly different approach." + } + ], + "5c9aed30-dec7-49af-9401-3ec6fa0e1334": [ + { + "document_id": "5c9aed30-dec7-49af-9401-3ec6fa0e1334", + "text": "Family Structure\n\nThe first re-identification method (FAMILY) employs genealogical data accompanying genomic data.Genealogies, rich in depth and structure, permit the construction of complex familial relationships.Consider a simple family structure of two parents and one child.Since the parental genders are guaranteed, there exist 2 variants of this structure, since the child's gender is either male or female.When disease status is taken into account, it is represented as a Boolean variable; either an individual afflicted or not afflicted.In this aspect, all three family members can be represented as three attributes {Father, Mother, Child}, and there exist (father's disease status)*(mother's disease status)*(child's disease status)*(child's gender) = 2*2*2*2 = 16 possible family-disease combinations.In reality, pedigrees are much more robust than a simple nuclear family.For example, a three-generation family of two children per family permits on the order of 10 5 distinct variants of the family-disease structure and 10 6 individuals that could be uniquely characterized.The number of combinationsk is larger when supplementary information, such as living status or medical/genetic features, is considered. 16e ability to determine unique family structures is only one part of the re-identification process.These structures must be linked to identifiable information, which, in many instances, is publicly available in the form of various genealogical databases.These databases are accessible both offline and via the World Wide Web.For example, genealogical records are available in many public databases, including ,Ancestry.com>,,Infospace.com>,,RootsWeb.com>,,GeneaNet.com>,,FamilySearch.org>, and ,Genealogy.com>. {From such data, it is not difficult to construct family structures and, with such information in hand, an adversary can link disease-labeled family structures to named individuals." + } + ], + "6041a1eb-5376-4e06-a4df-0563f1b8a724": [ + { + "document_id": "6041a1eb-5376-4e06-a4df-0563f1b8a724", + "text": "\n\nFig. 3. Illustrations of the three CEU pedigrees (black) showing how genetic information from distant patrilineal relatives (arrow; red, patrilineal lines) can identify individuals.Filled squares represent sequenced individuals.To respect the privacy of these families, only abbreviated versions are presented.The sex of the CEU grandchildren was randomized.The numbers of grandchildren are not given." + } + ], + "748cfe7e-e4f2-453f-8575-50dfe84e2538": [ + { + "document_id": "748cfe7e-e4f2-453f-8575-50dfe84e2538", + "text": "\n\nWhen I was in high school, I remember often trying to match my friends to their parents at various school functions and being surprised at how easy this was.As human geneticists, in spite of the enormous advances being made in our field, we still cannot answer many of the everyday questions that we are asked, such as: \"Why does he look just like his mother? \"Max Perutz [1], in a recent editorial comment in the New Scientist entitled \"The Molecular Biology of the Future,\" suggested some questions, for, as he put it, \"an examination in some future century. \"Here are two of them: (1) \"The time has come\" the Walrus said, \"To talk of many things ...And why the sea is boiling hot And whether pigs have wings. \"Calculate the amount of genetic information this would require in megacricks." + } + ], + "83a4ab87-f4a5-40b9-9297-5a3596e3636f": [ + { + "document_id": "83a4ab87-f4a5-40b9-9297-5a3596e3636f", + "text": "Using genetic markers, the pattern of inheritance can be tracked through\nfamilies. For example, by analyzing a marker linked to the eye color gene\nin several generations, it is possible to determine from which grandparents a\nchild has inherited its eye color alleles. More importantly, finding a marker\nlinked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance." + } + ], + "86b86235-b7a8-4dfc-be13-d119dc31b377": [ + { + "document_id": "86b86235-b7a8-4dfc-be13-d119dc31b377", + "text": "In\ncontrast, genomic imprinting is due to epigenetic changes within\nthe individual causing differential gene expression characterized\nby either complete or partial silencing of one parental allele\n(Barlow, 2011; Abramowitz and Bartolomei, 2012; Ashbrook and\nHager, 2013). As both mothers and fathers had contact with the\npups in our study, our observed PGEs could come from either\nparent. Among quantitative USV traits only peak amplitude of call\ndisplayed a possible parent-of-origin effect. For call number, call\nduration, mean peak frequency, and all morphological traits,\nthere were no significant parent-of-origin effect in reciprocal\nF1 females. In contrast, Thornton et al." + } + ], + "915ee14c-df93-4482-966a-fbf3db2c11ea": [ + { + "document_id": "915ee14c-df93-4482-966a-fbf3db2c11ea", + "text": "\n\nAnother way of avoiding stratification is to use family-based samples.This approach has several theoretical advantages: as well as being immune to stratification 114 , these samples can be used to determine whether an allele has different effects on disease when it is inherited maternally or paternally 115 , and DISCORDANT SIB designs [116][117][118] can control for the effects of shared environment.Furthermore, more complex family-based designs are possible 119 that might allow combined association and linkage analysis 120 , and family-based association tests have also been developed for quantitative traits [94][95][96][97][98] .However, pure sibship-based association studies are underpowered relative to case-control studies 107,116,117 , and the requirement for living parents might introduce an age-of-onset bias towards younger patients for diseases that usually arise late in life.Furthermore, family-based samples are often much more difficult to collect, particularly if larger pedigrees are sought.Finally, the most commonly used family-based design, the TRANSMISSION DISEQUILIBIRIUM TEST (TDT; see REF. 114) is susceptible to technical artefacts (see below)." + } + ], + "a12388bc-0a2c-4cf4-aa39-39eebabe9a7e": [ + { + "document_id": "a12388bc-0a2c-4cf4-aa39-39eebabe9a7e", + "text": "\n\nBecause mtDNA is not subjected (as far as we know) to sexual recombination and crossover at the time of nuclear meiosis, nature must call on other means to ensure that inevitable germ plasm mtDNA mutations (Medvedev, 1981) are not transmitted.These mutations among primary oocytes, on the face of it, can be expected to increase with time, that is with maternal age.Empirical data on this question are incomplete and conflicting, being mostly confined so far to searches for deletions rather than point mutations (Chen et al., 1995;Keefe et al., 1995).It is inevitable, however, that there will be such mutations and that there must therefore be a reliable physiological mechanism (a) for giving an opportunity for back-mutations to occur, (b) for selecting in favor of those back-mutations (thus preserving the genome) and in favor of rare advantageous mutations, and (c) for preventing the spread of persistent harmful mutations through the population -mutations that are too slight (or too late in origin) to have escaped intraovarian culling.The sheer conservation of the mitochondrial genome over 0.5 billion years or more, despite a mutation rate estimated at 10 -20 times that of nuclear DNA, is ample reason to conclude that such a physiological purification process must exist." + } + ], + "ab1a324f-3c9c-4b41-bb1d-5d5ca216a001": [ + { + "document_id": "ab1a324f-3c9c-4b41-bb1d-5d5ca216a001", + "text": "To scrutinize the polygenic networks underlying complex diseases, however, mouse resources\nthat are optimized to study the actions of isolated genetic loci on\na fixed background will be insufficient on their own. For example, predisposition to the metabolic syndrome is inherited in\na non-Mendelian fashion stressing genetic heterogeneity and\nmultigenetic pathogenesis (Nandi et al. , 2004). With the reawakening as to the extraordinary genetic resources and phenotypic\ndiversity archived in extant inbred strains, however, a foundation\nis in place for tracking down these complex traits and quantitative trait loci (QTL)." + } + ], + "b58ddaa8-9d41-4dc5-97d7-aca64de3685b": [ + { + "document_id": "b58ddaa8-9d41-4dc5-97d7-aca64de3685b", + "text": "Otherwise, tens of thousands or markers will appear significant in\nthe genome-wise association studies using up to one million genetic\nmarkers. Approaches to control for stratification include using of\nself report of ancestry or genetically derived principle components\nin the analysis. For studies using inbred mouse lines, a cladogram\nwhich is a hierarchical grouping based on phylogenetic analysis of\nstrain relatedness can be created to subdivide inbred strains into\nmore genetically homogenous subgroups." + } + ], + "dc2f6b02-5c9a-4764-b70e-d2321135e148": [ + { + "document_id": "dc2f6b02-5c9a-4764-b70e-d2321135e148", + "text": "\n\nAlthough bilateral descent is the norm in Western societies, it is not universal and there is variation with cultural practices around lineage.In certain societies, individuals place greater importance on (and have greater knowledge about) one side of the family than another (unilineal descent).Thus, individuals in patrilineal groups trace relationships through males only so that your father's brother's children are members of your family, but not your father's sisters (Kottak, 2007).They are members of their husband's group or family.Efforts to create a family pedigree may be hampered if the participant is not familiar with her mother's relatives, but her mother's brother's children (her cousins) may be able to supplement her overall family history.Knowledge about the cultural system of unilineal descent avoids assuming the universality of bilateral descent.Cultural beliefs such as these also have implications in the conduct of genetic research in terms of confidentiality and autonomy (Benkendorf et al., 1997;Wertz, 1997).One cannot assume that the named proband is in a position to speak for the extended family in agreeing to participate in any genetic research (DudokdeWit et al., 1997)." + } + ], + "dcc71b11-5668-4274-9f35-d9b7f01695a2": [ + { + "document_id": "dcc71b11-5668-4274-9f35-d9b7f01695a2", + "text": "In particular in polygynous species, a female’s\noffspring may have different fathers and are thus more closely related through the maternal\nthan the paternal line. Therefore, any fitness cost to mothers, such as increased provisioning\nand care, affect maternally derived genes more strongly than paternally derived genes,\nleading to the silencing of the maternal copy (i.e. paternal expression) of genes that increase\nresource transfer. 5. Coadaptation between offspring and maternal traits\nThe genetics of the co-evolution of parental and offspring traits has been investigated using\nquantitative genetics models and in several empirical studies (Agrawal et al." + }, + { + "document_id": "dcc71b11-5668-4274-9f35-d9b7f01695a2", + "text": "In this\nscenario, genes expressed in parents will be selected for their effects on parental behaviour\nwhile genes expressed in offspring will be selected for their effects on influencing parental\nbehaviour. At the genetic level the predicted conflict between paternal and maternal genomes is\nthought to have led to the evolution of genomic imprinting (monoallelic gene expression). Genomic imprinting effects are good examples of offspring genetic effects on maternal care\nbecause of the impact on the quality of maternal care and level of resource provisioning (e.g. Li et al. , 1999)." + } + ] + }, + "data_source": [], + "document_id": "9520AB55F5C8D714F196867C1A9C4702", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "mtDNA", + "patrilineal", + "matrilineal", + "genetic&markers", + "autosomal&DNA", + "Y-DNA", + "mitochondrial&DNA", + "genomic&imprinting", + "ancestry", + "haploid" + ], + "metadata": [], + "question": "Why is genetic tracing matrilineal rather than patrilineal?", + "subquestions": null, + "task_id": "9520AB55F5C8D714F196867C1A9C4702", + "usage": { + "chatgpt": 7241, + "gpt-4": 4620, + "gpt-4-turbo-preview": 3661 + }, + "user_id": 2 + }, + "document_id": "9520AB55F5C8D714F196867C1A9C4702", + "task_id": "9520AB55F5C8D714F196867C1A9C4702" +} diff --git a/gnqa/paper1_eval/src/data/responses/general/gen_resp10.json b/gnqa/paper1_eval/src/data/responses/general/gen_resp10.json new file mode 100644 index 0000000..11ff807 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/general/gen_resp10.json @@ -0,0 +1,152 @@ +{ + "created_at": "2024-04-01T12:16:23.401343Z", + "data": { + "amplify": false, + "answer": "The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.", + "context": { + "13036fc5-c31c-4d21-a5f0-5ddfe11663d2": [ + { + "document_id": "13036fc5-c31c-4d21-a5f0-5ddfe11663d2", + "text": "Genetic mapping in\nmouse strains enhances the power of detecting modifier genes and identifying complex\ngenetic interactions. Genomewide quantitative trait locus (QTL) analysis, as described in\nmore detail below, represents a promising approach to detect genetic variants that are\nassociated with specific phenotypes and interact with each other. 16\nACCEPTED MANUSCRIPT\nIn experimental crosses of two (inbred) strains the first generation (F1) of\noffsprings is genetically heterozygous but equal. Then in the next generation (F2) the\n\nPT\n\nstrain-specific genetic information is distributed across the genomes of their progeny and\n\nRI\n\neach offspring is genetically unique." + } + ], + "1fb6e4db-79c1-49c9-a358-3414f6a674da": [ + { + "document_id": "1fb6e4db-79c1-49c9-a358-3414f6a674da", + "text": "Second, and perhaps more\nimportant, is the difference in the size and types of the\ngenetic reference populations. In our previous study, we\nmapped the QTL with 36 F2 mice that were genotyped at\n82 markers. In the current study, by comparison, we were\nable to map QTLs after examining 342 mice from 55 strains\nthat were genotyped at approximately 4000 markers." + } + ], + "27e14ff3-b5a5-4f60-80a2-eaa2ab53e991": [ + { + "document_id": "27e14ff3-b5a5-4f60-80a2-eaa2ab53e991", + "text": "This contrast can be exploited to identify subregions that underlie the trans-QTLs [67]. SNPs were counted for all four pairs of parental haplotypes—B\nvs D, B vs H, B vs C, and L vs S—and SNP profiles for the four\ncrosses were compared (figure 6). Qrr1 is a highly polymorphic\nPLoS Genetics | www.plosgenetics.org\n\n8\n\nNovember 2008 | Volume 4 | Issue 11 | e1000260\nQTL Hotspot on Mouse Distal Chromosome 1\n\nFigure 5. QTL for aminoacyl-tRNA synthetases in distal Qrr1." + } + ], + "3485665e-4e33-481a-943e-d0fcb7c2f2ac": [ + { + "document_id": "3485665e-4e33-481a-943e-d0fcb7c2f2ac", + "text": "The traditional approach to QTL mapping is to use\ntwo strains that differ maximally in the phenotype as\nparental strains for genetic crosses, with the following\ncaveats. QTL analysis based on a single cross will most\nlikely reflect only a small portion of the net genetic\nvariation, and QTL detection will be limited to regions\nwhere the two progenitor strains have functional polymorphisms. Data from multiple crosses, or from an HS,\nwill overcome this limitation and can also be used to\nreduce QTL intervals [5,30]." + } + ], + "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce": [ + { + "document_id": "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce", + "text":"These candidate genes are then sequenced in the two parental inbred\nstrains looking for sequence di¡erences in coding or regulatory regions. After ¢ne mapping the QTL interval and shortening the list of plausible\ncandidate polymorphisms, the major challenge remains ␁ proving de¢nitively\nwhich nucleotide polymorphism underlies the QTL. The most direct proof\nwould be replacing one strain’s allele with another strain’s allele (creating a\nFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mouse\ngenerations are depicted." + } + ], + "516cc395-4e7c-4371-9444-24edb56a7233": [ + { + "document_id": "516cc395-4e7c-4371-9444-24edb56a7233", + "text": "Furthermore, splicing QTLs\n(sQTLs) rather than eQTLs could comprise the molecular mechanism linking DNA variants with YFP53; thus, sQTL analysis could uncover genes that would not normally be\ndetected at the level of differential gene expression (DGE),53 and thus, a differentially\n\n181\n182\n\nMolecular-Genetic and Statistical Techniques for Behavioral and Neural Research\n\nFigure 8.5 Schematic for immediate, rapid fine mapping in select F2 recombinants of the RCC-F2\ncross. Top panel: Genome-wide significant QTL (green trace; red dashed line ¼ significance threshold;\nblue vertical lines ¼ Bayes credible interval)." + } + ], + "7dc4230d-c0a3-484b-9fb4-04d5ff09956b": [ + { + "document_id": "7dc4230d-c0a3-484b-9fb4-04d5ff09956b", + "text": "Interval-specific haplotype analysis\nApproximately 97% of the genetic variation between\ninbred mouse strains is ancestral [22], so regions of\nidentity by descent (IBD) between two strains used to\ndetect a QTL are highly unlikely to contain the causal\ngenetic polymorphism underlying the QTL [28]. For\nexample, a cross between C57BL/6J and A/J mice detected\nwww.sciencedirect.com\n\na blood pressure QTL on Chr 1 [7]." + } + ], + "80eb54fe-0d83-4300-9fba-e17ce5d1e5b4": [ + { + "document_id": "80eb54fe-0d83-4300-9fba-e17ce5d1e5b4", + "text": "Interval-specific haplotype analysis\nApproximately 97% of the genetic variation between\ninbred mouse strains is ancestral [22], so regions of\nidentity by descent (IBD) between two strains used to\ndetect a QTL are highly unlikely to contain the causal\ngenetic polymorphism underlying the QTL [28]. For\nexample, a cross between C57BL/6J and A/J mice detected\nwww.sciencedirect.com\n\na blood pressure QTL on Chr 1 [7]." + } + ], + "92fa8f50-2923-41a1-812b-32d931c71684": [ + { + "document_id": "92fa8f50-2923-41a1-812b-32d931c71684", + "text": "At present, the BXD panel is composed of 80 different strains that all have been\nfully genotyped.26 Variation in any quantifiable trait can be associated with the\nsegregation of parental alleles, and linkage genetics can map this variation to\nquantitative trait loci (QTLs), thereby identifying the genomic region(s) affecting\nthat trait. An overview of the QTL mapping approach is depicted in Figure 2. Classical QTL analysis has permitted the identification of loci that are\nassociated with variation in HSC traits." + } + ], + "9981a933-8fdf-4107-a6fd-3f9ef71f5d08": [ + { + "document_id": "9981a933-8fdf-4107-a6fd-3f9ef71f5d08", + "text": "In general,\nlinking genetic variation with trait variation identifies QTL and a significant linkage of\nphenotype and genotype suggest that the DNA status helps to determine trait expression. As stated above, mouse QTL studies provide distinct advantages over human studies\nin the examination of genetic causes of a quantitative trait (e.g. alcoholism), even in the\nabsence of specific hypotheses regarding its aetiology or candidate genes." + }, + { + "document_id": "9981a933-8fdf-4107-a6fd-3f9ef71f5d08", + "text": "The progenitor mouse strains\nshould have sufficient variation for the traits of interest and they should be genetically diverse\nenough to enable genetic mapping (BENNETT et al. 2006; FLINT 2003; GRISEL 2000). The\nsample size required for the identification of QTL depends largely on the effect size that a\nQTL contributes to phenotypes on interest. Inference about QTL can be made if one or more\ngenetic markers are over- or underrepresented in the analysed individuals. Genotyping is\noften done by means of microsatellite markers, which contains mono, di-, tri-, or\ntetranucleotide tandem repeats flanked by specific sequences (Figure 4a)." + }, + { + "document_id": "9981a933-8fdf-4107-a6fd-3f9ef71f5d08", + "text": "This comparison gives information about the reliability of the observed genotype\ninformation: The more the marker locations differ between the two maps (which signifies\nvariation in marker positions), the higher the possibility of genotyping errors. QTL mapping was done in several stages to identify loci acting individually and QTL that\ninteracted, either additively or epistatically. To determine individually-acting QTL, a singleQTL genome scan was conducted with the function scanone." + } + ], + "9b830769-1d42-4dce-b529-4e07902c0743": [ + { + "document_id": "9b830769-1d42-4dce-b529-4e07902c0743", + "text": "Importantly, whereas\nthese studies required substantial labor, time, and resources, X-QTL is a quick and easy\napproach to achieve a comparable level of genetic dissection. The levels of complexity\nobserved here (e.g. 14 loci explaining 70% of the genetic variance for 4-NQO resistance) are\nstill dramatically lower than those seen in for some human traits in GWAS (e.g. 40 loci\nexplaining 5% of the variance for height 2,5). One obvious explanation is the difference in\nexperimental designs (line crosses vs. population association studies), but differences in\ngenetic architectures among species and traits may also contribute." + } + ], + "a64778cd-bff8-43dd-b5a3-d608ab8f4828": [ + { + "document_id": "a64778cd-bff8-43dd-b5a3-d608ab8f4828", + "text": "The method uses two pieces of information: mapping data from crosses that\ninvolve more than two inbred strains and sequence variants in the progenitor strains within the interval\ncontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that any\nsequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals except\nat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis." + } + ], + "c2efeeee-f71a-4292-8240-80a4518f820d": [ + { + "document_id": "c2efeeee-f71a-4292-8240-80a4518f820d", + "text": "The method uses two pieces of information: mapping data from crosses that\ninvolve more than two inbred strains and sequence variants in the progenitor strains within the interval\ncontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that any\nsequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals except\nat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis." + } + ], + "d1f04d58-2589-4183-aee4-569820dae052": [ + { + "document_id": "d1f04d58-2589-4183-aee4-569820dae052", + "text": "Genotyping all the individual progeny for\nmarkers that show allelic variation between the parental strains (either single nucleotide polymorphisms or simple sequence repeats) will allow the detection of associations between trait values and marker genotype, and in this way demonstrate to which\nset of markers a QTL is linked. To reduce the genotyping effort, selective genotyping\nof the individuals at the extremes of the phenotypic spectrum can be performed (20,23). Although these three approaches are in general considered to be the best to detect and\nmap QTL, they have several disadvantages for quantitative traits involving HSC." + } + ], + "da485354-fcdc-49b8-9a41-0f673610156a": [ + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "So, how do you go about planning and performing a QTL study, and how\ndo you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbred\nstrains that have a markedly different trait. One can now look up many different\ntraits of inbred mice online at the Mouse Phenome Database (http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). However, the trait you may\nwant to study may not be present in wild type mice, so you may want to cross\na mutant (or genetically engineered) strain onto several inbred strains." + }, + { + "document_id": "da485354-fcdc-49b8-9a41-0f673610156a", + "text": "QTL Theory and Planning\nThe theory behind the most basic form of QTL mapping is based upon intercrossing two inbred strains. The mouse genome consists of 19 pairs of autosomes (non sex-determining chromosome) and the X and Y chromosomes. In\nthe example shown in Fig. 18.1, we are intercrossing stain A (shown with a\nblack chromosome pair) with strain B (shown with a white chromosome pair). The initial F1 (filial generation 1) mice are true hybrids, with each individual\n\nFrom: Molecular Biomethods Handbook, 2nd Edition." + } + ], + "f253e087-e030-40a8-8400-3b6bf50c1fd6": [ + { + "document_id": "f253e087-e030-40a8-8400-3b6bf50c1fd6", + "text":"These candidate genes are then sequenced in the two parental inbred\nstrains looking for sequence di¡erences in coding or regulatory regions. After ¢ne mapping the QTL interval and shortening the list of plausible\ncandidate polymorphisms, the major challenge remains ␁ proving de¢nitively\nwhich nucleotide polymorphism underlies the QTL. The most direct proof\nwould be replacing one strain’s allele with another strain’s allele (creating a\nFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mouse\ngenerations are depicted." + } + ], + "f4e26cf0-d214-41bf-b392-9c63a903b0b8": [ + { + "document_id": "f4e26cf0-d214-41bf-b392-9c63a903b0b8", + "text": "QTL mapping studies then\nseek to detect the polymorphisms underlying the complex traits of interest by\nscanning for alleles that co-vary with\nthe traits. Similar experiments also can be conducted with special derivatives of inbred\nstrains known as recombinant inbred\n(RI) mice. These animals are derived\nby cross-breeding two or more distinct\nparental strains (which often diverge\nwidely for the trait of interest), followed\nby inbreeding of the offspring for several\ngenerations (Bailey 1971). Given the\ncorrect breeding strategy, this method\n1\n\nThis is an issue faced by GWASs researchers when classifying\nsamples as cases or controls." + } + ] + }, + "data_source": [], + "document_id": "1F8BA9562D43754B872EEE810B6D5676", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "QTL", + "DNA", + "mapping", + "genotype", + "phenotype", + "inbred&strains", + "polymorphism", + "allele", + "genetic&variation", + "quantitative&trait&loci" + ], + "metadata": [], + "question": "How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?", + "subquestions": null, + "task_id": "1F8BA9562D43754B872EEE810B6D5676", + "usage": { + "chatgpt": 4992, + "gpt-4": 3589, + "gpt-4-turbo-preview": 2616 + }, + "user_id": 2 + }, + "document_id": "1F8BA9562D43754B872EEE810B6D5676", + "task_id": "1F8BA9562D43754B872EEE810B6D5676" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response01.json b/gnqa/paper1_eval/src/data/responses/test/response01.json new file mode 100644 index 0000000..890f75a --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response01.json @@ -0,0 +1,150 @@ +{ + "created_at": "2024-03-29T08:12:04.175184Z", + "data": { + "amplify": false, + "answer": "The text mentions two severe asthma clusters, SC2 and SC3, but does not specify individual genes. SC2 is characterized by high T2 inflammation, high FeNO, bronchoalveolar lavage and blood eosinophilia, and up-regulated expression of T2 signature and mast cell genes. SC3 is characterized by lower levels of T2 clinical biomarkers and gene expression, a later age of onset, increased incidence of nasal polyposis, and higher levels of neutrophilic inflammation.", + "context": { + "00da4d7c-879c-47f8-8648-89b2fdb19eda": [ + { + "document_id": "00da4d7c-879c-47f8-8648-89b2fdb19eda", + "text": "\n\nWe present an analytical technique designed to test a priori defined gene sets (for example, pathways) for association with disease phenotypes.We apply this method to gene expression profiles of human diabetic muscle, identifying a set of genes whose expression is correlated with insulin resistance and aerobic capacity.These results suggest hypotheses about pathways contributing to human metabolic disease and, more generally, show the value of incorporating information about functional relationships among genes in the analysis of microarray data." + } + ], + "0af8f2bf-8caf-4459-823b-06e22e637cc8": [ + { + "document_id": "0af8f2bf-8caf-4459-823b-06e22e637cc8", + "text": "\n\nPathway and gene ontology analysis for select phenotypes and envionmental factors showing GxE interactions." + } + ], + "14cad5a7-e53a-4ab8-9d4f-8f0b827ae427": [ + { + "document_id": "14cad5a7-e53a-4ab8-9d4f-8f0b827ae427", + "text": "\n\nNext, the genes that correlated with FeNO (n = 549) were used to objectively cluster asthma subjects into subgroups.In agreement with Moore et al., most of the severe asthma patients clustered into 2 subject clusters (SCs) (SC2 and SC3).One severe asthma cluster (SC2) had high T2 inflammation, as evidence by a high FeNO, bronchoalveolar lavage and blood eosinophilia, and up-regulated expression of T2 signature and mast cell genes.The other severe asthma cluster (SC3) had lower levels of T2 clinical biomarkers and gene expression, in addition to a later age of onset, increased incidence of nasal polyposis and higher levels of neutrophilic inflammation.Roughly 1/2 of all asthma subjects had evidence of high T2 inflammatory response (by clinical biomarkers and gene expression), confirming the prior findings of Woodruff et al. in a more severe and steroid-treated patient population.In general, both severe asthma clusters (SC2 and SC3) were older and more obese than the other non-severe subclusters.Further, both of the severe SCs demonstrated suppression of genes associated with cilia function, neuronal function, cell adhesion and wound repair.These findings suggested that airway epithelial defense, repair, neuronal function are an integral part of a healthy epithelial layer and perhaps prevention of severe asthma." + } + ], + "18d12255-3cc6-415b-bd30-ff94bb087813": [ + { + "document_id": "18d12255-3cc6-415b-bd30-ff94bb087813", + "text": "These\ngenes are high priority candidates, although we acknowledge that causal variants may lie in non-coding\nregions. For each of these high priority candidates we then examined which GO:biological processes\n(Consortium, 2015) and KEGG pathways (Kanehisa et al. , 2012) the gene was annotated as being part of,\nand highlighted those which may relate to our phenotypes. We also reviewed known effects of mutations\nusing the Mouse Genome Informatics (MGI) Phenotypes, Alleles and Disease Models Search\n(www.informatics.jax.org/allele) (Bello et al. , 2015)." + } + ], + "19aeec76-3ae4-4039-a887-407738ad4298": [ + { + "document_id": "19aeec76-3ae4-4039-a887-407738ad4298", + "text": "Results were displayed as a matrix with all phenotypes/diseases associated with\n\n173\n\nmouse models and human genes found for the candidate gene list. 174\n175\n\n2.6. Expression-phenotype correlations\n\n176\n\nFor each gene discovered after filtering, an adequate probe within the well-curated INIA Amygdala\n\n177\n\nCohort Affy MoGene 1.0ST (Mar11) RMA, Hippocampus Consortium M430v2 (Jun06) PDNN,\n\n178\n\nVCU BXD Prefrontal Cortex M430 2.0 (Dec06) RMA, INIA Hypothalamus Affy MoGene 1.0ST\n\n179\n\n(Nov10), and INIA Adrenal Affy MoGene 1.0ST (Jun12) RMA Databases was identified using\n\n180\n\nGeneNetwork (http://www.genenetwork.org; Williams and Mulligan, 2012))." + } + ], + "1f2060d9-353b-4de8-9172-edf15881f40f": [ + { + "document_id": "1f2060d9-353b-4de8-9172-edf15881f40f", + "text": "\n\nThe GeneNetwork website contains extensive phenotypic datasets ranging from behavioral to morphological to pharmacological.To identify phenotypes associated with Gsto1 variation, we queried the BXD phenotype database in GeneNetwork, which contains nearly 3000 phenotypes, to look for the phenotypes that are most closely related to hippocampal expression of Gsto1 (probe set 1416531_at)." + } + ], + "36858807-1395-4b2f-a3ee-e054f9b0149d": [ + { + "document_id": "36858807-1395-4b2f-a3ee-e054f9b0149d", + "text": "\n\nTo examine known causal genes that have been reported in the literature, including related genes and pathways, a gene list was generated consisting of 6264 genes categorized by disorders, pathways, expression, AmiGO terms, and other into 26 sublists (supplemental data).This list was manually collected from different database sources covering all aspects of insulin-and glucose-related genes and disorders.This was done through an extensive literature review using PubMed, Ovid®, GeneCards®, and the National Center for Biotechnology Information (NCBI).Gene and protein expression databases such as BioGPS and The Human Protein Atlas were used.Protein interactions and gene network databases, such as AmiGO, BioGRID, GIANT, KEGG, and Reactome, were also used.Knockout mouse databases, such as MGI and IMPC, were also used.However, filtering against the gene list will not replace the manual screening for all variants called; therefore, we did not consider the results of our gene list alone.Once the raw data were obtained, they were filtered and investigated individually.As shown in Fig. 1, mutations went through serial steps ending up with a single nucleotide polymorphism mutation as a potential explanation.Pathogenicity scores were determined by SIFT, PolyPhen-2, PROVEAN, and PhD-SNP." + } + ], + "4049da4d-c7cf-4e30-9a21-c77609fad23d": [ + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "text": "Chesler, E. J., Wang, J., Lu, L., Qu, Y., Manly, K. F., and Williams, R. W. (2003). Genetic correlates\nof gene expression in recombinant inbred strains: a relational model system to explore\nneurobehavioral phenotypes. Neuroinformatics 1, 343–357. doi:10.1385/NI:1:4:343. Denny, J. C., Ritchie, M. D., Basford, M. A., Pulley, J. M., Bastarache, L., Brown-Gentry, K., et al. (2010). PheWAS: demonstrating the feasibility of a phenome-wide scan to discover genedisease associations. Bioinformatics 26, 1205–1210. doi:10.1093/bioinformatics/btq126. Farrar, C. A., Zhou, W., and Sacks, S. H. (2016). Role of the lectin complement pathway in kidney\ntransplantation. Immunobiology 221, 1068–1072. doi:10.1016/j.imbio.2016.05.004. Gene Ontology Consortium (2015)." + }, + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "text": "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In this\nmanuscript we will outline some simple use cases, and show how a small number of plausible\ncandidate genes can be identified for an immune phenotype. 1. Data\nOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. The\nfirst is to use the global search bar located at the top of the page (Figure 1). This is a new\nfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of the\ndatasets." + } + ], + "58714c13-954b-46b3-bd0e-69ccadd9dc6a": [ + { + "document_id": "58714c13-954b-46b3-bd0e-69ccadd9dc6a", + "text": "Protein interaction data: There is a growing body of protein-interaction data and this data is a useful\nextension to inferences of functional interaction between disease gene candidates and co-expressed genes. Ontologies for Functional Annotation: This project will lead to a small subset of genes of interest for asthma\nand AD.. Ontologies are key in making automated and vocabulary controlled statements about function and it\nwill be interesting to interface the analytical framework presented in the proposal with contemporary\nadvances in gene ontology methodology." + }, + { + "document_id": "58714c13-954b-46b3-bd0e-69ccadd9dc6a", + "text": "A network or interaction model will be generated using methods of graphical modelling\nwith both inhouse data and public databases to propose predictive models for epithelial cells and characterise critical\nmolecular interactions within asthma and AD biology. Finally, supporting and extending methodologies from above\nwill contribute to (E) Future Directions of the study and include interfacing and data exchange with contemporary\npublic databases. D(a) Disease Association and eQTL Mapping\nMapping the human genome for regions and positions that are responsible for disease susceptibility and\ndifferential gene expression is central to this project." + }, + { + "document_id": "58714c13-954b-46b3-bd0e-69ccadd9dc6a", + "text": "For example, time series data sets potentially capture relationships and\ndependencies of gene expression within and between time points which may suggest causative co-regulation. These\ndependencies and interactions could be better uncovered using statistical modelling approaches such as Bayesian\nmodel based methods that aim to identify co-expressed clusters of genes under a model of temporal dependence\nbetween observations, that is utilising gene expression measures in time to better judge cluster membership11,12. Secondly, the asthma and AD expression dataset of sibpairs inherently contains underlying structures of\nshared genetic disease risk." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "Genes are arranged based\non their genetic positions, and genes annotated to be involved in the module are colored red. Genes with absolute GMAS over 0.268 are\nconsidered significantly associated. DDT, BOLA3, and ARID1A are labeled. B, Venn diagram of novel genes associated with respiratory electron transport module in human, mouse and rat. 707 genes were predicted\nto be mito-proteins by G-MAD in all three species." + } + ], + "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d": [ + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "text": "Chesler, E. J., Wang, J., Lu, L., Qu, Y., Manly, K. F., and Williams, R. W. (2003). Genetic correlates\nof gene expression in recombinant inbred strains: a relational model system to explore\nneurobehavioral phenotypes. Neuroinformatics 1, 343–357. doi:10.1385/NI:1:4:343. Denny, J. C., Ritchie, M. D., Basford, M. A., Pulley, J. M., Bastarache, L., Brown-Gentry, K., et al. (2010). PheWAS: demonstrating the feasibility of a phenome-wide scan to discover genedisease associations. Bioinformatics 26, 1205–1210. doi:10.1093/bioinformatics/btq126. Farrar, C. A., Zhou, W., and Sacks, S. H. (2016). Role of the lectin complement pathway in kidney\ntransplantation. Immunobiology 221, 1068–1072. doi:10.1016/j.imbio.2016.05.004. Gene Ontology Consortium (2015)." + }, + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "text": "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In this\nmanuscript we will outline some simple use cases, and show how a small number of plausible\ncandidate genes can be identified for an immune phenotype. 1. Data\nOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. The\nfirst is to use the global search bar located at the top of the page (Figure 1). This is a new\nfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of the\ndatasets." + } + ], + "85ee9743-b34d-4d49-9017-d7d2e5d4b996": [ + { + "document_id": "85ee9743-b34d-4d49-9017-d7d2e5d4b996", + "text": "6\n\nPhenotype-matched reports\n\n7\n\nThe framework implementation we have presented uses only genomic\ninformation to generate a patient or research report. Of course, the\nclinical features of the sample offer vital clues as to which gene is\nlikely responsible for the disease. It would therefore make sense to include phenotype-based gene filtering or prioritization to the report. To\nmake this possible, associations of Human Phenotype Ontology (HPO)\nterms[292] to their known disease genes could be integrated into the\nsystem. Users can enter HPO terms that match the phenotypes observed in a patient to shorten their list of candidate genes." + } + ], + "98d443c7-8d99-4139-a27d-e447b0f6630f": [ + { + "document_id": "98d443c7-8d99-4139-a27d-e447b0f6630f", + "text": "Predicted transcriptome association test\n\nWe used the PrediXcan 16 framework to identify genes that might mediate associations between genetic variants and asthma risk.PrediXcan is a software tool that estimates tissue-specific gene expression profiles from an individual's SNP genotype profile by use of prediction models trained in large reference databases of genotypes and tissue-specific gene expression profiles.With these genotype-imputed expression profiles, PrediXcan can perform gene-based association tests that correlate predicted expression levels with phenotypes (eg, asthma) to identify candidate causal genes from GWAS data.We used a summary version of PrediXcan, which has high concordance with the individual-level version (r²>0•99). 17or predictions, we downloaded elastic net models trained with reference transcriptome data from the Genotype-Tissue Expression consortium 18 for 49 tissues (appendix pp 9, 47)." + } + ], + "b72caae5-bb5a-4317-8d4d-21b41d60df21": [ + { + "document_id": "b72caae5-bb5a-4317-8d4d-21b41d60df21", + "text": "\n\nGene selection was based on searches conducted using the Genetic Association Database (geneticassociationdb.nih.gov).Only genes with multiple, independent indicators of function were included.aPhenotype available for one cohort only." + } + ], + "ed140f66-fbad-4fd7-8ae3-4d9cac4f63ac": [ + { + "document_id": "ed140f66-fbad-4fd7-8ae3-4d9cac4f63ac", + "text": "The results from the phenotype-driven searches\nshould then be linked to gene names associated with a\ngiven phenotype. These genes are presented as a list\nfrom which the user can choose the genes of interest\nand save them in a shopping cart. It is then possible to\nfeed the genes into the gene-centric use-case and perform a more detailed data mining or meta-analysis. The description and further development of the phenotype-driven use-case may represent a very useful\nconcept for scientists and clinicians outside the mouse\ncommunity." + } + ], + "fcd522a5-43ad-413b-abd9-5e3c9ccaca9f": [ + { + "document_id": "fcd522a5-43ad-413b-abd9-5e3c9ccaca9f", + "text": "\n\nAs a demonstration of the utility of the web interface, we entered the 9 genes that reached suggestive significance in a recent genome-wide association study of opioid cessation (Cox et al. 2020).The graph view of the search results are shown in Fig. 3. Genes and keywords are all shown as circles and lines connecting them show the number of abstracts containing the 2 circles they connect.Keywords under the same main category are shown with the same color in the graphic output.Clicking on the lines brings up a new page that displays all sentences containing the keywords that line connects.An alternative tabular view of the same results is also available, where genes, the keywords, and number of abstracts are shown as separate columns." + } + ] + }, + "data_source": [], + "document_id": "C58C7981111D600F0D509F387DB6CCF7", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "asthma", + "genes", + "phenotype", + "FeNO", + "T2&inflammation", + "eosinophilia", + "gene&expression", + "PrediXcan", + "GWAS", + "Human&Phenotype&Ontology&HPO" + ], + "metadata": [], + "question": "List genes related to asthma with their phenotype descriptions.", + "subquestions": null, + "task_id": "C58C7981111D600F0D509F387DB6CCF7", + "usage": { + "chatgpt": 5108, + "gpt-4": 4004, + "gpt-4-turbo-preview": 2992 + }, + "user_id": 2 + }, + "document_id": "C58C7981111D600F0D509F387DB6CCF7", + "task_id": "C58C7981111D600F0D509F387DB6CCF7" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response02.json b/gnqa/paper1_eval/src/data/responses/test/response02.json new file mode 100644 index 0000000..f6fc655 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response02.json @@ -0,0 +1,205 @@ +{ + "created_at": "2024-03-25T11:43:39.700633Z", + "data": { + "amplify": false, + "answer": "Mouse studies popular in genenetwork often focus on ailments such as diabetes, glaucoma, obesity-associated diabetes, and metabolic diseases. These studies utilize genetically modified mouse models, inbred mouse models, and polygenic models. They also use specific strains like DBA/2J, Goto-Kakizaki (GK) rat, and KK mice.", + "context": { + "08a6ce71-133e-426d-adfe-600ff52802a2": [ + { + "document_id": "08a6ce71-133e-426d-adfe-600ff52802a2", + "text": "A major\nadvantage of the mouse as an animal model is the availability of\nwell-characterized inbred strains that enable functional genomics\non defined genetic backgrounds. Currently, however, exploiting\nthe full utility of mice to study human diseases is hampered by the\nlack of gene targeting resources for multiple inbred mouse strains. DBA/2J is a common inbred mouse strain critical in studying a\ndiverse range of human diseases. For example, it is widely used as\nan inherited model of glaucoma. Glaucoma is a neurodegenerative\ndisorder that affects 70 million people worldwide." + } + ], + "14a9de52-cff1-4397-bb2c-8c2e34bb05bf": [ + { + "document_id": "14a9de52-cff1-4397-bb2c-8c2e34bb05bf", + "text": "The\nnetwork is driven by a common regulator,\nEbi2 (also known as Gpr183), which is conserved in rats and humans, is expressed in\nmacrophages and is associated in GWASs\nwith human type 1 diabetes48. Such systemsgenetics studies are possible in rats because\nof the ready availability of ex vivo tissues and\nthe statistical power gained from studies of\ninbred strains in controlled environments. Overall, these vignettes provide clear\nexamples of the translational focus of the\nrat genetics community in an era of unprecedented scientific opportunity enabled\nby ultra-high-throughput genomics and\nmathematical biology." + } + ], + "1bf337a1-ffed-4199-a11f-c5a62df47980": [ + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "text": "\n\nInbred animal models with homogeneous genetic backgrounds have been a powerful adjunct to human studies, providing a sufficiently large number of samples required for an unconstrained genetic analysis.Several polygenic NIDDM rodent models have been developed.These include the Goto-Kakizaki (GK) rat, the Otsuka Long-Evans Tokushima Fatty (OLETF) rat, the Nagoya Shibata Yasuda mouse, the New Zealand Obese mouse (reviewed in Kim et al., 1998), and the Tsumura-Suzuki Obese Diabetes mouse (Suzuki et al., 1999).The underlying genetic factors in these animal models have been studied by quantitative trait locus (QTL) mapping analysis, and several QTLs associated with glucose intolerance, defective insulin secretion, or parameters defining glucose homeostasis have been located (reviewed in Kim et al., 1998;Hirayama et al., 1999;Ueda et al., 1999)." + } + ], + "2a7da18e-3756-45c5-b18c-a2231685fefd": [ + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "In as much\nas it is quite difficult to conduct certain infectious disease studies in humans, there has\nbeen a critical need for small animal models for infectious diseases. Appreciating the\nlimitations of existing models, we developed several novel and complementary mouse\nmodels that are ideal for use in systems genetics studies of complex diseases. These\nmodels not only allow biological validation of known genetic associations, but importantly they afford an unbiased tool for discovering novel genes and pathways contributing to disease outcomes, under different environments. 2008 Genetic effects on environmental vulnerability to disease." + } + ], + "3776e53f-5f7d-4cf4-ab7c-5fe06a1c0570": [ + { + "document_id": "3776e53f-5f7d-4cf4-ab7c-5fe06a1c0570", + "text": "Generalities\n\nMouse models have been developed to give new insights into human diseases.Mouse models can be classified into two main classes: 1) genetically modified mouse models, animals that lack (knockout) or overexpress a specific gene and the protein that is encoded for, 2) mice that acquire a disease/symptom following an experimental procedure, such as diet, chemical injections and specific surgery." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "4439ac39-e421-482f-9aa9-9ad11fa641c1": [ + { + "document_id": "4439ac39-e421-482f-9aa9-9ad11fa641c1", + "text": "In\nother cases, the rat phenotypes have proved more\nrobust and consistent, such as pristane-induced\narthritis as a model for rheumatoid arthritis\n(Holmdahl et al. 2001) and cresentic glomerulonephritis (Aitman et al. 2006). Decades of careful\nphenotyping and detailed analyses in rat experimental crosses have led to the localization of hundreds of rat physiological quantitative trait loci\n(pQTLs) containing genes that confer susceptibility\nto complex disease phenotypes, including hypertension, type 2 diabetes, autoimmune disorders, and\ncancer (Flint et al. 2005). The availability of the rat genome sequence in\nJune 2003 (Gibbs et al." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": ", et al. , Harnessing Genetic Complexity to Enhance Translatability of Alzheimer's Disease Mouse\nModels: A Path toward Precision Medicine. Neuron, 2019. 101(3): p. 399-411 e5. Beura, L.K. , et al. , Normalizing the environment recapitulates adult human immune traits in laboratory mice. Nature, 2016. 532(7600): p. 512-6. Kleinert, M., et al. , Animal models of obesity and diabetes mellitus. Nat Rev Endocrinol, 2018. 14(3): p. 140-162. Kebede, M.A. and A.D. Attie, Insights into obesity and diabetes at the intersection of mouse and human genetics. Trends Endocrinol Metab, 2014. 25(10): p. 493-501.\nvon Scheidt, M., et al." + }, + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "Researchers have access to all the tissue samples in mice, especially those highly relevant in\ndiseases, which is impossible in most human studies because of ethical issues. 8. Mouse models can be used to capture the disease progression stages in longitudinal studies. 9. Mouse genetic populations are able to model the genetic diversity of human populations, and require\nfewer individuals for genetic association analyses. 10. Unlike human genetic studies where data should always be kept highly confidential, data from mouse\nstudies can be made public available to facilitate its re-analysis to the fullest extent." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Knock-out and transgenic mice in diabetes research\n\nTransgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003)." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nPolygenic models of obesity.Polygenic models of obesity may provide a more accurate model of the human condition.A variety of different polygenic mouse models of obesity, glucose intolerance and diabetes exist, allowing a variety of genotypes and susceptibilities to be studied.However, unlike the monogenic models, there are no wild-type controls.In addition, the male sex bias is more extreme in these models (Leiter, 2009).These polygenic models have been used in a wide variety of studies that have aimed to reverse the symptoms of type 2 diabetes (Chen et al., 2009;Fukaya et al., 2009;Guo et al., 2010;Mochizuki et al., 2011;Yoshinari and Igarashi, 2011), understand more about the interplay of obesity and glucose homeostasis (Kluth et al., 2011) (Jurgens et al., 2007) or study diabetic complications (Cheng et al., 2007;Fang et al., 2010;Buck et al., 2011;Lee et al., 2011a).KK mice.KK mice are a mildly obese and hyperleptinaemic strain derived from wild-derived ddY mice in Japan by Kondo in 1957 (Clee and Attie, 2007).They develop severe hyperinsulinaemia and demonstrate insulin resistance in both muscle and adipose tissue.The pancreatic islets are hypertrophic and degranulated.This mouse strain also shows signs of diabetic nephropathy (Ikeda, 1994)." + } + ], + "785df64a-ebbf-4dca-94dd-0ae27f7ac815": [ + { + "document_id": "785df64a-ebbf-4dca-94dd-0ae27f7ac815", + "text": ", 2008) and specific genetic factors for predisposition to DN were\nrecently identified in several diabetic sibling studies (Bleyer et al. , 2008; Schelling et\nal.,2008; Tanaka et al. , 2005). Similar to humans, inbred strains of mice exhibit differences in their susceptibility to\ndiabetes, renal and cardiovascular diseases (Krolewski et al. , 1996). More recently,\ndifferential susceptibilities to DN have also been observed in well-defined strains of\n\n23" + } + ], + "84b037c5-8e75-434f-aad1-d270257963f6": [ + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\n\nThe third advantage of the mouse model is that after identification of a candidate gene, direct genetic evidence for its involvement in a pathophysiology can be obtained in mice, but very rarely in humans.Thus, inbred mouse models are ideally suited for the investigation of the obesity-associated diabetes.However, the genetic homogeneity of the inbred strains is not only an advantage, it also limits their potential.Individuals of an inbred mouse line are genetically identical, and it cannot be expected that a single strain carries more than a small portion of all relevant gene variants.Currently, more than 2000 mouse QTL for different traits have been identified in crosses between inbred stains, but only about 1 % has been characterized on molecular level (Flint et al. 2005).Thus, more than one model and new resources, e.g., systems biology may be required for a complete genetic analysis of complex traits.Previous and ongoing research supports the view that the combination of individual genomes-by intercross of inbred strains and by the generation of congenic lineswill reveal effects of many more genes and gene interactions than can be observed in a single inbred strain.Because the cross-breeding experiments are time consuming and expensive, selecting the ''right'' models of the obesity-associated diabetes is of crucial importance (Leiter 2009).Another advantage of mouse studies in comparison to human studies is the ability to control the environment and to investigate effects of diets, exercise, and intestinal microbiota." + } + ], + "8604652e-2477-4552-8f43-f5f19e421df2": [ + { + "document_id": "8604652e-2477-4552-8f43-f5f19e421df2", + "text": "Introduction\nRodents, particularly mouse and rat have been widely used for biomedical research in models of\nhuman diseases since it is known that almost of all of genes in mouse and rat are similar to that of\nhumans. However, not every genetic pathway or molecular mechanism of diseases or drugs discovered\nto be efficacious in these models can be extrapolated to human diseases. Thus, while much data from\nanimal studies have been successfully applied to humans, some have not. The present study aims to\nexplore the degrees of differences in the causal pathways for lung fibrosis between humans and mice." + } + ], + "90015638-c92d-4506-95b5-b789f08d613a": [ + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "text": "\n\nThese limitations support the increasing need of experimental systems to characterize the fundamental biological mechanisms responsible for diabetes inheritance and the function of risk genes.In the context of diabetes pathogenesis, in vitro systems are useful but often limited, in particular to assess glucose tolerance, insulin sensitivity, islet architecture and function and diabetes complications.The laboratory mouse provides a wide range of experimental models for diabetes gene discovery and for in vivo post-GWAS studies of diabetes that develops either spontaneously or following gene editing [5].The laboratory rat is also a powerful system to implement phenotyping methods required to record biological variables relevant to common chronic diseases.The rat is the preferred model to perform phenotyping procedures that are often technically challenging in mice or require the collection of large volumes of blood or organs.For these reasons, rat models of type 2 diabetes or hypertension have been successfully used to localise in the genome genes controlling endophenotypes relevant to these complex diseases.This review addresses strategies used to map the genetic determinants of physiological and molecular phenotypes relevant to type 2 diabetes pathogenesis and to characterize their biological function in vivo through examples derived from genetic and genomic research in the Goto-Kakizaki (GK) rat strain." + } + ], + "ab1a324f-3c9c-4b41-bb1d-5d5ca216a001": [ + { + "document_id": "ab1a324f-3c9c-4b41-bb1d-5d5ca216a001", + "text": "However, many of the phenotypes of the homozygous null mutations\nwere extreme and/or did not model the complexity of the metabolic syndrome. For example, IR knockout (IR2/2) mice died\nbecause of developmental effects (Accili et al. , 1996), which precluded analysis of adult mice. Likewise, GLUT42/2 mice exhibited only moderate insulin resistance and were not overtly diabetic, suggesting compensatory mechanisms (Katz et al. , 1995). Monogenic GEMMs furthermore ignore the polygenic nature of\nmetabolic diseases, resulting from genetic and environmental\nfactors impacting at multiple levels in signaling cascades. Oligogenic mouse models remedied some of these shortcomings." + }, + { + "document_id": "ab1a324f-3c9c-4b41-bb1d-5d5ca216a001", + "text": "Since glucokinase2/2 mice are embryonic lethal, this collection of glucokinase mutants is useful for dissecting the pathogenesis of MODY2. Genetic reference populations (GRPs)\nPerhaps the most ‘‘refreshing’’ mouse resource for investigating\ncomplex diseases is the construction of mouse crosses using\ninbred mice and the subsequent QTL mapping. Inbred mice\nhave an inherent wealth of variation due to past spontaneous\nmutation events, which have been preserved through systematic and uninterrupted brother-sister matings (Paigen, 2003). Inbred mice are appealing since they are genetically identical\nwithin a strain but are diverse between strains." + } + ], + "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65": [ + { + "document_id": "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65", + "text": "Mouse Models of Oxidative Stress and Mitochondrial\n\nDysfunction in Aging.Genetically engineered mouse models provide great systems to directly dissect the complex relationship between oxidative damage, mitochondrial dysfunction, and aging.Although it is difficult to manipulate mitochondrial genome, genetic engineering of nuclear genes that are involved in oxidative stress response and mitochondrial function has been utilized to study mitochondrial biology and aging." + } + ], + "dee36885-b2f4-4311-b70a-17e228034820": [ + { + "document_id": "dee36885-b2f4-4311-b70a-17e228034820", + "text": "Rodent models of glaucoma have gained favor in the research community due to their ease of handling and the lower costs associated with acquisition and care. In particular, the mouse provides a number of useful genetic\napproaches to create models and to test specific molecular interactions associated with the disease process. Furthermore, the mouse genome is relatively\nconserved compared to the human genome." + } + ], + "df542302-18b9-43c2-a421-cba1dba0b3be": [ + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "text": "Better Mouse Models. A key point to bear in mind in assessing the usefulness of mouse models is the relative plasticity displayed by rodents faced with gene deletions.Thus, differences between the penetrance of mutations in human genes linked to monogenic forms of diabetes, including maturity onset diabetes of the young (MODY), between humans and mice, are usually observed [114] with the mouse equivalents showing far less marked disturbances in glycemia or changes which are seen only after deletion of both alleles.This clearly reflects the limitations of the use of mice (weight ∼25 g, life expectancy ∼3 years) for comparisons with human subjects.Nonetheless, and although the phenotypes of the above murine models are thus often more subtle than the human counterparts, they remain useful models for the study of diabetes, allowing single-targeted gene deletions which are impossible in man.For example, human populations with different genetic backgrounds have different susceptibility to the R235W ZnT8 polymorphism.We should not, therefore, find surprising the results that different genetic backgrounds and different diet reveal different phenotypes in ZnT8 knockout models." + } + ] + }, + "data_source": [], + "document_id": "E7F8082FA9D839C33A51A833B8FC1A89", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "DBA/2J", + "glaucoma", + "genetically&modified&mouse&models", + "diet", + "chemical&injections", + "specific&surgery", + "diabetes", + "Goto-Kakizaki", + "Otsuka&Long-Evans&Tokushima&Fatty", + "Nagoya&Shibata&Yasuda&mouse", + "New&Zealand&Obese&mouse", + "Tsumura-Suzuki&Obese&Diabetes&mouse" + ], + "metadata": [ + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "Data suggest that urine AQP5/creatinine ratio is significantly higher in patients with diabetic nephropathy than in control subjects, subjects diabetes, or subjects with nephropathy of unknown etiology; urine AQP5/creatinine ratio increases with stage of diabetic nephropathy; this biomarker may improve clinical models in distinguishing diabetic nephropathy from normal controls and subjects with type 2 diabetic alone.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab213643" + }, + { + "object": "these pathological and molecular changes induced by diabetes were eliminated in JNK2-/- diabetic mice compared to JNK2-/- control mice, and C66 treatment did not further affect these parameters in JNK2-/- diabetic mice. Our results indicate that C66 ameliorates diabetic cardiomyopathy by inhibiting JNK2 relative pathways.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab908673" + }, + { + "object": "We used young, leptin receptor deficient Db/Db mice to mimic the effect of diet and diabetes on adolescents. Db/Db and Control mice were fed either Western or Control diets, and were sacrificed at 3 months of age. Db/Db mice were obese, while only female mice developed diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1014541" + }, + { + "object": "Blockade of IL-27 significantly delayed the onset of diabetic splenocyte-transferred diabetes, while IL-27-treated diabetic splenocytes promoted the onset of autoimmune diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab103352" + }, + { + "object": "Microarray analysis of Chlamydia psittaci infected C57BL/6J and DBA/2J mice shows that proinflammatory cytokine and chemokine Cxcl1 KC, Cxcl2 Mip2, and Cxcl11 were up-regulated >10-fold in susceptible DBA/2J mice. Upstream regulators of inflammation such as TLRs and MyD88 were increased in resistant C57BL/6J vs susceptible DBA/2J mice. >100-fold increase in ifi204 and ifi203 proliferation of macrophage and >10-fold for NK cell activating C57BL/6J vs DBA/2J.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab3623" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "MDM2 controls diabetic and non-diabetic renal antioxidant capacity via NRF2. NRF2 predominantly mediates MDM2's action on diabetic kidney disease. P53 is a key factor through which MDM2 activates NRF2 in diabetic kidney disease.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab473349" + }, + { + "object": "Data suggest expression of Sirt3 in developing skeletal muscle can be regulated by diet; maternal low-protein diet down-regulates Sirt3; postnatal high-fat diet up-regulates Sirt3; these two factors increase susceptibility of pups to type 2 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab203342" + }, + { + "object": "diabetic subjects with diabetic foot showed in comparison with diabetics without diabetic foot higher IL-6 and resistin plasma levels, lower adiponectin plasma levels", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab228757" + } + ], + "question": "Which types of mouse studies are popular in genenetwork, where a type an ailment, e.g. asthma, allergies, aging, heart failure, diabetes, glaucoma?", + "subquestions": null, + "task_id": "E7F8082FA9D839C33A51A833B8FC1A89", + "usage": { + "chatgpt": 6380, + "gpt-4": 4204, + "gpt-4-turbo-preview": 3247 + }, + "user_id": 2 + }, + "document_id": "E7F8082FA9D839C33A51A833B8FC1A89", + "task_id": "E7F8082FA9D839C33A51A833B8FC1A89" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response03.json b/gnqa/paper1_eval/src/data/responses/test/response03.json new file mode 100644 index 0000000..c14ab82 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response03.json @@ -0,0 +1,205 @@ +{ + "created_at": "2024-03-25T10:53:10.812694Z", + "data": { + "amplify": false, + "answer": "Yes, the studies identified significant quantitative trait loci associated with both type 1 and type 2 diabetes. These include loci such as TCF7L2, DNER, SRR, HNF1A, KCNQ1, CDKN2A/CDKN2B, JAZF1, CDC123, CAMK1D, TSPAN8, LGR5, THADA, ADAMTS9, NOTCH2, and others. However, the significance of some loci varied across different populations and studies.", + "context": { + "0bcb68e6-4da3-457c-a68c-981b14d1ded8": [ + { + "document_id": "0bcb68e6-4da3-457c-a68c-981b14d1ded8", + "text": "\n\nAdditional large-scale meta-analysis predicated upon increased sample size, were carried out on existing datasets from the WTCCC [18] and the Genetics of Kidneys in Diabetes (GoKinD) study [69][70][71] plus control data derived from the National Institute of Mental Health.These investigators observed significant association of previously observed loci.Importantly, they did not observe evidence of new T1D loci reaching the threshold for genome-wide significance.Instead they re-analyzed the most nominally significant associated SNP in an independent British cohort of approximately 6000 cases, 7000 controls and in 2800 families, where they uncovered four additional loci, BACH2 (previously reported [67]), 10p15 harboring protein kinase C theta (PRKCQ), 15q24 harboring nine genes including the cathepsin H (CTSH), complement 1q (C1q), tumor necrosis factor related protein 6 (C1QTNF6) and somatostatin receptor 3 (SSTR3) genes.Table 1 summarizes the 16 T1D loci reported to date.An example of a tag-SNP that captures the association with T1D in each instance is highlighted together with its relative minor allele frequency in controls and what magnitude of risk or protection it confers.Key references regarding the role of each locus in the context of the disease are included and along with the chromosomal band where each locus resides, the main candidate gene (symbol and full name) is highlighted." + } + ], + "0de85e11-dcbb-4538-b043-ee18a30e9f14": [ + { + "document_id": "0de85e11-dcbb-4538-b043-ee18a30e9f14", + "text": "Detection of established loci\n\nWe explored the extent to which previously reported type 2 diabetes association signals could be detected in African-descent individuals.Based on the previously reported effect sizes and the effect allele frequency and sample size from our African meta-analysis, we had sufficient power (80%) to detect three signals (TCF7L2, DNER and SRR) at genome-wide significance (p < 2.5 × 10 −8 ) (ESM Table 2).Only the TCF7L2 variant reached genome-wide significance in our study, whereas both variants in DNER (rs1861612) and SRR (rs391300), originally discovered in Pima Indians and East Asians, respectively, had p > 0.1 (ESM Table 2)." + } + ], + "1c2f4eb9-5880-418a-be08-4c33ec3a8889": [ + { + "document_id": "1c2f4eb9-5880-418a-be08-4c33ec3a8889", + "text": "\n\nOn the basis of the combined stage 1-3 analyses, we found that six signals reached compelling levels of evidence (P ¼ 5.0 Â 10 -8 or better) for association with T2D (Table 2).As in all linkage disequilibrium (LD)-mapping approaches, characterization of the causal variants responsible, their effect sizes and the genes through which they act will require extensive resequencing and fine-mapping.However, on the basis of current evidence, we found that the most associated variants in each of these signals map to intron 1 of JAZF1, between CDC123 and CAMK1D, between TSPAN8 and LGR5, in exon 24 of THADA, near ADAMTS9 and in intron 5 of NOTCH2." + } + ], + "33c5de8c-7efc-41df-a540-22729d8b7d2c": [ + { + "document_id": "33c5de8c-7efc-41df-a540-22729d8b7d2c", + "text": "\n\nReplication study of newly identified type 1 diabetes risk loci" + } + ], + "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f": [ + { + "document_id": "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f", + "text": "\n\nAlthough these are considered to be loci convincingly associated with susceptibility to type 2 diabetes in populations of European descent, other genes related to susceptibility to the disease are probably still unidentified, particularly those for populations of other ancestries.In order to uncover genetic variants that increase the risk of type 2 diabetes, we conducted a genome-wide association study in Japanese individuals with type 2 diabetes and unrelated controls.We first genotyped 268,068 SNPs, which covered approximately 56% of common SNPs in the Japanese, in 194 individuals with type 2 diabetes and diabetic retinopathy (case 1) and in 1,558 controls (control 1) collected in the BioBank Japan.We compared the allele frequencies of 207,097 successfully genotyped SNPs and selected the 8,323 SNPs showing the lowest P values.We then attempted to genotype these 8,323 SNPs in 1,367 individuals with type 2 diabetes and diabetic retinopathy (case 2) and for 1,266 controls (control 2) (stage 2), and successfully obtained data for 6,731 SNPs (the P value distribution in the second test is shown in Supplementary Fig. 1a online).The results of principal component analysis 8 in the stage 1 and 2 samples and HapMap samples revealed that there was no evidence for population stratification between the case and control groups throughout the present tests (Supplementary Fig. 1b,c).We selected the 9 SNP loci showing P values o0.0001 (additive model in stage 2, Table 1) and genotyped a third set of cases and controls comprising 3,557 Japanese individuals with type 2 diabetes (cases 3,4,5) and 1,352 controls (controls 3,4).We evaluated the differences in the population structure among these three sets of case and two sets of control groups by Wright's F test.As the results indicated that there was no difference in the population structure among these groups (Supplementary Table 1b online), we combined these populations for the third test of case-control study.The third set of analysis identified the significant associations for six SNPs (Table 1), including the CDKAL1 locus at 6p22.3 (rs4712524, rs9295475 and rs9460546), the IGF2BP2 locus at 3q27.2 (rs6769511 and rs4376068) and the KCNQ1 locus at 11p15.5 (rs2283228).The remaining three SNPs (rs13259803, rs612774 and rs10836097) had P values of 40.05 in the third test and were not further examined.CDKAL1 and IGF2BP2 were previously reported as susceptibility genes for type 2 diabetes in the Japanese population 9 .Therefore, we focused on the KCNQ1 locus, which was highly associated with type 2 diabetes." + } + ], + "3a066437-9d88-46c7-bc55-9992728847a7": [ + { + "document_id": "3a066437-9d88-46c7-bc55-9992728847a7", + "text": "\n\nWe consider these data as an interesting preliminary result that surely requires additional independent studies including a higher number of patients in order to confirm and clarify the possible contribution of this locus to the development of T2DM complications." + } + ], + "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a": [ + { + "document_id": "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a", + "text": "DISCUSSION\n\nTaken together, our full second-stage approach and combined meta-analysis have revealed additional loci associated with type 1 diabetes.Clearly the risks are relatively modest compared with previously described associations, and it was only with this sample size at our disposal that we could we detect and establish these signals as true positives through an independent validation effort." + } + ], + "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc": [ + { + "document_id": "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc", + "text": "Identification of susceptibility loci\n\nThe degree of evidence for all reported T2D loci was quantified as follows: a locus with a logarithm of odds ratio (LOD) score of 3 or more was considered significant, a LOD score between 2.2 and 3 was considered suggestive and a LOD score between 1 and 2.2 was considered nominal.For T2D, only those loci were included that were significant at least once, or were suggestive in at least one study and at least nominal in two or more studies.The inclusion of the second category of loci was based on a study by Wiltshire et al. [72], in which it was postulated that locus counting is a useful additional tool for the evaluation of genome scan data for complex trait loci.We used the same two criteria to determine the loci from the five papers published on obesity since 2004 and combined these loci with those from Bell et al. [7].As obesity phenotypes, BMI, serum leptin levels, abdominal subcutaneous and visceral fat, and percentage body fat were included.All of these phenotypes were used as continuous quantitative traits, as well as with various cut-off levels." + } + ], + "4be1d780-404a-4826-ba06-80b2c15e705b": [ + { + "document_id": "4be1d780-404a-4826-ba06-80b2c15e705b", + "text": "\n\nToday, more than 100 loci for type 2 diabetes and glycemic traits have been identified through numerous GWA studies of common and rare variation in populations of diverse ancestral origins [31]; however, to date, very few GWA studies have been published in cohorts of Mexican ancestry.The first GWA study performed in a non-European cohort was published in 2007 and comprised 561 Mexican American type 2 diabetes cases and controls drawn from the Starr County Health Studies [32].Although no loci reached genome-wide significance, several loci identified in prior GWA studies in Europeans were replicated [32].This analysis was subsequently expanded (N = 1273) and meta-analyzed with a cohort from Mexico City (N = 1310) in 2011 [33,34].The most significant variants observed in this meta-analysis included known regions near HNF1A and KCNQ1.Top association signals were then meta-analyzed with the DIAGRAM and DIAGRAM+ datasets of European ancestry individuals, resulting in two regions reaching genome-wide significance: HNF1A and CDKN2A/CDKN2B (Table 1).Top association signals in both studies were annotated to explore their roles as expression quantitative trait loci (eQTL) in both adipose and muscle tissues, revealing a marked excess of transacting eQTL in top signals in both tissue types." + } + ], + "5293f814-f4a7-48e0-b4e5-b1f13fdc8516": [ + { + "document_id": "5293f814-f4a7-48e0-b4e5-b1f13fdc8516", + "text": "\n\n75±79 The main conclusion is that there is no major locus for T2D (analogous to HLA in type 1 diabetes).This is not surprising given the modest l s for T2D (approximately 3.5 in Europeans), imposing a limit on the magnitude of any single gene eect. 4Many scans have consequently been signi®cantly underpowered to detect the modest gene eects anticipated.Certainly, few T2D scans have reported linkages meeting the established criteria for genomewide signi®cance. 80This modest power, combined with the diversity of the pedigrees sampled and the analytical techniques used, means that the replication of positive ®ndings between data sets has been the exception rather than the rule." + } + ], + "711e3d33-a196-4072-bc31-ffaa6bb3efa0": [ + { + "document_id": "711e3d33-a196-4072-bc31-ffaa6bb3efa0", + "text": "Quantitative Trait Analysis\n\nExploration of putative T2DM variants with quantitative glycemic traits in a subset of African-American samples (n = 671 from the IRAS and IRASFS control samples, Table S5) revealed limited insight into the biological mechanism associated with T2DM risk.In addition, the five putative African-American T2DM susceptibility loci were tested for association with quantitative measures of glucose homeostasis in the European Caucasian population, in silico, by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC; [16]).These results did not provide further insight into the probable role these variants may have in disease susceptibility (Table S6).The most significantly associated SNP in African Americans, rs7560163, failed quality controls filters and was not included in analysis likely due to being monomorphic as seen in a representative Caucasian population from the HapMap project (Table S4)." + } + ], + "91d6996a-319d-461e-ae78-3c64a70832cc": [ + { + "document_id": "91d6996a-319d-461e-ae78-3c64a70832cc", + "text": "\n\nDiscovery of novel loci for T2D susceptibility.We tested for T2D association with ~27 million variants passing quality-control filters, ~21 million of which had a minor allele frequency (MAF) < 5%.Our meta-analysis identified variants at 231 loci reaching genomewide significance (P < 5 × 10 −8 ) in the BMI-unadjusted analysis (N eff 231,436) and 152 in the smaller (N eff 157,401) BMI-adjusted analysis.Of the 243 loci identified across these two analyses, 135 mapped outside regions previously implicated in T2D risk (Methods, Fig. 1 and Supplementary Table 2)." + } + ], + "ad88aed6-75ba-469d-b96b-7be4a65be8fc": [ + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "text": "\n\nGenetic studies performed since 2012 have identified many additional T2D loci based on risk alleles common in one population but less common in others.Studies in African Americans identified RND3-RBM43 (28), HLA-B and INS-IGF2 (29).Studies in South Asians identified TMEM163 (30) and SGCG (31).One locus, SLC16A11-SLC16A13, was simultaneously identified in Japanese and Mexican Americans (32,33), and studies in East Asians identified ANK1 (34), GRK5 and RASGRP1 (35), LEP and GPSM1 (32), and CCDC63 and C12orf51 (36).A study of individuals from Greenland identified TBC1D4 (37), and a sequencing-based study of Danes with follow-up in other Europeans identified MACF1 (38).Finally, the largest GWAS to date in American Indians identified DNER at near genome-wide significance (P = 6.6 × 10 −8 ) (39).Three of these studies imputed GWAS data using the 1000 Genomes Project sequence-based reference panels, providing better genome coverage (29,32,33,40).Taken together, these studies highlight the value of diverse populations, including founder and historically isolated populations, to detect risk loci." + } + ], + "b973bd17-aac9-4d68-8ac4-1c683165b68f": [ + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nFinally, a recent study identified additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs. 21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds.Thus, by combining data involving 10,128 samples, the authors found in the initial stages of the analysis highly associated variants (they followed only 69 signals out of over 2 million metaanalyzed SNPs) with P values Ͻ10 Ϫ4 in unknown loci, and 11 of these type 2 diabetes' associated SNPs were taken forward to further stages of analysis.Large stage replication testing allowed the detection of at least six previously unknown loci with robust evidence for association with type 2 diabetes." + }, + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nSurprisingly, data about previous published loci associated with type 2 diabetes were not sufficiently powerful to reach a significant P value in individual scans.For example, variants at SLC30A8 and PPARG were significantly associated with type 2 diabetes only when pooling all the GWAs data, whereas in a single genome scan (DGI), no gene showed a positive signal (P value: 0.92 and 0.83, respectively).Thus, this may suggest that GWAs are still underpowered to find SNPs with small effect size." + } + ], + "d86525a8-0a2f-44a8-b343-61a5df8d6e68": [ + { + "document_id": "d86525a8-0a2f-44a8-b343-61a5df8d6e68", + "text": "\nBackground: The two genome-wide association studies published by us and by the Wellcome Trust Case-Control Consortium (WTCCC) revealed a number of novel loci, but neither had the statistical power to elucidate all of the genetic components of type 1 diabetes risk, a task for which larger effective sample sizes are needed.Methods: We analysed data from two sources: (1) The previously published second stage of our study, with a total sample size of the two stages consisting of 1046 Canadian case-parent trios and 538 multiplex families with 929 affected offspring from the Type 1 Diabetes Genetics Consortium (T1DGC); (2) the Rapid Response 2 (RR2) project of the T1DGC, which genotyped 4417 individuals from 1062 non-overlapping families, including 2059 affected individuals (mostly sibling pairs) for the 1536 markers with the highest statistical significance for type 1 diabetes in the WTCCC results.Results: One locus, mapping to a linkage disequilibrium (LD) block at chr15q14, reached statistical significance by combining results from two markers (rs17574546 and rs7171171) in perfect LD with each other (r 2 = 1).We obtained a joint p value of 1.3610 26 , which exceeds by an order of magnitude the conservative threshold of 3.26610 25 obtained by correcting for the 1536 single nucleotide polymorphisms (SNPs) tested in our study.Meta-analysis with the original WTCCC genome-wide data produced a p value of 5.83610 29 .Conclusions: A novel type 1 diabetes locus was discovered.It involves RASGRP1, a gene known to play a crucial role in thymocyte differentiation and T cell receptor (TCR) signalling by activating the Ras signalling pathway." + } + ], + "dad48e98-2dcc-41ae-866a-139f5540a24c": [ + { + "document_id": "dad48e98-2dcc-41ae-866a-139f5540a24c", + "text": "\n\nFinally, we examined whether genes identified using our association studies were enriched within diabetes-related pathways.We collated a list of 42 genes to which 53 CpG sites associated with T2D traits (CS score ≥1.77, combined P < 0.017) mapped.Even in this small dataset, pathway analysis (Supplementary Material, Table S12) indicated significant enrichment in 31 pathways (Fisher's exact P < 0.05), including those related to circadian clock (P = 0.005), adipocytokine signaling (P = 0.009), leptin pathway (P = 0.023), HDL-mediated lipid transport (P = 0.031) and insulin signaling (P = 0.033)." + } + ], + "e88b610f-8afa-46f7-a03c-d7bd579a7496": [ + { + "document_id": "e88b610f-8afa-46f7-a03c-d7bd579a7496", + "text": "\n\nIn recent years, progress has been made in following up mechanistic studies of GWAS type 2 diabetes-association signals [6,7,9,[25][26][27][28][29][30], but challenges remain in sifting through the many associated variants at a locus to identify those influencing disease.We hypothesized that a common variant with modest effect underlies the association at the CDC123/CAMK1D locus and evaluated the location of high LD variants (r 2 $.7; n = 11) at the locus relative to known transcripts and to putative DNA regulatory elements.We identified two variants that overlapped putative islet and/or liver regulatory regions and none located in exons.We did not assess variants in lower LD (r 2 ,.7), and additional functional SNPs may exist at this locus acting through alternate functional mechanisms untested in the current study." + } + ], + "fdbabc3c-ec60-45ce-9f5c-683f745c4d00": [ + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "\n\nMeta-analysis results for T2D SNPs for insulin and glucose-related traits." + }, + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "A r t i c l e s\n\nBy combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P < 5 × 10 −8 .These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A).The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation.We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits." + } + ] + }, + "data_source": [], + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "TCF7L2", + "DNER", + "SRR", + "HNF1A", + "KCNQ1", + "CDKN2A", + "CDKN2B", + "JAZF1", + "CDC123", + "CAMK1D" + ], + "metadata": [ + { + "object": "We identified a Congenital long QT syndrome LQTS family harboring three compound mutations in different genes KCNQ1-R174C, hERG-E1039X and SCN5A-E428K. IKs-like, IKr-like, INa-like currents and the functional interaction between KCNQ1-R174C and hERG-E1039X channels were studied using patch-clamp.Expression of KCNQ1-R174C alone showed no IKs. Co-expression of KCNQ1-WT + KCNQ1-R174C caused a loss-of-function in IKs", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007244" + }, + { + "object": "Pancreatic cancer was induced in adult mice by the combination of KRASG12D overexpression and loss of Tp53 and Cdkn2a only if Cdkn2b was concomitantly inactivated. inactivation of both Cdkn2b and Cdkn2a was necessary for Rb phosphorylation and to encompass oncogene-induced cellular senescence.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab580373" + }, + { + "object": "Twenty-five different variants were identified in GCK gene 30 probands-61% of positivity, and 7 variants in HNF1A 10 probands-17% of positivity. Fourteen of them were novel 12- GCK /2- HNF1A . ACMG guidelines were able to classify a large portion of variants as pathogenic 36%- GCK /86%- HNF1A and likely pathogenic 44%- GCK /14%- HNF1A , with 16% 5/32 as uncertain significance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab977086" + }, + { + "object": "We found that CDKN2B was a virtual target of miR-15a-5p with potential binding sites in the 3'UTR of CDKN2B 77-83 bp. We also showed that miR-15a-5p could bind to the CDKN2B 3'UTR. The data revealed a negative regulatory role of miR-15a-5p in the apoptosis of smooth muscle cells via targeting CDKN2B, and showed that miR-15a-5p could be a novel therapeutic target of AAA.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1004682" + }, + { + "object": "For each gene and the four pathways in which they occurred, we tested whether pancreatic cancer PC patients overall or CDKN2A+ and CDKN2A- cases separately had an increased number of rare nonsynonymous variants. Overall, we identified 35 missense variants in PC patients, 14 in CDKN2A+ and 21 in CDKN2A- PC cases.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab300370" + }, + { + "object": "we investigated the effects of KCNQ1 A340E, a loss-of-function mutant. J343 mice bearing KCNQ1 A340E demonstrated a much higher 24-h intake of electrolytes potassium, sodium, and chloride. KCNQ1, therefore, is suggested to play a central role in electrolyte metabolism. KCNQ1 A340E, with the loss-of-function phenotype, may dysregulate electrolyte homeostasis", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1008629" + }, + { + "object": "Results show that C-FOS directly binds to rs7074440 TCF7L2. Its knockdown decreases TCF7L2 gene expression proving evidence that c-FOS protein regulates TCF7L2 through its binding to rs7074440.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab661049" + }, + { + "object": "This review provides an update of the latest research advances on JAZF1 and its regulatory network in T2 diabetes mellitus T2DM. The association between JAZF1 polymorphisms and T2DM is discussed as well. The information provided is of importance for guiding future studies as well as for the design of JAZF1-based T2DM therapy. [review]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab935511" + }, + { + "object": "JAZF1 was identified as a direct miR-1275 target. miR-1275 supresses migration and invasion of gastric cancer cells in vitro and in vivo, which was restored by JAZF1 overexpression. Moreover, JAZF1 was recognized as a direct regulator of Vimentin.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab434327" + }, + { + "object": "DNA hypermethylation of KCNQ1 promoter resulted in its downregulation in hepatocellular carcinoma HCC. Bioinformatic analysis indicated a regulatory role of KCNQ1 in the epithelial-to-mesenchymal transition process. Gain-of-function study showed that KCNQ1 exhibited remarkable inhibitory roles on tumor metastasis in vitro and in vivo.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab235904" + } + ], + "question": "Do we find significant quantitative trait loci in the studies for diabetes?", + "subquestions": null, + "task_id": "B7084C90C3CF93908B3FB34BBA00743B", + "usage": { + "chatgpt": 9426, + "gpt-4": 4931, + "gpt-4-turbo-preview": 3991 + }, + "user_id": 2 + }, + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "task_id": "B7084C90C3CF93908B3FB34BBA00743B" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response04.json b/gnqa/paper1_eval/src/data/responses/test/response04.json new file mode 100644 index 0000000..ea08b61 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response04.json @@ -0,0 +1,189 @@ +{ + "created_at": "2024-03-25T14:00:01.668047Z", + "data": { + "amplify": false, + "answer": "Several environmental factors can affect mice and influence diabetes. These include the stress response, which can be greater in male mice and can confound feeding behavior, the unique gut microbiota of rodents from different production facilities, cage position, single versus group housing, the skill level of the researcher, ambient room temperature, and the type of cage bedding. Diet, particularly high-fat feeding, can also induce obesity and diabetes. Additionally, the intrauterine environment can impact the subsequent development of diabetes amongst offspring.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ae5d2bb-b09d-4646-922a-277188b53cbb": [ + { + "document_id": "0ae5d2bb-b09d-4646-922a-277188b53cbb", + "text": "\n\nIn these models, adult offspring of diabetic animals were noted to have normal development of the endocrine pancreas (Aerts et al., 1997;Ma et al., 2012).However, they develop glucose intolerance and impaired insulin response to glucose challenge, and display insulin resistance, mainly in the liver and muscle, highlighting the presence of both insulin resistance and b-cell dysfunction (Aerts et al., 1988;Holemans et al., 1991a,b).The key role of the intrauterine environment was demonstrated by a series of embryo transfer experiments, which showed that the diabetes risk in a low genetic risk strain can be substantially increased by the hyperglycaemic environment of a dam with a high genetic risk of diabetes (Gill-Randall et al., 2004)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nOther diet-induced rodent models of type 2 diabetes.Although rats and mice are the most commonly used models for studies of type 2 diabetes, other rodents have also been identified as useful models.These include the desert gerbil and the newly described Nile grass rat, both of which tend to develop obesity in captivity." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSince the obesity is induced by environmental manipulation rather than genes, it is thought to model the human situation more accurately than genetic models of obesityinduced diabetes.High fat feeding is often used in transgenic or knock-out models, which may not show an overt diabetic phenotype under normal conditions, but when the beta cells are 'pushed', the gene may be shown to be of importance.It should be noted that the background strain of the mice can determine the susceptibility to diet-induced metabolic changes, and thus, effects could be missed if a more resistant strain is used (Surwit et al., 1995;Bachmanov et al., 2001;Almind and Kahn, 2004).It has also been reported that there is heterogeneity of the response to high fat feeding within the inbred C57BL/6 strain, indicating that differential responses to a high-fat diet are not purely genetic (Burcelin et al., 2002)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "Other considerations and limitations\n\nA myriad of factors affect animal experiments.Men elicit a greater stress response in mice than women 292 , likely confounding feeding behaviour.Rodents from different production facilities (for example, Jackson Laboratory and Taconic) have unique gut microbiotas 293 , perhaps contributing to differences in their susceptibility to DIO and related diabetic complications 293 .Similarly, cage position within a rack of cages, single versus group housing, the skill level of the researcher, ambient room temperature or the type of cage bedding can all affect experimental outcomes." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nWe believe there are several factors that researchers should consider when conducting obesity and diabetes mellitus research in rodents (FIG.2).Although our list is by no means an exhaustive, it demonstrates the complexity and interconnectedness of the myriad of factors that can confound experimental outcomes.Although it is impossible to control for everything, researchers should accurately detail all experimental conditions and methods to allow for better interpretation of the results and, importantly, for better reproducibility." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nFigure2| Important experimental parameters and potential confounders of experimental outcomes in obesity and diabetes research and their interrelatedness.Countless factors influence experimental outcomes when using animal models, and what is enumerated here is by no means a complete list.This figure is one depiction of the multifactorial and interconnected genetic and environmental matrix that makes it virtually impossible to design the perfect experiment.For example, single-housing mice to obtain more accurate food intake data introduces a stress that in turn affects food intake.The severity of this stress response is both strain-specific and sex-dependent.What is important is to be aware of these challenges and to control for them in the most optimal manner.It is equally, if not more, important to accurately and comprehensively detail all experimental conditions in research papers, as these have bearing on the interpretation and reproducibility of the published results.DIO, diet-induced obesity." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAnother concern pertains to control mice.Compared with free-living mice in the wild, laboratory control mice with ad libitum access to food are sedentary, overweight, glucose intolerant and tend to die at a younger age 297 .Comparisons between mice with DIO and control mice might be analogous to investigating the genetic cause of obesity-resistance by comparing humans who are overweight or obese.This potential problem with control mice could explain why the use of DIO diets that have 40% to 60% of total energy from fat is so prevalent, as this might be necessary to achieve divergent weight gains.With free access to running wheels, C57BL/6J mice voluntarily run 5-10 km per day 298,299 .As is the case with humans 300 , mice get health benefits from regular physical activity including weight loss, decreased adiposity and improved insulin sensitivity 301,302 .Physical activity might also affect the epigenome over several generations 303 .An enriched physical and social cage environment alone improves leptin sensitivity and energy expenditure in mice, independent of physical activity 304,305 .Overall, these data suggest that with standard mouse husbandry, chow-fed laboratory mice are not the ideal healthy and lean control group for meaningful obesity research." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nTo better address these points, various animal models have been developed.For example, using HFD-T2DM male rats, the F1 female offspring showed reduced β cell area and insulin secretion, together with glucose intolerance, without changes in body weight [145].The islets of the F1 female offspring showed differential expression of many genes involved in Ca 2+ , mitogen-activated protein kinase and Wnt signaling, apoptosis and cell cycle regulation [145].Similarly, in pregnant C57BL6J mice, food deprivation resulted in β cell mass reduction and an increased risk of β cell failure in offspring [146]." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "They are probably typical of those\nfew mice that develop diabetes more slowly and do\nnot tax the pancreatic insulin supply as severely early\nin the course of the disease. Attempts at therapy. Attempts to keep the weight\nof diabetic mice within normal limits by total or\npartial food restriction resulted in premature deaths. After it was discovered that gluconeogenesis is greatly\nincreased in diabetic mice, attempts were made to\nregulate blood sugar levels and also weight gain by\nfeeding rations devoid of carbohydrate." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "The degree\nof dependence of adiposity, hyperglycemia, and islet\nhypertrophy on food consumption varies among these\nmice, but in all, the increase in islet volume and consequent fi-eell hyperplasia appears to be an effective\n\n247\n\nmeans of maintaining blood sugar concentrations at\nnear normal levels. I n contrast, neither the diabetic\nsand rat [5] nor the diabetic mouse has hypertrophied\nislets and neither effectively controls blood sugar levels." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "HV~MEI,: Studies with the Mutation, Diabetes\n\nalmost undetectable. Similarly, the activities of citrate\nlyase and glucose-6-phosphate dehydrogenase were\ngreatly decreased in these older diabetic as compared\n\nDiabetologia\n\nthe diabetic mice have attained m a x i m u m weight,\nafter which no further accumulation of adipose tissue\nis noted. Fig. 8." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Animal models of diabetes in pregnancy and the role of intrauterine environment\n\nAnother important field of diabetes research that has relied heavily on animal experimentation is the study of diabetes in pregnancy and the role of the intrauterine environment on the subsequent development of diabetes amongst offspring." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "obesity", + "insulin&resistance", + "glucose&intolerance", + "high-fat&diet", + "environmental&factors", + "mouse&models", + "genetic&background", + "intrauterine&environment", + "diet-induced&obesity" + ], + "metadata": [ + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "Data, including data from studies using knockout/transgenic mice, suggest that PrPC is involved in development of insulin resistance and obesity; PrPC knockout mice fed high-fat diet present all the symptoms associated with insulin resistance hyperglycemia, hyperinsulinemia, and obesity; transgenic mice overexpressing PrPC fed high-fat diet exhibit normal insulin sensitivity and reduced weight gain.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab215504" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "FNDC5 attenuates adipose tissue inflammation and insulin resistance via AMPK-mediated macrophage polarization in HFD-induced obesity. FNDC5 plays several beneficial roles in obesity and may be used as a therapeutic regimen for preventing inflammation and insulin resistance in obesity and diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab299408" + }, + { + "object": "WISP1 can be involved in glucose/lipid metabolism in obese youth, which may be modulated by IL-18. Increased WISP1 levels may be a risk factor of obesity and insulin resistance, and WISP1 has a potential therapeutic effect on insulin resistance in obese children and adolescents", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1017591" + }, + { + "object": "Obesity interacted with the TCF7L2-rs7903146 on Type 2 DiabetesT2D prevalence. Association of TCF7L2 polymorphism with T2D incidence was stronger in non-obese than in obese subjects. TCF7L2 predictive value was higher in non-obese subjects. We created obesity-specific genetic risk score with ten T2D-polymorphisms and demonstrated for the first time their higher strata-specific predictive value for T2D risk.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab541919" + }, + { + "object": "LCN-2 expression and serum levels could discriminate IGT from NGT and type 2 diabetes mellitus T2DMfrom IGT obese women and early predicting T2DM among obese women. While, LCN-2 expression level was the independent predictor of IGT in obese women. Combination of both LCN-2 expression and serum levels improved their diagnostic value in early detection of IGT and T2DM among obese women", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab445589" + }, + { + "object": "Gestational obesity and gestational diabetes mellitus may contribute to elevated serum chemerin. Serum chemerin in pregnancy was associated with insulin resistance and triglycerides. Chemerin gene may play a role both in obese and gestational diabetes mellitus patients", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321477" + }, + { + "object": "SGNE1 genetic variation does not contribute to obesity and common forms of Type 2 diabetes but may worsen glucose intolerance and insulin resistance, especially in the background of severe and early onset obesity", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab702715" + }, + { + "object": "our results indicate that UC-MSCs can alleviate insulin resistance in part via production of IL-6 that elicits M2 polarization. Additionally, human obesity and insulin resistance were associated with increased pro-inflammatory ATMs infiltration. Thus, MSCs may be a new treatment for obesity-related insulin resistance and T2D concerning macrophage polarized effects", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab560980" + } + ], + "question": "What environmental factors effect mice that also influence diabetes?", + "subquestions": null, + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "usage": { + "chatgpt": 6673, + "gpt-4": 3552, + "gpt-4-turbo-preview": 2599 + }, + "user_id": 2 + }, + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response05.json b/gnqa/paper1_eval/src/data/responses/test/response05.json new file mode 100644 index 0000000..9a49068 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response05.json @@ -0,0 +1,191 @@ +{ + "created_at": "2024-03-25T14:02:33.179370Z", + "data": { + "amplify": false, + "answer": "1. Diabetes incidence study where mice were tested for diabetes monthly by blood glucose and weekly by urine assessment.\n2. Studies involving transgenic/knockout animal models of type 2 diabetes to study the role of genes and their effects on peripheral insulin action.\n3. Study involving male type-2 diabetic db/db mice and genetic control non-diabetic db/ mice.\n4. Study involving young adult mice fed a high-fat or Western diet to elicit DIO and insulin resistance.\n5. Study involving Akita mouse genotyping.\n6. Study involving 12-week-old male type 2 diabetic db/db mice and contemporary control wild-type mice.\n7. Study involving transgenic mice to create specific models of type 1 and type 2 diabetes.\n8. Study involving AKITA mice derived from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene.\n9. Study monitoring mice for the development of diabetes.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + } + ], + "42e06cda-627e-46f2-a289-c4c1fb6af8f2": [ + { + "document_id": "42e06cda-627e-46f2-a289-c4c1fb6af8f2", + "text": "Animal group and study design\n\nFirst, one set of animals comprising 12-week-old male type 2 diabetic db/db (C57BL/KsJ-db−/db−, n = 8) and contemporary control wild-type (C57BL/KsJ-db+/db−, n = 8) mice (Jackson Laboratories) were included in this study.Their weights and blood glucose levels were analysed to eliminate variation.Erectile functions of the animals were evaluated by the apomorphine-induced penile erection test, according to a previously described protocol (Pan et al. 2014).Afterwards, intracavernous pressure (ICP) investigations and histological measurements were applied to further confirm the results of the function tests.Then, all mice were sacrificed and the corpus cavernosum (CC) was collected from each mouse.Because the tissue of the CC is difficult to crush, we randomly collected the CCs from two mice and mixed them into one subgroup.As a result, four diabetic subgroups (DB groups) and four normal control subgroups (NC groups) were used for molecular measurements.Second, another set of animals, including three T2DMED and three normal control mice that were independent from the original set of animals, were included in the validation experiments using qRT-PCR.Third, another separate set of animals, including five T2DMED and five control mice, were used to verify one of the predicted targets, IGF-1, using ELISA.A luciferase reporter assay was performed to verify the binding of the differentially expressed miRNAs to the target gene IGF-1.All procedures were approved by the Institutional Animal Care and Use committee at Nanjing Medical University." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 1 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Knock-out and transgenic mice in diabetes research\n\nTransgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003)." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Genetically induced insulin-dependent diabetes\n\nAKITA mice.The AKITA mouse was derived in Akita, Japan from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene preventing correct processing of proinsulin.This causes an overload of misfolded proteins and subsequent ER stress.This results in a severe insulindependent diabetes starting from 3 to 4 weeks of age, which is characterized by hyperglycaemia, hypoinsulinaemia, polyuria and polydipsia.Untreated homozygotes rarely survive longer than 12 weeks.The lack of beta cell mass in this model makes it an alternative to streptozotocin-treated mice in transplantation studies (Mathews et al., 2002).It has also been used as a model of type 1 diabetic macrovascular disease (Zhou et al., 2011) and neuropathy (Drel et al., 2011).In addition, this model is commonly used to study potential alleviators of ER stress in the islets and in this respect models some of the pathology of type 2 diabetes (Chen et al., 2011)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nTo achieve a slow pathogenesis of T2DM, young adult mice 284 or rats 285 are fed a high-fat or Western diet to elicit DIO and insulin resistance.Single or multiple injections with low-dose streptozotocin (~30-40 mg/kg intraperitoneally) then elicit partial loss of β-cells, which results in hypoinsulinaemia and hyperglycaemia.Protocols are being continuously refined and likely differ between species and even strains 283 .The HFD streptozotocin rat is sensitive to metformin, further demonstrating the utility of this model 285 .Downsides of streptozotocin treatment include liver and kidney toxicity and mild carcinogenic adverse effects (TABLE 1)." + } + ], + "785df64a-ebbf-4dca-94dd-0ae27f7ac815": [ + { + "document_id": "785df64a-ebbf-4dca-94dd-0ae27f7ac815", + "text": "Materials and methods\n2.1 Mouse models\n2.1.1 Mouse strains\n2.1.2 Induction of type 1 diabetes\n8\n2.1.3 Insulin treatment on diabetic mice\n2.1.4 Akita mouse genotyping\n2.2 Characterization of diabetic nephropathy in mice\n2.2.1 Proteinuria measurement\n2.2.2 Glomerular cells quantification\n2.2.3 Methenamine silver staining quantification\n\n3. 4. 5. 6." + } + ], + "7e809821-000d-4fff-971d-264650e3612b": [ + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "text": "\n\nii) Rodent models of diabetic retinopathy" + } + ], + "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d": [ + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "text": "\n\nThere are some good reviews available in the literatures describing the transgenic/knockout animal models of type 2 diabetes [114][115][116][117][118] .The transgenic and knockout models are developed for studying the role of genes and their effects on peripheral insulin action such as insulin receptor, IRS-1, IRS-2, glucose transporter (GLUT 4), peroxisome proliferator activated receptor-g (PPAR-g) and tumour necrosis factor-a (TNF-a) as well as in insulin secretion such as GLUT-2, glucokinase (GK), islet amyloid polypeptide (IAPP) and GLP-1 and in hepatic glucose production (expression of PEPCK) associated with development of type 2 diabetes.Further, combination or double knockout mouse models including defect in insulin action and insulin secretion (e.g., IRS-1 +/-/GK +/-double knockout) have been produced which clearly illustrate the mechanisms associated with development of insulin resistance and beta cell dysfunction leading to overt hyperglycaemic state in human type 2 diabetes.These above genetically modified animals exhibit various phenotypic features of type 2 diabetes varying from mild to severe hyperglycaemia, insulin resistance, hyperinsulinaemia, impaired glucose tolerance and others as explained in detail elsewhere 6,9,[114][115][116][117][118] .Very recently, tissue specific knockout mouse models have been achieved, allowing further insight into the insulin action with respect to particular target tissues (muscle, adipose tissue and liver) associated with insulin resistance and type 2 diabetes 115,117,118 .The transgenic/knockout animals are currently used mostly for the mechanistic study in diabetes research and not usually recommended for screening programme as they are more complicated and costly." + } + ], + "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6": [ + { + "document_id": "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6", + "text": "Functional deficits refs\n\nNon-Alzheimer-disease mouse [71][72][73][74]76,78,81,85,87 and rat 59,75,77 ,79,95,97 Mouse [81][82][83][84][85] and rat 79,111 Cerebral effects of inducing diabetes or insulin resistance in normal rodents (that is, non-Alzheimer-disease rodent models) and in rodents genetically modified to accumulate amyloidβ in the brain (that is, rodent models of Alzheimer disease). Common intervetions to induce diabetic conditions in rodents included recessive mutations in the leptin gene (Lep; also known as Ob), defects in the leptin receptor (LEPR; also known as OB-R), diet and administration of streptozotocin. Rodents with pancratic overexpression of human amylin spontaneously develop both type 2 diabetes mellitus and dementia-like pathology." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 1 diabetes" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Introduction\n\nAnimal experimentation has a long history in the field of diabetes research.The aim of this article is to review the commonly used animal models and discuss the recent technological advances that are being employed in the discipline.The review is based on an extensive literature search using the terms rodent, mouse, rat, animal model, transgenics, knockout, diabetes and pathogenesis, in scientific journal databases such as MEDLINE ®.In addition, abstracts presented at meetings of Diabetes UK, the European Association for the Study of Diabetes and the American Diabetes Association over the last 5 years were examined in order to gain an appreciation of recent and ongoing research projects." + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "mouse", + "insulin", + "db/db", + "streptozotocin", + "AKITA", + "transgenic", + "knockout", + "glucose", + "tolerance" + ], + "metadata": [ + { + "object": "Hyperglycemia and blood pressure were similar between Trpc6 knockout and wild-type Akita mice, but knockout mice were more insulin resistant. In cultured podocytes, knockout of Trpc6 inhibited expression of the Irs2 and decreased insulin responsiveness. Data suggest that knockout of Trpc6 in Akita mice promotes insulin resistance and exacerbates glomerular disease independent of hyperglycemia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab367197" + }, + { + "object": "High levels of IP6K3 mRNA were found in myotubes and muscle tissues. Expression was elevated under diabetic, fasting, and disuse conditions in mouse skeletal muscles. Ip6k3-/- mice had lower blood glucose, less insulin, decreased fat, lower weight, increased plasma lactate, enhanced glucose tolerance, lower glucose during an insulin tolerance test, and reduced muscle Pdk4 expression. Ip6k3 deletion extended lifespan.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab348326" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "Mice overexpressing protein S showed significant improvements in blood glucose level, glucose tolerance, insulin sensitivity, and insulin secretion compared with wild-type counterparts. diabetic protein S transgenic mice developed significantly less severe diabetic glomerulosclerosis than controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab482040" + }, + { + "object": "Sequence difference between C57BL/6J and C57BL/6N strains of mice. Pmch knockout mice display decreased circulating glucose, abnormal glucose tolerance and increased oxygen consumption. N carries a private missense variant in this gene isoleucine to threonine. N mice display increased oxygen consumption, but higher circulating glucose levels and normal glucose tolerance compared to J.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab5150" + }, + { + "object": "Ghrl-/- and Ghsr-/- male mice studied after either 6 or 16 h of fasting had blood glucose concentrations comparable with those of controls following intraperitoneal glucose, or insulin tolerance tests, or after mixed nutrient meals. Collectively, our data provide strong evidence against a paracrine ghrelin-GHSR axis mediating insulin secretion or glucose tolerance in lean, chow-fed adult mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab322269" + }, + { + "object": "Patients with type 2 diabetes have significantly higher concentrations of plasma fetuin-B compared with normal glucose tolerance subjects and plasma fetuin-B is strongly associated with glucose and lipid metabolism, chronic inflammation and first-phase glucose-stimulated insulin secretion and insulin resistance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab584502" + }, + { + "object": "In wild-type mice, elevated bile acids impair hepatic insulin sensitivity by blunting the insulin suppression of hepatic glucose production. The impaired hepatic insulin sensitivity could not be attributed to TGR5 signaling, as TGR5 knockout mice exhibited a similar inhibition of insulin suppression of hepatic glucose production.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab406411" + }, + { + "object": "Gestational glucose tolerance in WT mice, but not Ffar2-/- mice improved while on antibiotics. Gestational glucose tolerance worsened in Ffar2-/- mice during a second pregnancy. Maternal Ffar2 expression had no effect on the growth rates and glucose and glucose tolerance in the offspring.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321474" + }, + { + "object": "ApoA-I is inversely associated with insulin resistance in patients with impaired glucose tolerance, and low apoA-I is an independent risk factor for impaired glucose tolerance IGT. These results indicate that apoA-I plays an important role in regulating insulin sensitivity and glucose metabolism in patients with IGT.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab76907" + } + ], + "question": "List mouse studies that involve insulin.", + "subquestions": null, + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "usage": { + "chatgpt": 5772, + "gpt-4": 4159, + "gpt-4-turbo-preview": 3223 + }, + "user_id": 2 + }, + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response06.json b/gnqa/paper1_eval/src/data/responses/test/response06.json new file mode 100644 index 0000000..bb965b6 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response06.json @@ -0,0 +1,201 @@ +{ + "created_at": "2024-03-25T14:09:27.751236Z", + "data": { + "amplify": false, + "answer": "The typical weight of a mouse liver can vary, but in the context provided, it can weigh up to 4.5 grams in a 40 gram mouse and 1.2 grams in a 20 gram normal mouse.", + "context": { + "5c8c5649-dbaa-43cb-a8ce-7d3bc6458a38": [ + { + "document_id": "5c8c5649-dbaa-43cb-a8ce-7d3bc6458a38", + "text": "\n\nFIG. 6. Hepatic steatosis during DIO is associated with loss of eAT mass.A: Liver weight (adjusted for body weight) of mice fed a HF diet for 1, 4, 8, 12, 16, and 20 weeks.B: Inverse association of eAT mass and liver weight (as in A) between DIO weeks 12 and 20.C: Representative micrographs of hematoxylin and eosin-stained liver sections demonstrating that hepatic macrosteatosis in HF-fed mice is initially evident at DIO week 12 and increases through week 20." + }, + { + "document_id": "5c8c5649-dbaa-43cb-a8ce-7d3bc6458a38", + "text": "\n\nRESEARCH DESIGN AND METHODS-Male C57BL/6 mice were fed a high-fat diet for 20 weeks to induce obesity.Every 4 weeks, insulin resistance was assessed by intraperitoneal insulin tolerance tests, and epididymal (eAT) and inguinal subcutaneous AT (iAT) and livers were harvested for histological, immunohistochemical, and gene expression analyses." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "BXD and HMDP mouse strains, as well as HXB/BXH rat strains, with\nhigher Cd36 expression had increased fat mass and body weight, as well as decreased VO 2 and liver acid\nbeta−glucosidase activity (Figure S2.4B-C), confirming the involvement of Cd36 in metabolism [126] and\nsuggesting a potential role in Gaucher's disease, which results from the deficiency of acid beta−glucosidase\n[127]. An association between Abca8a liver transcripts and triglyceride levels was also revealed (Figure\nS2.4D)." + } + ], + "65d16255-3edd-46fb-a100-2ab8ba6abcdd": [ + { + "document_id": "65d16255-3edd-46fb-a100-2ab8ba6abcdd", + "text": "The mice were sacrificed at 9 am after a 4-hour fast. (A-E)\nPARPi reduced body weight (A; *, #, and $ indicates significant differences between\n\n27\nHFHS and CD, HFHS and PAPRi-Prev, and HFHS and PARPi-Ther, respectively),\nliver weight (B), epididymal fat pad (C), liver triglyceride content (D), and cholesterol\n(E) in both preventive and therapeutic cohorts (n=8-10). (F,G) Representative images\nof livers (F) and liver sections stained with H&E and Oil Red O (lipid content appears\nin red) (G), (n= 4-5)." + }, + { + "document_id": "65d16255-3edd-46fb-a100-2ab8ba6abcdd", + "text": "CD45 positive cells\nappear brown. (n=4). * P <0.05; ** P < 0.001; *** P< 0.0001. Data are expressed as\nthe mean ± SEM. One-way ANOVA with a post-hoc Bonferroni test was used for all\nstatistical analyses. Male mice were used in these experiments. Fig. 5. Liver damage in MCD diet-induced NAFLD was reversed by NAD+\nrepletion. C57BL/6J mice were fed with CD, MCD, or MCD+PARPi (PARPi, 50\nmg/kg/day). The mice were sacrificed at 9 am after a 4-hour fast. (A) PARPi reduces\nglobal protein PARylation and (B) recovers NAD+ levels in liver tissue (n=6)." + }, + { + "document_id": "65d16255-3edd-46fb-a100-2ab8ba6abcdd", + "text": "At\n10 weeks of age, male C57BL/6J mice were challenged with an MCD diet for 5\nweeks. Similar to the effects seen in mice on a HFHS diet, MCD-fed mice treated\nwith PARPi in a preventive manner exhibited reduced PARylation and increased\nhepatic NAD+ levels (Fig. 5A and B). Mice fed with a MCD diet for 5 weeks showed classical pathophysiological\ncharacteristics of NAFLD, including hepatic steatosis, inflammation and fibrosis. MCD\ndiet increased AST and ALT levels compared to a control diet, while PARPi treatment\nreduced their levels (Fig. 5C and D)." + } + ], + "8e92b2e3-b525-4c17-a0cb-5ca740a74c66": [ + { + "document_id": "8e92b2e3-b525-4c17-a0cb-5ca740a74c66", + "text": "\n\nThe left inguinal, gonadal, and retroperitoneal fat pads were dissected and weighed individually. (Prior data showed that weights of left and right fat pads are highly correlated. )The mesenteric fat pad was also dissected and weighed.An adiposity index (AI) was computed for each mouse as follows: the left inguinal, gonadal, and retroperitoneal fat pad weights were summed, doubled, added to mesenteric fat pad weight, divided by body weight, and multiplied by 100.The ratios of the individual fat pad weights divided by body weight and expressed as a percentage (for example, 200× left gonadal fat pad weight/body weight) were analyzed as separate traits, as were blood glucose level, plasma leptin level (log 10 transformed), body weight, and body length." + } + ], + "a5e25b91-4846-4a42-b9b4-838031ec19b7": [ + { + "document_id": "a5e25b91-4846-4a42-b9b4-838031ec19b7", + "text": "Metabolic phenotypes were compared between\nmice in the upper (Lonp1-high) and lower (Lonp1-low) quartiles with respect to WAT Lonp1 expression (n=9–10 mice per\nCopyright © 2021 Korean Endocrine Society\n\nVAT mRNA levels of OXPHOS-complex and UPRmt genes\nin relation to BMI\nAmong 48 patients, 11 were obese (≥25 kg/m2), 11 were overweight (23 to 24.9 kg/m2), and 26 were of normal or underweight (<22.9 kg/m2), according to the World Health Organization Asia-Pacific Obesity Classification [16]. Clinical characteristics of the participants stratified by BMI (<23 kg/m2 vs. ≥23\nkg/m2) are summarized in Table 1." + } + ], + "acfbb3e9-6eeb-4541-bd1f-9f460de09958": [ + { + "document_id": "acfbb3e9-6eeb-4541-bd1f-9f460de09958", + "text": "In an F2 cohort derived from these parental strains, we have\nshown that the range of blood glucose, insulin levels, and body\nweight exceeds that of either the C57BL/6 (B6) leptinob/ob or BTBR\nleptinob/ob parental strains. We went on to identify several diabetesrelated QTL in this F2 sample [21,22]. In the current study, we\nfocused on a subset of 60 F2 mice that have previously been\nevaluated in detail with regard to liver gene expression profiles\n[24] to ask if the abundances of hepatic metabolic intermediates\nwould show sufficient heritability to enable us to map metabolic\nQTL (mQTL)." + } + ], + "af4c6e19-fafe-4178-a9eb-213991f344d6": [ + { + "document_id": "af4c6e19-fafe-4178-a9eb-213991f344d6", + "text": "(E–G) Data from CTB6F2 (E) and HMDP (F) mouse cohorts, and the HXB/BXH rat cohort (G) indicate significant negative correlations between liver Rpl26 levels\nand body weight, and other metabolic traits. adipose tissue (subWAT) mass (Figure 2D), suggesting pleiotropic effects of Pten. The links between Pten and neurobiological and metabolic phenotypes have been confirmed by independent studies (Kwon et al. , 2006; Ortega-Molina et al. ,\n2012). Overall, PheWAS showed that 4,230 out of 11,548 genes\nwere associated with at least one phenotypic trait and all genes\nhad significant associated molecular traits after phenome-wide\ncorrection (Figures 2E; Table S3)." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Curves of weight ( • ... • ) and blood sugar concentration\nwith age in a less typical diabetic mouse\n\nDiabetologia\n\n(I\n\n--I\n\n)\n\nAside from the large accumulations of fat, subcutaneously in axillary and inguinal regions and intraabdominally in mescnteric and gonadal fat pads, the\nmost striking anatomical deviation is the size of the\nliver. The liver m a y weigh up to 4.5 grams in a 40\ngram mouse, compared with 1.2 grams in a 20 gram\nnormal mouse." + } + ], + "b71befbe-2a20-434e-907e-0ae581373243": [ + { + "document_id": "b71befbe-2a20-434e-907e-0ae581373243", + "text": "In mice, within hours after the last meal, the organs\nrespond with changes in gene expression mainly in general metabolism (70). The role of the liver is\nto provide energy for glucose-dependent tissues, by glycogenolysis, gluconeogenesis, ketogenesis,\nand fatty-acid β-oxidation (71). The basic architecture of the lobules and the zonation are not\naffected, but the cell size declines in prolonged fasting, when murine liver restores partly its glycogen\ndeposits, and much of gene expression returns to control values (72). In Abcb4-/- mice, collagens,\nfibronectin and vimentin, responsible for the structural integrity of the ECM, were strongly affected\nby fasting." + }, + { + "document_id": "b71befbe-2a20-434e-907e-0ae581373243", + "text": "James SJ, Muskhelishvili L. Rates of apoptosis and proliferation vary with caloric intake and may influence\nincidence of spontaneous hepatoma in C57BL/6 x C3H F1 mice. Cancer Res 1994 Nov 1;54(21):5508-5510. 50. Hakvoort TB, Moerland PD, Frijters R, Sokolovic A, Labruyere WT, Vermeulen JL, et al. Interorgan\ncoordination of the murine adaptive response to fasting. J Biol Chem 2011 May 6;286(18):16332-16343. 51. Lin S, Saxena NK, Ding X, Stein LL, Anania FA. Leptin increases tissue inhibitor of metalloproteinase I\n(TIMP-1) gene expression by a specificity protein 1/signal transducer and activator of transcription 3\nmechanism. Mol Endocrinol 2006 Dec;20(12):3376-3388. 52." + } + ], + "b942c082-a734-47d7-8494-8457ce995ce2": [ + { + "document_id": "b942c082-a734-47d7-8494-8457ce995ce2", + "text": "\n\nCharacterization of lean and obese control and mGHRKO mice" + } + ], + "c2df1cd8-c962-4fac-88c9-cad52f7753b0": [ + { + "document_id": "c2df1cd8-c962-4fac-88c9-cad52f7753b0", + "text": "\n\nConsistent with the broad up-regulation of genes associated with fatty acid synthesis (Table 1), Oil Red O staining of liver sections from 15-d-old pups and naturally aged mice revealed enhanced accumulation of triacylglycerides in both compared to control littermates and 8-wk-old mice (Figure 7C), indicating hepatic steatosis.This and the absence of adipose tissue suggest that Csb m/m /Xpa À/À mice display generalized lipodystrophy (loss and abnormal redistribution of body fat) [31]., and Csb m/m /Xpa À/À mice (n ¼ 6).The levels of IGF1 (ng/ml) and glucose (mmol/l) in the serum of Csb m/m /Xpa À/À mice are significantly lower than that of control littermates (p , 0.0004 and p , 0.04, respectively). (C) PAS staining for glycogen and Oil Red O staining for triglycerides in livers of 15-d-old wt and Csb m/m /Xpa À/À mice and 96-wk-old wt mice.Pictures were taken at 1003 magnification.Note the large polyploid nuclei in the 96-wk-old wt mouse liver and the reduced glycogen levels in the Csb m/m /Xpa À/À liver after overnight fasting.doi:10.1371/journal.pbio.0050002.g007" + } + ], + "ce2c68bf-878d-460c-8d9b-d45ce3034ef7": [ + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "text": "Association between lifespan and metabolic organ weights\nWe measured weight of certain metabolic organs and tissues of a subsample of cases on\nboth diets at ~500 days of age. HFD mice (n = 63) had 84% greater fat mass, 25% greater\nheart mass, 19% greater liver mass, and 18% greater kidney mass at ~500 days compared\nto controls (n = 71). However, HFD did not influence brain mass (Supplemental Table)." + } + ], + "ddd79d05-8140-48d7-a7fe-5685bb6b50f8": [ + { + "document_id": "ddd79d05-8140-48d7-a7fe-5685bb6b50f8", + "text": "\n\nYoung adult dwarf mice have more body fat than normal mice.But, with age, normal mice from this line accumulate fat at a higher rate, and the percent body fat in old DF mice does not differ from that of normal mice, as measured by dual energy X-ray absorptiometry (DEXA) (29).Downregulation of lipid biosynthetic genes and upregulation of -oxidation-related genes in the liver of DF mice may explain this slower rate of fat deposition." + } + ], + "dfebf2a5-8553-41f9-af2d-f781778d1342": [ + { + "document_id": "dfebf2a5-8553-41f9-af2d-f781778d1342", + "text": "(b) Serum levels of liver injury markers, triglyceride, and cholesterol profiles of 20-month-old WT (n = 6) and Gdf15 KO (n = 6)\nmice. (c) Serum levels of pro-inflammatory cytokines of 20-month-old WT (n = 6) and Gdf15 KO (n = 6) mice. (d) H&E staining for liver tissues\nof 20-month-old WT (n = 6) and Gdf15 KO (n = 6) mice. Scale bar, 200 μm. Arrows indicate fat accumulation. (e) Fixed adipose tissue from\n20-month-old WT (n = 6) and Gdf15 KO (n = 6) mice was stained for F4/80 antibodies. Scale bar, 200 μm." + } + ], + "e7a99e2b-a89f-4091-b6e0-c445fd4948bb": [ + { + "document_id": "e7a99e2b-a89f-4091-b6e0-c445fd4948bb", + "text": "(12) studied liver\ngene expression changes in Stat5b knockout and wild-type\nmice, finding 1,603 differentially regulated genes, with 850\nbeing male- and 753 female biased (P ⬍ 0.05 and FC ⬎ 1.5). A large study consisting of 344 mice comprising an F2 cross\nbetween C57B/6J.apoE⫺/⫺ and C3H/HeJ.apoE⫺/⫺ strains\n(⬃50% from each sex) produced two reports (57, 61) that\nexamined sexually dimorphic gene expression in adipose tissue, brain, liver, and muscle. It was reported that 9,250 genes\nare dimorphic in the liver (P ⬍ 0.01 and FC ⬎ 1)." + } + ], + "e7bc9d83-6c3b-405c-a552-29874b927860": [ + { + "document_id": "e7bc9d83-6c3b-405c-a552-29874b927860", + "text": "2006) studied liver gene expression\nchanges in Stat5b knockout and wild type mice, finding 1,603 differentially regulated genes,\nwith 850 being male- and 753 female-biased (p<0.05 and FC>1.5). A large study consisting\nof 344 mice comprising an F2 cross between C57B/6J.apoE-/- and C3H/HeJ.apoE-/- strains\n(~50% from each sex) produced two reports (Wang et al. 2006; Yang et al. 2006) which\nexamined sexually dimorphic gene expression in adipose tissue, brain, liver and muscle. It\nwas reported that 9,250 genes are dimorphic in the liver (p<0.01 and FC>1)." + } + ] + }, + "data_source": [], + "document_id": "6BFBCECDC0E8EC5D39EAE8D98049FDD9", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "mouse", + "liver", + "weight", + "grams", + "diabetic", + "fat", + "metabolic", + "diet", + "NAFLD", + "PARPi" + ], + "metadata": [ + { + "object": "our design showed an association between the rs9939609 DNA variant and weight loss after a high polyunsaturated fat hypocaloric diet. Also, an interaction with the type of the hypocaloric diets and metabolic changes secondary to weight loss was observed. Metabolic improvement was better in A carriers with a weight loss secondary to a P hypocaloric diet.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab554681" + }, + { + "object": "Sustained, elevated levels of SAA1 were correlated with metabolic parameters and local cytokine expression in the liver following 16 weeks on the high-fat diet. We suggest that SAA1-derived amyloid deposition under long-term high-fat diet exposure may be associated with the complications of high-fat diet-induced obesity and metabolic disorders.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab759501" + }, + { + "object": "Aging, metabolism: DEPRECATED, Lifespan, longevity difference low fat minus high fat of females at UTHSC on either a normal low fat chow diet or a high fat diet 60% calories from fat, 12 hr light cycle only computed if more than 4 cases per diet [difference, days]", + "predicate": "http://purl.org/dc/terms/description", + "subject": "http://genenetwork.org/id/traitBxd_17469" + }, + { + "object": "interactions of fat intake with the genetic rs11150675 and transcriptional ILMN_1725441 variations at the NFATC2IP locus on 2-year weight change. cis-DNA methylation at cg26663590 of the NFATC2IP locus showed an opposite impact on weight-loss in response to high-fat vs low-fat diet. baseline methylation at cg26663590 causally mediated 52.8% of the effect of rs11150675 on 2-year weight-loss in the high-fat diet group", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab422351" + }, + { + "object": "Aging, metabolism: Mean life span, longevity of females, combined data both diets, on either a standard chow diet Harlan Teklad 7912 chow diet, 6.2% fat or on a high fat diet Harlan Teklad 06414, 18.4% protein, 60.3% calories from fat, 5.1 kcal/g at UTHSC on a 12 hr light cycle in polypropylene cages 145 in2 with up to 10 animals/cage, Harlan Teklad 7087 soft cob bedding unweighted average, updated Feb 2023 [days]", + "predicate": "http://purl.org/dc/terms/description", + "subject": "http://genenetwork.org/id/traitBxd_21450" + }, + { + "object": "an initial accelerated increase in body weight and fat mass of Bmal1-/- mice on high-fat diet may have been offset by the effect of premature ageing on organ weight, resulting in comparable weights after 15 weeks of high-fat diet.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab65355" + }, + { + "object": "Expression of Sirt1, Cox2i2, Parg, Pank3, Rhoa, Mrs2, Arhgap5, Igfbp3, Derl1, and Immp1l are all controlled by a QTL that peaks at Igf1r in BXD liver. LRS peak for Sirt1 of 19.5 when data are combined; LRS of 15.6 on high fat diet and LRS of 6 on low fat diet. Used dataset: EPFL/LISP BXD CD+HFD Liver Affy Mouse Gene 1.0 ST Apr13 RMA", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1805" + }, + { + "object": "Uqcrg expression maps to Chr 1 near Numts LRS ~17, B high, old genotypes using high fat diet data set EPFL/LISP BXD HFD Liver Affy Mouse Gene 1.0 ST Apr13 RMA. Possible link to longevity on high fat diet. No eQTL to Chr 1 on chow diet using probe set 10385818.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab6482" + }, + { + "object": "The absence of UCP1 augmented obesity weight gain, body fat mass, %body fat, fat depot size in high-fat diet- and cafeteria-fed 129S mice, with a similar or lower food intake, indicating that, when present, UCP1 indeed decreases metabolic efficiency. The increased obesity was due to a decrease in energy expenditure.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab776097" + }, + { + "object": "Through phenotype analysis of high-fat diet-induced obesity and metabolic syndrome mice with muscle-, fat-, or liver-specific knockout of Ube2o, study identified Ube2o as an essential regulator of glucose and lipid metabolism programs in skeletal muscle, but not in adipose or liver tissue. Mechanistically, Ube2o acted as a ubiquitin ligase and targeted AMPKalpha2 for ubiquitin-dependent degradation in skeletal muscles.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab955251" + } + ], + "question": "What is the typical weight of a mouse liver?", + "subquestions": null, + "task_id": "6BFBCECDC0E8EC5D39EAE8D98049FDD9", + "usage": { + "chatgpt": 6192, + "gpt-4": 4109, + "gpt-4-turbo-preview": 3136 + }, + "user_id": 2 + }, + "document_id": "6BFBCECDC0E8EC5D39EAE8D98049FDD9", + "task_id": "6BFBCECDC0E8EC5D39EAE8D98049FDD9" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response07.json b/gnqa/paper1_eval/src/data/responses/test/response07.json new file mode 100644 index 0000000..a1d2f27 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response07.json @@ -0,0 +1,203 @@ +{ + "created_at": "2024-03-25T14:14:08.479589Z", + "data": { + "amplify": false, + "answer": "C57BL/6 and DBA/2 mice differ in several ways. C57BL/6 mice are more accurate in tasks with greater demands and make fewer impulsive responses. They also have larger brain weights and are more resistant to radiation-induced genomic instability. DBA/2 mice, on the other hand, make fewer omission errors, respond more vigorously to certain influenza viruses, and perform better in less spatially demanding tasks. They also have larger cortical areas when adjusted for brain weight and are more sensitive to acute nephrotoxicity. Genetic differences between the two strains affect various responses including activity, memory, learning tasks, and responses to alcohol and opioids.", + "context": { + "069247f3-7209-4dbb-85e3-e9f11e273812": [ + { + "document_id": "069247f3-7209-4dbb-85e3-e9f11e273812", + "text": "However, when the data were adjusted\nfor brain weight, there was a significant (p = 0.008) difference between DBA/2J and C57BL/6J (2.14 ± 0.06 mm2\nand 1.96 ± 0.03 mm2, respectively) making the DBA/2J\nlarger by 8.50%. Total brain weight of DBA/2J animals\nwas significantly (p < 0.0001) smaller than that of C57BL/\n6J animals (0.35 ± 0.01 g, 0.42 ± 0.01 g respectively)." + } + ], + "08a6ce71-133e-426d-adfe-600ff52802a2": [ + { + "document_id": "08a6ce71-133e-426d-adfe-600ff52802a2", + "text": "Phenotypes are often very different between mouse strains with\ndiverse genetic backgrounds and the strain characteristics of DBA/\n2J are often contrasted with other genetically distinct inbred strains\nsuch as C57BL/6J. These defined genetic backgrounds provide an\nexcellent system for mapping modifier genes [20,21,22]. To study\nthese differences a number of DBA/2J-relevant resources have\nbeen generated. For instance, a genome-wide panel of congenic\nstrains has been created that contain portions of DBA/2J\nchromosomes on a C57BL/6J background [23]. These 65 strains\ncontain more than 95% of the DBA/2J genome." + } + ], + "0de2ad4a-b7e3-484e-8778-5ea47e42a4e4": [ + { + "document_id": "0de2ad4a-b7e3-484e-8778-5ea47e42a4e4", + "text": "Well-documented behavioral differences between C57 and\nDBA, including enhanced closed-arm preference and deficits in conditional fear, were\nobserved. This suggests at a minimum that the influence of previous testing in the two\nparental strains was comparable. The use of DBA/2J donor segments for the GTM panel may have implications for loci\nidentified in tests involving auditory stimuli, as this strain is known to undergo progressive\nhearing loss with age. While no rigorous examination of hearing capacity in the GTM has\nbeen conducted, inspection of time course data for individual mice in both the general\n\nMol Psychiatry." + } + ], + "1d3f76c8-87f6-402c-a488-4f6266bb7c9c": [ + { + "document_id": "1d3f76c8-87f6-402c-a488-4f6266bb7c9c", + "text": "Particularly\nstriking is the difference in their locomotor response:\nthe C57BL/6J strain shows a marked locomotor activation following an acute opiate administration,\nwhich is virtually absent in DBA/2 mice [6, 25, 29]. After chronic morphine treatment, either tolerance or\nsensitization of the locomotor response was evidenced in C57BL/6J mice, depending on the treatment paradigm, whereas no altered responses were\nobserved in the DBA/2J strain [1, 22, 29, 31]. Other\ninter-strain differences in reactions to opioids have\nalso been reported, including a greater sensitivity to\nopioid reward and stronger withdrawal symptoms in\nthe C57BL/6J strain [2, 6, 17, 30, 35]." + } + ], + "27e062d0-d5ed-4ee9-8783-f22882284865": [ + { + "document_id": "27e062d0-d5ed-4ee9-8783-f22882284865", + "text": "Although\nno differences in attentional performance were detected\nbetween C57BL/6J and DBA/2J, in line with previous reports\nin the 5-CSRTT and five-choice CPT (Loos et al . 2010;\nYoung et al . 2009), we observed significant differences\namong BXD recombinant inbred strains that transgressed\nbeyond the phenotypes of the founders. This suggested the\ncontribution of multiple genetic loci to these phenotypes,\nof which we detected a significant one on chromosome 16\nfor response variability." + } + ], + "2a92d7b5-946c-4a22-a4b9-26e950b0f757": [ + { + "document_id": "2a92d7b5-946c-4a22-a4b9-26e950b0f757", + "text": "Given the large differences that we found\npreviously (Crusio 2013) between C57BL/6 and DBA/2, this is\nunexpected. One possible explanation for the lower than expected\nperformance of the C57BL/6 and (at least some) BXD strains\nlies in the housing conditions. Our animal facility was built to\nhouse about 500 cages in one large breeding room. However,\nthe cage-washing installation (and the available personnel)\ncould not handle that many cages at a time. As a result,\nevery day one or two racks of cages were changed. C57BL/6\nmice are sensitive to such disruptions and, indeed, breeding\nresults were only mediocre." + } + ], + "581f83bc-3521-4cb3-ad3c-d905a90ecc29": [ + { + "document_id": "581f83bc-3521-4cb3-ad3c-d905a90ecc29", + "text": "C57BL/6 and DBA/2 mice is not yet fully understood but\ninvolves multiple genetic differences between the two mouse\nlineages, affecting several pathways and processes (1). Certain\ninfluenza viruses grow to higher titers in DBA/2 mice (A/Hong\nKong/213/2003 [H5N1] or A/Memphis/33/2008 [H1N1]) (data\nnot shown) while others do not (H7N3 and H10N5) (this\nstudy). Irrespective of the difference in viral loads, DBA/2 mice\nrespond more vigorously, producing larger quantities of certain proinflammatory molecules like TNF-␣, which was shown\nto correlate with increased morbidity and mortality in humans\n(5)." + } + ], + "5e5b18da-984c-415e-b2ce-e33b3c44b731": [ + { + "document_id": "5e5b18da-984c-415e-b2ce-e33b3c44b731", + "text": "Additionally, in this protocol the strains DBA/2J, A/J, NOD/ShiLt/J,\nC57BL/10J, SM/J, and C57BR/cdJ are AA sensitive; the strains\nCAST/EiJ and BTBR T⫹ tf/J are resistant; and the strains\nNZW/LacJ, KK,HIJ, and SWR/J have intermediate resistance to\nAA-induced acute nephrotoxicity (supplementary data; all supplementary material for this article is available online at the\njournal web site.). For this QTL study, C57BL/6J and DBA/2J mice were used\nas resistant and sensitive strains, respectively. Each strain has\na complete genomic sequence available, and the genetic basis\nof differences in their ability to respond to xenobiotics is\nextensively studied (reviewed in Ref. 8)." + } + ], + "66baf01d-e081-4034-b7ec-03592eac90a7": [ + { + "document_id": "66baf01d-e081-4034-b7ec-03592eac90a7", + "text": "The C57BL/6J X DBA/2J (BXD) recombinant inbred (RI)\nmouse strains, which are unique mosaic of alleles derived from\nthe parental C57BL/6J (B6) and DBA/2J (D2) strains have been\nconstructed as a high precision genetic reference population\nfor systems genetics in unraveling the genetic architecture\nof polygenic traits (Ashbrook et al. , 2019). The BXD family\nconsists of more than 150 BXD fully inbred strains that\nsegregate for ∼6 million genetic variants and thus can be\nused as an informative murine genetic reference panel." + } + ], + "810a8c3f-5ec5-4ce8-9ad1-149ce98a573d": [ + { + "document_id": "810a8c3f-5ec5-4ce8-9ad1-149ce98a573d", + "text": "Because\nwe have now shown that the parental strains C57BL/6J\nand DBA/2J markedly differ in both quantitative measures\nof cortex area size [6] and shape, this assures variation in\nthe derivative BXD lines, and provides an empirical basis\nfor using the BXD panel to study cortical development. Conclusion\nC57BL/6J and DBA/2J have markedly different cortical\narea maps, in both size and shape. These differences suggest polymorphism in genetic factors underlying cortical\nspecification, even between common isogenic strains. Comparing cortical phenotypes between normally varying inbred mice or between genetically modified mice can\nidentify genetic contributions to cortical specification." + } + ], + "8df298ea-4052-4a4a-bcd3-2e36818844f4": [ + { + "document_id": "8df298ea-4052-4a4a-bcd3-2e36818844f4", + "text": "The\nC57BL/6 mice were more accurate than DBA/2 mice at the\nshorter SD where the task demands were greater, and they also\nmade anticipatory (impulsive) responses at a lower rate. In contrast, the DBA/2 mice made fewer omission errors than the\nC57BL/6 but this effect was not seen until the final stages of\nthe experimental procedures. These findings are in agreement\nwith those of Greco et al. [18]. Although they used different breeders as well as different test chambers, training protocols and reinforcers, the results were similar: DBA/2 males\nwere less accurate and made more anticipatory responses than\nC57BL/6 males." + }, + { + "document_id": "8df298ea-4052-4a4a-bcd3-2e36818844f4", + "text": "DBA/2 mice perform poorly in other\nspatial tasks as well as in the 5-CSRTT (see Section 1) but\nthis is by no means true for paradigms that are less spatially\ndemanding. For instance, in the four-arm baited and cued versions of the radial maze, as well as in auditory fear conditioning,\nC57BL/6 and DBA/2 do not differ [1,30]; DBA/2 mice even\nperform better than C57BL/6 with regard to two-way active\navoidance learning [37]." + }, + { + "document_id": "8df298ea-4052-4a4a-bcd3-2e36818844f4", + "text": "While the factorial structure\nof C57BL/6 mice remained the same as under low attentional\ndemands (two factors), there was only one factor for DBA2\nmice. This factor was characterised by high positive loadings\n(>0.78) from the percent of correct responses and omission\nerrors, and a high negative loading (0.87) from anticipatory\nresponses. 4. Discussion\nThe results indicated that both C57BL/6 and DBA/2 mice\nwere able to learn the complex 5-CSRTT task but there were\nconsiderable quantitative differences in their performance." + }, + { + "document_id": "8df298ea-4052-4a4a-bcd3-2e36818844f4", + "text": "It can be seen that at all SD, accuracy was\ngreater for C57BL/6 than for DBA/2 mice. The clearest difference was at 1 s SD where C57BL/6 mice were responding at\na mean accuracy of 80% compared with the DBA/2 group for\nwhich the mean was 59% (Fig. 1(A)). With a SD of 5 s there was\nno significant main effect for group (F1,28 = 3.13), whereas at 2\nand 1 s SD significant group effects were achieved (F1,28 = 5.44\nand 25.1; P < 0.05 and 0.001, respectively)." + } + ], + "a67372ac-02b7-41c4-bb55-5152444c5479": [ + { + "document_id": "a67372ac-02b7-41c4-bb55-5152444c5479", + "text": "In marked contrast, the C57BL/6J strain\nwas found to have the highest level of oral morphine consumption [6]. However, sensitivity to the reinforcing\neffects of morphine in conditioned place preference and\nintravenous self-administration paradigms was higher in\nDBA mice than in C57BL [10]. The two frequently used\nlaboratory strains of mice C57BL/6J and DBA/2J show\nremarkable differences in analgesic response to morphine. Moreover, several studies have reported profound\ndifferences in morphine induced locomotor activity\nbetween the sensitive C57BL/6 and insensitive DBA/2\nmice [3,7]." + } + ], + "b73879de-43a6-48b0-ad69-98afadbfb997": [ + { + "document_id": "b73879de-43a6-48b0-ad69-98afadbfb997", + "text": ", increased exploration of the open\nareas) in both tests. One explanation is that DBA/2J is “susceptible”\nto this stressor, whereas C57BL/6J is “resilient.” However, a more\ncircumscribed but potentially more accurate interpretation is that\nboth strains react strongly to this particular stress regime, but\ndiffer in the manner in which the response manifests behaviorally. Thus, DBA/2J may develop a classic “passive” anxiety-like\nsuppression of approach behavior, whereas C57BL/6J may exhibit more of an “active” response to stress. This could reflect an\nincreased panic-like escape drive or manic-like reaction to stress\nin C57BL/6J, rather than a decrease in anxiety-like behavior." + } + ], + "d608e1a6-2bf1-4ad6-993d-453a328896a0": [ + { + "document_id": "d608e1a6-2bf1-4ad6-993d-453a328896a0", + "text": "Differences in radiation sensitivity between the BXD parental strains were first described\nby Roderick more than 45 years ago, with DBA/2J succumbing more quickly than\nC57BL/6J to a lethal dose of radiation (26). At more modest doses, C57BL/6J mice\nwere shown to be more resistant to radiation-induced genomic instability than DBA/2J\n(38, 84, 85)." + } + ], + "dbe5a781-3561-48cb-9f63-cfb4f3246434": [ + { + "document_id": "dbe5a781-3561-48cb-9f63-cfb4f3246434", + "text": "Genetic differences between C57 and DBA mice have been shown to translate into a broad spectrum\nof CNS related functional and molecular correlates, for example, differences in activity, impulsive\naction, hippocampal related memory and learning tasks, post- and pre-synaptic protein expression,\nand synaptic transmission and plasticity [27–40]. Through genetic linkage analyses, the genetic and\nphenotypic differences in the BXD panel of RI strains have resulted in identification of genes and loci\ninvolved in complex CNS functions, such as impulsivity [41], reversal learning [42], attention [43],\nneuronal oscillations [44], hearing loss [45], and fear and spatial learning [39,40]." + } + ], + "f4e26cf0-d214-41bf-b392-9c63a903b0b8": [ + { + "document_id": "f4e26cf0-d214-41bf-b392-9c63a903b0b8", + "text": "For example, the\nC57BL/6J (B6) and DBA2/J (D2)\ninbred mice frequently are used in\nalcohol research because they clearly\ndiffer in various responses to alcohol,\nincluding development of functional\ntolerance (Grieve and Littleton 1979),\nlocomotor activation (Phillips et al. 1998), and sensitivity to withdrawal\nsymptoms (Metten and Crabbe 1994). Because the environmental conditions\nin these experiments can be controlled,\nany differences observed between the\nmouse strains in these phenotypes most\nlikely can be attributed to genetic differences." + } + ], + "f6abed2a-3182-46be-aae6-97d99f08e73e": [ + { + "document_id": "f6abed2a-3182-46be-aae6-97d99f08e73e", + "text": "For example, when subjected to HFD, DBA/2J had 12.5% more body fat compared\nto C57BL/6J (P < 0.0001, Fig 1A). Additionally, the F1 offspring generated by DBA/2J dams\n(DBA/2J x C57BL/6J) had 10.6% more body fat (P < 0.001) compared to the F1 from C57BL/\n2J dams (C57BL/6J x DBA/2J). While the source of these latter effects appears to be maternal,\nfurther studies are needed to identify the molecular basis of these differences. In general,\ngenetic differences between strains impacted body weight variation throughout the experiment\n(P < 0.05) (Fig 1B)." + } + ] + }, + "data_source": [], + "document_id": "393CA44A783B1B62AE4A38BDC3BA656E", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "C57BL/6", + "DBA/2", + "accuracy", + "anticipatory&responses", + "omission&errors", + "genetic&differences", + "cortical&area", + "alcohol&research", + "CNS&functions", + "AA-induced´&nephrotoxicity" + ], + "metadata": [ + { + "object": "A 2.8-kb cDNA encoding an 80-kDa melanoma Ag defined by a syngeneic anti-B16 melanoma mAb able to block anti-melanoma cytotoxic T cell responses. Mela transfectants are brightly stained with the antibody. Northern blot showed that this transcript was detected in mouse melanoma cells of C57BL/6 and DBA/2 origin, C1300 A/J neuroblastoma, L cell C3H and EL-4 T lymphoma C57BL/6, but not in other tumors, such as S913 fibrosarcoma C57BL/10, NIH3T3, 70 Z/3 pre-B lymphoma, and P3U1 plasmacytoma.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab89" + }, + { + "object": "findings indicate that hippocampal pCREB is closely tied to this form of associative conditioning only in C57BL/6 mice and that different neural substrates may support trace conditioning in C57BL/6 and DBA/2 strains", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab289786" + }, + { + "object": "KLK6 protein from 129 mice showed reduced SDS-PAGE mobility compared with that from C57BL/6 mice; recombinant KLK6 protein from 129 mice had a higher optimum pH and >15 times higher hydrolytic enzymatic activity for several substrates than that from C57BL/6 mice. These results suggest that KLKs may contribute to the genetic basis of the differences between mouse strains.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab439223" + }, + { + "object": "genetic differences in the VDR gene may be involved in the development of AITD and the activity of GD, whereas the genetic differences in the GC and CYP2R1 genes may be involved with the intractability of GD.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab316964" + }, + { + "object": "Using MDR and RF, the overall best classifier of lung cancer status were SNPs rs1799732 DRD2, rs5744256 IL-18, rs2306022 ITGA11 with training accuracy of 0.6592 and a testing accuracy of 0.6572 and a cross-validation consistency of 10/10 with permutation testing P<0.0001", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab989251" + }, + { + "object": "studied time course of TLR9 and BD-2 gene expression by corneal epithelial cells in herpetic keratitis in C57Bl/6 and BALB/c mice; reduced TLR9 gene expression in the cornea of C57Bl/6 mice was associated with high sensitivity to infection caused by HSV-1", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab733376" + }, + { + "object": "S100P achieved 96.4% sensitivity, 93.3% specificity, 98.2% PPV, 87.5% NPV and 95.8% total accuracy, while IMP3 achieved 91.2% sensitivity, 86.7% specificity, 96.2% PPV, 72.2% NPV and 90.3% total accuracy for pancreatic ductal adenocarcinoma PDA.Both markers were sensitive and specific for diagnosis of PDA.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab690502" + }, + { + "object": "The expression intensity for the aldehyde dehydrogenase 7A1 ALDH7A1 mRNA and protein was significantly higher in C57BL/6 mice than DBA/2 mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab807658" + }, + { + "object": "The capability to express IL-4 and other Th2 cytokines is greater in DBA/2 splenocytes and CD4+ T cells than in C57BL/6 cells, a difference that correlates with altered IL-4 mRNA stability.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab788438" + }, + { + "object": "Comparison of behavioral studies in wild-type C57BL/6 mice and hemizygous Drd2 mice backcrossed into C57BL/6 background finds open-field locomotion, conditioned place preference, and avoidance learning are indistinguishable in the transgenic lines.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab151446" + } + ], + "question": "How are C57BL/6 and DBA/2 different?", + "subquestions": null, + "task_id": "393CA44A783B1B62AE4A38BDC3BA656E", + "usage": { + "chatgpt": 5792, + "gpt-4": 3986, + "gpt-4-turbo-preview": 3013 + }, + "user_id": 2 + }, + "document_id": "393CA44A783B1B62AE4A38BDC3BA656E", + "task_id": "393CA44A783B1B62AE4A38BDC3BA656E" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response08.json b/gnqa/paper1_eval/src/data/responses/test/response08.json new file mode 100644 index 0000000..dc7117a --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response08.json @@ -0,0 +1,197 @@ +{ + "created_at": "2024-03-25T14:18:12.274204Z", + "data": { + "amplify": false, + "answer": "Several behavioral QTLs have been found in mice. These include QTLs affecting aggressive behaviors, which have been identified in strains such as the Turku aggressive (TA) and non-aggressive (TNS) strains, the NC900 and NC100 strains, and the short attack latency (SAL) and long attack latency (LAL) strains. Other QTLs have been associated with traits like anxiety-related behavior, seizure, hippocampal volume, and alcohol preference. Additionally, QTLs related to alcohol, morphine or other drugs, painful arthritis, emotionality/anxiety, and seizure susceptibility have been mapped. There are also QTLs that influence exploratory behavior in mice.", + "context": { + "1a041a89-4da8-4ad5-b241-da36df917930": [ + { + "document_id": "1a041a89-4da8-4ad5-b241-da36df917930", + "text": "\n\nQuantitative trait locus (QTL) mapping has been carried out in numerous species to associate regions of the genome to phenotypes even before the structure of the genome was well understood (e.g., [3]).Rodents, especially mice, have been the species most prominently used for biomedically relevant traits.Amongst these, the BXD family of recombinant inbred (RI) strains derived from crossing two inbred strains-C57BL/6J and DBA/2J mice-have been extensively used for almost 50 years in fields such as neuropharmacology [4][5][6], immunology [7][8][9][10][11][12][13], behaviour [13][14][15][16][17][18][19][20][21], aging [21][22][23][24][25][26][27][28][29], neurodegeneration [30][31][32][33], and gut microbiome-host interactions [34]." + } + ], + "27e14ff3-b5a5-4f60-80a2-eaa2ab53e991": [ + { + "document_id": "27e14ff3-b5a5-4f60-80a2-eaa2ab53e991", + "text": "Milhaud JM, Halley H, Lassalle JM (2002) Two QTLs located on\nchromosomes 1 and 5 modulate different aspects of the performance of mice\nof the B6D Ty RI strain series in the Morris navigation task. Behav Genet 32:\n69–78. 16. Buck KJ, Rademacher BS, Metten P, Crabbe JC (2002) Mapping murine loci\nfor physical dependence on ethanol. Psychopharmacology (Berl) 160: 398–407. 17. Ferraro TN, Golden GT, Smith GG, Schork NJ, St Jean P, et al. (1997)\nMapping murine loci for seizure response to kainic acid. Mamm Genome 8:\n200–208. 18." + } + ], + "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce": [ + { + "document_id": "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce", + "text": "Other aggression QTLs\nSeveral lines of mice have been selectively bred for high or low levels of o¡ensive\naggression, which con¢rms that a propensity for aggressive behaviours is partially\nheritable. These lines include the Turku aggressive (TA) and non-aggressive\n(TNS) strains bred in Finland, the NC900 and NC100 strains bred in North\nCarolina, and the short attack latency (SAL) and long attack latency (LAL)\nstrains bred in the Netherlands (Miczek et al 2001). In wild mice, there is evidence for a QTL a¡ecting aggressive behaviours in a\nregion of chromosome 17, the t region." + }, + { + "document_id": "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce", + "text": "QTL ANALYSIS OF AGGRESSIVE BEHAVIOURS IN MICE\n\n65\n\nProgress towards identifying QTLs that a¡ect\naggressive behaviours in mice\nAn example of aggression QTLs identi¢ed as part of a whole genome scan\nOne of the few studies to identify intermale aggression QTLs as part of a whole\ngenome scan was published recently (Brodkin et al 2002). This study used NZB/\nB1NJ (extremely aggressive) and A/J (extremely unaggressive) inbred mice as\nparental strains. The methods chosen for housing and aggression testing were\ndesigned to control the e¡ect of non-genetic factors on the phenotype." + }, + { + "document_id": "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce", + "text": "Neuroscientist 4:317^323\nBrodkin ES, Goforth SA, Keene AH, Fossella JA, Silver LM 2002 Identi¢cation of quantitative\ntrait loci that a¡ect aggressive behavior in mice. J Neurosci 22:1165^1170\nChesler EJ, Lu L, Wang J, Williams RW, Manly KF 2004 WebQTL: rapid exploratory analysis\nof gene expression and genetic networks for brain and behavior. Nat Neurosci 7:485^486\nDarvasi A 1997 Interval-speci¢c congenic strains (ISCS): an experimental design for mapping a\nQTL into a 1-centimorgan interval. Mamm Genome 8:163^167\nDarvasi A 1998 Experimental strategies for the genetic dissection of complex traits in animal\nmodels." + }, + { + "document_id": "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce", + "text": "Brodkin: Such a course mapping study with only about 400 mice would be\nunlikely to detect a QTL that accounts for only 2.5% of the phenotypic variance,\nQTL ANALYSIS OF AGGRESSIVE BEHAVIOURS IN MICE\n\n73\n\nbut it should detect a QTL that accounts for approximately 10% of the variance\n(Lynch & Walsh 1998, Darvasi 1998). QTLs of this magnitude of e¡ect on\nneurobiological or behavioural traits have been found fairly commonly in crosses\nbetween inbred mouse strains (see e.g. Wehner et al 1997)." + } + ], + "4de669b7-da76-42ef-a88a-afebf1e86734": [ + { + "document_id": "4de669b7-da76-42ef-a88a-afebf1e86734", + "text": "By correlating genotypes with phenotypes in quantitative trait locus (QTL)\nanalysis, a large number of polymorphic regions harboring\ntrait relevant allelic variation have been defined for a wide\nrange of behavioral phenotypes [17]. At present, there are\n340\n\n549 QTLs for behavioral phenotypes in the Mouse Genome\nInformatics database, which are largely derived from crosses\nof 2 inbred strains of mice [18]." + } + ], + "53a0a196-385a-47ba-9509-0d4f4b157cbf": [ + { + "document_id": "53a0a196-385a-47ba-9509-0d4f4b157cbf", + "text": "A search of the Mouse Genome\nInformatics database (www.informatics.jax.org, March 16,2006) revealed 34\nneurobehavioral- and/or pain-related QTLs mapped to >75 cM; these inc1ude seven traits\nrelated to alcohol, six to morphine or other drugs, two to painful arthritis, five to\nemotionality/anxiety, and one to seizure susceptibility. Several ofthese QTLs have been\nfinely mapped near the peak of linkage of our analgesia QTL." + } + ], + "60e08224-f0e8-409c-b00a-b9e7358d3548": [ + { + "document_id": "60e08224-f0e8-409c-b00a-b9e7358d3548", + "text": "The behavioral QTLs were determined from the MGI\ndatabase as of October 1, 2004. Alcrsp2 (Erwin et al. , 1997); Ap3q (Bachmanov et al. , 2002); Alcp12 (Gill et al. , 1998). Behavioral QTLs have been mapped using other\nmouse strains, and their validity in the ILS and ISS strains has not been tested. Mb, megabases. Table 4." + } + ], + "835a094d-9c2b-4686-8725-d3c4123175b0": [ + { + "document_id": "835a094d-9c2b-4686-8725-d3c4123175b0", + "text": "In the fourth step, we sought to identify DNA sequence variants that influence\nboth molecular phenotypes as well as phenotypes at the structural and behavioral level. A\nremarkable region located on the distal end of mouse Chr 1 (172–178 Mb) was the ideal\nsubject for such an integrative study. This region, which we have named as Qrr1 (QTL\nrich region on distal Chr 1), is known for its unusually high density of QTLs for neural\nand behavioral traits, e.g. , traits like anxiety-related behavior, seizure, hippocampal\nvolume, and alcohol preference consistently map to this region." + } + ], + "9ac0b7e7-6294-4cfb-97e3-e5a4546af324": [ + { + "document_id": "9ac0b7e7-6294-4cfb-97e3-e5a4546af324", + "text": "Overall, these studies reveal the existence of an extensive\npolygenic system influencing the exploratory behavior of\nmice similar to the kind of genetic architecture shown to\ninfluence behavior in tests of fear and anxiety (Caldarone\net al. 1997; Flint et al. 1995; Gill & Boyle 2005; Henderson\net al. 2004; Laarakker et al. 2008; Singer et al. 2005; Turri\net al. 2001a,b). The significance of the QTL, and also of the\npolygenic system, is heightened by the finding that roughly\nthe same set of genes has the potential to influence some\nbehaviors from early adulthood to old age." + } + ], + "bd221ae3-3994-4fe2-b22d-b050b0d62bbf": [ + { + "document_id": "bd221ae3-3994-4fe2-b22d-b050b0d62bbf", + "text": "The behavioral phenotypes with QTLs on distal\nChr 17 are (1) prepulse inhibition, assayed by McCaughran\net al.41 in a panel of 21 BXD strains (trait ID on Genenetwork\nis 10396), (2) anxiety trait measure by time spent in open\nquadrant of zero-maze, assayed in a larger panel of 57 BXD\nstrains42 (trait ID 11696) and (3) handling induced convulsion\nas an index of ethanol withdrawal severity, measured in 25\nBXD strains43 (trait ID 10065). Gene–gene interaction analysis." + } + ], + "d0deb53b-7286-4fd0-9188-b7b9f366fd76": [ + { + "document_id": "d0deb53b-7286-4fd0-9188-b7b9f366fd76", + "text": "Quantitative trait locus (QTL) mapping has been carried out in numerous species to associate\nregions of the genome to phenotypes even before the structure of the genome was well understood\n(e.g. , [3]). Rodents, especially mice, have been the species most prominently used for biomedically relevant traits. Amongst these, the BXD family of recombinant inbred (RI) strains derived\nfrom crossing two inbred strains—C57BL/6J and DBA/2J mice—have been extensively used for\nalmost 50 years in fields such as neuropharmacology [4–6], immunology [7–13], behaviour [13–21],\naging [21–29], neurodegeneration [30–33], and gut microbiome–host interactions [34]." + } + ], + "d2f9c5cf-835c-450a-bb42-a2454a99e058": [ + { + "document_id": "d2f9c5cf-835c-450a-bb42-a2454a99e058", + "text": "Two QTLs located on chromosomes 1 and 5 modulate different\naspects of the performance of mice of the BXD Ty RI strain series in the Morris navigation task. Behav Genet. 2002; 32:69–78. [PubMed: 11958544]\nMozhui RT, Ciobanu DC, Schikorski T, Wang XS, Lu L, Williams RW. Dissection of a QTL hotspot\non mouse distal chromosome 1 that modulates neurobehavioral phenotypes and gene expression. PLoS Genetics. 2008; 4:e1000260. [PubMed: 19008955]\nMulligan MK, Wang X, Adler AL, Mozhui K, Lu L, Williams RW. Complex control of GABA(A)\nreceptor subunit mRNA expression: variation, covariation, and genetic regulation. PLoS One. 2012; 7(4):e34586." + } + ], + "d6085c3a-6ade-499e-9fde-4c8ea682f20e": [ + { + "document_id": "d6085c3a-6ade-499e-9fde-4c8ea682f20e", + "text": "Type\nI and type II error rates for quantitative trait loci (QTL) mapping studies using\nrecombinant inbred mouse strains. Behav Genet, 26(2): 149-160. Bidwell, L. C., Willcutt, E. G., Defries, J. C., & Pennington, B. F. 2007. Testing for\nneuropsychological endophenotypes in siblings discordant for attentiondeficit/hyperactivity disorder. Biol Psychiatry, 62(9): 991-998. Bitanihirwe, B. K., Dubroqua, S., Singer, P., Feldon, J., & Yee, B. K. 2011. Sensorimotor\ngating and vigilance-dependent choice accuracy: a within-subject correlative\nanalysis in wild-type C57BL/6 mice. Behav Brain Res, 217(1): 178-187. 151\nReferences\nBitsios, P., & Giakoumaki, S. G. 2005." + } + ], + "d8993417-3a27-4000-b693-6cb4662b9f80": [ + { + "document_id": "d8993417-3a27-4000-b693-6cb4662b9f80", + "text": "Quantitative trait locus (QTL) mapping has been carried out in numerous species to associate\nregions of the genome to phenotypes even before the structure of the genome was well understood\n(e.g. , [3]). Rodents, especially mice, have been the species most prominently used for biomedically relevant traits. Amongst these, the BXD family of recombinant inbred (RI) strains derived\nfrom crossing two inbred strains—C57BL/6J and DBA/2J mice—have been extensively used for\nalmost 50 years in fields such as neuropharmacology [4–6], immunology [7–13], behaviour [13–21],\naging [21–29], neurodegeneration [30–33], and gut microbiome–host interactions [34]." + } + ], + "f253e087-e030-40a8-8400-3b6bf50c1fd6": [ + { + "document_id": "f253e087-e030-40a8-8400-3b6bf50c1fd6", + "text": "Other aggression QTLs\nSeveral lines of mice have been selectively bred for high or low levels of o¡ensive\naggression, which con¢rms that a propensity for aggressive behaviours is partially\nheritable. These lines include the Turku aggressive (TA) and non-aggressive\n(TNS) strains bred in Finland, the NC900 and NC100 strains bred in North\nCarolina, and the short attack latency (SAL) and long attack latency (LAL)\nstrains bred in the Netherlands (Miczek et al 2001). In wild mice, there is evidence for a QTL a¡ecting aggressive behaviours in a\nregion of chromosome 17, the t region." + }, + { + "document_id": "f253e087-e030-40a8-8400-3b6bf50c1fd6", + "text": "QTL ANALYSIS OF AGGRESSIVE BEHAVIOURS IN MICE\n\n65\n\nProgress towards identifying QTLs that a¡ect\naggressive behaviours in mice\nAn example of aggression QTLs identi¢ed as part of a whole genome scan\nOne of the few studies to identify intermale aggression QTLs as part of a whole\ngenome scan was published recently (Brodkin et al 2002). This study used NZB/\nB1NJ (extremely aggressive) and A/J (extremely unaggressive) inbred mice as\nparental strains. The methods chosen for housing and aggression testing were\ndesigned to control the e¡ect of non-genetic factors on the phenotype." + }, + { + "document_id": "f253e087-e030-40a8-8400-3b6bf50c1fd6", + "text": "Neuroscientist 4:317^323\nBrodkin ES, Goforth SA, Keene AH, Fossella JA, Silver LM 2002 Identi¢cation of quantitative\ntrait loci that a¡ect aggressive behavior in mice. J Neurosci 22:1165^1170\nChesler EJ, Lu L, Wang J, Williams RW, Manly KF 2004 WebQTL: rapid exploratory analysis\nof gene expression and genetic networks for brain and behavior. Nat Neurosci 7:485^486\nDarvasi A 1997 Interval-speci¢c congenic strains (ISCS): an experimental design for mapping a\nQTL into a 1-centimorgan interval. Mamm Genome 8:163^167\nDarvasi A 1998 Experimental strategies for the genetic dissection of complex traits in animal\nmodels." + }, + { + "document_id": "f253e087-e030-40a8-8400-3b6bf50c1fd6", + "text": "Brodkin: Such a course mapping study with only about 400 mice would be\nunlikely to detect a QTL that accounts for only 2.5% of the phenotypic variance,\nQTL ANALYSIS OF AGGRESSIVE BEHAVIOURS IN MICE\n\n73\n\nbut it should detect a QTL that accounts for approximately 10% of the variance\n(Lynch & Walsh 1998, Darvasi 1998). QTLs of this magnitude of e¡ect on\nneurobiological or behavioural traits have been found fairly commonly in crosses\nbetween inbred mouse strains (see e.g. Wehner et al 1997)." + } + ] + }, + "data_source": [], + "document_id": "7C515AA69FEB13DBCB2B87E949390D2A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "QTL", + "behavioral", + "mice", + "aggression", + "chromosome", + "Morris&navigation&task", + "neurobehavioral", + "ethanol&withdrawal", + "prepulse&inhibition", + "anxiety" + ], + "metadata": [ + { + "object": "Mice exposed to aggressive confrontations exhibited a similar pattern of species-typical aggressive and non-aggressive behaviors on the first and the last session. Repeated aggressive confrontations promoted an increase in plasma corticosterone. After 10 aggressive confrontation sessions, mice presented a non-significant trend toward reducing hippocampal levels of CRF, which inversely correlated with plasma corticosterone", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab481563" + }, + { + "object": "Overexpression of RGS2 in explicitly serotonergic neurons augments male aggression in control mice and rescues male aggression in Rgs2 -/- mice, while anxiety is not affected. Findings specifically identify that RGS2 expression in serotonergic neurons is sufficient to drive male aggression in mice and as a potential therapeutic target for treating aggression.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab572353" + }, + { + "object": "Dopamine and DOPAC were not changed in 3-mo-old mice but were decreased at 8 mo in the striatum of PIKE-KO mice compared with wild-type mice. DA and DOPAC in hippocampus and substantia nigra were significantly decreased in 3-mo-old and 8-mo-old PIKE-KO mice as compared with wild-type mice. More severe motor defects in PIKE-KO and Fyn-KO mice than in wild-type mice exposed to alpha synuclein and MPTP.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab237945" + }, + { + "object": "We found a significant reduction of testosterone levels in mGluR7 knockout KO mice. Social investigating behaviour of intact mGluR7 KO mice also differed from that of wild-type mice; e.g. the KO mice showing less frequent anogenital sniffing and more frequent grooming behaviour. Further, castrated mGluR7 KO mice have smaller seminal vesicles than those of castrated wild-type mice, although intact mice were no different.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1004015" + }, + { + "object": "Mice exposed to aggressive confrontations exhibited a similar pattern of species-typical aggressive and non-aggressive behaviors on the first and the last session. Repeated aggressive confrontations promoted an increase in plasma corticosterone. Repeated sessions of social instigation or aggressive confrontation did not alter BDNF concentrations at the prefrontal cortex and hippocampus.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab481564" + }, + { + "object": "Recognition memory improved with exercise in WT mice, was impaired in TNFR1-/- exercise mice, showed non-significant impairment with exercise in TNF-/- mice, and no changes in TNFR2-/- mice. In spatial learning there were exercise related improvements in WT mice, non-significant but meaningful impairments evident in TNFR1-/- exercise mice, modest improvement in TNF-/- exercise mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab632171" + }, + { + "object": "No difference between wild-type WT and v1a and v1b double knockout dKO mice was found in olfactory preferences for estrous female odor to male odor. Over all four mating tests, the number of mounts and pursuits after receptive females was significantly greater in dKO mice than in WT mice. In the elevated plus maze and the open field test, dKO mice showed lower anxiety-like behavior than WT mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab529453" + }, + { + "object": "Neonatal maternal separation MS lead to increased anxiety-like behavior in Cdh13-/- mice compared to the other two MS groups. Cdh13-/- mice showed a context-dependent effect on stress- & anxiety-related behavior, impaired extinction learning following contextual fear conditioning & decreased impulsivity, & a mild decrease in errors in the Barnes maze & reduced risk-taking in the light-dark transition test after MS.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab520848" + }, + { + "object": "We identified low activity and high anxiety-like behaviors in Il18r1-/- mice, whereas Il18-/- mice displayed little anxiety-like behavior.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007843" + }, + { + "object": "significantly higher binding of POLR2A to the Tmem132d promoter region of high anxiety behavior mice than in low anxiety behavior mice", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab707366" + } + ], + "question": "What behavioural QTLs have been found in mice?", + "subquestions": null, + "task_id": "7C515AA69FEB13DBCB2B87E949390D2A", + "usage": { + "chatgpt": 6048, + "gpt-4": 4191, + "gpt-4-turbo-preview": 3207 + }, + "user_id": 2 + }, + "document_id": "7C515AA69FEB13DBCB2B87E949390D2A", + "task_id": "7C515AA69FEB13DBCB2B87E949390D2A" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response09.json b/gnqa/paper1_eval/src/data/responses/test/response09.json new file mode 100644 index 0000000..22fa49e --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response09.json @@ -0,0 +1,195 @@ +{ + "created_at": "2024-03-25T14:30:20.651749Z", + "data": { + "amplify": false, + "answer": "You can look for genes that regulate hippocampal neural stem cell proliferation in the results of gene expression profiling studies, such as the one mentioned in the text. Specifically, the study identified LRP6 as a novel regulator of adult hippocampal neurogenesis. Other genes like Lef1, Mtdh, and Chchd8 may also be involved.", + "context": { + "2dfc2b82-b8eb-4e73-957a-0ea8a4401a84": [ + { + "document_id": "2dfc2b82-b8eb-4e73-957a-0ea8a4401a84", + "text": "Other cell cyclerelated genes, such as p21, p18 and p27, were also reported to be involved in\nregulating different types of hematopoietic cells (Cheng 2004; Steinman 2002). For example, p21 and p18 specifically control HSC proliferation, whereas p27\nonly affects hematopoietic progenitor cells. Further study of the chromosome 3\nQTL interval in the congenic mouse model may provide a platform leading to the\ndiscovery of novel cycle-active gene and/or functions of already known genes. The apoptotic analyses shown in Table 3.2 are novel." + } + ], + "3278febd-171a-485d-bd6e-0cbb523d73ec": [ + { + "document_id": "3278febd-171a-485d-bd6e-0cbb523d73ec", + "text": "Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT, Wiltshire T, Su AI,\nVellenga E, Wang J, Manly KF, Lu L, Chesler EJ, Alberts R, Jansen RC,\nWilliams RW, Cooke MP, de Haan G: Uncovering regulatory pathways that\naffect hematopoietic stem cell function using ‘genetical genomics’. Nat\nGenet 2005, 37(3):225-32. 29. Overall RW, Kempermann G, Peirce J, Lu L, Goldowitz D, Gage FH,\nGoodwin S, Smit AB, Airey DC, Rosen GD, Schalkwyk LC, Sutter TR,\nNowakowski RS, Whatley S, Williams RW: Genetics of the hippocampal\ntranscriptome in mouse: a systematic survey and online neurogenomics\nresource." + } + ], + "489539fd-f7c5-44eb-bb58-5fc19d50a7cf": [ + { + "document_id": "489539fd-f7c5-44eb-bb58-5fc19d50a7cf", + "text": "In summary, I have identified p107 and Snx5 as quantitative trait genes that\nregulate the number of HSCs in B6 and congenic mice. CAFC assays confirmed that\nincreased expression of both genes increases HSC number in an in vitro setting. Although the increased expression of both Snx5 and p107 resulted in small increases in\nHSC number, the changes are biologically significant given the extensive proliferative\npotential of primitive stem cells." + } + ], + "66fc5ee9-0126-431f-add0-819957499810": [ + { + "document_id": "66fc5ee9-0126-431f-add0-819957499810", + "text": "The molecular mechanisms that regulate progenitor cell division and\ndifferentiation in the RMS remain largely unknown. Here, we surveyed the mouse genome in an\nunbiased manner to identify candidate gene loci that regulate proliferation in the adult RMS. We\nquantified neurogenesis in adult C57BL/6J and A/J mice and 27 recombinant inbred lines derived\nfrom those parental strains. We showed that the A/J RMS had greater numbers of\nbromodeoxyuridine-labeled cells than that of C57BL/6J mice with similar cell cycle parameters,\nindicating that the differences in the number of bromodeoxyuridine-positive cells reflected the\nnumber of proliferating cells between the strains." + }, + { + "document_id": "66fc5ee9-0126-431f-add0-819957499810", + "text": "Page 10\n\nNIH-PA Author Manuscript\n\nSeptin 9 (Sept9) and cyclin-dependent kinase 3 (cdk3) and are two other genes that are\nworth mentioning because even though they are not directly linked to neurogenesis, they are\nboth cell cycle regulatory genes. Sept9 is involved in the progression through G1 of the cell\ncycle and it is highly expressed throughout the adult mouse brain (Gonzalez et al. , 2009). Whereas, cdk3 is expressed at low levels throughout the adult mouse brain and it is required\nfor G1-S transition (Braun et al. , 1998)." + } + ], + "835a094d-9c2b-4686-8725-d3c4123175b0": [ + { + "document_id": "835a094d-9c2b-4686-8725-d3c4123175b0", + "text": "Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT, Wiltshire T et al. (2005). Uncovering regulatory pathways that effect hematopoietic stem cell function using\n‘genetical genomics’. Nat Genet 37:225–232. Cai L, Morrow EM, Cepko CL (2000). Misexpression of basic helix-loop-helix genes in\nthe murine cerebral cortex affects cell fate choices and neuronal survival. Development\n127:3021–3030. Caldarone B, Saavedra C, Tartaglia K, Wehner JM, Dudek BC, Flaherty L (1997). Quantitative trait loci analysis affecting contextual conditioning in mice. Nat Genet\n17:335–337. Calder AJ, Lawrence AD, Young AW (2001). Neuropsychology of fear and loathing. Nature Rev Neurosci 2:352–363." + } + ], + "8fb56fda-e1a2-4407-acb2-9a5983861202": [ + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "As further step, this\nfinding opens the door to study the molecular networks via which LRP6 acts to\nregulate proliferation. ! '*! ! +&(/. ((&-*)\n\n5.2. Redox regulation of Adult Hippocampal Precursor Cells\n\n5.2.1. Hypoxia increases AHPCs proliferation and neuronal differentiation\nOxygen concentration plays an important role in cellular development and\ntissue homeostasis. In the brain, depending on the tissue, the oxygen\nconcentration varies from 0.1 to 5% and in the rat hippocampus it is around\n3.2% (Studer et al. , 2000)." + }, + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "While this study covers only one part in\nthe several conceptual levels of regulation we are confident that this work will\nlead to finding a central regulatory pathway that regulates adult hippocampal\nprecursor cell proliferation. ! &*! ! +&(/. ((&-*)\n\n5.1.1. Establishment of AHPCs\nIsolating the precursor cells has become extremely important in order to study\nthem in detail away from the influence of their in vivo niche. Once the cells are\nin culture they express their autonomous, intrinsic properties without the niche\ninfluences such as cell-cell contacts, blood vessels, known and unknown\ngrowth factors and network activities." + }, + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "Gene expression profiling\nusing RNA samples from proliferating cultures of the 20 BXD mice strains\nyielded two cis eQTL candidates that directly regulated proliferation, LRP6\nand Chchd8. LRP6 is well known as a co-receptor of Wnt signaling, but the\nfunction of Chchd8 is not known. Further experimentation, using over-\n\n! I! ! SUMMARY\n\nexpression and gene silencing demonstrated that LRP6 negatively regulates\nAHPCs proliferation. Thus, from this study using a system genetics approach,\nwe were able to identify, LRP6 as a novel regulator of adult hippocampal\nneurogenesis. ! V! ! INTRODUCTION\n\n2. INTRODUCTION\n2.1." + }, + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "Gene expression profiling ...............................................................68\n4.1.8. LRP6 is a novel regulator of AHPCs proliferation .........................73\n4.2. Redox regulation of Adult Hippocampal Precursor Cells................78\n4.2.1. AHPCs yield increased under hypoxic conditions..........................78\n\n! T! ! TABLE OF CONTENTS\n\n4.2.2. More neuronal differentiation under hypoxic conditions................79\n5. DISCUSSION ..............................................................................................81\n5.1. Systems genetic approach to identify genes regulating AHPCs\nproliferation .................................................................................................81\n5.1.1. Establishment of AHPCs................................................................82\n5.1.2. Variation in proliferative and differentiative properties of AHPCs83\n5.1.3. QTL analysis ...................................................................................86\n5.1.4. Candidate genes from gene expression profiling ............................87\n5.1.5. Lrp6 as negative regulator of AHPCs proliferation ........................89\n5.2. Redox regulation of Adult Hippocampal Precursor Cells................92\n5.2.1." + }, + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "Mapping determinants of human gene expression by\nregional and genome-wide association. Nature 437, 1365-1369. Chiasson, B.J. , Tropepe, V., Morshead, C.M. , and van der Kooy, D. (1999). Adult mammalian forebrain ependymal and subependymal cells\ndemonstrate proliferative potential, but only subependymal cells have neural\nstem cell characteristics. Journal of Neuroscience 19, 4462-4471. Cipolleschi, M.G. , Dello Sbarba, P., and Olivotto, M. (1993). The role of\nhypoxia in the maintenance of hematopoietic stem cells. Blood 82, 20312037. Clarke, D.L. , Johansson, C.B. , Wilbertz, J., Veress, B., Nilsson, E., Karlstrom,\nH., Lendahl, U., and Frisen, J. (2000)." + }, + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "List of BXD AHPC lines stored\n\nTable 3. List of eQTls in 0.6 threshold range\n\nTable 4. Cis acting genes regulating proliferation trait\n\n! U#! ! PUBLICATIONS\n\nPublications\n\nA protocol for isolation and enriched monolayer cultivation of neural precursor\ncells from mouse dentate gyrus. Harish Babu*, Jan-Hendrik Claasen*, Suresh\nKannan, Annette E. Rünker, Theo Palmer, Gerd Kempermann. Front. Neurosci. 5:89. doi: 10.3389/fnins.2011.00089\n\nSystem genetics approach yields candidate genes regulating adult hippocampal\nprecursor cells proliferation, Manuscript in preparation (first author paper)\n\n! U##! ! SUMMARY\n\n1. SUMMARY\nAdult hippocampal neurogenesis is regulated at various levels and by various\nfactors." + }, + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "A recent study suggesting the role of mitochondria and\n\n! &&! ! +&(/. ((&-*)\n\ncytochrome\n\noxidase\n\nin\n\nenhancing\n\nhippocampal\n\nneurogenesis\n\nduring\n\ninflammation (Voloboueva et al. , 2010) may reveal the link for Chchd8 gene in\nadult neurogenesis. 5.1.5. Lrp6 as negative regulator of AHPCs proliferation\nThe results from our gene expression profiling suggest that high expression\nlevel of Lrp6 is associated with slow proliferating AHPCs and vice versa. We\nconfirmed this result by over expressing LRP6 in AHPCs. This revealed that\nLRP6 over expression reduced the proliferation of AHPCs by more than 2fold." + } + ], + "9497cd3a-8b36-46d3-be18-d9a6f4c36a27": [ + { + "document_id": "9497cd3a-8b36-46d3-be18-d9a6f4c36a27", + "text": "Two types of collagen and N-Cadherin were also in this pathway. The top upstream regulators of this gene set were Huntingtin (HTT) which regulates 32 of\nthe 193 genes analyzed (p = 1.22 × 10−15), and β-estradiol which may regulate 39 out of 193\ngenes in the set (p = 4.06 × 10−10). 3.2.2. Genes regulated by ethanol in the NAC following CIE—Three hundred\nseventy-eight probesets were exclusively altered by ethanol in the NAC only following CIE\n(Supplemental Fig. 2 and Table 5)." + } + ], + "9b3b1f72-2b99-45ce-b61b-b861fcf84604": [ + { + "document_id": "9b3b1f72-2b99-45ce-b61b-b861fcf84604", + "text": "Expression of a\nsubset of these neurogenesis-associated transcripts was controlled\nin cis across the BXD set. These self-modulating genes are particularly interesting candidates to control neurogenesis. Among\nthese were musashi (Msi1h) and prominin1兾CD133 (Prom1), both\nof which are linked to stem-cell maintenance and division. Twelve\nneurogenesis-associated transcripts had significant cis-acting\nquantitative trait loci, and, of these, six had plausible biological\nassociation with adult neurogenesis (Prom1, Ssbp2, Kcnq2, Ndufs2,\nCamk4, and Kcnj9). Only one cis-acting candidate was linked to\nboth neurogenesis and gliogenesis, Rapgef6, a downstream target\nof ras signaling." + } + ], + "9c266a06-68f9-4e25-8de4-87d8ee02d929": [ + { + "document_id": "9c266a06-68f9-4e25-8de4-87d8ee02d929", + "text": "Other cell cyclerelated genes, such as p21, p18 and p27, were also reported to be involved in\nregulating different types of hematopoietic cells (Cheng 2004; Steinman 2002). For example, p21 and p18 specifically control HSC proliferation, whereas p27\nonly affects hematopoietic progenitor cells. Further study of the chromosome 3\nQTL interval in the congenic mouse model may provide a platform leading to the\ndiscovery of novel cycle-active gene and/or functions of already known genes. The apoptotic analyses shown in Table 3.2 are novel." + } + ], + "b7f409c2-5328-4bd5-94f5-cc7456252ef6": [ + { + "document_id": "b7f409c2-5328-4bd5-94f5-cc7456252ef6", + "text": "\n\nand Tgfbr3 (transforming growth factor beta receptor 3).Of the significant genes correlated with the hippocampal cell death phenotype, there were 107 genes that were significant for a strain × treatment interaction.Four of these genes also showed an FC > 1.5: Gadd45g (growth arrest and DNA-damage-inducible, gamma), Kcnj13 (potassium inwardly rectifying channel, subfamily J, member 13), Plekhg1 (pleckstrin homology domain containing, family G (with RhoGef domain) member 1), and Sgms2 (sphingomyelin synthase 2)." + } + ], + "db0459f8-6602-48d7-be9b-14863a88bbe1": [ + { + "document_id": "db0459f8-6602-48d7-be9b-14863a88bbe1", + "text": "111\nBystrykh, L., E. Weersing, et al. (2005). \"Uncovering regulatory pathways that\naffect hematopoietic stem cell function using 'genetical genomics'. \"Nat\nGenet 37(3): 225-32. Cashman, J., A. C. Eaves, et al. (1985). \"Regulated proliferation of primitive\nhematopoietic progenitor cells in long-term human marrow cultures. \"Blood\n66: 1002-1005. Celeste, A., O. Fernandez-Capetillo, et al. (2003). \"Histone H2AX phosphorylation\nis dispensable for the initial recognition of DNA breaks. \"Nat Cell Biol 5(7):\n675-9. Chen, J., B. A. Astle, et al. (1999). \"Development and aging of primitive\nhematopoietic stem cells in BALB/cBy mice.\"Exp. Hematol. 27: 928-935. Cheng, T., N. Rodrigues, et al." + } + ], + "ee850069-4957-4159-97b9-38253ef00b18": [ + { + "document_id": "ee850069-4957-4159-97b9-38253ef00b18", + "text": "\n\nThe next category was Cellular Growth and Proliferation, which includes growth, proliferation, expansion and differentiation of cells and is also pertinent to the possible formation of new cells in this area of the hippocampus.37 genes were associated with this function.Not surprisingly, in the Cell Cycle function (Supplementary Table 2) we found thirty genes involved in cell cycle progression indicating the activity of dividing cells in this region." + } + ], + "f92e167e-0375-45b7-9d91-f8a4d0e1fbba": [ + { + "document_id": "f92e167e-0375-45b7-9d91-f8a4d0e1fbba", + "text": "Lef1 is expressed in cultured hippocampal\nneural stem cells in response to activation of the Wnt signaling\npathway (Cui et al. , 2011). Our evidence and the literature both\nsuggest that genes known to be involved in hippocampal adult\nneurogenesis are targets of Lef1, an important factor in generating\ngranule cells in the dentate gyrus during development (Galceran\net al. , 2000). The only two genes not targeted by Lef1 can be\nclosely associated with it: Mtdh regulates the expression of Lef1\n(Hu et al. , 2009; Yoo et al." + } + ] + }, + "data_source": [], + "document_id": "225D40F9ABA26046B89A427FAA204F2A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "LRP6", + "Chchd8", + "Lef1", + "Mtdh", + "AHPCs", + "hippocampal&neural&stem&cells", + "Wnt&signaling&pathway", + "cell&cycle", + "neurogenesis", + "proliferation" + ], + "metadata": [ + { + "object": "Results show that MbTrxC-AhpC forms an NADPH-dependent peroxidase ensemble for efficient reduction of H2O2 inside the mycobacterial antioxidant defense system and identify the amino acids involved in TrxC and AhpC interaction. AhpC undergoes a redox-modulated dimer to dodecamer formation, in which the unique mycobacterial N-terminal stretch of AhpC place a fundamental role. [AhpC, TrxC]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab662541" + }, + { + "object": "Functional studies demonstrated that miR-27 overexpression promoted multiple myeloma cell proliferation, facilitated cell cycle progression, and expedited cell migration and invasion; whereas miR-27 knockdown inhibited cell proliferation, induced cell cycle arrest, and slowed down cell motility.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab307388" + }, + { + "object": "Cell cycle profiling and proliferation assays revealed that the proximal alternative polyadenylation sites of CCND1 accelerated the cell cycle and promoted cell proliferation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab132405" + }, + { + "object": "Loss-of-function assays demonstrated that silenced FAM83H-AS1 obviously suppressed cell proliferation via regulating the cell-cycle distribution and cell apoptosis rate, and mechanistic experiments revealed that FAM83H-AS1 could epidemically silence CDKN1A expression through recruiting EZH2 to the promoter of CDKN1A, thereby influencing the cell cycle and proliferation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab459131" + }, + { + "object": "This study showed that miR-30b-5p repressed cell proliferation and cell cycle of HCC cell lines and that miR-30b-5p mediated DNMT3A to repress proliferation, meanwhile it targeted USP37 for decelerating cell cycle.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab947658" + }, + { + "object": "Loss-of-function assays demonstrated that silenced FAM83H-AS1 obviously suppressed cell proliferation via regulating the cell-cycle distribution and cell apoptosis rate, and mechanistic experiments revealed that FAM83H-AS1 could epidemically silence CDKN1A expression through recruiting EZH2 to the promoter of CDKN1A, thereby influencing the cell cycle and proliferation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab459132" + }, + { + "object": "eIF3a expression oscillated with cell cycle and peaked in S phase. Reducing eIF3a expression also reduced cell proliferation rate by elongating cell cycle but did not change the cell cycle distribution.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab251875" + }, + { + "object": "eIF3a expression oscillated with cell cycle and peaked in S phase. Reducing eIF3a expression also reduced cell proliferation rate by elongating cell cycle but did not change the cell cycle distribution.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab251876" + }, + { + "object": "MiR-152-3p was highly expressed in the bone marrow of chronic myeloid leukemia patients and cell lines and promoted the proliferation and cell cycle progression of K562 cells. Bioinformatics analysis, luciferase reporter assay, and Western blot revealed p27Kip1 p27 is a direct target of miR-152-3p. Up-regulation of p27 in K562 cells attenuated the effect of miR-152-3p on cell proliferation and cell cycle progression.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab487214" + }, + { + "object": "the HOX protein LIN-39 regulates the vulval precursor cell cycle at two levels. First, LIN-39 acts as a permissive factor by maintaining the expression of the cell cycle machinery in the vulval precursor cells, keeping them competent to proliferate. Second, LIN-39 triggers the lateral NOTCH signaling pathway in the adjacent 2 degrees vulval precursor cells, which over comes the cell cycle inhibition by LIN-31 Forkhead.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab821517" + } + ], + "question": "Where do I look for genes that regulate hippocampal neural stem cell proliferation?", + "subquestions": null, + "task_id": "225D40F9ABA26046B89A427FAA204F2A", + "usage": { + "chatgpt": 6607, + "gpt-4": 4369, + "gpt-4-turbo-preview": 3300 + }, + "user_id": 2 + }, + "document_id": "225D40F9ABA26046B89A427FAA204F2A", + "task_id": "225D40F9ABA26046B89A427FAA204F2A" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response10.json b/gnqa/paper1_eval/src/data/responses/test/response10.json new file mode 100644 index 0000000..bf4dab7 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response10.json @@ -0,0 +1,158 @@ +{ + "created_at": "2024-03-25T14:33:03.505102Z", + "data": { + "amplify": false, + "answer": "QTL stands for Quantitative Trait Locus. It is a region of the genome that is linked to a trait and contains a gene or genes that regulate a portion of the genetic variation for a particular phenotype. QTLs are associated with quantitative traits, which are traits that have measurable phenotypic variation due to genetic and/or environmental influences.", + "context": { + "0666777d-a2c5-40f5-b506-e78888b6b121": [ + { + "document_id": "0666777d-a2c5-40f5-b506-e78888b6b121", + "text": "QTL Mapping and Identification of Candidate Genes\nA QTL is a region of the genome shown to be linked to a trait. The purpose of mapping\nthis region is to identify a region of a genome that has a higher probability of harbouring the\ngenetic variations controlling variability in trait values." + } + ], + "07d409f4-91f9-4701-bb39-1421845a6321": [ + { + "document_id": "07d409f4-91f9-4701-bb39-1421845a6321", + "text": "Often a local eQTL will be caused by allelic variation in the regulatory region of\nthe gene or within the gene itself. mQTL\nA metabolite Quantitative Trait Locus is a region in the genome at which allelic variation\ncorrelates with the abundance variation of a certain metabolite. pQTL\nA protein Quantitative Trait Locus is a region in the genome at which allelic variation correlates with the abundance variation of a certain protein. Just like eQTL, pQTL can be\nlocal or distant according to the genomic position of the gene encoding for the protein relative to the QTL." + } + ], + "29f5af5f-8dc7-4e53-b0fa-66d37317a3f4": [ + { + "document_id": "29f5af5f-8dc7-4e53-b0fa-66d37317a3f4", + "text": "QTLs are regions within the\ngenome whose genetic variation modulates quantitatively a phenotype characteristic of\nthe particular trait under study (Lynch and Walsh, 1998). Determining the association\nbetween variations in specific disease phenotypes or a trait, with variations in genotypes\nof a reference population can be used to locate a QTL. One of the methods used for\nmapping QTLs associated with complex traits is genetic markers-trait association. Genetic markers associated with certain loci can be inherited in linkage disequilibrium. Generating populations with linked loci in disequilibrium is achieved though either\ncrosses between inbred lines, or use of the out-bred populations." + } + ], + "2a92d7b5-946c-4a22-a4b9-26e950b0f757": [ + { + "document_id": "2a92d7b5-946c-4a22-a4b9-26e950b0f757", + "text": "Quantitative trait locus-mapping is a statistical method\nused to map chromosomal intervals (loci) that contribute to\nheritable variance in phenotypes. The method simply compares the inheritance of allelic variants (B or D genotypes\nin our case) with differences in phenotypes. A QTL will\ngenerally cover a region that includes 10–100 genes, and\nthese positional candidates can then be ranked roughly on\nthe basis of criteria such as the types of DNA variants, patterns of mRNA expression, data from complementary human\ngenetic cohorts (GWAS and linkage) and relevant literature\nabout gene effects on central nervous system structure and\nfunction." + } + ], + "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce": [ + { + "document_id": "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce", + "text": "Chromosomal\nregions containing a gene (or genes) that a¡ect the level of a quantitative trait are\ncalled quantitative trait loci (QTLs). The relevant genes in these regions have been\ncalled quantitative trait genes (QTGs) (Hitzemann et al 2003). Quantitative trait\nlocus (QTL) analysis is an experimental strategy for identifying QTLs, and\nultimately QTGs, that a¡ect quantitative traits. Because of the complexity of\nthese traits, progress in identifying QTGs has been slow compared to that in\ncloning genes underlying Mendelian traits (Glazier et al 2002)." + } + ], + "4049da4d-c7cf-4e30-9a21-c77609fad23d": [ + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "text": "Expression QTL\nNext, we will examine expression quantitative trait loci (eQTLs). These are QTLs for gene\nexpression traits, a subset of the molecular phenotypes mentioned above. Much like classical\nphenotypes, expression of transcripts can be influenced by variants within the genome. However, because we know the location of the gene, we can split these eQTL into two\ncategories, trans- (or distal) or cis- (or local) eQTL. A trans-eQTL (or distal-eQTL) describes when the expression of a gene is influenced by a locus\nfar away from that gene, and therefore indicates that the gene of interest is downstream of\nanother gene." + } + ], + "40ebee6a-ba5a-4f21-86d1-78d421288687": [ + { + "document_id": "40ebee6a-ba5a-4f21-86d1-78d421288687", + "text": "These loci\nwhich are associated with changes in transcript expression are often termed\nexpression QTL (eQTL): a variant (or variants) within the locus alters the\nexpression of the gene of interest. An eQTL found near to the location (~ ≤\n1Mbp) of the transcript is described as a local eQTL, and are often called ciseQTL. This is in contrast to trans-eQTL which are found more distally. Cis-eQTL\nare interesting when they are found for a gene within a QTL for another\nphenotype (e.g." + } + ], + "621d8b0a-821b-45f8-ae91-aba0cdcdda10": [ + { + "document_id": "621d8b0a-821b-45f8-ae91-aba0cdcdda10", + "text": "The location of these genotypes are quantitative trait loci (QTLs) [Abiola et al. , 2003]. Detected via statistical methods [Doerge, 2002], QTLs are stretches of DNA highly associated with a specific phenotype, analogous to genetic landmarks which roughly indicate\nthe position of the active gene. QTLs are not defined at very fine granularity; they usually\ncorrespond to areas large enough to hold several genes. The genetic polymorphism (genotypes) in neighboring areas of a set of loci, as a group, influence structure and function on\nboth molecular and organismic scales." + } + ], + "6d850ba3-9219-4250-b17f-7cf4867ca354": [ + { + "document_id": "6d850ba3-9219-4250-b17f-7cf4867ca354", + "text": "Quantitative trait loci (QTL)\n\n132\n\nanalysis is a means to query the entire genome for DNA variants (markers) that show significant\n\n133\n\nassociations with the phenotype (quantitative trait) under investigation. This is the first step to\n\n134\n\nidentify candidate genes whose variants (alleles) affect the value of the phenotype. QTL analysis\n\n135\n\nwas performed using WebQTL (http://www.genenetwork.org) for each PCA factor. WebQTL\n\n136\n\nperforms 2,000 or more permutations of the strain data and significant QTL are defined by the\n\n137\n\nlikelihood ratio statistic (LRS) score of correctly ordered data exceeding all other permutations\n\n138\n\n95% of the time, i.e." + } + ], + "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d": [ + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "text": "Expression QTL\nNext, we will examine expression quantitative trait loci (eQTLs). These are QTLs for gene\nexpression traits, a subset of the molecular phenotypes mentioned above. Much like classical\nphenotypes, expression of transcripts can be influenced by variants within the genome. However, because we know the location of the gene, we can split these eQTL into two\ncategories, trans- (or distal) or cis- (or local) eQTL. A trans-eQTL (or distal-eQTL) describes when the expression of a gene is influenced by a locus\nfar away from that gene, and therefore indicates that the gene of interest is downstream of\nanother gene." + } + ], + "95b99c09-c336-44fd-b378-f41991edb3aa": [ + { + "document_id": "95b99c09-c336-44fd-b378-f41991edb3aa", + "text": "These are referred to as expression QTLs, or\neQTLs (Schadt et al. , 2003), which control a portion of\nexpression variation of particular genes in a population. eQTLs result from genetic differences in regulatory elements close to or within the gene (apparent cis-acting\neQTLs) as well as those that map elsewhere in the genome\nfrom the gene whose expression is modulated (trans-acting\neQTLs). By combining microarray and QTL analysis on the\nsame mice, much can be learned about the genetic underpinnings of particular alcohol traits (Hitzemann et al. , 2004;\nTabakoff et al. , 2003)." + } + ], + "a8e16a9a-242b-492f-95f6-9e80a10e77cc": [ + { + "document_id": "a8e16a9a-242b-492f-95f6-9e80a10e77cc", + "text": "Working with complex traits that\ntypically vary in their manifestation across a continuous distribution, in contrast to the\nbinary nature of monogenic traits, QTLs are discovered by simply identifying loci with\nalleles that consistently covary with a phenotype across a population. Genomic regions that\nshow a sufficiently strong association with a phenotype are considered QTLs. The simplest,\nor most hopeful, interpretation of a mapped QTL is that the implicated region harbors a\nsingle gene affecting manifestation of the associated phenotype." + } + ], + "b078162f-a48d-405b-b2cf-3559fc3338c8": [ + { + "document_id": "b078162f-a48d-405b-b2cf-3559fc3338c8", + "text": "By definition, a\nquantitative trait locus is a chromosomal region that contains a gene, or genes, that\nregulate a portion of the genetic variation for a particular phenotype (Wehner et al. 2001). The goal of QTL mapping is to identify regions of the genome that harbour\ngenes relevant to a specified trait. QTL map locations are commonly determined by\ninitial screening of mice with specific genetic characteristics, such as recombinant\ninbred strains, the F2 of two inbred strains, or recombinant congenic strains (Flint\n2003)." + } + ], + "b103d0bf-16ab-4e53-bb3b-7c2af3cfd9f6": [ + { + "document_id": "b103d0bf-16ab-4e53-bb3b-7c2af3cfd9f6", + "text": "(2003)\nand others defined the expression QTLs (eQTLs) as either cis\n(mapping near the gene locus) or trans (mapping elsewhere in\nthe genome). When behavioral QTLs (bQTLs) and cis-eQTLs\noverlap, the cis-eQTL genes are inferred as strong quantitative\ntrait gene (QTG) candidates (see e.g. Farris et al. 2010). The\nsituation for trans-eQTLs is more complicated since the QTL\nconfidence interval is generally larger and any gene within the\nQTL interval could have a regulatory role. The application of genetical genomics to mouse has\ngenerally focused on segregating populations involving\nR. Hitzemann et al." + } + ], + "cb3f9967-9762-4a9b-96cb-0acccdc316d2": [ + { + "document_id": "cb3f9967-9762-4a9b-96cb-0acccdc316d2", + "text": "Page 2\n\nDefinition of a QTL\nNIH-PA Author Manuscript\n\nA quantitative trait is one that has measurable phenotypic variation owing to genetic and/or\nenvironmental influences. This variation can consist of discrete values, such as the number of\nseparate tumours in the intestine of a cancer-prone mouse, or can be continuous, such as\nmeasurements of height, weight and blood pressure. Sometimes a threshold must be crossed\nfor the quantitative trait to be expressed; this is common among complex diseases. A QTL is a genetic locus, the alleles of which affect this variation." + } + ], + "d09e59f1-14d1-4391-8419-90c6d6bc2fde": [ + { + "document_id": "d09e59f1-14d1-4391-8419-90c6d6bc2fde", + "text": "When the phenotype of interest is a quantitative trait, such as blood pressure or cholesterol levels, the underlying genetic locus is\nreferred to as a “QTL”. A common strategy investigates the\nassociation between quantitative traits of transcriptional responses and their underlying DNA loci called “response\nQTLs” (reQTLs) (Albert and Kruglyak 2015). Studies have\nprovided clear evidence for the colocalization of reQTLs\nand disease-related loci (Caliskan et al. 2015)." + } + ], + "e7bc9d83-6c3b-405c-a552-29874b927860": [ + { + "document_id": "e7bc9d83-6c3b-405c-a552-29874b927860", + "text": "81\nGene Expression Quantitative Trait Locus Analysis\nQuantitative trait locus (QTL) mapping is a statistical technique that finds\nassociations between phenotype and genotype in a genetically segregating population\n(Lander and Botstein 1989). Here, we performed eQTL mapping on the male and female\ndata separately. There were 1,137 significant (q≤0.5 and p≤0.025) male and 1,232\n\nfemale eQTLs. First, we explored differences in patterns of eQTL locations between sexes by\nplotting the genomic locations of each eQTL versus the transcript location (Figure 4.3a, b)." + } + ], + "f253e087-e030-40a8-8400-3b6bf50c1fd6": [ + { + "document_id": "f253e087-e030-40a8-8400-3b6bf50c1fd6", + "text": "Chromosomal\nregions containing a gene (or genes) that a¡ect the level of a quantitative trait are\ncalled quantitative trait loci (QTLs). The relevant genes in these regions have been\ncalled quantitative trait genes (QTGs) (Hitzemann et al 2003). Quantitative trait\nlocus (QTL) analysis is an experimental strategy for identifying QTLs, and\nultimately QTGs, that a¡ect quantitative traits. Because of the complexity of\nthese traits, progress in identifying QTGs has been slow compared to that in\ncloning genes underlying Mendelian traits (Glazier et al 2002)." + } + ], + "f67f291b-2ea5-4d78-9595-2cbbc35dc415": [ + { + "document_id": "f67f291b-2ea5-4d78-9595-2cbbc35dc415", + "text": "1.4\n\nQ u a n tita tiv e T rait L ocu s M a p p in g\n\nQ uantitative tra it loci (QTLs) are genetic regions on a chromosome th a t control\ncertain quantitative traits, such as crop yield or body fat. QTL m apping involves con\nstruction of genomic m aps and testing for association between tra its and polymorphic\nmarkers. A significant association provides evidence th a t a QTL is near th e m arker." + } + ], + "f8184d24-6bd9-4450-a13e-d656aa2afb02": [ + { + "document_id": "f8184d24-6bd9-4450-a13e-d656aa2afb02", + "text": "\n\nCurrent data processing capabilities have also made it possible to search genome-wide for QTL (quantitative trait loci) [109].QTL mapping seeks to identify the relationship between various genomic locations and a set of quantitative traits, leading to a chromosomal location and ultimately to identification of gene(s) with the final goal of looking at gene expression.Among other things, this will lead to a better understanding of genetic mechanisms of variation and adaptation [121].Results can then be applied to adjust conservation measures in response to rapid change, for example, by identifying the genetic adaptability potential of individuals to be used in assisted migration or reintroduction [122,123]." + } + ] + }, + "data_source": [], + "document_id": "C346DA54E54A8AE66035F3BA22439DC0", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "QTL&mapping", + "quantitative&trait&locus", + "gene&expression", + "genetic&variation", + "phenotype", + "eQTL", + "genomic®ions", + "allelic&variation", + "DNA&loci", + "statistical&methods" + ], + "metadata": [], + "question": "What does QTL mean?", + "subquestions": null, + "task_id": "C346DA54E54A8AE66035F3BA22439DC0", + "usage": { + "chatgpt": 5348, + "gpt-4": 3693, + "gpt-4-turbo-preview": 2691 + }, + "user_id": 2 + }, + "document_id": "C346DA54E54A8AE66035F3BA22439DC0", + "task_id": "C346DA54E54A8AE66035F3BA22439DC0" +} diff --git a/gnqa/paper1_eval/src/data/responses/volunteer/flavia.json b/gnqa/paper1_eval/src/data/responses/volunteer/flavia.json new file mode 100644 index 0000000..0832fa9 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/volunteer/flavia.json @@ -0,0 +1,154 @@ +{ + "created_at": "2024-06-26T17:52:34.575350Z", + "data": { + "amplify": false, + "answer": "You can use the Rat Genome Database (RGD) for genetic, genomic, phenotype, and disease-related data generated from rat research.", + "context": { + "071b4686-f5c4-4759-a038-14d79a45dac7": [ + { + "document_id": "071b4686-f5c4-4759-a038-14d79a45dac7", + "text": "The database contains trait data for several\nhundred phenotypes including common inbreds, consomics, 80 BXD recombinant inbreds,\nhybrids, and over 60,0000 mutagenised mice including ENU mutants and several knockout\nlines. SOPs are employed for phenotypic data acquisition. This publicly accessible database\nis an excellent example of one that can be made significantly more valuable to the\ncommunity with a standard in place for the reporting of these protocols. PhenoSITE (http://www.gsc.riken.go.jp/Mouse/phenotype/top.htm) provides baseline\nphenotype data for three inbred strains and their F1 hybrids." + } + ], + "23dcf284-7c19-4335-91e1-50c3b85e6bad": [ + { + "document_id": "23dcf284-7c19-4335-91e1-50c3b85e6bad", + "text": "The Mouse\nGenome Database (MGD) has structured their mouse genomic data in terms of the Mammalian Phenotype Ontology\n[10]. Similarly, the Rat Genome Database (RGD) [11] also\ndeveloped a phenome database, integrated with its genomic\ndata. In humans, the GeneNetwork (WebQTL) provides a\ndatabase of complex traits with mappings to quantitative trait\nloci [12]. And several studies have focused on integrating\nhuman phenome and genome resources. For example, Butte\net al. created a large-scale phenome–genome network by\nintegrating the Unified Medical Language System with human\nmicroarray gene expression data [13]; and Aerts et al." + }, + { + "document_id": "23dcf284-7c19-4335-91e1-50c3b85e6bad", + "text": "de la Cruz N, Bromberg S, Pasko D, Shimoyama M, Twigger S, et al. (2005)\nThe Rat Genome Database (RGD): Developments towards a phenome\ndatabase. Nucleic Acids Res 33: D485–D491. Wang J, Williams RW, Manly KF (2003) WebQTL: Web-based complex trait\nanalysis. Neuroinformatics 1: 299–308. Butte AJ, Kohane IS (2006) Creation and implications of a phenome–\ngenome network. Nat Biotechnol 24: 55–62. Aerts S, Lambrechts D, Maity S, Van Loo P, Coessens B, et al. (2006) Gene\nprioritization through genomic data fusion. Nat Biotechnol 24: 537–544." + } + ], + "40c30ce7-909d-4f40-9848-9e225f902bc1": [ + { + "document_id": "40c30ce7-909d-4f40-9848-9e225f902bc1", + "text": "\n\nShur-Jen Wang provided an overview of the Rat Genome Database, which provides a platform to improve model selection.The database includes a quantitative phenotype tool that provides expected ranges for a phenotype of interest across strain groups, drawing from published literature and other deposited data and resources.This tool can also be used to link phenotypic variation to damaging genomic variants, which are shown in parallel." + } + ], + "443efea1-ffe7-446e-b2fb-37d8ec3cb74a": [ + { + "document_id": "443efea1-ffe7-446e-b2fb-37d8ec3cb74a", + "text": "This is a\npublicly available database that contains phenotypes from hundreds of studies and also\nlists basal gene expression data for many tissues, including brain regions. 3.4. Why Mice? The European house mouse (Mus musculus) has served as human analogue in basic\nresearch for many decades. Ethical and logistic limitations preclude almost all toxicogenetic\nresearch in humans. Genome-wide association studies in humans have revealed the genetic\nbasis for individual differences in several diseases; however, the exact mechanisms for gene\naction are difficult to ascertain. Thus, the use of animal models to uncover mechanisms\nbecomes the approach [61,62]." + } + ], + "5edf84d0-c2d9-45eb-91b9-c35743b6a463": [ + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "A number of public data resources are also being established to provide freely\naccessible microarray data on drug- and toxicity-related phenotypes. For example,\nthe Chemical Effects in Biological Systems (CEBS) database (Mattes et al. , 2004) is\na highly recommended resource that accommodates gene-expression profiles, and\nproteomics and metabolomics data and allows very complex queries across more\nthan 100 experiments, mostly performed in rat liver. These experiments include data\ngenerated after exposure to members of key drug classes, including the antidiabetic,\ntroglitazone (Rezulin); the antiepileptic, valproic acid; and the antidepressive, fluoxetine (Prozac) among other drugs (Mattes et al. , 2004)." + } + ], + "5f10ca6d-3a51-4401-a808-9a90b432ca16": [ + { + "document_id": "5f10ca6d-3a51-4401-a808-9a90b432ca16", + "text": "Although these as yet include only a\n\nlimited number of laboratories and genotypes, they all try to enlist larger groups\nof researchers and to expand the animal\nmodels covered, and they are publicly available. It will be beneficial for the redesign of\nnew behavioral measures that raw behavioral data will be available as well in these\ndatabases. Access to this information will allow\nexperimenters to extract from the database\nthe size of the genotype-by-laboratory interaction relevant to their experiment." + } + ], + "75813bc2-f0b5-400c-92d7-0958df97a04f": [ + { + "document_id": "75813bc2-f0b5-400c-92d7-0958df97a04f", + "text": ", 2014; see Section 9). GeneNetwork is a database that enables searching for ∼4000 phenotypes from multiple studies in the BXD, HXB, and in other recombinant inbred rodent families, as well as in other model organisms\nand even humans (Mulligan et al. , 2017). GeneNetwork employed a\nsomewhat different strategy than MPD in that it did not rely solely on\nresearchers submitting their data. Instead the database operators extracted the data from the scientific literature and integrated them into a\nuniform format (Chesler et al. , 2003)." + }, + { + "document_id": "75813bc2-f0b5-400c-92d7-0958df97a04f", + "text": "In the future, these two data\nresources, the per strain phenotype data storage with thorough protocol\ndocumentation in MPD, the Rat Genome Database, and genetic analysis\nsuite in GeneNetwork.org will be more closely integrated (Mulligan\net al. , 2017). The public database of the International Mouse Phenotyping\n221\nNeuroscience and Biobehavioral Reviews 87 (2018) 218–232\n\nN. Kafkafi et al. Consortium (IMPC) is intended to be “the first truly comprehensive\nfunctional catalogue of a mammalian genome” (Morgan et al. , 2009;\nKoscielny et al. , 2014)." + } + ], + "778e63d4-18ec-4c0d-a221-bddffd5335f6": [ + { + "document_id": "778e63d4-18ec-4c0d-a221-bddffd5335f6", + "text": "\n\nUseful Databases for the Exploration of Relationships Among Genetic Variations and Specific Phenotypes." + } + ], + "90a19d89-daac-4de9-8213-d3047b1e4b65": [ + { + "document_id": "90a19d89-daac-4de9-8213-d3047b1e4b65", + "text": "Shimoyama M, De Pons J, Hayman GT, Laulederkind SJ, Liu W, Nigam R, Petri V, Smith JR,\nTutaj M, Wang S-J, The Rat Genome Database 2015: genomic, phenotypic and environmental\nvariations and disease, Nucleic acids research 43(D1) (2014) D743–D750. [PubMed: 25355511]\n[24]. Dickinson ME, Flenniken AM, Ji X, Teboul L, Wong MD, White JK, Meehan TF, Weninger WJ,\nWesterberg H, Adissu H, High-throughput discovery of novel developmental phenotypes, Nature\n537(7621) (2016) 508. [PubMed: 27626380]\n[25]." + } + ], + "92fa8f50-2923-41a1-812b-32d931c71684": [ + { + "document_id": "92fa8f50-2923-41a1-812b-32d931c71684", + "text": "All data presented in this paper were deposited in the online database\nGeneNetwork (www.genenetwork.org), an open web resource that contains\ngenotypic, gene expression, and phenotypic data from several genetic reference\npopulations of multiple species (e.g. mouse, rat and human) and various cell\ntypes and tissues.35;36 It provides a valuable tool to integrate gene networks and\nphenotypic traits, and also allows cross-cell type and cross-species comparative\ngene expression and eQTL analyses." + } + ], + "a1c91fbe-9f6c-45fe-af9a-46c162d340ed": [ + { + "document_id": "a1c91fbe-9f6c-45fe-af9a-46c162d340ed", + "text": "This is a\npublicly available database that contains phenotypes from hundreds of studies and also\nlists basal gene expression data for many tissues, including brain regions. 3.4. Why Mice? The European house mouse (Mus musculus) has served as human analogue in basic\nresearch for many decades. Ethical and logistic limitations preclude almost all toxicogenetic\nresearch in humans. Genome-wide association studies in humans have revealed the genetic\nbasis for individual differences in several diseases; however, the exact mechanisms for gene\naction are difficult to ascertain. Thus, the use of animal models to uncover mechanisms\nbecomes the approach [61,62]." + } + ], + "ba1c6c7e-9355-413a-947c-0bae330b58ba": [ + { + "document_id": "ba1c6c7e-9355-413a-947c-0bae330b58ba", + "text": "The Mouse Phenome Database would be a natural choice: it already provides a\ncontrolled vocabulary for representing phenotype measurements and enforces correct strain nomenclature to\nfacilitate accurate comparisons across studies. Effective\nintegration of phenotypic and genetic data, facilitated by\nthe databases and analytical tools presented in this review,\nis critical to realizing the promise of the CC as it exists\ntoday." + } + ], + "c12e853e-4f0d-48f9-93af-15db9ad2dfae": [ + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "A number of public data resources are also being established to provide freely\naccessible microarray data on drug- and toxicity-related phenotypes. For example,\nthe Chemical Effects in Biological Systems (CEBS) database (Mattes et al. , 2004) is\na highly recommended resource that accommodates gene-expression profiles, and\nproteomics and metabolomics data and allows very complex queries across more\nthan 100 experiments, mostly performed in rat liver. These experiments include data\ngenerated after exposure to members of key drug classes, including the antidiabetic,\ntroglitazone (Rezulin); the antiepileptic, valproic acid; and the antidepressive, fluoxetine (Prozac) among other drugs (Mattes et al. , 2004)." + } + ], + "dbe5a781-3561-48cb-9f63-cfb4f3246434": [ + { + "document_id": "dbe5a781-3561-48cb-9f63-cfb4f3246434", + "text": "The GeneNetwork database provides open access\nto BXD and other RI strain derived microarray data, single nucleotide polymorphism (SNP) data,\nand phenotypic data for quantitative trait loci analysis and gene expression correlation analyses. Gene expression data were exported for manually selected probes in the PDNN hippocampus\ndatabase (Hippocampus Consortium M430v2), and the PDNN whole brain database (INIA Brain\nmRNA M430). The Hippocampus database was chosen as one of the most elaborate brain databases,\nas well as most highly recommended dataset on GeneNetwork itself (http://www.genenetwork.org/\nwebqtl/main.py?FormID=sharinginfo&GN_AccessionId=112)." + } + ], + "e6fc60c2-8651-44d7-a4aa-b4090e2d59f2": [ + { + "document_id": "e6fc60c2-8651-44d7-a4aa-b4090e2d59f2", + "text": "The Mouse Phenome Database would be a\nnatural choice: it already provides a controlled vocabulary for representing phenotype\nmeasurements and enforces correct strain nomenclature to facilitate accurate comparisons\nacross studies. Effective integration of phenotypic and genetic data, facilitated by the\ndatabases and analytical tools presented in this review, is critical to realizing the promise of\nthe CC as it exists today." + } + ], + "ed937e0a-1b83-4400-9bb3-d61ef714a797": [ + { + "document_id": "ed937e0a-1b83-4400-9bb3-d61ef714a797", + "text": "RGD database (www.rgd.mcw.edu) provides updated genetic,\ngenomic, phenotype, and disease data generated from mouse, rat,\nand human. A total of 450 genes were downloaded using “cardiomyocyte”, “myocyte”, and “cardiomyopathy” as the keywords. GWAS Catalog (www.ebi.ac.uk/gwas) database provides published genome-wide association studies in human populations. A\ntotal of 126 genes associated with cardiomyopathy disease with p\nvalue ≤5 × 10 −6 were downloaded using “cardiomyopathy” as\nthe key word. IMPC database (http://www.mousephenotype.org/) provides detailed phenotype data for the knockout mouse. A total of 636\ngenes were downloaded using “cardiomyocyte”, “myocyte”, and\n“cardiomyopathy” as key words. collaborative effort [19]." + } + ], + "f35e02a1-3314-4663-913f-38a3fc072aa8": [ + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "A number of public data resources are also being established to provide freely\naccessible microarray data on drug- and toxicity-related phenotypes. For example,\nthe Chemical Effects in Biological Systems (CEBS) database (Mattes et al. , 2004) is\na highly recommended resource that accommodates gene-expression profiles, and\nproteomics and metabolomics data and allows very complex queries across more\nthan 100 experiments, mostly performed in rat liver. These experiments include data\ngenerated after exposure to members of key drug classes, including the antidiabetic,\ntroglitazone (Rezulin); the antiepileptic, valproic acid; and the antidepressive, fluoxetine (Prozac) among other drugs (Mattes et al. , 2004)." + } + ], + "fca531d0-d45b-495f-a02c-fbd437617b20": [ + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "A number of public data resources are also being established to provide freely\naccessible microarray data on drug- and toxicity-related phenotypes. For example,\nthe Chemical Effects in Biological Systems (CEBS) database (Mattes et al. , 2004) is\na highly recommended resource that accommodates gene-expression profiles, and\nproteomics and metabolomics data and allows very complex queries across more\nthan 100 experiments, mostly performed in rat liver. These experiments include data\ngenerated after exposure to members of key drug classes, including the antidiabetic,\ntroglitazone (Rezulin); the antiepileptic, valproic acid; and the antidepressive, fluoxetine (Prozac) among other drugs (Mattes et al. , 2004)." + } + ] + }, + "data_source": [], + "document_id": "26681F93BA485656CF56BD71682E7C77", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "Rat&Genome&Database", + "Rat", + "Genetic", + "Genomic", + "Phenotype", + "Disease", + "GeneNetwork", + "Mouse", + "Human", + "Chemical&Effects&in&Biological&Systems" + ], + "metadata": [], + "question": "Which database can I use for genetic, genomic, phenotype, and disease-related data generated from rat research?", + "subquestions": null, + "task_id": "26681F93BA485656CF56BD71682E7C77", + "usage": { + "chatgpt": 5545, + "gpt-4": 3743, + "gpt-4-turbo-preview": 2749 + }, + "user_id": 2 + }, + "document_id": "26681F93BA485656CF56BD71682E7C77", + "task_id": "26681F93BA485656CF56BD71682E7C77" +} diff --git a/gnqa/paper1_eval/src/data/results/eval2_general1.json b/gnqa/paper1_eval/src/data/results/eval2_general1.json new file mode 100644 index 0000000..9c8dd91 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/eval2_general1.json @@ -0,0 +1,7 @@ +, +{ + "faithfulness": 0.7428571428571429, + "answer_relevancy": 0.9780678036268498, + "context_relevancy": 0.09343441716165339, + "context_utilization": 0.816596788224676 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/eval2_general2.json b/gnqa/paper1_eval/src/data/results/eval2_general2.json new file mode 100644 index 0000000..face395 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/eval2_general2.json @@ -0,0 +1,13 @@ +, +{ + "faithfulness": NaN, + "answer_relevancy": NaN, + "context_relevancy": 0.10210226586398571, + "context_utilization": NaN +}, +{ + "faithfulness": 0.85, + "answer_relevancy": 0.6948351748903157, + "context_relevancy": 0.09669216181532704, + "context_utilization": 0.7730960707226785 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/eval_aging1.json b/gnqa/paper1_eval/src/data/results/eval_aging1.json new file mode 100644 index 0000000..7f020f8 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/eval_aging1.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "answer_relevancy": 0.90332619492291, + "context_relevancy": 0.16311053327554975, + "context_utilization": 0.9695800984320362 +}, +{ + "faithfulness": 0.9777777777777779, + "answer_relevancy": 0.9152650172290191, + "context_relevancy": 0.17545621228789543, + "context_utilization": 0.9695800984320362 +}, +{ + "faithfulness": 1.0, + "answer_relevancy": 0.9207411197703179, + "context_relevancy": 0.19377271060439374, + "context_utilization": 0.9695800984320362 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/eval_aging2.json b/gnqa/paper1_eval/src/data/results/eval_aging2.json new file mode 100644 index 0000000..5cf1f31 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/eval_aging2.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "answer_relevancy": 0.9131945711490829, + "context_relevancy": 0.0843248379163872, + "context_utilization": 0.8269904041235476 +}, +{ + "faithfulness": 1.0, + "answer_relevancy": 0.9073113293523962, + "context_relevancy": 0.0843248379163872, + "context_utilization": 0.833091604265284 +}, +{ + "faithfulness": 1.0, + "answer_relevancy": 0.909257413921701, + "context_relevancy": 0.0843248379163872, + "context_utilization": 0.833091604265284 +}
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\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/eval_suga2.json b/gnqa/paper1_eval/src/data/results/eval_suga2.json new file mode 100644 index 0000000..4ea2aa2 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/eval_suga2.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "answer_relevancy": 0.9318400456917242, + "context_relevancy": 0.12194071444495894, + "context_utilization": 0.9657545215065534 +}, +{ + "faithfulness": 1.0, + "answer_relevancy": 0.9269052398452946, + "context_relevancy": 0.12194071444495894, + "context_utilization": 0.9657545215065534 +}, +{ + "faithfulness": 1.0, + "answer_relevancy": 0.9326698973133014, + "context_relevancy": 0.11492317058530979, + "context_utilization": 0.9717723548657957 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/eval_sugaA.json b/gnqa/paper1_eval/src/data/results/eval_sugaA.json new file mode 100644 index 0000000..fda4de7 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/eval_sugaA.json @@ -0,0 +1,7 @@ +, +{ + "faithfulness": 1.0, + "answer_relevancy": 0.9332465603795168, + "context_relevancy": 0.17527404777829225, + "context_utilization": 0.9832121070042665 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gemma_eval_general1.json b/gnqa/paper1_eval/src/data/results/gemma_eval_general1.json new file mode 100644 index 0000000..6b13c83 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gemma_eval_general1.json @@ -0,0 +1,7 @@ +, +{ + "faithfulness": NaN, + "answer_relevancy": NaN, + "context_relevancy": 0.017839778759088275, + "context_utilization": NaN +}
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\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_aging_2.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_aging_2.json new file mode 100644 index 0000000..16e0754 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_aging_2.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.96, + "context_utilization": 0.999999999991935, + "context_relevancy": 0.135272921108742, + "answer_relevancy": 0.9479744529828181 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.999999999991935, + "context_relevancy": 0.135272921108742, + "answer_relevancy": 0.951711024285933 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.999999999991935, + "context_relevancy": 0.14987988628287136, + "answer_relevancy": 0.9541549710773409 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_aging_3.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_aging_3.json new file mode 100644 index 0000000..566613d --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_aging_3.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "context_utilization": 0.97675568021047, + "context_relevancy": 0.2259505726726024, + "answer_relevancy": 0.9448278057931704 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.97675568021047, + "context_relevancy": 0.21568920951760603, + "answer_relevancy": 0.9444115188658463 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.97675568021047, + "context_relevancy": 0.22922926119719259, + "answer_relevancy": 0.9444470134072755 +}
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\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_diabetes_1.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_diabetes_1.json new file mode 100644 index 0000000..63646cf --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_diabetes_1.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.8533333333333333, + "context_utilization": 0.9438491717704647, + "context_relevancy": 0.20436440992383947, + "answer_relevancy": 0.957861571692806 +}, +{ + "faithfulness": 0.8355555555555556, + "context_utilization": 0.9438491717704647, + "context_relevancy": 0.2012874868469164, + "answer_relevancy": 0.9533191002746577 +}, +{ + "faithfulness": 0.8533333333333333, + "context_utilization": 0.9438491717704647, + "context_relevancy": 0.18389618249909034, + "answer_relevancy": 0.9498105973186146 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_diabetes_2.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_diabetes_2.json new file mode 100644 index 0000000..02fe10f --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_diabetes_2.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.9583333333333334, + "context_utilization": 0.7194444444356269, + "context_relevancy": 0.45524315840105317, + "answer_relevancy": 0.9496830965502638 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.7220833333238528, + "context_relevancy": 0.3970421001999949, + "answer_relevancy": 0.947827635665291 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.7194444444356269, + "context_relevancy": 0.3941849573428521, + "answer_relevancy": 0.9388702679644993 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_diabetes_3.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_diabetes_3.json new file mode 100644 index 0000000..6566e51 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_diabetes_3.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "context_utilization": 0.9237332568786083, + "context_relevancy": 0.2418398640689662, + "answer_relevancy": 0.9914901338443677 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9237332568786083, + "context_relevancy": 0.2352516287748486, + "answer_relevancy": 0.9926324858517163 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9295047961859101, + "context_relevancy": 0.2352516287748486, + "answer_relevancy": 0.9942151664950669 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_diabetes_4.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_diabetes_4.json new file mode 100644 index 0000000..29e72c0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_diabetes_4.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.96, + "context_utilization": 0.8382274392203959, + "context_relevancy": 0.21850226437090842, + "answer_relevancy": 0.9268774561175513 +}, +{ + "faithfulness": 0.96, + "context_utilization": 0.8289482840320825, + "context_relevancy": 0.21792356066720475, + "answer_relevancy": 0.9264507966486306 +}, +{ + "faithfulness": 0.96, + "context_utilization": 0.8382274392203959, + "context_relevancy": 0.22104856066720474, + "answer_relevancy": 0.9306530537050953 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_gn_1.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_gn_1.json new file mode 100644 index 0000000..25a71b0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_gn_1.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "context_utilization": 0.19999999999900003, + "context_relevancy": 0.05, + "answer_relevancy": 0.1823656883581401 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.19999999999900003, + "context_relevancy": 0.05, + "answer_relevancy": 0.1823656883581401 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.19999999999900003, + "context_relevancy": 0.05, + "answer_relevancy": 0.1823656883581401 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_gn_3.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_gn_3.json new file mode 100644 index 0000000..580e854 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_gn_3.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "context_utilization": 0.5999999999959664, + "context_relevancy": 0.22450090744101633, + "answer_relevancy": 0.562411241022707 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.5999999999959664, + "context_relevancy": 0.1687443284936479, + "answer_relevancy": 0.5643801560995779 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.5999999999959664, + "context_relevancy": 0.1687443284936479, + "answer_relevancy": 0.5617108358354678 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_gn_4.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_gn_4.json new file mode 100644 index 0000000..bcfc652 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_cs_gn_4.json @@ -0,0 +1,19 @@ +[ +{ + "faithfulness": 1.0, + "context_utilization": 0.19999999999882354, + "context_relevancy": 0.065625, + "answer_relevancy": 0.1834019127645967 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.1999999999988889, + "context_relevancy": 0.065625, + "answer_relevancy": 0.18443207660654864 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.19999999999882354, + "context_relevancy": 0.065625, + "answer_relevancy": 0.18442316533105405 +}] diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_aging_1.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_aging_1.json new file mode 100644 index 0000000..f719092 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_aging_1.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.96, + "context_utilization": 0.9479350312277262, + "context_relevancy": 0.21303541253345637, + "answer_relevancy": 0.9224404704070004 +}, +{ + "faithfulness": 0.96, + "context_utilization": 0.9479350312277262, + "context_relevancy": 0.21303541253345637, + "answer_relevancy": 0.9204895776596349 +}, +{ + "faithfulness": 0.975, + "context_utilization": 0.9479350312277262, + "context_relevancy": 0.21303541253345637, + "answer_relevancy": 0.9233177482569399 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_aging_2.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_aging_2.json new file mode 100644 index 0000000..6539d02 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_aging_2.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "context_utilization": 0.9999999999917659, + "context_relevancy": 0.12455653962641092, + "answer_relevancy": 0.9215002061256425 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9999999999917659, + "context_relevancy": 0.11027082534069661, + "answer_relevancy": 0.9238905660966263 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9999999999917659, + "context_relevancy": 0.10345264352251479, + "answer_relevancy": 0.9236938936685843 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_aging_3.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_aging_3.json new file mode 100644 index 0000000..13c967f --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_aging_3.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "context_utilization": 0.9017950700460371, + "context_relevancy": 0.15025391166567637, + "answer_relevancy": 0.9080233205044008 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9017950700460371, + "context_relevancy": 0.1521235888294712, + "answer_relevancy": 0.9183172871520828 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9017950700460371, + "context_relevancy": 0.14271182412358882, + "answer_relevancy": 0.914051539296523 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_aging_4.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_aging_4.json new file mode 100644 index 0000000..b40e032 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_aging_4.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "context_utilization": 0.873908075621365, + "context_relevancy": 0.13236286714496703, + "answer_relevancy": 0.9379656935564172 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.873908075621365, + "context_relevancy": 0.13236286714496703, + "answer_relevancy": 0.9291571366744364 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.873908075621365, + "context_relevancy": 0.13236286714496703, + "answer_relevancy": 0.9374908833538264 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_diabetes_1.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_diabetes_1.json new file mode 100644 index 0000000..d06530b --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_diabetes_1.json @@ -0,0 +1,20 @@ +[ +{ + "faithfulness": 1.0, + "context_utilization": 0.9898660740877201, + "context_relevancy": 0.31265901349702185, + "answer_relevancy": 0.9236030246314068 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9898660740877201, + "context_relevancy": 0.14113303947104788, + "answer_relevancy": 0.9150252742414604 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9728819471034, + "context_relevancy": 0.13863303947104788, + "answer_relevancy": 0.9148789006153158 +} +] diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_diabetes_2.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_diabetes_2.json new file mode 100644 index 0000000..e9fee86 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_diabetes_2.json @@ -0,0 +1,20 @@ +[ +{ + "faithfulness": 1.0, + "context_utilization": 0.7124087573371619, + "context_relevancy": 0.22621316914080075, + "answer_relevancy": 0.9046933431898141 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.7004998969667501, + "context_relevancy": 0.23871316914080074, + "answer_relevancy": 0.9058328551471282 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.7124087573371619, + "context_relevancy": 0.24675410481331536, + "answer_relevancy": 0.9079384840142384 +} +] diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_diabetes_3.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_diabetes_3.json new file mode 100644 index 0000000..e39107d --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_diabetes_3.json @@ -0,0 +1,20 @@ +[ +{ + "faithfulness": 0.96, + "context_utilization": 0.7479011200345999, + "context_relevancy": 0.2814642730385713, + "answer_relevancy": 0.8930647394153285 +}, +{ + "faithfulness": 0.9099999999999999, + "context_utilization": 0.7479011200345999, + "context_relevancy": 0.2814642730385713, + "answer_relevancy": 0.896847471293901 +}, +{ + "faithfulness": 0.9099999999999999, + "context_utilization": 0.7479011200345999, + "context_relevancy": 0.2814642730385713, + "answer_relevancy": 0.8912330225043821 +} +] diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_diabetes_4.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_diabetes_4.json new file mode 100644 index 0000000..2be82a9 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_diabetes_4.json @@ -0,0 +1,20 @@ +[ +{ + "faithfulness": 0.9333333333333332, + "context_utilization": 0.7297725885164278, + "context_relevancy": 0.17196237023200656, + "answer_relevancy": 0.8650648136737542 +}, +{ + "faithfulness": 0.9333333333333332, + "context_utilization": 0.7297725885164278, + "context_relevancy": 0.19056702139479725, + "answer_relevancy": 0.877389474552466 +}, +{ + "faithfulness": 0.9333333333333332, + "context_utilization": 0.7297725885164278, + "context_relevancy": 0.12413628327548483, + "answer_relevancy": 0.8783898419790906 +} +] diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_gn_3.json b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_gn_3.json new file mode 100644 index 0000000..8f33b47 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/gpt4o_eval_de_gn_3.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "context_utilization": 0.3914232592779822, + "context_relevancy": 0.05517979452054794, + "answer_relevancy": 0.39015395726757396 +}, +{ + "faithfulness": 0.6666666666666666, + "context_utilization": 0.3914232592779822, + "context_relevancy": 0.05517979452054794, + "answer_relevancy": 0.3864361192318465 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.3914232592779822, + "context_relevancy": 0.05517979452054794, + "answer_relevancy": 0.3901540653386376 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/gpt4o/scores_cs_diabetes.json b/gnqa/paper1_eval/src/data/results/gpt4o/scores_cs_diabetes.json new file mode 100644 index 0000000..ef8c661 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/gpt4o/scores_cs_diabetes.json @@ -0,0 +1,37 @@ +, +{ + "faithfulness": 0.8836363636363636, + "context_utilization": 0.9533674463200074, + "context_relevancy": 0.1906017620560349, + "answer_relevancy": 0.9629314894517702 +}, +{ + "faithfulness": 0.8436363636363637, + "context_utilization": 0.9533674463200074, + "context_relevancy": 0.20364480596864404, + "answer_relevancy": 0.9495337378736439 +}, +{ + "faithfulness": 0.9292861989650555, + "context_utilization": 0.9651063978998563, + "context_relevancy": 0.7109415961877185, + "answer_relevancy": 0.6638464088279047 +}, +{ + "faithfulness": 0.4690747444442785, + "context_utilization": 0.7745118439410044, + "context_relevancy": 0.7140014395170777, + "answer_relevancy": 0.9322560108422944 +}, +{ + "faithfulness": 0.7745118439410044, + "context_utilization": 0.3333333333333333, + "context_relevancy": 0.3538011695906433, + "answer_relevancy": 0.5456168066603103 +}, +{ + "faithfulness": 0.5657894736779605, + "context_utilization": 1.0, + "context_relevancy": 0.22142857142857142, + "answer_relevancy": 0.7181594110215056 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_aging_1.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_aging_1.json new file mode 100644 index 0000000..f37296e --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_aging_1.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.9428571428571428, + "context_utilization": 0.9352808378906239, + "context_relevancy": 0.07125660926343383, + "answer_relevancy": 0.9523107847972947 +}, +{ + "faithfulness": 0.9428571428571428, + "context_utilization": 0.9355754170487147, + "context_relevancy": 0.07125660926343383, + "answer_relevancy": 0.9549674105661919 +}, +{ + "faithfulness": 0.9428571428571428, + "context_utilization": 0.9211814776549062, + "context_relevancy": 0.07125660926343383, + "answer_relevancy": 0.9499741000488516 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_aging_2.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_aging_2.json new file mode 100644 index 0000000..f7dae45 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_aging_2.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.7742138364779875, + "context_utilization": 0.9894163077459343, + "context_relevancy": 0.04506568948673187, + "answer_relevancy": 0.9408685212116719 +}, +{ + "faithfulness": 0.7742138364779875, + "context_utilization": 0.9894163077459343, + "context_relevancy": 0.04506568948673187, + "answer_relevancy": 0.9443348131121218 +}, +{ + "faithfulness": 0.7742138364779875, + "context_utilization": 0.9894163077459343, + "context_relevancy": 0.04506568948673187, + "answer_relevancy": 0.9373602976132769 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_aging_3.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_aging_3.json new file mode 100644 index 0000000..b844e70 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_aging_3.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.5714285714285715, + "context_utilization": 0.8007295763340471, + "context_relevancy": 0.17757604714126454, + "answer_relevancy": 0.9624406549445811 +}, +{ + "faithfulness": 0.5714285714285715, + "context_utilization": 0.8256406991618427, + "context_relevancy": 0.17757604714126454, + "answer_relevancy": 0.9624295953235836 +}, +{ + "faithfulness": 0.5714285714285715, + "context_utilization": 0.8256406991618427, + "context_relevancy": 0.17757604714126454, + "answer_relevancy": 0.9622154472101722 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_diabetes_1.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_diabetes_1.json new file mode 100644 index 0000000..8316988 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_diabetes_1.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.875, + "context_utilization": 0.6983276538190184, + "context_relevancy": 0.12429532403609515, + "answer_relevancy": 0.9112620728936985 +}, +{ + "faithfulness": 0.875, + "context_utilization": 0.6983276538190184, + "context_relevancy": 0.09929532403609516, + "answer_relevancy": 0.9153897050102227 +}, +{ + "faithfulness": 0.875, + "context_utilization": 0.6983276538190184, + "context_relevancy": 0.10864315012305167, + "answer_relevancy": 0.917767867097622 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_diabetes_2.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_diabetes_2.json new file mode 100644 index 0000000..7020070 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_diabetes_2.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.96, + "context_utilization": 0.9677256242806254, + "context_relevancy": 0.21125490196078428, + "answer_relevancy": 0.96903893567995 +}, +{ + "faithfulness": 0.96, + "context_utilization": 0.9769465411060386, + "context_relevancy": 0.2143799019607843, + "answer_relevancy": 0.9657737286038965 +}, +{ + "faithfulness": 0.96, + "context_utilization": 0.9769465411060386, + "context_relevancy": 0.2143799019607843, + "answer_relevancy": 0.9662487631948171 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_diabetes_3.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_diabetes_3.json new file mode 100644 index 0000000..1b57ac7 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_diabetes_3.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.8400000000000001, + "context_utilization": 0.9538081741417747, + "context_relevancy": 0.11497132693854006, + "answer_relevancy": 0.9169018406443659 +}, +{ + "faithfulness": 0.8400000000000001, + "context_utilization": 0.9538081741417747, + "context_relevancy": 0.2016379936052067, + "answer_relevancy": 0.9187380038134432 +}, +{ + "faithfulness": 0.8400000000000001, + "context_utilization": 0.9434457191364413, + "context_relevancy": 0.11497132693854006, + "answer_relevancy": 0.9169054522175759 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_diabetes_4.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_diabetes_4.json new file mode 100644 index 0000000..e54895e --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_diabetes_4.json @@ -0,0 +1,39 @@ +[ +{ + "faithfulness": 0.75, + "context_utilization": 0.49586940836114385, + "context_relevancy": 0.4489795918367347, + "answer_relevancy": 0.9050522628722737 +}, +{ + "faithfulness": 0.75, + "context_utilization": 0.5332560296769832, + "context_relevancy": 0.4489795918367347, + "answer_relevancy": 0.9274337314167257 +}, +{ + "faithfulness": 0.75, + "context_utilization": 0.49586940836114385, + "context_relevancy": 0.4489795918367347, + "answer_relevancy": 0.9274337314167257 +} +] +, +{ + "faithfulness": 0.75, + "context_utilization": 0.49586940836114385, + "context_relevancy": 0.2857142857142857, + "answer_relevancy": 0.9050522628722737 +}, +{ + "faithfulness": 0.75, + "context_utilization": 0.49586940836114385, + "context_relevancy": 0.4489795918367347, + "answer_relevancy": 0.9050692102679129 +}, +{ + "faithfulness": 0.75, + "context_utilization": 0.49586940836114385, + "context_relevancy": 0.4489795918367347, + "answer_relevancy": 0.9050522628722737 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_1.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_1.json new file mode 100644 index 0000000..4481bdb --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_1.json @@ -0,0 +1,14 @@ +[ +{ + "faithfulness": 0.9099999999999999, + "context_utilization": 0.7636817432217684, + "context_relevancy": 0.1880278568582262, + "answer_relevancy": 0.9423280729066063 +}, +{ + "faithfulness": 0.9099999999999999, + "context_utilization": 0.7357044805156637, + "context_relevancy": 0.15469452352489288, + "answer_relevancy": 0.9486310766041234 +} +] diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_2.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_2.json new file mode 100644 index 0000000..f0733da --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_2.json @@ -0,0 +1,20 @@ +[ +{ + "faithfulness": 1.0, + "context_utilization": 0.6326643990778912, + "context_relevancy": 0.1347400263302517, + "answer_relevancy": 0.8746783013952267 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.6683786847884866, + "context_relevancy": 0.1508690585883162, + "answer_relevancy": 0.8703116371547157 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.6326643990778912, + "context_relevancy": 0.1332248748151002, + "answer_relevancy": 0.8689393391315343 +} +] diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_3.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_3.json new file mode 100644 index 0000000..7258a04 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_3.json @@ -0,0 +1,25 @@ +, +{ + "faithfulness": 0.5677966101694916, + "context_utilization": 0.4561270844811867, + "context_relevancy": 0.5560185185148071, + "answer_relevancy": 0.5052295687739448 +}, +{ + "faithfulness": 0.75, + "context_utilization": 0.5643129043087701, + "context_relevancy": 0.05599820060366845, + "answer_relevancy": 0.7414497144046052 +}, +{ + "faithfulness": 0.75, + "context_utilization": 0.5729415276879585, + "context_relevancy": 0.05599820060366845, + "answer_relevancy": 0.5544292034718707 +}, +{ + "faithfulness": 0.75, + "context_utilization": 0.5643129043087701, + "context_relevancy": 0.05599820060366845, + "answer_relevancy": 0.5571557447633533 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_4.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_4.json new file mode 100644 index 0000000..15b1eb4 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_4.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.9428571428571428, + "context_utilization": 0.789441709521905, + "context_relevancy": 0.136784410468621, + "answer_relevancy": 0.8500389108331188 +}, +{ + "faithfulness": 0.9142857142857143, + "context_utilization": 0.7921665772467545, + "context_relevancy": 0.15115688010424852, + "answer_relevancy": 0.8317623611813637 +}, +{ + "faithfulness": 0.9142857142857143, + "context_utilization": 0.789441709521905, + "context_relevancy": 0.1713997950840056, + "answer_relevancy": 0.8295033051724321 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_5.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_5.json new file mode 100644 index 0000000..03713c2 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_5.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.9333333333333332, + "context_utilization": 0.6801836614504664, + "context_relevancy": 0.06454107195486505, + "answer_relevancy": 0.7372449377189451 +}, +{ + "faithfulness": 0.888888888888889, + "context_utilization": 0.6582554717950728, + "context_relevancy": 0.06454107195486505, + "answer_relevancy": 0.7372493726798736 +}, +{ + "faithfulness": 0.8761904761904763, + "context_utilization": 0.6582554717950728, + "context_relevancy": 0.06454107195486505, + "answer_relevancy": 0.7372449377189451 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_6.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_6.json new file mode 100644 index 0000000..0d67e80 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_6.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.75, + "context_utilization": 0.45564199508207504, + "context_relevancy": 0.06005275024001898, + "answer_relevancy": 0.8915679391851077 +}, +{ + "faithfulness": 0.75, + "context_utilization": 0.45564199508207504, + "context_relevancy": 0.05215801339791372, + "answer_relevancy": 0.7064299254450507 +}, +{ + "faithfulness": 0.75, + "context_utilization": 0.45564199508207504, + "context_relevancy": 0.0707670359543047, + "answer_relevancy": 0.705077643467664 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_7.json b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_7.json new file mode 100644 index 0000000..a30782a --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_cs_gn_7.json @@ -0,0 +1,18 @@ +{ + "faithfulness": 1.0, + "context_utilization": 0.9178474303338136, + "context_relevancy": 0.09082338152105594, + "answer_relevancy": 0.9524284122181226 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9178474303338136, + "context_relevancy": 0.09082338152105594, + "answer_relevancy": 0.9492709094955006 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9178474303338136, + "context_relevancy": 0.09082338152105594, + "answer_relevancy": 0.9524270517859097 +} diff --git a/gnqa/paper1_eval/src/data/results/human/scores_de_aging_1.json b/gnqa/paper1_eval/src/data/results/human/scores_de_aging_1.json new file mode 100644 index 0000000..0700cc3 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_de_aging_1.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.7428571428571429, + "context_utilization": 0.811213861888054, + "context_relevancy": 0.2314977832798794, + "answer_relevancy": 0.9433409234117335 +}, +{ + "faithfulness": 0.7428571428571429, + "context_utilization": 0.7983208584270672, + "context_relevancy": 0.24114933391503665, + "answer_relevancy": 0.9213466964486724 +}, +{ + "faithfulness": 0.7142857142857142, + "context_utilization": 0.7928499698879043, + "context_relevancy": 0.25367860791972047, + "answer_relevancy": 0.9318615626710995 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_de_aging_2.json b/gnqa/paper1_eval/src/data/results/human/scores_de_aging_2.json new file mode 100644 index 0000000..b7f8cc0 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_de_aging_2.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "context_utilization": 0.9999999999923077, + "context_relevancy": 1.0, + "answer_relevancy": 0.8836732547434365 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9999999999923077, + "context_relevancy": 1.0, + "answer_relevancy": 0.8836732547434365 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9999999999923077, + "context_relevancy": 1.0, + "answer_relevancy": 0.8836732547434365 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_de_diabetes_1.1.json b/gnqa/paper1_eval/src/data/results/human/scores_de_diabetes_1.1.json new file mode 100644 index 0000000..0e46a7f --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_de_diabetes_1.1.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "context_utilization": 0.7777403152338384, + "context_relevancy": 0.06084656084656084, + "answer_relevancy": 0.9645121106959694 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.7777403152338384, + "context_relevancy": 0.06084656084656084, + "answer_relevancy": 0.9545089573441493 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.7719252969185456, + "context_relevancy": 0.05026455026455026, + "answer_relevancy": 0.9327156331092903 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_de_diabetes_1.json b/gnqa/paper1_eval/src/data/results/human/scores_de_diabetes_1.json new file mode 100644 index 0000000..0b621e2 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_de_diabetes_1.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.9166666666666667, + "context_utilization": 0.7671392748688641, + "context_relevancy": 0.33561602418745273, + "answer_relevancy": 0.90324232280188 +}, +{ + "faithfulness": 0.9166666666666667, + "context_utilization": 0.8555804271901495, + "context_relevancy": 0.2314914450628736, + "answer_relevancy": 0.7214993293693964 +}, +{ + "faithfulness": 0.9666666666666668, + "context_utilization": 0.8080409996869443, + "context_relevancy": 0.2837641723356009, + "answer_relevancy": 0.9014349074286775 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_de_diabetes_2.json b/gnqa/paper1_eval/src/data/results/human/scores_de_diabetes_2.json new file mode 100644 index 0000000..bd6159a --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_de_diabetes_2.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.96, + "context_utilization": 0.9407265478802447, + "context_relevancy": 0.36922494182022314, + "answer_relevancy": 0.9364702737085768 +}, +{ + "faithfulness": 0.96, + "context_utilization": 0.9344763371477345, + "context_relevancy": 0.386466321130568, + "answer_relevancy": 0.944903559928554 +}, +{ + "faithfulness": 0.96, + "context_utilization": 0.9344763371477345, + "context_relevancy": 0.36922494182022314, + "answer_relevancy": 0.9355512181399582 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_de_gn_1.1.json b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_1.1.json new file mode 100644 index 0000000..d47c31f --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_1.1.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.9609375, + "context_utilization": 0.6937871661149843, + "context_relevancy": 0.13637360626722328, + "answer_relevancy": 0.7491735530216923 +}, +{ + "faithfulness": 0.9609375, + "context_utilization": 0.6937871661149843, + "context_relevancy": 0.13637360626722328, + "answer_relevancy": 0.8902254519253692 +}, +{ + "faithfulness": 0.9296875, + "context_utilization": 0.6937871661149843, + "context_relevancy": 0.13637360626722328, + "answer_relevancy": 0.7491716987687886 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_de_gn_1.json b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_1.json new file mode 100644 index 0000000..9b8aea1 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_1.json @@ -0,0 +1,18 @@ +{ + "faithfulness": 1.0, + "context_utilization": 0.9596645021564207, + "context_relevancy": 0.1634286630390054, + "answer_relevancy": 0.8973761639776056 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9596645021564207, + "context_relevancy": 0.1634286630390054, + "answer_relevancy": 0.9038434542970721 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9561079845997444, + "context_relevancy": 0.1634286630390054, + "answer_relevancy": 0.8983469111948426 +} diff --git a/gnqa/paper1_eval/src/data/results/human/scores_de_gn_2.json b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_2.json new file mode 100644 index 0000000..30be099 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_2.json @@ -0,0 +1,18 @@ +{ + "faithfulness": 0.8, + "context_utilization": 0.7266600180799679, + "context_relevancy": 0.12599664343008876, + "answer_relevancy": 0.7320068044307713 +}, +{ + "faithfulness": 0.8, + "context_utilization": 0.7266600180799679, + "context_relevancy": 0.13234584977929512, + "answer_relevancy": 0.7198147208663943 +}, +{ + "faithfulness": 0.8, + "context_utilization": 0.7266600180799679, + "context_relevancy": 0.12849969593314126, + "answer_relevancy": 0.7325464661134955 +} diff --git a/gnqa/paper1_eval/src/data/results/human/scores_de_gn_3.json b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_3.json new file mode 100644 index 0000000..33a94ff --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_3.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.8666666666666666, + "context_utilization": 0.6480859663109396, + "context_relevancy": 0.1510877797535341, + "answer_relevancy": 0.915240518467451 +}, +{ + "faithfulness": 0.8666666666666666, + "context_utilization": 0.6480859663109396, + "context_relevancy": 0.11387847742795269, + "answer_relevancy": 0.9124757388808369 +}, +{ + "faithfulness": 0.9333333333333332, + "context_utilization": 0.6480859663109396, + "context_relevancy": 0.1510877797535341, + "answer_relevancy": 0.9141762748312928 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_de_gn_4.json b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_4.json new file mode 100644 index 0000000..345f566 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_4.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 0.65, + "context_utilization": 0.354120538187183, + "context_relevancy": 0.1120026888642334, + "answer_relevancy": 0.7376780691990237 +}, +{ + "faithfulness": 0.5333333333333333, + "context_utilization": 0.34712053818788413, + "context_relevancy": 0.1120026888642334, + "answer_relevancy": 0.7455570356847625 +}, +{ + "faithfulness": 0.65, + "context_utilization": 0.34712053818788413, + "context_relevancy": 0.0993042761658207, + "answer_relevancy": 0.7376780609996703 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_de_gn_5.json b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_5.json new file mode 100644 index 0000000..5148d68 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_5.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "context_utilization": 0.8007395937295169, + "context_relevancy": 0.049944862903025335, + "answer_relevancy": 0.8599243307705603 +}, +{ + "faithfulness": 0.8, + "context_utilization": 0.806603791260579, + "context_relevancy": 0.049944862903025335, + "answer_relevancy": 0.6986715526356269 +}, +{ + "faithfulness": 0.9, + "context_utilization": 0.806603791260579, + "context_relevancy": 0.049944862903025335, + "answer_relevancy": 0.8579006890252776 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/human/scores_de_gn_6.json b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_6.json new file mode 100644 index 0000000..25d04cf --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/human/scores_de_gn_6.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": 1.0, + "context_utilization": 0.9999999999919545, + "context_relevancy": 0.20662768031189083, + "answer_relevancy": 0.9302858689849556 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9999999999919545, + "context_relevancy": 0.2584795321637427, + "answer_relevancy": 0.9258655139523131 +}, +{ + "faithfulness": 1.0, + "context_utilization": 0.9999999999919545, + "context_relevancy": 0.1992202729044834, + "answer_relevancy": 0.9219977486705678 +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/llamaeval_general1.json b/gnqa/paper1_eval/src/data/results/llamaeval_general1.json new file mode 100644 index 0000000..d9d134f --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/llamaeval_general1.json @@ -0,0 +1,13 @@ +, +{ + "faithfulness": NaN, + "answer_relevancy": NaN, + "context_relevancy": 0.924645390070922, + "context_utilization": NaN +}, +{ + "faithfulness": NaN, + "answer_relevancy": NaN, + "context_relevancy": 0.8, + "context_utilization": NaN +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/results.json b/gnqa/paper1_eval/src/data/results/results.json new file mode 100644 index 0000000..4b30b95 --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/results.json @@ -0,0 +1,20 @@ +{'faithfulness': nan, 'answer_relevancy': nan, 'context_relevancy': 0.7412, 'context_utilization': nan} +, +{ + "faithfulness": NaN, + "answer_relevancy": NaN, + "context_relevancy": 0.5342715544752126, + "context_utilization": NaN +}, +{ + "faithfulness": NaN, + "answer_relevancy": NaN, + "context_relevancy": 0.523524948140371, + "context_utilization": NaN +}, +{ + "faithfulness": NaN, + "answer_relevancy": NaN, + "context_relevancy": 0.6374515308316596, + "context_utilization": NaN +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/data/results/results_aging.json b/gnqa/paper1_eval/src/data/results/results_aging.json new file mode 100644 index 0000000..7fad8ff --- /dev/null +++ b/gnqa/paper1_eval/src/data/results/results_aging.json @@ -0,0 +1,19 @@ +, +{ + "faithfulness": NaN, + "answer_relevancy": NaN, + "context_relevancy": 0.726235827137375, + "context_utilization": NaN +}, +{ + "faithfulness": NaN, + "answer_relevancy": NaN, + "context_relevancy": 0.7121415843797659, + "context_utilization": NaN +}, +{ + "faithfulness": NaN, + "answer_relevancy": NaN, + "context_relevancy": 0.7374184453992012, + "context_utilization": NaN +}
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/errors/__pycache__/rag_err.cpython-310.pyc b/gnqa/paper1_eval/src/errors/__pycache__/rag_err.cpython-310.pyc Binary files differnew file mode 100644 index 0000000..240b761 --- /dev/null +++ b/gnqa/paper1_eval/src/errors/__pycache__/rag_err.cpython-310.pyc diff --git a/gnqa/paper1_eval/src/errors/rag_err.py b/gnqa/paper1_eval/src/errors/rag_err.py new file mode 100644 index 0000000..e9f7c02 --- /dev/null +++ b/gnqa/paper1_eval/src/errors/rag_err.py @@ -0,0 +1,62 @@ + +# pylint: skip-file +import json + +from requests import HTTPError + + +class UnprocessableEntity(HTTPError): + """An HTTP 422 Unprocessable Entity error occurred. + + https://help.helpjuice.com/en_US/api-v3/api-v3#errors + + The request could not be processed, usually due to a missing or invalid parameter. + + The response will also include an error object with an explanation of fields that + are missing or invalid. Here is an example: + + .. code-block:: + + HTTP/1.1 422 Unprocessable Entity + + + { + "errors": [ + { + "email": "is not valid." + } + ] + } + """ + + def __init__(self, request, response): + """UnprocessableEntity constructor. + + Parses out error information from the error object and passes on to the + :obj:`HTTPError` constructor. + + Args: + exc (:obj:`HTTPError`): Original exception. + """ + rq_json = next(iter(json.loads(request.body.decode()).values())) + errors = response.json() + + for field, error in errors.items(): + rq_field = rq_json.get(field, None) + if not rq_field: + continue + + if isinstance(error, list): + error = error.insert(0, rq_field) + elif isinstance(error, str): + error = f"{rq_field} {error}" + + msg = json.dumps(errors) + super(HTTPError, self).__init__( + msg, request=request, response=response) + + +class LLMError(HTTPError): + def __init__(self, request, response, msg): + super(HTTPError, self).__init__( + msg, request=request, response=response) diff --git a/gnqa/paper1_eval/src/parsejson.py b/gnqa/paper1_eval/src/parsejson.py new file mode 100644 index 0000000..b49a898 --- /dev/null +++ b/gnqa/paper1_eval/src/parsejson.py @@ -0,0 +1,63 @@ +import json +import sys + + +def iterate_json(obj, thedict): + if isinstance(obj, dict): + for key, val in obj.items(): + if (key == "text"): + thedict["contexts"].append(val.replace("\n", " ").strip()) + elif (key == "answer"): + thedict["answer"] = val.replace("\n", " ").strip() + elif (key == "question"): + thedict["question"] = val.replace("\n", " ").strip() + else: + if (len(obj.items()) == 1 ): + print(key, " --> ", val) + iterate_json(val, thedict) + elif isinstance(obj, list): + for item in obj: + iterate_json(item, thedict) + +def create_dataset_from_files(tag, file_name, rag_out): + for the_file in file_name[tag]: + ragas_output = { + "contexts": [], + "answer": "", + "question": ""} + #print(the_file) + with open("./data/"+the_file, "r") as r_file: + data_file = json.load(r_file) + iterate_json(data_file, ragas_output) + rag_out["answer"].append(ragas_output["answer"]) + rag_out["question"].append(ragas_output["question"]) + rag_out["contexts"].append(ragas_output["contexts"]) + +def create_resultset_from_file(file_name): + with open("./data/"+the_file, "r") as r_file: + data_file = json.load(r_file) + iterate_json(data_file, ragas_output) + + +file_list_tag = str(sys.argv[1]) +read_file = str(sys.argv[2]) # e.g. doc_list.json +outp_file = str(sys.argv[3]) + +rag_out = { + "question": [], + "answer": [], + "contexts": [] +} + +cntxt_lst = [] + +# this should be a json file with a list of input files and an output file +with open(read_file, "r") as r_file: + file_lst = json.load(r_file) + +create_dataset_from_files(file_list_tag, file_lst, rag_out) + +with open(outp_file, "a") as the_data: + #json.dump(ragas_output, the_data) + the_data.write(",\n") + the_data.write(json.dumps(rag_out, indent=2)) diff --git a/gnqa/paper1_eval/src/parsejson_ratings.py b/gnqa/paper1_eval/src/parsejson_ratings.py new file mode 100644 index 0000000..bd20417 --- /dev/null +++ b/gnqa/paper1_eval/src/parsejson_ratings.py @@ -0,0 +1,106 @@ +import json +import sys + +""" +This file converts the json report from GNQA into a list of individual users +and their interactions with the system. At the moment we are getting their +questions, the systems answers, and their ratings of the overall system response. +Unfortunately the context is not saved with the answer. +""" +# report_data, ratings_dict +def reorg_json_report(obj, resp_lst, ratings): + if isinstance(obj, dict): + user_id = '' + for key, val in obj.items(): + if (key == "user_id"): + user_id = val + if isKeyInList(resp_lst, val) == 0: + resp_lst.append({val: ratings}) + else: + print("\nKey {0} is already present".format(val)) + elif (key in ["query","answer","weight","task_id"]): + ratings[key].append(val) + #else: + # print('These are the current ratings --> {0}'.format(ratings)) + print('The ratings before being pushed to user_responses -> {0}'.format(ratings)) + # add query to dictionary, if it is an update then don't update the ratings + qcount = query_dict.setdefault(ratings["query"][0], 0) + query_dict.update({ratings["query"][0]: qcount+1}) + update_ratings(resp_lst, user_id, ratings) + if qcount == 0: + taskquery_dict.setdefault(ratings["task_id"][0], ratings["query"][0]) + #update_ratings(resp_lst, user_id, ratings) + #reorg_json_report(val, resp_lst, ratings) + elif isinstance(obj, list): + for item in obj: + ratings = reset_ratings() + reorg_json_report(item, resp_lst, ratings) + + + +def create_resultset_from_file(resp_lst, file_name, output): + with open(file_name, "r") as r_file: + the_data = json.load(r_file) + reorg_json_report(the_data, resp_lst, output) + +def isKeyInList(the_lst, the_key): + result = 0 + result_item = {} + for the_item in the_lst: + if the_key in the_item: + result = 1 + result_item = the_item + return result, result_item + +def update_ratings(ratings, user_id, input_dict): + key_ndx, ratings_dict = isKeyInList(ratings, user_id) + if key_ndx == 0: + ratings.append({user_id: input_dict}) + else: + for key, val in input_dict.items(): + if isinstance(val,list): + ratings_dict[user_id][key].append(val[0]) + else: + ratings_dict[user_id][key].append(val) + +def reset_ratings(): + return { + "task_id": [], + "weight": [], + "answer": [], + "query": [] + } + +user_responses = [] +ratings_out = { + "task_id": [], + "weight": [], + "answer": [], + "query": [] +} + +query_dict = {} +taskquery_dict = {} + +try: + read_file = str(sys.argv[1]) # e.g. doc_list.json + outp_file = str(sys.argv[2]) +except: + exit('Example use "python3 parsejson_ratings.py data/ratings/2024_06_25-gnqa_responses.json data/ratings/[date]-out.json"') + +#print('The input file is {0}, the output file is {1}'.format(read_file, outp_file)) + +create_resultset_from_file(user_responses, read_file, ratings_out) +print('The number of users is {0}'.format(len(user_responses))) +#print(json.dumps(ratings_out, indent=2)) +with open(outp_file, "a") as the_data: + the_data.write(",\n") + the_data.write(json.dumps(user_responses, indent=2)) + +print("Greetings shabes!") +print('There are {0} unique queries.'.format(len(taskquery_dict))) +print(json.dumps(taskquery_dict, indent=2)) +#get number of users +# get number of questions asked per user +# get average ratings + diff --git a/gnqa/paper1_eval/src/ragas_fahamuRAG.py b/gnqa/paper1_eval/src/ragas_fahamuRAG.py new file mode 100644 index 0000000..345483e --- /dev/null +++ b/gnqa/paper1_eval/src/ragas_fahamuRAG.py @@ -0,0 +1,66 @@ +import os +import sys +import json +import time +import configparser +import pandas as pd + +from pandas import DataFrame as df +from langchain_together import Together +from langchain_together.embeddings import TogetherEmbeddings +from ragas.metrics import (faithfulness, answer_relevancy, context_relevancy, context_utilization) +from ragas import evaluate +from datasets import Dataset#, load_dataset + +def evaluateDataset(num_evaluations, dataset, output_file): + for n in range(0,num_evaluations): + + results = evaluate(dataset, metrics=[faithfulness,context_utilization,context_relevancy,answer_relevancy], raise_exceptions=False) + print(results) + with open(output_file, "a") as the_data: + the_data.write(",\n") + the_data.write(json.dumps(results, indent=2)) + time.sleep(20) + + + +config = configparser.ConfigParser() +config.read('_config.cfg') + +os.environ["OPENAI_API_KEY"] = config['key.api']['openai2'] +together_key = config['key.api']['togetherai'] + +#embeddings = TogetherEmbeddings(model="togethercomputer/m2-bert-80M-8k-retrieval") +embeddings = TogetherEmbeddings(model="togethercomputer/m2-bert-80M-32k-retrieval") + +together_completion = Together( + #model="NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT", + #model="togethercomputer/Llama-2-7B-32K-Instruct", + #model="meta-llama/Llama-3-70b-chat-hf", + model="google/gemma-7b-it", + temperature=0.8, + max_tokens=4000, + top_k=1, + together_api_key=together_key +) + +read_file = str(sys.argv[1]) +outp_file = str(sys.argv[2]) +num_evals = int(sys.argv[3]) + +print(read_file) +print(outp_file) + +with open(read_file, "r") as r_file: + data = json.load(r_file) + +dataset = Dataset.from_dict(data) +print(dataset) +evaluateDataset(num_evals, dataset, outp_file) +""" +results = evaluate( + dataset, + metrics=[faithfulness,answer_relevancy,context_relevancy,context_utilization], + llm=together_completion, + embeddings=embeddings) +""" diff --git a/gnqa/paper1_eval/src/retrieve_context.py b/gnqa/paper1_eval/src/retrieve_context.py new file mode 100644 index 0000000..58b9d47 --- /dev/null +++ b/gnqa/paper1_eval/src/retrieve_context.py @@ -0,0 +1,171 @@ +import os +import sys +import json +import time +import configparser +import apis.process as gnqa +from apis.process import get_gnqa, get_response_from_taskid + + +config = configparser.ConfigParser() +config.read('_config.cfg') + +''' +the refs object is a list of items containing doc_id, bibInfo, and comboTxt +We only need comboTxt +''' +def simplifyContext(refs): + result = [] + for item in refs: + combo_text = item['comboTxt'] + combo_text = combo_text.replace('\n','') + combo_text = combo_text.replace('\t','') + result.append(combo_text) + return result + +def writeDatasetFile(responses, outp_file): + print(outp_file) + output = json.dumps(responses, indent=2) + if os.path.exists(outp_file): + with open(outp_file, "a") as the_data: + the_data.write('' + output) + else: + with open(outp_file, "a") as the_data: + the_data.write(output) + + +def reset_responses(): + return { + 'question': [], + 'answer': [], + 'contexts': [], + 'task_id': [] + } + +def parse_document(jsonfile): + print('Parse document') + for item in jsonfile: + level = item['level'] + domain = item['domain'] + query_lst = item['query'] + create_datasets(query_lst, domain, level) + +def create_datasets(query_list, domain, level): + print('Creating dataset') + responses = reset_responses() + ndx = 0 + for query in query_list: + print(query) + task_id, answer, refs = get_gnqa(query, config['key.api']['fahamuai'], config['DEFAULT']['DATA_DIR']) + responses['question'].append(query) + responses['answer'].append(answer) + responses['task_id'].append(task_id) + responses['contexts'].append(simplifyContext(refs)) + ndx+=1 + time.sleep(10) # sleep a bit to not overtask the api + if ndx % 5 == 0: + print('Will print to file number {0}'.format(int(ndx/5))) + outp_file = '{0}dataset_{1}_{2}_{3}.json'.format(config['out.response.dataset']['gpt4o_dir'],level,domain,str(int(ndx/5))) + writeDatasetFile(responses, outp_file) + responses = reset_responses() + if len(responses['question']) > 0: + outp_file = '{0}dataset_{1}_{2}_{3}.json'.format(config['out.response.dataset']['gpt4o_dir'],level,domain,str(int(ndx/5)+1)) + writeDatasetFile(responses, outp_file) + +def parse_responses(jsonfile): + print('Parsing human responses') + de_dict_general = {"level": "domainexpert", "domain": "general", "query": [], "task_id": []} + de_dict_aging = {"level": "domainexpert", "domain": "aging", "query": [], "task_id": []} + de_dict_diabetes = {"level": "domainexpert", "domain": "diabetes", "query": [], "task_id": []} + cs_dict_general = {"level": "citizenscientist", "domain": "general", "query": [], "task_id": []} + cs_dict_aging = {"level": "citizenscientist", "domain": "aging", "query": [], "task_id": []} + cs_dict_diabetes = {"level": "citizenscientist", "domain": "diabetes", "query": [], "task_id": []} + j = 0 + for _, val in jsonfile.items(): + ndx = 0 + lvl = val.get("level") + for qry in val.get("query"): + ans = val.get("answer")[ndx] if "answer" in val else "" + tpc = val.get("topic")[ndx] + tpc = "general" if tpc==0 else "aging" if tpc==1 else "diabetes" + tskd = val.get("task_id")[ndx] + if lvl == 'cs' and tpc == 'general': + addToDataList(cs_dict_general, qry, ans, tskd) + elif lvl == 'cs' and tpc == 'aging': + addToDataList(cs_dict_aging, qry, ans, tskd) + elif lvl == 'cs' and tpc == 'diabetes': + addToDataList(cs_dict_diabetes, qry, ans, tskd) + elif lvl == 'de' and tpc == 'general': + addToDataList(de_dict_general, qry, ans, tskd) + elif lvl == 'de' and tpc == 'aging': + addToDataList(de_dict_aging, qry, ans, tskd) + elif lvl == 'de' and tpc == 'diabetes': + addToDataList(de_dict_diabetes, qry, ans, tskd) + else: + print('Somehow there is a query without a topic or expertise level') + ndx+=1 + j+=1 + create_datasets_from_taskid(de_dict_general) + create_datasets_from_taskid(de_dict_aging) + create_datasets_from_taskid(de_dict_diabetes) + create_datasets_from_taskid(cs_dict_general) + create_datasets_from_taskid(cs_dict_aging) + create_datasets_from_taskid(cs_dict_diabetes) + +def addToDataList(data_lst, qry, ans, tskd): + data_lst["query"].append(qry) + data_lst["task_id"].append(tskd) + if "answer" not in data_lst.keys(): + data_lst["answer"] = [] + data_lst["answer"].append(ans) + + +def create_datasets_from_taskid(info_dict):#task_list, query_list, answers, domain, level): + print('Creating dataset of questions from {0} in the topic of {1}'.format(info_dict["level"], info_dict["domain"])) + responses = reset_responses() + ndx = 0 + query_list = info_dict["query"] + if "answer" in info_dict: + answers = info_dict["answer"] + else: + info_dict["answer"] = [] + answers = [] + + for task_id in info_dict["task_id"]: + _, an_answer, refs = get_response_from_taskid(config['key.api']['fahamuai'], task_id) + responses['question'].append(query_list[ndx]) + if answers[ndx] == "": + responses['answer'].append(an_answer) + else: + responses['answer'].append(answers[ndx]) + responses['task_id'].append(task_id) + responses['contexts'].append(simplifyContext(refs)) + ndx+=1 + time.sleep(10) # sleep a bit to not overtask the api + if ndx % 5 == 0: + #print('Will print to file number {0}'.format(int(ndx/5))) + outp_file = '{0}dataset_{1}_{2}_{3}_two.json'.format(config['out.response.dataset']['human_dir'],info_dict["level"],info_dict["domain"],str(int(ndx/5))) + writeDatasetFile(responses, outp_file) + responses = reset_responses() + if len(responses['question']) > 0: + #print('Will print to file number {0}'.format(int((ndx/5)+1))) + #print(responses) + outp_file = '{0}dataset_{1}_{2}_{3}_two.json'.format(config['out.response.dataset']['human_dir'],info_dict["level"],info_dict["domain"],str(int(ndx/5)+1)) + writeDatasetFile(responses, outp_file) + +try: + + read_file = str(sys.argv[1]) + file_type = str(sys.argv[2]) + +except: + exit('Example use "python3 retrieve_context.py data/queries/qlist.json human/gpt4o"') + + +print('Read input file') +with open(read_file, "r") as r_file: + file_lst = json.load(r_file) +if file_type == "gpt4o": + parse_document(file_lst) +else: + parse_responses(file_lst)
\ No newline at end of file diff --git a/gnqa/paper1_eval/src/run_human_datasets.sh b/gnqa/paper1_eval/src/run_human_datasets.sh new file mode 100755 index 0000000..6f55d6d --- /dev/null +++ b/gnqa/paper1_eval/src/run_human_datasets.sh @@ -0,0 +1,63 @@ +#!/bin/bash + +#echo "Evaluating dataset of questions from domainexperts on the topic of general GN information." +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_domainexpert_general_1.json data/results/human/scores_de_gn_1.json 3 +#sleep 60 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_domainexpert_general_2.json data/results/human/scores_de_gn_2.json 3 +#sleep 60 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_domainexpert_general_3.json data/results/human/scores_de_gn_3.json 3 +#sleep 60 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_domainexpert_general_4.json data/results/human/scores_de_gn_4.json 3 +#sleep 60 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_domainexpert_general_5.json data/results/human/scores_de_gn_5.json 3 +#sleep 60 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_domainexpert_general_6.json data/results/human/scores_de_gn_6.json 3 +#sleep 60 +#echo "Evaluating dataset of questions from domainexpert in the topic of aging" +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_domainexpert_aging_1.json data/results/human/scores_de_aging_1.json 3 +#sleep 60 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_domainexpert_aging_2.json data/results/human/scores_de_aging_2.json 3 +#sleep 60 +#echo "Evaluating dataset of questions from domainexpert in the topic of diabetes" +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_domainexpert_diabetes_1.json data/results/human/scores_de_diabetes_1.json 3 +#sleep 60 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_domainexpert_diabetes_2.json data/results/human/scores_de_diabetes_2.json 3 +#sleep 60 +#echo "Evaluating dataset of questions from citizenscientist in the topic of general" +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_general_1.json data/results/human/scores_cs_gn_1.json 3 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_general_2.json data/results/human/scores_cs_gn_2.json 3 +#sleep 30 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_general_3.json data/results/human/scores_cs_gn_3.json 3 +#sleep 30 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_general_4.json data/results/human/scores_cs_gn_4.json 3 +#sleep 30 +##python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_general_5.json data/results/human/scores_cs_gn_5.json 3 +##sleep 30 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_general_6.json data/results/human/scores_cs_gn_6.json 3 +#sleep 30 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_general_7.json data/results/human/scores_cs_gn_7.json 3 +#echo "Evaluating dataset of questions from citizenscientist in the topic of aging" +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_aging_1.json data/results/human/scores_cs_aging_1.json 3 +#sleep 30 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_aging_2.json data/results/human/scores_cs_aging_2.json 3 +#sleep 30 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_aging_3.json data/results/human/scores_cs_aging_3.json 3 +#sleep 30 +#echo "Evaluating dataset of questions from citizenscientist in the topic of diabetes" +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_1.json data/results/human/scores_cs_diabetes_1.json 3 +#sleep 30 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_2.json data/results/human/scores_cs_diabetes_2.json 3 +#sleep 30 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_3.json data/results/human/scores_cs_diabetes_3.json 3 +#sleep 30 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_4.json data/results/human/scores_cs_diabetes_4.json 3 +#sleep 30 + +echo "Evaluating dataset of queriestion from citizen scientist on the diabetes topics, generated by gpt4o" +python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/gpt4o/dataset_citizenscientist_gn_2.json data/results/gpt4o/scores_cs_gn.json 3 +#sleep 60 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_2.json data/results/gpt4o/scores_cs_diabetes.json 3 +#sleep 60 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_3.json data/results/gpt4o/scores_cs_diabetes.json 3 +#sleep 60 +#python3 ragas_fahamuRAG.py /home/shebes/code/GN-LLM-Hacking/gnqa_eval/src/data/datasets/gpt4o/dataset_citizenscientist_diabetes_4.json data/results/gpt4o/scores_cs_diabetes.json 3 |